A trap-and-haul program is operated to move anadromous Pacific salmon (Oncorhynchus spp.) around dams and reservoirs in the Cowlitz River basin, Washington. The primary release site for adult fish is in Lake Scanewa, a small reservoir created by Cowlitz Falls Dam, the uppermost dam in the basin. Releases in the reservoir are terminated when reservoir water temperature is 16 degrees Celsius (°C) or greater to reduce the risk of exposing adult salmon and steelhead (O. mykiss) to thermal conditions known to be stressful for these species. When the water temperature limit is reached, fish are released into the Cowlitz and Cispus Rivers upstream of the reservoir. Concerns have been raised about fish presence in the reservoir during warm conditions, so an empirical evaluation of water temperatures selected by spring Chinook salmon (O. tshawytscha) in Lake Scanewa was requested by resource managers. We conducted the evaluation during June–October 2012. Water temperature in the reservoir was measured using a series of thermographs combined with daily measurements at the Cowlitz Falls Fish Facility. Temperature-use data were collected from 50 adult hatchery-origin spring Chinook salmon tagged with temperature-sensing radio transmitters and released into the reservoir. This approach yielded a total of 930,056 individual temperature-use records in the reservoir during the study. Tagged fish occupied water temperatures less than 16 °C during most of the study period. Water temperatures ranged from 5 to 18.5 °C during the study, peaking in mid-August. Overall, about 25 percent of the detections of tagged fish were in water temperatures in the 16–20 °C range. In August, tagged fish detections in the 18–20 °C range comprised 13 percent of the total detections, and the remainder of the detections were in water less than or equal to 17 °C. Results from the study indicate that adult hatchery-origin spring Chinook salmon were able to locate and use water temperatures in the reservoir less than levels associated with stress. However, we also observed that thermal conditions in the reservoir were cool relative to most other years during 2008–16. Additional research may be warranted to better understand thermal exposure of adult spring Chinook salmon during warmer years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191055","collaboration":"Prepared in cooperation with Public Utility District Number 1 of Lewis County, Washington","usgsCitation":"Kock, T.J., Ekstrom, B.K., and Liedtke, T.L., 2018, Distribution of adult Chinook salmon (Oncorhynchus tshawytscha) in relation to water temperatures, Lake Scanewa, Cowlitz River, Washington, 2012: U.S. Geological Survey Open-File Report 2019-1055, 14 p., https://doi.org/10.3133/ofr20191055.","productDescription":"iv, 14 p.","numberOfPages":"22","onlineOnly":"Y","ipdsId":"IP-106983","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":364309,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1055/coverthb2.jpg"},{"id":364310,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1055/ofr20191055.pdf","text":"Report","size":"1.7 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\"}}]}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
Large spatial databases often labeled as geospatial big data exceed the capacity of commonly used computing systems as a result of data volume, variety, velocity, and veracity. Additional problems also labeled with V’s are cited, but the four primary ones are the most problematic and focus of this chapter (Li et al., 2016, Panimalar et al., 2017). Sources include satellites, aircraft and drone platforms, vehicles, geosocial networking services, mobile devices, and cameras. The problems in processing these data to extract useful information include query, analysis, and visualization. Data mining techniques and machine learning algorithms, such as deep convolutional neural networks, often are used with geospatial big data. The obvious problem is handling the large data volumes, particularly for input and output operations, requiring parallel read and write of the data, as well as high speed computers, disk services, and network transfer speeds. Additional problems of large spatial databases include the variety and heterogeneity of data requiring advanced algorithms to handle different data types and characteristics, and integration with other data. The velocity at which the data are acquired is a challenge, especially using today’s advanced sensors and the Internet of Things that includes millions of devices creating data on short temporal scales of micro seconds to minutes. Finally, the veracity, or truthfulness of large spatial databases is difficult to establish and validate, particularly for all data elements in the database.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Geographic Information Science & Technology Body of Knowledge","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"UCGIS","doi":"10.22224/gistbok/2019.2.13","usgsCitation":"Usery, E., 2019, Problems of Large Spatial Databases, chap. of Geographic Information Science & Technology Body of Knowledge, https://doi.org/10.22224/gistbok/2019.2.13.","onlineOnly":"Y","ipdsId":"IP-101554","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true},{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":460367,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.22224/gistbok/2019.2.13","text":"Publisher Index Page"},{"id":365010,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-04-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Usery, E. Lynn 0000-0002-2766-2173","orcid":"https://orcid.org/0000-0002-2766-2173","contributorId":204684,"corporation":false,"usgs":true,"family":"Usery","given":"E. Lynn","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true},{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":762758,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70203209,"text":"fs20193026 - 2019 - Geological and geophysical data for a three-dimensional view—Inside the San Juan and Silverton Calderas, Southern Rocky Mountains Volcanic Field, Silverton, Colorado","interactions":[],"lastModifiedDate":"2019-06-05T14:01:16","indexId":"fs20193026","displayToPublicDate":"2019-06-04T11:10:00","publicationYear":"2019","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":"2019-3026","title":"Geological and geophysical data for a three-dimensional view—Inside the San Juan and Silverton Calderas, Southern Rocky Mountains Volcanic Field, Silverton, Colorado","docAbstract":"The San Juan-Silverton caldera complex located near Silverton, Colorado, in the Southern Rocky Mountains volcanic field is an ideal natural laboratory for furthering the understanding of shallow-to-deep volcanic-related mineral systems. Recent advances in geophysical data processing and three-dimensional (3D) model construction will help to characterize shallow properties important for understanding surface water and groundwater quality issues and will also improve knowledge of deep geological structures that may have been conduits for hydrothermal fluids that formed mineral deposits. The study has general applications to mineral resource assessments in other areas of the world and to identifying possible groundwater flow paths and associated geochemistry important in abandoned mine lands cleanup.
Silverton, Colorado, is the site of a spectacular succession of igneous rocks that formed beginning about 35 million years ago (Ma). Base metals (copper, lead, and zinc) and precious metals (silver and gold) mined from the late 1870s to 1991 owe their existence to a 25-million-year cycle of igneous activity. The presence of economic, base, and precious metal deposits within a complex geological setting were largely responsible for stimulating studies by the U.S. Geological Survey (USGS) conducted during the early 20th century. The focus of investigations in the late 20th and 21st centuries have broadened in scope to include abandoned mine lands (AML) investigations. The legacy of hard rock mining in headwater catchment areas caused environmental challenges for local communities and downstream water resource users. The Gold King Mine, located a few kilometers north of Silverton, illustrates the potential environmental effects of abandoned mines. On August 5, 2015, during reclamation efforts at the Gold King Mine, a breach of collapsed workings sent approximately 3 million gallons of acidic and metal-rich mine water into the upper Animas River, a tributary to the Colorado River Basin. Mining-related sources of metals and acidity add to geological sources of metals in surface water and groundwater. Weathering processes of altered and mineralized rock have been a source of acid rock drainage that have been ongoing for millennia.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/fs20193026","collaboration":"Prepared in collaboration with U.S. Forest Service, Bureau of Land Management, U.S. Environmental Protection Agency, Colorado Division of Reclamation Mining and Safety, Colorado Department of Public Health and Environment, and Animas River Stakeholders Group","usgsCitation":"Yager, D.B., Anderson, E.D., Rodriguez, B.D., Deszcz-Pan, M., and Smith, B.D., 2019, Geological and geophysical data for a three-dimensional view—Inside the San Juan and Silverton calderas, Southern Rocky Mountains volcanic field, Silverton, Colorado: U.S. Geological Survey Fact Sheet 2019-3026, 4 p., https://doi.org/10.3133/fs20193026.","productDescription":"4 p.","onlineOnly":"N","ipdsId":"IP-103569","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":364304,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3026/coverthb.jpg"},{"id":364305,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3026/fs20193026.pdf","text":"Report","size":"8.83 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019-3026"}],"country":"United States","state":"Colorado","county":"San Juan County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-107.5857,37.9702],[-107.5786,37.9667],[-107.5721,37.9636],[-107.5632,37.9573],[-107.5584,37.9524],[-107.5549,37.9493],[-107.5502,37.9475],[-107.5361,37.9445],[-107.5319,37.9414],[-107.5324,37.9378],[-107.5347,37.9337],[-107.5352,37.9291],[-107.5351,37.9237],[-107.532,37.9178],[-107.5278,37.9088],[-107.5247,37.9039],[-107.5212,37.9007],[-107.5211,37.8967],[-107.5279,37.8875],[-107.5324,37.8806],[-107.5329,37.8748],[-107.5317,37.8734],[-107.5305,37.8716],[-107.5204,37.8618],[-107.5179,37.8554],[-107.5184,37.8486],[-107.5176,37.84],[-107.5146,37.8342],[-107.5127,37.8288],[-107.5121,37.8265],[-107.5109,37.8256],[-107.5068,37.8243],[-107.491,37.8236],[-107.4828,37.8223],[-107.4757,37.817],[-107.4705,37.8143],[-107.4669,37.8107],[-107.4627,37.8044],[-107.4578,37.7918],[-107.457,37.785],[-107.4581,37.7791],[-107.4666,37.7668],[-107.4677,37.7645],[-107.4695,37.7645],[-107.4777,37.768],[-107.4812,37.7684],[-107.4829,37.7675],[-107.484,37.7648],[-107.4824,37.7407],[-107.4832,37.6374],[-107.6698,37.6372],[-107.6849,37.6375],[-107.6867,37.6375],[-107.9686,37.6377],[-107.9628,37.6401],[-107.96,37.6415],[-107.9583,37.6429],[-107.9572,37.6456],[-107.9572,37.6479],[-107.9579,37.6524],[-107.9604,37.6592],[-107.9629,37.6646],[-107.966,37.6718],[-107.9685,37.6777],[-107.9698,37.6822],[-107.9699,37.6867],[-107.9688,37.6899],[-107.966,37.6936],[-107.9615,37.6977],[-107.9575,37.7005],[-107.9534,37.7024],[-107.9505,37.7029],[-107.9471,37.7029],[-107.9389,37.7017],[-107.936,37.7017],[-107.9331,37.7027],[-107.9274,37.706],[-107.9239,37.7074],[-107.9181,37.7079],[-107.9135,37.7098],[-107.9094,37.7112],[-107.9049,37.7154],[-107.9014,37.7168],[-107.8968,37.7173],[-107.8904,37.717],[-107.8817,37.7162],[-107.8764,37.7163],[-107.8747,37.7172],[-107.873,37.7213],[-107.8726,37.7259],[-107.8733,37.7317],[-107.8717,37.7368],[-107.8684,37.7431],[-107.8644,37.7477],[-107.8627,37.7509],[-107.8622,37.7537],[-107.8629,37.7559],[-107.8641,37.7582],[-107.8659,37.76],[-107.8677,37.7617],[-107.8683,37.7635],[-107.8672,37.7663],[-107.8615,37.7732],[-107.8592,37.7737],[-107.854,37.7742],[-107.8493,37.7734],[-107.8446,37.7721],[-107.8423,37.7721],[-107.84,37.7726],[-107.8354,37.7767],[-107.8275,37.7859],[-107.8224,37.7915],[-107.8213,37.7928],[-107.8225,37.7955],[-107.8268,37.8063],[-107.8263,37.8082],[-107.8258,37.81],[-107.8085,37.8207],[-107.8056,37.8212],[-107.8004,37.8212],[-107.7975,37.8213],[-107.7952,37.8222],[-107.7935,37.8236],[-107.7918,37.8277],[-107.7885,37.8332],[-107.7868,37.8355],[-107.7845,37.8378],[-107.7812,37.8451],[-107.7762,37.8556],[-107.7756,37.857],[-107.7768,37.8592],[-107.7781,37.8615],[-107.7741,37.8656],[-107.7655,37.8739],[-107.7553,37.8845],[-107.7479,37.8923],[-107.7422,37.8982],[-107.7359,37.9038],[-107.7188,37.8977],[-107.7077,37.8955],[-107.7024,37.892],[-107.6977,37.8912],[-107.6942,37.8917],[-107.6897,37.8967],[-107.6879,37.8976],[-107.6862,37.899],[-107.6839,37.9],[-107.681,37.9],[-107.6682,37.9011],[-107.6595,37.9039],[-107.6514,37.9081],[-107.6422,37.9146],[-107.6394,37.9187],[-107.6389,37.9237],[-107.6404,37.9368],[-107.6405,37.9404],[-107.6407,37.9491],[-107.6385,37.9545],[-107.635,37.9586],[-107.6263,37.9588],[-107.6216,37.9588],[-107.6077,37.9636],[-107.5961,37.9669],[-107.588,37.9688],[-107.5857,37.9702]]]},\"properties\":{\"name\":\"San Juan\",\"state\":\"CO\"}}]}","contact":"Director, Geology, Geophysics, and Geochemistry Science Center
U.S. Geological Survey
Box 25046, MS-964
Denver, CO 80225-0046
Bringing Bayesian Models to Life empowers the reader to extend, enhance, and implement statistical models for ecological and environmental data analysis. We open the black box and show the reader how to connect modern statistical models to computer algorithms. These algorithms allow the user to fit models that answer their scientific questions without needing to rely on automated Bayesian software. We show how to handcraft statistical models that are useful in ecological and environmental science including: linear and generalized linear models, spatial and time series models, occupancy and capture-recapture models, animal movement models, spatio-temporal models, and integrated population-models.
","language":"English","publisher":"Taylor & Francis","doi":"10.1201/9780429243653","usgsCitation":"Hooten, M., and Hefley, T.J., 2019, Bringing Bayesian models to life, 590 p., https://doi.org/10.1201/9780429243653.","productDescription":"590 p.","ipdsId":"IP-103401","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":395444,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2019-05-15","publicationStatus":"PW","contributors":{"authors":[{"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":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":833185,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hefley, Trevor J.","contributorId":147146,"corporation":false,"usgs":false,"family":"Hefley","given":"Trevor","email":"","middleInitial":"J.","affiliations":[{"id":16796,"text":"Dept Fish, Wildlife & Cons Biol, Colorado St Univ, Fort Collins, CO","active":true,"usgs":false}],"preferred":false,"id":833186,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70203599,"text":"fs20193032 - 2019 - The National Map—New data delivery homepage, advanced viewer, lidar visualization","interactions":[],"lastModifiedDate":"2019-06-05T14:09:30","indexId":"fs20193032","displayToPublicDate":"2019-06-03T12:49:53","publicationYear":"2019","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":"2019-3032","displayTitle":"The National Map—New Data Delivery Homepage, Advanced Viewer, Lidar Visualization","title":"The National Map—New data delivery homepage, advanced viewer, lidar visualization","docAbstract":"As one of the cornerstones of the U.S. Geological Survey’s (USGS) National Geospatial Program, The National Map is a collaborative effort among the USGS and other Federal, State, and local partners to improve and deliver topographic information for the Nation. The National Map is featuring direct links to new and improved GIS data access utilities on a refreshed data delivery homepage at https://www.usgs.gov/NationalMap/data. From the homepage, users can now quickly access topographical map products, GIS data, and the full suite of The National Map’s applications and visualization services.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193032","usgsCitation":"U.S. Geological Survey, 2019, The National Map—New data delivery homepage, advanced viewer, lidar visualization: US. Geological Survey Fact Sheet 2019–3032, 2 p., https://doi.org/10.3133/fs20193032.\n","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-106375","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":364298,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3032/coverthb.jpg"},{"id":364299,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3032/fs20193032.pdf","text":"Report","size":"629 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019–3032"}],"contact":"National Geospatial Program
Core Science Systems
U.S. Geological Survey
12201 Sunrise Valley Dr., MS 511
Reston, VA 20192
Dispersal shapes demographic processes and therefore is fundamental to understanding biological, ecological, and evolutionary processes acting within populations. However, assessing population connectivity in scoters (Melanitta sp.) is challenging as these species have large spatial distributions that span remote landscapes, have varying nesting distributions (disjunct vs. continuous), exhibit unknown levels of dispersal, and vary in the timing of the formation of pair bonds (winter vs. fall/spring migration) that may influence the distribution of genetic diversity. Here, we used double‐digest restriction‐associated DNA sequence (ddRAD) and microsatellite genotype data to assess population structure within the three North American species of scoter (black scoter, M. americana; white‐winged scoter, M. deglandi; surf scoter, M. perspicillata), and between their European congeners (common scoter, M. nigra; velvet scoter, M. fusca). We uncovered no or weak genomic structure (ddRAD ΦST < 0.019; microsatellite FST < 0.004) within North America but high levels of structure among European congeners (ddRAD ΦST > 0.155, microsatellite FST > 0.086). The pattern of limited genomic structure within North America is shared with other sea duck species and is often attributed to male‐biased dispersal. Further, migratory tendencies (east vs. west) of female surf and white‐winged scoters in central Canada are known to vary across years, providing additional opportunities for intracontinental dispersal and a mechanism for the maintenance of genomic connectivity across North America. In contrast, the black scoter had relatively elevated levels of divergence between Alaska and Atlantic sites and a second genetic cluster found in Alaska at ddRAD loci was concordant with its disjunct breeding distribution suggestive of a dispersal barrier (behavioral or physical). Although scoter populations appear to be connected through a dispersal network, a small percentage (<4%) of ddRAD loci had elevated divergence which may be useful in linking areas (nesting, molting, staging, and wintering) throughout the annual cycle.
Porosity is one of the most important parameters to assess in-place oil or gas in reservoirs, and to evaluate recovery from enhanced production operations. Since it is relatively well-established to determine porosity using different laboratory and field methods, its value is usually determined at many locations across a reservoir as part of the common practice to capture reservoir heterogeneity and the variability in values. This suite of measurements and the distribution of values are most valuable for probabilistic reservoir assessments, and for spatial modeling if the exact data locations are known.
Despite the importance of individual measurements to set the range of values for probabilistic studies, it is not always possible to access these data due to confidentiality. In most cases, commercial or publicly available databases that assessments may rely on usually report only mean values of porosity, like any other reservoir data, or they may not report a value at all. This makes both quantifying the mean value and the uncertainty around it difficult for probabilistic assessments.
In this study, the TORIS (Tertiary Oil Recovery Information System) dataset of the National Petroleum Council and the U.S. Department of Energy was used to model porosity and the uncertainty around predicted values. TORIS is an integrated dataset of production data, reservoir properties, and project databases of crude oil reservoirs in the United States. The model presented in the paper was based on ANCOVA (Analysis of Co-Variance) of data from 1038 reservoirs from the TORIS dataset for porosity prediction, validation and testing for quantitative and qualitative parameters that may be readily available in most cases, and to estimate uncertainty around the mean values. This model also explored association of porosity values to different parameters, and to different depositional systems and diagenetic overprint conditions. Furthermore, an ANN (Artificial Neural Network) model was created to compare the predicted values of both models. Results showed that the ANN model was able to represent more of the variability, however it lacked the insights that might be gained from the ANCOVA model.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.petrol.2019.05.071","usgsCitation":"Karacan, C.O., 2019, An ANCOVA model for porosity and its uncertainty for oil reservoirs based on TORIS dataset: Journal of Petroleum Science and Engineering, 24 p., https://doi.org/10.1016/j.petrol.2019.05.071.","productDescription":"24 p.","ipdsId":"IP-103341","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":364378,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":364371,"type":{"id":15,"text":"Index Page"},"url":"https://www.sciencedirect.com/science/article/pii/S092041051930525X"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Karacan, C. Ozgen 0000-0002-0947-8241","orcid":"https://orcid.org/0000-0002-0947-8241","contributorId":201991,"corporation":false,"usgs":true,"family":"Karacan","given":"C.","email":"","middleInitial":"Ozgen","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":763708,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70204255,"text":"70204255 - 2019 - Application of sediment end-member analysis for understanding sediment fluxes, northern Chandeleur Islands, Louisiana","interactions":[],"lastModifiedDate":"2019-07-16T14:41:07","indexId":"70204255","displayToPublicDate":"2019-06-01T14:32:32","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"title":"Application of sediment end-member analysis for understanding sediment fluxes, northern Chandeleur Islands, Louisiana","docAbstract":"We analyzed grain-size distributions (GSDs) from a time-series of sediment samples to evaluate sediment transport following anthropogenic sand-berm emplacement at the northern Chandeleur Islands, Louisiana. End-member analysis (EMA) was applied to compare the end-member (EM) GSD of a known sediment source to GSDs from surrounding environments and characterize the physical redistribution of source sediment over time. Although we successfully modeled a proxy borrow-source EM using pre-emplacement (2007-2008) datasets, this EM is not easily distinguishable from the modeled emergent-island EM, possibly because the baseline dataset did not provide the necessary sample distribution to distinguish the range of depositional environments. Comparison of post-emplacement (2012) samples from the berm and natural island with the proxy borrow-source EM suggests that this application of EMA can be a valuable tool for understanding sediment redistribution subsequent to restoration efforts, especially if the GSDs of the emplaced and naturally-occurring sediments are dissimilar and adequately sampled.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Coastal Sediments 2019—Proceedings of the 9th International Conference","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Coastal Sediments 2019 ","conferenceDate":"May 27-31, 2019","conferenceLocation":"Tampa/St. Petersburg, Florida","language":"English","publisher":"World Scientific Co. Pte. Ltd.","doi":"10.1142/9789811204487_0003","usgsCitation":"Bernier, J., Miselis, J.L., Buster, N.A., and Flocks, J.G., 2019, Application of sediment end-member analysis for understanding sediment fluxes, northern Chandeleur Islands, Louisiana, in Coastal Sediments 2019—Proceedings of the 9th International Conference, Tampa/St. Petersburg, Florida, May 27-31, 2019, p. 25-38, https://doi.org/10.1142/9789811204487_0003.","productDescription":"14 p.","startPage":"25","endPage":"38","ipdsId":"IP-105796","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":365627,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Northern Chandeleur Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.0277099609375,\n 29.58540020340835\n ],\n [\n -88.77777099609375,\n 29.58540020340835\n ],\n [\n -88.77777099609375,\n 30.063151406016434\n ],\n [\n -89.0277099609375,\n 30.063151406016434\n ],\n [\n -89.0277099609375,\n 29.58540020340835\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bernier, Julie 0000-0002-9918-5353 jbernier@usgs.gov","orcid":"https://orcid.org/0000-0002-9918-5353","contributorId":3549,"corporation":false,"usgs":true,"family":"Bernier","given":"Julie","email":"jbernier@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":766198,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miselis, Jennifer L. 0000-0002-4925-3979 jmiselis@usgs.gov","orcid":"https://orcid.org/0000-0002-4925-3979","contributorId":3914,"corporation":false,"usgs":true,"family":"Miselis","given":"Jennifer","email":"jmiselis@usgs.gov","middleInitial":"L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":766199,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Buster, Noreen A. 0000-0001-5069-9284 nbuster@usgs.gov","orcid":"https://orcid.org/0000-0001-5069-9284","contributorId":3750,"corporation":false,"usgs":true,"family":"Buster","given":"Noreen","email":"nbuster@usgs.gov","middleInitial":"A.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":766200,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Flocks, James G. 0000-0002-6177-7433 jflocks@usgs.gov","orcid":"https://orcid.org/0000-0002-6177-7433","contributorId":816,"corporation":false,"usgs":true,"family":"Flocks","given":"James","email":"jflocks@usgs.gov","middleInitial":"G.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":766201,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70204585,"text":"70204585 - 2019 - Ground-motion residuals, path effects, and crustal properties: A pilot study in southern California","interactions":[],"lastModifiedDate":"2019-08-07T09:05:26","indexId":"70204585","displayToPublicDate":"2019-06-01T11:55:39","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2314,"text":"Journal of Geophysical Research B: Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Ground-motion residuals, path effects, and crustal properties: A pilot study in southern California","docAbstract":"To improve models of ground motion estimation and probabilistic seismic hazard analyses, the engineering seismology field is moving toward developing fully nonergodic ground motion models, models specific for individual source‐to‐site paths. Previous work on this topic has examined systematic variations in ground‐motion along particular paths (from either recorded or simulated earthquake data) and has not included physical properties of the path. We present here a framework to include physical path properties, by seeking correlations between ground motion amplitudes along specific paths and crustal properties, specifically seismic velocity and anelastic attenuation, along that path. Using a large data set of small‐magnitude earthquakes recorded in Southern California, we find a correlation between the gradient of seismic S wave velocity and the path term residual, after accounting for an average geometric spreading and anelastic attenuation, indicating that heterogeneity in crustal velocity primarily controls the path‐specific attenuation. Even in aseismic regions, details of path‐specific ground motion prediction equations can be developed from crustal structure and property data.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2018JB016796","usgsCitation":"Sahakian, V., Baltay Sundstrom, A.S., Hanks, T.C., Bueler, J., Vernon, F., Kilb, D.L., and Abrahamson, N., 2019, Ground-motion residuals, path effects, and crustal properties: A pilot study in southern California: Journal of Geophysical Research B: Solid Earth, v. 124, no. 6, p. 5738-5753, https://doi.org/10.1029/2018JB016796.","productDescription":"16 p.","startPage":"5738","endPage":"5753","ipdsId":"IP-101300","costCenters":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":366300,"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 -122.67333984374999,\n 32.45415593941475\n ],\n [\n -114.3017578125,\n 32.45415593941475\n ],\n [\n -114.3017578125,\n 37.23032838760387\n ],\n [\n -122.67333984374999,\n 37.23032838760387\n ],\n [\n -122.67333984374999,\n 32.45415593941475\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"124","issue":"6","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-06-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Sahakian, Valerie J.","contributorId":208097,"corporation":false,"usgs":false,"family":"Sahakian","given":"Valerie J.","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":767639,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baltay Sundstrom, Annemarie S. 0000-0002-6514-852X abaltay@usgs.gov","orcid":"https://orcid.org/0000-0002-6514-852X","contributorId":4932,"corporation":false,"usgs":true,"family":"Baltay Sundstrom","given":"Annemarie","email":"abaltay@usgs.gov","middleInitial":"S.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":767638,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hanks, Thomas C. 0000-0003-0928-0056 thanks@usgs.gov","orcid":"https://orcid.org/0000-0003-0928-0056","contributorId":3065,"corporation":false,"usgs":true,"family":"Hanks","given":"Thomas","email":"thanks@usgs.gov","middleInitial":"C.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":767640,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bueler, Janine","contributorId":217838,"corporation":false,"usgs":false,"family":"Bueler","given":"Janine","email":"","affiliations":[{"id":27208,"text":"UC San Diego","active":true,"usgs":false}],"preferred":false,"id":767641,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vernon, Frank","contributorId":217839,"corporation":false,"usgs":false,"family":"Vernon","given":"Frank","affiliations":[{"id":27208,"text":"UC San Diego","active":true,"usgs":false}],"preferred":false,"id":767642,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kilb, Deborah L.","contributorId":216380,"corporation":false,"usgs":false,"family":"Kilb","given":"Deborah","email":"","middleInitial":"L.","affiliations":[{"id":37799,"text":"SCRIPPS","active":true,"usgs":false}],"preferred":false,"id":767643,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Abrahamson, Norm A.","contributorId":217840,"corporation":false,"usgs":false,"family":"Abrahamson","given":"Norm A.","affiliations":[{"id":6609,"text":"UC Berkeley","active":true,"usgs":false}],"preferred":false,"id":767644,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70206732,"text":"70206732 - 2019 - Assessment of site-specific agricultural Best Management Practices in the Upper East River watershed, Wisconsin, using a field-scale SWAT model","interactions":[],"lastModifiedDate":"2019-11-19T10:28:19","indexId":"70206732","displayToPublicDate":"2019-06-01T10:23:23","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Assessment of site-specific agricultural Best Management Practices in the Upper East River watershed, Wisconsin, using a field-scale SWAT model","docAbstract":"The Great Lakes “Priority Watershed” effort targeted the Upper East River watershed, a 116.5 km2 tributary watershed to Green Bay in Wisconsin, to reduce sediment and nutrients entering Green Bay. A Soil and Water Assessment Tool (SWAT) model was created to determine the effectiveness of Best Management Practices (BMPs) derived from the U.S. Department of Agriculture-Natural Resources Conservation Service National Conservation Planning (NCP) Database. The model was calibrated at the monthly timestep for flow, sediment, dissolved reactive phosphorus (DRP), total phosphorus (TP), and total nitrogen (TN). Field- and watershed-scale sediment and nutrient reductions were calculated due to the implementation of 74 BMP combinations on dairy and cash grain rotations. Modeling results indicated that when multiple BMPs are placed on a field, especially those that included filter strips and grassed waterways, generally reduced sediment and nutrient loads more than a single BMP implementation. The most effective in-field practice at reducing DRP and TP on dairy fields was a combination of 5 different BMPs: cover crops, crop rotation, nutrient management plan, reduced tillage, and a filter strip. Conservation cover was the most effective practice at reducing sediment and nutrient yields. Sediment and nutrient loads decreased at the watershed scale as the quantity and coverage of BMPs increased. When all contracted NCP BMPs were simulated at the watershed scale, sediment loads were reduced 2%, while TP, DRP, TN and nitrate loads were reduced 20%, 9%, 24%, and 17%, respectively. Modeling scenarios indicated that as the number and area of BMPs were increased, sediment and nutrient load reductions were also increased.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2019.02.004","usgsCitation":"Merriman, K.R., Prasad Daggupati, Raghavan Srinivasan, and Hayhurst, B.A., 2019, Assessment of site-specific agricultural Best Management Practices in the Upper East River watershed, Wisconsin, using a field-scale SWAT model: Journal of Great Lakes Research, v. 3, no. 45, p. 619-641, https://doi.org/10.1016/j.jglr.2019.02.004.","productDescription":"23 p.","startPage":"619","endPage":"641","ipdsId":"IP-095539","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":467568,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2019.02.004","text":"Publisher Index Page"},{"id":369326,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"East River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.0609130859375,\n 44.67646564865964\n ],\n [\n -88.9947509765625,\n 44.31205742666618\n ],\n [\n -89.307861328125,\n 44.036269809534616\n ],\n [\n -89.1375732421875,\n 43.70362249839005\n ],\n [\n -88.61572265625,\n 43.432977075795606\n ],\n [\n -88.1378173828125,\n 43.504736854976954\n ],\n [\n -87.7642822265625,\n 44.48866833139464\n ],\n [\n -88.0609130859375,\n 44.67646564865964\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"3","issue":"45","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Merriman, Katherine R. 0000-0002-1303-2410","orcid":"https://orcid.org/0000-0002-1303-2410","contributorId":203352,"corporation":false,"usgs":true,"family":"Merriman","given":"Katherine","email":"","middleInitial":"R.","affiliations":[{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":775579,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Prasad Daggupati","contributorId":220739,"corporation":false,"usgs":false,"family":"Prasad Daggupati","affiliations":[{"id":12660,"text":"University of Guelph","active":true,"usgs":false}],"preferred":false,"id":775580,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Raghavan Srinivasan","contributorId":220740,"corporation":false,"usgs":false,"family":"Raghavan Srinivasan","affiliations":[{"id":40264,"text":"Texas A& M University","active":true,"usgs":false}],"preferred":false,"id":775581,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hayhurst, Brett A. 0000-0002-1717-2015 bhayhurs@usgs.gov","orcid":"https://orcid.org/0000-0002-1717-2015","contributorId":3398,"corporation":false,"usgs":true,"family":"Hayhurst","given":"Brett","email":"bhayhurs@usgs.gov","middleInitial":"A.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":775582,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70203889,"text":"70203889 - 2019 - Spatio-temporal population change of Arctic-breeding waterbirds on the Arctic Coastal Plain of Alaska","interactions":[],"lastModifiedDate":"2019-08-15T12:25:18","indexId":"70203889","displayToPublicDate":"2019-06-01T10:12:14","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":947,"text":"Avian Conservation and Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Spatio-temporal population change of Arctic-breeding waterbirds on the Arctic Coastal Plain of Alaska","docAbstract":"Rapid physical changes that are occurring in the Arctic are primary drivers of landscape change and thus may drive population dynamics of Arctic-breeding birds. Despite the importance of this region to breeding and molting waterbirds, lack of a comprehensive analysis of historic data has hindered quantifying avian population change. We estimated distribution, abundance, and spatially explicit population trend of 20 breeding waterbird species using 25 years (1992–2016) of aerial survey data collected on the Arctic Coastal Plain (ACP), Alaska. The ACP is an extensive wetland complex on Alaska’s North Slope that supports millions of breeding waterbirds and includes portions of the National Petroleum Reserve—Alaska and the Arctic National Wildlife Refuge. We summarized annual counts into approximately 6-km by 6-km grid cells and analyzed data with generalized linear mixed models that accounted for survey timing and spatio-temporal autocorrelation. Geese and swans were most abundant along the coast between Admiralty Bay and Prudhoe Bay. Sea ducks, generalist predators (i.e., jaeger, gulls, terns), and loons were most abundant between Utqiaġvik and Point Lay, Alaska. Important areas for most species included the coastal fringe near Teshekpuk Lake, the Colville River Delta, and Admiralty Bay. The National Petroleum Reserve—Alaska was an important area for all species examined. Conversely, density on the coastal plain of the Arctic National Wildlife Refuge was greater than average for 20% of species. Annual population growth rates over the 25-year survey period were variable: 13 increased (range: 1.4%–13.8%), one decreased (-3.4%), and six were stable. However, even species with no overall population trend had areas of changing population size, suggesting localized conditions affected waterbird distributions on the ACP. Our results can be used to better inform land use decisions, improve monitoring of waterbird populations, and increase understanding of avian response to ecological change in the Arctic.","language":"English","publisher":"Resilience Alliance","doi":"10.5751/ACE-01383-140118","usgsCitation":"Amundson, C.L., Flint, P.L., Stehn, R., Platte, R., Wilson, H.M., Larned, W.W., and Fischer, J., 2019, Spatio-temporal population change of Arctic-breeding waterbirds on the Arctic Coastal Plain of Alaska: Avian Conservation and Ecology, v. 14, no. 1, 18, 198 p., https://doi.org/10.5751/ACE-01383-140118.","productDescription":"18, 198 p.","ipdsId":"IP-095978","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":467569,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5751/ace-01383-140118","text":"Publisher Index Page"},{"id":364827,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Arctic Coastal Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -142.294921875,\n 69.56522590149099\n ],\n [\n -142.294921875,\n 70.1403642720717\n ],\n [\n -145.107421875,\n 70.31873847853124\n ],\n [\n -147.48046875,\n 70.4367988185464\n ],\n [\n -149.765625,\n 70.78690984117928\n ],\n [\n -151.962890625,\n 70.8734913192635\n ],\n [\n -153.544921875,\n 71.10254274232307\n ],\n [\n -154.86328125,\n 71.27259471233448\n ],\n [\n -156.4453125,\n 71.52490903732816\n ],\n [\n -158.466796875,\n 71.10254274232307\n ],\n [\n -161.54296875,\n 70.52489722821652\n ],\n [\n -164.00390625,\n 69.65708627301174\n ],\n [\n -162.158203125,\n 69.31832006949072\n ],\n [\n -160.48828125,\n 69.80930869552193\n ],\n [\n -160.75195312499997,\n 70.1403642720717\n ],\n [\n -159.345703125,\n 70.11048478105927\n ],\n [\n -147.744140625,\n 69.62651016802958\n ],\n [\n -142.294921875,\n 69.56522590149099\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Amundson, Courtney L. 0000-0002-0166-7224 camundson@usgs.gov","orcid":"https://orcid.org/0000-0002-0166-7224","contributorId":4833,"corporation":false,"usgs":true,"family":"Amundson","given":"Courtney","email":"camundson@usgs.gov","middleInitial":"L.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":764601,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flint, Paul L. 0000-0002-8758-6993 pflint@usgs.gov","orcid":"https://orcid.org/0000-0002-8758-6993","contributorId":3284,"corporation":false,"usgs":true,"family":"Flint","given":"Paul","email":"pflint@usgs.gov","middleInitial":"L.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":764602,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stehn, Robert A","contributorId":216354,"corporation":false,"usgs":false,"family":"Stehn","given":"Robert A","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":764603,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Platte, Robert","contributorId":216355,"corporation":false,"usgs":false,"family":"Platte","given":"Robert","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":764604,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wilson, Heather M.","contributorId":37056,"corporation":false,"usgs":false,"family":"Wilson","given":"Heather","email":"","middleInitial":"M.","affiliations":[{"id":13236,"text":"U.S. Fish and Wildlife Service, Migratory Bird Management","active":true,"usgs":false}],"preferred":false,"id":764605,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Larned, William W.","contributorId":75206,"corporation":false,"usgs":false,"family":"Larned","given":"William","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":764606,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fischer, Julian B.","contributorId":207042,"corporation":false,"usgs":false,"family":"Fischer","given":"Julian B.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":764607,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70204529,"text":"70204529 - 2019 - Bayesian analysis of the impact of rainfall data product on simulated slope failure for North Carolina locations","interactions":[],"lastModifiedDate":"2019-08-01T08:41:22","indexId":"70204529","displayToPublicDate":"2019-06-01T08:40:08","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1308,"text":"Computational Geosciences","active":true,"publicationSubtype":{"id":10}},"title":"Bayesian analysis of the impact of rainfall data product on simulated slope failure for North Carolina locations","docAbstract":"In the past decades, many different approaches have been developed in the literature to quantify the load-carrying capacity and geotechnical stability (or the Factor of Safety, F_s) of variably saturated hillslopes. Much of this work has focused on a deterministic characterization of hillslope stability. Yet, simulated F_s values are subject to considerable uncertainty due to our inability to characterize accurately the soil mantle’s properties (hydraulic, geotechnical and geomorphologic) and spatiotemporal variability of the moisture content of the hillslope interior. This is particularly true at larger spatial scales. Thus, uncertainty-incorporating analyses of physically based models of rain-induced landslides are rare in the literature. Such landslide modeling is typically conducted at the hillslope scale using gauge-based rainfall forcing data with rather poor spatiotemporal coverage. For regional landslide modeling, the specific advantages and/or disadvantages of gauge-only, radar-merged and satellite-based rainfall products are not clearly established. Here, we compare and evaluate the performance of the Transient Rainfall Infiltration and Grid-based Regional Slope-stability analysis (TRIGRS) model for three different rainfall products using 112 observed landslides in the period between 2004 and 2011 from the North Carolina Geological Survey database. Our study includes the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis Version 7 (TMPA V7), the North American Land Data Assimilation System Phase 2 (NLDAS-2) analysis, and the reference ‘truth’ Stage IV precipitation. TRIGRS model performance was rather inferior with the use of literature values of the geotechnical parameters and soil hydraulic properties from ROSETTA using soil textural and bulk density data from SSURGO (Soil Survey Geographic database). The performance of TRIGRS improved considerably after Bayesian estimation of the parameters with the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm using Stage IV precipitation data. Hereto, we use a likelihood function that combines binary slope failure information from landslide event and ‘null’ periods using multivariate frequency distribution-based metrics such as the False Discovery and False Omission Rates. Our results demonstrate that the Stage IV-inferred TRIGRS parameter distributions generalize well to TMPA and NLDAS-2 precipitation data, particularly at sites with considerably larger TMPA and NLDAS-2 rainfall amounts during landslide events than null periods. TRIGRS model performance is then rather similar for all three rainfall products. At higher elevations, however, the TMPA and NLDAS-2 precipitation volumes are insufficient and their performance with the Stage IV-derived parameter distributions indicate their inability to accurately characterize hillslope stability.","language":"English","publisher":"Springer","doi":"10.1007/s10596-018-9804-y","usgsCitation":"Yatheendradas, S., Kirschbaum, D., Nearing, G., Vrugt, J.A., Baum, R.L., Wooten, R., Lu, N., and Godt, J.W., 2019, Bayesian analysis of the impact of rainfall data product on simulated slope failure for North Carolina locations: Computational Geosciences, v. 23, no. 3, p. 495-522, https://doi.org/10.1007/s10596-018-9804-y.","productDescription":"28 p.","startPage":"495","endPage":"522","ipdsId":"IP-103255","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":467571,"rank":0,"type":{"id":41,"text":"Open Access External Repository 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Carolina\",\"nation\":\"USA \"}}]}","volume":"23","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-01-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Yatheendradas, Soni","contributorId":217737,"corporation":false,"usgs":false,"family":"Yatheendradas","given":"Soni","email":"","affiliations":[{"id":39690,"text":"University of Maryland; NASA GSFC","active":true,"usgs":false}],"preferred":false,"id":767407,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kirschbaum, Dalia","contributorId":217738,"corporation":false,"usgs":false,"family":"Kirschbaum","given":"Dalia","email":"","affiliations":[{"id":39055,"text":"NASA GSFC","active":true,"usgs":false}],"preferred":false,"id":767408,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nearing, Grey","contributorId":217739,"corporation":false,"usgs":false,"family":"Nearing","given":"Grey","email":"","affiliations":[{"id":37195,"text":"The University of Alabama","active":true,"usgs":false}],"preferred":false,"id":767409,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vrugt, Jasper A.","contributorId":217740,"corporation":false,"usgs":false,"family":"Vrugt","given":"Jasper","email":"","middleInitial":"A.","affiliations":[{"id":36629,"text":"University of California","active":true,"usgs":false}],"preferred":false,"id":767410,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Baum, Rex L. 0000-0001-5337-1970 baum@usgs.gov","orcid":"https://orcid.org/0000-0001-5337-1970","contributorId":1288,"corporation":false,"usgs":true,"family":"Baum","given":"Rex","email":"baum@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":767411,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wooten, 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Hazards","active":true,"usgs":true}],"preferred":true,"id":767414,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70204730,"text":"70204730 - 2019 - Earthquake-induced chains of geologic hazards: Patterns, mechanisms, and impacts","interactions":[],"lastModifiedDate":"2019-08-13T07:59:39","indexId":"70204730","displayToPublicDate":"2019-06-01T07:58:52","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3283,"text":"Reviews of Geophysics","active":true,"publicationSubtype":{"id":10}},"title":"Earthquake-induced chains of geologic hazards: Patterns, mechanisms, and impacts","docAbstract":"Large earthquakes initiate chains of surface processes that last much longer than the brief moments of strong shaking. Most moderate- and large-magnitude earthquakes trigger landslides, ranging from small failures in the soil cover to massive, devastating rock avalanches. Some landslides dam rivers and impound lakes, which can collapse days to centuries later, and flood mountain valleys for hundreds of kilometers downstream. Landslide deposits on slopes can remobilize during heavy rainfall and evolve into debris flows. Cracks and fractures can form and widen on mountain crests and flanks, promoting increased frequency of landslides that lasts for decades. More gradual impacts involve the flushing of excess debris downstream by rivers, which can generate bank erosion and floodplain accretion as well as channel avulsions that affect flooding frequency, settlements, ecosystems, and infrastructure. Ultimately, earthquake sequences and their geomorphic consequences alter mountain landscapes over both human and geologic time scales. Two recent events have attracted intense research into earthquake-induced landslides and their consequences: the magnitude M 7.6 Chi-Chi, Taiwan earthquake of 1999, and the M 7.9 Wenchuan, China earthquake of 2008. Using data and insights from these and several other earthquakes, we analyze how such events initiate processes that change mountain landscapes, highlight research gaps, and suggest pathways toward a more complete understanding of the seismic effects on the Earth’s surface.","language":"English","publisher":"Wiley","doi":"10.1029/2018RG000626","usgsCitation":"Fan, X., Scaringi, G., Korup, O., West, A.J., Westen, C.J., Tanyas, H., Hovius, N., Hales, T.C., Jibson, R.W., Allstadt, K.E., Zhang, L., Evans, S.G., Xu, C., , L., Pei, X., Xu, Q., and Huang, R., 2019, Earthquake-induced chains of geologic hazards: Patterns, mechanisms, and impacts: Reviews of Geophysics, v. 57, p. 421-503, https://doi.org/10.1029/2018RG000626.","productDescription":"83 p.","startPage":"421","endPage":"503","ipdsId":"IP-107091","costCenters":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":467572,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2018rg000626","text":"Publisher Index Page"},{"id":366490,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":366480,"type":{"id":15,"text":"Index Page"},"url":"https://doi.org/10.1029/2018RG000626"}],"volume":"57","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-06-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Fan, Xuanmei","contributorId":218069,"corporation":false,"usgs":false,"family":"Fan","given":"Xuanmei","email":"","affiliations":[{"id":39733,"text":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, Sichuan, China","active":true,"usgs":false}],"preferred":false,"id":768218,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Scaringi, Gianvito","contributorId":218070,"corporation":false,"usgs":false,"family":"Scaringi","given":"Gianvito","email":"","affiliations":[{"id":39734,"text":"University of Technology, Chengdu, Sichuan, China and Science, and Science, Charles University, Prague, Czech Republic","active":true,"usgs":false}],"preferred":false,"id":768219,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Korup, Oliver","contributorId":218071,"corporation":false,"usgs":false,"family":"Korup","given":"Oliver","email":"","affiliations":[{"id":39735,"text":"Institute of Earth and Environmental Science, University of Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":768220,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"West, A. Joshua","contributorId":200289,"corporation":false,"usgs":false,"family":"West","given":"A.","email":"","middleInitial":"Joshua","affiliations":[],"preferred":false,"id":768221,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Westen, Cees J. van","contributorId":218072,"corporation":false,"usgs":false,"family":"Westen","given":"Cees","email":"","middleInitial":"J. van","affiliations":[{"id":39272,"text":"University of Twente","active":true,"usgs":false}],"preferred":false,"id":768222,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tanyas, Hakan","contributorId":198731,"corporation":false,"usgs":false,"family":"Tanyas","given":"Hakan","affiliations":[],"preferred":false,"id":768223,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hovius, Niels","contributorId":198733,"corporation":false,"usgs":false,"family":"Hovius","given":"Niels","email":"","affiliations":[],"preferred":false,"id":768224,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hales, Tristram C","contributorId":218073,"corporation":false,"usgs":false,"family":"Hales","given":"Tristram","email":"","middleInitial":"C","affiliations":[{"id":39736,"text":"Cardiff University, Cardiff, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":768225,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Jibson, Randall W. 0000-0003-3399-0875 jibson@usgs.gov","orcid":"https://orcid.org/0000-0003-3399-0875","contributorId":2985,"corporation":false,"usgs":true,"family":"Jibson","given":"Randall","email":"jibson@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":768217,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Allstadt, Kate E. 0000-0003-4977-5248 kallstadt@usgs.gov","orcid":"https://orcid.org/0000-0003-4977-5248","contributorId":167684,"corporation":false,"usgs":true,"family":"Allstadt","given":"Kate","email":"kallstadt@usgs.gov","middleInitial":"E.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":false,"id":768226,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Zhang, Limin","contributorId":218074,"corporation":false,"usgs":false,"family":"Zhang","given":"Limin","email":"","affiliations":[{"id":39737,"text":"The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China","active":true,"usgs":false}],"preferred":false,"id":768227,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Evans, Stephen G.","contributorId":179140,"corporation":false,"usgs":false,"family":"Evans","given":"Stephen","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":768228,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Xu, Chong","contributorId":196191,"corporation":false,"usgs":false,"family":"Xu","given":"Chong","email":"","affiliations":[],"preferred":false,"id":768229,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":" Li","contributorId":203216,"corporation":false,"usgs":false,"given":"Li","email":"","affiliations":[],"preferred":false,"id":768230,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Pei, Xiangjun","contributorId":218075,"corporation":false,"usgs":false,"family":"Pei","given":"Xiangjun","email":"","affiliations":[{"id":39738,"text":"Chengdu University of Technology, Chengdu, Sichuan, China","active":true,"usgs":false}],"preferred":false,"id":768231,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Xu, Qiang","contributorId":214818,"corporation":false,"usgs":false,"family":"Xu","given":"Qiang","email":"","affiliations":[{"id":39123,"text":"Key Laboratory of Continental Collision and Plateau Uplift, Institute of Tibetan Plateau Research and Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":768232,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Huang, Runqiu","contributorId":218076,"corporation":false,"usgs":false,"family":"Huang","given":"Runqiu","email":"","affiliations":[{"id":39733,"text":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, Sichuan, China","active":true,"usgs":false}],"preferred":false,"id":768233,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70204635,"text":"70204635 - 2019 - Comparison of beaver density estimates from aerial surveys of waterways versus transects","interactions":[],"lastModifiedDate":"2019-09-03T18:40:09","indexId":"70204635","displayToPublicDate":"2019-05-31T18:30:22","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5015,"text":"Canadian Wildlife Biology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Comparison of beaver density estimates from aerial surveys of waterways versus transects","docAbstract":"Historic beaver-sign (Castor canadensis) survey flights were often conducted over waterways to maximize beaver detections. However, densities determined from strip transect surveys are more useful to compare across and within study areas than waterway indices based on observations per distance flown because transects are more representative of the wider landscape. Yet, it is unknown if, and to what extent, aerial waterway surveys are reflective of transect densities. I conducted aerial surveys for active beaver sign each fall during 2015–2018 over two waterway routes and two corresponding strip-transect routes. The simple linear regression of transect densities on waterway densities (n=8) yielded a reasonable (R2=0.79) preliminary equation for converting historic waterway data to transect densities. Additionally, visual inspection indicated that converted waterway densities reasonably reflected the trend in transect densities in an area where the wider habitat was similar in terms of beaver harvest, land use, and proportion of water features. Although trend was well-reflected, individual waterway densities in this area were only 57-75% of transect densities. In other areas, where water features were limited, visual inspection suggested the trend of waterway densities was less reflective of transect density trend and individual waterway densities overestimated transect densities (up to 309%). Nevertheless, while transect densities are better for comparisons within and across study areas, waterway surveys are still important for timely and specific within-study area insights. This research provides useful benchmark examples of reliability regarding waterway observation indices converted to densities for conservation, research, and management of beaver and their ecosystems. Because these conclusions are based on a small sample, additional research is recommended to better define this relationship especially in areas with differing habitat, beaver harvest, land use patterns, etc.","language":"English","publisher":"Alpha Wildlife","usgsCitation":"Barber-Meyer, S., 2019, Comparison of beaver density estimates from aerial surveys of waterways versus transects: Canadian Wildlife Biology and Management, v. 8, no. 1, p. 9-16.","productDescription":"8 p.","startPage":"9","endPage":"16","ipdsId":"IP-104885","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":367170,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":367167,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://cwbm.name/comparison-of-beaver-density-estimates-from-aerial-surveys-of-waterways-versus-transects/"}],"country":"United States","state":"Minnesota","otherGeospatial":"Boundary Waters Canoe Area Wilderness, Superior National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.8182373046875,\n 47.585789182379905\n ],\n [\n -90.13458251953125,\n 47.585789182379905\n ],\n [\n -90.13458251953125,\n 48.206371336358906\n ],\n [\n -91.8182373046875,\n 48.206371336358906\n ],\n [\n -91.8182373046875,\n 47.585789182379905\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","issue":"1","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Barber-Meyer, Shannon 0000-0002-3048-2616 sbarber-meyer@usgs.gov","orcid":"https://orcid.org/0000-0002-3048-2616","contributorId":191875,"corporation":false,"usgs":true,"family":"Barber-Meyer","given":"Shannon","email":"sbarber-meyer@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":767853,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70205810,"text":"70205810 - 2019 - Handbook to the partners in flight population estimates database, version 3.0","interactions":[],"lastModifiedDate":"2019-10-08T07:08:41","indexId":"70205810","displayToPublicDate":"2019-05-31T11:45:35","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesTitle":{"id":5870,"text":"Partners in Flight Technical Publication","active":true,"publicationSubtype":{"id":4}},"seriesNumber":"7","title":"Handbook to the partners in flight population estimates database, version 3.0","docAbstract":"This document describes the content of Version 3.0 of the Partners in Flight (PIF) Population Estimates Database, which provides population estimates for breeding USA/Canada landbirds at several geographic scales following the Partners in Flight approach described initially in Rich et al. (2004) and by Rosenberg and Blancher (2005) and most recently refined by Stanton et al. (2019). The Handbook also provides details about how the estimates were derived, information on limitations and caveats, a guide to using the estimates, and future desired directions for improving the estimates.\n\nThis version of the database is intended as a companion to the Partners in Flight Landbird Conservation Plan: 2016 Revision for Canada and Continental United States (Rosenberg et al. 2016), although estimates for most species included in the database have been updated from those used in the 2016 Plan. Most of the estimates in the database are based on North American Breeding Bird Survey (BBS) data for landbirds from the decade 2006–2015. The information in this 2019 Version of the Handbook, for Version 3.0 of the database, describes all changes that have been made to the database since 2007 (Version 1.0, cf. Blancher et al. 2007)—including the additions documented in the Handbook for Version 2.0 (Blancher et al. 2013)—and therefore serves as a single source document describing the current database.\n\nVersion 3.0 of the database addresses some of the recommendations suggested by Thogmartin et al. (2006) but does not yet fully address other limitations noted by Thogmartin et al. (2006), Blancher et al. (2007), Thogmartin (2010), Matsuoka et al. (2012), and Twedt (2015). By far, the most substantial change to the database comprises the incorporation of quantitative uncertainty bounds around population estimates for most species (see Stanton et al. 2019 for details). PIF Science anticipates that future versions of the database will occur in stages: first, improving elements of the basic PIF approach (e.g., incorporating more recent BBS data, updating Time of Day Adjustments, and refining Pair Adjustments); next, addressing additional concerns inherent in the PIF approach (e.g., replacing average maximum detection distance bins with research-derived species-specific effective detection radii); and later, perhaps replacing the sample-based PIF approach with a spatially-explicit, model-based (pixel-based) approach that more deliberately incorporates habitat and road biases and the proportion of birds available but not detected by current sampling methodology.\n\nThis current Version 3.1 of the Handbook updates citations in the Literature Cited and incorporates relatively minor improvements in text clarity over the previous 3.0 version. The content of the Population Estimates Database itself remains the same except for changes to the global and USA/Canada estimates for Northern Bobwhite and Black Vulture.","language":"English","publisher":"Partners in Flight and Bird Conservancy of the Rockies","usgsCitation":"Will, T., Stanton, J.C., Rosenberg, K.V., Panjabi, A.O., Camfield, A., Shaw, A., Thogmartin, W.E., and Blancher, P.J., 2019, Handbook to the partners in flight population estimates database, version 3.0: Partners in Flight Technical Publication 7, 38 p.","productDescription":"38 p.","ipdsId":"IP-106689","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":368039,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":368002,"type":{"id":15,"text":"Index Page"},"url":"https://pif.birdconservancy.org/PopEstimates/"}],"publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Will, Tom","contributorId":149777,"corporation":false,"usgs":false,"family":"Will","given":"Tom","email":"","affiliations":[{"id":17821,"text":"U.S. Fish and Wildlife Service, Division of Migratory Birds","active":true,"usgs":false}],"preferred":false,"id":772445,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stanton, Jessica C. 0000-0002-6225-3703 jcstanton@usgs.gov","orcid":"https://orcid.org/0000-0002-6225-3703","contributorId":5634,"corporation":false,"usgs":true,"family":"Stanton","given":"Jessica","email":"jcstanton@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":772444,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rosenberg, Kenneth V.","contributorId":171463,"corporation":false,"usgs":false,"family":"Rosenberg","given":"Kenneth","email":"","middleInitial":"V.","affiliations":[{"id":27615,"text":"Cornell Lab of Ornithology, Conservation Science Program","active":true,"usgs":false}],"preferred":false,"id":772446,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Panjabi, Arvind O.","contributorId":169967,"corporation":false,"usgs":false,"family":"Panjabi","given":"Arvind","email":"","middleInitial":"O.","affiliations":[{"id":25644,"text":"Bird Conservancy of the Rockies","active":true,"usgs":false}],"preferred":false,"id":772447,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Camfield, Alaine","contributorId":219517,"corporation":false,"usgs":false,"family":"Camfield","given":"Alaine","email":"","affiliations":[{"id":12590,"text":"Canadian Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":772448,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shaw, Allison","contributorId":219518,"corporation":false,"usgs":false,"family":"Shaw","given":"Allison","email":"","affiliations":[{"id":25644,"text":"Bird Conservancy of the Rockies","active":true,"usgs":false}],"preferred":false,"id":772449,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":772450,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Blancher, Peter J.","contributorId":175182,"corporation":false,"usgs":false,"family":"Blancher","given":"Peter","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":772451,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70223766,"text":"70223766 - 2019 - Forest restoration, wildfire, and habitat selection by female mule deer","interactions":[],"lastModifiedDate":"2021-09-07T15:42:59.985176","indexId":"70223766","displayToPublicDate":"2019-05-31T10:32:37","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Forest restoration, wildfire, and habitat selection by female mule deer","docAbstract":"Decades of fire suppression, logging, and overgrazing have led to increased densities of small diameter trees which have been associated with decreases in biodiversity, reduced habitat quality for wildlife species, degraded foraging conditions for ungulates, and more frequent and severe wildfires. In response, land managers are implementing forest restoration treatments using prescribed fire and thinning to mitigate the risk of catastrophic wildfires and improve habitat conditions for a variety of wildlife species. We monitored habitat selection by female mule deer (Odocoileus hemionus) in relation to forest restoration treatments and wildfires in northern New Mexico in 2015. Our specific objectives were to assess changes in forage abundance and determine habitat selection patterns of mule deer in relation to recent wildfires, forest restoration treatments, including duration of the post-treatment recovery, and other habitat characteristics. Herbaceous forage biomass was greater in wildfire-burned areas than in untreated areas or those treated with prescribed burns or thinning. Oak forage biomass was greater in wildfire-burned areas compared to prescribed burns, forest thinning, and untreated areas. However, thinned areas tended to have higher oak forage biomass than untreated areas. Mule deer selected for areas burned by prescribed fire and generally avoided wildfire-burned and thinned areas <5 years old. Mule deer strongly selected for thinned areas ≥5 years old. At both the landscape and home-range scale, grasslands were avoided during most seasons, pinyon-juniper woodlands were selected in winter, and oak vegetation and mixed-conifer forests were selected during summer. Our data suggests that mule deer may benefit from recent prescribed burns and older forest thinning, but the duration of post-treatment vegetation recovery influences the strength and direction of selection. Knowledge of the short- and long-term effects of restoration treatments will provide managers with the means to make more informed decisions regarding implementation of forest restoration and other vegetation treatments intended to benefit mule deer.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2019.05.067","usgsCitation":"Roerick, T.M., Cain, J.W., and Gedir, J., 2019, Forest restoration, wildfire, and habitat selection by female mule deer: Forest Ecology and Management, v. 447, p. 169-179, https://doi.org/10.1016/j.foreco.2019.05.067.","productDescription":"11 p.","startPage":"169","endPage":"179","ipdsId":"IP-105337","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":467576,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.foreco.2019.05.067","text":"Publisher Index Page"},{"id":388880,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Jemez Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.88873291015625,\n 35.575799570297406\n ],\n [\n -106.63192749023438,\n 35.575799570297406\n ],\n [\n -106.63192749023438,\n 35.94688293218141\n ],\n [\n -106.88873291015625,\n 35.94688293218141\n ],\n [\n -106.88873291015625,\n 35.575799570297406\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"447","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Roerick, Tanya M.","contributorId":265344,"corporation":false,"usgs":false,"family":"Roerick","given":"Tanya","email":"","middleInitial":"M.","affiliations":[{"id":27575,"text":"NMSU","active":true,"usgs":false}],"preferred":false,"id":822584,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cain, James W. III 0000-0003-4743-516X jwcain@usgs.gov","orcid":"https://orcid.org/0000-0003-4743-516X","contributorId":4063,"corporation":false,"usgs":true,"family":"Cain","given":"James","suffix":"III","email":"jwcain@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":822585,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gedir, J. V.","contributorId":265345,"corporation":false,"usgs":false,"family":"Gedir","given":"J. V.","affiliations":[{"id":27575,"text":"NMSU","active":true,"usgs":false}],"preferred":false,"id":822586,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70203744,"text":"70203744 - 2019 - The influence of body size, condition, and age on recruitment of four Alaskan brown bear populations","interactions":[],"lastModifiedDate":"2019-06-07T15:18:25","indexId":"70203744","displayToPublicDate":"2019-05-29T15:06:53","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3671,"text":"Ursus","active":true,"publicationSubtype":{"id":10}},"title":"The influence of body size, condition, and age on recruitment of four Alaskan brown bear populations","docAbstract":"Recruitment of brown bear (Ursus arctos) offspring into a population is the product of initial cub production and subsequent survival and is a critical component of overall population status and trend. We investigated the relationship between maternal body size, body condition, and age (as a surrogate for gained experience) and recruitment of dependent offspring (≥1 yr old) in 4 Alaska, USA (2014–2017), brown bear populations using logistic regression. Body size alone was our top predictor of the presence of offspring and appeared in all top models. Our data suggest that bear size is the primary driver of productivity across all 4 study populations, with larger bears having a greater chance of being observed with offspring. The effect of body condition was likely confounded by the increased energetic costs of supporting cubs through time and had a negative relationship with recruitment. Age (experience) was positively related to recruitment. Understanding the relative importance of body size, body condition, and age on the recruitment of offspring provides insights into life-history trade-offs female bears must manage as they strive to meet the nutritional costs of cub production and rearing, while minimizing risks to themselves and their offspring. Further assessment of long-term longitudinal studies of brown bears that assess the lifetime reproductive output of individuals would be highly informative to further assess the effect of experience on recruitment and to support the management of brown bear populations for recovery, conservation, sustained yield, and ecosystem function.
","language":"English","publisher":"BioOne","doi":"10.2192/URSUS-D-18-00008.1","usgsCitation":"Hilderbrand, G., Gustine, D., Joly, K., Mangipane, B., Leacock, W., Cameron, M., Sorum, M., Mangipane, L., and Erlenbach, J., 2019, The influence of body size, condition, and age on recruitment of four Alaskan brown bear populations: Ursus, v. 2, no. 29, p. 111-118, https://doi.org/10.2192/URSUS-D-18-00008.1.","productDescription":"8 p.","startPage":"111","endPage":"118","ipdsId":"IP-081284","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":467583,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2192/ursus-d-18-00008.1","text":"Publisher Index Page"},{"id":364525,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Brooks Range, Gates of the Arctic National Park & Preserve, Katmai National Park & Preserve, Kodiak National Wildlife Refuge, Lake Clark National Park & Preserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -158.33496093749997,\n 55.99838095535963\n ],\n [\n -150.9521484375,\n 55.99838095535963\n ],\n [\n -150.9521484375,\n 62.67414334669093\n ],\n [\n -158.33496093749997,\n 62.67414334669093\n ],\n [\n -158.33496093749997,\n 55.99838095535963\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.13769531249997,\n 66.31986144668052\n ],\n [\n -147.3046875,\n 66.31986144668052\n ],\n [\n -147.3046875,\n 68.70448628851169\n ],\n [\n -156.13769531249997,\n 68.70448628851169\n ],\n [\n -156.13769531249997,\n 66.31986144668052\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"2","issue":"29","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hilderbrand, Grant V. 0000-0002-0051-8315 ghilderbrand@usgs.gov","orcid":"https://orcid.org/0000-0002-0051-8315","contributorId":199764,"corporation":false,"usgs":true,"family":"Hilderbrand","given":"Grant V.","email":"ghilderbrand@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":false,"id":763923,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gustine, David","contributorId":200449,"corporation":false,"usgs":false,"family":"Gustine","given":"David","affiliations":[],"preferred":false,"id":763924,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Joly, Kyle","contributorId":53117,"corporation":false,"usgs":false,"family":"Joly","given":"Kyle","email":"","affiliations":[{"id":12462,"text":"U.S. Department of the Interior, National Park Service","active":true,"usgs":false}],"preferred":false,"id":763925,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mangipane, Buck","contributorId":211731,"corporation":false,"usgs":false,"family":"Mangipane","given":"Buck","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":763926,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Leacock, William","contributorId":192123,"corporation":false,"usgs":false,"family":"Leacock","given":"William","affiliations":[],"preferred":false,"id":763927,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cameron, Matthew","contributorId":204963,"corporation":false,"usgs":false,"family":"Cameron","given":"Matthew","email":"","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":763928,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sorum, Mathew","contributorId":204962,"corporation":false,"usgs":false,"family":"Sorum","given":"Mathew","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":763929,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mangipane, Lindsey","contributorId":201731,"corporation":false,"usgs":false,"family":"Mangipane","given":"Lindsey","affiliations":[{"id":36244,"text":"MSU","active":true,"usgs":false}],"preferred":false,"id":763930,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Erlenbach, Joy","contributorId":200750,"corporation":false,"usgs":false,"family":"Erlenbach","given":"Joy","affiliations":[],"preferred":false,"id":763931,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70205183,"text":"70205183 - 2019 - Impacts of saltwater intrusion on wetland prey production and composition in a historically freshwater marsh","interactions":[],"lastModifiedDate":"2019-10-11T15:05:58","indexId":"70205183","displayToPublicDate":"2019-05-29T12:25:57","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1584,"text":"Estuaries and Coasts","active":true,"publicationSubtype":{"id":10}},"title":"Impacts of saltwater intrusion on wetland prey production and composition in a historically freshwater marsh","docAbstract":"Sea level rise is a fundamental driver of ecosystem change and has the potential to shift the spatial distributions of habitats more rapidly than species can adapt. Rapid sea level rise and associated saltwater intrusion have negative impacts on coastal environments, including loss of habitat for species such as sea turtles and shorebirds. In Florida’s largest wetland, the Everglades, the availability of small fishes that are prey for wading birds serve as an indicator of ecosystem health. In the southwestern part of the ecosystem, sea level rise and a reduction in freshwater flow have converted formerly freshwater marsh into an estuarine mangrove-dominated landscape. In this study, we sampled fish communities in this southwest portion of the Everglades exposed to a range of saline conditions. Biomass of native fishes was highest at a salinity value of approximately 25 parts per thousand, while non-native biomass decreased with increasing salinity. Applying salinity change scenarios, we determined that a 10% reduction from the salinity levels recorded in this study would be optimal for biomass of native fishes. The baseline data we collected on non-native fishes at the site will be useful for monitoring as restoration progresses and for managers working on invasive species control. This region has been actively undergoing ecosystem restoration for the last decade, but restored freshwater flow closer to historical conditions is still years away. Without increased freshwater inflow from hydrologic restoration, rising sea levels and saltwater intrusion may provide conservation challenges for wading birds and their prey-base fishes.
","language":"English","publisher":"Springer","doi":"10.1007/s12237-019-00572-8","usgsCitation":"Romanach, S., Beerens, J.M., Patton, B., Chapman, J.P., and Hanson, M., 2019, Impacts of saltwater intrusion on wetland prey production and composition in a historically freshwater marsh: Estuaries and Coasts, v. 42, no. 6, p. 1600-1611, https://doi.org/10.1007/s12237-019-00572-8.","productDescription":"12 p.","startPage":"1600","endPage":"1611","ipdsId":"IP-088911","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":437441,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F73B5ZCD","text":"USGS data release","linkHelpText":"Fish data collected during 2015 and 2016 at 9 sites at the Ten Thousand Islands National Wildlife Refuge, Florida."},{"id":367221,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.47186279296875,\n 25.109227976892218\n ],\n [\n -80.55038452148438,\n 25.109227976892218\n ],\n [\n -80.55038452148438,\n 25.88393659458397\n ],\n [\n -81.47186279296875,\n 25.88393659458397\n ],\n [\n -81.47186279296875,\n 25.109227976892218\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"42","issue":"6","noUsgsAuthors":false,"publicationDate":"2019-05-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Romanach, Stephanie 0000-0003-0271-7825","orcid":"https://orcid.org/0000-0003-0271-7825","contributorId":216117,"corporation":false,"usgs":true,"family":"Romanach","given":"Stephanie","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":770267,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beerens, James M. 0000-0001-8143-916X jbeerens@usgs.gov","orcid":"https://orcid.org/0000-0001-8143-916X","contributorId":143722,"corporation":false,"usgs":true,"family":"Beerens","given":"James","email":"jbeerens@usgs.gov","middleInitial":"M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":770268,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Patton, Brett 0000-0002-7396-3452 pattonb@usgs.gov","orcid":"https://orcid.org/0000-0002-7396-3452","contributorId":5458,"corporation":false,"usgs":true,"family":"Patton","given":"Brett","email":"pattonb@usgs.gov","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":770269,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chapman, Julia P. 0000-0002-7073-0012","orcid":"https://orcid.org/0000-0002-7073-0012","contributorId":218788,"corporation":false,"usgs":true,"family":"Chapman","given":"Julia","email":"","middleInitial":"P.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":770270,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hanson, Matt 0000-0002-2859-3878 mhanson@usgs.gov","orcid":"https://orcid.org/0000-0002-2859-3878","contributorId":208997,"corporation":false,"usgs":true,"family":"Hanson","given":"Matt","email":"mhanson@usgs.gov","affiliations":[],"preferred":true,"id":770271,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70203531,"text":"sir20195035 - 2019 - Simulation of groundwater flow in the Brunswick Area, Georgia, for 2004 and 2015, and selected groundwater-management scenarios","interactions":[],"lastModifiedDate":"2019-05-30T15:56:39","indexId":"sir20195035","displayToPublicDate":"2019-05-29T11:15:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5035","displayTitle":"Simulation of Groundwater Flow in the Brunswick Area, Georgia, for 2004 and 2015, and Selected Groundwater-Management Scenarios","title":"Simulation of groundwater flow in the Brunswick Area, Georgia, for 2004 and 2015, and selected groundwater-management scenarios","docAbstract":"The Upper Floridan aquifer (UFA) is the principal water source for industrial and public supply in Glynn County, Georgia. Wells in active pumping centers that tap the UFA for industries near the city of Brunswick have created an upward hydraulic-head gradient in the Floridan aquifer system, which has allowed high chloride (saline) groundwater from the Fernandina permeable zone of the Lower Floridan aquifer (LFA) to migrate upward into freshwater zones. Chloride concentrations of more than 250 milligrams per liter—the State and Federal secondary drinking-water standard—have been measured in a 2-square-mile area near downtown Brunswick.
An existing regional U.S. Geological Survey modular finite-difference groundwater-flow model (MODFLOW-2000) was modified using greater horizontal and vertical resolution to enable more detailed simulation of the effects of pumping in the vicinity of chloride contamination. Modifications to the regional model consisted of (1) limiting grid size to a maximum of 500 feet (ft) per side in the vicinity of the chloride plume; (2) representing the upper and lower Brunswick aquifers with distinct model layers; (3) similarly, representing upper and lower water-bearing zones of the UFA with distinct model layers in Glynn and Camden Counties, Ga.; and (4) establishing new hydraulic-property geographic zones in the UFA within Glynn County. The revised groundwater-flow model was calibrated to steady-state conditions that were assumed to exist during 2000 and 2004. The calibration and framework of the revised groundwater-flow model were documented in a separate report. For the current study, steady-state conditions were calibrated using October 2015 pumping rates in the Brunswick/Glynn County area as a 2015 Base Case. The 2015 Base Case simulation was used as the basis to evaluate seven groundwater-management scenarios in the Brunswick/Glynn County area.
Seven groundwater management-scenarios were developed on the basis of short- and long-term groundwater-use projections for the UFA in the Brunswick/Glynn County area. Scenarios A and B simulated additional pumping in the upper water-bearing zone (UWBZ) of the UFA at existing public-supply wells located near a chloride plume and planned public-supply wells to be constructed north of downtown Brunswick. Scenario C simulated a shutdown at Brunswick Cellulose Inc. and Pinova Inc. and the resulting deactivation of nine production wells, with a combined total pumping of 31.3 million gallons per day (Mgal/d) for the 2015 Base Case simulation. Scenario D (three scenarios) simulated 12.5, 25, and 50 percent (designated Scenarios D1, D2, and D3) of the total pumping of 31.3 Mgal/d at Brunswick Cellulose and Pinova. The objective of Scenario D was to determine pumping rates that may reverse groundwater-flow directions toward the Brunswick Cellulose well field and potentially allow groundwater with higher chloride concentration to migrate toward nearby public-supply wells. Scenario E simulated an additional pumping of 5 Mgal/d from the UWBZ of the UFA at a recently constructed production well within the Brunswick Cellulose well field.
Backward particle-tracking (MODPATH) analysis in public-supply wells located just outside the chloride plume to the north shows that predominant groundwater-flow directions are from the northeast toward the Brunswick Cellulose well field. The analysis covered 20- and 50-year periods for the 2015 Base Case and Scenario C simulations with 100 percent of backtracked particles remaining in the UWBZ and lower water-bearing zone of the UFA. Groundwater-flow directions are characterized by some vertical movement and dominant horizontal movement away from the chloride plume in the northern Brunswick area. For the 2015 Base Case simulation, the mean rate of particle movement ranged from 268 to 413 feet per year. For the Scenario C simulation, the mean rate of particle movement ranged from 89 to 182 feet per year with 50 percent of particles migrating from the chloride plume area. The rate of particle movement is influenced most by the horizontal hydraulic-head gradient in the UWBZ of the UFA.
The revised groundwater-flow model is subject to the limitations documented in the original model. In addition, the values used for the specified-head boundaries in the Floridan aquifer system for the 2004 calibrated model were based on the sparse data available and were not changed for the 2015 update to the model. These model boundaries control 80 percent of the inflows and about 60 percent of the outflows. Composite-scaled sensitivities of the model parameters indicate the revised model is most sensitive to pumping rates, followed by the horizontal hydraulic conductivity in the UFA for zones along coastal Georgia.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195035","collaboration":"Prepared in cooperation with the Brunswick-Glynn County Joint Water and Sewer Commission and the Georgia Environmental Protection Division","usgsCitation":"Cherry, G.S., 2019, Simulation of groundwater flow in the Brunswick area, Georgia, for 2004 and 2015, and selected groundwater-management scenarios: U.S. Geological Survey Scientific Investigations Report 2019–5035, 70 p., https://doi.org/10.3133/sir20195035.","productDescription":"Report: vii, 70 p.; Data Release","numberOfPages":"82","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-089920","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":364052,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5035/coverthb.jpg"},{"id":364054,"rank":3,"type":{"id":30,"text":"Data 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\"}}]}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Stephenson Center, Suite 129
Columbia, SC 29210
The Mississippi Alluvial Plain (MAP) aquifer system is a vital resource that supports agriculture in one of the most productive regions of the country. The U.S. Geological Survey Water Availability and Use Science Program (WAUSP) is conducting a multi-discipline investigation of the MAP aquifer system. The investigation is utilizing borehole, surface, and airborne geophysical methods to improve the characterization and understanding of the aquifer. The combination of geophysical data collected over a range of spatial scales with varying depths of investigation and resolution is key to determining the distribution of sand and clay within the aquifer. Mobile geophysical methods that enable continuous measurements over large areas improve aquifer characterization with their increased spatial coverage. In support of the MAP investigation, a new towed Time-Domain Electromagnetic (tTEM) imaging system developed by Aarhus University was used near Shellmound, Mississippi (MS), to delineate the distribution of coarse- and fine-grained sediments underlying the site. The tTEM results compare favorably with the results of airborne EM (AEM) surveys flown in the study and improve the resolution of sand and gravel distribution within the tTEM depth of investigation.
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Utilizing a moderate-magnitude database of recordings, we simulate ground motions for larger-magnitude scenarios M6.0, 6.5, 7.0, 7.5, and 8.0. For the physics-based method, we vary the faulting mechanism, slip, stress drop, rupture velocity, source depth, and 1D velocity structure. For the stochastic method, we simulate realizations using a set of six model parameters, each of which has a pre-assigned probability distribution. The median spectral accelerations over all synthetic realizations are compared with the NGA-East models. The synthetic standard deviation for deterministic simulations ranges from approximately 0.4 to 0.85 for various magnitudes and distances, whereas that for stochastic simulations is between 0.48 and 1.04. Based on the simulation results and comparisons with NGA-East variability models, a range for ground motion variability in the CEUS is discussed.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"ICASP 13 Proceedings","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"13th International Conference on Applications of Statistics and Probability in Civil Engineering (ICASP13)","conferenceDate":"May 26-30, 2019","conferenceLocation":"Seoul, South Korea","language":"English","publisher":"Seoul National University","doi":"10.22725/ICASP13.075","usgsCitation":"Sun, X., Rezaeian, S., Clayton, B., and Hartzell, S.H., 2019, Estimation of ground motion variability in the CEUS using simulations, in ICASP 13 Proceedings, Seoul, South Korea, May 26-30, 2019, 75; 8 p., https://doi.org/10.22725/ICASP13.075.","productDescription":"75; 8 p.","ipdsId":"IP-105826","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":365065,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Hartzell, Stephen H. 0000-0003-0858-9043 shartzell@usgs.gov","orcid":"https://orcid.org/0000-0003-0858-9043","contributorId":2594,"corporation":false,"usgs":true,"family":"Hartzell","given":"Stephen","email":"shartzell@usgs.gov","middleInitial":"H.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":758280,"contributorType":{"id":2,"text":"Editors"},"rank":4}],"authors":[{"text":"Sun, Xiaodan","contributorId":139583,"corporation":false,"usgs":false,"family":"Sun","given":"Xiaodan","email":"","affiliations":[{"id":6672,"text":"former: USGS Southwest Biological Science Center, Colorado Plateau Research Station, Flagstaff, AZ. Current address: TN-SCORE, Univ of Tennessee, Knoxville, TN, e-mail: jennen@gmail.com","active":true,"usgs":false}],"preferred":false,"id":758278,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rezaeian, Sanaz 0000-0001-7589-7893 srezaeian@usgs.gov","orcid":"https://orcid.org/0000-0001-7589-7893","contributorId":4395,"corporation":false,"usgs":true,"family":"Rezaeian","given":"Sanaz","email":"srezaeian@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":758277,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clayton, Brandon 0000-0003-0502-7184 bclayton@usgs.gov","orcid":"https://orcid.org/0000-0003-0502-7184","contributorId":197196,"corporation":false,"usgs":true,"family":"Clayton","given":"Brandon","email":"bclayton@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":758279,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hartzell, Stephen H. 0000-0003-0858-9043 shartzell@usgs.gov","orcid":"https://orcid.org/0000-0003-0858-9043","contributorId":2594,"corporation":false,"usgs":true,"family":"Hartzell","given":"Stephen","email":"shartzell@usgs.gov","middleInitial":"H.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":765123,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}