This statistic shows a ranking of the *** lowest places on earth based on elevation below sea level. The world's lowest place on earth is the Dead Sea located in Jordan and Israel, with an elevation amounting to approximately *** meters below sea level.

This statistic shows a ranking of the ten lowest dry land points on earth. The lowest land point is the Dead Sea Depression with an elevation amounting to approximately *** meters below sea level, however, this elevation is an estimate and tends to fluctuate. The shoreline of the Dead Sea is the lowest dry land in the world.

This statistic shows the ten lowest points on earth. The world's lowest point is the Kola Borehole in Russia with a depth of ****** feet. The Kola Borehole is a result of a Soviet Union's drilling project which started in 1970 and was abandoned in 1989 due to temperatures that reached *** degrees Celsius. The only purpose for this project was to drill as deep as possible into the Earth's crust.

At 282 feet below sea level, Death Valley in the Mojave Desert, California is the lowest point of elevation in the United States (and North America). Coincidentally, Death Valley is less than 85 miles from Mount Whitney, the highest point of elevation in the mainland United States. Death Valley is one of the hottest places on earth, and in 1913 it was the location of the highest naturally occurring temperature ever recorded on Earth (although some meteorologists doubt its legitimacy). New Orleans Louisiana is the only other state where the lowest point of elevation was below sea level. This is in the city of New Orleans, on the Mississippi River Delta. Over half of the city (up to two-thirds) is located below sea level, and recent studies suggest that the city is sinking further - man-made efforts to prevent water damage or flooding are cited as one reason for the city's continued subsidence, as they prevent new sediment from naturally reinforcing the ground upon which the city is built. These factors were one reason why New Orleans was so severely impacted by Hurricane Katrina in 2005 - the hurricane itself was one of the deadliest in history, and it destroyed many of the levee systems in place to prevent flooding, and the elevation exacerbated the damage caused. Highest low points The lowest point in five states is over 1,000 feet above sea level. Colorado's lowest point, at 3,315 feet, is still higher than the highest point in 22 states or territories. For all states whose lowest points are found above sea level, these points are located in rivers, streams, or bodies of water.

IntroductionClimate Central’s Surging Seas: Risk Zone map shows areas vulnerable to near-term flooding from different combinations of sea level rise, storm surge, tides, and tsunamis, or to permanent submersion by long-term sea level rise. Within the U.S., it incorporates the latest, high-resolution, high-accuracy lidar elevation data supplied by NOAA (exceptions: see Sources), displays points of interest, and contains layers displaying social vulnerability, population density, and property value. Outside the U.S., it utilizes satellite-based elevation data from NASA in some locations, and Climate Central’s more accurate CoastalDEM in others (see Methods and Qualifiers). It provides the ability to search by location name or postal code.The accompanying Risk Finder is an interactive data toolkit available for some countries that provides local projections and assessments of exposure to sea level rise and coastal flooding tabulated for many sub-national districts, down to cities and postal codes in the U.S. Exposure assessments always include land and population, and in the U.S. extend to over 100 demographic, economic, infrastructure and environmental variables using data drawn mainly from federal sources, including NOAA, USGS, FEMA, DOT, DOE, DOI, EPA, FCC and the Census.This web tool was highlighted at the launch of The White House's Climate Data Initiative in March 2014. Climate Central's original Surging Seas was featured on NBC, CBS, and PBS U.S. national news, the cover of The New York Times, in hundreds of other stories, and in testimony for the U.S. Senate. The Atlantic Cities named it the most important map of 2012. Both the Risk Zone map and the Risk Finder are grounded in peer-reviewed science.Back to topMethods and QualifiersThis map is based on analysis of digital elevation models mosaicked together for near-total coverage of the global coast. Details and sources for U.S. and international data are below. Elevations are transformed so they are expressed relative to local high tide lines (Mean Higher High Water, or MHHW). A simple elevation threshold-based “bathtub method” is then applied to determine areas below different water levels, relative to MHHW. Within the U.S., areas below the selected water level but apparently not connected to the ocean at that level are shown in a stippled green (as opposed to solid blue) on the map. Outside the U.S., due to data quality issues and data limitations, all areas below the selected level are shown as solid blue, unless separated from the ocean by a ridge at least 20 meters (66 feet) above MHHW, in which case they are shown as not affected (no blue).Areas using lidar-based elevation data: U.S. coastal states except AlaskaElevation data used for parts of this map within the U.S. come almost entirely from ~5-meter horizontal resolution digital elevation models curated and distributed by NOAA in its Coastal Lidar collection, derived from high-accuracy laser-rangefinding measurements. The same data are used in NOAA’s Sea Level Rise Viewer. (High-resolution elevation data for Louisiana, southeast Virginia, and limited other areas comes from the U.S. Geological Survey (USGS)). Areas using CoastalDEM™ elevation data: Antigua and Barbuda, Barbados, Corn Island (Nicaragua), Dominica, Dominican Republic, Grenada, Guyana, Haiti, Jamaica, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, San Blas (Panama), Suriname, The Bahamas, Trinidad and Tobago. CoastalDEM™ is a proprietary high-accuracy bare earth elevation dataset developed especially for low-lying coastal areas by Climate Central. Use our contact form to request more information.Warning for areas using other elevation data (all other areas)Areas of this map not listed above use elevation data on a roughly 90-meter horizontal resolution grid derived from NASA’s Shuttle Radar Topography Mission (SRTM). SRTM provides surface elevations, not bare earth elevations, causing it to commonly overestimate elevations, especially in areas with dense and tall buildings or vegetation. Therefore, the map under-portrays areas that could be submerged at each water level, and exposure is greater than shown (Kulp and Strauss, 2016). However, SRTM includes error in both directions, so some areas showing exposure may not be at risk.SRTM data do not cover latitudes farther north than 60 degrees or farther south than 56 degrees, meaning that sparsely populated parts of Arctic Circle nations are not mapped here, and may show visual artifacts.Areas of this map in Alaska use elevation data on a roughly 60-meter horizontal resolution grid supplied by the U.S. Geological Survey (USGS). This data is referenced to a vertical reference frame from 1929, based on historic sea levels, and with no established conversion to modern reference frames. The data also do not take into account subsequent land uplift and subsidence, widespread in the state. As a consequence, low confidence should be placed in Alaska map portions.Flood control structures (U.S.)Levees, walls, dams or other features may protect some areas, especially at lower elevations. Levees and other flood control structures are included in this map within but not outside of the U.S., due to poor and missing data. Within the U.S., data limitations, such as an incomplete inventory of levees, and a lack of levee height data, still make assessing protection difficult. For this map, levees are assumed high and strong enough for flood protection. However, it is important to note that only 8% of monitored levees in the U.S. are rated in “Acceptable” condition (ASCE). Also note that the map implicitly includes unmapped levees and their heights, if broad enough to be effectively captured directly by the elevation data.For more information on how Surging Seas incorporates levees and elevation data in Louisiana, view our Louisiana levees and DEMs methods PDF. For more information on how Surging Seas incorporates dams in Massachusetts, view the Surging Seas column of the web tools comparison matrix for Massachusetts.ErrorErrors or omissions in elevation or levee data may lead to areas being misclassified. Furthermore, this analysis does not account for future erosion, marsh migration, or construction. As is general best practice, local detail should be verified with a site visit. Sites located in zones below a given water level may or may not be subject to flooding at that level, and sites shown as isolated may or may not be be so. Areas may be connected to water via porous bedrock geology, and also may also be connected via channels, holes, or passages for drainage that the elevation data fails to or cannot pick up. In addition, sea level rise may cause problems even in isolated low zones during rainstorms by inhibiting drainage.ConnectivityAt any water height, there will be isolated, low-lying areas whose elevation falls below the water level, but are protected from coastal flooding by either man-made flood control structures (such as levees), or the natural topography of the surrounding land. In areas using lidar-based elevation data or CoastalDEM (see above), elevation data is accurate enough that non-connected areas can be clearly identified and treated separately in analysis (these areas are colored green on the map). In the U.S., levee data are complete enough to factor levees into determining connectivity as well.However, in other areas, elevation data is much less accurate, and noisy error often produces “speckled” artifacts in the flood maps, commonly in areas that should show complete inundation. Removing non-connected areas in these places could greatly underestimate the potential for flood exposure. For this reason, in these regions, the only areas removed from the map and excluded from analysis are separated from the ocean by a ridge of at least 20 meters (66 feet) above the local high tide line, according to the data, so coastal flooding would almost certainly be impossible (e.g., the Caspian Sea region).Back to topData LayersWater Level | Projections | Legend | Social Vulnerability | Population | Ethnicity | Income | Property | LandmarksWater LevelWater level means feet or meters above the local high tide line (“Mean Higher High Water”) instead of standard elevation. Methods described above explain how each map is generated based on a selected water level. Water can reach different levels in different time frames through combinations of sea level rise, tide and storm surge. Tide gauges shown on the map show related projections (see just below).The highest water levels on this map (10, 20 and 30 meters) provide reference points for possible flood risk from tsunamis, in regions prone to them.

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;

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.

This groundwater level elevation and temperature data package is aimed at improving the predictive understanding of hydro-biogeochemical processes at the lower montane site in the East River Watershed, Colorado. The dataset is obtained using pressure transducers placed in shallow wells in the floodplain. This dataset contains data from wells with Location ID's ER-DOW (alias DO1West), ER-DOE (alias DO2East), ER-MBA1 (alias M1Bend1), ER-MBA2 (alias M1Bend2), ER-UPW (alias UP1West), ER-UPM (alias UP2), ER-UPE (alias UP3East). Another dataset contains the data from wells with Location ID's ER-CPA1 to ER-CPA6. Each file contains the water level elevation and the water temperature. Water level elevation have been obtained using the barometric pressure from the pressure transducer (Hobos sensor) in the well, barometric pressure from a sensor in air located at the same site (lower montane), depth from top-of-casing (TOC) to sensor measurement point, and TOC elevation. Data have been checked with a few measurements of water table depths. A real-time kinematic (RTK) global positioning system (GPS) has been used to survey the TOC (data in file WellLocation_WLdataArchive2018). The water level elevation is given in UTM13N Geoid2012AB. While depth to water level is not present in the data files, it can be easily calculated with the TOC and distance to ground provided in the GPS coordinate file. The dataset quality is discussed in Collection/Analysis section of the methods. Time-series of measurements will be added to this data package, so please check back for updates. If you have questions, please contact the author.

GLOBE is a project to develop the best available 30-arc-second (nominally 1 kilometer) global digital elevation data set. This version of GLOBE contains data from 11 sources, and 17 combinations of source and lineage. It continues much in the tradition of the National Geophysical Data Center's TerrainBase (FGDC 1090), as TerrainBase served as a generally lower-resolution prototype of GLOBE data management and compilation techniques. The GLOBE mosaic has been compiled onto CD-ROMs for the international user community. It is also available from the World Wide Web (linked from the online linkage noted above and anonymous ftp. Improvements to the global model are anticipated, as appropriate data and/or methods are made available. In addition, individual contributions to GLOBE (several areas have more than one candidate) should become available at the same website. GLOBE may be used for technology development, such as helping plan infrastructure for cellular communications networks, other public works, satellite data processing, and environmental monitoring and analysis. GLOBE prototypes (and probably GLOBE itself after its release) have been used to help develop terrain avoidance systems for aircraft. In all cases, GLOBE data should be treated as any potentially useful but guaranteed imperfect data set. Mission- or life-critical applications should consider the documented artifacts, as well as likely undocumented imperfections, in the data.

This dataset contains a seamless high resolution, two-meter, topographic lidar digital elevation model (DEM) of the Upper Texas Coast. The elevations in this DEM represent the topographic bare-earth surface. The dataset is a fusion of several airborne topographic light detection and ranging (lidar) surveys acquired by various surveyors between the years 2015 – 2019. The landward extent of the lidar surveys selected for the creation of this DEM is determined by the boundary of the ADvanced CIRCulation (ADCIRC) TX2008_R35H computational mesh obtained from the Computational Hydraulics Group at The University of Texas at Austin. The spatial reference used for the tiles in the DEM is in Universal Transverse Mercator (UTM) Zone 15 in units of meters and in conformance with the North American Datum of 1983 (NAD83). All bare earth elevations are referenced to the North American Datum of 1988 (NAVD88). The 2-meter DEM of the lower Texas coast is available under GRIIDC Unique Dataset Identifier (UDI): HI.x833.000:0010 (DOI: 10.7266/Z7WG9EGN).

This data release contains topographic information compiled for the Lower Roanoke River corridor located in eastern North Carolina. The Lower Roanoke River corridor includes the mainstem of the Roanoke River from Roanoke Rapids, NC (below the Roanoke Rapids dam) to the mouth of the Roanoke River at its confluence with the Albemarle Sound, and the associated floodplains and wetland areas surrounding the river. All datasets were derived from publicly available airborne light detection and radar (lidar) data collected in years 2003, 2014, and 2020. Data are organized into four categories: Digital Elevation Models (DEMs), Slopes, Digital Elevation Models of Difference (DoDs) in numeric format, and one DoD in categoric format. The DEM and Slope datasets represent static topographic conditions in 2003, 2014, or 2020. The DoD datasets reflect changes to topographic conditions between the years of 2003 and 2014, 2014 and 2020, and 2003 and 2020. These datasets can be used to support future examination of geomorphologic changes in the Lower Roanoke River basin. Due to the Lower Roanoke River corridor's extensive low-lying floodplain network, the lidar data and subsequent estimations of ground elevations (DEMs) were sensitive to differences in river stages across airborne lidar flight dates. Dense canopy cover and changes to lidar collection and processing techniques across the data acquisition years may also have affected the quality of data contained in this release. This data release contains four .zip files: (1) "DEM_3m.zip" contains three digital elevation model raster datasets in GeoTIFF format representing bare earth ground elevations in the years 2003, 2014, and 2020 and one metadata file in .xml format that describes the three digital elevation models. (2) "Slope_3m.zip" contains three slope raster datasets in GeoTIFF format representing bare earth ground slopes in the years 2003, 2014, and 2020 and one metadata file in .xml format that describes the three slope rasters. (3) "DoD_numeric_3m.zip" contains three digital elevation difference model raster datasets in GeoTIFF format representing bare earth ground elevation changes between the years 2003 and 2014, 2014 and 2020, and 2003 and 2020 and one metadata file in .xml format that describes the three digital elevation difference models. (4) "DoD_categoric_3m.zip" contains one digital elevation class difference raster dataset in GeoTIFF format representing bare earth ground class changes between the years 2003 and 2014, 2014 and 2020, and 2003 and 2020 and one metadata file in .xml format that describes the digital elevation class change models.

A height-above-river raster produced from 2008 lidar bare-earth elevations for the lower 10 kilometers of the Snow River flood plain is used as part of a geomorphic assessment to identify areas susceptible to inundation and erosion during outburst floods from Snow Lake. This dataset presents flood plain elevations above the approximate elevation of the main river channel low water surface.

The topographic accessibility index is a measure of elevation in relation to valley floor corrected for variation in valley floor elevation across the western United States (i.e., valley floor elevation at California's coast is much lower compared to a valley floor in Wyoming). This index was based on 90-m resolution digital elevation model grids (DEM; National Elevation Dataset, USGS EROS [Earth Resources Observation Systems]),3 and a landform model of the western United States (Manis et al. 2001), which delineates valley flats and near level plateaus or terraces (cell values, 1 or 4). We reclassified the landform model (cell value, 1) and multiplied this layer by the DEM to derive valley floor elevation. Using a moving window analysis (303 x 303 cells; 743.65 km2), we computed mean regional valley floor elevations and subtracted them from the DEM, to derive the difference between the local elevation and regional valley floor elevation. This data set was then resampled to 180 m resolution for analysis.

Only results from the best model are shown. β st. is the standardized β.

This is a tiled collection of the 3D Elevation Program (3DEP) and is 1/3 arc-second (approximately 10 m) resolution. The 3DEP data holdings serve as the elevation layer of The National Map, and provide foundational elevation information for earth science studies and mapping applications in the United States. Scientists and resource managers use 3DEP data for hydrologic modeling, resource monitoring, mapping and visualization, and many other applications. The elevations in this DEM represent the topographic bare-earth surface. The seamless 1/3 arc-second DEM layers are derived from diverse source data that are processed to a common coordinate system and unit of vertical measure. These data are distributed in geographic coordinates in units of decimal degrees, and in conformance with the North American Datum of 1983 (NAD 83). All elevation values are in meters and, over the continental United States, are referenced to the North American Vertical Datum of 1988 (NAVD88). The seamless ...

The Digital SAR Mosaic and Elevation Map of the Greenland Ice Sheet combines the most detailed synthetic aperture radar (SAR) image mosaic available with the best current digital elevation model. The mosaic image shows both the location of the ice edge and the distribution of melt-related 'scatterers' on the surface. These scatterers include ice lenses and complex layered structure in the percolation zone and bare ice of the ablation zone. Other melt-related features that can be seen include lake and surface meltwater stream channels at lower elevations, as well as ice-marginal lakes.

This characterization of the ice sheet provides a reference against which future change can be measured. Changing conditions resulting from climatic variation should show up as changes in the ice margin and shifts in the hydrologic zones. It is hoped that the standard reference provided by this data set can facilitate activities aimed at change detection and promote other work aimed at understanding the processes operating on the ice sheet.

The image data are derived from SAR image swaths acquired by the ERS-1 satellite during August of 1992. The mosaic was assembled at the Jet Propulsion Laboratory (JPL) and Goddard Space Flight Center (GSFC). Its component images are a copyrighted product of the European Space Agency. The mosaic, a value-added derived product, is available to individuals and non-profit organizations for research oriented purposes only. The Danish geodetic and cadastral agency Kort-og Matrikelstyrelsen (KMS) compiled the elevation data provided with the product from a number of sources, including field surveys, aerial photographs, and the ERS-1 radar altimeter.

Unmanned Aerial System (UAS) flights were conducted over four stream catchments in Rio Blanco County, Colorado, during the summer of 2016. Two sties had active oil and gas operations within the basin whereas the other two sites did not. Structure from motion (SfM) was used to align raw images and create a dense point cloud, georectified orthoimage, and Digital Elevation Model (DEM) for each basin. A Digital Terrain Model (DTM), or bare earth model, for each basin was created by reclassifying the dense point cloud as either bare ground or other (vegetation, oil and gas infrastructure, etc.) and interpolating the land surface between bare ground points. Ideally, the DTM would always be equal or lower than the DEM; however, the interpolated surface can sometimes be higher than the DEM if bare ground points surround depressions with vegetation or in thick vegetation strands with an undulating surface. Therefore, a final surface model, created by merging the DTM with the DEM for all areas where the DTM was greater than the DEM, was produced for each basin. Lastly, a random forest classification approach was used to classify the orthoimagery on a pixel level into five vegetation/land cover classifications - bare ground, grass, litter, shrub/woody vegetation, and shadow.

This collection provides a seamlessly merged, hydrologically robust Digital Elevation Model (DEM) for the Murray Darling Basin (MDB), Australia, at 5 m and 25 m grid cell resolution.

This composite DEM has been created from all the publicly available high resolution DEMs in the Geoscience Australia (GA) elevation data portal Elvis (https://elevation.fsdf.org.au/) as at November 2022. The input DEMs, also sometimes referred to as digital terrain models (DTMs), are bare-earth products which represent the ground surface with buildings and vegetation removed. The DEMs were either from lidar (0.5 to 2 m resolution) or photogrammetry (5 m resolution) and totalled 852 individual DEMs.

The merging process involved ranking the DEMs, pairing the DEMs with overlaps, and adjusting and smoothing the elevations of the lower ranked DEM to make the edge elevations compatible with the higher-ranked DEM. This method is adapted from Gallant 2019 with modifications to work with hundreds of DEMs and have a variable number of gaussian smoothing steps.

Where there were gaps in the high-resolution DEM extents, the Forests and Buildings removed DEM (FABDEM; Hawker et al. 2022), a bare-earth radar-derived, 1 arc-second resolution global elevation model was used as the underlying base DEM. FABDEM is based on the Copernicus global digital surface model.

Additionally, hillshade datasets created from both the 5 m and 25 m DEMs are provided.

Note: the FABDEM dataset is available publicly for non-commercial purposes and consequently the data files available with this Collection are also available with a Creative Commons NonCommercial ShareAlike 4.0 Licence (CC BY-NC-SA 4.0). See https://data.bris.ac.uk/datasets/25wfy0f9ukoge2gs7a5mqpq2j7/license.txt Lineage: For a more detailed lineage see the supporting document Composite_MDB_DEM_Lineage.

DATA SOURCES 1. Geoscience Australia elevation data (https://elevation.fsdf.org.au/) via Amazon Web Service s3 bucket. Of the 852 digital elevation models (DEMs) from the GA elevation data portal, 601 DEMs were from lidar and 251 were from photogrammetry. The latest date of download was Nov 2022. The oldest input DEM was from 2008 and the newest from 2022.

1. FABDEM - Forests and buildings removed DEM based on the 1 arc-second Copernicus global digital surface model. Hawker, L., Uhe, P., Paulo, L., Sosa, J., Savage, J., Sampson, C., Neal, J., 2022. A 30 m global map of elevation with forests and buildings removed. Environ. Res. Lett. 17, 024016. https://doi.org/10.1088/1748-9326/ac4d4f

METHODS Part I. Preprocessing The input DEMs were prepared for merging with the following steps: 1. Metadata for all input DEMs was collated in a single file and the DEMs were ranked from finest resolution/newest to coarsest resolution/oldest 2. Tiled input DEMs were combined into single files 3. Input DEMs were reprojected to GA LCC conformal conic projection (EPSG:7845) and bilinearly resampled to 5 m 4. Input DEMs were shifted vertically to the Australian Vertical Working Surface (AVWS; EPSG:9458) 5. The input DEMs were stacked (without any merging and/or smoothing at DEM edges) based on rank so that higher ranking DEMs preceded the lower ranking DEMs, i.e. the elevation value in a grid cell came from the highest rank DEM which had a value in that cell 6. An index raster dataset was produced, where the value assigned to each grid cell was the rank of the DEM which contributed the elevation value to the stacked DEM (see Collection Files - Index_5m_resolution) 7. A metadata file describing each input dataset was linked to the index dataset via the rank attribute (see Collection Files - Metadata)

Vertical height reference surface https://icsm.gov.au/australian-vertical-working-surface

Part II. DEM Merging The method for seamlessly merging DEMs to create a composite dataset is based on Gallant 2019, with modifications to work with hundreds of input DEMs. Within DEM pairs, the elevations of the lower ranked DEM are adjusted and smoothed to make the edge elevations compatible with the higher-ranked DEM. Processing was on the CSIRO Earth Analytics and Science Innovation (EASI) platform. Code was written in python and dask was used for task scheduling.

Part III. Postprocessing 1. A minor correction was made to the 5 m composite DEM in southern Queensland to replace some erroneous elevation values (-8000 m a.s.l.) with the nearest values from the surrounding grid cells 2. A 25 m version of the composite DEM was created by aggregating the 5m DEM, using a 5 x 5 grid cell window and calculating the mean elevation 3. Hillshade datasets were produced for the 5 m and 25 m DEMs using python code from https://github.com/UP-RS-ESP/DEM-Consistency-Metrics

Part IV. Validation Six validation areas were selected across the MDB for qualitative checking of the output at input dataset boundaries. The hillshade datasets were used to look for linear artefacts. Flow direction and flow accumulation rasters and drainage lines were derived from the stacked DEM (step 5 in preprocessing) and the post-merge composite DEM. These were compared to determine whether the merging process had introduced additional errors.

OUTPUTS 1. seamlessly merged composite DEMs at 5 m and 25 m resolutions (geotiff) 2. hillshade datasets for the 5 m and 25 m DEMs (geotiff) 3. index raster dataset at 5 m resolution (geotiff) 4. metadata file containing input dataset information and rank (the rank column values link to the index raster dataset values) 5. figure showing a map of the index dataset and 5m composite DEM (jpeg)

DATA QUALITY STATEMENT Note that we did not attempt to improve the quality of the input DEMs, they were not corrected prior to merging and any errors will be retained in the composite DEM.

The dataset is a digital elevation model (DEM), in GeoTiff format, of the bathymetric surface of Blue Mountain Lake, Arkansas, within the extent of pool elevation 420 feet (ft) above the North American Vertical Datum of 1988 (NAVD88). The DEM was derived from a terrain dataset created by merging bare earth point data from an aerial LiDAR survey conducted in December 2010 for the U.S. Army Corps of Engineers (USACE), Little Rock District, with point data from a bathymetric survey conducted in May 2017 by the Lower Mississippi-Gulf Water Science Center of the U.S. Geological Survey (USGS) using methodology similar to that described by Wilson and Richards (2006).

This is a tiled collection of the 3D Elevation Program (3DEP) and is one meter resolution. The 3DEP data holdings serve as the elevation layer of The National Map, and provide foundational elevation information for earth science studies and mapping applications in the United States. Scientists and resource managers use 3DEP data for hydrologic modeling, resource monitoring, mapping and visualization, and many other applications. The elevations in this DEM represent the topographic bare-earth surface. USGS standard one-meter DEMs are produced exclusively from high resolution light detection and ranging (lidar) source data of one-meter or higher resolution. One-meter DEM surfaces are seamless within collection projects, but, not necessarily seamless across projects. The spatial reference used for tiles of the one-meter DEM within the conterminous United States (CONUS) is Universal Transverse Mercator (UTM) in units of meters, and in conformance with the North American Datum of 1983 ...