The U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, monitored suspended sediment within constructed Missouri River chutes during March through October 2012. Chutes were constructed at selected river bends by the U.S. Army Corps of Engineers to help mitigate aquatic habitat lost through the creation and maintenance of the navigation channel on the Missouri River. The restoration and development of chutes is one method for creating shallow-water habitat within the Missouri River to meet requirements established by the amended 2000 Biological Opinion. Understanding geomorphic channel-evolution processes and sediment transport is important for the design of chutes, monitoring and maintenance of existing chutes, and characterizing the habitat that the chutes provide. This report describes the methods used to monitor suspended sediment at two Missouri River chutes and presents the results of the data analysis to help understand the suspended-sediment characteristics of each chute and the effect the chutes have on the Missouri River. Upper Hamburg chute, near Nebraska City, Nebraska, and Kansas chute, near Peru, Nebraska, were selected for monitoring. At each study site, monthly discrete samples were collected from April through October in the Missouri River main-channel transects upstream from the chute inlet, downstream from the chute outlet, at the outlet (downstream transect) of both chutes, and at the inlet (upstream transect) of Kansas chute. In addition, grab samples from all chute sampling locations were collected using autosamplers. Suspended-sediment concentration (SSC) and grain-size metrics were determined for all samples (discrete and grab). Continuous water-quality monitors recorded turbidity and water temperature at 15-minute intervals at the three chute sampling locations. Two acoustic Doppler velocimeters, one within each chute, measured water depth and current velocities continuously. The depth and velocity data were used to estimate streamflow within each chute. The sampling design was developed to understand the suspended-sediment differences within each chute and between the chute and the Missouri River main channel during discrete sampling. The sampling design also allowed for site-specific surrogate relations between SSC and turbidity to be developed, which could be used to compute real-time estimates of SSC and sediment loads within the chutes. Real-time estimates of SSC and sediment loads enable a better understanding of sediment transport within the chutes during times when physical samples are not collected, including periods of high flow.
\nHigh flows during the summer of 2011 resulted in substantial alterations to both studied chutes; therefore, the U.S. Army Corps of Engineers repaired and modified both chutes during 2012. These unforeseen repairs and modifications within the chutes added uncertainty to the analysis because concentrations were altered by construction equipment and flow alteration.
\nDaily suspended-sediment and suspended-silt loads were estimated based on surrogate relations with turbidity. A linear regression was used to estimate equal-width increment (EWI)-equivalent SSC from autosampler SSC before using the model-calibration dataset to determine the best-fit model for prediction of SSC from the turbidity and, in some cases, discharge. Correlation between suspended-sand concentration (SSandC) in EWI samples and concurrent samples collected by an autosampler was low; therefore, SSandC was excluded from development of surrogate relations because a large part of the calibration dataset was from autosamples. Instead, SSandC was estimated as SSC minus suspended-silt-clay concentration (SSiltC). At all sites, the best-fit models included the base-10 logarithm of concentration and turbidity, and at Kansas chute upstream, the base-10 logarithm of streamflow was also included in the best-fit models. These surrogate models were used to estimate continuous time series of SSC and SSiltC. Estimated concentrations of suspended sediment were used to estimate instantaneous and daily loads for total suspended sediment, suspended silt-clay, and suspended sand. Estimated daily suspended-sediment loads were not significantly different between upstream and downstream transects within the Kansas chute, and most individual daily loads within the chute were not significantly different between upstream and downstream transects when evaluated using overlap in daily 95-percent confidence intervals. The comparison of daily load values for upstream and downstream chute transects, as estimated from turbidity-based surrogate models for Kansas chute, documents the daily dynamic nature of sediment transport within the chute with a temporal resolution that is not practical with discrete suspended-sediment sampling alone.
\nComparisons of concentrations and loads from EWI samples collected from different transects within a study site resulted in few significant differences, but comparisons are limited by small sample sizes and large within-transect variability. When comparing the Missouri River upstream transect to the chute inlet transect, similar results were determined in 2012 as were determined in 2008—the chute inlet affected the amount of sediment entering the chute from the main channel. In addition, the Kansas chute is potentially affecting the sediment concentration within the Missouri River main channel, but small sample size and construction activities within the chute limit the ability to fully understand either the effect of the chute in 2012 or the effect of the chute on the main channel during a year without construction. Finally, some differences in SSC were detected between the Missouri River upstream transects and the chute downstream transects; however, the effect of the chutes on the Missouri River main-channel sediment transport was difficult to isolate because of construction activities and sampling variability.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165002","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Omaha District","usgsCitation":"Densmore, B.K., Rus, D.L., Moser, M.T., Hall, B.M., and Andersen, M.J., 2016, Sediment loads and transport at constructed chutes along the Missouri River—Upper Hamburg chute near Nebraska City, Nebraska, and Kansas chute near Peru, Nebraska, 2012: U.S. Geological Survey Scientific Investigations Report 2016–5002, 47 p. https://dx.doi.org/10.3133/sir20165002.","productDescription":"vii, 47 p.","numberOfPages":"60","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-064671","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":316553,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5002/coverthb.jpg"},{"id":316554,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5002/sir20165002.pdf","text":"Report","size":"20.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5002"}],"country":"United States","state":"Nebraska","city":"Nebraska City, Peru","otherGeospatial":"Missouri River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.78601837158203,\n 40.564937785967224\n ],\n [\n -95.78601837158203,\n 40.61681920737131\n ],\n [\n -95.74241638183592,\n 40.61681920737131\n ],\n [\n -95.74241638183592,\n 40.564937785967224\n ],\n [\n -95.78601837158203,\n 40.564937785967224\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.73881149291991,\n 40.513277131087484\n ],\n [\n -95.73881149291991,\n 40.53050177574321\n ],\n [\n -95.70259094238281,\n 40.53050177574321\n ],\n [\n -95.70259094238281,\n 40.513277131087484\n ],\n [\n -95.73881149291991,\n 40.513277131087484\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, USGS Nebraska Water Science Center
5231 South 19th Street
Lincoln, NE 68512
Hydrodynamic connectivity describes the sources and destinations of water parcels within a domain over a given time. When combined with biological models, it can be a powerful concept to explain the patterns of constituent dispersal within marine ecosystems. However, providing connectivity metrics for a given domain is a three-dimensional problem: two dimensions in space to define the sources and destinations and a time dimension to evaluate connectivity at varying temporal scales. If the time scale of interest is not predefined, then a general approach is required to describe connectivity over different time scales. For this purpose, we have introduced the concept of a “retention clock” that highlights the change in connectivity through time. Using the example of connectivity between protected areas within Barnegat Bay, New Jersey, we show that a retention clock matrix is an informative tool for multitemporal analysis of connectivity.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2015GL066888","usgsCitation":"Defne, Z., Ganju, N.K., and Aretxabaleta, A., 2016, Estimating time-dependent connectivity in marine systems: Geophysical Research Letters, v. 43, no. 3, p. 1193-1201, https://doi.org/10.1002/2015GL066888.","productDescription":"9 p.","startPage":"1193","endPage":"1201","ipdsId":"IP-068617","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":471257,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015gl066888","text":"Publisher Index Page"},{"id":328842,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"43","issue":"3","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-04","publicationStatus":"PW","scienceBaseUri":"57f7c6cfe4b0bc0bec09cb72","contributors":{"authors":[{"text":"Defne, Zafer 0000-0003-4544-4310 zdefne@usgs.gov","orcid":"https://orcid.org/0000-0003-4544-4310","contributorId":5520,"corporation":false,"usgs":true,"family":"Defne","given":"Zafer","email":"zdefne@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":649215,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ganju, Neil K. 0000-0002-1096-0465 nganju@usgs.gov","orcid":"https://orcid.org/0000-0002-1096-0465","contributorId":174763,"corporation":false,"usgs":true,"family":"Ganju","given":"Neil","email":"nganju@usgs.gov","middleInitial":"K.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":649216,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aretxabaleta, Alfredo 0000-0002-9914-8018 aaretxabaleta@usgs.gov","orcid":"https://orcid.org/0000-0002-9914-8018","contributorId":140090,"corporation":false,"usgs":true,"family":"Aretxabaleta","given":"Alfredo","email":"aaretxabaleta@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":649217,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70207057,"text":"70207057 - 2016 - Climate change implications for tropical islands: Interpolating and interpreting statistically downscaled GCM projections for management and planning","interactions":[],"lastModifiedDate":"2019-12-04T15:26:55","indexId":"70207057","displayToPublicDate":"2016-02-03T15:19:57","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5202,"text":"Journal of Applied Meteorology and Climatology","onlineIssn":"1558-8432","printIssn":"1558-8424","active":true,"publicationSubtype":{"id":10}},"title":"Climate change implications for tropical islands: Interpolating and interpreting statistically downscaled GCM projections for management and planning","docAbstract":"The potential ecological and economic effects of climate change for tropical islands were studied using output from 12 statistically downscaled general circulation models (GCMs) taking Puerto Rico as a test case. Two model selection/model averaging strategies were used: the average of all available GCMs and the average of the models that are able to reproduce the observed large-scale dynamics that control precipitation over the Caribbean. Five island-wide and multidecadal averages of daily precipitation and temperature were estimated by way of a climatology-informed interpolation of the site-specific downscaled climate model output. Annual cooling degree-days (CDD) were calculated as a proxy index for air-conditioning energy demand, and two measures of annual no-rainfall days were used as drought indices. Holdridge life zone classification was used to map the possible ecological effects of climate change. Precipitation is predicted to decline in both model ensembles, but the decrease was more severe in the “regionally consistent” models. The precipitation declines cause gradual and linear increases in drought intensity and extremes. The warming from the 1960–90 period to the 2071–99 period was 4.6°–9°C depending on the global emission scenarios and location. This warming may cause increases in CDD, and consequently increasing energy demands. Life zones may shift from wetter to drier zones with the possibility of losing most, if not all, of the subtropical rain forests and extinction risks to rain forest specialists or obligates.
","language":"English","publisher":"American Meteorological Society","doi":"10.1175/JAMC-D-15-0182.1","usgsCitation":"Henareh Khalyani, A., Gould, W.A., Harmsen, E., Terando, A.J., Quinones, M., and Collazo, J., 2016, Climate change implications for tropical islands: Interpolating and interpreting statistically downscaled GCM projections for management and planning: Journal of Applied Meteorology and Climatology, v. 55, no. 2, p. 265-282, https://doi.org/10.1175/JAMC-D-15-0182.1.","productDescription":"18 p.","startPage":"265","endPage":"282","ipdsId":"IP-069429","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":471258,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1175/jamc-d-15-0182.1","text":"Publisher Index Page"},{"id":369918,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.4066162109375,\n 17.814071002942764\n ],\n [\n -65.56915283203125,\n 17.814071002942764\n ],\n [\n -65.56915283203125,\n 18.609807415471877\n ],\n [\n -67.4066162109375,\n 18.609807415471877\n ],\n [\n -67.4066162109375,\n 17.814071002942764\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"55","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Henareh Khalyani, Azad","contributorId":194189,"corporation":false,"usgs":false,"family":"Henareh Khalyani","given":"Azad","email":"","affiliations":[],"preferred":false,"id":776658,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gould, William A.","contributorId":103535,"corporation":false,"usgs":true,"family":"Gould","given":"William","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":776659,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harmsen, Eric 0000-0003-1462-1281","orcid":"https://orcid.org/0000-0003-1462-1281","contributorId":212206,"corporation":false,"usgs":false,"family":"Harmsen","given":"Eric","email":"","affiliations":[{"id":38459,"text":"Department of Agricultural and Biosystems Engineering, University of Puerto Rico","active":true,"usgs":false}],"preferred":false,"id":776660,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Terando, Adam J. 0000-0002-9280-043X aterando@usgs.gov","orcid":"https://orcid.org/0000-0002-9280-043X","contributorId":173447,"corporation":false,"usgs":true,"family":"Terando","given":"Adam","email":"aterando@usgs.gov","middleInitial":"J.","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":776661,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Quinones, Maya","contributorId":221026,"corporation":false,"usgs":false,"family":"Quinones","given":"Maya","email":"","affiliations":[],"preferred":false,"id":776662,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Collazo, Jaime A.","contributorId":191545,"corporation":false,"usgs":false,"family":"Collazo","given":"Jaime A.","affiliations":[],"preferred":false,"id":776663,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70159631,"text":"ofr20151219 - 2016 - A seasonal comparison of surface sediment characteristics in Chincoteague Bay, Maryland and Virginia, USA","interactions":[],"lastModifiedDate":"2025-05-13T16:52:04.747944","indexId":"ofr20151219","displayToPublicDate":"2016-02-03T14:45:00","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":"2015-1219","title":"A seasonal comparison of surface sediment characteristics in Chincoteague Bay, Maryland and Virginia, USA","docAbstract":"Scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center conducted a seasonal collection of surficial sediments from Chincoteague Bay and Tom's Cove, between Assateague Island and the Delmarva Peninsula in late March/early April 2014 and October 2014. The sampling efforts were part of a larger U.S. Geological Survey study to assess the effects of storm events on sediment distribution in back-barrier environments of the United States. By sampling during the spring and fall, a more complete understanding of seasonal variability in the area can help determine baseline conditions. The objective of this study was to characterize the sediments of Chincoteague Bay in order to create baseline conditions to incorporate with the hydrodynamic and sediment transport models used to evaluate pre- and post-storm change and compare with future field measurements.
\nThis report is an archive for sedimentological data derived from the surface sediment of Chincoteague Bay. Data are available for the spring (March/April 2014) and fall (October 2014) samples collected. Downloadable data are provided as Excel spreadsheets and as JPEG files. Additional files include ArcGIS shapefiles of the sampling sites, detailed results of sediment grain-size analyses, and formal Federal Geographic Data Committee metadata (data downloads).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151219","usgsCitation":"Ellis, A.M., Marot, M.E., Wheaton, C.J., Bernier, J.C., and Smith, C.G., 2015, A seasonal comparison of surface sediment characteristics in Chincoteague Bay, Maryland and Virginia, USA: U.S. Geological Survey Open-File Report 2015-1219, https://dx.doi.org/10.3133/ofr20151219.","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-065701","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":315341,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2015/1219","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2015-1219"},{"id":316535,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"}],"country":"United States","state":"Maryland, Virginia","otherGeospatial":"Chincoteague Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.19317626953125,\n 38.28778081436419\n ],\n [\n -75.50628662109375,\n 37.88677656291023\n ],\n [\n -75.3717041015625,\n 37.83364941345968\n ],\n [\n -75.17532348632812,\n 38.09241741843045\n ],\n [\n -75.09292602539062,\n 38.272688535980976\n ],\n [\n -75.19317626953125,\n 38.28778081436419\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"St. Petersburg Coastal and Marine Science Center
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600 4th Street South
St. Petersburg, FL 33701
(727) 502-8000
http://coastal.er.usgs.gov/
Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, scientific research, national security, recreation, and many others. For the Commonwealth of Puerto Rico, elevation data are critical for flood risk management, landslide mitigation, natural resources conservation, sea level rise and subsidence, coastal zone management, infrastructure and construction management, and other business uses. Today, high-density light detection and ranging (lidar) data are the primary sources for deriving elevation models and other datasets. Federal, State, Tribal, U.S. territorial, and local agencies work in partnership to (1) replace data that are older and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data.
The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States, Hawaii, and selected U.S. territories, and quality level 5 interferometric synthetic aperture radar (IfSAR) data for Alaska, all with a 6- to 10-year acquisition cycle, provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey (USGS), the Office of Management and Budget Circular A‒16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other three-dimensional (3D) representations of the Nation’s natural and constructed features.
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U.S. Geological Survey
511 National Center
Reston, VA 20192
http://www.usgs.gov/ngpo/
http://nationalmap.gov/3DEP/
The ability to infect a host is a key trait of a virus, and differences in infectivity could put one virus at an evolutionary advantage over another. In this study we have quantified the infectivity of two strains of infectious hematopoietic necrosis virus (IHNV) that are known to differ in fitness and virulence. By exposing juvenile rainbow trout (Oncorhynchus mykiss) hosts to a wide range of virus doses, we were able to calculate the infectious dose in terms of ID50 values for the two genotypes. Lethal dose experiments were also conducted to confirm the virulence difference between the two virus genotypes, using a range of virus doses and holding fish either in isolation or in batch so as to calculate LD50values. We found that infectivity is positively correlated with virulence, with the more virulent genotype having higher infectivity. Additionally, infectivity increases more steeply over a short range of doses compared to virulence, which has a shallower increase. We also examined the data using models of virion interaction and found no evidence to suggest that virions have either an antagonistic or a synergistic effect on each other, supporting the independent action hypothesis in the process of IHNV infection of rainbow trout.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.virusres.2015.12.020","usgsCitation":"McKenney, D., Kurath, G., and Wargo, A., 2016, Characterization of infectious dose and lethal dose of two strains of infectious hematopoietic necrosis virus (IHNV): Virus Research, v. 214, p. 80-89, https://doi.org/10.1016/j.virusres.2015.12.020.","productDescription":"10 p.","startPage":"80","endPage":"89","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068876","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":471260,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://scholarworks.wm.edu/vimsarticles/810","text":"Publisher Index Page"},{"id":316512,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"214","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56b324a9e4b0cc79997f04d3","contributors":{"authors":[{"text":"McKenney, Douglas dmckenney@usgs.gov","contributorId":156278,"corporation":false,"usgs":true,"family":"McKenney","given":"Douglas","email":"dmckenney@usgs.gov","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":false,"id":597135,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kurath, Gael 0000-0003-3294-560X gkurath@usgs.gov","orcid":"https://orcid.org/0000-0003-3294-560X","contributorId":2629,"corporation":false,"usgs":true,"family":"Kurath","given":"Gael","email":"gkurath@usgs.gov","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":597136,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wargo, Andrew","contributorId":73480,"corporation":false,"usgs":true,"family":"Wargo","given":"Andrew","affiliations":[],"preferred":false,"id":597137,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70161652,"text":"fs20163002 - 2016 - New insights into the Edwards Aquifer—Brackish-water simulation, drought, and the role of uncertainty analysis","interactions":[],"lastModifiedDate":"2016-02-03T11:08:03","indexId":"fs20163002","displayToPublicDate":"2016-02-03T11:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-3002","title":"New insights into the Edwards Aquifer—Brackish-water simulation, drought, and the role of uncertainty analysis","docAbstract":"The Edwards aquifer is an important water resource in south-central Texas, providing water for residents, businesses, and ecosystems. The aquifer is a highly complex karst system characterized by areas of rapid groundwater flow, faulted and fractured Cretaceous-age rocks, and multiple water-quality zones. Karst aquifer systems include soluble rocks such as limestone and dolomite that can convey tremendous amounts of water through dissolution-enhanced faults and fractures. Recent sustained droughts (2011–15) have heightened concerns about the possible effects of drought on this vital water resource.
\nThe Edwards aquifer consists of three water-quality zones. The freshwater zone of the Edwards aquifer is bounded to the south by a zone of brackish water (transition zone) where the aquifer transitions from fresh to saline water. The saline zone is downdip from the transition zone. There is concern that a recurrence of extreme drought, such as the 7-year drought from 1950 through 1956, could cause the transition zone to move toward (encroach upon) the freshwater zone, causing production wells near the transition zone to pump saltier water. There is also concern of drought effects on spring flows from Comal and San Marcos Springs. These concerns were evaluated through the development of a new numerical model of the Edwards aquifer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163002","collaboration":"Prepared in cooperation with the San Antonio Water System","usgsCitation":"Foster, L.K., and White, J.T., 2016, New insights into the Edwards aquifer—Brackish-water simulation, drought, and the role of uncertainty analysis: U.S. Geological Survey Fact Sheet 2016–3002, 6 p., https://dx.doi.org/10.3133/fs20163002.","productDescription":"6 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-070683","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":316352,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3002/coverthbr.jpg"},{"id":316353,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3002/fs20163002.pdf","text":"Report","size":"5.11 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016-3002"}],"country":"United States","state":"Texas","otherGeospatial":"Edwards Aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.1298828125,\n 30.774878871959746\n ],\n [\n -100.62377929687499,\n 29.080175989623203\n ],\n [\n -99.82177734375,\n 27.994401411046173\n ],\n [\n -96.866455078125,\n 29.864465259258\n ],\n [\n -98.1298828125,\n 30.774878871959746\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754
http://tx.usgs.gov
Animal telemetry is the science of elucidating the movements and behavior of animals in relation to their environment or habitat. Here, we focus on telemetry of aquatic species (marine mammals, sharks, fish, sea birds and turtles) and so are concerned with animal movements and behavior as they move through and above the world’s oceans, coastal rivers, estuaries and great lakes. Animal telemetry devices (“tags”) yield detailed data regarding animal responses to the coupled ocean–atmosphere and physical environment through which they are moving. Animal telemetry has matured and we describe a developing US Animal Telemetry Network (ATN) observing system that monitors aquatic life on a range of temporal and spatial scales that will yield both short- and long-term benefits, fill oceanographic observing and knowledge gaps and advance many of the U.S. National Ocean Policy Priority Objectives. ATN has the potential to create a huge impact for the ocean observing activities undertaken by the U.S. Integrated Ocean Observing System (IOOS) and become a model for establishing additional national-level telemetry networks worldwide.
","language":"English","publisher":"BioMed Central","doi":"10.1186/s40317-015-0092-1","usgsCitation":"Block, B.A., Holbrook, C., Simmons, S.E., Holland, K.N., Ault, J.S., Costa, D.P., Mate, B., Seitz, A.C., Arendt, M.D., Payne, J., Mahmoudi, B., Moore, P.L., Price, J., Levenson, J.J., Wilson, D., and Kochevar, R.E., 2016, Toward a national animal telemetry network for aquatic observations in the United States: Animal Biotelemetry, v. 4, no. 6, 8 p., https://doi.org/10.1186/s40317-015-0092-1.","productDescription":"8 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069947","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":471261,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40317-015-0092-1","text":"Publisher Index 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Management","active":true,"usgs":false}],"preferred":false,"id":597431,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Levenson, J. J.","contributorId":156326,"corporation":false,"usgs":false,"family":"Levenson","given":"J.","email":"","middleInitial":"J.","affiliations":[{"id":20318,"text":"Bureau of Ocean Energy Management","active":true,"usgs":false}],"preferred":false,"id":597429,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Wilson, Doug","contributorId":7581,"corporation":false,"usgs":true,"family":"Wilson","given":"Doug","email":"","affiliations":[],"preferred":false,"id":597432,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Kochevar, Randall E","contributorId":156328,"corporation":false,"usgs":false,"family":"Kochevar","given":"Randall","email":"","middleInitial":"E","affiliations":[{"id":16719,"text":"Hopkins Marine Station, Stanford University, Pacific Grove, CA 909350, USA","active":true,"usgs":false}],"preferred":false,"id":597433,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70176571,"text":"70176571 - 2016 - Colored dissolved organic matter in shallow estuaries: relationships between carbon sources and light attenuation","interactions":[],"lastModifiedDate":"2016-09-21T16:35:19","indexId":"70176571","displayToPublicDate":"2016-02-02T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1011,"text":"Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Colored dissolved organic matter in shallow estuaries: relationships between carbon sources and light attenuation","docAbstract":"Light availability is of primary importance to the ecological function of shallow estuaries. For example, benthic primary production by submerged aquatic vegetation is contingent upon light penetration to the seabed. A major component that attenuates light in estuaries is colored dissolved organic matter (CDOM). CDOM is often measured via a proxy, fluorescing dissolved organic matter (fDOM), due to the ease of in situ fDOM sensor measurements. Fluorescence must be converted to CDOM absorbance for use in light attenuation calculations. However, this CDOM–fDOM relationship varies among and within estuaries. We quantified the variability in this relationship within three estuaries along the mid-Atlantic margin of the eastern United States: West Falmouth Harbor (MA), Barnegat Bay (NJ), and Chincoteague Bay (MD/VA). Land use surrounding these estuaries ranges from urban to developed, with varying sources of nutrients and organic matter. Measurements of fDOM (excitation and emission wavelengths of 365 nm (±5 nm) and 460 nm (±40 nm), respectively) and CDOM absorbance were taken along a terrestrial-to-marine gradient in all three estuaries. The ratio of the absorption coefficient at 340 nm (m−1) to fDOM (QSU) was higher in West Falmouth Harbor (1.22) than in Barnegat Bay (0.22) and Chincoteague Bay (0.17). The CDOM : fDOM absorption ratio was variable between sites within West Falmouth Harbor and Barnegat Bay, but consistent between sites within Chincoteague Bay. Stable carbon isotope analysis for constraining the source of dissolved organic matter (DOM) in West Falmouth Harbor and Barnegat Bay yielded δ13C values ranging from −19.7 to −26.1 ‰ and −20.8 to −26.7 ‰, respectively. Concentration and stable carbon isotope mixing models of DOC (dissolved organic carbon) indicate a contribution of 13C-enriched DOC in the estuaries. The most likely source of 13C-enriched DOC for the systems we investigated is Spartina cordgrass. Comparison of DOC source to CDOM : fDOM absorption ratios at each site demonstrates the relationship between source and optical properties. Samples with 13C-enriched carbon isotope values, indicating a greater contribution from marsh organic material, had higher CDOM : fDOM absorption ratios than samples with greater contribution from terrestrial organic material. Applying a uniform CDOM : fDOM absorption ratio and spectral slope within a given estuary yields errors in modeled light attenuation ranging from 11 to 33 % depending on estuary. The application of a uniform absorption ratio across all estuaries doubles this error. This study demonstrates that light attenuation coefficients for CDOM based on continuous fDOM records are highly dependent on the source of DOM present in the estuary. Thus, light attenuation models for estuaries would be improved by quantification of CDOM absorption and DOM source identification.
","language":"English","publisher":"European Geosciences Union","doi":"10.5194/bg-13-583-2016","usgsCitation":"Oestreich, W., Ganju, N.K., Pohlman, J.W., and Suttles, S., 2016, Colored dissolved organic matter in shallow estuaries: relationships between carbon sources and light attenuation: Biogeosciences, v. 13, no. 2, p. 583-595, https://doi.org/10.5194/bg-13-583-2016.","productDescription":"13 p.","startPage":"583","endPage":"595","ipdsId":"IP-065243","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":471265,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/bg-13-583-2016","text":"Publisher Index Page"},{"id":328840,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"2","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-02","publicationStatus":"PW","scienceBaseUri":"57f7c6cfe4b0bc0bec09cb74","contributors":{"authors":[{"text":"Oestreich, W.K.","contributorId":174765,"corporation":false,"usgs":false,"family":"Oestreich","given":"W.K.","email":"","affiliations":[{"id":27509,"text":"Dept. of Civil and Environmental Engineering, Northwestern University, Evanston, IL","active":true,"usgs":false}],"preferred":false,"id":649224,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ganju, Neil K. 0000-0002-1096-0465 nganju@usgs.gov","orcid":"https://orcid.org/0000-0002-1096-0465","contributorId":174763,"corporation":false,"usgs":true,"family":"Ganju","given":"Neil","email":"nganju@usgs.gov","middleInitial":"K.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":649223,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pohlman, John W. 0000-0002-3563-4586 jpohlman@usgs.gov","orcid":"https://orcid.org/0000-0002-3563-4586","contributorId":145771,"corporation":false,"usgs":true,"family":"Pohlman","given":"John","email":"jpohlman@usgs.gov","middleInitial":"W.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":649225,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Suttles, Steven E. 0000-0002-4119-8370 ssuttles@usgs.gov","orcid":"https://orcid.org/0000-0002-4119-8370","contributorId":174766,"corporation":false,"usgs":true,"family":"Suttles","given":"Steven E. ","email":"ssuttles@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":649226,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70178683,"text":"70178683 - 2016 - Rock-avalanche dynamics revealed by large-scale field mapping and seismic signals at a highly mobile avalanche in the West Salt Creek valley, western Colorado","interactions":[],"lastModifiedDate":"2017-03-15T14:51:01","indexId":"70178683","displayToPublicDate":"2016-02-02T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"Rock-avalanche dynamics revealed by large-scale field mapping and seismic signals at a highly mobile avalanche in the West Salt Creek valley, western Colorado","docAbstract":"On 25 May 2014, a rain-on-snow–induced rock avalanche occurred in the West Salt Creek valley on the northern flank of Grand Mesa in western Colorado (United States). The avalanche mobilized from a preexisting rock slide in the Green River Formation and traveled 4.6 km down the confined valley, killing three people. The avalanche was rare for the contiguous United States because of its large size (54.5 Mm3) and high mobility (height/length = 0.14). To understand the avalanche failure sequence, mechanisms, and mobility, we conducted a forensic analysis using large-scale (1:1000) structural mapping and seismic data. We used high-resolution, unmanned aircraft system imagery as a base for field mapping, and analyzed seismic data from 22 broadband stations (distances < 656 km from the rock-slide source area) and one short-period network. We inverted broadband data to derive a time series of forces that the avalanche exerted on the earth and tracked these forces using curves in the avalanche path. Our results revealed that the rock avalanche was a cascade of landslide events, rather than a single massive failure. The sequence began with an early morning landslide/debris flow that started ∼10 h before the main avalanche. The main avalanche lasted ∼3.5 min and traveled at average velocities ranging from 15 to 36 m/s. For at least two hours after the avalanche ceased movement, a central, hummock-rich core continued to move slowly. Since 25 May 2014, numerous shallow landslides, rock slides, and rock falls have created new structures and modified avalanche topography. Mobility of the main avalanche and central core was likely enhanced by valley floor material that liquefied from undrained loading by the overriding avalanche. Although the base was likely at least partially liquefied, our mapping indicates that the overriding avalanche internally deformed predominantly by sliding along discrete shear surfaces in material that was nearly dry and had substantial frictional strength. These results indicate that the West Salt Creek avalanche, and probably other long-traveled avalanches, could be modeled as two layers: a thin, liquefied basal layer, and a thicker and stronger overriding layer.","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES01265.1","usgsCitation":"Coe, J.A., Baum, R.L., Allstadt, K.E., Kochevar, B., Schmitt, R.G., Morgan, M.L., White, J.L., Stratton, B.T., Hayashi, T.A., and Kean, J.W., 2016, Rock-avalanche dynamics revealed by large-scale field mapping and seismic signals at a highly mobile avalanche in the West Salt Creek valley, western Colorado: Geosphere, v. 12, no. 2, p. 607-631, https://doi.org/10.1130/GES01265.1.","productDescription":"25 p.","startPage":"607","endPage":"631","ipdsId":"IP-071133","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":471264,"rank":4,"type":{"id":40,"text":"Open Access Publisher Index 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\"coordinates\": [\n [\n [\n -108.3,\n 38.7\n ],\n [\n -108.3,\n 39.3\n ],\n [\n -107.8,\n 39.3\n ],\n [\n -107.8,\n 38.7\n ],\n [\n -108.3,\n 38.7\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-29","publicationStatus":"PW","scienceBaseUri":"58492df4e4b06d80b7b093ae","contributors":{"authors":[{"text":"Coe, Jeffrey A. 0000-0002-0842-9608 jcoe@usgs.gov","orcid":"https://orcid.org/0000-0002-0842-9608","contributorId":1333,"corporation":false,"usgs":true,"family":"Coe","given":"Jeffrey","email":"jcoe@usgs.gov","middleInitial":"A.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":655185,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baum, Rex L. 0000-0001-5337-1970 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Other potential applications include the simulation of biogeochemical processes in variably-saturated systems that track the transport and fate of agricultural pollutants, nutrients, natural and xenobiotic organic compounds and micropollutants such as pharmaceuticals, as well as the evolution of isotope patterns.
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We hypothesize that the climatic impact of boreal fires depends on local landscape heterogeneity such as burn severity, prefire vegetation type, and soil properties. To test this hypothesis, spatially explicit emission of greenhouse gases (GHGs) and aerosols and their resulting radiative forcing are required as an important and necessary component towards a full assessment. In this study, we integrated remote sensing (Landsat and MODIS) and models (carbon consumption model, emission factors model, and radiative forcing model) to calculate the carbon consumption, GHGs and aerosol emissions, and their radiative forcing of 2001–2010 fires at 30 m resolution in the Yukon River Basin of Alaska. Total carbon consumption showed significant spatial variation, with a mean of 2,615 g C m−2 and a standard deviation of 2,589 g C m−2. The carbon consumption led to different amounts of GHGs and aerosol emissions, ranging from 593.26 Tg (CO2) to 0.16 Tg (N2O). When converted to equivalent CO2 based on global warming potential metric, the maximum 20 years equivalent CO2 was black carbon (713.77 Tg), and the lowest 20 years equivalent CO2 was organic carbon (−583.13 Tg). The resulting radiative forcing also showed significant spatial variation: CO2, CH4, and N2O can cause a 20-year mean radiative forcing of 7.41 W m−2 with a standard deviation of 2.87 W m−2. This emission forcing heterogeneity indicates that different boreal fires have different climatic impacts. When considering the spatial variation of other forcings, such as surface shortwave forcing, we may conclude that some boreal fires, especially boreal deciduous fires, can warm the climate.
","language":"English","publisher":"Springer","publisherLocation":"New York","doi":"10.1007/s00704-015-1379-0","usgsCitation":"Huang, S., Liu, H., Dahal, D., Jin, S., Li, S., and Liu, S., 2016, Spatial variations in immediate greenhouse gases and aerosol emissions and resulting radiative forcing from wildfires in interior Alaska: Theoretical and Applied Climatology, v. 123, no. 3, p. 581-592, https://doi.org/10.1007/s00704-015-1379-0.","startPage":"581","endPage":"592","numberOfPages":"12","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-058246","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":326645,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","volume":"123","issue":"3","noUsgsAuthors":false,"publicationDate":"2015-01-18","publicationStatus":"PW","scienceBaseUri":"57b58b5de4b03bcb0104bc6e","contributors":{"authors":[{"text":"Huang, Shengli shuang@usgs.gov","contributorId":1926,"corporation":false,"usgs":true,"family":"Huang","given":"Shengli","email":"shuang@usgs.gov","affiliations":[],"preferred":true,"id":519096,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Liu, Heping","contributorId":117909,"corporation":false,"usgs":true,"family":"Liu","given":"Heping","affiliations":[],"preferred":false,"id":519100,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dahal, Devendra 0000-0001-9594-1249 ddahal@usgs.gov","orcid":"https://orcid.org/0000-0001-9594-1249","contributorId":5622,"corporation":false,"usgs":true,"family":"Dahal","given":"Devendra","email":"ddahal@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":519098,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jin, Suming 0000-0001-9919-8077 sjin@usgs.gov","orcid":"https://orcid.org/0000-0001-9919-8077","contributorId":4397,"corporation":false,"usgs":true,"family":"Jin","given":"Suming","email":"sjin@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":519097,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Li, Shuang","contributorId":116219,"corporation":false,"usgs":true,"family":"Li","given":"Shuang","email":"","affiliations":[],"preferred":false,"id":519099,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Liu, Shu-Guang sliu@usgs.gov","contributorId":984,"corporation":false,"usgs":true,"family":"Liu","given":"Shu-Guang","email":"sliu@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":519095,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70164312,"text":"70164312 - 2016 - Post-eruptive inflation of Okmok Volcano, Alaska, from InSAR, 2008–2014","interactions":[],"lastModifiedDate":"2016-02-01T11:00:28","indexId":"70164312","displayToPublicDate":"2016-02-01T12:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Post-eruptive inflation of Okmok Volcano, Alaska, from InSAR, 2008–2014","docAbstract":"Okmok, a ~10-km wide caldera that occupies most of the northeastern end of Umnak Island, is one of the most active volcanoes in the Aleutian arc. The most recent eruption at Okmok during July-August 2008 was by far its largest and most explosive since at least the early 19th century. We investigate post-eruptive magma supply and storage at the volcano during 2008–2014 by analyzing all available synthetic aperture radar (SAR) images of Okmok acquired during that time period using the multi-temporal InSAR technique. Data from the C-band Envisat and X-band TerraSAR-X satellites indicate that Okmok started inflating very soon after the end of 2008 eruption at a time-variable rate of 48-130 mm/y, consistent with GPS measurements. The “model-assisted” phase unwrapping method is applied to improve the phase unwrapping operation for long temporal baseline pairs. The InSAR time-series is used as input for deformation source modeling, which suggests magma accumulating at variable rates in a shallow storage zone at ~3.9 km below sea level beneath the summit caldera, consistent with previous studies. The modeled volume accumulation in the 6 years following the 2008 eruption is ~75% of the 1997 eruption volume and ~25% of the 2008 eruption volume.
","language":"English","publisher":"Multidisciplinary Digital Publishing Institute","doi":"10.3390/rs71215839","usgsCitation":"Qu, F., Lu, Z., Poland, M.P., Freymueller, J.T., Zhang, Q., and Jung, H., 2016, Post-eruptive inflation of Okmok Volcano, Alaska, from InSAR, 2008–2014: Remote Sensing, v. 7, no. 12, p. 16778-16794, https://doi.org/10.3390/rs71215839.","productDescription":"17 p.","startPage":"16778","endPage":"16794","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069602","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":471268,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs71215839","text":"Publisher Index Page"},{"id":316377,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Okmok Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -168.46572875976562,\n 53.26685566290742\n ],\n [\n -168.46572875976562,\n 53.570491879287\n ],\n [\n -167.772216796875,\n 53.570491879287\n ],\n [\n -167.772216796875,\n 53.26685566290742\n ],\n [\n -168.46572875976562,\n 53.26685566290742\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","issue":"12","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-09","publicationStatus":"PW","scienceBaseUri":"56b081bde4b010e2af2a11ad","contributors":{"authors":[{"text":"Qu, Feifei","contributorId":156236,"corporation":false,"usgs":false,"family":"Qu","given":"Feifei","email":"","affiliations":[{"id":20301,"text":"SMU","active":true,"usgs":false}],"preferred":false,"id":596945,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lu, Zhong 0000-0001-9181-1818 lu@usgs.gov","orcid":"https://orcid.org/0000-0001-9181-1818","contributorId":901,"corporation":false,"usgs":true,"family":"Lu","given":"Zhong","email":"lu@usgs.gov","affiliations":[],"preferred":true,"id":596946,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Poland, Michael P. 0000-0001-5240-6123 mpoland@usgs.gov","orcid":"https://orcid.org/0000-0001-5240-6123","contributorId":146118,"corporation":false,"usgs":true,"family":"Poland","given":"Michael","email":"mpoland@usgs.gov","middleInitial":"P.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":596944,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Freymueller, Jeffrey T.","contributorId":97458,"corporation":false,"usgs":true,"family":"Freymueller","given":"Jeffrey","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":596947,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zhang, Qin","contributorId":156237,"corporation":false,"usgs":false,"family":"Zhang","given":"Qin","email":"","affiliations":[{"id":20301,"text":"SMU","active":true,"usgs":false}],"preferred":false,"id":596948,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jung, Hyung-Sup","contributorId":58382,"corporation":false,"usgs":true,"family":"Jung","given":"Hyung-Sup","email":"","affiliations":[],"preferred":false,"id":596949,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70164490,"text":"70164490 - 2016 - Conifer density within lake catchments predicts fish mercury concentrations in remote subalpine lakes","interactions":[],"lastModifiedDate":"2018-08-07T11:52:31","indexId":"70164490","displayToPublicDate":"2016-02-01T10:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1555,"text":"Environmental Pollution","active":true,"publicationSubtype":{"id":10}},"title":"Conifer density within lake catchments predicts fish mercury concentrations in remote subalpine lakes","docAbstract":"Remote high-elevation lakes represent unique environments for evaluating the bioaccumulation of atmospherically deposited mercury through freshwater food webs, as well as for evaluating the relative importance of mercury loading versus landscape influences on mercury bioaccumulation. The increase in mercury deposition to these systems over the past century, coupled with their limited exposure to direct anthropogenic disturbance make them useful indicators for estimating how changes in mercury emissions may propagate to changes in Hg bioaccumulation and ecological risk. We evaluated mercury concentrations in resident fish from 28 high-elevation, sub-alpine lakes in the Pacific Northwest region of the United States. Fish total mercury (THg) concentrations ranged from 4 to 438 ng/g wet weight, with a geometric mean concentration (±standard error) of 43 ± 2 ng/g ww. Fish THg concentrations were negatively correlated with relative condition factor, indicating that faster growing fish that are in better condition have lower THg concentrations. Across the 28 study lakes, mean THg concentrations of resident salmonid fishes varied as much as 18-fold among lakes. We used a hierarchal statistical approach to evaluate the relative importance of physiological, limnological, and catchment drivers of fish Hg concentrations. Our top statistical model explained 87% of the variability in fish THg concentrations among lakes with four key landscape and limnological variables: catchment conifer density (basal area of conifers within a lake's catchment), lake surface area, aqueous dissolved sulfate, and dissolved organic carbon. Conifer density within a lake's catchment was the most important variable explaining fish THg concentrations across lakes, with THg concentrations differing by more than 400 percent across the forest density spectrum. These results illustrate the importance of landscape characteristics in controlling mercury bioaccumulation in fish.
","language":"English","publisher":"Applied Science Publishers","publisherLocation":"Essex, England","doi":"10.1016/j.envpol.2016.01.049","usgsCitation":"Eagles-Smith, C.A., Herring, G., Johnson, B., and Graw, R., 2016, Conifer density within lake catchments predicts fish mercury concentrations in remote subalpine lakes: Environmental Pollution, v. 212, p. 279-289, https://doi.org/10.1016/j.envpol.2016.01.049.","productDescription":"11 p.","startPage":"279","endPage":"289","numberOfPages":"11","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-071287","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":471270,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envpol.2016.01.049","text":"Publisher Index Page"},{"id":316722,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"212","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56bb1bbde4b08d617f654de4","chorus":{"doi":"10.1016/j.envpol.2016.01.049","url":"http://dx.doi.org/10.1016/j.envpol.2016.01.049","publisher":"Elsevier BV","authors":"Eagles-Smith Collin A., Herring Garth, Johnson Branden, Graw Rick","journalName":"Environmental Pollution","publicationDate":"5/2016","publiclyAccessibleDate":"2/5/2017"},"contributors":{"authors":[{"text":"Eagles-Smith, Collin A. 0000-0003-1329-5285 ceagles-smith@usgs.gov","orcid":"https://orcid.org/0000-0003-1329-5285","contributorId":505,"corporation":false,"usgs":true,"family":"Eagles-Smith","given":"Collin","email":"ceagles-smith@usgs.gov","middleInitial":"A.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":597576,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Herring, Garth 0000-0003-1106-4731 gherring@usgs.gov","orcid":"https://orcid.org/0000-0003-1106-4731","contributorId":4403,"corporation":false,"usgs":true,"family":"Herring","given":"Garth","email":"gherring@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":597577,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Branden L. branden_johnson@usgs.gov","contributorId":4168,"corporation":false,"usgs":true,"family":"Johnson","given":"Branden L.","email":"branden_johnson@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":597578,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Graw, Rick","contributorId":77824,"corporation":false,"usgs":true,"family":"Graw","given":"Rick","email":"","affiliations":[],"preferred":false,"id":597579,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70168703,"text":"70168703 - 2016 - Endangered species management and ecosystem restoration: Finding the common ground","interactions":[],"lastModifiedDate":"2017-10-30T09:54:00","indexId":"70168703","displayToPublicDate":"2016-02-01T10:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1468,"text":"Ecology and Society","active":true,"publicationSubtype":{"id":10}},"title":"Endangered species management and ecosystem restoration: Finding the common ground","docAbstract":"Management actions to protect endangered species and conserve ecosystem function may not always be in precise alignment. Efforts to recover the California Ridgway’s Rail (Rallus obsoletus obsoletus; hereafter, California rail), a federally and state-listed species, and restoration of tidal marsh ecosystems in the San Francisco Bay estuary provide a prime example of habitat restoration that has conflicted with species conservation. On the brink of extinction from habitat loss and degradation, and non-native predators in the 1990s, California rail populations responded positively to introduction of a non-native plant, Atlantic cordgrass (Spartina alterniflora). California rail populations were in substantial decline when the non-native Spartina was initially introduced as part of efforts to recover tidal marshes. Subsequent hybridization with the native Pacific cordgrass (Spartina foliosa) boosted California rail populations by providing greater cover and increased habitat area. The hybrid cordgrass (S. alterniflora × S. foliosa) readily invaded tidal mudflats and channels, and both crowded out native tidal marsh plants and increased sediment accretion in the marsh plain. This resulted in modification of tidal marsh geomorphology, hydrology, productivity, and species composition. Our results show that denser California rail populations occur in invasive Spartina than in native Spartina in San Francisco Bay. Herbicide treatment between 2005 and 2012 removed invasive Spartina from open intertidal mud and preserved foraging habitat for shorebirds. However, removal of invasive Spartina caused substantial decreases in California rail populations. Unknown facets of California rail ecology, undesirable interim stages of tidal marsh restoration, and competing management objectives among stakeholders resulted in management planning for endangered species or ecosystem restoration that favored one goal over the other. We have examined this perceived conflict and propose strategies for moderating harmful effects of restoration while meeting the needs of both endangered species and the imperiled native marsh ecosystem.
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M.","contributorId":127686,"corporation":false,"usgs":false,"family":"Hull","given":"Joshua","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":621342,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Albertson, Joy D.","contributorId":167182,"corporation":false,"usgs":false,"family":"Albertson","given":"Joy","email":"","middleInitial":"D.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":621343,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bloom, Valary K.","contributorId":167183,"corporation":false,"usgs":false,"family":"Bloom","given":"Valary","email":"","middleInitial":"K.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":621344,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bobzien, Steven","contributorId":167184,"corporation":false,"usgs":false,"family":"Bobzien","given":"Steven","email":"","affiliations":[{"id":24634,"text":"East Bay Regional Park District","active":true,"usgs":false}],"preferred":false,"id":621345,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McBroom, Jennifer","contributorId":167185,"corporation":false,"usgs":false,"family":"McBroom","given":"Jennifer","email":"","affiliations":[{"id":24635,"text":"Invasive Spartina Project","active":true,"usgs":false}],"preferred":false,"id":621346,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Latta, Marilyn","contributorId":167186,"corporation":false,"usgs":false,"family":"Latta","given":"Marilyn","email":"","affiliations":[{"id":24636,"text":"California State Coastal Conservancy","active":true,"usgs":false}],"preferred":false,"id":621347,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Olofson, Peggy","contributorId":167187,"corporation":false,"usgs":false,"family":"Olofson","given":"Peggy","email":"","affiliations":[{"id":24637,"text":"San Francisco Estuary Invasive Spartina Project","active":true,"usgs":false}],"preferred":false,"id":621348,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Rohmer, Tobias M.","contributorId":167188,"corporation":false,"usgs":false,"family":"Rohmer","given":"Tobias","email":"","middleInitial":"M.","affiliations":[{"id":24638,"text":"Invasive Spartina Project; Olofson Environmental Inc.","active":true,"usgs":false}],"preferred":false,"id":621349,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Schwarzbach, Steven E. steven_schwarzbach@usgs.gov","contributorId":1025,"corporation":false,"usgs":true,"family":"Schwarzbach","given":"Steven","email":"steven_schwarzbach@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":621350,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Strong, Donald R.","contributorId":17933,"corporation":false,"usgs":true,"family":"Strong","given":"Donald R.","affiliations":[],"preferred":false,"id":621351,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Grijalva, Erik","contributorId":167189,"corporation":false,"usgs":false,"family":"Grijalva","given":"Erik","email":"","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":621352,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Wood, Julian K.","contributorId":167190,"corporation":false,"usgs":false,"family":"Wood","given":"Julian","email":"","middleInitial":"K.","affiliations":[{"id":17734,"text":"Point Blue Conservation Science","active":true,"usgs":false}],"preferred":false,"id":621353,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Skalos, Shannon 0000-0003-1229-8580 sskalos@usgs.gov","orcid":"https://orcid.org/0000-0003-1229-8580","contributorId":167191,"corporation":false,"usgs":true,"family":"Skalos","given":"Shannon","email":"sskalos@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":621354,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Takekawa, John Y. 0000-0003-0217-5907 john_takekawa@usgs.gov","orcid":"https://orcid.org/0000-0003-0217-5907","contributorId":176168,"corporation":false,"usgs":true,"family":"Takekawa","given":"John","email":"john_takekawa@usgs.gov","middleInitial":"Y.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":621355,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70175167,"text":"70175167 - 2016 - Continental Shelf Morphology and Stratigraphy Offshore San Onofre, CA: The Interplay Between Rates of Eustatic Change and Sediment Supply","interactions":[],"lastModifiedDate":"2016-08-02T11:25:26","indexId":"70175167","displayToPublicDate":"2016-02-01T06:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2667,"text":"Marine Geology","active":true,"publicationSubtype":{"id":10}},"title":"Continental Shelf Morphology and Stratigraphy Offshore San Onofre, CA: The Interplay Between Rates of Eustatic Change and Sediment Supply","docAbstract":"New high-resolution CHIRP seismic data acquired offshore San Onofre, southern California reveal that shelf sediment distribution and thickness are primarily controlled by eustatic sea level rise and sediment supply. Throughout the majority of the study region, a prominent abrasion platform and associated shoreline cutoff are observed in the subsurface from ~ 72 to 53 m below present sea level. These erosional features appear to have formed between Melt Water Pulse 1A and Melt Water Pulse 1B, when the rate of sea-level rise was lower. There are three distinct sedimentary units mapped above a regional angular unconformity interpreted to be the Holocene transgressive surface in the seismic data. Unit I, the deepest unit, is interpreted as a lag deposit that infills a topographic low associated with an abrasion platform. Unit I thins seaward by downlap and pinches out landward against the shoreline cutoff. Unit II is a mid-shelf lag deposit formed from shallower eroded material and thins seaward by downlap and landward by onlap. The youngest, Unit III, is interpreted to represent modern sediment deposition. Faults in the study area do not appear to offset the transgressive surface. The Newport Inglewood/Rose Canyon fault system is active in other regions to the south (e.g., La Jolla) where it offsets the transgressive surface and creates seafloor relief. Several shoals observed along the transgressive surface could record minor deformation due to fault activity in the study area. Nevertheless, our preferred interpretation is that the shoals are regions more resistant to erosion during marine transgression. The Cristianitos fault zone also causes a shoaling of the transgressive surface. This may be from resistant antecedent topography due to an early phase of compression on the fault. The Cristianitos fault zone was previously defined as a down-to-the-north normal fault, but the folding and faulting architecture imaged in the CHIRP data are more consistent with a strike-slip fault with a down-to-the-northwest dip-slip component. A third area of shoaling is observed off of San Mateo and San Onofre creeks. This shoaling has a constructional component and could be a relict delta or beach structure. (C) 2015 Elsevier B.V. All rights reserved.
","language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam, Netherlands","doi":"10.1016/j.margeo.2015.08.003","usgsCitation":"Klotsko, S., Driscoll, N.W., Kent, G., and Brothers, D.S., 2016, Continental Shelf Morphology and Stratigraphy Offshore San Onofre, CA: The Interplay Between Rates of Eustatic Change and Sediment Supply: Marine Geology, v. 369, p. 116-126, https://doi.org/10.1016/j.margeo.2015.08.003.","productDescription":"11 p.","startPage":"116","endPage":"126","numberOfPages":"11","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064476","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":325909,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Onofre State Beach, Southern California, between Los Angeles and San Diego","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.5665855407715,\n 33.37913595905522\n ],\n [\n -117.5430679321289,\n 33.36444180060303\n ],\n [\n -117.52504348754881,\n 33.351680957199115\n ],\n [\n -117.50916481018065,\n 33.340495758384954\n ],\n [\n -117.50144004821779,\n 33.3333250034563\n ],\n [\n -117.50555992126465,\n 33.33038482330389\n ],\n [\n -117.57113456726073,\n 33.37583894926043\n ],\n [\n -117.5665855407715,\n 33.37913595905522\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"369","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57a1c42ee4b006cb45552c00","contributors":{"authors":[{"text":"Klotsko, Shannon","contributorId":173303,"corporation":false,"usgs":false,"family":"Klotsko","given":"Shannon","email":"","affiliations":[{"id":27208,"text":"UC San Diego","active":true,"usgs":false}],"preferred":false,"id":644187,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Driscoll, Neal W.","contributorId":140186,"corporation":false,"usgs":false,"family":"Driscoll","given":"Neal","email":"","middleInitial":"W.","affiliations":[{"id":12888,"text":"Scripps Institution of Oceanography, Univ of California","active":true,"usgs":false}],"preferred":false,"id":644188,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kent, Graham","contributorId":7608,"corporation":false,"usgs":true,"family":"Kent","given":"Graham","affiliations":[],"preferred":false,"id":644189,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brothers, Daniel S. 0000-0001-7702-157X dbrothers@usgs.gov","orcid":"https://orcid.org/0000-0001-7702-157X","contributorId":167089,"corporation":false,"usgs":true,"family":"Brothers","given":"Daniel","email":"dbrothers@usgs.gov","middleInitial":"S.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":644186,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70171476,"text":"70171476 - 2016 - Integrated risk and recovery monitoring of ecosystem restorations on contaminated sites","interactions":[],"lastModifiedDate":"2018-08-07T12:46:43","indexId":"70171476","displayToPublicDate":"2016-02-01T01:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2006,"text":"Integrated Environmental Assessment and Management","active":true,"publicationSubtype":{"id":10}},"title":"Integrated risk and recovery monitoring of ecosystem restorations on contaminated sites","docAbstract":"Ecological restorations of contaminated sites balance the human and ecological risks of residual contamination with the benefits of ecological recovery and the return of lost ecological function and ecosystem services. Risk and recovery are interrelated dynamic conditions, changing as remediation and restoration activities progress through implementation into long-term management and ecosystem maturation. Monitoring restoration progress provides data critical to minimizing residual contaminant risk and uncertainty, while measuring ecological advancement toward recovery goals. Effective monitoring plans are designed concurrently with restoration plan development and implementation and are focused on assessing the effectiveness of activities performed in support of restoration goals for the site. Physical, chemical, and biotic measures characterize progress toward desired structural and functional ecosystem components of the goals. Structural metrics, linked to ecosystem functions and services, inform restoration practitioners of work plan modifications or more substantial adaptive management actions necessary to maintain desired recovery. Monitoring frequency, duration, and scale depend on specific attributes and goals of the restoration project. Often tied to restoration milestones, critical assessment of monitoring metrics ensures attainment of risk minimization and ecosystem recovery. Finally, interpretation and communication of monitoring findings inform and engage regulators, other stakeholders, the scientific community, and the public. Because restoration activities will likely cease before full ecosystem recovery, monitoring endpoints should demonstrate risk reduction and a successional trajectory toward the condition established in the restoration goals. A detailed assessment of the completed project's achievements, as well as unrealized objectives, attained through project monitoring, will determine if contaminant risk has been minimized, if injured resources have recovered, and if ecosystem services have been returned. Such retrospective analysis will allow better planning for future restoration goals and strengthen the evidence base for quantifying injuries and damages at other sites in the future.
","language":"English","publisher":"SETAC","publisherLocation":"Pensacola, FL","doi":"10.1002/ieam.1731","usgsCitation":"Hooper, M.J., Glomb, S.J., Harper, D., Hoelzle, T.B., McIntosh, L.M., and Mulligan, D.R., 2016, Integrated risk and recovery monitoring of ecosystem restorations on contaminated sites: Integrated Environmental Assessment and Management, v. 12, no. 2, p. 284-295, https://doi.org/10.1002/ieam.1731.","productDescription":"12 p.","startPage":"284","endPage":"295","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-062929","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":471274,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ieam.1731","text":"Publisher Index Page"},{"id":322008,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2015-10-01","publicationStatus":"PW","scienceBaseUri":"57500767e4b0ee97d51bb663","contributors":{"authors":[{"text":"Hooper, Michael J. 0000-0002-4161-8961 mhooper@usgs.gov","orcid":"https://orcid.org/0000-0002-4161-8961","contributorId":3251,"corporation":false,"usgs":true,"family":"Hooper","given":"Michael","email":"mhooper@usgs.gov","middleInitial":"J.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":631238,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Glomb, Stephen J.","contributorId":169847,"corporation":false,"usgs":false,"family":"Glomb","given":"Stephen","email":"","middleInitial":"J.","affiliations":[{"id":25606,"text":"Office of Restoration and Damage Assessment, U.S. Department of the Interior, 1849 C Street NW, Washington, DC","active":true,"usgs":false}],"preferred":false,"id":631239,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harper, David 0000-0001-7061-8461 david_harper@usgs.gov","orcid":"https://orcid.org/0000-0001-7061-8461","contributorId":169848,"corporation":false,"usgs":true,"family":"Harper","given":"David","email":"david_harper@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":631240,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hoelzle, Timothy B.","contributorId":169849,"corporation":false,"usgs":false,"family":"Hoelzle","given":"Timothy","email":"","middleInitial":"B.","affiliations":[{"id":25607,"text":"Great Ecology, 3459 Ringsby Court, Suite 421, Denver, CO","active":true,"usgs":false}],"preferred":false,"id":631241,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McIntosh, Lisa M.","contributorId":169850,"corporation":false,"usgs":false,"family":"McIntosh","given":"Lisa","email":"","middleInitial":"M.","affiliations":[{"id":25608,"text":"Woodard & Curran, 980 Washington Street, Suite 325N, Dedham, MA","active":true,"usgs":false}],"preferred":false,"id":631242,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mulligan, David R.","contributorId":169851,"corporation":false,"usgs":false,"family":"Mulligan","given":"David","email":"","middleInitial":"R.","affiliations":[{"id":25609,"text":"Centre for Mined Land Rehabilitation, Sustainable Minerals Institute, The University of Queensland, Brisbane, QLD 4072 Australia","active":true,"usgs":false}],"preferred":false,"id":631243,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70168700,"text":"70168700 - 2016 - Quantifying pollen-vegetation relationships to reconstruct ancient forests using 19th-century forest composition and pollen data","interactions":[],"lastModifiedDate":"2018-03-26T13:37:12","indexId":"70168700","displayToPublicDate":"2016-02-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3219,"text":"Quaternary Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying pollen-vegetation relationships to reconstruct ancient forests using 19th-century forest composition and pollen data","docAbstract":"Mitigation of climate change and adaptation to its effects relies partly on how effectively land-atmosphere interactions can be quantified. Quantifying composition of past forest ecosystems can help understand processes governing forest dynamics in a changing world. Fossil pollen data provide information about past forest composition, but rigorous interpretation requires development of pollen-vegetation models (PVMs) that account for interspecific differences in pollen production and dispersal. Widespread and intensified land-use over the 19th and 20th centuries may have altered pollen-vegetation relationships. Here we use STEPPS, a Bayesian hierarchical spatial PVM, to estimate key process parameters and associated uncertainties in the pollen-vegetation relationship. We apply alternate dispersal kernels, and calibrate STEPPS using a newly developed Euro-American settlement-era calibration data set constructed from Public Land Survey data and fossil pollen samples matched to the settlement-era using expert elicitation. Models based on the inverse power-law dispersal kernel outperformed those based on the Gaussian dispersal kernel, indicating that pollen dispersal kernels are fat tailed. Pine and birch have the highest pollen productivities. Pollen productivity and dispersal estimates are generally consistent with previous understanding from modern data sets, although source area estimates are larger. Tests of model predictions demonstrate the ability of STEPPS to predict regional compositional patterns.
A new 3D seismic reflection data volume acquired in 2012 has allowed for the detailed mapping and characterization of gas hydrate distribution in the Pearl River Mouth Basin in the South China Sea. Previous studies of core and logging data showed that gas hydrate occurrence at high concentrations is controlled by the presence of relatively coarse-grained sediment and the upward migration of thermogenic gas from the deeper sediment section into the overlying gas hydrate stability zone (BGHSZ); however, the spatial distribution of the gas hydrate remains poorly defined. We used a constrained sparse spike inversion technique to generate acoustic-impedance images of the hydrate-bearing sedimentary section from the newly acquired 3D seismic data volume. High-amplitude reflections just above the bottom-simulating reflectors (BSRs) were interpreted to be associated with the accumulation of gas hydrate with elevated saturations. Enhanced seismic reflections below the BSRs were interpreted to indicate the presence of free gas. The base of the BGHSZ was established using the occurrence of BSRs. In areas absent of well-developed BSRs, the BGHSZ was calculated from a model using the inverted P-wave velocity and subsurface temperature data. Seismic attributes were also extracted along the BGHSZ that indicate variations reservoir properties and inferred hydrocarbon accumulations at each site. Gas hydrate saturations estimated from the inversion of acoustic impedance of conventional 3D seismic data, along with well-log-derived rock-physics models were also used to estimate gas hydrate saturations. Our analysis determined that the gas hydrate petroleum system varies significantly across the Pearl River Mouth Basin and that variability in sedimentary properties as a product of depositional processes and the upward migration of gas from deeper thermogenic sources control the distribution of gas hydrates in this basin.
","language":"English","publisher":"Society of Exploration Geophysicists","doi":"10.1190/INT-2015-0020.1","usgsCitation":"Wang, X., Qiang, J., Collett, T.S., Shi, H., Yang, S., Yan, C., Li, Y., Wang, Z., and Chen, D., 2016, Characterization of gas hydrate distribution using conventional 3D seismic data in the Pearl River Mouth Basin, South China Sea: Interpretation, v. 4, no. 1, p. 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Here we use geodetic observations including GPS and InSAR to characterize the Loma Prieta earthquake postseismic displacements from 1989 to 2013. Pre-earthquake deformation rates are constrained by nearly 20 yr of USGS trilateration measurements and removed from the postseismic measurements prior to the analysis. We observe GPS horizontal displacements at mean rates of 1–4 mm/yr toward Loma Prieta Mountain until 2000, and ∼2 mm/yr surface subsidence of the northern Santa Cruz Mountains between 1992 and 2002 shown by InSAR, which is not associated with the seasonal and longer-term hydrological deformation in the adjoining Santa Clara Valley. Previous work indicates afterslip dominated in the early (1989–1994) postseismic period, so we focus on modeling the postseismic viscoelastic relaxation constrained by the geodetic observations after 1994. The best fitting model shows an elastic 19-km-thick upper crust above an 11-km-thick viscoelastic lower crust with viscosity of ∼6 × 1018 Pas, underlain by a viscous upper mantle with viscosity between 3 × 1018 and 2 × 1019 Pas. The millimeter-scale postseismic deformation does not resolve the viscosity in the different layers very well, and the lower-crustal relaxation may be localized in a narrow shear zone. However, the inferred lithospheric rheology is consistent with previous estimates based on post-1906 San Francisco earthquake measurements along the San Andreas fault system. The viscoelastic relaxation may also contribute to the enduring increase of aseismic slip and repeating earthquake activity on the San Andreas fault near San Juan Bautista, which continued for at least a decade after the Loma Prieta event.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2015.12.018","usgsCitation":"Huang, M., Burgmann, R., and Pollitz, F., 2016, Lithospheric rheology constrained from twenty-five years of postseismic deformation following the 1989 Mw 6.9 Loma Prieta earthquake: Earth and Planetary Science Letters, v. 435, p. 147-158, https://doi.org/10.1016/j.epsl.2015.12.018.","productDescription":"12 p.","startPage":"147","endPage":"158","ipdsId":"IP-068757","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":471290,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.epsl.2015.12.018","text":"Publisher Index Page"},{"id":342215,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United states","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.35,\n 37.6\n ],\n [\n -121.25,\n 37.6\n ],\n [\n -121.25,\n 36.8\n ],\n [\n -122.35,\n 36.8\n ],\n [\n -122.35,\n 37.6\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"435","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"593910ade4b0764e6c5e8863","contributors":{"authors":[{"text":"Huang, Mong-Han","contributorId":192699,"corporation":false,"usgs":false,"family":"Huang","given":"Mong-Han","email":"","affiliations":[],"preferred":false,"id":697433,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burgmann, Roland","contributorId":192700,"corporation":false,"usgs":false,"family":"Burgmann","given":"Roland","affiliations":[],"preferred":false,"id":697420,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pollitz, Frederick 0000-0002-4060-2706 fpollitz@usgs.gov","orcid":"https://orcid.org/0000-0002-4060-2706","contributorId":139578,"corporation":false,"usgs":true,"family":"Pollitz","given":"Frederick","email":"fpollitz@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":697418,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70182721,"text":"70182721 - 2016 - Comparison of measurement- and proxy-based Vs30 values in California","interactions":[],"lastModifiedDate":"2017-02-27T14:42:27","indexId":"70182721","displayToPublicDate":"2016-02-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1436,"text":"Earthquake Spectra","active":true,"publicationSubtype":{"id":10}},"title":"Comparison of measurement- and proxy-based Vs30 values in California","docAbstract":"This study was prompted by the recent availability of a significant amount of openly accessible measured VS30 values and the desire to investigate the trend of using proxy-based models to predict VS30 in the absence of measurements. Comparisons between measured and model-based values were performed. The measured data included 503 VS30 values collected from various projects for 482 seismographic station sites in California. Six proxy-based models—employing geologic mapping, topographic slope, and terrain classification—were also considered. Included was a new terrain class model based on the Yong et al. (2012) approach but recalibrated with updated measured VS30 values. Using the measured VS30 data as the metric for performance, the predictive capabilities of the six models were determined to be statistically indistinguishable. This study also found three models that tend to underpredict VS30 at lower velocities (NEHRP Site Classes D–E) and overpredict at higher velocities (Site Classes B–C).
","language":"English","publisher":"Earthquake Engineering Research Institute ","doi":"10.1193/013114EQS025M","usgsCitation":"Yong, A.K., 2016, Comparison of measurement- and proxy-based Vs30 values in California: Earthquake Spectra, v. 32, no. 1, p. 171-192, https://doi.org/10.1193/013114EQS025M.","productDescription":"22 p. ","startPage":"171","endPage":"192","ipdsId":"IP-056058","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":336289,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"32","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-01","publicationStatus":"PW","scienceBaseUri":"58b548c1e4b01ccd54fddfba","contributors":{"authors":[{"text":"Yong, Alan K. 0000-0003-1807-5847 yong@usgs.gov","orcid":"https://orcid.org/0000-0003-1807-5847","contributorId":1554,"corporation":false,"usgs":true,"family":"Yong","given":"Alan","email":"yong@usgs.gov","middleInitial":"K.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":673454,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70173661,"text":"70173661 - 2016 - Dynamic occupancy models for explicit colonization processes","interactions":[],"lastModifiedDate":"2016-06-08T10:22:41","indexId":"70173661","displayToPublicDate":"2016-02-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Dynamic occupancy models for explicit colonization processes","docAbstract":"The dynamic, multi-season occupancy model framework has become a popular tool for modeling open populations with occupancies that change over time through local colonizations and extinctions. However, few versions of the model relate these probabilities to the occupancies of neighboring sites or patches. We present a modeling framework that incorporates this information and is capable of describing a wide variety of spatiotemporal colonization and extinction processes. A key feature of the model is that it is based on a simple set of small-scale rules describing how the process evolves. The result is a dynamic process that can account for complicated large-scale features. In our model, a site is more likely to be colonized if more of its neighbors were previously occupied and if it provides more appealing environmental characteristics than its neighboring sites. Additionally, a site without occupied neighbors may also become colonized through the inclusion of a long-distance dispersal process. Although similar model specifications have been developed for epidemiological applications, ours formally accounts for detectability using the well-known occupancy modeling framework. After demonstrating the viability and potential of this new form of dynamic occupancy model in a simulation study, we use it to obtain inference for the ongoing Common Myna (Acridotheres tristis) invasion in South Africa. Our results suggest that the Common Myna continues to enlarge its distribution and its spread via short distance movement, rather than long-distance dispersal. Overall, this new modeling framework provides a powerful tool for managers examining the drivers of colonization including short- vs. long-distance dispersal, habitat quality, and distance from source populations.
","language":"English","publisher":"Ecological Society of America","doi":"10.1890/15-0416.1","usgsCitation":"Broms, K.M., Hooten, M., Johnson, D., Altwegg, R., and Conquest, L., 2016, Dynamic occupancy models for explicit colonization processes: Ecology, v. 97, no. 1, p. 194-204, https://doi.org/10.1890/15-0416.1.","productDescription":"11 p.","startPage":"194","endPage":"204","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064209","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":471292,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1890/15-0416.1","text":"External Repository"},{"id":323254,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"97","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-01-29","publicationStatus":"PW","scienceBaseUri":"575941d6e4b04f417c256803","contributors":{"authors":[{"text":"Broms, Kristin M.","contributorId":171524,"corporation":false,"usgs":false,"family":"Broms","given":"Kristin","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":637841,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false}],"preferred":true,"id":637469,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Devin S.","contributorId":47524,"corporation":false,"usgs":true,"family":"Johnson","given":"Devin S.","affiliations":[],"preferred":false,"id":637842,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Altwegg, Res","contributorId":171528,"corporation":false,"usgs":false,"family":"Altwegg","given":"Res","email":"","affiliations":[],"preferred":false,"id":637843,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Conquest, Loveday","contributorId":86624,"corporation":false,"usgs":true,"family":"Conquest","given":"Loveday","email":"","affiliations":[],"preferred":false,"id":637844,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}