description: This database contains locations of day care centers for 39 states which include the states of AZ, CA, , NV, NY, HI. This is a work in progress and data for remaining states will be added as they become available. The dataset only includes center based day care locations (including schools and religious institutes) and does not include home and family based day cares. All the data was acquired from respective states departments or their open source websites and then geocoded and converted into a spatial database, data for Washington D.C., Puerto Rico, Delaware and Louisiana was obtained in a GIS format. Information on the source of data for each state is available in the database itself. After geocoding the exact spatial location of each point is being verified using high resolution imagery and ancillary dataset and points are being moved to rooftops wherever possible, this is an ongoing work and points which have been physically verified have been labeled "Geocode", "Imagery", "Imagery with other" and "Unverified" depending on the methodology used to move the points. "Unverified" data points have still not being physically examined even though each of the points has been street geocoded as mentioned above. "Unverified" points for Puerto Rico, Washington DC and the states of Louisiana and Delaware may have better positional accuracy as data for these was obtained in GIS format. The "TYPE" attribute has not been populated yet, this will be populated once a common classification of day care for all states has been decided. The "O_TYPE" attribute contains the classification provided by individual states.; abstract: This database contains locations of day care centers for 39 states which include the states of AZ, CA, , NV, NY, HI. This is a work in progress and data for remaining states will be added as they become available. The dataset only includes center based day care locations (including schools and religious institutes) and does not include home and family based day cares. All the data was acquired from respective states departments or their open source websites and then geocoded and converted into a spatial database, data for Washington D.C., Puerto Rico, Delaware and Louisiana was obtained in a GIS format. Information on the source of data for each state is available in the database itself. After geocoding the exact spatial location of each point is being verified using high resolution imagery and ancillary dataset and points are being moved to rooftops wherever possible, this is an ongoing work and points which have been physically verified have been labeled "Geocode", "Imagery", "Imagery with other" and "Unverified" depending on the methodology used to move the points. "Unverified" data points have still not being physically examined even though each of the points has been street geocoded as mentioned above. "Unverified" points for Puerto Rico, Washington DC and the states of Louisiana and Delaware may have better positional accuracy as data for these was obtained in GIS format. The "TYPE" attribute has not been populated yet, this will be populated once a common classification of day care for all states has been decided. The "O_TYPE" attribute contains the classification provided by individual states.

The site suitability criteria included in the techno-economic land use screens are listed below. As this list is an update to previous cycles, tribal lands, prime farmland, and flood zones are not included as they are not technically infeasible for development. The techno-economic site suitability exclusion thresholds are presented in table 1. Distances indicate the minimum distance from each feature for commercial scale wind developmentAttributes: Steeply sloped areas: change in vertical elevation compared to horizontal distancePopulation density: the number of people living in a 1 km2 area Urban areas: defined by the U.S. Census. Water bodies: defined by the U.S. National Atlas Water Feature Areas, available from Argonne National Lab Energy Zone Mapping Tool Railways: a comprehensive database of North America's railway system from the Federal Railroad Administration (FRA), available from Argonne National Lab Energy Zone Mapping Tool Major highways: available from ESRI Living Atlas Airports: The Airports dataset including other aviation facilities as of July 13, 2018 is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics's (BTS's) National Transportation Atlas Database (NTAD). The Airports database is a geographic point database of aircraft landing facilities in the United States and U.S. Territories. Attribute data is provided on the physical and operational characteristics of the landing facility, current usage including enplanements and aircraft operations, congestion levels and usage categories. This geospatial data is derived from the FAA's National Airspace System Resource Aeronautical Data Product. Available from Argonne National Lab Energy Zone Mapping Tool Active mines: Active Mines and Mineral Processing Plants in the United States in 2003Military Lands: Land owned by the federal government that is part of a US military base, camp, post, station, yard, center, or installation. Table 1 Wind Steeply sloped areas >10o Population density >100/km2 Capacity factor <20% Urban areas <1000 m Water bodies <250 m Railways <250 m Major highways <125 m Airports <5000 m Active mines <1000 m Military Lands <3000m For more information about the processes and sources used to develop the screening criteria see sources 1-7 in the footnotes. Data updates occur as needed, corresponding to typical 3-year CPUC IRP planning cyclesFootnotes:[1] Lopez, A. et. al. “U.S. Renewable Energy Technical Potentials: A GIS-Based Analysis,” 2012. https://www.nrel.gov/docs/fy12osti/51946.pdf[2] https://greeningthegrid.org/Renewable-Energy-Zones-Toolkit/topics/social-environmental-and-other-impacts#ReadingListAndCaseStudies[3] Multi-Criteria Analysis for Renewable Energy (MapRE), University of California Santa Barbara. https://mapre.es.ucsb.edu/[4] Larson, E. et. al. “Net-Zero America: Potential Pathways, Infrastructure, and Impacts, Interim Report.” Princeton University, 2020. https://environmenthalfcentury.princeton.edu/sites/g/files/toruqf331/files/2020-12/Princeton_NZA_Interim_Report_15_Dec_2020_FINAL.pdf.[5] Wu, G. et. al. “Low-Impact Land Use Pathways to Deep Decarbonization of Electricity.” Environmental Research Letters 15, no. 7 (July 10, 2020). https://doi.org/10.1088/1748-9326/ab87d1.[6] RETI Coordinating Committee, RETI Stakeholder Steering Committee. “Renewable Energy Transmission Initiative Phase 1B Final Report.” California Energy Commission, January 2009.[7] Pletka, Ryan, and Joshua Finn. “Western Renewable Energy Zones, Phase 1: QRA Identification Technical Report.” Black & Veatch and National Renewable Energy Laboratory, 2009. https://www.nrel.gov/docs/fy10osti/46877.pdf.[8]https://www.census.gov/cgi-bin/geo/shapefiles/index.php?year=2019&layergroup=Urban+Areas[9]https://ezmt.anl.gov/[10]https://www.arcgis.com/home/item.html?id=fc870766a3994111bce4a083413988e4[11]https://mrdata.usgs.gov/mineplant/Credits Title: Techno-economic screening criteria for utility-scale wind energy installations for Integrated Resource Planning Purpose for creation: These site suitability criteria are for use in electric system planning, capacity expansion modeling, and integrated resource planning. Keywords: wind energy, resource potential, techno-economic, IRP Extent: western states of the contiguous U.S. Use Limitations The geospatial data created by the use of these techno-economic screens inform high-level estimates of technical renewable resource potential for electric system planning and should not be used, on their own, to guide siting of generation projects nor assess project-level impacts.Confidentiality: Public ContactEmily Leslie Emily@MontaraMtEnergy.comSam Schreiber sam.schreiber@ethree.com Jared Ferguson Jared.Ferguson@cpuc.ca.govOluwafemi Sawyerr femi@ethree.com

Filtered (5% slope and less) direct normal solar resource data for the Southwest United States

This data provides filtered solar resource averaged over surface cells of 0.1 degrees in both latitude and longitude, or about 10 km in size. Exclusions: Contiguous Area 1km squared; US Census Urban Areas; MRLC Multi Resolution Land Characteristics Consortium http://www.mrlc.gov/; Argone National Lab ACEC Lands (Areas of Critical Environmental Concern) Federally Protected lands (FS - IRA (Inventoried Roadless Areas, FS - National Monument, FS - National Scenic Area, FS - Wilderness, FS - Wilderness Study Area, BLM - Wilderness, BLM - National Recreation Area, BLM - Forest Reserve, BLM - Wilderness Study Area, BLM - National Monument, BLM - National Conservation Area, FWS - National Wildlife Refuge, FWS - Waterfowl Production Area, FWS - Wildlife Management Area, FWS - Wilderness, FWS - Wilderness Study Area, FWS - Fish Hatchery, NPS - National Battlefield, NPS - National Battlefield Park, NPS - National Capital Park, NPS - National Historic Park, NPS - National Historic Site, NPS - National Lakeshore, NPS - National Mall, NPS - National Memorial, NPS - National Military Park, NPS - National Monument, NPS - National Park, NPS - National Parkway, NPS - National Preserve, NPS - National Recreation Area, NPS - National Reserve, NPS - National River, NPS - National Seashore, NPS - Wilderness, NPS - Wilderness Study Area, NPS - National Wild and Scenic River).

License Info

DISCLAIMER NOTICE This GIS data was developed by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC for the U.S. Department of Energy ("DOE"). The user is granted the right, without any fee or cost, to use, copy, modify, alter, enhance and distribute this data for any purpose whatsoever, provided that this entire notice appears in all copies of the data. Further, the user of this data agrees to credit NREL in any publications or software that incorporate or use the data.

Access to and use of the GIS data shall further impose the following obligations on the User. The names DOE/NREL may not be used in any advertising or publicity to endorse or promote any product or commercial entity using or incorporating the GIS data unless specific written authorization is obtained from DOE/NREL. The User also understands that DOE/NREL shall not be obligated to provide updates, support, consulting, training or assistance of any kind whatsoever with regard to the use of the GIS data.

THE GIS DATA IS PROVIDED "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL DOE/NREL BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER, INCLUDING BUT NOT LIMITED TO CLAIMS ASSOCIATED WITH THE LOSS OF DATA OR PROFITS, WHICH MAY RESULT FROM AN ACTION IN CONTRACT, NEGLIGENCE OR OTHER TORTIOUS CLAIM THAT ARISES OUT OF OR IN CONNECTION WITH THE ACCESS OR USE OF THE GIS DATA.

The User acknowledges that access to the GIS data is subject to U.S. Export laws and regulations and any use or transfer of the GIS data must be authorized under those regulations. The User shall not use, distribute, transfer, or transmit GIS data or any products incorporating the GIS data except in compliance with U.S. export regulations. If requested by DOE/NREL, the User agrees to sign written assurances and other export-related documentation as may be required to comply with U.S. export regulations.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. A three-year program was initiated to complete the task of classifying and mapping the vegetation at HAFO; the results of the HAFO project are being used to develop this volunteer project for MIIN. Phase One which was directed by HAFO and network staff in conjunction with NatureServe developed a vegetation classification using the National Vegetation Classification System (NVCS). Phase Two, directed by Northwest Management, Inc.’s (NMI) GIS Laboratory and Cogan Technology, Incorporated (CTI) produced a digital vegetation map for HAFO. To classify the HAFO vegetation, 85 representative plots located throughout the monument were sampled during the summer of 2006. Analysis of the plot data by the Idaho Conservation Data Center (ICDC) in the winter of 2006-2007 produced 34 distinct plant associations.

This dataset contains information about the biomass resources generated by county in the United States. It includes the following feedstock categories: crop residues, forest residues, primary mill residues, secondary mill residues, and urban wood waste.

The estimates are based on county-level statistics and/or point-source data gathered from the U.S. Department of Agriculture (USDA), USDA Forest Service, EPA and other organizations, which are further processed using relevant assumptions and conversions.

The United States Fish and Wildlife Service (USFWS) National Wild Fish Health Survey Database (NWFHSDb) has been available to the public since September 2001. The database contains data on pathogen occurrence in free-ranging (wild) populations of fish. This data is collected via the National Wild Fish Health Survey, initiated in 1996 as a collaborative effort among natural resource agencies. The survey is maintained and managed by the nine USFWS National Fish Health Centers. The database is part of an effort to create an information system that will be a valuable tool for the management, protection, and recovery of aquatic ecosystems. The NWFHSDb consists of two distinct components: an internal database maintained and utilized by the Fish Health Centers for entering, tracking, and reporting data, and this publicly accessible website. Data from each Fish Health Center is available on this site for display and download. The NWFHSDb displays pathogen distribution information and is based on the spatial data generated by the Fish Health Centers. The NWFHSDb itself is a geographic information system (GIS) designed to be accessed via a web browser. It offers users the ability to obtain maps of NWFHS data based on user-defined queries. Individual case reports are available for each record and search results may be downloaded in several formats for further analysis. The feature layer and related tables contain data from 2021-present. Depending on data integration issues, data may not be complete. Please reach out to the identified Fish Health Center for questions or more information.

This dataset was developed by the National Renewable Energy Laboratory (NREL) for the U.S. Agency for International Development's (USAID) South Asia Regional Initiative for Energy Cooperation (SARI/E). The dataset contains Wind Power Density at 50-m Above Ground Level in the form of a GIS shapefile. The data were output in Geographic Information Systems (GIS) format and incorporated into a Geospatial Toolkit (GsT) which is provided in data resources. The GsT allows the user to examine the resource data in a geospatial context along with other key information relevant to renewable energy development, such as transportation networks, transmission corridors, existing power facilities, load centers, terrain conditions, and land use.

The National Carbon Sequestration Database and Geographic Information System (NATCARB) Saline spatial database is a small-scale (large-area) overview of carbon dioxide (CO2) geologic storage potential in saline formations across the USA and parts of Canada. Saline formations are composed of brine-saturated porous rock and capped by one or more regionally extensive, low-permeability rock formations. Only saline formations containing formation fluid with total dissolved solids (TDS) greater than 10,000 ppm merited evaluation for potential CO2 storage. A saline storage resource can include one named geologic stratigraphic unit or be defined as only a part of a stratigraphic unit. This data layer reflects the best available knowledge regarding the location of carbon sequestration potential in the USA and Canada, both onshore and offshore. NATCARB is administered by the US Dept. of Energy (DOE) National Energy Technology Laboratory (NETL) and contains data provided by several Regional Carbon Sequestration Partnerships (RCSP). RCSPs originally developed the data per individual geologic storage resource, or as continuous surface models, and then converted these data into a 10 km X 10 km vector "grid". The NATCARB Team at the Kansas Geological Survey compiled the regional datasets into a single, seamless layer.

GIS-datasets for the Street networks of Stockholm, Gothenburg and Eskilstuna produced as part of the Spatial Morphology Lab (SMoL).

The goal of the SMoL project is to develop a strong theory and methodology for urban planning & design research with an analytical approach. Three frequently recurring variables of spatial urban form are studied that together quite well capture and describe the central characteristics and qualities of the built environment: density, diversity and proximity.

The first measure describes how intensive a place can be used depending on how much built up area is found there. The second measure captures how differentiated the use of a place can be depending on the division in smaller units such as plots. The third measure describes how accessible a place is depending on how it relates with other places. Empirical studies have shown strong links between these metrics and people's use of cities such as pedestrian movement patterns.

To support this goal, a central objective of the project is the establishment of an international platform of GIS data models for comparative studies in spatial urban form comprising three European capitals: London in the UK, Amsterdam in the Netherlands and Stockholm in Sweden, as well as two additional Swedish cities of smaller size than Stockholm: Gothenburg and Eskilstuna.

The result of the project is a GIS database for the five cities covering the three basic layers of urban form: street network (motorised and non-motorised), buildings and plots systems.

The data is shared via SND to create a research infrastructure that is open to new study initiatives. The datasets for Amsterdam will also be uploaded to SND. The datasets of London cannot be uploaded because of licensing restrictions.

The street network GIS-maps include motorised and non-motorised networks. The motorised networks exclude all streets that are pedestrian-only and were cars are excluded. The network layers are based on the Swedish national road database, NVDB (Nationell Vägdatabas), downloaded from Trafikverket (https://lastkajen.trafikverket.se, date of download 15-5-2016, last update 8-11-2015). The original road-centre-line maps of all cities were edited based on the same basic representational principles and were converted into line-segment maps, using the following software: FME, Mapinfo professional and PST (Place Syntax Tool). The coordinate system is SWEREF99TM. The map covers all Västra Götaland region (Västra Götalands län).

In the final line-segment maps (GIS-layers) all roads are represented with one line irrespectively of the number of lanes, except from Motorways and Highways which are represented with two lines, one for each direction, again irrespectively of the number of lanes. We followed the same editing and generalizing procedure for all maps aiming to remove errors and to increase comparability between networks. This process included removing duplicate and isolated lines, snapping and generalizing. The snapping threshold used was 2m (end points closer than 2m were snapped together). The generalizing threshold used was 1m (successive line segments with angular deviation less than 1m were merged into one). In the final editing step, all road polylines were segmented to their constituting line-segments. The aim was to create appropriate line-segment maps to be analysed using Angular Segment Analysis, a network centrality analysis method introduced in Space Syntax.

All network layers are complemented with an “Unlink points” layer; a GIS point layer with the locations of all non-level intersections, such as overpasses and underpasses, bridges, tunnels, flyovers and the like. The Unlink point layer is necessary to conduct network analysis that takes into account the non-planarity of the street network, using such software as PST (Place Syntax Tool).

Chemical, physical, profile and laboratory analysis oceanographic data were collected aboard the Brooks McCall in the Gulf of Mexico from 2010-05-30 to 2010-06-02 in response to the Deepwater Horizon Oil Spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consisted of multiple government and corporate agencies. These data include Attenuation/Transmission, CDOM fluorescence, Total Petroleum Hydrocarbons (TPH), Volatile Organic Compounds, conductivity, dissolved oxygen, hydrostatic pressure, salinity, sound velocity, suspended solids, temperature and water density. The instruments used to collect these data included CTD, Laser In-Situ Scattering and Transmissometer (LISST), Transmissometer, bottle, fluorometer and oxygen meter along with other physical sampling devices. More specific information about each data set is located in their individual metadata records. This data set also contains products created for use in real time analysis and decision support. These products may include charts, graphs, maps, plots, and GIS formatted data files. The CTD data underwent preliminary quality assurance and control procedures at the National Coastal Data Development Center (NCDDC). The analytical chemistry data are provisional and provide results of onshore laboratory analysis of water and sediment samples. Cruise level information consisting of data management documents, cruise reports and plans, videos and pictures, and other miscellaneous documentation were gathered by the data managers. (NODC Accession 0069046)

National Risk Index Version: March 2023 (1.19.0)A Heat Wave is a period of abnormally and uncomfortably hot and unusually humid weather typically lasting two or more days with temperatures outside the historical averages for a given area. Annualized frequency values for Heat Waves are in units of event-days per year.The National Risk Index is a dataset and online tool that helps to illustrate the communities most at risk for 18 natural hazards across the United States and territories: Avalanche, Coastal Flooding, Cold Wave, Drought, Earthquake, Hail, Heat Wave, Hurricane, Ice Storm, Landslide, Lightning, Riverine Flooding, Strong Wind, Tornado, Tsunami, Volcanic Activity, Wildfire, and Winter Weather. The National Risk Index provides Risk Index values, scores and ratings based on data for Expected Annual Loss due to natural hazards, Social Vulnerability, and Community Resilience. Separate values, scores and ratings are also provided for Expected Annual Loss, Social Vulnerability, and Community Resilience. For the Risk Index and Expected Annual Loss, values, scores and ratings can be viewed as a composite score for all hazards or individually for each of the 18 hazard types.Sources for Expected Annual Loss data include: Alaska Department of Natural Resources, Arizona State University’s (ASU) Center for Emergency Management and Homeland Security (CEMHS), California Department of Conservation, California Office of Emergency Services California Geological Survey, Colorado Avalanche Information Center, CoreLogic’s Flood Services, Federal Emergency Management Agency (FEMA) National Flood Insurance Program, Humanitarian Data Exchange (HDX), Iowa State University's Iowa Environmental Mesonet, Multi-Resolution Land Characteristics (MLRC) Consortium, National Aeronautics and Space Administration’s (NASA) Cooperative Open Online Landslide Repository (COOLR), National Earthquake Hazards Reduction Program (NEHRP), National Oceanic and Atmospheric Administration’s National Centers for Environmental Information (NCEI), National Oceanic and Atmospheric Administration's National Hurricane Center, National Oceanic and Atmospheric Administration's National Weather Service (NWS), National Oceanic and Atmospheric Administration's Office for Coastal Management, National Oceanic and Atmospheric Administration's National Geophysical Data Center, National Oceanic and Atmospheric Administration's Storm Prediction Center, Oregon Department of Geology and Mineral Industries, Pacific Islands Ocean Observing System, Puerto Rico Seismic Network, Smithsonian Institution's Global Volcanism Program, State of Hawaii’s Office of Planning’s Statewide GIS Program, U.S. Army Corps of Engineers’ Cold Regions Research and Engineering Laboratory (CRREL), U.S. Census Bureau, U.S. Department of Agriculture's (USDA) National Agricultural Statistics Service (NASS), U.S. Forest Service's Fire Modeling Institute's Missoula Fire Sciences Lab, U.S. Forest Service's National Avalanche Center (NAC), U.S. Geological Survey (USGS), U.S. Geological Survey's Landslide Hazards Program, United Nations Office for Disaster Risk Reduction (UNDRR), University of Alaska – Fairbanks' Alaska Earthquake Center, University of Nebraska-Lincoln's National Drought Mitigation Center (NDMC), University of Southern California's Tsunami Research Center, and Washington State Department of Natural Resources.Data for Social Vulnerability are provided by the Centers for Disease Control (CDC) Agency for Toxic Substances and Disease Registry (ATSDR) Social Vulnerability Index, and data for Community Resilience are provided by University of South Carolina's Hazards and Vulnerability Research Institute’s (HVRI) 2020 Baseline Resilience Indicators for Communities.The source of the boundaries for counties and Census tracts are based on the U.S. Census Bureau’s 2021 TIGER/Line shapefiles. Building value and population exposures for communities are based on FEMA’s Hazus 6.0. Agriculture values are based on the USDA 2017 Census of Agriculture.

Abstract: Monthly and annual average solar resource potential for the lower 48 states of the United States of America.

Purpose: Provide information on the solar resource potential for the United States of America lower 48 states. The insolation values represent the average solar energy available to a flat plate collector, such as a photovoltaic panel, oriented due south at an angle from horizontal equal to the latitude of the collector location.

Supplemental Information: This data provides monthly average and annual average daily total solar resource averaged over surface cells of approximatley 40 km by 40 km in size. This data was developed from the Climatological Solar Radiation (CSR) Model. The CSR model was developed by the National Renewable Energy Laboratory for the U.S. Department of Energy. Specific information about this model can be found in Maxwell, George and Wilcox (1998) and George and Maxwell (1999). This model uses information on cloud cover, atmostpheric water vapor and trace gases, and the amount of aerosols in the atmosphere to calculate the monthly average daily total insolation (sun and sky) falling on a horizontal surface. The cloud cover data used as input to the CSR model are an 7-year histogram (1985-1991) of monthly average cloud fraction provided for grid cells of approximately 40km x 40km in size. Thus, the spatial resolution of the CSR model output is defined by this database. The data are obtained from the National Climatic Data Center in Ashville, North Carolina, and were developed from the U.S. Air Force Real Time Nephanalysis (RTNEPH) program. Atmospheric water vapor, trace gases, and aerosols are derived from a variety of sources. The procedures for converting the collector at latitude tilt are described in Marion and Wilcox (1994). Where possible, existing ground measurement stations are used to validate the data. Nevertheless, there is uncertainty associated with the meterological input to the model, since some of the input parameters are not avalible at a 40km resolution. As a result, it is believed that the modeled values are accurate to approximately 10% of a true measured value within the grid cell. Due to terrain effects and other micoclimate influences, the local cloud cover can vary significantly even within a single grid cell. Furthermore, the uncertainty of the modeled estimates increase with distance from reliable measurement sources and with the complexity of the terrain. Units are in kilowatt hours per meter squared per day.

OtherCitation Details:

George, R, and E. Maxwell, 1999: "High-Resolution Maps of Solar Collector Performance Using A Climatological Solar Radiation Model", Proceedings of the 1999 Annual Conference, American Solar Energy Society, Portland, ME.

Maxwell, E, R. George and S. Wilcox, "A Climatological Solar Radiation Model", Proceedings of the 1998 Annual Conference, American Solar Energy Society, Albuquerque NM.

Marion, William and Stephen Wilcox, 1994: "Solar Radiation Data Manual for Flat-plate and Concentrating Collectors". NREL/TP-463-5607, National Renewable Energy Laboratory, 1617 Cole Boulevard, Golden, CO 80401.

License Info

DISCLAIMER NOTICE This GIS data was developed by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC for the U.S. Department of Energy ("DOE"). The user is granted the right, without any fee or cost, to use, copy, modify, alter, enhance and distribute this data for any purpose whatsoever, provided that this entire notice appears in all copies of the data. Further, the user of this data agrees to credit NREL in any publications or software that incorporate or use the data.

Access to and use of the GIS data shall further impose the following obligations on the User. The names DOE/NREL may not be used in any advertising or publicity to endorse or promote any product or commercial entity using or incorporating the GIS data unless specific written authorization is obtained from DOE/NREL. The User also understands that DOE/NREL shall not be obligated to provide updates, support, consulting, training or assistance of any kind whatsoever with regard to the use of the GIS data.

THE GIS DATA IS PROVIDED "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL DOE/NREL BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER, INCLUDING BUT NOT LIMITED TO CLAIMS ASSOCIATED WITH THE LOSS OF DATA OR PROFITS, WHICH MAY RESULT FROM AN ACTION IN CONTRACT, NEGLIGENCE OR OTHER TORTIOUS CLAIM THAT ARISES OUT OF OR IN CONNECTION WITH THE ACCESS OR USE OF THE GIS DATA.

The User acknowledges that access to the GIS data is subject to U.S. Export laws and regulations and any use or transfer of the GIS data must be authorized under those regulations. The User shall not use, distribute, transfer, or transmit GIS data or any products incorporating the GIS data except in compliance with U.S. export regulations. If requested by DOE/NREL, the User agrees to sign written assurances and other export-related documentation as may be required to comply with U.S. export regulations.

This data set contains boundary layers for the Nomans Land Island National Wildlife Refuge in Massachusetts.

National Wildlife Refuges are federal lands managed by the U.S. Fish and Wildlife Service (USFWS). The primary source for boundary information is the USFWS Realty program (status maps, legal surveys). A joint effort between the Region 5 (northeast - ME, MA, NH, VT, RI, CT, NY, PA, NJ, MD, VA, WV) GIS Lab and Realty program, has resulted in digital refuge boundaries for all refuges in the northeast at a 1:24,000 scale.

The purpose of this data is to serve as a spatial reference of refuge boundaries for other data layers in GIS and mapping applications. It is specifically not intended to be used as a land survey or representation of land for conveyance or tax purposes. The Realty Survey program in USFWS is developing cadastral information (boundary and acreage data) appropriate for legal purposes. It is expected that data created in this project will be replaced as better survey information is collected.

Status maps were registered to geographic coordinates, boundaries digitized and labeled, then stepped through 3 levels of quality review for spatial and thematic accuracy.

Refuge boundaries define areas that are approved by U.S. Congress for acquisition in the National Wildlife Refuge System, or are currently owned by USFWS. Arcs are coded with an item "boundary" that the type of boundary line and polygons are coded with an item "status" that describes their ownership status.

[Summary provided by U.S. Fish & Wildlife Service]

This submission contains a number of maps and shapefiles related to the Utah FORGE site. Examples include geologic maps (several variations) and GIS data for the Utah FORGE site outline. All data are georeferenced to UTM, zone 12N, NAD 83, NAVD 88.

Abstract: Monthly and annual average solar resource potential for the lower 48 states of the United States of America.

Purpose: Provide information on the solar resource potential for the for the lower 48 states of the United States of America.

Supplemental Information: This data provides monthly average and annual average daily total solar resource averaged over surface cells of approximatley 40 km by 40 km in size. This data was developed from the Climatological Solar Radiation (CSR) Model. The CSR model was developed by the National Renewable Energy Laboratory for the U.S. Department of Energy. Specific information about this model can be found in Maxwell, George and Wilcox (1998) and George and Maxwell (1999). This model uses information on cloud cover, atmostpheric water vapor and trace gases, and the amount of aerosols in the atmosphere to calculate the monthly average daily total insolation (sun and sky) falling on a horizontal surface. The cloud cover data used as input to the CSR model are an 7-year histogram (1985-1991) of monthly average cloud fraction provided for grid cells of approximately 40km x 40km in size. Thus, the spatial resolution of the CSR model output is defined by this database. The data are obtained from the National Climatic Data Center in Ashville, North Carolina, and were developed from the U.S. Air Force Real Time Nephanalysis (RTNEPH) program. Atmospheric water vapor, trace gases, and aerosols are derived from a variety of sources. The procedures for converting the collector at latitude tilt are described in Marion and Wilcox (1994). Where possible, existing ground measurement stations are used to validate the data. Nevertheless, there is uncertainty associated with the meterological input to the model, since some of the input parameters are not avalible at a 40km resolution. As a result, it is believed that the modeled values are accurate to approximately 10% of a true measured value within the grid cell. Due to terrain effects and other micoclimate influences, the local cloud cover can vary significantly even within a single grid cell. Furthermore, the uncertainty of the modeled estimates increase with distance from reliable measurement sources and with the complexity of the terrain.

Other Citation Details: George, R, and E. Maxwell, 1999: "High-Resolution Maps of Solar Collector Performance Using A Climatological Solar Radiation Model", Proceedings of the 1999 Annual Conference, American Solar Energy Society, Portland, ME.

License Info

This GIS data was developed by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC for the U.S. Department of Energy ("DOE"). The user is granted the right, without any fee or cost, to use, copy, modify, alter, enhance and distribute this data for any purpose whatsoever, provided that this entire notice appears in all copies of the data. Further, the user of this data agrees to credit NREL in any publications or software that incorporate or use the data.

Access to and use of the GIS data shall further impose the following obligations on the User. The names DOE/NREL may not be used in any advertising or publicity to endorse or promote any product or commercial entity using or incorporating the GIS data unless specific written authorization is obtained from DOE/NREL. The User also understands that DOE/NREL shall not be obligated to provide updates, support, consulting, training or assistance of any kind whatsoever with regard to the use of the GIS data.

THE GIS DATA IS PROVIDED "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL DOE/NREL BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER, INCLUDING BUT NOT LIMITED TO CLAIMS ASSOCIATED WITH THE LOSS OF DATA OR PROFITS, WHICH MAY RESULT FROM AN ACTION IN CONTRACT, NEGLIGENCE OR OTHER TORTIOUS CLAIM THAT ARISES OUT OF OR IN CONNECTION WITH THE ACCESS OR USE OF THE GIS DATA.

The User acknowledges that access to the GIS data is subject to U.S. Export laws and regulations and any use or transfer of the GIS data must be authorized under those regulations. The User shall not use, distribute, transfer, or transmit GIS data or any products incorporating the GIS data except in compliance with U.S. export regulations. If requested by DOE/NREL, the User agrees to sign written assurances and other export-related documentation as may be required to comply with U.S. export regulations.

Purpose: This is a web map used for a situational awareness viewer. Click on links below for more information, this is just a summary of the layers in this map as of 09/14/2018.Live Data Live Feed - Storm Reports (NOAA) - This map contains continuously updated U.S. tornado reports, wind storm reports and hail storm reports. You can click on each to receive information about the specific location and read a short description about the issue. Live Feed - Observed Weather (NOAA METAR) - Current wind and weather conditions at all METAR stations.Live Feed: Open Shelters (FEMA / Red Cross National Shelter System) - his web service displays data from the FEMA National Shelter System database. The FEMA NSS database is synchronized every morning with the American Red Cross shelter database. After this daily refresh, FEMA GIS connects every 20 minutes to the FEMA NSS database looking for any shelter updates that occur throughout the day in the the FEMA NSS.Live Feed: Active Hurricanes - Hurricane tracks and positions provide information on where the storm has been, where is it going, where it is currently located and the category as defined by wind speed. This data is provided by NOAA National Hurricane Center (NHC).Live Feed Action Level Stream Gauges (USGS) - This map service shows those gauges from the Live Stream Gauge layer that are currently flooding. It only includes those gauges where flood stages have been defined by the contributing agencies. Action stage represents the river depth at which the agency begins preparing for a flood and taking mitigative action.Live Feed: USA Short-Term Weather Warnings - This layer presents continuously updated US weather warnings. You can click on each to receive information about the specific location and read a short description about the issue. Each layer is updated every minute with data provided by NOAA’s National Weather Service - http://www.nws.noaa.gov/regsci/gis/shapefiles/.Live Feed: Power Outages - Current power outage data reported by the EARSS system.Live Feed: Radar (NOAA) - Quality Controlled 1km x 1km CONUS Radar Base Reflectivity. This data is provided by Mutil-Radar-Multi-Sensor (MRMS) algorithm.Flood Prediction / Simulation (Created on 09/13 by Pacific Northwest National Laboratory RIFT Model) - Pacific Northwest National Laboratory RIFT Model: The simulations, based on NOAA weather forecasts, are used to improve understanding of the storm and its potential flood impacts. The simulations were created with PNNL's Rapid Inundation Flood Tool, a two-dimensional hydrodynamic computer model.Base Data - FEMA National Flood Hazard Layer - The National Flood Hazard Layer (NFHL) dataset represents the current effective flood data for the country, where maps have been modernized. It is a compilation of effective Flood Insurance Rate Map (FIRM) databases and Letters of Map Change (LOMCs). The NFHL is updated as studies go effective. For more information, visit FEMA's Map Service Center (MSC). Base Data - Storm Surge Scenarios (NOAA) - This mapping service displays near worst case storm surge flooding (inundation) scenarios for the Gulf and Atlantic coasts. This map service was derived from an experimental storm surge data product developed by the National Hurricane Center (NHC).

The Digital Geologic-GIS Map of National Park American Samoa, American Samoa is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (npsa_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (npsa_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (npsa_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (npsa_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (npsa_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (npsa_geology_metadata_faq.pdf). Please read the npsa_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: University of Hawaii, Cartographic Laboratory. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (npsa_geology_metadata.txt or npsa_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scales of 1:56,000 (Ofu/Olosega and Tau maps) or 1:63,000 (Tutuila map) and United States National Map Accuracy Standards features are within 28.45 meters or 93.33 feet (1:56,000 scale maps) or 32.0 meters or 105 feet (1:63,000 scale maps) of their actual _location as presented by this dataset. Users of this data should thus not assume the _location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

This data set consists of GIS data documenting the location, species composition, and other habitat characteristics of submerged aquatic vegetation (SAV) in coastal Alabama. This was collected as part of a remote sensing investigation of SAV distribution using summer and fall ortho imagery as base maps.

Digital Object Identifier: 10.25573/data.11919276

The Isthmus of Panama is a narrow strip of land that connects the continents of North and South America and separates the Atlantic (Caribbean) and Pacific oceans. It is rich in native biodiversity, with tropical rain and cloud forests, island archipelagos, coral reefs, mangrove and estuarine ecosystems.

In this study our aim is to model how post-glacial sea-level rise changed the land- and sea-scapes of the Isthmus of Panama. This helps provide more precise historical context to better understand modern biogeographical patterns of both terrestrial and marine ecosystems on the Isthmus. In collaboration with the "https://stridata-si.opendata.arcgis.com/">GIS unit at the Smithsonian Tropical Research Institute we combined bathymetric and topographic data with historical sea level data of the Western Atlantic and Tropical Eastern Pacific to produce digital elevation models, maps, and animated videos showing the changing land and sea-scapes of the Isthmus through time.

A more detailed description of the project and methods can be found here: https://storymaps.arcgis.com/stories/1d730fe1f6e443eab423a5aeea51dfb9

Aaron O'Dea Lab - Max Titcomb

Smithsonian Tropical Research Institute

References:

O’Dea A, Aguilera O, Aubry M-P, Berggren WA, Budd AF, Cione AL, Coates AG, Collins LS, Coppard SE, Cozzuol MA, de Queiroz A, Duque-Caro H, Eytan RI, Farris DW, Finnegan S, Gasparini GM, Grossman EL, Johnson KG, Keigwin LD, Knowlton N, Leigh EG, Leonard-Pingel JS, Lessios HA, Marko PB, Norris RD, Rachello-Dolmen PG, Restrepo-Moreno SA, Soibelzon E, Soibelzon L, Stallard RF, Todd JA, Vermeij GJ, Woodburne MO, Jackson JBC. 2016. Formation of the Isthmus of Panama. Science Advances. e1600883.

Kenneth G. Miller, Michelle A. Kominz, James V. Browning, James D. Wright, Gregory S. Mountain, Miriam E. Katz, Peter J. Sugarman, Benjamin S. Cramer, Nicholas Christie-Blick, Stephen F. Pekar. 2005. The Phanerozoic Record of Global Sea-Level Change. SCIENCE. 10.1126/science.1116412

Redwood, S. D. 2020. Late Pleistocene to Holocene sea level rise in the Gulf of Panama, Panama, and its influence on early human migration through the Isthmus. Caribbean Journal of Earth Science, 51, 15-31. Geological Society of Jamaica. http://caribjes.com/CJESpdf/CJES51-3-RedwoodPanamaSealevel.pdf

This data set contains boundary layers for the Metompkin Island Division of the Chincoteague National Wildlife Refuge in Virginia.

National Wildlife Refuges are federal lands managed by the U.S. Fish and Wildlife Service (USFWS). The primary source for boundary information is the USFWS Realty program (status maps, legal surveys). A joint effort between the Region 5 (northeast - ME, MA, NH, VT, RI, CT, NY, PA, NJ, MD, VA, WV) GIS Lab and Realty program, has resulted in digital refuge boundaries for all refuges in the northeast at a 1:24,000 scale.

The purpose if this data is to serve as a spatial reference of refuge boundaries for other data layers in GIS and mapping applications. It is specifically not intended to be used as a land survey or representation of land for conveyance or tax purposes. The Realty Survey program in USFWS is developing cadastral information (boundary and acreage data) appropriate for legal purposes. It is expected that data created in this project will be replaced as better survey information is collected.

Status maps were registered to geographic coordinates, boundaries digitized and labeled, then stepped through 3 levels of quality review for spatial and thematic accuracy.

Refuge boundaries define areas that are approved by U.S. Congress for acquisition in the National Wildlife Refuge System, or are currently owned by USFWS. Arcs are coded with an item "boundary" that the type of boundary line and polygons are coded with an item "status" that describes their ownership status.

[Summary provided by U.S. Fish & Wildlife Service]