In 2023, there were *** flood disaster events recorded worldwide. This marks a slight decrease from the *** disasters in 2022 but remains significantly higher than the average ** floods per year reported in the 1990s. The peak in the past three decades occurred in 2006, with *** flood disasters.

Devastating human and economic toll Floods continue to take a heavy toll on human lives and economies worldwide. In 2023, approximately ** million people were impacted by flooding, including injuries and displacement. Although the number of people affected by floods has decreased since the beginning of the century, due in large part to an improvement in flood protection, better warning systems, and forecasting, the economic burden they cause has increased. Economic loss caused by floods amounted to *** billion U.S. dollars in the past decade, the highest since the *****. Five of the ten costliest floods since 1900 have occurred after 2010, underscoring the increasing financial burden of these events.

Regional disparities in flood impact The impact of floods varies significantly across regions. In 2023, Africa bore the brunt of flood-related fatalities, accounting for over ** percent of global flood deaths. Asia also suffered severely, with over ***** casualties in 2023. Southeast Asian countries, including Bangladesh, Vietnam, and Thailand, are among the most exposed to river flood risk worldwide due to factors such as low elevations, frequent tropical cyclones, and prolonged monsoons.

With a risk index score of 9.9, Bangladesh, Egypt, and Vietnam are the top countries worldwide regarding river flood risk, based on their physical exposure to this type of event. Thailand followed a close second, with a risk index score of 9.8. Where are flooding events most common? In 2024, nine out of the top 10 countries in terms of exposure to river flood risk were located in Asia, in particular in the south and eastern regions of the continent. Southeast Asia is prone to frequent and intense flooding events due to several factors, which include low average elevations, high incidence of tropical storms and heavy rains, prolonged monsoons, and underdeveloped flood protection infrastructure. In addition, climate change is also contributing to the increase in frequency and severity of these events. It is estimated that the global population exposed to flooding incidents will increase by 30 percent in a two-degrees-Celsius warming scenario. Record-breaking floods in Pakistan and Bangladesh Amongst the countries most exposed to floods in Southeast Asia, Bangladesh and Pakistan were particularly affected by floods in 2022. Torrential rain and unceasing downpours struck the countries from early June that year, leading to one of the worst flooding events in their history. In Pakistan, the floods have caused more than 1,700 deaths. In Bangladesh, an estimated 7.2 million people were affected by widespread damage to homes, infrastructure, croplands, and sanitation facilities. Overall, Pakistan and Bangladesh had some of the largest populations exposed to flood risk worldwide.

In 2023, the United States experienced 25 natural disasters, which made it the most natural catastrophe-prone country in the world that year. India and China came second on that list with 17 natural disasters occurring in the same year. Floods was the most common type of natural disaster in 2023. Types of natural disasters There are many different types of natural disasters that occur worldwide, including earthquakes, droughts, storms, floods, volcanic activity, extreme temperatures, landslides, and wild fires. Overall, there were 398 natural disasters registered all over the world in 2023. Costs of natural disasters Due to their destructive nature, natural disasters take a severe toll on populations and countries. Storms and floods, which tend to occur most regularly, have the biggest economic impact in the countries that they occur. In 2023, storms caused damages estimated at more than 100 billion U.S. dollars. Meanwhile, the number of deaths due to natural disasters neared 100,000 that year. The earthquake in Turkey in February had the highest death toll, with more than 50,000 fatalities. Scientists predict that some natural disasters such as storms, floods, landslides, and wildfires will be more frequent and more intense in the future, creating both human and financial losses.

In 2023, floods caused the death of roughly ***** people across the globe. Nevertheless, this death toll is dwarfed in comparison to the peak recorded in 1999. That year, some ****** people died as a result of floods, mostly the consequence of one of the deadliest flood incidents of the previous century, which hit Venezuela in December. The effects of flooding While the death toll is the most critical impact of flooding incidents, it is not the only one. For example, more than ** million people were impacted by floods worldwide in 2023, including those injured, affected or left homeless. In 2010 alone, almost *** million people were affected. Floods also incur massive economic damage by destroying buildings and infrastructure. Asian countries such as Bangladesh, Vietnam, and Egypt were the most exposed to river flooding as of 2024. Climate change and flooding As climate change causes global temperatures to rise, floods are expected to increase in frequency and severity. A warmer atmosphere is able to retain more moisture, leading to an increase in intense downpours. It also affects snowmelt patterns. According to a 2022 report, the global population exposed to flood was expected to rise by ** percent in the case of an increase in global temperatures of *** degrees Celsius.

The impact of global warming, caused by climate change, is significantly affecting the occurrence of heavy flooding worldwide, including in China. The extent of flooding in China is greater than in any other country. Each year, this devastating flooding claims numerous lives, damages countless homes, destroys roads, bridges, and buildings, and affects millions of people, resulting in substantial economic losses. Our research reveals that approximately 66% of China’s landmass is submerged by flooding, affecting about 50% of the population. Furthermore, the financial toll of flooding now accounts for approximately 1.42% of the annual gross domestic product (GDP), which is almost 40 times higher than the corresponding figure for the United States. In recent years, numerous regions in China have been affected by a series of devastating floods that have caused significant damage. This includes central northern Henan province and various areas in southern China. Our study specifically concentrated on these regions due to their high susceptibility to flooding. Our study indicates that floods in China exhibit a wide range of variations in terms of their extent, ranging from localized incidents in specific areas to more extensive regional or basin-wide occurrences. We have observed that Zhengzhou city in Henan province, which faced a devastating flood in 2021, received a significant amount of rainfall, specifically a total of 552.5 mm within a 24-hour period. In a similar manner, the significant flooding that occurred in southern China in 2020 impacted approximately 7.1 million individuals across eight provinces and resulted in 54 fatalities, the collapse of 6,700 houses, and incurred a direct economic loss of US$3.33 billion. In this paper, we have stridden to analyze the causes and impacts of flooding in China’s flood-prone regions and pointed out some mitigation strategies to reduce the repercussions of distressing flood events.

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-low scenario represented here, the modeled water level for a 20-day frequency is 144 cm (108 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.

This dataset is a merged and unified one from seven individual datasets, making it the longest records ever and wide coverage in the US for flood studies. All individual databases and a unified database are provided to accommodate different user needs. It is anticipated that this database can support a variety of flood-related research, such as a validation resource for hydrologic or hydraulic simulations, climatic studies concerning spatiotemporal patterns of floods given this long-term and U.S.-wide coverage, and flood susceptibility analysis for vulnerable geophysical locations.

Description of filenames:

1. cyberFlood_1104.csv – web-based crowdsourced flood database, developed at the University of Oklahoma (Wan et al., 2014). 203 flood events from 1998 to 2008 are retrieved with the latest version. Data accessed on 11/04/2020.

Data attributes: ID, Year, Month, Day, Duration, fatality, Severity, Cause, Lat, Long, Country Code, Continent Code

2. DFO.xlsx – the Dartmouth Flood Observatory flood database. It is a tabular form of global flood database, collected from news, government agencies, stream gauges, and remote sensing instruments from 1985 to the present. Data accessed on 10/27/2020.

Data attributes: ID, GlodeNumber, Country, OtherCountry, long, lat, Area, Began, Ended, Validation, Dead, Displaced, MainCause, Severity

3. emdat_public_2020_11_01_query_uid-MSWGVQ.xlsx – Emergency Events Database (EM-DAT). This flood report is managed by the Centre for Research on the Epidemiology of Disasters in Belgium, which contains all types of global natural disasters from 1900 to the present. Data accessed on 11/01/2020.

Data attributes: Dis No, Year, Seq, Disaster Group, Disaster Subgroup, Disaster Type, Disaster Subtype, Disaster Subsubtype, Event Nane, Entity Criteria, Country, ISO, Region, Continent, Location, Origin, Associated Disaster, Associated Disaster2, OFDA Response, Appeal, Declaration, Aid Contribution, Disaster Magnitude, Latitude, Longitude, Local Time, River Basin, Start Year, Start Month, Start Day, End Year, End Month, End Day, Total Death, No. Injured, No. Affected, No. Homeless, Total Affected, Reconstruction, Insured Damages, Total Damages, CPI

4. extracted_events_NOAA.csv – The national weather service storm reports. The NOAA NWS team collects weather-related natural hazards from 1950 to the present. Data accessed on 10/27/2020.

Data attributes: BEGIN_YEARMONTH, BEGIN_DAY, BEGIN_TIME, END_YEARMONTH, END_DAY, END_TIME, EPISODE_ID, EVENT_ID, STATE, STATE_FIPS, YEAR, MONTH_NAME, EVENT_TYPE, CZ_TYPE, CZ_FIPS, CZ_NAME, WFO, BEGIN_DATETIME, CZ_TIMEZONE, END_DATE_TIME, INJURIES_DIRECT, INJURIES_INDIRECT, DEATHS_DIRECT, DEATHS_INDIRECT, DAMAGE_PROPERTY, DAMAGE_CROPS, SOURCE, MAGNITUDE, MAGNITUDE_TYPE, FLOOD CAUSE, CATEGORY, TOR_F_SCALE< TOR_LENGTH, TOR_WIDTH, TOR_OTHER_WFO, TOR_OTHER_CZ_STATE, TOR_OTHER_CZ_FIPS, BEGIN_RANGE, BEGIN_AZIMUTH, BEGIN_LOCATION, END_RANGE, END_AZIMUTH, END_LOCATION, BEGIN_LAT, BEGIN_LON, END_LAT, END_LON, EPISODE_NARRATIVE, EVENT_NARRATIVE, DATA_SOURCE

5. FEDB_1118.csv – The University of Connecticut Flood Events Database. Floods retrieved from 6,301 stream gauges in the U.S. after flow separation from 2002 to 2013 (Shen et al., 2017). Data accessed on 11/18/2020.

Data attributes: STCD, StartTimeP, EndTimeP, StartTimeF, EndTimeF, Perc, Peak, RunoffCoef, IBF, Vp, Vb, Vt, Pmean, ETr, ELs, VarTr, VarLs, EQ, Q2, CovTrLs, Category, Geometry

6. GFM_events.csv – Global Flood Monitoring dataset. It is a crowdsourcing flood database derived from Twitter tweets over the globe since 2014. Data accessed on 11/9/2020.

Data attributes: event_id, location_ID, location_ID_url, name, type, country_location_ID, country_ISO3, start, end, time of detection

7. mPing_1030.csv – meteorological Phenomena Identification Near the Ground (mPing). The mPing app is a crowdsourcing, weather-reporting software jointly developed by NOAA National Severe Storms Laboratory (NSSL) and the University of Oklahoma (Elmore et al., 2014). Data accessed on 10/30/2020.

Data attributes: id, obtime, category, description, description_id, lon, lat

8. USFD_v1.0.csv – A merged United States Flood Database from 1900 to the present.

Data attributes: DATE_BEGIN, DATE_END, DURATION, LON, LAT, COUNTRY, STATE, AREA, FATALITY, DAMAGE, SEVERITY, SOURCE, CAUSE, SOURCE_DB, SOURCE_ID, DESCRIPTION, SLOPE, DEM, LULC, DISTANCE_RIVER, CONT_AREA, DEPTH, YEAR.

Details of attributes:

DATE_BEGIN: begin datetime of an event. yyyymmddHHMMSS

DATE_END: end datetime of an event. yyyymmddHHMMSS

DURATION: duration of an event in hours

LON: longitude in degrees

LAT: latitude in degrees

COUNTRY: United States of America

STATE: US state name

AREA: affected areas in km^2

FATALITY: number of fatalities

DAMAGE: economic damages in US dollars

SEVERITY: event severity, (1/1.5/2) according to DFO.

SOURCE: flood information source.

CAUSE: flood cause.

SOURCE_DB: source database from item 1-7.

SOURCE_ID: original ID in the source database.

DESCRIPTION: event description

SLOPE: calculated slope based on SRTM DEM 90m

DEM: Digital Elevation Model

LULC: Land Use Land Cover

DISTANCE_RIVER: distance to major river network in km,

CONT_AREA: contributing area (km^2), from MERIT Hydro

DEPTH: 500-yr flood depth

YEAR: year of the event.

The script to merge all sources and figure plots can be found in https://github.com/chrimerss/USFD.

This dataset is derived from the state-of-the-art global daily streamflow reanalysis GloFAS Reanalysis v3.0 at 0.1° resolution. River floods occurred in all major world rivers in 1980-2018 are selected using a peak over threshold routine. The set of flood peaks is then analyzed to identify the seasonality of floods, including the start, peak and end of the flood events, assuming the 1 in 2 year event as flood threshold.

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.

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-low scenario represented here, the modeled water level for a 1-day frequency is 136 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.

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.

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-high scenario represented here, the modeled water level for a 50-day frequency is 116 cm (85 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.

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-low scenario represented here, the modeled water level for a 20-day frequency is 116 cm (85 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.

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.

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.

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-low scenario represented here, the modeled water level for a 50-day frequency is 131 cm (96 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.

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-low scenario represented here, the modeled water level for a 1-day frequency is 163 cm (127 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.

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 low scenario represented here, the modeled water level for a 50-day frequency is 106 cm (75 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.

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 low scenario represented here, the modeled water level for a 50-day frequency is 106 cm (75 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.

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 low scenario represented here, the modeled water level for a 20-day frequency is 135 cm (99 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.