An important predictive variable for site amplification is the site dominant frequency (ƒd). At seismic monitoring stations, ƒd can be calculated from the peak of the horizontal‐to‐vertical spectral ratios (HVSRs) obtained from earthquake recordings (eHVSR). For other sites, ƒd can be estimated from microseismic (mHVSR) observations. We compare the ƒd values derived from eHVSR (5% damped response spectra from the Next Generation Attenuation‐West2 [NGA‐West2] database; Ancheta et al., 2014) with those derived from mHVSR (Fourier spectra from Yong et al., 2013) for seismic stations in California. We show that the logarithm of eHVSR ƒd scales linearly with the logarithm of mHVSR ƒd, with a standard deviation of 0.14log10 units for mHVSR ƒd larger than 0.2 Hz. The relationship holds for microseismic surveys at distances up to 300 m away from the seismic stations. The results of this study have beneficial implications for the characterization of site response in modern ground‐motion models as well as in building codes.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120180267","usgsCitation":"Behzad Hassani, Yong, A., Gail M. Atkinson, Feng, T., and Meng, L., 2019, Comparison of site dominant frequency from earthquake and microseismic data in California: Bulletin of the Seismological Society of America, v. 109, no. 3, p. 1034-1040, https://doi.org/10.1785/0120180267.","productDescription":"7 p.","startPage":"1034","endPage":"1040","ipdsId":"IP-100202","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482282,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Atkinson","affiliations":[{"id":83925,"text":"Western University, Department of Earth Sciences 1151 Richmond Street London, Ontario Canada N6A 5B7 of Earth Sciences 1151 Richmond Street London, Ontario Canada N6A 5B7","active":true,"usgs":false}],"preferred":false,"id":927971,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Feng, Tian","contributorId":351139,"corporation":false,"usgs":false,"family":"Feng","given":"Tian","affiliations":[{"id":83926,"text":"University of California, Los Angeles, Los Angeles, California, USA","active":true,"usgs":false}],"preferred":false,"id":928000,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Meng, Lingseng","contributorId":351143,"corporation":false,"usgs":false,"family":"Meng","given":"Lingseng","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":928001,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70205205,"text":"70205205 - 2019 - Relationships between diatom metrics based on species nutrient traits and agricultural land use","interactions":[],"lastModifiedDate":"2019-09-06T10:11:09","indexId":"70205205","displayToPublicDate":"2019-03-19T09:55:11","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1552,"text":"Environmental Monitoring and Assessment","onlineIssn":"1573-2959","printIssn":"0167-6369","active":true,"publicationSubtype":{"id":10}},"title":"Relationships between diatom metrics based on species nutrient traits and agricultural land use","docAbstract":"We assessed how diatom metrics were related to different ranges of agricultural land use. Diatom assemblage composition, nutrients, and landscape characteristics were determined at 232 sites in eight agriculturally dominated study areas of the continental United States. Two regional groups based on differences in diatom relations to human disturbance were determined. Changes in diatom species composition were related to nutrients,pH,and conductivity in the eastern study areas (due to more wetlands) and more exclusively to nutrients in the west-central study areas. Homogenization of diatom flora among streams was related to high agricultural disturbance at this transcontinental scale. Species traits were developed separately for the east and west central study groups and calculated two ways: indicator species analysis for taxa in low and high TN or TP conditions and weighted average partial least squares models of TN and TP concentration. These diatom metrics were significantly related to many indicators of agricultural land use in watersheds, especially percent row crops. Further analysis was conducted on only the west-central region due to its larger sample size.Overall, diatom metrics using species responses to N gradients were better related to agricultural land use than were species responses to P gradients. Most nutrient-based diatom metrics changed greatly in response to low ranges of percent row crops, but only a few high N diatom metrics responded to high row crop conditions. 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Jan","contributorId":139110,"corporation":false,"usgs":false,"family":"Stevenson","given":"R.","email":"","middleInitial":"Jan","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":770352,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Munn, Mark D. 0000-0002-7154-7252","orcid":"https://orcid.org/0000-0002-7154-7252","contributorId":205360,"corporation":false,"usgs":true,"family":"Munn","given":"Mark D.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":770349,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Waite, Ian R. 0000-0003-1681-6955 iwaite@usgs.gov","orcid":"https://orcid.org/0000-0003-1681-6955","contributorId":616,"corporation":false,"usgs":true,"family":"Waite","given":"Ian","email":"iwaite@usgs.gov","middleInitial":"R.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":770351,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70206912,"text":"70206912 - 2019 - Integrated assessment of wastewater reuse, exposure risk, and fish endocrine disruption in the Shenandoah River watershed","interactions":[],"lastModifiedDate":"2019-11-27T08:18:04","indexId":"70206912","displayToPublicDate":"2019-03-19T07:55:27","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Integrated assessment of wastewater reuse, exposure risk, and fish endocrine disruption in the Shenandoah River watershed","docAbstract":"Reuse of municipal and industrial wastewater treatment plant (WWTP) effluent is an important component in augmenting global freshwater supplies. The Shenandoah River Watershed was selected to conduct on-site exposure experiments to assess endocrine disrupting characteristics of different source waters. This investigation of the Shenandoah River Watershed integrates WWTP wastewater reuse modeling, hydrological and chemical characterization, and in vivo endocrine disruption bioassessment to assess contaminant sources, exposure pathways, and biological effects. The percentage of accumulated WWTP effluent in each river reach (ACCWW) was used to predict environmental concentrations for consumer product chemicals (boron), pharmaceutical compounds (carbamazepine), and steroidal estrogens (estrone, 17-beta-estradiol, estriol, and 17-alpha-ethinylestradiol). Fish endocrine disruption was evaluated using vitellogenin induction in male or juvenile fathead minnows. Water samples were analyzed for >500 inorganic and organic constituents to characterize the complex contaminant mixtures. Municipal ACCWW at drinking water treatment plant surface-water intakes ranged from <0.01 to 2.1 % under mean-annual streamflow and up to 4.7 % under August streamflow. Measured and predicted environmental concentrations resulted in 17-beta-estradiol equivalency quotients ranging from <0.05 to 5.1 ng L-1 indicating low-to-moderate risk of fish endocrine disruption. Results from the fish exposure experiments also showed limited estrogenic effects as indicated by the low (0.5- to 3.2-fold) vitellogenin induction.","language":"English","publisher":"ACS","doi":"10.1021/acs.est.8b05655","usgsCitation":"Barber, L., Krstolic, J.L., Kandel, C., Keefe, S.H., Rice, J., Westerhoff, P., Bertolatus, D., and Vajda, A.M., 2019, Integrated assessment of wastewater reuse, exposure risk, and fish endocrine disruption in the Shenandoah River watershed: Environmental Science & Technology, v. 53, no. 7, p. 3429-3440, https://doi.org/10.1021/acs.est.8b05655.","productDescription":"12 p.","startPage":"3429","endPage":"3440","ipdsId":"IP-099041","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":437536,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7QF8S22","text":"USGS data release","linkHelpText":"Assessment of Endocrine Disruption in the Shenandoah River Watershed - Chemical and Biological Data from Mobile Laboratory Fish Exposures and Other Experiments Conducted during 2014, 2015, and 2016"},{"id":369690,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Shenandoah River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.8828125,\n 41.64007838467894\n ],\n [\n -77.255859375,\n 42.00032514831621\n ],\n [\n -78.75,\n 40.27952566881291\n ],\n [\n -81.650390625,\n 36.491973470593685\n ],\n [\n -80.2880859375,\n 36.59788913307022\n ],\n [\n -76.5087890625,\n 36.491973470593685\n ],\n [\n -75.234375,\n 39.232253141714885\n ],\n [\n -74.8828125,\n 41.64007838467894\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"53","issue":"7","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-03-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Barber, Larry B. 0000-0002-0561-0831","orcid":"https://orcid.org/0000-0002-0561-0831","contributorId":218953,"corporation":false,"usgs":true,"family":"Barber","given":"Larry B.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":38175,"text":"Toxics Substances Hydrology Program","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":776234,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Krstolic, Jennifer L. 0000-0003-2253-9886 jkrstoli@usgs.gov","orcid":"https://orcid.org/0000-0003-2253-9886","contributorId":3677,"corporation":false,"usgs":true,"family":"Krstolic","given":"Jennifer","email":"jkrstoli@usgs.gov","middleInitial":"L.","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true},{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":776235,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kandel, Chintamani 0000-0002-3932-9247 ckandel@usgs.gov","orcid":"https://orcid.org/0000-0002-3932-9247","contributorId":197343,"corporation":false,"usgs":true,"family":"Kandel","given":"Chintamani","email":"ckandel@usgs.gov","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true},{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":776236,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Keefe, Steffanie H. 0000-0002-3805-6101 shkeefe@usgs.gov","orcid":"https://orcid.org/0000-0002-3805-6101","contributorId":2843,"corporation":false,"usgs":true,"family":"Keefe","given":"Steffanie","email":"shkeefe@usgs.gov","middleInitial":"H.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":776237,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rice, Jacelyn","contributorId":204155,"corporation":false,"usgs":false,"family":"Rice","given":"Jacelyn","email":"","affiliations":[{"id":36866,"text":"University of North Carolina Charlotte","active":true,"usgs":false}],"preferred":false,"id":776238,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Westerhoff, Paul","contributorId":204153,"corporation":false,"usgs":false,"family":"Westerhoff","given":"Paul","email":"","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":776239,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bertolatus, David 0000-0002-6829-9454","orcid":"https://orcid.org/0000-0002-6829-9454","contributorId":220848,"corporation":false,"usgs":false,"family":"Bertolatus","given":"David","email":"","affiliations":[{"id":16824,"text":"University of Colorado Denver","active":true,"usgs":false}],"preferred":false,"id":776240,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Vajda, Alan M.","contributorId":179189,"corporation":false,"usgs":false,"family":"Vajda","given":"Alan","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":776241,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70202692,"text":"70202692 - 2019 - Dynamic flood modeling essential to assess the coastal impacts of climate change","interactions":[],"lastModifiedDate":"2019-03-18T16:41:02","indexId":"70202692","displayToPublicDate":"2019-03-18T16:40:57","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Dynamic flood modeling essential to assess the coastal impacts of climate change","docAbstract":"Coastal inundation due to sea level rise (SLR) is projected to displace hundreds of millions of people worldwide over the next century, creating significant economic, humanitarian, and national-security challenges. However, the majority of previous efforts to characterize potential coastal impacts of climate change have focused primarily on long-term SLR with a static tide level, and have not comprehensively accounted for dynamic physical drivers such as tidal non-linearity, storms, short-term climate variability, erosion response and consequent flooding responses. Here we present a dynamic modeling approach that estimates climate-driven changes in flood-hazard exposure by integrating the effects of SLR, tides, waves, storms, and coastal change (i.e. beach erosion and cliff retreat). We show that for California, USA, the world’s 5th largest economy, over $150 billion of property equating to more than 6% of the state’s GDP and 600,000 people could be impacted by dynamic flooding by 2100; a three-fold increase in exposed population than if only SLR and a static coastline are considered. The potential for underestimating societal exposure to coastal flooding is greater for smaller SLR scenarios, up to a seven-fold increase in exposed population and economic interests when considering storm conditions in addition to SLR. These results highlight the importance of including climate-change driven dynamic coastal processes and impacts in both short-term hazard mitigation and long-term adaptation planning.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-019-40742-z","usgsCitation":"Barnard, P., Erikson, L.H., Foxgrover, A.C., Finzi Hart, J., Limber, P.W., O'Neill, A., van Ormondt, M., Vitousek, S., Wood, N.J., Hayden, M.K., and Jones, J.M., 2019, Dynamic flood modeling essential to assess the coastal impacts of climate change: Scientific Reports, v. 9, p. 1-13, https://doi.org/10.1038/s41598-019-40742-z.","productDescription":"Article number: 4309; 13 p.","startPage":"1","endPage":"13","ipdsId":"IP-092817","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":467802,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-019-40742-z","text":"Publisher Index Page"},{"id":362162,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-03-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Barnard, Patrick L. 0000-0003-1414-6476 pbarnard@usgs.gov","orcid":"https://orcid.org/0000-0003-1414-6476","contributorId":147147,"corporation":false,"usgs":true,"family":"Barnard","given":"Patrick L.","email":"pbarnard@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":759499,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Erikson, Li H. 0000-0002-8607-7695 lerikson@usgs.gov","orcid":"https://orcid.org/0000-0002-8607-7695","contributorId":149963,"corporation":false,"usgs":true,"family":"Erikson","given":"Li","email":"lerikson@usgs.gov","middleInitial":"H.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":759500,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Foxgrover, Amy C. 0000-0003-0638-5776 afoxgrover@usgs.gov","orcid":"https://orcid.org/0000-0003-0638-5776","contributorId":3261,"corporation":false,"usgs":true,"family":"Foxgrover","given":"Amy","email":"afoxgrover@usgs.gov","middleInitial":"C.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":759503,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Finzi Hart, Juliette A.","contributorId":214270,"corporation":false,"usgs":false,"family":"Finzi Hart","given":"Juliette A.","affiliations":[],"preferred":false,"id":759504,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Limber, Patrick W. 0000-0002-8207-3750 plimber@usgs.gov","orcid":"https://orcid.org/0000-0002-8207-3750","contributorId":196794,"corporation":false,"usgs":true,"family":"Limber","given":"Patrick","email":"plimber@usgs.gov","middleInitial":"W.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science 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To assess the impact of methodology on the detectable diversity of the coral’s microbiome, we obtained near full-length Sanger sequences from clone libraries constructed from a subset of the same A. poculata samples. Eight samples were analyzed: two sets of paired symbiotic (brown) and aposymbiotic (white) colonies collected in the fall (September) and two sets collected in the spring (April). Analysis of the Sanger sequences revealed that the microbiome of A. poculataexhibited a high level of richness; 806 OTUs were identified among 1390 bacterial sequences. While the Illumina study revealed that A. poculata’s microbial communities did not significantly vary according to symbiotic state, but did vary by season, Sanger sequencing did not expose seasonal or symbiotic differences in the microbiomes. Proteobacteria dominated the microbiome, forming the majority (55% to 80%) of classifiable bacteria in every sample, and the five bacterial classes with the highest mean relative portion (5% to 35%) were the same as those determined by prior Illumina sequencing. Sanger sequencing also captured the same core taxa previously identified by next-generation sequencing. Alignment of all sequences and construction of a phylogenetic tree revealed that both sequencing methods provided similar portrayals of the phylogenetic diversity within A. poculata’s bacterial associates. Consistent with previous findings, the results demonstrated that the Astrangia microbiome is stable notwithstanding the choice of sequencing method and the far fewer sequences generated by clone libraries (46 to 326 sequences per sample) compared to next-generation sequencing (3634 to 48481 sequences per sample). Moreover, the near-full length 16S rRNA sequences produced by this study are presented as a resource for the community studying this model system since they provide necessary information for designing primers and probes to further our understanding of this coral’s microbiome.
","language":"English","publisher":"AIMS Press","doi":"10.3934/microbiol.2019.1.62","usgsCitation":"Goldsmith, D.B., Pratte, Z.A., Kellogg, C.A., Snader, S.E., and Sharp, K.H., 2019, Stability of temperate coral Astrangia poculata microbiome is reflected across different sequencing methodologies: AIMS Microbiology, v. 5, no. 1, p. 62-76, https://doi.org/10.3934/microbiol.2019.1.62.","productDescription":"15 p.","startPage":"62","endPage":"76","ipdsId":"IP-102024","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":467804,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3934/microbiol.2019.1.62","text":"Publisher Index Page"},{"id":437538,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9C2XCQQ","text":"USGS data release","linkHelpText":"Cold-water Coral Microbiomes (Astrangia poculata) from Narragansett Bay: Sequence Data"},{"id":362160,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Goldsmith, Dawn B. 0000-0003-0080-5346 dgoldsmith@usgs.gov","orcid":"https://orcid.org/0000-0003-0080-5346","contributorId":191764,"corporation":false,"usgs":true,"family":"Goldsmith","given":"Dawn","email":"dgoldsmith@usgs.gov","middleInitial":"B.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":759486,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pratte, Zoe A.","contributorId":214260,"corporation":false,"usgs":false,"family":"Pratte","given":"Zoe","email":"","middleInitial":"A.","affiliations":[{"id":27526,"text":"Georgia Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":759487,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kellogg, Christina A. 0000-0002-6492-9455 ckellogg@usgs.gov","orcid":"https://orcid.org/0000-0002-6492-9455","contributorId":391,"corporation":false,"usgs":true,"family":"Kellogg","given":"Christina","email":"ckellogg@usgs.gov","middleInitial":"A.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"preferred":true,"id":759488,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Snader, Sara E.","contributorId":214261,"corporation":false,"usgs":false,"family":"Snader","given":"Sara","email":"","middleInitial":"E.","affiliations":[{"id":25340,"text":"Cherokee Nation Technologies","active":true,"usgs":false}],"preferred":false,"id":759489,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sharp, Koty H.","contributorId":214262,"corporation":false,"usgs":false,"family":"Sharp","given":"Koty","email":"","middleInitial":"H.","affiliations":[{"id":39003,"text":"Roger Williams University","active":true,"usgs":false}],"preferred":false,"id":759490,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70202446,"text":"fs20193009 - 2019 - The Missouri groundwater-level observation network","interactions":[],"lastModifiedDate":"2025-05-15T13:22:59.054456","indexId":"fs20193009","displayToPublicDate":"2019-03-18T14:30:00","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":"2019-3009","displayTitle":"The Missouri Groundwater-level Observation Network","title":"The Missouri groundwater-level observation network","docAbstract":"The Missouri groundwater-level observation well network is a series of wells across the State of Missouri in which groundwater levels are monitored in real time and periodically. The wells monitor the water levels in multiple key aquifers, such as the Ozark aquifer in the Salem and Springfield Plateaus and the Mississippi Alluvial Plain aquifer in the South-eastern Lowlands. As of 2018, 150 real-time sites are operated as a cooperative effort between the Missouri Department of Natural Resources (MoDNR) and the U.S. Geological Survey. This fact sheet describes the network and well data from the network.
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\"}}]}","edition":"Version 1.0: March 18, 2019; Version 1.1: March 22, 2019","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road, MS-100
Rolla, MO 65401
Mercury monitoring results from about 300 Morone saxatilis (striped bass) muscle tissue samples collected by the State of Utah from Lake Powell resulted in a Utah/Arizona fish consumption advisory issued in 2012 for approximately the lower 100 kilometers of the reservoir. Chemical, physical, and biological data were collected during two synoptic sampling cruises on Lake Powell during May/June 2014 and August 2015 to test three hypotheses associated with a conceptual model developed to explain the observed geographic concentration gradient of Hg in fish tissue samples. This model proposes that in the transition from a primarily riverine system to a reservoir, there is a change in the concentration and composition of water-column particulate material, increasing in the proportion of organic content moving downstream, as the larger size fractions of the inorganic particulate load are deposited in the upper reservoir. This change alleviates light limitation of phytoplankton production and leads to a higher proportion of autochthonous primary production in the downstream direction. This, in turn, drives increased microbial methylmercury (MeHg) production in the benthos and potentially the water column, in the downstream direction, and results in the observed elevated fish Hg levels in the lower part of the reservoir. The model also proposes that there are differences between the main stem of Lake Powell and side canyons, embayments, or secondary rivers entering the reservoir, in terms of Hg cycling dynamics and bioaccumulations, driven mainly by differences in hydrology. Finally, seasonal differences in Hg dynamics within the reservoir are proposed, based on seasonal dynamics associated with primary production and the physical process of seasonal stratification.
A total of three statistically testable hypotheses were proposed and postulated that measurable differences in key Hg and non-Hg metrics exist between: (1) the upper and lower reservoir; (2) main stem and river arm/side canyon/embayment sites; and (3) early-season (May/June 2014, less stratified) and late-season (August 2015, stratified) conditions. Statistically modeled least square means in combination with the graphical analysis of Hg and non-Hg parameters were used to examine the data collected during the study and test these hypotheses. Data collected during the study are included in a U.S. Geological Survey data release and are available online at https://doi.org/10.5066/F74X560J.
In general, water-column, plankton, and surface sediment samples collected during the synoptic sampling cruises are supportive of the three hypotheses associated with the conceptual model. In support of hypothesis 1 (comparing upper and lower reservoir sites), the least square mean for turbidity was higher in the upper reservoir. In contrast, surface water particulate organic carbon (as a percentage of total particulate mass), particulate MeHg (by mass [in nanograms per gram] and as a percentage of total mercury [THg]), and particulate-dissolved partitioning coefficients for THg and MeHg were higher in the lower reservoir. Plankton THg concentrations also were significantly (probability [p] less than (<) 0.05) higher in the lower reservoir. Surface sediment metrics in support of hypothesis 1 include higher MeHg production potential rates in the lower reservoir. In contrast, there were no statistically significant differences between the upper and lower reservoir for surface sediment percent of MeHg and MeHg concentration, percent MeHg, or methylation rate constants. These spatial trends associated with hypothesis 1 indicate a pathway for enhanced Hg bioavailability in the lower reservoir.
Hypothesis 2, which tested for differences between main stem and river arm/side canyon/embayment sites, was supported by a number of water-column parameters, including particulate THg and MeHg concentrations by mass (in nanograms per gram) and percent particulate MeHg being significantly (p<0.05) higher in the river arms, side canyons, and embayments relative to the main stem channel. Plankton MeHg concentrations (by mass [in nanograms per gram] and volume [in nanograms per liter] and as a percentage of THg) were elevated in river arm/side canyon/embayment sites compared to main stem sites, indicating an enhanced potential for MeHg bioaccumulation at the base of the pelagic food web in river arms, side canyons, and embayments. In contrast, few of the sediment metrics differed between main stem and river arm/side canyon/embayment sampling sites; however, the potential for MeHg degradation in surface sediment was significantly higher in the main stem. The data indicate that river arm/side canyon/embayment sites may experience enhanced Hg bioaccumulation, compared to the main stem, because of higher MeHg levels at the base of the pelagic food web. This conclusion is supported by the elevated Hg detected in striped bass muscle tissue samples collected in the San Juan Arm during this study (2014). Fish collected from the lower reservoir exhibited a distinct Hg isotopic signature that was enriched in delta (δ)202Hg and capital delta (Δ)199Hg relative to fish samples collected from either Good Hope Bay or the San Juan Arm.
Hypothesis 3 tested for differences between early (May/June) high-flow and late (August) low-flow seasons. This test was supported by a range of non-Hg metrics (nitrate, phosphate, chlorophyll a, dissolved oxygen, fluorescent dissolved organic matter, temperature, and pH) that reflect the increase in chlorophyll a, decrease in nutrients, and buildup of stratified conditions in the transition from early- to late-season sampling periods. Significant seasonal differences also were noted for multiple Hg metrics, including (a) water-column filtered and particulate (by mass) MeHg and THg concentrations; (b) plankton MeHg and THg concentration (by mass); and (c) sediment percent MeHg, Hg(II)-methylation rate constant, and microbial ribosomal ribonucleic acid, small subunit 16 (16S rRNA) abundance, all of which were higher during the late-season synoptic sampling. Overall, the surface sediment metrics are consistent with a seasonal shift from the early-season synoptic results, when the availability of Hg(II) exerts a primary control on MeHg production, to the late-season synoptic sampling, when microbial activity is a dominant driver of MeHg production.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181159","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Naftz, D.L., Marvin-DiPasquale, M., Krabbenhoft, D.P., Aiken, G., Boyd, E.S., Conaway, C.H., Ogorek, J., and Anderson, G.M., 2019, Biogeochemical and physical processes controlling mercury methylation and bioaccumulation in Lake Powell, Glen Canyon National Recreation Area, Utah and Arizona, 2014 and 2015: U.S. Geological Survey Open-File Report 2018–1159, 81 p., https://doi.org/10.3133/ofr20181159.","productDescription":"Report: xi, 81 p.; Data Release","numberOfPages":"98","onlineOnly":"Y","ipdsId":"IP-095917","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":359576,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1159/coverthb.jpg"},{"id":359577,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1159/ofr20181159.pdf","text":"Report","size":"9.11 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018–1159"},{"id":359578,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F74X560J","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data for Biogeochemical and Physical Processes Controlling Mercury Methylation and Bioaccumulation in Lake Powell, Glen Canyon National Recreation Area, Utah and Arizona, 2014–2015"}],"country":"United States","state":"Arizona, Utah","otherGeospatial":"Glen Canyon, Lake Powell","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.63551330566406,\n 36.75594019674357\n ],\n [\n -111.14044189453124,\n 36.75594019674357\n ],\n [\n -111.14044189453124,\n 37.020646433887805\n ],\n [\n -111.63551330566406,\n 37.020646433887805\n ],\n [\n -111.63551330566406,\n 36.75594019674357\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Utah Water Science Center
U.S. Geological Survey
2329 West Orton Circle West
Valley City, UT 84119
Context. Visual encounter surveying is a standard animal inventory method, modifications of which (e.g. distance sampling and repeated count surveys) are used for modelling population density. However, a variety of factors may bias visual survey counts.
Aims. The aim of the present study was to evaluate three observer-related biases: (1) whether fatigue compromises detection rate as a survey occasion progresses; (2) whether long-term fatigue or boredom compromise detection rates over the course of a survey period; and (3) whether observers exhibit biases in detection rates of different animal taxa.
Methods. We analysed >2.3 × 104 observations of lizards and small mammals from nocturnal pedestrian visual encounter surveys, each 4 h in duration, conducted by a pool of 29 observers, each of whom surveyed for up to 31 nights.
Key results. Detections of sleeping (diurnal) emerald tree skinks (Lamprolepis smaragdina) exhibited a small but statistically verified decline as the evening progressed, whereas detections of sleeping (diurnal) green anoles (Anolis carolinensis) increased as the evening progressed. Detections of nocturnal geckos (several species pooled) showed a weak and non-significant declining trend. Small mammal sightings (rats, shrews and mice pooled) declined strongly over the course of an evening. The participants saw greater or equal numbers of animals the more nights they surveyed. Most participants exhibited statistically significant, and often strong, taxonomic detection bias compared with the pool of peer observers. The skills of some observers appeared to be consistently above average; others consistently below average.
Conclusions. Data on sleeping lizards suggest that neither short-term nor long-term observer fatigue is of much concern for 4-h visual searches. On the contrary, differences among observers in taxonomic bias and overall detection skills pose a problem for data interpretation.
Implications. By comparing temporal detection patterns of immobile (e.g. sleeping) with actively moving animal taxa, sampling biases attributable to searcher fatigue versus the animals’ circadian rhythm can be disentangled and, if need be, statistically corrected for. Observer skill differences and observer-specific taxonomic biases may hamper efforts to statistically evaluate survey results, unless explicitly included as covariates in population models.
","language":"English","publisher":"BioOne","doi":"10.1071/WR18016","usgsCitation":"Lardner, B., Yackel Adams, A.A., Knox, A.J., Savidge, J.A., and Reed, R., 2019, Do observer fatigue and taxon-bias compromise visual encounter surveys for small vertebrates?: Wildlife Research, v. 46, no. 2, p. 127-135, https://doi.org/10.1071/WR18016.","productDescription":"9 p.","startPage":"127","endPage":"135","ipdsId":"IP-102306","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":467807,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1071/wr18016","text":"Publisher Index Page"},{"id":437539,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QTSAHY","text":"USGS data release","linkHelpText":"Visual Surveys Rapid Response Saipan 2016"},{"id":382552,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"46","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lardner, Bjorn","contributorId":225066,"corporation":false,"usgs":false,"family":"Lardner","given":"Bjorn","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":803720,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yackel Adams, Amy A. 0000-0002-7044-8447 yackela@usgs.gov","orcid":"https://orcid.org/0000-0002-7044-8447","contributorId":3116,"corporation":false,"usgs":true,"family":"Yackel Adams","given":"Amy","email":"yackela@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":803721,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Knox, Adam J","contributorId":244306,"corporation":false,"usgs":false,"family":"Knox","given":"Adam","email":"","middleInitial":"J","affiliations":[{"id":40374,"text":"Maui Invasive Species Committee","active":true,"usgs":false}],"preferred":false,"id":803722,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Savidge, Julie A.","contributorId":175196,"corporation":false,"usgs":false,"family":"Savidge","given":"Julie","email":"","middleInitial":"A.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":803723,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reed, Robert 0000-0001-8349-6168 reedr@usgs.gov","orcid":"https://orcid.org/0000-0001-8349-6168","contributorId":152301,"corporation":false,"usgs":true,"family":"Reed","given":"Robert","email":"reedr@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":803724,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70216101,"text":"70216101 - 2019 - Field-level characteristics influence wild bee functional guilds on public lands managed for conservation","interactions":[],"lastModifiedDate":"2020-11-04T16:03:00.897758","indexId":"70216101","displayToPublicDate":"2019-03-18T09:53:06","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3871,"text":"Global Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Field-level characteristics influence wild bee functional guilds on public lands managed for conservation","docAbstract":"Throughout the Midwestern US, many public lands set aside for conservation engage in management activities (e.g., agriculture) that may act as stressors on wild bee populations. Several studies have investigated how wild bees respond to large-scale agriculture production; however, there has been limited assessment of how wild bees may be impacted by agricultural activity on public lands or how local variables may influence bee communities in these same areas. In this study, we assessed the abundance and richness of wild bee floral and nesting guilds at 30 agricultural field margins located on five Conservation Areas in Missouri. Generally, regardless of guild, bee abundance and richness was greater in field margins with more floral diversity and taller vegetation. Bee guilds responded negatively to agricultural production in Conservation Areas with fewer soil- and cavity-nesting bees collected in margins adjacent to annually cropped fields. Although fewer diet specialists were collected, specialist bee abundance and richness was greater in margins near fields that were uncropped (i.e., vegetated, but not row-cropped) during the previous year. Overall, the percentage of trees and shrubs within 800 m of study fields (i.e., “woodland”) was negatively associated with abundance and richness of bees, but specifically, reduced richness of soil-nesters and diet specialists. Our findings indicate agricultural management activities on public lands may lead to decreased abundance and richness of wild bee guilds. If public lands are to be managed for species diversity, including wild bees, maintaining diverse plant communities with taller vegetation (>100 cm) near cultivated fields and/or modifying agricultural production practices on public lands may greatly improve the conservation of local bee communities.
Natural and anthropogenic hazards have the potential to impact all aspects of society including its economy and the environment. Diagnostic data to inform decision-making are critical for hazard management whether for emergency response, routine monitoring or assessments of potential risks. Imaging spectroscopy (IS) has unique contributions to make via the ability to provide some key quantitative diagnostic information. In this paper, we examine a selection of key case histories representing the state of the art to gain an insight into the achievements and perspectives in the use of visible to shortwave infrared IS for the detection, assessment and monitoring of a selection of significant natural and anthropogenic hazards. The selected key case studies examined provide compelling evidence for the use of the IS technology and its ability to contribute diagnostic information currently unattainable from operational spaceborne Earth observation systems. User requirements for the applications were also evaluated. The evaluation showed that the projected launch of spaceborne IS sensors in the near-, mid and long term future, together with the increasing availability, quality and moderate cost of off the shelf sensors, the possibilities to couple unmanned autonomous systems with miniaturized sensors, should be able to meet these requirements. The challenges and opportunities for the scientific community in the future when such data become available will then be ensuring consistency between data from different sensors, developing techniques to efficiently handle, process, integrate and deliver the large volumes of data, and most importantly translating the data to information that meets specific needs of the user community in a form that can be digested/understood by them. The latter is especially important to transforming the technology from a scientific to an operational tool. Additionally, the information must be independently validated using current trusted practices and uncertainties quantified before IS derived measurement can be integrated into operational monitoring services.
Aggregation of territorial individuals within a species can be facilitated via conspecific signals, wherein settlement implies habitat suitability, ease of resource acquisition and/or increased predator detection. The black-tailed prairie dog is a colonial small mammal with alarm vocalizations that confer benefits via group vigilance against predators and increased foraging time. Although prairie dog alarm calls are relatively well understood, the information embedded in their jump-yip call, which includes both a distinct cry and a bodily gesture, remains less clear. We evaluated prairie dog behaviour in response to conspecific acoustic signals using playbacks of alarm and jump-yip calls at 26 sites in northeastern Wyoming, U.S.A. Recorded calls from an isolated colony were broadcast to a mean of five individuals per site, and behavioural responses were compared against uninfluenced behaviour and a control playback of ambient sounds. The alarm playback caused prairie dogs to increase vigilance 122% and decrease foraging time 23%, demonstrating prairie dogs will shift behaviour based on signals from individuals of an unfamiliar colony. However, the alarm call playback reduced frequency of the jump-yip behaviour only at colonies nearest the recording source. The jump-yip playback caused unfamiliar prairie dogs to display 339% more jump-yips than uninfluenced behaviour. The jump-yip playback did not alter recipients' foraging or vigilance behaviours relative to control treatments, suggesting that although prairie dogs can understand and reciprocate an unfamiliar, single modality signal, they may not shift other behaviours based on this stimulus. As such, the purpose and benefits of the jump-yip call remain unclear. Playback efficacy also had a nonlinear relationship with distance from recording source. Our work improves understanding of communication at the metapopulation level, examines the potential role of the jump-yip and provides insights for how conspecific signals might be used as a management tool.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.anbehav.2019.02.004","usgsCitation":"Chalfoun, A.D., Connell, L.C., Porensky, L., and Scasta, J.D., 2019, Black-tailed prairie dog, Cynomys ludovicianus (Sciuridae), metapopulation response to novel sourced conspecific signals: Animal Behaviour, v. 150, p. 189-199, https://doi.org/10.1016/j.anbehav.2019.02.004.","productDescription":"11 p.","startPage":"189","endPage":"199","ipdsId":"IP-104048","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":467810,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.anbehav.2019.02.004","text":"Publisher Index Page"},{"id":388691,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Thunder Basin National Grassland","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.369873046875,\n 44.56699093657141\n ],\n [\n -105.084228515625,\n 44.56699093657141\n ],\n [\n -105.084228515625,\n 44.83249999349062\n ],\n [\n -105.369873046875,\n 44.83249999349062\n ],\n [\n -105.369873046875,\n 44.56699093657141\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.32617187499999,\n 43.52465500687185\n ],\n [\n -104.38110351562499,\n 43.88205730390537\n ],\n [\n -104.43603515624999,\n 44.20583500104184\n ],\n [\n -104.908447265625,\n 44.190082025040525\n ],\n [\n -104.94140625,\n 43.929549935614595\n ],\n [\n -105.303955078125,\n 43.84245116699039\n ],\n [\n -105.35888671875,\n 43.77109381775651\n ],\n [\n -105.699462890625,\n 43.77109381775651\n ],\n [\n -105.71044921875,\n 43.34116005412307\n ],\n [\n -105.732421875,\n 43.26920624914964\n ],\n [\n -105.66650390625,\n 43.213183300738876\n ],\n [\n -105.205078125,\n 43.229195113965005\n ],\n [\n -105.062255859375,\n 43.07691312608711\n ],\n [\n -104.8974609375,\n 43.141078106345866\n ],\n [\n -104.86450195312499,\n 43.42898792344155\n ],\n [\n -104.765625,\n 43.50872101129684\n ],\n [\n -104.578857421875,\n 43.46089378008257\n ],\n [\n -104.38110351562499,\n 43.46089378008257\n ],\n [\n -104.32617187499999,\n 43.52465500687185\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"150","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Chalfoun, Anna D. 0000-0002-0219-6006 achalfoun@usgs.gov","orcid":"https://orcid.org/0000-0002-0219-6006","contributorId":197589,"corporation":false,"usgs":true,"family":"Chalfoun","given":"Anna","email":"achalfoun@usgs.gov","middleInitial":"D.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":822191,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Connell, Lauren C.","contributorId":264923,"corporation":false,"usgs":false,"family":"Connell","given":"Lauren","email":"","middleInitial":"C.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":822192,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Porensky, Lauren M.","contributorId":264925,"corporation":false,"usgs":false,"family":"Porensky","given":"Lauren M.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":822193,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Scasta, John D.","contributorId":264927,"corporation":false,"usgs":false,"family":"Scasta","given":"John","email":"","middleInitial":"D.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":822194,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70219067,"text":"70219067 - 2019 - Understanding organic matter heterogeneity and maturation rate by Raman spectroscopy","interactions":[],"lastModifiedDate":"2021-03-23T14:44:11.665397","indexId":"70219067","displayToPublicDate":"2019-03-17T09:38:46","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2033,"text":"International Journal of Coal Geology","active":true,"publicationSubtype":{"id":10}},"title":"Understanding organic matter heterogeneity and maturation rate by Raman spectroscopy","docAbstract":"Solid organic matter (OM) in sedimentary rocks produces petroleum and solid bitumen when it undergoes thermal maturation. The solid OM is a ‘geomacromolecule’, usually representing a mixture of various organisms with distinct biogenic origins, and can have high heterogeneity in composition. Programmed pyrolysis is a common method to reveal bulk geochemical characteristics of the dominant OM, while detailed organic petrography is required to reveal information about the biogenic origin of contributing macerals. Despite the advantages of programmed pyrolysis, it cannot provide information about the heterogeneity of chemical compositions present in the individual OM types. Therefore, other analytical techniques such as Raman spectroscopy are necessary.
In this study, we compared geochemical characteristics and Raman spectra of two sets of naturally and artificially matured Bakken source rock samples. A continuous Raman spectral map on solid bitumen particles was created from the artificially matured hydrous pyrolysis residues, in particular, to show the systematic chemical modifications in microscale. Spectroscopic data was plotted for both sets against thermal maturity to compare maturation rate/path for these two separate groups. The outcome showed that artificial maturation through hydrous pyrolysis does not follow the same trend as naturally-matured samples although having similar solid bitumen reflectance values (%SBRo).
Furthermore, Raman spectroscopy of solid bitumen from artificially matured samples indicated the heterogeneity of OM decreases as maturity increases. This may represent an alteration in chemical structure towards more uniform compounds at higher maturity. This study may emphasize the necessity of using analytical methods such as Raman spectroscopy along with conventional geochemical methods to better reveal the underlying chemical structure of OM. Finally, observation by Raman spectroscopy of chemical alteration of OM during artificial maturation may assist in the proposal of improved pyrolysis protocols to better resemble natural geologic processes.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coal.2019.03.009","usgsCitation":"Khatibi, S., Ostadhassan, M., Hackley, P.C., Tuschel, D., Abarghani, A., and Bubach, B., 2019, Understanding organic matter heterogeneity and maturation rate by Raman spectroscopy: International Journal of Coal Geology, v. 206, p. 46-64, https://doi.org/10.1016/j.coal.2019.03.009.","productDescription":"19 p.","startPage":"46","endPage":"64","ipdsId":"IP-101108","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":467811,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.coal.2019.03.009","text":"Publisher Index Page"},{"id":437540,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P975KILE","text":"USGS data release","linkHelpText":"Analyzing Heterogeneity in Artificially Matured Samples of Bakken Shales (2018)"},{"id":384583,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota","otherGeospatial":"Williston Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.029541015625,\n 46.28622391806706\n ],\n [\n -98.93188476562499,\n 46.28622391806706\n ],\n [\n -98.93188476562499,\n 49.001843917978526\n ],\n [\n -104.029541015625,\n 49.001843917978526\n ],\n [\n -104.029541015625,\n 46.28622391806706\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"206","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Khatibi, Seyedalireza","contributorId":255596,"corporation":false,"usgs":false,"family":"Khatibi","given":"Seyedalireza","email":"","affiliations":[{"id":51594,"text":"Univ. North Dakota","active":true,"usgs":false}],"preferred":false,"id":812636,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ostadhassan, Mehdi","contributorId":255578,"corporation":false,"usgs":false,"family":"Ostadhassan","given":"Mehdi","email":"","affiliations":[{"id":17628,"text":"University of North Dakota","active":true,"usgs":false}],"preferred":false,"id":812637,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hackley, Paul C. 0000-0002-5957-2551 phackley@usgs.gov","orcid":"https://orcid.org/0000-0002-5957-2551","contributorId":592,"corporation":false,"usgs":true,"family":"Hackley","given":"Paul","email":"phackley@usgs.gov","middleInitial":"C.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":812638,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tuschel, David","contributorId":255597,"corporation":false,"usgs":false,"family":"Tuschel","given":"David","email":"","affiliations":[{"id":51595,"text":"HORIBA Scientific","active":true,"usgs":false}],"preferred":false,"id":812639,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Abarghani, Arash","contributorId":255576,"corporation":false,"usgs":false,"family":"Abarghani","given":"Arash","email":"","affiliations":[{"id":17628,"text":"University of North Dakota","active":true,"usgs":false}],"preferred":false,"id":812640,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bubach, Bailey","contributorId":255598,"corporation":false,"usgs":false,"family":"Bubach","given":"Bailey","email":"","affiliations":[{"id":51594,"text":"Univ. North Dakota","active":true,"usgs":false}],"preferred":false,"id":812641,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70202626,"text":"70202626 - 2019 - Manipulating wild and tamed phytobiomes: Challenges and opportunities","interactions":[],"lastModifiedDate":"2019-06-18T10:56:13","indexId":"70202626","displayToPublicDate":"2019-03-14T16:36:27","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5816,"text":"Phytobiomes Journal","active":true,"publicationSubtype":{"id":10}},"title":"Manipulating wild and tamed phytobiomes: Challenges and opportunities","docAbstract":"This white paper presents a series of perspectives on current and future phytobiome management, discussed at the Wild and Tamed Phytobiomes Symposium in University Park, PA, USA, in June 2018. To enhance plant productivity and health, and to translate lab- and greenhouse-based phytobiome research to field applications, the academic community and end-users need to address a variety of scientific, practical, and social challenges. Prior discussion of phytobiomes has focused heavily on plant-associated bacterial and fungal assemblages, but the phytobiomes concept covers all factors that influence plant function. Here we discuss various management considerations, including abiotic conditions (e.g. soil, nutrient applications), microorganisms (e.g. bacterial and fungal assemblages, bacterial and fungal inoculants, viruses), macroorganisms (e.g. arthropods, plant genetics), and societal factors (e.g. communication approaches, technology diffusion). An important near-term goal for this field should be to estimate the potential relative contribution of different components of the phytobiome to plant health, as well as the potential and risk of modifying each in the near-future.
","language":"English","publisher":"The American Phytopathological Society","doi":"10.1094/PBIOMES-01-19-0006-W","usgsCitation":"Bell, T.H., Hockett, K.L., Alcala-Briseno, R.I., Barbercheck, M., Beattie, G.A., Bruns, M.A., Carlson, J.E., Chung, T., Collins, A., Emmett, B., Esker, P., Garrett, K., Glenna, L., Gugino, B.K., Jimenez-Gasco, M.D., Kinkel, L., Kovac, J., Kowalski, K., Kuldau, G., Leveau, J.H., Michalska-Smith, M.J., Myrick, J., Peter, K., Salazar, M.F., Shade, A., Stopnisek, N., Tan, X., Welty, A.T., Wickings, K., and Yergeau, E., 2019, Manipulating wild and tamed phytobiomes: Challenges and opportunities: Phytobiomes Journal, v. 3, no. 1, p. 3-21, https://doi.org/10.1094/PBIOMES-01-19-0006-W.","productDescription":"19 p.","startPage":"3","endPage":"21","ipdsId":"IP-104782","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":467813,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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University","active":true,"usgs":false}],"preferred":false,"id":759265,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alcala-Briseno, Ricardo Ivan","contributorId":214154,"corporation":false,"usgs":false,"family":"Alcala-Briseno","given":"Ricardo","email":"","middleInitial":"Ivan","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":759266,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barbercheck, Mary","contributorId":214155,"corporation":false,"usgs":false,"family":"Barbercheck","given":"Mary","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":759267,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Beattie, Gwyn A.","contributorId":214156,"corporation":false,"usgs":false,"family":"Beattie","given":"Gwyn","email":"","middleInitial":"A.","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":759268,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bruns, Mary Ann","contributorId":214157,"corporation":false,"usgs":false,"family":"Bruns","given":"Mary","email":"","middleInitial":"Ann","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":759269,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Carlson, John E.","contributorId":214158,"corporation":false,"usgs":false,"family":"Carlson","given":"John","email":"","middleInitial":"E.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":759270,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Chung, Taejung","contributorId":214159,"corporation":false,"usgs":false,"family":"Chung","given":"Taejung","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State 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University","active":true,"usgs":false}],"preferred":false,"id":759274,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Garrett, Karen","contributorId":214195,"corporation":false,"usgs":false,"family":"Garrett","given":"Karen","email":"","affiliations":[],"preferred":false,"id":759325,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Glenna, Leland","contributorId":214164,"corporation":false,"usgs":false,"family":"Glenna","given":"Leland","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":759275,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Gugino, Beth K.","contributorId":214165,"corporation":false,"usgs":false,"family":"Gugino","given":"Beth","email":"","middleInitial":"K.","affiliations":[{"id":7260,"text":"Pennsylvania State 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J.","affiliations":[{"id":36629,"text":"University of California","active":true,"usgs":false}],"preferred":false,"id":759281,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Michalska-Smith, Matthew J.","contributorId":214171,"corporation":false,"usgs":false,"family":"Michalska-Smith","given":"Matthew","email":"","middleInitial":"J.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":759282,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Myrick, Jessica","contributorId":214172,"corporation":false,"usgs":false,"family":"Myrick","given":"Jessica","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":759283,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Peter, Kari","contributorId":214173,"corporation":false,"usgs":false,"family":"Peter","given":"Kari","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":759284,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Salazar, Maria Fernanda Vivanco","contributorId":214174,"corporation":false,"usgs":false,"family":"Salazar","given":"Maria","email":"","middleInitial":"Fernanda Vivanco","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":759285,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Shade, Ashley","contributorId":214175,"corporation":false,"usgs":false,"family":"Shade","given":"Ashley","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":759286,"contributorType":{"id":1,"text":"Authors"},"rank":26},{"text":"Stopnisek, Nejc","contributorId":214176,"corporation":false,"usgs":false,"family":"Stopnisek","given":"Nejc","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":759287,"contributorType":{"id":1,"text":"Authors"},"rank":27},{"text":"Tan, Xiaoquing","contributorId":214177,"corporation":false,"usgs":false,"family":"Tan","given":"Xiaoquing","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":759288,"contributorType":{"id":1,"text":"Authors"},"rank":28},{"text":"Welty, Amy T.","contributorId":214178,"corporation":false,"usgs":false,"family":"Welty","given":"Amy","email":"","middleInitial":"T.","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":759289,"contributorType":{"id":1,"text":"Authors"},"rank":29},{"text":"Wickings, Kyle","contributorId":214179,"corporation":false,"usgs":false,"family":"Wickings","given":"Kyle","email":"","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":759290,"contributorType":{"id":1,"text":"Authors"},"rank":30},{"text":"Yergeau, Etienne","contributorId":214180,"corporation":false,"usgs":false,"family":"Yergeau","given":"Etienne","email":"","affiliations":[{"id":25321,"text":"Institut National de la Recherche Scientifique","active":true,"usgs":false}],"preferred":false,"id":759291,"contributorType":{"id":1,"text":"Authors"},"rank":30}]}} ,{"id":70202629,"text":"70202629 - 2019 - Detrital K-feldspar Pb isotopic evaluation of extraregional sediment transported through an Eocene tectonic breach of southern California's Cretaceous batholith","interactions":[],"lastModifiedDate":"2019-03-14T16:30:35","indexId":"70202629","displayToPublicDate":"2019-03-14T16:30:31","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1427,"text":"Earth and Planetary Science Letters","active":true,"publicationSubtype":{"id":10}},"title":"Detrital K-feldspar Pb isotopic evaluation of extraregional sediment transported through an Eocene tectonic breach of southern California's Cretaceous batholith","docAbstract":"Sedimentary provenance studies have come to be overwhelmingly based upon U–Pb geochronologic measurements performed with detrital zircon while alternative and potentially complementary approaches such as conglomerate clast studies and heavy mineral analysis have faded in importance. Measurement of Pb isotopic compositions in detrital K-feldspar is among the under-utilized approaches available to ascertain sedimentary source regions. While it has been long recognized that common Pb isotope compositions recorded by K-feldspar vary widely and reflect the crustal provinces from which the host basement rocks crystallized, use of the approach has suffered due to a lack of appropriate statistical models and ground truth compositional data from source regions. In this paper, we: (1) present high-throughput LA-ICPMS analysis protocols needed to generate statistically meaningful detrital K-feldspar Pb isotope data sets; (2) develop an interpretative approach based upon 208Pb/206Pb vs. 207Pb/206Pb that incorporate information from the U- and Th-decay systems into one two-dimensional plot that is amenable to analysis using two-dimensional Kolmogorov–Smirnoff statistical tests; (3) generate new Pb isotopic data from basement rocks from southwestern North America to improve knowledge of the Pb isotopic properties of potential source regions; and (4) generate new Pb isotopic data from Lower Eocene to Lower Miocene sedimentary rocks to evaluate changes in drainage patterns that occurred in response to deformation that affected the southern California margin. Through this case study, we demonstrate how our new analytical and interpretative methods could be profitably applied to future geochemical and provenance studies and tectonically driven re-organization of drainage patterns.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2018.11.040","usgsCitation":"Shulaker, D.Z., Grove, M., Hourigan, J.K., Van Buer, N., Sharman, G.R., Howard, K.A., Miller, J., and Barth, A.P., 2019, Detrital K-feldspar Pb isotopic evaluation of extraregional sediment transported through an Eocene tectonic breach of southern California's Cretaceous batholith: Earth and Planetary Science Letters, v. 508, p. 4-17, https://doi.org/10.1016/j.epsl.2018.11.040.","productDescription":"14 p.","startPage":"4","endPage":"17","ipdsId":"IP-103612","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":362078,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"508","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Shulaker, Danielle Ziva","contributorId":214181,"corporation":false,"usgs":false,"family":"Shulaker","given":"Danielle","email":"","middleInitial":"Ziva","affiliations":[{"id":38987,"text":"Stanford U.","active":true,"usgs":false}],"preferred":false,"id":759295,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grove, Marty","contributorId":211570,"corporation":false,"usgs":false,"family":"Grove","given":"Marty","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":759296,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hourigan, Jeremy K.","contributorId":99023,"corporation":false,"usgs":true,"family":"Hourigan","given":"Jeremy","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":759297,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Van Buer, Nicholas","contributorId":214183,"corporation":false,"usgs":false,"family":"Van Buer","given":"Nicholas","email":"","affiliations":[{"id":38988,"text":"Cal State Poly Pomona","active":true,"usgs":false}],"preferred":false,"id":759298,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sharman, Glenn R.","contributorId":196537,"corporation":false,"usgs":false,"family":"Sharman","given":"Glenn","email":"","middleInitial":"R.","affiliations":[{"id":34621,"text":"Bureau of Economic Geology, Jackson School of Geosciences, The University of Texas at Austin, Austin, TX, USA","active":true,"usgs":false}],"preferred":false,"id":759299,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Howard, Keith A. 0000-0002-6462-2947 khoward@usgs.gov","orcid":"https://orcid.org/0000-0002-6462-2947","contributorId":3439,"corporation":false,"usgs":true,"family":"Howard","given":"Keith","email":"khoward@usgs.gov","middleInitial":"A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":759294,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Miller, Jonathan","contributorId":214184,"corporation":false,"usgs":false,"family":"Miller","given":"Jonathan","affiliations":[{"id":38989,"text":"San Jose State U.","active":true,"usgs":false}],"preferred":false,"id":759300,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Barth, Andrew P.","contributorId":214136,"corporation":false,"usgs":false,"family":"Barth","given":"Andrew","email":"","middleInitial":"P.","affiliations":[{"id":38983,"text":"Indiana University - Purdue University","active":true,"usgs":false}],"preferred":false,"id":759301,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70202630,"text":"70202630 - 2019 - Modeling elk‐to‐livestock transmission risk to predict hotspots of brucellosis spillover","interactions":[],"lastModifiedDate":"2019-06-18T10:54:15","indexId":"70202630","displayToPublicDate":"2019-03-14T16:27:15","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Modeling elk‐to‐livestock transmission risk to predict hotspots of brucellosis spillover","docAbstract":"Wildlife reservoirs of infectious disease are a major source of human‐wildlife conflict because of the risk of potential spillover associated with commingling of wildlife and livestock. In the Greater Yellowstone Ecosystem, the presence of brucellosis (Brucella abortus) in free‐ranging elk (Cervus canadensis) populations is of significant management concern because of the risk of disease transmission from elk to livestock. We identified how spillover risk changes through space and time by developing resource selection functions using telemetry data from 223 female elk to predict the relative probability of female elk occurrence daily during the transmission risk period. We combined these spatiotemporal predictions with elk seroprevalence, demography, and transmission timing data to identify when and where abortions (the primary transmission route of brucellosis) were most likely to occur. Additionally, we integrated our predictions of transmission risk with spatiotemporal data on areas of potential livestock use to estimate the daily risk to livestock. We predicted that approximately half of the transmission risk occurred on areas where livestock may be present (i.e., private property or grazing allotments). Of the transmission risk that occurred in livestock areas, 98% of it was on private ranchlands as opposed to state or federal grazing allotments. Disease prevalence, transmission timing, host abundance, and host distribution were all important factors in determining the potential for spillover risk. Our fine‐resolution (250‐m spatial, 1‐day temporal), large‐scale (17,732 km2) predictions of potential elk‐to‐livestock transmission risk provide wildlife and livestock managers with a useful tool to identify higher risk areas in space and time and proactively focus actions in these areas to separate elk and livestock to reduce spillover risk.
","language":"English","publisher":"Wiley","doi":"10.1002/jwmg.21645","usgsCitation":"Rayl, N., Proffitt, K., Almberg, E.S., Jones, J.D., Merkle, J., Gude, J., and Cross, P.C., 2019, Modeling elk‐to‐livestock transmission risk to predict hotspots of brucellosis spillover: Journal of Wildlife Management, v. 83, no. 4, p. 817-829, https://doi.org/10.1002/jwmg.21645.","productDescription":"13 p.","startPage":"817","endPage":"829","ipdsId":"IP-100336","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":467814,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.21645","text":"Publisher Index Page"},{"id":362077,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","volume":"83","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-03-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Rayl, Nathaniel D.","contributorId":199082,"corporation":false,"usgs":false,"family":"Rayl","given":"Nathaniel D.","affiliations":[],"preferred":false,"id":759303,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Proffitt, Kelly 0000-0001-5528-3309","orcid":"https://orcid.org/0000-0001-5528-3309","contributorId":210093,"corporation":false,"usgs":false,"family":"Proffitt","given":"Kelly","email":"","affiliations":[{"id":38065,"text":"Montana Fish, Wildlife and Parks, Bozeman, Montana","active":true,"usgs":false}],"preferred":false,"id":759305,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Almberg, Emily S.","contributorId":207014,"corporation":false,"usgs":false,"family":"Almberg","given":"Emily","email":"","middleInitial":"S.","affiliations":[{"id":37431,"text":"Montana Fish, Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":759304,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jones, Jennifer D.","contributorId":145754,"corporation":false,"usgs":false,"family":"Jones","given":"Jennifer","email":"","middleInitial":"D.","affiliations":[{"id":16227,"text":"Institute on Ecosystems,Montana State University MT, 59715 USA","active":true,"usgs":false}],"preferred":false,"id":759308,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Merkle, Jerod","contributorId":172972,"corporation":false,"usgs":false,"family":"Merkle","given":"Jerod","affiliations":[{"id":35288,"text":"Wyoming Cooperative Fish and Wildlife Research Unit, University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":759306,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gude, Justin A.","contributorId":210094,"corporation":false,"usgs":false,"family":"Gude","given":"Justin A.","affiliations":[{"id":38066,"text":"Montana Fish, Wildlife and Parks,","active":true,"usgs":false}],"preferred":false,"id":759307,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cross, Paul C. 0000-0001-8045-5213 pcross@usgs.gov","orcid":"https://orcid.org/0000-0001-8045-5213","contributorId":2709,"corporation":false,"usgs":true,"family":"Cross","given":"Paul","email":"pcross@usgs.gov","middleInitial":"C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":759302,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70202298,"text":"sir20195003 - 2019 - Climate, streamflow, and lake-level trends in the Great Lakes Basin of the United States and Canada, water years 1960–2015","interactions":[],"lastModifiedDate":"2019-03-15T16:14:59","indexId":"sir20195003","displayToPublicDate":"2019-03-14T16:15:18","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":"2019-5003","displayTitle":"Climate, Streamflow, and Lake-Level Trends in the Great Lakes Basin of the United States and Canada, Water Years 1960–2015","title":"Climate, streamflow, and lake-level trends in the Great Lakes Basin of the United States and Canada, water years 1960–2015","docAbstract":"Water levels in the Great Lakes fluctuate substantially because of complex interactions among inputs (precipitation and streamflow), outputs (evaporation and outflow), and other factors. This report by the U.S. Geological Survey in cooperation with the Great Lakes Restoration Initiative was completed to describe trends in climate, streamflow, lake levels, and major water-budget components within the Great Lakes Basin for water years (WYs) 1960–2015 (study period). Resulting trends are applicable only to the study period and should not be considered indicative of longer-term trends.
Analyses of climate trends used monthly data from the Parameter-elevation Regressions on Independent Slopes Model, which are available only for the United States. Trend tests were completed for annual and seasonal time series of monthly means for total precipitation, daily minimum air temperature (Tmin), and daily maximum air temperature (Tmax). Statistical significance for all time-trend tests (climate, streamflow, and lake levels) was determined using the Mann‑Kendall test for probability values less than or equal to 0.10. Trend analyses were completed without adjustments for serial correlation; however, a modified Mann-Kendall test was subsequently used to examine potential effects of short-term persistence in time-series data. Effects of short-term persistence were considered inconsequential for climate data and minor for streamflow data; however, the presence of short-term persistence in water-budget components had more substantial effects on trend analyses.
Spatial distributions of trends in climatic data for WYs 1960–2015 for the U.S. part of the Great Lakes Basin (land only) indicate (1) generally ubiquitous upward trends in Tmin and (2) a sharp transition from neutral or downward trends in precipitation northwest of Lake Michigan to generally upward trends east of Lake Michigan. Trends in Tmax were not statistically significant. Analyses of annual climatic data aggregated for the U.S. land part of the Great Lakes Basin indicated statistically significant upward trends for precipitation and Tmin, and similar statistically significant trends existed for all the individual lake subbasins except Lake Superior.
Of 103 U.S. Geological Survey streamgages analyzed for streamflow trends, 71 had significant annual trends (54 upward and 17 downward). Downward trends in annual streamflow are concentrated northwest of Lake Michigan (16 streamgages), and upward trends are concentrated east of Lake Michigan (53 streamgages). Of the 71 streamgages with significant annual trends, 70 had at least one season with a significant trend that matched the annual trend direction.
Of 35 Environment and Climate Change Canada streamgages analyzed, 22 had significant upward trends in annual streamflow, and all but 1 of these 22 had at least one season with a significant upward trend. None of the Environment and Climate Change Canada streamgages had significant downward annual trends, and only one had a significant downward seasonal trend.
Trends in lake levels and several major water-budget components affecting lake levels were analyzed for the study period. Significant downward trends in lake level and outflow for Lake Superior are driven primarily by low lake levels and outflows during WYs 1998–2014. A significant downward trend in runoff from the contributing drainage area also is indicated, which is consistent with numerous streamgages northwest of Lake Michigan with significant downward trends in annual streamflow. A significant upward trend in annual overlake evaporation also is indicated, which is consistent with the spatially distributed upward trends in annual Tmin.
The sum of overlake precipitation and runoff from the contributing drainage area for each of the Great Lakes, less overlake evaporation, composes a variable called net basin supply (NBS). A significant downward trend in NBS is indicated for Lake Superior, which is consistent with significant trends for individual components of runoff (downward) and evaporation (upward) that contributed to a significant downward trend for lake outflow. Statistically significant upward trends in NBS for Lake Saint Clair and Lake Ontario offset the downward trend for Lake Superior and combine with nonsignificant upward trends in NBS for Lakes Michigan and Huron and Lake Erie to produce a neutral trend in NBS for the basin.
A predictable pattern in monthly mean lake levels is noted for Lake Superior, with the minimum for each year usually during or near March and the maximum commonly during or near September or October. When an October lake level is in a period of substantial decline, potential for an ensuing short-term period of below-mean lake levels is enhanced. Downstream from Lake Superior, monthly lake levels have sawtooth patterns that somewhat resemble those for Lake Superior but with decreased predictability in timing.
Similar to Lake Superior, Lakes Michigan and Huron, Lake Saint Clair, and Lake Erie all have a prolonged period of low lake levels around WYs 1998–2014; however, a significant downward trend is indicated only for Lakes Michigan and Huron. All these lakes also have a period of low lake levels before about WY 1968, when minimum lake levels were lower than during WYs 1998–2014. The significant downward trend of outflow from Lake Superior is carried downstream into Lakes Michigan and Huron; however, trends in outflow from the next three lakes downstream (Lakes Saint Clair, Erie, and Ontario) are offset by increased precipitation and runoff and are not significant.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195003","collaboration":"Prepared in cooperation with the Great Lakes Restoration Initiative","usgsCitation":"Norton, P.A., Driscoll, D.G., and Carter, J.M., 2019, Climate, streamflow, and lake-level trends in the Great Lakes Basin of the United States and Canada, water years 1960–2015: Scientific Investigations Report 2019–5003, 47 p., https://doi.org/10.3133/sir20195003.","productDescription":"Report: vi, 47 p.; Appendix Figures; Appendix Tables: 5","numberOfPages":"58","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-089551","costCenters":[{"id":5068,"text":"Midwest Regional Director's Office","active":true,"usgs":true}],"links":[{"id":362031,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5003/coverthb.jpg"},{"id":362032,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5003/sir20195003.pdf","text":"Report","size":"22.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5003"},{"id":362033,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2019/5003/sir20195003_appendix_figs_1.1_to_1.103.pdf","text":"Appendix figures 1.1–1.103","size":"940 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5003"},{"id":362034,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2019/5003/sir20195003_appendix_figs_1.104_to_1.138.pdf","text":"Appendix figures 1.104–1.138","size":"333 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5003"},{"id":362035,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2019/5003/sir20195003_appendix_tables_1.1_to_1.5.xlsx","text":"Appendix tables 1.1–1.5","size":"132 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2019–5003"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.4716796875,\n 41.44272637767212\n ],\n [\n -75.7177734375,\n 41.44272637767212\n ],\n [\n -75.7177734375,\n 50.035973672195496\n ],\n [\n -93.4716796875,\n 50.035973672195496\n ],\n [\n -93.4716796875,\n 41.44272637767212\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702
Measurements of the stable isotope ratios of nitrogen (15N/14N) and oxygen (18O/16O) in nitrate (NO3–) enable identification of sources, dispersal, and fate of natural and contaminant NO3– in aquatic environments. The 18O/16O of NO3– produced by nitrification is often assumed to reflect the proportional contribution of oxygen atom sources, water, and molecular oxygen, in a 2:1 ratio. Culture and seawater incubations, however, indicate oxygen isotopic equilibration between nitrite (NO2–) and water, and kinetic isotope effects for oxygen atom incorporation, which modulate the NO3– 18O/16O produced during nitrification. To investigate the influence of kinetic and equilibrium effects on the isotopic composition of NO3– produced from the nitrification of ammonia (NH3), we incubated streamwater supplemented with ammonium (NH4+) and increments of 18O-enriched water. Resulting NO3– 18O/16O ratios showed (1) a disproportionate sensitivity to the 18O/16O ratio of water, mediated by isotopic equilibration between water and NO2–, as well as (2) kinetic isotope discrimination during O atom incorporation from molecular oxygen and water. Empirically, the NO3– 18O/16O ratios thus produced fortuitously converge near the 18O/16O ratio of water. More elevated NO3– 18O/16O values commonly reported in soils and oxic groundwater may thus derive from processes additional to nitrification, including NO3– reduction.
","language":"English","publisher":"ACS","doi":"10.1021/acs.est.8b03386","usgsCitation":"Boshers, D.S., Granger, J., Tobias, C.R., Bohlke, J., and Smith, R.L., 2019, Constraining the oxygen isotopic composition of nitrate produced by nitrification: Environmental Science & Technology, v. 53, no. 3, p. 1206-1216, https://doi.org/10.1021/acs.est.8b03386.","productDescription":"11 p.","startPage":"1206","endPage":"1216","ipdsId":"IP-099166","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":362074,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"53","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-01-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Boshers, Danielle S.","contributorId":214193,"corporation":false,"usgs":false,"family":"Boshers","given":"Danielle","email":"","middleInitial":"S.","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":759321,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Granger, Julie","contributorId":214194,"corporation":false,"usgs":false,"family":"Granger","given":"Julie","email":"","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":759322,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tobias, Craig R.","contributorId":191283,"corporation":false,"usgs":false,"family":"Tobias","given":"Craig","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":759323,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bohlke, J.K. 0000-0001-5693-6455 jkbohlke@usgs.gov","orcid":"https://orcid.org/0000-0001-5693-6455","contributorId":191103,"corporation":false,"usgs":true,"family":"Bohlke","given":"J.K.","email":"jkbohlke@usgs.gov","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":759320,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, Richard L. 0000-0002-3829-0125 rlsmith@usgs.gov","orcid":"https://orcid.org/0000-0002-3829-0125","contributorId":1592,"corporation":false,"usgs":true,"family":"Smith","given":"Richard","email":"rlsmith@usgs.gov","middleInitial":"L.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":38175,"text":"Toxics Substances Hydrology Program","active":true,"usgs":true}],"preferred":true,"id":759324,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70202372,"text":"ds1106 - 2019 - Groundwater and surface-water data collection for Mason County, western Washington, 2016–18","interactions":[],"lastModifiedDate":"2019-03-15T13:42:19","indexId":"ds1106","displayToPublicDate":"2019-03-14T10:55:37","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1106","displayTitle":"Groundwater and Surface-Water Data Collection for Mason County, Western Washington, 2016–18","title":"Groundwater and surface-water data collection for Mason County, western Washington, 2016–18","docAbstract":"Groundwater levels and surface water flow measurements were collected from August 2016 to September 2018 to provide the Mason Conservation District and other stakeholders with basic knowledge of existing water resources in Mason County, Washington. Additionally, the data were collected with the intent of contributing to informed decision making about groundwater use, management, and conservation throughout the county and for future inclusion in a groundwater model. Data were collected and compiled for 130 sites—110 wells and 20 miscellaneous surface-water discharge sites. In the spring of 2016, field reconnaissance was conducted to locate suitable locations for baseflow discharge measurements to be used for estimating groundwater contribution to surface flow. In the summer of 2016, a field inventory of wells was conducted to acquire locational data and to assess the suitability of the wells for inclusion in a monthly groundwater-level monitoring network. Groundwater levels were measured bimonthly in the 64 wells over 2 years. Streamflow measurements were conducted two times each summer during two summers for each of the 20 surface water sites.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1106","collaboration":"Prepared in cooperation with the Mason Conservation District","usgsCitation":"Tecca, A.E., and Frans, L.M., 2019, Groundwater and surface-water data collection for Mason County, western Washington, 2016–18: U.S. Geological Survey Data Series 1106, 26 p., https://doi.org/10.3133/ds1106.","productDescription":"v, 26 p.","numberOfPages":"36","onlineOnly":"Y","ipdsId":"IP-102744","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":362071,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1106/coverthb.jpg"},{"id":362072,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1106/ds1106.pdf","text":"Report","size":"4.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1106"}],"country":"United States","state":"Washington","county":"Mason County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.51379394531249,\n 46.97556750833867\n ],\n [\n -122.61016845703124,\n 46.97556750833867\n ],\n [\n -122.61016845703124,\n 47.66261271615866\n ],\n [\n -123.51379394531249,\n 47.66261271615866\n ],\n [\n -123.51379394531249,\n 46.97556750833867\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
Climate change can have strong effects on aquatic ecosystems, including disrupting nutrient cycling and mediating processes that affect primary production. Past studies have been conducted mostly on individual or small groups of ecosystems, making it challenging to predict how future climate change will affect water quality at broad scales. We used a subcontinental-scale database to address three objectives: (1) identify which climate metrics best predict lake water quality, (2) examine whether climate influences different nutrient and productivity measures similarly, and (3) quantify the potential effects of a changing climate on lakes. We used climate data to predict lake water quality in ~11,000 north temperate lakes across 17 U.S. states. We developed a novel machine learning method that jointly models different measures of water quality using 48 climate metrics and accounts for properties inherent in macroscale data (e.g., spatial autocorrelation). Our results suggest that climate metrics related to winter precipitation and summer temperature were strong predictors of lake nutrients and productivity. However, we found variation in the magnitude and direction of the relationship between climate and water quality. We predict that a likely future climate change scenario of warmer summer temperatures will lead to increased nutrient concentrations and algal biomass across lakes (median ~3%–9% increase), whereas increased winter precipitation will have highly variable effects. Our results emphasize the importance of heterogeneity in the response of individual ecosystems to climate and are a caution to extrapolating relationships across space.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2018WR023088","usgsCitation":"Collins, S.M., Yuan, S., Tan, P.N., Oliver, S.K., Lapierre, J.F., Cheruvelil, K., Fergus, C., Skaff, N.K., Stachelek, J., Wagner, T., and Soranno, P., 2019, Winter precipitation and summer temperature predict lake water quality at macroscales: Water Resources Research, v. 55, no. 4, p. 2708-2721, https://doi.org/10.1029/2018WR023088.","productDescription":"14 p.","startPage":"2708","endPage":"2721","ipdsId":"IP-095436","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":395273,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"55","issue":"4","noUsgsAuthors":false,"publicationDate":"2019-04-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Collins, S. M.","contributorId":273184,"corporation":false,"usgs":false,"family":"Collins","given":"S.","email":"","middleInitial":"M.","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":832670,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yuan, S.","contributorId":273185,"corporation":false,"usgs":false,"family":"Yuan","given":"S.","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":832671,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tan, P. N.","contributorId":273186,"corporation":false,"usgs":false,"family":"Tan","given":"P.","email":"","middleInitial":"N.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":832672,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Oliver, S. K.","contributorId":273187,"corporation":false,"usgs":false,"family":"Oliver","given":"S.","email":"","middleInitial":"K.","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":832673,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lapierre, J. F.","contributorId":273188,"corporation":false,"usgs":false,"family":"Lapierre","given":"J.","email":"","middleInitial":"F.","affiliations":[{"id":41192,"text":"Université de Montreal","active":true,"usgs":false}],"preferred":false,"id":832674,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cheruvelil, K. S.","contributorId":273189,"corporation":false,"usgs":false,"family":"Cheruvelil","given":"K. S.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":832675,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fergus, C. E.","contributorId":273190,"corporation":false,"usgs":false,"family":"Fergus","given":"C. E.","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":832676,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Skaff, N. K.","contributorId":273191,"corporation":false,"usgs":false,"family":"Skaff","given":"N.","email":"","middleInitial":"K.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":832677,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Stachelek, J.","contributorId":273193,"corporation":false,"usgs":false,"family":"Stachelek","given":"J.","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":832678,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":832679,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Soranno, P. A.","contributorId":273195,"corporation":false,"usgs":false,"family":"Soranno","given":"P. A.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":832680,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70202411,"text":"sir20195009 - 2019 - Flood-inundation maps for the Yellow River from River Drive to Centerville Highway, Gwinnett County, Georgia","interactions":[],"lastModifiedDate":"2019-03-13T16:10:00","indexId":"sir20195009","displayToPublicDate":"2019-03-13T09:00:00","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":"2019-5009","displayTitle":"Flood-Inundation Maps for the Yellow River from River Drive to Centerville Highway, Gwinnett County, Georgia","title":"Flood-inundation maps for the Yellow River from River Drive to Centerville Highway, Gwinnett County, Georgia","docAbstract":"Digital flood-inundation maps for a 16.4-mile reach of the Yellow River in Gwinnett County, Georgia, from 0.5 mile upstream from River Drive to Centerville Highway (Georgia State Route 124) were developed to depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at two U.S. Geological Survey (USGS) streamgages in the mapped area. The maps for the 9.0-mile reach from 0.5 mile upstream from River Drive to Stone Mountain Highway (U.S. Route 78) are referenced to the streamgage Yellow River near Snellville, Ga. (station 02206500), and the maps for the 7.4-mile reach from Stone Mountain Highway to Centerville Highway are referenced to the streamgage Yellow River at Ga. 124, near Lithonia, Ga. (02207120). Real-time stage information from these streamgages can be used with these maps to estimate near real-time areas of inundation. The forecasted peak-stage information for the USGS streamgages Yellow River near Snellville, Ga. (02206500), and Yellow River at Ga. 124, near Lithonia, Ga. (02207120), can be used in conjunction with the maps developed for this study to show predicted areas of flood inundation.
A one-dimensional step-backwater model was developed using the U.S. Army Corps of Engineers Hydrologic Engineering Center's River Analysis System (HEC–RAS) software for the Yellow River and was used to compute flood profiles for a 16.4-mile reach of the Yellow River. The hydraulic model was then used to simulate 16 water-surface profiles at 1.0-foot (ft) intervals at the Yellow River near Snellville streamgage and 17 water-surface profiles at 1.0-ft intervals at the Yellow River near Lithonia streamgage. At the Yellow River near Snellville streamgage, the profiles ranged from a stage of 18.0 ft, which is 819.1 ft above the North American Vertical Datum of 1988 (NAVD 88), to a stage of 33.0 ft, which is 834.1 ft above NAVD 88. At the Yellow River near Lithonia streamgage, the profiles ranged from the National Weather Service action stage of 13.0 ft, which is 732.5 ft above NAVD 88, to a stage of 29.0 ft, which is 748.5 ft above NAVD 88. The simulated water-surface profiles were then combined with a geographic information system digital elevation model—derived from light detection and ranging (lidar) data having a 5.0-ft horizontal resolution—to delineate the area flooded at each 1.0-ft interval of stream stage for both streamgages.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195009","collaboration":"Prepared in cooperation with Gwinnett County, Georgia","usgsCitation":"Musser, J.W., 2019, Flood-inundation maps for the Yellow River from River Drive to Centerville Highway, Gwinnett County, Georgia: U.S. Geological Survey Scientific Investigations Report 2019–5009, 15 p., https://doi.org/10.3133/sir20195009.","productDescription":"Report: vi, 15 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-100804","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":361975,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KKB3H2","text":"USGS data release","description":"USGS data release","linkHelpText":"Flood inundation and flood depth for the Yellow River in Gwinnett County, Georgia based on water-surface elevation at the U.S. Geological Survey streamgages Yellow River, near Snellville, Georgia (02206500) and Yellow River at Ga. 124, near Lithonia, Georgia (02207120)"},{"id":361973,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5009/coverthb.jpg"},{"id":361974,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5009/sir20195009.pdf","text":"Report","size":"1.88 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5009"}],"country":"United States","state":"Georgia","county":"Gwinnett County","otherGeospatial":"Yellow River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.1667,\n 33.75\n ],\n [\n -84,\n 33.75\n ],\n [\n -84,\n 33.9167\n ],\n [\n -84.1667,\n 33.9167\n ],\n [\n -84.1667,\n 33.75\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Columbia, SC 29210
Glacial−interglacial variations in CO2 and methane in polar ice cores have been attributed, in part, to changes in global wetland extent, but the wetland distribution before the Last Glacial Maximum (LGM, 21 ka to 18 ka) remains virtually unknown. We present a study of global peatland extent and carbon (C) stocks through the last glacial cycle (130 ka to present) using a newly compiled database of 1,063 detailed stratigraphic records of peat deposits buried by mineral sediments, as well as a global peatland model. Quantitative agreement between modeling and observations shows extensive peat accumulation before the LGM in northern latitudes (>40°N), particularly during warmer periods including the last interglacial (130 ka to 116 ka, MIS 5e) and the interstadial (57 ka to 29 ka, MIS 3). During cooling periods of glacial advance and permafrost formation, the burial of northern peatlands by glaciers and mineral sediments decreased active peatland extent, thickness, and modeled C stocks by 70 to 90% from warmer times. Tropical peatland extent and C stocks show little temporal variation throughout the study period. While the increased burial of northern peats was correlated with cooling periods, the burial of tropical peat was predominately driven by changes in sea level and regional hydrology. Peat burial by mineral sediments represents a mechanism for long-term terrestrial C storage in the Earth system. These results show that northern peatlands accumulate significant C stocks during warmer times, indicating their potential for C sequestration during the warming Anthropocene.
","language":"English","publisher":"PNAS","doi":"10.1073/pnas.1813305116","usgsCitation":"Treat, C.C., Kleinen, T., Broothaerts , N., Dalton, A.S., Dommain, R., Douglas, T.A., Drexler, J.Z., Finkelstein, S., Grosse, G., Hope, G., Hutchings, J., Jones, M.C., Kuhry, P., Lacourse, T., Lahteenoja, O., Loisel, J., Notebaert, B., Payne, R., Peteet, D.M., Sannel, A.B., Stelling, J.M., Strauss, J., Swindles, G.T., Talbot, J., Tarnocai, C., Verstraeten, G., Williams , C., Xia, Z., Yu, Z., Valiranta, M., Hattestrand, M., Alexanderson, H., and Brovkin, V., 2019, Widespread global peatland establishment and persistence over the last 130,000 y: PNAS, v. 116, no. 11, p. 4822-4827, https://doi.org/10.1073/pnas.1813305116.","productDescription":"6 p.","startPage":"4822","endPage":"4827","ipdsId":"IP-091584","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":467822,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1073/pnas.1813305116","text":"Publisher Index Page"},{"id":373582,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"116","issue":"11","noUsgsAuthors":false,"publicationDate":"2019-02-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Treat, Claire C.","contributorId":96606,"corporation":false,"usgs":true,"family":"Treat","given":"Claire","email":"","middleInitial":"C.","affiliations":[{"id":25501,"text":"University of Eastern Finland","active":true,"usgs":false}],"preferred":false,"id":785865,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kleinen, Thomas 0000-0001-9550-5164","orcid":"https://orcid.org/0000-0001-9550-5164","contributorId":50427,"corporation":false,"usgs":true,"family":"Kleinen","given":"Thomas","email":"","affiliations":[{"id":32387,"text":"Max Planck Institute for Meteorology, Hamburg, Germany","active":true,"usgs":false}],"preferred":false,"id":785866,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Broothaerts , Nils ","contributorId":223665,"corporation":false,"usgs":true,"family":"Broothaerts ","given":"Nils ","affiliations":[],"preferred":false,"id":785867,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dalton, April S.","contributorId":223666,"corporation":false,"usgs":true,"family":"Dalton","given":"April","email":"","middleInitial":"S.","affiliations":[{"id":13300,"text":"3Department of Earth Sciences, University of Toronto, Toronto, Ontario M5S 3B1, Canada.","active":true,"usgs":false}],"preferred":false,"id":785868,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dommain, Rene","contributorId":220666,"corporation":false,"usgs":false,"family":"Dommain","given":"Rene","email":"","affiliations":[{"id":40220,"text":"University of Potsdam, Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":785869,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Douglas, Thomas A. 0000-0003-1314-1905","orcid":"https://orcid.org/0000-0003-1314-1905","contributorId":64553,"corporation":false,"usgs":false,"family":"Douglas","given":"Thomas","email":"","middleInitial":"A.","affiliations":[{"id":33087,"text":"Cold Regions Research and Engineering Laboratory","active":true,"usgs":false}],"preferred":true,"id":785870,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Drexler, Judith Z. 0000-0002-0127-3866 jdrexler@usgs.gov","orcid":"https://orcid.org/0000-0002-0127-3866","contributorId":167492,"corporation":false,"usgs":true,"family":"Drexler","given":"Judith","email":"jdrexler@usgs.gov","middleInitial":"Z.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - 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In Osage County—a seismically quiet part of the state—continuous measurements of fluid pressure reveal that pressure in the reservoir is increasing by at least 5 kPa annually and sometimes at a much higher rate. Tidal analysis reveals that fluid level changes lead the local strain tides, with no apparent influence from transient permeability changes; this indicates a response that is inconsistent with flow in a radially extensive, confined reservoir. We investigate whether this is due to vertical flow to the water table, vertical flow within the Arbuckle, or local distortions from fractures. While none of these alternative models can fully explain both the observed tidal phases and amplitude ratios, the observed response to teleseismic waves supports a mechanism related to leakage rather than fracture effects. At this location fluid influx associated with wastewater disposal is offset by migration into surrounding layers, which include the Precambrian basement below. Thus, our findings suggest the need to monitor for changes in the induced seismicity hazard, while pore pressures increase in a leaky disposal reservoir.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019JB017327","usgsCitation":"Barbour, A.J., Xu, L., Roeloffs, E., and Rubinstein, J., 2019, Leakage and increasing fluid pressure detected in Oklahoma's wastewater disposal reservoir: Journal of Geophysical Research, no. 124, p. 2896-2919, https://doi.org/10.1029/2019JB017327.","productDescription":"24 p.","startPage":"2896","endPage":"2919","ipdsId":"IP-093460","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":363494,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma","county":"Osage 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Science Center","active":true,"usgs":true}],"preferred":true,"id":762077,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rubinstein, Justin 0000-0003-1274-6785","orcid":"https://orcid.org/0000-0003-1274-6785","contributorId":215341,"corporation":false,"usgs":true,"family":"Rubinstein","given":"Justin","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":762078,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70202202,"text":"sir20195004 - 2019 - Flood-inundation maps of the Meramec River from Eureka to Arnold, Missouri, 2018","interactions":[],"lastModifiedDate":"2019-10-23T09:27:27","indexId":"sir20195004","displayToPublicDate":"2019-03-11T13:38:08","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":"2019-5004","displayTitle":"Flood-Inundation Maps of the Meramec River from Eureka to Arnold, Missouri, 2018","title":"Flood-inundation maps of the Meramec River from Eureka to Arnold, Missouri, 2018","docAbstract":"Libraries of digital flood-inundation maps that spanned a combined 37.2-mile reach of the Meramec River that extended upstream from Eureka, Missouri, to downstream near the confluence of the Meramec and Mississippi Rivers were created by the U.S. Geological Survey (USGS) in cooperation with the U.S. Army Corps of Engineers, Metropolitan St. Louis Sewer District, Missouri Department of Transportation, Missouri American Water, Federal Emergency Management Agency Region 7, and the cities of Pacific, Eureka, Wildwood, and Arnold. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science website at https://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the cooperative USGS streamgages for the Meramec River near Eureka, Mo. (USGS station 07019000), the Meramec River at Valley Park, Mo. (USGS station 07019130), the Meramec River at Fenton, Mo. (USGS station 07019210), and the Meramec River at Arnold, Mo. (USGS station 07019300). Near-real-time stage data at these streamgages may be obtained from the USGS National Water Information System at https://doi.org/10.5066/F7P55KJN or the National Weather Service (NWS) Advanced Hydrologic Prediction Service at https://water.weather.gov/ahps/, which also forecasts flood hydrographs at these sites (listed as NWS sites erkm7, vllm7, fnnm7, and arnm7, respectively).
Flood profiles were computed for the stream reach by means of a calibrated one-dimensional step-backwater hydraulic model. The model was calibrated using a stage-discharge relation at the Meramec River near Eureka, Mo., streamgage (USGS station 07019000) and documented high-water marks from the flood of December 2015 through January 2016.
The calibrated hydraulic model was used to compute water-surface profiles: 1 set for the Meramec River near Eureka, Mo., streamgage (USGS station 07019000); 1 set for the Meramec River at Valley Park, Mo., streamgage (USGS station 07019130); 7 sets for the Meramec River at Fenton, Mo., streamgage (USGS station 07019210) for a range of Mississippi River conditions; and 8 sets for the Meramec River at Arnold, Mo., streamgage (USGS station 07019300) for a range of Mississippi River conditions. The water-surface profiles were produced for stages at 1-foot (ft) intervals referenced to the datum from each streamgage and ranging from the NWS action stage, or near bankfull discharge, to the stage corresponding to the estimated 0.2-percent annual exceedance probability (500-year recurrence interval) flood, as determined at the Meramec River near Eureka, Mo., streamgage (USGS station 07019000). The simulated water-surface profiles then were combined with a geographic information system digital elevation model (derived from light detection and ranging data having a 0.28-ft vertical accuracy and 3.28-ft horizontal resolution) to delineate the area flooded at each simulated 1-ft stage increment. Previously published flood-inundation maps were updated and incorporated in the flood map libraries for USGS stations 07019130 and 07019210 to complete the map sets corresponding to eight Mississippi River conditions.
The availability of these maps, along with internet information regarding current stage from the USGS streamgages and forecasted high-flow stages from the NWS, will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures and for postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195004","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Metropolitan St. Louis Sewer District, Missouri Department of Transportation, Missouri American Water, Federal Emergency Management Agency Region 7, the city of Pacific, the city of Eureka, the city of Wildwood, and the city of Arnold","usgsCitation":"Dietsch, B.J., and Strauch, K.R., 2019, Flood-inundation maps of the Meramec River from Eureka to Arnold, Missouri, 2018: U.S. Geological Survey Scientific Investigations Report 2019–5004, 12 p., https://doi.org/10.3133/sir20195004.","productDescription":"Report: vi, 12 p.; Data Release","numberOfPages":"22","onlineOnly":"Y","ipdsId":"IP-096951","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":361924,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5004/sir20195004.pdf","text":"Report","size":"1.32 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5004"},{"id":361925,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9B0XLJL","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Flood-Inundation Maps of the Meramec River from Eureka to Arnold, Missouri, 2018"},{"id":361923,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5004/coverthb.jpg"}],"country":"United States","state":"Missouri","city":"Arnold, Eureka","otherGeospatial":"Meramec River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.66398620605469,\n 38.38795699631396\n ],\n [\n -90.33405303955078,\n 38.38795699631396\n ],\n [\n -90.33405303955078,\n 38.565347844885466\n ],\n [\n -90.66398620605469,\n 38.565347844885466\n ],\n [\n -90.66398620605469,\n 38.38795699631396\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Water levels in lakes and wetlands in the central North Dakota Missouri Coteau region that were either dry or only sporadically held water since before the 1930s have been rising since the early 1990s in response to an extended wet period. The lakes have remained full since the mid-1990s, which has provided benefits to migratory waterfowl, fisheries, and wildlife. A small shift in climate conditions, either to drier or wetter conditions, can have a large effect on the lake levels of these water bodies. The North Dakota Game and Fish Department identified five lakes as candidates for sustaining long-term fisheries. The lakes are in Kidder, Stutsman, and Logan Counties, and some lakes might receive inflow from mostly freshwater aquifers, such as the Central Dakota and Streeter aquifers, and were mostly dry during the early 1990s. After about 1995, the lakes had filled up and were deep enough to sustain populations of game fish such as walleye, perch, and northern pike. Before investing in development of permanent fisheries and associated infrastructure, such as campgrounds and boat ramps, fisheries biologists needed to know if the lake levels are likely to remain high in coming decades.
The U.S. Geological Survey, in cooperation with the North Dakota Game and Fish Department, developed a water-balance model to determine the effects of precipitation, evapotranspiration, and groundwater interaction on lake volumes. The model was developed using climate input data and lake volumes for the calibration period 1992 through 2016, during which historical lake volumes could be estimated using land surface elevation data and Landsat images. Long-term (1940–2018) climate input data were used with the water-balance model to reconstruct historical lake volumes prior to the calibration period, and block-bootstrapping was used to simulate potential future climate input data and lake volumes for 2017 through 2067. The simulated future lake volumes were used to estimate the likelihood of annual lake volumes remaining consistent, increasing, or decreasing through the year 2067.
Of the five lakes, Sibley Lake was the most likely to sustain a long-term fishery for a period longer than 50 years. The simulated lake volumes for Alkaline Lake, Big Mallard Marsh, and Remmick Lake indicated the lakes have a 50-percent chance to fall below 75 percent of their 2016 volume by about 2030, 2067, and 2025, respectively. Simulation results for Marvin Miller Lake were substantially different compared to the other four lakes and indicated the lake has a 50-percent chance to fall below 75 percent of its 2016 volume prior to 2025.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195007","collaboration":"Prepared in cooperation with the North Dakota Game and Fish Department","usgsCitation":"Lundgren, R.F., York, B.C., Stroh, N.A., and Vecchia, A.V., 2019, Water-balance modeling of selected lakes for evaluating viability as long-term fisheries in Kidder, Logan, and Stutsman Counties, North Dakota: U.S. Geological Survey Scientific Investigations Report 2019–5007, 22 p., https://doi.org/10.3133/sir20195007.","productDescription":"Report: v, 22 p.; Downloads","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-102504","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":361913,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5007/coverthb.jpg"},{"id":361914,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5007/sir20195007.pdf","text":"Report","size":"1.82 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5007"},{"id":361915,"rank":3,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sir/2019/5007/downloads/","text":"Water-Balance Model R code scripts","linkFileType":{"id":6,"text":"zip"},"description":"Water-Balance Model R code scripts"}],"country":"United States","state":"North Dakota","county":"Kidder County, Logan County, Stutsman County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.1667,\n 46.5\n ],\n [\n -99.1667,\n 46.5\n ],\n [\n -99.1667,\n 47.5\n ],\n [\n -100.1667,\n 47.5\n ],\n [\n -100.1667,\n 46.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702