Pacific Islands Ocean Observing System (PacIOOS) GeoServer Web Map Service (WMS)

Marine Protected Areas - American Samoa (as_comp_all_mpa)

Marine Protected Areas (MPAs) of American Samoa, including National Parks, National Marine Sanctuaries, National Wildlife Refuges, ecological reserves, and Territorial Seashore Parks. Compiled from multiple sources, including the National Park Service (NPS), National Marine Sanctuaries (NMS), Fish and Wildlife Service (FWS), and the World Database of Protected Areas (WDPA). For use in planning purposes only, not for use in litigation.

Building Footprints - Aunuu, American Samoa (as_dw_aun_bldngs)

Building Footprints of Aunu'u, American Samoa

Shoreline - Aunuu, American Samoa (as_dw_aun_shore)

Shoreline of Aunu'u, American Samoa

Malaeimi Special Management Area - Tutuila, American Samoa (as_dw_mal_sma)

Malaeimi Special Management Area, Tutuila, American Samoa

County Boundaries - Manua, American Samoa (as_dw_manall_cntybndrs)

County boundaries of Manu'a, American Samoa: Ofu, Olosega, and Ta'u.

Elevation Contours, 20m - Manua, American Samoa (as_dw_manall_cont_20m)

20m elevation contours of Manu'a, American Samoa: Ofu, Olosega and Ta'u.

Flood Hazard Zones - Ofu and Olosega, American Samoa (as_dw_manall_femafirm)

FEMA Flood hazard zones for the islands of Ofu and Olosega, American Samoa

Village Boundaries - Manua, American Samoa (as_dw_manall_vilbndrs)

Village boundaries of Manu'a, American Samoa: Ofu, Olosega and Ta'u.

Wetland, Jurisdictional Line - Nuuuli, Tutuila, American Samoa (as_dw_nuu_jwl)

Nuuuli Jurisdictional Wetland Line - Tutuila, American Samoa

Shoreline - Olosega, American Samoa (as_dw_olo_shore)

Shoreline of Olosega, American Samoa

Shoreline - Swains, American Samoa (as_dw_swa_shore)

Shoreline of Swains Atoll, American Samoa

Building Footprints - Tau, American Samoa (as_dw_tau_bldngs)

Building Footprints of Ta'u, American Samoa

Shoreline - Tau, American Samoa (as_dw_tau_shore)

Shoreline of Ta'u, American Samoa

Building Footprints - Tutuila, American Samoa (as_dw_tut_bldngs)

Building Footprints of Tutuila, American Samoa

Elevation Contours, 20m - Tutuila, American Samoa (as_dw_tut_cont_20m)

20m elevation contours of Tutuila, American Samoa

Flood Hazard Zones - Tutuila, American Samoa (as_dw_tut_femafirm)

FEMA Flood hazard zones for the island of Tutuila, American Samoa

Village Populations, 1960-2000 - Tutuila, American Samoa (as_dw_tut_pop_19602000)

Tutuila, American Samoa: Village Populations 1960 - 2000.

County Boundaries - Tutuila, American Samoa (as_dw_tutaun_cntybndrs)

County boundaries of Tutuila, American Samoa.

Wetlands - American Samoa (as_dw_tutma_ppcwshedstdy)

American Samoa wetlands from American Samoa Environmental Protection Agency (ASEPA) watershed study by Pederson Planning Consultants.

NOAA/NCEI 10-m Bathymetry: American Samoa: Tutuila: Hillshade (as_ngdc_tutaun_bathy10m_hillshade)

A 10-m (1/3 arc-second) resolution gridded digital elevation model (DEM) for the bathymetry (ocean depth) surrounding the island of Tutuila in American Samoa. It is referenced to a vertical tidal datum of Mean High Water (MHW) and was compiled from various data sources including: NOAA National Centers for Environmental Information (NCEI), formerly the National Geophysical Data Center (NGDC); the U.S. Geological Survey (USGS); Naval Oceanographic Office (NAVOCEANO); Gaia Geo Analytical; and other federal, state, and local government agencies, academic institutions, and private companies. Developed for the National Tsunami Hazard Mitigation Program (NTHMP) to support NOAA's tsunami forecasting and modeling efforts. Not to be used for navigational purposes. For the related dataset containing numeric bathymetry values for this image layer, see http://pacioos.org/metadata/ngdc_bathy_10m_tutuila.html

Chlorophyll-a Average Annual Frequency of Anomalies, 1998-2018 - American Samoa (as_noaa_all_chlor_anom_freq)

Chlorophyll-a, is a widely used proxy for phytoplankton biomass and an indicator for changes in phytoplankton production. As an essential source of energy in the marine environment, the extent and availability of phytoplankton biomass can be highly influential for fisheries production and dictate trophic structure in marine ecosystems. Changes in phytoplankton biomass are predominantly effected by changes in nutrient availability, through either natural (e.g., turbulent ocean mixing) or anthropogenic (e.g., agricultural runoff) processes. This layer represents the annual average number of anomalies of chlorophyll-a (mg/m3) from 1998-2018, with values presented as fraction of a year. Data products generated by the Ocean Colour component of the European Space Agency (ESA) Climate Change Initiative (CCI) project. These files are 8-day 4-km composites of merged sensor products: Global Area Coverage (GAC), Local Area Coverage (LAC), MEdium Resolution Imaging Spectrometer (MERIS), Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua, Ocean and Land Colour Instrument (OLCI), Sea-viewing Wide Field-of-view Sensor (SeaWiFS), and Visible Infrared Imaging Radiometer Suite (VIIRS). The chlorophyll-a average annual frequency of anomalies was calculated by taking the average number of times that the 8-day time series exceeded the maximum monthly climatological chlorophyll-a value from 1998-2018 for each pixel. A quality control mask was applied to remove spurious data associated with shallow water, following Gove et al., 2013. Nearshore map pixels with no data were filled with values from the nearest neighboring valid offshore pixel by using a grid of points and the Near Analysis tool in ArcGIS then converting points to raster. Data source: https://oceanwatch.pifsc.noaa.gov/erddap/griddap/esa-cci-chla-8d-v5-0.graph

Chlorophyll-a Long-term Mean, 1998-2018 - American Samoa (as_noaa_all_chlor_avg)

Chlorophyll-a, is a widely used proxy for phytoplankton biomass and an indicator for changes in phytoplankton production. As an essential source of energy in the marine environment, the extent and availability of phytoplankton biomass can be highly influential for fisheries production and dictate trophic structure in marine ecosystems. Changes in phytoplankton biomass are predominantly effected by changes in nutrient availability, through either natural (e.g., turbulent ocean mixing) or anthropogenic (e.g., agricultural runoff) processes. This layer represents the mean of the 8-day time series of chlorophyll-a (mg/m3) from 1998-2018. Data products generated by the Ocean Colour component of the European Space Agency (ESA) Climate Change Initiative (CCI) project. These files are 8-day 4-km composites of merged sensor products: Global Area Coverage (GAC), Local Area Coverage (LAC), MEdium Resolution Imaging Spectrometer (MERIS), Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua, Ocean and Land Colour Instrument (OLCI), Sea-viewing Wide Field-of-view Sensor (SeaWiFS), and Visible Infrared Imaging Radiometer Suite (VIIRS). The long-term mean was calculated by taking the average of all 8-day data from 1998-2018 for each pixel. A quality control mask was applied to remove spurious data associated with shallow water, following Gove et al., 2013. Nearshore map pixels with no data were filled with values from the nearest neighboring valid offshore pixel by using a grid of points and the Near Analysis tool in ArcGIS then converting points to raster. Data source: https://oceanwatch.pifsc.noaa.gov/erddap/griddap/esa-cci-chla-8d-v5-0.graph

Chlorophyll-a Standard Deviation of Long-Term Mean, 1998-2018 - American Samoa (as_noaa_all_chlor_std)

Chlorophyll-a, is a widely used proxy for phytoplankton biomass and an indicator for changes in phytoplankton production. As an essential source of energy in the marine environment, the extent and availability of phytoplankton biomass can be highly influential for fisheries production and dictate trophic structure in marine ecosystems. Changes in phytoplankton biomass are predominantly effected by changes in nutrient availability, through either natural (e.g., turbulent ocean mixing) or anthropogenic (e.g., agricultural runoff) processes. This layer represents the standard deviation of the 8-day time series of chlorophyll-a (mg/m3) from 1998-2018. Data products generated by the Ocean Colour component of the European Space Agency (ESA) Climate Change Initiative (CCI) project. These files are 8-day 4-km composites of merged sensor products: Global Area Coverage (GAC), Local Area Coverage (LAC), MEdium Resolution Imaging Spectrometer (MERIS), Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua, Ocean and Land Colour Instrument (OLCI), Sea-viewing Wide Field-of-view Sensor (SeaWiFS), and Visible Infrared Imaging Radiometer Suite (VIIRS). The standard deviation was calculated over all 8-day chlorophyll-a data from 1998-2018 for each pixel. A quality control mask was applied to remove spurious data associated with shallow water, following Gove et al., 2013. Nearshore map pixels with no data were filled with values from the nearest neighboring valid offshore pixel by using a grid of points and the Near Analysis tool in ArcGIS then converting points to raster. Data source: https://oceanwatch.pifsc.noaa.gov/erddap/griddap/esa-cci-chla-8d-v5-0.graph

Photosynthetically Active Radiation (PAR) Average Annual Frequency of Anomalies, 2003-2018 - American Samoa (as_noaa_all_par_anom_freq)

Solar irradiance is one of the most important factors influencing coral reefs. As a majority of their nutrients are obtained from symbiotic photosynthesizing organisms, reef-building corals need sunlight as a fundamental source of energy. Seasonally low irradiance at high latitudes may be linked to reduced growth rates in corals and may limit reef calcification to shallower depths than that observed at lower latitudes. However, high levels of irradiance can lead to light-induced damage, production of free radicals, and in combination with increased temperatures, can exacerbate coral bleaching. Irradiance is here represented by PAR (photosynthetically active radiation), which is the spectrum of light that is important for photosynthesis. This layer represents the annual average number of anomalies of PAR (mol/m2/day) from 2003-2018, with values presented as fraction of a year. Data for PAR for the time period 2003-2018 were obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua satellite instrument from the NASA OceanColor website as 8-day 4-km composites. The PAR average annual frequency of anomalies was calculated by taking the average number of weeks that exceeded the maximum monthly climatological PAR value from 2003-2018 for each pixel. A quality control mask was applied to remove spurious data associated with shallow water, following Gove et al., 2013. Time series of anomalies were calculated by quantifying the number and magnitude of events from the 8-day time series that exceed the maximum climatological monthly mean. Nearshore map pixels with no data were filled with values from the nearest neighboring valid offshore pixel by using a grid of points and the Near Analysis tool in ArcGIS then converting points to raster. Data source: https://oceanwatch.pifsc.noaa.gov/erddap/griddap/aqua_par_8d_2018_0.graph

Photosynthetically Active Radiation (PAR) Long-term Mean, 2003-2018 - American Samoa (as_noaa_all_par_avg)

Solar irradiance is one of the most important factors influencing coral reefs. As a majority of their nutrients are obtained from symbiotic photosynthesizing organisms, reef-building corals need sunlight as a fundamental source of energy. Seasonally low irradiance at high latitudes may be linked to reduced growth rates in corals and may limit reef calcification to shallower depths than that observed at lower latitudes. However, high levels of irradiance can lead to light-induced damage, production of free radicals, and in combination with increased temperatures, can exacerbate coral bleaching. Irradiance is here represented by PAR (photosynthetically active radiation), which is the spectrum of light that is important for photosynthesis. This layer represents the mean of 8-day time series of PAR (mol/m2/day) from 2003-2018. Data for PAR for the time period 2003-2018 were obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua satellite instrument from the NASA OceanColor website as 8-day 4-km composites. The PAR long-term mean was calculated by taking the average of all 8-day data from 2003-2018 for each pixel. A quality control mask was applied to remove spurious data associated with shallow water, following Gove et al., 2013. Nearshore map pixels with no data were filled with values from the nearest neighboring valid offshore pixel by using a grid of points and the Near Analysis tool in ArcGIS then converting points to raster. Data source: https://oceanwatch.pifsc.noaa.gov/erddap/griddap/aqua_par_8d_2018_0.graph

Photosynthetically Active Radiation (PAR) Standard Deviation of Long-term Mean, 2003-2018 - American Samoa (as_noaa_all_par_std)

Solar irradiance is one of the most important factors influencing coral reefs. As a majority of their nutrients are obtained from symbiotic photosynthesizing organisms, reef-building corals need sunlight as a fundamental source of energy. Seasonally low irradiance at high latitudes may be linked to reduced growth rates in corals and may limit reef calcification to shallower depths than that observed at lower latitudes. However, high levels of irradiance can lead to light-induced damage, production of free radicals, and in combination with increased temperatures, can exacerbate coral bleaching. Irradiance is here represented by PAR (photosynthetically active radiation), which is the spectrum of light that is important for photosynthesis. This layer represents the standard deviation of the 8-day time series of PAR (mol/m2/day) from 2003-2018. Data for PAR for the time period 2003-2018 were obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua satellite instrument from the NASA OceanColor website as 8-day 4-km composites. The standard deviation of the long-term mean of PAR was calculated by taking the standard deviation over all 8-day data from 2003-2018 for each pixel. A quality control mask was applied to remove spurious data associated with shallow water, following Gove et al., 2013. Nearshore map pixels with no data were filled with values from the nearest neighboring valid offshore pixel by using a grid of points and the Near Analysis tool in ArcGIS then converting points to raster. Data source: https://oceanwatch.pifsc.noaa.gov/erddap/griddap/aqua_par_8d_2018_0.graph

Sea Surface Temperature (SST) Average Annual Frequency of Anomalies, 1985-2018 - American Samoa (as_noaa_all_sst_anom_freq)

Sea surface temperature (SST) plays an important role in a number of ecological processes and can vary over a wide range of time scales, from daily to decadal changes. SST influences primary production, species migration patterns, and coral health. If temperatures are anomalously warm for extended periods, drastic changes in the surrounding ecosystem can result, including harmful effects such as coral bleaching. This layer represents the annual average frequency of anomalies of SST from 1985-2018, with values presented as fraction of a year. These SST dataset are derived from CoralTemp 5-km gap-free analyzed blended sea surface temperature over the global ocean. CoralTemp is derived from three different but related 5-km daily gap-free SST data sets and provides an internally consistent SST product that stretches from 1985 to present. 1) Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) Sea Surface Temperature Reanalysis (1985-2002). 2) Geo-Polar Blended Night-Only Sea Surface Temperature Reanalysis (2002-2016). 3) Geo-Polar Blended Night-Only Sea Surface Temperature Near Real-Time (2017 to present). The 8-day composites are generated from daily Coral Reef Watch (CRW) files by OceanWatch Central Pacific. The SST average annual frequency of anomalies was calculated by taking the average number of weeks that exceeded the maximum monthly climatological SST value from 1985-2018 for each pixel. Data source: https://oceanwatch.pifsc.noaa.gov/erddap/griddap/CRW_sst_v1_0_8day.graph

Sea Surface Temperature (SST) Long-term Mean, 1985-2018 - American Samoa (as_noaa_all_sst_avg)

Sea surface temperature (SST) plays an important role in a number of ecological processes and can vary over a wide range of time scales, from daily to decadal changes. SST influences primary production, species migration patterns, and coral health. If temperatures are anomalously warm for extended periods, drastic changes in the surrounding ecosystem can result, including harmful effects such as coral bleaching. This layer represents the mean SST (degrees Celsius) of the weekly time series from 1985-2018. These SST dataset are derived from CoralTemp 5-km gap-free analyzed blended sea surface temperature over the global ocean. CoralTemp is derived from three different but related 5-km daily gap-free SST data sets and provides an internally consistent SST product that stretches from 1985 to present. 1) Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) Sea Surface Temperature Reanalysis (1985-2002). 2) Geo-Polar Blended Night-Only Sea Surface Temperature Reanalysis (2002-2016). 3) Geo-Polar Blended Night-Only Sea Surface Temperature Near Real-Time (2017 to present). The 8-day composites are generated from daily Coral Reef Watch (CRW) files by OceanWatch Central Pacific. The SST long-term mean was calculated by taking the average of all weekly data from 1985-2018 for each pixel. Data source: https://oceanwatch.pifsc.noaa.gov/erddap/griddap/CRW_sst_v1_0_8day.graph

Sea Surface Temperature (SST) Standard Deviation of Long-term Mean, 1985-2018 - American Samoa (as_noaa_all_sst_std)

Sea surface temperature (SST) plays an important role in a number of ecological processes and can vary over a wide range of time scales, from daily to decadal changes. SST influences primary production, species migration patterns, and coral health. If temperatures are anomalously warm for extended periods, drastic changes in the surrounding ecosystem can result, including harmful effects such as coral bleaching. This layer represents the standard deviation of SST (degrees Celsius) of the weekly time series from 1985-2018. These SST dataset are derived from CoralTemp 5-km gap-free analyzed blended sea surface temperature over the global ocean. CoralTemp is derived from three different but related 5-km daily gap-free SST data sets and provides an internally consistent SST product that stretches from 1985 to present. 1) Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) Sea Surface Temperature Reanalysis (1985-2002). 2) Geo-Polar Blended Night-Only Sea Surface Temperature Reanalysis (2002-2016). 3) Geo-Polar Blended Night-Only Sea Surface Temperature Near Real-Time (2017 to present). The 8-day composites are generated from daily Coral Reef Watch (CRW) files by OceanWatch Central Pacific. The standard deviation of the long-term mean SST was calculated by taking the standard deviation over all weekly data from 1985-2018 for each pixel. Data source: https://oceanwatch.pifsc.noaa.gov/erddap/griddap/CRW_sst_v1_0_8day.graph

Turbidity (Kd490) Average Annual Maximum Anomaly, 1998-2018 - American Samoa (as_noaa_all_turb_anom_max)

Spectrally resolved water-leaving radiances (ocean color) and inferred chlorophyll concentration are key to studying phytoplankton dynamics at seasonal and inter-annual scales, for a better understanding of the role of phytoplankton in marine biogeochemistry; the global carbon cycle; and the response of marine ecosystems to climate variability, change, and feedback processes. Ocean color data also have a critical role in operational observation systems monitoring coastal eutrophication, harmful algal blooms, and sediment plumes. The contiguous ocean color record reached 21 years in 2018. However, it is comprised of a number of one-off missions such that creating a consistent time series of ocean color data requires merging of the individual sensors without introducing artifacts. The diffuse attenuation coefficient at 490 nm (Kd490) indicates the turbidity of the water column: i.e., how well visible light in the blue to green region of the spectrum penetrates the water column. The value of Kd490 represents the rate at which light at 490 nm is attenuated with depth. For example, a Kd490 of 0.1 per meter means that light intensity is reduced by one natural log within 10 meters of water. Thus, for a Kd490 of 0.1, one attenuation length is 10 meters. Higher Kd490 values mean shallower attenuation depths and thus higher turbidity, or lower clarity, of ocean water. This layer represents the annual average of the maximum anomaly of Kd490 (m-1) from 1998-2018. Data products generated by the Ocean Colour component of the European Space Agency (ESA) Climate Change Initiative (CCI) project. These files are 8-day 4-km composites of merged sensor products: Global Area Coverage (GAC), Local Area Coverage (LAC), MEdium Resolution Imaging Spectrometer (MERIS), Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua, Ocean and Land Colour Instrument (OLCI), Sea-viewing Wide Field-of-view Sensor (SeaWiFS), and Visible Infrared Imaging Radiometer Suite (VIIRS). The Kd490 average annual maximum anomaly was calculated by taking the average of the Kd490 values from the 8-day time series in exceedance of the maximum monthly climatological Kd490 from 1998-2018 for each pixel. A quality control mask was applied to remove spurious data associated with shallow water, following Gove et al., 2013. Time series of anomalies were calculated by quantifying the number and magnitude of events from the 8-day time series that exceed the maximum climatological monthly mean. Nearshore map pixels with no data were filled with values from the nearest neighboring valid offshore pixel by using a grid of points and the Near Analysis tool in ArcGIS then converting points to raster. Data source: https://oceanwatch.pifsc.noaa.gov/erddap/griddap/esa-cci-kd-8d-v5-0.graph

Turbidity (Kd490) Maximum Monthly Climatological Mean, 1998-2018 - American Samoa (as_noaa_all_turb_clim_max)

Spectrally resolved water-leaving radiances (ocean color) and inferred chlorophyll concentration are key to studying phytoplankton dynamics at seasonal and inter-annual scales, for a better understanding of the role of phytoplankton in marine biogeochemistry; the global carbon cycle; and the response of marine ecosystems to climate variability, change, and feedback processes. Ocean color data also have a critical role in operational observation systems monitoring coastal eutrophication, harmful algal blooms, and sediment plumes. The contiguous ocean color record reached 21 years in 2018. However, it is comprised of a number of one-off missions such that creating a consistent time series of ocean color data requires merging of the individual sensors without introducing artifacts. The diffuse attenuation coefficient at 490 nm (Kd490) indicates the turbidity of the water column: i.e., how well visible light in the blue to green region of the spectrum penetrates the water column. The value of Kd490 represents the rate at which light at 490 nm is attenuated with depth. For example, a Kd490 of 0.1 per meter means that light intensity is reduced by one natural log within 10 meters of water. Thus, for a Kd490 of 0.1, one attenuation length is 10 meters. Higher Kd490 values mean shallower attenuation depths and thus higher turbidity, or lower clarity, of ocean water. This layer represents the maximum monthly climatological mean of Kd490 (m-1) from 1998-2018. Data products generated by the Ocean Colour component of the European Space Agency (ESA) Climate Change Initiative (CCI) project. These files are 8-day 4-km composites of merged sensor products: Global Area Coverage (GAC), Local Area Coverage (LAC), MEdium Resolution Imaging Spectrometer (MERIS), Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua, Ocean and Land Colour Instrument (OLCI), Sea-viewing Wide Field-of-view Sensor (SeaWiFS), and Visible Infrared Imaging Radiometer Suite (VIIRS). A quality control mask was applied to remove spurious data associated with shallow water, following Gove et al., 2013. Monthly climatologies were calculated from monthly time series averaging for all same-months (e.g., January). Nearshore map pixels with no data were filled with values from the nearest neighboring valid offshore pixel by using a grid of points and the Near Analysis tool in ArcGIS then converting points to raster. Data source: https://oceanwatch.pifsc.noaa.gov/erddap/griddap/esa-cci-kd-8d-v5-0.graph

Wave Power Long-term Mean, 2002-2012 - American Samoa (as_noaa_all_wave_avg)

Wave power is a major environmental forcing mechanism that influences a number of marine ecosystem processes including coral reef community development, structure, and persistence. By driving mixing of the upper water column, wave forcing can also play a role in nutrient availability and ocean temperature reduction during warming events. Wave forcing can be highly seasonal, with winter months typically experiencing far greater wave power than that experienced during the summer months. This layer represents the mean of maximum daily wave power (kW/m) from 2002-2012. In the absence of numerical wave model and wave forcing observational site-level data at the desired spatial resolution, a wave exposure proxy, developed by S. Jeanette Clark, is used to examine wave exposure. Wave energy estimates were derived at 1-km resolution utilizing NOAA WaveWatch III (WW3) global 0.5-deg wave model data and coastline analysis of wave exposure. This is achieved by: 1) Determining the incident wave swath for a specific site at an island using a 360-degree radial plot and degree-bin elimination based on a swath's intersection with land or relevant bathymetric contour. 2) Selecting the closest WW3 pixel and extracting the time series for significant wave height, peak period, and peak direction. 3) Calculating wave power (kW/m) with significant wave height and peak period using the following equation: Ef = pg / 64pi * Hs^2 * Tp / 1000 where p is the density of sea water (1024 kg m-3), g is the acceleration of gravity (9.8 m s-2), Hs is the offshore significant wave height, and Tp is the dominant wave period (1/wavelength). 4) Lastly, annual wave power data are filtered and organized into respective degree bins based on peak direction and summed to give a wave power estimate at each site. The wave power metric calculated here is based on offshore wave height and does not account for variation with depth.

NOAA Shallow-Water Benthic Habitats: American Samoa: Rose Atoll (as_noaa_ros_benthic_habitats)

Benthic habitat maps for the nearshore, shallow (< 30 m) coastal waters of Rose Atoll in American Samoa. NOAA's National Centers for Coastal Ocean Science (NCCOS) produced these data to support coral reef research and management. Habitat regions were digitally identified using visual interpretation of orthorectified satellite imagery with a minimum mapping unit (MMU) of approximately 1 acre. Includes biological cover types, geomorphological structure types, and geographic zones. Eighteen distinct and non-overlapping biological cover types were identified. Habitats or features that cover areas smaller than the minimal mapping unit of 1 acre were not considered. For example, uncolonized sand halos surrounding coral patch reefs are too small to be mapped independently. Cover type refers only to the predominant biological component colonizing the surface of the feature and does not address location (e.g., on the shelf or in the lagoon). The cover types are defined in a collapsible hierarchy ranging from eight major classes (live coral, seagrass, macroalgae, encrusting/coralline algae, turf algae, emergent vegetation, uncolonized, and unknown), combined with a density modifier representing the percentage of the predominant cover type (10%-<50% sparse, 50%-<90% patchy, 90%-100% continuous). Similarly, 14 distinct and non-overlapping geomorphological structure types were identified. The structure types are defined in a collapsible hierarchy ranging from four major classes (coral reef and hardbottom, unconsolidated sediment, other delineations, and unknown), to thirteen detailed classes: sand, mud, spur and groove, individual and aggregated patch reef, aggregate reef, scattered coral/rock in unconsolidated sediment, pavement, rock/boulder (volcanic and carbonate), reef rubble, pavement with sand channels, artificial, and unknown. Lastly, 13 mutually exclusive geographic zones were identified from land to open water corresponding to typical insular shelf and coral reef geomorphology. These zones include: shoreline intertidal, vertical wall (none identified), lagoon, back reef, reef flat, reef crest, fore reef, bank/shelf, bank/shelf escarpment, channel, dredged (since this condition eliminates natural geomorphology), unknown, and land. Zone refers only to each benthic community's location and does not address substrate or cover types within. For example, the lagoon zone may include patch reefs, sand, and seagrass beds; however, these are considered structural elements that may or may not occur within the lagoon zone and therefore, are not used to define it.

Channel Exclusion - Swains, American Samoa (as_noaa_swa_channel_exclusion)

The boundaries of the National Marine Sanctuary of American Samoa exclude these two channels at Swains Island to provide access to the island.

Coral Resilience By NOAA NCRMP Sector - American Samoa (as_noaa_tutma_coral_resilience)

Records of coral cover from the recent past can inform management strategies for reef restoration and protection. When combined with data on where current or future environmental conditions are most favorable, we can learn where corals are thriving because of or in spite of a healthy marine environment. The National Oceanic and Atmospheric Administration (NOAA) regularly surveys the health of coral reefs in the Pacific Islands as part of the National Coral Reef Monitoring Program (NCRMP). Divers record coral cover at a series of sites across different reef zones and depths. These surveys are then aggregated across spatial sectors, which divide the waters around the island into ecological units useful for management and monitoring. Resilience can be defined as the ability of a system to resist change during a disturbance or as the ability to recover quickly after a disturbance-induced change. This project analyzed trends in coral cover from the NCRMP from 2009 to 2018 to identify sectors that demonstrated either of these criteria for resilience. Coral sectors that maintained stable coral cover at relatively high levels were considered highly resilient. Sectors that demonstrated relatively rapid increases in coral cover over time were considered moderately resilient, and sectors that lost coral cover were considered to have low resilience. This project examined how the spatial distribution of highly resilient sectors related to areas with high environmental favorability. This layer represents geospatial polygons of the NCRMP coral sectors divided into three categories: high, moderate, and low coral resilience.

Volcanic Point Features - American Samoa (as_nps_all_volcanpts)

Volcanic Point Features of American Samoa

NOAA/PIBHMC 5-m Bathymetry: American Samoa: Northeast Bank: Hillshade (as_pibhmc_neb_bathy5m_hillshade)

A 5-m resolution gridded digital elevation model (DEM) grayscale hillshade for the bathymetry (ocean depth) surrounding Northeast Bank in American Samoa compiled from ship-borne multibeam sonar surveys. Northeast Bank (also called Muli Seamount) lies approximately 60 km northeast of Tutuila and 50 km northwest of Ofu Island. Almost complete bottom coverage was achieved in depths between 48 and 150 m. Data collected by the Pacific Islands Benthic Habitat Mapping Center (PIBHMC) in support of NOAA's Coral Reef Conservation Program. Not to be used for navigational purposes. For the related dataset containing numeric bathymetry values for this image layer, see http://pacioos.org/metadata/pibhmc_bathy_5m_nebank.html

NOAA/PIBHMC 40-m Bathymetry: American Samoa: Rose Atoll: Hillshade (as_pibhmc_ros_bathy40m_hillshade)

A 40-m resolution gridded digital elevation model (DEM) grayscale hillshade for the bathymetry (ocean depth) surrounding Rose Atoll in American Samoa compiled from ship-borne multibeam sonar surveys. Almost complete bottom coverage was achieved in depths between 2 and 4655 m. Data collected by the Pacific Islands Benthic Habitat Mapping Center (PIBHMC) in support of NOAA's Coral Reef Conservation Program. Not to be used for navigational purposes. For the related dataset containing numeric bathymetry values for this image layer, see http://pacioos.org/metadata/pibhmc_bathy_40m_rose.html

Bathymetric Position Index (BPI) Structures, 40m - Swains, American Samoa (as_pibhmc_swa_40m_bpi_structures)

Bathymetric position index (BPI) structures are derived from gridded (40 m cell size) multibeam bathymetry, collected aboard R/V AHI and NOAA ship Hi'ialakai, and it was created using the Benthic Terrain Modeler. Cell values represent one of 13 classes in an index of seafloor terrains. This data set is for Swains Island, American Samoa.

Hard-Soft Seafloor Classification, 40m - Swains, American Samoa (as_pibhmc_swa_40m_hardsoft)

Preliminary hard and soft seafloor substrate map derived from an unsupervised classification of multibeam backscatter and bathymetry derivatives at Swains Island, American Samoa. The dataset was created from gridded (40 m cell size) multibeam bathymetry derivatives collected aboard R/V AHI, and NOAA ship Hi'ialakai; two scales of bathymetric variance and bathymetric rugosity. Backscatter data were from a 300 kHz Simrad EM300 and a 240 kHz Reson 8101 sonar, gridded at 5 m. Very limited seafloor photographs for groundtruthing are available for Swains Island and therefore no supervised classification was performed and we are unable to visually or empirically evaluate the accuracy of the unsupervised classification seafloor substrate map. However, in locations such French Frigate Shoals, NWHI, and Tutuila, American Samoa, where ground truth data are available, the unsupervised classification method is a robust predictor of substrate type in similar depth ranges and seafloor environments. Since groundtruthing was not used to validate the unsupervised classification at Swains Island extreme caution should be used when examining these data to locate habitat of biological significance. The map should be used in conjunction with bathymetric derivatives such as rugosity, slope, and Bathymetric Position Index (BPI).

Seafloor Slope, 40m - Swains, American Samoa (as_pibhmc_swa_40m_slope)

Slope is derived from gridded (40 m cell size) multibeam bathymetry, collected aboard R/V AHI, and NOAA ship Hi'ialakai. Cell values reflect the maximum rate of change (in degrees) in elevation between neighboring cells derived with the ArcGIS Spatial Analyst extension. This data set is for Swains Island, American Samoa.

NOAA/PIBHMC 40-m Bathymetry: American Samoa: Swains: Hillshade (as_pibhmc_swa_bathy40m_hillshade)

A 40-m resolution gridded digital elevation model (DEM) for the bathymetry (ocean depth) surrounding Swains Island in American Samoa compiled from ship-borne multibeam sonar surveys. Almost complete bottom coverage was achieved in depths between 7 and 4800 m. Data collected by the Pacific Islands Benthic Habitat Mapping Center (PIBHMC) in support of NOAA's Coral Reef Conservation Program. Not to be used for navigational purposes. For the related dataset containing numeric bathymetry values for this image layer, see http://pacioos.org/metadata/pibhmc_bathy_40m_swains.html

NOAA/PIBHMC 5-m Bathymetry: American Samoa: Tau: Hillshade (as_pibhmc_tau_bathy5m_hillshade)

A 5-m resolution gridded digital elevation model (DEM) grayscale hillshade for the bathymetry (ocean depth) surrounding Tau Island in American Samoa compiled from ship-borne multibeam sonar surveys merged with coastal bathymetry derived from multipectral IKONOS satellite data. Almost complete bottom coverage was achieved in depths between 0 and 350 m. Data collected by the Pacific Islands Benthic Habitat Mapping Center (PIBHMC) in support of NOAA's Coral Reef Conservation Program. Not to be used for navigational purposes. For the related dataset containing numeric bathymetry values for this image layer, see http://pacioos.org/metadata/pibhmc_bathy_5m_tau.html

Bathymetric Position Index (BPI) Zones, 5m - Tutuila, American Samoa (as_pibhmc_tut_5m_bpi_zones)

Bathymetric position index (BPI) zones are derived from a focal mean analysis on bathymetry and slope. The grid is based on gridded (5 m cell size) multibeam bathymetry, collected aboard NOAA Ship Hi'ialakai and R/V AHI, and it was creating using the Benthic Terrain Modeler. Cell values represent one of 4 classes in an index of seafloor terrains. This data set is for the shelf and slope environments of Tutuila Island, American Samoa, South Pacific.

Hard-Soft Seafloor Classification, 5m (Simrad) - Tutuila, American Samoa (as_pibhmc_tut_5m_hardsoft_simrad)

Preliminary hard and soft seafloor substrate map derived from an unsupervised classification of multibeam backscatter and bathymety derivatives at Tutuila Island, American Samoa, South Pacific. The dataset was derived using a combination of Simrad em3002d backscatter, bathymetric variance, and bathymetric rugosity. The sonar frequencies was 300 kHz and all data were resampled to 5 m grid cell size prior to the classification. Initial supervised classifications of the backscatter data into hard and soft seafloor substrates, using seafloor photographs for groundtruthing and to define regions of interest, were used to define unsupervised class types and to visually evaluate the accuracy of the unsupervised classification seafloor substrate map.

Seafloor Slope, 5m - Tutuila, American Samoa (as_pibhmc_tut_5m_slope)

Slope is derived from gridded (5 m cell size) multibeam bathymetry, collected aboard NOAA Ship Hiialaka'i and R/V AHI, Cell values reflect the maximum rate of change (in degrees) in elevation between neighboring cells derived with the ArcGIS Spatial Analyst extension. This data set is for the shelf and slope environments of Tutuila Island, American Samoa, South Pacific.

NOAA/PIBHMC 40-m Bathymetry: American Samoa: Two Percent Bank: Hillshade (as_pibhmc_two_bathy40m_hillshade)

A 40-m resolution gridded digital elevation model (DEM) grayscale hillshade for the bathymetry (ocean depth) surrounding Two Percent Bank in American Samoa compiled from ship-borne multibeam sonar surveys. Two Percent Bank (also called Tulaga Seamount) lies approximately 70 km southeast of Tutuila and 60 km southwest of Ofu Island. Almost complete bottom coverage was achieved in depths between 78 and 2221 m. Data collected by the Pacific Islands Benthic Habitat Mapping Center (PIBHMC) in support of NOAA's Coral Reef Conservation Program. Not to be used for navigational purposes. For the related dataset containing numeric bathymetry values for this image layer, see http://pacioos.org/metadata/pibhmc_bathy_40m_twoperbank.html

Shoreline - American Samoa (as_rd_all_shore)

Shoreline of American Samoa

Sea Level Rise: American Samoa: High-Tide Flooding: 10-Ft Scenario (as_uhslc_all_slr_flood_10ft_v2)

This high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. As an alternative to the SLR scenarios in other data layers that we provide, our project also provides the ability to select specific amounts of SLR in increments of one foot, independent of any particular scenario. This information can be used if guidance for a project requires planning for a specific amount of SLR rather than a time horizon. The present layer models a sea level rise of 10 feet (305 cm). We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: High-Tide Flooding: 2030 High ScenarioSea Level Rise: American Samoa: High-Tide Flooding: 2030 High Scenario (as_uhslc_all_slr_flood_2030_high_v2)

This high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. In the 2030 high scenario represented here, the modeled water level is 36 cm (9 cm for Rose and Swains). In this scenario, world-wide society continues to increase emissions. Tipping points (large and sudden changes) are triggered, and worst-case possibilities arise. It is recommended using this scenario for planning construction of infrastructure with highly critical use and longer lifespans, such as a new hospital. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: High-Tide Flooding: 2030 Intermediate-High Scenario (as_uhslc_all_slr_flood_2030_inthigh_v2)

This high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. In the 2030 intermediate-high scenario represented here, the modeled water level is 35 cm (8 cm for Rose and Swains). In this scenario, world-wide society continues to increase emissions rate. Tipping points, i.e. large and sudden changes, are triggered; ice loss increases rapidly but is not catastrophic. It is recommended using this scenario for planning construction of infrastructure with medium-to-high critical use and longer lifespans, such as a new government office. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: High-Tide Flooding: 2030 Low Scenario (as_uhslc_all_slr_flood_2030_low_v2)

This high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. In the 2030 low scenario represented here, the modeled water level is 31 cm (4 cm for Rose and Swains). In this scenario, significant world-wide emissions reductions are implemented now, which is highly unlikely. It is not recommended to use this scenario for planning purposes. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: High-Tide Flooding: 2040 Intermediate Scenario (as_uhslc_all_slr_flood_2040_int_v2)

This high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. In the 2040 intermediate scenario represented here, the modeled water level is 44 cm (13 cm for Rose and Swains). In this scenario, world-wide society continues current emissions rates, and sea level rises at increased rates compared to the intermediate-low scenario. Tipping points, i.e. large and sudden changes, are still not crossed. It is recommended using this scenario for planning construction of infrastructure with low-to-medium critical use and lifespans extending into the second half of the century, such as a new storefront. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: High-Tide Flooding: 2040 Intermediate-Low Scenario (as_uhslc_all_slr_flood_2040_intlow_v2)

This high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. In the 2040 intermediate-low scenario represented here, the modeled water level is 42 cm (10 cm for Rose and Swains). In this scenario, world-wide society limits increase of emissions, and sea level rises without reaching any tipping points, i.e. large and sudden changes such as a rapid increase in ice sheets melting. It is recommended to use this scenario only for planning construction of non-critical infrastructure that owners can afford to lose, such as a beach "fale". Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: High-Tide Flooding: 2050 High Scenario (as_uhslc_all_slr_flood_2050_high_v2)

This high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. In the 2050 high scenario represented here, the modeled water level is 69 cm (35 cm for Rose and Swains). In this scenario, world-wide society continues to increase emissions. Tipping points (large and sudden changes) are triggered, and worst-case possibilities arise. It is recommended using this scenario for planning construction of infrastructure with highly critical use and longer lifespans, such as a new hospital. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: High-Tide Flooding: 2050 Intermediate-High Scenario (as_uhslc_all_slr_flood_2050_inthigh_v2)

This high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. In the 2050 intermediate-high scenario represented here, the modeled water level is 62 cm (29 cm for Rose and Swains). In this scenario, world-wide society continues to increase emissions rate. Tipping points, i.e. large and sudden changes, are triggered; ice loss increases rapidly but is not catastrophic. It is recommended using this scenario for planning construction of infrastructure with medium-to-high critical use and longer lifespans, such as a new government office. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: High-Tide Flooding: 2050 Low Scenario (as_uhslc_all_slr_flood_2050_low_v2)

This high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. In the 2050 low scenario represented here, the modeled water level is 44 cm (10 cm for Rose and Swains). In this scenario, significant world-wide emissions reductions are implemented now, which is highly unlikely. It is not recommended to use this scenario for planning purposes. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: High-Tide Flooding: 2060 Intermediate Scenario (as_uhslc_all_slr_flood_2060_int_v2)

This high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. In the 2060 intermediate scenario represented here, the modeled water level is 65 cm (30 cm for Rose and Swains). In this scenario, world-wide society continues current emissions rates, and sea level rises at increased rates compared to the intermediate-low scenario. Tipping points, i.e. large and sudden changes, are still not crossed. It is recommended using this scenario for planning construction of infrastructure with low-to-medium critical use and lifespans extending into the second half of the century, such as a new storefront. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: High-Tide Flooding: 2060 Intermediate-Low Scenario (as_uhslc_all_slr_flood_2060_intlow_v2)

This high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. In the 2060 intermediate-low scenario represented here, the modeled water level is 56 cm (21 cm for Rose and Swains). In this scenario, world-wide society limits increase of emissions, and sea level rises without reaching any tipping points, i.e. large and sudden changes such as a rapid increase in ice sheets melting. It is recommended to use this scenario only for planning construction of non-critical infrastructure that owners can afford to lose, such as a beach "fale". Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: High-Tide Flooding: 2070 High Scenario (as_uhslc_all_slr_flood_2070_high_v2)

This high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. In the 2070 high scenario represented here, the modeled water level is 128 cm (92 cm for Rose and Swains). In this scenario, world-wide society continues to increase emissions. Tipping points (large and sudden changes) are triggered, and worst-case possibilities arise. It is recommended using this scenario for planning construction of infrastructure with highly critical use and longer lifespans, such as a new hospital. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: High-Tide Flooding: 2070 Intermediate-High Scenario (as_uhslc_all_slr_flood_2070_inthigh_v2)

This high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. In the 2070 intermediate-high scenario represented here, the modeled water level is 104 cm (69 cm for Rose and Swains). In this scenario, world-wide society continues to increase emissions rate. Tipping points, i.e. large and sudden changes, are triggered; ice loss increases rapidly but is not catastrophic. It is recommended using this scenario for planning construction of infrastructure with medium-to-high critical use and longer lifespans, such as a new government office. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: High-Tide Flooding: 2070 Low Scenario (as_uhslc_all_slr_flood_2070_low_v2)

This high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. In the 2070 low scenario represented here, the modeled water level is 53 cm (17 cm for Rose and Swains). In this scenario, significant world-wide emissions reductions are implemented now, which is highly unlikely. It is not recommended to use this scenario for planning purposes. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: High-Tide Flooding: 2080 Intermediate Scenario (as_uhslc_all_slr_flood_2080_int_v2)

This high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. In the 2080 intermediate scenario represented here, the modeled water level is 92 cm (56 cm for Rose and Swains). In this scenario, world-wide society continues current emissions rates, and sea level rises at increased rates compared to the intermediate-low scenario. Tipping points, i.e. large and sudden changes, are still not crossed. It is recommended using this scenario for planning construction of infrastructure with low-to-medium critical use and lifespans extending into the second half of the century, such as a new storefront. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: High-Tide Flooding: 2080 Intermediate-Low Scenario (as_uhslc_all_slr_flood_2080_intlow_v2)

This high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. In the 2080 intermediate-low scenario represented here, the modeled water level is 70 cm (34 cm for Rose and Swains). In this scenario, world-wide society limits increase of emissions, and sea level rises without reaching any tipping points, i.e. large and sudden changes such as a rapid increase in ice sheets melting. It is recommended to use this scenario only for planning construction of non-critical infrastructure that owners can afford to lose, such as a beach "fale". Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: High-Tide Flooding: 2090 High Scenario (as_uhslc_all_slr_flood_2090_high_v2)

This high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. In the 2090 high scenario represented here, the modeled water level is 208 cm (172 cm for Rose and Swains). In this scenario, world-wide society continues to increase emissions. Tipping points (large and sudden changes) are triggered, and worst-case possibilities arise. It is recommended using this scenario for planning construction of infrastructure with highly critical use and longer lifespans, such as a new hospital. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: High-Tide Flooding: 2090 Intermediate-High Scenario (as_uhslc_all_slr_flood_2090_inthigh_v2)

This high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. In the 2090 intermediate-high scenario represented here, the modeled water level is 162 cm (126 cm for Rose and Swains). In this scenario, world-wide society continues to increase emissions rate. Tipping points, i.e. large and sudden changes, are triggered; ice loss increases rapidly but is not catastrophic. It is recommended using this scenario for planning construction of infrastructure with medium-to-high critical use and longer lifespans, such as a new government office. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: High-Tide Flooding: 2090 Low Scenario (as_uhslc_all_slr_flood_2090_low_v2)

This high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. In the 2090 low scenario represented here, the modeled water level is 60 cm (24 cm for Rose and Swains). In this scenario, significant world-wide emissions reductions are implemented now, which is highly unlikely. It is not recommended to use this scenario for planning purposes. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: High-Tide Flooding: 2100 Intermediate Scenario (as_uhslc_all_slr_flood_2100_int_v2)

This high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. In the 2100 intermediate scenario represented here, the modeled water level is 136 cm (99 cm for Rose and Swains). In this scenario, world-wide society continues current emissions rates, and sea level rises at increased rates compared to the intermediate-low scenario. Tipping points, i.e. large and sudden changes, are still not crossed. It is recommended using this scenario for planning construction of infrastructure with low-to-medium critical use and lifespans extending into the second half of the century, such as a new storefront. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: High-Tide Flooding: 2100 Intermediate-Low Scenario (as_uhslc_all_slr_flood_2100_intlow_v2)

This high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. In the 2100 intermediate-low scenario represented here, the modeled water level is 85 cm (48 cm for Rose and Swains). In this scenario, world-wide society limits increase of emissions, and sea level rises without reaching any tipping points, i.e. large and sudden changes such as a rapid increase in ice sheets melting. It is recommended to use this scenario only for planning construction of non-critical infrastructure that owners can afford to lose, such as a beach "fale". Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: High-Tide Flooding: 2-Ft Scenario (as_uhslc_all_slr_flood_2ft_v2)

This high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. As an alternative to the SLR scenarios in other data layers that we provide, our project also provides the ability to select specific amounts of SLR in increments of one foot, independent of any particular scenario. This information can be used if guidance for a project requires planning for a specific amount of SLR rather than a time horizon. The present layer models a sea level rise of 2 feet (61 cm). We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: High-Tide Flooding: 4-Ft Scenario (as_uhslc_all_slr_flood_4ft_v2)

This high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. As an alternative to the SLR scenarios in other data layers that we provide, our project also provides the ability to select specific amounts of SLR in increments of one foot, independent of any particular scenario. This information can be used if guidance for a project requires planning for a specific amount of SLR rather than a time horizon. The present layer models a sea level rise of 4 feet (122 cm). We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: High-Tide Flooding: 6-Ft Scenario (as_uhslc_all_slr_flood_6ft_v2)

This high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. As an alternative to the SLR scenarios in other data layers that we provide, our project also provides the ability to select specific amounts of SLR in increments of one foot, independent of any particular scenario. This information can be used if guidance for a project requires planning for a specific amount of SLR rather than a time horizon. The present layer models a sea level rise of 6 feet (183 cm). We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: High-Tide Flooding: 8-Ft Scenario (as_uhslc_all_slr_flood_8ft_v2)

This high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. As an alternative to the SLR scenarios in other data layers that we provide, our project also provides the ability to select specific amounts of SLR in increments of one foot, independent of any particular scenario. This information can be used if guidance for a project requires planning for a specific amount of SLR rather than a time horizon. The present layer models a sea level rise of 8 feet (244 cm). We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 10-Ft Scenario: 1 Day Per Year (as_uhslc_all_slr_xflood_10ft_1day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. As an alternative to the SLR scenarios in other data layers that we provide, our project also provides the ability to select specific amounts of SLR in increments of one foot, independent of any particular scenario. This information can be used if guidance for a project requires planning for a specific amount of SLR rather than a time horizon. The present layer models a sea level rise of 10 feet (305 cm). We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of one flooding day per year, a good indicator of the flooding extent and depth during the most extreme "King Tide" annually. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 10-ft scenario represented here, the modeled water level for a 1-day frequency is 387 cm. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 10-Ft Scenario: 50 Days Per Year (as_uhslc_all_slr_xflood_10ft_50day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. As an alternative to the SLR scenarios in other data layers that we provide, our project also provides the ability to select specific amounts of SLR in increments of one foot, independent of any particular scenario. This information can be used if guidance for a project requires planning for a specific amount of SLR rather than a time horizon. The present layer models a sea level rise of 10 feet (305 cm). We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 50 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 10-ft scenario represented here, the modeled water level for a 50-day frequency is 369 cm. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 1-Ft Scenario: 20 Days Per Year (as_uhslc_all_slr_xflood_1ft_20day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. As an alternative to the SLR scenarios in other data layers that we provide, our project also provides the ability to select specific amounts of SLR in increments of one foot, independent of any particular scenario. This information can be used if guidance for a project requires planning for a specific amount of SLR rather than a time horizon. The present layer models a sea level rise of 1 foot (30 cm). We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 20 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 1-ft scenario represented here, the modeled water level for a 20-day frequency is 104 cm. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2030 High Scenario: 1 Day Per Year (as_uhslc_all_slr_xflood_2030_high_1day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of one flooding day per year, a good indicator of the flooding extent and depth during the most extreme "King Tide" annually. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2030 high scenario represented here, the modeled water level for a 1-day frequency is 123 cm (96 cm for Rose and Swains). In this scenario, world-wide society continues to increase emissions. Tipping points (large and sudden changes) are triggered, and worst-case possibilities arise. It is recommended using this scenario for planning construction of infrastructure with highly critical use and longer lifespans, such as a new hospital. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2030 High Scenario: 50 Days Per Year (as_uhslc_all_slr_xflood_2030_high_50day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 50 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2030 high scenario represented here, the modeled water level for a 50-day frequency is 105 cm (78 cm for Rose and Swains). In this scenario, world-wide society continues to increase emissions. Tipping points (large and sudden changes) are triggered, and worst-case possibilities arise. It is recommended using this scenario for planning construction of infrastructure with highly critical use and longer lifespans, such as a new hospital. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2030 Intermediate Scenario: 20 Days Per Year (as_uhslc_all_slr_xflood_2030_int_20day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 20 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2030 intermediate scenario represented here, the modeled water level for a 20-day frequency is 107 cm (80 cm for Rose and Swains). In this scenario, world-wide society continues current emissions rates, and sea level rises at increased rates compared to the intermediate-low scenario. Tipping points, i.e. large and sudden changes, are still not crossed. It is recommended using this scenario for planning construction of infrastructure with low-to-medium critical use and lifespans extending into the second half of the century, such as a new storefront. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2030 Intermediate-High Scenario: 1 Day Per Year (as_uhslc_all_slr_xflood_2030_inthigh_1day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of one flooding day per year, a good indicator of the flooding extent and depth during the most extreme "King Tide" annually. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2030 intermediate-high scenario represented here, the modeled water level for a 1-day frequency is 122 cm (95 cm for Rose and Swains). In this scenario, world-wide society continues to increase emissions rate. Tipping points, i.e. large and sudden changes, are triggered; ice loss increases rapidly but is not catastrophic. It is recommended using this scenario for planning construction of infrastructure with medium-to-high critical use and longer lifespans, such as a new government office. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2030 Intermediate-High Scenario: 50 Days Per Year (as_uhslc_all_slr_xflood_2030_inthigh_50day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 50 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2030 intermediate-high scenario represented here, the modeled water level for a 50-day frequency is 103 cm (76 cm for Rose and Swains). In this scenario, world-wide society continues to increase emissions rate. Tipping points, i.e. large and sudden changes, are triggered; ice loss increases rapidly but is not catastrophic. It is recommended using this scenario for planning construction of infrastructure with medium-to-high critical use and longer lifespans, such as a new government office. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2030 Intermediate-Low Scenario: 20 Days Per Year (as_uhslc_all_slr_xflood_2030_intlow_20day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 20 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2030 intermediate-low scenario represented here, the modeled water level for a 20-day frequency is 106 cm (80 cm for Rose and Swains). In this scenario, world-wide society limits increase of emissions, and sea level rises without reaching any tipping points, i.e. large and sudden changes such as a rapid increase in ice sheets melting. It is recommended to use this scenario only for planning construction of non-critical infrastructure that owners can afford to lose, such as a beach "fale". Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2030 Low Scenario: 1 Day Per Year (as_uhslc_all_slr_xflood_2030_low_1day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of one flooding day per year, a good indicator of the flooding extent and depth during the most extreme "King Tide" annually. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2030 low scenario represented here, the modeled water level for a 1-day frequency is 118 cm (91 cm for Rose and Swains). In this scenario, significant world-wide emissions reductions are implemented now, which is highly unlikely. It is not recommended to use this scenario for planning purposes. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2030 Low Scenario: 50 Days Per Year (as_uhslc_all_slr_xflood_2030_low_50day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 50 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2030 low scenario represented here, the modeled water level for a 50-day frequency is 99 cm (73 cm for Rose and Swains). In this scenario, significant world-wide emissions reductions are implemented now, which is highly unlikely. It is not recommended to use this scenario for planning purposes. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2040 High Scenario: 20 Days Per Year (as_uhslc_all_slr_xflood_2040_high_20day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 20 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2040 high scenario represented here, the modeled water level for a 20-day frequency is 125 cm (94 cm for Rose and Swains). In this scenario, world-wide society continues to increase emissions. Tipping points (large and sudden changes) are triggered, and worst-case possibilities arise. It is recommended using this scenario for planning construction of infrastructure with highly critical use and longer lifespans, such as a new hospital. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2040 Intermediate Scenario: 1 Day Per Year (as_uhslc_all_slr_xflood_2040_int_1day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of one flooding day per year, a good indicator of the flooding extent and depth during the most extreme "King Tide" annually. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2040 intermediate scenario represented here, the modeled water level for a 1-day frequency is 131 cm (100 cm for Rose and Swains). In this scenario, world-wide society continues current emissions rates, and sea level rises at increased rates compared to the intermediate-low scenario. Tipping points, i.e. large and sudden changes, are still not crossed. It is recommended using this scenario for planning construction of infrastructure with low-to-medium critical use and lifespans extending into the second half of the century, such as a new storefront. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2040 Intermediate Scenario: 50 Days Per Year (as_uhslc_all_slr_xflood_2040_int_50day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 50 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2040 intermediate scenario represented here, the modeled water level for a 50-day frequency is 112 cm (81 cm for Rose and Swains). In this scenario, world-wide society continues current emissions rates, and sea level rises at increased rates compared to the intermediate-low scenario. Tipping points, i.e. large and sudden changes, are still not crossed. It is recommended using this scenario for planning construction of infrastructure with low-to-medium critical use and lifespans extending into the second half of the century, such as a new storefront. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2040 Intermediate-High Scenario: 20 Days Per Year (as_uhslc_all_slr_xflood_2040_inthigh_20day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 20 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2040 intermediate-high scenario represented here, the modeled water level for a 20-day frequency is 122 cm (90 cm for Rose and Swains). In this scenario, world-wide society continues to increase emissions rate. Tipping points, i.e. large and sudden changes, are triggered; ice loss increases rapidly but is not catastrophic. It is recommended using this scenario for planning construction of infrastructure with medium-to-high critical use and longer lifespans, such as a new government office. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2040 Intermediate-Low Scenario: 1 Day Per Year (as_uhslc_all_slr_xflood_2040_intlow_1day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of one flooding day per year, a good indicator of the flooding extent and depth during the most extreme "King Tide" annually. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2040 intermediate-low scenario represented here, the modeled water level for a 1-day frequency is 129 cm (98 cm for Rose and Swains). In this scenario, world-wide society limits increase of emissions, and sea level rises without reaching any tipping points, i.e. large and sudden changes such as a rapid increase in ice sheets melting. It is recommended to use this scenario only for planning construction of non-critical infrastructure that owners can afford to lose, such as a beach "fale". Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2040 Intermediate-Low Scenario: 50 Days Per Year (as_uhslc_all_slr_xflood_2040_intlow_50day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 50 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2040 intermediate-low scenario represented here, the modeled water level for a 50-day frequency is 110 cm (79 cm for Rose and Swains). In this scenario, world-wide society limits increase of emissions, and sea level rises without reaching any tipping points, i.e. large and sudden changes such as a rapid increase in ice sheets melting. It is recommended to use this scenario only for planning construction of non-critical infrastructure that owners can afford to lose, such as a beach "fale". Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2040 Low Scenario: 20 Days Per Year (as_uhslc_all_slr_xflood_2040_low_20day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 20 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2040 low scenario represented here, the modeled water level for a 20-day frequency is 112 cm (81 cm for Rose and Swains). In this scenario, significant world-wide emissions reductions are implemented now, which is highly unlikely. It is not recommended to use this scenario for planning purposes. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2050 High Scenario: 1 Day Per Year (as_uhslc_all_slr_xflood_2050_high_1day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of one flooding day per year, a good indicator of the flooding extent and depth during the most extreme "King Tide" annually. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2050 high scenario represented here, the modeled water level for a 1-day frequency is 155 cm (122 cm for Rose and Swains). In this scenario, world-wide society continues to increase emissions. Tipping points (large and sudden changes) are triggered, and worst-case possibilities arise. It is recommended using this scenario for planning construction of infrastructure with highly critical use and longer lifespans, such as a new hospital. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2050 High Scenario: 50 Days Per Year (as_uhslc_all_slr_xflood_2050_high_50day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 50 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2050 high scenario represented here, the modeled water level for a 50-day frequency is 138 cm (104 cm for Rose and Swains). In this scenario, world-wide society continues to increase emissions. Tipping points (large and sudden changes) are triggered, and worst-case possibilities arise. It is recommended using this scenario for planning construction of infrastructure with highly critical use and longer lifespans, such as a new hospital. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2050 Intermediate Scenario: 20 Days Per Year (as_uhslc_all_slr_xflood_2050_int_20day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 20 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2050 intermediate scenario represented here, the modeled water level for a 20-day frequency is 128 cm (95 cm for Rose and Swains). In this scenario, world-wide society continues current emissions rates, and sea level rises at increased rates compared to the intermediate-low scenario. Tipping points, i.e. large and sudden changes, are still not crossed. It is recommended using this scenario for planning construction of infrastructure with low-to-medium critical use and lifespans extending into the second half of the century, such as a new storefront. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2050 Intermediate-High Scenario: 1 Day Per Year (as_uhslc_all_slr_xflood_2050_inthigh_1day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of one flooding day per year, a good indicator of the flooding extent and depth during the most extreme "King Tide" annually. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2050 intermediate-high scenario represented here, the modeled water level for a 1-day frequency is 149 cm (116 cm for Rose and Swains). In this scenario, world-wide society continues to increase emissions rate. Tipping points, i.e. large and sudden changes, are triggered; ice loss increases rapidly but is not catastrophic. It is recommended using this scenario for planning construction of infrastructure with medium-to-high critical use and longer lifespans, such as a new government office. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2050 Intermediate-High Scenario: 50 Days Per Year (as_uhslc_all_slr_xflood_2050_inthigh_50day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 50 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2050 intermediate-high scenario represented here, the modeled water level for a 50-day frequency is 131 cm (98 cm for Rose and Swains). In this scenario, world-wide society continues to increase emissions rate. Tipping points, i.e. large and sudden changes, are triggered; ice loss increases rapidly but is not catastrophic. It is recommended using this scenario for planning construction of infrastructure with medium-to-high critical use and longer lifespans, such as a new government office. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2050 Intermediate-Low Scenario: 20 Days Per Year (as_uhslc_all_slr_xflood_2050_intlow_20day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 20 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2050 intermediate-low scenario represented here, the modeled water level for a 20-day frequency is 124 cm (90 cm for Rose and Swains). In this scenario, world-wide society limits increase of emissions, and sea level rises without reaching any tipping points, i.e. large and sudden changes such as a rapid increase in ice sheets melting. It is recommended to use this scenario only for planning construction of non-critical infrastructure that owners can afford to lose, such as a beach "fale". Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2050 Low Scenario: 1 Day Per Year (as_uhslc_all_slr_xflood_2050_low_1day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of one flooding day per year, a good indicator of the flooding extent and depth during the most extreme "King Tide" annually. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2050 low scenario represented here, the modeled water level for a 1-day frequency is 130 cm (97 cm for Rose and Swains). In this scenario, significant world-wide emissions reductions are implemented now, which is highly unlikely. It is not recommended to use this scenario for planning purposes. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2050 Low Scenario: 50 Days Per Year (as_uhslc_all_slr_xflood_2050_low_50day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 50 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2050 low scenario represented here, the modeled water level for a 50-day frequency is 112 cm (79 cm for Rose and Swains). In this scenario, significant world-wide emissions reductions are implemented now, which is highly unlikely. It is not recommended to use this scenario for planning purposes. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2060 High Scenario: 20 Days Per Year (as_uhslc_all_slr_xflood_2060_high_20day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 20 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2060 high scenario represented here, the modeled water level for a 20-day frequency is 169 cm (134 cm for Rose and Swains). In this scenario, world-wide society continues to increase emissions. Tipping points (large and sudden changes) are triggered, and worst-case possibilities arise. It is recommended using this scenario for planning construction of infrastructure with highly critical use and longer lifespans, such as a new hospital. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2060 Intermediate Scenario: 1 Day Per Year (as_uhslc_all_slr_xflood_2060_int_1day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of one flooding day per year, a good indicator of the flooding extent and depth during the most extreme "King Tide" annually. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2060 intermediate scenario represented here, the modeled water level for a 1-day frequency is 151 cm (115 cm for Rose and Swains). In this scenario, world-wide society continues current emissions rates, and sea level rises at increased rates compared to the intermediate-low scenario. Tipping points, i.e. large and sudden changes, are still not crossed. It is recommended using this scenario for planning construction of infrastructure with low-to-medium critical use and lifespans extending into the second half of the century, such as a new storefront. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2060 Intermediate Scenario: 50 Days Per Year (as_uhslc_all_slr_xflood_2060_int_50day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 50 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2060 intermediate scenario represented here, the modeled water level for a 50-day frequency is 133 cm (98 cm for Rose and Swains). In this scenario, world-wide society continues current emissions rates, and sea level rises at increased rates compared to the intermediate-low scenario. Tipping points, i.e. large and sudden changes, are still not crossed. It is recommended using this scenario for planning construction of infrastructure with low-to-medium critical use and lifespans extending into the second half of the century, such as a new storefront. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2060 Intermediate-High Scenario: 20 Days Per Year (as_uhslc_all_slr_xflood_2060_inthigh_20day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 20 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2060 intermediate-high scenario represented here, the modeled water level for a 20-day frequency is 154 cm (119 cm for Rose and Swains). In this scenario, world-wide society continues to increase emissions rate. Tipping points, i.e. large and sudden changes, are triggered; ice loss increases rapidly but is not catastrophic. It is recommended using this scenario for planning construction of infrastructure with medium-to-high critical use and longer lifespans, such as a new government office. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2060 Intermediate-Low Scenario: 1 Day Per Year (as_uhslc_all_slr_xflood_2060_intlow_1day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of one flooding day per year, a good indicator of the flooding extent and depth during the most extreme "King Tide" annually. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2060 intermediate-low scenario represented here, the modeled water level for a 1-day frequency is 141 cm (106 cm for Rose and Swains). In this scenario, world-wide society limits increase of emissions, and sea level rises without reaching any tipping points, i.e. large and sudden changes such as a rapid increase in ice sheets melting. It is recommended to use this scenario only for planning construction of non-critical infrastructure that owners can afford to lose, such as a beach "fale". Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2060 Intermediate-Low Scenario: 50 Days Per Year (as_uhslc_all_slr_xflood_2060_intlow_50day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 50 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2060 intermediate-low scenario represented here, the modeled water level for a 50-day frequency is 124 cm (89 cm for Rose and Swains). In this scenario, world-wide society limits increase of emissions, and sea level rises without reaching any tipping points, i.e. large and sudden changes such as a rapid increase in ice sheets melting. It is recommended to use this scenario only for planning construction of non-critical infrastructure that owners can afford to lose, such as a beach "fale". Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2060 Low Scenario: 20 Days Per Year (as_uhslc_all_slr_xflood_2060_low_20day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 20 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2060 low scenario represented here, the modeled water level for a 20-day frequency is 121 cm (86 cm for Rose and Swains). In this scenario, significant world-wide emissions reductions are implemented now, which is highly unlikely. It is not recommended to use this scenario for planning purposes. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2070 High Scenario: 1 Day Per Year (as_uhslc_all_slr_xflood_2070_high_1day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of one flooding day per year, a good indicator of the flooding extent and depth during the most extreme "King Tide" annually. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2070 high scenario represented here, the modeled water level for a 1-day frequency is 216 cm (180 cm for Rose and Swains). In this scenario, world-wide society continues to increase emissions. Tipping points (large and sudden changes) are triggered, and worst-case possibilities arise. It is recommended using this scenario for planning construction of infrastructure with highly critical use and longer lifespans, such as a new hospital. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2070 High Scenario: 50 Days Per Year (as_uhslc_all_slr_xflood_2070_high_50day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 50 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2070 high scenario represented here, the modeled water level for a 50-day frequency is 197 cm (162 cm for Rose and Swains). In this scenario, world-wide society continues to increase emissions. Tipping points (large and sudden changes) are triggered, and worst-case possibilities arise. It is recommended using this scenario for planning construction of infrastructure with highly critical use and longer lifespans, such as a new hospital. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2070 Intermediate Scenario: 20 Days Per Year (as_uhslc_all_slr_xflood_2070_int_20day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 20 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2070 intermediate scenario represented here, the modeled water level for a 20-day frequency is 152 cm (116 cm for Rose and Swains). In this scenario, world-wide society continues current emissions rates, and sea level rises at increased rates compared to the intermediate-low scenario. Tipping points, i.e. large and sudden changes, are still not crossed. It is recommended using this scenario for planning construction of infrastructure with low-to-medium critical use and lifespans extending into the second half of the century, such as a new storefront. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2070 Intermediate-High Scenario: 1 Day Per Year (as_uhslc_all_slr_xflood_2070_inthigh_1day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of one flooding day per year, a good indicator of the flooding extent and depth during the most extreme "King Tide" annually. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2070 intermediate-high scenario represented here, the modeled water level for a 1-day frequency is 193 cm (156 cm for Rose and Swains). In this scenario, world-wide society continues to increase emissions rate. Tipping points, i.e. large and sudden changes, are triggered; ice loss increases rapidly but is not catastrophic. It is recommended using this scenario for planning construction of infrastructure with medium-to-high critical use and longer lifespans, such as a new government office. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2070 Intermediate-High Scenario: 50 Days Per Year (as_uhslc_all_slr_xflood_2070_inthigh_50day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 50 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2070 intermediate-high scenario represented here, the modeled water level for a 50-day frequency is 173 cm (138 cm for Rose and Swains). In this scenario, world-wide society continues to increase emissions rate. Tipping points, i.e. large and sudden changes, are triggered; ice loss increases rapidly but is not catastrophic. It is recommended using this scenario for planning construction of infrastructure with medium-to-high critical use and longer lifespans, such as a new government office. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2070 Intermediate-Low Scenario: 20 Days Per Year (as_uhslc_all_slr_xflood_2070_intlow_20day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 20 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2070 intermediate-low scenario represented here, the modeled water level for a 20-day frequency is 138 cm (102 cm for Rose and Swains). In this scenario, world-wide society limits increase of emissions, and sea level rises without reaching any tipping points, i.e. large and sudden changes such as a rapid increase in ice sheets melting. It is recommended to use this scenario only for planning construction of non-critical infrastructure that owners can afford to lose, such as a beach "fale". Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2070 Low Scenario: 1 Day Per Year (as_uhslc_all_slr_xflood_2070_low_1day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of one flooding day per year, a good indicator of the flooding extent and depth during the most extreme "King Tide" annually. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2070 low scenario represented here, the modeled water level for a 1-day frequency is 139 cm (104 cm for Rose and Swains). In this scenario, significant world-wide emissions reductions are implemented now, which is highly unlikely. It is not recommended to use this scenario for planning purposes. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2070 Low Scenario: 50 Days Per Year (as_uhslc_all_slr_xflood_2070_low_50day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 50 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2070 low scenario represented here, the modeled water level for a 50-day frequency is 121 cm (85 cm for Rose and Swains). In this scenario, significant world-wide emissions reductions are implemented now, which is highly unlikely. It is not recommended to use this scenario for planning purposes. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2080 High Scenario: 20 Days Per Year (as_uhslc_all_slr_xflood_2080_high_20day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 20 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2080 high scenario represented here, the modeled water level for a 20-day frequency is 242 cm (206 cm for Rose and Swains). In this scenario, world-wide society continues to increase emissions. Tipping points (large and sudden changes) are triggered, and worst-case possibilities arise. It is recommended using this scenario for planning construction of infrastructure with highly critical use and longer lifespans, such as a new hospital. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2080 Intermediate Scenario: 1 Day Per Year (as_uhslc_all_slr_xflood_2080_int_1day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of one flooding day per year, a good indicator of the flooding extent and depth during the most extreme "King Tide" annually. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2080 intermediate scenario represented here, the modeled water level for a 1-day frequency is 179 cm (144 cm for Rose and Swains). In this scenario, world-wide society continues current emissions rates, and sea level rises at increased rates compared to the intermediate-low scenario. Tipping points, i.e. large and sudden changes, are still not crossed. It is recommended using this scenario for planning construction of infrastructure with low-to-medium critical use and lifespans extending into the second half of the century, such as a new storefront. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2080 Intermediate Scenario: 50 Days Per Year (as_uhslc_all_slr_xflood_2080_int_50day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 50 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2080 intermediate scenario represented here, the modeled water level for a 50-day frequency is 161 cm (125 cm for Rose and Swains). In this scenario, world-wide society continues current emissions rates, and sea level rises at increased rates compared to the intermediate-low scenario. Tipping points, i.e. large and sudden changes, are still not crossed. It is recommended using this scenario for planning construction of infrastructure with low-to-medium critical use and lifespans extending into the second half of the century, such as a new storefront. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2080 Intermediate-High Scenario: 20 Days Per Year (as_uhslc_all_slr_xflood_2080_inthigh_20day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 20 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2080 intermediate-high scenario represented here, the modeled water level for a 20-day frequency is 207 cm (170 cm for Rose and Swains). In this scenario, world-wide society continues to increase emissions rate. Tipping points, i.e. large and sudden changes, are triggered; ice loss increases rapidly but is not catastrophic. It is recommended using this scenario for planning construction of infrastructure with medium-to-high critical use and longer lifespans, such as a new government office. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2080 Intermediate-Low Scenario: 1 Day Per Year (as_uhslc_all_slr_xflood_2080_intlow_1day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of one flooding day per year, a good indicator of the flooding extent and depth during the most extreme "King Tide" annually. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2080 intermediate-low scenario represented here, the modeled water level for a 1-day frequency is 156 cm (121 cm for Rose and Swains). In this scenario, world-wide society limits increase of emissions, and sea level rises without reaching any tipping points, i.e. large and sudden changes such as a rapid increase in ice sheets melting. It is recommended to use this scenario only for planning construction of non-critical infrastructure that owners can afford to lose, such as a beach "fale". Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2080 Intermediate-Low Scenario: 50 Days Per Year (as_uhslc_all_slr_xflood_2080_intlow_50day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 50 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2080 intermediate-low scenario represented here, the modeled water level for a 50-day frequency is 138 cm (102 cm for Rose and Swains). In this scenario, world-wide society limits increase of emissions, and sea level rises without reaching any tipping points, i.e. large and sudden changes such as a rapid increase in ice sheets melting. It is recommended to use this scenario only for planning construction of non-critical infrastructure that owners can afford to lose, such as a beach "fale". Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2080 Low Scenario: 20 Days Per Year (as_uhslc_all_slr_xflood_2080_low_20day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 20 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2080 low scenario represented here, the modeled water level for a 20-day frequency is 130 cm (95 cm for Rose and Swains). In this scenario, significant world-wide emissions reductions are implemented now, which is highly unlikely. It is not recommended to use this scenario for planning purposes. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2090 High Scenario: 1 Day Per Year (as_uhslc_all_slr_xflood_2090_high_1day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of one flooding day per year, a good indicator of the flooding extent and depth during the most extreme "King Tide" annually. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2090 high scenario represented here, the modeled water level for a 1-day frequency is 295 cm (259 cm for Rose and Swains). In this scenario, world-wide society continues to increase emissions. Tipping points (large and sudden changes) are triggered, and worst-case possibilities arise. It is recommended using this scenario for planning construction of infrastructure with highly critical use and longer lifespans, such as a new hospital. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2090 High Scenario: 50 Days Per Year (as_uhslc_all_slr_xflood_2090_high_50day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 50 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2090 high scenario represented here, the modeled water level for a 50-day frequency is 278 cm (242 cm for Rose and Swains). In this scenario, world-wide society continues to increase emissions. Tipping points (large and sudden changes) are triggered, and worst-case possibilities arise. It is recommended using this scenario for planning construction of infrastructure with highly critical use and longer lifespans, such as a new hospital. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2090 Intermediate Scenario: 20 Days Per Year (as_uhslc_all_slr_xflood_2090_int_20day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 20 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2090 intermediate scenario represented here, the modeled water level for a 20-day frequency is 187 cm (151 cm for Rose and Swains). In this scenario, world-wide society continues current emissions rates, and sea level rises at increased rates compared to the intermediate-low scenario. Tipping points, i.e. large and sudden changes, are still not crossed. It is recommended using this scenario for planning construction of infrastructure with low-to-medium critical use and lifespans extending into the second half of the century, such as a new storefront. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2090 Intermediate-High Scenario: 1 Day Per Year (as_uhslc_all_slr_xflood_2090_inthigh_1day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of one flooding day per year, a good indicator of the flooding extent and depth during the most extreme "King Tide" annually. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2090 intermediate-high scenario represented here, the modeled water level for a 1-day frequency is 249 cm (213 cm for Rose and Swains). In this scenario, world-wide society continues to increase emissions rate. Tipping points, i.e. large and sudden changes, are triggered; ice loss increases rapidly but is not catastrophic. It is recommended using this scenario for planning construction of infrastructure with medium-to-high critical use and longer lifespans, such as a new government office. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2090 Intermediate-High Scenario: 50 Days Per Year (as_uhslc_all_slr_xflood_2090_inthigh_50day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 50 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2090 intermediate-high scenario represented here, the modeled water level for a 50-day frequency is 232 cm (196 cm for Rose and Swains). In this scenario, world-wide society continues to increase emissions rate. Tipping points, i.e. large and sudden changes, are triggered; ice loss increases rapidly but is not catastrophic. It is recommended using this scenario for planning construction of infrastructure with medium-to-high critical use and longer lifespans, such as a new government office. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2090 Intermediate-Low Scenario: 20 Days Per Year (as_uhslc_all_slr_xflood_2090_intlow_20day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 20 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2090 intermediate-low scenario represented here, the modeled water level for a 20-day frequency is 152 cm (115 cm for Rose and Swains). In this scenario, world-wide society limits increase of emissions, and sea level rises without reaching any tipping points, i.e. large and sudden changes such as a rapid increase in ice sheets melting. It is recommended to use this scenario only for planning construction of non-critical infrastructure that owners can afford to lose, such as a beach "fale". Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2090 Low Scenario: 1 Day Per Year (as_uhslc_all_slr_xflood_2090_low_1day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of one flooding day per year, a good indicator of the flooding extent and depth during the most extreme "King Tide" annually. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2090 low scenario represented here, the modeled water level for a 1-day frequency is 146 cm (110 cm for Rose and Swains). In this scenario, significant world-wide emissions reductions are implemented now, which is highly unlikely. It is not recommended to use this scenario for planning purposes. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2090 Low Scenario: 50 Days Per Year (as_uhslc_all_slr_xflood_2090_low_50day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 50 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2090 low scenario represented here, the modeled water level for a 50-day frequency is 129 cm (93 cm for Rose and Swains). In this scenario, significant world-wide emissions reductions are implemented now, which is highly unlikely. It is not recommended to use this scenario for planning purposes. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2100 High Scenario: 20 Days Per Year (as_uhslc_all_slr_xflood_2100_high_20day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 20 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2100 high scenario represented here, the modeled water level for a 20-day frequency is 327 cm (290 cm for Rose and Swains). In this scenario, world-wide society continues to increase emissions. Tipping points (large and sudden changes) are triggered, and worst-case possibilities arise. It is recommended using this scenario for planning construction of infrastructure with highly critical use and longer lifespans, such as a new hospital. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2100 Intermediate Scenario: 1 Day Per Year (as_uhslc_all_slr_xflood_2100_int_1day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of one flooding day per year, a good indicator of the flooding extent and depth during the most extreme "King Tide" annually. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2100 intermediate scenario represented here, the modeled water level for a 1-day frequency is 224 cm (187 cm for Rose and Swains). In this scenario, world-wide society continues current emissions rates, and sea level rises at increased rates compared to the intermediate-low scenario. Tipping points, i.e. large and sudden changes, are still not crossed. It is recommended using this scenario for planning construction of infrastructure with low-to-medium critical use and lifespans extending into the second half of the century, such as a new storefront. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2100 Intermediate Scenario: 50 Days Per Year (as_uhslc_all_slr_xflood_2100_int_50day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 50 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2100 intermediate scenario represented here, the modeled water level for a 50-day frequency is 205 cm (168 cm for Rose and Swains). In this scenario, world-wide society continues current emissions rates, and sea level rises at increased rates compared to the intermediate-low scenario. Tipping points, i.e. large and sudden changes, are still not crossed. It is recommended using this scenario for planning construction of infrastructure with low-to-medium critical use and lifespans extending into the second half of the century, such as a new storefront. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2100 Intermediate-High Scenario: 20 Days Per Year (as_uhslc_all_slr_xflood_2100_inthigh_20day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 20 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2100 intermediate-high scenario represented here, the modeled water level for a 20-day frequency is 268 cm (232 cm for Rose and Swains). In this scenario, world-wide society continues to increase emissions rate. Tipping points, i.e. large and sudden changes, are triggered; ice loss increases rapidly but is not catastrophic. It is recommended using this scenario for planning construction of infrastructure with medium-to-high critical use and longer lifespans, such as a new government office. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2100 Intermediate-Low Scenario: 1 Day Per Year (as_uhslc_all_slr_xflood_2100_intlow_1day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of one flooding day per year, a good indicator of the flooding extent and depth during the most extreme "King Tide" annually. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2100 intermediate-low scenario represented here, the modeled water level for a 1-day frequency is 171 cm (134 cm for Rose and Swains). In this scenario, world-wide society limits increase of emissions, and sea level rises without reaching any tipping points, i.e. large and sudden changes such as a rapid increase in ice sheets melting. It is recommended to use this scenario only for planning construction of non-critical infrastructure that owners can afford to lose, such as a beach "fale". Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2100 Intermediate-Low Scenario: 50 Days Per Year (as_uhslc_all_slr_xflood_2100_intlow_50day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 50 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2100 intermediate-low scenario represented here, the modeled water level for a 50-day frequency is 153 cm (116 cm for Rose and Swains). In this scenario, world-wide society limits increase of emissions, and sea level rises without reaching any tipping points, i.e. large and sudden changes such as a rapid increase in ice sheets melting. It is recommended to use this scenario only for planning construction of non-critical infrastructure that owners can afford to lose, such as a beach "fale". Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2100 Low Scenario: 20 Days Per Year (as_uhslc_all_slr_xflood_2100_low_20day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 20 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2100 low scenario represented here, the modeled water level for a 20-day frequency is 137 cm (100 cm for Rose and Swains). In this scenario, significant world-wide emissions reductions are implemented now, which is highly unlikely. It is not recommended to use this scenario for planning purposes. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2-Ft Scenario: 1 Day Per Year (as_uhslc_all_slr_xflood_2ft_1day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. As an alternative to the SLR scenarios in other data layers that we provide, our project also provides the ability to select specific amounts of SLR in increments of one foot, independent of any particular scenario. This information can be used if guidance for a project requires planning for a specific amount of SLR rather than a time horizon. The present layer models a sea level rise of 2 feet (61 cm). We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of one flooding day per year, a good indicator of the flooding extent and depth during the most extreme "King Tide" annually. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2-ft scenario represented here, the modeled water level for a 1-day frequency is 147 cm. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 2-Ft Scenario: 50 Days Per Year (as_uhslc_all_slr_xflood_2ft_50day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. As an alternative to the SLR scenarios in other data layers that we provide, our project also provides the ability to select specific amounts of SLR in increments of one foot, independent of any particular scenario. This information can be used if guidance for a project requires planning for a specific amount of SLR rather than a time horizon. The present layer models a sea level rise of 2 feet (61 cm). We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 50 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2-ft scenario represented here, the modeled water level for a 50-day frequency is 129 cm. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 3-Ft Scenario: 20 Days Per Year (as_uhslc_all_slr_xflood_3ft_20day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. As an alternative to the SLR scenarios in other data layers that we provide, our project also provides the ability to select specific amounts of SLR in increments of one foot, independent of any particular scenario. This information can be used if guidance for a project requires planning for a specific amount of SLR rather than a time horizon. The present layer models a sea level rise of 3 feet (91 cm). We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 20 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 3-ft scenario represented here, the modeled water level for a 20-day frequency is 164 cm. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 4-Ft Scenario: 1 Day Per Year (as_uhslc_all_slr_xflood_4ft_1day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. As an alternative to the SLR scenarios in other data layers that we provide, our project also provides the ability to select specific amounts of SLR in increments of one foot, independent of any particular scenario. This information can be used if guidance for a project requires planning for a specific amount of SLR rather than a time horizon. The present layer models a sea level rise of 4 feet (122 cm). We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of one flooding day per year, a good indicator of the flooding extent and depth during the most extreme "King Tide" annually. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 4-ft scenario represented here, the modeled water level for a 1-day frequency is 207 cm. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 4-Ft Scenario: 50 Days Per Year (as_uhslc_all_slr_xflood_4ft_50day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. As an alternative to the SLR scenarios in other data layers that we provide, our project also provides the ability to select specific amounts of SLR in increments of one foot, independent of any particular scenario. This information can be used if guidance for a project requires planning for a specific amount of SLR rather than a time horizon. The present layer models a sea level rise of 4 feet (122 cm). We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 50 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 4-ft scenario represented here, the modeled water level for a 50-day frequency is 189 cm. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 5-Ft Scenario: 20 Days Per Year (as_uhslc_all_slr_xflood_5ft_20day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. As an alternative to the SLR scenarios in other data layers that we provide, our project also provides the ability to select specific amounts of SLR in increments of one foot, independent of any particular scenario. This information can be used if guidance for a project requires planning for a specific amount of SLR rather than a time horizon. The present layer models a sea level rise of 5 feet (152 cm). We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 20 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 5-ft scenario represented here, the modeled water level for a 20-day frequency is 224 cm. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 6-Ft Scenario: 1 Day Per Year (as_uhslc_all_slr_xflood_6ft_1day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. As an alternative to the SLR scenarios in other data layers that we provide, our project also provides the ability to select specific amounts of SLR in increments of one foot, independent of any particular scenario. This information can be used if guidance for a project requires planning for a specific amount of SLR rather than a time horizon. The present layer models a sea level rise of 6 feet (183 cm). We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of one flooding day per year, a good indicator of the flooding extent and depth during the most extreme "King Tide" annually. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 6-ft scenario represented here, the modeled water level for a 1-day frequency is 267 cm. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 6-Ft Scenario: 50 Days Per Year (as_uhslc_all_slr_xflood_6ft_50day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. As an alternative to the SLR scenarios in other data layers that we provide, our project also provides the ability to select specific amounts of SLR in increments of one foot, independent of any particular scenario. This information can be used if guidance for a project requires planning for a specific amount of SLR rather than a time horizon. The present layer models a sea level rise of 6 feet (183 cm). We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 50 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 6-ft scenario represented here, the modeled water level for a 50-day frequency is 249 cm. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 7-Ft Scenario: 20 Days Per Year (as_uhslc_all_slr_xflood_7ft_20day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. As an alternative to the SLR scenarios in other data layers that we provide, our project also provides the ability to select specific amounts of SLR in increments of one foot, independent of any particular scenario. This information can be used if guidance for a project requires planning for a specific amount of SLR rather than a time horizon. The present layer models a sea level rise of 7 feet (213 cm). We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 20 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 7-ft scenario represented here, the modeled water level for a 20-day frequency is 284 cm. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 8-Ft Scenario: 1 Day Per Year (as_uhslc_all_slr_xflood_8ft_1day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. As an alternative to the SLR scenarios in other data layers that we provide, our project also provides the ability to select specific amounts of SLR in increments of one foot, independent of any particular scenario. This information can be used if guidance for a project requires planning for a specific amount of SLR rather than a time horizon. The present layer models a sea level rise of 8 feet (244 cm). We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of one flooding day per year, a good indicator of the flooding extent and depth during the most extreme "King Tide" annually. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 8-ft scenario represented here, the modeled water level for a 1-day frequency is 327 cm. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 8-Ft Scenario: 50 Days Per Year (as_uhslc_all_slr_xflood_8ft_50day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. As an alternative to the SLR scenarios in other data layers that we provide, our project also provides the ability to select specific amounts of SLR in increments of one foot, independent of any particular scenario. This information can be used if guidance for a project requires planning for a specific amount of SLR rather than a time horizon. The present layer models a sea level rise of 8 feet (244 cm). We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 50 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 8-ft scenario represented here, the modeled water level for a 50-day frequency is 309 cm. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 9-Ft Scenario: 20 Days Per Year (as_uhslc_all_slr_xflood_9ft_20day_v2)

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. As an alternative to the SLR scenarios in other data layers that we provide, our project also provides the ability to select specific amounts of SLR in increments of one foot, independent of any particular scenario. This information can be used if guidance for a project requires planning for a specific amount of SLR rather than a time horizon. The present layer models a sea level rise of 9 feet (274 cm). We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 20 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 9-ft scenario represented here, the modeled water level for a 20-day frequency is 344 cm. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Vegetation - American Samoa (as_usfs_comp_veg)

Vegetation of American Samoa, compiled from individual files for Ofu and Olosega, Rose Atoll, Swains Island, Ta'u, and Tutuila

USGS 10-m Digital Elevation Model (DEM): American Samoa: Tau: Hillshade (as_usgs_tau_dem10m_hillshade)

A 10-meter resolution land surface digital elevation model (DEM) grayscale hillshade for the Manua Island of Tau in American Samoa derived from United States Geological Survey (USGS) 1/3 arc-second DEM quadrangles. For the related dataset containing numeric elevation values for this image layer, see http://pacioos.org/metadata/usgs_dem_10m_tau.html

Coral Favorability: Overall Environmental Conditions: Present - American Samoa (as_usgs_tutma_coralscore)

The overall condition of the environment is a combination of managed and non-managed factors. While it is difficult for managers to prevent coral bleaching events, reefs experiencing fewer stressors may recover more quickly than reefs that were highly stressed at the time of bleaching. The scores for managed (see layer "as_usgs_tutma_coralscore_mgt") and non-managed conditions (see layer "as_usgs_tutma_coralscore_nonmgt") were averaged to produce an overall environmental favorability score. This layer represents a relative score for how favorable overall conditions are for coral growth and survival from a scale of 0 (worst) to 1 (best) in the present climate scenario. These data are provided as a raster with a resolution of 500 m for American Samoa, including Tutuila and the Manua Islands (Ofu, Olosega, and Tau).

Coral Favorability: Non-Managed Conditions: Present - American Samoa (as_usgs_tutma_coralscore_nonmgt)

Many aspects of the environment are outside the control of local or regional resource managers. These conditions may require concerted global action to affect change (e.g., water temperatures) or cannot be controlled at all (e.g., wave power). This layer synthesized spatial information for several non-managed conditions to create a relative score for how favorable a given location is for coral growth and survival. Present-day environmental conditions contributing to this layer included: marine calcite concentration (a proxy for ocean acidification), irradiance (photosynthetically available radiation, or PAR), thermal stress (Degree Heating Weeks), and wave power (per meter of wave front). Covariation in these conditions was accounted for using principal component analysis (PCA) to form composite variables of conditions that have strong relationships with one another. The resulting principal components were averaged and scaled from 0 (worst) to 1 (best) to produce the coral favorability score for non-managed conditions. These data are provided as a raster with a resolution of 500 m for American Samoa, including Tutuila and the Manua Islands (Ofu, Olosega, and Tau).

Coral Favorability: Non-Managed Conditions: Worst Case Emissions - American Samoa (as_usgs_tutma_coralscore_nonmgt_rcp85)

Many aspects of the environment are outside the control of local or regional resource managers. These conditions may require concerted global action to affect change (e.g., water temperatures) or cannot be controlled at all (e.g., wave power). This layer synthesized spatial information for several non-managed conditions to create a relative score for how favorable a given location is for coral growth and survival. Environmental conditions contributing to this layer included: marine calcite concentration (a proxy for ocean acidification), irradiance (photosynthetically available radiation, or PAR), thermal stress (annual severe bleaching threshold), wave power (per meter of wave front), and proximity to soils eroded by sea level rise. Projections exist for how some of these conditions may change over the next century based on the trajectory of global greenhouse gas emissions. This project explored how the relative favorability of non-managed conditions could change between the present climate scenario and the rest of the 21st century. This layer represents the future climate scenario for a worst case emissions scenario (Representative Concentration Pathway 8.5), in which no action is taken to reduce global greenhouse gas emissions. Covariation in these conditions was accounted for using principal component analysis (PCA) to form composite variables of conditions that have strong relationships with one another. The resulting principal components were averaged and scaled from 0 (worst) to 1 (best) to produce the coral favorability score for non-managed conditions. These data are provided as a raster with a resolution of 500 m for American Samoa, including Tutuila and the Manua Islands (Ofu, Olosega, and Tau).

Coral Favorability: Overall Environmental Conditions: Worst Case Emissions - American Samoa (as_usgs_tutma_coralscore_rcp85)

The overall condition of the environment is a combination of managed and non-managed factors. While it is difficult for managers to prevent coral bleaching events, reefs experiencing fewer stressors may recover more quickly than reefs that were highly stressed at the time of bleaching. The scores for managed (see layer "as_usgs_tutma_coralscore_mgt") and non-managed conditions (see layer "as_usgs_tutma_coralscore_nonmgt_rcp85") were averaged to produce an overall environmental favorability score. This layer represents a relative score for how favorable overall conditions are for coral growth and survival from a scale of 0 (worst) to 1 (best) in the future climate scenario for a worst case emissions scenario (Representative Concentration Pathway 8.5), in which no action is taken to reduce global greenhouse gas emissions. These data are provided as a raster with a resolution of 500 m for American Samoa, including Tutuila and the Manua Islands (Ofu, Olosega, and Tau).

Sites of Biodiversity, Conservation, and Tourism - Federated States of Micronesia (fm_ej_all_conservsites)

Sites of biodiversity, conservation, and tourism relevance in the Federated States of Micronesia.

Protected and Managed Areas - Federated States of Micronesia (fm_ej_all_pmas)

Protected and managed areas of the Federated States of Micronesia.

Coral Monitoring Sites - Pohnpei, Federated States of Micronesia (fm_ej_poh_csp_coralsurvey2005)

Coral monitoring sites 2004-2005 in Pohnpei, Federated States of Micronesia

State Boundaries - Federated States of Micronesia (fm_pac_statebdry_created)

State boundaries of the Federated States of Micronesia (FSM), delimiting the states of Yap, Chuuk, and Kosrae. These are approximated from the map image on Wikipedia at: http://en.wikipedia.org/wiki/File:Map_of_the_Federated_States_of_Micronesia_CIA.jpg

Vegetation - Federated States of Micronesia (fm_usfs_comp_veg)

Vegetation of the Federated States of Micronesia, compiled from individual files for Chuuk, Kosrae, Pohnpei and Yap.

USGS 10-m Digital Elevation Model (DEM): FSM: Kosrae: Hillshade (fm_usgs_kos_dem10m_hillshade)

A 10-meter resolution land surface digital elevation model (DEM) grayscale hillshade for the island of Kosrae in the Federated States of Micronesia (FSM) derived from United States Geological Survey (USGS) 1/3 arc-second DEM quadrangles. For the related dataset containing numeric elevation values for this image layer, see http://pacioos.org/metadata/usgs_dem_10m_kosrae.html

Marine Protected Areas - Guam (gu_comp_all_mpa)

Marine Protected Areas (MPAs) of Guam, including National Parks, National Marine Sanctuaries, National Wildlife Refuges, ecological reserves, and Territorial Seashore Parks. Compiled from multiple sources, including the National Park Service (NPS), National Marine Sanctuaries (NMS), Fish and Wildlife Service (FWS), World Database of Protected Areas (WDPA), Guam Bureau of Statistics and Plans (BSP), and the Guam Coastal Management Program (GCMP). For use in planning purposes only, not for use in litigation.

Conservation Areas - Guam (gu_db_all_conservareas)

Conservation areas of Guam

Ecological Reserves - Guam (gu_db_all_ecoresareas)

Ecological Reserves in Guam, Mariana Islands

Geology - Guam (gu_db_all_geol)

Geology of Guam, Mariana Islands

Marine Protected Areas - Guam (gu_db_all_mpas_2006)

Marine Protected Areas (2006) - Guam, Mariana Islands

Shoreline - Guam (gu_db_all_shore)

Shoreline of Guam, Mariana Islands

Shallow Water Mooring Buoys - Guam (gu_db_all_swmooring)

Shallow water mooring (SWM) buoys around Guam, Mariana Islands

NOAA Shallow-Water Benthic Habitats: Guam (gu_noaa_all_benthic_habitats)

Benthic habitat maps for the nearshore, shallow (< 30 m) coastal waters of the island of Guam. NOAA's National Centers for Coastal Ocean Science (NCCOS) produced these data to support coral reef research and management. Habitat regions were digitally identified using visual interpretation of orthorectified satellite imagery with a minimum mapping unit (MMU) of approximately 1 acre. Includes biological cover types, geomorphological structure types, and geographic zones. Eighteen distinct and non-overlapping biological cover types were identified. Habitats or features that cover areas smaller than the minimal mapping unit of 1 acre were not considered. For example, uncolonized sand halos surrounding coral patch reefs are too small to be mapped independently. Cover type refers only to the predominant biological component colonizing the surface of the feature and does not address location (e.g., on the shelf or in the lagoon). The cover types are defined in a collapsible hierarchy ranging from eight major classes (live coral, seagrass, macroalgae, encrusting/coralline algae, turf algae, emergent vegetation, uncolonized, and unknown), combined with a density modifier representing the percentage of the predominant cover type (10%-<50% sparse, 50%-<90% patchy, 90%-100% continuous). Similarly, 14 distinct and non-overlapping geomorphological structure types were identified. The structure types are defined in a collapsible hierarchy ranging from four major classes (coral reef and hardbottom, unconsolidated sediment, other delineations, and unknown), to thirteen detailed classes: sand, mud, spur and groove, individual and aggregated patch reef, aggregate reef, scattered coral/rock in unconsolidated sediment, pavement, rock/boulder (volcanic and carbonate), reef rubble, pavement with sand channels, artificial, and unknown. Lastly, 13 mutually exclusive geographic zones were identified from land to open water corresponding to typical insular shelf and coral reef geomorphology. These zones include: shoreline intertidal, vertical wall (none identified), lagoon, back reef, reef flat, reef crest, fore reef, bank/shelf, bank/shelf escarpment, channel, dredged (since this condition eliminates natural geomorphology), unknown, and land. Zone refers only to each benthic community's location and does not address substrate or cover types within. For example, the lagoon zone may include patch reefs, sand, and seagrass beds; however, these are considered structural elements that may or may not occur within the lagoon zone and therefore, are not used to define it.

Aerial Mosaic, 1km - Guam (gu_noaa_all_swbh_mosaic_clip1km)

Aerial mosaic of Guam.

Geological Faults - Guam (gu_nps_all_geofaults)

Geological Faults in Guam, Mariana Islands. National Park Service (NPS) data.

Geologic Cross Sections - War in the Pacific National Park, Guam (gu_nps_wapa_geoxsectlines)

Geologic cross section lines of War in the Pacific National Historical Park and vicinity, Guam.

NOAA/PIBHMC 5-m Bathymetry: Guam: Hillshade (gu_pibhmc_all_bathy5m_hillshade)

A 5-m resolution gridded digital elevation model (DEM) grayscale hillshade for the bathymetry (ocean depth) surrounding Guam compiled from ship-borne multibeam sonar surveys merged with bathymetry derived from aerial LiDAR data. Almost complete bottom coverage was achieved in depths between 0 and 400 m. Data collected by the Pacific Islands Benthic Habitat Mapping Center (PIBHMC) in support of NOAA's Coral Reef Conservation Program. Not to be used for navigational purposes. For the related dataset containing numeric bathymetry values for this image layer, see http://pacioos.org/metadata/pibhmc_bathy_5m_guam.html

NOAA/PIBHMC 1-m Bathymetry: Guam: Apra Harbor: Hillshade (gu_pibhmc_apra_bathy1m_hillshade)

A 1-m resolution gridded digital elevation model (DEM) grayscale hillshade for the bathymetry (ocean depth) surrounding Apra Harbor on the west coast of Guam compiled from ship-borne multibeam sonar surveys. Data collected by the Pacific Islands Benthic Habitat Mapping Center (PIBHMC) in support of NOAA's Coral Reef Conservation Program. Not to be used for navigational purposes. For the related dataset containing numeric bathymetry values for this image layer, see http://pacioos.org/metadata/pibhmc_bathy_1m_apra.html

Coral Favorability: Overall Environmental Conditions: Present - Guam (gu_usgs_all_coralscore)

The overall condition of the environment is a combination of managed and non-managed factors. While it is difficult for managers to prevent coral bleaching events, reefs experiencing fewer stressors may recover more quickly than reefs that were highly stressed at the time of bleaching. The scores for managed (see layer "gu_usgs_all_coralscore_mgt") and non-managed conditions (see layer "gu_usgs_all_coralscore_nonmgt") were averaged to produce an overall environmental favorability score. This layer represents a relative score for how favorable overall conditions are for coral growth and survival from a scale of 0 (worst) to 1 (best) in the present climate scenario. These data are provided for the island of Guam as a raster with a resolution of 1500 m.

Coral Favorability: Non-Managed Conditions: Present - Guam (gu_usgs_all_coralscore_nonmgt)

Many aspects of the environment are outside the control of local or regional resource managers. These conditions may require concerted global action to affect change (e.g., water temperatures) or cannot be controlled at all (e.g., wave power). This layer synthesized spatial information for several non-managed conditions to create a relative score for how favorable a given location is for coral growth and survival. Present-day environmental conditions contributing to this layer included: marine calcite concentration (a proxy for ocean acidification), irradiance (photosynthetically available radiation, or PAR), thermal stress (Degree Heating Weeks), and wave power (per meter of wave front). Covariation in these conditions was accounted for using principal component analysis (PCA) to form composite variables of conditions that have strong relationships with one another. The resulting principal components were averaged and scaled from 0 (worst) to 1 (best) to produce the coral favorability score for non-managed conditions. These data are provided for the island of Guam as a raster with a resolution of 1500 m.

Coral Favorability: Non-Managed Conditions: Worst Case Emissions - Guam (gu_usgs_all_coralscore_nonmgt_rcp85)

Many aspects of the environment are outside the control of local or regional resource managers. These conditions may require concerted global action to affect change (e.g., water temperatures) or cannot be controlled at all (e.g., wave power). This layer synthesized spatial information for several non-managed conditions to create a relative score for how favorable a given location is for coral growth and survival. Environmental conditions contributing to this layer included: marine calcite concentration (a proxy for ocean acidification), irradiance (photosynthetically available radiation, or PAR), thermal stress (annual severe bleaching threshold), wave power (per meter of wave front), and proximity to soils eroded by sea level rise. Projections exist for how some of these conditions may change over the next century based on the trajectory of global greenhouse gas emissions. This project explored how the relative favorability of non-managed conditions could change between the present climate scenario and the rest of the 21st century. This layer represents the future climate scenario for a worst case emissions scenario (Representative Concentration Pathway 8.5), in which no action is taken to reduce global greenhouse gas emissions. Covariation in these conditions was accounted for using principal component analysis (PCA) to form composite variables of conditions that have strong relationships with one another. The resulting principal components were averaged and scaled from 0 (worst) to 1 (best) to produce the coral favorability score for non-managed conditions. These data are provided for the island of Guam as a raster with a resolution of 1500 m.

Coral Favorability: Overall Environmental Conditions: Worst Case Emissions - Guam (gu_usgs_all_coralscore_rcp85)

The overall condition of the environment is a combination of managed and non-managed factors. While it is difficult for managers to prevent coral bleaching events, reefs experiencing fewer stressors may recover more quickly than reefs that were highly stressed at the time of bleaching. The scores for managed (see layer "gu_usgs_all_coralscore_mgt") and non-managed conditions (see layer "gu_usgs_all_coralscore_nonmgt_rcp85") were averaged to produce an overall environmental favorability score. This layer represents a relative score for how favorable overall conditions are for coral growth and survival from a scale of 0 (worst) to 1 (best) in the future climate scenario for a worst case emissions scenario (Representative Concentration Pathway 8.5), in which no action is taken to reduce global greenhouse gas emissions. These data are provided for the island of Guam as a raster with a resolution of 1500 m.

Wildlife Refuge Boundary - Guam (gu_weri_gnwr_bndry)

Wildlife Refuge Boundary - Guam

Drainage, All Streams - Southern Guam (gu_weri_sog_drain_allstrms)

Drainage, all streams - Southern Guam.

Drainage, Stream Network - Southern Guam (gu_weri_sog_drain_network)

Drainage, stream network - Southern Guam.

Drainage, River Mouths - Southern Guam (gu_weri_sog_drain_rvrmouths)

Drainage, river mouths - Southern Guam.

Wetlands - Southern Guam (gu_weri_sog_env_wlands)

Wetlands - Southern Guam

Villages - Southern Guam (gu_weri_sog_pop_villages)

Villages - Southern Guam

Elevation Contours, 6m - Southern Guam (gu_weri_sog_topo_6m)

Elevation Contours, 6m - Southern Guam

Watersheds, Minor - Southern Guam (gu_weri_sog_wshed_subwsheds)

Watersheds, minor - Southern Guam

Sport Fishing Boat Areas - Guam (gu_yl_all_boatsprfishnareas)

Sport fishing boat areas around Guam, Mariana Islands

Coastal Protection Values, Eastern Storms - Guam (gu_yl_all_coastalprotecteast)

Coastal Protection Value of coral reefs from tropical storms coming from the east for Guam, Mariana Islands

Coral Tourism Value - Guam (gu_yl_all_coraltourvalue)

Coral Tourism Value around Guam, Mariana Islands

Hook Line Fishing Areas - Guam (gu_yl_all_hooklinefishnareas)

Hook line fishing areas around Guam, Mariana Islands

Park Locations - Guam (gu_yl_all_parks)

Parks locations in Guam, Mariana Islands

Roads - Guam (gu_yl_all_streets)

Roads of Guam, Mariana Islands

Shoreline - Baker Island (hbi_pac_bak_shoreline)

Shoreline of Baker Island.

NOAA/PIBHMC 40-m Bathymetry: USMOI: Baker Island: Hillshade (hbi_pibhmc_bak_bathy40m_hillshade)

A 40-m resolution gridded digital elevation model (DEM) grayscale hillshade compiled from ship-borne multibeam sonar surveys for the bathymetry (ocean depth) surrounding Baker Island in the Central Pacific Ocean, a U.S. Minor Outlying Island (USMOI). Almost complete bottom coverage was achieved in depths between 8 and 4700 m. Data collected by the Pacific Islands Benthic Habitat Mapping Center (PIBHMC) in support of NOAA's Coral Reef Conservation Program. Not to be used for navigational purposes. For the related dataset containing numeric bathymetry values for this image layer, see http://pacioos.org/metadata/pibhmc_bathy_40m_baker.html

NOAA/PIBHMC 40-m Bathymetry: USMOI: Howland Island: Hillshade (hbi_pibhmc_how_bathy40m_hillshade)

A 40-m resolution gridded digital elevation model (DEM) grayscale hillshade compiled from ship-borne multibeam sonar surveys for the bathymetry (ocean depth) surrounding Howland Island in the Central Pacific Ocean, a U.S. Minor Outlying Island (USMOI). Almost complete bottom coverage was achieved in depths between 8 and 3800 m. Data collected by the Pacific Islands Benthic Habitat Mapping Center (PIBHMC) in support of NOAA's Coral Reef Conservation Program. Not to be used for navigational purposes. For the related dataset containing numeric bathymetry values for this image layer, see http://pacioos.org/metadata/pibhmc_bathy_40m_howland.html

NOAA/PIFSC Rapid Ecological Assessment (REA) Reef Fish Survey Locations: Main Hawaiian Islands (hi_cred_all_rea_sites)

To support a long-term NOAA Coral Reef Conservation Program (CRCP) for sustainable management and conservation of coral reef ecosystems, reef fish assessment surveys are conducted as part of Rapid Ecological Assessments (REA) during Pacific Reef Assessment and Monitoring Program (Pacific RAMP) cruises in the Main Hawaiian Islands region by the NOAA Pacific Islands Fisheries Science Center (PIFSC). REA is a useful method for gathering data pertaining to ecologically significant biological components of a reef habitat over small spatial scales. Because the method provides a quick "snapshot" of major reef biota during a single dive or snorkel survey, it is particularly useful in assessing remote areas that are only rarely visited and where little time can be spent. Surveys are conducted along a set of transect lines. With their high level of taxonomic resolution over small spatial scales, REAs are a good complement to towed diver surveys, which are conducted over larger spatial scales but with a lower level of taxonomic resolution. For more information, please see: https://www.fisheries.noaa.gov/resource/document/ecosystem-sciences-division-standard-operating-procedures-data-collection-rapid

Multi-Hazard Inundation: Honolulu, Hawaii (hi_csp_hono_allflood_slr0m)

Multi-hazard inundation around Honolulu. The study area includes the urban corridor stretching from Honolulu International Airport to Waikiki and Diamond Head along the south shore of Oahu. Shows inundation from the following two hazards: 1) Tsunami Run-Up Inundation Computer model simulation of tsunami run-up inundation using current sea level at mean higher high water (MHHW) as its baseline water level. The model simulates maximum inundation based on five major historical tsunamis that have impacted Hawaii: 1) The 1946 Aleutian earthquake (8.2 Mw), 2) 1952 Kamchatka earthquake (9.0 Mw), 3) 1957 Aleutian earthquake (8.6 Mw), 4) 1960 Chile earthquake (9.5 Mw), and 5) the 1964 Alaska earthquake (9.2 Mw). 2) Hurricane Storm Surge Inundation Computer model simulation of hurricane storm surge inundation using current sea level at mean higher high water (MHHW) as its baseline water level. The model simulates a Category 4 hurricane, similar to Hurricane Iniki which devastated the island of Kauai in 1992, with a central pressure ranging from 910 to 970 mbar and maximum sustained winds ranging from 90 to 150 mph as it tracked from open ocean to land to open ocean again. The model result shows the Maximum of the Maximum Envelope of High Water (MEOW), or MOM, providing a worst-case snapshot for a particular storm category under "perfect" storm conditions. Data produced in 2014 by Dr. Kwok Fai Cheung of the department of Ocean and Resources Engineering (ORE) in the School of Ocean and Earth Science and Technology (SOEST) of the University of Hawaii at Manoa. Supported in part by the NOAA Coastal Storms Program (CSP) and the University of Hawaii Sea Grant College Program. While considerable effort has been made to implement all model components in a thorough, correct, and accurate manner, numerous sources of error are possible. The entire risk associated with the results and performance of these data is assumed by the user. These data should be used strictly as a planning reference and not for navigation, permitting, or other legal purposes.

Multi-Hazard Inundation With 0.5-m Sea Level Rise: Honolulu, Hawaii (hi_csp_hono_allflood_slrhm)

Multi-hazard inundation around Honolulu, Hawaii resulting from future sea level rise. The study area includes the urban corridor stretching from Honolulu International Airport to Waikiki and Diamond Head along the south shore of Oahu. Shows inundation from the following three hazards: 1) Sea Level Rise Inundation: 0.5-m Scenario Coastal flooding due to 0.5 meter of sea level rise. This scenario was derived using a National Geospatial Agency (NGA)-provided digital elevation model (DEM) based on LiDAR data of the Honolulu area collected in 2009. This "bare earth" DEM (vegetation and structures removed) was used to represent the current topography of the study area. The accuracy of the DEM was validated using a selection of 16 Tidal Benchmarks located within the study area. The single value tidal water surface of mean higher high water (MHHW) modeled at the Honolulu tide gauge was used to represent sea level for the purposes of this study. Water levels are shown as they would appear during the highest high tides (excluding wind-driven tides). 2) Tsunami Run-Up Inundation With 0.5-m Sea Level Rise Computer model simulation of tsunami run-up inundation including half a meter of sea level rise at mean higher high water (MHHW) as its baseline water level. The model simulates maximum inundation based on five major historical tsunamis that have impacted Hawaii: 1) The 1946 Aleutian earthquake (8.2 Mw), 2) 1952 Kamchatka earthquake (9.0 Mw), 3) 1957 Aleutian earthquake (8.6 Mw), 4) 1960 Chile earthquake (9.5 Mw), and 5) the 1964 Alaska earthquake (9.2 Mw). 3) Hurricane Storm Surge Inundation With 0.5-m Sea Level Rise Computer model simulation of hurricane storm surge inundation including half a meter of sea level rise at mean higher high water (MHHW) as its baseline water level. The model simulates a Category 4 hurricane, similar to Hurricane Iniki which devastated the island of Kauai in 1992, with a central pressure ranging from 910 to 970 mbar and maximum sustained winds ranging from 90 to 150 mph as it tracked from open ocean to land to open ocean again. The model result shows the Maximum of the Maximum Envelope of High Water (MEOW), or MOM, providing a worst-case snapshot for a particular storm category under "perfect" storm conditions. Data produced in 2014 by Dr. Charles "Chip" Fletcher of the department of Geology & Geophysics (G&G) (1) and Dr. Kwok Fai Cheung of the department of Ocean and Resources Engineering (ORE) (2 & 3) in the School of Ocean and Earth Science and Technology (SOEST) of the University of Hawaii at Manoa. Supported in part by the NOAA Coastal Storms Program (CSP) and the University of Hawaii Sea Grant College Program. These data do not consider future changes in coastal geomorphology and natural processes such as erosion, subsidence, or future construction. These data do not specify timing of inundation depths and are not appropriate for conducting detailed spatial analysis. The entire risk associated with the results and performance of these data is assumed by the user. These data should be used strictly as a planning reference and not for navigation, permitting, or other legal purposes.

Hurricane Storm Surge Inundation With 1-m Sea Level Rise: Honolulu, Hawaii (hi_csp_hono_hurflood_slr1m)

Computer model simulation of hurricane storm surge inundation around Honolulu, Hawaii including one meter of sea level rise at mean higher high water (MHHW) as its baseline water level. The study area includes the urban corridor stretching from Pearl Harbor to Waikiki and Diamond Head along the south shore of the island of Oahu. The model simulates a Category 4 hurricane, similar to Hurricane Iniki which devastated the island of Kauai in 1992, with a central pressure ranging from 910 to 970 mbar and maximum sustained winds ranging from 90 to 150 mph as it tracked from open ocean to land to open ocean again. The model result shows the Maximum of the Maximum Envelope of High Water (MEOW), or MOM, providing a worst-case snapshot for a particular storm category under "perfect" storm conditions. Model results produced in 2014 by Dr. Kwok Fai Cheung of the department of Ocean and Resources Engineering (ORE) in the School of Ocean and Earth Science and Technology (SOEST) of the University of Hawaii at Manoa. Supported in part by the NOAA Coastal Storms Program (CSP) and the University of Hawaii Sea Grant College Program. While considerable effort has been made to implement all model components in a thorough, correct, and accurate manner, numerous sources of error are possible. These data do not consider future changes in coastal geomorphology and natural processes such as erosion, subsidence, or future construction. These data do not specify timing of inundation depths and are not appropriate for conducting detailed spatial analysis. The entire risk associated with the results and performance of these data is assumed by the user. These data should be used strictly as a planning reference and not for navigation, permitting, or other legal purposes.

Mean Higher High Water (MHHW) Sea Level: Honolulu, Hawaii (hi_csp_hono_mhhw)

The single value tidal water surface of mean higher high water (MHHW) modeled at the Honolulu tide gauge is used to represent present-day sea level for the urban corridor stretching from Honolulu International Airport to Waikiki and Diamond Head along the south shore of Oahu in the state of Hawaii. Water levels are shown as they would appear during the highest high tides (excluding wind-driven tides). Land elevation was derived using a National Geospatial Agency (NGA)-provided digital elevation model (DEM) based on LiDAR data of the Honolulu area collected in 2009. This "bare earth" DEM (vegetation and structures removed) was used to represent the current topography of the study area above zero elevation. The accuracy of the DEM was validated using a selection of 16 Tidal Benchmarks located within the study area. Data produced in 2014 by Dr. Charles "Chip" Fletcher of the department of Geology & Geophysics (G&G) in the School of Ocean and Earth Science and Technology (SOEST) of the University of Hawaii at Manoa. Supported in part by the NOAA Coastal Storms Program (CSP) and the University of Hawaii Sea Grant College Program. These data should be used strictly as a planning reference and not for navigation, permitting, or other legal purposes.

Sea Level Rise Inundation: 1-m Scenario: Honolulu, Hawaii (hi_csp_hono_slr1m)

This map shows coastal flooding around Honolulu, Hawaii due to 1 meter of sea level rise. This scenario was derived using a National Geospatial Agency (NGA)-provided digital elevation model (DEM) based on LiDAR data of the Honolulu area collected in 2009. This "bare earth" DEM (vegetation and structures removed) was used to represent the current topography of the study area above zero elevation for the urban corridor stretching from Honolulu International Airport to Waikiki and Diamond Head along the south shore of Oahu. The accuracy of the DEM was validated using a selection of 16 Tidal Benchmarks located within the study area. The single value tidal water surface of mean higher high water (MHHW) modeled at the Honolulu tide gauge was used to represent sea level for the purposes of this study. Water levels are shown as they would appear during the highest high tides (excluding wind-driven tides). Data produced in 2014 by Dr. Charles "Chip" Fletcher of the department of Geology & Geophysics (G&G) in the School of Ocean and Earth Science and Technology (SOEST) of the University of Hawaii at Manoa. Supported in part by the NOAA Coastal Storms Program (CSP) and the University of Hawaii Sea Grant College Program. These data do not consider future changes in coastal geomorphology and natural processes such as erosion, subsidence, or future construction. These data do not specify timing of inundation depths and are not appropriate for conducting detailed spatial analysis. The entire risk associated with the results and performance of these data is assumed by the user. These data should be used strictly as a planning reference and not for navigation, permitting, or other legal purposes.

Sea Level Rise Inundation: 3-ft Scenario: Honolulu, Hawaii (hi_csp_hono_slr3ft)

This map shows coastal flooding around Honolulu, Hawaii due to 3 feet (0.914 m) of sea level rise. This scenario was derived using a National Geospatial Agency (NGA)-provided digital elevation model (DEM) based on LiDAR data of the Honolulu area collected in 2009. This "bare earth" DEM (vegetation and structures removed) was used to represent the current topography of the study area above zero elevation for the urban corridor stretching from Honolulu International Airport to Waikiki and Diamond Head along the south shore of Oahu. The accuracy of the DEM was validated using a selection of 16 Tidal Benchmarks located within the study area. The single value tidal water surface of mean higher high water (MHHW) modeled at the Honolulu tide gauge was used to represent sea level for the purposes of this study. Water levels are shown as they would appear during the highest high tides (excluding wind-driven tides). Data produced in 2014 by Dr. Charles "Chip" Fletcher of the department of Geology & Geophysics (G&G) in the School of Ocean and Earth Science and Technology (SOEST) of the University of Hawaii at Manoa. Supported in part by the NOAA Coastal Storms Program (CSP) and the University of Hawaii Sea Grant College Program. These data do not consider future changes in coastal geomorphology and natural processes such as erosion, subsidence, or future construction. These data do not specify timing of inundation depths and are not appropriate for conducting detailed spatial analysis. The entire risk associated with the results and performance of these data is assumed by the user. These data should be used strictly as a planning reference and not for navigation, permitting, or other legal purposes.

Sea Level Rise Inundation: 5-ft Scenario: Honolulu, Hawaii (hi_csp_hono_slr5ft)

This map shows coastal flooding around Honolulu, Hawaii due to 5 feet (1.524 m) of sea level rise. This scenario was derived using a National Geospatial Agency (NGA)-provided digital elevation model (DEM) based on LiDAR data of the Honolulu area collected in 2009. This "bare earth" DEM (vegetation and structures removed) was used to represent the current topography of the study area above zero elevation for the urban corridor stretching from Honolulu International Airport to Waikiki and Diamond Head along the south shore of Oahu. The accuracy of the DEM was validated using a selection of 16 Tidal Benchmarks located within the study area. The single value tidal water surface of mean higher high water (MHHW) modeled at the Honolulu tide gauge was used to represent sea level for the purposes of this study. Water levels are shown as they would appear during the highest high tides (excluding wind-driven tides). Data produced in 2014 by Dr. Charles "Chip" Fletcher of the department of Geology & Geophysics (G&G) in the School of Ocean and Earth Science and Technology (SOEST) of the University of Hawaii at Manoa. Supported in part by the NOAA Coastal Storms Program (CSP) and the University of Hawaii Sea Grant College Program. These data do not consider future changes in coastal geomorphology and natural processes such as erosion, subsidence, or future construction. These data do not specify timing of inundation depths and are not appropriate for conducting detailed spatial analysis. The entire risk associated with the results and performance of these data is assumed by the user. These data should be used strictly as a planning reference and not for navigation, permitting, or other legal purposes.

Sea Level Rise Inundation: 0.5-m Scenario: Honolulu, Hawaii (hi_csp_hono_slrhm)

This map shows coastal flooding around Honolulu, Hawaii due to 0.5 meter of sea level rise. This scenario was derived using a National Geospatial Agency (NGA)-provided digital elevation model (DEM) based on LiDAR data of the Honolulu area collected in 2009. This "bare earth" DEM (vegetation and structures removed) was used to represent the current topography of the study area above zero elevation for the urban corridor stretching from Honolulu International Airport to Waikiki and Diamond Head along the south shore of Oahu. The accuracy of the DEM was validated using a selection of 16 Tidal Benchmarks located within the study area. The single value tidal water surface of mean higher high water (MHHW) modeled at the Honolulu tide gauge was used to represent sea level for the purposes of this study. Water levels are shown as they would appear during the highest high tides (excluding wind-driven tides). Data produced in 2014 by Dr. Charles "Chip" Fletcher of the department of Geology & Geophysics (G&G) in the School of Ocean and Earth Science and Technology (SOEST) of the University of Hawaii at Manoa. Supported in part by the NOAA Coastal Storms Program (CSP) and the University of Hawaii Sea Grant College Program. These data do not consider future changes in coastal geomorphology and natural processes such as erosion, subsidence, or future construction. These data do not specify timing of inundation depths and are not appropriate for conducting detailed spatial analysis. The entire risk associated with the results and performance of these data is assumed by the user. These data should be used strictly as a planning reference and not for navigation, permitting, or other legal purposes.

Tsunami Run-Up Inundation With 1-m Sea Level Rise: Honolulu, Hawaii (hi_csp_hono_tsuflood_slr1m)

Computer model simulation of tsunami run-up inundation around Honolulu, Hawaii including one meter of sea level rise at mean higher high water (MHHW) as its baseline water level. The study area includes the urban corridor stretching from Pearl Harbor to Waikiki and Diamond Head along the south shore of the island of Oahu. The model simulates maximum inundation based on five major historical tsunamis that have impacted Hawaii: 1) The 1946 Aleutian earthquake (8.2 Mw), 2) 1952 Kamchatka earthquake (9.0 Mw), 3) 1957 Aleutian earthquake (8.6 Mw), 4) 1960 Chile earthquake (9.5 Mw), and 5) the 1964 Alaska earthquake (9.2 Mw). Model results produced in 2014 by Dr. Kwok Fai Cheung of the department of Ocean and Resources Engineering (ORE) in the School of Ocean and Earth Science and Technology (SOEST) of the University of Hawaii at Manoa. Supported in part by the NOAA Coastal Storms Program (CSP) and the University of Hawaii Sea Grant College Program. While considerable effort has been made to implement all model components in a thorough, correct, and accurate manner, numerous sources of error are possible. These data do not consider future changes in coastal geomorphology and natural processes such as erosion, subsidence, or future construction. These data do not specify timing of inundation depths and are not appropriate for conducting detailed spatial analysis. The entire risk associated with the results and performance of these data is assumed by the user. These data should be used strictly as a planning reference and not for navigation, permitting, or other legal purposes.

Fish Aggregation Devices (FADs) - Hawaii (hi_dar_all_fads)

Location of fish aggregation device (FAD) buoys within the Main Hawaiian Islands. Fishermen in Hawaii and other parts of the world have long known that tunas and other pelagic fishes are attracted to floating objects. Fishermen have benefited from this behavior by fishing around floating logs, nets, debris and other flotsam. Hawaii has capitalized on this phenomena by placing FADs in the waters surrounding the Hawaiian Islands. In these waters, schools of tunas and other important pelagic fishes such as dolphin-fish (mahi-mahi), wahoo (ono), and billfishes can be induced to congregate and remain for periods of time in an area so that fishers can easily locate them. Thus, the FADs are used to "attract" and "hold" pelagic fishes in areas to enhance fishing. NOTE: Please do not rely on these buoys to check your position. Most buoys have only been located to the nearest 0.1 minute (6 seconds). In addition, each buoy has a watch circle that can be as much as 1 mile in diameter.

Hawaii Division of Aquatic Resources (DAR) Marine Monitoring Sites: West Hawaii (hi_dar_bigi_marine_sites)

The State of Hawaii Department of Land and Natural Resources (DLNR) Division of Aquatic Resources (DAR) is the primary agency responsible for coordinating Hawaii's reef management efforts in the main Hawaiian Islands. The DAR marine monitoring program employs numerous methodologies developed by DAR scientists in collaboration with NOAA, USGS and the University of Hawaii (UH). Specific methods are used at study sites depending on the resource management concerns that DAR is looking to address, and include surveys of abundance of resource and herbivorous fish, smaller cryptic fish and recruits, urchins and larger mobile invertebrates, benthic habitat cover, coral health, and biological diversity. This layer includes the locations of DAR monitoring sites along the west coast of Hawaii Island (Big Island). For further information, please see: http://dlnr.hawaii.gov/coralreefs/monitoring/

Predicted Coral Cover of Montipora capitata in the Hawaiian Islands (hi_ef_all_coralreefs_mcap)

Output from a model to predict the benthic cover of Montipora capitata in the Hawaiian Islands as a proportion (0-1.0). Coral cover was modeled with boosted regression trees (BRT) in R software using data from coral cover surveys and environmental covariates derived from models and/or observations including wave height, benthic geomorphology, and downwelled irradiance. The best performing BRT model was used to predict M. capitata cover for the entire geographic study domain. Data and model output represent conditions for shallow coral reefs in the Hawaiian Islands from 0 m to -30 m depth over the time period 2000-2009. Further details on methodology and results are contained in Franklin et al. (2013).

Predicted Coral Cover of Montipora patula in the Hawaiian Islands (hi_ef_all_coralreefs_mpat)

Output from a model to predict the benthic cover of Montipora patula in the Hawaiian Islands as a proportion (0-1.0). Coral cover was modeled with boosted regression trees (BRT) in R software using data from coral cover surveys and environmental covariates derived from models and/or observations including wave height, benthic geomorphology, and downwelled irradiance. The best performing BRT model was used to predict M. patula cover for the entire geographic study domain. Data and model output represent conditions for shallow coral reefs in the Hawaiian Islands from 0 m to -30 m depth over the time period 2000-2009. Further details on methodology and results are contained in Franklin et al. (2013).

Predicted Coral Cover of Porites lobata in the Hawaiian Islands (hi_ef_all_coralreefs_plob)

Output from a model to predict the benthic cover of Porites lobata in the Hawaiian Islands as a proportion (0-1.0). Coral cover was modeled with boosted regression trees (BRT) in R software using data from coral cover surveys and environmental covariates derived from models and/or observations including wave height, benthic geomorphology, and downwelled irradiance. The best performing BRT model was used to predict P. lobata cover for the entire geographic study domain. Data and model output represent conditions for shallow coral reefs in the Hawaiian Islands from 0 m to -30 m depth over the time period 2000-2009. Further details on methodology and results are contained in Franklin et al. (2013).

Midway Atoll National Wildlife Refuge - Hawaii (hi_fws_nwhi_midway_refuge)

One of the few regions within the U.S. Fish and Wildlife Service (USFWS) National Wildlife Refuge System (NWRS) with a marine component, Midway Atoll is one of the most remote coral atolls on Earth, located near the edge of the Northwestern Hawaiian Islands (NWHI). Nearly two million birds call it home for much of each year, including the world's largest population of Laysan Albatrosses, or "gooney birds". Hawaiian monk seals, green sea turtles, and spinner dolphins all frequent the lagoon.

Government Land Ownership - Hawaii (hi_hcgg_all_govjurisdiction)

Government land ownership in the State of Hawaii: Federal, State, State Department of Hawaiian Home Lands (DHHL), and County. Source: City and County (C&C) of Honolulu (July 2013), Kauai County (January 2012), Maui County (July 2013), Hawaii County (June 2013). This dataset was created using the Large Landowners layer that was derived from the Tax Map Key (TMK) Parcel shapefiles from the counties of Honolulu, Kauai, Maui and Hawaii. Lands were selected for Type = "Public".

Coral Reef Assessment and Monitoring Program (CRAMP) Study Sites: Main Hawaiian Islands (hi_himb_all_cramp_sites)

The Hawaii Coral Reef Assessment and Monitoring Program (CRAMP) was created during 1997-98 by leading coral reef researchers, managers and educators in Hawaii. The initial task was to develop a statewide network consisting of over 30 long-term coral reef monitoring sites and an associated database. Upon completion of the monitoring network the focus was expanded to include rapid quantitative assessments and habitat mapping on a statewide spatial scale. Today the emphasis is on using these tools to understand the ecology of Hawaiian coral reefs in relation to other geographic areas. CRAMP study sites, including all areas of concern designated by the State of Hawaii Division of Aquatic Resources (DAR), were selected from throughout the State of Hawaii based on information provided by a wide spectrum of managers, scientists, and educators. These sites represent a full range of reef habitats subjected to various degrees of anthropogenic influences ranging from severely impacted to relatively pristine sites held in conservation status. CRAMP is based at the Hawaii Institue of Marine Biology (HIMB) of the University of Hawaii at Manoa and is led by Dr. Ku'ulei S. Rodgers (kuuleir@hawaii.edu). For further information, please see: http://cramp.wcc.hawaii.edu

Northwestern Hawaiian Islands Multibeam Bathymetry Synthesis: 60-m Bathymetry: Hillshade (hi_hurl_nwhi_bathy60m_hillshade)

A 60-m resolution grayscale hillshade of all available bathymetry (ocean depth) data for the Northwestern Hawaiian Islands (NWHI), including ship-borne multibeam sonar surveys merged with lower resolution (1-km) global bathymetry from SRTM30+. Because of its remote location, dedicated multibeam mapping of the NWHI region began only in the year 2000. In an effort to consolidate the more recent systematic surveys with older transit data swaths for this region, a synthesis of all existing multibeam data was undertaken beginning in 2009, with a fourth revision completed in mid 2015. Data provided by the Hawaii Undersea Research Laboratory (HURL) in the School of Ocean and Earth Science and Technology (SOEST) at the University of Hawaii at Manoa. For the related dataset containing numeric bathymetry values for this image layer, see http://pacioos.org/metadata/hurl_bathy_60m_nwhi.html

NOAA Shallow-Water Benthic Habitats: Hawaii: Big Island (hi_noaa_bigi_benthic_habitats)

Benthic habitat maps for the nearshore, shallow (< 30 m) coastal waters of Hawaii Island in the State of Hawaii. NOAA's National Centers for Coastal Ocean Science (NCCOS) produced these data to support coral reef research and management. Habitat regions were digitally identified using visual interpretation of orthorectified satellite imagery with a minimum mapping unit (MMU) of approximately 1 acre. Includes biological cover types, geomorphological structure types, and geographic zones. Eighteen distinct and non-overlapping biological cover types were identified. Habitats or features that cover areas smaller than the minimal mapping unit of 1 acre were not considered. For example, uncolonized sand halos surrounding coral patch reefs are too small to be mapped independently. Cover type refers only to the predominant biological component colonizing the surface of the feature and does not address location (e.g., on the shelf or in the lagoon). The cover types are defined in a collapsible hierarchy ranging from eight major classes (live coral, seagrass, macroalgae, encrusting/coralline algae, turf algae, emergent vegetation, uncolonized, and unknown), combined with a density modifier representing the percentage of the predominant cover type (10%-<50% sparse, 50%-<90% patchy, 90%-100% continuous). Similarly, 14 distinct and non-overlapping geomorphological structure types were identified. The structure types are defined in a collapsible hierarchy ranging from four major classes (coral reef and hardbottom, unconsolidated sediment, other delineations, and unknown), to thirteen detailed classes: sand, mud, spur and groove, individual and aggregated patch reef, aggregate reef, scattered coral/rock in unconsolidated sediment, pavement, rock/boulder (volcanic and carbonate), reef rubble, pavement with sand channels, artificial, and unknown. Lastly, 13 mutually exclusive geographic zones were identified from land to open water corresponding to typical insular shelf and coral reef geomorphology. These zones include: shoreline intertidal, vertical wall (none identified), lagoon, back reef, reef flat, reef crest, fore reef, bank/shelf, bank/shelf escarpment, channel, dredged (since this condition eliminates natural geomorphology), unknown, and land. Zone refers only to each benthic community's location and does not address substrate or cover types within. For example, the lagoon zone may include patch reefs, sand, and seagrass beds; however, these are considered structural elements that may or may not occur within the lagoon zone and therefore, are not used to define it.

Coastal Water Quality Monitoring Sites - West Hawaii Island, Hawaii (hi_noaa_bigi_coastal_waterquality)

The relative resilience of coral reef sites was assessed at two depths in 2015, 2016, and 2017 (note: this map layer only includes the shallow locations). The surveys were conducted as a collaborative effort by SymbioSeas, Hawaii Division of Aquatic Resources (DAR), The Nature Conservancy (TNC), NOAA Coral Reef Ecosystem Program (CREP), and community organizations.

Hawaiian Islands Sentinel Site Cooperative: West Hawaii Focus Area (hi_noaa_bigi_sentinel)

The Hawaiian Islands Sentinel Site Cooperative (SSC) is one of five areas across the country that make up NOAA's Sentinel Site Program. This program brings together a network of people from across different levels of government, community involvement, NGOs and other stakeholders, expertise, and existing NOAA tools and services within specific geographic regions to tackle problems faced by coastal communities. The initial focus areas for the Hawaiian Islands SSC are to restore damaged wetlands by monitoring rainfall, stream flow, and salt water intrusion; balance human needs with ecosystem health; and to find solutions to local problems related to coastal inundation and sea level change. This layer outlines the SSC project boundary for a portion of the west coast of Hawaii Island (Big Island). For further information, please see: http://oceanservice.noaa.gov/sentinelsites/hawaii.html

NOAA Shallow-Water Benthic Habitats: Hawaii: Kahoolawe (hi_noaa_kaho_benthic_habitats)

Benthic habitat maps for the nearshore, shallow (< 30 m) coastal waters of the island of Kahoolawe in the State of Hawaii. NOAA's National Centers for Coastal Ocean Science (NCCOS) produced these data to support coral reef research and management. Habitat regions were digitally identified using visual interpretation of orthorectified satellite imagery with a minimum mapping unit (MMU) of approximately 1 acre. Includes biological cover types, geomorphological structure types, and geographic zones. Eighteen distinct and non-overlapping biological cover types were identified. Habitats or features that cover areas smaller than the minimal mapping unit of 1 acre were not considered. For example, uncolonized sand halos surrounding coral patch reefs are too small to be mapped independently. Cover type refers only to the predominant biological component colonizing the surface of the feature and does not address location (e.g., on the shelf or in the lagoon). The cover types are defined in a collapsible hierarchy ranging from eight major classes (live coral, seagrass, macroalgae, encrusting/coralline algae, turf algae, emergent vegetation, uncolonized, and unknown), combined with a density modifier representing the percentage of the predominant cover type (10%-<50% sparse, 50%-<90% patchy, 90%-100% continuous). Similarly, 14 distinct and non-overlapping geomorphological structure types were identified. The structure types are defined in a collapsible hierarchy ranging from four major classes (coral reef and hardbottom, unconsolidated sediment, other delineations, and unknown), to thirteen detailed classes: sand, mud, spur and groove, individual and aggregated patch reef, aggregate reef, scattered coral/rock in unconsolidated sediment, pavement, rock/boulder (volcanic and carbonate), reef rubble, pavement with sand channels, artificial, and unknown. Lastly, 13 mutually exclusive geographic zones were identified from land to open water corresponding to typical insular shelf and coral reef geomorphology. These zones include: shoreline intertidal, vertical wall (none identified), lagoon, back reef, reef flat, reef crest, fore reef, bank/shelf, bank/shelf escarpment, channel, dredged (since this condition eliminates natural geomorphology), unknown, and land. Zone refers only to each benthic community's location and does not address substrate or cover types within. For example, the lagoon zone may include patch reefs, sand, and seagrass beds; however, these are considered structural elements that may or may not occur within the lagoon zone and therefore, are not used to define it.

NOAA Shallow-Water Benthic Habitats: Northwestern Hawaiian Islands: Kure Atoll (hi_noaa_kure_benthic_habitats)

Benthic habitat maps for the nearshore, shallow (< 30 m) coastal waters of Kure Atoll in the Northwestern Hawaiian Islands (NWHI) and the Papahanaumokuakea Marine National Monument. NOAA's National Centers for Coastal Ocean Science (NCCOS) produced these data to support coral reef research and management. Habitat regions were digitally identified using visual interpretation of orthorectified satellite imagery with a minimum mapping unit (MMU) of approximately 100 square meters (1/40 acre). A slightly different benthic habitat classification scheme was used for the NWHI compared to the Main Hawaiian Islands (MHI) and other regions across the Pacific (e.g., American Samoa, Guam, and CNMI). While many classes are similar, they are not categorized as biological cover types, geomorphological structure types, and geographic zones. Instead, a hierarchical scheme was used to flexibly denote substrate category (e.g., unconsolidated and hardbottom), structure (e.g., linear reef or pavement), and cover (e.g., coral, coralline algae, or macroalgae). A total of 45 detailed benthic habitat classes were identified within the NWHI. For simplification and to more easily distinguish cover types, these are presented in a set of 7 aggregated benthic habitat classes including 5 hardbottom substrate classes (live coral, coralline algae, macroalgae, uncolonized, and unknown biological cover) and 2 unconsolidated substrate classes (macroalgae and uncolonized). Query an aggregated polygon to get the detailed benthic habitat classification at the clicked location.

NOAA Shallow-Water Benthic Habitats: Northwestern Hawaiian Islands: Lisianski (hi_noaa_lski_benthic_habitats)

Benthic habitat maps for the nearshore, shallow (< 30 m) coastal waters of Lisianski Island in the Northwestern Hawaiian Islands (NWHI) and the Papahanaumokuakea Marine National Monument. NOAA's National Centers for Coastal Ocean Science (NCCOS) produced these data to support coral reef research and management. Habitat regions were digitally identified using visual interpretation of orthorectified satellite imagery with a minimum mapping unit (MMU) of approximately 100 square meters (1/40 acre). A slightly different benthic habitat classification scheme was used for the NWHI compared to the Main Hawaiian Islands (MHI) and other regions across the Pacific (e.g., American Samoa, Guam, and CNMI). While many classes are similar, they are not categorized as biological cover types, geomorphological structure types, and geographic zones. Instead, a hierarchical scheme was used to flexibly denote substrate category (e.g., unconsolidated and hardbottom), structure (e.g., linear reef or pavement), and cover (e.g., coral, coralline algae, or macroalgae). A total of 45 detailed benthic habitat classes were identified within the NWHI. For simplification and to more easily distinguish cover types, these are presented in a set of 7 aggregated benthic habitat classes including 5 hardbottom substrate classes (live coral, coralline algae, macroalgae, uncolonized, and unknown biological cover) and 2 unconsolidated substrate classes (macroalgae and uncolonized). Query an aggregated polygon to get the detailed benthic habitat classification at the clicked location.

NOAA Shallow-Water Benthic Habitats: Northwestern Hawaiian Islands: Maro Reef (hi_noaa_maro_benthic_habitats)

Benthic habitat maps for the nearshore, shallow (< 30 m) coastal waters of Maro Reef in the Northwestern Hawaiian Islands (NWHI) and the Papahanaumokuakea Marine National Monument. NOAA's National Centers for Coastal Ocean Science (NCCOS) produced these data to support coral reef research and management. Habitat regions were digitally identified using visual interpretation of orthorectified satellite imagery with a minimum mapping unit (MMU) of approximately 100 square meters (1/40 acre). A slightly different benthic habitat classification scheme was used for the NWHI compared to the Main Hawaiian Islands (MHI) and other regions across the Pacific (e.g., American Samoa, Guam, and CNMI). While many classes are similar, they are not categorized as biological cover types, geomorphological structure types, and geographic zones. Instead, a hierarchical scheme was used to flexibly denote substrate category (e.g., unconsolidated and hardbottom), structure (e.g., linear reef or pavement), and cover (e.g., coral, coralline algae, or macroalgae). A total of 45 detailed benthic habitat classes were identified within the NWHI. For simplification and to more easily distinguish cover types, these are presented in a set of 7 aggregated benthic habitat classes including 5 hardbottom substrate classes (live coral, coralline algae, macroalgae, uncolonized, and unknown biological cover) and 2 unconsolidated substrate classes (macroalgae and uncolonized). Query an aggregated polygon to get the detailed benthic habitat classification at the clicked location.

NOAA Shallow-Water Benthic Habitats: Northwestern Hawaiian Islands: Midway (hi_noaa_mdwy_benthic_habitats)

Benthic habitat maps for the nearshore, shallow (< 30 m) coastal waters of Midway Atoll in the Northwestern Hawaiian Islands (NWHI) and the Papahanaumokuakea Marine National Monument. NOAA's National Centers for Coastal Ocean Science (NCCOS) produced these data to support coral reef research and management. Habitat regions were digitally identified using visual interpretation of orthorectified satellite imagery with a minimum mapping unit (MMU) of approximately 100 square meters (1/40 acre). A slightly different benthic habitat classification scheme was used for the NWHI compared to the Main Hawaiian Islands (MHI) and other regions across the Pacific (e.g., American Samoa, Guam, and CNMI). While many classes are similar, they are not categorized as biological cover types, geomorphological structure types, and geographic zones. Instead, a hierarchical scheme was used to flexibly denote substrate category (e.g., unconsolidated and hardbottom), structure (e.g., linear reef or pavement), and cover (e.g., coral, coralline algae, or macroalgae). A total of 45 detailed benthic habitat classes were identified within the NWHI. For simplification and to more easily distinguish cover types, these are presented in a set of 7 aggregated benthic habitat classes including 5 hardbottom substrate classes (live coral, coralline algae, macroalgae, uncolonized, and unknown biological cover) and 2 unconsolidated substrate classes (macroalgae and uncolonized). Query an aggregated polygon to get the detailed benthic habitat classification at the clicked location.

NOAA Shallow-Water Benthic Habitats: Northwestern Hawaiian Islands: Necker (hi_noaa_nckr_benthic_habitats)

Benthic habitat maps for the nearshore, shallow (< 30 m) coastal waters of Necker Island in the Northwestern Hawaiian Islands (NWHI) and the Papahanaumokuakea Marine National Monument. NOAA's National Centers for Coastal Ocean Science (NCCOS) produced these data to support coral reef research and management. Habitat regions were digitally identified using visual interpretation of orthorectified satellite imagery with a minimum mapping unit (MMU) of approximately 100 square meters (1/40 acre). A slightly different benthic habitat classification scheme was used for the NWHI compared to the Main Hawaiian Islands (MHI) and other regions across the Pacific (e.g., American Samoa, Guam, and CNMI). While many classes are similar, they are not categorized as biological cover types, geomorphological structure types, and geographic zones. Instead, a hierarchical scheme was used to flexibly denote substrate category (e.g., unconsolidated and hardbottom), structure (e.g., linear reef or pavement), and cover (e.g., coral, coralline algae, or macroalgae). A total of 45 detailed benthic habitat classes were identified within the NWHI. For simplification and to more easily distinguish cover types, these are presented in a set of 7 aggregated benthic habitat classes including 5 hardbottom substrate classes (live coral, coralline algae, macroalgae, uncolonized, and unknown biological cover) and 2 unconsolidated substrate classes (macroalgae and uncolonized). Query an aggregated polygon to get the detailed benthic habitat classification at the clicked location.

NOAA Shallow-Water Benthic Habitats: Hawaii: Niihau (hi_noaa_niih_benthic_habitats)

Benthic habitat maps for the nearshore, shallow (< 30 m) coastal waters of the island of Niihau in Hawaii. NOAA's National Centers for Coastal Ocean Science (NCCOS) produced these data to support coral reef research and management. Habitat regions were digitally identified using visual interpretation of orthorectified satellite imagery with a minimum mapping unit (MMU) of approximately 1 acre. Includes biological cover types, geomorphological structure types, and geographic zones. Eighteen distinct and non-overlapping biological cover types were identified. Habitats or features that cover areas smaller than the minimal mapping unit of 1 acre were not considered. For example, uncolonized sand halos surrounding coral patch reefs are too small to be mapped independently. Cover type refers only to the predominant biological component colonizing the surface of the feature and does not address location (e.g., on the shelf or in the lagoon). The cover types are defined in a collapsible hierarchy ranging from eight major classes (live coral, seagrass, macroalgae, encrusting/coralline algae, turf algae, emergent vegetation, uncolonized, and unknown), combined with a density modifier representing the percentage of the predominant cover type (10%-<50% sparse, 50%-<90% patchy, 90%-100% continuous). Similarly, 14 distinct and non-overlapping geomorphological structure types were identified. The structure types are defined in a collapsible hierarchy ranging from four major classes (coral reef and hardbottom, unconsolidated sediment, other delineations, and unknown), to thirteen detailed classes: sand, mud, spur and groove, individual and aggregated patch reef, aggregate reef, scattered coral/rock in unconsolidated sediment, pavement, rock/boulder (volcanic and carbonate), reef rubble, pavement with sand channels, artificial, and unknown. Lastly, 13 mutually exclusive geographic zones were identified from land to open water corresponding to typical insular shelf and coral reef geomorphology. These zones include: shoreline intertidal, vertical wall (none identified), lagoon, back reef, reef flat, reef crest, fore reef, bank/shelf, bank/shelf escarpment, channel, dredged (since this condition eliminates natural geomorphology), unknown, and land. Zone refers only to each benthic community's location and does not address substrate or cover types within. For example, the lagoon zone may include patch reefs, sand, and seagrass beds; however, these are considered structural elements that may or may not occur within the lagoon zone and therefore, are not used to define it.

Papahanaumokuakea Marine National Monument (PMNM), 2006-2016 - Northwestern Hawaiian Islands (NWHI) (hi_noaa_nwhi_papahanaumokuakea_2006)

Boundary of the Papahanaumokuakea Marine National Monument (PMNM), located in the Northwestern Hawaiian Islands (NWHI). Created in 2006, Papahanaumokuakea is the largest conservation area in the U.S. and one of the largest marine conservation areas in the world. It is home to extensive coral reefs harboring over 7,000 marine species, one quarter of which are found only in Hawaii. Many of the islands and shallow water environments are important habitats for rare species such as the threatened green sea turtle and the endangered Hawaiian monk seal. Significant cultural Native Hawaiian sites can also be found on the islands of Nihoa and Mokumanamana (Necker Island). Co-managed with the State of Hawaii and the U.S. Department of the Interior, the monument preserves one of the most untouched areas of coral reef in the world. This layer shows the PMNM boundary prior to its expansion in 2016. On August 26, 2016, President Obama signed a proclamation expanding the monument from 139,797 square miles (362,073 square kilometers) to 582,578 square miles (1,508,870 square kilometers). This extended the monument boundary westward of -163 degrees longitude out to Hawaii's Exclusive Economic Zone (EEZ) at 200 nautical miles offshore. The present layer shows the monument boundary as it existed *before* this expansion. To view the expanded boundary, access the data layers for "hi_noaa_nwhi_papahanaumokuakea" or "hi_noaa_nwhi_papahanaumokuakea_line" instead.

NOAA Shallow-Water Benthic Habitats: Hawaii: Oahu (hi_noaa_oahu_benthic_habitats)

Benthic habitat maps for the nearshore, shallow (< 30 m) coastal waters of the island of Oahu in the State of Hawaii. NOAA's National Centers for Coastal Ocean Science (NCCOS) produced these data to support coral reef research and management. Habitat regions were digitally identified using visual interpretation of orthorectified satellite imagery with a minimum mapping unit (MMU) of approximately 1 acre. Includes biological cover types, geomorphological structure types, and geographic zones. Eighteen distinct and non-overlapping biological cover types were identified. Habitats or features that cover areas smaller than the minimal mapping unit of 1 acre were not considered. For example, uncolonized sand halos surrounding coral patch reefs are too small to be mapped independently. Cover type refers only to the predominant biological component colonizing the surface of the feature and does not address location (e.g., on the shelf or in the lagoon). The cover types are defined in a collapsible hierarchy ranging from eight major classes (live coral, seagrass, macroalgae, encrusting/coralline algae, turf algae, emergent vegetation, uncolonized, and unknown), combined with a density modifier representing the percentage of the predominant cover type (10%-<50% sparse, 50%-<90% patchy, 90%-100% continuous). Similarly, 14 distinct and non-overlapping geomorphological structure types were identified. The structure types are defined in a collapsible hierarchy ranging from four major classes (coral reef and hardbottom, unconsolidated sediment, other delineations, and unknown), to thirteen detailed classes: sand, mud, spur and groove, individual and aggregated patch reef, aggregate reef, scattered coral/rock in unconsolidated sediment, pavement, rock/boulder (volcanic and carbonate), reef rubble, pavement with sand channels, artificial, and unknown. Lastly, 13 mutually exclusive geographic zones were identified from land to open water corresponding to typical insular shelf and coral reef geomorphology. These zones include: shoreline intertidal, vertical wall (none identified), lagoon, back reef, reef flat, reef crest, fore reef, bank/shelf, bank/shelf escarpment, channel, dredged (since this condition eliminates natural geomorphology), unknown, and land. Zone refers only to each benthic community's location and does not address substrate or cover types within. For example, the lagoon zone may include patch reefs, sand, and seagrass beds; however, these are considered structural elements that may or may not occur within the lagoon zone and therefore, are not used to define it.

Lighthouses - Hawaii (hi_ocs_all_lighthouses)

Locations of lighthouses and minor coastal lights for the State of Hawaii. These aids to navigation (ATONs) are used to mark dangerous coastlines, hazardous shoals and reefs, and safe entries to harbors and can assist in both marine and aerial navigation. Information regarding the lamp light (color, range, elevation, etc.) is also provided as determined from NOAA nautical charts.

Chlorophyll-a Average Annual Maximum Anomaly, 2002-2013 - Hawaii (hi_otp_all_chlor_anom_max)

Chlorophyll-a is a widely used proxy for phytoplankton biomass and an indicator for changes in phytoplankton production. As an essential source of energy in the marine environment, the extent and availability of phytoplankton biomass can be highly influential for fisheries production and dictate trophic structure in marine ecosystems. Changes in phytoplankton biomass are predominantly effected by changes in nutrient availability, through either natural (e.g., turbulent ocean mixing) or anthropogenic (e.g., agricultural runoff) processes. This layer represents the annual average of the maximum anomaly of chlorophyll-a (mg/m3) from 2002-2013. Monthly and 8-day 4-km (0.0417-degree) spatial resolution data were obtained from the MODIS (Moderate-resolution Imaging Spectroradiometer) Aqua satellite instrument from the NASA OceanColor website (http://oceancolor.gsfc.nasa.gov). The chlorophyll-a average annual maximum anomaly was calculated by taking the average of the chlorophyll-a values from the 8-day time series in exceedance of the maximum monthly climatological chlorophyll-a from 2002-2013 for each pixel. A quality control mask was applied to remove spurious data associated with shallow water, following Gove et al., 2013. Monthly Climatologies were calculated from monthly time series using only full years over the Ocean Tipping Points (OTP) project time frame of interest (2002-2013). Time series of anomalies were calculated by quantifying the number and magnitude of events from the 8-day time series that exceed the maximum climatological monthly mean. Nearshore map pixels with no data were filled with values from the nearest neighboring valid offshore pixel by using a grid of points and the Near Analysis tool in ArcGIS then converting points to raster.

Chlorophyll-a Maximum Monthly Climatological Mean, 2002-2013 - Hawaii (hi_otp_all_chlor_clim_max)

Chlorophyll-a is a widely used proxy for phytoplankton biomass and an indicator for changes in phytoplankton production. As an essential source of energy in the marine environment, the extent and availability of phytoplankton biomass can be highly influential for fisheries production and dictate trophic structure in marine ecosystems. Changes in phytoplankton biomass are predominantly effected by changes in nutrient availability, through either natural (e.g., turbulent ocean mixing) or anthropogenic (e.g., agricultural runoff) processes. This layer represents the maximum monthly climatological mean of chlorophyll-a (mg/m3) from 2002-2013. Monthly and 8-day 4-km (0.0417-degree) spatial resolution data were obtained from the MODIS (Moderate-resolution Imaging Spectroradiometer) Aqua satellite instrument from the NASA OceanColor website (http://oceancolor.gsfc.nasa.gov). A quality control mask was applied to remove spurious data associated with shallow water, following Gove et al., 2013. Monthly climatologies were calculated from monthly time series using only full years over the Ocean Tipping Points (OTP) project time frame of interest (2002-2013), averaging for all same-months (e.g., January). Nearshore map pixels with no data were filled with values from the nearest neighboring valid offshore pixel by using a grid of points and the Near Analysis tool in ArcGIS then converting points to raster.

Coastal Habitat Modification - Hawaii (hi_otp_all_coastal_mod)

Coastal habitats are utilized and altered for a suite of human uses. Habitat modification is here defined as the alteration or removal of geomorphic structure as a result of human use. This includes several habitat-modifying features like seawalls, piers, breakwaters, dredged areas, artificial land (i.e., filled wetlands), and offshore structures. This data layer represents the presence of habitat modification in shallow waters of the Main Hawaiian Islands. The Ocean Tipping Points (OTP) project mapped the presence of habitat-modifying features by combining several existing datasets derived primarily from satellite and aerial imagery, including the following datasets: benthic habitat maps (NOAA Center for Coastal Monitoring and Assessment (CCMA), 2007); NOAA Environmental Sensitivity Index (ESI) line data (NOAA Office of Response and Restoration (OR&R), 2001); maintained channels (NOAA, US Army Corps of Engineers (USACE), MarineCadastre.gov); and locations of offshore aquaculture. The layer represents the presence or absence of habitat modification, with a cell size of 500 m. Relevant man-made features were extracted from each individual dataset and saved (features classified as artificial and dredged areas in NOAA benthic habitat maps; coastal segments designated as man-made structures and riprap in NOAA ESI line data; all features from the maintained channels and aquaculture datasets). The resulting polygon datasets were merged together. A field was added to all vector layers with a value of 1 for each feature to represent the presence of habitat modification. Vector data were then converted to 500-m rasters and combined into a mosaic.

Commercial Fishing Estimated Average Annual Catch of Reef Fish, 2003-2013 - Hawaii (hi_otp_all_fishing_com)

Nearshore fisheries in the Main Hawaiian Islands encompass a diverse group of fishers using a wide array of gears and targeting many different species. Communities in Hawaii often rely on these fisheries for economic, social, and cultural services. However, the stress from overfishing can cause ecosystem degradation and long-term economic loss. This layer represents the average annual commercial catch of reef fish over the years 2003-2013 as reported in commercial catch data collected by the State of Hawaii Department of Aquatic Resources (DAR) Commercial Marine Landings Database (CML). Commercial catch is reported to DAR in large irregular reporting blocks by gear and by species. This layer is the sum of the three gear-specific Ocean Tipping Points (OTP) commercial fishing rasters (line, net, and spear). This layer's spatial footprint aligns with the inshore commercial reporting blocks from the shapefile served on the Hawaii Statewide GIS Program website (Fishchart2008.shp) (http://planning.hawaii.gov/gis/download-gis-data/). Data are filtered by DAR before release such that reporting blocks with less than three fishers reporting are excluded in order to protect fisher identities. It is not possible to explicitly distinguish between boat-based and shore-based fishing based on the gear types reported in CML data. OTP filtered the data for reef fish species only and calculated average annual catch in kilograms by reporting block and gear type to match with data from the Marine Recreational Information Program (MRIP): line, net, and spear. In marine protected areas (MPAs) where boat-based fishing is not allowed, catch was set to zero; and inside de facto MPAs with restricted access, catch was reduced according to expert input and local knowledge. Average annual commercial catch data were converted from polygon to raster for each gear type and then divided by the number of 100-m raster cells within each reporting block so that units are comparable to non-commercial fishing layers (kg/ha). The result assumes commercial catch is evenly distributed spatially across each reporting block.

Commercial Net Fishing Estimated Average Annual Catch of Reef Fish, 2003-2013 - Hawaii (hi_otp_all_fishing_com_net)

Nearshore fisheries in the Main Hawaiian Islands encompass a diverse group of fishers using a wide array of gears and targeting many different species. Communities in Hawaii often rely on these fisheries for economic, social, and cultural services. However, the stress from overfishing can cause ecosystem degradation and long-term economic loss. This layer represents the average annual commercial catch of reef fish by net fishing over the years 2003-2013 as reported in commercial catch data collected by the State of Hawaii Department of Aquatic Resources (DAR) Commercial Marine Landings Database (CML). "Net fishing" is a fairly broad category that can include everything from hand nets and throw nets to gill nets and seine nets; however, it was not possible to parse out more specific gears due to how DAR reports gear type. Commercial catch is reported to DAR in large irregular reporting blocks by gear and by species. This layer's spatial footprint aligns with the inshore commercial reporting blocks from the shapefile served on the Hawaii Statewide GIS Program website (Fishchart2008.shp) (http://planning.hawaii.gov/gis/download-gis-data/). Data are filtered by DAR before release such that reporting blocks with less than three fishers reporting are excluded in order to protect fisher identities. It is not possible to explicitly distinguish between boat-based and shore-based fishing based on the gear types reported in CML data. The Ocean Tipping Points (OTP) project filtered the data for net fishing and reef fish species only and calculated average annual catch in kilograms by reporting block to match with data from the Marine Recreational Information Program (MRIP). In marine protected areas (MPAs) where boat-based fishing is not allowed, catch was set to zero; and inside de facto MPAs with restricted access, catch was reduced according to expert input and local knowledge. Average annual commercial catch data were converted from polygon to raster and then divided by the number of 100-m raster cells within each reporting block so that units are comparable to non-commercial fishing layers (kg/ha). The result assumes commercial catch is evenly distributed spatially across each reporting block.

Non-commercial Fishing Estimated Average Annual Catch of Reef Fish, 2004-2013 - Hawaii (hi_otp_all_fishing_rec)

Nearshore fisheries in the Main Hawaiian Islands encompass a diverse group of fishers using a wide array of gears and targeting many different species. Communities in Hawaii often rely on these fisheries for economic, social, and cultural services. However, the stress from overfishing can cause ecosystem degradation and long-term economic loss. This layer represents the average annual catch of reef fish by non-commercial fishing methods. Average annual catch at the island scale from 2004-2013 was estimated from Marine Recreational Information Program (MRIP) combined fisher intercept and phone survey data (McCoy et al., 2018). These island-scale estimates were spatially distributed offshore by combining two different proxies for shoreline accessibility (terrain steepness and presence of roads) while accounting for marine protected areas (MPAs) and de facto MPAs (e.g., military danger zones) where access is restricted. This layer's spatial footprint aligns with the inshore commercial reporting blocks for commercial fish catch reporting to the State of Hawaii Department of Aquatic Resources (DAR). This layer is the sum of the non-commercial boat-based and shore-based Ocean Tipping Points (OTP) rasters for all gear types (line, net, and spear); for specific details, see respective layers. Final pixels values are in units of kg/ha such that the sum of all pixels for each island is equal to the estimates of average annual catch from McCoy et al. (2018). Units, pixel size, and grid alignment are consistent with all other OTP fishing layers so that they can be compared directly or added together for various uses.

Non-commercial Boat-based Line Fishing Estimated Average Annual Catch of Reef Fish, 2004-2013 - Hawaii (hi_otp_all_fishing_rec_boat_line)

Nearshore fisheries in the Main Hawaiian Islands encompass a diverse group of fishers using a wide array of gears and targeting many different species. Communities in Hawaii often rely on these fisheries for economic, social, and cultural services. However, the stress from overfishing can cause ecosystem degradation and long-term economic loss. This layer represents the average annual catch of reef fish by non-commercial boat-based line fishing methods. Average annual catch at the island scale from 2004-2013 was estimated from Marine Recreational Information Program (MRIP) combined fisher intercept and phone survey data (McCoy et al., 2018). These island-scale estimates were spatially distributed offshore using distance to boat harbors and launch ramps while accounting for marine protected areas (MPAs) and de facto MPAs (e.g., military danger zones) where access is restricted. A Gaussian decay function assumed the majority of the catch occurs within 10-20 km of each harbor. Additionally, the Ocean Tipping Points (OTP) project weighted boat harbors by the human population present within 30 km. This layer's spatial footprint aligns with the inshore commercial reporting blocks for commercial fish catch reporting to the State of Hawaii Department of Aquatic Resources (DAR). Point data for boat harbors and launch ramps were combined from two datasets available from the Hawaii Statewide GIS Program website (Harbors.shp and BoatingFacilities.shp) (http://planning.hawaii.gov/gis/download-gis-data/). Data were checked for quality to ensure only operational boat harbors and launch ramps were included and geographic positions were accurate. Anchorages, fishing piers, historic, and disused ramps/harbors were removed prior to analysis. Boat facility weighting factors were calculated based on total human population within 30 km of each boat harbor or ramp. Human population was mapped based on 2010 census data and LANDFIRE land use/land cover data using the USGS Dasymetric Mapping Tool to gain a more accurate representation of population distribution. A 30-km buffer was then created around each boating facility and a Zonal Statistics tool was used to sum the human population within each buffer. These population values were then used to assign weights to each boating facility in order to allocate a proportion of total island catch estimates to each boat harbor or ramp. These weights sum to 1 for each island. In order to allocate catch proportionally to each boat harbor/ramp, estimated annual catch at the island scale and the human population-based weighting factor were joined to the attribute table of each boating facility's cost allocation footprint and used in a Gaussian decay function with each distance surface. This decay function assumes the majority of catch occurs within 10-20 km of a harbor or ramp and declines more rapidly with increasing distance. Catch in full no-take MPAs were set to zero and other areas with restricted access were reduced according to expert input and local knowledge. Pixel values within each boating facility's footprint were then rescaled such that the sum in each footprint was equal to the respective boat facility's weighting factor times the MRIP catch estimate for that island in units of kg per pixel. Finally, all raster layers for each boat harbor/ramp were summed together. Final pixels values are in units of kg/ha such that the sum of all pixels for each island is equal to the estimates of average annual catch from McCoy et al. (2018). Units, pixel size, and grid alignment are consistent with all other OTP fishing layers so that they can be compared directly or added together for various uses.

Non-commercial Boat-based Spear Fishing Estimated Average Annual Catch of Reef Fish, 2004-2013 - Hawaii (hi_otp_all_fishing_rec_boat_spear)

Nearshore fisheries in the Main Hawaiian Islands encompass a diverse group of fishers using a wide array of gears and targeting many different species. Communities in Hawaii often rely on these fisheries for economic, social, and cultural services. However, the stress from overfishing can cause ecosystem degradation and long-term economic loss. This layer represents the average annual catch of reef fish by non-commercial boat-based spear fishing methods. Average annual catch at the island scale from 2004-2013 was estimated from Marine Recreational Information Program (MRIP) combined fisher intercept and phone survey data (McCoy et al., 2018). These island-scale estimates were spatially distributed offshore using distance to boat harbors and launch ramps while accounting for marine protected areas (MPAs) and de facto MPAs (e.g., military danger zones) where access is restricted. A Gaussian decay function assumed the majority of the catch occurs within 10-20 km of each harbor. Additionally, the Ocean Tipping Points (OTP) project weighted boat harbors by the human population present within 30 km. This layer's spatial footprint aligns with the inshore commercial reporting blocks for commercial fish catch reporting to the State of Hawaii Department of Aquatic Resources (DAR). Point data for boat harbors and launch ramps were combined from two datasets available from the Hawaii Statewide GIS Program website (Harbors.shp and BoatingFacilities.shp) (http://planning.hawaii.gov/gis/download-gis-data/). Data were checked for quality to ensure only operational boat harbors and launch ramps were included and geographic positions were accurate. Anchorages, fishing piers, historic, and disused ramps/harbors were removed prior to analysis. Boat facility weighting factors were calculated based on total human population within 30 km of each boat harbor or ramp. Human population was mapped based on 2010 census data and LANDFIRE land use/land cover data using the USGS Dasymetric Mapping Tool to gain a more accurate representation of population distribution. A 30-km buffer was then created around each boating facility and a Zonal Statistics tool was used to sum the human population within each buffer. These population values were then used to assign weights to each boating facility in order to allocate a proportion of total island catch estimates to each boat harbor or ramp. These weights sum to 1 for each island. In order to allocate catch proportionally to each boat harbor/ramp, estimated annual catch at the island scale and the human population-based weighting factor were joined to the attribute table of each boating facility's cost allocation footprint and used in a Gaussian decay function with each distance surface. This decay function assumes the majority of catch occurs within 10-20 km of a harbor or ramp and declines more rapidly with increasing distance. Catch in full no-take MPAs were set to zero and other areas with restricted access were reduced according to expert input and local knowledge. Pixel values within each boating facility's footprint were then rescaled such that the sum in each footprint was equal to the respective boat facility's weighting factor times the MRIP catch estimate for that island in units of kg per pixel. Finally, all raster layers for each boat harbor/ramp were summed together. Final pixels values are in units of kg/ha such that the sum of all pixels for each island is equal to the estimates of average annual catch from McCoy et al. (2018). Units, pixel size, and grid alignment are consistent with all other OTP fishing layers so that they can be compared directly or added together for various uses.

Non-commercial Shore-based Line Fishing Estimated Average Annual Catch of Reef Fish, 2004-2013 - Hawaii (hi_otp_all_fishing_rec_shore_line)

Nearshore fisheries in the Main Hawaiian Islands encompass a diverse group of fishers using a wide array of gears and targeting many different species. Communities in Hawaii often rely on these fisheries for economic, social, and cultural services. However, the stress from overfishing can cause ecosystem degradation and long-term economic loss. This layer represents the average annual catch of reef fish by non-commercial shore-based line fishing methods. Average annual catch at the island scale from 2004-2013 was estimated from Marine Recreational Information Program (MRIP) combined fisher intercept and phone survey data (McCoy et al., 2018). These island-scale estimates were spatially distributed offshore by combining two different proxies for shoreline accessibility (terrain steepness and presence of roads) while accounting for marine protected areas (MPAs) and de facto MPAs (e.g., military danger zones) where access is restricted. This layer's spatial footprint aligns with the inshore commercial reporting blocks for commercial fish catch reporting to the State of Hawaii Department of Aquatic Resources (DAR). Slope of the shoreline was calculated in degrees using a USGS 10-m Digital Elevation Model (DEM). Topologically Integrated Geographic Encoding and Referencing (TIGER) road data from the US Census Bureau were used to classify the presence and type of roads. Attributes for slope and road accessibility were then combined into a single accessibility criterion. A weighting scheme was created that assumes easily accessible shorelines with flat slopes and paved public road access have the highest catch and that catch decreases incrementally with level of accessibility. Any combination that includes no accessibility due to steep slopes received a zero weight and therefore zero fishing. Weights sum to 1 for each island. These weights were then multiplied by the MRIP island-scale estimates of annual catch from line fishing at each coastal point. Catch was then extended offshore 200 m. Catch in full no-take MPAs were set to zero and other areas with restricted access were reduced according to expert input and local knowledge. Final pixels values are in units of kg/ha such that the sum of all pixels for each island is equal to the estimates of average annual catch from McCoy et al. (2018). Units, pixel size, and grid alignment are consistent with all other OTP fishing layers so that they can be compared directly or added together for various uses.

Non-commercial Shore-based Spear Fishing Estimated Average Annual Catch of Reef Fish, 2004-2013 - Hawaii (hi_otp_all_fishing_rec_shore_spear)

Nearshore fisheries in the Main Hawaiian Islands encompass a diverse group of fishers using a wide array of gears and targeting many different species. Communities in Hawaii often rely on these fisheries for economic, social, and cultural services. However, the stress from overfishing can cause ecosystem degradation and long-term economic loss. This layer represents the average annual catch of reef fish by non-commercial shore-based spear fishing methods. Average annual catch at the island scale from 2004-2013 was estimated from Marine Recreational Information Program (MRIP) combined fisher intercept and phone survey data (McCoy et al., 2018). These island-scale estimates were spatially distributed offshore by combining two different proxies for shoreline accessibility (terrain steepness and presence of roads) while accounting for marine protected areas (MPAs) and de facto MPAs (e.g., military danger zones) where access is restricted. This layer's spatial footprint aligns with the inshore commercial reporting blocks for commercial fish catch reporting to the State of Hawaii Department of Aquatic Resources (DAR). Slope of the shoreline was calculated in degrees using a USGS 10-m Digital Elevation Model (DEM). Topologically Integrated Geographic Encoding and Referencing (TIGER) road data from the US Census Bureau were used to classify the presence and type of roads. Attributes for slope and road accessibility were then combined into a single accessibility criterion. A weighting scheme was created that assumes easily accessible shorelines with flat slopes and paved public road access have the highest catch and that catch decreases incrementally with level of accessibility. Any combination that includes no accessibility due to steep slopes received a zero weight and therefore zero fishing. Weights sum to 1 for each island. These weights were then multiplied by the MRIP island-scale estimates of annual catch from spear fishing at each coastal point. A logistic decay function was used so spear fishing catch decreases with depth to 40 m or a maximum distance of 2 km from shore. Catch in full no-take MPAs were set to zero and other areas with restricted access were reduced according to expert input and local knowledge. Final pixels values are in units of kg/ha such that the sum of all pixels for each island is equal to the estimates of average annual catch from McCoy et al. (2018). Units, pixel size, and grid alignment are consistent with all other OTP fishing layers so that they can be compared directly or added together for various uses.

Observed Presence of Alien and Invasive Reef Fish, 2000-2013 - Hawaii (hi_otp_all_invasive_fish)

This raster data layer represents the presence of alien and invasive reef fish species within 2 km of an observation, including roi or bluespotted grouper (Cephalopholis argus), ta'ape or bluestripe snapper (Lutjanus kasmira), and to'au or blacktail snapper (Lutjanus fulvus). Original data were queried from the University of Hawaii at Manoa (UH) Fisheries Ecology Research Laboratory (FERL) Hawaii Fish and Benthic Biological Synthesis Database (2000-2013), which is synthesized from NOAA, State of Hawaii Division of Aquatic Resources (DAR), Coral Reef Assessment and Monitoring Program (CRAMP), The Nature Conservancy (TNC), and other surveys conducted across the Main Hawaiian Islands. These data should be considered presence only. Areas with no presence may be due to lack of survey data or observed absence. Raster values of 1 represent areas within 2 km of positive invasive fish observations while values of 0 represent the remaining area. The cell size is 500 m and the area of interest is from the shoreline of the Main Hawaiian Islands extending 5 km offshore and 1 km inshore.

Sediment Export to Nearshore Waters - Hawaii (hi_otp_all_nearshore_sediment)

This raster data layer represents sediment plumes originating from stream mouths and coastal pour points. The Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model for sediment retention was modified for Hawaii, parameterized, and run for each of the Main Hawaiian Islands to determine sediment export from subwatershed hydrologic units (Falinski, 2016). Results from this model were aggregated into larger drainage areas that flow to single coastal pour points. From these points sediment was dispersed offshore using the Kernel Density tool in ArcGIS with a 1.5-km search radius. The resulting raster depicts simplistic sediment plumes with units in tons of sediment per year per hectare. The InVEST model predicts the average annual amount of sediment (tons/yr) retained in and exported from each map pixel as a function of many landscape variables. Data inputs to InVEST included: 1) USGS 10-m Digital Elevation Model (DEM); 2) NOAA Coastal Change Analysis Program (C-CAP) land use/land cover data; 3) R factor (old USGS maps and interpolation); 4) K factor (USDA Natural Resources Conservation Service (NRCS) Soil Survey Geographic database (SSURGO)); 5) University of Hawaii at Manoa (UH) rainfall atlas; 6) ArcHydro-derived subwatersheds such that flow lines approximately match the State of Hawaii streams layer; and 7) derived products from the above and more. See Falinski (2016) for detailed methodology. Coastal pour points were created by intersecting streams and coastline features from the National Hydrography Dataset (NHD), resulting in points where streams flow to the shoreline. The NHD was used rather than flow lines generated from the DEM because there are many instances in Hawaii where streams flow into man-made ditch systems and never reach the coast or simply dry up and go underground before reaching the coast. To determine the amount of sediment load at the coastline, resulting coastal points were given a unique drainage identifier. Next, the stream segment features were buffered by 1 m and dissolved so that connecting stream networks became single features. These polygon stream features were then assigned the drainage ID from the coastal points using a spatial join and subsequently used to assign that drainage ID to the subwatershed polygons. Finally, subwatersheds were dissolved by drainage ID and sediment export from each subwatershed was summed up to yield the total sediment export for each larger drainage basin, which was then joined back to the corresponding coastal drainage points. Each step in the process required quality control to ensure that: no pour points are left out, subwatersheds are not erroneously connected to the wrong drainage or left out, each drainage has only 1 pour point, and drainages do not erroneously span a ridgeline that should divide basins.

Nitrogen Flux from Onsite Sewage Disposal Systems (OSDS) - Hawaii (hi_otp_all_osds_nitrogen)

This layer represents the nitrogen flux coming from onsite sewage disposal systems (OSDS) (e.g., cesspools and septic tanks). OSDS point data were obtained from the University of Hawaii at Manoa (UH) (Aly El-Kadi) and the State of Hawaii Department of Health (DOH) (Bob Whittier) that estimates nitrogen flux from each property, or Tax Map Key (TMK) parcel, with OSDS. The Ocean Tipping Points (OTP) project converted the points to raster by summing nutrient flux values within 500-m pixels. A Focal Statistics tool was then used to calculate the total flux within a 1.5-km radius of each offshore pixel. Units are in grams per day per km squared. The area of interest is from the shoreline of the Main Hawaiian Islands extending 5 km offshore and 1 km inshore.

Photosynthetically Active Radiation (PAR) Average Annual Frequency of Anomalies, 2002-2013 - Hawaii (hi_otp_all_par_anom_freq)

Solar irradiance is one of the most important factors influencing coral reefs. As the majority of their nutrients are obtained from symbiotic photosynthesizing organisms, reef-building corals need irradiance as a fundamental source of energy. Seasonally-low irradiance at high latitudes may be linked to reduced growth rates in corals and may limit reef calcification to shallower depths than that observed at lower latitudes. However, high levels of irradiance can lead to light-induced damage, production of free radicals, and in combination with increased temperatures, can exacerbate coral bleaching. This layer represents the annual average number of anomalies of irradiance from 2002-2013, with values presented as fraction of a year. Irradiance is here represented by PAR (photosynthetically active radiation), which is the spectrum of light that is important for photosynthesis. Monthly and 8-day 4-km (0.0417-degree) spatial resolution data were obtained from the MODIS (Moderate-resolution Imaging Spectroradiometer) Aqua satellite instrument from the NASA OceanColor website (http://oceancolor.gsfc.nasa.gov). The PAR average annual frequency of anomalies was calculated by taking the average number of weeks that exceeded the maximum monthly climatological PAR value from 2002-2013 for each pixel. A quality control mask was applied to remove spurious data associated with shallow water, following Gove et al., 2013. Monthly climatologies were calculated from monthly time series using only full years over the Ocean Tipping Points (OTP) project time frame of interest (2002-2013). Time series of anomalies were calculated by quantifying the number and magnitude of events from the 8-day time series that exceed the maximum climatological monthly mean. Nearshore map pixels with no data were filled with values from the nearest neighboring valid offshore pixel by using a grid of points and the Near Analysis tool in ArcGIS then converting points to raster.

Photosynthetically Active Radiation (PAR) Long-term Mean, 2002-2013 - Hawaii (hi_otp_all_par_avg)

Solar irradiance is one of the most important factors influencing coral reefs. As the majority of their nutrients are obtained from symbiotic photosynthesizing organisms, reef-building corals need irradiance as a fundamental source of energy. Seasonally-low irradiance at high latitudes may be linked to reduced growth rates in corals and may limit reef calcification to shallower depths than that observed at lower latitudes. However, high levels of irradiance can lead to light-induced damage, production of free radicals, and in combination with increased temperatures, can exacerbate coral bleaching. This layer represents the mean of 8-day time series of irradiance (mol/m2/day) from July 2002 to December 31, 2013. Irradiance is here represented by PAR (photosynthetically active radiation), which is the spectrum of light that is important for photosynthesis. Monthly and 8-day 4-km (0.0417-degree) spatial resolution data were obtained from the MODIS (Moderate-resolution Imaging Spectroradiometer) Aqua satellite instrument from the NASA OceanColor website (http://oceancolor.gsfc.nasa.gov). The PAR long-term mean was calculated by taking the average of all 8-day data from 2002-2013 for each pixel. A quality control mask was applied to remove spurious data associated with shallow water, following Gove et al., 2013. Nearshore map pixels with no data were filled with values from the nearest neighboring valid offshore pixel by using a grid of points and the Near Analysis tool in ArcGIS then converting points to raster.

Photosynthetically Active Radiation (PAR) Standard Deviation of Long-term Mean, 2002-2013 - Hawaii (hi_otp_all_par_std)

Solar irradiance is one of the most important factors influencing coral reefs. As the majority of their nutrients are obtained from symbiotic photosynthesizing organisms, reef-building corals need irradiance as a fundamental source of energy. Seasonally-low irradiance at high latitudes may be linked to reduced growth rates in corals and may limit reef calcification to shallower depths than that observed at lower latitudes. However, high levels of irradiance can lead to light-induced damage, production of free radicals, and in combination with increased temperatures, can exacerbate coral bleaching. This layer represents the standard deviation of the 8-day time series of irradiance (mol/m2/day) from July 2002 to December 31, 2013. Irradiance is here represented by PAR (photosynthetically active radiation), which is the spectrum of light that is important for photosynthesis. Monthly and 8-day 4-km (0.0417-degree) spatial resolution data were obtained from the MODIS (Moderate-resolution Imaging Spectroradiometer) Aqua satellite instrument from the NASA OceanColor website (http://oceancolor.gsfc.nasa.gov). The standard deviation of the long-term mean of PAR was calculated by taking the standard deviation over all 8-day data from 2002-2013 for each pixel. A quality control mask was applied to remove spurious data associated with shallow water, following Gove et al., 2013. Nearshore map pixels with no data were filled with values from the nearest neighboring valid offshore pixel by using a grid of points and the Near Analysis tool in ArcGIS then converting points to raster.

Sea Surface Temperature (SST) Average Annual Maximum Anomaly, 2000-2013 - Hawaii (hi_otp_all_sst_anom_max)

Sea surface temperature (SST) plays an important role in a number of ecological processes and can vary over a wide range of time scales, from daily to decadal changes. SST influences primary production, species migration patterns, and coral health. If temperatures are anomalously warm for extended periods of time, drastic changes in the surrounding ecosystem can result, including harmful effects such as coral bleaching. This layer represents the annual average of the maximum anomaly of SST (degrees Celsius) from 2000-2013. Three SST datasets were combined to provide continuous coverage from 1985-2013. The concatenation applies bias adjustment derived from linear regression to the overlap periods of datasets, with the final representation matching the 0.05-degree (~5-km) near real-time SST product. First, a weekly composite, gap-filled SST dataset from the NOAA Pathfinder v5.2 SST 1/24-degree (~4-km), daily dataset (a NOAA Climate Data Record) for each location was produced following Heron et al. (2010) for January 1985 to December 2012. Next, weekly composite SST data from the NOAA/NESDIS/STAR Blended SST 0.1-degree (~11-km), daily dataset was produced for February 2009 to October 2013. Finally, a weekly composite SST dataset from the NOAA/NESDIS/STAR Blended SST 0.05-degree (~5-km), daily dataset was produced for March 2012 to December 2013. The SST average annual maximum anomaly was calculated by taking the average of the annual maximum SST values in exceedance of the maximum monthly climatological SST from 2000-2013 for each pixel.

Sea Surface Temperature (SST) Maximum Monthly Climatological Mean, 1985-2013 - Hawaii (hi_otp_all_sst_clim_max)

Sea surface temperature (SST) plays an important role in a number of ecological processes and can vary over a wide range of time scales, from daily to decadal changes. SST influences primary production, species migration patterns, and coral health. If temperatures are anomalously warm for extended periods of time, drastic changes in the surrounding ecosystem can result, including harmful effects such as coral bleaching. This layer represents the maximum of the monthly mean climatology of SST (degrees Celsius) from 1985-2013. Three SST datasets were combined to provide continuous coverage from 1985-2013. The concatenation applies bias adjustment derived from linear regression to the overlap periods of datasets, with the final representation matching the 0.05-degree (~5-km) near real-time SST product. First, a weekly composite, gap-filled SST dataset from the NOAA Pathfinder v5.2 SST 1/24-degree (~4-km), daily dataset (a NOAA Climate Data Record) for each location was produced following Heron et al. (2010) for January 1985 to December 2012. Next, weekly composite SST data from the NOAA/NESDIS/STAR Blended SST 0.1-degree (~11-km), daily dataset was produced for February 2009 to October 2013. Finally, a weekly composite SST dataset from the NOAA/NESDIS/STAR Blended SST 0.05-degree (~5-km), daily dataset was produced for March 2012 to December 2013. An SST climatology was first calculated by taking the average of the 5-km weekly SST data for each month, and then averaging for all same-months (e.g., January) over the 1985-2013 time period.

Sea Surface Temperature (SST) Standard Deviation of Long-term Mean, 2000-2013 - Hawaii (hi_otp_all_sst_std)

Sea surface temperature (SST) plays an important role in a number of ecological processes and can vary over a wide range of time scales, from daily to decadal changes. SST influences primary production, species migration patterns, and coral health. If temperatures are anomalously warm for extended periods of time, drastic changes in the surrounding ecosystem can result, including harmful effects such as coral bleaching. This layer represents the standard deviation of SST (degrees Celsius) of the weekly time series from 2000-2013. Three SST datasets were combined to provide continuous coverage from 1985-2013. The concatenation applies bias adjustment derived from linear regression to the overlap periods of datasets, with the final representation matching the 0.05-degree (~5-km) near real-time SST product. First, a weekly composite, gap-filled SST dataset from the NOAA Pathfinder v5.2 SST 1/24-degree (~4-km), daily dataset (a NOAA Climate Data Record) for each location was produced following Heron et al. (2010) for January 1985 to December 2012. Next, weekly composite SST data from the NOAA/NESDIS/STAR Blended SST 0.1-degree (~11-km), daily dataset was produced for February 2009 to October 2013. Finally, a weekly composite SST dataset from the NOAA/NESDIS/STAR Blended SST 0.05-degree (~5-km), daily dataset was produced for March 2012 to December 2013. The standard deviation of the long-term mean SST was calculated by taking the standard deviation over all weekly data from 2000-2013 for each pixel.

Wave Power Average Annual Maximum Anomaly, 2000-2013 - Hawaii (hi_otp_all_wave_anom_max)

Wave power is a major environmental forcing mechanism in Hawaii that influences a number of marine ecosystem processes including coral reef community development, structure, and persistence. By driving mixing of the upper water column, wave forcing can also play a role in nutrient availability and ocean temperature reduction during warming events. Wave forcing in Hawaii is highly seasonal, with winter months typically experiencing far greater wave power than that experienced during the summer months. This layer represents the annual average of the maximum anomaly of wave power (kW/m) from 2000-2013. Data were obtained from the University of Hawaii at Manoa (UH) School of Ocean and Earth Science and Technology (SOEST) SWAN model (Simulating WAves Nearshore) following Li et al. (2016). Hourly 500-m SWAN model runs of wave power were converted to maximum daily wave power from 1979-2013 and then averaged over each month from 1979-2013, creating a monthly time series from which monthly climatologies were made. Time series of anomalies were calculated by quantifying the number and magnitude of events from the maximum daily data set that exceeded the maximum climatological monthly mean. Pixels were removed directly adjacent to coastlines owing to the model being too coarse to handle extreme refraction and dissipation. Nearshore map pixels with no data were filled with values from the nearest neighboring valid offshore pixel by using a grid of points and the Near Analysis tool in ArcGIS then converting points to raster. The average annual maximum wave power anomaly was calculated by taking the average of the annual maximum wave power values in exceedance of the maximum monthly climatological wave power from 2000-2013 for each 500-m grid cell.

Wave Power Maximum Monthly Climatological Mean, 1979-2013 - Hawaii (hi_otp_all_wave_clim_max)

Wave power is a major environmental forcing mechanism in Hawaii that influences a number of marine ecosystem processes including coral reef community development, structure, and persistence. By driving mixing of the upper water column, wave forcing can also play a role in nutrient availability and ocean temperature reduction during warming events. Wave forcing in Hawaii is highly seasonal, with winter months typically experiencing far greater wave power than that experienced during the summer months. This layer represents the maximum monthly climatological mean of wave power (kW/m) from 1979-2013. Data were obtained from the University of Hawaii at Manoa (UH) School of Ocean and Earth Science and Technology (SOEST) SWAN model (Simulating WAves Nearshore) following Li et al. (2016). Hourly 500-m SWAN model runs of wave power were converted to maximum daily wave power from 1979-2013 and then averaged over each month from 1979-2013, creating a monthly time series from which monthly climatologies were made. Pixels were removed directly adjacent to coastlines owing to the model being too coarse to handle extreme refraction and dissipation. Nearshore map pixels with no data were filled with values from the nearest neighboring valid offshore pixel by using a grid of points and the Near Analysis tool in ArcGIS then converting points to raster.

Species Distribution: Cetaceans - Hawaii (hi_pacioos_all_cetaceans)

This dataset contains a collection of known point locations of cetaceans (dolphins and whales) identified either via automated satellite tracking of tagged organisms or through direct human observation via shipborne and aerial surveys. This can be useful for assessing species abundance, population structure, habitat use, and behavior. This collection is aggregated from multiple data sources and survey periods. Each data point contains attributes for further information about the time and source of the observation. This dataset was compiled by the Pacific Islands Ocean Observing System (PacIOOS) and may be updated in the future if additional data sources are acquired.

Species Distribution: Fraser's Dolphin - Hawaii (hi_pacioos_all_dolphin_frasers)

This dataset contains two known point locations of the elusive Fraser's dolphin identified through direct human observation via shipborne surveys in 2008 and 2012. Cascadia Research Collective (CRC) has been undertaking surveys for odontocetes in Hawaiian waters since 2000. For further information, please see: http://www.cascadiaresearch.org/hawaiian-cetacean-studies/frasers-dolphins-hawaii

Species Distribution: Rough-Toothed Dolphin - Hawaii (hi_pacioos_all_dolphin_rough_toothed)

This dataset contains a collection of known point locations of rough-toothed dolphins identified either via automated satellite tracking of tagged organisms or through direct human observation via shipborne and aerial surveys. This can be useful for assessing species abundance, population structure, habitat use, and behavior. This collection is aggregated from multiple data sources and survey periods listed below. Each data point contains attributes for further information about the time and source of the observation. This dataset was compiled by the Pacific Islands Ocean Observing System (PacIOOS) and may be updated in the future if additional data sources are acquired. Cascadia Research Collective (CRC) has been undertaking surveys for odontocetes in Hawaiian waters since 2000. Rough-toothed dolphins are the most abundant odontocetes around the islands of Kauai and Niihau although uncommon elsewhere. There is a small resident population of this species off of Hawaii Island. Satellite tagging has been undertaken off Kauai to examine movements of individuals in relation to the U.S. Navy's Pacific Missile Range Facility. In addition, Dr. Joseph Mobley of the Marine Mammal Research Consultants (MMRC) led aerial surveys for cetaceans in Hawaiian waters from 1993-2003. For further information, please see: http://www.cascadiaresearch.org/hawaiian-cetacean-studies/rough-toothed-dolphins-hawaii

Species Distribution: Pantropical Spotted Dolphin - Hawaii (hi_pacioos_all_dolphin_spotted_pantropical)

This dataset contains a collection of known point locations of pantropical spotted dolphins identified through direct human observation via shipborne and aerial surveys. This can be useful for assessing species abundance, population structure, habitat use, and behavior. This collection is aggregated from multiple data sources and survey periods listed below. Each data point contains attributes for further information about the time and source of the observation. This dataset was compiled by the Pacific Islands Ocean Observing System (PacIOOS) and may be updated in the future if additional data sources are acquired. Cascadia Research Collective (CRC) has been undertaking shipborne surveys for odontocetes in Hawaiian waters since 2000. Pantropical spotted dolphins are among the most frequently encountered odontocetes in Hawaii, using both shallow and deep waters. Genetic evidence indicates three separate island-associated populations, off Oahu, the four islands (Maui, Molokai, Lanai, Kahoolawe), and Hawaii Island. In addition, Dr. Joseph Mobley of the Marine Mammal Research Consultants (MMRC) led aerial surveys for cetaceans in Hawaiian waters from 1993-2003. For further information, please see: http://www.cascadiaresearch.org/hawaiian-cetacean-studies/pantropical-spotted-dolphins-hawaii

Species Distribution: Dolphins - Hawaii (hi_pacioos_all_dolphins)

This dataset contains a collection of known point locations of dolphins identified either via automated satellite tracking of tagged organisms or through direct human observation via shipborne and aerial surveys. This can be useful for assessing species abundance, population structure, habitat use, and behavior. This collection is aggregated from multiple data sources and survey periods. Each data point contains attributes for further information about the time and source of the observation. This dataset was compiled by the Pacific Islands Ocean Observing System (PacIOOS) and may be updated in the future if additional data sources are acquired.

Species Distribution: Green Sea Turtle - Hawaii (hi_pacioos_all_turtle_green_sea)

This dataset contains a collection of 13 known point locations of green sea turtles identified through direct human observation via aerial surveys between March and April of 1995. Dr. Joseph Mobley of the Marine Mammal Research Consultants (MMRC) led aerial surveys for turtles and cetaceans in Hawaiian waters from 1993-2003.

Species Distribution: Cuvier's Beaked Whale - Hawaii (hi_pacioos_all_whale_beaked_cuviers)

This dataset contains a collection of known point locations of Cuvier's beaked whales identified through direct human observation via shipborne and aerial surveys. This can be useful for assessing species abundance, population structure, habitat use, and behavior. This collection is aggregated from multiple data sources and survey periods listed below. Each data point contains attributes for further information about the time and source of the observation. This dataset was compiled by the Pacific Islands Ocean Observing System (PacIOOS) and may be updated in the future if additional data sources are acquired. Cascadia Research Collective (CRC) has been undertaking shipborne surveys for odontocetes in Hawaiian waters since 2000. Photo-identification and satellite-tagging indicate a small resident population of Cuvier's beaked whales off of Hawaii Island. Less is known about this species around the other Hawaiian islands. In addition, Dr. Joseph Mobley of the Marine Mammal Research Consultants (MMRC) led aerial surveys for cetaceans in Hawaiian waters from 1993-2003. For further information, please see: http://www.cascadiaresearch.org/hawaiian-cetacean-studies/beaked-whales-hawaii

Species Distribution: Fin Whale - Hawaii (hi_pacioos_all_whale_fin)

This dataset contains a collection of two known point locations of fin whales identified through direct human observation via shipborne and aerial surveys in 2012 and 1998, respectively. This collection is aggregated from multiple data sources and survey periods listed below. Each data point contains attributes for further information about the time and source of the observation. This dataset was compiled by the Pacific Islands Ocean Observing System (PacIOOS) and may be updated in the future if additional data sources are acquired. Cascadia Research Collective (CRC) has been undertaking shipborne surveys for cetaceans in Hawaiian waters since 2000. In addition, Dr. Joseph Mobley of the Marine Mammal Research Consultants (MMRC) led aerial surveys for cetaceans in Hawaiian waters from 1993-2003.

Species Distribution: Killer Whale - Hawaii (hi_pacioos_all_whale_killer)

This dataset contains a collection of three known point locations of killer whales identified through direct human observation via shipborne and aerial surveys. This collection is aggregated from multiple data sources and survey periods listed below. Each data point contains attributes for further information about the time and source of the observation. This dataset was compiled by the Pacific Islands Ocean Observing System (PacIOOS) and may be updated in the future if additional data sources are acquired. Cascadia Research Collective (CRC) has been undertaking shipborne surveys for odontocetes in Hawaiian waters since 2000. Killer whales are rare visitors to the main Hawaiian Islands, and are likely part of a widely-ranging open-ocean population. In addition, Dr. Joseph Mobley of the Marine Mammal Research Consultants (MMRC) led aerial surveys for cetaceans in Hawaiian waters from 1993-2003. For further information, please see: http://www.cascadiaresearch.org/hawaiian-cetacean-studies/killer-whales-hawaii

Species Distribution: Pygmy Killer Whale - Hawaii (hi_pacioos_all_whale_killer_pygmy)

This dataset contains a collection of known point locations of pygmy killer whales identified either via automated satellite tracking of tagged organisms or through direct human observation via shipborne and aerial surveys. This can be useful for assessing species abundance, population structure, habitat use, and behavior. This collection is aggregated from multiple data sources and survey periods listed below. Each data point contains attributes for further information about the time and source of the observation. This dataset was compiled by the Pacific Islands Ocean Observing System (PacIOOS) and may be updated in the future if additional data sources are acquired. Cascadia Research Collective (CRC) has been undertaking surveys for odontocetes in Hawaiian waters since 2000. Pygmy killer whales are one of the least abundant odontocetes in Hawaiian waters. Photo-identification and satellite tagging suggests there are small resident populations of pygmy killer whales off several of the main Hawaiian Islands. In addition, Dr. Joseph Mobley of the Marine Mammal Research Consultants (MMRC) led aerial surveys for cetaceans in Hawaiian waters from 1993-2003. For further information, please see: http://www.cascadiaresearch.org/hawaiian-cetacean-studies/pygmy-killer-whales-hawaii

Species Distribution: Short-Finned Pilot Whale - Hawaii (hi_pacioos_all_whale_pilot_short_finned)

This dataset contains a collection of known point locations of short-finned pilot whales identified either via automated satellite tracking of tagged organisms or through direct human observation via shipborne and aerial surveys. This can be useful for assessing species abundance, population structure, habitat use, and behavior. This collection is aggregated from multiple data sources and survey periods listed below. Each data point contains attributes for further information about the time and source of the observation. This dataset was compiled by the Pacific Islands Ocean Observing System (PacIOOS) and may be updated in the future if additional data sources are acquired. Cascadia Research Collective (CRC) has been undertaking shipborne surveys for odontocetes in Hawaiian waters since 2000. Short-finned pilot whales are among the most frequently encountered odontocetes in deep water around the main Hawaiian Islands. There appear to be island-resident populations in deep waters around all of the main Hawaiian Islands. From photo-identification and satellite tagging there is evidence of both an island-associated and a pelagic population of short-finned pilot whales in Hawaii, and several different communities of island-associated pilot whales around the islands, with individuals largely remaining associated with one or two islands. In adition, Dr. Joseph Mobley of the Marine Mammal Research Consultants (MMRC) led aerial surveys for cetaceans in Hawaiian waters from 1993-2003. For further information, please see: http://www.cascadiaresearch.org/short-finned-pilot-whales-hawaii

Species Distribution: Dwarf Sperm Whale - Hawaii (hi_pacioos_all_whale_sperm_dwarf)

This dataset contains a collection of known point locations of dwarf sperm whales identified through direct human observation via shipborne surveys. This can be useful for assessing species abundance, population structure, habitat use, and behavior. This collection is aggregated from multiple survey periods. Each data point contains attributes for further information about the time and source of the observation. This dataset was compiled by the Pacific Islands Ocean Observing System (PacIOOS) and may be updated in the future if additional data sources are acquired. Cascadia Research Collective (CRC) has been undertaking shipborne surveys for odontocetes in Hawaiian waters since 2000. Photo-identification indicates a small resident population of dwarf sperm whales off of Hawaii Island as well as an offshore population. Less is known about this species around the other Hawaiian islands. For further information, please see: http://www.cascadiaresearch.org/hawaiian-cetacean-studies/dwarf-and-pygmy-sperm-whales-hawaii

Species Distribution: Pygmy Sperm Whale - Hawaii (hi_pacioos_all_whale_sperm_pygmy)

This dataset contains a collection of known point locations of pygmy sperm whales identified through direct human observation via shipborne surveys. This can be useful for assessing species abundance, population structure, habitat use, and behavior. This collection is aggregated from multiple survey periods. Each data point contains attributes for further information about the time and source of the observation. This dataset was compiled by the Pacific Islands Ocean Observing System (PacIOOS) and may be updated in the future if additional data sources are acquired. Cascadia Research Collective (CRC) has been undertaking shipborne surveys for odontocetes in Hawaiian waters since 2000. Pygmy sperm whales have been sighted much less frequently in Hawaiian waters than dwarf sperm whales. For further information, please see: http://www.cascadiaresearch.org/hawaiian-cetacean-studies/dwarf-and-pygmy-sperm-whales-hawaii

West Maui Wave Run-up Forecast Validation (hi_pacioos_maui_westmaui_photos)

The Pacific Islands Ocean Observing System (PacIOOS), University of Hawaii School of Ocean and Earth Science and Technology Department of Oceanography, and Hawaii Sea Grant are developing a high-resolution, real-time wave run-up forecast and notification system for West Maui's coastline. In order to validate the site-specific, short-term forecasts and to determine meaningful thresholds associated with each forecast domain, citizen scientists contribute crowd-sourced photographs by documenting the shoreline of West Maui during predicted wave run-up events. Photos, observations, date, time, location, and other metadata are submitted online in this free, publicy-accessible dataset. Site-specific, short- and long-term forecasts, will strengthen West Maui's coastal community and economy by enhancing preparedness and response operations, and by informing future land use planning. A combination of high water levels and large wave swells can result in significant coastal erosion, damage to infrastructure and properties, and land-based sedimentation that impairs coastal water quality. The State of Hawaii has experienced an increase in wave plus tide-driven flooding in recent years, and these events are expected to grow in numbers and duration due to sea level rise and changing wave energies. When sharing these photographs, please cite this project with the following attribution: (c) PacIOOS, (year of photo). Some rights reserved. Licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

Anchorages - Hawaii (hi_sohdop_all_anchor_pts)

Displays the point location of anchorages and non-anchorages for the State of Hawaii as recorded on NOAA nautical charts. These are offshore areas outside of harbors where ships and boats can lie at anchor; many offer natural shelter from the effects of storms. Some anchorage zones have usage restrictions while others are prohibited entirely. These are indicated herein by marker color. See also the associated "Anchorage Zones - Hawaii" layer for related polygon boundaries.

Aids To Navigation (ATON) Buoys - Hawaii (hi_sohdop_all_aton_buoys)

Displays the location of buoys for the State of Hawaii as recorded on NOAA nautical charts, which are used as aids to navigation (ATONs) to indicate traffic channels, to warn against potential dangers, or for other navigational purposes. Some include lights or radar reflectors to make them visible at night and in conditions of reduced visibility. In the IALA B maritime buoyage system (North and South America), red lateral markers have even numbers and should remain on your right when returning from sea ("red, right, returning") while green lateral markers have odd numbers and should remain on your left. Other colors and color combinations are used for non-lateral markers with a variety of purposes.

Bottomfish Restricted Fishing Areas - Hawaii (hi_sohdop_all_brfa)

Boundaries of bottomfish restricted fishing areas (BRFA) for the State of Hawaii. It is unlawful for any person to take or possess bottomfish while in a vessel that is drifting or anchored within any BRFA. Bottomfish species covered by these rules include: a) 'ula'ula koa'e or onaga (Etelis coruscans); b) 'ula'ula or ehu (Etelis carbunculus); c) kalekale (Pristipomoides sieboldii); d) 'opakapaka (Pristipomoides filamentosus); e) 'ukikiki or gindai (Pristipomoides zonatus); f) hapu'u (Epinephelus quernus); and g) lehi (Aphareus rutilans). These species are often referred to as the "Deep 7".

Marine Dumping Zones - Hawaii (hi_sohdop_all_dump)

Boundaries of available marine dumping zones for the State of Hawaii as recorded on NOAA nautical charts. Regulations for ocean dumping sites are contained in Title 40 ("Protection of Environment), Subchapter H ("Ocean Dumping"), Parts 220-229 of the U.S. Code of Federal Regulations (40 CFR 220-229). Additional information concerning the regulations and requirements for use of these sites may be obtained from the U.S. Environmental Protection Agency (EPA).

Fisheries Management Areas - Hawaii (hi_sohdop_all_fish_mgmt)

Boundaries of fisheries management areas (FMA) for the State of Hawaii. The mission of the Hawaii Division of Aquatic Resources (DAR) is to manage, conserve and restore the state's unique aquatic resources and ecosystems for present and future generations. Major program areas include projects to manage or enhance fisheries for long-term sustainability of the resources, protect and restore the aquatic environment, protect native and resident aquatic species and their habitat, and provide facilities and opportunities for recreational fishing. The areas outlined in this layer have fishing regulations. For details and further information, please see: http://dlnr.hawaii.gov/dar/fishing/fishing-regulations/

Marine Sewer Lines - Hawaii (hi_sohdop_all_marine_sewers)

Displays the locations of some of the submarine sewer pipelines for the State of Hawaii as recorded on NOAA nautical charts, including Oahu as well as one each on Maui (Kihei) and the Big Island (Kona). Not all submarine pipelines are required to be buried, and those that were originally buried may have become exposed. Mariners should use extreme caution when operating vessels in depths of water comparable to their draft in areas where pipelines may exist and when anchoring, dragging, or trawling.

Shoreline - Hawaii (hi_sohdop_all_shore)

Coastlines for the main Hawaiian islands. Source: USGS Digital Line Graphs, 1983 version. History: Extracted by OSP staff from the 1983 1:24,000 USGS Digital Line Graphs.

Three Nautical Mile Limit - Hawaii (hi_sohdop_all_three_nmi)

The three nautical mile (3 nmi) limit refers to a traditional and now largely obsolete maritime boundary that defined a country's territorial waters, for the purposes of trade regulation and exclusivity, as extending as far as the reach of cannons fired from land. In its place, the Territorial Sea boundary at 12 nmi was established as the international norm by the 1982 United Nations Convention on the Law of the Sea.

Watersheds - Hawaii (hi_sohdop_all_wshed)

Watershed boundaries for the eight main Hawaiian Islands, generated by the State of Hawaii Office of Planning in Arc/Info and GRID using USGS DEM data.

Discontinued Marine Explosives Dumping Zones - Hawaii (hi_sohdop_all_xdump_hist)

Boundary of a marine explosive dumping area that is no longer used by the State of Hawaii as recorded on NOAA nautical charts. Regulations for ocean dumping sites are contained in Title 40 ("Protection of Environment), Subchapter H ("Ocean Dumping"), Parts 220-229 of the U.S. Code of Federal Regulations (40 CFR 220-229).

Kahoolawe Island Reserve - Hawaii (hi_sohdop_kaho_reserve)

Boundary of the marine portions of the Kahoolawe Island Reserve for the island of Kahoolawe in Hawaii. Beginning in World War II, Kahoolawe was used as a training ground and bombing range by the U.S. military. After decades of protests, the Navy ended live-fire training in 1990 and the island was transferred to the State of Hawaii in 1994. The state legislature established the Kahoolawe Island Reserve to restore and oversee the island and its surrounding waters. Today, Kahoolawe can be used only for native Hawaiian cultural, spiritual, and subsistence purposes. All entry to the reserve must be authorized by the Kahoolawe Island Reserve Commission. For more information, please see: http://www.kahoolawe.hawaii.gov

Hawaii Marine Laboratory Refuge - Coconut Island, Hawaii (hi_sohdop_oahu_coconut_island)

This marine refuge consists of the coral reefs and bay waters surrounding Coconut Island (Moku o Loe) located in Kaneohe Bay on the east coast of Oahu in Hawaii. Except for scientific research conducted by the Hawaii Institute of Marine Biology (HIMB) of the University of Hawaii, it is unlawful to take any aquatic life from within the boundaries of the refuge.

Piers - Oahu, Hawaii (hi_sohdop_oahu_piers)

Displays the locations of two piers for the State of Hawaii. While harbors may also have piers, these sites solely provide a pier and no other facilities. A pier is a platform built out from the shore into the water and supported by piles to provide access to ships and boats. This overlay includes two such piers: Lilipuna Pier in Kaneohe Bay and Makai Pier in Waimanalo, both on Oahu.

Fish Ponds - Big Island, Hawaii (hi_tnc_bigi_fish_ponds)

Locations of known and historic fish ponds on the island of Hawaii (Big Island). For some fish ponds, data includes their condition, ownership, and references used to map them.

Kaupulehu Proposed Marine Reserve Area - West Hawaii Island, Hawaii (hi_tnc_bigi_kaupulehu_reserve)

Proposed marine reserve area at Kaupulehu in the northern part of the Kona district on the west coast of Hawaii Island. The community proposal includes no-take for 10 years to allow for the reef to rest and replenish.

Puako Coral Health And Water Quality Sites - West Hawaii Island, Hawaii (hi_tnc_bigi_puako_monitoring)

To understand and mitigate the impacts of land-based pollution on coral reef health, The Nature Conservancy (TNC), University of Hawaii at Hilo (UH), and other researchers collected data to: 1) identify where high groundwater flows occur on the Puako reef system; 2) determine what is in the water by monitoring levels of bacteria and nutrients; and 3) assess whether degraded water quality can compromise coral health - and if so, which sites are most vulnerable.

Geology - Hawaii (hi_usgs_all_geology)

Geology of the eight major islands of the State of Hawaii, a useful guide to its geologic setting and history, ground- and surface-water resources, economic deposits, and landslide or volcanic hazards. Maps the lithologic characteristics and distribution of geologic deposits and their stratigraphic relationships, including radiometric ages and geochemical analyses compiled from findings published over the past 100 years. Data compiled in 2007 by the United States Geological Survey (USGS) from multiple sources.

USGS 10-m Digital Elevation Model (DEM): Hawaii: Kahoolawe: Hillshade (hi_usgs_kaho_dem10m_hillshade)

A 10-meter resolution land surface digital elevation model (DEM) grayscale hillshade for the island of Kahoolawe in Hawaii derived from United States Geological Survey (USGS) 1/3 arc-second DEM quadrangles. For the related dataset containing numeric elevation values for this image layer, see http://pacioos.org/metadata/usgs_dem_10m_kahoolawe.html

USGS 10-m Digital Elevation Model (DEM): Hawaii: Lanai: Hillshade (hi_usgs_lana_dem10m_hillshade)

A 10-meter resolution land surface digital elevation model (DEM) grayscale hillshade for the island of Lanai in Hawaii derived from United States Geological Survey (USGS) 1/3 arc-second DEM quadrangles. For the related dataset containing numeric elevation values for this image layer, see http://pacioos.org/metadata/usgs_dem_10m_lanai.html

USGS 10-m Digital Elevation Model (DEM): Hawaii: Molokai: Hillshade (hi_usgs_molo_dem10m_hillshade)

A 10-meter resolution land surface digital elevation model (DEM) grayscale hillshade for the island of Molokai in Hawaii derived from United States Geological Survey (USGS) 1/3 arc-second DEM quadrangles. For the related dataset containing numeric elevation values for this image layer, see http://pacioos.org/metadata/usgs_dem_10m_molokai.html

USGS 10-m Digital Elevation Model (DEM): Hawaii: Oahu: Hillshade (hi_usgs_oahu_dem10m_hillshade)

A 10-meter resolution land surface digital elevation model (DEM) grayscale hillshade for the island of Oahu in Hawaii derived from United States Geological Survey (USGS) 1/3 arc-second DEM quadrangles. For the related dataset containing numeric elevation values for this image layer, see http://pacioos.org/metadata/usgs_dem_10m_oahu.html

NOAA/PIBHMC 20-m Bathymetry: USMOI: Jarvis Island: Hillshade (jai_pibhmc_all_bathy20m_hillshade)

A 20-m resolution gridded digital elevation model (DEM) grayscale hillshade compiled from ship-borne multibeam sonar surveys for the bathymetry (ocean depth) surrounding Jarvis Island in the Central Pacific Ocean, a U.S. Minor Outlying Island (USMOI). Almost complete bottom coverage was achieved in depths between 3 and 3600 m. Data collected by the Pacific Islands Benthic Habitat Mapping Center (PIBHMC) in support of NOAA's Coral Reef Conservation Program. Not to be used for navigational purposes. For the related dataset containing numeric bathymetry values for this image layer, see http://pacioos.org/metadata/pibhmc_bathy_20m_jarvis.html

Shoreline - Johnson Atoll (jat_ocs_all_shoreline)

Shoreline of Johnson Atoll

NOAA/PIBHMC 5-m Bathymetry: USMOI: Johnston Atoll: Hillshade (jat_pibhmc_all_bathy5m_hillshade)

A 5-m resolution gridded digital elevation model (DEM) grayscale hillshade for the bathymetry (ocean depth) surrounding Johnston Atoll in the Central Pacific Ocean, a U.S. Minor Outlying Island (USMOI), compiled from ship-borne multibeam sonar surveys merged with coastal bathymetry derived from multipectral IKONOS satellite data. Almost complete bottom coverage was achieved in depths between 0 and 150 m. Data collected by the Pacific Islands Benthic Habitat Mapping Center (PIBHMC) in support of NOAA's Coral Reef Conservation Program. Not to be used for navigational purposes. For the related dataset containing numeric bathymetry values for this image layer, see http://pacioos.org/metadata/pibhmc_bathy_5m_johnston.html

NOAA/PIBHMC 5-m Bathymetry: USMOI: Kingman Reef: Hillshade (kng_pibhmc_all_bathy5m_hillshade)

A 5-m resolution gridded digital elevation model (DEM) grayscale hillshade for the bathymetry (ocean depth) surrounding Kingman Reef in the Central Pacific Ocean, a U.S. Minor Outlying Island (USMOI), compiled from ship-borne multibeam sonar surveys merged with coastal bathymetry derived from multipectral IKONOS satellite data. Almost complete bottom coverage was achieved in depths between 0 and 345 m. Data collected by the Pacific Islands Benthic Habitat Mapping Center (PIBHMC) in support of NOAA's Coral Reef Conservation Program. Not to be used for navigational purposes. For the related dataset containing numeric bathymetry values for this image layer, see http://pacioos.org/metadata/pibhmc_bathy_5m_kingman.html

Airports - Marshall Islands (mh_mgd_all_airports)

Airports - Marshall Islands

Bathymetry Contours, 200m - Marshall Islands (mh_mgd_all_bathy_contours_200m)

Bathymetry Contours, 200m - Marshall Islands

Conservation Targets (Reefs) - Marshall Islands (mh_mgd_all_conservtarg_reefs)

Conservation Targets (Reefs) - Marshall Islands

Local Government Areas - Marshall Islands (mh_mgd_all_local_gov_areas)

Local Government Areas - Marshall Islands

Protected Areas - Marshall Islands (mh_mgd_all_protected_areas)

Protected Areas - Marshall Islands

Territorial Seas - Marshall Islands (mh_mgd_all_territorial_seas)

Territorial seas for the Republic of the Marshall Islands (RMI). Territorial waters, or a territorial sea, as defined by the 1982 United Nations Convention on the Law of the Sea, is a belt of coastal waters extending at most twelve nautical miles (12 nmi) from the baseline (usually the mean low-water mark) of a coastal state. The territorial sea is regarded as the sovereign territory of the state, although foreign ships are allowed innocent passage through it; this sovereignty also extends to the airspace over and seabed below.

Bathymetry With Contours - Majuro, Marshall Islands (mh_sopac_maj_bathy_color)

From multibeam bathymetry surveys carried out by the Pacific Community (SPC) Geoscience, Energy and Maritime (GEM) Division, formerly the South Pacific Applied Geoscience Commission (SOPAC), from 2003-2006. This work was implemented through the project "Reducing Vulnerability of Pacific States", funded by the European Development Fund. Ocean depth measured in meters.

SPC/GEM 50-m Bathymetry: RMI: Majuro: Reef: Hillshade (mh_sopac_maj_bathy_reef_east_hillshade)

A 50-m resolution gridded digital elevation model (DEM) grayscale hillshade for the bathymetry (ocean depth) of the eastern reef of Majuro Atoll in the Republic of the Marshall Islands (RMI). Compiled from ship-borne multibeam sonar surveys conducted from 2003-2006 by the Pacific Community (SPC) Geoscience, Energy and Maritime (GEM) Division, formerly the South Pacific Applied Geoscience Commission (SOPAC. This work was implemented through the project "Reducing Vulnerability of Pacific States" funded by the European Development Fund. Not to be used for navigational purposes. For the related dataset containing numeric bathymetry values for this image layer, see http://pacioos.org/metadata/sopac_bathy_50m_majuro_reef.html

Vegetation - Marshall Islands (mh_usfs_comp_veg)

Draft U.S. Forest Service (USFS) vegetation classification for the Marshall Islands.

QuickBird Imagery, 2006 - Pagan, CNMI (mp_hw_pag_qbird_2006)

QuickBird satellite imagery of Pagan, Commonwealth of the Northern Mariana Islands (CNMI), 2006.

NOAA Shallow-Water Benthic Habitats: CNMI: Agrihan (mp_noaa_agr_benthic_habitats)

Benthic habitat maps for the nearshore, shallow (< 30 m) coastal waters of the island of Agrihan in the Commonwealth of the Northern Mariana Islands (CNMI). NOAA's National Centers for Coastal Ocean Science (NCCOS) produced these data to support coral reef research and management. Habitat regions were digitally identified using visual interpretation of orthorectified satellite imagery with a minimum mapping unit (MMU) of approximately 1 acre. Includes biological cover types, geomorphological structure types, and geographic zones. Eighteen distinct and non-overlapping biological cover types were identified. Habitats or features that cover areas smaller than the minimal mapping unit of 1 acre were not considered. For example, uncolonized sand halos surrounding coral patch reefs are too small to be mapped independently. Cover type refers only to the predominant biological component colonizing the surface of the feature and does not address location (e.g., on the shelf or in the lagoon). The cover types are defined in a collapsible hierarchy ranging from eight major classes (live coral, seagrass, macroalgae, encrusting/coralline algae, turf algae, emergent vegetation, uncolonized, and unknown), combined with a density modifier representing the percentage of the predominant cover type (10%-<50% sparse, 50%-<90% patchy, 90%-100% continuous). Similarly, 14 distinct and non-overlapping geomorphological structure types were identified. The structure types are defined in a collapsible hierarchy ranging from four major classes (coral reef and hardbottom, unconsolidated sediment, other delineations, and unknown), to thirteen detailed classes: sand, mud, spur and groove, individual and aggregated patch reef, aggregate reef, scattered coral/rock in unconsolidated sediment, pavement, rock/boulder (volcanic and carbonate), reef rubble, pavement with sand channels, artificial, and unknown. Lastly, 13 mutually exclusive geographic zones were identified from land to open water corresponding to typical insular shelf and coral reef geomorphology. These zones include: shoreline intertidal, vertical wall (none identified), lagoon, back reef, reef flat, reef crest, fore reef, bank/shelf, bank/shelf escarpment, channel, dredged (since this condition eliminates natural geomorphology), unknown, and land. Zone refers only to each benthic community's location and does not address substrate or cover types within. For example, the lagoon zone may include patch reefs, sand, and seagrass beds; however, these are considered structural elements that may or may not occur within the lagoon zone and therefore, are not used to define it.

NOAA Shallow-Water Benthic Habitats: CNMI: Aguijan (mp_noaa_agu_benthic_habitats)

Benthic habitat maps for the nearshore, shallow (< 30 m) coastal waters of the island of Aguijan in the Commonwealth of the Northern Mariana Islands (CNMI). NOAA's National Centers for Coastal Ocean Science (NCCOS) produced these data to support coral reef research and management. Habitat regions were digitally identified using visual interpretation of orthorectified satellite imagery with a minimum mapping unit (MMU) of approximately 1 acre. Includes biological cover types, geomorphological structure types, and geographic zones. Eighteen distinct and non-overlapping biological cover types were identified. Habitats or features that cover areas smaller than the minimal mapping unit of 1 acre were not considered. For example, uncolonized sand halos surrounding coral patch reefs are too small to be mapped independently. Cover type refers only to the predominant biological component colonizing the surface of the feature and does not address location (e.g., on the shelf or in the lagoon). The cover types are defined in a collapsible hierarchy ranging from eight major classes (live coral, seagrass, macroalgae, encrusting/coralline algae, turf algae, emergent vegetation, uncolonized, and unknown), combined with a density modifier representing the percentage of the predominant cover type (10%-<50% sparse, 50%-<90% patchy, 90%-100% continuous). Similarly, 14 distinct and non-overlapping geomorphological structure types were identified. The structure types are defined in a collapsible hierarchy ranging from four major classes (coral reef and hardbottom, unconsolidated sediment, other delineations, and unknown), to thirteen detailed classes: sand, mud, spur and groove, individual and aggregated patch reef, aggregate reef, scattered coral/rock in unconsolidated sediment, pavement, rock/boulder (volcanic and carbonate), reef rubble, pavement with sand channels, artificial, and unknown. Lastly, 13 mutually exclusive geographic zones were identified from land to open water corresponding to typical insular shelf and coral reef geomorphology. These zones include: shoreline intertidal, vertical wall (none identified), lagoon, back reef, reef flat, reef crest, fore reef, bank/shelf, bank/shelf escarpment, channel, dredged (since this condition eliminates natural geomorphology), unknown, and land. Zone refers only to each benthic community's location and does not address substrate or cover types within. For example, the lagoon zone may include patch reefs, sand, and seagrass beds; however, these are considered structural elements that may or may not occur within the lagoon zone and therefore, are not used to define it.

NOAA Shallow-Water Benthic Habitats: CNMI: Anatahan (mp_noaa_ana_benthic_habitats)

Benthic habitat maps for the nearshore, shallow (< 30 m) coastal waters of the island of Anatahan in the Commonwealth of the Northern Mariana Islands (CNMI). NOAA's National Centers for Coastal Ocean Science (NCCOS) produced these data to support coral reef research and management. Habitat regions were digitally identified using visual interpretation of orthorectified satellite imagery with a minimum mapping unit (MMU) of approximately 1 acre. Includes biological cover types, geomorphological structure types, and geographic zones. Eighteen distinct and non-overlapping biological cover types were identified. Habitats or features that cover areas smaller than the minimal mapping unit of 1 acre were not considered. For example, uncolonized sand halos surrounding coral patch reefs are too small to be mapped independently. Cover type refers only to the predominant biological component colonizing the surface of the feature and does not address location (e.g., on the shelf or in the lagoon). The cover types are defined in a collapsible hierarchy ranging from eight major classes (live coral, seagrass, macroalgae, encrusting/coralline algae, turf algae, emergent vegetation, uncolonized, and unknown), combined with a density modifier representing the percentage of the predominant cover type (10%-<50% sparse, 50%-<90% patchy, 90%-100% continuous). Similarly, 14 distinct and non-overlapping geomorphological structure types were identified. The structure types are defined in a collapsible hierarchy ranging from four major classes (coral reef and hardbottom, unconsolidated sediment, other delineations, and unknown), to thirteen detailed classes: sand, mud, spur and groove, individual and aggregated patch reef, aggregate reef, scattered coral/rock in unconsolidated sediment, pavement, rock/boulder (volcanic and carbonate), reef rubble, pavement with sand channels, artificial, and unknown. Lastly, 13 mutually exclusive geographic zones were identified from land to open water corresponding to typical insular shelf and coral reef geomorphology. These zones include: shoreline intertidal, vertical wall (none identified), lagoon, back reef, reef flat, reef crest, fore reef, bank/shelf, bank/shelf escarpment, channel, dredged (since this condition eliminates natural geomorphology), unknown, and land. Zone refers only to each benthic community's location and does not address substrate or cover types within. For example, the lagoon zone may include patch reefs, sand, and seagrass beds; however, these are considered structural elements that may or may not occur within the lagoon zone and therefore, are not used to define it.

NOAA Shallow-Water Benthic Habitats: CNMI: Asuncion (mp_noaa_asu_benthic_habitats)

Benthic habitat maps for the nearshore, shallow (< 30 m) coastal waters of the island of Asuncion in the Commonwealth of the Northern Mariana Islands (CNMI). NOAA's National Centers for Coastal Ocean Science (NCCOS) produced these data to support coral reef research and management. Habitat regions were digitally identified using visual interpretation of orthorectified satellite imagery with a minimum mapping unit (MMU) of approximately 1 acre. Includes biological cover types, geomorphological structure types, and geographic zones. Eighteen distinct and non-overlapping biological cover types were identified. Habitats or features that cover areas smaller than the minimal mapping unit of 1 acre were not considered. For example, uncolonized sand halos surrounding coral patch reefs are too small to be mapped independently. Cover type refers only to the predominant biological component colonizing the surface of the feature and does not address location (e.g., on the shelf or in the lagoon). The cover types are defined in a collapsible hierarchy ranging from eight major classes (live coral, seagrass, macroalgae, encrusting/coralline algae, turf algae, emergent vegetation, uncolonized, and unknown), combined with a density modifier representing the percentage of the predominant cover type (10%-<50% sparse, 50%-<90% patchy, 90%-100% continuous). Similarly, 14 distinct and non-overlapping geomorphological structure types were identified. The structure types are defined in a collapsible hierarchy ranging from four major classes (coral reef and hardbottom, unconsolidated sediment, other delineations, and unknown), to thirteen detailed classes: sand, mud, spur and groove, individual and aggregated patch reef, aggregate reef, scattered coral/rock in unconsolidated sediment, pavement, rock/boulder (volcanic and carbonate), reef rubble, pavement with sand channels, artificial, and unknown. Lastly, 13 mutually exclusive geographic zones were identified from land to open water corresponding to typical insular shelf and coral reef geomorphology. These zones include: shoreline intertidal, vertical wall (none identified), lagoon, back reef, reef flat, reef crest, fore reef, bank/shelf, bank/shelf escarpment, channel, dredged (since this condition eliminates natural geomorphology), unknown, and land. Zone refers only to each benthic community's location and does not address substrate or cover types within. For example, the lagoon zone may include patch reefs, sand, and seagrass beds; however, these are considered structural elements that may or may not occur within the lagoon zone and therefore, are not used to define it.

Shorelines - CNMI (mp_noaa_comp_swbh_shore)

Shorelines of the Commonwealth of the Northern Mariana Islands (CNMI).

NOAA Shallow-Water Benthic Habitats: CNMI: Farallon de Pajaros (mp_noaa_fdp_benthic_habitats)

Benthic habitat maps for the nearshore, shallow (< 30 m) coastal waters of the island of Farallon de Pajaros in the Commonwealth of the Northern Mariana Islands (CNMI). NOAA's National Centers for Coastal Ocean Science (NCCOS) produced these data to support coral reef research and management. Habitat regions were digitally identified using visual interpretation of orthorectified satellite imagery with a minimum mapping unit (MMU) of approximately 1 acre. Includes biological cover types, geomorphological structure types, and geographic zones. Eighteen distinct and non-overlapping biological cover types were identified. Habitats or features that cover areas smaller than the minimal mapping unit of 1 acre were not considered. For example, uncolonized sand halos surrounding coral patch reefs are too small to be mapped independently. Cover type refers only to the predominant biological component colonizing the surface of the feature and does not address location (e.g., on the shelf or in the lagoon). The cover types are defined in a collapsible hierarchy ranging from eight major classes (live coral, seagrass, macroalgae, encrusting/coralline algae, turf algae, emergent vegetation, uncolonized, and unknown), combined with a density modifier representing the percentage of the predominant cover type (10%-<50% sparse, 50%-<90% patchy, 90%-100% continuous). Similarly, 14 distinct and non-overlapping geomorphological structure types were identified. The structure types are defined in a collapsible hierarchy ranging from four major classes (coral reef and hardbottom, unconsolidated sediment, other delineations, and unknown), to thirteen detailed classes: sand, mud, spur and groove, individual and aggregated patch reef, aggregate reef, scattered coral/rock in unconsolidated sediment, pavement, rock/boulder (volcanic and carbonate), reef rubble, pavement with sand channels, artificial, and unknown. Lastly, 13 mutually exclusive geographic zones were identified from land to open water corresponding to typical insular shelf and coral reef geomorphology. These zones include: shoreline intertidal, vertical wall (none identified), lagoon, back reef, reef flat, reef crest, fore reef, bank/shelf, bank/shelf escarpment, channel, dredged (since this condition eliminates natural geomorphology), unknown, and land. Zone refers only to each benthic community's location and does not address substrate or cover types within. For example, the lagoon zone may include patch reefs, sand, and seagrass beds; however, these are considered structural elements that may or may not occur within the lagoon zone and therefore, are not used to define it.

Aerial Mosaic, 1km - Guguan, CNMI (mp_noaa_gug_swbh_mosaic_clip1km)

Aerial Mosaic of Guguan, Commonwealth of the Northern Mariana Islands (CNMI).

Aerial Mosaic, 1km - Maug, CNMI (mp_noaa_mau_swbh_mosaic_clip1km)

Aerial mosaic of Maug, Commonwealth of the Northern Mariana Islands (CNMI).

NOAA Shallow-Water Benthic Habitats: CNMI: Rota (mp_noaa_rot_benthic_habitats)

Benthic habitat maps for the nearshore, shallow (< 30 m) coastal waters of the island of Rota in the Commonwealth of the Northern Mariana Islands (CNMI). NOAA's National Centers for Coastal Ocean Science (NCCOS) produced these data to support coral reef research and management. Habitat regions were digitally identified using visual interpretation of orthorectified satellite imagery with a minimum mapping unit (MMU) of approximately 1 acre. Includes biological cover types, geomorphological structure types, and geographic zones. Eighteen distinct and non-overlapping biological cover types were identified. Habitats or features that cover areas smaller than the minimal mapping unit of 1 acre were not considered. For example, uncolonized sand halos surrounding coral patch reefs are too small to be mapped independently. Cover type refers only to the predominant biological component colonizing the surface of the feature and does not address location (e.g., on the shelf or in the lagoon). The cover types are defined in a collapsible hierarchy ranging from eight major classes (live coral, seagrass, macroalgae, encrusting/coralline algae, turf algae, emergent vegetation, uncolonized, and unknown), combined with a density modifier representing the percentage of the predominant cover type (10%-<50% sparse, 50%-<90% patchy, 90%-100% continuous). Similarly, 14 distinct and non-overlapping geomorphological structure types were identified. The structure types are defined in a collapsible hierarchy ranging from four major classes (coral reef and hardbottom, unconsolidated sediment, other delineations, and unknown), to thirteen detailed classes: sand, mud, spur and groove, individual and aggregated patch reef, aggregate reef, scattered coral/rock in unconsolidated sediment, pavement, rock/boulder (volcanic and carbonate), reef rubble, pavement with sand channels, artificial, and unknown. Lastly, 13 mutually exclusive geographic zones were identified from land to open water corresponding to typical insular shelf and coral reef geomorphology. These zones include: shoreline intertidal, vertical wall (none identified), lagoon, back reef, reef flat, reef crest, fore reef, bank/shelf, bank/shelf escarpment, channel, dredged (since this condition eliminates natural geomorphology), unknown, and land. Zone refers only to each benthic community's location and does not address substrate or cover types within. For example, the lagoon zone may include patch reefs, sand, and seagrass beds; however, these are considered structural elements that may or may not occur within the lagoon zone and therefore, are not used to define it.

NOAA Shallow-Water Benthic Habitats: CNMI: Saipan (mp_noaa_sai_benthic_habitats)

Benthic habitat maps for the nearshore, shallow (< 30 m) coastal waters of the island of Saipan in the Commonwealth of the Northern Mariana Islands (CNMI). NOAA's National Centers for Coastal Ocean Science (NCCOS) produced these data to support coral reef research and management. Habitat regions were digitally identified using visual interpretation of orthorectified satellite imagery with a minimum mapping unit (MMU) of approximately 1 acre. Includes biological cover types, geomorphological structure types, and geographic zones. Eighteen distinct and non-overlapping biological cover types were identified. Habitats or features that cover areas smaller than the minimal mapping unit of 1 acre were not considered. For example, uncolonized sand halos surrounding coral patch reefs are too small to be mapped independently. Cover type refers only to the predominant biological component colonizing the surface of the feature and does not address location (e.g., on the shelf or in the lagoon). The cover types are defined in a collapsible hierarchy ranging from eight major classes (live coral, seagrass, macroalgae, encrusting/coralline algae, turf algae, emergent vegetation, uncolonized, and unknown), combined with a density modifier representing the percentage of the predominant cover type (10%-<50% sparse, 50%-<90% patchy, 90%-100% continuous). Similarly, 14 distinct and non-overlapping geomorphological structure types were identified. The structure types are defined in a collapsible hierarchy ranging from four major classes (coral reef and hardbottom, unconsolidated sediment, other delineations, and unknown), to thirteen detailed classes: sand, mud, spur and groove, individual and aggregated patch reef, aggregate reef, scattered coral/rock in unconsolidated sediment, pavement, rock/boulder (volcanic and carbonate), reef rubble, pavement with sand channels, artificial, and unknown. Lastly, 13 mutually exclusive geographic zones were identified from land to open water corresponding to typical insular shelf and coral reef geomorphology. These zones include: shoreline intertidal, vertical wall (none identified), lagoon, back reef, reef flat, reef crest, fore reef, bank/shelf, bank/shelf escarpment, channel, dredged (since this condition eliminates natural geomorphology), unknown, and land. Zone refers only to each benthic community's location and does not address substrate or cover types within. For example, the lagoon zone may include patch reefs, sand, and seagrass beds; however, these are considered structural elements that may or may not occur within the lagoon zone and therefore, are not used to define it.

NOAA Shallow-Water Benthic Habitats: CNMI: Tinian (mp_noaa_tin_benthic_habitats)

Benthic habitat maps for the nearshore, shallow (< 30 m) coastal waters of the island of Tinian in the Commonwealth of the Northern Mariana Islands (CNMI). NOAA's National Centers for Coastal Ocean Science (NCCOS) produced these data to support coral reef research and management. Habitat regions were digitally identified using visual interpretation of orthorectified satellite imagery with a minimum mapping unit (MMU) of approximately 1 acre. Includes biological cover types, geomorphological structure types, and geographic zones. Eighteen distinct and non-overlapping biological cover types were identified. Habitats or features that cover areas smaller than the minimal mapping unit of 1 acre were not considered. For example, uncolonized sand halos surrounding coral patch reefs are too small to be mapped independently. Cover type refers only to the predominant biological component colonizing the surface of the feature and does not address location (e.g., on the shelf or in the lagoon). The cover types are defined in a collapsible hierarchy ranging from eight major classes (live coral, seagrass, macroalgae, encrusting/coralline algae, turf algae, emergent vegetation, uncolonized, and unknown), combined with a density modifier representing the percentage of the predominant cover type (10%-<50% sparse, 50%-<90% patchy, 90%-100% continuous). Similarly, 14 distinct and non-overlapping geomorphological structure types were identified. The structure types are defined in a collapsible hierarchy ranging from four major classes (coral reef and hardbottom, unconsolidated sediment, other delineations, and unknown), to thirteen detailed classes: sand, mud, spur and groove, individual and aggregated patch reef, aggregate reef, scattered coral/rock in unconsolidated sediment, pavement, rock/boulder (volcanic and carbonate), reef rubble, pavement with sand channels, artificial, and unknown. Lastly, 13 mutually exclusive geographic zones were identified from land to open water corresponding to typical insular shelf and coral reef geomorphology. These zones include: shoreline intertidal, vertical wall (none identified), lagoon, back reef, reef flat, reef crest, fore reef, bank/shelf, bank/shelf escarpment, channel, dredged (since this condition eliminates natural geomorphology), unknown, and land. Zone refers only to each benthic community's location and does not address substrate or cover types within. For example, the lagoon zone may include patch reefs, sand, and seagrass beds; however, these are considered structural elements that may or may not occur within the lagoon zone and therefore, are not used to define it.

Beacons - Saipan, CNMI (mp_ocs_sai_beacons)

Beacon locations around Saipan, Commonwealth of the Northern Mariana Islands (CNMI).

Ocean Depth Soundings - Saipan, Tinian, and Aguijan, CNMI (mp_ocs_sta_soundings)

Ocean depth soundings in meters around Saipan, Tinian, and Aguijan, Commonwealth of the Northern Mariana Islands (CNMI).

Channels - Saipan and Tinian, CNMI (mp_ocs_sti_channels)

Channels around Saipan and Tinian, Commonwealth of the Northern Mariana Islands (CNMI).

Marine Obstructions - Saipan and Tinian, CNMI (mp_ocs_sti_obstructpt)

Marine obstruction points around Saipan and Tinian, Commonwealth of the Northern Mariana Islands (CNMI).

NOAA/PIBHMC 10-m Bathymetry: CNMI: Agrihan: Hillshade (mp_pibhmc_agr_bathy10m_hillshade)

A 10-m resolution gridded digital elevation model (DEM) grayscale hillshade for the bathymetry (ocean depth) surrounding Agrihan Island in the Commonwealth of the Northern Mariana Islands (CNMI) compiled from ship-borne multibeam sonar surveys. Almost complete bottom coverage was achieved in depths between 4 and 800 m. Data collected by the Pacific Islands Benthic Habitat Mapping Center (PIBHMC) in support of NOAA's Coral Reef Conservation Program. Not to be used for navigational purposes. For the related dataset containing numeric bathymetry values for this image layer, see http://pacioos.org/metadata/pibhmc_bathy_10m_agrihan.html

NOAA/PIBHMC 10-m Bathymetry: CNMI: Alamagan: Hillshade (mp_pibhmc_ala_bathy10m_hillshade)

A 10-m resolution gridded digital elevation model (DEM) grayscale hillshade for the bathymetry (ocean depth) surrounding Alamagan Island in the Commonwealth of the Northern Mariana Islands (CNMI) compiled from ship-borne multibeam sonar surveys merged with coastal bathymetry derived from multipectral IKONOS satellite data. Almost complete bottom coverage was achieved in depths between 0 and 800 m. Data collected by the Pacific Islands Benthic Habitat Mapping Center (PIBHMC) in support of NOAA's Coral Reef Conservation Program. Not to be used for navigational purposes. For the related dataset containing numeric bathymetry values for this image layer, see http://pacioos.org/metadata/pibhmc_bathy_10m_alamagan.html

NOAA/PIBHMC 10-m Bathymetry: CNMI: Asuncion: Hillshade (mp_pibhmc_asu_bathy10m_hillshade)

A 10-m resolution gridded digital elevation model (DEM) grayscale hillshade for the bathymetry (ocean depth) surrounding Asuncion Island in the Commonwealth of the Northern Mariana Islands (CNMI) compiled from ship-borne multibeam sonar surveys merged with coastal bathymetry derived from multipectral IKONOS satellite data. Almost complete bottom coverage was achieved in depths between 0 and 800 m. Data collected by the Pacific Islands Benthic Habitat Mapping Center (PIBHMC) in support of NOAA's Coral Reef Conservation Program. Not to be used for navigational purposes. For the related dataset containing numeric bathymetry values for this image layer, see http://pacioos.org/metadata/pibhmc_bathy_10m_asuncion.html

NOAA/PIBHMC 5-m Bathymetry: CNMI: Farallon De Medinilla: Hillshade (mp_pibhmc_fdm_bathy5m_hillshade)

A 5-m resolution gridded digital elevation model (DEM) grayscale hillshade for the bathymetry (ocean depth) surrounding the island of Farallon de Medinilla in the Commonwealth of the Northern Mariana Islands (CNMI) compiled from ship-borne multibeam sonar surveys. Almost complete bottom coverage was achieved in depths between 31 and 468 m. Data collected by the Pacific Islands Benthic Habitat Mapping Center (PIBHMC) in support of NOAA's Coral Reef Conservation Program. Not to be used for navigational purposes. For the related dataset containing numeric bathymetry values for this image layer, see http://pacioos.org/metadata/pibhmc_bathy_5m_medinilla.html

NOAA/PIBHMC 10-m Bathymetry: CNMI: Guguan: Hillshade (mp_pibhmc_gug_bathy10m_hillshade)

A 10-m resolution gridded digital elevation model (DEM) grayscale hillshade for the bathymetry (ocean depth) surrounding Guguan Island in the Commonwealth of the Northern Mariana Islands (CNMI) compiled from ship-borne multibeam sonar surveys. Almost complete bottom coverage was achieved in depths between 7 and 800 m. Data collected by the Pacific Islands Benthic Habitat Mapping Center (PIBHMC) in support of NOAA's Coral Reef Conservation Program. Not to be used for navigational purposes. For the related dataset containing numeric bathymetry values for this image layer, see http://pacioos.org/metadata/pibhmc_bathy_10m_guguan.html

NOAA/PIBHMC 10-m Bathymetry: CNMI: Maug: Hillshade (mp_pibhmc_mau_bathy10m_hillshade)

A 10-m resolution gridded digital elevation model (DEM) grayscale hillshade for the bathymetry (ocean depth) surrounding Maug Island in the Commonwealth of the Northern Mariana Islands (CNMI) compiled from ship-borne multibeam sonar surveys merged with coastal bathymetry derived from multipectral IKONOS satellite data. Almost complete bottom coverage was achieved in depths between 0 and 800 m. Data collected by the Pacific Islands Benthic Habitat Mapping Center (PIBHMC) in support of NOAA's Coral Reef Conservation Program. Not to be used for navigational purposes. For the related dataset containing numeric bathymetry values for this image layer, see http://pacioos.org/metadata/pibhmc_bathy_10m_maug.html

NOAA/PIBHMC 10-m Bathymetry: CNMI: Pagan: Hillshade (mp_pibhmc_pag_bathy10m_hillshade)

A 10-m resolution gridded digital elevation model (DEM) grayscale hillshade for the bathymetry (ocean depth) surrounding Pagan Island in the Commonwealth of the Northern Mariana Islands (CNMI) compiled from ship-borne multibeam sonar surveys. Almost complete bottom coverage was achieved in depths between 5 and 800 m. Data collected by the Pacific Islands Benthic Habitat Mapping Center (PIBHMC) in support of NOAA's Coral Reef Conservation Program. Not to be used for navigational purposes. For the related dataset containing numeric bathymetry values for this image layer, see http://pacioos.org/metadata/pibhmc_bathy_10m_pagan.html

NOAA/PIBHMC 60-m Bathymetry: CNMI: Rota: Hillshade (mp_pibhmc_rot_bathy60m_hillshade)

A 60-m resolution gridded digital elevation model (DEM) grayscale hillshade for the bathymetry (ocean depth) surrounding Rota Island in the Commonwealth of the Northern Mariana Islands (CNMI) compiled from ship-borne multibeam sonar surveys. Almost complete bottom coverage was achieved in depths between 5 and 1890 m. Data collected by the Pacific Islands Benthic Habitat Mapping Center (PIBHMC) in support of NOAA's Coral Reef Conservation Program. Not to be used for navigational purposes. For the related dataset containing numeric bathymetry values for this image layer, see http://pacioos.org/metadata/pibhmc_bathy_60m_rota.html

NOAA/PIBHMC 10-m Bathymetry: CNMI: Sarigan: Hillshade (mp_pibhmc_sar_bathy10m_hillshade)

A 10-m resolution gridded digital elevation model (DEM) grayscale hillshade for the bathymetry (ocean depth) surrounding Sarigan Island in the Commonwealth of the Northern Mariana Islands (CNMI) compiled from ship-borne multibeam sonar surveys merged with coastal bathymetry derived from multipectral WorldView-2 (WV-2) satellite data. Almost complete bottom coverage was achieved in depths between 0 and 800 m. Data collected by the Pacific Islands Benthic Habitat Mapping Center (PIBHMC) in support of NOAA's Coral Reef Conservation Program. Not to be used for navigational purposes. For the related dataset containing numeric bathymetry values for this image layer, see http://pacioos.org/metadata/pibhmc_bathy_10m_sarigan.html

NOAA/PIBHMC 5-m Bathymetry: CNMI: Tinian: Hillshade (mp_pibhmc_tin_bathy5m_hillshade)

A 5-m resolution gridded digital elevation model (DEM) grayscale hillshade for the bathymetry (ocean depth) surrounding Tinian Island in the Commonwealth of the Northern Mariana Islands (CNMI) compiled from ship-borne multibeam sonar surveys merged with coastal bathymetry derived from multipectral IKONOS satellite data and aerial LiDAR data. Includes nearby Aguijan Island and Tatsumi Bank. Almost complete bottom coverage was achieved in depths between 0 and 400 m. Data collected by the Pacific Islands Benthic Habitat Mapping Center (PIBHMC) in support of NOAA's Coral Reef Conservation Program. Not to be used for navigational purposes. For the related dataset containing numeric bathymetry values for this image layer, see http://pacioos.org/metadata/pibhmc_bathy_5m_tinian.html

USGS 10-m Digital Elevation Model (DEM): CNMI: Aguijan: Hillshade (mp_usgs_agu_dem10m_hillshade)

A 10-meter resolution land surface digital elevation model (DEM) grayscale hillshade for the island of Aguijan in the Commonwealth of the Northern Mariana Islands (CNMI) derived from United States Geological Survey (USGS) 1/3 arc-second DEM quadrangles. For the related dataset containing numeric elevation values for this image layer, see http://pacioos.org/metadata/usgs_dem_10m_aguijan.html

USGS 10-m Digital Elevation Model (DEM): CNMI: Saipan: Hillshade (mp_usgs_sai_dem10m_hillshade)

A 10-meter resolution land surface digital elevation model (DEM) grayscale hillshade for the island of Saipan in the Commonwealth of the Northern Mariana Islands (CNMI) derived from United States Geological Survey (USGS) 1/3 arc-second DEM quadrangles. For the related dataset containing numeric elevation values for this image layer, see http://pacioos.org/metadata/usgs_dem_10m_saipan.html

Shoreline - U.S. Pacific and Affiliated Territories (pac_comp_all_shore)

Shorelines of all U.S. Pacific and affiliated territories. Compiled from individual shoreline layers.

Pacific Islands Network (PACN) Marine Monitoring Sites (pac_nps_all_pacn_sites)

Locations of monitoring sites related to the Pacific Islands Network (PACN) Benthic Marine and Marine Fish monitoring protocols administered by the U.S. National Park Service (NPS). These monitoring sites are located within nearshore waters of the following National Parks: * Kaloko-Honokohau National Historical Park (NHP) on the western shore of Hawaii Island (Big Island) * Kalaupapa National Historical Park (NHP) on the nothern shore of Molokai in Hawaii * War in the Pacific National Historical Park (NHP) on the western shore of Guam * National Park (NP) of American Samoa on the northern shore of Tutuila The benthic marine community within PACN is a complex ecologic system and a diverse taxonomic environment, including algae and corals and other invertebrates. Reef-building corals are the primary architectural organism and are sensitive to environmental degradation; therefore, they are a good indicator of overall health for nearshore marine ecosystems. Primary stressors to coral reefs include disease, bleaching, sedimentation, eutrophication, storms, and global climate change. The United Nations Environment Programme (UNEP) has proposed using coral reefs as a worldwide indicator ecosystem for global climate change (Spalding et al. 2004). For these reasons, the PACN has chosen to implement long-term monitoring of benthic marine communities. Benthic marine communities are most closely linked with marine fish, and monitoring efforts will be conducted at the same time and location to maximize data value. Fish are a major component of coral reef ecosystems. This highly diverse assemblage of carnivores, planktivores, herbivores, and detritovores serves a variety of ecological functions. Fish affect ecosystem structure, productivity, and sustainability. Selected species can act as indicators of general reef health, environmental stress, and potential ecosystem changes. Fishing is increasingly recognized as a principal threat to Pacific Ocean coral reefs and other marine ecosystems worldwide. In this respect, it is highly probable that most of the Pacific island national parks can be categorized as "impaired" to "seriously impaired" in terms of their fish communities. While the harvest of fish and other marine creatures will be addressed in a separate (fisheries-dependent) monitoring protocol, data collected through PACN marine fish monitoring contributes to the overall fish analyses by providing an in-water (fisheries-independent) assessment of the size and abundance of species within park waters.

Exclusive Economic Zones (EEZs) - U.S.-Affiliated Pacifc Islands (pac_ocs_usa_eez)

These boundaries represent the exclusive economic zones (EEZ) for U.S.-affiliated Pacific Islands, including Hawaii, American Samoa, the Commonwealth of the Northern Mariana Islands (CNMI), the Federated States of Micronesia (FSM), Guam, the Republic of the Marshall Islands (RMI), the Republic of Palau, as well as the U.S. Minor Outlying Islands of Baker Island, Howland Island, Jarvis Island, Johnston Atoll, Kingman Reef, Midway Atoll, Palmyra Atoll, and Wake Island. Under the law of the sea, an EEZ is a sea zone over which a state has special rights over the exploration and use of marine resources. It stretches out to 200 nautical miles from its coast.

Country Labels - U.S. Pacific and Affiliated Territories (pac_pac_labels_country_created)

Labels of country names for U.S. Pacific and affiliated territories.

Political Boundaries - Oceania (pac_rd_political_bndry)

Political boundaries of Oceania, Pacific Ocean.

Hawaii and Pacific Islands King Tides Project (pac_uhsg_usa_kingtides_photos)

The University of Hawaii Sea Grant College Program's Hawaii and Pacific Islands King Tides Project documents high water level events known as King Tides to better understand future impacts from sea level rise and other coastal hazards. King Tides provide a window into the future because today's high tides are predicted to become tomorrow's average sea levels. Citizen scientists have contributed to this free, publicly-accessible, and crowd-sourced dataset by photographing King Tides at places important to them throughout Hawaii and Oceania. Photos, observations, date, time, location, and other metadata are submitted online. This publicly-accessible online database informs research, policy, and decision making across the State of Hawaii and the wider Pacific region. King tides are the highest astronomical tides of the year. The scientific term for a King Tide is a perigean spring tide. King Tides in the Hawaiian Islands tend to occur during the summer (e.g., July and August) and winter months (e.g., December and January) in conjunction with new moons and full moons. King Tides, or the highest high tides of the year, are a unique coastal hazard. The timing of these extreme water level events can be anticipated through the use of tidal predictions, yet their impacts (e.g., coastal flooding and inundation in low-lying areas) can have devastating consequences for coastal inhabitants, particularly when combined with severe weather or high wave events. It is a common misconception that King Tides are the result of man-made climate change. When in reality, they are not byproducts of climate change, rather they are windows for us to see what the future of sea level rise from global climate change might look like along our coastlines. With future sea level rise we can expect more frequent high tide flooding and monthly and even daily high tides exceeding coastal inundation thresholds. When sharing these photographs, please cite this project with the following attribution: (c) Hawaii Sea Grant King Tides Project, (year of photo). Some rights reserved. Licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

Ocean Depth Soundings - Palmyra Atoll (pat_ocs_all_soundings)

Ocean depth soundings in meters around Palmyra Atoll.

NOAA/PIBHMC 5-m Bathymetry: USMOI: Palmyra Atoll: Hillshade (pat_pibhmc_all_bathy5m_hillshade)

A 5-m resolution gridded digital elevation model (DEM) grayscale hillshade for the bathymetry (ocean depth) surrounding Palmyra Atoll in the Central Pacific Ocean, a U.S. Minor Outlying Island (USMOI), compiled from ship-borne multibeam sonar surveys merged with coastal bathymetry derived from multipectral IKONOS satellite data. Almost complete bottom coverage was achieved in depths between 0 and 300 m. Data collected by the Pacific Islands Benthic Habitat Mapping Center (PIBHMC) in support of NOAA's Coral Reef Conservation Program. Not to be used for navigational purposes. For the related dataset containing numeric bathymetry values for this image layer, see http://pacioos.org/metadata/pibhmc_bathy_5m_palmyra.html

Shoreline - Palau (pw_noaa_all_shoreline)

Shoreline of Palau

Buildings - Palau (pw_plrs_all_bldngs)

Facility buildings in Palau.

Conservation Points - Palau (pw_plrs_all_conservsites)

Conservation points in Palau.

Dive Sites - Palau (pw_plrs_all_divesites)

Dive sites in Palau.

Historic Sites - Palau (pw_plrs_all_historicsites)

Historic sites in Palau.

Marsh Habitat - Palau (pw_plrs_all_marsh)

Habitat layer for marsh areas in Palau.

Political Boundary Lines - Palau (pw_plrs_all_political_bndry_line)

Political boundary lines for Palau.

Roads - Palau (pw_plrs_all_roads)

Roads in Palau.

Schools - Palau (pw_plrs_all_schools)

Schools in Palau.

Sewer Branch Lines - Palau (pw_plrs_all_sewer_lines)

Infrastructure layer for sewer branch lines in Palau.

Tourist Sites - Palau (pw_plrs_all_touristsites)

Tourist sites in Palau.

Shoreline - Melekeok, Palau (pw_plrs_mele_shore)

Melekeok Risk Assessment layer for normal shoreline in Palau.

Administrative Boundary - Ngaremeduu, Palau (pw_plrs_ngar_bndry)

Conservation boundary layer for Ngaremeduu, Palau.

Vegetation - Palau (pw_usfs_all_veg)

Draft U.S. Forest Service (USFS) vegetation classification for the Republic of Palau.

Submarine Cable Areas - U.S. and Affiliated Territories (usa_noaa_all_submarine_cable_areas)

These data show the general location of commercial and research submarine cables within U.S. waters. The majority of these cables are for telecommunications, and the remaining are for power transmission. The geographic footprint for each cable may vary and is dependent on the original source data. In the nearshore, cables are routinely buried below the seabed. In the offshore, they are placed directly on the seabed. A submarine cable area may contain one or more physical cables. 30 CFR 585.301 defines a minimum 100-foot-wide right of way grant on each side of a cable.

Shoreline - Wake Island (wai_ocs_all_shoreline)

Shoreline of Wake Island

Antimeridian (world_antimeridian)

The Antimeridian is the +/-180 degree line of longitude, exactly opposite the Prime Meridian. It is often used as the basis for the International Date Line (IDL) because it passes through the open waters of the Pacific Ocean. However, this is a simplification of the actual IDL, which curtails several countries.

Prime Meridian (world_prime_meridian)

The Prime Meridian is the meridian (line of longitude) at which the longitude is defined to be 0 degrees. The Prime Meridian and its opposite, the Antimeridian (at +/-180 degrees longitude), form a "great circle" that divides the Earth into the Eastern and Western Hemispheres. By international convention, the Prime Meridian passes through the Royal Observatory, Greenwich, in southeast London, known as the International Meridian or Greenwich Meridian.

SRTM30+ Global 1-km Digital Elevation Model (DEM): Version 11: Land Surface: Hillshade (world_srtm30plus_dem1km_hillshade)

A global 1-km resolution land surface digital elevation model (DEM) grayscale hillshade. Derived from the SRTM30+ v11 dataset produced at Scripps Institution of Oceanography from United States Geological Survey (USGS) 30 arc-second SRTM30 gridded DEM data, itself a product of NASA's Shuttle Radar Topography Mission (SRTM). GTOPO30 data are used for high latitudes where SRTM data are not available.

Tropic of Capricorn (world_tropic_of_capricorn)

The Tropic of Capricorn lies at 23d 26' 22" (23.4394 degrees) south of the Equator and marks the most southerly latitude at which the sun can appear directly overhead at noon. This event occurs at the December solstice, when the southern hemisphere is tilted towards the sun to its maximum extent. The Earth's tropical zone ("the tropics") includes everything between the Tropic of Cancer and the Tropic of Capricorn.