Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Skip to content
  • Feedback

    • Appalachian Basin Temperature-Depth Maps and Structured Data in support of Feasibility Study of Direct District Heating for the Cornell Campus Utilizing Deep Geothermal Energy

    Metadata Updated: April 26, 2022

    This dataset contains shapefiles and rasters that summarize the results of a stochastic analysis of temperatures at depth in the Appalachian Basin states of New York, Pennsylvania, and West Virginia. This analysis provides an update to the temperature-at-depth maps provided in the Geothermal Play Fairway Analysis of the Appalachian Basin (GPFA-AB) Thermal Quality Analysis (GDR repository 879: https://gdr.openei.org/submissions/879). This dataset improves upon the GPFA-AB dataset by considering several additional uncertainties in the temperature-at-depth calculations, including geologic properties and thermal properties. A Monte Carlo analysis of these uncertain properties and the GPFA-AB estimated surface heat flow was used to predict temperatures at depth using a 1-D heat conduction model. In this data submission, temperatures are provided for depths from 1-5 km in 0.5 km increments. The mean, standard deviation, and selected quantiles of temperatures at these depths are provided as shapefiles with attribute tables that contain the data. Rasters are provided for the mean and standard deviation data. Figures and maps that summarize the data are also provided. For the pixel corresponding to Cornell University, Ithaca, NY, a .csv file containing the 10,000 temperature-depth profiles estimated from the Monte Carlo analysis is provided. These data are summarized in a figure containing violin plots that illustrate the probability of obtaining certain temperatures at depths below Cornell.

    Access & Use Information

    Public: This dataset is intended for public access and use. License: Creative Commons Attribution

    Downloads & Resources

    Dates

    Metadata Created Date June 24, 2021
    Metadata Updated Date April 26, 2022

    Metadata Source

    Harvested from OpenEI data.json

    Other Data Resources

    Additional Metadata

    Resource Type Dataset
    Metadata Created Date June 24, 2021
    Metadata Updated Date April 26, 2022
    Publisher Cornell University
    Maintainer
    Doi 10.15121/1632873
    Identifier https://data.openei.org/submissions/3824
    Data First Published 2019-10-29T06:00:00Z
    Data Last Modified 2021-09-02T17:32:15Z
    Public Access Level public
    Bureau Code 019:20
    Metadata Context https://openei.org/data.json
    Metadata Catalog ID https://openei.org/data.json
    Schema Version https://project-open-data.cio.gov/v1.1/schema
    Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
    Data Quality True
    Harvest Object Id f44d2cba-a829-4657-a5b8-c44f1b2a181d
    Harvest Source Id 7cbf9085-0290-4e9f-bec1-91653baeddfd
    Harvest Source Title OpenEI data.json
    Homepage URL https://gdr.openei.org/submissions/1182
    License https://creativecommons.org/licenses/by/4.0/
    Old Spatial {"type":"Polygon","coordinates":-82.8683640625,36.985225701196,-74.310115625,36.985225701196,-74.310115625,43.861667818031,-82.8683640625,43.861667818031,-82.8683640625,36.985225701196}
    Program Code 019:006
    Projectlead Arlene Anderson
    Projectnumber EE0008103
    Projecttitle EARTH SOURCE HEAT: A CASCADED SYSTEMS APPROACH TO DDU OF GEOTHERMAL ENERGY ON THE CORNELL CAMPUS
    Source Datajson Identifier True
    Source Hash 7777cbf71478b6534b6d7337c918bc5344a37e9a
    Source Schema Version 1.1
    Spatial {"type":"Polygon","coordinates":-82.8683640625,36.985225701196,-74.310115625,36.985225701196,-74.310115625,43.861667818031,-82.8683640625,43.861667818031,-82.8683640625,36.985225701196}

    Didn't find what you're looking for? Suggest a dataset here.