Fluoride is a naturally occurring element in groundwater that supports bone and dental health at low concentrations but can cause health problems at elevated concentrations in drinking water. This study investigates spatial and temporal trends for fluoride concentrations in untreated groundwater from over 20,000 public supply wells in California. The presence of a significant temporal trend in fluoride concentrations in a well was assessed using the Mann-Kendall test and a spatial-weighting approach was used to identify the areal extent of the groundwater resources with significant trends. Less than 2% of the groundwater resources used for public supply in the state have concentrations above the California maximum contaminant level of 2 mg/L. Approximately 14% of the groundwater resource used for public supply show a significant recent trend (2000–2019), with decreasing trends occurring in 10% of the resource. Potential drivers for trends were evaluated in two of the areas in southern California with the greatest prevalence of trends but distinct climate and hydrogeological characteristics. Aquifers in the Mojave and Whitewater River watersheds, located in the desert region, and the Los Angeles Basin, located on the coast, have been replenished with imported and treated water over the last half century to maintain sustainable groundwater levels. The recharge of imported water with different chemistries has altered the geochemical conditions in the aquifers, driving changes in fluoride concentrations.
Ecosystems are dynamic systems with complex responses to environmental variation. In response to pervasive stressors of changing climate and disturbance regimes, many ecosystems are realigning rapidly across spatial scales, in many cases moving outside of their observed historical range of variation into alternative ecological states. In some cases, these new states are transitory and represent successional stages that may ultimately revert to the pre-disturbance condition; in other cases, alternative states are persistent and potentially self-reinforcing, especially under conditions of altered climate, disturbance regimes, and influences of non-native species. These reorganized states may appear novel, but reorganization is a characteristic ecosystem response to environmental variation that has been expressed and documented throughout the paleoecological record. Resilience, the ability of an ecosystem to recover or adapt following disturbance, is an emergent property that results from the expression of multiple mechanisms operating across levels of organism, population, and community. We outline a unifying framework of ecological resilience based on ecological mechanisms that lead to outcomes of persistence, recovery, and reorganization. Persistence is the ability of individuals to tolerate exposure to environmental stress, disturbance, or competitive interactions. As a direct expression of life history evolution and adaptation to environmental variation and stress, persistence is manifested most directly in survivorship and continued growth and reproduction of established individuals. When persistence has been overcome (e.g., following mortality from stress, disturbance, or both), populations must recover by reproduction. Recovery requires the establishment of new individuals from seed or other propagules following dispersal from the parent plant. When recovery fails to re-establish the pre-disturbance community, the ecosystem will assemble into a new state. Reorganization occurs along a gradient of magnitude, from changes in the relative dominance of species present in a community, to individual species replacements within an essentially intact community, to complete species turnover and shift to dominance by plants of different functional types, e.g. transition from forest to shrub or grass dominance. When this latter outcome is persistent and involves reinforcing mechanisms, the resulting state represents a vegetation type conversion (VTC), which in this framework represents an end member of reorganization processes. We explore reorganization in greater detail as this phase is increasingly observed but the least understood of the resilience responses. This resilience framework provides a direct and actionable basis for ecosystem management in a rapidly changing world, by targeting specific components of ecological response and managing for sustainable change.
Despite several secretive marsh bird (SMB) species being listed as critically imperiled throughout the mid-continent of North America, limited information on SMB distribution and habitat use within primary migratory corridors results in uncertainty on contributions of wetlands in mid-latitude states toward their annual cycle needs. Our objectives were to quantify temporal patterns of SMB wetland occupancy during spring migration at a mid-latitude state and evaluate the relationships between SMB colonization probability and water-level management practices, and the resulting habitat conditions during spring migration. We conducted a 2-year, dynamic occupancy study (2013–2014) that included 6 rounds of repeated call-back surveys to detect the presence of 5 SMB species (i.e., Virginia rail [Rallus limicola], sora [Porzana carolina], king rail [R. elegans], least bittern [Ixobrychus exilis], and American bittern [Botaurus lentiginosus]) during spring (Apr–Jun) on 107 wetlands across 8 conservation areas and 4 national wildlife refuges throughout Missouri, USA. We detected sora most frequently, followed by least bittern, American bittern, Virginia rail, and king rail. Coefficient estimates indicated colonization probability for all species was positively associated with emergent vegetation cover and negatively associated with amount of open water. Open water was the only variable in the best supported model explaining American bittern site colonization, to which they were negatively associated. Virginia rail colonization had a strong positive association with vegetation height, whereas least bittern and sora site colonization were influenced positively by water depth and agriculture, respectively. Based on the habitat associations within and among SMB species identified in this study, wetland managers can tailor management strategies to optimize spring migration habitat for single- or multi-species objectives.
On August 8, 2020 northwest North Carolina experienced a Mw 5.1 earthquake that caused damage to buildings and roads in the city of Sparta. A regional centroid moment tensor solution shows the earthquake was the result of slip on a reverse fault with a minor strike-slip component. InSAR data, from the Japan Aerospace Exploration Agency’s ALOS2 satellite, reveal a deformation field that is more complex than expected from a single reverse fault earthquake. The data also reveal an apparent fault rupture at the Earth’s surface that caused damage to local roads. Modeling of the InSAR deformation field indicates the fault rupture is associated with a very shallow normal faulting event with an equivalent Mw of about 5.1, that overprinted the reverse fault deformation field and possibly occurred aseismically.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0320210044","usgsCitation":"Wicks, C., and Chiu, J., 2022, Surface rupture on a secondary fault associated with the August 8, 2020, Mw 5.1 Sparta North Carolina Earthquake: The Seismic Record, v. 2, no. 1, p. 59-67, https://doi.org/10.1785/0320210044.","productDescription":"9 p.","startPage":"59","endPage":"67","ipdsId":"IP-135349","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":489955,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1785/0320210044","text":"Publisher Index Page"},{"id":482384,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","city":"Sparta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.17200973627948,\n 36.532296644809335\n ],\n [\n -81.17200973627948,\n 36.456479302893044\n ],\n [\n -81.0462093796463,\n 36.456479302893044\n ],\n [\n -81.0462093796463,\n 36.532296644809335\n ],\n [\n -81.17200973627948,\n 36.532296644809335\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"2","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-03-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Wicks, Charles 0000-0002-0809-1328","orcid":"https://orcid.org/0000-0002-0809-1328","contributorId":9023,"corporation":false,"usgs":true,"family":"Wicks","given":"Charles","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":928303,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chiu, Jer-Ming","contributorId":351278,"corporation":false,"usgs":false,"family":"Chiu","given":"Jer-Ming","affiliations":[{"id":83945,"text":"Univ. of Memphis, CERI","active":true,"usgs":false}],"preferred":false,"id":928304,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70229681,"text":"sir20225019 - 2022 - Bedload and suspended-sediment transport in lower Vance Creek, western Washington, water years 2019–20","interactions":[],"lastModifiedDate":"2026-04-09T16:38:11.743054","indexId":"sir20225019","displayToPublicDate":"2022-03-23T15:38:15","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5019","displayTitle":"Bedload and Suspended-Sediment Transport in Lower Vance Creek, Western Washington, Water Years 2019–20","title":"Bedload and suspended-sediment transport in lower Vance Creek, western Washington, water years 2019–20","docAbstract":"Vance Creek drains a 24 square mile area of the Olympic Mountains in western Washington. The lower 4 miles of the creek often go dry in discontinuous patches during the summer, limiting salmon rearing success. To better understand sediment transport dynamics in the creek and aid in potential restoration design, bedload and suspended-sediment concentration samples were collected for water years 2019–20 at a site about 2 miles upstream from the creek’s confluence with the South Fork Skokomish River.
Fifty bedload samples and 7 suspended-sediment concentration samples were collected over 7 sampling days. These samples were used to develop rating curves relating bedload flux or suspended-sediment concentration to discharge. Mean annual bedload flux was estimated to be 12,200 ± 2,300 tons per year for water years 1930–2020 period of record, based on application of the derived bedload rating curve to an extrapolated daily discharge record. The mean annual suspended-sediment load over the same period was estimated to be 9,000 tons per year with large, but unquantified, uncertainty. Bedload material was predominantly gravel from 0.08 to 2.5 inches (2 to 64 millimeters) in diameter. At the highest sampled discharges, approximately equivalent to a 50 percent annual exceedance probability (2-year peak-flow event), the bedload grain-size distribution was similar to that of the local channel bed. Bedload grain-size distributions generally coarsened as discharge increased. The suspended-sediment load was consistently one-half sand and one-half silt and clay, regardless of discharge. Bedload constituted about 60 percent of the total sediment flux (bedload plus suspended load). This is near the upper limit of values observed in a global compilation of long-term load partitioning data.
Sediment transport at the Vance Creek sampling site was compared with sediment-transport data from five other watersheds in the region. To facilitate comparisons, mean annual loads were divided by mean annual runoff volume to obtain an effective average sediment concentration. This normalization accounts for differences in both drainage area and mean runoff depth between the comparison watersheds. At the three comparison watershed sites with relatively complete sediment-transport data, mean bedload concentrations ranged from 44 to 109 milligrams per liter (mg/L) and mean suspended-sediment concentrations ranged from 139 to 374 mg/L; bedload constituted 21 to 29 percent of the total sediment load. The mean bedload concentration at the Vance Creek sampling site (69 mg/L) fell in the middle of the range observed in comparison watersheds, whereas the mean suspended-sediment concentration (50 mg/L) was markedly lower. Bedload samples at the Vance Creek sampling site also were generally less sand rich (sample-average sand fraction was 13 percent at Vance Creek versus 20 to 37 percent for comparison waters). Bedload transport rates at the Vance Creek sampling site appear relatively average for the region, given the drainage basin area and average runoff. In contrast, the supply and transport of finer material, both in the suspended load and the sand fraction of the bedload, are relatively low.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225019","collaboration":"Prepared in cooperation with the Mason Conservation District","usgsCitation":"Anderson, S.W., 2022, Bedload and suspended-sediment transport in lower Vance Creek, western Washington, water\nyears 2019–20: U.S. Geological Survey Scientific Investigations Report 2022–5019, 25 p., https://doi.org/10.3133/sir20225019.","productDescription":"vii, 25 p.","onlineOnly":"Y","ipdsId":"IP-119859","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":502372,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112712.htm","linkFileType":{"id":5,"text":"html"}},{"id":397070,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5019/images"},{"id":397068,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5019/coverthb.jpg"},{"id":397069,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5019/sir20225019.pdf","text":"Report","size":"2.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5019"},{"id":397071,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5019/sir20225019.XML"}],"country":"United States","state":"Washington","otherGeospatial":"Vance Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.2889,\n 47.3208\n ],\n [\n -123.2833,\n 47.3208\n ],\n [\n -123.2833,\n 47.325\n ],\n [\n -123.2889,\n 47.325\n ],\n [\n -123.2889,\n 47.3208\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
Understanding dispersion in rivers is critical for numerous applications, such as characterizing larval drift for endangered fish species and responding to spills of hazardous materials. Injecting a visible dye into the river can yield insight on dispersion processes, but conventional field instrumentation yields limited data on variations in dye concentration over time at a few, fixed points. Remote sensing can provide more detailed, spatially distributed information on the dye's motion, but this approach has only been tested in clear-flowing streams. The purpose of this study was to assess the potential of remote sensing to facilitate tracer studies in more turbid rivers. To pursue this objective, we injected Rhodamine WT dye into the Missouri River and collected field spectra from a boat, videos from a small unoccupied aircraft system (sUAS), and orthophotos from an airplane. Applying an optimal band ratio analysis (OBRA) algorithm to the field spectra revealed strong correlations (R2 = 0.936) between a spectrally based quantity and in situ concentration measurements. OBRA also performed well for broadband RGB (red, green, blue) images extracted from the sUAS-based videos; the resulting concentration maps were used to produce animations that captured movement of the dye pulse. Spectral mixture analysis of repeat orthophoto coverage yielded relative concentration estimates that provided a synoptic perspective on dispersion of the dye throughout the entire 13.8 km reach over the full 2.5-hr duration of the experiment. The results of this study demonstrate the potential to remotely sense tracer dye concentrations in large, highly turbid rivers.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021WR031396","usgsCitation":"Legleiter, C.J., Sansom, B.J., and Jacobson, R., 2022, Remote sensing of visible dye concentrations during a tracer experiment on a large, turbid river: Water Resources Research, v. 58, no. 4, e2021WR031396, 23 p., https://doi.org/10.1029/2021WR031396.","productDescription":"e2021WR031396, 23 p.","ipdsId":"IP-133418","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":448396,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021wr031396","text":"Publisher Index Page"},{"id":435912,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JDISO3","text":"USGS data release","linkHelpText":"Remotely sensed data and field measurements for mapping visible dye concentrations during a tracer experiment on the Missouri River near Columbia, MO, May 5, 2021"},{"id":398020,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","city":"Columbia","otherGeospatial":"Missouri River, Searcy's Bend","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.50162124633789,\n 38.856552783257754\n ],\n [\n -92.45372772216797,\n 38.856552783257754\n ],\n [\n -92.45372772216797,\n 38.91467806459576\n ],\n [\n -92.50162124633789,\n 38.91467806459576\n ],\n [\n -92.50162124633789,\n 38.856552783257754\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"58","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":839523,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sansom, Brandon James 0000-0001-7999-9547","orcid":"https://orcid.org/0000-0001-7999-9547","contributorId":289636,"corporation":false,"usgs":true,"family":"Sansom","given":"Brandon","email":"","middleInitial":"James","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":839524,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jacobson, R. B. 0000-0002-8368-2064","orcid":"https://orcid.org/0000-0002-8368-2064","contributorId":92614,"corporation":false,"usgs":true,"family":"Jacobson","given":"R. B.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":839525,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70230024,"text":"70230024 - 2022 - Bridging the gap between spatial modeling and management of invasive annual grasses in the imperiled sagebrush biome","interactions":[],"lastModifiedDate":"2023-03-24T16:54:57.706489","indexId":"70230024","displayToPublicDate":"2022-03-23T11:26:31","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6002,"text":"Rangeland Ecology & Management","active":true,"publicationSubtype":{"id":10}},"title":"Bridging the gap between spatial modeling and management of invasive annual grasses in the imperiled sagebrush biome","docAbstract":"Invasions of native plant communities by non-native species present major challenges for ecosystem management and conservation. Invasive annual grasses such as cheatgrass, medusahead, and ventenata are pervasive and continue to expand their distributions across imperiled sagebrush-steppe communities of the western United States. These invasive grasses alter native plant communities, ecosystem function, and fire regimes, threatening sagebrush ecosystem persistence. Spatial data describing the distribution and abundance of invasive species are often used by resource managers to identify, target, and determine needed interventions. However, there are challenges associated with translating these datasets into management actions. We conducted a review of available spatial products to assess advances in, and barriers to, applying contemporary model-based maps to support rangeland management. We found dozens of regional data products describing cheatgrass or annual herbaceous cover and few maps describing ventenata or medusahead. Over the past decade, IAG spatial data increased in spatial and temporal resolution and increasingly used response variables that indicate the severity of infestation such as percent cover. Despite improvements, use of such data is limited by the time required to find, compare, understand, and translate model-based maps into management strategy. There is also a need for products with higher spatial resolution and accuracy. In collaboration with a multipartner stakeholder group, we identified key considerations that guide selection of IAG spatial data products for use by land managers and other users. On the basis of these considerations, we discuss issues that contribute to a research-implementation gap between users and product developers and suggest future directions for improved development of management-ready spatial products.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rama.2022.01.006","usgsCitation":"Tarbox, B.C., Van Schmidt, N.D., Shyvers, J.E., Saher, D., Heinrichs, J., and Aldridge, C.L., 2022, Bridging the gap between spatial modeling and management of invasive annual grasses in the imperiled sagebrush biome: Rangeland Ecology & Management, v. 82, p. 104-115, https://doi.org/10.1016/j.rama.2022.01.006.","productDescription":"12 p.","startPage":"104","endPage":"115","ipdsId":"IP-129019","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":435913,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VW97AO","text":"USGS data release","linkHelpText":"Database of invasive annual grass spatial products for the western United States January 2010 to February 2021"},{"id":397530,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"82","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Tarbox, Bryan C. 0000-0001-5040-3949","orcid":"https://orcid.org/0000-0001-5040-3949","contributorId":288930,"corporation":false,"usgs":true,"family":"Tarbox","given":"Bryan","email":"","middleInitial":"C.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":838720,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Van Schmidt, Nathan D. 0000-0002-5973-7934","orcid":"https://orcid.org/0000-0002-5973-7934","contributorId":288931,"corporation":false,"usgs":true,"family":"Van Schmidt","given":"Nathan","email":"","middleInitial":"D.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":838721,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shyvers, Jessica E. 0000-0002-4307-0004","orcid":"https://orcid.org/0000-0002-4307-0004","contributorId":288929,"corporation":false,"usgs":true,"family":"Shyvers","given":"Jessica","email":"","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":838722,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Saher, D. Joanne 0000-0002-2452-2570","orcid":"https://orcid.org/0000-0002-2452-2570","contributorId":288928,"corporation":false,"usgs":false,"family":"Saher","given":"D. Joanne","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":838723,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Heinrichs, Julie A. 0000-0001-7733-5034","orcid":"https://orcid.org/0000-0001-7733-5034","contributorId":240888,"corporation":false,"usgs":false,"family":"Heinrichs","given":"Julie A.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":838724,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":838725,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70267427,"text":"70267427 - 2022 - How lions move at night when they hunt?","interactions":[],"lastModifiedDate":"2025-05-23T16:02:05.636688","indexId":"70267427","displayToPublicDate":"2022-03-23T10:57:34","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2373,"text":"Journal of Mammalogy","onlineIssn":"1545-1542","printIssn":"0022-2372","active":true,"publicationSubtype":{"id":10}},"title":"How lions move at night when they hunt?","docAbstract":"Movement patterns of lions (Panthera leo) reveal how they hunt large herbivores in heterogeneous landscapes such as the Kruger National Park in South Africa. Large herbivores are distributed differently on the landscape and therefore have different vulnerabilities as prey for lions. For instance, blue wildebeest (Connochaetes taurinus) occupy small grazing lawns at night but are difficult for lions to capture because open areas lack cover for stalking. African buffalo (Syncerus caffer) aggregate in large herds but are less available because these herds only intermittently enter the home ranges of individual lion prides. Unlike large herds of wildebeest and buffalo, plains zebra (Equus quagga) move widely in small herds while browsing greater kudus (Tragelaphus strepsiceros) and giraffes (Giraffa camelopardalis giraffa) generally occur in lower densities. We used spatial data derived from GPS collars to investigate several hypotheses regarding the movements of three lion prides in response to their prey. We found that lions were most active and moved longer distances during nighttime than during daytime. Lions remained within their core home ranges on 87% of nights and wandered to the outlying areas of the home ranges every second night. Lions visited grazing lawns, that is, area of short grass, where wildebeest herds resided every second night, and moved toward the direction of buffalo herds within 2 km of vicinity. Lions spent more time near riverbanks that provided dense woody cover at night than expected but concentrated only weakly near sites with surface water where herbivores drank in the dry season. Our study contributes to understanding how lions vary their movements in response to the spatial and temporal heterogeneity in the relative availability and vulnerability of multiple prey species.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/jmammal/gyac025","usgsCitation":"Yiu, S., Owen-Smith, N., and Cain, J.W., 2022, How lions move at night when they hunt?: Journal of Mammalogy, v. 103, no. 4, p. 855-864, https://doi.org/10.1093/jmammal/gyac025.","productDescription":"10 p.","startPage":"855","endPage":"864","ipdsId":"IP-109457","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":486522,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"South Africa","otherGeospatial":"Kruger National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 30.72137474888291,\n -22.35002959432397\n ],\n [\n 31.220577742146247,\n -25.52321749917516\n ],\n [\n 32.13088908280099,\n -25.529841746852213\n ],\n [\n 31.940017350082087,\n -23.896450066908073\n ],\n [\n 31.323354828993274,\n -22.370399291095367\n ],\n [\n 30.72137474888291,\n -22.35002959432397\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"103","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-03-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Yiu, Sze-Wing","contributorId":355799,"corporation":false,"usgs":false,"family":"Yiu","given":"Sze-Wing","affiliations":[{"id":12729,"text":"UW","active":true,"usgs":false}],"preferred":false,"id":938170,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Owen-Smith, Norman","contributorId":355800,"corporation":false,"usgs":false,"family":"Owen-Smith","given":"Norman","affiliations":[{"id":12729,"text":"UW","active":true,"usgs":false}],"preferred":false,"id":938171,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cain, James W. III 0000-0003-4743-516X jwcain@usgs.gov","orcid":"https://orcid.org/0000-0003-4743-516X","contributorId":4063,"corporation":false,"usgs":true,"family":"Cain","given":"James","suffix":"III","email":"jwcain@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":938169,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70229992,"text":"70229992 - 2022 - Positively selected genes in the hoary bat (Lasiurus cinereus) lineage: Prominence of thymus expression, immune and metabolic function, and regions of ancient synteny","interactions":[],"lastModifiedDate":"2022-03-24T15:18:53.662638","indexId":"70229992","displayToPublicDate":"2022-03-23T09:13:21","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3840,"text":"PeerJ","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Positively selected genes in the hoary bat (Lasiurus cinereus) lineage: Prominence of thymus expression, immune and metabolic function, and regions of ancient synteny","title":"Positively selected genes in the hoary bat (Lasiurus cinereus) lineage: Prominence of thymus expression, immune and metabolic function, and regions of ancient synteny","docAbstract":"Background
Bats of the genus Lasiurus occur throughout the Americas and have diversified into at least 20 species among three subgenera. The hoary bat (Lasiurus cinereus) is highly migratory and ranges farther across North America than any other wild mammal. Despite the ecological importance of this species as a major insect predator, and the particular susceptibility of lasiurine bats to wind turbine strikes, our understanding of hoary bat ecology, physiology, and behavior remains poor.
Methods
To better understand adaptive evolution in this lineage, we used whole-genome sequencing to identify protein-coding sequence and explore signatures of positive selection. Gene models were predicted with Maker and compared to seven well-annotated and phylogenetically representative species. Evolutionary rate analysis was performed with PAML.
Results
Of 9,447 single-copy orthologous groups that met evaluation criteria, 150 genes had a significant excess of nonsynonymous substitutions along the L. cinereus branch (P < 0.001 after manual review of alignments). Selected genes as a group had biased expression, most strongly in thymus tissue. We identified 23 selected genes with reported immune functions as well as a divergent paralog of Steep1 within suborder Yangochiroptera. Seventeen genes had roles in lipid and glucose metabolic pathways, partially overlapping with 15 mitochondrion-associated genes; these adaptations may reflect the metabolic challenges of hibernation, long-distance migration, and seasonal variation in prey abundance. The genomic distribution of positively selected genes differed significantly from background expectation by discrete Kolmogorov–Smirnov test (P < 0.001). Remarkably, the top three physical clusters all coincided with islands of conserved synteny predating Mammalia, the largest of which shares synteny with the human cat-eye critical region (CECR) on 22q11. This observation coupled with the expansion of a novel Tbx1-like gene family may indicate evolutionary innovation during pharyngeal arch development: both the CECR and Tbx1 cause dosage-dependent congenital abnormalities in thymus, heart, and head, and craniodysmorphy is associated with human orthologs of other positively selected genes as well.
","language":"English","publisher":"PeerJ","doi":"10.7717/peerj.13130","usgsCitation":"Cornman, R.S., and Cryan, P.M., 2022, Positively selected genes in the hoary bat (Lasiurus cinereus) lineage: Prominence of thymus expression, immune and metabolic function, and regions of ancient synteny: PeerJ, v. 10, e13130, 39 p., https://doi.org/10.7717/peerj.13130.","productDescription":"e13130, 39 p.","ipdsId":"IP-132867","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":448401,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.13130","text":"Publisher Index Page"},{"id":435914,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OZAGYU","text":"USGS data release","linkHelpText":"Gene annotations for the hoary bat (Lasiurus [Aeorestes] cinereus) and alignments with other bat gene sets for evolutionary analysis"},{"id":397455,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","noUsgsAuthors":false,"publicationDate":"2022-03-17","publicationStatus":"PW","contributors":{"editors":[{"text":"Meegaskumbura, Madhava","contributorId":289186,"corporation":false,"usgs":false,"family":"Meegaskumbura","given":"Madhava","email":"","affiliations":[],"preferred":false,"id":838650,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Cornman, Robert S. 0000-0001-9511-2192 rcornman@usgs.gov","orcid":"https://orcid.org/0000-0001-9511-2192","contributorId":5356,"corporation":false,"usgs":true,"family":"Cornman","given":"Robert","email":"rcornman@usgs.gov","middleInitial":"S.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":838600,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cryan, Paul M. 0000-0002-2915-8894 cryanp@usgs.gov","orcid":"https://orcid.org/0000-0002-2915-8894","contributorId":147942,"corporation":false,"usgs":true,"family":"Cryan","given":"Paul","email":"cryanp@usgs.gov","middleInitial":"M.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":838601,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70229989,"text":"70229989 - 2022 - Mass balance of two perennial snowfields: Niwot Ridge, Colorado and the Ulaan Taiga, Mongolia.","interactions":[],"lastModifiedDate":"2022-03-23T14:11:47.712745","indexId":"70229989","displayToPublicDate":"2022-03-23T08:59:12","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":899,"text":"Arctic, Antarctic, and Alpine Research","active":true,"publicationSubtype":{"id":10}},"title":"Mass balance of two perennial snowfields: Niwot Ridge, Colorado and the Ulaan Taiga, Mongolia.","docAbstract":"Perennial snowfields are generally receding worldwide, though the precise mechanisms causing recessions are not always well understood. Here we apply a numerical snowpack model to identify the leading factors controlling the mass balance of two perennial snowfields that have significant human interest: Arapaho glacier, located at Niwot Ridge in the Colorado Rocky Mountains (United States), and a snowfield located in the Ulaan Taiga (Mongolia). The two locations were chosen because they differ in elevation, slope and aspect. However, both have sub-arctic climates and are located within semi-arid regions. We show that for these two locations the snowfield mass balance is primarily sensitive to air temperature and wind speed, followed by precipitation and dust deposition amounts. We find that the sensitivities are similar for the center of the snowfield as well as the margins.","language":"English","publisher":"Taylor & Francis","doi":"10.1080/15230430.2022.2027591","usgsCitation":"Williams, K.E., McKay, C.P., Toon, O.B., and Jennings, K.S., 2022, Mass balance of two perennial snowfields: Niwot Ridge, Colorado and the Ulaan Taiga, Mongolia.: Arctic, Antarctic, and Alpine Research, v. 54, no. 1, p. 41-61, https://doi.org/10.1080/15230430.2022.2027591.","productDescription":"21 p.","startPage":"41","endPage":"61","ipdsId":"IP-125219","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":448403,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/15230430.2022.2027591","text":"Publisher Index Page"},{"id":397454,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mongolia, United States","state":"Colorado","otherGeospatial":"Arapaho Glacier, Rocky Mountains, Ulaan Taiga Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.65105438232422,\n 40.02022014033094\n ],\n [\n -105.65028190612793,\n 40.019825755305554\n ],\n [\n -105.64877986907958,\n 40.02097603859142\n ],\n [\n -105.64865112304688,\n 40.022126302485596\n ],\n [\n -105.64830780029297,\n 40.02183052219348\n ],\n [\n -105.648136138916,\n 40.020680253312854\n ],\n [\n -105.64774990081787,\n 40.02074598348557\n ],\n [\n -105.64714908599852,\n 40.0214032817301\n ],\n [\n -105.64556121826172,\n 40.02107463339935\n ],\n [\n -105.64478874206543,\n 40.021731928477855\n ],\n [\n -105.64435958862305,\n 40.02206057364261\n ],\n [\n -105.64418792724608,\n 40.02166619925493\n ],\n [\n -105.64324378967285,\n 40.02192911576667\n ],\n [\n -105.64332962036133,\n 40.02258640261354\n ],\n [\n -105.64298629760742,\n 40.023046499638816\n ],\n [\n -105.64375877380371,\n 40.02330941083162\n ],\n [\n -105.64405918121338,\n 40.023802366587425\n ],\n [\n -105.64491748809814,\n 40.023703775721195\n ],\n [\n -105.64521789550781,\n 40.02327654698791\n ],\n [\n -105.64607620239258,\n 40.02350659356135\n ],\n [\n -105.64641952514648,\n 40.02373663935909\n ],\n [\n -105.64641952514648,\n 40.023966684381186\n ],\n [\n -105.64581871032713,\n 40.02390095731116\n ],\n [\n -105.64423084259033,\n 40.02524834959007\n ],\n [\n -105.64410209655762,\n 40.02567556597571\n ],\n [\n -105.64427375793457,\n 40.02620136708539\n ],\n [\n -105.64538955688477,\n 40.026431403796515\n ],\n [\n -105.64603328704834,\n 40.02616850463476\n ],\n [\n -105.6471061706543,\n 40.02606991718791\n ],\n [\n -105.64796447753906,\n 40.02603705467397\n ],\n [\n -105.65015316009521,\n 40.024361045471494\n ],\n [\n -105.65096855163574,\n 40.022652130949986\n ],\n [\n -105.65105438232422,\n 40.02022014033094\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 98.7945556640625,\n 50.40851753069726\n ],\n [\n 99.8602294921875,\n 50.40851753069726\n ],\n [\n 99.8602294921875,\n 51.68958500811337\n ],\n [\n 98.7945556640625,\n 51.68958500811337\n ],\n [\n 98.7945556640625,\n 50.40851753069726\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"54","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-03-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Williams, Kaj E. 0000-0003-1755-1872 kewilliams@usgs.gov","orcid":"https://orcid.org/0000-0003-1755-1872","contributorId":196988,"corporation":false,"usgs":true,"family":"Williams","given":"Kaj","email":"kewilliams@usgs.gov","middleInitial":"E.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":838596,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McKay, Christopher P.","contributorId":197097,"corporation":false,"usgs":false,"family":"McKay","given":"Christopher","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":838597,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Toon, Owen B. 0000-0002-1394-3062","orcid":"https://orcid.org/0000-0002-1394-3062","contributorId":289134,"corporation":false,"usgs":false,"family":"Toon","given":"Owen","email":"","middleInitial":"B.","affiliations":[{"id":12502,"text":"University of Colorado - Boulder","active":true,"usgs":false}],"preferred":false,"id":838598,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jennings, Keith S. 0000-0002-4660-1472","orcid":"https://orcid.org/0000-0002-4660-1472","contributorId":289136,"corporation":false,"usgs":false,"family":"Jennings","given":"Keith","email":"","middleInitial":"S.","affiliations":[{"id":36969,"text":"Lynker Technologies","active":true,"usgs":false}],"preferred":false,"id":838599,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70230158,"text":"70230158 - 2022 - Geophysical imaging of the Yellowstone hydrothermal plumbing system","interactions":[],"lastModifiedDate":"2022-04-11T11:01:05.08063","indexId":"70230158","displayToPublicDate":"2022-03-23T08:53:07","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2840,"text":"Nature","active":true,"publicationSubtype":{"id":10}},"title":"Geophysical imaging of the Yellowstone hydrothermal plumbing system","docAbstract":"The nature of Yellowstone National Park’s plumbing system linking deep thermal fluids to its legendary thermal features is virtually unknown. The prevailing concepts of Yellowstone hydrology and chemistry are that fluids reside in reservoirs with unknown geometries, flow laterally from distal sources and emerge at the edges of lava flows. Here we present a high-resolution synoptic view of pathways of the Yellowstone hydrothermal system derived from electrical resistivity and magnetic susceptibility models of airborne geophysical data. Groundwater and thermal fluids containing appreciable total dissolved solids significantly reduce resistivities of porous volcanic rocks and are differentiated by their resistivity signatures. Clay sequences mapped in thermal areas and boreholes typically form at depths of less than 1,000 metres over fault-controlled thermal fluid and/or gas conduits. We show that most thermal features are located above high-flux conduits along buried faults capped with clay that has low resistivity and low susceptibility. Shallow subhorizontal pathways feed groundwater into basins that mixes with thermal fluids from vertical conduits. These mixed fluids emerge at the surface, controlled by surficial permeability, and flow outwards along deeper brecciated layers. These outflows, continuing between the geyser basins, mix with local groundwater and thermal fluids to produce the observed geochemical signatures. Our high-fidelity images inform geochemical and groundwater models for hydrothermal systems worldwide.
","language":"English","publisher":"Nature","doi":"10.1038/s41586-021-04379-1","usgsCitation":"Finn, C., Bedrosian, P.A., Holbrook, W.S., Auken, E., Bloss, B.R., and Crosbie, K.J., 2022, Geophysical imaging of the Yellowstone hydrothermal plumbing system: Nature, v. 603, p. 643-647, https://doi.org/10.1038/s41586-021-04379-1.","productDescription":"5 p.","startPage":"643","endPage":"647","ipdsId":"IP-126049","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":448406,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1038/s41586-021-04379-1","text":"External Repository"},{"id":435916,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LVAU7W","text":"USGS data release","linkHelpText":"Airborne Electromagnetic Survey Processed Data and Models Data Release, Yellowstone National Park, Wyoming, 2016"},{"id":435915,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MCJ9B6","text":"USGS data release","linkHelpText":"Airborne Electromagnetic and Magnetic Survey, Yellowstone National Park, 2016 - Minimally Processed Data"},{"id":397933,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Wyoming","otherGeospatial":"Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.1,\n 44.25\n ],\n [\n -110.25,\n 44.25\n ],\n [\n -110.25,\n 45\n ],\n [\n -111.1,\n 45\n ],\n [\n -111.1,\n 44.25\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"603","noUsgsAuthors":false,"publicationDate":"2022-03-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Finn, Carol A. 0000-0002-6178-0405","orcid":"https://orcid.org/0000-0002-6178-0405","contributorId":229711,"corporation":false,"usgs":true,"family":"Finn","given":"Carol A.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":839331,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bedrosian, Paul A. 0000-0002-6786-1038 pbedrosian@usgs.gov","orcid":"https://orcid.org/0000-0002-6786-1038","contributorId":839,"corporation":false,"usgs":true,"family":"Bedrosian","given":"Paul","email":"pbedrosian@usgs.gov","middleInitial":"A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":839332,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holbrook, W. Steven","contributorId":175481,"corporation":false,"usgs":false,"family":"Holbrook","given":"W.","email":"","middleInitial":"Steven","affiliations":[],"preferred":false,"id":839333,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Auken, Esben","contributorId":193991,"corporation":false,"usgs":false,"family":"Auken","given":"Esben","email":"","affiliations":[],"preferred":false,"id":839334,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bloss, Benjamin R. 0000-0002-1678-8571 bbloss@usgs.gov","orcid":"https://orcid.org/0000-0002-1678-8571","contributorId":139981,"corporation":false,"usgs":true,"family":"Bloss","given":"Benjamin","email":"bbloss@usgs.gov","middleInitial":"R.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":839335,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Crosbie, Kayla J 0000-0002-2724-1264","orcid":"https://orcid.org/0000-0002-2724-1264","contributorId":289565,"corporation":false,"usgs":true,"family":"Crosbie","given":"Kayla","email":"","middleInitial":"J","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":839336,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70230330,"text":"70230330 - 2022 - Reframing groundwater hydrology as a data-driven science","interactions":[],"lastModifiedDate":"2022-08-01T16:58:15.067632","indexId":"70230330","displayToPublicDate":"2022-03-23T06:43:52","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Reframing groundwater hydrology as a data-driven science","docAbstract":"No abstract available.
","language":"English","publisher":"Wiley","doi":"10.1111/gwat.13195","usgsCitation":"Shapiro, A.M., and Day-Lewis, F., 2022, Reframing groundwater hydrology as a data-driven science: Groundwater, v. 60, no. 4, p. 455-456, https://doi.org/10.1111/gwat.13195.","productDescription":"2 p.","startPage":"455","endPage":"456","ipdsId":"IP-138143","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":398301,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"60","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-03-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Shapiro, Allen M. 0000-0002-6425-9607 ashapiro@usgs.gov","orcid":"https://orcid.org/0000-0002-6425-9607","contributorId":2164,"corporation":false,"usgs":true,"family":"Shapiro","given":"Allen","email":"ashapiro@usgs.gov","middleInitial":"M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":840001,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Day-Lewis, F.D. 0000-0003-3526-886X","orcid":"https://orcid.org/0000-0003-3526-886X","contributorId":222721,"corporation":false,"usgs":false,"family":"Day-Lewis","given":"F.D.","affiliations":[],"preferred":false,"id":840002,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70230010,"text":"70230010 - 2022 - MIS 5e sea-level history along the Pacific coast of North America","interactions":[],"lastModifiedDate":"2022-03-23T14:23:16.315291","indexId":"70230010","displayToPublicDate":"2022-03-22T09:16:01","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1426,"text":"Earth System Science Data","active":true,"publicationSubtype":{"id":10}},"title":"MIS 5e sea-level history along the Pacific coast of North America","docAbstract":"The primary last interglacial, marine isotope substage (MIS) 5e records on the Pacific coast of North America, from Washington (USA) to Baja California Sur (Mexico), are found in the deposits of erosional marine terraces. Warmer coasts along the southern Golfo de California host both erosional marine terraces and constructional coral reef terraces. Because the northern part of the region is tectonically active, MIS 5e terrace elevations vary considerably, from a few meters above sea level to as much as 70 m above sea level. The primary paleo-sea-level indicator is the shoreline angle, the junction of the wave-cut platform with the former sea cliff, which forms very close to mean sea level. Most areas on the Pacific coast of North America have experienced uplift since MIS 5e time, but the rate of uplift varies substantially as a function of tectonic setting. Chronology in most places is based on uranium-series ages of the solitary coral Balanophyllia elegans (erosional terraces) or the colonial corals Porites and Pocillopora (constructional reefs). In areas lacking corals, correlation to MIS 5e often can be accomplished using amino acid ratios of fossil mollusks, compared to similar ratios in mollusks that also host dated corals. Uranium-series (U-series) analyses of corals that have experienced largely closed-system histories range from ∼124 to ∼118 ka, in good agreement with ages from MIS 5e reef terraces elsewhere in the world. There is no geomorphic, stratigraphic, or geochronological evidence for more than one high-sea stand during MIS 5e on the Pacific coast of North America. However, in areas of low uplift rate, the outer parts of MIS 5e terraces apparently were re-occupied by the high-sea stand at ∼100 ka (MIS 5c), evident from mixes of coral ages and mixes of molluscan faunas with differing thermal aspects. This sequence of events took place because glacial isostatic adjustment processes acting on North America resulted in regional high-sea stands at ∼100 and ∼80 ka that were higher than is the case in far-field regions, distant from large continental ice sheets. During MIS 5e time, sea surface temperatures (SSTs) off the Pacific coast of North America were higher than is the case at present, evident from extralimital southern species of mollusks found in dated deposits. Apparently, no wholesale shifts in faunal provinces took place, but in MIS 5e time, some species of bivalves and gastropods lived hundreds of kilometers north of their present northern limits, in good agreement with SST estimates derived from foraminiferal records and alkenone-based reconstructions in deep-sea cores. Because many areas of the Pacific coast of North America have been active tectonically for much or all of the Quaternary, many earlier interglacial periods are recorded as uplifted, higher-elevation terraces. In addition, from southern Oregon to northern Baja California, there are U-series-dated corals from marine terraces that formed at ∼80 ka, during MIS 5a. In contrast to MIS 5e, these terrace deposits host molluscan faunas that contain extralimital northern species, indicating cooler SST at the end of MIS 5. Here I present a review and standardized database of MIS 5e sea-level indicators along the Pacific coast of North America and the corresponding dated samples. The database is available in Muhs et al. (2021b; https://doi.org/10.5281/zenodo.5903285).
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/essd-14-1271-2022","usgsCitation":"Muhs, D.R., 2022, MIS 5e sea-level history along the Pacific coast of North America: Earth System Science Data, v. 14, p. 1271-1330, https://doi.org/10.5194/essd-14-1271-2022.","productDescription":"60 p.","startPage":"1271","endPage":"1330","ipdsId":"IP-127889","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":448410,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/essd-14-1271-2022","text":"Publisher Index Page"},{"id":397456,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Pacific coast of North America","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.265625,\n 8.754794702435618\n ],\n [\n -89.296875,\n 14.604847155053898\n ],\n [\n -103.71093749999999,\n 21.289374355860424\n ],\n [\n -112.8515625,\n 32.84267363195431\n ],\n [\n -121.640625,\n 38.54816542304656\n ],\n [\n -120.58593749999999,\n 49.38237278700955\n ],\n [\n -135.703125,\n 60.930432202923335\n ],\n [\n -149.765625,\n 61.938950426660604\n ],\n [\n -156.4453125,\n 60.58696734225869\n ],\n [\n -163.125,\n 55.57834467218206\n ],\n [\n -169.1015625,\n 53.74871079689897\n ],\n [\n -176.48437499999997,\n 53.12040528310657\n ],\n [\n -172.6171875,\n 48.922499263758255\n ],\n [\n -145.8984375,\n 52.696361078274485\n ],\n [\n -131.8359375,\n 39.639537564366684\n ],\n [\n -115.31249999999999,\n 19.31114335506464\n ],\n [\n -99.140625,\n 8.754794702435618\n ],\n [\n -87.890625,\n 5.61598581915534\n ],\n [\n -82.265625,\n 8.754794702435618\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","noUsgsAuthors":false,"publicationDate":"2022-03-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Muhs, Daniel R. 0000-0001-7449-251X dmuhs@usgs.gov","orcid":"https://orcid.org/0000-0001-7449-251X","contributorId":1857,"corporation":false,"usgs":true,"family":"Muhs","given":"Daniel","email":"dmuhs@usgs.gov","middleInitial":"R.","affiliations":[{"id":218,"text":"Denver Federal Center","active":false,"usgs":true}],"preferred":true,"id":838649,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70239153,"text":"70239153 - 2022 - Long-term hydrologic sustainability of calcareous fens along the Glacial Lake Agassiz beach ridges, northwestern Minnesota, USA","interactions":[],"lastModifiedDate":"2022-12-30T13:36:42.11684","indexId":"70239153","displayToPublicDate":"2022-03-22T07:25:21","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Long-term hydrologic sustainability of calcareous fens along the Glacial Lake Agassiz beach ridges, northwestern Minnesota, USA","docAbstract":"Calcareous fens are peat-accumulating wetlands fed by calcium-rich groundwater that support several threatened species of plants that thrive in these geochemical conditions. This investigation characterized the hydrology of two calcareous fens in the Glacial Lake Agassiz beach ridge complex in northwestern Minnesota, USA. Sandy surficial beach ridge aquifers and underlying buried glacial aquifers were considered as sources of groundwater to the fen. A combination of the two sources influenced by seasonal hydrology was also considered. Synchronous hydrologic responses to rainfall events and hydraulic gradients indicate the calcareous fens are well-connected to the beach-ridge aquifers. Chemistry of water discharging to the fens is calcium-magnesium-bicarbonate type similar to the beach ridge aquifers, and distinct from buried aquifers that have significant sodium and chloride. High tritium values and oxygen isotope signatures similar to the beach ridge aquifers characterized fen water. Beach ridge aquifer complexes are relatively thin (8–10 m) and overlie thick clay/clay loam till. These beach ridges exhibit high seasonal recharge and have permanent saturated zones, providing a continual source of calcium-rich water for the fens. Electrical resistivity profiles characterized the glacial stratigraphy and highlighted the well-developed physical connection between beach ridge aquifers and calcareous fens. The results of this study allow evaluation of the potential impacts of irrigation and aggregate quarrying on calcareous fens along sand and gravel beach ridges.
Maxent models were run using the B. anthracis presence data and/or the animal outbreak presence data. Models run using the animal outbreak data alone utilized two scales: the Outbreak State scale which included only states reporting animal anthrax outbreaks from 2001 to 2013 and the National scale which included all states in the contiguous United States. Three iterations of the environmental data were used and included the Sample Location dataset which utilized the environmental variable data with assigned latitude and longitude locations from the USGS NASGLP project; the Normalized dataset which scaled the environmental variables so that the values fell between 0 and 1; and the Interpolated dataset which provided an interpolation of the environmental variables averaged for each county and assigned to a point for that county at the centroid (rather than using the NASGLP latitude and longitude location). Two metrics were used to measure model performance including the widely used area under the curve (AUC) and an alternative method, the True Skill Statistic (TSS). The AUC gives the probability that a randomly chosen presence location has been correctly ranked higher than the absence/background site. AUC values at 0.5 or lower mean the ranking is no better than random, while the AUC values nearer to 1 mean the model is a better predictor. The TSS provides a comparison of how well the background predictions made by the model match the model results at the test dataset (presence) locations. TSS values near +1 means the model approaches perfect agreement, while values near −1 indicate the model is no better than random.
Maxent models to determine the influence of environmental factors on the B. anthracis distribution using the PCR data yielded a low TSS, which suggested the model might be underfitting the data. This was not surprising due to the difficulty in recovering B. anthracis in soil samples as well as the samples themselves being discrete in nature and only capturing a snapshot in time. Therefore, the distribution of B. anthracis and its niche in the contiguous United States could not be determined in this study. However, efforts to investigate environmental factors that would have a higher potential of supporting an anthrax outbreak in wildlife and livestock yielded better results. Results showed that most of the Maxent models in this study performed best when using the Outbreak State scale. When the models were scaled up to the National scale, model performance declined, except for the Normalized variable dataset. At the Outbreak State scale, a large proportion of the area was predicted to be of higher probability for wildlife/livestock anthrax outbreaks, and the statistical measures assumed the model was underfitting the data. The model with the highest AUC and TSS scores for this study was the Outbreak State scale using Sample Location dataset (AUC = 0.918 and TSS = 0.82). Some of the variables found to be closely related to the occurrence of B. anthracis in this study included pH, drainage potential, and concentration of elements including Na, Ca, Sr, and Mg, which have also been found to be related to animal outbreaks or to the occurrence of B. anthracis in previous studies.
The models in the current study indicated possible regions that have not had recent wildlife/livestock anthrax outbreaks but contained environmental conditions that could potentially support an outbreak if one were to occur (Michigan and Maine). This work provides an extension to the use of ecological niche modeling to outbreak potential in livestock/wildlife in the United States because it utilizes additional soil geochemistry data and has shown that further validation techniques, such as the TSS, should be considered in addition to AUC. Results from this study could be used by animal and public health officials to identify areas with a higher potential for anthrax outbreak in wildlife and livestock due to naturally occurring soil and environmental conditions.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Geospatial Technology for Human Well-Being and Health","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-030-71377-5_19","usgsCitation":"Silvestri, E., Douglas, S., Luna, V., Jean-Babtiste, C., Harbin, D., Hempel, L., Boe, T., Nichols, T., and Griffin, D.W., 2022, Spatially integrating microbiology and geochemistry to reveal complex environmental health issues: Anthrax in the contiguous United States, chap. of Geospatial Technology for Human Well-Being and Health, p. 355-377, https://doi.org/10.1007/978-3-030-71377-5_19.","productDescription":"23 p.","startPage":"355","endPage":"377","ipdsId":"IP-092852","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":409412,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n [\n [\n [\n -94.81758,\n 49.38905\n ],\n [\n -94.64,\n 48.84\n ],\n [\n -94.32914,\n 48.67074\n ],\n [\n -93.63087,\n 48.60926\n ],\n [\n -92.61,\n 48.45\n ],\n [\n -91.64,\n 48.14\n ],\n [\n -90.83,\n 48.27\n ],\n [\n -89.6,\n 48.01\n ],\n [\n -89.27292,\n 48.01981\n ],\n [\n -88.37811,\n 48.30292\n ],\n [\n -87.43979,\n 47.94\n ],\n [\n -86.46199,\n 47.55334\n ],\n [\n -85.65236,\n 47.22022\n ],\n [\n -84.87608,\n 46.90008\n ],\n [\n -84.77924,\n 46.6371\n ],\n [\n -84.54375,\n 46.53868\n ],\n [\n -84.6049,\n 46.4396\n ],\n [\n -84.3367,\n 46.40877\n ],\n [\n -84.14212,\n 46.51223\n ],\n [\n -84.09185,\n 46.27542\n ],\n [\n -83.89077,\n 46.11693\n ],\n [\n -83.61613,\n 46.11693\n ],\n [\n -83.46955,\n 45.99469\n ],\n [\n -83.59285,\n 45.81689\n ],\n [\n -82.55092,\n 45.34752\n ],\n [\n -82.33776,\n 44.44\n ],\n [\n -82.13764,\n 43.57109\n ],\n [\n -82.43,\n 42.98\n ],\n [\n -82.9,\n 42.43\n ],\n [\n -83.12,\n 42.08\n ],\n [\n -83.142,\n 41.97568\n ],\n [\n -83.02981,\n 41.8328\n ],\n [\n -82.69009,\n 41.67511\n ],\n [\n -82.43928,\n 41.67511\n ],\n [\n -81.27775,\n 42.20903\n ],\n [\n -80.24745,\n 42.3662\n ],\n [\n -78.93936,\n 42.86361\n ],\n [\n -78.92,\n 42.965\n ],\n [\n -79.01,\n 43.27\n ],\n [\n -79.17167,\n 43.46634\n ],\n [\n -78.72028,\n 43.62509\n ],\n [\n -77.73789,\n 43.62906\n ],\n [\n -76.82003,\n 43.62878\n ],\n [\n -76.5,\n 44.01846\n ],\n [\n -76.375,\n 44.09631\n ],\n [\n -75.31821,\n 44.81645\n ],\n [\n -74.867,\n 45.00048\n ],\n [\n -73.34783,\n 45.00738\n ],\n [\n -71.50506,\n 45.0082\n ],\n [\n -71.405,\n 45.255\n ],\n [\n -71.08482,\n 45.30524\n ],\n [\n -70.66,\n 45.46\n ],\n [\n -70.305,\n 45.915\n ],\n [\n -69.99997,\n 46.69307\n ],\n [\n -69.23722,\n 47.44778\n ],\n [\n -68.905,\n 47.185\n ],\n [\n -68.23444,\n 47.35486\n ],\n [\n -67.79046,\n 47.06636\n ],\n [\n -67.79134,\n 45.70281\n ],\n [\n -67.13741,\n 45.13753\n ],\n [\n -66.96466,\n 44.8097\n ],\n [\n -68.03252,\n 44.3252\n ],\n [\n -69.06,\n 43.98\n ],\n [\n -70.11617,\n 43.68405\n ],\n [\n -70.64548,\n 43.09024\n ],\n [\n -70.81489,\n 42.8653\n ],\n [\n -70.825,\n 42.335\n ],\n [\n -70.495,\n 41.805\n ],\n [\n -70.08,\n 41.78\n ],\n [\n -70.185,\n 42.145\n ],\n [\n -69.88497,\n 41.92283\n ],\n [\n -69.96503,\n 41.63717\n ],\n [\n -70.64,\n 41.475\n ],\n [\n -71.12039,\n 41.49445\n ],\n [\n -71.86,\n 41.32\n ],\n [\n -72.295,\n 41.27\n ],\n [\n -72.87643,\n 41.22065\n ],\n [\n -73.71,\n 40.9311\n ],\n [\n -72.24126,\n 41.11948\n ],\n [\n -71.945,\n 40.93\n ],\n [\n -73.345,\n 40.63\n ],\n [\n -73.982,\n 40.628\n ],\n [\n -73.95232,\n 40.75075\n ],\n [\n -74.25671,\n 40.47351\n ],\n [\n -73.96244,\n 40.42763\n ],\n [\n -74.17838,\n 39.70926\n ],\n [\n -74.90604,\n 38.93954\n ],\n [\n -74.98041,\n 39.1964\n ],\n [\n -75.20002,\n 39.24845\n ],\n [\n -75.52805,\n 39.4985\n ],\n [\n -75.32,\n 38.96\n ],\n [\n -75.07183,\n 38.78203\n ],\n [\n -75.05673,\n 38.40412\n ],\n [\n -75.37747,\n 38.01551\n ],\n [\n -75.94023,\n 37.21689\n ],\n [\n -76.03127,\n 37.2566\n ],\n [\n -75.72205,\n 37.93705\n ],\n [\n -76.23287,\n 38.31921\n ],\n [\n -76.35,\n 39.15\n ],\n [\n -76.54272,\n 38.71762\n ],\n [\n -76.32933,\n 38.08326\n ],\n [\n -76.99,\n 38.23999\n ],\n [\n -76.30162,\n 37.91794\n ],\n [\n -76.25874,\n 36.9664\n ],\n [\n -75.9718,\n 36.89726\n ],\n [\n -75.86804,\n 36.55125\n ],\n [\n -75.72749,\n 35.55074\n ],\n [\n -76.36318,\n 34.80854\n ],\n [\n -77.39763,\n 34.51201\n ],\n [\n -78.05496,\n 33.92547\n ],\n [\n -78.55435,\n 33.86133\n ],\n [\n -79.06067,\n 33.49395\n ],\n [\n -79.20357,\n 33.15839\n ],\n [\n -80.30132,\n 32.50935\n ],\n [\n -80.86498,\n 32.0333\n ],\n [\n -81.33629,\n 31.44049\n ],\n [\n -81.49042,\n 30.72999\n ],\n [\n -81.31371,\n 30.03552\n ],\n [\n -80.98,\n 29.18\n ],\n [\n -80.53558,\n 28.47213\n ],\n [\n -80.53,\n 28.04\n ],\n [\n -80.05654,\n 26.88\n ],\n [\n -80.08801,\n 26.20576\n ],\n [\n -80.13156,\n 25.81677\n ],\n [\n -80.38103,\n 25.20616\n ],\n [\n -80.68,\n 25.08\n ],\n [\n -81.17213,\n 25.20126\n ],\n [\n -81.33,\n 25.64\n ],\n [\n -81.71,\n 25.87\n ],\n [\n -82.24,\n 26.73\n ],\n [\n -82.70515,\n 27.49504\n ],\n [\n -82.85526,\n 27.88624\n ],\n [\n -82.65,\n 28.55\n ],\n [\n -82.93,\n 29.1\n ],\n [\n -83.70959,\n 29.93656\n ],\n [\n -84.1,\n 30.09\n ],\n [\n -85.10882,\n 29.63615\n ],\n [\n -85.28784,\n 29.68612\n ],\n [\n -85.7731,\n 30.15261\n ],\n [\n -86.4,\n 30.4\n ],\n [\n -87.53036,\n 30.27433\n ],\n [\n -88.41782,\n 30.3849\n ],\n [\n -89.18049,\n 30.31598\n ],\n [\n -89.59383,\n 30.15999\n ],\n [\n -89.41373,\n 29.89419\n ],\n [\n -89.43,\n 29.48864\n ],\n [\n -89.21767,\n 29.29108\n ],\n [\n -89.40823,\n 29.15961\n ],\n [\n -89.77928,\n 29.30714\n ],\n [\n -90.15463,\n 29.11743\n ],\n [\n -90.88022,\n 29.14854\n ],\n [\n -91.62678,\n 29.677\n ],\n [\n -92.49906,\n 29.5523\n ],\n [\n -93.22637,\n 29.78375\n ],\n [\n -93.84842,\n 29.71363\n ],\n [\n -94.69,\n 29.48\n ],\n [\n -95.60026,\n 28.73863\n ],\n [\n -96.59404,\n 28.30748\n ],\n [\n -97.14,\n 27.83\n ],\n [\n -97.37,\n 27.38\n ],\n [\n -97.38,\n 26.69\n ],\n [\n -97.33,\n 26.21\n ],\n [\n -97.14,\n 25.87\n ],\n [\n -97.53,\n 25.84\n ],\n [\n -98.24,\n 26.06\n ],\n [\n -99.02,\n 26.37\n ],\n [\n -99.3,\n 26.84\n ],\n [\n -99.52,\n 27.54\n ],\n [\n -100.11,\n 28.11\n ],\n [\n -100.45584,\n 28.69612\n ],\n [\n -100.9576,\n 29.38071\n ],\n [\n -101.6624,\n 29.7793\n ],\n [\n -102.48,\n 29.76\n ],\n [\n -103.11,\n 28.97\n ],\n [\n -103.94,\n 29.27\n ],\n [\n -104.45697,\n 29.57196\n ],\n [\n -104.70575,\n 30.12173\n ],\n [\n -105.03737,\n 30.64402\n ],\n [\n -105.63159,\n 31.08383\n ],\n [\n -106.1429,\n 31.39995\n ],\n [\n -106.50759,\n 31.75452\n ],\n [\n -108.24,\n 31.75485\n ],\n [\n -108.24194,\n 31.34222\n ],\n [\n -109.035,\n 31.34194\n ],\n [\n -111.02361,\n 31.33472\n ],\n [\n -113.30498,\n 32.03914\n ],\n [\n -114.815,\n 32.52528\n ],\n [\n -114.72139,\n 32.72083\n ],\n [\n -115.99135,\n 32.61239\n ],\n [\n -117.12776,\n 32.53534\n ],\n [\n -117.29594,\n 33.04622\n ],\n [\n -117.944,\n 33.62124\n ],\n [\n -118.4106,\n 33.74091\n ],\n [\n -118.51989,\n 34.02778\n ],\n [\n -119.081,\n 34.078\n ],\n [\n -119.43884,\n 34.34848\n ],\n [\n -120.36778,\n 34.44711\n ],\n [\n -120.62286,\n 34.60855\n ],\n [\n -120.74433,\n 35.15686\n ],\n [\n -121.71457,\n 36.16153\n ],\n [\n -122.54747,\n 37.55176\n ],\n [\n -122.51201,\n 37.78339\n ],\n [\n -122.95319,\n 38.11371\n ],\n [\n -123.7272,\n 38.95166\n ],\n [\n -123.86517,\n 39.76699\n ],\n [\n -124.39807,\n 40.3132\n ],\n [\n -124.17886,\n 41.14202\n ],\n [\n -124.2137,\n 41.99964\n ],\n [\n -124.53284,\n 42.76599\n ],\n [\n -124.14214,\n 43.70838\n ],\n [\n -124.02053,\n 44.6159\n ],\n [\n -123.89893,\n 45.52341\n ],\n [\n -124.07963,\n 46.86475\n ],\n [\n -124.39567,\n 47.72017\n ],\n [\n -124.68721,\n 48.18443\n ],\n [\n -124.5661,\n 48.37971\n ],\n [\n -123.12,\n 48.04\n ],\n [\n -122.58736,\n 47.096\n ],\n [\n -122.34,\n 47.36\n ],\n [\n -122.5,\n 48.18\n ],\n [\n -122.84,\n 49\n ],\n [\n -120,\n 49\n ],\n [\n -117.03121,\n 49\n ],\n [\n -116.04818,\n 49\n ],\n [\n -113,\n 49\n ],\n [\n -110.05,\n 49\n ],\n [\n -107.05,\n 49\n ],\n [\n -104.04826,\n 48.99986\n ],\n [\n -100.65,\n 49\n ],\n [\n -97.22872,\n 49.0007\n ],\n [\n -95.15907,\n 49\n ],\n [\n -95.15609,\n 49.38425\n ],\n [\n -94.81758,\n 49.38905\n ]\n ]\n ]\n ]\n },\n \"properties\": {\n \"name\": \"United States\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2022-03-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Silvestri, Erin","contributorId":299154,"corporation":false,"usgs":false,"family":"Silvestri","given":"Erin","affiliations":[{"id":12772,"text":"USEPA","active":true,"usgs":false}],"preferred":false,"id":857184,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Douglas, Stephen 0000-0001-9078-538X","orcid":"https://orcid.org/0000-0001-9078-538X","contributorId":299155,"corporation":false,"usgs":false,"family":"Douglas","given":"Stephen","affiliations":[{"id":64780,"text":"Versar Inc","active":true,"usgs":false}],"preferred":false,"id":857185,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Luna, Vicky 0000-0003-0558-5557","orcid":"https://orcid.org/0000-0003-0558-5557","contributorId":299156,"corporation":false,"usgs":false,"family":"Luna","given":"Vicky","email":"","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":857186,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jean-Babtiste, C.A.O.","contributorId":299157,"corporation":false,"usgs":false,"family":"Jean-Babtiste","given":"C.A.O.","email":"","affiliations":[{"id":64781,"text":"Citizen","active":true,"usgs":false}],"preferred":false,"id":857187,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Harbin, D.","contributorId":299158,"corporation":false,"usgs":false,"family":"Harbin","given":"D.","email":"","affiliations":[{"id":64781,"text":"Citizen","active":true,"usgs":false}],"preferred":false,"id":857188,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hempel, Laura 0000-0001-5020-6056","orcid":"https://orcid.org/0000-0001-5020-6056","contributorId":299159,"corporation":false,"usgs":false,"family":"Hempel","given":"Laura","affiliations":[{"id":64782,"text":"Oregon State","active":true,"usgs":false}],"preferred":false,"id":857189,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Boe, Timothy","contributorId":299160,"corporation":false,"usgs":false,"family":"Boe","given":"Timothy","affiliations":[{"id":12772,"text":"USEPA","active":true,"usgs":false}],"preferred":false,"id":857190,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Nichols, Tonya","contributorId":299161,"corporation":false,"usgs":false,"family":"Nichols","given":"Tonya","affiliations":[{"id":12772,"text":"USEPA","active":true,"usgs":false}],"preferred":false,"id":857191,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Griffin, Dale W. 0000-0003-1719-5812 dgriffin@usgs.gov","orcid":"https://orcid.org/0000-0003-1719-5812","contributorId":2178,"corporation":false,"usgs":true,"family":"Griffin","given":"Dale","email":"dgriffin@usgs.gov","middleInitial":"W.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":857192,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70229813,"text":"fs20223001 - 2022 - A user guide to selecting invasive annual grass spatial products for the western United States","interactions":[],"lastModifiedDate":"2022-09-27T13:55:40.551929","indexId":"fs20223001","displayToPublicDate":"2022-03-21T17:45:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-3001","displayTitle":"A User Guide to Selecting Invasive Annual Grass Spatial Products for the Western United States","title":"A user guide to selecting invasive annual grass spatial products for the western United States","docAbstract":"Invasive annual grasses (IAGs)—including Bromus tectorum (cheatgrass), Taeniatherum caput-medusae (medusahead), and Ventenata dubia (ventenata) species—present significant challenges for rangeland management by altering plant communities, impacting ecosystem function, reducing forage for wildlife and livestock, and increasing fire risk. Numerous spatial data products are used to map IAGs, and understanding the similarities, differences, and potential tradeoffs among these products is key to selecting the right maps for specific applications. This short guide outlines considerations for selecting regional- and national-scale spatial data to support the management of IAGs.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223001","usgsCitation":"Van Schmidt, N.D., Shyvers, J.E., Saher, D.J., Tarbox, B.C., Heinrichs, J.A., and Aldridge, C.L., 2022, A user guide to selecting invasive annual grass spatial products for the western United States: U.S. Geological Survey Fact Sheet 2022-3001, 6 p., https://doi.org/10.3133/fs20223001.","productDescription":"Fact Sheet: 6 p.; Data Release","ipdsId":"IP-128971","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":405558,"rank":8,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223001/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2022-3001"},{"id":397245,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3001/fs20223001.xml"},{"id":397242,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3001/coverthb.jpg"},{"id":397267,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VW97AO","text":"USGS Data Release","linkHelpText":"Database of invasive annual grass spatial products for the western United States January 2010 to February 2021"},{"id":397289,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.1016/j.rama.2022.01.006","text":"Rangeland Ecology and Management","linkHelpText":"Bridging the gap between spatial modeling and management of invasive annual grasses in the imperiled sagebrush biome"},{"id":397243,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3001/fs20223001.pdf","text":"Report","size":"936 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022-3001"},{"id":397246,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3001/images"},{"id":397247,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/dr1152","text":"Data Report 1152","linkHelpText":"Compendium to Invasive Annual Grass Spatial Products for the Western United States, January 2010–February 2021"}],"country":"United States","otherGeospatial":"western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.04296874999999,\n 29.99300228455108\n ],\n [\n -93.42773437499999,\n 31.203404950917395\n ],\n [\n -94.21875,\n 33.284619968887675\n ],\n [\n -94.5703125,\n 34.08906131584994\n ],\n [\n -94.5703125,\n 36.94989178681327\n ],\n [\n -94.658203125,\n 39.50404070558415\n ],\n [\n -95.712890625,\n 40.64730356252251\n ],\n [\n -96.416015625,\n 42.48830197960227\n ],\n [\n -96.591796875,\n 43.96119063892024\n ],\n [\n -96.591796875,\n 45.82879925192134\n ],\n [\n -97.294921875,\n 49.15296965617042\n ],\n [\n -122.607421875,\n 49.03786794532644\n ],\n [\n -123.3984375,\n 48.40003249610685\n ],\n [\n -124.98046874999999,\n 48.516604348867475\n ],\n [\n -124.62890625,\n 45.9511496866914\n ],\n [\n -125.068359375,\n 41.11246878918088\n ],\n [\n -124.01367187499999,\n 38.685509760012\n ],\n [\n -120.32226562500001,\n 33.578014746143985\n ],\n [\n -117.0703125,\n 32.39851580247402\n ],\n [\n -114.9609375,\n 32.62087018318113\n ],\n [\n -114.521484375,\n 32.24997445586331\n ],\n [\n -110.56640625,\n 31.203404950917395\n ],\n [\n -108.1494140625,\n 31.372399104880525\n ],\n [\n -108.17138671875,\n 31.74685416292141\n ],\n [\n -106.4794921875,\n 31.74685416292141\n ],\n [\n -104.74365234375,\n 30.41078179084589\n ],\n [\n -104.4580078125,\n 29.57345707301757\n ],\n [\n -103.095703125,\n 28.97931203672246\n ],\n [\n -102.54638671875,\n 29.707139348134145\n ],\n [\n -102.3046875,\n 29.916852233070173\n ],\n [\n -101.3818359375,\n 29.783449456820605\n ],\n [\n -99.8876953125,\n 27.916766641249065\n ],\n [\n -99.38232421875,\n 27.21555620902969\n ],\n [\n -99.140625,\n 26.352497858154024\n ],\n [\n -97.6904296875,\n 25.97779895546436\n ],\n [\n -96.9873046875,\n 26.05678288577881\n ],\n [\n -97.27294921875,\n 27.547241546253268\n ],\n [\n -96.6796875,\n 28.14950321154457\n ],\n [\n -94.68017578125,\n 29.171348850951507\n ],\n [\n -93.93310546875,\n 29.611670115197377\n ],\n [\n -93.97705078125,\n 29.935895213372444\n ],\n [\n -94.04296874999999,\n 29.99300228455108\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Building C
Fort Collins, CO 80526-8118
Invasive annual grasses (IAGs) degrade native plant communities, alter fire cycles, impact ecosystem processes, and threaten the persistence of some species. Therefore, controlling the spread of IAGs has become a land management priority in the western United States. A wide array of geospatial data has been developed in the last decade to help land managers combat the invasion and expansion of non-native grasses by identifying areas where these species are likely to occur. However, choosing the most appropriate spatial product to address specific management concerns is a daunting task for many land managers, particularly with the rapid increase in the number of IAG spatial products available. To aid potential users in assessing these products, we reviewed and summarized 23 datasets that captured the three IAG species of most concern to rangeland management—Bromus tectorum (cheatgrass), Taeniatherum caput-medusae (medusahead), and Ventenata dubia (ventenata). To be included in this review, products were required to include part of the western United States, be regional or National in scale, and have been published between January 2010 and February 2021. This review, part of a series of informational data resources, is the compendium to an Excel-readable database and provides a 2-page summary of each spatial data product to assist land managers in understanding and selecting the best available spatial data for their management needs.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1152","usgsCitation":"Saher, D.J., Shyvers, J.E., Tarbox, B.C., Van Schmidt, N.D., Heinrichs, J.A., and Aldridge, C.L., 2022, Compendium to invasive annual grass spatial products for the western United States, January 2010–February 2021: Data Report 1152, 63 p., https://doi.org/10.3133/dr1152.","productDescription":"Report: ix, 63 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-129089","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":397230,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1152/dr1152.xml"},{"id":397229,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1152/dr1152.pdf","text":"Report","size":"8.10 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DR 1152"},{"id":397231,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1152/images"},{"id":397228,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1152/coverthb.jpg"},{"id":397237,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/fs20223001","text":"Fact Sheet 2022-3001","linkHelpText":"A User Guide to Selecting Invasive Annual Grass Spatial Products for the Western United States"},{"id":397290,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.1016/j.rama.2022.01.006","text":"Rangeland Ecology and Management","linkHelpText":"Bridging the gap between spatial modeling and management of invasive annual grasses in the imperiled sagebrush biome"},{"id":397269,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VW97AO","text":"USGS Data Release","linkHelpText":"Database of invasive annual grass spatial products for the western United States January 2010 to February 2021"}],"country":"United States","otherGeospatial":"western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.04296874999999,\n 29.99300228455108\n ],\n [\n -93.42773437499999,\n 31.203404950917395\n ],\n [\n -94.21875,\n 33.284619968887675\n ],\n [\n -94.5703125,\n 34.08906131584994\n ],\n [\n -94.5703125,\n 36.94989178681327\n ],\n [\n -94.658203125,\n 39.50404070558415\n ],\n [\n -95.712890625,\n 40.64730356252251\n ],\n [\n -96.416015625,\n 42.48830197960227\n ],\n [\n -96.591796875,\n 43.96119063892024\n ],\n [\n -96.591796875,\n 45.82879925192134\n ],\n [\n -97.294921875,\n 49.15296965617042\n ],\n [\n -122.607421875,\n 49.03786794532644\n ],\n [\n -123.3984375,\n 48.40003249610685\n ],\n [\n -124.98046874999999,\n 48.516604348867475\n ],\n [\n -124.62890625,\n 45.9511496866914\n ],\n [\n -125.068359375,\n 41.11246878918088\n ],\n [\n -124.01367187499999,\n 38.685509760012\n ],\n [\n -120.32226562500001,\n 33.578014746143985\n ],\n [\n -117.0703125,\n 32.39851580247402\n ],\n [\n -114.9609375,\n 32.62087018318113\n ],\n [\n -114.521484375,\n 32.24997445586331\n ],\n [\n -110.56640625,\n 31.203404950917395\n ],\n [\n -108.1494140625,\n 31.372399104880525\n ],\n [\n -108.17138671875,\n 31.74685416292141\n ],\n [\n -106.4794921875,\n 31.74685416292141\n ],\n [\n -104.74365234375,\n 30.41078179084589\n ],\n [\n -104.4580078125,\n 29.57345707301757\n ],\n [\n -103.095703125,\n 28.97931203672246\n ],\n [\n -102.54638671875,\n 29.707139348134145\n ],\n [\n -102.3046875,\n 29.916852233070173\n ],\n [\n -101.3818359375,\n 29.783449456820605\n ],\n [\n -99.8876953125,\n 27.916766641249065\n ],\n [\n -99.38232421875,\n 27.21555620902969\n ],\n [\n -99.140625,\n 26.352497858154024\n ],\n [\n -97.6904296875,\n 25.97779895546436\n ],\n [\n -96.9873046875,\n 26.05678288577881\n ],\n [\n -97.27294921875,\n 27.547241546253268\n ],\n [\n -96.6796875,\n 28.14950321154457\n ],\n [\n -94.68017578125,\n 29.171348850951507\n ],\n [\n -93.93310546875,\n 29.611670115197377\n ],\n [\n -93.97705078125,\n 29.935895213372444\n ],\n [\n -94.04296874999999,\n 29.99300228455108\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Building C
Fort Collins, CO 80526-8118
Groundwater withdrawals provide most public-water supplies and all private-domestic users in Fauquier County, Virginia, a fast-growing rural area southwest of Washington, D.C. Groundwater levels were measured in 129 wells during a county-wide synoptic survey from October 29 through November 2, 2018. Field measurements, combined with datapoints from the National Hydrography Dataset, were used to develop a county-wide groundwater-level contour map. Groundwater levels and withdrawals during the synoptic survey were near or slightly above long-term medians. Error analysis indicated that the estimated groundwater-level contours generally were lower than observed measurements, with a root-mean-squared error of 33.52 feet. Groundwater levels in Fauquier County are controlled largely by topography: low levels in the crystalline Blue Ridge aquifers in the northwestern part of the county contrast markedly with higher levels in the sedimentary Mesozoic Basin aquifers in the southeast. At current levels of groundwater withdrawal, and at the scale and scope of the synoptic survey, no cones of depression in the groundwater surface were detected. The Fauquier County groundwater-level contour map is available as a U.S. Geological Survey data release.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225014","collaboration":"Prepared in cooperation with the Fauquier County Board of Supervisors and the Virginia Department of Environmental Quality","usgsCitation":"Kearns, M.R., and McCoy, K.J., 2022, Groundwater-level contour map of Fauquier County, Virginia, October–November 2018: U.S. Geological Survey Scientific Investigations Report 2022–5014, 17 p., https://doi.org/10.3133/sir20225014.","productDescription":"Report: vi, 17 p.; Data Release","numberOfPages":"17","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-131366","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":397359,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225014/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":397301,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5014/images/"},{"id":397299,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JM7GYZ","text":"USGS data release","linkHelpText":"Raster and vector geospatial data of interpolated groundwater level altitude associated with a groundwater-level map of Fauquier County, Virginia, October - November 2018"},{"id":502300,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112714.htm","linkFileType":{"id":5,"text":"html"}},{"id":397298,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5014/sir20225014.pdf","text":"Report","size":"5.57 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5014"},{"id":397300,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5014/sir20225014.XML"},{"id":397297,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5014/coverthb.jpg"}],"country":"United States","state":"Virginia","county":"Fauquier County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-77.9612,39.0154],[-77.6568,38.9437],[-77.6617,38.937],[-77.6674,38.9203],[-77.6787,38.8978],[-77.6875,38.8752],[-77.7032,38.8868],[-77.7041,38.8718],[-77.7169,38.8562],[-77.7164,38.8285],[-77.6594,38.7483],[-77.6247,38.6979],[-77.5599,38.6036],[-77.5378,38.5715],[-77.534,38.5606],[-77.5271,38.5546],[-77.5602,38.5283],[-77.5722,38.5195],[-77.5842,38.5088],[-77.6174,38.4753],[-77.6289,38.4627],[-77.6314,38.4583],[-77.6299,38.4492],[-77.6335,38.4433],[-77.629,38.4383],[-77.6311,38.4243],[-77.6342,38.4189],[-77.6338,38.4089],[-77.642,38.4099],[-77.653,38.416],[-77.6682,38.4198],[-77.6792,38.425],[-77.6892,38.4256],[-77.6945,38.4252],[-77.704,38.4213],[-77.7111,38.4209],[-77.7158,38.421],[-77.7205,38.4192],[-77.7235,38.417],[-77.7277,38.4134],[-77.7307,38.4121],[-77.7342,38.4126],[-77.7365,38.4149],[-77.7392,38.4218],[-77.7415,38.425],[-77.7491,38.4282],[-77.7549,38.4315],[-77.7589,38.4366],[-77.7617,38.4429],[-77.7627,38.4497],[-77.7619,38.4565],[-77.7624,38.4593],[-77.7659,38.4607],[-77.7712,38.4621],[-77.7752,38.4658],[-77.7763,38.4681],[-77.775,38.4744],[-77.7802,38.4804],[-77.7824,38.4867],[-77.7864,38.4891],[-77.7893,38.4909],[-77.7927,38.4973],[-77.8002,38.5042],[-77.8018,38.512],[-77.8069,38.5184],[-77.8103,38.5238],[-77.8132,38.5289],[-77.8196,38.5312],[-77.8213,38.5326],[-77.8265,38.5377],[-77.8341,38.5382],[-77.8364,38.5396],[-77.8439,38.5474],[-77.8473,38.552],[-77.8508,38.5561],[-77.8583,38.5617],[-77.8612,38.5653],[-77.8634,38.5699],[-77.8645,38.5749],[-77.8674,38.5781],[-77.872,38.5818],[-77.8737,38.5854],[-77.8742,38.59],[-77.8711,38.594],[-77.8664,38.5953],[-77.864,38.598],[-77.8651,38.6007],[-77.8691,38.6044],[-77.8714,38.6085],[-77.8701,38.6121],[-77.8612,38.6161],[-77.8588,38.6188],[-77.8599,38.6206],[-77.8669,38.6239],[-77.8698,38.628],[-77.8703,38.6316],[-77.8767,38.6331],[-77.8767,38.6362],[-77.8754,38.6394],[-77.873,38.6439],[-77.8705,38.6498],[-77.8751,38.6553],[-77.8802,38.6621],[-77.8813,38.6653],[-77.8854,38.6667],[-77.8914,38.6632],[-77.8961,38.6632],[-77.9007,38.6669],[-77.9036,38.6706],[-77.9023,38.6751],[-77.8975,38.6787],[-77.898,38.6832],[-77.9033,38.6869],[-77.9055,38.6919],[-77.9083,38.6969],[-77.9136,38.6993],[-77.9248,38.6999],[-77.9307,38.699],[-77.9449,38.697],[-77.9602,38.6994],[-77.9655,38.6999],[-77.9707,38.7023],[-77.976,38.7046],[-77.9812,38.7069],[-77.9865,38.7093],[-77.9918,38.7116],[-77.9952,38.7144],[-77.9999,38.7176],[-78.0045,38.7213],[-78.0085,38.7295],[-78.0142,38.7382],[-78.0171,38.7418],[-78.0158,38.7459],[-78.0163,38.7504],[-78.0198,38.7541],[-78.0251,38.7551],[-78.0273,38.7614],[-78.0261,38.7659],[-78.0254,38.77],[-78.0241,38.7745],[-78.0211,38.7786],[-78.024,38.7822],[-78.0292,38.7859],[-78.0297,38.7905],[-78.0284,38.7963],[-78.0313,38.7991],[-78.0402,38.7978],[-78.0449,38.8002],[-78.0496,38.8007],[-78.0518,38.8057],[-78.074,38.8209],[-78.0974,38.8293],[-78.1043,38.8403],[-78.1166,38.8472],[-78.1223,38.8563],[-78.1317,38.8633],[-78.1268,38.8718],[-78.117,38.8862],[-78.1141,38.8871],[-78.1118,38.8821],[-78.1083,38.8793],[-78.1024,38.8801],[-78.0934,38.8855],[-78.0875,38.8863],[-78.0787,38.8821],[-78.0763,38.8821],[-78.0692,38.8866],[-78.0596,38.8887],[-78.0578,38.8901],[-78.0578,38.8928],[-78.0619,38.896],[-78.0641,38.9015],[-78.074,38.9088],[-78.074,38.9115],[-78.0672,38.9233],[-78.0641,38.9318],[-78.0617,38.9336],[-78.0504,38.9367],[-78.0379,38.9415],[-78.0336,38.9505],[-78.0233,38.959],[-78.0172,38.9703],[-78.01,38.9765],[-78.0045,38.9819],[-77.9882,38.9994],[-77.969,39.01],[-77.9612,39.0154]]]},\"properties\":{\"name\":\"Fauquier\",\"state\":\"VA\"}}]}","contact":"Director, Virginia and West Virginia Water Science Center
U.S. Geological Survey
730 East Parham Road
Richmond, Virginia 23228
This study is part of the Coastal Protection and Restoration Authority (CPRA) Louisiana Barrier Island Comprehensive Monitoring (BICM) program. The goal of the BICM program is to provide long-term data on the barrier islands of Louisiana for monitoring change and assisting in coastal management. The BICM program uses historical data and acquires new data to map and monitor shoreline position, sediment properties, topography, bathymetry, and habitat. Since 2006, the U.S. Geological Survey (USGS) has collected geophysical and sedimentologic data across the Breton National Wildlife Refuge (BNWR) through the BICM program and collaborative USGS projects such as the Barrier Island Evolution Research project (under CPRA contract number 2000339324, BICM2–Chandeleurs TopoBathy DEM), which builds upon the previous BICM physical assessment of the BNWR outlined in a separate report. This project uses topographic and bathymetric data from three periods (1917–1922, 2006–2007, and 2013–2015) to develop digital elevation models (DEMs), measure elevation change, and calculate sediment budgets for the barrier island system. The sediment budget analysis, derived from the volumetric change between the three periods, is necessary for understanding sediment transport dynamics along barrier islands and providing information for effective coastal management. This report describes the methods used to acquire, process, and produce these products.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221020","collaboration":"Prepared in cooperation with the Coastal Protection and Restoration Authority of Louisiana","programNote":"Louisiana Barrier Island Comprehensive Monitoring Program 2015–2020","usgsCitation":"Flocks, J.G., Forde, A.S., and Bernier, J.C., 2022, Chandeleur Islands to Breton Island bathymetric and topographic datasets and operational sediment budget development—Methodology and analysis report: U.S. Geological Survey Open-File Report 2022–1020, 48 p., https://doi.org/10.3133/ofr20221020.","productDescription":"ix, 48 p.","numberOfPages":"48","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-122915","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":397352,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20231020/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":397307,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1020/coverthb.jpg"},{"id":397308,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1020/ofr20221020.pdf","text":"Report","size":"47.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1020"},{"id":397309,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1020/images/"},{"id":397310,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1020/ofr20221020.XML"},{"id":501765,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112713.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Louisiana","otherGeospatial":"Breton Island, Breton National Wildlife Refuge, Chandeleur Islands, Curlew Shoals, Grand Gosier Shoals, Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.22477722167967,\n 29.351656186711196\n ],\n [\n -88.83064270019531,\n 29.438999582891338\n ],\n [\n -88.61228942871094,\n 29.685070141332993\n ],\n [\n -88.59992980957031,\n 29.956124387148986\n ],\n [\n -88.72833251953125,\n 30.19439868711761\n ],\n [\n -89.09431457519531,\n 30.064934211006477\n ],\n [\n -89.00230407714844,\n 29.854341876042557\n ],\n [\n -89.14306640625,\n 29.664189403696138\n ],\n [\n -89.36073303222656,\n 29.467101009006807\n ],\n [\n -89.22477722167967,\n 29.351656186711196\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
Fisheries managers and ecologists use statistical models to estimate population-level relations and demographic rates (e.g., length-maturity curves, growth curves, and mortality rates). These relations and rates provide insight into populations and inputs for other models. For example, growth curves may vary across lakes showing fish populations differ due to management actions or underlying environmental conditions. A fisheries manager could use this information to set lake-specific harvest limits or an ecologist could use this information to test scientific hypotheses about fish populations. The above example also demonstrates how populations exist within hierarchical structures where sub-populations may be nested within a meta-population. More generally, these hierarchical structures may be both biological (e.g., different lakes or river pools) and statistical (e.g., correlated error structures). Currently, limited options exist for fitting these hierarchical models and people seeking to use them often must program their own implementations. Furthermore, many fisheries managers and researchers may not have Bayesian programming skills, but many can use interactive languages such as R. Additionally, programs such as JAGS often require long run times (e.g., hours if not days) to fit hierarchical models and programs such as Stan can be more difficult to program because it is a compiled language. We created fishStan to share hierarchical models for fisheries and ecology in an easy-to-use R package.
","language":"English","publisher":"Open Journals","doi":"10.21105/joss.03444","usgsCitation":"Erickson, R.A., Stich, D.S., and Hebert, J.L., 2022, FishStan: Hierarchical Bayesian models for fisheries: Journal of Open Source Software, v. 7, no. 71, 3444, 2 p., https://doi.org/10.21105/joss.03444.","productDescription":"3444, 2 p.","ipdsId":"IP-125667","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":448415,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.21105/joss.03444","text":"Publisher Index Page"},{"id":397534,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"71","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":838685,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stich, Daniel S.","contributorId":280276,"corporation":false,"usgs":false,"family":"Stich","given":"Daniel","email":"","middleInitial":"S.","affiliations":[{"id":33660,"text":"SUNY Oneonta","active":true,"usgs":false}],"preferred":false,"id":838686,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hebert, Jillian Lee 0000-0003-4893-8287","orcid":"https://orcid.org/0000-0003-4893-8287","contributorId":289197,"corporation":false,"usgs":true,"family":"Hebert","given":"Jillian","email":"","middleInitial":"Lee","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":838687,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70229834,"text":"70229834 - 2022 - Using carbon, nitrogen, and mercury isotope values to distinguish mercury sources to Alaskan lake trout","interactions":[],"lastModifiedDate":"2022-04-26T12:13:45.041649","indexId":"70229834","displayToPublicDate":"2022-03-21T11:32:50","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7485,"text":"Environmental Science and Technology Letters","active":true,"publicationSubtype":{"id":10}},"title":"Using carbon, nitrogen, and mercury isotope values to distinguish mercury sources to Alaskan lake trout","docAbstract":"Lake trout (Salvelinus namaycush), collected from 13 remote lakes located in southwestern Alaska, were analyzed for carbon, nitrogen, and mercury (Hg) stable isotope values to assess the importance of migrating oceanic salmon, volcanic activity, and atmospheric deposition to fish Hg burden. Methylmercury (MeHg) bioaccumulation in phytoplankton (5.0–6.9 kg L–1) was also measured to quantify the basal uptake of MeHg to these aquatic food webs. Hg isotope values in lake trout revealed that while the extent of precipitation-delivered Hg was similar across the entire study area, volcanic Hg is likely an important additional source to lake trout in proximate lakes. In contrast, migratory salmon (Oncorhynchus nerka) deliver little MeHg to lake trout directly, although indirect delivery processes via decay could exist. A high level of variability in carbon, nitrogen, and Hg isotope values indicates niche partitioning in lake trout populations within each lake and that a complex suite of ecological interactions is occurring, complicating the conceptually linear assessment of the contaminant source to the receiving organism. Without connecting energy and contaminant isotope axes, we would not have understood why lake trout from these pristine lakes have highly variable Hg burdens despite consistently low water Hg and comparable age-length dynamics.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.estlett.2c00096","usgsCitation":"Lepak, R., Ogorek, J.M., Bartz, K.K., Janssen, S., Tate, M., Runsheng, Y., Hurley, J., Young, D.B., Eagles-Smith, C., and Krabbenhoft, D.P., 2022, Using carbon, nitrogen, and mercury isotope values to distinguish mercury sources to Alaskan lake trout: Environmental Science and Technology Letters, v. 9, no. 4, p. 312-319, https://doi.org/10.1021/acs.estlett.2c00096.","productDescription":"8 p.","startPage":"312","endPage":"319","ipdsId":"IP-132699","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":448420,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9171711","text":"External Repository"},{"id":435917,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UEP9C5","text":"USGS data release","linkHelpText":"Assessment of mercury sources in Alaskan lake food webs (version 1.1, September 2023)"},{"id":397405,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Katmai National Park and preserve, Lake Clark Nation Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -152.325439453125,\n 60.31606836555203\n ],\n [\n -152.138671875,\n 60.8663124746226\n ],\n [\n -152.75390624999997,\n 60.94644199944748\n ],\n [\n -152.874755859375,\n 61.554109444927185\n ],\n [\n -153.643798828125,\n 61.543641475549954\n ],\n [\n -154.017333984375,\n 61.26495144723964\n ],\n [\n -154.368896484375,\n 61.079544234557304\n ],\n [\n -154.53369140625,\n 61.01040072727077\n ],\n [\n -154.44580078125,\n 60.71619779357714\n ],\n [\n -154.786376953125,\n 60.392147922518845\n ],\n [\n -155.072021484375,\n 60.08676274626006\n ],\n [\n -154.742431640625,\n 59.84481485969105\n ],\n [\n -154.127197265625,\n 59.938504253195234\n ],\n [\n -153.5009765625,\n 59.89444803635239\n ],\n [\n -153.1494140625,\n 59.84481485969105\n ],\n [\n -152.68798828125,\n 59.866883195210214\n ],\n [\n -152.325439453125,\n 60.31606836555203\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.0830078125,\n 57.80965135970151\n ],\n [\n -154.09423828124997,\n 58.15911242952296\n ],\n [\n -153.182373046875,\n 58.876263846088314\n ],\n [\n -153.841552734375,\n 59.12522577945005\n ],\n [\n -154.40185546875,\n 59.06315402462662\n ],\n [\n -154.84130859375,\n 59.108308258604964\n ],\n [\n -155.0390625,\n 59.16466752496466\n ],\n [\n -155.225830078125,\n 59.02924933736396\n ],\n [\n -155.709228515625,\n 59.00662762374203\n ],\n [\n -156.55517578125,\n 58.78528524510292\n ],\n [\n -156.55517578125,\n 58.57398108438837\n ],\n [\n -156.412353515625,\n 58.56252272853734\n ],\n [\n -156.236572265625,\n 58.23438030939462\n ],\n [\n -155.7421875,\n 57.97315745102812\n ],\n [\n -155.0830078125,\n 57.80965135970151\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-03-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Lepak, Ryan F. 0000-0003-2806-1895","orcid":"https://orcid.org/0000-0003-2806-1895","contributorId":210990,"corporation":false,"usgs":false,"family":"Lepak","given":"Ryan F.","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":838498,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ogorek, Jacob M. 0000-0002-6327-0740 jmogorek@usgs.gov","orcid":"https://orcid.org/0000-0002-6327-0740","contributorId":4960,"corporation":false,"usgs":true,"family":"Ogorek","given":"Jacob","email":"jmogorek@usgs.gov","middleInitial":"M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":838499,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bartz, Krista K.","contributorId":200705,"corporation":false,"usgs":false,"family":"Bartz","given":"Krista","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":838500,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Janssen, Sarah E. 0000-0003-4432-3154","orcid":"https://orcid.org/0000-0003-4432-3154","contributorId":210991,"corporation":false,"usgs":true,"family":"Janssen","given":"Sarah E.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":838501,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tate, Michael T. 0000-0003-1525-1219 mttate@usgs.gov","orcid":"https://orcid.org/0000-0003-1525-1219","contributorId":3144,"corporation":false,"usgs":true,"family":"Tate","given":"Michael T.","email":"mttate@usgs.gov","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":838502,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Runsheng, Yin","contributorId":288959,"corporation":false,"usgs":false,"family":"Runsheng","given":"Yin","email":"","affiliations":[{"id":32415,"text":"Chinese Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":838503,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hurley, James P.","contributorId":147931,"corporation":false,"usgs":false,"family":"Hurley","given":"James P.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":838504,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Young, Daniel","contributorId":58468,"corporation":false,"usgs":false,"family":"Young","given":"Daniel","affiliations":[{"id":35763,"text":"National Park Service, Lake Clark National Park and Preserve, Port Alsworth, AK","active":true,"usgs":false}],"preferred":false,"id":838505,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Eagles-Smith, Collin A. 0000-0003-1329-5285","orcid":"https://orcid.org/0000-0003-1329-5285","contributorId":221745,"corporation":false,"usgs":true,"family":"Eagles-Smith","given":"Collin A.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":838506,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Krabbenhoft, David P. 0000-0003-1964-5020 dpkrabbe@usgs.gov","orcid":"https://orcid.org/0000-0003-1964-5020","contributorId":1658,"corporation":false,"usgs":true,"family":"Krabbenhoft","given":"David","email":"dpkrabbe@usgs.gov","middleInitial":"P.","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":838507,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70229832,"text":"ofr20221010 - 2022 - Documentation of models describing relations between continuous real-time and discrete water-quality constituents in the Little Arkansas River, south-central Kansas, 1998–2019","interactions":[],"lastModifiedDate":"2026-03-27T19:46:42.747184","indexId":"ofr20221010","displayToPublicDate":"2022-03-21T10:33:31","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1010","displayTitle":"Documentation of Models Describing Relations Between Continuous Real-Time and Discrete Water-Quality Constituents in the Little Arkansas River, South-Central Kansas, 1998–2019","title":"Documentation of models describing relations between continuous real-time and discrete water-quality constituents in the Little Arkansas River, south-central Kansas, 1998–2019","docAbstract":"Data were collected at two monitoring sites along the Little Arkansas River in south-central Kansas that bracket most of the easternmost part of the Equus Beds aquifer. The data were used as part of the city of Wichita’s aquifer storage and recovery project to evaluate source water quality. The U.S. Geological Survey, in cooperation with the City of Wichita, has continued to monitor the water quality of these sites through 2019 to update previously published regression-based models using continuously measured physicochemical properties and discretely sampled water-quality constituents of interest. The purpose of this report is to provide an update of the previously published linear regression models that have been used to continuously compute estimates of water-quality constituent concentrations or densities at these two sites. Water-quality constituent model updates include those for dissolved and suspended solids, suspended-sediment concentration, hardness, alkalinity, primary ions (bicarbonate, calcium, sodium, chloride, and sulfate), nutrients (total Kjeldahl nitrogen and total phosphorus), total organic carbon, indicator bacteria (Escherichia coli and fecal coliform bacteria), a trace element (arsenic), and a pesticide (atrazine).
Regression analyses were used to develop surrogate models that related continuously measured physicochemical properties, streamflow, and seasonal components to discretely sampled water-quality constituent concentrations or densities. Specific conductance was an explanatory variable for dissolved solids, primary ions, and atrazine. Turbidity was an explanatory variable for total suspended solids and sediment, nutrients, total organic carbon, and indicator bacteria. Streamflow and water temperature were explanatory variables for dissolved arsenic. Seasonal components were included as explanatory variables for atrazine models. The amount of variance explained by most of the updated models was within 5 percent of previously published models.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221010","collaboration":"Prepared in cooperation with the City of Wichita, Kansas","usgsCitation":"Stone, M.L., and Klager, B.J., 2022, Documentation of models describing relations between continuous real-time and discrete water-quality constituents in the Little Arkansas River, south-central Kansas, 1998–2019: U.S. Geological Survey Open-File Report 2022–1010, 34 p., https://doi.org/10.3133/ofr20221010.","productDescription":"Report: vii, 34 p.; 2 Appendixes; Dataset","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-126572","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":397345,"rank":8,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20221010/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":397333,"rank":7,"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":397331,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2022/1010/ofr20221010_appendix1.zip","text":"Appendix 1","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"—Model Archive Summaries for the Little Arkansas River at Highway 50 near Halstead, Kansas (Halstead Site; U.S. Geological Survey Station Number 07143672)"},{"id":501756,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112715.htm","linkFileType":{"id":5,"text":"html"}},{"id":397330,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1010/images"},{"id":397332,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2022/1010/ofr20221010_appendix2.zip","text":"Appendix 2","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"—Model Archive Summaries for the Little Arkansas River near Sedgwick, Kansas (Sedgwick Site; U.S. Geological Survey Station Number 07144100)"},{"id":397329,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1010/ofr20221010.XML"},{"id":397328,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1010/ofr20221010.pdf","text":"Report","size":"1.82 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1010"},{"id":397327,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1010/coverthb.jpg"}],"country":"United States","state":"Kansas","otherGeospatial":"Little Arkansas River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.1667,\n 37.714244967649265\n ],\n [\n -97.1667,\n 37.714244967649265\n ],\n [\n -97.1667,\n 38.533333\n ],\n [\n -98.1667,\n 38.533333\n ],\n [\n -98.1667,\n 37.714244967649265\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
Lake Powell is the second largest constructed water reservoir by storage capacity in the United States and represents a critical component in management of water resources in the Colorado River Basin. The reservoir provides hydroelectric power generation at Glen Canyon Dam, banks water storage for the Upper Colorado River Basin, stabilizes water commitments downstream, and buffers the Lower Colorado River Basin, including Lake Mead, against sedimentation and fluctuations in hydrological conditions. With completion of the dam in 1963, Lake Powell steadily filled with water before reaching full pool in 1980 and has become a popular destination for recreation, welcoming more than 4 million visitors per year. Since the early 2000s, severe drought and increases in water demand have resulted in a significant drop in reservoir elevation and stored water, prompting a heightened level of interest in the current state and future of Lake Powell.
Beginning in 2017, the U.S. Geological Survey, in cooperation with the Bureau of Reclamation, completed topobathymetric surveys of Lake Powell for the first update of elevation-area-capacity relationships since 1986. This report presents results of these surveys and comparisons with estimates from previous surveys. The storage volume and surface area, as of completion of the topobathymetric survey in spring 2018, are calculated at 0.33-foot (0.10-meter) increments for elevations ranging from 3,120.08 to 3,717.19 feet above the North American Vertical Datum of 1988 (NAVD 88). Between 0.33-foot increments, the storage volumes and areas were linearly interpolated at 0.01-foot intervals. Interpolation error in the 0.01-foot interval estimates was assessed at lower (3,160.00–3,161.00 feet above NAVD 88), middle (3,400.00–3,401.00 feet above NAVD 88), and upper (3,700.00–3,711.00 feet above NAVD 88) elevations. The interpolated storage capacity and area estimates are comparable to the measured values with differences ranging from 0.00 to 0.02 percent and from −0.01 to 0.03 percent, respectively.
Current storage capacity at full pool (3702.91 feet above NAVD 88) is 25,160,000 acre-feet. Compared to previously published estimates, this volume represents a 6.79 percent or 1,833,000-acre-foot decrease in storage capacity from 1963 to 2018 and a 4.00 percent or 1,049,000-acre-foot decrease from 1986 to 2018. Areal extent, as of spring 2018, at full pool is 159,200 acres, which represents a 1.33-percent decrease from 1963 to 2018 and a 0.96 percent decrease from 1986 to 2018.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225017","collaboration":"Prepared in cooperation with the Bureau of Reclamation","programNote":"Water Resources Mission Area","usgsCitation":"Root, J.C., and Jones, D.K., 2022, Elevation-area-capacity relationships of Lake Powell in 2018 and estimated loss of storage capacity since 1963: U.S. Geological Survey Scientific Investigations Report 2022–5017, 21 p., https://doi.org/10.3133/sir20225017.","productDescription":"Report: vii, 21 p.; 2 Data Releases","numberOfPages":"21","onlineOnly":"Y","ipdsId":"IP-120332","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":502368,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112711.htm","linkFileType":{"id":5,"text":"html"}},{"id":396681,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O3IPG3","text":"Elevation-area-capacity tables for Lake Powell, 2018","description":"Jones, D.K., and Root, J.C, 2022, Elevation-area-capacity tables for Lake Powell, 2018: U.S. Geological Survey data release, https://doi.org/10.5066/P9O3IPG3."},{"id":396676,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5017/coverthb.jpg"},{"id":396677,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5017/sir20225017.pdf","text":"Report","size":"7 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":396678,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5017/sir20225017.xml"},{"id":396679,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5017/images"},{"id":396680,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9H60YCF","text":"Modified topobathymetric elevation data for Lake Powell","description":"Jones, D.K., and Root, J.C., 2021, Modified topobathymetric elevation data for Lake Powell: U.S. Geological Survey data release, https://doi.org/10.5066/P9H60YCF."}],"country":"United States","state":"Arizona, Utah","otherGeospatial":"Colorado River, Glen Canyon Dam, Glen Canyon National Recreation Area, Lake Powell","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.06054687499999,\n 36.39033486213649\n ],\n [\n -109.566650390625,\n 36.39033486213649\n ],\n [\n -109.566650390625,\n 38.49229419236133\n ],\n [\n -112.06054687499999,\n 38.49229419236133\n ],\n [\n -112.06054687499999,\n 36.39033486213649\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Utah Water Science Center
U.S. Geological Survey
2329 West Orton Circle
Salt Lake City, Utah 84119-2047
Bennett et al. (2021, Science 373, 1528–1531) reported that ancient human footprints discovered in White Sands National Park, New Mexico date to between ∼23,000 and 21,000 years ago. Haynes (2022, PaleoAmerica, this issue) proposes two alternate hypotheses to explain the antiquity of the footprints. One is that they were made by humans crossing over older sediments sometime during the Holocene. This is incorrect as there are Pleistocene megafauna tracks interspersed with the human footprints, so they cannot be Holocene in age. The other hypothesis maintains seeds used to date the human footprints were exhumed from older sediments, transported across the Tularosa Basin, and deposited on moist ground that was traversed by Clovis people at ∼13,000 years ago. This scenario requires a series of events that are highly unlikely, if not impossible. We maintain the seeds were collected from their original depositional context and the ages of the footprints fall within the Last Glacial Maximum.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/20555563.2022.2039863","usgsCitation":"Pigati, J.S., Springer, K.B., Holliday, V.T., Bennett, M.R., Bustos, D., Urban, T.M., Reynolds, S.C., and Odess, D., 2022, Reply to “Evidence for humans at White Sands National Park during the Last Glacial Maximum could actually be for Clovis people ~13,000 years ago” by C. Vance Haynes, Jr.: PaleoAmerica, v. 8, no. 2, p. 99-101, https://doi.org/10.1080/20555563.2022.2039863.","productDescription":"13 p.","startPage":"99","endPage":"101","ipdsId":"IP-137468","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":397391,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"White Sands National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.48223876953125,\n 32.676372772089834\n ],\n [\n -106.12792968749999,\n 32.676372772089834\n ],\n [\n -106.12792968749999,\n 32.88189375925038\n ],\n [\n -106.48223876953125,\n 32.88189375925038\n ],\n [\n -106.48223876953125,\n 32.676372772089834\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-03-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Pigati, Jeffrey S. 0000-0001-5843-6219 jpigati@usgs.gov","orcid":"https://orcid.org/0000-0001-5843-6219","contributorId":201167,"corporation":false,"usgs":true,"family":"Pigati","given":"Jeffrey","email":"jpigati@usgs.gov","middleInitial":"S.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":838585,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Springer, Kathleen B. 0000-0002-2404-0264 kspringer@usgs.gov","orcid":"https://orcid.org/0000-0002-2404-0264","contributorId":149826,"corporation":false,"usgs":true,"family":"Springer","given":"Kathleen","email":"kspringer@usgs.gov","middleInitial":"B.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":838586,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holliday, Vance T.","contributorId":265971,"corporation":false,"usgs":false,"family":"Holliday","given":"Vance","email":"","middleInitial":"T.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":838587,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bennett, Matthew R.","contributorId":265968,"corporation":false,"usgs":false,"family":"Bennett","given":"Matthew","email":"","middleInitial":"R.","affiliations":[{"id":54847,"text":"Bournemouth University, U.K.","active":true,"usgs":false}],"preferred":false,"id":838588,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bustos, David","contributorId":265969,"corporation":false,"usgs":false,"family":"Bustos","given":"David","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":838589,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Urban, Thomas M.","contributorId":271168,"corporation":false,"usgs":false,"family":"Urban","given":"Thomas","email":"","middleInitial":"M.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":838590,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Reynolds, Sally C.","contributorId":265972,"corporation":false,"usgs":false,"family":"Reynolds","given":"Sally","email":"","middleInitial":"C.","affiliations":[{"id":54847,"text":"Bournemouth University, U.K.","active":true,"usgs":false}],"preferred":false,"id":838591,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Odess, Daniel","contributorId":265975,"corporation":false,"usgs":false,"family":"Odess","given":"Daniel","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":838592,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70229815,"text":"sir20225015 - 2022 - Distribution of streamflow, sediment, and nutrients entering Galveston Bay from the Trinity River, Texas, 2016–19","interactions":[],"lastModifiedDate":"2026-04-08T17:35:39.5195","indexId":"sir20225015","displayToPublicDate":"2022-03-21T07:50:59","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5015","displayTitle":"Distribution of Streamflow, Sediment, and Nutrients Entering Galveston Bay from the Trinity River, Texas, 2016–19","title":"Distribution of streamflow, sediment, and nutrients entering Galveston Bay from the Trinity River, Texas, 2016–19","docAbstract":"The U.S. Geological Survey (USGS), in cooperation with the Texas Water Development Board, collected streamflow and water-quality data at USGS monitoring stations in the lower Trinity River Basin from January 2016 to December 2019 to characterize streamflow, nutrients, and suspended sediment entering Galveston Bay from the Trinity River. Results from previous studies indicate that water from the main channel of the Trinity River is diverted into surrounding wetlands and water bodies and is stored or discharged directly into Galveston Bay through distributary channels in the delta. This study provides an assessment of the distribution of streamflow in the various channels that form the delta of the Trinity River to evaluate the effects of streamflow diversions on the eventual supply of freshwater, nutrients, and suspended sediment to Galveston Bay.
Instantaneous streamflow data and continuous streamflow records from USGS monitoring stations in the delta of the Trinity River were used to quantify freshwater inflow into Galveston Bay and assess the distribution of streamflow in the lowermost reaches of the Trinity River Basin. In this report, periods in which releases from Lake Livingston caused a rise in streamflow farther downstream at USGS station 08067000 Trinity River at Liberty, Tex. (hereinafter referred to as the “Liberty site”) that did not exceed 20,000 cubic feet per second (ft3/s) are referred to as “low-flow events,” and periods in which streamflow at the Liberty site exceeded 20,000 ft3/s are referred to as “high-flow events.”
During this study, it was estimated that only about 55 percent of the total water volume released from Lake Livingston was accounted for at USGS station 08067252 Trinity River at Wallisville, Tex. (hereinafter referred to as the “Wallisville site”), which is approximately 8 river miles upstream from where the Trinity River enters Galveston Bay. The difference in water volumes between what is released from Lake Livingston and what is measured at the Wallisville site is consistent with findings from previous studies and indicates that a large part of the volume released from Lake Livingston does not reach Galveston Bay through the main channel of the Trinity River.
To assess the distribution of streamflow and estimate the amount of water diverted from the main channel of the Trinity River into distributary channels, instantaneous streamflow measurements were made at USGS station 08067230 Old River Lake near Wallisville, Tex. (hereinafter referred to as the “Old River Lake site”) and the Wallisville site during a range of hydrologic conditions. Results indicate that a large portion of the freshwater inflow was likely delivered to Galveston Bay through pathways other than the main channel of the Trinity River, including Old River Lake. When streamflow at the Liberty site, located upstream from the Wallisville site, exceeded approximately 40,000 ft3/s, Old River Lake and its network of hydrologically connected channels likely became the primary pathway for freshwater inflow entering Galveston Bay.
Water quality was characterized from discrete samples collected during a range of hydrologic conditions at the Old River Lake site and the Wallisville site in order to evaluate the effects of streamflow diversions on the supply of suspended sediment and nutrients into Galveston Bay. Suspended-sediment concentrations were typically higher at the Wallisville site than at the Old River Lake site, likely because of lower water velocities at the Old River Lake site than at the Wallisville site; low water velocities allow suspended sediment to settle, thus reducing concentrations. Suspended-sediment loads were also typically higher at the Wallisville site than at the Old River Lake site during high-flow events. However, when streamflows at the Liberty site exceeded approximately 60,000 ft3/s, suspended-sediment loads were higher at the Old River Lake, which likely became the primary pathway for suspended-sediment delivery into Galveston Bay.
Suspended-sediment concentrations and loads were computed at the Wallisville and Liberty sites for the duration of 11 hydrologic events representing different streamflows by using the regression equations developed for each monitoring station. Overall, approximately 25 percent of the total sediment load measured during events at the Liberty site was measured at the Wallisville site, indicating that only a portion of the suspended-sediment load from the Liberty site reached Galveston Bay through the main channel of the Trinity River during the measured events. Based on data from discrete samples, some of this sediment load was diverted into Old River Lake and associated distributary channels.
Results from analysis of nutrient samples indicate that streamflow conditions affect the nitrogen concentrations in the delta of the Trinity River. At the Old River Lake site, nitrate plus nitrite and total dissolved nitrogen concentrations were typically lower during low-flow conditions than during high-flow events; low-flow conditions represent low-flow events or tidal-flow conditions (during low-flow conditions the streamflow at the Liberty site was less than 20,000 ft3/s). Lower concentrations of nitrate plus nitrite and total dissolved nitrogen at the Old River Lake site may be associated with various physical and biogeochemical processes, including the transformation and biological uptake of nitrate, nitrite, and other species of nitrogen resulting from extended water residence times and relatively small inputs of nitrogen from the upstream reaches of the Trinity River Basin. During high-flow events, the proportions of nitrogen species were similar among sites, indicating that the travel path through wetlands and channels surrounding Old River Lake likely does not affect the relative concentrations of the various nitrogen species present in freshwater inflow to Galveston Bay.
Results from analysis of nutrient samples also indicate that the pathways for nutrient delivery from the Trinity River into Galveston Bay are dependent on event magnitude. When streamflows at the Liberty site were low (approximately 20,000 ft3/s), the main channel of the Trinity River was the primary pathway for nitrogen and phosphorus entering Galveston Bay. Once streamflow at the Liberty site exceeded 20,000 ft3/s, however, the contribution of nutrient loading through Old River Lake to Galveston Bay increased proportionally to the nutrient loading in the main channel, and when streamflow at the Liberty site exceeded approximately 50,000 ft3/s, Old River Lake likely became the primary pathway for nutrient delivery into Galveston Bay.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225015","collaboration":"Prepared in cooperation with the Texas Water Development Board","usgsCitation":"Lucena, Z., and Lee, M.T., 2022, Distribution of streamflow, sediment, and nutrients entering Galveston Bay from the Trinity River, Texas, 2016–19: U.S. Geological Survey Scientific Investigations Report 2022–5015, 55 p., https://doi.org/10.3133/sir20225015.","productDescription":"Report: vi, 55 p.; Dataset","numberOfPages":"66","onlineOnly":"N","ipdsId":"IP-126129","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":397341,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225015/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":397266,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":397264,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5015/sir20225015.XML"},{"id":397265,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5015/images"},{"id":397263,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5015/sir20225015.pdf","text":"Report","size":"4.08 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5015"},{"id":397262,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5015/coverthb.jpg"},{"id":502301,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112716.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Texas","otherGeospatial":"Galveston Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.86145019531249,\n 29.260044678228486\n ],\n [\n -94.50714111328125,\n 29.537619205973428\n ],\n [\n -94.71038818359375,\n 29.84302629154662\n ],\n [\n -95.03173828125,\n 29.752455480021393\n ],\n [\n -95.0592041015625,\n 29.59017705987947\n ],\n [\n -94.98504638671875,\n 29.489815619374962\n ],\n [\n -94.921875,\n 29.401319510041485\n ],\n [\n -94.8944091796875,\n 29.305561325527698\n ],\n [\n -94.86145019531249,\n 29.260044678228486\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754-4501