This statistic shows a ranking of the ten 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 414 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.

Soil Landscapes of the United States (SOLUS)metadataDescriptionSoil Landscapes of the United States, or SOLUS, is a national map product developed by the National Cooperative Soil Survey that is focused on providing a consistent set of spatially continuous soil property maps to support large scope soil investigations and land use decisions. SOLUS maps use a digital soil mapping framework that combines multiple sources of soil survey data with environmental covariate data and machine learning. Digital soil mapping is the production of georeferenced soil databases based on the quantitative relationships between soil measurements made in the field or laboratory and environmental data. Numerical models use the quantitative relationships to predict the spatial distribution of either discrete soil classes, such as map units, or continuous soil properties, such as clay content. SOLUS maps use continuous property mapping, which predicts soil physical or chemical properties in horizontal and vertical dimensions. The soil properties are represented across a continuous range of values. Raster datasets of select soil properties can be predicted at specified depths or depth intervals. Continuous soil property maps such as SOLUS provide critical natural resource information to support environmental researchers and modelers, conservationists, and others making land management decisions. SOLUS will be updated annually with improved data and methodology. SOLUS100The first version of SOLUS, called SOLUS100, is 100 m spatial resolution. Each 100 m raster cell represents a 100 m by 100 m square on the ground with soil property values estimated at seven depths: 0, 5, 15, 30, 60, 100, and 150 cm. The next version will be 30 m spatial resolution and called SOLUS30. SOLUS100 predicts 20 soil properties (listed below with units) at seven depths for the continental United States for a total of 512 maps.Very fine sand (%)Fine sand (%)Medium sand (%)Coarse sand (%)Very coarse sand (%)Total sand (%)Silt (%)Clay (%)pHSoil organic carbon (%)Calcium carbonate equivalent (%)Gypsum content (% by weight)Electrical conductivity (mmhos/cm)Sodium adsorption ratioCation exchange capacity (meq/100g)Effective cation exchange capacity (meq/100g)Oven dry bulk density (g/cm3)Depth to bedrock (cm)Depth to restriction (cm)Rock fragment volume (%)Property Prediction and Uncertainty LayersEach property-depth prediction is accompanied by estimates of uncertainty expressed as prediction interval low and high and relative prediction interval (RPI). Prediction interval low and high define the range within which future predictions may occur. The relative prediction interval ranges from 0 to 1 and is a relative measure of uncertainty with high values being more uncertain. It is computed as the ratio of the 95% prediction interval width to the training set 95% quantile width (97.5% quantile value – 2.5% quantile value). Values closer to 0 indicate lower uncertainty and values closer to 1 indicate higher uncertainty. Values greater than 1 indicate that the prediction at that location is outside the range of the training data used for that property at that depth. The Soil and Plant Science Division delivers each property-depth combination through Google Cloud Platform as four raster data layers: the property prediction, the prediction interval low and high, and the RPI. Property prediction and uncertainty layers follow the naming convention: propertyname_depth_cm_p (predicted property values)propertyname_depth_cm_rpi (relative prediction interval)propertyname_depth_cm_l (prediction interval low)propertyname_depth_cm_h (prediction interval high)SOLUS100 map of clay content predicted at the 0 cm depth for the continental U.S.AccessSOLUS100 maps are available for download or use within scripting or GIS software environments: SOLUS100 Cloud Storage BucketDetails on background, methodology, accuracy, uncertainty, and other results and discussion of SOLUS100 maps are available at SOLUS100 Ag Data Commons Repository and in the following publication:Nauman, T. W., Kienast-Brown, S., Roecker, S. M., Brungard, C., White, D., Philippe, J., & Thompson, J. A. (2024). Soil landscapes of the United States (SOLUS): developing predictive soil property maps of the conterminous United States using hybrid training sets. Soil Science Society of America Journal, 1–20. https://acsess.onlinelibrary.wiley.com/doi/10.1002/saj2.20769Data CitationsSoil Survey Staff. Soil Landscapes of the United States. United States Department of Agriculture, Natural Resources Conservation Service. Available online at storage.googleapis.com/solus100pub/index.html. Month, day, year accessed (year of official release).Citation ExampleThe following example is for the 2024 SOLUS maps. Such citations should appear in the reference section of your document.Soil Survey Staff. Soil Landscapes of the United States. United States Department of Agriculture, Natural Resources Conservation Service. Available online at storage.googleapis.com/solus100pub/index.html. May 22, 2024 (2024 official release).

Overview: Land cover mapping represents the coverage of vegetation, bare, wet and built surfaces (developed and natural surfaces) at a given point in time. The existing land cover map was developed by Whatcom County Planning and Development Services (PDS) during spring of 2012 for the Lower Nooksack Water Budget. The dataset represents ground conditions between 2006 and 2010. The project team created the existing condition land cover dataset by combining local and regional datasets to get the most accurate and current data for the U.S. and Canadian portions of WRIA 1. The development of the existing land cover map includes 14 land cover categories; each has a unique impact on the water balance. The agricultural land cover class was further classified into crop types.

Land cover and crop types influence evapotranspiration and infiltration, playing an important role in determining the watershed’s water balance. Land cover data provides information used to parameterize the water movement through the vegetation canopy and water demand of plant evapotranspiration in the estimation of the water budget by the hydrology model.

Land cover changes over time, as exemplified by comparing the existing and historic land cover data in WRIA 1, displayed in Figure 1 and Figure 2. Historic land cover mapping developed by Utah State University (Winkelaar, 2004) as part of the WRIA 1 Watershed Management Project was used to represent land cover/land use for the undepleted flow simulations. This work was done using a suite of studies and ancillary datasets, including turn of the century GLO maps and NRCS soils data. Methods and sources more thoroughly described in Mapping Methodology and Data Sources for Historic Conditions Landuse/ Landcover Within Water Resource Inventory Area 1 (WRIA1) Washington, U.S.A. The historic land cover map includes 10 land cover classes.

Purpose: Within the Topnet-WM hydrologic model used to estimate the Lower Nooksack Water Budget, the local land cover type is used to parameterize the water movement through the vegetation canopy and water demand for plant evapotranspiration, as described in detail in Chapter 2: Water Budget Model. Water input to the canopy comes from rainfall, snowmelt, and irrigation. The process of some water retention by the canopy is known as interception. Potential evapotranspiration is first satisfied from the canopy interception storage. Water that passes through the canopy to the soil becomes input to the vadose zone soil storage. The vadose zone is the unsaturated soil region above the water table. Potential evapotranspiration not satisfied from the interception storage becomes potential evapotranspiration from the vadose zone soil storage. The model calculates crop evapotranspiration using the Penman-Monteith method. Irrigation requirements are calculated using potential crop evapotranspiration and irrigation efficiency. Land cover mapping also identifies impervious surfaces where water directly runs off, as well as lakes and wetlands where water is stored and evaporates.

This resource is a subset of the Lower Nooksack Water Budget (LNWB) Collection Resource.

This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from multiple Infra-Red (IR) instruments on satellites in Geostationary Earth Orbit (GEO) and Low Earth Orbiting (LEO) sun-synchronous (a.k.a. polar orbiting) satellites. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water. LST fields are provided at 3 hourly intervals each day (00:00 UTC, 03:00 UTC, 06:00 UTC, 09:00 UTC, 12:00 UTC, 15:00 UTC, 18:00 UTC and 21:00 UTC). Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and the solar geometry angles. The product is based on merging of available GEO data and infilling with available LEO data outside of the GEO discs. Inter-instrument biases are accounted for by cross-calibration with the IASI instruments on METOP and LSTs are retrieved using a Generalised Split Window algorithm from all instruments. As data towards the edge of the GEO disc is known to have greater uncertainty, any datum with a satellite zenith angle of more than 60 degrees is discarded. All LSTs included have an observation time that lies within +/- 30 minutes of the file nominal Universal Time. Data from the following instruments is included in the dataset: geostationary, Imagers on Geostationary Operational Environmental Satellite (GOES) 12 and GOES 13, Advanced Baseline Imager (ABI) on GOES 16, Spinning Enhanced Visible Infra-Red Imager (SEVIRI) on Meteosat Second Generation (MSG) 1, MSG 2, MSG 3, and MSG 4, Japanese Advanced Meteorological Imager (JAMI) on Multifunctional Transport Satellite MTSAT) 1, and MTSAT 2; and polar, Advanced Along-Track Scanning Radiometer (AATSR) on Environmental Satellite (Envisat), Moderate-resolution Imaging Spectroradiometer (MODIS) on Earth Observation System (EOS) - Aqua and EOS - Terra, Sea and Land Surface Temperature Radiometer SLSTR on Sentinel-3A and Sentinel-3B. However, it should be noted that which instruments contribute to a particular product file depends on depends on mission start and end dates and instrument downtimes. Dataset coverage starts on 1st January 2009 and ends on 31st December 2020. LSTs are provided on a global equal angle grid at a resolution of 0.05° longitude and 0.05° latitude. The dataset coverage is nominally global over the land surface but varies depending on satellite and instrument availability and coverage. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface. The dataset was produced by the University of Leicester (UoL) and data were processed in the UoL processing chain. The Geostationary data were produced by the Instituto Português do Mar e da Atmosfera (IPMA) before being merged into the final dataset. The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.

The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) LEOLSTCMG30 version 1 Climate Modeling Grid (CMG) product provides Land Surface Temperature (LST) derived from the Low Earth Orbit (LEO) satellite data record from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) instruments as well as LST error estimates for both day and night. The product will include global LST produced on CMG at monthly timesteps from 2002 to 2020. The MEaSUREs LEOLST product is generated by regridding the monthly LST CMG products from MODIS (MYD21C3) and VIIRS (VNP21C3). The product will be available on 0.25, 0.5, and 1 degree optimized climate grids with well characterized per-pixel uncertainties. A low-resolution browse is also available showing LST as an RGB (red, green, blue) image in PNG format.Known Issues Users should be aware that in v001 of the Low Earth Orbit Land Surface Temperature Monthly Global Gridded product (LEOLSTCMG30) the nighttime LST error estimates (LST_Night_err_) in the one degree, half degree, and quarter degree SDS layers were erroneously filled with zero values, and should not be used for any scientific data analyses.

This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from multiple Infra-Red (IR) instruments on Low Earth Orbiting (LEO) sun-synchronous (a.k.a. polar orbiting) satellites. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water. Daytime and night-time temperatures are provided in separate files corresponding to 10:30 and 22:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class. The dataset is comprised of LSTs from a series of instruments with a common heritage: the Along-Track Scanning Radiometer 2 (ATSR-2), the Advanced Along-Track Scanning Radiometer (AATSR) and the Sea and Land Surface Temperature Radiometer on Sentinel 3A (SLSTRA); and data from the Moderate Imaging Spectroradiometer on Earth Observation System - Terra (MODIS Terra) to fill the gap between AATSR and SLSTR. So, the instruments contributing to the time series are: ATSR-2 from August 1995 to July 2002; AATSR from August 2002 to March 2012; MODIS Terra from April 2012 to July 2016; and SLSTRA from August 2016 to December 2020. Inter-instrument biases are accounted for by cross-calibration with the Infrared Atmospheric Sounding Interferometer (IASI) instruments on Meteorological Operational (METOP) satellites. For consistency, a common algorithm is used for LST retrieval for all instruments. Furthermore, an adjustment is made to the LSTs to account for the half-hour difference between satellite equator crossing times. For consistency through the time series, coverage is restricted to the narrowest instrument swath width. The dataset coverage is near global over the land surface. During the period covered by ATSR-2, small regions were not covered due to downlinking constraints (most noticeably a track extending southwards across central Asia through India – further details can be found on the ATSR project webpages at http://www.atsr.rl.ac.uk/dataproducts/availability/coverage/atsr-2/index.shtml). LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. Full Earth coverage is achieved in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface. Dataset coverage starts on 1st August 1995 and ends on 31st December 2020. There are two gaps of several months in the dataset: no data were acquired from ATSR-2 between 23 December 1995 and 30 June 1996 due to a scan mirror anomaly; and the ERS-2 gyro failed in January 2001, data quality was less good between 17th Jan 2001 and 5th July 2001 and are not used in this dataset. Also, there is a twelve day gap in the dataset due to Envisat mission extension orbital manoeuvres from 21st October 2010 to 1st November 2010. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies. The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain. The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.

From the abstract of the referenced publication:

Five possible runway sites have been proposed within 4 km of Davis on the northwestern part of Broad Peninsula, Vestfold Hills. Most are on on thin, young sedimentary sequences on low level flat areas, although two are dominantly on Precambrian basement, one of which is at low elevation.

This report reviews the geology of each site as a background summary for use by engineers in the event of a decision to build.

Permafrost level in the area is normally within 100+/- 20-30 cm of the surface and appears to vary depending on location and proximity to water masses.

The report uses as much information as can be assembled from earlier dispersed reports and adds detailed grain size data from eight sites cored during the 1993-94 summer.

Stratigraphy of the sediment sections is not well understood and is best documented in the Heidemann Valley. Maximum sediment thickness known is about 4 m. All sediments appear to be younger than one million years and probably are much younger.

Speculation is given about the origin and significance of some of the features of the area.

Available for download:

1 The ANARE Research Notes 98 publication as a pdf.

2 AutoCAD drawing file data digitised in November 1996 from original survey plans of the possible runway sites survey undertaken in February 1984 by the Australian Survey Office. The original survey plan drawing number is 3276/001 in 9 sheets. The coordinates system for the drawing file is WGS84 UTM grid Zone 44.

3 Eleven maps of the possible runway sites in Adobe Illustrator (.ai) format. The maps were produced by AUSLIG (Australian Survey and Land Information Group) using the digitised survey data and are included in the publication. The maps are also available from the Antarctic Map Catalogue as pdf. The map catalogue numbers are 13363 to 13373. See a Related URL for a link to the Antarctic Map Catalogue.

This work was completed as part of The Bahamas GEF 2020: Meeting the Challenge of 2020 project, The Nature Conservancy was tasked alongside Bahamas National Trust to draft zoning plans for five marine parks including:1. Bonefish Pond National Park (BPNP) – New Providence Island2. Lucayan National Park (LNP) – Grand Bahama Island3. Moriah Harbour Cay National Park (MHCNP) – The Exumas4. Exuma Cays Land and Sea Park (ECLSP) – The Exumas5. Andros West Side National Park (AWSNP) – Andros IslandBenthic habitat classification was completed using Planet SuperDove imagery captured on 20 Jan 2023, for Lucayan National Park. Land cover classification was completed with Planet SkySat imagery captured on 9 Sept. 2019. Image classification of the five National Parks was performed using Trimble eCognition v10 software. A custom rule set for classifying both the land and benthic habitat classes was developed and tested for each park within eCognition Developer using an Object-based Image Analysis (OBIA) approach. Classes were extracted using both spectral and non-spectral attributes, including bathymetry and corresponding spatial and contextual information.Land cover and Benthic Habitat Description for the Bahamas GEF Project:Barren - Above ground areas that are dry and absent of vegetation cover (e.g. sand, impervious)Grass - Grassy or pasture areas where low levels of short scrub/shrub vegetation may exist Forest - Coppice or mixed forest cover. In Andros Westside NP, the forests are distinguished between pine and coppice.Mangrove - Mangrove forests with taller, fuller canopies (less canopy gaps/low ground detection)Mangrove Sparse - Mangrove forests with shorter, thin canopies (more canopy gaps/high ground detection)Mud/Muddy Bottom - Inland or near-shore shallow areas such as coastal lagoons, estuaries, and inundated mud flats with higher levels of sediment and turbidity.Vegetation Sparse - Areas of intermittent vegetation including sparse forest cover and scrub/shrubWater Inland - Water bodies including coastal lagoons, tidal creeks, inlets, ponds, and blue holes surrounded by land. Includes both brackish and freshwater features.Coral/Algal - Includes fringing, patch, and deeper bank/shelf reefs with presence of live coral colonies or structure that is extensive or patchy with gorgonians, sponges and sparse seagrass and/or algae dominate the substrate between coral colonies.Hardbottom Dense - Low relief and scoured hardground with higher densities of mixed assemblages of gorgonians, sponges and macroalgae that vary with depth and location.Hardbottom Sparse - Low relief, scoured hardground with lower densities of algae, gorgonians with remaining substrate sparsely covered with hard corals and sponges.Sand - Low relief, sand substrate found in depths up to 30m with a bare to sparse living community cover (<10%). These areas can be covered by a layer of cyanobacteria and commonly includes green algae genera.Seagrass Dense - Found in shallow lagoons or relatively sheltered zones at a depth of 2-10m, characterized by a low relief, sand substrate with dense seagrass species cover with >50% cover.Seagrass Sparse - Found in shallow lagoons or relatively sheltered zones at a depth of 2-10m, characterized by a low relief, sand substrate with sparse seagrass species cover with <50% cover. Cyanobacteria often form dense mats between macroalgal stalks covering the underlying sandy substrate.Spur and Groove - Alternating reef formations (spurs) and accumulating sand channels (groove) that are oriented perpendicular to the shore with medium–high relief, starting at about 10m dropping to depth reaching about 25 to 30m.Water Deep - Ocean Areas beyond the range of optical seafloor reflectance >30m) recorded by the satellite system. Habitats are unknown and require active sensing to be mapped.

Statewide Ecopia 3 foot Land Cover (2021-2022)This raster land cover data is based off of high-resolution statewide imagery from 2021-2022. It was used by Ecopia to extract and digitize the entire state into 7 different land cover classes. Download Notes:This service can be entered into ArcGIS Pro where "Download Rasters" can be used to download approximately 20 square miles at a time. (Rt. click layer in TOC > Data > Download Rasters)Alternatively, the entire statewide 3ft dataset is available as a zipped download from here (includes colormap file): Ecopia_Statewide_3ft_Raster_TilesClasses available at bottom of this pages.Data SpecificationImagery Used for Extraction: Pixel resolution: 15 cm (6")Camera sensor: Hexagon Pushbroom (Content Mapper)Date of capture: 06/25/2021 - 08/14/2022Date of Vector Extraction: June 2023Extraction Methodology:Ecopia uses proprietary extraction and modeling software to process raw images into high-resolution land cover classifications.Quality Measurements:Measure Name - Threshold across Impervious Polygons:False Negatives <= 5% All PolygonsFalse Positives <= 5% All PolygonsValid Interpretation >= 95% All PolygonsMinimum Area 100% All PolygonsValid Geometry 100% All PolygonsMeasure Name - Threshold across Natural Polygons:False Negatives <=5% All PolygonsFalse Positives <=5% All PolygonsValid Interpretation >=90% All PolygonsMinimum Area 100% All PolygonsValid Geometry 100% All PolygonsLand Cover Classes:UnclassifiedImperviousImpervious, covered by treesShrub/low vegetationTree/forest/high vegetationOpen waterRailroadVegetation (Canopy Mapping)Tree canopy will be captured as a unique polygon layer. It can therefore overlap impervious layers.High vegetation is distinguished from low vegetation based on crown, texture, and derived height models. Leveraging stereo imagery produces results using 3D elevation models used to aid the distinction of vegetation categories. Distinguishing low from high vegetation is based on a 5m threshold, but this is not always feasible, especially in areas where heavy canopy prevents a visualization of the ground. In these circumstances, high vegetation will be given the priority over low vegetation. For more information visit: www.ecopiatech.comClasses:0: No data - Null, clear1: Unclassified2: Impervious3: Impervious, Covered by Tree Canopy6: Shrub/Low Vegetation7: Tree/Forest/High Vegetation8: Open Water12: Railroad

The soil number (soil points) for arable land estimation and the grassland base number for grassland estimation are used to estimate the natural yield capacity of the soils. The classification of the soil in value figures is based on the arable or grassland estimation framework of the soil estimation. The soil numbers or grassland base numbers shown here in 19 classes are part of the class sign. Increases and reductions in the determination of the number of soils or grassland bases also take into account differences in yield due to climatic and water conditions, terrain or stone content and other factors. These are taken into account with the number of arable land or grassland. The higher the value, the higher the natural yield of the soil. The area boundaries correspond to those of the estimation map of the ground estimate (class sign map).

The map shows the average annual leachate rate for the 30-year period 1991-2020. The rate of leachate (mm/year) from the soil is the essential quantity for the recharge of groundwater and the transfer of substances from the soil into the groundwater. It depends on the use (arable land, grassland or forest), the climate and the soil characteristics. It describes the amount of water that seeps from the soil body into the deeper subsoil. Methodologically, the size is derived according to the TUB-BGR method (DWA, 2016). The main soil characteristic for the leachate rate is the amount of soil water available to plants (Wpfl), important climate parameters are precipitation and potential evaporation according to FAO. The degree of sealing of the soils is not taken into account in the evaluation on a medium scale. The method is only applicable for arable land with a slope of

Seasonal variations in vegetation, rainfall, and soil moisture conditions have the potential to impact the slope stability of locally forested coastal bluffs in the Atlantic Highlands of New Jersey. Both the seasonality and rainfall amounts of the two types of storms that induce shallow landslides in the area vary considerably. Most of the documented historical landslides are the result of heavy rainfall caused by late summer-fall tropical cyclones. The majority of the remaining documented landslides are related to spring nor’easters and total storm rainfall amounts for these storms are generally lower than the rainfall amounts for the tropical cyclones. In order to assess how conditions that may affect the potential for shallow landslide initiation vary seasonally, we are monitoring shallow pore-water pressure and soil moisture, precipitation, and slope movement. Our monitoring is located at two sites of previously documented historical landsliding. The Mount Mitchill Scenic Overlook (MMSO) site is located on a slope interpreted to be the main scarp of a deep-seated, rotational landslide or slump that occurred in April 1782. The scarp slope is covered by a veneer of colluvium that has accumulated over the past few centuries. Shallow landslides have been documented nearby on the bluff and other evidence for shallow landslide movement includes scars on the upper slope and convex lobes lower on the slope. The Ocean Boulevard Bridge (OBB) site has experienced recurrent episodes of shallow landslide movement including in 2007 and May 2012. This data release presents the time series data from instrumentation installed at MMSO and OBB for a monitoring period that began on August 1, 2016, and lasted through December 31, 2018. Monitoring data collected in the time period prior to this data release is available in Fiore and others (2017). The instrumentation includes three observation wells, nine soil moisture probes, two rain gauges, and a cable extension transducer. At MMSO, we monitored shallow pore-water pressure in the lower part of the bluff, rainfall, effective rainfall beneath the deciduous forest canopy, and shallow soil moisture at three locations on the lower and middle parts of the bluff. At OBB, we monitored the shallow pore-water pressure at one location in the upper bluff and another location in the landslide deposit, shallow soil moisture at one location in the middle bluff and another location in the landslide deposit, and movement along the west flank of the May 2012 landslide where a displaced soil block forms the upper part of the landslide deposit. Soil samples were collected at six locations on September 28, 2017 using a stainless steel core barrel pushed vertically into sediments along leveled ground below the organic horizon. Samples were analyzed in the laboratory for various hydraulic and geotechnical properties. This data release presents the output for each instrument sensor type as recorded on the datalogger, as well as the laboratory results of the hydraulic and geotechnical properties of the soil samples collected. In some cases the output requires conversion to engineering units and we provide all the necessary factors, values, and equations to facilitate these conversions. The most significant storm during this monitoring period was a cloudburst on August 7, 2018, that produced numerous shallow slope failures in sandy colluvium across the bluffs. Data included in this release support an interpretive paper published in the Quarterly Journal of Engineering Geology and Hydrogeology regarding this cloudburst.

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.

Aerial coverage of hummocks and hollows was estimated in July 2012 in ten 4 m x 4 m plots each along three 60-m transects in the S1 Bog. Hollows were defined as the lowest elevation within plots and were typically at or near the height of the water table, hummocks included area above hollows, including the sides of the hummocks. The experimental work was conducted in a Picea mariana [black spruce] - Sphagnum spp. bog forest in northern Minnesota, 40 km north of Grand Rapids, in the USDA Forest Service Marcell Experimental Forest (MEF).

The interoperable INSPIRE-WFS Soil / Characteristics of Water Binding Brandenburg is a download service that provides data in the Annex Scheme Soil (derived from the original data set: Characteristic values of the water binding Brandenburg) is provided. It provides an overview of the characteristics of water retention (water content at field capacity, usable field capacity, usable field capacity in the effective root area) in the state of Brandenburg. The map is based on the legend units of the soil overview map (BÜK300) with corresponding assignment of parameterized surface soil shapes, which were determined by terrain and laboratory tests. In the case of insufficient data, Table 43 using the linking rule 1.11 (methodological documentation of soil science, Hennings et al 2000) was used for water retention due to the better consistency of the derived values with measured data from the characteristic value tables of the Soil Science Mapping Instructions, 3rd edition, Hanover 1982 (Soil Science Working Group). According to the INSPIRE data specification Soil (D2.8.III.3_v3.0), the contents of the card are INSPIRE compliant. The WFS includes the following feature types: - Observation process (ompr:Process) with information on the organisation LBGR involved in the process, - derived soil object (so:SoilDerivedObject) with information on the observation of the soil property to describe the derived soil object (soilDerivedObjectObservation), - observation of a soil property (om:OM_Observation) with information on the character of the soil-derived object, the observed property, the soil-derived observation of soil-related properties, the result of observations of the soil-derived object, - Soil body, a demarcated part of the floor covering that is homogeneous with respect to certain soil characteristics and/or spatial patterns, and

- Soil layer (so:SoilLayer) with information on the assignment of the layer to a term corresponding to its type, to the derived profile that serves as the reference profile for a specific type of soil in a specific geographical area, the upper and lower depth of the profile element, measured from the surface (0 cm) of a soil profile (in cm).

The compliant INSPIRE-WFS Soil / Kennwerte der Wasserbindung Brandenburg is a download service that delivers data in the annex schema Soil (derived from the original data set: Parameters of water retention Brandenburg). It provides an overview of the parameters of water retention (water content at field capacity, available field capacity, available field capacity in the effective root zone) in the federal state of Brandenburg. The map is based on the legend units of the soil map (BÜK300) with corresponding assignment of parameterized soil forms, which were determined by field and laboratory investigations. In the case of inadequate data bases, the data were obtained using the values from the tables of the Bodenkundliche Kartieranleitung, 3rd edition, Hanover 1982 (AG Bodenkunde) for water retention, Tab. 43 using method 1.11 (method documentation Bodenkunde, Hennings et al 2000). The content of the soil map is compliant to the INSPIRE data specification for the annex theme Soil (D2.8.III.3_v3.0). The WFS includes the following feature types: - Observation process (ompr:Process) with information about the organization LBGR involved in the process, - Soil derived object (so:SoilDerivedObject) with information on the observation of the soil property for characterizing the soil derived object(soilDerivedObjectObservation), Observations of a soil derived object (om:OM_Observation) with information about the character of the soil derived object, the observed property, the soil derived observation of soil related properties, the result of the observations of the soil derived object, - Soil body (so:SoilBody), part of the soil cover that is delineated and that is homogeneous with regard to certain soil properties and/or spatial patterns, and - Soil layer (so:SoilLayer) with information about the assignment of the layer according to the concept that fits its kind, to the derived soil profile, which serves as a reference profile for a particular type of soil in a specific geographical area, including the upper and lower depth of the profile element from the surface (0 cm) of a soil profile (in cm).

A central goal of the Regional Master Plan is to determine the amount and type of human development and activity that the ecosystem of the Highlands Region can sustain while still maintaining the overall ecological values thereof, with special reference to surface and ground water quality and supply. Based on an analysis of available methods and available data, the Low Flow Margin method was selected as the best scientific approach available at this time for estimating capacity of ground water supplies across the entire Highlands Region, to maintain both ecological flow needs and estimate sustainable levels of human consumption. The Low Flow Margin method uses two low flow statistics, and is derived using statistical analyses of data from reference drainage basins with minimal consumptive water uses. The HUC14 subwatershed was selected as the smallest drainage area available for application of the method. The Highlands Council collaborated with the US Geological Survey, Water Resource Science Unit to develop Low Flow Margin results for each HUC14 subwatershed based on data from reference drainage basins with stream flow gaging stations to determine the Ground Water Capacity for each of the 183 HUC14 subwatersheds that occur within the Highlands Region. A key issue for water availability estimates is to what extent Ground Water Capacity should be made available for both current and future human uses. It is important to recognize that the Highlands Act emphasizes that human water uses should be constrained by ecological needs. Therefore, only a portion of Ground Water Capacity is considered available for human use, with the majority being reserved for ecosystem integrity. That amount, called Ground Water Availability, is defined as the portion of Ground Water Capacity that is available for consumptive and depletive human use without harm to ecosystems of the Highlands Region. Utilizing this method, Ground Water Availability is obtained by multiplying Ground Water Capacity by a percentage threshold, of water availability as shown below: Ground Water Availability = (Ground Water Capacity) * (% Water Availability Threshold) In the most ecologically sensitive HUC14 subwatersheds, Ground Water Availability should be severely limited to protect aquatic ecosystems and the related terrestrial ecosystems. For other HUC14s, a graduated scale is appropriately based on ecological values. HUC14s with concentrated development or agriculture and limited ecological constraints would be assigned a higher portion of Ground Water Capacity. To avoid having a highly complex system, few water availability thresholds should exist in the entire system. Implementation of the Regional Master Plan is guided by a Land Use Capability Map that identifies geographic "zones" based on a comprehensive evaluation of resource constraints and development opportunity. The Land Use Capability Map identifies those resource constrained lands where development should be limited, and as such, where it is appropriate to reserve more water for ecosystem function in order to maintain ecological value. Therefore, the thresholds established in the calculation of Ground Water Availability are determined based on the corresponding zone of the Land Use Capability Map.

This dataset contains high-resolution aerial imagery from the USDA NAIP program, high-resolution land cover labels from the Chesapeake Conservancy, low-resolution land cover labels from the USGS NLCD 2011 dataset, low-resolution multi-spectral imagery from Landsat 8, and high-resolution building footprint masks from Microsoft Bing, formatted to accelerate machine learning research into land cover mapping. The Chesapeake Conservancy spent over 10 months and $1.3 million creating a consistent six-class land cover dataset covering the Chesapeake Bay watershed. While the purpose of the mapping effort by the Chesapeake Conservancy was to create land cover data to be used in conservation efforts, the same data can be used to train machine learning models that can be applied over even wider areas.