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.
About this Dataset
| Title | Sea Level Rise: American Samoa: Extreme High-Tide Flooding: 4-Ft Scenario: 1 Day Per Year |
|---|---|
| Description | 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. |
| Modified | 2025-04-18T00:15:34.263Z |
| Publisher Name | N/A |
| Contact | N/A |
| Keywords | Earth Science > Climate Indicators > Atmospheric/Ocean Indicators > Sea Level Rise > Inundation , Earth Science > Human Dimensions > Environmental Impacts , Earth Science > Human Dimensions > Natural Hazards > Floods , Earth Science > Oceans > Coastal Processes > Sea Level Rise , Ocean > Pacific Ocean > South Pacific Ocean > Polynesia > American Samoa > Aunuu , Ocean > Pacific Ocean > South Pacific Ocean > Polynesia > American Samoa > Manua , Ocean > Pacific Ocean > South Pacific Ocean > Polynesia > American Samoa > Ofu , Ocean > Pacific Ocean > South Pacific Ocean > Polynesia > American Samoa > Olosega , Ocean > Pacific Ocean > South Pacific Ocean > Polynesia > American Samoa > Rose Atoll , Ocean > Pacific Ocean > South Pacific Ocean > Polynesia > American Samoa > Swains , Ocean > Pacific Ocean > South Pacific Ocean > Polynesia > American Samoa > Tau , Ocean > Pacific Ocean > South Pacific Ocean > Polynesia > American Samoa > Tutuila , PacIOOS > Pacific Islands Ocean Observing System , PacIOOS > Pacific Islands Ocean Observing System , oceans |
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