In 2001, a rare swarm of small, shallow earthquakes beneath the city of Spokane, Washington, caused ground shaking as well as audible booms over a five‐month period. Subsequent Interferometric Synthetic Aperture Radar (InSAR) data analysis revealed an area of surface uplift in the vicinity of the earthquake swarm. To investigate the potential faults that may have caused both the earthquakes and the topographic uplift, we collected ∼3 km of high‐resolution seismic‐reflection profiles to image the upper‐source region of the swarm. The two profiles reveal a complex deformational pattern within Quaternary alluvial, fluvial, and flood deposits, underlain by Tertiary basalts and basin sediments. At least 100 m of arching on a basalt surface in the upper 500 m is interpreted from both the seismic profiles and magnetic modeling. Two west‐dipping faults deform Quaternary sediments and project to the surface near the location of the Spokane fault defined from modeling of the InSAR data.
","language":"English","publisher":" Seismological Society of America","doi":"10.1785/0120150295","usgsCitation":"Stephenson, W.J., Odum, J., Wicks, C.W., Pratt, T.L., and Blakely, R.J., 2016, Seismic imaging beneath an InSAR anomaly in eastern Washington State: Shallow faulting associated with an earthquake swarm in a low-hazard area: Bulletin of the Seismological Society of America, v. 106, no. 4, p. 1461-1469, https://doi.org/10.1785/0120150295.","productDescription":"9 p.","startPage":"1461","endPage":"1469","ipdsId":"IP-074071","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":330355,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","volume":"106","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-06-21","publicationStatus":"PW","scienceBaseUri":"58106f98e4b0f497e7961115","contributors":{"authors":[{"text":"Stephenson, William J. 0000-0001-8699-0786 wstephens@usgs.gov","orcid":"https://orcid.org/0000-0001-8699-0786","contributorId":695,"corporation":false,"usgs":true,"family":"Stephenson","given":"William","email":"wstephens@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":651942,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Odum, Jackson K. 0000-0003-4697-2430 odum@usgs.gov","orcid":"https://orcid.org/0000-0003-4697-2430","contributorId":1365,"corporation":false,"usgs":true,"family":"Odum","given":"Jackson K.","email":"odum@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":651943,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wicks, Charles W. Jr. 0000-0002-0809-1328 cwicks@usgs.gov","orcid":"https://orcid.org/0000-0002-0809-1328","contributorId":127701,"corporation":false,"usgs":true,"family":"Wicks","given":"Charles","suffix":"Jr.","email":"cwicks@usgs.gov","middleInitial":"W.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":651944,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pratt, Thomas L. 0000-0003-3131-3141 tpratt@usgs.gov","orcid":"https://orcid.org/0000-0003-3131-3141","contributorId":3279,"corporation":false,"usgs":true,"family":"Pratt","given":"Thomas","email":"tpratt@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":651945,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Blakely, Richard J. 0000-0003-1701-5236 blakely@usgs.gov","orcid":"https://orcid.org/0000-0003-1701-5236","contributorId":1540,"corporation":false,"usgs":true,"family":"Blakely","given":"Richard","email":"blakely@usgs.gov","middleInitial":"J.","affiliations":[{"id":662,"text":"Western Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":651946,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70177878,"text":"70177878 - 2016 - Dynamic distributions and population declines of Golden-winged Warblers","interactions":[],"lastModifiedDate":"2020-08-25T17:09:33.774891","indexId":"70177878","displayToPublicDate":"2016-10-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"seriesTitle":{"id":5103,"text":"Studies in Avian Biology","printIssn":"0197-9922","active":true,"publicationSubtype":{"id":24}},"chapter":"1","title":"Dynamic distributions and population declines of Golden-winged Warblers","docAbstract":"With an estimated breeding population in 2010 of 383,000 pairs, the Golden-winged Warbler (Vermivora chrysoptera) is among the most vulnerable and steeply declining of North American passerines. This species also has exhibited among the most dynamic breeding distributions, with populations expanding and then contracting over the past 150 years in response to regional habitat changes, interactions with closely related Blue-winged Warblers (V. cyanoptera), and possibly climate change. Since 1966, the rangewide population has declined by >70% (-2.3% per year; latest North American Breeding Bird Survey data), with much steeper declines in the Appalachian Mountains bird conservation region (-8.3% per year, 98% overall decline). Despite apparently stable or increasing populations in the northwestern part of the range (Minnesota, Manitoba), population estimates for Golden-winged Warbler have continued to decline by 18% from the decade of the 1990s to the 2000s. Population modeling predicts a further decline to roughly 37,000 individuals by 2100, with the species likely to persist only in Manitoba, Minnesota, and possibly Ontario. To delineate the present-day distribution and to identify population concentrations that could serve as conservation focus areas, we compiled rangewide survey data collected in 2000-2006 in 21 states and 3 Canadian provinces, as part of the Golden-winged Warbler Atlas Project (GOWAP), supplemented by state and provincial Breeding Bird Atlas data and more recent observations in eBird. Based on >8,000 GOWAP surveys for Golden-winged and Blue-winged warblers and their hybrids, we mapped occurrence of phenotypically pure and mixed populations in a roughly 0.5-degree grid across the species’ ranges. Hybrids and mixed Golden-winged-Blue-winged populations occurred in a relatively narrow zone across Minnesota, Wisconsin, Michigan, southern Ontario, and northern New York. Phenotypically pure Golden-winged Warbler populations occurred north of this hybrid zone, but the future of northern populations in the Great Lakes states and Canada (where >80% of the species occurs at present) is highly uncertain because of continued northward expansion of Blue-winged Warblers and hybridization. A second, now-disjunct band of Golden-winged Warbler populations exists in the Appalachian Mountains from southeastern New York to northern Georgia, surrounded at lower elevations by Blue-winged Warblers. Important concentrations of Golden-winged Warblers persist in the Allegheny Mountains region of West Virginia, the Cumberland Mountains in Tennessee, Blue Ridge Mountains of western North Carolina, Allegheny Plateau and Pocono Mountains in Pennsylvania, and in the Hudson Highlands of southern New York. These high-elevation Appalachian populations have escaped contact with Blue-winged Warblers until very recently and represent important refugia for conservation and management; other Appalachian populations are rapidly declining. In addition, based on historical records and standardized surveys across the wintering grounds, we identified three regions of concentration: highlands and Caribbean slopes from Guatemala and Belize to northwestern Nicaragua; middle elevations (both slopes) in Costa Rica and western Panama; and in an arc of the northern Andes from central Colombia to northern Venezuela. It is possible that the winter range has been shifting towards the northwest in recent decades, paralleling shifts in the breeding distribution. Future conservation efforts for Golden-winged Warbler need to include close monitoring of the dynamic phenotypic and genetic distributional shifts, and may need to consider the “winged warbler” complex together as a highly adaptable evolutionary unit.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Golden-winged Warbler ecology, conservation, and habitat management (Studies in Avian Biology, volume 49)","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"CRC Press","publisherLocation":"Boca Raton, FL","isbn":"978-1-4822-4068-9","usgsCitation":"Rosenberg, K.V., Will, T., Buehler, D.A., Barker Swarthout, S., Thogmartin, W.E., Bennett, R.E., and Chandler, R., 2016, Dynamic distributions and population declines of Golden-winged Warblers, chap. 1 of Golden-winged Warbler ecology, conservation, and habitat management (Studies in Avian Biology, volume 49): Studies in Avian Biology, v. 49, p. 3-28.","productDescription":"26 p.","startPage":"3","endPage":"28","ipdsId":"IP-059605","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":330436,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":330361,"type":{"id":15,"text":"Index Page"},"url":"https://hdl.handle.net/11299/189700"}],"volume":"49","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5811c0f2e4b0f497e79a5a6b","contributors":{"authors":[{"text":"Rosenberg, Kenneth V.","contributorId":171463,"corporation":false,"usgs":false,"family":"Rosenberg","given":"Kenneth","email":"","middleInitial":"V.","affiliations":[{"id":27615,"text":"Cornell Lab of Ornithology, Conservation Science Program","active":true,"usgs":false}],"preferred":false,"id":651970,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Will, Tom","contributorId":149777,"corporation":false,"usgs":false,"family":"Will","given":"Tom","email":"","affiliations":[{"id":17821,"text":"U.S. Fish and Wildlife Service, Division of Migratory Birds","active":true,"usgs":false}],"preferred":false,"id":651971,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Buehler, David A.","contributorId":169746,"corporation":false,"usgs":false,"family":"Buehler","given":"David","email":"","middleInitial":"A.","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":651972,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barker Swarthout, Sara","contributorId":176239,"corporation":false,"usgs":false,"family":"Barker Swarthout","given":"Sara","email":"","affiliations":[{"id":34544,"text":"Cornell Lab of Ornithology, Cornell University","active":true,"usgs":false}],"preferred":false,"id":651973,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":651969,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bennett, Ruth E.","contributorId":94622,"corporation":false,"usgs":false,"family":"Bennett","given":"Ruth","email":"","middleInitial":"E.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":709867,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Chandler, Richard rchandler@usgs.gov","contributorId":2511,"corporation":false,"usgs":true,"family":"Chandler","given":"Richard","email":"rchandler@usgs.gov","affiliations":[{"id":13266,"text":"Warnell School of Forestry and Natural Resources, The University of Georgia","active":true,"usgs":false}],"preferred":false,"id":709868,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70177882,"text":"70177882 - 2016 - Synthesising empirical results to improve predictions of post-wildfire runoff and erosion response","interactions":[],"lastModifiedDate":"2016-10-25T15:51:47","indexId":"70177882","displayToPublicDate":"2016-10-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2083,"text":"International Journal of Wildland Fire","active":true,"publicationSubtype":{"id":10}},"title":"Synthesising empirical results to improve predictions of post-wildfire runoff and erosion response","docAbstract":"Advances in research into wildfire impacts on runoff and erosion have demonstrated increasing complexity of controlling factors and responses, which, combined with changing fire frequency, present challenges for modellers. We convened a conference attended by experts and practitioners in post-wildfire impacts, meteorology and related research, including modelling, to focus on priority research issues. The aim was to improve our understanding of controls and responses and the predictive capabilities of models. This conference led to the eight selected papers in this special issue. They address aspects of the distinctiveness in the controls and responses among wildfire regions, spatiotemporal rainfall variability, infiltration, runoff connectivity, debris flow formation and modelling applications. Here we summarise key findings from these papers and evaluate their contribution to improving understanding and prediction of post-wildfire runoff and erosion under changes in climate, human intervention and population pressure on wildfire-prone areas.
","language":"English","publisher":"International Association of Wildland Fire","doi":"10.1071/WF16021","usgsCitation":"Shakesby, R.A., Moody, J.A., Martin, D.A., and Robichaud, P.R., 2016, Synthesising empirical results to improve predictions of post-wildfire runoff and erosion response: International Journal of Wildland Fire, v. 25, no. 3, p. 257-261, https://doi.org/10.1071/WF16021.","productDescription":"5 p.","startPage":"257","endPage":"261","ipdsId":"IP-073495","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":462069,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1071/wf16021","text":"Publisher Index Page"},{"id":330382,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"25","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58106f98e4b0f497e7961113","contributors":{"authors":[{"text":"Shakesby, Richard A.","contributorId":176258,"corporation":false,"usgs":false,"family":"Shakesby","given":"Richard","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":652007,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moody, John A. 0000-0003-2609-364X jamoody@usgs.gov","orcid":"https://orcid.org/0000-0003-2609-364X","contributorId":771,"corporation":false,"usgs":true,"family":"Moody","given":"John","email":"jamoody@usgs.gov","middleInitial":"A.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":652006,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martin, Deborah A. 0000-0001-8237-0838 damartin@usgs.gov","orcid":"https://orcid.org/0000-0001-8237-0838","contributorId":1900,"corporation":false,"usgs":true,"family":"Martin","given":"Deborah","email":"damartin@usgs.gov","middleInitial":"A.","affiliations":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"preferred":false,"id":652008,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Robichaud, Peter R.","contributorId":176259,"corporation":false,"usgs":false,"family":"Robichaud","given":"Peter","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":652009,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70178381,"text":"70178381 - 2016 - Geologic history of Martian regolith breccia Northwest Africa 7034: Evidence for hydrothermal activity and lithologic diversity in the Martian crust","interactions":[],"lastModifiedDate":"2016-11-15T17:02:35","indexId":"70178381","displayToPublicDate":"2016-10-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2317,"text":"Journal of Geophysical Research E: Planets","active":true,"publicationSubtype":{"id":10}},"title":"Geologic history of Martian regolith breccia Northwest Africa 7034: Evidence for hydrothermal activity and lithologic diversity in the Martian crust","docAbstract":"The timing and mode of deposition for Martian regolith breccia Northwest Africa (NWA) 7034 were determined by combining petrography, shape analysis, and thermochronology. NWA 7034 is composed of igneous, impact, and brecciated clasts within a thermally annealed submicron matrix of pulverized crustal rocks and devitrified impact/volcanic glass. The brecciated clasts are likely lithified portions of Martian regolith with some evidence of past hydrothermal activity. Represented lithologies are primarily ancient crustal materials with crystallization ages as old as 4.4 Ga. One ancient zircon was hosted by an alkali-rich basalt clast, confirming that alkalic volcanism occurred on Mars very early. NWA 7034 is composed of fragmented particles that do not exhibit evidence of having undergone bed load transport by wind or water. The clast size distribution is similar to terrestrial pyroclastic deposits. We infer that the clasts were deposited by atmospheric rainout subsequent to a pyroclastic eruption(s) and/or impact event(s), although the ancient ages of igneous components favor mobilization by impact(s). Despite ancient components, the breccia has undergone a single pervasive thermal event at 500–800°C, evident by groundmass texture and concordance of ~1.5 Ga dates for bulk rock K-Ar, U-Pb in apatite, and U-Pb in metamict zircons. The 1.5 Ga age is likely a thermal event that coincides with rainout/breccia lithification. We infer that the episodic process of regolith lithification dominated sedimentary processes during the Amazonian Epoch. The absence of pre-Amazonian high-temperature metamorphic events recorded in ancient zircons indicates source domains of static southern highland crust punctuated by episodic impact modification.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2016JE005143","usgsCitation":"McCubbin, F.M., Boyce, J.W., Novak-Szabo, T., Santos, A., Tartese, R., Muttik, N., Domokos, G., Vazquez, J.A., Keller, L.P., Moser, D.E., Jerolmack, D.J., Shearer, C.K., Steele, A., Elardo, S.M., Rahman, Z., Anand, M., Delhaye, T., and Agee, C.B., 2016, Geologic history of Martian regolith breccia Northwest Africa 7034: Evidence for hydrothermal activity and lithologic diversity in the Martian crust: Journal of Geophysical Research E: Planets, v. 121, no. 10, p. 2120-2149, https://doi.org/10.1002/2016JE005143.","productDescription":"30 p.","startPage":"2120","endPage":"2149","ipdsId":"IP-072126","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":470534,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/2016je005143","text":"External 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Timea","contributorId":176888,"corporation":false,"usgs":false,"family":"Novak-Szabo","given":"Timea","email":"","affiliations":[],"preferred":false,"id":653883,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Santos, Alison","contributorId":176883,"corporation":false,"usgs":false,"family":"Santos","given":"Alison","email":"","affiliations":[],"preferred":false,"id":653884,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tartese, Romain","contributorId":176884,"corporation":false,"usgs":false,"family":"Tartese","given":"Romain","email":"","affiliations":[],"preferred":false,"id":653885,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Muttik, Nele","contributorId":176890,"corporation":false,"usgs":false,"family":"Muttik","given":"Nele","email":"","affiliations":[],"preferred":false,"id":653886,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Domokos, Gabor","contributorId":176885,"corporation":false,"usgs":false,"family":"Domokos","given":"Gabor","email":"","affiliations":[],"preferred":false,"id":653887,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Vazquez, Jorge A. 0000-0003-2754-0456 jvazquez@usgs.gov","orcid":"https://orcid.org/0000-0003-2754-0456","contributorId":4458,"corporation":false,"usgs":true,"family":"Vazquez","given":"Jorge","email":"jvazquez@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true},{"id":5056,"text":"Office of the AD Energy and Minerals, and Environmental Health","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":true,"id":653888,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Keller, Lindsay P.","contributorId":176886,"corporation":false,"usgs":false,"family":"Keller","given":"Lindsay","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":653889,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Moser, Desmond E.","contributorId":176887,"corporation":false,"usgs":false,"family":"Moser","given":"Desmond","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":653890,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Jerolmack, Douglas J.","contributorId":78622,"corporation":false,"usgs":true,"family":"Jerolmack","given":"Douglas","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":653891,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Shearer, Charles K.","contributorId":111575,"corporation":false,"usgs":true,"family":"Shearer","given":"Charles","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":653892,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Steele, Andrew","contributorId":23830,"corporation":false,"usgs":true,"family":"Steele","given":"Andrew","email":"","affiliations":[],"preferred":false,"id":653893,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Elardo, Stephen M.","contributorId":176891,"corporation":false,"usgs":false,"family":"Elardo","given":"Stephen","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":653894,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Rahman, Zia","contributorId":176892,"corporation":false,"usgs":false,"family":"Rahman","given":"Zia","email":"","affiliations":[],"preferred":false,"id":653895,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Anand, Mahesh","contributorId":176893,"corporation":false,"usgs":false,"family":"Anand","given":"Mahesh","email":"","affiliations":[],"preferred":false,"id":653896,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Delhaye, Thomas","contributorId":176894,"corporation":false,"usgs":false,"family":"Delhaye","given":"Thomas","email":"","affiliations":[],"preferred":false,"id":653897,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Agee, Carl B.","contributorId":176895,"corporation":false,"usgs":false,"family":"Agee","given":"Carl","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":653898,"contributorType":{"id":1,"text":"Authors"},"rank":18}]}} ,{"id":70178034,"text":"70178034 - 2016 - Optimization of scat detection methods for a social ungulate, the wild pig, and experimental evaluation of factors affecting detection of scat","interactions":[],"lastModifiedDate":"2016-11-01T13:42:50","indexId":"70178034","displayToPublicDate":"2016-10-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Optimization of scat detection methods for a social ungulate, the wild pig, and experimental evaluation of factors affecting detection of scat","docAbstract":"Collection of scat samples is common in wildlife research, particularly for genetic capture-mark-recapture applications. Due to high degradation rates of genetic material in scat, large numbers of samples must be collected to generate robust estimates. Optimization of sampling approaches to account for taxa-specific patterns of scat deposition is, therefore, necessary to ensure sufficient sample collection. While scat collection methods have been widely studied in carnivores, research to maximize scat collection and noninvasive sampling efficiency for social ungulates is lacking. Further, environmental factors or scat morphology may influence detection of scat by observers. We contrasted performance of novel radial search protocols with existing adaptive cluster sampling protocols to quantify differences in observed amounts of wild pig (Sus scrofa) scat. We also evaluated the effects of environmental (percentage of vegetative ground cover and occurrence of rain immediately prior to sampling) and scat characteristics (fecal pellet size and number) on the detectability of scat by observers. We found that 15- and 20-m radial search protocols resulted in greater numbers of scats encountered than the previously used adaptive cluster sampling approach across habitat types, and that fecal pellet size, number of fecal pellets, percent vegetative ground cover, and recent rain events were significant predictors of scat detection. Our results suggest that use of a fixed-width radial search protocol may increase the number of scats detected for wild pigs, or other social ungulates, allowing more robust estimation of population metrics using noninvasive genetic sampling methods. Further, as fecal pellet size affected scat detection, juvenile or smaller-sized animals may be less detectable than adult or large animals, which could introduce bias into abundance estimates. Knowledge of relationships between environmental variables and scat detection may allow researchers to optimize sampling protocols to maximize utility of noninvasive sampling for wild pigs and other social ungulates.
The Alligator Gar Atractosteus spatula is a species of conservation concern throughout its range, and better definition of otoliths during early development would aid understanding its life history and ecology. We conducted X-ray computed tomography scans, scanning electron microscopy, and light microscopy to examine the three pairs of otoliths and how they developed over time in relation to fish size and age. The sagittae are the largest, possessing distinct dorsal and ventral lobes covered with small otoconia concentrated in the sulcul region. The sagittae exhibited allometric growth, increasing more rapidly in the ventral lobe than in the dorsal. The asterisci were smaller and also exhibited small otoconia on their surface, but much less than the sagittae. The lapilli were oriented laterally, in contrast to the sagittae and asterisci, which were oriented vertically, with a hump on the dorsum and very large otoconia on the lateral surface that appeared to fuse into the main otolith as the fish grew. Based on size measurements and ring counts in all three pairs of otoliths from 101 known-age Alligator Gar sampled weekly through 91 d after hatch, we developed regression models to examine otolith growth and predict age. All relationships were significant and highly explanatory, but the strongest relationships were between otolith and fish size (for measurements from sagittae) and for age predictions from the lapillus. Age prediction models all resulted in a slope near unity, indicating that ring deposition occurred approximately daily. The first ring in sagittae and lapilli corresponded to swim-up, whereas the first ring formed in asterisci approximately 8 d after swim-up. These results fill a gap in knowledge and can aid understanding of evolutionary processes as well as provide useful information for management and conservation.
","language":"English","publisher":"Taylor & Frances","doi":"10.1080/00028487.2015.1135189","usgsCitation":"Long, J.M., and Snow, R.A., 2016, Ontogenetic development of otoliths in Alligator Gar: Transactions of the American Fisheries Society, v. 145, no. 3, p. 537-544, https://doi.org/10.1080/00028487.2015.1135189.","productDescription":"8 p.","startPage":"537","endPage":"544","ipdsId":"IP-058724","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":330339,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"145","issue":"3","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-19","publicationStatus":"PW","scienceBaseUri":"580f1db9e4b0f497e794e4cf","contributors":{"authors":[{"text":"Long, James M. 0000-0002-8658-9949 jmlong@usgs.gov","orcid":"https://orcid.org/0000-0002-8658-9949","contributorId":3453,"corporation":false,"usgs":true,"family":"Long","given":"James","email":"jmlong@usgs.gov","middleInitial":"M.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":651896,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Snow, Richard A.","contributorId":176213,"corporation":false,"usgs":false,"family":"Snow","given":"Richard","email":"","middleInitial":"A.","affiliations":[{"id":27443,"text":"Oklahoma Department of Wildlife Conservation","active":true,"usgs":false}],"preferred":false,"id":651909,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70177947,"text":"70177947 - 2016 - Near-real-time cheatgrass percent cover in the Northern Great Basin, USA, 2015","interactions":[],"lastModifiedDate":"2017-01-17T19:09:07","indexId":"70177947","displayToPublicDate":"2016-10-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3230,"text":"Rangelands","active":true,"publicationSubtype":{"id":10}},"title":"Near-real-time cheatgrass percent cover in the Northern Great Basin, USA, 2015","docAbstract":"The Paleocene-Eocene Thermal Maximum (PETM) is characterized by a transient group of nannoplankton, belonging to the genus Discoaster. Our investigation of expanded shelf sections provides unprecedented detail of the morphology and phylogeny of the transient Discoasterduring the PETM and their relationship with environmental change. We observe a much larger range of morphological variation than previously documented suggesting that the taxa belonged to a plexus of highly gradational morphotypes rather than individual species. We propose that the plexus represents malformed ecophenotypes of a single species that migrated to a deep photic zone refuge during the height of PETM warming and eutrophication. Anomalously, high rates of organic matter remineralization characterized these depths during the event and led to lower saturation levels, which caused malformation. The proposed mechanism explains the co-occurrence of malformed Discoaster with pristine species that grew in the upper photic zone; moreover, it illuminates why malformation is a rare phenomenon in the paleontological record.
","language":"English","publisher":"AGU","doi":"10.1002/2016PA002980","usgsCitation":"Bralower, T., and Self-Trail, J., 2016, Nannoplankton malformation during the Paleocene-Eocene Thermal Maximum and its paleoecological and paleoceanographic significance: Paleoceanography, v. 31, no. 10, p. 1423-1439, https://doi.org/10.1002/2016PA002980.","productDescription":"17 p.","startPage":"1423","endPage":"1439","ipdsId":"IP-074908","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":331560,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"10","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-10-25","publicationStatus":"PW","scienceBaseUri":"5847dc7de4b06d80b7af6ab1","contributors":{"authors":[{"text":"Bralower, Timothy J.","contributorId":177196,"corporation":false,"usgs":false,"family":"Bralower","given":"Timothy J.","affiliations":[],"preferred":false,"id":654962,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Self-Trail, Jean 0000-0002-3018-4985 jstrail@usgs.gov","orcid":"https://orcid.org/0000-0002-3018-4985","contributorId":147370,"corporation":false,"usgs":true,"family":"Self-Trail","given":"Jean","email":"jstrail@usgs.gov","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":654961,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70185034,"text":"70185034 - 2016 - Controls on selenium distribution and mobilization in an irrigated shallow groundwater system underlain by Mancos Shale, Uncompahgre River Basin, Colorado, USA","interactions":[],"lastModifiedDate":"2017-03-15T11:16:36","indexId":"70185034","displayToPublicDate":"2016-10-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Controls on selenium distribution and mobilization in an irrigated shallow groundwater system underlain by Mancos Shale, Uncompahgre River Basin, Colorado, USA","docAbstract":"Elevated selenium (Se) concentrations in surface water and groundwater have become a concern in areas of the Western United States due to the deleterious effects of Se on aquatic ecosystems. Elevated Se concentrations are most prevalent in irrigated alluvial valleys underlain by Se-bearing marine shales where Se can be leached from geologic materials into the shallow groundwater and surface water systems. This study presents groundwater chemistry and solid-phase geochemical data from the Uncompahgre River Basin in Western Colorado, an irrigated alluvial landscape underlain by Se-rich Cretaceous marine shale. We analyzed Se species, major and trace elements, and stable nitrogen and oxygen isotopes of nitrate in groundwater and aquifer sediments to examine processes governing selenium release and transport in the shallow groundwater system. Groundwater Se concentrations ranged from below detection limit (< 0.5 μg L− 1) to 4070 μg L− 1, and primarily are controlled by high groundwater nitrate concentrations that maintain oxidizing conditions in the aquifer despite low dissolved oxygen concentrations. High nitrate concentrations in non-irrigated soils and nitrate isotopes indicate nitrate is largely derived from natural sources in the Mancos Shale and alluvial material. Thus, in contrast to areas that receive substantial NO3 inputs through inorganic fertilizer application, Se mitigation efforts that involve limiting NO3 application might have little impact on groundwater Se concentrations in the study area. Soluble salts are the primary source of Se to the groundwater system in the study area at-present, but they constitute a small percentage of the total Se content of core material. Sequential extraction results indicate insoluble Se is likely composed of reduced Se in recalcitrant organic matter or discrete selenide phases. Oxidation of reduced Se species that constitute the majority of the Se pool in the study area could be a potential source of Se in the future as soluble salts are progressively depleted.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2016.06.063","usgsCitation":"Mills, T.J., Mast, M.A., Thomas, J.C., and Keith, G.L., 2016, Controls on selenium distribution and mobilization in an irrigated shallow groundwater system underlain by Mancos Shale, Uncompahgre River Basin, Colorado, USA: Science of the Total Environment, v. 566-567, p. 1621-1631, https://doi.org/10.1016/j.scitotenv.2016.06.063.","productDescription":"11 p.","startPage":"1621","endPage":"1631","ipdsId":"IP-072320","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":337598,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Uncompahgre River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.17550659179688,\n 38.41378642476067\n ],\n [\n -107.78961181640625,\n 38.41378642476067\n ],\n [\n -107.78961181640625,\n 38.79476766282312\n ],\n [\n -108.17550659179688,\n 38.79476766282312\n ],\n [\n -108.17550659179688,\n 38.41378642476067\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"566-567","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58ca52cee4b0849ce97c86aa","contributors":{"authors":[{"text":"Mills, Taylor J. 0000-0001-7252-0521 tmills@usgs.gov","orcid":"https://orcid.org/0000-0001-7252-0521","contributorId":4658,"corporation":false,"usgs":true,"family":"Mills","given":"Taylor","email":"tmills@usgs.gov","middleInitial":"J.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":684023,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mast, M. Alisa 0000-0001-6253-8162 mamast@usgs.gov","orcid":"https://orcid.org/0000-0001-6253-8162","contributorId":827,"corporation":false,"usgs":true,"family":"Mast","given":"M.","email":"mamast@usgs.gov","middleInitial":"Alisa","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":684024,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thomas, Judith C. 0000-0001-7883-1419 juthomas@usgs.gov","orcid":"https://orcid.org/0000-0001-7883-1419","contributorId":1468,"corporation":false,"usgs":true,"family":"Thomas","given":"Judith","email":"juthomas@usgs.gov","middleInitial":"C.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":684025,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Keith, Gabrielle L. gkeith@usgs.gov","contributorId":5247,"corporation":false,"usgs":true,"family":"Keith","given":"Gabrielle","email":"gkeith@usgs.gov","middleInitial":"L.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":684026,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70189238,"text":"70189238 - 2016 - Inter-comparison of three-dimensional models of volcanic plumes","interactions":[],"lastModifiedDate":"2017-07-06T13:11:53","indexId":"70189238","displayToPublicDate":"2016-10-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"Inter-comparison of three-dimensional models of volcanic plumes","docAbstract":"We performed an inter-comparison study of three-dimensional models of volcanic plumes. A set of common volcanological input parameters and meteorological conditions were provided for two kinds of eruptions, representing a weak and a strong eruption column. From the different models, we compared the maximum plume height, neutral buoyancy level (where plume density equals that of the atmosphere), and level of maximum radial spreading of the umbrella cloud. We also compared the vertical profiles of eruption column properties, integrated across cross-sections of the plume (integral variables). Although the models use different numerical procedures and treatments of subgrid turbulence and particle dynamics, the inter-comparison shows qualitatively consistent results. In the weak plume case (mass eruption rate 1.5 × 106 kg s− 1), the vertical profiles of plume properties (e.g., vertical velocity, temperature) are similar among models, especially in the buoyant plume region. Variability among the simulated maximum heights is ~ 20%, whereas neutral buoyancy level and level of maximum radial spreading vary by ~ 10%. Time-averaging of the three-dimensional (3D) flow fields indicates an effective entrainment coefficient around 0.1 in the buoyant plume region, with much lower values in the jet region, which is consistent with findings of small-scale laboratory experiments. On the other hand, the strong plume case (mass eruption rate 1.5 × 109 kg s− 1) shows greater variability in the vertical plume profiles predicted by the different models. Our analysis suggests that the unstable flow dynamics in the strong plume enhances differences in the formulation and numerical solution of the models. This is especially evident in the overshooting top of the plume, which extends a significant portion (~ 1/8) of the maximum plume height. Nonetheless, overall variability in the spreading level and neutral buoyancy level is ~ 20%, whereas that of maximum height is ~ 10%. This inter-comparison study has highlighted the different capabilities of 3D volcanic plume models, and identified key features of weak and strong plumes, including the roles of jet stability, entrainment efficiency, and particle non-equilibrium, which deserve future investigation in field, laboratory, and numerical studies.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2016.06.011","usgsCitation":"Suzuki, Y., Costa, A., Cerminara, M., Esposti Ongaro, T., Herzog, M., Van Eaton, A.R., and Denby, L., 2016, Inter-comparison of three-dimensional models of volcanic plumes: Journal of Volcanology and Geothermal Research, v. 326, p. 26-42, https://doi.org/10.1016/j.jvolgeores.2016.06.011.","productDescription":"17 p.","startPage":"26","endPage":"42","ipdsId":"IP-071593","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":470540,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.17863/cam.1638","text":"External Repository"},{"id":343414,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"326","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"595f4c3ee4b0d1f9f057e345","contributors":{"authors":[{"text":"Suzuki, Yujiro","contributorId":194289,"corporation":false,"usgs":false,"family":"Suzuki","given":"Yujiro","email":"","affiliations":[],"preferred":false,"id":703662,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Costa, Antonio","contributorId":194290,"corporation":false,"usgs":false,"family":"Costa","given":"Antonio","email":"","affiliations":[{"id":27088,"text":"Istituto Nazionale di Geofisica e Vulcanologia (INGV)","active":true,"usgs":false}],"preferred":false,"id":703663,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cerminara, Matteo","contributorId":194291,"corporation":false,"usgs":false,"family":"Cerminara","given":"Matteo","email":"","affiliations":[],"preferred":false,"id":703664,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Esposti Ongaro, Tomaso","contributorId":194292,"corporation":false,"usgs":false,"family":"Esposti Ongaro","given":"Tomaso","email":"","affiliations":[],"preferred":false,"id":703665,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Herzog, Michael","contributorId":194293,"corporation":false,"usgs":false,"family":"Herzog","given":"Michael","email":"","affiliations":[],"preferred":false,"id":703666,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Van Eaton, Alexa R. 0000-0001-6646-4594 avaneaton@usgs.gov","orcid":"https://orcid.org/0000-0001-6646-4594","contributorId":184079,"corporation":false,"usgs":true,"family":"Van Eaton","given":"Alexa","email":"avaneaton@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":703661,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Denby, Leif","contributorId":194294,"corporation":false,"usgs":false,"family":"Denby","given":"Leif","email":"","affiliations":[],"preferred":false,"id":703667,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70188153,"text":"70188153 - 2016 - Lateral and subsurface flows impact arctic coastal plain lake water budgets","interactions":[],"lastModifiedDate":"2018-10-25T16:43:24","indexId":"70188153","displayToPublicDate":"2016-10-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Lateral and subsurface flows impact arctic coastal plain lake water budgets","docAbstract":"Arctic thaw lakes are an important source of water for aquatic ecosystems, wildlife, and humans. Many recent studies have observed changes in Arctic surface waters related to climate warming and permafrost thaw; however, explaining the trends and predicting future responses to warming is difficult without a stronger fundamental understanding of Arctic lake water budgets. By measuring and simulating surface and subsurface hydrologic fluxes, this work quantified the water budgets of three lakes with varying levels of seasonal drainage, and tested the hypothesis that lateral and subsurface flows are a major component of the post-snowmelt water budgets. A water budget focused only on post-snowmelt surface water fluxes (stream discharge, precipitation, and evaporation) could not close the budget for two of three lakes, even when uncertainty in input parameters was rigorously considered using a Monte Carlo approach. The water budgets indicated large, positive residuals, consistent with up to 70% of mid-summer inflows entering lakes from lateral fluxes. Lateral inflows and outflows were simulated based on three processes; supra-permafrost subsurface inflows from basin-edge polygonal ground, and exchange between seasonally drained lakes and their drained margins through runoff and evapotranspiration. Measurements and simulations indicate that rapid subsurface flow through highly conductive flowpaths in the polygonal ground can explain the majority of the inflow. Drained lakes were hydrologically connected to marshy areas on the lake margins, receiving water from runoff following precipitation and losing up to 38% of lake efflux to drained margin evapotranspiration. Lateral fluxes can be a major part of Arctic thaw lake water budgets and a major control on summertime lake water levels. Incorporating these dynamics into models will improve our ability to predict lake volume changes, solute fluxes, and habitat availability in the changing Arctic.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.10917","usgsCitation":"Koch, J.C., 2016, Lateral and subsurface flows impact arctic coastal plain lake water budgets: Hydrological Processes, v. 30, no. 21, p. 3918-3931, https://doi.org/10.1002/hyp.10917.","productDescription":"14 p.","startPage":"3918","endPage":"3931","ipdsId":"IP-064008","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":342033,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","volume":"30","issue":"21","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-06-21","publicationStatus":"PW","scienceBaseUri":"59327926e4b0e9bd0eab5513","contributors":{"authors":[{"text":"Koch, Joshua C. 0000-0001-7180-6982 jkoch@usgs.gov","orcid":"https://orcid.org/0000-0001-7180-6982","contributorId":202532,"corporation":false,"usgs":true,"family":"Koch","given":"Joshua","email":"jkoch@usgs.gov","middleInitial":"C.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":696929,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70182773,"text":"70182773 - 2016 - The timing of compositionally-zoned magma reservoirs and mafic 'priming' weeks before the 1912 Novarupta-Katmai rhyolite eruption","interactions":[],"lastModifiedDate":"2017-03-01T14:43:11","indexId":"70182773","displayToPublicDate":"2016-10-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1427,"text":"Earth and Planetary Science Letters","active":true,"publicationSubtype":{"id":10}},"title":"The timing of compositionally-zoned magma reservoirs and mafic 'priming' weeks before the 1912 Novarupta-Katmai rhyolite eruption","docAbstract":"The June 6, 1912 eruption of more than 13 km3 of dense rock equivalent (DRE) magma at Novarupta vent, Alaska was the largest of the 20th century. It ejected >7 km3 of rhyolite, ~1.3 km3 of andesite and ~4.6 km3 of dacite. Early ideas about the origin of pyroclastic flows and magmatic differentiation (e.g., compositional zonation of reservoirs) were shaped by this eruption. Despite being well studied, the timing of events that led to the chemically and mineralogically zoned magma reservoir remain poorly known. Here we provide new insights using the textures and chemical compositions of plagioclase and orthopyroxene crystals and by reevaluating previous U-Th isotope data. Compositional zoning of the magma reservoir likely developed a few thousand years before the eruption by several additions of mafic magma below an extant silicic reservoir. Melt compositions calculated from Sr contents in plagioclase fill the compositional gap between 68 and 76% SiO2 in whole pumice clasts, consistent with uninterrupted crystal growth from a continuum of liquids. Thus, our findings support a general model in which large volumes of crystal-poor rhyolite are related to intermediate magmas through gradual separation of melt from crystal-rich mush. The rhyolite is incubated by, but not mixed with, episodic recharge pulses of mafic magma that interact thermochemically with the mush and intermediate magmas. Hot, Mg-, Ca-, and Al-rich mafic magma intruded into, and mixed with, deeper parts of the reservoir (andesite and dacite) multiple times. Modeling the relaxation of the Fe-Mg concentrations in orthopyroxene and Mg in plagioclase rims indicates that the final recharge event occurred just weeks prior to the eruption. Rapid addition of mass, volatiles, and heat from the recharge magma, perhaps aided by partial melting of cumulate mush below the andesite and dacite, pressurized the reservoir and likely propelled a ~10 km lateral dike that allowed the overlying rhyolite to reach the surface.","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2016.07.015","usgsCitation":"Singer, B.S., Costa, F., Herrin, J.S., Hildreth, W., and Fierstein, J., 2016, The timing of compositionally-zoned magma reservoirs and mafic 'priming' weeks before the 1912 Novarupta-Katmai rhyolite eruption: Earth and Planetary Science Letters, v. 451, p. 125-137, https://doi.org/10.1016/j.epsl.2016.07.015.","productDescription":"13 p. ","startPage":"125","endPage":"137","ipdsId":"IP-078234","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":470525,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.epsl.2016.07.015","text":"Publisher Index Page"},{"id":336778,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"451","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58b7eba6e4b01ccd5500bb03","contributors":{"authors":[{"text":"Singer, Brad S.","contributorId":184168,"corporation":false,"usgs":false,"family":"Singer","given":"Brad","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":673703,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Costa, Fidel","contributorId":184169,"corporation":false,"usgs":false,"family":"Costa","given":"Fidel","email":"","affiliations":[],"preferred":false,"id":673704,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Herrin, Jason S.","contributorId":184170,"corporation":false,"usgs":false,"family":"Herrin","given":"Jason","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":673705,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hildreth, Wes 0000-0002-7925-4251 hildreth@usgs.gov","orcid":"https://orcid.org/0000-0002-7925-4251","contributorId":2221,"corporation":false,"usgs":true,"family":"Hildreth","given":"Wes","email":"hildreth@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":680460,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fierstein, Judith 0000-0001-8024-1426 jfierstn@usgs.gov","orcid":"https://orcid.org/0000-0001-8024-1426","contributorId":147000,"corporation":false,"usgs":true,"family":"Fierstein","given":"Judith","email":"jfierstn@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":673707,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70178043,"text":"70178043 - 2016 - Simulation modeling to explore the effects of length-based harvest regulations for Ictalurus fisheries","interactions":[],"lastModifiedDate":"2016-11-01T12:54:35","indexId":"70178043","displayToPublicDate":"2016-10-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Simulation modeling to explore the effects of length-based harvest regulations for Ictalurus fisheries","docAbstract":"Management of Blue Catfish Ictalurus furcatus and Channel Catfish I. punctatus for trophy production has recently become more common. Typically, trophy management is attempted with length-based regulations that allow for the moderate harvest of small fish but restrict the harvest of larger fish. However, the specific regulations used vary considerably across populations, and no modeling efforts have evaluated their effectiveness. We used simulation modeling to compare total yield, trophy biomass (Btrophy), and sustainability (spawning potential ratio [SPR] > 0.30) of Blue Catfish and Channel Catfish populations under three scenarios: (1) current regulation (typically a length-based trophy regulation), (2) the best-performing minimum length regulation (MLRbest), and (3) the best-performing length-based trophy catfish regulation (LTRbest; “best performing” was defined as the regulation that maximized yield, Btrophy, and sustainability). The Btrophy produced did not differ among the three scenarios. For each fishery, the MLRbest and LTRbest produced greater yield (>22% more) than the current regulation and maintained sustainability at higher finite exploitation rates (>0.30) than the current regulation. The MLRbest and LTRbest produced similar yields and SPRs for Channel Catfish and similar yields for Blue Catfish; however, the MLRbest for Blue Catfish produced more resilient fisheries (higher SPR) than the LTRbest. Overall, the variation in yield, Btrophy, and SPR among populations was greater than the variation among regulations applied to any given population, suggesting that population-specific regulations may be preferable to regulations applied to geographic regions. We conclude that LTRs are useful for improving catfish yield and maintaining sustainability without overly restricting harvest but are not effective at increasing the Btrophy of catfish.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/02755947.2016.1204391","usgsCitation":"Stewart, D., Long, J.M., and Shoup, D.E., 2016, Simulation modeling to explore the effects of length-based harvest regulations for Ictalurus fisheries: North American Journal of Fisheries Management, v. 36, no. 5, p. 1190-1204, https://doi.org/10.1080/02755947.2016.1204391.","productDescription":"15 p.","startPage":"1190","endPage":"1204","ipdsId":"IP-068502","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":330607,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","issue":"5","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-13","publicationStatus":"PW","scienceBaseUri":"5819a9c2e4b0bb36a4c91015","contributors":{"authors":[{"text":"Stewart, David R.","contributorId":141323,"corporation":false,"usgs":false,"family":"Stewart","given":"David R.","affiliations":[],"preferred":false,"id":652624,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Long, James M. 0000-0002-8658-9949 jmlong@usgs.gov","orcid":"https://orcid.org/0000-0002-8658-9949","contributorId":3453,"corporation":false,"usgs":true,"family":"Long","given":"James","email":"jmlong@usgs.gov","middleInitial":"M.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":652588,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shoup, Daniel E.","contributorId":141325,"corporation":false,"usgs":false,"family":"Shoup","given":"Daniel","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":652625,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70184980,"text":"70184980 - 2016 - Fragmented patterns of flood change across the United States","interactions":[],"lastModifiedDate":"2017-03-14T15:38:22","indexId":"70184980","displayToPublicDate":"2016-10-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Fragmented patterns of flood change across the United States","docAbstract":"Trends in the peak magnitude, frequency, duration, and volume of frequent floods (floods occurring at an average of two events per year relative to a base period) across the United States show large changes; however, few trends are found to be statistically significant. The multidimensional behavior of flood change across the United States can be described by four distinct groups, with streamgages experiencing (1) minimal change, (2) increasing frequency, (3) decreasing frequency, or (4) increases in all flood properties. Yet group membership shows only weak geographic cohesion. Lack of geographic cohesion is further demonstrated by weak correlations between the temporal patterns of flood change and large-scale climate indices. These findings reveal a complex, fragmented pattern of flood change that, therefore, clouds the ability to make meaningful generalizations about flood change across the United States.
","language":"English","publisher":"AGU Publications","doi":"10.1002/2016GL070590","usgsCitation":"Archfield, S.A., Hirsch, R.M., Viglione, A., and Blöschl, G., 2016, Fragmented patterns of flood change across the United States: Geophysical Research Letters, v. 43, no. 19, p. 10232-10239, https://doi.org/10.1002/2016GL070590.","productDescription":"8 p.","startPage":"10232","endPage":"10239","ipdsId":"IP-079542","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":470526,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016gl070590","text":"Publisher Index Page"},{"id":337538,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"43","issue":"19","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-10-09","publicationStatus":"PW","scienceBaseUri":"58c90125e4b0849ce97abcd7","contributors":{"authors":[{"text":"Archfield, Stacey A. 0000-0002-9011-3871 sarch@usgs.gov","orcid":"https://orcid.org/0000-0002-9011-3871","contributorId":1874,"corporation":false,"usgs":true,"family":"Archfield","given":"Stacey","email":"sarch@usgs.gov","middleInitial":"A.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":true,"id":683811,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hirsch, Robert M. 0000-0002-4534-075X rhirsch@usgs.gov","orcid":"https://orcid.org/0000-0002-4534-075X","contributorId":2005,"corporation":false,"usgs":true,"family":"Hirsch","given":"Robert","email":"rhirsch@usgs.gov","middleInitial":"M.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":683812,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Viglione, A.","contributorId":189084,"corporation":false,"usgs":false,"family":"Viglione","given":"A.","affiliations":[],"preferred":false,"id":683813,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Blöschl, G.","contributorId":189085,"corporation":false,"usgs":false,"family":"Blöschl","given":"G.","affiliations":[],"preferred":false,"id":683814,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70194549,"text":"70194549 - 2016 - Climate change and indigenous peoples: A synthesis of current impacts and experiences","interactions":[],"lastModifiedDate":"2017-12-15T11:06:35","indexId":"70194549","displayToPublicDate":"2016-10-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":32,"text":"General Technical Report","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"PNW-GTR-944","title":"Climate change and indigenous peoples: A synthesis of current impacts and experiences","docAbstract":"A growing body of literature examines the vulnerability, risk, resilience, and adaptation of indigenous peoples to climate change. This synthesis of literature brings together research pertaining to the impacts of climate change on sovereignty, culture, health, and economies that are currently being experienced by Alaska Native and American Indian tribes and other indigenous communities in the United States. The knowledge and science of how climate change impacts are affecting indigenous peoples contributes to the development of policies, plans, and programs for adapting to climate change and reducing greenhouse gas emissions. This report defines and describes the key frameworks that inform indigenous understandings of climate change impacts and pathways for adaptation and mitigation, namely, tribal sovereignty and self-determination, culture and cultural identity, and indigenous community health indicators. It also provides a comprehensive synthesis of climate knowledge, science, and strategies that indigenous communities are exploring, as well as an understanding of the gaps in research on these issues. This literature synthesis is intended to make a contribution to future efforts such as the 4th National Climate Assessment, while serving as a resource for future research, tribal and agency climate initiatives, and policy development.
","language":"English","publisher":"U.S. Department of Agriculture, Forest Service","usgsCitation":"Norton-Smith, K., Lynn, K., Chief, K., Cozetto, K., Donatuto, J., Hiza, M., Kruger, L., Maldonado, J., Viles, C., and Whyte, K., 2016, Climate change and indigenous peoples: A synthesis of current impacts and experiences: General Technical Report PNW-GTR-944, 136 p.","productDescription":"136 p.","ipdsId":"IP-077840","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":350029,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":349669,"type":{"id":15,"text":"Index Page"},"url":"https://www.fs.fed.us/pnw/pubs/pnw_gtr944.pdf"}],"publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fcb7e4b06e28e9c2415d","contributors":{"authors":[{"text":"Norton-Smith, Kathryn","contributorId":201144,"corporation":false,"usgs":false,"family":"Norton-Smith","given":"Kathryn","email":"","affiliations":[],"preferred":false,"id":724430,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lynn, Kathy","contributorId":201145,"corporation":false,"usgs":false,"family":"Lynn","given":"Kathy","email":"","affiliations":[],"preferred":false,"id":724431,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chief, Karletta","contributorId":147055,"corporation":false,"usgs":false,"family":"Chief","given":"Karletta","email":"","affiliations":[{"id":6624,"text":"University of Arizona, Laboratory of Tree-Ring Research","active":true,"usgs":false}],"preferred":false,"id":724432,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cozetto, Karen","contributorId":147057,"corporation":false,"usgs":false,"family":"Cozetto","given":"Karen","email":"","affiliations":[{"id":6709,"text":"University of Colorado, Denver","active":true,"usgs":false}],"preferred":false,"id":724433,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Donatuto, Jamie","contributorId":201146,"corporation":false,"usgs":false,"family":"Donatuto","given":"Jamie","email":"","affiliations":[],"preferred":false,"id":724434,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hiza, Margaret 0000-0003-2851-2502 mhiza@usgs.gov","orcid":"https://orcid.org/0000-0003-2851-2502","contributorId":198449,"corporation":false,"usgs":true,"family":"Hiza","given":"Margaret","email":"mhiza@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":724429,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kruger, Linda","contributorId":168546,"corporation":false,"usgs":false,"family":"Kruger","given":"Linda","email":"","affiliations":[{"id":6679,"text":"US Forest Service, Rocky Mountain Research Station","active":true,"usgs":false}],"preferred":false,"id":724435,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Maldonado, Julie","contributorId":168542,"corporation":false,"usgs":false,"family":"Maldonado","given":"Julie","email":"","affiliations":[{"id":25327,"text":"Livelihoods Knowledge Network, Santa Barbara, CA","active":true,"usgs":false}],"preferred":false,"id":724436,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Viles, Carson","contributorId":201147,"corporation":false,"usgs":false,"family":"Viles","given":"Carson","email":"","affiliations":[],"preferred":false,"id":724437,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Whyte, Kyle P.","contributorId":168548,"corporation":false,"usgs":false,"family":"Whyte","given":"Kyle P.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":724438,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70175670,"text":"ofr20161124 - 2016 - Laboratory evaluation of the Design Analysis Associates DAA H-3613i radar water-level sensor—Results of temperature, distance, and SDI-12 tests","interactions":[],"lastModifiedDate":"2016-10-03T11:42:46","indexId":"ofr20161124","displayToPublicDate":"2016-09-30T16:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-1124","title":"Laboratory evaluation of the Design Analysis Associates DAA H-3613i radar water-level sensor—Results of temperature, distance, and SDI-12 tests","docAbstract":"The Design Analysis Associates (DAA) DAA H-3613i radar water-level sensor (DAA H-3613i), manufactured by Xylem Incorporated, was evaluated by the U.S. Geological Survey (USGS) Hydrologic Instrumentation Facility (HIF) for conformance to manufacturer’s accuracy specifications for measuring a distance throughout the sensor’s operating temperature range, for measuring distances from 3 to 15 feet at ambient temperatures, and for compliance with the SDI-12 serial-to-digital interface at 1200-baud communication standard. The DAA H-3613i is a noncontact water-level sensor that uses pulsed radar to measure the distance between the radar and the water surface from 0.75 to 131 feet over a temperature range of −40 to 60 degrees Celsius (°C). Manufacturer accuracy specifications that were evaluated, the test procedures that followed, and the results obtained are described in this report. The sensor’s accuracy specification of ± 0.01 feet (± 3 millimeters) meets USGS requirements for a primary water-stage sensor used in the operation of a streamgage. The sensor met the manufacturer’s stated accuracy specifications for water-level measurements during temperature testing at a distance of 8 feet from the target over its temperature-compensated operating range of −40 to 60 °C, except at 60 °C. At 60 °C, about half the measurements exceeded the manufacturer’s accuracy specification by not more than 0.005 feet.The sensor met the manufacturer’s stated accuracy specifications for water-level measurements during distance-accuracy testing at the tested distances from 3 to 15 feet above the water surface at the HIF.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161124","usgsCitation":"Carnley, M.V., 2016, Laboratory evaluation of the Design Analysis Associates DAA H-3613i radar water-level sensor—Results of temperature, distance, and SDI-12 tests: U.S. Geological Survey Open-File Report 2016–1124, 7 p., https://dx.doi.org/10.3133/ofr20161124. ","productDescription":"iii, 7 p.","numberOfPages":"16","onlineOnly":"Y","ipdsId":"IP-071442","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":329212,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1124/coverthb.jpg"},{"id":329213,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1124/ofr20161124.pdf","text":"Report","size":"2.83 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1124"}],"contact":"Hydrologic Instrumentation Facility
U.S. Geological Survey
Building 2101
Stennis Space Center, MS 39529
http://water.usgs.gov/hif/
In the 1970s and 1980s, C.S. Venable Barclay conducted geologic mapping of areas primarily underlain by Cretaceous coals in the eastern part of the Little Snake River coal field (LSR) in Carbon County, southwest Wyoming. With some exceptions, most of the mapping data were never published. Subsequently, after his retirement from the U.S. Geological Survey (USGS), his field maps and field notebooks were archived in the USGS Field Records. Due to a pending USGS coal assessment of the Little Snake River coal field area and planned geological mapping to be conducted by the Wyoming State Geological Survey, Barclay’s mapping data needed to be published to support these efforts. Subsequently, geologic maps were scanned and georeferenced into a geographic information system, and project and field notes were scanned into Portable Document Format (PDF) files. Data for seventeen 7½-minute quadrangles are presented in this report. This publication is solely intended to compile the mapping data as it was last worked on by Barclay and provides no interpretation or modification of his work.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161170","usgsCitation":"Haacke, J.E., Barclay, C.S.V., and Hettinger, R.D., 2016, Preliminary geologic mapping of Cretaceous and Tertiary formations in the eastern part of the Little Snake River coal field, Carbon County, Wyoming: U.S. Geological Survey Open-File Report 2016–1170, 9 p., https://dx.doi.org/10.3133/ofr20161170.","productDescription":"Report: iii, 9 p.; Field Notes; Metadata; Read Me; Spatial Data","numberOfPages":"12","onlineOnly":"Y","ipdsId":"IP-070800","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":329139,"rank":3,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/of/2016/1170/ofr20161170_Field Notes.zip","text":"Field Notes","size":"444 MB","linkFileType":{"id":6,"text":"zip"},"description":"OFR 2016-1170 Field 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Director, USGS Central Energy Resources Science Center
Box 25046, Mail Stop 939
Denver, CO 80225
Bridge-scour equations presented in the Federal Highway Administration Hydraulic Engineering Circular No. 18 reflect the current state-of-the practice for predicting scour at bridges. Although these laboratory-derived equations provide an important resource for assessing scour potential, there is a measure of uncertainty when applying these equations to field conditions. The uncertainty and limitations have been acknowledged by laboratory researchers and confirmed in field investigations.
Because of the uncertainty associated with bridge-scour equations, HEC-18 recommends that engineers evaluate the computed scour depths obtained from the equations and modify the resulting data if they appear unreasonable. Perhaps the best way to evaluate the reasonableness of predicted scour is to compare it to field measurements of historic scour. Historic field data show scour depths resulting from high flows and provide a reference for evaluating predicted scour. It is rare, however, that such data are available at or near a site of interest, making the evaluation of predicted scour as compared to field data difficult if not impossible. Realizing the value of historic scour measurements, the U.S. Geological Survey (USGS), in cooperation with the South Carolina Department of Transportation (SCDOT), conducted a series of three field investigations to collect historic scour data with the goal of understanding regional trends of scour at riverine bridges in South Carolina.
Historic scour measurements, including measurements of clear-water abutment, contraction, and pier scour, as well as live-bed contraction and pier scour, were made at more than 200 bridges. These field investigations provided valuable insights into regional scour trends and yielded regional bridge-scour envelope curves that can be used as supplementary tools for assessing all components of scour at riverine bridges in South Carolina.
The application and limitations of these envelope curves were documented in four reports. Because each report addresses different components of bridge scour, it was recognized that there was a need to develop an integrated procedure for applying the envelope curves to help assess scour potential at riverine bridges in South Carolina. The result of that effort is detailed in Benedict and others (2016) and summarized in this fact sheet.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163065","collaboration":"Prepared in cooperation with the South Carolina Department of Transportation","usgsCitation":"Benedict, S.T., Feaster, T.D., and Caldwell, A.W., 2016, Assessing potential scour using the South Carolina bridge-scour envelope curves: U.S. Geological Survey Fact Sheet 2016-3065, 2 p., https://dx.doi.org/10.3133/fs20163065.","productDescription":"2 p. 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Carolina\",\"nation\":\"USA \"}}]}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road, Suite 129
Columbia, SC 29210
http://sc.water.usgs.gov/
A daily watershed model of the Sacramento River Basin of northern California was developed to simulate streamflow and suspended sediment transport to the San Francisco Bay-Delta. To compensate for sparse data, a unique combination of model inputs was developed, including meteorological variables, potential evapotranspiration, and parameters defining hydraulic geometry. A slight decreasing trend of sediment loads and concentrations was statistically significant in the lowest 50% of flows, supporting the observed historical sediment decline. Historical changes in climate, including seasonality and decline of snowpack, contribute to changes in streamflow, and are a significant component describing the mechanisms responsible for the decline in sediment. Several wet and dry hypothetical climate change scenarios with temperature changes of 1.5 °C and 4.5 °C were applied to the base historical conditions to assess the model sensitivity of streamflow and sediment to changes in climate. Of the scenarios evaluated, sediment discharge for the Sacramento River Basin increased the most with increased storm magnitude and frequency and decreased the most with increases in air temperature, regardless of changes in precipitation. The model will be used to develop projections of potential hydrologic and sediment trends to the Bay-Delta in response to potential future climate scenarios, which will help assess the hydrological and ecological health of the Bay-Delta into the next century.
","language":"English","publisher":"Molecular Diversity Preservation International","publisherLocation":"Basel, Switzerland","doi":"10.3390/w8100432","usgsCitation":"Stern, M.A., Flint, L.E., Minear, J.T., Flint, A.L., and Wright, S., 2016, Characterizing changes in streamflow and sediment supply in the Sacramento River Basin, California, using hydrological simulation program—FORTRAN (HSPF): Water, v. 8, no. 10, https://doi.org/10.3390/w8100432.","startPage":"432","numberOfPages":"21","ipdsId":"IP-073991","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":552,"text":"San Francisco Bay-Delta","active":false,"usgs":true}],"links":[{"id":462073,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w8100432","text":"Publisher Index Page"},{"id":329512,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.5,\n 38.25\n ],\n [\n -123.5,\n 41\n ],\n [\n -121,\n 41\n ],\n [\n -121,\n 38.25\n ],\n [\n -123.5,\n 38.25\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","issue":"10","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-30","publicationStatus":"PW","scienceBaseUri":"57ffdefee4b0824b2d179cf4","contributors":{"authors":[{"text":"Stern, Michelle A. 0000-0003-3030-7065 mstern@usgs.gov","orcid":"https://orcid.org/0000-0003-3030-7065","contributorId":4244,"corporation":false,"usgs":true,"family":"Stern","given":"Michelle","email":"mstern@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":650712,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flint, Lorraine E. 0000-0002-7868-441X lflint@usgs.gov","orcid":"https://orcid.org/0000-0002-7868-441X","contributorId":1184,"corporation":false,"usgs":true,"family":"Flint","given":"Lorraine","email":"lflint@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":650713,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Minear, Justin Toby jminear@usgs.gov","contributorId":3736,"corporation":false,"usgs":true,"family":"Minear","given":"Justin","email":"jminear@usgs.gov","middleInitial":"Toby","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":650714,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Flint, Alan L. 0000-0002-5118-751X aflint@usgs.gov","orcid":"https://orcid.org/0000-0002-5118-751X","contributorId":1492,"corporation":false,"usgs":true,"family":"Flint","given":"Alan","email":"aflint@usgs.gov","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":650715,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wright, Scott 0000-0002-0387-5713 sawright@usgs.gov","orcid":"https://orcid.org/0000-0002-0387-5713","contributorId":1536,"corporation":false,"usgs":true,"family":"Wright","given":"Scott","email":"sawright@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":650716,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70156288,"text":"tm6A53 - 2016 - MT3D-USGS version 1: A U.S. Geological Survey release of MT3DMS updated with new and expanded transport capabilities for use with MODFLOW","interactions":[],"lastModifiedDate":"2016-10-03T11:14:03","indexId":"tm6A53","displayToPublicDate":"2016-09-30T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"6-A53","title":"MT3D-USGS version 1: A U.S. Geological Survey release of MT3DMS updated with new and expanded transport capabilities for use with MODFLOW","docAbstract":"MT3D-USGS, a U.S. Geological Survey updated release of the groundwater solute transport code MT3DMS, includes new transport modeling capabilities to accommodate flow terms calculated by MODFLOW packages that were previously unsupported by MT3DMS and to provide greater flexibility in the simulation of solute transport and reactive solute transport. Unsaturated-zone transport and transport within streams and lakes, including solute exchange with connected groundwater, are among the new capabilities included in the MT3D-USGS code. MT3D-USGS also includes the capability to route a solute through dry cells that may occur in the Newton-Raphson formulation of MODFLOW (that is, MODFLOW-NWT). New chemical reaction Package options include the ability to simulate inter-species reactions and parent-daughter chain reactions. A new pump-and-treat recirculation package enables the simulation of dynamic recirculation with or without treatment for combinations of wells that are represented in the flow model, mimicking the above-ground treatment of extracted water. A reformulation of the treatment of transient mass storage improves conservation of mass and yields solutions for better agreement with analytical benchmarks. Several additional features of MT3D-USGS are (1) the separate specification of the partitioning coefficient (Kd) within mobile and immobile domains; (2) the capability to assign prescribed concentrations to the top-most active layer; (3) the change in mass storage owing to the change in water volume now appears as its own budget item in the global mass balance summary; (4) the ability to ignore cross-dispersion terms; (5) the definition of Hydrocarbon Spill-Source Package (HSS) mass loading zones using regular and irregular polygons, in addition to the currently supported circular zones; and (6) the ability to specify an absolute minimum thickness rather than the default percent minimum thickness in dry-cell circumstances.
Benchmark problems that implement the new features and packages test the accuracy of new code through comparison to analytical benchmarks, as well as to solutions from other published codes. The input file structure for MT3D-USGS adheres to MT3DMS conventions for backward compatibility: the new capabilities and packages described herein are readily invoked by adding three-letter package name acronyms to the name file or by setting input flags as needed. Memory is managed in MT3D-USGS using FORTRAN modules in order to simplify code development and expansion.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section A: Ground water in Book 6: Modeling techniques","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm6A53","collaboration":"Prepared in collaboration with S.S. Papadopulos & Associates, Inc.","usgsCitation":"Bedekar, Vivek, Morway, E.D., Langevin, C.D., and Tonkin, Matt, 2016, MT3D-USGS version 1: A U.S. Geological Survey release of MT3DMS updated with new and expanded transport capabilities for use with MODFLOW:\nU.S. Geological Survey Techniques and Methods 6-A53, 69 p., https://dx.doi.org/10.3133/tm6A53.","productDescription":"Report: x, 69 p.; Application Site","numberOfPages":"84","onlineOnly":"Y","ipdsId":"IP-053896","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":329190,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/06/a53/coverthb.jpg"},{"id":329191,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/06/a53/tm06a53.pdf","text":"Report","size":"4.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 6-A53"},{"id":329192,"rank":3,"type":{"id":4,"text":"Application Site"},"url":"https://dx.doi.org/10.5066/F75T3HKD","text":"MT3D-USGS Version 1","description":"TM 6-A53 MT3D-USGS Version 1"}],"publicComments":"Ground Water Resources Program\nThis report is Chapter 53 of Section A: Ground water in Book 6: Modeling techniques.","contact":"Office of Groundwater
U.S. Geological Survey
Mail Stop 411
12201 Sunrise Valley Drive
Reston, VA 20192
http://water.usgs.gov/ogw/
Multiple sampling trips during calendar years 2013 through 2015 were coordinated to provide measurements of interdependent benthic processes that potentially affect contaminant transport in Upper Klamath Lake (UKL), Oregon. The measurements were motivated by recognition that such internal processes (for example, solute benthic flux, bioturbation and solute efflux by benthic invertebrates, and physical groundwater-surface water interactions) were not integrated into existing management models for UKL. Up until 2013, all of the benthic-flux studies generally had been limited spatially to a number of sites in the northern part of UKL and limited temporally to 2–3 samplings per year. All of the benthic invertebrate studies also had been limited to the northern part of the lake; however, intensive temporal (weekly) studies had previously been completed independent of benthic-flux studies. Therefore, knowledge of both the spatial and temporal variability in benthic flux and benthic invertebrate distributions for the entire lake was lacking. To address these limitations, we completed a lakewide spatial study during 2013 and a coordinated temporal study with weekly sampling of benthic flux and benthic invertebrates during 2014. Field design of the spatially focused study in 2013 involved 21 sites sampled three times as the summer cyanobacterial bloom developed (that is, May 23, June 13, and July 3, 2013). Results of the 27-week, temporally focused study of one site in 2014 were summarized and partitioned into three periods (referred to herein as pre-bloom, bloom and post-bloom periods), each period involving 9 weeks of profiler deployments, water column and benthic sampling. Partitioning of the pre-bloom, bloom, and post-bloom periods were based on water-column chlorophyll concentrations and involved the following date intervals, respectively: April 15 through June 10, June 17 through August 13, and August 20 through October 16, 2014.
To examine dissolved-solute (0.2-micrometer [μm] filtered) benthic flux, sets of nonmetallic pore-water profilers (U.S. Patent 8,051,727 B1) were deployed. In 2013, the deployment of profilers at 21 UKL sites occurred at the beginning of the annual cyanobacterial bloom of Aphanizomenon flos–aquae (AFA), in the middle of the bloom period, and at the peak of the bloom. Coordinated benthic invertebrate collections also were made. Based on results from 2013, weekly deployments of profilers and collection of benthic invertebrate samples from late spring to early autumn were used to estimate temporal trends in solute flux and benthic invertebrate densities. Estimates of nutrient efflux by benthic invertebrates were determined in the spring and autumn from 2011 through 2013 and three times (spring, summer, and autumn) in 2015. This work extends UKL studies that began in 2006 to quantify the importance of benthic solute sources in the lake. In 2015, piezometers and thermistor sets were deployed to quantify potential groundwater exchange with the lake water column.
Analysis of the 2013 soluble reactive phosphorus (SRP) benthic flux indicated no effect of location (lake region), habitat, or sampling period, and the average lakewide flux values were consistent with earlier studies that had been confined to the northern region of UKL and adjacent wetlands. The 2014 study therefore focused on estimating temporal trends at a site within Ball Bay. During both 2013 and 2014 field studies, fluxes of macronutrients (soluble reactive phosphorus (SRP) and ammonia) and micronutrients (iron [Fe] and manganese [Mn]) were consistently positive and increased prior to the initial AFA bloom, varied or lagged with water-column chlorophyll during the summer bloom period, then decreased after the cyanobacterial blooms, only to rebound toward pre-bloom conditions in the final weeks of sampling. These four solutes exhibited benthic loads greater than maximum riverine loads estimated during the spring and early summers of 2013 and 2014. However, consistently detectable concentrations for all four solutes provide no evidence that they consistently serve as the limiting nutrient for primary production in the lake. In contrast to the four solutes (SRP, ammonia, Fe, and Mn), benthic fluxes of dissolved arsenic (As) were both negative and positive (that is, the lakebed currently serves as both a source and a sink for dissolved As, depending on season). In a further contrast with SRP, ammonia, dissolved Fe, and Mn, dissolved-As riverine loads to UKL were of similar magnitude to benthic loads. A negative relationship between dissolved-As flux and water-column As over the 2014 temporal study provides a potential advantage for the management of water-quality in contrast to solutes, like SRP or ammonia, with consistently positive flux.
The mean total benthic invertebrate density during 2013 was 12,610 individuals per square meter (n=63). Although benthic invertebrate density did not change over the study period, it was higher in littoral habitats than open-lake or trench habitats and higher in the northern region compared to the central or southern regions of UKL. Mean total benthic invertebrate density during 2014 was 19,726 individuals m−2 (n=27). Density during the pre-bloom and bloom periods of April 15 to August 13, 2014 (the first two thirds of the 2014 sampling period), were similar to 2013. However, benthic invertebrate density more than doubled during the latter one-third of the study, that is, the post-bloom period between August 20 to October 16, 2014. Oligochaeta, Chironomidae and Hirudinea represented well over 90 percent of the benthic fauna; Oligochaeta were twice as abundant as Chironomidae or Hirudinea, the latter two of which were similar in density.
Benthic invertebrates may enhance dissolved-nutrient (or toxicant) transport across the sediment-water interface by (1) modifying diffusion-layer thicknesses and permeability through bioturbation, (2) enhancing advective flow across the interface through bioirrigation, and (3) excreting or expelling dissolved or particulate solutes directly into the overlying water column (Boudreau and Jorgensen, 2001). We evaluated SRP efflux via excretion for approximately 15 different major taxa in UKL. Once these measures were scaled, it was evident that benthic invertebrates potentially contribute approximately 1.5 times the amount of SRP to the water column of Upper Klamath Lake as diffusive SRP flux alone, measured in profiler deployments.
Sets of piezometers and temperature loggers were deployed in UKL to obtain estimates of vertical advective solute flux. The pressure transducer installations, within the piezometers, did not perform as designed, rendering the head gradient data unreliable. However, in terms of future research, this field work did demonstrate the feasibility of collecting vertical gradient data with piezometer deployments. Advective flux estimates herein are based solely on heat-flow modeling based on temperature data from four lake sites, without use of transducer data. Given the magnitudes (both positive or negative) of the heat-transfer fluxes for SRP, relative to diffusive-flux and macroinvertebrate efflux measurements (all positive but spanning the same orders of magnitude), further examination of solute advective flux is recommended as a potential transport process to integrate into existing water-quality (for example, Total Maximum Daily Load [TMDL]) models.
As a complement to the biogeochemical focus of this study, initial analyses of suspended-particle (floc) characteristics and settling velocities from the water column were derived near the surface and lakebed at two UKL sites. To better understand changing particle characteristics during the AFA-bloom period, suspended particles were examined in 2015 using a LabSFLOC (LF), which is a Laboratory Spectral Flocculation Characteristics version of an In-Situ Settling Velocity instrument (INSSEV-LF). Particle characteristics and settling velocities were analyzed from the water column near the surface (sample dp_10) and lakebed (sample dp_90) at two lake sites (open-lake site ML and littoral site LS01). The term “floc” refers herein to suspended particles that may aggregate or disaggregate to change in size, composition, and settling velocity.
During pre-bloom (May) conditions, where maximum suspended particulate matter concentration (SPMC) was 140 milligrams per liter (mg L−1) was now observed at site LS01 in close proximity to the bed, where Dmean peaked at 305 μm, and the corresponding Wsmean was 3.9 millimeters per second (mm s−1). The high near-bed SPMC (828 mg L−1) experienced during post-bloom October 2015 at LS01 formed a benthic nepheloid layer (BNL) above the lake’s bed. Numerous low density, fast settling macrofloc-sized organic aggregates (D >160 μm) were observed (some up to 1 mm in size) near bed at LS01 both during the bloom and post-bloom conditions; many of these flocs displayed fibrous organic structures. In terms of mass settling fluxes, the post-bloom BNL produced a total MSF of 4,139 milligrams per square meter per second (mg m−2 s−1) (92.1 percent of MSF credited to the macrofloc-sized organic aggregates/cyanobacterial colonies); that was nearly three times the corresponding near-bed settling flux observed during the July 2015 bloom and 360 times greater than the pre-bloom conditions from May 2015 (98.8 percent and 14 percent of MSF credited to the macrofloc-sized fractions for those respective months). Such changes in the near-bed settling flux demonstrate the highly significant seasonal effects that the AFA bloom has on the floc depositional fluxes in UKL and highlights the importance of seasonal monitoring of these conditions in order to correctly parameterize the wide range in depositional characteristics and floc properties measured throughout UKL.
Collectively, floc populations observed within UKL demonstrated a wide range in settling velocity (Ws) for a given particle size, D. Similarly, a given settling velocity was not associated with a specific particle size. This variability in particle characteristics and properties indicates the influence of varying floc effective density and its effect on mass and mass settling fluxes (MSF). The use of instruments, such as the INSSEV-LF, enables measuring the variability of settling velocity and its relation to particle density and size.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161175","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Kuwabara, J.S., Topping, B.R., Carter, J.L., Carlson, R.A., Parchaso, F., Fend, S.V., Stauffer-Olsen, N., Manning, A.J., Land, J.M., 2016, Benthic processes affecting contaminant transport in Upper Klamath Lake, Oregon (ver. 1.1, October 2016): U.S. Geological Survey Open-File Report 2016–1175, 103 p., https://dx.doi.org/10.3133/ofr20161175. ","productDescription":"Report: viii, 103 p.; 2 Tables","numberOfPages":"115","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":329222,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1175/coverthb.jpg"},{"id":329223,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1175/ofr20161175.pdf","text":"Report","size":"4.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1175"},{"id":329224,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2016/1175/ofr20161175_table4.xlsx","text":"Table 4","size":"96 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2016-1175 Table 4"},{"id":329425,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2016/1175/versionHist.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2016-1175 Version History"},{"id":329424,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2016/1175/ofr20161175_table19.xlsx","text":"Table 19","size":"18 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2016-1175 Table 19"}],"country":"United States","state":"Oregon","otherGeospatial":"Upper Klamath Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.09793090820311,\n 42.231567925608616\n ],\n [\n -122.09793090820311,\n 42.70464124398721\n ],\n [\n -121.79992675781249,\n 42.70464124398721\n ],\n [\n -121.79992675781249,\n 42.231567925608616\n ],\n [\n -122.09793090820311,\n 42.231567925608616\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: Originally posted September 30, 2016; Version 1.1: October 11, 2016","contact":"NRP staff
Water Resources National Research Program
U.S. Geological Survey
345 Middlefield Road, MS-435
Menlo Park, CA 94025
National Research Program
In the spring of 2009, the U.S. Geological Survey, in cooperation with the San Bernardino Valley Municipal Water District, began working on a gravity survey in the Yucaipa area to explore the three-dimensional shape of the sedimentary fill (alluvial deposits) and the surface of the underlying crystalline basement rocks. As water use has increased in pace with rapid urbanization, water managers have need for better information about the subsurface geometry and the boundaries of groundwater subbasins in the Yucaipa area. The large density contrast between alluvial deposits and the crystalline basement complex permits using modeling of gravity data to estimate the thickness of alluvial deposits. The bottom of the alluvial deposits is considered to be the top of crystalline basement rocks. The gravity data, integrated with geologic information from surface outcrops and 51 subsurface borings (15 of which penetrated basement rock), indicated a complex basin configuration where steep slopes coincide with mapped faults―such as the Crafton Hills Fault and the eastern section of the Banning Fault―and concealed ridges separate hydrologically defined subbasins.
Gravity measurements and well logs were the primary data sets used to define the thickness and structure of the groundwater basin. Gravity measurements were collected at 256 new locations along profiles that totaled approximately 104.6 km (65 mi) in length; these data supplemented previously collected gravity measurements. Gravity data were reduced to isostatic anomalies and separated into an anomaly field representing the valley fill. The ‘valley-fill-deposits gravity anomaly’ was converted to thickness by using an assumed, depth-varying density contrast between the alluvial deposits and the underlying bedrock.
To help visualize the basin geometry, an animation of the elevation of the top of the basement-rocks was prepared. The animation “flies over” the Yucaipa groundwater basin, viewing the land surface, geology, faults, and ridges and valleys of the shaded-relief elevation of the top of the basement complex.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161127","collaboration":"Prepared in cooperation with the San Bernardino Valley Municipal Water District","usgsCitation":"Mendez, G.O., Langenheim, V.E., Morita, Andrew, and Danskin, W.R., 2016, Geologic structure of the Yucaipa area inferred from gravity data, San Bernardino and Riverside Counties, California: U.S. Geological Survey Open-File Report 2016–1127, 22 p., https://dx.doi.org/10.3133/ofr20161127.","productDescription":"Report: vii, 23 p.; Video Animation","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-077241","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":329070,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1127/ofr20161127.pdf","text":"Report","size":"34 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1127"},{"id":329071,"rank":3,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/of/2016/1127/ofr20161127_gravity.mp4","text":"Video animation","size":"47.3 MB mp4","description":"OFR 2016-1127 Video Animation","linkHelpText":"Land surface, geology, faults, wells, and elevation of the basement rocks in the Yucaipa area, California."},{"id":329069,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1127/coverthb.jpg"}],"country":"United States","state":"California","county":"San Bernardino County, Riverside County","otherGeospatial":"Yucaipa Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.15888977050781,\n 33.96842016198477\n ],\n [\n -117.15888977050781,\n 34.08962997133382\n ],\n [\n -116.97212219238281,\n 34.08962997133382\n ],\n [\n -116.97212219238281,\n 33.96842016198477\n ],\n [\n -117.15888977050781,\n 33.96842016198477\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, CA 95819
http://ca.water.usgs.gov
The effects of earthquake shaking on the population and infrastructure across the State of Hawaii could be catastrophic, and the high seismic hazard in the region emphasizes the likelihood of such an event. Earthquake early warning (EEW) has the potential to give several seconds of warning before strong shaking starts, and thus reduce loss of life and damage to property. The two approaches to EEW are (1) a network approach (such as ShakeAlert or ElarmS) where the regional seismic network is used to detect the earthquake and distribute the alarm and (2) a local approach where a critical facility has a single seismometer (or small array) and a warning system on the premises.
The network approach, also referred to here as ShakeAlert or ElarmS, uses the closest stations within a regional seismic network to detect and characterize an earthquake. Most parameters used for a network approach require observations on multiple stations (typically 3 or 4), which slows down the alarm time slightly, but the alarms are generally more reliable than with single-station EEW approaches. The network approach also benefits from having stations closer to the source of any potentially damaging earthquake, so that alarms can be sent ahead to anyone who subscribes to receive the notification. Thus, a fully implemented ShakeAlert system can provide seconds of warning for both critical facilities and general populations ahead of damaging earthquake shaking.
The cost to implement and maintain a fully operational ShakeAlert system is high compared to a local approach or single-station solution, but the benefits of a ShakeAlert system would be felt statewide—the warning times for strong shaking are potentially longer for most sources at most locations.
The local approach, referred to herein as “single station,” uses measurements from a single seismometer to assess whether strong earthquake shaking can be expected. Because of the reliance on a single station, false alarms are more common than when using a regional network of seismometers. Given the current network, a single-station approach provides more warning for damaging earthquakes that occur close to the station, but it would have limited benefit compared to a fully implemented ShakeAlert system. For Honolulu, for example, the single-station approach provides an advantage over ShakeAlert only for earthquakes that occur in a narrow zone extending northeast and southwest of O‘ahu. Instrumentation and alarms associated with the single-station approach are typically maintained and assessed within the target facility, and thus no outside connectivity is required. A single-station approach, then, is unlikely to help broader populations beyond the individuals at the target facility, but they have the benefit of being commercially available for relatively little cost.
The USGS Hawaiian Volcano Observatory (HVO) is the Advanced National Seismic System (ANSS) regional seismic network responsible for locating and characterizing earthquakes across the State of Hawaii. During 2014 and 2015, HVO tested a network-based EEW algorithm within the current seismic network in order to assess the suitability for building a full EEW system. Using the current seismic instrumentation and processing setup at HVO, it is possible for a network approach to release an alarm a little more than 3 seconds after the earthquake is recorded on the fourth seismometer. Presently, earthquakes having M≥3 detected with the ElarmS algorithm have an average location error of approximately 4.5 km and an average magnitude error of -0.3 compared to the reviewed catalog locations from the HVO. Additional stations and upgrades to existing seismic stations would serve to improve solution precision and warning times and additional staffing would be required to provide support for a robust, network-based EEW system.
For a critical facility on the Island of Hawaiʻi, such as the telescopes atop Mauna Kea, one phased approach to mitigate losses could be to immediately install a single station system to establish some level of warning. Subsequently, supporting the implementation of a full network-based EEW system on the Island of Hawaiʻi would provide additional benefit in the form of improved warning times once the system is fully installed and operational, which may take several years.
Distributed populations across the Hawaiian Islands, including those outside the major cities and far from the likely earthquake source areas, would likely only benefit from a network approach such as ShakeAlert to provide warnings of strong shaking.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161172","usgsCitation":"Thelen, W.A., Hotovec-Ellis, A.J., Bodin, P., 2016, Feasibility study of earthquake early warning (EEW) in Hawaii: U.S. Geological Survey Open-File Report 2016–1172, 33 p., https://dx.doi.org/10.3133/ofr20161172.","productDescription":"iii, 30 p.","numberOfPages":"35","onlineOnly":"Y","ipdsId":"IP-079231","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":329083,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1172/ofr20161172.pdf","text":"Report","size":"2.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1172"},{"id":329082,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1172/coverthb.jpg"}],"country":"United 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\"}}]}","contact":"Contact HVO
Volcano Science Center, Hawaiian Volcano Observatory
U.S. Geological Survey
P.O. Box 51, 1 Crater Rim Road
Hawaiʻi Volcanoes National Park, HI 96718-0051
http://hvo.wr.usgs.gov/
The generation of Everglades Depth Estimation Network (EDEN) daily water-level and water-depth maps is dependent on high quality real-time data from over 240 water-level stations. To increase the accuracy of the daily water-surface maps, the Automated Data Assurance and Management (ADAM) tool was created by the U.S. Geological Survey as part of Greater Everglades Priority Ecosystems Science. The ADAM tool is used to provide accurate quality-assurance review of the real-time data from the EDEN network and allows estimation or replacement of missing or erroneous data. This user’s manual describes how to install and operate the ADAM software. File structure and operation of the ADAM software is explained using examples.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161116","collaboration":"Greater Everglades Priority Ecosystems Science","usgsCitation":"Petkewich, M.D., Daamen, R.C., Roehl, E.A., and Conrads, P.A., 2016, User’s manual for the Automated Data Assurance and Management application developed for quality control of Everglades Depth Estimation Network water-level data: U.S. Geological Survey Open-File Report 2016–1116, 28 p., https://dx.doi.org/10.3133/ofr20161116.","productDescription":"Report: vi, 28 p.; Companion File","numberOfPages":"38","onlineOnly":"Y","ipdsId":"IP-076311","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":329016,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/sir20165094","text":"Scientific Investigations Report 2016–5094","description":"Scientific Investigations Report 2016–5094","linkHelpText":"- Using Inferential Sensors for Quality Control of Everglades Depth Estimation Network Water-Level Data"},{"id":328990,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1116/ofr20161116.pdf","text":"Report","size":"13.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1116"},{"id":328989,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1116/coverthb.jpg"},{"id":329002,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2016/1116/downloads","text":"Executable files for Automated Data Assurance and Management application","description":"OFR 2016-1116"}],"contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
Stephenson Center, Suite 129
Gracern Road
Columbia, SC 29210
https://www2.usgs.gov/water/southatlantic
The Everglades Depth Estimation Network (EDEN), with over 240 real-time gaging stations, provides hydrologic data for freshwater and tidal areas of the Everglades. These data are used to generate daily water-level and water-depth maps of the Everglades that are used to assess biotic responses to hydrologic change resulting from the U.S. Army Corps of Engineers Comprehensive Everglades Restoration Plan. The generation of EDEN daily water-level and water-depth maps is dependent on high quality real-time data from water-level stations. Real-time data are automatically checked for outliers by assigning minimum and maximum thresholds for each station. Small errors in the real-time data, such as gradual drift of malfunctioning pressure transducers, are more difficult to immediately identify with visual inspection of time-series plots and may only be identified during on-site inspections of the stations. Correcting these small errors in the data often is time consuming and water-level data may not be finalized for several months. To provide daily water-level and water-depth maps on a near real-time basis, EDEN needed an automated process to identify errors in water-level data and to provide estimates for missing or erroneous water-level data.
The Automated Data Assurance and Management (ADAM) software uses inferential sensor technology often used in industrial applications. Rather than installing a redundant sensor to measure a process, such as an additional water-level station, inferential sensors, or virtual sensors, were developed for each station that make accurate estimates of the process measured by the hard sensor (water-level gaging station). The inferential sensors in the ADAM software are empirical models that use inputs from one or more proximal stations. The advantage of ADAM is that it provides a redundant signal to the sensor in the field without the environmental threats associated with field conditions at stations (flood or hurricane, for example). In the event that a station does malfunction, ADAM provides an accurate estimate for the period of missing data. The ADAM software also is used in the quality assurance and quality control of the data. The virtual signals are compared to the real-time data, and if the difference between the two signals exceeds a certain tolerance, corrective action to the data and (or) the gaging station can be taken. The ADAM software is automated so that, each morning, the real-time EDEN data are compared to the inferential sensor signals and digital reports highlighting potential erroneous real-time data are generated for appropriate support personnel. The development and application of inferential sensors is easily transferable to other real-time hydrologic monitoring networks.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165094","collaboration":"Greater Everglades Priority Ecosystems Science","usgsCitation":"Petkewich, M.D., Daamen, R.C., Roehl, E.A., and Conrads, P.A., 2016, Using inferential sensors for quality control of Everglades Depth Estimation Network water-level data: U.S. Geological Survey Scientific Investigations Report 2016–5094, 25 p., https://dx.doi.org/10.3133/sir20165094.","productDescription":"v, 25 p.","onlineOnly":"Y","ipdsId":"IP-066447","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":329015,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/ofr20161116","text":"Open-File Report 2016–1116","description":"Open-File Report 2016–1116","linkHelpText":"- User’s Manual for the Automated Data Assurance and Management Application Developed for Quality Control of Everglades Depth Estimation Network Water-Level Data"},{"id":329013,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5094/coverthb.jpg"},{"id":329014,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5094/sir20165094.pdf","text":"Report","size":"14.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5094"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.36749267578125,\n 25.916055815010868\n ],\n [\n -81.3482666015625,\n 26.25893609446839\n ],\n [\n -80.88958740234375,\n 26.261399213411483\n ],\n [\n -80.88134765625,\n 26.152972606566966\n ],\n [\n -80.84564208984375,\n 26.09625490696853\n ],\n [\n -80.82916259765625,\n 26.335268473041793\n ],\n [\n -80.53802490234375,\n 26.337729970839195\n ],\n [\n -80.4583740234375,\n 26.477948705162902\n ],\n [\n -80.45562744140625,\n 26.681821407205977\n ],\n [\n -80.35400390625,\n 26.681821407205977\n ],\n [\n -80.34576416015625,\n 26.64745870265938\n ],\n [\n -80.2716064453125,\n 26.5958952526538\n ],\n [\n -80.22491455078125,\n 26.519735305660795\n ],\n [\n -80.2166748046875,\n 26.423849388805877\n ],\n [\n -80.233154296875,\n 26.369724677109726\n ],\n [\n -80.2880859375,\n 26.35988109485539\n ],\n [\n -80.299072265625,\n 26.194876675795218\n ],\n [\n -80.3594970703125,\n 26.123384198664976\n ],\n [\n -80.4364013671875,\n 26.14804172600284\n ],\n [\n -80.43090820312499,\n 25.94816628853973\n ],\n [\n -80.48309326171875,\n 25.8962911755466\n ],\n [\n -80.4913330078125,\n 25.668760323272966\n ],\n [\n -80.5572509765625,\n 25.57465306409282\n ],\n [\n -80.4803466796875,\n 25.29437116258816\n ],\n [\n -80.75225830078125,\n 25.227304826281653\n ],\n [\n -81.36749267578125,\n 25.916055815010868\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
Stephenson Center, Suite 129
Gracern Road
Columbia, SC 29210
https://www2.usgs.gov/water/southatlantic/