The U.S. Geological Survey (USGS) Bee Lab is a collaborative interagency joint venture and international leader for bee (Hymenoptera: Apoidea) identification, survey design, quantification of bee and plant interrelations, and development and maintenance of occurrence databases. Each of these objectives supports native bee conservation by providing critical data and tools for the United States and other countries. The Bee Lab is part of the USGS Eastern Ecological Science Center (EESC) and located in Laurel, Maryland, at the U.S. Fish and Wildlife Service (USFWS) Patuxent Research Refuge. The laboratory houses scientists from the EESC, USGS’s Cooperative Fish and Wildlife Research Units, and the USFWS to develop identification tools and survey design support for State, Federal, Tribal, and nongovernment organization partners. In addition to the development of identification tools, important objectives include developing keys for native and nonnative bee species and making those tools accessible to partners and the public. Among the most visible and reused products produced during the development of the tools are the detailed photographs of the bees themselves. Accurate bee identification allows for better monitoring of bee species and examination of environmental factors that may influence their populations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233023","programNote":"Cooperative Research Units Program","usgsCitation":"Droege, S., Irwin, E., Malpass, J., and Mawdsley, J., 2023, The bee lab: U.S. Geological Survey Fact Sheet 2023–3023, 2 p., https://doi.org/10.3133/fs20233023.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-152934","costCenters":[{"id":203,"text":"Cooperative Research Unit Atlanta","active":false,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":417785,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2023/3023/fs20233023.XML"},{"id":417784,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2023/3023/images/"},{"id":417783,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20233023/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 20230-3023"},{"id":417782,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2023/3023/fs20233023.pdf","text":"Report","size":"8.05 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 20230-3023"},{"id":417781,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2023/3023/coverthb.jpg"}],"contact":"Eastern Ecological Science Center
U.S. Geological Survey
12100 Beech Forest Road
Laurel, Maryland 20708
Species We Study: Pollinators
Contact Pubs Warehouse
The Delaware River Basin provides drinking water to 13.3 million people and supports endangered species, provides recreational opportunities, and is an essential resource to regional industries. The efforts of Federal and State governments have substantially improved overall water quality in the basin, which had been severely degraded prior to the mid-20th century. Recent trend analyses of water-quality data reveal negative and positive changes: increasing rates of salinization and improvements in nutrient conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233014","issn":"2327-6932","programNote":"Water Availability and Use Science Program","usgsCitation":"Shoda, M., Gain, E.G., and Murphy, J.C., 2023, River water quality in the Delaware River Basin—Concentrations and trends through 2018: U.S. Geological Survey Fact Sheet 2023–3014, 4 p., https://doi.org/10.3133/fs20233014.","productDescription":"Report: 4 p., 2 Data Releases","numberOfPages":"4","onlineOnly":"Y","ipdsId":"IP-133306","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":417697,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2023/3014/images/"},{"id":417699,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PX8LZO","text":"USGS—Multisource surface-water-quality data and U.S. Geological Survey streamgage match for the Delaware River Basin"},{"id":417695,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2023/3014/fs20233014.XML","linkFileType":{"id":8,"text":"xml"},"description":"FS 2023-3014 XML"},{"id":417694,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2023/3014/fs20233014.pdf","size":"5.94 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2023-3014"},{"id":417693,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2023/3014/coverthb.jpg"},{"id":417698,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KMWNJ5","text":"USGS—Water-quality trends for rivers and streams in the Delaware River Basin using Weighted Regressions on Time, Discharge, and Season (WRTDS) models, Seasonal Kendall Trend (SKT) tests, and multisource data, water year 1978–2018"},{"id":417700,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20225097","text":"USGS SIR 2022–5097"},{"id":417696,"rank":4,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20233014/full","linkFileType":{"id":5,"text":"html"},"description":"FS 2023-3014 HTML"},{"id":499664,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114765.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Delaware, New Jersey, New York, Pennsylvania","otherGeospatial":"Delaware River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.67732588371656,\n 38.47315961720821\n ],\n [\n -74.18382604899824,\n 38.47315961720821\n ],\n [\n -74.18382604899824,\n 42.626372631803235\n ],\n [\n -75.67732588371656,\n 42.626372631803235\n ],\n [\n -75.67732588371656,\n 38.47315961720821\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"For more information about this publication, contact
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Email: wausp-info@usgs.gov
For additional information, visit
https://www.usgs.gov/programs/national-water-quality-program
Title VII of Division N in the Consolidated Appropriations Act, 2023 (Public Law 117–328), was enacted on December 29, 2022. The U.S. Geological Survey received $41.04 million in disaster emergency supplemental funding for repairing and replacing facilities and equipment, collecting high-resolution elevation data in affected areas, and completing scientific assessments to support direct recovery and rebuilding decisions in the wake of declared disasters related to hurricanes and typhoons in 2022.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233025","usgsCitation":"Hinck, J.E., and Stachyra, J., 2023, Consolidated Appropriations Act, 2023—USGS disaster emergency recovery activities: U.S. Geological Survey Fact Sheet 2023–3025, 4 p., https://doi.org/10.3133/fs20233025.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"Y","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":41100,"text":"Coastal and Marine Hazards and Resources Program","active":true,"usgs":true}],"links":[{"id":417777,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2023/3025/coverthb.jpg"},{"id":417778,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2023/3025/fs20233025.pdf","text":"Report","size":"3.09 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2023–3025"},{"id":417779,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2023/3025/fs20233025.XML"}],"contact":"Associate Director, Natural Hazards Mission Area
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Using a geology-based assessment methodology, the U.S. Geological Survey estimated undiscovered, technically recoverable mean conventional resources of 7.3 billion barrels of oil and 463.7 trillion cubic feet of gas within the Barents Sea area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/fs20233019","programNote":"National and Global Petroleum Assessment","usgsCitation":"Schenk, C.J., Mercier, T.J., Woodall, C.A., Ellis, G.S., Finn, T.M., Le, P.A., Marra, K.R., Leathers-Miller, H.M., and Drake, R.M., II, 2023, Assessment of undiscovered conventional oil and gas resources of the Barents Sea area, 2021: U.S. Geological Survey Fact Sheet 2023–3019, 2 p., https://doi.org/10.3133/fs20233019.","productDescription":"Report: 2 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-133737","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":417394,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2023/3019/coverthb.jpg"},{"id":417396,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9L0W7ST","text":"USGS data release","linkHelpText":"USGS National and Global Oil and Gas Assessment Project—Barents Sea Area: Assessment Unit Boundaries, Assessment Input Data, and Fact Sheet Data Tables"},{"id":417395,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2023/3019/fs20233019.pdf","text":"Report","size":"1.33 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2023-3019"},{"id":417518,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/fs20233019/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2023-3019"},{"id":417517,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2023/3019/fs20233019.xml"},{"id":417516,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2023/3019/images"}],"country":"Norway, Russia","otherGeospatial":"Barents Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 4.550542070460438,\n 79.47846463932942\n ],\n [\n 4.550542070460438,\n 68.68292262514223\n ],\n [\n 70.64781874733308,\n 68.68292262514223\n ],\n [\n 70.64781874733308,\n 79.47846463932942\n ],\n [\n 4.550542070460438,\n 79.47846463932942\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Energy Resources Science Center
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Director, National Geospatial Program
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Reston, VA 20192
Email: 3DEP@usgs.gov
","tableOfContents":"Reliable flood-frequency estimates are important for hydraulic structure design and floodplain management in Georgia, South Carolina, and North Carolina. Annual peak streamflows (hereafter, referred to as peak flows) measured at 965 U.S. Geological Survey streamgages were used to compute flood-frequency estimates with annual exceedance probabilities (AEPs) of 50, 20, 10, 4, 2, 1, 0.5, and 0.2 percent. These AEPs correspond to flood-recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years, respectively. A subset of these streamgages (801) were used to develop equations to predict the AEP flood flows at ungaged stream locations. This study was completed by the USGS in cooperation with the Georgia, South Carolina, and North Carolina Departments of Transportation and the North Carolina Department of Crime Control and Public Safety, and the results are summarized in this fact sheet. The complete results and the supporting data are presented in the companion scientific investigations report and data release.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233011","collaboration":"Prepared in cooperation with the Georgia Department of Transportation (Engineering Division, Office of Bridge Design and Maintenance), South Carolina Department of Transportation (Hydraulic Design Support Office), North Carolina Department of Transportation (Division of Highways, Hydraulics Unit), and the North Carolina Department of Crime Control and Public Safety (Division of Emergency Management, Floodplain Mapping Program)","usgsCitation":"Feaster, T.D., Gotvald, A.J., Musser, J.W., Weaver, J.C., and Kolb, K.R., 2023, Magnitude and frequency of floods for rural streams in Georgia, South Carolina, and North Carolina, 2017—Summary: U.S. Geological Survey Fact Sheet 2023–3011, 6 p., https://doi.org/10.3133/fs20233011.","productDescription":"Report: 6 p.; 2 Data 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\"}}]}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
1770 Corporate Drive, Suite 500
Norcross, GA 30093
The U.S. Geological Survey (USGS) serves a principal role in conducting wildlife disease outbreak investigations, surveillance, and ecological research to support management of diseases in free-ranging native wildlife. Approximately 60 percent of emerging human infectious diseases such as COVID-19, are zoonotic, meaning they are transmitted between animals and humans and 70 percent of these diseases originate in wildlife (Jones and others, 2008). The effects of emerging wildlife diseases are global and profound, often resulting in economic and agricultural impacts, declines in wildlife populations, and ecological disturbances.
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U.S. Geological Survey
12201 Sunrise Valley Drive
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Biological Threats and Invasive Species Research Program
Contact Pubs Warehouse
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Box 25046, Mail Stop 302
Denver, CO 80225
The U.S. Geological Survey and The Nature Conservancy previously collaborated to evaluate changes and trends in the concentrations and flux of nutrients (total phosphorus, as phosphorus; nitrate plus nitrite, as nitrogen; and total Kjeldahl nitrogen, as nitrogen) and total suspended solids (TSS) at three study gages located on the upper White River at Muncie, near Nora, and near Centerton, Indiana. That work is extended and updated using 3 additional years of data (through 2020) and newer estimation methods. In addition, information is provided about climatic and anthropogenic factors that could influence the concentrations and fluxes of nutrients and TSS in the upper White River Basin.
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6460 Busch Blvd, Suite 100
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U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
The Blackfeet Nation seeks an increased scientific understanding of the water resources within the Blackfeet Indian Reservation of northwestern Montana. Hydrologic information is needed to better inform water-management decisions as the Blackfeet Nation implements the Blackfeet Water Rights Compact, initiates new water-use projects, and improves the Blackfeet Irrigation Project.
The U.S. Geological Survey and the Blackfeet Water Department began cooperating in 2019 to design and implement a hydrologic data-collection program. The program is being implemented in phases that include discrete and continuous discharge measurements of streams and canals, installation, operation of streamgages, groundwater-level monitoring, and database management. Data collected will be used to characterize current hydrologic conditions on the reservation and will act as a baseline for comparison as Blackfeet Nation water projects are implemented.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233012","collaboration":"Prepared in cooperation with the Blackfeet Water Department","usgsCitation":"Lawlor, S.M., Caldwell, R.R., Bartos, T.T., and Price, B., 2023, U.S. Geological Survey and Blackfeet Water Department Hydrologic Assessment of the Blackfeet Indian Reservation, Montana: U.S. Geological Survey Fact Sheet 2023–3012, 4 p., https://doi.org/10.3133/fs20233012.","productDescription":"Report: 4 p.; Dataset","numberOfPages":"4","onlineOnly":"Y","ipdsId":"IP-141394","costCenters":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"links":[{"id":414914,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2023/3012/coverthb2.jpg"},{"id":414918,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2023/3012/images"},{"id":414919,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":415102,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/fs20233012/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":499662,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114650.htm","linkFileType":{"id":5,"text":"html"}},{"id":414917,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2023/3012/fs20233012.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":414916,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2023/3012/fs20233012.pdf","text":"Report","size":"2.78 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2023–3012"}],"country":"United States","state":"Montana","otherGeospatial":"Blackfeet Indian Reservation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -113.92382337824559,\n 49.00115786005651\n ],\n [\n -113.92382337824559,\n 47.9492225969058\n ],\n [\n -111.72750252269083,\n 47.9492225969058\n ],\n [\n -111.72750252269083,\n 49.00115786005651\n ],\n [\n -113.92382337824559,\n 49.00115786005651\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
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Nuisance levels of benthic algae are becoming increasingly common in surface waters of the western United States and can compromise aesthetic quality, limit recreational activities, block water infrastructure, and negatively affect aquatic life. In cooperation with the White River and Douglas Creek Conservation Districts, the Colorado River Basin Salinity Control Forum, and the Colorado River Water Conservation District, the U.S. Geological Survey studied physical, chemical, and biological factors potentially controlling the occurrence of benthic algae in the upper White River Basin, Colorado, from 2018 through 2021. Multiple approaches were used to assess nutrients and physical conditions in the upper White River Basin. A linear mixed-effects model was used to evaluate the relative effect of different factors on algal biomass across water-quality sites.
The frequency and severity of algal blooms in the upper White River Basin may be affected by long-term changes in nutrient availability and streamflow, specifically changes in the timing and magnitude of high and low streamflow. The effects of large peak streamflow, including movement of the streambed, may be the dominant control on the occurrence of algal blooms through years. Large, late, and long-lasting peak streamflow may limit algal blooms during the same year and into subsequent years. Without streambed disturbance, other factors such as nutrients and water temperature may have a larger effect on algal biomass.
Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
Denver, CO 80225
Des Moines Water Works (DMWW) is one of the largest water providers in Iowa and as population growth continues, demand for drinking water is increasing. DMWW uses groundwater and surface water as raw water sources to supply the City of Des Moines and surrounding communities. In response to current and future demands, DMWW is in need of a thorough understanding of local groundwater resources, specifically the Des Moines River alluvial aquifer. The Des Moines River alluvial aquifer is hydraulically connected to the Des Moines River and consists of alluvial deposits and glacial outwash sands and gravels. To ensure a sustainable groundwater supply, additional information to better understand and manage groundwater availability within the Des Moines River alluvial aquifer would be beneficial. Beginning in 2018, DMWW partnered with the U.S. Geological Survey to construct a groundwater-flow model to increase understanding of the hydrologic system in the Des Moines area. The model hydrogeologic framework will be enhanced by using multiple geophysical methods of data collection in the Des Moines River, Beaver Creek, and the Des Moines River alluvial aquifer that could provide a better understanding of the geology in the model area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233006","usgsCitation":"Thomas, J.C., Spring, M.A., Gruhn, L.R., and Bristow, E.L., 2023, Application of geophysical methods to enhance aquifer characterization and groundwater-flow model development, Des Moines River alluvial aquifer, Des Moines, Iowa, 2022: U.S. Geological Survey Fact Sheet 2023–3006, 4 p., https://doi.org/10.3133/fs20233006.","productDescription":"Report: 4 p.; 2 Data Releases; Dataset","numberOfPages":"4","onlineOnly":"Y","ipdsId":"IP-136349","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":414104,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2023/3006/fs20233006.pdf","text":"Report","size":"2.95 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2023–3006"},{"id":414105,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2023/3006/fs20233006.XML","description":"FS 2023–3006"},{"id":414103,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2023/3006/coverthb2.jpg"},{"id":414112,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://www.usgs.gov/national-hydrography/access-national-hydrography-products","text":"USGS dataset","linkHelpText":"—National Hydrography Dataset— USGS National Hydrography Dataset Best Resolution for Hydrologic Unit 4 – 2001"},{"id":414109,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9B9AVKJ","text":"USGS data release","linkHelpText":"Geophysical data collected in the Des Moines River, Beaver Creek, and the Des Moines River floodplain, Des Moines, Iowa, 2018"},{"id":414110,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9F3CKLC","text":"USGS data release","linkHelpText":"MODFLOW-NWT model used to simulate groundwater levels in the Des Moines River alluvial aquifer near Des Moines, Iowa"},{"id":499566,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114475.htm","linkFileType":{"id":5,"text":"html"}},{"id":414138,"rank":8,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/fs20233006/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":414113,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2023/3006/images"}],"country":"United States","state":"Iowa","city":"Des Moines","otherGeospatial":"Des Moines River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.69895287733283,\n 41.66093681949087\n ],\n [\n -93.69895287733283,\n 41.52388190639587\n ],\n [\n -93.51363729010005,\n 41.52388190639587\n ],\n [\n -93.51363729010005,\n 41.66093681949087\n ],\n [\n -93.69895287733283,\n 41.66093681949087\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
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As the United States transitions away from fossil fuels, its economy will rely on more renewable energy. Because current renewable energy sources sometimes produce variable power supplies, it is important to store energy for use when power supply drops below power demand. Battery storage is one method to store power. However, geologic (underground) energy storage may be able to retain vastly greater quantities of energy over much longer durations compared to typical battery storage. Geologic energy storage also has high flexibility; many different types of materials can be used to store chemical, thermal, or mechanical energy in a variety of underground settings. The U.S. Geological Survey (USGS) has the capability to research and assess possible domestic geologic energy storage resources to help prepare the United States for the future of renewable energy.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223082","programNote":"Energy Resources Program","usgsCitation":"Buursink, M.L., Anderson, S.T., Brennan, S.T., Burns, E.R., Freeman, P.A., Gallotti, J.S., Lohr, C.D., Merrill, M. D., Morrissey, E.A., Plampin, M.R., and Warwick, P.D., 2023, Geologic energy storage: U.S. Geological Survey Fact Sheet 2022–3082, 4 p., https://doi.org/10.3133/fs20223082.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-137766","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":413716,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3082/coverthb2.jpg"},{"id":413720,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3082/images/"},{"id":413719,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3082/fs20223082.XML"},{"id":413711,"rank":6,"type":{"id":18,"text":"Project Site"},"url":"https://www.usgs.gov/centers/geology-energy-and-minerals-science-center/science/geologic-energy-storage","text":"Geologic Energy Storage","linkFileType":{"id":5,"text":"html"}},{"id":413717,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3082/fs20223082.pdf","text":"Report","size":"6.57 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022-3082"},{"id":413718,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223082/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2022-3082"}],"contact":"Geology, Energy & Minerals Science Center
U.S. Geological Survey
Mail Stop 954
12201 Sunrise Valley Drive
Reston, VA 20192
Email: AskEnergyProgram@usgs.gov
Floods are one of the most frequent and expensive natural disasters that occur across the United States. Rapid, high-water events that occur in local areas—flash floods—are especially difficult for emergency managers to predict and provide advance warning to the public, and insufficient data can hamper postflood recovery efforts. Central Texas is hilly, and it is known as a “flash flood alley” because of its high-intensity rains, shallow soils, and steep terrain, all of which combined can result in loss of life and property damage. For example, the flash flood event during July 2002 claimed 12 lives in central Texas, including 1 in the town of Utopia, which is on the east bank of the Sabinal River in a flash-flood-prone area along the Balcones Escarpment. During the flood event, the peak discharge recorded on July 5, 2002, at U.S. Geological Survey (USGS) streamgage 08198000 Sabinal River near Sabinal, Tex. (hereinafter referred to as the “Sabinal gage”), was 108,000 cubic feet per second (corresponding to a stream stage [also called gage height] of 33.74 feet). To put the 2002 flood into context, during a typical year the median daily discharge in the Sabinal River at the Sabinal gage is only about 23 cubic feet per second. In 2021, the USGS, in cooperation with the Bandera County River Authority and Groundwater District and the Texas Water Development Board, developed a flood warning toolset for the Sabinal River near Utopia. This study builds on earlier USGS flood work on the Medina River in Bandera County. The newly developed toolset consists of a newly installed USGS streamgage to collect continuous stream stage data (streamgage 08197970 Sabinal River at Utopia, Tex.; hereinafter referred to as the “Utopia gage”) 13 miles upstream from the Sabinal gage, a hydraulic model developed for the Sabinal River near Utopia, and an online library of digital flood-inundation maps referenced to the stream stage at the Utopia gage.
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U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
Overview
Creation of Flood Warning Toolset
References Cited
The U.S. Geological Survey (USGS) launched the Earth Mapping Resources Initiative (Earth MRI) to modernize the surface and subsurface geologic mapping of the United States, with a focus on identifying areas that may have the potential to contain critical mineral resources. EarthMRI can inform strategies to ensure secure and reliable domestic critical mineral supplies for the United States as mandated by Executive Order 13817 and the Infrastructure and Jobs Act of 2021 (Public Law 117–58, 135 Stat. 529). Earth MRI is a collaborative effort between the USGS and the State geological surveys as represented by the Association of American State Geologists to identify, prioritize, and acquire new geoscience data for geographic areas, or focus areas, across the Nation that have potential to host critical mineral resources. Mapping of focus areas was based on a framework of mineral systems and their associated mineral deposit types that could possibly host critical minerals. Using readily available geologic, geophysical, geochemical, and mineral deposit data, teams of USGS scientists worked with representatives of State geological surveys in a series of workshops to outline focus areas that contain evidence of key features for one or more mineral systems. These areas can be used to guide future efforts to collect new geologic, geophysical, geochemical, and topographic data that focus on critical minerals through Earth MRI.
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Mineral Resources Program
U.S. Geological Survey
913 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
Email: minerals@usgs.gov
“The Bay Connects us, the Bay reflects us” writes Tom Horton in the book “Turning the Tide—Saving the Chesapeake Bay”. The Chesapeake Bay watershed contains the largest estuary in the United States. The watershed stretches north to Cooperstown, New York, south to Lynchburg and Virginia Beach, Virginia, west to Pendleton County, West Virginia, and east to Seaford, Delaware, and Scranton, Pennsylvania. The watershed is more than 64,000 square miles that contain 150 major rivers and streams, hereafter referred to collectively as streams, that total more than 100,000 miles in length. The watershed contains thousands of smaller creeks and tributaries, large numbers of plants and animals, and, in 2020, more than 18.4 million people. As changes occur in population, land use, and climate within the watershed, so too do the diversity and health of the Bay's ecosystems.
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U.S. Geological Survey
1730 East Parham Road
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The U.S. Geological Survey (USGS) is initiating a study approach focused on building cross-disciplinary connections to weave together the scientific knowledge related to drought conditions and effects in the Colorado River Basin. The basin is experiencing the worst drought in recorded history, posing unprecedented new challenges in the basin and in areas relying on water from the basin. Science is continually advancing, and there is an increasing need to interpret the connections between studies to predict the effects of drought and other changes affecting the Earth system. The USGS primarily works in independent disciplines and science centers to provide cutting-edge science to advance research and science applications worldwide. The complexity and volume of research that has been conducted related to drought in the Colorado River Basin is difficult to quantify. To complicate matters, studies, models, and datasets are cataloged and may be available in multiple, unrelated locations, across various internal systems, data repositories, and local offices. Furthermore, there are limited interactions and interfaces between scientists and partners working in different science disciplines; in many cases, individual science products require stakeholders to integrate complex interdisciplinary data across geographical and topical extents. The diverse array of interdisciplinary science and science products produced by the USGS highlights the need for a wide ranging collaborative support structure.
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Box 25046, MS 911
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The Rangeland Condition Monitoring Assessment and Projection (RCMAP) project quantifies the percentage cover of rangeland components across the western United States using Landsat imagery from 1985 to 2021. The RCMAP product suite consists of nine fractional components: annual herbaceous, bare ground, herbaceous, litter, nonsagebrush shrub, perennial herbaceous, sagebrush, shrub, and tree, in addition to the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. First, we have trained time-series predictions directly from 331 high-resolution sites collected from 2013 to 2018 and additional field data; for example, Bureau of Land Management Assessment, Inventory, and Monitoring instead of using the 2016 “base” map as an intermediary. This removes one level of model error and allows the direct association of high-resolution derived training data to the corresponding year of Landsat imagery. Neural network models have replaced Cubist models as our classifier. Continuous Change Detection and Classification synthetic Landsat images were obtained for six monthly periods for each region and were added as predictors. These data enhance the phenologic detail of imagery, improving discrimination among components. Postprocessing has been improved with updated fire recovery equations stratified by ecosystem resistance and resilience classes. Additionally, postprocessing has been enhanced through a revised noise detection model, based on third order polynomial models for each component and each pixel. These data can be used to answer critical questions regarding the effect of climate change and the suitability of management practices. Component products can be downloaded from the Multi-Resolution Land Characteristics Consortium website at https://www.mrlc.gov/data.
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47914 252nd Street
Sioux Falls, SD 57198
The U.S. Geological Survey (USGS) National Civil Applications Center (NCAC) analyzes remote-sensing data from the Intelligence Community (IC) and the U.S. Department of Defense (DOD) to support public safety missions and to study land-surface and environmental changes. The NCAC provides remotely sensed images to USGS scientists and other civilian Federal agencies; the images come from intelligence and military sensors, referred to as U.S. National Imagery Systems (USNIS), and unclassified commercial satellite data purchased by the DOD. Often these data are referred to as Geospatial Intelligence (GEOINT), which is defined in U.S. Code, title 10, section 467 as “the exploitation and analysis of imagery and geospatial information to describe, assess, and visually depict physical features and geographically referenced activities on or about the earth. Geospatial intelligence consists of imagery, imagery intelligence, and geospatial information.” The NCAC also provides the secretariat with staff and manages the U.S. interagency Civil Applications Committee (CAC), which oversees and facilitates the appropriate civilian uses of overhead remote-sensing technology and data collected by military and intelligence systems and commercial sources. Funded by the USGS National Land Imaging Program, the NCAC operates facilities in Reston, Virginia, and Lakewood, Colorado.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223085","programNote":"National Land Imaging Program","usgsCitation":"Young, P.M., 2023, National Civil Applications Center (ver. 1.1, March 2023): U.S. Geological Survey Fact Sheet 2022–3085, 4 p., https://doi.org/10.3133/fs20223085.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-129954","costCenters":[{"id":36171,"text":"National Civil Applications Center","active":true,"usgs":true}],"links":[{"id":412228,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3085/fs20223085.pdf","text":"Report","size":"1.92 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022-3085"},{"id":412227,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3085/coverthb3.jpg"},{"id":414012,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2022/3085/versionHist.txt","size":"653 B"}],"edition":"Version 1.0: January 2023; Version 1.1: March 2023","contact":"Director, National Civil Applications Center
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 562
Reston, VA 20192
Email: cac@usgs.gov
Detailed geologic maps are the basis of most earth science investigations and can be used for natural hazard mitigation, resource identification and exploration, infrastructure planning, and more. As a part of the U.S. Geological Survey (USGS) congressionally mandated National Cooperative Geologic Mapping Program (NCGMP), the EDMAP program (referred to as EDMAP) is a partnership between the USGS and participating colleges and universities that provides mentorship and training opportunities for earth science students nationwide. EDMAP supports graduate students and upper-level undergraduate students—under the guidance of a faculty member who serves as a “principal investigator”—for the training of students to become geologic mappers. Between 1996 and 2021, EDMAP funded geologic mapping educational experiences and training for 1,373 students at more than 170 universities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233002","programNote":"National Cooperative Geologic Mapping Program","usgsCitation":"Shelton, J.L., Swezey, C.S., and Marketti, M., 2023, The EDMAP Program: Training the next generation of geologic mappers: U.S. Geological Survey Fact Sheet 2023–3002, 4 p., https://doi.org/10.3133/fs20233002.","productDescription":"4 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-131886","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":64806,"text":"National Cooperative Geologic Mapping","active":true,"usgs":true}],"links":[{"id":411786,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20233002/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2023-3002"},{"id":411784,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2023/3002/coverthb.jpg"},{"id":411787,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2023/3002/fs20233002.XML"},{"id":411788,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2023/3002/images/"},{"id":411785,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2023/3002/fs20233002.pdf","text":"Report","size":"1.92 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2023-3002"}],"contact":"National Cooperative Geologic Mapping Program
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 908
Reston, VA 20192
Unconventional oil and gas development that uses horizontal drilling and hydraulic fracturing is rapidly changing the landscape and exponentially increasing oil production within the Williston Basin, especially in North Dakota and eastern Montana. The activities associated with unconventional oil and gas development are complex and wide reaching and include, in part, road and well-pad construction, leaks from pits or tanks, chemical spills, discharge of wastewater, drilling before casing installation, leaks during or after hydraulic fracturing, failed casing seals, pipeline breaks, abandoned wells, deep-well disposal of flowback or produced wastewater, and induced subsurface migration pathways that can potentially adversely affect the environmental resources within the Williston Basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223088","usgsCitation":"Delzer, G.C., and Post van der Burg, M., 2023, Research needs identified for potential effects of energy development activities on environmental resources of the Williston Basin, United States: U.S. Geological Survey Fact Sheet 2022–3088, 6 p., https://doi.org/10.3133/fs20223088.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"Y","ipdsId":"IP-142397","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":499561,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114229.htm","linkFileType":{"id":5,"text":"html"}},{"id":411643,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3088/images"},{"id":411641,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3088/fs20223088.pdf","text":"Report","size":"1.84 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022–3088"},{"id":411642,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3088/fs20223088.XML"},{"id":411640,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3088/coverthb.jpg"},{"id":411720,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223088/full","text":"Report","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Montana, North Dakota, South Dakota","otherGeospatial":"Williston Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -101.85992433948707,\n 44.748962924301054\n ],\n [\n -100.27178237770465,\n 45.62378897009327\n ],\n [\n -99.72386467913583,\n 47.01666497234379\n ],\n [\n -98.92422494544115,\n 48.99070414841606\n ],\n [\n -106.85334014872393,\n 49.00199336720374\n ],\n [\n -106.20557284548465,\n 47.60278245148987\n ],\n [\n -105.42666681945474,\n 46.15028300648845\n ],\n [\n -104.32046634105996,\n 45.350667945249626\n ],\n [\n -103.35976723012234,\n 45.170260895064786\n ],\n [\n -102.7400085664068,\n 44.590251713951545\n ],\n [\n -101.96303401243244,\n 44.60899148746665\n ],\n [\n -101.85992433948707,\n 44.748962924301054\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702
Using a geology-based assessment methodology, the U.S. Geological Survey estimated undiscovered, technically recoverable mean resources of 321 million barrels of shale oil and 435 billion cubic feet of shale gas in the Lower Saxony Basin, Germany.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223081","usgsCitation":"Schenk, C.J., Mercier, T.J., Woodall, C.A., Finn, T.M., Marra, K.R., Leathers-Miller, H.M., Le, P.A., Drake, R.M., II, and Ellis, G.S., 2022, Assessment of continuous oil and gas resources in the Lower Saxony Basin of Germany, 2020: U.S. Geological Survey Fact Sheet 2022−3081, 2 p., https://doi.org/10.3133/fs20223081.","productDescription":"Report: 2 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-118094","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":411300,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3081/fs20223081.pdf","text":"Report","size":"3.32 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022-3081"},{"id":411301,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9X92PDD","text":"USGS data release","linkHelpText":"USGS National and Global Oil and Gas Assessment Project-Lower Saxony Basin: Assessment Unit Boundaries, Assessment Input Data, and Fact Sheet Data Tables"},{"id":411299,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3081/coverthb.jpg"}],"country":"Germany","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 6.5034584138108755,\n 54.966657240625636\n ],\n [\n 6.5034584138108755,\n 52.042035677810105\n ],\n [\n 11.774657339550146,\n 52.042035677810105\n ],\n [\n 11.774657339550146,\n 54.966657240625636\n ],\n [\n 6.5034584138108755,\n 54.966657240625636\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
The U.S. Geological Survey (USGS) has continuously monitored real-time water quality and suspended-sediment transport in San Francisco Bay (the Bay) since 1989 as part of a multi-agency effort (see “Acknowledgments” section) to address estuary management, water supply, and ecological concerns. The San Francisco Bay area is home to millions of people and biologically diverse marine and terrestrial flora and fauna. Freshwater mixes with saltwater in the Bay and is subject to riverine influences (floods, droughts, managed reservoir releases, and freshwater diversions) and marine influences (tides, waves, and effects of saltwater).
Water temperature, salinity, suspended-sediment concentration (SSC), and turbidity, are used by State and Federal resources managers and are monitored at eight key locations throughout the Bay (fig. 1). Water temperature and salinity affect the density of water, which controls gravity-driven circulation patterns and stratification in the water column. Salinity indicates the relative mixing of fresh and ocean waters in the Bay and is derived from specific conductance measurements. Turbidity is a measure of light scattered from suspended particles in the water that is used to estimate suspended-sediment concentration. Suspended-sediment concentrations also are directly measured through depth-integrated water sampling.
Suspended sediment affects Bay water quality in multiple ways. Suspended sediment affects phytoplankton growth by attenuating sunlight in the water column. Suspended sediment deposition on tidal marshes and intertidal mudflats helps to restore and sustain these habitats as sea level rises. Settling of suspended sediment in ports and shipping channels creates the need for more dredging. In addition, suspended sediment often carries adsorbed contaminants as it is transported in the water column, which affects the distributions and concentrations of adsorbed contaminants in the environment. Excessive concentrations of sediment-adsorbed contaminants in deposits on the bottom of the Bay can affect ecosystem health.
External factors, such as tidal currents, waves, and wind can also affect water quality in the Bay. Tidal currents in the Bay change direction four times daily, and wind direction and intensity typically fluctuate on a daily cycle. Consequently, salinity, water temperature, and suspended-sediment concentration differ spatially and temporally throughout the Bay. Therefore, high-frequency measurements at multiple locations are needed to monitor these changes. Data collected at eight stations throughout the Bay are transmitted in near real-time using cellular telemetry and posted to the USGS National Water Information System (NWIS; https://waterdata.usgs.gov/usa/nwis). The purposes of this fact sheet are to (1) provide information about the USGS San Francisco Bay water-quality monitoring network; (2) highlight various applications in which these data can be used; and (3) provide internet links to access the resulting continuous water-quality data collected by the USGS.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223087","usgsCitation":"Palm, D.L., Einhell, D.C., Davila Olivera, S.M., 2022, Continuous water-quality and suspended-sediment transport monitoring in San Francisco Bay, California, water years 2020–21: U.S. Geological Survey Fact Sheet 2022–3087, 4 p., https://doi.org/10.3133/fs20223087.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-138273","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":411162,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3087/coverthb.jpg"},{"id":411163,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3087/fs20223087.pdf","text":"Report","size":"2.03 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022-3087"},{"id":411164,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/fs20223087/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2022-3087"},{"id":411165,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3087/images/"},{"id":411166,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3087/fs20223087.XML"},{"id":501514,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114175.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.7032269260365,\n 38.2575437837898\n ],\n [\n -122.64478458511374,\n 38.2575437837898\n ],\n [\n -122.64478458511374,\n 37.373118003359465\n ],\n [\n -121.7032269260365,\n 37.373118003359465\n ],\n [\n -121.7032269260365,\n 38.2575437837898\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, CA 95819
The Landsat Collection 2 Level-3 Fractional Snow Covered Area science product indicates the percentage of pixels covered by snow for Landsat 4–9 imagery. Landsat’s spatial resolution offers the capability to map snow cover patterns across topographically complex mountainous regions. Snow cover is spatially and temporally variable and is often concentrated in remote or inaccessible land regions, making spaceborne remote sensing the most feasible approach to measure and monitor snow cover change.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223086","usgsCitation":"U.S. Geological Survey, 2022, Landsat Collection 2 Level-3 Fractional Snow Covered Area science product (ver. 1.1, June 2023): U.S. Geological Survey Fact Sheet 2022–3086, 2 p., https://doi.org/10.3133/fs20223086.","productDescription":"Report: 2 p.; Dataset","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-139626","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":418251,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3086/fs20223086.pdf","text":"Report","size":"1.03 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022–3086"},{"id":410799,"rank":1,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS database","linkHelpText":"—EarthExplorer"},{"id":418250,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3086/coverthb2.jpg"},{"id":418252,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2022/3086/versionHist.txt","size":"1 kB","linkFileType":{"id":2,"text":"txt"}}],"edition":"Version 1.0: December 20, 2022; Version 1.1: June 21, 2023","contact":"Customer Services, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198