The United States has an average elevation of roughly 2,500 feet (763m) above sea level, however there is a stark contrast in elevations across the country. Highest states Colorado is the highest state in the United States, with an average elevation of 6,800 feet (2,074m) above sea level. The 10 states with the highest average elevation are all in the western region of the country, as this is, by far, the most mountainous region in the country. The largest mountain ranges in the contiguous western states are the Rocky Mountains, Sierra Nevada, and Cascade Range, while the Appalachian Mountains is the longest range in the east - however, the highest point in the U.S. is Denali (Mount McKinley), found in Alaska. Lowest states At just 60 feet above sea level, Delaware is the state with the lowest elevation. Delaware is the second smallest state, behind Rhode Island, and is located on the east coast. Larger states with relatively low elevations are found in the southern region of the country - both Florida and Louisiana have an average elevation of just 100 feet (31m) above sea level, and large sections of these states are extremely vulnerable to flooding and rising sea levels, as well as intermittent tropical storms.

United States US: Urban Population Living in Areas Where Elevation is Below 5 meters: % of Total Population data was reported at 2.264 % in 2010. This records an increase from the previous number of 2.246 % for 2000. United States US: Urban Population Living in Areas Where Elevation is Below 5 meters: % of Total Population data is updated yearly, averaging 2.264 % from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 2.329 % in 1990 and a record low of 2.246 % in 2000. United States US: Urban Population Living in Areas Where Elevation is Below 5 meters: % of Total Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Land Use, Protected Areas and National Wealth. Urban population below 5m is the percentage of the total population, living in areas where the elevation is 5 meters or less.; ; Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.; Weighted Average;

United States US: Land Area Where Elevation is Below 5 Meters: % of Total Land Area data was reported at 1.168 % in 2010. This stayed constant from the previous number of 1.168 % for 2000. United States US: Land Area Where Elevation is Below 5 Meters: % of Total Land Area data is updated yearly, averaging 1.168 % from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 1.168 % in 2010 and a record low of 1.168 % in 2010. United States US: Land Area Where Elevation is Below 5 Meters: % of Total Land Area data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Land Use, Protected Areas and National Wealth. Land area below 5m is the percentage of total land where the elevation is 5 meters or less.; ; Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.; Weighted Average;

This statistic shows a ranking of the estimated average elevation of the land area in 2020 in Latin America, differentiated by country.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

United States US: Population Living in Areas Where Elevation is Below 5 Meters: % of Total Population data was reported at 2.513 % in 2010. This records an increase from the previous number of 2.502 % for 2000. United States US: Population Living in Areas Where Elevation is Below 5 Meters: % of Total Population data is updated yearly, averaging 2.513 % from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 2.575 % in 1990 and a record low of 2.502 % in 2000. United States US: Population Living in Areas Where Elevation is Below 5 Meters: % of Total Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Land Use, Protected Areas and National Wealth. Population below 5m is the percentage of the total population living in areas where the elevation is 5 meters or less.; ; Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.; Weighted Average;

The dataset is a digital elevation model (DEM), in GeoTiff format, of the bathymetry of DeQueen Lake, Sevier County, Arkansas, below a pool elevation of 474 ft above the North American Vertical Datum of 1988 (NAVD88). The DEM was derived from a terrain (digital terrain model, or DTM) created from a feature dataset of XYZ (point) data from an aerial LiDAR survey conducted in March, 2008 for the U.S. Army Corps of Engineers, Little Rock District, and a bathymetric survey conducted in July, 2015, by the Lower Mississippi-Gulf Water Science Center of the U.S. Geological Survey (USGS) using methodologies for multi-beam sonar surveys similar to those described by Lee, K.G. (2013) and Huizinga (2016). References: Lee, K.G., 2013, Estimation of reservoir storage capacity using multibeam sonar and terrestrial LiDAR, Randy Poynter Lake, Rockdale County, Georgia, 2012: U.S. Geological Survey Scientific Investigations Map 3265, 1 sheet, https://pubs.usgs.gov/sim/3265/; Huizinga, R.J., 2016, Bathymetric and velocimetric surveys at highway bridges crossing the Missouri River near Kansas City, Missouri, June 2–4, 2015: U.S. Geological Survey Scientific Investigations Report 2016–5061, 93 p., http://dx.doi.org/10.3133/sir20165061.

The sea level rise (SLR) coastal inundation layers were created using existing federal products: the (1) NOAA Coastal Digital Elevation Models (DEMs) and (2) 2022 Interagency Sea Level Rise Technical Report Data Files. The DEMs for the Continental United States (CONUS) are provided in North American Vertical Datum 1988 (NAVD 88) and were converted to Mean Higher High Water (MHHW) using the NOAA VDatum conversion surfaces; the elevation values are in meters (m). The NOAA Scenarios of Future Mean Sea Level are provided in centimeters (cm). The MHHW DEMs for CONUS were merged and converted to cm and Scenarios of Future Mean Sea Level were subtracted from the merged DEM. Values below 0 represent areas that are below sea level and are “remapped” to 1, all values above 0 are remapped to “No Data”, creating a map that shows only areas impacted by SLR. Areas protected by levees in Louisiana and Texas were then masked or removed from the results. This was done for each of the emissions scenarios (Lower Emissions = 2022 Intermediate SLR Scenario Higher Emissions = 2022 Intermediate High SLR Scenario) at each of the mapped time intervals (Early Century - Year 2030, Middle Century - Year 2050, and Late Century - Year 2090). The resulting maps are displayed in the CMRA Assessment Tool. County, tract, and tribal geographies summaries of percentage SLR inundation were also calculated using Zonal Statistics tools. The Sea Level Rise Scenario year 2020 is considered “baseline” and the impacts are calculated by subtracting the baseline value from each of the near-term, mid-term and long-term timeframes. General Disclaimer The data and maps in this tool illustrate the scale of potential flooding, not the exact location, and do not account for erosion, subsidence, or future construction. Water levels are relative to Mean Higher High Water (MHHW) (excludes wind driven tides). The data, maps, and information provided should be used only as a screening-level tool for management decisions. As with all remotely sensed data, all features should be verified with a site visit. Hydroconnectivity was not considered in the mapping process. The data and maps in this tool are provided “as is,” without warranty to their performance, merchantable state, or fitness for any particular purpose. The entire risk associated with the results and performance of these data is assumed by the user. This tool should be used strictly as a planning reference tool and not for navigation, permitting, or other legal purposes. SLR visualizations and statistics are not available in CMRA for Hawaii, Alaska, or U.S. territories at this time. Levees Disclaimer Enclosed levee areas are displayed as gray areas on the maps. Major federal leveed areas were assumed high enough and strong enough to protect against inundation depicted in this viewer, and therefore no inundation was mapped in these regions. Major federal leveed areas were taken from the National Levee Database. Minor (nonfederal) leveed areas were mapped using the best available elevation data that capture leveed features. In some cases, however, breaks in elevation occur along leveed areas because of flood control features being removed from elevation data, limitations of the horizontal and vertical resolution of the elevation data, the occurrence of levee drainage features, and so forth. Flooding behind levees is only depicted if breaks in elevation data occur or if the levee elevations are overtopped by the water surface. At some flood levels, alternate pathways around—not through—levees, walls, dams, and flood gates may exist that allow water to flow into areas protected at lower levels. In general, imperfect levee and elevation data make assessing protection difficult, and small data errors can have large consequences. Citations 2022 Sea Level Rise Technical Report - Sweet, W.V., B.D. Hamlington, R.E. Kopp, C.P. Weaver, P.L. Barnard, D. Bekaert, W. Brooks, M. Craghan, G. Dusek, T. Frederikse, G. Garner, A.S. Genz, J.P. Krasting, E. Larour, D. Marcy, J.J. Marra, J. Obeysekera, M. Osler, M. Pendleton, D. Roman, L. Schmied, W. Veatch, K.D. White, and C. Zuzak, 2022: Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean Projections and Extreme Water Level Probabilities Along U.S. Coastlines. NOAA Technical Report NOS 01. National Oceanic and Atmospheric Administration, National Ocean Service, Silver Spring, MD, 111 pp. https://oceanservice.noaa.gov/hazards/sealevelrise/noaa-nostechrpt01-global-regional-SLR-scenarios-US.pdf

Increased frequency and intensity of storms, saltwater intrusion, sea level rise, and warming temperatures are affecting forests along the mid-Atlantic, Southeastern, and Gulf coasts of the US. However, we still lack a clear understanding of how the structure of coastal forests is being altered by climate change drivers. Here, we used data from the Forest Inventory and Analyses program of the United States Forest Service to examine structure and biomass change in forests along the mid-Atlantic, Southeastern, and Gulf coasts of the US. We selected plots that have been resampled at low (5 m) and mid (30-50 m) elevations in coastal areas of states from Texas to New Jersey, allowing us to determine change in live trees, standing dead wood, and downed dead wood biomass (and carbon) stocks across a decade. We estimated forest attributes at the county level for each elevational class. Forest area increased by 1.9% in low elevation counties and by 0.3% in mid elevation counties. Live tree biomass density increased by 13% in low elevation counties, and by 16% in mid elevation counties. Standing dead biomass decreased in low elevation counties by 9.2% and by 2.8% in mid elevation counties. On average, downed dead wood increased by 22% in low elevation counties and decreased by 50% in mid elevation counties. Changes in the stock of C in standing and downed dead wood (0.45 to 9.1 Tg C) are similar to soil marsh C loss (9.54 Tg C). Annualized growth and harvest were both higher (16% and 58% respectively) in mid elevation counties than low elevation counties, while annualized mortality was 25% higher in low elevation counties. Annualized growth in low elevation counties was negatively correlated to sea level rise rates, and positively correlated to number of storms, illustrating tradeoffs associated with different climate change drivers. Overall, our results illustrate the vulnerability of US southeastern coastal low and mid elevation forests to climate change and sea level rise with indications that the complexity and rate of change in associated ecosystem functions (e.g., growth, mortality, and carbon storage) within the greater social environment (e.g., agricultural abandonment) may increase. Methods We used data from the National Forest Inventory and Analysis (FIA) program which, administered by the USDA Forest Service, provides a comprehensive statistical inventory and associated database of forests across the United States. The program applies standardized techniques to measure forest characteristics across a national plot sampling network of approximately one plot per 2,428 ha, with plot locations determined using a hexagonal sampling framework designed to be as spatially balanced as possible. The plot location within each 2,428 ha hexagon was visited by field crews if remotely sensed data indicated it was in forest land use (having ≥ 10% tree canopy cover, or evidence of such cover) that was at least 0.4 ha in area and 37 m wide. We focused on data for the mid-Atlantic and Southeast coast of the US, from Texas to New Jersey. We selected information from forested plots located in low (~5m) and mid elevation (30-50 m) areas with slopes less than 15%, and had either hydric conditions, or were near a water feature, which are indicative of forested wetlands. We used the FIA methodologies to estimate forest resources attributes from plot level to the county level. We looked at changes in live trees (biomass and C), standing dead wood (SD, biomass and C), and downed dead wood (biomass and C, DD). The systematic FIA sample design further allowed for statistical population-level estimates of various forest attributes, such as the area of a low-elevation forest in a county, using an “expansion factor” assigned to each plot condition. Using a design-based approach to population inference, expansion factors can be summed across plots in a population to provide an estimate of the total area within that population. Similarly, the FIA sample design allows individual trees inventoried on plots to be scaled via an expansion factor to estimate the total C of trees within an area. In this case, we calculated the area and biomass (from standing live, standing dead, and downed dead) of low-elevation and mid-elevation forests in low-elevation and mid-elevation counties, respectively, and within each state. Field crews collected a wide variety of data using standardized protocols from each FIA plot, which covered 0.067 hectares within four 7.31-m radius subplots arranged at the vertices and center of a triangle. This included the diameter, height, and species for every live and dead tree with a diameter at breast height (DBH) ≥ 12.7 cm. All trees with DBH ≥ 2.54 cm but < 12.7 cm were measured in a single 2.07-m-radius microplot within each of the plot’s four subplots. Using the component ratio method, the FIA program estimates the aboveground dry biomass of each tree with DBH ≥ 2.54 cm in pounds. Biomass and C densities were calculated by scaling plot-level data to per hectare estimates for the counties. We estimated change in the stocks of different pools by subtracting time 2 from time 1. We also looked at changes in different size classes and decay classes (for dead wood). We used data from the two latest survey evaluation periods, spanning a decade of change (Table 1). We estimated forest biomass standing stocks and change among key structural components using data from 1700 plots in low elevation counties and 3200 plots in mid elevation counties. We estimated population level values for 126 low elevation counties and 179 mid elevation counties (Fig 1). We excluded counties for which there were less than three plots in any survey year. To examine potential climate change drivers of forest dynamics we used publicly available datasets. We obtained sea level rise rates for 43 of the National Oceanic and Atmospheric Administration (NOAA) tide gauges from the Permanent Service for Mean Sea Level (PSMSL, Supplementary Table 1). We used the website to estimate rates of sea level rise from 2010-2020 to match the FIA dataset, given reports of accelerating rates of sea level rise in the Southeastern US. We calculated mean annual temperature and mean annual precipitation from the GridMet dataset (4 km2 spatial resolution), accessed through the Climate Engine portal (https://app.climateengine.org/climateEngine) for the period 2010-2020, to roughly match the FIA measurements. We also estimated change in temperature and precipitation by estimating the sen slope of each factor over the same time period using the Climate Engine portal. We used the NOAA National Hurricane Center Atlantic Hurricane Catalog (HURDAT2), accessed through Google Earth Engine, to count the number of tropical cyclones that passed through a 100 km radius buffer of the NOAA tide gauges for the same period. On average, low elevation counties were located 22.4 ± 2.6 km, while mid elevation counties were 108 ± 5.9 km from the NOAA tide gauges.

Projected Hazard: Model-derived water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the model summary and inspect output carefully. Data are complete for the information presented. Details: Model background: The CoSMoS model comprises three tiers. Tier I consists of one Delft3D hydrodynamics FLOW grid for computation of tides, water level variations, flows, and currents and one SWAN grid for computation of wave generation and propagation across the continental shelf. The FLOW and SWAN models are two-way coupled so that tidal currents are accounted for in wave propagation and growth and conversely, so that orbital velocities generated by waves impart changes on tidal currents. The Tier I SWAN and FLOW models consist of identical structured curvilinear grids that extend from far offshore to the shore and range in resolution from 0.5 km in the offshore to 0.2 km in the nearshore. Spatially varying astronomic tidal amplitudes and phases and steric rises in water levels due to large-scale effects (for example, a prolonged rise in sea level) are applied along all open boundaries of the Tier I FLOW grid. Winds (split into eastward and northward components) and sea-level pressure (SLP) fields from CaRD10 (Dr. Dan Cayan, Scripps Institute of Oceanography, San Diego, California, written commun., 2014) that vary in both space and time are applied to all grid cells at each model time-step. Deep-water wave conditions, applied at the open boundaries of the Tier I SWAN model runs, were projected for the 21st century Representative Concentration Pathway (RCP) 4.5 climate scenario (2011-2100) using the WaveWatch III numerical wave model (Tolman and others, 2002) and 3-hourly winds from the GFDL-ESM2M Global Climate Model (GCM). Tier II provides higher resolution near the shore and in areas that require greater resolution of physical processes (such as bays, harbors, and estuaries). A single nested outer grid and multiple two-way coupled domain decomposition (DD) structured grids allow for local grid refinement and higher resolution where needed. Tier II was segmented into 11 sections along the Southern California Bight, to reduce computation time and complete runs within computational limitations. Water-level and Neumann time-series, extracted from Tier I simulations, are applied to the shore-parallel and lateral open boundaries of each Tier II sub-model outer grid respectively. Several of the sub-models proved to be unstable with lateral Neumann boundaries; for those cases one or both of the lateral boundaries were converted to water-level time-series or left unassigned. The open-boundary time-series are extracted from completed Tier I simulations so that there is no communication from Tier II to Tier I. Because this one-way nesting could produce erroneous results near the boundaries of Tier II and because data near any model boundary are always suspect, Tier II sub-model extents were designed to overlap in the along-coast direction. In the landward direction, Tier II DD grids extend to the 10-m topographic contour; exceptions exist where channels (such as the Los Angeles River) or other low-lying regions extend very far inland. Space- and time-varying wind and SLP fields, identical to those used in Tier I simulations, are applied to all Tier II DD grids to allow for wind-setup and local inverse barometer effects (IBE, rise or depression of water levels in response to atmospheric pressure gradients). A total of 42 time-series fluvial discharges are included in the Tier II FLOW domains in an effort to simulate exacerbated flooding caused by backflow at the confluence of high river seaward flows and elevated coastal surge levels migrating inland. Time-varying fluvial discharges are applied either at the closed boundaries or distributed as point sources within the relevant model domains. Wave computations are accomplished with the SWAN model using two grids for each Tier II sub-model: one larger grid covering the same area as the outer FLOW grid and a second finer resolution two-way coupled nearshore nested grid. The nearshore grid extends from approximately 800-1,000 m water depth up to 8-10 m elevations onshore. The landward extension is included to allow for wave computations of the higher SLR scenarios. Time- and space-varying 2D wave spectra extracted from previously completed Tier I simulations are applied approximately every kilometer along the open boundaries of the outer Tier II sub-model SWAN grids. The same space- and time-varying wind fields used in Tier I simulations are also applied to both Tier II SWAN grids to allow for computation of local wave generation. Tier III for the entire Southern California Bight consists of 4,802 cross-shore transects (CST) spaced approximately 100 m apart in the along-shore direction. The profiles extend from the -15 m isobath to at least 10 m above NAVD88. The CSTs are truncated for cases where a lagoon or other waterway exists on the landward end of the profile. Time-varying water levels and wave parameters (significant wave heights, Hs; peak periods, Tp; and peak incident wave directions, Dp), extracted from Tier II grid cells that coincide with the seaward end of the CSTs, are applied at the open boundary of each CST. The XBeach model is run in a hydrostatic (no vertical pressure gradients) mode including event-based morphodynamic change. Wave propagation, two-way wave-current interaction, water-level variations, and wave runup are computed at each transect. XBeach simulations are included in the CoSMoS model to account for infragravity waves that can significantly extend the reach of wave runup (Roelvink and others, 2009) compared to short-wave incident waves. The U.S. west coast is particularly susceptible to infragravity waves at the shore due to breaking of long-period swell waves (Tp > 15). Resulting water levels (WLs) from both Delft3D (high interest bays and marshes) and open-coast XBeach (CSTs) were spatially combined and interpolated to a 10 m grid. These WL elevations are differenced from the originating 2 m digital elevation model (DEM) to determine final flooding extent and depth of flooding. Events: The model system is run for pre-determined scenarios of interest such as the 1-yr or 100-yr storm event in combination with sea-level rise. Storms are first identified from time-series of total water level proxies (TWLpx) at the shore. TWLpx are computed for the majority of the 21st century (2010-2100), assuming a linear super-position of the major processes that contribute to the overall total water level. TWLpx time-series are then evaluated for extreme events, which define the boundary conditions for subsequent modeling with CoSMoS. Multiple 100-yr events are determined (varying Hs, Tp, Dp) and used for multiple model runs to better account for regional and directional flooding affects. Model results are combined and compiled into scenario-specific composites of flood projection. Digital Elevation Model (DEM): Our seamless, topobathymetric digital elevation model (DEM) was based largely upon the Coastal California TopoBathy Merge Project DEM, with some modifications performed by the USGS Earth Resources Observation and Science (EROS) Center to incorporate the most recent, high-resolution topographic and bathymetric datasets available. Topography is derived from bare-earth light detection and ranging (lidar) data collected in 2009-2011 for the CA Coastal Conservancy Lidar Project and bathymetry from 2009-2010 bathymetric lidar as well as acoustic multi- and single-beam data collected primarily between 2001 and 2013. The DEM was constructed to define the shape of nearshore, beach, and cliff surfaces as accurately as possible, utilizing dozens of bathymetric and topographic data sets. These data were used to populate the majority of the Tier I and II grids. To describe and include impacts from long-term shoreline evolution, including cumulative storm activity, seasonal trends, ENSO, and SLR, the DEM was modified for each SLR scenario. Long-term shoreline (Vitousek and Barnard, 2015) and cliff (Limber and others, 2015) erosion projections were efficiently combined along the cross-shore transects to evolve the shore-normal profiles. Elevation changes from the profiles were spatially-merged for a cohesive, 3D depiction of coastal evolution used to modify the DEM. These data are used to generate initial profiles of the 4,802 CSTs used for Phase 2 Tier III XBeach modeling and determining final projected flood depths in each SLR scenario. All data are referenced to NAD83 horizontal datum and NAVD88 vertical datum. Data for Tiers II and III are projected in UTM, zone 11. Outputs include: Projected water levels for the storm and sea-level rise scenario indicated. Data correspond to the near-shore region including areas vulnerable to coastal flooding due to storm surge, sea-level anomalies, tide elevation, and wave run-up during the same storm and sea-level rise simulation. References Cited: Howell, S., Smith-Konter, B., Frazer, N., Tong, X., and Sandwell, D., 2016, The vertical fingerprint of earthquake cycle loading in southern

The dataset is a digital elevation model (DEM), in GeoTiff format, of the bathymetry of Dierks Lake, Howard and Sevier Counties, Arkansas. The extent of the DEM represents the area encompassing the extent of the aerial Light Detection And Ranging (LiDAR) data used in the project. Horizontal and vertical units are expressed in meters. The DEM was derived from an LAS dataset (an industry-standard binary format for storing aerial LiDAR data) created from point datasets stored in “Dierks2018_gdb”. The point datasets include aerial LiDAR data from a survey conducted in 2016 by the National Resources Conservation Service (U.S. Geological Survey, 2017), point data from digitized historical topographic maps, and bathymetric data from a survey conducted in June 2018 by the Lower Mississippi-Gulf Water Science Center of the U.S. Geological Survey (USGS) using methodologies for single- and multi-beam sonar surveys similar to those described by Wilson and Richards (2006) and Richards and Huizinga (2018). In April 2019, it was discovered that some of the bathymetric data collected in shallow and/or tree-ridden areas of the lake had been omitted, resulting in errors in the final products. The missing data were located and added to the geodatabase, the final products re-created, metadata edited accordingly, and the data release reviewed. In response to the second review, in select shallow and/or tree-ridden tributary arms of the lake where bathymetric data were sparse, points along the stream channels, digitized from historical topographic maps representing the pre-impoundment topography, were added to the dataset; select areas of erroneous bathymetric data were edited; and contours at the dam were adjusted based on the historical topographic maps. First release: March 2019; revised August 2019 (version 1.1). The previous version can be obtained by contacting the USGS Lower Mississippi-Gulf Water Science Center using the "Point of Contact" link on the landing page on ScienceBase. References: Richards, J.M. and Huizinga, R.J., 2018, Bathymetric contour map, surface area and capacity table, and bathymetric difference map for Clearwater Lake near Piedmont, Missouri, 2017: U.S. Geological Survey Scientific Investigations Map 3409: 1 sheet, https://doi.org/10.3133/sim3409; U.S. Geological Survey, 2017, Lidar Point Cloud - USGS National Map 3DEP Downloadable Data Collection: U.S. Geological Survey, https://nationalmap.gov/3DEP; Wilson, G.L., and Richards, J.M., 2006, Procedural Documentation and Accuracy Assessment of Bathymetric Maps and Area/Capacity Tables for Small Reservoirs: U.S. Geological Survey Scientific Investigations Report 2006-5208, https://pubs.usgs.gov/sir/2006/5208/.

This dataset was created to represent the land surface elevation at 1:24,000 scale for Florida. The elevation contour lines representing the land surface elevation were digitized from United States Geological survey 1:24,000 (7.5 minute) quadrangles and were compiled by South Florida, South West Florida, St. Johns River and Suwannee River Water Management Districts and FDEP. QA and corrections to the data were supplied by the Florida Department of Environmental Protection's Florida Geological Survey and the Division of Water Resource Management. This data, representing over 1,000 USGS topographic maps, spans a variety of contour intervals including 1 and 2 meter and 5 and 10 foot. The elevation values have been normalized to feet in the final data layer. Attributes for closed topographic depressions were also captured where closed (hautchered) features were identified and the lowest elevation determined using the closest contour line minus one-half the contour interval. This data was derived from the USGS 1:24,000 topographic map series. The data is more than 20 years old and is likely out-of-date in areas of high human activity.

These data were created as part of the National Oceanic and Atmospheric Administration Office for Coastal Management's efforts to create an online mapping viewer called the Sea Level Rise and Coastal Flooding Impacts Viewer. It depicts potential sea level rise and its associated impacts on the nation's coastal areas. The purpose of the mapping viewer is to provide coastal managers and scientists with a preliminary look at sea level rise and coastal flooding impacts. The viewer is a screening-level tool that uses nationally consistent data sets and analyses. Data and maps provided can be used at several scales to help gauge trends and prioritize actions for different scenarios. The Sea Level Rise and Coastal Flooding Impacts Viewer may be accessed at: https://coast.noaa.gov/slr. This metadata record describes the Florida Panhandle, West digital elevation model (DEM), which is a part of a series of DEMs produced for the National Oceanic and Atmospheric Administration Office for Coastal Management's Sea Level Rise and Coastal Flooding Impacts Viewer described above. This DEM includes the best available lidar known to exist at the time of DEM creation that met project specifications. This DEM includes data for Escambia, Santa Rosa, Okaloosa and Walton Counties. The DEM was produced from the following lidar data sets: 1. 2017 NWFWMD Escambia and Santa Rosa FL Lidar 2. 2017 NWFWMD Lower Choctawhatchee Lidar 3. 2018 USGS Panhandle FL Lidar 4. 2018 Leon County 5. 2008 NWFWMD Lidar: Walton County (Eglin AFB) 6. 2007 FDEM Coastal Lidar: Coastal Okaloosa County 7. 2008 NWFWMD Lidar: Inland Okaloosa County 8. 2006 NWFWMD Lidar: Escambia, Santa Rosa, and Walton Counties The DEM is referenced vertically to the North American Vertical Datum of 1988 (NAVD88) with vertical units of meters and horizontally to the North American Datum of 1983 (NAD83). The resolution of the DEM is approximately 3 meters.

In 2016, the U.S. Army Corps of Engineers (USACE) started collecting high-resolution multibeam echosounder (MBES) data on Lake Koocanusa. The survey originated near the International Boundary (River Mile (RM) 271.0) and extended down the reservoir, hereinafter referred to as downstream, about 1.4 miles downstream of the Montana 37 Highway Bridge near Boulder Creek (about RM 253). USACE continued the survey in 2017, completing a reach that extended from about RM 253 downstream to near Tweed Creek (RM 244.5). In 2018, the U.S. Geological Survey (USGS) Idaho Water Science Center completed the remaining portion of the reservoir from RM 244.5 downstream to Libby Dam (RM 219.9). The MBES data collected in 2016 and 2017 by the USACE was combined with the MBES data collected in 2018 by the USGS. The USGS also developed an elevation-area-capacity table at one-foot intervals from the minimum pool elevation (2,290.84 ft) to the maximum pool elevation (2462.84 ft) using the new bathymetry data. The updated stage-capacity table will be compared to the current usable storage estimate of 4,979,500 acre-feet and published in a USGS Scientific Investigations Report. A 10-ft digital elevation model (DEM) and minimum and maximum pool contours also were generated from the bathymetric data and are provided in this data release.

Between January and March 2010, lidar data was collected in southeast/coastal Georgia under a multi-agency partnership between the Coastal Georgia Regional Development Center, USGS, FEMA, NOAA and local county governments. Data acquisition is for the full extent of coastal Georgia, approximately 50 miles inland, excluding counties with existing high-resolution lidar derived elevation data. The data capture area consists of an area of approximately 5703 square miles. This project is within the Atlantic Coastal Priority Area as defined by the National Geospatial Program (NGP) and supports homeland security requirements of the National Geospatial-Intelligence Agency (NGA). This project also supports the National Spatial Data Infrastructure (NSDI) and will advance USGS efforts related to The National Map and the National Elevation Dataset. The data were delivered in LAS format version 1.2 in 5000 x 5000 foot tiles. The data are classified according to ASPRS LAS 1.2 classification scheme: Class 1 - Unclassified Class 2 - Bare Earth Class 7 - Low Point (Noise) Class 9 - Water Class 10 - Reserved Class 12 - Overlap

United States US: Rural Land Area Where Elevation is Below 5 Meters: % of Total Land Area data was reported at 0.980 % in 2010. This stayed constant from the previous number of 0.980 % for 2000. United States US: Rural Land Area Where Elevation is Below 5 Meters: % of Total Land Area data is updated yearly, averaging 0.980 % from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 0.980 % in 2010 and a record low of 0.980 % in 2010. United States US: Rural Land Area Where Elevation is Below 5 Meters: % of Total Land Area data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Land Use, Protected Areas and National Wealth. Rural land area below 5m is the percentage of total land where the rural land elevation is 5 meters or less.; ; Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.; Weighted average;

United States US: Urban Land Area Where Elevation is Below 5 Meters: % of Total Land Area data was reported at 0.187 % in 2010. This stayed constant from the previous number of 0.187 % for 2000. United States US: Urban Land Area Where Elevation is Below 5 Meters: % of Total Land Area data is updated yearly, averaging 0.187 % from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 0.187 % in 2010 and a record low of 0.187 % in 2010. United States US: Urban Land Area Where Elevation is Below 5 Meters: % of Total Land Area data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Land Use, Protected Areas and National Wealth. Urban land area below 5m is the percentage of total land where the urban land elevation is 5 meters or less.; ; Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.; Weighted Average;

The dataset is a digital elevation model (DEM), in GeoTiff format, of the bathymetry of Gillham Lake, Sevier County, Arkansas. The extent of the DEM represents the area encompassing the extent of the aerial Light Detection And Ranging (LiDAR) data used in the project. Horizontal and vertical units are expressed in meters. The DEM was derived from an LAS dataset (an industry-standard binary format for storing aerial LiDAR data) created from point datasets stored in “Gillham2018_gdb”. The point datasets include aerial LiDAR data from a survey conducted in 2016 by the National Resources Conservation Service (U.S. Geological Survey, 2017), point data from digitized historical topographic maps, and bathymetric data from a survey conducted in June 2018 by the Lower Mississippi-Gulf Water Science Center of the U.S. Geological Survey (USGS) using methodologies for single and multi-beam sonar surveys similar to those described by Wilson and Richards (2006) and Richards and Huizinga (20 ...

United States US: Rural Land Area Where Elevation is Below 5 Meters data was reported at 91,676.458 sq km in 2010. This stayed constant from the previous number of 91,676.458 sq km for 2000. United States US: Rural Land Area Where Elevation is Below 5 Meters data is updated yearly, averaging 91,676.458 sq km from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 91,676.458 sq km in 2010 and a record low of 91,676.458 sq km in 2010. United States US: Rural Land Area Where Elevation is Below 5 Meters data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Land Use, Protected Areas and National Wealth. Rural land area below 5m is the total rural land area in square kilometers where the elevation is 5 meters or less.; ; Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.; Sum;

United States US: Rural Population Living in Areas Where Elevation is Below 5 Meters: % of Total Population data was reported at 0.249 % in 2010. This records a decrease from the previous number of 0.256 % for 2000. United States US: Rural Population Living in Areas Where Elevation is Below 5 Meters: % of Total Population data is updated yearly, averaging 0.249 % from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 0.256 % in 2000 and a record low of 0.246 % in 1990. United States US: Rural Population Living in Areas Where Elevation is Below 5 Meters: % of Total Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Land Use, Protected Areas and National Wealth. Rural population below 5m is the percentage of the total population, living in areas where the elevation is 5 meters or less.; ; Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.; Weighted Average;

United States US: Urban Land Area Where Elevation is Below 5 Meters data was reported at 17,520.222 sq km in 2010. This stayed constant from the previous number of 17,520.222 sq km for 2000. United States US: Urban Land Area Where Elevation is Below 5 Meters data is updated yearly, averaging 17,520.222 sq km from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 17,520.222 sq km in 2010 and a record low of 17,520.222 sq km in 2010. United States US: Urban Land Area Where Elevation is Below 5 Meters data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Land Use, Protected Areas and National Wealth. Urban land area below 5m is the total urban land area in square kilometers where the elevation is 5 meters or less.; ; Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.; Sum;