AndreaBozzo/ceres-open-data-index huggingface dataset:
Sea Surface Temperature (SST) Maximum Monthly Climatological Mean, 1985-2018 - American Samoa

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 maximum of the monthly mean climatology of SST (degrees Celsius) 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.

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 1985-2018 period.

Data source: https://oceanwatch.pifsc.noaa.gov/erddap/griddap/CRW_sst_v1_0_8day.graph

Tags: american samoa, aquatic ecosystems, biosphere, coastal areas, coastal processes, coral reef, coral reefs, earth science, ecosystems, human dimensions, human settlements, manua, marine ecosystems, ocean, ocean temperature, oceans, ofu, olosega, pacific islands ocean observing system, pacific ocean, pacioos, polynesia, reef, reef habitat, rose atoll, sea surface temperature, south pacific ocean, swains, tau, tutuila
Organization: National Oceanic and Atmospheric Administration, Department of Commerce
Vertical Land Motion - Tutuila and Aunuu, American Samoa

Land subsidence rates across Tutuila and Aunuu are drawn from recent work by Huang et al. (2022) showing that the average rate of subsidence on these islands is spatially variable and ranges from 6 to 9 mm/yr. The study analyzed all available radar data from the European Space Agency (ESA) Sentinel-1 satellite, which covers Tutuila and Aunuu only, and derived an average subsidence rate for the 6-year period between September 2015 to December 2022. Data are available over areas where radar data was successfully used to retrieve unambiguous measurements. Vertical land motion (VLM) measurements are provided by this data layer in millimeters per year (mm/yr), with negative values indicating land subsidence.

Tags: american samoa, aunuu, coastal processes, earth science, geomorphic landforms/processes, human dimensions, land subsidence, land surface, natural hazards, ocean, pacific islands ocean observing system, pacific ocean, pacioos, polynesia, solid earth, south pacific ocean, subsidence, tutuila
Organization: National Oceanic and Atmospheric Administration, Department of Commerce
Marine Protected Areas - Guam

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.

Tags: administrative divisions, boundaries, conservation, earth science, environmental governance/management, environmental impacts, guam, human dimensions, micronesia, ocean, pacific islands ocean observing system, pacific ocean, pacioos, western pacific ocean
Organization: National Oceanic and Atmospheric Administration, Department of Commerce
Sea Level Rise: American Samoa: High-Tide Flooding: 2070 High Scenario

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.

Tags: american samoa, atmospheric/ocean indicators, aunuu, climate indicators, coastal processes, earth science, environmental impacts, floods, human dimensions, inundation, manua, natural hazards, ocean, oceans, ofu, olosega, pacific islands ocean observing system, pacific ocean, pacioos, polynesia, rose atoll, sea level rise, south pacific ocean, swains, tau, tutuila
Organization: National Oceanic and Atmospheric Administration, Department of Commerce
Sea Level Rise: American Samoa: High-Tide Flooding: 2060 Low Scenario

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 low scenario represented here, the modeled water level is 48 cm (13 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.

Tags: american samoa, atmospheric/ocean indicators, aunuu, climate indicators, coastal processes, earth science, environmental impacts, floods, human dimensions, inundation, manua, natural hazards, ocean, oceans, ofu, olosega, pacific islands ocean observing system, pacific ocean, pacioos, polynesia, rose atoll, sea level rise, south pacific ocean, swains, tau, tutuila
Organization: National Oceanic and Atmospheric Administration, Department of Commerce
Coral Reef Assessment and Monitoring Program (CRAMP) Study Sites: Main Hawaiian Islands

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

Tags: aquatic ecosystems, biosphere, central pacific ocean, coastal processes, continent, coral reefs, earth science, environmental assessments, environmental governance/management, hawaii, hawaiian islands, human dimensions, marine environment monitoring, north america, ocean, oceans, pacific islands ocean observing system, pacific ocean, pacioos, reef habitat, united states of america
Organization: National Oceanic and Atmospheric Administration, Department of Commerce
Turbidity (Kd490) Standard Deviation of Long-term Mean, 1998-2018 - American Samoa

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 standard deviation of the 8-day time series 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 standard deviation was calculated over all 8-day Kd490 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-kd-8d-v5-0.graph

Tags: american samoa, aquatic ecosystems, biosphere, coastal areas, coastal processes, coral reef, coral reefs, earth science, ecosystems, human dimensions, human settlements, kd490, manua, marine ecosystems, ocean, ocean optics, oceans, ofu, olosega, pacific islands ocean observing system, pacific ocean, pacioos, polynesia, reef, reef habitat, rose atoll, south pacific ocean, swains, tau, turbidity, tutuila, water quality
Organization: National Oceanic and Atmospheric Administration, Department of Commerce
Chlorophyll-a Average Annual Maximum Anomaly, 1998-2018 - American Samoa

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 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 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 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-chla-8d-v5-0.graph

Tags: american samoa, aquatic ecosystems, biosphere, chlorophyll, coastal areas, coastal processes, coral reef, coral reefs, earth science, ecosystems, human dimensions, human settlements, manua, marine ecosystems, ocean, ocean chemistry, ocean color, ocean optics, oceans, ofu, olosega, pacific islands ocean observing system, pacific ocean, pacioos, polynesia, reef, reef habitat, rose atoll, south pacific ocean, swains, tau, tutuila
Organization: National Oceanic and Atmospheric Administration, Department of Commerce
Sea Level Rise: American Samoa: High-Tide Flooding: 3-Ft Scenario

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 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.

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.

Tags: american samoa, atmospheric/ocean indicators, aunuu, climate indicators, coastal processes, earth science, environmental impacts, floods, human dimensions, inundation, manua, natural hazards, ocean, oceans, ofu, olosega, pacific islands ocean observing system, pacific ocean, pacioos, polynesia, rose atoll, sea level rise, south pacific ocean, swains, tau, tutuila
Organization: National Oceanic and Atmospheric Administration, Department of Commerce
Coral Favorability: Non-Managed Conditions: Worst Case Emissions - American Samoa

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).

Tags: american samoa, aquatic ecosystems, biosphere, coastal areas, coastal processes, conservation, coral reef, coral reef assessment, coral reefs, coupled climate models, earth science, earth science services, ecosystems, environmental impacts, environmental sustainability, human dimensions, human settlements, manua, marine ecosystems, models, ocean, oceans, ofu, olosega, pacific islands ocean observing system, pacific ocean, pacioos, polynesia, reef, reef habitat, south pacific ocean, sustainability, tau, tutuila
Organization: National Oceanic and Atmospheric Administration, Department of Commerce