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":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":70238338,"text":"70238338 - 2022 - Spatially integrating microbiology and geochemistry to reveal complex environmental health issues: Anthrax in the contiguous United States","interactions":[],"lastModifiedDate":"2022-11-17T12:39:57.630159","indexId":"70238338","displayToPublicDate":"2022-03-22T06:38:24","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Spatially integrating microbiology and geochemistry to reveal complex environmental health issues: Anthrax in the contiguous United States","docAbstract":"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":70229812,"text":"dr1152 - 2022 - Compendium to invasive annual grass spatial products for the western United States, January 2010-February 2021","interactions":[],"lastModifiedDate":"2022-03-22T15:22:07.660435","indexId":"dr1152","displayToPublicDate":"2022-03-21T17:45:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":9318,"text":"Data Report","code":"DR","onlineIssn":"2771-9448","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1152","displayTitle":"Compendium to Invasive Annual Grass Spatial Products for the Western United States, January 2010–February 2021","title":"Compendium to invasive annual grass spatial products for the western United States, January 2010-February 2021","docAbstract":"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
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
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
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
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
Attempts to mitigate shoreline microbial contamination require a thorough understanding of pollutant sources, which often requires multiple years of data collection (e.g., point/nonpoint) and the interacting factors that influence water quality. Because restoration efforts can alter shoreline or beach morphology, revisiting source inputs is often necessary. Microbial source tracking (MST) using source-specific molecular markers, genomic community analyses, and physical modeling was used to identify contamination sources along three Lake Michigan beaches of the Laurentian Great Lakes with historically high fecal indicator bacteria (FIB, E. coli) concentrations. Genetic markers for human (Bacteroides HF183) and mixed gull species (Catellicoccus marimammalium) fecal sources were tested from water and sediment. Gene sequencing (16S rRNA) was used to identify similarities in bacterial communities in nearshore water, river inputs, sand, sediment, and groundwater. Synoptic surveys of water exchange were conducted to determine nearshore-offshore interactions of FIB. In addition to these MST studies, best management practices to mitigate FIB, including gull deterrence, slope grading, wetland establishment, and shoreline plantings, were reviewed for their effectiveness at reducing FIB concentrations over time. Using multiple tools for MST helped identify primary and secondary sources of FIB (gulls, stormwater inputs) and the physical processes that exacerbate FIB concentrations (onshore currents, limited circulation). Management actions were successful in the short-term at reducing FIB, but scope of success was temporally limited, with FIB concentrations often rebounding. Results highlight the usefulness of MST to inform best management practices and the need for a sustained adaptive approach that adjusts for changes in the coastal system.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2022.01.009","usgsCitation":"Nevers, M., Buszka, P.M., Byappanahalli, M., Cole, T., Corsi, S., Jackson, P.R., Kinzelman, J.L., Nakatsu, C.H., and Phanikumar, M.S., 2022, Microbial source tracking and evaluation of best management practices for restoring degraded beaches of Lake Michigan: Journal of Great Lakes Research, v. 48, no. 2, p. 441-454, https://doi.org/10.1016/j.jglr.2022.01.009.","productDescription":"14 p.","startPage":"441","endPage":"454","ipdsId":"IP-127246","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":448429,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2022.01.009","text":"Publisher Index Page"},{"id":397344,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Wisconsin","city":"Chicago, Racine","otherGeospatial":"Jeorse Park Beach, Lake Michigan, North Beach, 63rd Street Beach","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.9345703125,\n 41.60928183876483\n ],\n [\n -86.7095947265625,\n 41.60928183876483\n ],\n [\n -86.7095947265625,\n 42.84777884235988\n ],\n [\n -87.9345703125,\n 42.84777884235988\n ],\n [\n -87.9345703125,\n 41.60928183876483\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"48","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nevers, Meredith B. 0000-0001-6963-6734","orcid":"https://orcid.org/0000-0001-6963-6734","contributorId":201531,"corporation":false,"usgs":true,"family":"Nevers","given":"Meredith B.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":838533,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Buszka, Paul M. 0000-0001-8218-826X pmbuszka@usgs.gov","orcid":"https://orcid.org/0000-0001-8218-826X","contributorId":1786,"corporation":false,"usgs":true,"family":"Buszka","given":"Paul","email":"pmbuszka@usgs.gov","middleInitial":"M.","affiliations":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true},{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":838534,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Byappanahalli, Muruleedhara 0000-0001-5376-597X","orcid":"https://orcid.org/0000-0001-5376-597X","contributorId":241924,"corporation":false,"usgs":true,"family":"Byappanahalli","given":"Muruleedhara","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":838535,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cole, Travis 0000-0002-0935-381X","orcid":"https://orcid.org/0000-0002-0935-381X","contributorId":289099,"corporation":false,"usgs":false,"family":"Cole","given":"Travis","affiliations":[{"id":62047,"text":"Crane Environmental Services, LLC","active":true,"usgs":false}],"preferred":false,"id":838536,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Corsi, Steven R. 0000-0003-0583-5536 srcorsi@usgs.gov","orcid":"https://orcid.org/0000-0003-0583-5536","contributorId":172002,"corporation":false,"usgs":true,"family":"Corsi","given":"Steven R.","email":"srcorsi@usgs.gov","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":838537,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jackson, P. Ryan 0000-0002-3154-6108 pjackson@usgs.gov","orcid":"https://orcid.org/0000-0002-3154-6108","contributorId":194529,"corporation":false,"usgs":true,"family":"Jackson","given":"P.","email":"pjackson@usgs.gov","middleInitial":"Ryan","affiliations":[{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"preferred":true,"id":838538,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kinzelman, Julie L.","contributorId":236944,"corporation":false,"usgs":false,"family":"Kinzelman","given":"Julie","email":"","middleInitial":"L.","affiliations":[{"id":37612,"text":"City of Racine Health Department","active":true,"usgs":false}],"preferred":false,"id":838539,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Nakatsu, Cindy H 0000-0003-0663-180X","orcid":"https://orcid.org/0000-0003-0663-180X","contributorId":215593,"corporation":false,"usgs":false,"family":"Nakatsu","given":"Cindy","email":"","middleInitial":"H","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":838540,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Phanikumar, Mantha S.","contributorId":147924,"corporation":false,"usgs":false,"family":"Phanikumar","given":"Mantha","email":"","middleInitial":"S.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":838541,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70231859,"text":"70231859 - 2022 - Estimating detection and occupancy of secretive marsh bird species in low and high saline marshes in southwestern Louisiana using automated recording units","interactions":[],"lastModifiedDate":"2023-06-09T13:47:49.119252","indexId":"70231859","displayToPublicDate":"2022-03-19T06:58:02","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":"Estimating detection and occupancy of secretive marsh bird species in low and high saline marshes in southwestern Louisiana using automated recording units","docAbstract":"Secretive marsh birds (SMBs) are important indicator species of coastal wetlands but are difficult to detect and monitor. In coastal Louisiana, an important stronghold for these species, climate and hydrological models predict that freshwater and intermediate marshes will expand in the next 50 years, while brackish marshes will shrink. We used a multi-species Bayesian hierarchical occupancy model to estimate detection and occupancy probabilities for 11 SMB species in low and high saline marshes using data from automated recording units at 33 sites in southwestern Louisiana from February–June 2012. A quadratic effect of Julian date, but not minimum daily temperature nor precipitation affected detection of SMB species. King Rail (Rallus elegans), American Bittern (Botaurus lentiginosus), Common Gallinule (Gallinula galeata), and Pied-billed Grebe (Podilymbus podiceps) occupied mainly freshwater and intermediate marshes. Clapper Rail (Rallus crepitans), Seaside Sparrow (Ammospiza maritima), and Sora (Porzana carolina) predominantly occupied brackish and salt marshes. American Coot (Fulica americana), Purple Gallinule (Porphyrio martinica), Least Bittern (Ixobrychus exilis), and Marsh Wren (Cistothorus palustris) occupied both low and high saline marshes, showing flexibility that could maintain populations of these species as marsh salinities change in the future. If the current distribution of SMB species persists as marsh availability changes under future conditions, populations of the 4 species we found in low saline marshes may increase, whereas populations of at least 2 species found primarily in high saline marshes may decrease. Our modeling indicates that automatic recording units can produce comparable detection probabilities to other studies using traditional SMB sampling methods.
Accurate estimates of animal abundance are essential for guiding effective management, and poor survey data can produce misleading inferences. Aerial surveys are an efficient survey platform, capable of collecting wildlife data across large spatial extents in short timeframes. However, these surveys can yield unreliable data if not carefully executed. Despite a long history of aerial survey use in ecological research, problems common to aerial surveys have not yet been adequately resolved. Through an extensive review of the aerial survey literature over the last 50 years, we evaluated how common problems encountered in the data (including nondetection, counting error, and species misidentification) can manifest, the potential difficulties conferred, and the history of how these challenges have been addressed. Additionally, we used a double-observer case study focused on waterbird data collected via aerial surveys and an online group (flock) counting quiz to explore the potential extent of each challenge and possible resolutions. We found that nearly three quarters of the aerial survey methodology literature focused on accounting for nondetection errors, while issues of counting error and misidentification were less commonly addressed. Through our case study, we demonstrated how these challenges can prove problematic by detailing the extent and magnitude of potential errors. Using our online quiz, we showed that aerial observers typically undercount group size and that the magnitude of counting errors increases with group size. Our results illustrate how each issue can act to bias inferences, highlighting the importance of considering individual methods for mitigating potential problems separately during survey design and analysis. We synthesized the information gained from our analyses to evaluate strategies for overcoming the challenges of using aerial survey data to estimate wildlife abundance, such as digital data collection methods, pooling species records by family, and ordinal modeling using binned data. Recognizing conditions that can lead to data collection errors and having reasonable solutions for addressing errors can allow researchers to allocate resources effectively to mitigate the most significant challenges for obtaining reliable aerial survey data.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.8733","usgsCitation":"Davis, K.L., Silverman, E., Sussman, A., Wilson, R., and Zipkin, E.F., 2022, Errors in aerial survey count data: Identifying pitfalls and solutions: Ecology and Evolution, v. 12, no. 3, e8733, 14 p., https://doi.org/10.1002/ece3.8733.","productDescription":"e8733, 14 p.","ipdsId":"IP-128816","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":448446,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/ece3.8733","text":"External Repository"},{"id":397342,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Florida, Louisiana, Mississippi, Texas","otherGeospatial":"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 -81.0791015625,\n 25.025884063244828\n ],\n [\n -80.85937499999999,\n 25.304303764403617\n ],\n [\n -81.82617187499999,\n 26.78484736105119\n ],\n [\n -82.72705078125,\n 27.819644755099446\n ],\n [\n -82.50732421875,\n 29.017748018496047\n ],\n [\n -84.0673828125,\n 30.240086360983426\n ],\n [\n -85.14404296875,\n 29.783449456820605\n ],\n [\n -85.62744140625,\n 30.315987718557867\n ],\n [\n -86.572265625,\n 30.543338954230222\n ],\n [\n -87.8466796875,\n 30.65681556429287\n ],\n [\n -89.736328125,\n 30.391830328088137\n ],\n [\n -90.7470703125,\n 30.4297295750316\n ],\n [\n -90.54931640625,\n 29.783449456820605\n ],\n [\n -90.85693359375,\n 29.49698759653577\n ],\n [\n -91.95556640625,\n 30.088107753367257\n ],\n [\n -92.4169921875,\n 29.783449456820605\n ],\n [\n -93.7353515625,\n 29.897805610155874\n ],\n [\n -94.52636718749999,\n 29.80251790576445\n ],\n [\n -94.7900390625,\n 29.954934549656144\n ],\n [\n -95.0537109375,\n 29.783449456820605\n ],\n [\n -95.0537109375,\n 29.34387539941801\n ],\n [\n -96.13037109375,\n 28.86391842622456\n ],\n [\n -96.6796875,\n 28.748396571187406\n ],\n [\n -96.92138671875,\n 28.497660832963472\n ],\n [\n -96.9873046875,\n 28.304380682962783\n ],\n [\n -97.42675781249999,\n 28.110748760633534\n ],\n [\n -97.646484375,\n 27.547241546253268\n ],\n [\n -97.6904296875,\n 27.15692045688088\n ],\n [\n -97.3828125,\n 26.05678288577881\n ],\n [\n -95.888671875,\n 25.97779895546436\n ],\n [\n -94.46044921875,\n 27.293689224852407\n ],\n [\n -91.58203125,\n 27.780771643348196\n ],\n [\n -88.11035156249999,\n 27.46928747369202\n ],\n [\n -86.28662109375,\n 27.839076094777816\n ],\n [\n -83.64990234375,\n 26.05678288577881\n ],\n [\n -82.8369140625,\n 24.926294766395593\n ],\n [\n -82.46337890625,\n 24.56710835257599\n ],\n [\n -81.0791015625,\n 25.025884063244828\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-03-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Davis, Kayla L.","contributorId":177595,"corporation":false,"usgs":false,"family":"Davis","given":"Kayla","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":838512,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Silverman, Emily D","contributorId":288964,"corporation":false,"usgs":false,"family":"Silverman","given":"Emily D","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":838513,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sussman, Allison 0000-0002-6996-9982","orcid":"https://orcid.org/0000-0002-6996-9982","contributorId":211294,"corporation":false,"usgs":true,"family":"Sussman","given":"Allison","email":"","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":838511,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wilson, R. Randy","contributorId":288965,"corporation":false,"usgs":false,"family":"Wilson","given":"R. Randy","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":838514,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zipkin, Elise F. 0000-0003-4155-6139","orcid":"https://orcid.org/0000-0003-4155-6139","contributorId":192755,"corporation":false,"usgs":false,"family":"Zipkin","given":"Elise","email":"","middleInitial":"F.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":838515,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70229768,"text":"70229768 - 2022 - Inherit the kingdom or storm the castle? Breeding strategies in a social carnivore","interactions":[],"lastModifiedDate":"2022-03-17T14:59:55.730675","indexId":"70229768","displayToPublicDate":"2022-03-17T09:54:03","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1589,"text":"Ethology","active":true,"publicationSubtype":{"id":10}},"title":"Inherit the kingdom or storm the castle? Breeding strategies in a social carnivore","docAbstract":"Breeding opportunities are inherently limited for animals that live and breed in groups. Turnover in breeding positions can have marked effects on groups of cooperative breeders, particularly social carnivores. We generally know little about how breeding vacancies are filled in social carnivores and what factors might influence an individual's ability to successfully fill a vacancy. I used a long-term (11 years) genetic dataset from gray wolves to ask whether breeding vacancies were filled by individuals from within groups or by adoptees (i.e., adult animals immigrating into the group) from outside the group. Males were three times more likely than females to be adopted into breeding positions outside their group. Females typically inherited breeding positions within their natal groups (80%, n = 20), while males obtained breeding positions outside their group (76%, n = 17). Group size did not influence whether a breeding vacancy was filled by an adoptee or inherited by an individual from within the group. Prior to adoption, genetic relatedness was 30% higher in groups when females were adopted into breeding positions compared to when they inherited breeding positions from within groups. Thus, genetic relatedness within groups appears to play a role in whether females are adopted into groups or not. Because of their strong reliance on dispersal to secure a breeding position, male wolves appear to be the couriers of genetic diversity in populations of gray wolves. Many states in the United States have recently implemented hunting and trapping seasons for gray wolves. If dispersing male wolves are disproportionately harvested, genetic connectivity and diversity in populations may be affected.
Regional-scale liquefaction hazard analyses are necessary for resilience planning and prioritization of seismic upgrades for critical distributed infrastructure such as levees, pipelines, roadways, and electrical transmission facilities. Two approaches are often considered for liquefaction hazard analysis of distributed infrastructure: (1) conventional, site-specific probe or borehole-based analyses, which do not quantify the uncertainty between investigation locations; or (2) surface geology-based analyses, which often neglect localized geotechnical properties and include a great amount of uncertainty. We describe an analytical method to unify the disparate site-specific and deposit-scale approaches using Gaussian processes. We use borehole data to produce spatial fields of random variables for liquefaction triggering analyses, such as groundwater elevation, soil texture classification, penetration resistance, and cyclic resistance ratio that converge to the site-specific uncertainty at sampling locations but also quantify the uncertainty in-between sampling locations. We demonstrate the effectiveness of Gaussian process models for regional-scale liquefaction hazard analyses in two example studies in Washington state and California, US.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Geo-Congress 2022: Geophysical and earthquake engineering and soil dynamics","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Geo-Congress 2022","conferenceDate":"March 20-23, 2022","conferenceLocation":"Charlotte, NC","language":"English","publisher":"American Society of Civil Engineers","doi":"10.1061/9780784484043.039","usgsCitation":"Greenfield, M.W., and Grant, A.R., 2022, Regional-scale liquefaction analyses, in Geo-Congress 2022: Geophysical and earthquake engineering and soil dynamics, Charlotte, NC, March 20-23, 2022, p. 401-410, https://doi.org/10.1061/9780784484043.039.","productDescription":"10 p.","startPage":"401","endPage":"410","ipdsId":"IP-130480","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":405788,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2022-03-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Greenfield, Michael W.","contributorId":267916,"corporation":false,"usgs":false,"family":"Greenfield","given":"Michael","email":"","middleInitial":"W.","affiliations":[{"id":40903,"text":"Greenfield Geotechnical, Portland, OR","active":true,"usgs":false}],"preferred":false,"id":849956,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grant, Alex R. 0000-0002-5096-4305","orcid":"https://orcid.org/0000-0002-5096-4305","contributorId":219066,"corporation":false,"usgs":true,"family":"Grant","given":"Alex","middleInitial":"R.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":849957,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70239283,"text":"70239283 - 2022 - Using ensemble data assimilation to estimate transient hydrologic exchange flow under highly dynamic flow conditions","interactions":[],"lastModifiedDate":"2023-01-06T12:40:06.995804","indexId":"70239283","displayToPublicDate":"2022-03-17T06:34:22","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Using ensemble data assimilation to estimate transient hydrologic exchange flow under highly dynamic flow conditions","docAbstract":"Quantifying dynamic hydrologic exchange flows (HEFs) within river corridors that experience high-frequency flow variations caused by dam regulations is important for understanding the biogeochemical processes at the river water and groundwater interfaces. Heat has been widely used as a tracer to infer steady-state flow velocities through analytical solutions of heat transport defined by the diurnal temperature signals. Under sub-daily dynamic flow conditions, however, such analytical solutions are not applicable due to the violation of their fundamental assumptions. In this study, we developed a data assimilation-based approach to estimate the sub-daily flux under highly dynamic flow conditions using multi-depth temperature observations at a 5-min resolution. If the hydraulic gradient is measured, Darcy's law was used to calculate the flux with permeability estimated from temperature responses below the riverbed. Otherwise, flux was estimated directly by assimilating multi-depth temperature data at 1- or 2-hr time intervals assuming one-dimensional flow and heat transport governing equation. By comparing estimated fluxes with model-generated synthetic truth, we demonstrated that both schemes have robust performance in estimating fluxes under highly dynamic flow conditions. This data assimilation-based flux estimation method was able to capture the vertical sub-daily fluxes using multi-depth high-resolution temperature data alone, even in the presence of multi-dimensional flow. This approach has been successfully applied to real field temperature data collected at the Hanford site, which experiences highly dynamic HEFs. Our study shows the promise of adopting distributed 1-D temperature monitoring to capture spatial and temporal exchange dynamics in river corridors at a watershed scale or beyond.
Missouri, one of only two States that borders eight different States, lies in the heart of the United States. Distinguished by its farm fields and forests, substantial rivers and lakes, and cities filled with culture and industry, the “Show Me State” has abundant beauty and a long history of connecting the East and the West. The Pony Express, Oregon Trail, Santa Fe Trail, and California Trail all began in Missouri.
The land and the people of Missouri contribute to its resiliency. Landsat data provide important tools for Missourians to protect their landscapes and waterways and enhance their economy under a variety of circumstances, from fast-arising natural disasters to longer-term environmental phenomena.
Here are several ways that Landsat data benefit Missouri.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223015","usgsCitation":"U.S. Geological Survey, 2022, Missouri and Landsat (ver. 1.1, January 2023): U.S. Geological Survey Fact Sheet 2022–3015, 2 p., https://doi.org/10.3133/fs20223015.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-126132","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":412077,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3015/fs20223015.XML"},{"id":412078,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3015/images"},{"id":412076,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2022/3015/versionHist.txt","size":"1.92 kB","linkFileType":{"id":2,"text":"txt"}},{"id":412075,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3015/fs20223015.pdf","text":"Report","size":"5.63 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022-3015"},{"id":397074,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3015/coverthb2.jpg"},{"id":412084,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223015/full","text":"Report","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Missouri","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-89.545006,36.336809],[-89.605668,36.342234],[-89.615841,36.336085],[-89.620255,36.323006],[-89.611819,36.309088],[-89.578492,36.288317],[-89.554289,36.277751],[-89.539487,36.277368],[-89.534507,36.261802],[-89.539229,36.248821],[-89.562206,36.250909],[-89.577544,36.242262],[-89.602374,36.238106],[-89.642182,36.249486],[-89.678046,36.248284],[-89.695235,36.252766],[-89.705328,36.239898],[-89.69263,36.224959],[-89.607004,36.171179],[-89.591605,36.144096],[-89.59307,36.129699],[-89.601936,36.11947],[-89.666598,36.095802],[-89.678821,36.084636],[-89.688577,36.029238],[-89.706932,36.000981],[-90.37789,35.995683],[-90.351732,36.025347],[-90.34909,36.040131],[-90.339343,36.047112],[-90.333261,36.067504],[-90.320746,36.071326],[-90.320662,36.087138],[-90.29991,36.098236],[-90.294492,36.112949],[-90.266256,36.120559],[-90.235585,36.139474],[-90.231386,36.147348],[-90.23537,36.159153],[-90.220425,36.184764],[-90.21128,36.183392],[-90.188189,36.20536],[-90.152497,36.215582],[-90.14224,36.227522],[-90.126366,36.229367],[-90.130114,36.240307],[-90.118219,36.253491],[-90.114922,36.265595],[-90.086471,36.271531],[-90.06398,36.303038],[-90.081961,36.322097],[-90.074074,36.342895],[-90.077695,36.348478],[-90.066297,36.3593],[-90.064514,36.382085],[-90.078671,36.399116],[-90.138512,36.413952],[-90.134231,36.422827],[-90.143743,36.424433],[-90.143798,36.428483],[-90.134136,36.436602],[-90.137323,36.455411],[-90.141101,36.461791],[-90.155804,36.463555],[-90.152888,36.47093],[-90.142222,36.470554],[-90.143683,36.476029],[-90.158838,36.479558],[-90.159305,36.492446],[-90.152481,36.497952],[-94.617919,36.499414],[-94.617975,37.722176],[-94.607354,39.113444],[-94.589933,39.140403],[-94.591933,39.155003],[-94.608834,39.160503],[-94.640035,39.153103],[-94.662435,39.157603],[-94.663835,39.179103],[-94.680336,39.184303],[-94.714137,39.170403],[-94.741938,39.170203],[-94.763138,39.179903],[-94.781518,39.206146],[-94.811663,39.206594],[-94.831679,39.215938],[-94.835056,39.220658],[-94.825663,39.241729],[-94.831471,39.256273],[-94.84632,39.268481],[-94.887056,39.28648],[-94.905329,39.311952],[-94.910017,39.352543],[-94.88136,39.370383],[-94.879281,39.37978],[-94.885026,39.389801],[-94.901823,39.392798],[-94.92311,39.384492],[-94.942039,39.389499],[-94.946293,39.405646],[-94.972952,39.421705],[-94.982144,39.440552],[-95.0375,39.463689],[-95.045716,39.472459],[-95.052177,39.499996],[-95.082714,39.516712],[-95.109304,39.542285],[-95.113077,39.559133],[-95.103228,39.577783],[-95.089515,39.581028],[-95.064519,39.577115],[-95.049277,39.589583],[-95.046361,39.599557],[-95.055152,39.621657],[-95.053367,39.630347],[-95.027644,39.665454],[-95.018318,39.672869],[-94.984149,39.67785],[-94.971317,39.68641],[-94.971206,39.729305],[-94.965318,39.739065],[-94.948726,39.745593],[-94.902612,39.724202],[-94.875643,39.730494],[-94.862943,39.742994],[-94.860743,39.763094],[-94.869644,39.772894],[-94.912293,39.759338],[-94.934262,39.773642],[-94.935206,39.78313],[-94.929654,39.788282],[-94.884084,39.794234],[-94.875944,39.813294],[-94.878677,39.826522],[-94.886933,39.833098],[-94.916918,39.836138],[-94.942567,39.856602],[-94.928466,39.876344],[-94.929574,39.888754],[-94.95154,39.900533],[-94.986975,39.89667],[-95.00844,39.900596],[-95.024389,39.891202],[-95.027931,39.871522],[-95.037767,39.865542],[-95.085003,39.861883],[-95.128166,39.874165],[-95.140601,39.881688],[-95.143802,39.901918],[-95.149657,39.905948],[-95.179453,39.900062],[-95.199347,39.902709],[-95.206326,39.912121],[-95.20069,39.928155],[-95.204428,39.938949],[-95.250254,39.948644],[-95.269886,39.969396],[-95.302507,39.984357],[-95.315271,40.01207],[-95.356876,40.031522],[-95.387195,40.02677],[-95.40726,40.033112],[-95.416824,40.043235],[-95.42164,40.058952],[-95.409856,40.07432],[-95.407591,40.09803],[-95.394216,40.108263],[-95.39284,40.115887],[-95.398667,40.126419],[-95.428749,40.135577],[-95.436348,40.15872],[-95.460746,40.169173],[-95.479193,40.185652],[-95.482757,40.197346],[-95.469718,40.227908],[-95.477501,40.24272],[-95.490333,40.248966],[-95.521925,40.24947],[-95.552473,40.261904],[-95.556325,40.267714],[-95.550966,40.285947],[-95.562157,40.297359],[-95.581787,40.29958],[-95.610439,40.31397],[-95.642262,40.306025],[-95.657328,40.310856],[-95.653729,40.322582],[-95.625204,40.334288],[-95.623728,40.346567],[-95.641027,40.366399],[-95.643934,40.386849],[-95.659134,40.40869],[-95.65819,40.44188],[-95.693133,40.469396],[-95.699969,40.505275],[-95.661687,40.517309],[-95.652262,40.538114],[-95.655848,40.546609],[-95.671754,40.562626],[-95.678718,40.56256],[-95.694147,40.556942],[-95.69505,40.533124],[-95.708591,40.521551],[-95.722444,40.528118],[-95.75711,40.52599],[-95.769281,40.536656],[-95.763366,40.550797],[-95.773549,40.578205],[-95.765645,40.585208],[-94.632035,40.571186],[-94.080463,40.572899],[-92.689854,40.589884],[-91.729115,40.61364],[-91.716769,40.59853],[-91.686357,40.580875],[-91.690804,40.559893],[-91.681714,40.553035],[-91.6219,40.542292],[-91.618028,40.53403],[-91.621353,40.510072],[-91.590817,40.492292],[-91.574746,40.465664],[-91.52509,40.457845],[-91.524053,40.448437],[-91.533623,40.43832],[-91.519935,40.433673],[-91.526555,40.419872],[-91.522333,40.409648],[-91.498093,40.401926],[-91.489816,40.404317],[-91.484507,40.3839],[-91.465116,40.385257],[-91.465009,40.376223],[-91.452458,40.375501],[-91.441243,40.386255],[-91.419422,40.378264],[-91.444833,40.36317],[-91.46214,40.342414],[-91.492727,40.278217],[-91.490524,40.259498],[-91.505828,40.238839],[-91.505495,40.195606],[-91.512974,40.181062],[-91.508224,40.157665],[-91.510322,40.127994],[-91.489606,40.057435],[-91.494878,40.036453],[-91.465315,39.983995],[-91.41936,39.927717],[-91.41988,39.916533],[-91.443513,39.893583],[-91.446922,39.883034],[-91.436051,39.84551],[-91.377971,39.811273],[-91.361571,39.787548],[-91.370009,39.732524],[-91.3453,39.709402],[-91.27614,39.665759],[-91.229317,39.620853],[-91.181936,39.602677],[-91.174651,39.593313],[-91.168419,39.564928],[-91.153628,39.548248],[-91.100307,39.538695],[-91.079769,39.507728],[-91.064305,39.494643],[-91.059439,39.46886],[-91.03827,39.448436],[-90.993789,39.422959],[-90.940766,39.403984],[-90.928745,39.387544],[-90.904862,39.379403],[-90.893777,39.367343],[-90.8475,39.345272],[-90.816851,39.320496],[-90.793461,39.309498],[-90.751599,39.265432],[-90.72996,39.255894],[-90.717113,39.213912],[-90.707902,39.15086],[-90.686051,39.117785],[-90.681086,39.10059],[-90.681994,39.090066],[-90.712541,39.057064],[-90.71158,39.046798],[-90.678193,38.991851],[-90.675949,38.96214],[-90.657254,38.92027],[-90.639917,38.908272],[-90.625122,38.888654],[-90.583388,38.86903],[-90.555693,38.870785],[-90.500117,38.910408],[-90.486974,38.925982],[-90.482419,38.94446],[-90.472122,38.958838],[-90.440078,38.967364],[-90.395816,38.960037],[-90.309454,38.92412],[-90.250248,38.919344],[-90.109407,38.843548],[-90.123107,38.798048],[-90.166409,38.772649],[-90.176309,38.754449],[-90.20991,38.72605],[-90.20921,38.70275],[-90.18641,38.67475],[-90.181325,38.660381],[-90.17801,38.63375],[-90.18451,38.611551],[-90.196011,38.594451],[-90.222112,38.576451],[-90.260314,38.528352],[-90.285215,38.443453],[-90.295316,38.426753],[-90.349743,38.377609],[-90.368219,38.340254],[-90.373929,38.281853],[-90.353902,38.213855],[-90.331554,38.18758],[-90.290765,38.170453],[-90.274928,38.157615],[-90.243116,38.112669],[-90.218708,38.094365],[-90.17222,38.069636],[-90.158533,38.074735],[-90.130788,38.062341],[-90.126612,38.043981],[-90.11052,38.026547],[-90.08826,38.015772],[-90.059367,38.015543],[-90.051357,38.003584],[-90.03241,37.995258],[-90.00011,37.964563],[-89.978919,37.962791],[-89.942099,37.970121],[-89.933797,37.959143],[-89.925085,37.960021],[-89.932467,37.947497],[-89.959646,37.940196],[-89.974918,37.926719],[-89.952499,37.883218],[-89.923185,37.870672],[-89.901832,37.869822],[-89.844786,37.905572],[-89.799333,37.881517],[-89.796087,37.859505],[-89.786369,37.851734],[-89.782035,37.855092],[-89.739873,37.84693],[-89.71748,37.825724],[-89.669644,37.799922],[-89.660227,37.781032],[-89.667993,37.759484],[-89.665546,37.752095],[-89.64953,37.745498],[-89.617278,37.74972],[-89.612478,37.740036],[-89.596566,37.732886],[-89.583316,37.713261],[-89.516685,37.692762],[-89.51204,37.680985],[-89.517718,37.641217],[-89.478399,37.598869],[-89.47603,37.590226],[-89.486062,37.580853],[-89.519808,37.582748],[-89.521925,37.560735],[-89.517051,37.537278],[-89.475525,37.471388],[-89.439769,37.4372],[-89.421054,37.387668],[-89.432836,37.347056],[-89.489005,37.333368],[-89.511842,37.310825],[-89.51834,37.285497],[-89.489915,37.251315],[-89.470525,37.253357],[-89.458827,37.248661],[-89.467631,37.2182],[-89.456105,37.18812],[-89.42558,37.138235],[-89.37871,37.094586],[-89.375712,37.080505],[-89.384681,37.048251],[-89.362397,37.030156],[-89.322982,37.01609],[-89.29213,36.992189],[-89.278628,36.98867],[-89.263527,37.00005],[-89.257608,37.015496],[-89.260003,37.023288],[-89.304752,37.047565],[-89.310819,37.057897],[-89.30829,37.068371],[-89.259936,37.064071],[-89.25493,37.072014],[-89.234053,37.037277],[-89.200793,37.016164],[-89.192097,36.979995],[-89.185491,36.973518],[-89.170008,36.970298],[-89.125069,36.983499],[-89.109498,36.976563],[-89.099594,36.964543],[-89.100762,36.944002],[-89.117567,36.887356],[-89.131944,36.857437],[-89.137969,36.847349],[-89.1704,36.841522],[-89.178888,36.831368],[-89.179229,36.812915],[-89.171069,36.798119],[-89.155891,36.789126],[-89.12353,36.785309],[-89.116563,36.767557],[-89.126134,36.751735],[-89.166888,36.759633],[-89.184523,36.753638],[-89.197808,36.739412],[-89.19948,36.716045],[-89.169522,36.688878],[-89.169467,36.674596],[-89.15908,36.666352],[-89.197654,36.628936],[-89.202607,36.601576],[-89.217447,36.576159],[-89.236542,36.566824],[-89.258318,36.564948],[-89.278935,36.577699],[-89.326731,36.632186],[-89.365548,36.625059],[-89.375453,36.615719],[-89.382762,36.583603],[-89.41977,36.493896],[-89.448468,36.46442],[-89.464153,36.457189],[-89.486215,36.46162],[-89.494248,36.475972],[-89.465888,36.529946],[-89.467761,36.546847],[-89.479093,36.568206],[-89.500076,36.576305],[-89.542459,36.580566],[-89.566817,36.564216],[-89.571241,36.547343],[-89.560344,36.525436],[-89.519501,36.475419],[-89.523427,36.456572],[-89.543406,36.43877],[-89.545255,36.427079],[-89.509722,36.373626],[-89.519,36.3486],[-89.545006,36.336809]]]},\"properties\":{\"name\":\"Missouri\",\"nation\":\"USA \"}}]}","edition":"Version 1.0: March 16, 2022; Version 1.1: January 19, 2023","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Semi-arid urban environments are undergoing an increase in both average air temperatures and in the frequency and intensity of extreme heat events. Within cities, different composition and densities of urban landcovers (ULC) influence local air temperatures, either mitigating or increasing heat. Currently, understanding how combinations of ULC influence air temperature at the block to neighborhood scale is necessary for heat mitigation plans, and yet limited due to the complexities integrating high-resolution ULC with spatial and temporally high-resolution microclimate data. We quantify how ULC influences air temperature at 60 m resolution for day and nighttime climate normals and extreme heat conditions by integrating microclimate sensor data sensor and high-resolution (1 m2) ULC for Denver, Colorado's urban core. We derive ULC drivers of air temperature using a structural equation model, then use a random forest algorithm to predict air temperatures for 30-year climate normals and an extreme heat condition. We find that, in conjunction with other ULC, urban tree canopy reduces daytime air temperatures (−0.026 °C per % cover), and the combination of impervious surfaces and buildings increases daytime air temperature (0.021 °C per % cover). Compared to daytime hours, nighttime irrigated turf temperature cooling effects are increased from being non-significant to −0.022 °C per % cover, while tree canopy effects are reduced from −0.026 °C during the day to −0.016 °C at night. Overall, ULC drives ~17% and 25% of local air temperature during the day and night, respectively. ULC influence on daytime air temperatures is altered in extreme heat events, both depending on the ULC type and time of day. Our findings inform urban planners seeking to identify potential hot and cool spots within a semi-arid city and mitigate high urban air temperatures through using ULC within larger urban climate mitigation strategies.
The single-station microtremor horizontal-to-vertical spectral ratio (MHVSR) method was initially proposed to retrieve the site amplification function and its resonance frequencies produced by unconsolidated sediments overlying high-velocity bedrock. Presently, MHVSR measurements are predominantly conducted to obtain an estimate of the fundamental site frequency at sites where a strong subsurface impedance contrast exists. Of the earthquake site characterization methods presented in this special issue, the MHVSR method is the furthest behind in terms of consensus towards standardized guidelines and commercial use. The greatest challenges to an international standardization of MHVSR acquisition and analysis are (1) the what — the underlying composition of the microtremor wavefield is site-dependent, and thus, the appropriate theoretical (forward) model for inversion is still debated; and (2) the how — many factors and options are involved in the data acquisition, processing, and interpretation stages. This paper reviews briefly a historical development of the MHVSR technique and the physical basis of an MHVSR (the what). We then summarize recommendations for MHVSR acquisition and analysis (the how). Specific sections address MHVSR interpretation and uncertainty assessment.
","language":"English","publisher":"Springer","doi":"10.1007/s10950-021-10062-9","usgsCitation":"Molnar, S., Sirohey, A., Assaf, J., Bard, P., Castellaro, C., Cornou, C., Cox, B., Guillier, B., Hassani, B., Kawase, H., Matsushima, S., Sánchez-Sesma, F., and Yong, A., 2022, A review of the microtremor horizontal-to-vertical spectral ratio (MHVSR) method: Journal of Seismology, v. 26, p. 653-685, https://doi.org/10.1007/s10950-021-10062-9.","productDescription":"33 p.","startPage":"653","endPage":"685","ipdsId":"IP-127918","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":448472,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10950-021-10062-9","text":"Publisher Index Page"},{"id":406138,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"26","noUsgsAuthors":false,"publicationDate":"2022-03-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Molnar, S.","contributorId":203574,"corporation":false,"usgs":false,"family":"Molnar","given":"S.","email":"","affiliations":[{"id":13255,"text":"University of Western Ontario","active":true,"usgs":false}],"preferred":false,"id":850685,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sirohey, A.","contributorId":296125,"corporation":false,"usgs":false,"family":"Sirohey","given":"A.","email":"","affiliations":[],"preferred":false,"id":850686,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Assaf, J.","contributorId":296126,"corporation":false,"usgs":false,"family":"Assaf","given":"J.","email":"","affiliations":[],"preferred":false,"id":850708,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bard, P.-Y.","contributorId":296110,"corporation":false,"usgs":false,"family":"Bard","given":"P.-Y.","email":"","affiliations":[{"id":63992,"text":"Université Grenoble Alpes","active":true,"usgs":false}],"preferred":false,"id":850687,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Castellaro, C.","contributorId":296111,"corporation":false,"usgs":false,"family":"Castellaro","given":"C.","email":"","affiliations":[{"id":63993,"text":"Alma Mater Studiorum Università di Bologna","active":true,"usgs":false}],"preferred":false,"id":850688,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cornou, C.","contributorId":296112,"corporation":false,"usgs":false,"family":"Cornou","given":"C.","affiliations":[{"id":63992,"text":"Université Grenoble Alpes","active":true,"usgs":false}],"preferred":false,"id":850689,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cox, B.","contributorId":296113,"corporation":false,"usgs":false,"family":"Cox","given":"B.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":850690,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Guillier, B.","contributorId":296114,"corporation":false,"usgs":false,"family":"Guillier","given":"B.","email":"","affiliations":[{"id":63992,"text":"Université Grenoble Alpes","active":true,"usgs":false}],"preferred":false,"id":850691,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hassani, B.","contributorId":296115,"corporation":false,"usgs":false,"family":"Hassani","given":"B.","email":"","affiliations":[{"id":37568,"text":"BC Hydro","active":true,"usgs":false}],"preferred":false,"id":850692,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Kawase, H.","contributorId":296116,"corporation":false,"usgs":false,"family":"Kawase","given":"H.","email":"","affiliations":[{"id":36662,"text":"Kyoto University","active":true,"usgs":false}],"preferred":false,"id":850693,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Matsushima, S.","contributorId":296117,"corporation":false,"usgs":false,"family":"Matsushima","given":"S.","affiliations":[{"id":36662,"text":"Kyoto University","active":true,"usgs":false}],"preferred":false,"id":850694,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Sánchez-Sesma, F. J.","contributorId":296118,"corporation":false,"usgs":false,"family":"Sánchez-Sesma","given":"F. J.","affiliations":[{"id":25354,"text":"Universidad Nacional Autónoma de México","active":true,"usgs":false}],"preferred":false,"id":850695,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Yong, Alan 0000-0003-1807-5847","orcid":"https://orcid.org/0000-0003-1807-5847","contributorId":204730,"corporation":false,"usgs":true,"family":"Yong","given":"Alan","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":850696,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70236346,"text":"70236346 - 2022 - A review of near-surface QS estimation methods using active and passive sources","interactions":[],"lastModifiedDate":"2022-09-02T14:12:47.588487","indexId":"70236346","displayToPublicDate":"2022-03-16T09:10:53","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2453,"text":"Journal of Seismology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"A review of near-surface QS estimation methods using active and passive sources","title":"A review of near-surface QS estimation methods using active and passive sources","docAbstract":"Seismic attenuation and the associated quality factor (Q) have long been studied in various sub-disciplines of seismology, ranging from observational and engineering seismology to near-surface geophysics and soil/rock dynamics with particular emphasis on geotechnical earthquake engineering and engineering seismology. Within the broader framework of seismic site characterization, various experimental techniques have been adopted over the years to measure the near-surface shear-wave quality factor (QS). Common methods include active- and passive-source recording techniques performed at the free surface of soil deposits and within boreholes, as well as laboratory tests. This paper intends to provide an in-depth review of what Q is and, in particular, how QS is estimated in the current practice. After motivating the importance of this parameter in seismology, we proceed by recalling various theoretical definitions of Q and its measurement through laboratory tests, considering various deformation modes, most notably QP and QS. We next provide a review of the literature on QS estimation methods that use data from surface and borehole sensor recordings. We distinguish between active- and passive-source approaches, along with their pros and cons, as well as the state-of-the-practice and state-of-the-art. Finally, we summarize the phenomena associated with the high-frequency shear-wave attenuation factor (kappa) and its relation to Q, as well as other lesser-known attenuation parameters.
","language":"English","publisher":"Springer","doi":"10.1007/s10950-021-10066-5","usgsCitation":"Parolai, S., Lai, C.G., Dreossi, I., Ktenidou, O., and Yong, A., 2022, A review of near-surface QS estimation methods using active and passive sources: Journal of Seismology, v. 26, p. 823-862, https://doi.org/10.1007/s10950-021-10066-5.","productDescription":"40 p.","startPage":"823","endPage":"862","ipdsId":"IP-132752","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":448475,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10950-021-10066-5","text":"Publisher Index Page"},{"id":406135,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"26","noUsgsAuthors":false,"publicationDate":"2022-03-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Parolai, Stefano 0000-0002-9084-7488","orcid":"https://orcid.org/0000-0002-9084-7488","contributorId":296105,"corporation":false,"usgs":false,"family":"Parolai","given":"Stefano","email":"","affiliations":[{"id":63989,"text":"Instituto Nazionale di Oceonografia","active":true,"usgs":false}],"preferred":false,"id":850680,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lai, Carlo G.","contributorId":296106,"corporation":false,"usgs":false,"family":"Lai","given":"Carlo","email":"","middleInitial":"G.","affiliations":[{"id":63990,"text":"Department of Civil and Architectural Engineering, University of Pavia, Pavia, Italy","active":true,"usgs":false}],"preferred":false,"id":850681,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dreossi, Ilaria","contributorId":296107,"corporation":false,"usgs":false,"family":"Dreossi","given":"Ilaria","email":"","affiliations":[{"id":63991,"text":"National Institute of Oceanography and Applied Geophysics – OGS, Udine, Italy","active":true,"usgs":false}],"preferred":false,"id":850682,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ktenidou, Olga-Joan","contributorId":271026,"corporation":false,"usgs":false,"family":"Ktenidou","given":"Olga-Joan","email":"","affiliations":[{"id":56255,"text":"National Observatory of Athens","active":true,"usgs":false}],"preferred":false,"id":850683,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Yong, Alan K. 0000-0003-1807-5847","orcid":"https://orcid.org/0000-0003-1807-5847","contributorId":296108,"corporation":false,"usgs":true,"family":"Yong","given":"Alan K.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":850684,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70239267,"text":"70239267 - 2022 - Average kinship within bighorn sheep populations is associated with connectivity, augmentation, and bottlenecks","interactions":[],"lastModifiedDate":"2023-01-06T14:34:50.408914","indexId":"70239267","displayToPublicDate":"2022-03-16T08:29:19","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Average kinship within bighorn sheep populations is associated with connectivity, augmentation, and bottlenecks","docAbstract":"Understanding the influence of population attributes on genetic diversity is important to advancement of biological conservation. Because bighorn sheep (Ovis canadensis) populations vary in size and management history, the species provides a unique opportunity to observe the response of average pairwise kinship, inversely related to genetic diversity, to a spectrum of natural and management influences. We estimated average pairwise kinship of bighorn sheep herds and compared estimates with population origin (native/indigenous/extant or reintroduced), historical minimum count, connectivity, and augmentation history, to determine which predictors were the most important. We evaluated 488 bighorn sheep from 19 wild populations with past minimum counts of 16–562 animals, including native and reintroduced populations that received 0–165 animals in augmentations. Using the Illumina High Density Ovine array, we generated a dataset of 7728 single nucleotide polymorphisms and calculated average pairwise kinship for each population. Multiple linear regression analysis determined that connectivity between populations via dispersal, greater number of animals received in augmentations, and greater minimum count were correlated with lower average pairwise kinship at the population level, and whether the population was extant or reintroduced was less important. Thus, our results indicated that genetic isolation of populations can result in increased levels of inbreeding. By determining that natural and human-assisted gene flow were likely the most important influences of average pairwise kinship at the population level, this study can serve as a benchmark for future management of bighorn sheep populations and aid in identifying populations of genetic concern to define priorities for conservation of wild populations.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3972","usgsCitation":"Flesch, E.P., Graves, T., Thomson, J., Proffitt, K., and Garrott, R.A., 2022, Average kinship within bighorn sheep populations is associated with connectivity, augmentation, and bottlenecks: Ecosphere, v. 13, no. 3, e3972, 19 p., https://doi.org/10.1002/ecs2.3972.","productDescription":"e3972, 19 p.","ipdsId":"IP-123033","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":488768,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3972","text":"Publisher Index Page"},{"id":411486,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.78094692827673,\n 43.20819189911293\n ],\n [\n -107.87478246478094,\n 43.46432073825207\n ],\n [\n -105.38522276615836,\n 47.31273522708753\n ],\n [\n -106.94850703787552,\n 48.95098006865928\n ],\n [\n -116.04022455322576,\n 48.995371199230505\n ],\n [\n -115.43136405543936,\n 47.5027919613581\n ],\n [\n -114.77745582859464,\n 46.867640242508855\n ],\n [\n -114.42311195285694,\n 46.465058107358715\n ],\n [\n -114.4197823602259,\n 45.56395759707192\n ],\n [\n -113.70839351291886,\n 45.38622209814852\n ],\n [\n -112.22430921762538,\n 44.50809933614741\n ],\n [\n -111.26330199120804,\n 44.53201508738698\n ],\n [\n -110.78094692827673,\n 43.20819189911293\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"13","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-03-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Flesch, Elizabeth P 0000-0002-7592-8124","orcid":"https://orcid.org/0000-0002-7592-8124","contributorId":222685,"corporation":false,"usgs":false,"family":"Flesch","given":"Elizabeth","email":"","middleInitial":"P","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":860960,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Graves, Tabitha A. 0000-0001-5145-2400","orcid":"https://orcid.org/0000-0001-5145-2400","contributorId":202084,"corporation":false,"usgs":true,"family":"Graves","given":"Tabitha A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":860961,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thomson, Jennifer 0000-0003-1921-0975","orcid":"https://orcid.org/0000-0003-1921-0975","contributorId":248418,"corporation":false,"usgs":false,"family":"Thomson","given":"Jennifer","email":"","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":860962,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Proffitt, Kelly M.","contributorId":275167,"corporation":false,"usgs":false,"family":"Proffitt","given":"Kelly M.","affiliations":[{"id":48627,"text":"mtfwp","active":true,"usgs":false}],"preferred":false,"id":860963,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Garrott, Robert A.","contributorId":171537,"corporation":false,"usgs":false,"family":"Garrott","given":"Robert","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":860964,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70232907,"text":"70232907 - 2022 - Coupling validation effort with in situ bioacoustic data improves estimating relative activity and occupancy for multiple species with cross-species misclassifications","interactions":[],"lastModifiedDate":"2022-07-13T12:14:14.991953","indexId":"70232907","displayToPublicDate":"2022-03-16T07:07:28","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Coupling validation effort with in situ bioacoustic data improves estimating relative activity and occupancy for multiple species with cross-species misclassifications","docAbstract":"Tropical regions are experiencing high rates of forest cover loss coupled with changes in the volume and timing of rainfall. These shifts can compromise streamflow and water provision, highlighting the need to identify how forest cover influences streamflow generation under variable rainfall conditions. Although rainfall is the key driver of streamflow regimes, the role of forests is less clear, particularly in tropical regions where forest loss is an ongoing risk. Forest cover loss alters evapotranspiration, rainfall infiltration and storage, and may increase stream ecosystem vulnerability to rainfall extremes. Puerto Rico, an island with spatially heterogenous forest cover and a marked geographic rainfall gradient, is projected to experience more frequent droughts and flash flooding. Using 15-minute streamflow data collected between 2005 and 2016 from 20 USGS stream gages and 3-hourly Multi-Source Weighted-Ensemble Precipitation rainfall estimates, we utilized flow-duration curves and linear mixed regression models to examine the role of forest cover in regulating the timing and volume of streamflow. The mixed model approach helps to account for differences in watershed characteristics. We determined the effects of rainfall and forest cover on low and peak flows in Puerto Rican streams, then evaluated changes in these relationships under dry and wet antecedent rainfall conditions. Watersheds with high forest cover had consistently greater low and peak streamflow than deforested ones under all rainfall conditions, although the effect was more marked during wet antecedent conditions, suggesting that peak flow is largely the result of saturation excess overland flow. During dry antecedent rainfall conditions, highly forested watersheds had higher streamflow than deforested ones, suggesting greater hillslope storage and release may also be at play. Our results demonstrate that forest cover generated a net increase in hillslope infiltration and storage and may lessen drought impacts on streamflow in Puerto Rico. Resilience to prolonged drought may be limited by finite water storage potential in this steep, mountainous setting, highlighting maintenance of forest cover as an important water management strategy to increase infiltration.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.14551","usgsCitation":"Hall, J.S., Scholl, M.A., Gorokhovich, Y., and Uriarte, M., 2022, Forest cover lessens the impact of drought on streamflow in Puerto Rico: Hydrological Processes, v. 36, no. 5, e14551, 16 p., https://doi.org/10.1002/hyp.14551.","productDescription":"e14551, 16 p.","ipdsId":"IP-122081","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":399811,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Puerto Rico","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-65.3277,18.295843],[-65.337451,18.308308],[-65.327318,18.323666],[-65.342068,18.34529],[-65.335701,18.349535],[-65.329334,18.341955],[-65.321754,18.338316],[-65.309833,18.337973],[-65.304409,18.332054],[-65.298328,18.330529],[-65.255933,18.342117],[-65.221568,18.320959],[-65.222853,18.310464],[-65.249857,18.296691],[-65.260282,18.290823],[-65.283269,18.280214],[-65.3277,18.295843]]],[[[-67.89174,18.11397],[-67.887099,18.112574],[-67.87643,18.114157],[-67.869804,18.118851],[-67.861548,18.122144],[-67.848245,18.10832],[-67.843202,18.094858],[-67.843615,18.085099],[-67.845293,18.081938],[-67.853098,18.078195],[-67.865598,18.06544],[-67.871462,18.0578],[-67.895921,18.052342],[-67.904431,18.05913],[-67.918778,18.063116],[-67.927841,18.068572],[-67.940799,18.079716],[-67.934479,18.111306],[-67.932185,18.113221],[-67.91088,18.119668],[-67.89174,18.11397]]],[[[-65.308717,18.145172],[-65.302295,18.141089],[-65.294896,18.14283],[-65.287962,18.148097],[-65.275165,18.13443],[-65.276214,18.131936],[-65.283248,18.132999],[-65.296036,18.12799],[-65.322794,18.126589],[-65.327184,18.124106],[-65.338506,18.112439],[-65.342037,18.11138],[-65.350493,18.111914],[-65.364733,18.120377],[-65.397837,18.110873],[-65.399791,18.108832],[-65.411767,18.106211],[-65.423765,18.097764],[-65.426311,18.093749],[-65.45138,18.086096],[-65.45681,18.087778],[-65.465849,18.087715],[-65.468768,18.092643],[-65.47979,18.096352],[-65.507265,18.091646],[-65.524209,18.081977],[-65.542087,18.081177],[-65.558646,18.08566],[-65.569305,18.091616],[-65.570628,18.097325],[-65.57686,18.103224],[-65.575579,18.115669],[-65.546199,18.119329],[-65.511712,18.13284],[-65.489829,18.135912],[-65.46791,18.143767],[-65.437058,18.15766],[-65.399517,18.161935],[-65.371373,18.157517],[-65.334289,18.147761],[-65.313476,18.144296],[-65.308717,18.145172]]],[[[-66.438813,18.485713],[-66.420921,18.488639],[-66.410344,18.489886],[-66.394287,18.489748],[-66.377286,18.488044],[-66.37282,18.487726],[-66.349647,18.486335],[-66.337728,18.48562],[-66.315477,18.474724],[-66.31503,18.47468],[-66.291225,18.472347],[-66.283675,18.472203],[-66.276599,18.478129],[-66.269799,18.480281],[-66.258015,18.476906],[-66.251547,18.472464],[-66.241797,18.46874],[-66.220148,18.466],[-66.199032,18.466163],[-66.192664,18.466212],[-66.183886,18.460506],[-66.179218,18.455305],[-66.172315,18.451462],[-66.159796,18.451706],[-66.153037,18.454457],[-66.14395,18.459761],[-66.139572,18.462317],[-66.139451,18.462387],[-66.139443,18.462315],[-66.138532,18.453305],[-66.133085,18.445881],[-66.127938,18.444632],[-66.125198,18.451209],[-66.124284,18.456324],[-66.123188,18.45943],[-66.123343,18.460363],[-66.125015,18.470435],[-66.118338,18.469581],[-66.092098,18.466535],[-66.083254,18.462022],[-66.073987,18.4581],[-66.043272,18.453655],[-66.03944,18.454441],[-66.036559,18.450216],[-66.036491,18.450117],[-66.023221,18.443875],[-66.006523,18.444347],[-65.99718,18.449895],[-65.992935,18.457489],[-65.992793,18.458102],[-65.992349,18.460024],[-65.99079,18.460419],[-65.958492,18.451354],[-65.92567,18.444881],[-65.916843,18.444619],[-65.907756,18.446893],[-65.904988,18.450926],[-65.878683,18.438322],[-65.838825,18.431865],[-65.831476,18.426849],[-65.828457,18.423543],[-65.816691,18.410663],[-65.794556,18.402845],[-65.787666,18.402544],[-65.774937,18.413951],[-65.77053,18.41294],[-65.769749,18.409473],[-65.771695,18.406277],[-65.750455,18.385208],[-65.750179,18.38505],[-65.742154,18.380459],[-65.733567,18.382211],[-65.699069,18.368156],[-65.669636,18.362102],[-65.668845,18.361939],[-65.634431,18.369835],[-65.627246,18.376436],[-65.626527,18.381728],[-65.624975,18.386553],[-65.622761,18.387771],[-65.618229,18.386496],[-65.614891,18.382473],[-65.619068,18.367755],[-65.628198,18.353711],[-65.63419,18.338965],[-65.628047,18.328252],[-65.626456,18.298982],[-65.634389,18.292349],[-65.635826,18.288271],[-65.634893,18.283923],[-65.630833,18.264989],[-65.623111,18.248012],[-65.597618,18.234289],[-65.589947,18.228225],[-65.593795,18.224059],[-65.615981,18.227389],[-65.626731,18.235484],[-65.638181,18.229121],[-65.637565,18.224444],[-65.628414,18.205149],[-65.635281,18.199975],[-65.639688,18.205656],[-65.662185,18.207018],[-65.664127,18.207136],[-65.690749,18.19499],[-65.694515,18.187011],[-65.691021,18.178998],[-65.695856,18.179324],[-65.710895,18.186963],[-65.712533,18.189146],[-65.717999,18.190176],[-65.728471,18.185588],[-65.734664,18.180368],[-65.738834,18.174066],[-65.739125,18.173453],[-65.743632,18.163957],[-65.758728,18.156601],[-65.766919,18.148424],[-65.777584,18.129239],[-65.796711,18.083746],[-65.796289,18.079835],[-65.794686,18.078607],[-65.795028,18.073561],[-65.796711,18.069842],[-65.801831,18.058527],[-65.809174,18.056818],[-65.817107,18.063378],[-65.825848,18.057482],[-65.83109,18.050664],[-65.834274,18.038988],[-65.832429,18.014916],[-65.839591,18.015077],[-65.850913,18.011954],[-65.870335,18.006597],[-65.875122,18.002826],[-65.884937,17.988521],[-65.896102,17.99026],[-65.905319,17.983974],[-65.910537,17.981855],[-65.924738,17.976087],[-65.976611,17.967669],[-65.98455,17.969411],[-65.985358,17.971854],[-65.995185,17.978989],[-66.007731,17.980541],[-66.017308,17.979583],[-66.019539,17.978354],[-66.024,17.975896],[-66.046585,17.954853],[-66.049033,17.954561],[-66.058217,17.959238],[-66.068678,17.966335],[-66.069979,17.966357],[-66.08141,17.966552],[-66.116194,17.949141],[-66.127009,17.946953],[-66.140661,17.94102],[-66.147912,17.933963],[-66.155387,17.929406],[-66.159742,17.928613],[-66.161232,17.931747],[-66.175626,17.933565],[-66.186914,17.935363],[-66.189726,17.933936],[-66.200174,17.929515],[-66.206961,17.932268],[-66.213374,17.944614],[-66.202655,17.944753],[-66.185554,17.940997],[-66.179548,17.943727],[-66.174839,17.948214],[-66.176814,17.950438],[-66.206207,17.96305],[-66.206807,17.963307],[-66.215355,17.959376],[-66.218081,17.95729],[-66.231519,17.943912],[-66.229181,17.934651],[-66.232013,17.931154],[-66.252737,17.934574],[-66.260684,17.936083],[-66.270905,17.947098],[-66.275651,17.94826],[-66.290782,17.946491],[-66.297679,17.959148],[-66.31695,17.976683],[-66.323659,17.978536],[-66.338152,17.976492],[-66.33839,17.976458],[-66.362511,17.968231],[-66.365098,17.964832],[-66.368777,17.957717],[-66.371591,17.951469],[-66.385059,17.939004],[-66.391227,17.945819],[-66.398945,17.950925],[-66.412131,17.957286],[-66.445481,17.979379],[-66.450368,17.983226],[-66.454888,17.986784],[-66.461342,17.990273],[-66.491396,17.990262],[-66.510143,17.985618],[-66.540537,17.975476],[-66.583233,17.961229],[-66.589658,17.969386],[-66.594392,17.970682],[-66.605035,17.969015],[-66.623788,17.98105],[-66.631944,17.982746],[-66.645651,17.98026],[-66.657797,17.974605],[-66.664391,17.968259],[-66.672819,17.966451],[-66.699115,17.977568],[-66.709856,17.982109],[-66.713394,17.987763],[-66.716957,17.990344],[-66.731118,17.991658],[-66.746248,17.990349],[-66.750427,17.995443],[-66.753964,17.99959],[-66.755341,18.001203],[-66.764491,18.006317],[-66.770307,18.005955],[-66.799656,17.99245],[-66.806866,17.983786],[-66.807924,17.979606],[-66.806903,17.976046],[-66.805683,17.975052],[-66.795106,17.977438],[-66.789302,17.980793],[-66.784953,17.978326],[-66.787245,17.972914],[-66.80827,17.965635],[-66.8224,17.954499],[-66.838584,17.949931],[-66.852288,17.955004],[-66.856474,17.956553],[-66.859471,17.954316],[-66.862545,17.952022],[-66.871697,17.952707],[-66.88344,17.952526],[-66.899639,17.948298],[-66.904585,17.950527],[-66.906532,17.955356],[-66.906276,17.963368],[-66.924529,17.972808],[-66.928651,17.970204],[-66.930414,17.963127],[-66.916127,17.959102],[-66.909483,17.952559],[-66.909359,17.94988],[-66.912522,17.947446],[-66.930313,17.943389],[-66.932636,17.939998],[-66.931581,17.9369],[-66.919298,17.932062],[-66.923826,17.926923],[-66.927261,17.926875],[-66.959998,17.940216],[-66.980516,17.951648],[-66.98105,17.952505],[-66.982669,17.9551],[-66.982206,17.961192],[-66.987287,17.970663],[-66.996738,17.972899],[-67.003972,17.970799],[-67.014744,17.968468],[-67.024522,17.970722],[-67.062478,17.973819],[-67.076534,17.967759],[-67.089827,17.951418],[-67.101468,17.946621],[-67.109985,17.945806],[-67.109986,17.945806],[-67.128251,17.948153],[-67.133733,17.951919],[-67.167031,17.963073],[-67.178566,17.964792],[-67.183508,17.962706],[-67.188717,17.950989],[-67.187474,17.946252],[-67.183694,17.937982],[-67.183457,17.931135],[-67.194785,17.932826],[-67.196924,17.935651],[-67.197273,17.937461],[-67.197517,17.941514],[-67.197668,17.943549],[-67.198988,17.94782],[-67.200973,17.949896],[-67.210034,17.953595],[-67.212101,17.956027],[-67.21433,17.962436],[-67.215271,17.983464],[-67.211973,17.992993],[-67.207694,17.998019],[-67.177893,18.008882],[-67.174299,18.011149],[-67.172397,18.014906],[-67.172138,18.021422],[-67.173761,18.024548],[-67.193269,18.03185],[-67.209887,18.035439],[-67.196694,18.066491],[-67.190656,18.064269],[-67.184589,18.06775],[-67.183938,18.069914],[-67.186465,18.074195],[-67.192999,18.076877],[-67.198212,18.076828],[-67.199314,18.091135],[-67.19529,18.096149],[-67.183921,18.103683],[-67.182182,18.108507],[-67.176554,18.151046],[-67.178618,18.159318],[-67.180822,18.168055],[-67.180701,18.168182],[-67.155185,18.195001],[-67.152665,18.203493],[-67.158001,18.216719],[-67.173,18.230666],[-67.175429,18.248008],[-67.187843,18.266671],[-67.187873,18.266874],[-67.189971,18.281015],[-67.196056,18.290443],[-67.209963,18.294974],[-67.225403,18.296648],[-67.226081,18.296722],[-67.235137,18.299935],[-67.267484,18.353149],[-67.27135,18.362329],[-67.268259,18.366989],[-67.260671,18.370197],[-67.23909,18.375318],[-67.226744,18.378247],[-67.216998,18.382078],[-67.202167,18.389908],[-67.160144,18.415587],[-67.159608,18.415915],[-67.156599,18.418983],[-67.155245,18.424401],[-67.156619,18.439562],[-67.161746,18.453462],[-67.169011,18.466352],[-67.169016,18.478488],[-67.164144,18.487396],[-67.14283,18.505485],[-67.138249,18.507776],[-67.125655,18.511706],[-67.103468,18.514523],[-67.093752,18.515757],[-67.07929,18.513256],[-67.020276,18.510603],[-66.988958,18.497724],[-66.95954,18.489878],[-66.957733,18.489129],[-66.957517,18.489171],[-66.944636,18.491693],[-66.906872,18.483556],[-66.90143,18.484552],[-66.867386,18.490785],[-66.849673,18.490745],[-66.83694,18.487659],[-66.836635,18.487701],[-66.79932,18.492775],[-66.780311,18.491411],[-66.764893,18.484097],[-66.749301,18.476701],[-66.742067,18.474681],[-66.733986,18.473457],[-66.710743,18.472611],[-66.683719,18.481367],[-66.679876,18.484944],[-66.664364,18.487809],[-66.645839,18.488777],[-66.624618,18.494199],[-66.586778,18.484948],[-66.584074,18.484287],[-66.565241,18.485523],[-66.562916,18.48845],[-66.563485,18.490512],[-66.558503,18.489987],[-66.53484,18.481253],[-66.533487,18.481663],[-66.529476,18.482877],[-66.511609,18.476848],[-66.470292,18.46907],[-66.456486,18.46892],[-66.449184,18.470991],[-66.441852,18.479751],[-66.439961,18.485525],[-66.438813,18.485713]]]]},\"properties\":{\"name\":\"Puerto Rico\",\"nation\":\"USA \"}}]}","volume":"36","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hall, Jazlynn S. 0000-0002-8782-0065","orcid":"https://orcid.org/0000-0002-8782-0065","contributorId":290688,"corporation":false,"usgs":false,"family":"Hall","given":"Jazlynn","email":"","middleInitial":"S.","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":841585,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Scholl, Martha A. 0000-0001-6994-4614 mascholl@usgs.gov","orcid":"https://orcid.org/0000-0001-6994-4614","contributorId":1920,"corporation":false,"usgs":true,"family":"Scholl","given":"Martha","email":"mascholl@usgs.gov","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":841586,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gorokhovich, Yuri","contributorId":290689,"corporation":false,"usgs":false,"family":"Gorokhovich","given":"Yuri","email":"","affiliations":[{"id":39562,"text":"City University of New York","active":true,"usgs":false}],"preferred":false,"id":841587,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Uriarte, Maria","contributorId":287019,"corporation":false,"usgs":false,"family":"Uriarte","given":"Maria","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":841588,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}