4 datasets found
  1. u

    Probabilities of Adjusted Elevation for 2080s

    • marine.usgs.gov
    Updated Jul 30, 2025
    + more versions
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    (2025). Probabilities of Adjusted Elevation for 2080s [Dataset]. https://marine.usgs.gov/coastalchangehazardsportal/ui/info/item/EXf3LkWP
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    Dataset updated
    Jul 30, 2025
    Area covered
    Description

    The U.S. Geological Survey has been forecasting sea-level rise impacts on the landscape to evaluate where coastal land will be available for future use. The purpose of this project is to develop a spatially explicit, probabilistic model of coastal response for the Northeastern U.S. to a variety of sea-level scenarios that take into account the variable nature of the coast and provides outputs at spatial and temporal scales suitable for decision support. Model results provide predictions of adjusted land elevation ranges (AE) with respect to forecast sea-levels, a likelihood estimate of this outcome (PAE), and a probability of coastal response (CR) characterized as either static or dynamic. The predictions span the coastal zone vertically from -12 meters (m) to 10 m above mean high water (MHW). Results are produced at a horizontal resolution of 30 meters for four decades (the 2020s, 2030s, 2050s and 2080s). Adjusted elevations and their respective probabilities are generated using regional geospatial datasets of current sea-level forecasts, vertical land movement rates, and current elevation data. Coastal response type predictions incorporate adjusted elevation predictions with land cover data and expert knowledge to determine the likelihood that an area will be able to accommodate or adapt to water level increases and maintain its initial land class state or transition to a new non-submerged state (dynamic) or become submerged (static). Intended users of these data include scientific researchers, coastal planners, and natural resource management communities.

    These GIS layers provide the probability of observing the forecast of adjusted land elevation (PAE) with respect to predicted sea-level rise or the Northeastern U.S. for the 2020s, 2030s, 2050s and 2080s. These data are based on the following inputs: sea-level rise, vertical land movement rates due to glacial isostatic adjustment and elevation data. The output displays the highest probability among the five adjusted elevation ranges (-12 to -1, -1 to 0, 0 to 1, 1 to 5, and 5 to 10 m) to be observed for the forecast year as defined by a probabilistic framework (a Bayesian network), and should be used concurrently with the adjusted land elevation layer (AE), also available from http://woodshole.er.usgs.gov/project-pages/coastal_response/, which provides users with the forecast elevation range occurring when compared with the four other elevation ranges. These data layers primarily show the distribution of adjusted elevation range probabilities over a large spatial scale and should therefore be used qualitatively.

  2. E

    Data from: Boundary Dataset for the Jazira Region of Syria

    • dtechtive.com
    • find.data.gov.scot
    • +2more
    xml, zip
    Updated Feb 21, 2017
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    University of Edinburgh (2017). Boundary Dataset for the Jazira Region of Syria [Dataset]. http://doi.org/10.7488/ds/1786
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    zip(0.0093 MB), xml(0.0075 MB)Available download formats
    Dataset updated
    Feb 21, 2017
    Dataset provided by
    University of Edinburgh
    License

    ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
    License information was derived automatically

    Area covered
    Syria
    Description

    This boundary dataset complements 13 other datasets as part of a study that compared ancient settlement patterns with modern environmental conditions in the Jazira region of Syria. This study examined settlement distribution and density patterns over the past five millennia using archaeological survey reports and French 1930s 1:200,000 scale maps to locate and map archaeological sites. An archaeological site dataset was created and compared to and modelled with soil, geology, terrain (contour), surface and subsurface hydrology and normal and dry year precipitation pattern datasets; there are also three spreadsheet datasets providing 1963 precipitation and temperature readings collected at three locations in the region. The environmental datasets were created to account for ancient and modern population subsistence activities, which comprise barley and wheat farming and livestock grazing. These environmental datasets were subsequently modelled with the archaeological site dataset, as well as, land use and population density datasets for the Jazira region. Ancient trade routes were also mapped and factored into the model, and a comparison was made to ascertain if there was a correlation between ancient and modern settlement patterns and environmental conditions; the latter influencing subsistence activities. This boundary dataset was generated to define the extent of the study area, which comprises the border between Syria and Turkey, Syria and Iraq, the River Tigris and the River Euphrates. All related data collected was confined within this boundary dataset with the exception of the archaeological dataset. Archaeological sites were identified and mapped along both banks of the River Euphrates. Also, the town of Dayr az-Zawr, where the 1963 precipitation and temperature monthly values were collected for one of the datasets, falls outside the Jazira Region. Derived from 1:200,000 French Levant Map Series (Further Information element in this metadata record provides list of sheets).The boundary line dataset was captured from 11 map sheets, which were based on the French Levant surveys conducted in Syria during the 1930s and mapped at a scale of 1:200,000. The size of each map measures 69 x 59 cm. The boundary line on each sheet was traced to mylar. Subsequently, each mylar sheet was photocopied and reduced in size to an 11 x 17 inch sheet. These sheets were merged to form the contiguous area comprising the full extent of the boundary for the study area. This was then traced again to another mylar sheet and subsequently scanned and cleaned for further processing and use in a GIS as a polygon coverage. Thesis M 2001 MATH, Ohio University Mathys, Antone J 'A GIS comparative analysis of bronze age settlement patterns and the contemporary physical landscape in the Jazira Region of Syria'., French Levant Map Series (1:200,000) for Syrie (Syria). Projected to Lambert grid. These are colour maps measuring to 69 x 59 cm in size. The dataset was created from the following sheet numbers and titles: 1) NI-37 XVII, Abou Kemal 2) NI-37 XVIII, Ana 3) NI-37 XXI, Ressafe 4) NI-37 XXII, Raqqa 5) NI-37 XXIII, Deir ez Zoir 6) NI-37 XXIV, Bouara 7) NI-37-III, Djerablous 8) NJ-37 IV, Toual Aaba 9) NJ-37 V, Hassetche 10) NJ-37 VI, Qamishliye-Sinjar 11) (No sheet number), Qaratchok-Darh Dressepar la Service Geographique des F.F.L. en 1945 Reimprime par l'Institut Geographique National en 1950 (Originally produced by this Geographic Service of the F.F.L. (Forces Francaises Libres) in 1945 and reprinted by the National Geographic Institute in 1950). Paris: France. Institut Geographique National, 1945-1950. Original map series might be traced to Beirut: Bureau Topographique des Troupes francaises du Levant, 1933-1938. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2010-06-09 and migrated to Edinburgh DataShare on 2017-02-21.

  3. d

    Stream Segments Used With the Precipitation Runoff Modeling System for...

    • search.dataone.org
    • data.usgs.gov
    • +2more
    Updated Oct 5, 2017
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    Jacob LaFontaine (2017). Stream Segments Used With the Precipitation Runoff Modeling System for Hydrologic Simulations of the Upper Chattahoochee River Basin in Northeast Georgia, United States [Dataset]. https://search.dataone.org/view/e2527537-98bf-46b8-a6b6-b6e103e396c3
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    Dataset updated
    Oct 5, 2017
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Authors
    Jacob LaFontaine
    Area covered
    Variables measured
    seg_id
    Description

    The stream segments available here were extracted from a previous application of the Precipitation Runoff Modeling System (PRMS) in the Apalachicola-Chattahoochee-Flint River Basin by LaFontaine and others (2013). A Geographic Information System (GIS) file for the stream segments is provided as a shapefile with attribute seg_id identifying the numbering convention used in the PRMS models of the upper Chattahoochee River Basin in northeast Georgia. These GIS files represent the watershed area upstream of U.S. Geological Survey streamgage 02335000.

  4. a

    3rd Graders Meeting or Exceeding California Standards for Math

    • hub.arcgis.com
    • ph-lacounty.hub.arcgis.com
    Updated Dec 21, 2023
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    County of Los Angeles (2023). 3rd Graders Meeting or Exceeding California Standards for Math [Dataset]. https://hub.arcgis.com/datasets/4a426ccd341943cfbc8a4fe2f9f854db
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    Dataset updated
    Dec 21, 2023
    Dataset authored and provided by
    County of Los Angeles
    Area covered
    Description

    Note, information is based on location of the school, not students’ community of residence.Children found to have low math skills are at increased risk for poor academic outcomes, which can have profound consequences for future health and longevity.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

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(2025). Probabilities of Adjusted Elevation for 2080s [Dataset]. https://marine.usgs.gov/coastalchangehazardsportal/ui/info/item/EXf3LkWP

Probabilities of Adjusted Elevation for 2080s

Probabilities of Adjusted Elevation for 2080s

Explore at:
Dataset updated
Jul 30, 2025
Area covered
Description

The U.S. Geological Survey has been forecasting sea-level rise impacts on the landscape to evaluate where coastal land will be available for future use. The purpose of this project is to develop a spatially explicit, probabilistic model of coastal response for the Northeastern U.S. to a variety of sea-level scenarios that take into account the variable nature of the coast and provides outputs at spatial and temporal scales suitable for decision support. Model results provide predictions of adjusted land elevation ranges (AE) with respect to forecast sea-levels, a likelihood estimate of this outcome (PAE), and a probability of coastal response (CR) characterized as either static or dynamic. The predictions span the coastal zone vertically from -12 meters (m) to 10 m above mean high water (MHW). Results are produced at a horizontal resolution of 30 meters for four decades (the 2020s, 2030s, 2050s and 2080s). Adjusted elevations and their respective probabilities are generated using regional geospatial datasets of current sea-level forecasts, vertical land movement rates, and current elevation data. Coastal response type predictions incorporate adjusted elevation predictions with land cover data and expert knowledge to determine the likelihood that an area will be able to accommodate or adapt to water level increases and maintain its initial land class state or transition to a new non-submerged state (dynamic) or become submerged (static). Intended users of these data include scientific researchers, coastal planners, and natural resource management communities.

These GIS layers provide the probability of observing the forecast of adjusted land elevation (PAE) with respect to predicted sea-level rise or the Northeastern U.S. for the 2020s, 2030s, 2050s and 2080s. These data are based on the following inputs: sea-level rise, vertical land movement rates due to glacial isostatic adjustment and elevation data. The output displays the highest probability among the five adjusted elevation ranges (-12 to -1, -1 to 0, 0 to 1, 1 to 5, and 5 to 10 m) to be observed for the forecast year as defined by a probabilistic framework (a Bayesian network), and should be used concurrently with the adjusted land elevation layer (AE), also available from http://woodshole.er.usgs.gov/project-pages/coastal_response/, which provides users with the forecast elevation range occurring when compared with the four other elevation ranges. These data layers primarily show the distribution of adjusted elevation range probabilities over a large spatial scale and should therefore be used qualitatively.

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