This statistic shows the total land and water area of the United States by state and territory. Alabama covers an area of 52,420 square miles.

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Tree Ring. The data include parameters of tree ring with a geographic location of Alaska, United States Of America. The time period coverage is from 38 to -51 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

Surface ocean velocities estimated from HF-Radar are representative of the upper 0.3 - 2.5 meters of the ocean. The main objective of near-real time processing is to produce the best product from available data at the time of processing. Radial velocity measurements are obtained from individual radar sites through the U.S. HF-Radar Network. Hourly radial data are processed by unweighted least-squares on a 1 km resolution grid of the U.S. West Coast to produce near real-time surface current maps.Surface ocean velocities estimated from HF-Radar are representative of the upper 0.3 - 2.5 meters of the ocean. The main objective of near-real time processing is to produce the best product from available data at the time of processing. Radial velocity measurements are obtained from individual radar sites through the U.S. HF-Radar Network. Hourly radial data are processed by unweighted least-squares on a 1 km resolution grid of the U.S. West Coast to produce near real-time surface current maps.Surface ocean velocities estimated from HF-Radar are representative of the upper 0.3 - 2.5 meters of the ocean. The main objective of near-real time processing is to produce the best product from available data at the time of processing. Radial velocity measurements are obtained from individual radar sites through the U.S. HF-Radar Network. Hourly radial data are processed by unweighted least-squares on a 1 km resolution grid of the U.S. West Coast to produce near real-time surface current maps.Surface ocean velocities estimated from HF-Radar are representative of the upper 0.3 - 2.5 meters of the ocean. The main objective of near-real time processing is to produce the best product from available data at the time of processing. Radial velocity measurements are obtained from individual radar sites through the U.S. HF-Radar Network. Hourly radial data are processed by unweighted least-squares on a 1 km resolution grid of the U.S. West Coast to produce near real-time surface current maps.

These data represent the centerline and measured increments at hundredths, tenths and whole miles, along the centerline of the Colorado River beginning at Glen Canyon Dam near Page, Arizona and terminating near the inflow s of Lake Mead in the Grand Canyon region of Arizona, USA. The centerline was digitized using Color Infra-Red (CIR) orthophotography collected in March 2000 as source information and a LiDAR-derived river shoreline representing 8,000 cubic feet per second (CFS)as the defined extent of the river. Every effort was made to follow the main flow of the river while keeping the line approximately equidistant from both shorelines. The centerline feature class has been created to more accurately map locations along the Colorado River downstream of the Glen Canyon Dam. River miles and river kilometers were developed from measurements along this line. The incremental point feature classes were derived from the centerline of the Colorado River datasets. Specifically, the points were generated from nodes extracted from the centerline endpoints of the tenth mile line feature class. The Grand Canyon Monitoring and Research Center (GCMRC) river mileage was cross-checked with commercially available river guides and always fell within one mile of the guides, usually corresponding within a half mile. Additionally, these data were subjected to internal review by GCMRC scientists and commercial boatmen with decades of river travel experience on the Colorado River. River Mile 0 was measured from the USGS concrete gage and cableway at Lees Ferry, Arizona -- as per the Colorado River Compact of 1922 -- with negative river mile numbers used in Glen Canyon and positive river mile numbers downstream in Marble and Grand Canyons. These data were updated in March 2015 using newer ortho-rectified imagery collected in May of 2009 and 2013, both at approximately 8,000 CFS. Due to extended drought conditions that have persisted in the U.S. Southwest, lake levels have dropped dramatically, especially at Lake Mead. A stretch of the Colorado River corridor that was part of Lake Mead in year 2000 has returned to a flowing river once again, and with a different channel that has not previously existed. All changes to the original centerline are downstream of River Mile 260 which is just upstream of Quartermaster Canyon in western Grand Canyon. New river miles and river kilometers were developed from this updated centerline.

Product shows local sea surface temperatures (degrees C). It is a composite gridded-image derived from 8-km resolution SST Observations. It is generated every 48 hours for North America. SST is defined as the skin temperature of the ocean surface water.

This data set provides a 38-year, 1-km resolution inventory of annual on-road CO2 emissions for the conterminous United States based on roadway-level vehicle traffic data and state-specific emissions factors for multiple vehicle types on urban and rural roads as compiled in the Database of Road Transportation Emissions (DARTE). CO2 emissions from the on-road transportation sector are provided annually for 1980-2017 as a continuous surface at a spatial resolution of 1 km.

Sillimanite deposits were inspected on November 1st, 1949. The deposits are reported to extend over a length of approximately 3 miles and a width of ½ mile. Mr Sandland of Morialpa Station conducted us to the more important outcrops. Sillimanite deposits were inspected on November 1st, 1949. The deposits are reported to extend over a length of approximately 3 miles and a width of ½ mile. Mr Sandland of Morialpa Station conducted us to the more important outcrops.

The purpose of this project is to assess the variability of near shore surface circulation (upper 1 meter) off the coast of Barrow and Wainwright. The North Slope Borough has jurisdiction over waters within 3 miles from its coast. Direct surface flow measurements are taken by CODE (1 m drogue) drifters which were deployed within 3 to 15 nautical miles from shore in July and/or August 2011-2014 during the open water season in the Arctic. These measurements will help us to better understand the flow, shear and dispersion of near shore surface currents in the Chukchi Sea.

This dataset contains the polygons that make up the geodatabase for Miramar Landuse. The City of Miramar is a linear city 14 miles in length from east to west and 1.5 to 2.5 miles in width, comprising approximately 31 square miles. The boundaries of the City are delineated by Pembroke Road to the north, U.S. 441 to the east, the Broward County line to the south, and they also extend 1/2 mile west of U.S. 27 into Everglades Water Conservation Area 3A. The City’s development pattern has occurred from east to west with approximately one-third of the land area currently developed. The predominate land use is low density residential. Updated on 7/1/2009 related to Ord no. 09-15, on 1/14/2015 related to Ord no.15-07, on 7/10/2019 related to Ord no.19-01 and Ord no.19-18.

Surface ocean velocities estimated from HF-Radar (HFR) are representative of the upper 0.3 - 2.5 meters of the ocean. The main objective of near-real time processing is to produce the best product from available data at the time of processing. Radial velocity measurements are obtained from individual radar sites through the U.S. HF-Radar Network. Hourly radial data are processed by unweighted least-squares on a 2 km resolution grid of Hawaii to produce near real-time surface current maps.

Annual and growing-season weather data and expanded description of methods for flux measurements, chamber volume estimation, and CO2-balance calculations. The appendix also contains nine supplementary figures (pictures and a map of the field site, soil temperature, thaw depth, monthly fluxes, ANPP, NDVI, water table depth) and five tables (statistical results and summaries of warming effects on environmental variables, monthly fluxes, biomass/ANPP/canopy N, and model parameters).

Sample and Specific Conductance Monitoring Site Approximately 2.5 kilometers downstream from the USGS stream gage 13046995 (Latitude N 44°03'43", Longitude W 111°09'07", NAD83). Methods Specific Conductance Data An In-Situ Aqua Troll 100 Data Logger was used to measure and store specific conductance measurements. Specific conductance measurements were made every 15 minutes. The specific conductance monitoring data were periodically checked against discrete measurements. The hand-held field meter used for discrete measurements and the continuous specific conductance probe were calibrated using NIST traceable standards and measurements were made following the procedure described in the USGS National Field Manual (USGS, 2015). Water Quality Data Samples were collected near the specific conductance monitoring sites. At the time of collection, all waters samples were filtered through a syringe filter (0.45-micrometer). Two splits of the filtered water were retained for chemical analyses, including an unacidified (FU) sample for determination of anion concentrations and a nitric acid preserved (FA; 1% volume-to-volume concentrated trace-metal grade nitric acid) sample for cation and trace metal analyses. During sample collection, the water temperature, specific conductance, and pH were measured. Concentrations of chloride, fluoride, bromide, and sulfate were determined with an ion chromatograph (Dionex DX600). Analytical errors for these constituents were typically less than 2%. Total alkalinity as bicarbonate was determined by titration with sulfuric acid to the bicarbonate end-point. The analytical error in alkalinity concentrations was approximately ± 3%. Concentrations of cations and trace metals were determined with an inductively coupled plasma-optical emission spectroscopy (Perkin Elmer Optima 7300 DV) following the methods described in Ball and others (2010). Arsenic concentrations for selected samples was determined by hydride generation atomic absorption spectroscopy (Perkin Elmer PinAAcle 900T). Quality Control (QC) analyses included standard reference water samples, sample replicates, and blanks. The accuracy of the water chemistry data was checked by calculating charge and specific conductance balance using PHREEQCI (McCleskey, 2018; McCleskey and others, 2012). Results Specific Conductance Data The file FallSC.csv contains the date and time of each measurement and the specific conductance in units of microSiemens per centimeter. The entries in the data file appear in the following columns: A. Date and Time (format: MM/DD/YYYY HH:MM; MDT, mountain daylight time) B. Specific conductance (µS/cm, microSiemens per centimeter) C. Temperature (degree Celsius) Water Quality Data The file FallWQ.csv contains sample collection date and time, pH, specific conductance, solute concentrations, and calculated charge and specific conductance balance. The entries in the water quality data file appear in the following columns: A. Sample location B. Collection Date C. Collection Time D. pH (standard units) E. Specific conductance (microSiemens per centimeter) F. Calcium concentration (milligrams per liter) G. Magnesium concentration (milligrams per liter) H. Sodium concentration (milligrams per liter) I. Potassium concentration (milligrams per liter) J. Chloride concentration (milligrams per liter) K. Sulfate concentration (milligrams per liter) L. Alkalinity (milligrams per liter as bicarbonate) M. Iron concentration (milligrams per liter) N. Silica concentration (milligrams per liter) O. Boron concentration (milligrams per liter) P. Aluminum concentration (milligrams per liter) Q. Fluoride concentration (milligrams per liter) R. Lithium concentration (milligrams per liter) S. Strontium concentration (milligrams per liter) T. Barium concentration (milligrams per liter) U. Rubidium concentration (milligrams per liter) V. Bromide concentration (milligrams per liter) W. Manganese concentration (milligrams per liter) X. Copper concentration (milligrams per liter) Y. Zinc concentration (milligrams per liter) Z. Cadmium concentration (milligrams per liter) AA. Chromium concentration (milligrams per liter) AB. Cobalt concentration (milligrams per liter) AC. Lead concentration (milligrams per liter) AD. Nickel concentration (milligrams per liter) AE. Vanadium concentration (milligrams per liter) AF. Arsenic concentration (milligrams per liter) AG. Antimony concentration (milligrams per liter) AH. Charge Balance (percent) AI. Specific Conductance Imbalance (percent) References Ball, J.W., McCleskey, R.B., and Nordstrom, D.K., 2010, Water-chemistry data for selected springs, geysers, and streams in Yellowstone National Park, Wyoming, 2006-2008: U.S. Geological Survey Open-File Report 2010-1192, 109 p. McCleskey, R.B., 2018, Calculated specific conductance using PHREEQCI: U.S. Geological Survey software release, https://doi.org/10.5066/F7M907VD. McCleskey, R.B., Nordstrom, D.K., Ryan, J.N., and Ball, J.W., 2012, A New Method of Calculating Electrical Conductivity With Applications to Natural Waters: Geochimica et Cosmochimica Acta, v. 77, p. 369-382. [http://www.sciencedirect.com/science/article/pii/S0016703711006181] USGS, 2015. A.6 Field Measurements. 6.3 Specific Electrical Conductance, National field manual for the collection of water-quality data: U.S. Geological Survey Techniques of Water-Resources Investigations, book 9, chaps. A1-A9, available online at https://water.usgs.gov/owq/FieldManual/compiled/NFM_complete.pdf.

The reported soil temperature profile measurements (24 locations - 2 probes with 5 and 6 thermistors, respectively - Fig. 1 and Fig. 2) were initiated as part of a soil micro-warming experiment at the Council Road Mile Marker 71 Site (CN_MM71) in September 2017. From 2017 through August 2019, these were measurements of ambient pre-treatment plot conditions. This tussock tundra site (with underlying permafrost) experiences annual frost heaving that causes a vertical movement of the ground surface, hence causing some of the upper most temperature thermistors to be at surface level or above ground and recorded therefore air temperatures near the surface and not below ground soil temperature (see section on Quality Assurance). After August 2019, these measurements ended in preparation for transitioning to the experimental warming of individual plots. The reported temperature data are nominally 3-hour averages of measurements made at varying frequencies (3-hour maximum) with frequencies that depended upon power (solar) availability to operate the sensors (see section in documentation on Methods). The number of values and the standard deviation for each 3-hour average (where appropriate) are also provided. These measurements are located near the NGEE-Arctic CO2 and CH4 eddy covariance tower at the Council Road Site (US-NGC: NGEE Arctic Council, https://ameriflux.lbl.gov/sites/siteinfo/US-NGC#overview) and auxiliary data at https://doi.org/10.5440/1526749. This dataset contains four *.csv files and one *.pdf file. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

The Hawaii Ocean Time-series (HOT) program makes repeated observations of the physics, biology and chemistry at a site approximately 100 km north of Oahu, Hawaii. Two stations are visited about once a month: Kahe Point (Station 1: 21.34N, 158.27W) and Station ALOHA (Station 2: 22.75N, 158W). Various other stations are made intermittently in support of similar research objectives or mooring deployments.

This NODC Accession contains Carbon Assimilation data consisting of Primary Production and Sediment Trap Particle Flux measurements and calculations during HOT cruises 1-227 occurring in 1988-2010. These data are only taken at Station ALOHA. There are over a dozen cruises without data. Files are organized on a yearly basis of each type.

In separate NODC accessions, there are Water Column Chemical data (JGOFS parameters), CTD, Niskin bottle, and thermosalinograph data sets over HOT cruises 1-227 for Station Aloha and other stations and during transit.

In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within California’s State Waters. The program supports a large number of coastal-zone- and ocean-management issues, including the California Marine Life Protection Act (MLPA) (California Department of Fish and Wildlife, 2008), which requires information about the distribution of ecosystems as part of the design and proposal process for the establishment of Marine Protected Areas. A focus of CSMP is to map California’s State Waters with consistent methods at a consistent scale. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data (the undersea equivalent of satellite remote-sensing data in terrestrial mapping), acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow (to about 100 m) subsurface geology. It is emphasized that the more interpretive habitat and geology data rely on the integration of multiple, new high-resolution datasets and that mapping at small scales would not be possible without such data. This approach and CSMP planning is based in part on recommendations of the Marine Mapping Planning Workshop (Kvitek and others, 2006), attended by coastal and marine managers and scientists from around the state. That workshop established geographic priorities for a coastal mapping project and identified the need for coverage of “lands” from the shore strand line (defined as Mean Higher High Water; MHHW) out to the 3-nautical-mile (5.6-km) limit of California’s State Waters. Unfortunately, surveying the zone from MHHW out to 10-m water depth is not consistently possible using ship-based surveying methods, owing to sea state (for example, waves, wind, or currents), kelp coverage, and shallow rock outcrops. Accordingly, some of the data presented in this series commonly do not cover the zone from the shore out to 10-m depth. This data is part of a series of online U.S. Geological Survey (USGS) publications, each of which includes several map sheets, some explanatory text, and a descriptive pamphlet. Each map sheet is published as a PDF file. Geographic information system (GIS) files that contain both ESRI ArcGIS raster grids (for example, bathymetry, seafloor character) and geotiffs (for example, shaded relief) are also included for each publication. For those who do not own the full suite of ESRI GIS and mapping software, the data can be read using ESRI ArcReader, a free viewer that is available at http://www.esri.com/software/arcgis/arcreader/index.html (last accessed September 20, 2013). The California Seafloor Mapping Program is a collaborative venture between numerous different federal and state agencies, academia, and the private sector. CSMP partners include the California Coastal Conservancy, the California Ocean Protection Council, the California Department of Fish and Wildlife, the California Geological Survey, California State University at Monterey Bay’s Seafloor Mapping Lab, Moss Landing Marine Laboratories Center for Habitat Studies, Fugro Pelagos, Pacific Gas and Electric Company, National Oceanic and Atmospheric Administration (NOAA, including National Ocean Service–Office of Coast Surveys, National Marine Sanctuaries, and National Marine Fisheries Service), U.S. Army Corps of Engineers, the Bureau of Ocean Energy Management, the National Park Service, and the U.S. Geological Survey. These web services for the Offshore of Salt Point map area includes data layers that are associated to GIS and map sheets available from the USGS CSMP web page at https://walrus.wr.usgs.gov/mapping/csmp/index.html. Each published CSMP map area includes a data catalog of geographic information system (GIS) files; map sheets that contain explanatory text; and an associated descriptive pamphlet. This web service represents the available data layers for this map area. Data was combined from different sonar surveys to generate a comprehensive high-resolution bathymetry and acoustic-backscatter coverage of the map area. These data reveal a range of physiographic including exposed bedrock outcrops, large fields of sand waves, as well as many human impacts on the seafloor. To validate geological and biological interpretations of the sonar data, the U.S. Geological Survey towed a camera sled over specific offshore locations, collecting both video and photographic imagery; these “ground-truth” surveying data are available from the CSMP Video and Photograph Portal at https://doi.org/10.5066/F7J1015K. The “seafloor character” data layer shows classifications of the seafloor on the basis of depth, slope, rugosity (ruggedness), and backscatter intensity and which is further informed by the ground-truth-survey imagery. The “potential habitats” polygons are delineated on the basis of substrate type, geomorphology, seafloor process, or other attributes that may provide a habitat for a specific species or assemblage of organisms. Representative seismic-reflection profile data from the map area is also include and provides information on the subsurface stratigraphy and structure of the map area. The distribution and thickness of young sediment (deposited over the past about 21,000 years, during the most recent sea-level rise) is interpreted on the basis of the seismic-reflection data. The geologic polygons merge onshore geologic mapping (compiled from existing maps by the California Geological Survey) and new offshore geologic mapping that is based on integration of high-resolution bathymetry and backscatter imagery seafloor-sediment and rock samplesdigital camera and video imagery, and high-resolution seismic-reflection profiles. The information provided by the map sheets, pamphlet, and data catalog has a broad range of applications. High-resolution bathymetry, acoustic backscatter, ground-truth-surveying imagery, and habitat mapping all contribute to habitat characterization and ecosystem-based management by providing essential data for delineation of marine protected areas and ecosystem restoration. Many of the maps provide high-resolution baselines that will be critical for monitoring environmental change associated with climate change, coastal development, or other forcings. High-resolution bathymetry is a critical component for modeling coastal flooding caused by storms and tsunamis, as well as inundation associated with longer term sea-level rise. Seismic-reflection and bathymetric data help characterize earthquake and tsunami sources, critical for natural-hazard assessments of coastal zones. Information on sediment distribution and thickness is essential to the understanding of local and regional sediment transport, as well as the development of regional sediment-management plans. In addition, siting of any new offshore infrastructure (for example, pipelines, cables, or renewable-energy facilities) will depend on high-resolution mapping. Finally, this mapping will both stimulate and enable new scientific research and also raise public awareness of, and education about, coastal environments and issues. Web services were created using an ArcGIS service definition file. The ArcGIS REST service and OGC WMS service include all Offshore of Salt Point map area data layers. Data layers are symbolized as shown on the associated map sheets.