The use of single nucleotide polymorphism (SNP) arrays to generate large SNP datasets for comparison purposes have recently become an attractive alternative to other genotyping methods. Although most SNP arrays were originally developed for domestic organisms, they can be effectively applied to wild relatives to obtain large panels of SNPs. In this study, we tested the cross-species application of the Affymetrix 600K Chicken SNP array in five species of North American prairie grouse (Centrocercus and Tympanuchus genera). Two individuals were genotyped per species for a total of ten samples. A high proportion (91%) of the total 580 961 SNPs were genotyped in at least one individual (73–76% SNPs genotyped per species). Principal component analysis with autosomal SNPs separated the two genera, but failed to clearly distinguish species within genera. Gene ontology analysis identified a set of genes related to morphogenesis and development (including genes involved in feather development), which may be primarily responsible for large phenotypic differences between Centrocercus and Tympanuchus grouse. Our study provided evidence for successful cross-species application of the chicken SNP array in grouse which diverged ca. 37 mya from the chicken lineage. As far as we are aware, this is the first reported application of a SNP array in non-passerine birds, and it demonstrates the feasibility of using commercial SNP arrays in research on non-model bird species.
Adaptive management has been applied to problems with multiple conflicting objectives in various natural resources settings to learn how management actions affect divergent values regarding system response. Hydropower applications have only recently begun to emerge in the field, yet in the specific example reported herein, stakeholders invested in determining the best management alternatives for attainment of a suite of objectives outlined in a long-term adaptive management program below R.L. Harris Dam, a large, privately owned dam in Alabama. Stakeholders convened an objective-setting workshop to engage a governance structure and developed a decision support model to determine appropriate actions that optimized stakeholder values. The process led to implemented change in dam operation inclusive of incorporating hypothetical responses in system parameters to management. To account for the iterative loop of adaptive management, yearly monitoring of state variables that approximated many stakeholder objectives was performed from 2005 to 2016 and data collected were incorporated into the decision model. Specific analysis of fish and macroinvertebrate population responses indicated a less than satisfactory response for some stakeholders to the flow-management changes at the dam. Uncertainty regarding the best management to provide adequate hydrologic and thermal habitats for fauna and boatable days for recreationists still exists. The project led to a Federal Energy Regulatory Commission process for renewing the license to operate the dam (beginning in 2018); adaptive management could be a viable path forward to ensure stakeholder satisfaction related to new management options.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191026","collaboration":"Prepared in cooperation with the Alabama Department of Conservation and Natural Resources, Alabama Power Company, U.S. Fish and Wildlife Service, and R.L. Harris Dam Adaptive Management Stakeholders","usgsCitation":"Irwin, E.R., ed., 2019, Adaptive management of flows from R.L. Harris Dam (Tallapoosa River, Alabama)—Stakeholder process and use of biological monitoring data for decision making: U.S. Geological Survey Open-File Report 2019–1026, 93 p., https://doi.org/10.3133/ofr20191026.","productDescription":"Report: x, 93 p.; 4 Appendixes; 1 Table","numberOfPages":"108","onlineOnly":"Y","ipdsId":"IP-096592","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":363058,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1026/ofr20191026_appendix_A2.pdf","text":"Appendix A2","size":"302 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1026 Appendix A2","linkHelpText":"– Initial Bayesian Belief Network (2005), Training Cases and Learned Networks (2005–16)"},{"id":363057,"rank":2,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1026/ofr20191026_appendix_A1.pdf","text":"Appendix A1","size":"1.14 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1026 Appendix A1","linkHelpText":"– Transcripts from the Adaptive Management Workshop, April 30–May 1, 2003"},{"id":363061,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2019/1026/ofr20191026_table_C2.1.pdf","text":"Table C2.1","size":"198 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1026 Table C2.1","linkHelpText":"– Sum of total observations for each macroinvertebrate taxon at all sites, listed alphabetically by class, order, family and taxon"},{"id":363060,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1026/ofr20191026_appendix_B.pdf","text":"Appendix B","size":"296 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1026 Appendix B","linkHelpText":"– R code used to conduct metapopulation analyses"},{"id":363056,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1026/ofr20191026.pdf","text":"Report","size":"5.82 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1026"},{"id":363053,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1026/coverthb3.jpg"},{"id":363059,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1026/ofr20191026_appendix_A3.pdf","text":"Appendix A3","size":"112 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1026 Appendix A3","linkHelpText":"– Charter of the R.L. Harris Stakeholders Board"}],"country":"United States","state":"Alabama","otherGeospatial":"Tallapoosa River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.7208251953125,\n 32.93953889877841\n ],\n [\n -85.48324584960936,\n 32.93953889877841\n ],\n [\n -85.48324584960936,\n 33.6283419913718\n ],\n [\n -85.7208251953125,\n 33.6283419913718\n ],\n [\n -85.7208251953125,\n 32.93953889877841\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Alabama Cooperative Fish and Wildlife Research Unit
School of Forestry and Wildlife Sciences
Auburn University
602 Duncan Dr.
Auburn, AL 36849–5418
A binational study was initiated to update statistical equations that are used to estimate the magnitude and frequency of peak flows on streams in Manitoba and Ontario, Canada, and Minnesota that are contained within the binational Lake of the Woods–Rainy River Basin upstream from Kenora, Ontario, Canada. Hydraulic engineers use peak streamflow data to inform designs of bridges, culverts, and dams, and water managers use peak streamflow data to inform regulation and planning activities. However, long-term streamflow measurements are available at few locations along the more than 20,000 miles of stream/ditch networks within the binational Lake of the Woods–Rainy River Basin upstream from Kenora, Ontario, Canada.
Estimates of peak-flow magnitudes for 66.7-, 50-, 20-, 10-, 4-, 2-, 1-, and 0.2-percent annual exceedance probabilities equivalent to annual flood-frequency recurrence intervals of 1.5-, 2-, 5-, 10-, 25-, 50-, 100-, and 500-year recurrence intervals, respectively, are presented for 49 streamgages in Minnesota and adjacent areas in the Province of Ontario, Canada, based on data collected through water year 2013. Peak-flow frequency information was subsequently used in regression analyses to develop equations relating peak flows for selected recurrence intervals to various basin and climatic characteristics.
The study area includes 49 streamgages located in the binational Lake of the Woods–Rainy River Basin upstream from Kenora, Ontario, Canada, and is represented by southern portions of the Canadian Provinces of Manitoba (2 percent) and Ontario (56 percent) and the northern portion of the U.S. State of Minnesota (42 percent). The study area was represented by three regions that were defined in previous studies in the U.S. State of Minnesota and another in the Canadian Province of Ontario. The two Minnesota regions A and B were developed using a multiple regression method and hydrologic landscape units were used to validate regions in Minnesota. The Ontario region A was developed using a multiple regression method and standardized residuals from the 100-year recurrence intervals.
Canadian maximum instantaneous peak-flow data were converted from a calendar year to a water year (October 1 to September 30) and where the annual maximum instantaneous peak-flow value was not available in HYDAT, the Sangal method was applied to known average daily flow values to estimate an annual maximum instantaneous peak-flow value. Geographic information system software was used to calculate eight characteristics investigated as potential explanatory variables in the regression analyses.
The procedure for estimating peak-flow frequency for selected exceedance probabilities for a specific ungaged site depends on whether the site is near a streamgage on the same stream or is on an ungaged stream. For an ungaged site near a streamgage on the same stream, the drainage-area ratio method can be used. For an ungaged site on an ungaged stream, the regional regression equations developed for this study should be used.
All equations presented in this study will be incorporated into StreamStats, a web-based geographic information system tool developed by the U.S. Geological Survey. StreamStats allows users to obtain streamflow statistics, basin characteristics, and other information for user-selected locations on streams through an interactive map.
Director, Upper Midwest Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, MN 55112
Director, Central Midwest Water Science Center
U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240
The U.S. Geological Survey, the National Oceanic and Atmospheric Administration, the European Association for Remote Sensing Companies, and the European Space Agency in coordination with the GEOValue Community hosted a side event to the Group on Earth Observations Plenary on October 23–24, 2017, in Washington, D.C. The workshop, entitled “Demonstrating the Value of Earth Observations: Methods, Practical Applications and Solutions,” brought together more than 60 international experts including economists, scientists, and engineers to consider the state of the science and applications of valuing Earth observations (EO).
This 2-day workshop built upon previous activities developed under the GEOValue initiative. This workshop brought together expert analysts from multiple disciplines and backgrounds who are developing methods to identify and measure the value of information generated from the use of satellite and in-situ data. The mix of government agencies, international financial institutions, and independent consultants who participated in the workshop blended to develop a rich mix of views, approaches, and outcomes.
During the first part of the workshop, the focus was on the latest science in valuing EO. A number of methodologies were described. Approaches generally assess the societal benefits of specific actions (for example, investments in EO). Some methods focus on broad measures of economic activity (for example, gross domestic product) or methods to assess total economic value such as contingent valuation surveys. Alternatively, use-case approaches (a use case is defined as an evaluation in which one or more decisions, applications, or other uses of data, information, and information products are specifically considered) start with the specific actions and how information is used to support decision making and affect outcomes.
The second part of the meeting was focused on the use and development of value chains and decision trees. A value chain can be defined as the set of value-adding activities that one or more organizations perform in creating and distributing goods and services. In terms of EO, the value chain approach can be applied to consider societal benefits of the data and assess the value of data and data features. The EO value chain considers the geospatial data sources and the processing of the data into value added information to be incorporated into decision-support systems, leading to decision makers’ actions. To understand the value of EO, one would also need to recognize the demand side of the equation or how EO benefits users. Extending the value chain concept and incorporating tenets of Bayesian decision making, a decision tree would include one or more use cases. The value provided by the marginal increase in information could flow from one or several parts of the supply side of the value chain. The decision tree is based on the premise that information has no value if it is not used in at least one decision. By connecting the value chain and the decision tree, a framework is created that allows for conceptualizing the value of EO in its many uses. One can then apply economic techniques to monetize the marginal benefit of an outcome with information versus one without.
A third part of the meeting applied the value chain and decision-tree frameworks to five specific thematic areas, each with the focus of using information for a decision point:
During the working session, five separate groups worked to define and delineate the value chains and decision trees associated with each topic, discussing the related challenges and data needs. The outcomes were reported back to the full group. Because of the complexity of the topics, most groups first identified a network of value chains and then narrowed the scope to develop a single value chain to address their group’s topic. Although they worked separately and on different topics, the groups came to similar conclusions, concurring that the value chain and decision-tree frameworks are very effective for informing quantitative impact assessments and developing a relatable narrative to assist the public in understanding the link between EO and citizens.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191033","collaboration":"Prepared in cooperation with the National Oceanic and Atmospheric Administration, FourBridges, European Space Agency, and European Association of Remote Sensing Companies","usgsCitation":"Pearlman, F., Lawrence, C.B., Pindilli, E.J., Geppi, D., Shapiro, C.D., Grasso, M., Pearlman, J., Adkins, J., Sawyer, G., and Tassa, A., 2019, Demonstrating the value of Earth observations—Methods, practical applications, and solutions—Group on Earth Observations side event proceedings: U.S. Geological Survey Open-File Report 2019–1033, 33 p., https://doi.org/10.3133/ofr20191033.","productDescription":"vi, 33 p.","numberOfPages":"44","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-102614","costCenters":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"links":[{"id":363044,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1033/coverthb.jpg"},{"id":363045,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1033/ofr20191033.pdf","text":"Report","size":"1.22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1033"}],"contact":"Science and Decisions Center
U.S. Geological Survey
913 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
Email: gs_emeh_sdc@usgs.gov
The tropics are a region encircling the equator, delineated to the north by the Tropic of Cancer (23°26′14.0″N) and to the south by the Tropic of Capricorn (23°26′14.0″S). While we often think of the tropics as consistently warm and wet throughout the year, in reality, the tropics maintain a myriad of climates. Of the 116 Holdridge life zones (a global bioclimatic classification scheme), the tropics contain more life zones than the sum of all the planet's other geographic regions combined (Holdridge, 1967). In addition to high climatic diversity, the tropics support a wide range of parent materials, landforms, geomorphic characteristics, and soil ages, and maintain all 12 soil types of the USDA soil taxonomy system (Palm et al., 2007; Porder et al., 2007; Quesada et al., 2010; Richter and Babbar, 1991; Sanchez, 1977; Soil Survey Staff, 2006; Townsend et al., 2008). Accordingly, there is no single representative tropical ecosystem. Given the diversity of tropical biomes, this chapter will focus specifically on tropical forested ecosystems and their responses to warming because of their global importance, potential sensitivity to change, and the fact that an improved understanding of how these ecosystems may respond to warmer climate conditions is of significant importance to ecology and society. Furthermore, while generally considering all tropical forest types, emphasis in this chapter is on the humid tropics for which we have most data.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Ecosystem consequences of soil warming: Microbes, vegetation, fauna and soil biogeochemistry","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Elsevier","doi":"10.1016/B978-0-12-813493-1.00015-6","usgsCitation":"Wood, T.E., Cavaleri, M., Giardina, C.P., Khan, S., Mohan, J., Nottingham, A.T., Reed, S., and Slot, M., 2019, Soil warming effects on tropical forests with highly weathered soils, chap. 14 of Ecosystem consequences of soil warming: Microbes, vegetation, fauna and soil biogeochemistry, p. 385-439, https://doi.org/10.1016/B978-0-12-813493-1.00015-6.","productDescription":"55 p.","startPage":"385","endPage":"439","ipdsId":"IP-102016","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":389955,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wood, Tana E.","contributorId":197805,"corporation":false,"usgs":false,"family":"Wood","given":"Tana","middleInitial":"E.","affiliations":[],"preferred":false,"id":824198,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cavaleri, Molly A.","contributorId":67381,"corporation":false,"usgs":true,"family":"Cavaleri","given":"Molly A.","affiliations":[],"preferred":false,"id":824199,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Giardina, Christian P. 0000-0002-3431-5073","orcid":"https://orcid.org/0000-0002-3431-5073","contributorId":182695,"corporation":false,"usgs":false,"family":"Giardina","given":"Christian","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":824200,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Khan, Shafkat","contributorId":266048,"corporation":false,"usgs":false,"family":"Khan","given":"Shafkat","email":"","affiliations":[],"preferred":false,"id":824201,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mohan, Jacqueline","contributorId":62924,"corporation":false,"usgs":true,"family":"Mohan","given":"Jacqueline","email":"","affiliations":[],"preferred":false,"id":824202,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nottingham, Andrew T.","contributorId":266049,"corporation":false,"usgs":false,"family":"Nottingham","given":"Andrew","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":824203,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Reed, Sasha C. 0000-0002-8597-8619","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":205372,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":824204,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Slot, Martijn","contributorId":266050,"corporation":false,"usgs":false,"family":"Slot","given":"Martijn","email":"","affiliations":[],"preferred":false,"id":824205,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70203199,"text":"70203199 - 2019 - Evaluating and using existing models to map probable suitable habitat for rare plants to inform management of multiple-use public lands in the California desert","interactions":[],"lastModifiedDate":"2019-04-29T08:53:31","indexId":"70203199","displayToPublicDate":"2019-04-19T08:53:00","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating and using existing models to map probable suitable habitat for rare plants to inform management of multiple-use public lands in the California desert","docAbstract":"Multiple-use public lands require balancing diverse resource uses and values across landscapes. In the California desert, there is strong interest in renewable energy development and important conservation concerns. The Bureau of Land Management recently completed a land-use plan for the area that provides protection for modeled suitable habitat for multiple rare plants. Three sets of habitat models were commissioned for plants of conservation concern as part of the planning effort. The Bureau of Land Management then needed to determine which model or combination of models to use to implement plan requirements. Our goals were to: 1) develop a process for evaluating the existing habitat models and 2) use the evaluation results to map probable and potential suitable habitat. We developed a method for evaluating the construction (input data and methods) and performance of existing models and applied it to 88 habitat models for 43 rare plant species. We also developed a process for mapping probable and potential suitable habitat based on the existing models; potential habitat maps are intended only to guide future field surveys. We were able to map probable suitable habitat for 26 of the 43 species and potential suitable habitat for 41 species. Forty percent of the project area contains probable suitable habitat for at least one species (43,338 km2), with much of that habitat (43%) occurring on lands managed by the Bureau of Land Management. Lands prioritized for renewable energy development contain 3% of the habitat modeled as suitable for at least one species. Our products can be used by agencies to review proposed projects and plan future plant surveys and by developers to target sites likely to minimize conflicts with rare plant conservation goals. Our methods can be broadly applied to understand and quantify the defensibility of models used in conservation and regulatory contexts.","language":"English","publisher":"PLoS ONE","doi":"10.1371/journal.pone.0214099","usgsCitation":"Reese, G., Carter, S.K., Lunch, C., and Walterscheid, S., 2019, Evaluating and using existing models to map probable suitable habitat for rare plants to inform management of multiple-use public lands in the California desert: PLoS ONE, v. 14, no. 4, 26 p., https://doi.org/10.1371/journal.pone.0214099.","productDescription":"26 p.","ipdsId":"IP-099792","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":467685,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0214099","text":"Publisher Index Page"},{"id":437493,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NDA9YC","text":"USGS data release","linkHelpText":"Probable and potential suitable habitat for 43 rare plant species in the California desert"},{"id":363287,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.27783203125,\n 31.87755764334002\n ],\n [\n -113.88427734374999,\n 31.87755764334002\n ],\n [\n -113.88427734374999,\n 38.22091976683121\n ],\n [\n -122.27783203125,\n 38.22091976683121\n ],\n [\n -122.27783203125,\n 31.87755764334002\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-04-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Reese, Gordon 0000-0002-5191-7770 greese@usgs.gov","orcid":"https://orcid.org/0000-0002-5191-7770","contributorId":215093,"corporation":false,"usgs":true,"family":"Reese","given":"Gordon","email":"greese@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":761613,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carter, Sarah K. 0000-0003-3778-8615","orcid":"https://orcid.org/0000-0003-3778-8615","contributorId":192418,"corporation":false,"usgs":true,"family":"Carter","given":"Sarah","email":"","middleInitial":"K.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":761612,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lunch, Christina","contributorId":215094,"corporation":false,"usgs":false,"family":"Lunch","given":"Christina","email":"","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":761614,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Walterscheid, Steve","contributorId":215095,"corporation":false,"usgs":false,"family":"Walterscheid","given":"Steve","email":"","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":761615,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70202389,"text":"sir20185170 - 2019 - Drinking water health standards comparison and chemical analysis of groundwater for 72 domestic wells in Bradford County, Pennsylvania, 2016","interactions":[],"lastModifiedDate":"2019-06-12T10:00:24","indexId":"sir20185170","displayToPublicDate":"2019-04-19T08:45:00","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":"2018-5170","displayTitle":"Drinking Water Health Standards Comparison and Chemical Analysis of Groundwater for 72 Domestic Wells in Bradford County, Pennsylvania, 2016","title":"Drinking water health standards comparison and chemical analysis of groundwater for 72 domestic wells in Bradford County, Pennsylvania, 2016","docAbstract":"Pennsylvania has the second highest number of residential wells of any state in the Nation with approximately 2.4 million residents that depend on groundwater for their domestic water supply. Despite the widespread reliance on groundwater in rural areas of the state, publicly available data to characterize the quality of private well water are limited. In Bradford County, more than half of the residents use groundwater from private domestic-supply wells as their primary drinking source. The quality of private well water is influenced by the regional and local setting, including the surrounding soil, geology, land use, household plumbing, and well construction. The groundwater used for domestic water supply in Bradford County is obtained primarily from shallow bedrock and from unconsolidated (glacial) deposits that overlie the bedrock. Historical land use has been predominately forested, agricultural, and residential, but more recently unconventional oil/gas development has been distributed throughout the landscape. Pennsylvania is one of only two states in the Nation without statewide water-well construction standards.
To better assess the quality of groundwater used for drinking water supply in Bradford County, data for 72 domestic wells were collected and analyzed for a wide range of constituents that could be evaluated in relation to drinking water health standards, geology, land use, and other environmental factors. Groundwater samples were collected from May through August 2016 and analyzed for physical and chemical properties, including major ions, nutrients, trace elements, volatile organic compounds, ethylene and propylene glycol, alcohols, gross-alpha/beta-particle activity, uranium, radon-222, and dissolved gases. A subset of samples was analyzed for radium isotopes (radium-226 and -228) and for the isotopic composition of methane. This study was conducted by the U.S. Geological Survey in cooperation with the Northern Tier Regional Planning and Development Commission and is part of a regional effort to characterize groundwater in rural areas of Pennsylvania.
Results of the 2016 study show that groundwater quality generally met most drinking-water standards. However, a percentage of samples failed to meet maximum contaminant levels (MCLs) for total coliform bacteria (49.3 percent), Escherichia coli (8.5 percent), barium (2.8 percent), and arsenic (2.8 percent); and secondary maximum contaminant levels (SMCL) for sodium (48.6 percent), manganese (30.6 percent), gross alpha and beta activity (16.7 percent), iron (11.1 percent), pH (8.3 percent), total dissolved solids (5.6 percent), chloride (1.4 percent), and aluminum (1.4 percent). Radon-222 activities exceeded the proposed drinking-water standard of 300 picocuries per liter (pCi/L) in 70.4 percent of the samples. There were no exceedances of drinking water health standards for any volatile organic compounds, and the only detections were for three trihalomethanes in one sample.
The pH of the groundwater had a large influence on chemical characteristics and ranged from 6.18 to 9.31. Generally, the higher pH samples had higher potential for elevated concentrations of several constituents, including total dissolved solids, sodium, lithium, chloride, fluoride, boron, arsenic, and methane. For the Bradford County well-water samples, calcium/bicarbonate type waters were most abundant, with others classified as sodium/bicarbonate or mixed water types including calcium-sodium/bicarbonate, calcium-sodium/bicarbonate-chloride, sodium/bicarbonate-chloride, sodium/bicarbonate-sulfate, or sodium/chloride types. Six principal components (pH, redox, hardness, chloride-bromide, strontium-barium, and molybdenum-arsenic) explained nearly 78.3 percent of the variance in the groundwater dataset.
Groundwater from 12.5 percent of the wells had concentrations of methane greater than the Pennsylvania action level of 7 milligrams per liter (mg/L); detectable methane concentrations ranged from 0.01 to 77 mg/L. In addition, low levels of ethane (as much as 0.13 mg/L) were present in seven samples with the highest methane concentrations. The isotopic composition of methane in five of these groundwater samples was consistent with the isotopic compositions reported for mud-gas logging samples from these geologic units and a thermogenic source. Isotopic composition from a sixth sample suggested the methane in that sample may be of microbial origin. Well-water samples with the higher methane concentrations also had higher pH values and elevated concentrations of sodium, lithium, boron, fluoride, arsenic, and bromide. Relatively elevated concentrations of some other constituents, such as barium and chloride, commonly were present in, but not limited to, those well-water samples with elevated methane.
Four of the six groundwater samples with the highest methane concentrations had chloride/bromide ratios that indicate mixing with a small amount of brine (0.02 percent or less) similar in composition to those reported for gas and oil well brines in Pennsylvania. In several other eastern Pennsylvania counties where gas drilling is absent, groundwater with comparable chloride/bromide ratios and chloride concentrations have been reported, implying a potential natural source of brine. Most of Bradford County well-water samples have chloride concentrations less than 20 mg/L, and those with higher chloride concentrations have chloride/bromide ratios that indicate anthropogenic sources (such as road-deicing salt and septic effluent) or brine. Brines that are naturally present may originate from deeper parts of the aquifer system, whereas anthropogenic sources are more likely to affect shallow groundwater because they occur on or near the land surface.
The available data for this study indicate that no one physical factor, such as the topographic setting, well depth, or altitude at the bottom of the well, was particularly useful for predicting those well locations with an elevated dissolved concentration of methane. The 2016 assessment of groundwater quality in Bradford County shows groundwater is generally of good quality, but methane and some constituents that occur in high concentration in naturally occurring brine and also in produced waters may be present at low to moderate concentrations in groundwater in various parts of the aquifer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185170","collaboration":"Prepared in cooperation with the Northern Tier Regional Planning and Development Commission","usgsCitation":"Clune, J.W., and Cravotta, C.A., III, 2019, Drinking water health standards comparison and chemical analysis of groundwater for 72 domestic wells in Bradford County, Pennsylvania, 2016 (ver 1.2, May 30, 2019): U.S. Geological Survey Scientific Investigations Report 2018–5170, 66 p., https://doi.org/10.3133/sir20185170.","productDescription":"Report: vi, 66 p.; Data Release","numberOfPages":"76","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-098593","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":363039,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5170/coverthb4.jpg"},{"id":363132,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2018/5170/versionHist.txt","text":"Version History","size":"1.24 KB","linkFileType":{"id":2,"text":"txt"}},{"id":363047,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VRV6US","text":"USGS data release","description":"USGS data release","linkHelpText":"Compilation of Data Not Available in the National Water Information System for Domestic Wells Sampled by the U.S. Geological Survey in Bradford County, Pennsylvania, May-August 2016"},{"id":363040,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5170/sir20185170.pdf","text":"Report","size":"8.01 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5170"}],"country":"United States","state":"Pennsylvania","county":"Bradford County ","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-76.9291,42.0024],[-76.9095,42.0025],[-76.8966,42.0026],[-76.6476,42.0019],[-76.6334,42.0017],[-76.5964,42.0013],[-76.5618,42.0009],[-76.5531,42.0008],[-76.5229,42.0005],[-76.466,41.9999],[-76.3826,41.9989],[-76.1467,41.9991],[-76.1382,41.898],[-76.1336,41.8467],[-76.1285,41.7935],[-76.1258,41.773],[-76.1219,41.7217],[-76.1171,41.6531],[-76.1959,41.648],[-76.1996,41.6467],[-76.2015,41.6435],[-76.2015,41.6426],[-76.2015,41.6408],[-76.2016,41.6353],[-76.2016,41.6344],[-76.2023,41.6335],[-76.2029,41.6322],[-76.2063,41.6145],[-76.209,41.6004],[-76.2091,41.5982],[-76.2184,41.5579],[-76.2217,41.5447],[-76.2383,41.5458],[-76.2432,41.5463],[-76.2487,41.5468],[-76.3277,41.5526],[-76.4454,41.5608],[-76.5,41.5649],[-76.5975,41.5715],[-76.6367,41.5745],[-76.6478,41.5755],[-76.6619,41.5765],[-76.679,41.578],[-76.6938,41.579],[-76.6993,41.5795],[-76.7496,41.5834],[-76.7569,41.5839],[-76.787,41.5872],[-76.7949,41.5882],[-76.8005,41.5887],[-76.8103,41.5896],[-76.8133,41.5901],[-76.8219,41.5911],[-76.8379,41.593],[-76.8747,41.5968],[-76.8747,41.599],[-76.8805,41.6363],[-76.8833,41.6681],[-76.8838,41.6717],[-76.885,41.6781],[-76.8873,41.6999],[-76.8907,41.7267],[-76.8936,41.7503],[-76.8976,41.783],[-76.8987,41.8007],[-76.8993,41.808],[-76.9022,41.8248],[-76.9022,41.8257],[-76.9051,41.8466],[-76.9162,41.918],[-76.9209,41.9507],[-76.9238,41.9711],[-76.9291,42.0024]]]},\"properties\":{\"name\":\"Bradford\",\"state\":\"PA\"}}]}","edition":"Version 1.2: May 30, 2019; Version 1.1: April 23, 2019; Version 1.0: April 19, 2019","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070
Landsat 9 is a partnership between the National Aeronautics and Space Administration and the U.S. Geological Survey that will continue the Landsat program’s critical role of repeat global observations for monitoring, understanding, and managing Earth’s natural resources. Since 1972, Landsat data have provided a unique resource for those who work in agriculture, geology, forestry, regional planning, education, mapping, and global-change research. Landsat images have also proved invaluable to the International Charter: Space and Major Disasters, supporting emergency response and disaster relief to save lives. With the addition of Landsat 9, the Landsat program’s record of land imaging will be extended to over half a century.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193008","usgsCitation":"U.S. Geological Survey, 2019, Landsat 9 (ver. 1.3, August 2022): U.S. Geological Survey Fact Sheet 2019–3008, 2 p., https://doi.org/10.3133/fs20193008.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-102185","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":363027,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3008/coverthb4.jpg"},{"id":404567,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3008/fs20193008.pdf","text":"Report","size":"2.17 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019–3008"},{"id":404568,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2019/3008/versionHist.txt","text":"Version History","size":"8.43 kB","linkFileType":{"id":2,"text":"txt"},"description":"Version History"}],"edition":"Version 1.0: April 18, 2019; Version 1.1: May 1, 2019; Version 1.2: April 8, 2020; Version 1.3: August 3, 2022","contact":"Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Historical training and operational activities at Joint Base Cape Cod (JBCC) on western Cape Cod, Massachusetts, have resulted in the release of contaminants into an underlying glacial aquifer that is the sole source of water to the surrounding communities. Remedial systems have been installed to contain and remove contamination from the aquifer. Groundwater withdrawals for public supply are expected to increase as the region continues to urbanize. Increases in water-supply withdrawals and wastewater return flow likely will affect the hydrologic system around JBCC and could affect the transport of any contamination that may remain in the aquifer following remediation of contamination from the JBCC. The U.S. Geological Survey, in cooperation with the Air Force Civil Engineer Center, developed a numerical, steady-state regional model of the Sagamore flow lens on western Cape Cod and evaluated the potential effects of future (2030) groundwater withdrawals on water levels, streamflows, hydraulic gradients, and advective transport near the JBCC.
The aquifer consists generally of sandy sediments underlain by impermeable bedrock and is bounded laterally by a freshwater/saltwater interface. Data on the altitude of the bedrock surface, position of the freshwater/saltwater interface, lithology of the aquifer, spatial distribution of recharge, and hydrologic boundaries were incorporated into the three-dimensional, finite-difference groundwater flow model.
Some inputs into the numerical model—aquifer properties, leakances, and recharge—are represented as parameters to facilitate estimation of optimal parameter values in an inverse calibration. A hybrid parameterization scheme, with both zones of piecewise constancy and pilot points, is used to represent hydraulic conductivity; other adjustable parameters include recharge, boundary leakance, and porosity. Data on water levels, the distribution of subsurface contamination, and groundwater ages were compiled, evaluated, and used to develop observations of long-term average hydraulic gradients and advective-transport patterns. These observations of steady-state hydrologic conditions were combined with the parameterized groundwater model in an inverse calibration to estimate model parameters that best fit the observations.
Current (2010) and future (2030) conditions were simulated in the calibrated model to characterize the groundwater flow system and to determine potential effects of increased groundwater withdrawals on advective-transport patterns at the JBCC. Groundwater flow and advective transport are radially outward from a water-table divide in the northern part of the JBCC; flow diverges from the divide toward all points of the compass. Most groundwater flow and contaminant transport occur in shallow parts of the aquifer. On average, about one-half of the groundwater flux occurs in the shallowest 20 percent of the saturated thickness; shallow flow is even more predominant near streams and lakes. Projected (2030) increases in groundwater withdrawals decrease water levels by a maximum of about 1.2 feet in the northern part of the JBCC; drawdowns exceeding 1 foot generally are limited to areas near the largest increases in withdrawals, such as in the northern part of the JBCC, near Long Pond in Falmouth, and in eastern Barnstable. Streamflow decreases average about 6 percent; the largest decreases are in areas with the largest drawdowns. Changes in hydraulic-gradient directions at the water table exceed 1 degree in about 13 percent of the aquifer, generally near groundwater divides where gradient magnitudes are small and near large groundwater withdrawals. Predictions of advective transport from randomly selected locations at the water table are similar for current (2010) and future (2030) groundwater withdrawals. The results indicate that projected increases in groundwater withdrawals affect water levels and streamflows, but effects on hydraulic gradients and advective transport at the JBCC likely are small.
Several underlying assumptions inherent in the model, including observations and weights used in the calibration, representation of local-scale heterogeneity, and simulation of the freshwater/saltwater interface, could affect model calibration and predictions; these assumptions were evaluated with alternative models and alternative inverse calibrations. Eight alternative calibrations were performed in which different, but reasonable, observations and weights were used. The preferred calibrated model had the best overall fit to the observations.
Fine-grained silty sediments occur in many parts of the aquifer, and silt lenses can locally affect hydraulic gradients. A set of alternative models in which silts were represented with different correlation distances and hydraulic conductivities indicated that explicitly representing silt lenses could affect model calibration but that the implicit representation of local-scale heterogeneity may be sufficient at the regional scale to represent regional-scale hydraulic gradients. For the coastal boundary, two alternative models representing silty and sandy seabeds and their associated interface positions were developed to test the importance of the assumed coastal-boundary condition. The two alternative models resulted in different predictions of streamflow—streamflows increase with smaller (silty) seabed leakances. However, predictions of advective transport, particularly near the JBCC, generally were similar between the alternative and preferred calibrated models, indicating that the seabed leakance and associated interface position at the coastal boundary does not affect simulations of advective transport in inland parts of the aquifer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185139","collaboration":"Prepared in cooperation with the Air Force Civil Engineer Center","usgsCitation":"Walter, D.A., McCobb, T.D., and Fienen, M.N., 2019, Use of a numerical model to simulate the hydrologic system and transport of contaminants near Joint Base Cape Cod, western Cape Cod, Massachusetts: U.S. Geological Survey Scientific Investigations Report 2018–5139, 98 p., https://doi.org/10.3133/sir20185139.","productDescription":"Report: xi, 98 p.; Data Release","numberOfPages":"114","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-077209","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":362939,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F77P8XCT ","text":"USGS data release ","description":"USGS data release ","linkHelpText":"MODFLOW–2005 and MODPATH Used to Simulate the Hydrologic System and Transport of Contaminants Near Joint Base Cape Cod, Western Cape Cod, Massachusetts"},{"id":437495,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F77P8XCT","text":"USGS data release","linkHelpText":"MODFLOW2005 and MODPATH used to simulate the hydrologic system and transport contaminants near Joint Base Cape Cod, Western Cape Cod, Massachusetts"},{"id":362937,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5139/coverthb2.jpg"},{"id":362938,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5139/sir20185139.pdf","text":"Report","size":"43.8 MB ","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5139"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Cape Cod","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.026611328125,\n 41.21172151054787\n ],\n [\n -69.840087890625,\n 41.21172151054787\n ],\n [\n -69.840087890625,\n 42.21224516288584\n ],\n [\n -71.026611328125,\n 42.21224516288584\n ],\n [\n -71.026611328125,\n 41.21172151054787\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
331 Commerce Way, Suite 2
Pembroke, NH 03275
The uplift history of the Colorado Plateau has been debated for over a century with still no unified hypotheses for the cause, timing, and rate of uplift. 40Ar/39Ar and K/Ar dating of recurrent basaltic volcanism over the past ∼6 Ma within the Virgin River drainage system, southwest Utah, northwest Arizona, and southern Nevada, provides a way to reconstruct paleoprofiles and quantify differential river incision across the boundary faults of the Colorado Plateau–Basin and Range boundary. We compare differential incision data with patterns of channel steepness, bedrock erodibility, basaltic migration, and mantle velocity structure to understand the birth and evolution of the Virgin River system.
New detrital sanidine ages constrain the arrival of the Virgin River across the Virgin Mountains to less than 5.9 Ma. Virgin River incision rates and amounts show an eastward stair-step increase in bedrock incision across multiple N-S–trending normal faults. Using block incision values away from fault-related flexures, average bedrock incision rates are near zero since 4.6 Ma in the Lower Colorado River corridor, 23 m/Ma from 6.8 to 3.6 Ma in the Lake Mead block, 85 m/Ma from 3 to 0.4 Ma in the combined St. George and Hurricane blocks, and 338 m/Ma from 1 to 0.1 Ma in the Zion block. Steady incision within each block is documented by incision constraints that span these age ranges. We test two end-member hypotheses to explain the observed differential incision magnitudes and rates along the Virgin River system over the past ∼5 Ma: (1) as a measure of mantle-driven differential uplift of the Colorado Plateau relative to sea level; or (2) due to river integration across previously uplifted topography and differential rock types with down-dropping of Transition Zone blocks but no post–5 Ma uplift.
We favor headwater uplift of the Colorado Plateau because basalt-preserved paleoprofiles indicate that eastern fault blocks have been the “active” blocks that moved upwards relative to western blocks with little base-level change of the lower Colorado River corridor in the past 4.6 Ma. Block-to-block differential incision adds cumulatively such that the Zion block (Colorado Plateau edge) has been deeply incised 880–1200 m (∼338 m/Ma) over the 2.6–3.6 Ma period of Hurricane fault neotectonic movement, which has a slip magnitude of 1100 m. Mantle-driven uplift is implicated by a strong correlation throughout the Virgin River drainage between high normalized channel steepness (ksn) and low underlying mantle velocity, whereas there is a weaker correlation between high ksn and resistant lithologies. Basaltic volcanism has migrated northeastward at a rate of ∼18 km/Ma parallel to the Virgin River between ca. 13 and 0.5 Ma, also suggesting a mantle-driven mechanism for the combined epeirogenic uplift of the western Colorado Plateau, recurrent slip on its bounding faults, and headward propagation and differential incision of the Virgin River. Thus, we interpret the Virgin River to be a <5 Ma disequilibrium river system responding to ongoing upper-mantle modification and related basalt extraction that has driven ∼1 km of young (and ongoing) surface uplift of the western Colorado Plateau.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02019.1","usgsCitation":"Walk, C., Karlstrom, K., Crow, R.S., and Heizler, M., 2019, Birth and evolution of the Virgin River fluvial system: ∼1 km of post–5 Ma uplift of the western Colorado Plateau: Geosphere, v. 15, no. 3, p. 759-782, https://doi.org/10.1130/GES02019.1.","productDescription":"24 p.","startPage":"759","endPage":"782","ipdsId":"IP-102339","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":467690,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02019.1","text":"Publisher Index Page"},{"id":380958,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Nevada, Utah","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.927734375,\n 35.460669951495305\n ],\n [\n -111.6650390625,\n 35.460669951495305\n ],\n [\n -111.6650390625,\n 38.09998264736481\n ],\n [\n -115.927734375,\n 38.09998264736481\n ],\n [\n -115.927734375,\n 35.460669951495305\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"15","issue":"3","noUsgsAuthors":false,"publicationDate":"2019-04-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Walk, Cory","contributorId":245362,"corporation":false,"usgs":false,"family":"Walk","given":"Cory","email":"","affiliations":[{"id":16658,"text":"UNM","active":true,"usgs":false}],"preferred":false,"id":806037,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Karlstrom, Karl","contributorId":245363,"corporation":false,"usgs":false,"family":"Karlstrom","given":"Karl","affiliations":[{"id":16658,"text":"UNM","active":true,"usgs":false}],"preferred":false,"id":806038,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Crow, Ryan S. 0000-0002-2403-6361 rcrow@usgs.gov","orcid":"https://orcid.org/0000-0002-2403-6361","contributorId":5792,"corporation":false,"usgs":true,"family":"Crow","given":"Ryan","email":"rcrow@usgs.gov","middleInitial":"S.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":806039,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Heizler, Matt","contributorId":245364,"corporation":false,"usgs":false,"family":"Heizler","given":"Matt","affiliations":[{"id":7026,"text":"New Mexico Tech","active":true,"usgs":false}],"preferred":false,"id":806040,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70203113,"text":"70203113 - 2019 - Carbon dioxide enhanced oil recovery and residual oil zone studies at the U.S. Geological Survey","interactions":[],"lastModifiedDate":"2019-05-01T10:23:33","indexId":"70203113","displayToPublicDate":"2019-04-17T10:23:23","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Carbon dioxide enhanced oil recovery and residual oil zone studies at the U.S. Geological Survey","docAbstract":"The U.S. Geological Survey (USGS) is preparing a national resource assessment of the potential hydrocarbons recoverable after injection of carbon dioxide (CO2) into conventional oil reservoirs in the United States. The implementation of CO2-enhanced oil recovery (CO2-EOR) techniques can increase hydrocarbon production, and lead to incidental retention of CO2 in reservoir pore space allowing long-term storage of anthropogenic CO2. A Comprehensive Resource Database (CRD) containing proprietary data on location, geologic, petrophysical, and reservoir parameters, plus production and well counts for major oil and gas reservoirs in onshore areas and State waters of the conterminous United States and Alaska, was developed to support the USGS assessment. Residual oil zones (ROZs) also can provide potential pore space for long-term storage of anthropogenic CO2. However, ROZs are not included in the upcoming USGS national CO2-EOR assessment because assessment methods for ROZs still are being developed. Additional ROZ CO2-EOR and CO2 retention data and reservoir simulations are needed to calibrate national ROZ assessment estimates.
","conferenceTitle":"14th International Conference on Greenhouse Gas Control Technologies, GHGT-14","conferenceDate":"October 21-25, 2018","conferenceLocation":"Melbourne, Australia","language":"English","publisher":"Social Science Research Network (SSRN)","usgsCitation":"Warwick, P., Attanasi, E., Blondes, M., Brennan, S.T., Buursink, M., Doolan, C.A., Freeman, P., Jahediesfanjani, H., Karacan, C.O., Lohr, C., Merrill, M., Olea, R.A., Roueche, J.N., Shelton, J., Slucher, E., Varela, B.A., and Verma, M.K., 2019, Carbon dioxide enhanced oil recovery and residual oil zone studies at the U.S. Geological Survey, 14th International Conference on Greenhouse Gas Control Technologies, GHGT-14, Melbourne, Australia, October 21-25, 2018, p. 1-4.","productDescription":"4 p.","startPage":"1","endPage":"4","ipdsId":"IP-100919","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":363428,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":363097,"type":{"id":15,"text":"Index Page"},"url":"https://ssrn.com/abstract=3366202"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Warwick, Peter D. 0000-0002-3152-7783","orcid":"https://orcid.org/0000-0002-3152-7783","contributorId":205928,"corporation":false,"usgs":true,"family":"Warwick","given":"Peter D.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":761225,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Attanasi, Emil D. 0000-0001-6845-7160 attanasi@usgs.gov","orcid":"https://orcid.org/0000-0001-6845-7160","contributorId":198728,"corporation":false,"usgs":true,"family":"Attanasi","given":"Emil D.","email":"attanasi@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":761226,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Blondes, Madalyn S. 0000-0003-0320-0107 mblondes@usgs.gov","orcid":"https://orcid.org/0000-0003-0320-0107","contributorId":3598,"corporation":false,"usgs":true,"family":"Blondes","given":"Madalyn S.","email":"mblondes@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":761227,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brennan, Sean T. 0000-0002-9381-6863 sbrennan@usgs.gov","orcid":"https://orcid.org/0000-0002-9381-6863","contributorId":205926,"corporation":false,"usgs":true,"family":"Brennan","given":"Sean","email":"sbrennan@usgs.gov","middleInitial":"T.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":761228,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Buursink, Marc L. 0000-0001-6491-386X","orcid":"https://orcid.org/0000-0001-6491-386X","contributorId":203357,"corporation":false,"usgs":true,"family":"Buursink","given":"Marc L.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":761229,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Doolan, Colin A. 0000-0002-7595-7566 cdoolan@usgs.gov","orcid":"https://orcid.org/0000-0002-7595-7566","contributorId":3046,"corporation":false,"usgs":true,"family":"Doolan","given":"Colin","email":"cdoolan@usgs.gov","middleInitial":"A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":761230,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Freeman, Philip A. 0000-0002-0863-7431","orcid":"https://orcid.org/0000-0002-0863-7431","contributorId":206294,"corporation":false,"usgs":true,"family":"Freeman","given":"Philip A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":761231,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jahediesfanjani, Hossein 0000-0001-6281-5166","orcid":"https://orcid.org/0000-0001-6281-5166","contributorId":201000,"corporation":false,"usgs":false,"family":"Jahediesfanjani","given":"Hossein","affiliations":[],"preferred":false,"id":761232,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Karacan, C. 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However, their long‐term population dynamics are poorly known, even though these dynamics are critical to determining impacts and effective management. We gathered and analyzed 67 long‐term (>10 yr) data sets on dreissenid populations from lakes and rivers across Europe and North America. We addressed five questions: (1) How do Dreissena populations change through time? (2) Specifically, do Dreissena populations decline substantially after an initial outbreak phase? (3) Do different measures of population performance (biomass or density of settled animals, veliger density, recruitment of young) follow the same patterns through time? (4) How do the numbers or biomass of zebra mussels or of both species combined change after the quagga mussel arrives? (5) How does body size change over time? We also considered whether current data on long‐term dynamics of Dreissena populations are adequate for science and management. Individual Dreissena populations showed a wide range of temporal dynamics, but we could detect only two general patterns that applied across many populations: (1) Populations of both species increased rapidly in the first 1–2 yr after appearance, and (2) quagga mussels appeared later than zebra mussels and usually quickly caused large declines in zebra mussel populations. We found little evidence that combined Dreissena populations declined over the long term. Different measures of population performance were not congruent; the temporal dynamics of one life stage or population attribute cannot generally be accurately inferred from the dynamics of another. We found no consistent patterns in the long‐term dynamics of body size. The long‐term dynamics of Dreissena populations probably are driven by the ecological characteristics (e.g., predation, nutrient inputs, water temperature) and their temporal changes at individual sites rather than following a generalized time course that applies across many sites. Existing long‐term data sets on dreissenid populations, although clearly valuable, are inadequate to meet research and management needs. Data sets could be improved by standardizing sampling designs and methods, routinely collecting more variables, and increasing support.
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Steinman, and Jeschke, J.M., 2019, Long-term population dynamics of dreissenid mussels (Dreissena polymorpha and D. rostriformis): A cross-system analysis: Ecosphere, v. 10, no. 4, e02701, 22 p., https://doi.org/10.1002/ecs2.2701.","productDescription":"e02701, 22 p.","ipdsId":"IP-100985","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":467692,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2701","text":"Publisher Index Page"},{"id":377520,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"4","noUsgsAuthors":false,"publicationDate":"2019-04-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Strayer, David L.","contributorId":238531,"corporation":false,"usgs":false,"family":"Strayer","given":"David L.","affiliations":[{"id":47722,"text":"Cary Institute of Ecosystem Studies, Millbrook, NY","active":true,"usgs":false}],"preferred":false,"id":796360,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Adamovich, Boris V.","contributorId":238532,"corporation":false,"usgs":false,"family":"Adamovich","given":"Boris","email":"","middleInitial":"V.","affiliations":[{"id":47723,"text":"Biological Department, Belarusian State University, Minsk, Belarus","active":true,"usgs":false}],"preferred":false,"id":796361,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rita Adrian","contributorId":238533,"corporation":false,"usgs":false,"family":"Rita Adrian","affiliations":[{"id":47724,"text":"Freie Universität Berlin, Berlin, Germany","active":true,"usgs":false}],"preferred":false,"id":796362,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Aldridge, David C.","contributorId":238534,"corporation":false,"usgs":false,"family":"Aldridge","given":"David","email":"","middleInitial":"C.","affiliations":[{"id":47725,"text":"Department of Zoology, University of Cambridge, Cambridge, UK","active":true,"usgs":false}],"preferred":false,"id":796363,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Balogh, Csilla","contributorId":238535,"corporation":false,"usgs":false,"family":"Balogh","given":"Csilla","email":"","affiliations":[{"id":47726,"text":"Centre for Ecological Research, Balaton Limnological Institute, Hungarian Academy of Sciences, Tihany, Hungary","active":true,"usgs":false}],"preferred":false,"id":796364,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Burlakova, Lyubov E.","contributorId":238536,"corporation":false,"usgs":false,"family":"Burlakova","given":"Lyubov","email":"","middleInitial":"E.","affiliations":[{"id":47728,"text":"Great Lakes Center, SUNY Buffalo State, Buffalo, NY","active":true,"usgs":false}],"preferred":false,"id":796365,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fried-Petersen, Hannah","contributorId":238537,"corporation":false,"usgs":false,"family":"Fried-Petersen","given":"Hannah","email":"","affiliations":[{"id":47729,"text":"Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences, Uppsala, Sweden","active":true,"usgs":false}],"preferred":false,"id":796366,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"G.-Toth, Laszlo","contributorId":238538,"corporation":false,"usgs":false,"family":"G.-Toth","given":"Laszlo","email":"","affiliations":[{"id":47726,"text":"Centre for Ecological Research, Balaton Limnological Institute, Hungarian Academy of Sciences, Tihany, Hungary","active":true,"usgs":false}],"preferred":false,"id":796367,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Amy L. 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Schwalb","affiliations":[{"id":47740,"text":"Department of Biology, Texas State University, San Marcos, TX","active":true,"usgs":false}],"preferred":false,"id":796380,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Smith, David R. 0000-0001-6074-9257 drsmith@usgs.gov","orcid":"https://orcid.org/0000-0001-6074-9257","contributorId":168442,"corporation":false,"usgs":true,"family":"Smith","given":"David","email":"drsmith@usgs.gov","middleInitial":"R.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":796381,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Alan D. Steinman","contributorId":238552,"corporation":false,"usgs":false,"family":"Alan D. Steinman","affiliations":[{"id":47741,"text":"Annis Water Resources Institute, Grand Valley State University, Muskegon, MI","active":true,"usgs":false}],"preferred":false,"id":796382,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Jeschke, Jonathan M.","contributorId":238553,"corporation":false,"usgs":false,"family":"Jeschke","given":"Jonathan","email":"","middleInitial":"M.","affiliations":[{"id":47724,"text":"Freie Universität Berlin, Berlin, Germany","active":true,"usgs":false}],"preferred":false,"id":796383,"contributorType":{"id":1,"text":"Authors"},"rank":24}]}} ,{"id":70203174,"text":"70203174 - 2019 - Methane emissions from artificial waterbodies dominate the carbon footprint of irrigation: A study of transitions in the food-energy-water-climate nexus (Spain, 1900-2014)","interactions":[],"lastModifiedDate":"2019-04-25T08:39:36","indexId":"70203174","displayToPublicDate":"2019-04-16T16:27:34","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":"Methane emissions from artificial waterbodies dominate the carbon footprint of irrigation: A study of transitions in the food-energy-water-climate nexus (Spain, 1900-2014)","docAbstract":"Irrigation in the Mediterranean region has been used for millennia and has greatly expanded with industrialization. Irrigation is critical for climate change adaptation, but it is also an important source of greenhouse gas emissions. This study analyzes the carbon (C) footprint of irrigation in Spain, covering the complete historical process of mechanization. A 21-fold total, 6-fold area-based, and 4-fold product-based increase in the carbon footprint was observed during the 20th century, despite an increase in water use efficiency. CH4 emissions from waterbodies, which had not previously been considered in the C footprint of irrigation systems, dominated the emission budget during most of the analyzed period. Technologies to save water and tap new water resources greatly increased energy and infrastructure demand, while improvements in power generation efficiency had a limited influence on irrigation emissions. Electricity production from irrigation dams may contribute to climate change mitigation, but the amount produced in relation to that consumed in irrigation has greatly declined. High uncertainty in CH4 emission estimates from waterbodies stresses a need for more spatially resolved data and an improved empirical knowledge of the links between water quality, water level fluctuations, and emissions at the regional scale.
The Albuquerque Basin, located in central New Mexico, is about 100 miles long and 25–40 miles wide. The basin is hydrologically defined as the extent of consolidated and unconsolidated deposits of Tertiary and Quaternary age that encompasses the structural Rio Grande Rift between San Acacia to the south and Cochiti Lake to the north. A 20-percent population increase in the basin from 1990 to 2000 and a 22-percent population increase from 2000 to 2010 resulted in an increased demand for water in areas within the basin. Drinking-water supplies throughout the basin were obtained solely from groundwater resources until December 2008, when the Albuquerque Bernalillo County Water Utility Authority (ABCWUA) began treatment and distribution of surface water from the Rio Grande through the San Juan-Chama Drinking Water Project.
An initial network of wells was established by the U.S. Geological Survey (USGS) in cooperation with the City of Albuquerque from April 1982 through September 1983 to monitor changes in groundwater levels throughout the Albuquerque Basin. In 1983, this network consisted of 6 wells with analog-to-digital recorders and 27 wells where water levels were measured monthly. The network currently (2017) consists of 122 wells and piezometers. (A piezometer is a specialized well open to a specific depth in the aquifer, often of small diameter and nested with other piezometers open to different depths.) The USGS, in cooperation with the ABCWUA and the New Mexico Office of the State Engineer, currently (2017) measures and reports water levels from the 122 wells and piezometers in the network; this report presents water-level data collected by USGS personnel at those 122 sites through water years 2016 and 2017 (October 1, 2015, through September 30, 2017). Water levels that were collected from wells in previous water years were published in previous USGS reports.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1113","collaboration":"Prepared in cooperation with the Albuquerque Bernalillo County Water Utility Authority","usgsCitation":"Beman, J.E., Ritchie, A.B., and Galanter, A.E., 2019, Water-level data for the Albuquerque Basin and adjacent areas, central New Mexico, period of record through September 30, 2017 (ver. 1.1, August 2021): U.S. Geological Survey Data Series 1113, 39 p., https://doi.org/10.3133/ds1113.","productDescription":"iii, 39 p.","numberOfPages":"48","onlineOnly":"Y","ipdsId":"IP-106011","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":362978,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1113/coverthb2.jpg"},{"id":388366,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1113/ds1113.pdf","text":"Report","size":"5.66 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1113"},{"id":388367,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/ds/1113/versionHist.txt","text":"Version History","size":"575 B","linkFileType":{"id":2,"text":"txt"},"description":"DS 1113 Version History"}],"country":"United States","state":"New Mexico","otherGeospatial":"Albuquerque Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.57812499999999,\n 33.710632271492095\n ],\n [\n -106.14990234375,\n 33.710632271492095\n ],\n [\n -106.14990234375,\n 35.764343479667176\n ],\n [\n -107.57812499999999,\n 35.764343479667176\n ],\n [\n -107.57812499999999,\n 33.710632271492095\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.1: August 2021","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
Previous acoustic surveys, netting, and count data have shown that overall bat activity patterns have shifted among most species between pre- and post-white-nose syndrome (WNS) years in much of North America where WNS has occurred. However, the significance of these changes is based on the species-specific susceptibility to WNS. We used acoustically recorded echolocation passes obtained at Fort Drum, New York to describe changes in bat activity pre-WNS (2004–2007) to post-WNS (2008–2018). We examined seasonal and yearly changes in bat activity as they relate to the presence of WNS at hibernacula near (<25 km) Fort Drum. A priori, we expected that overall activity for communal hibernating species would be less in years following WNS, and migratory bats or those hibernating bats that are less affected by WNS would show no response or a positive response, due to niche relaxation/competitive release. Our results indicated both an overall and seasonal decrease in activity for Myotis spp. post-WNS. For WNS-susceptible species, our results reflect the high level of mortality in regional winter hibernacula post-WNS and possibly variable reproductive effort and recruitment thereafter. Although migratory bats did show increases in post-WNS activity throughout the summer, we found little evidence that community displacement was occurring on a nightly level by any species. The continuous spread of WNS across North America has had strong negative effects on bat populations of affected species, and our research identifies how individual species (both impacted and non-impacted) respond to WNS.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2019.e00633","usgsCitation":"Nocera, T., Ford, W., Silvis, A., and Dobony, C., 2019, Patterns of acoustical activity of bats prior to and 10 years after WNS on Fort Drum Army Installation, New York: Global Ecology and Conservation, v. 18, e00633, 9 p., https://doi.org/10.1016/j.gecco.2019.e00633.","productDescription":"e00633, 9 p.","ipdsId":"IP-101098","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467700,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2019.e00633","text":"Publisher Index Page"},{"id":388163,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Fort Drum Army Installation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.574951171875,\n 44.006644643819655\n ],\n [\n -75.36895751953125,\n 44.188112606916484\n ],\n [\n -75.56121826171875,\n 44.268804788566165\n ],\n [\n -75.8660888671875,\n 44.05403780323783\n ],\n [\n -75.75897216796875,\n 43.98688630934305\n ],\n [\n -75.574951171875,\n 44.006644643819655\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"18","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nocera, Tomás","contributorId":264429,"corporation":false,"usgs":false,"family":"Nocera","given":"Tomás","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":821525,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":821524,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Silvis, Alexander","contributorId":264430,"corporation":false,"usgs":false,"family":"Silvis","given":"Alexander","affiliations":[{"id":54475,"text":"RES Inc","active":true,"usgs":false}],"preferred":false,"id":821526,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dobony, Christopher A.","contributorId":264431,"corporation":false,"usgs":false,"family":"Dobony","given":"Christopher A.","affiliations":[{"id":54476,"text":"Fort Drum","active":true,"usgs":false}],"preferred":false,"id":821527,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70203402,"text":"70203402 - 2019 - Peak ground displacement saturates exactly when expected: Implications for earthquake early warning","interactions":[],"lastModifiedDate":"2019-12-22T14:25:52","indexId":"70203402","displayToPublicDate":"2019-04-16T09:27:23","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2314,"text":"Journal of Geophysical Research B: Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Peak ground displacement saturates exactly when expected: Implications for earthquake early warning","docAbstract":"The scaling of rupture properties with magnitude is of critical importance to earthquake early warning (EEW) systems that rely on source characterization using limited snapshots of waveform data. ShakeAlert, a prototype EEW system that is being developed for the western United States, provides real-time estimates of earthquake magnitude based on P-wave peak ground displacements measured at stations triggered by the event. The algorithms used in ShakeAlert assume that the displacement measurements at each station are statistically independent and that there exists a linear and time-independent relation between log peak ground displacement and earthquake magnitude. Here we challenge this basic assumption using a comprehensive database of more than 130,000 vertical component waveforms from M4.5-M9 earthquakes occurring near Japan from 1997 through 2017 and recorded by the K-NET and KiK-net strong-motion networks. By analyzing the time-evolution of P-wave peak ground displacements for these earthquakes, we show that there is a break, or saturation, in the magnitude-displacement scaling that depends on the length of the measurement time window. We demonstrate that the magnitude at which this saturation occurs is well-explained by a simple and non-deterministic model of earthquake rupture growth. We then use the predictions of this saturation model to develop a Bayesian framework for estimating posterior uncertainties in real-time magnitude estimates which incorporates the expected time-dependence of the peak displacement measurements.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2018JB017093","usgsCitation":"Trugman, D.T., Page, M.T., Minson, S.E., and Cochran, E.S., 2019, Peak ground displacement saturates exactly when expected: Implications for earthquake early warning: Journal of Geophysical Research B: Solid Earth, v. 124, no. 5, p. 4642-4653, https://doi.org/10.1029/2018JB017093.","productDescription":"12 p.","startPage":"4642","endPage":"4653","ipdsId":"IP-103663","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":460405,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2018jb017093","text":"Publisher Index Page"},{"id":363713,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Japan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 128.2763671875,\n 32.69486597787505\n ],\n [\n 130.3857421875,\n 29.57345707301757\n ],\n [\n 141.8115234375,\n 35.496456056584165\n ],\n [\n 142.734375,\n 41.50857729743935\n ],\n [\n 146.42578125,\n 43.26120612479979\n ],\n [\n 144.84375,\n 44.465151013519616\n ],\n [\n 141.6796875,\n 45.82879925192134\n ],\n [\n 140.9765625,\n 45.24395342262324\n ],\n [\n 138.9111328125,\n 41.934976500546604\n ],\n [\n 138.9111328125,\n 38.238180119798635\n ],\n [\n 130.166015625,\n 34.88593094075317\n ],\n [\n 128.2763671875,\n 32.69486597787505\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"124","issue":"5","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Trugman, Daniel T.","contributorId":197011,"corporation":false,"usgs":false,"family":"Trugman","given":"Daniel","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":762534,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Page, Morgan T. 0000-0001-9321-2990 mpage@usgs.gov","orcid":"https://orcid.org/0000-0001-9321-2990","contributorId":3762,"corporation":false,"usgs":true,"family":"Page","given":"Morgan","email":"mpage@usgs.gov","middleInitial":"T.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":762533,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Minson, Sarah E. 0000-0001-5869-3477 sminson@usgs.gov","orcid":"https://orcid.org/0000-0001-5869-3477","contributorId":5357,"corporation":false,"usgs":true,"family":"Minson","given":"Sarah","email":"sminson@usgs.gov","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":762535,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cochran, Elizabeth S. 0000-0003-2485-4484 ecochran@usgs.gov","orcid":"https://orcid.org/0000-0003-2485-4484","contributorId":2025,"corporation":false,"usgs":true,"family":"Cochran","given":"Elizabeth","email":"ecochran@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":762536,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70202231,"text":"ofr20191013 - 2019 - Monitoring storm tide and flooding from Hurricane Irma along the U.S. Virgin Islands, Puerto Rico, and the Southeastern United States, September 2017","interactions":[],"lastModifiedDate":"2019-07-26T10:14:44","indexId":"ofr20191013","displayToPublicDate":"2019-04-16T08:13:37","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-1013","displayTitle":"Monitoring Storm Tide and Flooding From Hurricane Irma Along the U.S. Virgin Islands, Puerto Rico, and the Southeastern United States, September 2017","title":"Monitoring storm tide and flooding from Hurricane Irma along the U.S. Virgin Islands, Puerto Rico, and the Southeastern United States, September 2017","docAbstract":"Hurricane Irma skirted the northern coasts of the U.S. Virgin Islands and Puerto Rico, with maximum sustained winds of 185 miles per hour (mi/h) on September 6, 2017. The hurricane first made landfall in Florida near Cudjoe Key, in the lower Florida Keys, with maximum sustained winds of 130 mi/h on September 10, 2017. The hurricane made a second Florida landfall on Marco Island, Florida, with maximum sustained winds of 115 mi/h on September 10, 2017. The U.S. Geological Survey (USGS), in cooperation with Federal Emergency Management Agency, deployed a temporary monitoring network of water-level and barometric pressure sensors at 249 locations along the Puerto Rico, Florida, Georgia, and South Carolina coasts to record the timing, areal extent, and magnitude of hurricane storm tide and coastal flooding generated by the hurricane. Immediately following the passage of Hurricane Irma, the sensors were retrieved, and the data were disseminated on the USGS Flood Event Viewer (https://stn.wim.usgs.gov/FEV/#IrmaSeptember2017). The storm-tide peak data values were verified by comparing data from hydrologic recorders and nearby high-water marks (HWMs). Following the hurricane, 508 independent HWM locations were flagged and surveyed relative to the North American Vertical Datum of 1988, National Geodetic Vertical Datum of 1929, or a local datum along the southeastern U.S. coast, and to Puerto Rico Vertical Datum of 2002 in Puerto Rico. Most HWMs were in Florida because of the path of the hurricane. The data from the Hurricane Irma storm-tide network are available on a provisional basis in tab-delimited, American Standard Code for Information Interchange (ASCII) format and Network Common Data Form (NetCDF) format by site for each sensor by using the USGS Flood Event Viewer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191013","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Byrne, M.J., Sr., and Dickman, M.R., 2019, Monitoring storm tide and flooding from Hurricane Irma along the U.S. Virgin Islands, Puerto Rico, and the Southeastern United States, September 2017 (ver. 1.1, July 2019): U.S. Geological Survey Open-File Report 2019–1013, 35 p., https://doi.org/10.3133/ofr20191013.","productDescription":"vi, 35 p.","numberOfPages":"46","onlineOnly":"N","ipdsId":"IP-095711","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":365693,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1013/ofr20191013.pdf","text":"Report","size":"9.26 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1013"},{"id":365694,"rank":2,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2019/1013/versionHist.txt","text":"Version History","size":"1.00 kB","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2019–1013 Version History"},{"id":365697,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1013/coverthb2.jpg"}],"country":"United States","otherGeospatial":"Puerto Rico, U.S. Virgin Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.9892578125,\n 24.00632619875113\n ],\n [\n -79.4970703125,\n 24.00632619875113\n ],\n [\n -79.4970703125,\n 32.0639555946604\n ],\n [\n -88.9892578125,\n 32.0639555946604\n ],\n [\n -88.9892578125,\n 24.00632619875113\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -68.02734375,\n 16.04581345375217\n ],\n [\n -63.45703124999999,\n 16.04581345375217\n ],\n [\n -63.45703124999999,\n 20.96143961409684\n ],\n [\n -68.02734375,\n 20.96143961409684\n ],\n [\n -68.02734375,\n 16.04581345375217\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: April 16, 2019; Version 1.1: July 25, 2019 ","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559