This data release, RI_WRpts.gdb, consists of information from Rhode Island Ground-water maps published by the Rhode Island Water Resources Coordinating Board, the Rhode Island Port and Industrial Development Commission, Rhode Island Industrial Commission, and the Rhode Island Development Council; in cooperation with the U.S. Geological Survey. The point data on these maps have been digitized into a standard ArcGIS geodatabase format. Data about wells and test borings consists of geographic _location, identification number, geologic material (bedrock or unconsolidated), altitude in feet of the bedrock surface or altitude of the bottom of well, and data source. Seismic survey locations and bedrock outcrops where they are shown as points on the source maps are also included. The Ground-water maps, published between 1948 and 1964, also show geologic information which is being used to create a revised surficial materials database for future publication.

Data was compiled from published sources by US Geological Survey geoscientists Mark J. Mihalasky, Susan M. Hall and Robert A. Zielinski. This dataset was provided to the U.S. Energy Information Administration in February of 2019 to facilitate updating of national uranium resource distribution maps. The location of uranium provinces, districts and select important deposits located outside of these broader regions was taken from a variety of sources listed alphabetically below.Adams S.S.; Smith R.B., 1981, Geology and recognition criteria for sandstone uranium deposits in mixed fluvial-shallow marine sedimentary sequences, South Texas; U.S. Department of Energy Report GJBX-4(81), 145 p.Colorado Geological Survey, 2018, Uranium Districts – Colorado; published on the Colorado Geological Survey website at http://coloradogeologicalsurvey.org/energy-resources/uranium2/map/.Chenoweth, W.L., 1980, Uranium in Colorado; Rocky Mountain Association of Geologists, 1980 Symposium, p. 217-224Gloyn, R.W.; Bon, R.L.; Wakefield, S.; Krahulec, K., 2005, Uranium and vanadium map of Utah; Map 215, Utah Department of Natural Resources, Utah Geological Survey, 1:750,000 scale, 1 sheet. Metadata download at: https://gis.utah.gov/data/energy/uranium/Gregory R.W., 2016, Uranium: Geology and Applications; Wyoming State Geological Survey Public Information Circular No 46, 36 p.Keith, S.B.; Gest, D.E.; DeWitt, E; 1983, Metallic mineral districts of Arizona; Arizona Bureau of Geology and Mineral Technology, Geological Survey Branch, Tucson, AZ, 1:1,000,000 scale, 1 sheetKyle L, Beahm D, 2013, NI 43-101 preliminary economic assessment update (revised), Coles Hill uranium property, Pittsylvania County, VA USA; prepared by Lyntek Incorporated, Lakewood, CO; 2013, 126 p. Figure 1.1.McLemore, V.T. and Chenoweth, W.L., 1989, Uranium resources in New Mexico; New Mexico Bureau of Mines and Minerals Resources, Resource Map 18, 36 p. Available at: https://geoinfo.nmt.edu/faq/mining/home.html

FEMA, as the administrator of the National Flood Insurance Program (NFIP), has created Advisory Base Flood Elevations (ABFEs) and storm erosion areas for the United States Virgin Islands (USVI). The ABFE information, storm erosion data, and related layers depicted on this web service for the USVI can serve as a guide to understanding current flood and erosion hazard conditions that communities should build to in order to reduce impacts of similar events in the future. All elevations included on the map are referenced to the Virgin Island Vertical Datum of 2009 (VIVD 09).Data DownloadGIS data and PDF maps that support this web map can be downloaded at the locations indicated below:GIS Data in shapefile format can be downloaded by clicking hereGIS Data in ESRI's File GeoDatabase format can be downloaded by clicking herePDF Maps:Map panels for the entire territory, in Portable Document Format (PDF) can be downloaded by clicking here. The downloaded zip file contains map panels for the entire study area. A grid of all map panels (panel index) in PDF format for St.Thomas and St.John can be accessed here.A grid of all map panels (panel index) in PDF format for St.Croix can be accessed here.Individual map panels can be accessed directly from the map viewer, by locating the panel of interest and by clicking on the panel to activate a pop-up that contains the link to the panel.

The NRCS National Water and Climate Center's Interactive Map displays both current and historic hydrometeorological data in an easy-to-use, visual interface. The information on the map comes from many sources. Natural Resources Conservation Service snowpack and precipitation data are derived from manually-collected snow courses and automated Snow Telemetry (SNOTEL) and Soil Climate Analysis Network (SCAN) stations. Other data sources include precipitation, streamflow, and reservoir data from the U.S. Bureau of Reclamation (BoR), the Applied Climate Information System (ACIS), the U.S. Geological Survey (USGS), and other hydrometeorological monitoring entities. The Interactive Map has two regions: the map display itself, and the map controls which determine both the display mode and the types of data and stations to show on the map: Display Modes; Map Components; Station Conditions Controls; Basin Conditions Controls; Station Inventory Controls. Resources in this dataset:Resource Title: Interactive Map home. File Name: Web Page, url: https://www.nrcs.usda.gov/wps/portal/wcc/home/quicklinks/predefinedMaps/ The Interactive Map provides spatial visualization of current and historic hydrometeorological data collected by the Natural Resources Conservation Service and other monitoring agencies. The map also provides station inventories based on sensor and geographic filters. This page has links to pre-defined maps organized by data type. After opening a map, users can zoom to area of interest, customize the map, and then bookmark the URL to save the settings.

As a result of a Latin American Coal Assessment, the USGS published the first Coal Map of South America (Weaver and Wood, 1994) and developed a cooperative inter-American exchange of geologic information which lead to a better understanding of the potential for coal resource utilization in the western hemisphere. This coal study was started by the late Gordon H. Wood, Jr. The original compilation, completed before his death, was a result of library research and it did not include updated information from scientists and others in the coal-bearing countries of South America. During the Fall of 1991, Jean N. Weaver visited Uruguay, Argentina, Chile, Peru, Ecuador, Colombia, Venezuela, Brazil, and Bolivia. The purpose of the nine-country visit was twofold: (1) to discuss with geologists and other authorities in each country the quantity, quality, and distribution of known coal resources and the status of coal recovery and utilization and (2) to inform them of the current role of ...

Land resource areas used in the United States, Caribbean, and Pacific Basin Major Land Resource Areas (MLRA) Geographic Database serve as the geospatial expression of the map products presented and described in Agriculture Handbook 296 (2022). Land resource categories historically used at state and national levels are land resource units, major land resource areas, and land resource regions (National Soil Survey Handbook, Part 649; Land Resource Hierarchy). Although Agriculture Handbook 296 (AH 296) does not describe land resource units (LRUs) directly, they are the basic units from which major land resource areas are determined. They are also the basic units for state land resource maps. LRUs are commonly but not necessarily coextensive with state general soil map units. LRUs generally are several thousand acres in size. A unit can be one continuous area or several separate areas that are near each other. In 2005, these areas were designated as common resource areas (CRAs) within the Natural Resources Conservation Service (NRCS). Like LRUs, common resource areas are not described in AH 296 and are not shown on the national mapbut are mentioned for historical purposes. Major land resource areas are geographically associated land resource units at a broader scale and higher hierarchical level than LRUs. Land resource regions (LRR) are a group of geographically associated major land resource areasat the highest hierarchical level shown at the continental scale. Identification of these large areas is important in statewide agricultural planning and has value in interstate, regional, and national planning.In AH 296, major land resource areas are generally designated by numbers and identified by a descriptive geographic name. Examples are MLRA 1 (Northern Pacific Coast Range, Foothills, and Valleys), MLRA 154 (South-Central Florida Ridge), and MLRA 230 (Yukon-Kuskokwim Highlands). Some MLRAs are designated by a letter in addition to a number because a previously established MLRA had been divided into smaller, more homogeneous areas, for example, MLRAs 102A, 102B, and 102C. Other MLRAs, especially smaller ones approaching the LRU scale, have been recombined. The use of numbers and letters to identify the newly created MLRAs requires fewer changes in existing information in records and in databases. A few MLRAs consist of two or more parts separated for short distances by other land resource areas. In some places one of the parts is widely separated from the main body of the MLRA and is in an adjoining LRR. The description of the respective MLRA also applies to these outlying parts. The spatial illustration of the MLRAs has been smoothed for the contiguous United States and Alaska to better reflect the scale at which the MLRA resource attributes (climate, soils, land use, vegetation, geology, and physiography) were aggregated for delineation.

National-scale geologic, geophysical, and mineral resource raster and vector data covering the United States, Canada, and Australia are provided in this data release. The data were compiled as part of the tri-national Critical Minerals Mapping Initiative (CMMI). The CMMI, established in 2019, is an international science collaboration between the U.S. Geological Survey (USGS), Geoscience Australia (GA), and the Geological Survey of Canada (GSC). One aspect of the CMMI is to use national- to global-scale earth science data to map where critical mineral prospectivity may exist using advanced machine learning approaches (Kelley, 2020). The geoscience information presented in this report include the training and evidential layers that cover all three countries and underpin the resultant prospectivity models for basin-hosted Pb-Zn mineralization described in Lawley and others (2021). It is expected that these data layers will be useful to many regional- to continental-scale studies related to a wide range of earth science research. Therefore, the data layers are organized using widely accepted GIS formats in the same map projection to increase efficiency and effectiveness of future studies. All datasets have a common geographic projection in decimal degrees using a WGS84 datum. Data for the various training and evidential layers were either derived for this study or were extracted from previous national to global-scale compilations. Data from outside work are provided here as a courtesy for completeness of the model and should be cited as the original source. Original references are provided on each child page. Where possible, data for the United States were merged to data for Canada to provide composite data that allow for continuity and seamless analyses of the earth science data across the two countries. Earth science data provided in this report include training data for the models. Training data include a mineral resource database of Pb-Zn deposits and occurrences related to either carbonate-hosted (Mississippi Valley type-MVT) or clastic-dominated (aka sedex) Pb-Zn mineralization. Evidential layers that were used as input to the models include GeoTIFF grid files consisting of ground, airborne, and satellite geophysical data (magnetic, gravity, tomography, seismic) and several related derivative products. Geologic layers incorporated into the models include shapefiles of modified lithology and faults for the United States, Canada and Australia. A global database of ancient and modern passive margins is provided here as well as a link to a database mapping the global distribution of black shale units from a previous USGS study. GeoTIFF grids of the final prospectivity models for MVT and for clastic-dominated Pb-Zn mineralization across the US, Canada, and Australia from Lawley and others (2021) are also included. Each child page describes the particular data layer and related derivative products if applicable. Kelley, K.D., 2020, International geoscience collaboration to support critical mineral discovery: U.S. Geological Survey Fact Sheet 2020–3035, 2 p., https://doi.org/10.3133/fs20203035. Lawley, C.J.M., McCafferty, A.E., Graham, G.E., Huston, D.L., Kelley, K.D., Czarnota, K., Paradis, S., Peter, J.M., Hayward, N., Barlow, M., Emsbo, P., Coyan, J., San Juan, C.A., and Gadd, M.G., 2022, Data-driven prospectivity modelling of sediment-hosted Zn-Pb mineral systems and their critical raw materials: Ore Geology Reviews, v. 141, no. 104635, https://doi.org/10.1016/j.oregeorev.2021.104635.

A collection of geospatial files, map images, publication documentation, and informational resources in support of the Geologic Map of North America.

Datasets associated with the data publication "A map of pollinator floral resource habitats in the agricultural landscape of Central New York". Floral resource maps were produced for 12 counties in New York State (USA): Cayuga, Chemung, Cortland, Monroe, Onondaga, Ontario, Schuyler, Seneca, Tioga, Tompkins, Wayne, and Yates. The dataset covers 8 years from 2012 to 2019.

Each year has two alternative versions that represent urban areas in Monroe, Seneca, and Wayne counties differently. Datasets with the prefix "final_cat_" use categorical variables and datasets with the prefix "final_cont_" use a continuous value. See the associated publication for more values about the meaning and interpretation of these values.

This cartographic quality series of 1:50 000 scale monochrome maps cover the provincial extent of Alberta comprised of 764 maps that are individually named using the National Topographic System (NTS) map sheet identifier. These maps display the Alberta Township System (ATS), hydrographic features, municipalities, roads, cutlines, facilities, pipelines, powerlines, railways, select geo-administrative features (parks, reserves, etc.). At this scale 1.0 cm on the map represents 0.5 km on the ground. Each map covers an area of 0.50 degree longitude by 0.25 degree latitude. This product can be viewed on a computer, printed or be plotted in part or in whole. All maps contained within a 1:250 000 block (generally up to 16 map sheets) will be included in the NTS Block download. This series is not updated on a regular basis and may contain a range of publication dates.

The .csv table is part of a dataset package that was compiled for use as mineral assessment guidance in the Sagebrush Mineral-Resource Assessment project (SaMiRA). Mineral potential maps from previous mineral-resource assessments which included areas of the SaMiRA project areas were georeferenced. The images were clipped to the extent of the map and all explanatory text, gathered from map explanations or report text, was recorded into this table. This table is to be used in conjunction with the individual georeferenced raster images. It includes the image file name, map title and figure caption when appropriate. The images are also classified according to the legal definition of mineral resources: metallic, non-metallic, leasable non-fuel, leasable fuel, geothermal, paleontological, and saleable.

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.

USDA/NRCS SSURGO: This layer shows the Soil Survey Geographic (SSURGO) by the United States Department of Agriculture’s Natural Resources Conservation Service. SSURGO digitizing duplicates the original soil survey maps. This level of mapping is designed for use by landowners, townships, and county natural resource planning and management. The user should be knowledgeable of soils data and their characteristics. The soil units are symbolized by Esri to show the dominant condition for the 12 soil orders according to Soil Taxonomy. Dominant condition was determined by evaluating each of the components in a map unit; the percentage of the component that each soil order represented was accumulated for all the soil orders present in the map unit. The soil order with the highest accumulated percentage is then characterized as the dominant condition for that unit. If a tie was found between soil orders, a “tie-break” rule was applied. The tie-break was based on the component’s “slope_r” attribute value, which represents the Slope Gradient – Representative Value. The slope_r values were accumulated in the same fashion as the soil order attributes, i.e., by soil order, and the order with the lowest slope_r value was selected as dominant because that represented the lower slope value, and therefore we assumed the soils were more likely to be staying in that area or being deposited in that area. USDA/NRCS STATSGO This layer shows the U.S. General Soil Map of general soil association units by the United States Department of Agriculture’s Natural Resources Conservation Service. It was developed by the National Cooperative Soil Survey and supersedes the State Soil Geographic (STATSGO) dataset published in 1994. It consists of a broad-based inventory of soils and non-soil areas that occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. The soil units are symbolized by Esri to show the dominant condition for the 12 soil orders according to Soil Taxonomy. Dominant condition was determined by evaluating each of the components in a map unit; the percentage of the component that each soil order represented was accumulated for all the soil orders present in the map unit. The soil order with the highest accumulated percentage is then characterized as the dominant condition for that unit. If a tie was found between soil orders, a “tie-break” rule was applied. The tie-break was based on the component’s “slope_r” attribute value, which represents the Slope Gradient – Representative Value. The slope_r values were accumulated in the same fashion as the soil order attributes, i.e., by soil order, and the order with the lowest slope_r value was selected as dominant because that represented the lower slope value, and therefore we assumed the soils were more likely to be staying in that area or being deposited in that area. USDA/NRCS GLOBAL SOIL REGIONS This layer shows the Global Soil Regions map by the United States Department of Agriculture’s Natural Resources Conservation Service. The data and symbology are based on a reclassification of the FAO-UNESCO Soil Map of the World combined with a soil climate map. The soils data is symbolized to show the distribution of the 12 soil orders according to Soil Taxonomy. For more information on this map, including the terms of use, visit us online.Website Link: https://www.nrcs.usda.gov/wps/portal/nrcs/site/national/home/

The overall goal of the project was to systematically gather and quantify seafloor mapping data needs within the Southeast US study region (estuary to Exclusive Economic Zone (EEZ) of North Carolina, South Carolina, and Georgia). The results identify locations where stakeholder interests overlap with other organizations, leading to improved coordination of data needs, and leveraging collective resources to meet these shared goals. Already, priority areas identified by this study are being used by NOAA to focus planned fiscal year 2021 seafloor mapping missions. The web mapping application incorporating these results can be found here: https://noaa.maps.arcgis.com/home/item.html?id=04cdd2a68c4f427f893f2042f326dc80Spatial information on the arrangement of geological features, habitats and living marine resources on the seabed are often the foundation for decision-making in ecosystem management and ocean planning. Collecting information on the seabed depths and geomorphology is an expensive operation requiring airborne platforms like satellites, planes or drones, or small vessels to large research ships. Coordinating these data needs and data collection efforts will better leverage collective resources and meet shared goals. To help enable this coordination, in 2020 the National Oceanic and Atmospheric Administration (NOAA) National Centers for Coastal Ocean Science (NCCOS) developed a spatial framework, process, and online application to identify common data collection priorities for seafloor mapping, sampling, and visual surveys along shore and offshore of the Southeast United States (North Carolina, South Carolina, and Georgia).Twenty-five representatives from federal and state agencies, academic institutions, and non-governmental conservation groups, designated seafloor mapping priorities using an online prioritization tool. Participants allocated virtual coins across 5x5 km grid cells to denote their organization’s regions of seafloor mapping needs. Grid cells with more coins were higher priorities than cells with fewer coins. Participants also reported why these locations were important and what data types were needed. Results were analyzed and mapped using statistical techniques to identify significant relationships between priorities, reasons for those priorities and data needs. Several common areas of interest were identified in the spatially explicit analysis of the responses. Nearshore surfzone along Georgia, South Carolina, and North Carolina were highlighted by several agencies and organizations interested in sediment and sand resources as well as potential for rocky reef habitats. Inshore estuarine areas were highlighted by state agencies and conservation groups interested in monitoring change in managed areas like National Estuarine Reserves. On the outer continental shelf, areas near Blake Plateau off South Carolina and the continental shelf break off North Carolina were identified by federal agencies and conservation organizations as areas of sensitive habitats or historically significantly shipwrecks and maritime resources.The seafloor mapping prioritization approach described in the Buckel et al. (2021) report associated with these data provides recommendations to organizations charged with mapping the seabed for navigation and commerce as well as resource assessments and management. Already, the priority areas identified in this exercise are being used by NOAA to focus planned seafloor mapping missions. Furthermore, the outcomes from this regional exercise contribute into a National Mapping Prioritization under the lead of NOAA to coordinate mapping activities across the entire US EEZ. Together, these quantitative seafloor mapping prioritization approaches will enable improved coordination and more efficient allocation of resources needed to conduct seafloor mapping providing data to support environmental stewardship, safe navigation and commerce.

Wetlands within EPA Region 9

No Description Was Provided. Link Function: 375-- download.

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:

no abstract provided

This map features the Soil Survey Geographic (SSURGO) by the United States Department of Agriculture's Natural Resources Conservation Service. It also shows data that was developed by the National Cooperative Soil Survey and supersedes the State Soil Geographic (STATSGO) dataset published in 1994. SSURGO digitizing duplicates the original soil survey maps. This level of mapping is designed for use by landowners, townships, and county natural resource planning and management. The user should be knowledgeable of soils data and their characteristics. The smallest scale map shows the Global Soil Regions map by the United States Department of Agriculture’s Natural Resources Conservation Service.The web map combines with the soil survey with the terrain basemap and a hydro overlay layer for reference purposes. This basemap is ideal for display of thematic data such as the soil survey map, providing a neutral terrain background with an overlay layer for reference purposes.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information