This data layer contains geothermal resource areas and their technical potential used in long-term electric system modeling for Integrated Resource Planning and SB 100. Geothermal resource areas are delineated by Known Geothermal Resource Areas (KGRAs) (Geothermal Map of California, 2002), other geothermal fields (CalGEM Field Admin Boundaries, 2020), and Bureau of Land Management (BLM) Geothermal Leasing Areas (California BLM State Office GIS Department, 2010). The fields that are considered in our assessment have enough information known about the geothermal reservoir that an electric generation potential was estimated by USGS (Williams et al. 2008) or estimated by a BLM Environmental Impact Statement (El Centro Field Office, 2007). For the USGS identified geothermal systems, any point that lies within 2 km of a field is summed to represent the total mean electrical generation potential from the entire field.

Geothermal field boundaries are constructed for identified geothermal systems that lie outside of an established geothermal field. A circular footprint is assumed with a radius determined by the area needed to support the mean resource potential estimate, assuming a 10 MW/km² power density.

Several geothermal fields have power plants that are currently generating electricity from the geothermal source. The total production for each geothermal field is estimated by the CA Energy Commission’s Quarterly Fuel and Energy Report that tracks all power plants greater than 1 MW. The nameplate capacity of all generators in operation as of 2021 were used to inform how much of the geothermal fields are currently in use. This source yields inconsistent results for the power plants in the Geysers. Instead, an estimate from the net energy generation from those power plants is used. Using these estimates, the net undeveloped geothermal resource potential can be calculated.

Finally, we apply the protected area layer for geothermal to screen out those geothermal fields that lie entirely within a protected area. The protected area layer is compiled from public and private lands that have special designations prohibiting or not aligning with energy development.

This layer is featured in the CEC 2023 Land-Use Screens for Electric System Planning data viewer.

For more information about this layer and its use in electric system planning, please refer to the Land Use Screens Staff Report in the CEC Energy Planning Library.

Change Log:

Version 1.1 (January 18, 2024)

• ProtectedArea_Exclusion field was updated to correct for the changes to the Protected Area Layer. A Development Focus Area on Bureau of Land Management (BLM) land that overlays the Coso Hot Springs allows its resource potential to be considered in the statewide estimate.

Data Dictionary:

Total_MWe_Mean: The estimated resource potential from each geothermal field. All geothermal fields, except for Truckhaven, was given an estimate by Williams et al. 2008. If more than one point resource intersects (within 2km of) the field, the sum of the individual geothermal systems was used to estimate the magnitude of the resource coming from the entire geothermal field. Estimates are given in MW.

Total_QFER_NameplateCapacity: The total nameplate capacities of all generators in operation as of 2021 that intersects (within 2 km of) a geothermal field. The resource potential already in use for the Geysers is determined by Lovekin et al. 2004. Estimates are given in MW.

ProtectedArea_Exclusion: Binary value representing whether a field is excluded by the land-use screen or not. Fields that are excluded have a value of 1; those that aren’t have a value of 0.

NetUndevelopedRP: The net undeveloped resource potential for each geothermal field. This field is determined by subtracting the total resource potential in use (Total_QFER_NameplateCapacity) from the total estimated resource potential (Total_MWe_Mean). Estimates are given in MW.

Acres_GeothermalField: This is the geodesic acreage of each geothermal field. Values are reported in International Acres using a NAD 1983 California (Teale) Albers (Meters) projection.

References:

1. Geothermal Map of California, S-11. California Department of Conservation, 2002. https://www.conservation.ca.gov/calgem/geothermal/maps/Pages/index.aspx
2. CalGEM Field Admin Boundaries, 2020. https://gis.conservation.ca.gov/server/rest/services/CalGEM/Admin_Bounds/MapServer
   
3. California BLM State Office GIS Department, California BLM Verified and Potential Geothermal Leases in California, 2010. https://databasin.org/datasets/5ec77a1438ab4402bf09ef9bfd7f04d9/
   
4. Williams, Colin F., Reed, Marshall J., Mariner, Robert H., DeAngelo, Jacob, Galanis, S. Peter, Jr. 2008. "Assessment of moderate- and high-temperature geothermal resources of the United States: U.S. Geological Survey Fact Sheet 2008-3082." 4 p. https://certmapper.cr.usgs.gov/server/rest/services/geothermal/westus_favoribility_systems/MapServer/0
5. El Centro Field Office, Bureau of Land Management (2007). Final Environmental Impact Statement for the Truckhaven Geothermal Leasing Area (Publication Index Number: BLM/CA/ES-2007-017+3200). United States Department of the Interior Bureau of Land Management.
6. Lovekin, James W., Subir K. Sanyal, Christopher W. Klein. 2004. “New Geothermal Site Identification and Qualification.” Richmond, California:

Geothermal well data from Southern Methodist University (SMU, 2021) and the U.S. Geological Survey (Sass et al., 2005) were used to create maps of estimated background conductive heat flow across the greater Great Basin region of the western US. The heat flow maps in this data release were created using a process that sought to remove hydrothermal convective influence from predictions of background conductive heat flow. Heat flow maps were constructed using a custom-developed iterative process using weighted regression, where convectively influenced outliers were de-emphasized by assigning lower weights to measurements that are very different from the estimated local trend (e.g., local convective influence). The weighted regression algorithm is 2D LOESS (locally estimated scatterplot smoothing; Cleveland et al., 1992), which was used for local linear regression, and smoothness was controlled by varying the number of nearby points used for each local interpolation. Three maps are included in this data release, allowing comparison of the influence of measurement confidence: all wells are equal-weight, and two different published categorizations of measurement quality were used to de-emphasize low-quality measurements. Each map is an estimate of background conductive heat flow as a function of assumed data quality, and a point coverage is also provided for all wells in the compiled dataset. The point coverage includes an important new attribute for geothermal wells: the residual, which can be interpreted as the well’s departure from estimated background heat flow conditions, and the value of residual may be useful in identifying hydrothermal or groundwater influence on conductive heat flow. References Cleveland, W. S., Grosse, E., Shyu, W. M, 1992, Local regression models. Chapter 8 of Statistical Models in S eds J.M. Chambers and T.J. Hastie, Wadsworth & Brooks/Cole. Sass, J. H., S.S. Priest, A.H. Lachenbruch, S.P. Galanis, Jr., T.H. Moses, Jr., J.P. Kennelly, Jr., R.J. Munroe, E.P. Smith, F.V. Grubb, R.H. Husk, Jr., and C.W. Mase, 2005, Summary of supporting data for USGS regional heat flow studies of the Great Basin, 1970-1990, USGS Open file Report, 2005-1207. SMU Regional Heat Flow Database, retrieved from http://geothermal.smu.edu on March 29, 2021.

This is a surface showing relative favorability for the presence of geothermal systems in the western United States. It is an average of 12 models that correlates different geological and geophysical factors to the known presence of moderate (90 - 150° C) to high (> 150° C) temperature geothermal systems. as discussed in the reference in the 'Larger Work' section of this metadata file. The data is represented as a polygon contour file as well as a raster.

The NOAA National Centers for Environmental Information ceased providing support for this product on May 05, 2025. Note this metadata record is accompanied by another newer version of metadata for the same product. Geothermics is the study of heat generated in Earth's interior and its manifestation at the surface. The NOAA National Centers for Environmental Information has a variety of publications and data sets which provide information on the location, magnitude, and potential uses of geothermal resources. The publication, "Thermal Springs List for the United States" (1981) is a compilation of 1,700 thermal springs locations in 23 states. The list gives the geographic locations of thermal springs by state, and is sorted by degrees of latitude and longitude within the state. It contains the name of each spring (where available), maximum surface temperature (in both degrees Fahrenheit and degrees Celsius), name of corresponding USGS 1:2,500,000-scale (AMS) map, largest scale USGS topographic map coverage available (either 7.5 or 15-min. quadrangle), and cross-references. Thermal springs listed include natural surface hydrothermal features (springs, pools, mud pots, mud volcanoes, geysers, fumaroles, and steam vents) at temperatures of 20 degrees Celsius (68 degrees Fahrenheit) or higher. They do not include wells or mines, except at sites where they supplement or replace natural vents that have been active recently or at sites where orifices are indistinguishable as natural or artificial. The thermal springs data from this publication are also available on-line."Geothermal Gradient Map of the United States" (1982) shows 1,700 wells, with accompanying heat flow and conductivity data. This map was produced in cooperation with Los Alamos National Laboratory. Thermal aspect data (1991) from the Decade of North American Geology project, are available on diskette. These data were compiled by Dr. David Blackwell of Southern Methodist University. Global heat flow data (1993) were compiled by Dr. Henry Pollack of the University of Michigan. Data were collected through the World Heat Flow Committee of the International Council of Scientific Unions. These are available on-line.

A geothermal gradient map is needed in order to determine the hot dry rock (HDR) geothermal resource of the United States. Based on published and unpublished data (including new measurements) the HDR program will produce updated gradient maps annually, to be used as a "tool" for resource evaluation and exploration. The 1980 version of this map will be presented in a poster session at this meeting.

NREL, as part of the Play Fairway Analysis Retrospective, compiled and mapped publicly available geologic and geophysical data in relation to the 2008 USGS geothermal potential analysis. Included in this submission are maps displaying the publicly available data for LIDAR coverage, aeromagnetic coverage, gravity station locations, and geologic map coverage over the Western United States.

Geothermics is the study of heat generated in Earth's interior and its manifestation at the surface. The National Geophysical Data Center (NGDC) has a variety of publications and data sets which provide information on the location, magnitude, and potential uses of geothermal resources. The publication, "Thermal Springs List for the United States" (1981) is a compilation of 1,700 thermal springs locations in 23 states. The list gives the geographic locations of thermal springs by state, and is sorted by degrees of latitude and longitude within the state. It contains the name of each spring (where available), maximum surface temperature (in both degrees Fahrenheit and degrees Celsius), name of corresponding USGS 1:2,500,000-scale (AMS) map, largest scale USGS topographic map coverage available (either 7.5 or 15-min. quadrangle), and cross-references. Thermal springs listed include natural surface hydrothermal features (springs, pools, mud pots, mud volcanoes, geysers, fumaroles, and steam vents) at temperatures of 20 degrees Celsius (68 degrees Fahrenheit) or higher. They do not include wells or mines, except at sites where they supplement or replace natural vents that have been active recently or at sites where orifices are indistinguishable as natural or artificial. The thermal springs data from this publication are also available on-line."Geothermal Gradient Map of the United States" (1982) shows 1,700 wells, with accompanying heat flow and conductivity data. This map was produced in cooperation with Los Alamos National Laboratory. Thermal aspect data (1991) from the Decade of North American Geology project, are available on diskette. These data were compiled by Dr. David Blackwell of Southern Methodist University. Global heat flow data (1993) were compiled by Dr. Henry Pollack of the University of Michigan. Data were collected through the World Heat Flow Committee of the International Council of Scientific Unions. These are available on-line.

The Thermal Springs List for the United States contains data from 1661 hot springs in the continental U.S., Hawaii, and Alaska. The content has not been updated since its initial publication in 1980. Geothermics is the study of heat generated in Earth's interior and its manifestation at the surface. The NOAA National Centers for Environmental Information has a variety of publications and data sets which provide information on the location, magnitude, and potential uses of geothermal resources. The publication, "Thermal Springs List for the United States" (1981) is a compilation of 1,700 thermal springs locations in 23 states. The list gives the geographic locations of thermal springs by state, and is sorted by degrees of latitude and longitude within the state. It contains the name of each spring (where available), maximum surface temperature (in both degrees Fahrenheit and degrees Celsius), name of corresponding USGS 1:2,500,000-scale (AMS) map, largest scale USGS topographic map coverage available (either 7.5 or 15-min. quadrangle), and cross-references. Thermal springs listed include natural surface hydrothermal features (springs, pools, mud pots, mud volcanoes, geysers, fumaroles, and steam vents) at temperatures of 20 degrees Celsius (68 degrees Fahrenheit) or higher. They do not include wells or mines, except at sites where they supplement or replace natural vents that have been active recently or at sites where orifices are indistinguishable as natural or artificial. The thermal springs data from this publication are also available on-line. This dataset has been archived in the framework of the PANGAEA US data rescue initiative 2025.

GIS datasets that were used in the study. The GIS data can be opened in either QGIS or ArcGIS. The files are separated into the folders 'CGF' and 'BGF-DPGF'. The root folders contain the research areas (ROI) and locations. The 'Indicator mineral' folder contains the initial Mineral Mapping raster of the five target minerals (Alu., Chd., Hem., Kln., and Opl.), which were converted to the point data of Two-Class Mineral Maps (including a Non-Prediction dataset for areas without mapped minerals). The folder also includes normalized Mineral Density Maps. The 'Fault' folder contains the initial Fault Traces, Fault Distance Maps, and normalized Fault Density Maps. The 'LST' folder contains normalized Multiclass Temperature Maps, and the Two-class Temperature Maps that include both converted point data of high temperature (HT) areas and low temperature (LT) areas.

The data contained herein are five input features (i.e., heat flow, distance to the nearest quaternary fault, distance to the nearest quaternary magma body, seismic event density, maximum horizontal stress) and labels (i.e., where known geothermal systems have been identified) from Williams and DeAngelo (2008) and nine favorability maps from Mordensky et al. (2023). The favorability maps are the untransformed predictions from models resulting from the features and labels used with either the methods presented in Williams and DeAngelo (2008) or the machine learning approaches presented in Mordensky et al. (2023). Each favorability map depicts an estimate of relative favorability with respect to the other locations (i.e., cells), allowing for a comparison of the influence the different methods and machine learning approaches produced when predicting geothermal favorability. The machine learning approaches sought to minimize the influence of expert bias imparted by the methods from Williams and DeAngelo (2008). The favorability maps presented from the models that used the methods from Williams and DeAngelo (2008) are provided for comparative purposes.

This paper describes an update of the Geothermal Gradient Map of the Conterminous United States (Kron and Heiken 1980) and compares the changes made since the first map. The second map presents a compilation of over 1700 wells that have been measured for temperature below 50 m and whose temperature/depth profiles are linear, or composed of linear segments which reflect changes in the thermal conductivity of the rocks rather than hydrology. The data are displayed at an enlarged scale of 1:2,500,000 and in a new format which shows the location, depth, and gradient of each well in a single color coded symbol. This edition contains over two times the amount of data shown on the first map and is accompanied by a table, listing for each well its location, depth, gradient, heat flow (where available), thermal conductivity (where available) and a reference. Over 200 references have been consulted and are presented with the data.

In the development of geothermal resources in the Eastern United States, mapping of heat flow may be the first major step towards discovering geothermal anomalies. The sparseness of conventional heat flow measurements in the region has in the past made mapping of surface heat flow and subsurface temperature problematic. As part of this study a new procedure for calculating heat flow from bottom-hole temperature (BHT) data has been developed and applied to the eastern US. The focus of this paper are results for the New York, Pennsylvania, West Virginia, and Eastern Ohio areas. Using existing heat flow measurements with the addition of 2950 bottom hole temperature points a 5 X 5 contour map of surface heat flow for the Eastern United States was generated. Based on the preliminary results from this work, the Appalachian Basin may contain some of the most favorable potential targets for EGS geothermal exploration in the eastern 1/3 of the United Stated and especially in eastern West Virginia. Temperatures of at least 150?C exist at a depth of 4.5km.

Between 1979 and 1982, the Alaska Division of Geological & Geophysical Surveys (DGGS) and the Geophysical Institute, University of Alaska Fairbanks, undertook an assessment of the states geothermal resources under a program jointly sponsored by the U.S. Department of Energy and the State of Alaska. During this period, reconnaissance investigations of more than 100 thermal spring sites and fumarole fields located in Alaska were conducted by DGGS.More recently, DGGS completed a cooperative project with the National Geothermal Data System on thermal springs and related geothermal data of Alaska. The Alaska geothermal database modules include comprehensive information on thermal springs, including temperature and flow rates, direct use, aqueous spring chemistry, physical samples, volcanic vents, geothermal wells, borehole temperature data and geothermal references.DGGS developed and maintains an interactive geothermal web application that brings together various data services related to geothermal sites throughout Alaska.Geothermal springs: Includes temperature and flow information. Spring location uncertainty and data sources are also included.Geothermal wells: Location and name onlyVolcanoes: Direct from the Historically Active Volcanoes of Alaska service

This dataset contains geothermal leases cases derived from Legal Land Descriptions (LLD) contained in the US Bureau of Land Management's, BLM, Mineral and Land Record System(MLRS) and geocoded (mapped) using the Public Land Survey System (PLSS) derived from the most accurate survey data available through BLM Cadastral Survey workforce. Geospatial representations might be missing for some cases that can not be geocoded using the MLRS algorithm. Each case is given a data quality score based on how well it mapped. These can be lumped into seven groups to provide a simplified way to understand the scores.Group 1: Direct PLSS Match. Scores “0”, “1”, “2”, “3” should all have a match to the PLSS data. There are slight differences, but the primary expectation is that these match the PLSS.Group 2: Calculated PLSS Match. Scores “4”, “4.1”, “5”, “6”, “7” and “8” were generated through a process of creating the geometry that is not a direct capture from the PLSS. They represent a best guess based on the underlining PLSSGroup 3 – Mapped to Section. Score of “8.1”, “8.2”, “8.3”, “9” and “10” are mapped to the Section for various reasons (refer to log information in data quality field).Group 4- Combination of mapped and unmapped areas. Score of 15 represents a case that has some portions that would map and others that do not.Group 5 – No NLSDB Geometry, Only Attributes. Scores “11”, “12”, “20”, “21” and “22” do not have a match to the PLSS and no geometry is in the NLSDB, and only attributes exist in the data.Group 6 – Mapped to County. Scores of “25” map to the County.Group 7 – Improved Geometry. Scores of “100” are cases that have had their geometry edited by BLM staff using ArcGIS Pro or MLRS bulk upload tool.

Table S1. The K–S test result. The null hypothesis was rejected in most cases, showing that the distributions of datasets are primarily different. Table S2. The p-values of the coefficients and intercepts from the linear regression models of Fault Density Maps fit by Mineral Density Maps. Table S3. The p-values of the coefficients and intercepts from the linear regression models of Multiclass Temperature Maps fit by Fault Density Map and Mineral Density Maps. Table S4. The AIC values from the regression models of Fault Density Maps fit by Mineral Density Maps. Table S5. The AIC values from the regression models of Multiclass Temperature Maps fit by Fault Density Map and Mineral Density Maps.

Topography provides information about the structural controls of the Great Basin and therefore information that may be used to identify favorable structural settings for geothermal systems. Specifically, local relative topography gives information about locations of faults and fault intersections relative to mountains, valleys, or at the transitions between. As part of U.S. Geological Survey efforts to engineer features that are useful for predicting geothermal resources, we construct a detrended elevation map that emphasizes local relative topography and highlights features that geologists use for identifying geothermal systems (i.e., providing machine learning algorithms with features that may improve predictive skill by emphasizing the information used by geologists). Herein, we provide the trend and local relative elevation maps documented in DeAngelo and others (2023), describing the process of removal of the regional trend and the resulting detrended elevation maps that emphasize basin-and-range scale structural features. Regional elevation trends were estimated using a local linear regression and subtracted from a 30-m digital elevation model (DEM) of topography to create the detrended elevation (i.e., local relative topography) map; therefore one could add the detrended surface to the corresponding trend surface to construct the original DEM. In an effort to optimize the detrended surface, alternate versions were produced with different rates of smoothness resulting in three detrended elevation maps. The resulting detrended elevation maps emphasize geologic structure and relative displacement, and these products may be useful for other geologic research including mineral exploration, hydrologic research, and defining geologic provinces. References DeAngelo, J., Burns, E.R., Lindsey, C.R., and Mordensky, S.P., (2023), Detrending Great Basin elevation to identify structural patterns for identifying geothermal favorability, Geothermal Rising Conference Transactions, 47, Reno, Nevada, October 1-5, 2023.

Figure S1. Illustration of indicator mineral map datasets. Figure S2. Illustration of fault map datasets. Figure S3. Fault system at CGF. Figure S4. Fault system at BGF and DPGF. Figure S5. Illustration of LST datasets. Figure S6. Histograms and CDF plots of Two-Class Mineral Maps versus Fault Distance Maps. Figure S7. Histograms and CDF plots of Two-Class Mineral Maps versus Fault Density Maps. Figure S8. Histograms and CDF plots of Two-class Temperature Maps versus fault datasets. The top two rows correspond to Fault Distance Maps, while the bottom two rows correspond to Fault Density Maps. Figure S9. Histograms and CDF plots of Two-Class Mineral Maps versus Multiclass Temperature Map. Figure S10. The multiple comparisons of ANOVA. The plots show the mean estimates (circles) and 95% confidence intervals (bars) for each group of SGP. Red symbols highlight groups with Significant Differences from the control group (blue). Grey symbols indicate groups with Insignificant Differences where confidence intervals overlap with the control group.

This report is one in a series of digital maps, data files, and reports generated by the US Geological Survey (USGS) to provide geologic information for the Interior Columbia Basin Ecosystem Management Project (ICBEMP), a US Forest Service and Bureau of Land Management interagency project. The various digital maps and data files that were provided by teh USGS and that are available in this and other reports are being used in a geographic information system (GIS)-based ecosystem assessment. The assessment will include a comprehensive analysis of past, present, and future ecosystem conditions within the general area of the Columbia River Basin east of the Cascade Mountains.

This map depicts the relative favorability or geothermal potential as well as well defined geothermal systems located in the western US.

Images of subsurface electrical conductivity are useful for locating fluids and other electrically conductive phases at depth in the Earth. This data release presents electrical conductance maps estimated from a 3D model of the Great Basin, USA, in five different depth ranges, spanning 2 to 200 km depth. Electrical conductance is the integration of electrical conductivity in a depth range. Great Basin electrical conductivity is estimated through 3D inverse modeling of over 800 publicly available magnetotelluric (MT) transfer functions. The transfer functions can be found on the electromagnetic transfer function repository hosted by the Incorporated Research Institutions of Seismology (IRIS) data management center (https://ds.iris.edu/spud/emtf, Kelbert et al. (2011) and Kelbert et al. (2018)), the geothermal data repository (https://gdr.openei.org/home), and Science Base. The inversion code ModEM (Egbert et al., 2012; Kelbert et al., 2014), run on the U.S. Geological Survey's high-performance computer Yeti, provides estimate of subsurface 3D electrical conductivity. The following conductance layers are estimated: - gb_conductance_surface_tp.tif [2 - 12 km depth] - gb_conductance_middle_crust_tp.tif [12 - 20 km depth] - gb_conductance_lower_crust_tp.tif [20 - 50 km depth] - gb_conductance_upper_mantle_tp.tif [50 - 90 km depth] - gb_conductance_mantle_tp.tif [90 - 200 km depth] This work was undertaken as part of the INGENIOUS (Innovative Geothermal Exploration through Novel Investigations of Undiscovered Systems) project funded by the U.S. Department of Energy Geothermal Technologies Office awarded to the University of Nevada, Reno. INGENIOUS is a multi-disciplinary, multi-institution effort to develop new methodologies and best practices to accelerate the discovery of new, commercially viable geothermal resources. This work was also supported by the U.S. Geological Survey Geothermal Resources Investigations Project (GRIP).