A steady-state three-dimensional groundwater-flow model based on present conditions is coupled with the particle-tracking program MODPATH to assess the fate and transport of volatile organic-compound plumes within the Magothy and upper glacial aquifers in southeastern Nassau County, New York. Particles are forward tracked from locations within plumes defined by surfaces of equal concentration. Particles move toward ultimate well capture and discharge to the general head and drain boundaries representing natural receptors in the models. Because rates of advection within coarse-grained sediments typically exceed 0.1 foot per day, mechanisms of dispersion and diffusion were assumed to be negligible. Resulting particle pathlines are influenced by hydrogeologic framework features and the interplay of nearby hydrologic stresses. Simulated hydrologic effects include cones of depression near pumping wells and water-table mounding near points of treated water recharge; however, remedial pumping amounts are balanced by treated-water return, and net effects at distant regional boundaries, including freshwater/saltwater interfaces, are minor.
Once a steady-state model was developed and calibrated, eight hypothetical remedial scenarios were evaluated to hydraulically contain the volatile organic-compound plumes. Specifically, the remedial scenarios were optimized to achieve full containment by altering the pumping-well locations, adjusting the pumping rates, and adjusting the discharge locations and rates. Based on the results, total hypothetical extraction rates varied from about 5,462 gallons per minute during an anticipated near-future condition to about 13,340 gallons per minute during full hydraulic containment of all site-related compounds identified by the New York State standards, criteria, and guidance for environmental investigations and cleanup. Targeting of high-concentration zones of the plume increases the total amount of remedial pumpage necessary to capture all parts of the plume but may decrease the total amount of time necessary to operate a remedial system. Simulated time frames of advective transport ranged from about 12 years to capture zones with elevated concentrations of volatile organic compounds (mean particle travel time plus the standard deviation of travel time) to more than 100 years to capture all zones.
Groundwater-flow model analysis indicates that all the optimal plume-containment scenarios would have negligible effects on streams and the saltwater-freshwater interface along the south shore of Long Island. Massapequa, Bellmore, Seaman, and Seaford Creeks are represented by using MODFLOW drain-boundary conditions. Saltwater-freshwater interfaces are represented by using MODFLOW general head-boundary conditions where the Magothy aquifer discharges upward into saline groundwater across the Gardiners clay confining unit and the Lloyd aquifer discharges upward into saline groundwater across the Raritan confining unit.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205090","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Misut, P.E., Walter, D., Schubert, C., and Dressler, S., 2020, Analysis of remedial scenarios affecting plume movement through a sole-source aquifer system, southeastern Nassau County, New York: U.S. Geological Survey Scientific Investigations Report 2020–5090, 83 p., https://doi.org/10.3133/sir20205090.","productDescription":"Report: vi, 83 p.; Data Release; 5 Figures","numberOfPages":"83","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-105143","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":380266,"rank":4,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2020/5090/sir20205090_figures.zip","text":"High-resolution figures","size":"159 MB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Figures 16, 18, 20, 22, and 24"},{"id":380264,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DOBQ8N","text":"USGS data release","linkHelpText":"MODFLOW–NWT and MODPATH6 model use to analyze remedial scenarios affecting plume movement through a sole-source aquifer system, southeastern Nassau County, New York"},{"id":380262,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5090/coverthb.jpg"},{"id":380263,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5090/sir20205090.pdf","text":"Report","size":"18.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5090"}],"country":"United States","state":"New York","county":"Nassau County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.87619018554688,\n 40.482470524589516\n ],\n [\n -73.289794921875,\n 40.482470524589516\n ],\n [\n -73.289794921875,\n 40.81796653313175\n ],\n [\n -73.87619018554688,\n 40.81796653313175\n ],\n [\n -73.87619018554688,\n 40.482470524589516\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
On September 20, 2017, Hurricane Maria triggered widespread debris flows in Puerto Rico. We used field observations and pre- and post-Maria lidar to study the volumetric growth of long-travelled (>400 m) debris flows in four basins. We found overall growth rates that ranged from 0.7 to 30.4 m3 per meter of channel length. We partitioned the rates into two growth mechanisms, aggregation of multiple landslides, or erosion and entrainment of channel sediment. In three basins, landslides accounted for more than 80% of the total debris-flow volumes. In one basin, entrainment accounted for 96% of the volume. These results indicate that forecasting volumes for regional debris-flow inundation modeling is more complicated than estimating the number and volume of contributing landslide source areas, although this task is difficult by itself. In this preliminary analysis, we did not find geologic, topographic, or morphometric variables that correlated with the growth observations. We suspect that the observed growth rates were heavily influenced by local variations in environmental conditions, including antecedent soil moisture conditions, duration and intensity of rainfall, and availability of channel material. Given these considerations, regional debris-flow inundation modeling may be best achieved by using a suite of scenarios that capture possible mechanisms of debris-flow growth.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 13th International Symposium on Landslides","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"13th International Symposium on Landslides","conferenceDate":"February 22-26, 2021","language":"English","publisher":"International Society for Soil Mechanics and Geotechnical Engineering","usgsCitation":"Coe, J.A., Bessette-Kirton, E., Brien, D.L., and Reid, M.E., 2020, Debris-flow growth in Puerto Rico during Hurricane Maria: Preliminary results from analyses of pre- and post-event lidar data, in Proceedings of the 13th International Symposium on Landslides, February 22-26, 2021, 9 p.","productDescription":"9 p.","ipdsId":"IP-113885","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":385190,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":383574,"type":{"id":15,"text":"Index Page"},"url":"https://www.issmge.org/uploads/publications/105/106/ISL2020-7.pdf"}],"country":"United States","state":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.30499267578125,\n 17.85590441431915\n ],\n [\n -65.5828857421875,\n 17.85590441431915\n ],\n [\n -65.5828857421875,\n 18.578568865536027\n ],\n [\n -67.30499267578125,\n 18.578568865536027\n ],\n [\n -67.30499267578125,\n 17.85590441431915\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Coe, Jeffrey A. 0000-0002-0842-9608 jcoe@usgs.gov","orcid":"https://orcid.org/0000-0002-0842-9608","contributorId":1333,"corporation":false,"usgs":true,"family":"Coe","given":"Jeffrey","email":"jcoe@usgs.gov","middleInitial":"A.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":810789,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bessette-Kirton, Erin K. 0000-0002-2797-0694","orcid":"https://orcid.org/0000-0002-2797-0694","contributorId":225097,"corporation":false,"usgs":false,"family":"Bessette-Kirton","given":"Erin K.","affiliations":[{"id":13252,"text":"University of Utah","active":true,"usgs":false}],"preferred":false,"id":810790,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brien, Dianne L. 0000-0003-3227-7963 dbrien@usgs.gov","orcid":"https://orcid.org/0000-0003-3227-7963","contributorId":229851,"corporation":false,"usgs":true,"family":"Brien","given":"Dianne","email":"dbrien@usgs.gov","middleInitial":"L.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":810791,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reid, Mark E. 0000-0002-5595-1503 mreid@usgs.gov","orcid":"https://orcid.org/0000-0002-5595-1503","contributorId":1167,"corporation":false,"usgs":true,"family":"Reid","given":"Mark","email":"mreid@usgs.gov","middleInitial":"E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":810792,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70218222,"text":"70218222 - 2020 - Testing hypotheses on signatures of precipitation variability in the river and floodplain deposits of the Paleogene San Juan Basin, New Mexico, USA","interactions":[],"lastModifiedDate":"2021-02-22T12:54:08.541165","indexId":"70218222","displayToPublicDate":"2021-02-18T13:16:19","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2451,"text":"Journal of Sedimentary Research","onlineIssn":"1938-3681","printIssn":"1527-1404","active":true,"publicationSubtype":{"id":10}},"title":"Testing hypotheses on signatures of precipitation variability in the river and floodplain deposits of the Paleogene San Juan Basin, New Mexico, USA","docAbstract":"Much progress has been made in recent years towards a set of recognition criteria for river discharge variability in river channel deposits, and thus sedimentary proxies for precipitation variability. Despite this progress, there is currently no consensus on how different styles of discharge variability are reflected in river sedimentary records, and whether variable-discharge river records from different climate types can be distinguished. Herein, river discharge and precipitation variability in the Paleogene is investigated using associations between river channel and floodplain deposits across the Paleocene-Eocene boundary from the Paleocene upper Nacimiento Formation and the early Eocene San Jose Formation in the San Juan Basin, New Mexico, USA. \n\nThe succession is identified as deposits of variable-discharge river systems based on shared channel-deposit characteristics with modern and ancient variable-discharge river systems and the proposed facies models, in addition to alternations of poorly drained and well-drained floodplain deposits and/or slickensides indicating alternating wet-dry cycles. A long-term stratigraphic trend toward increasingly well-drained floodplain deposits is also observed and hypothesized to indicate successively more arid conditions from the Paleocene into the early Eocene. Comparisons with modern rivers from various climate zones suggest a long-term shift from a monsoonal climate in the Paleocene, to a fluctuating subhumid climate, ultimately leading to semiarid to arid conditions in the early Eocene. These observations suggest that floodplain deposits may be a better indicator of ambient climate, whereas channel deposits are records for frequency and magnitude of high-intensity precipitation events. Therefore, the existing facies models for variable-discharge rivers that consider only channel facies may not capture critical information needed to make accurate interpretations of paleoclimatic conditions. This study also adds to a growing body of evidence from geologic records of mid-latitude Paleogene river systems suggesting increases in the magnitude or variability of river discharge coinciding with established climate perturbations.","language":"English","publisher":"SEPM (Society for Sedimentary Geology)","doi":"10.2110/jsr.2020.75","usgsCitation":"Zellman, K.L., Plink-Bjorklund, P., and Fricke, H., 2020, Testing hypotheses on signatures of precipitation variability in the river and floodplain deposits of the Paleogene San Juan Basin, New Mexico, USA: Journal of Sedimentary Research, v. 90, no. 12, p. 1770-1801, https://doi.org/10.2110/jsr.2020.75.","productDescription":"32 p.","startPage":"1770","endPage":"1801","ipdsId":"IP-107514","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":383385,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"San Juan basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.907470703125,\n 36.23762751669998\n ],\n [\n -107.82806396484375,\n 36.23762751669998\n ],\n [\n -107.82806396484375,\n 37.00035919622158\n ],\n [\n -108.907470703125,\n 37.00035919622158\n ],\n [\n -108.907470703125,\n 36.23762751669998\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"90","issue":"12","noUsgsAuthors":false,"publicationDate":"2021-02-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Zellman, Kristine L. 0000-0002-7088-429X kzellman@usgs.gov","orcid":"https://orcid.org/0000-0002-7088-429X","contributorId":4849,"corporation":false,"usgs":true,"family":"Zellman","given":"Kristine","email":"kzellman@usgs.gov","middleInitial":"L.","affiliations":[],"preferred":true,"id":810474,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Plink-Bjorklund, Piret","contributorId":251748,"corporation":false,"usgs":false,"family":"Plink-Bjorklund","given":"Piret","email":"","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":810475,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fricke, Henry","contributorId":251749,"corporation":false,"usgs":false,"family":"Fricke","given":"Henry","email":"","affiliations":[{"id":37163,"text":"Colorado College","active":true,"usgs":false}],"preferred":false,"id":810476,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70229515,"text":"70229515 - 2020 - Landsat surface reflectance validation site selection","interactions":[],"lastModifiedDate":"2022-03-11T15:35:55.452537","indexId":"70229515","displayToPublicDate":"2021-02-17T09:31:32","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Landsat surface reflectance validation site selection","docAbstract":"An investigation was conducted to determine optimal locations within the continental United States for insitu measurements to validate the U.S. Landsat Analysis Ready Data (ARD) Surface Reflectance product. Site assessment involved analysis of aerosol optical depth, precipitable water vapor, land cover, cloud cover, and elevation models. Nineteen sites were selected for further month-by-month ranking to identify those sites most likely to capture simple or complex atmosphere.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"IGARSS 2020 - 2020 IEEE international geoscience and remote sensing symposium","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"IEEE","doi":"10.1109/IGARSS39084.2020.9323374","usgsCitation":"Maddox, E.M., and Zavesky, L.D., 2020, Landsat surface reflectance validation site selection, in IGARSS 2020 - 2020 IEEE international geoscience and remote sensing symposium, p. 6133-6136, https://doi.org/10.1109/IGARSS39084.2020.9323374.","productDescription":"4 p.","startPage":"6133","endPage":"6136","ipdsId":"IP-115451","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":397023,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Maddox, Emily M. 0000-0001-5649-1193","orcid":"https://orcid.org/0000-0001-5649-1193","contributorId":288315,"corporation":false,"usgs":true,"family":"Maddox","given":"Emily","email":"","middleInitial":"M.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":837721,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zavesky, Landon Douglas 0000-0003-1109-8149","orcid":"https://orcid.org/0000-0003-1109-8149","contributorId":288316,"corporation":false,"usgs":true,"family":"Zavesky","given":"Landon","email":"","middleInitial":"Douglas","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":837722,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70217282,"text":"70217282 - 2020 - Management of the brown-headed cowbird: Implications for endangered species and agricultural damage mitigation","interactions":[],"lastModifiedDate":"2021-01-18T14:16:03.109888","indexId":"70217282","displayToPublicDate":"2021-01-11T08:12:31","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1914,"text":"Human-Wildlife Interactions","active":true,"publicationSubtype":{"id":10}},"title":"Management of the brown-headed cowbird: Implications for endangered species and agricultural damage mitigation","docAbstract":"The brown-headed cowbird (Molothrus ater; cowbird) is unique among North American blackbirds (Icteridae) because it is managed to mitigate the negative effects on endangered songbirds and economic losses in agricultural crops. Cowbird brood parasitism can further affect species that are considered threatened or endangered due to anthropogenic land uses. Historically, cowbirds have often been culled without addressing ultimate causes of songbird population declines. Similar to other North American blackbirds, cowbirds depredate agricultural crops, albeit at a lower rate reported for other blackbird species. Conflicting information exists on the extent of agricultural damage caused by cowbirds and the effectiveness of mitigation measures for application to management. In this paper, we reviewed the progress that has been made in cowbird management from approximately 2005 to 2020 in relation to endangered species. We also reviewed losses to the rice (Oryza sativa) crop attributed to cowbirds and the programs designed to reduce depredation. Of the 4 songbird species in which cowbirds have been managed, both the Kirtland’s warbler (Dendroica kirtlandii) and black-capped vireo (Vireo atricapilla) have been removed from the endangered species list following population increases in response to habitat expansion. Cowbird trapping has ceased for Kirtland’s warbler but continues for the vireo. In contrast, least Bell’s vireo (V. bellii pusillus) and southwestern willow flycatcher (Empidonax traillii extimus) still require cowbird control after modest increases in suitable habitat. Our review of rice depredation by cowbirds revealed models that have been created to determine the number of cowbirds that can be taken to decrease rice loss have been useful but require refinement with new data that incorporate cowbird population changes in the rice growing region, dietary preference studies, and current information on population sex ratios and female cowbird egg laying. Once this information has been gathered, bioenergetic and economic models would increase our understanding of the damage caused by cowbirds.
DGMETA (Dissolved Gas Modeling and Environmental Tracer Analysis) is a Microsoft Excel-based computer program that is used for modeling air-water equilibrium conditions from measurements of dissolved gases and for computing concentrations of environmental tracers that rely on air-water equilibrium model results. DGMETA can solve for the temperature, salinity, excess air, fractionation of gases, or pressure/elevation of water when it is equilibrated with the atmosphere. Models are calibrated inversely using one or more measurements of dissolved gases such as helium, neon, argon, krypton, xenon, and nitrogen. Excess nitrogen gas, originating from denitrification or other sources, also can be included as a fitted parameter or as a separate calculation from the dissolved gas modeling results. DGMETA uses the air-water equilibrium models to separate measured concentrations of gases and isotopes of gases into components that are used for tracing water in the environment. DGMETA calculates atmospheric dry-air mole fractions (mixing ratios) for transient atmospheric gas tracers such as chlorofluorocarbons, sulfur hexafluoride, and bromotrifluoromethane (Halon-1301); and concentrations of tritiogenic helium-3 and radiogenic helium-4, which accumulate from the decay of tritium in water and the decay of uranium and thorium in rocks, respectively.
Sample data can be graphed to identify applicable models of excess air, samples that contain excess nitrogen gas, or samples that have partially degassed, for example. Monte Carlo analysis of errors associated with dissolved gas equilibrium model results can be carried through computations of environmental tracer concentrations to provide robust estimates of error. In addition, graphical routines for separating helium sources using helium isotopes are included to refine estimates of tritiogenic helium-3 when terrigenic helium from mantle or crustal sources is present in samples. Environmental tracer concentrations and their errors computed from DGMETA can be used with other programs, such as TracerLPM (Jurgens and others, 2012), to determine groundwater ages and biogeochemical reaction rates. DGMETA also produces output files in a format that meets the U.S. Geological Survey open data requirements for documentation of model inputs and outputs.
DGMETA is a versatile and adaptable program that allows users to add solubility data for new gases, modify the existing set of gas solubility data, modify the default set of gases used for modeling, choose calculations based on real (non-ideal) gas behavior, and select various concentration units for data entry and results to match laboratory reports and study objectives. DGMETA comes with a set of gases widely used in hydrology and oceanography and many gases include multiple solubilities from previous work. Seventeen dissolved gases are included in the default version of the program: noble gases (helium, neon, argon, krypton, and xenon), reactive gases (nitrogen, oxygen, methane, carbon dioxide, carbon monoxide, hydrogen, and nitrous oxide), and environmental tracers (chlorofluorocarbon-11, chlorofluorocarbon-12, chlorofluorocarbon-113, sulfur hexafluoride, and Halon-1301).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm4F5","collaboration":"National Water Quality Assessment Project","usgsCitation":"Jurgens, B.C., Böhlke, J., Haase, K., Busenberg, E., Hunt, A.G., and Hansen, J.A., 2020, DGMETA (version 1)—Dissolved gas modeling and environmental tracer analysis computer program: U.S. Geological Survey Techniques and Methods 4-F5, 50 p., https://doi.org/10.3133/tm4F5.","productDescription":"Report: viii, 50 p.; Software Release","onlineOnly":"Y","ipdsId":"IP-100912","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":436689,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NQ1RFY","text":"USGS data release","linkHelpText":"DGMETA (Version 1): Dissolved Gas Modeling and Environmental Tracer Analysis Computer Program"},{"id":382045,"rank":3,"type":{"id":35,"text":"Software Release"},"url":"https://code.usgs.gov/cawsc/DGMETA","text":"DGMETA","linkHelpText":"- DGMETA (Dissolved Gas Modeling and Environmental Tracer Analysis) is a Microsoft Excel-based computer program that is used for modeling air-water equilibrium conditions from measurements of dissolved gases and for computing concentrations of environmental tracers that rely on air-water equilibrium model results."},{"id":382038,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/04/f05/coverthb.jpg"},{"id":382039,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/04/f05/tm4f5.pdf","text":"Report","size":"8.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 4-F5"}],"contact":"NAWQA Science Team
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 413
Reston, VA 20192–0002
Biodiversity metrics are frequently used to guide conservation planning because they can summarize biogeographical attributes of plant and animal communities quickly and at multiple scales. Attributes include habitat features of high conservation value, representativeness, and redundancy of biological communities. We conducted a rapid ecological assessment of resident avian species in the west-central mountainous region of Puerto Rico in 2015, a landscape dominated by coffee cultivation. We focused on this landscape because shade-grown and restored shade-grown coffee plantations offer an opportunity to complement protected habitat (e.g., reserves) to enhance species persistence. We used species richness, which tallies the number of unique species, and a quadratic entropy index of diversity, which incorporates interspecific taxonomic differentiation to evaluate species representativeness and redundancy across sun- and shade-grown coffee plantations and secondary forest. We surveyed 120 sites, calculating both metrics using species-specific occupancy probabilities estimated from community-level occupancy models. Species representativeness and redundancy were high as neither metric was able to discriminate among habitat types, possibly because plant communities were redundant, and the avian community was dominated by species adept at exploiting altered habitats. Similarly, we could not discriminate among avian communities modeling each biodiversity metric as a function of site-specific habitat covariates. Our findings and available knowledge on avian community demographics suggest that conservation strategies could couple protected habitat (e.g., reserves) and restored habitat (e.g., coffee plantations) to enhance species diversity and persistence across human-modified landscapes.
","language":"English","publisher":"Eagle Hill Publications","usgsCitation":"Battle, K.E., Pacifici, K., Collazo, J.A., and Reigh, B.J., 2020, Using biodiversity metrics to guide conservation planning in altered tropical landscapes: Caribbean Naturalist, v. 77.","ipdsId":"IP-088027","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":394983,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"77","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Battle, K. E.","contributorId":272295,"corporation":false,"usgs":false,"family":"Battle","given":"K.","email":"","middleInitial":"E.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":831924,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pacifici, Krishna","contributorId":244494,"corporation":false,"usgs":false,"family":"Pacifici","given":"Krishna","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":831925,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Collazo, Jaime A. 0000-0002-1816-7744","orcid":"https://orcid.org/0000-0002-1816-7744","contributorId":217287,"corporation":false,"usgs":true,"family":"Collazo","given":"Jaime","email":"","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":831926,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reigh, B. J.","contributorId":272296,"corporation":false,"usgs":false,"family":"Reigh","given":"B.","email":"","middleInitial":"J.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":831927,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70236941,"text":"70236941 - 2020 - Response of the tallest California building during the Mw7.1 July 5, 2019 Ridgecrest, California earthquake","interactions":[],"lastModifiedDate":"2022-09-29T16:35:21.297382","indexId":"70236941","displayToPublicDate":"2020-12-31T11:34:52","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"title":"Response of the tallest California building during the Mw7.1 July 5, 2019 Ridgecrest, California earthquake","docAbstract":"The 73-story Wilshire Grand in downtown Los Angeles is the recently constructed tallest building in California. It is designed in conformance with performance-based design procedures. The lateral load resisting system of the building is designed with concrete core shear walls, three outriggers with buckling restrained braces (BRBs) located along the height and two three-story truss-belt structural systems. The building is equipped with a 36-channel accelerometric seismic monitoring array that recorded the recent Mw7.1 Ridgecrest earthquake of July 5, 2019, as well as the Mw6.4 July 4, 2019 Ridgecrest Earthquake. In this paper, only the Mw7.1 July 5, 2019 event is studied because of a larger response of the subject building during that earthquake. The earthquake records of July 5, 2019 are specifically studied to determine its dynamic characteristics and building specific behavior. The structure exhibits torsional behavior most likely due to abrupt asymmetrical changes in the thickness and size in-plan of the core shear wall system. Modal shapes, frequencies and critical damping percentages of the building are identified. The translational and torsional modes during the earthquake are not closely coupled with fundamental NS, EW and torsional frequencies (periods) of 0.16 (6.25), 0.27(3.70) and 0.42 (2.38) Hz (seconds). This does not lead to a beating effect even though there is an appearance of it in the displacement records. Due to the relatively low amplitude of shaking during the earthquake, the drift ratios are too small to cause any damage. It is expected that during stronger shaking levels likely to be caused by future events, these characteristics may change and the effect of BRB’s can be better assessed.","conferenceTitle":"17th World Conference on Earthquake Engineering","conferenceDate":"Sept. 13-18, 2020","conferenceLocation":"Sendai, Japan","language":"English","publisher":"International Association for Earthquake Engineering","usgsCitation":"Celebi, M., Ghahari, S., Haddadi, H., and Taciroglu, E., 2020, Response of the tallest California building during the Mw7.1 July 5, 2019 Ridgecrest, California earthquake, 17th World Conference on Earthquake Engineering, Sendai, Japan, Sept. 13-18, 2020, C000253.","productDescription":"C000253","ipdsId":"IP-114622","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":407616,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Los Angeles","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.2613956928253,\n 34.04933483636836\n ],\n [\n -118.25936794281006,\n 34.04933483636836\n ],\n [\n -118.25936794281006,\n 34.05107714851305\n ],\n [\n -118.2613956928253,\n 34.05107714851305\n ],\n [\n -118.2613956928253,\n 34.04933483636836\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Celebi, Mehmet 0000-0002-4769-7357 celebi@usgs.gov","orcid":"https://orcid.org/0000-0002-4769-7357","contributorId":200969,"corporation":false,"usgs":true,"family":"Celebi","given":"Mehmet","email":"celebi@usgs.gov","affiliations":[],"preferred":true,"id":852751,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ghahari, S. F.","contributorId":296773,"corporation":false,"usgs":false,"family":"Ghahari","given":"S. F.","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":852752,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haddadi, Hamid","contributorId":296690,"corporation":false,"usgs":false,"family":"Haddadi","given":"Hamid","affiliations":[{"id":12640,"text":"California Geological Survey","active":true,"usgs":false}],"preferred":false,"id":852753,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Taciroglu, Ertugrul","contributorId":176616,"corporation":false,"usgs":false,"family":"Taciroglu","given":"Ertugrul","email":"","affiliations":[],"preferred":false,"id":852754,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70215999,"text":"70215999 - 2020 - The response of streams to changes in atmospheric deposition of sulfur and nitrogen in the Adirondack Mountains","interactions":[],"lastModifiedDate":"2021-10-01T15:52:52.924612","indexId":"70215999","displayToPublicDate":"2020-12-31T10:48:48","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":9141,"text":"Final Report","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"20-19","title":"The response of streams to changes in atmospheric deposition of sulfur and nitrogen in the Adirondack Mountains","docAbstract":"Acidic deposition is the result of upwind sulfur (S) and nitrogen (N) emissions into the atmosphere from human activities. Environmental impacts from acidic deposition across forested landscapes include acidification of soil and drainage water, depletion of available soil nutrient bases, and impacts to and changes in forest and aquatic species composition and biodiversity. Acidic deposition can mobilize aluminum (Al) from soil-to-soil solution and subsequently to drainage water in forms that can be toxic to aquatic life. When exposed to decreasing levels of acidic deposition, which has been occurring in New York since the late 1970s, some soils and drainage waters have become gradually less acidic. Remaining questions relate to effects on stream resources, anticipated resource recovery under increasingly lower levels of deposition, and the levels of deposition (target loads, TLs) needed to reach a range of stream ecosystem recovery targets. Environmental scientists commonly estimate thresholds of air pollutant emissions and resulting atmospheric deposition at which adverse ecological effects are manifested. This analysis is often done using critical loads (CL) and/or TLs, using approaches that account for the spatial and temporal aspects of acidification and recovery. Exceedance represents the extent to which current levels of acidic deposition exceed the level expected to cause ecological harm. The research reported here is intended to help address S and N deposition TLs and ecosystem recovery of Adirondack streams, a resource that has been less thoroughly investigated than lakes. The overarching goal of this work is to highlight key considerations that will help inform decision-makers and ecosystem managers who are responsible for environmental policy in New York State and beyond. Salient aspects of stream TL modeling are discussed with an aim of informing not only scientists, but also policymakers, ecosystem managers, and nonscientists who are required to make decisions related to the effects of acidic deposition on natural ecosystems. Analyses reported herein quantify relations among chemical indicators and metrics of fish community health and biodiversity in streams of the Adirondack Park. This information is used to indicate levels of atmospheric deposition necessary to alleviate harmful effects on fish populations. Results of this investigation provide a framework that can be applied to better understand how modeled stream acid neutralizing capacity (ANC) values that are developed to support TL investigations can be adjusted to reflect high-flow ANC values that may be associated with toxic conditions. Since process models are often calibrated to a low-flow or average flow condition, the magnitude and spatial extent of TL exceedances increase substantially when episodic acidification is considered.
","language":"English","publisher":"New York State Energy Research and Development Authority","usgsCitation":"Driscoll, C., Shao, S., Sullivan, T.J., McDonnell, T.C., Baldigo, B.P., Burns, D., and Lawrence, G.B., 2020, The response of streams to changes in atmospheric deposition of sulfur and nitrogen in the Adirondack Mountains: Final Report 20-19, 166 p.","productDescription":"166 p.","ipdsId":"IP-103637","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":390128,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":390127,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://www.nyserda.ny.gov/-/media/Files/Publications/Research/Environmental/20-19-Responses-of-Streams-in-the-Adirondack-Mountains.pdf"}],"country":"United States","state":"New York","otherGeospatial":"Adirondack Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.00390625,\n 43.11702412135048\n ],\n [\n -73.41064453125,\n 43.44494295526125\n ],\n [\n -73.3447265625,\n 44.000717834282774\n ],\n [\n -73.32275390625,\n 44.32384807250689\n ],\n [\n -73.6083984375,\n 44.84029065139799\n ],\n [\n -74.014892578125,\n 44.933696389694674\n ],\n [\n -74.564208984375,\n 44.793530904744074\n ],\n [\n -75.091552734375,\n 44.53567453241317\n ],\n [\n -75.487060546875,\n 44.06390660801779\n ],\n [\n -75.35522460937499,\n 43.492782808225\n ],\n [\n -74.893798828125,\n 43.18114705939968\n ],\n [\n -74.520263671875,\n 43.068887774169625\n ],\n [\n -74.00390625,\n 43.11702412135048\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Driscoll, Charles T.","contributorId":240874,"corporation":false,"usgs":false,"family":"Driscoll","given":"Charles T.","affiliations":[{"id":5082,"text":"Syracuse University","active":true,"usgs":false}],"preferred":false,"id":803731,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shao, Shuai","contributorId":222597,"corporation":false,"usgs":false,"family":"Shao","given":"Shuai","email":"","affiliations":[{"id":5082,"text":"Syracuse University","active":true,"usgs":false}],"preferred":false,"id":803735,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sullivan, Timothy J.","contributorId":196720,"corporation":false,"usgs":false,"family":"Sullivan","given":"Timothy","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":803732,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McDonnell, Todd C.","contributorId":127622,"corporation":false,"usgs":false,"family":"McDonnell","given":"Todd","email":"","middleInitial":"C.","affiliations":[{"id":7087,"text":"Scientist, E&S Environmental Chemistry Inc, Corvallis OR","active":true,"usgs":false}],"preferred":false,"id":803736,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Baldigo, Barry P. 0000-0002-9862-9119 bbaldigo@usgs.gov","orcid":"https://orcid.org/0000-0002-9862-9119","contributorId":1234,"corporation":false,"usgs":true,"family":"Baldigo","given":"Barry","email":"bbaldigo@usgs.gov","middleInitial":"P.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":803733,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Burns, Douglas A. 0000-0001-6516-2869","orcid":"https://orcid.org/0000-0001-6516-2869","contributorId":202943,"corporation":false,"usgs":true,"family":"Burns","given":"Douglas A.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":803734,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lawrence, Gregory B. 0000-0002-8035-2350 glawrenc@usgs.gov","orcid":"https://orcid.org/0000-0002-8035-2350","contributorId":867,"corporation":false,"usgs":true,"family":"Lawrence","given":"Gregory","email":"glawrenc@usgs.gov","middleInitial":"B.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":803737,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70215354,"text":"70215354 - 2020 - Smallmouth buffalo (Ictiobus bubalus) growth across a 1200km human use and ecological disturbance gradient in the Upper Mississippi River System","interactions":[],"lastModifiedDate":"2021-10-01T15:35:34.106947","indexId":"70215354","displayToPublicDate":"2020-12-31T10:25:19","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":9371,"text":"Mississippi River Restoration Program","active":true,"publicationSubtype":{"id":1}},"displayTitle":"Smallmouth buffalo (Ictiobus bubalus) growth across a 1200km human use and ecological disturbance gradient in the Upper Mississippi River System","title":"Smallmouth buffalo (Ictiobus bubalus) growth across a 1200km human use and ecological disturbance gradient in the Upper Mississippi River System","docAbstract":"Smallmouth buffalo (Ictiobus bubalus) is a common and widely distributed large-bodied species of the family Catostomidae. It inhabits large rivers and reservoirs of the eastern continental United States (east of the continental Divide) and is most abundant and common in the large rivers of the Midwest and Central Plains, though it does occur as far north and east as the Hudson Bay drainage and as far south and west as Arizona (Edwards and Twoney 1982).\n\nHistorically, smallmouth buffalo were an important component of commercial fisheries on both the Mississippi and Illinois Rivers. However, following the introduction of common carp (Cyprinus carpio) in the mid-1800s (Carlander 1954), the construction of a system of navigation dams on Upper Mississippi and Illinois River in the 1930s (USGS 1999), and water quality/pollution issues through the 1980s (Weiner 2010), the role of smallmouth buffalo in the overall UMRS fish community and commercial fishery has generally diminished relative to historical standards. Still, smallmouth buffalo remains an important and valued component of the UMRS commercial fishery.\n\nThe study area is represented by three study reaches on the Illinois River and three study reaches on the Upper Mississippi River (Figure 1). Collectively, these study reaches represent nearly 1200 river km and exist across strong and pronounced ecological and disturbance gradients. For example, habitat composition, water quality, commercial navigation intensity, aquatic plant prominence, and the number and abundance of nonnative fish species vary strongly across the study domain, with northern Mississippi River reaches exhibiting less navigation traffic, better water quality, markedly greater aquatic plant prominence, more diverse habitat composition, and comparably much smaller numbers of nonnative species than the lower Mississippi River study reach and those on the Illinois River (USGS 1999; Johnson and Hagerty [eds] 2008; Irons et al. 2009).\n\nLong term monitoring efforts conducted under the auspices of the Upper Mississippi River Restoration program over the past 27 years have provided tremendous insights into shifts and changes of the overall UMRS fish community (Ickes et al. 2005; Garvey et al. 2010; Schramm and Ickes 2016). However, these monitoring efforts observe only the most basic aspects of the UMRS fish community (i.e., catch, length, weight, distribution, and occurrence). To gain a greater understanding of forces driving community level shifts and changes, more directed study is needed on the functional attributes of fish populations (i.e., growth, mortality, recruitment). Collectively, these functional attributes of populations are termed population dynamics and/or vital rates.\n\nIt is important to note, the population dynamics of fishes in large rivers is generally poorly understood, especially for non-game species (Ickes 2018). The prevailing view is that abiotic factors largely govern inter-annual population dynamics, typically based upon rather short-term observations and correlations with assorted abiotic river attributes that vary on a seasonal or annual basis (for example, Risotto and Turner 1985). However, the role that longer-term abiotic factors play in regulating population abundance, or that biotic factors internal to the population (e.g., spawner-recruit dynamics, growth dynamics) or external to the population (e.g., predator-prey dynamics, sympatric competitors, disease) remain poorly understood. Achieving a greater understanding of these dynamics is important for stock, game, and invasive species management.\n\nIn 2017, as part of a larger study designed to gain vital population rate information for smallmouth buffalo in the Upper Mississippi and Illinois Rivers (“Smallmouth Buffalo population demographics of the Upper Mississippi River System”; UMRR LTRM 2018SOW project items 2018MMBF1-2018MMBF6) annual growth patterns in smallmouth buffalo were determined and evaluated. This was accomplished by measuring growth histories recorded in annual growth increments on hard bony parts (here otoliths), a method known generically as biochronology, and somewhat analogous to dendrochronology practiced by foresters. These methods allow one to generate time-series of annual growth histories that depend upon age, year class (i.e., cohort), and annual environmental conditions experienced by the population over time (Weisberg, 1993).\n\nBiochronology methods were used to develop a 36-year time series of smallmouth buffalo growth in the Upper Mississippi and Illinois Rivers across a 1200 km ecological and human use disturbance gradient. Annual growth intervals were identified and measured from otoliths to determine fish age and growth history. A mixed model that parses the growth increment into age and year effects was fit to these data.\n\nGiven the pronounced ecological and disturbance gradients inherent to the UMRS and the study domain, an a priori expectation of differing patterns in growth is accepted as a null hypothesis to test.\n\nThe goal of this study was to model smallmouth buffalo growth as a function of the age of the fish and the growth year in which the growth was gained. The primary modeling objective was to parse growth observed on each annulus into a portion attributable to the age of the fish and the portion attributable to the year in which the growth was gained. In effect, this modeling approach removes the somewhat trivial age effects on growth so that a non-confounded growth year effect can be gained. Results attributable to growth year provide a time series of growth information that is of the same duration as the oldest fish observed and solely reflects environmental influences on growth. These model responses can then be investigated relative to environmental covariate time-series suspected of influencing growth of smallmouth buffalo in the Upper Mississippi and Illinois Rivers (e.g., temperature, discharge, population density, population mortality, forage availability, sympatric competition, habitat composition, navigation intensity, nonnative fish prominence, etc.). Thus, the primary scientific objective was to investigate if and how smallmouth buffalo growth varies in accordance with innate ecological and disturbance gradients across the study domain.","language":"English","publisher":"US Army Corps of Engineers","usgsCitation":"Ickes, B., 2020, Smallmouth buffalo (Ictiobus bubalus) growth across a 1200km human use and ecological disturbance gradient in the Upper Mississippi River System: Mississippi River Restoration Program, 16 p.","productDescription":"16 p.","ipdsId":"IP-111767","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":390126,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":390125,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://umesc.usgs.gov/reports_publications/ltrmp_rep_list.html"}],"country":"United States","state":"Illinois, Iowa, Minnesota, Missouri, Wisconsin","otherGeospatial":"Illinois river, upper Mississippi River system","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.285400390625,\n 40.329795743702064\n ],\n [\n -90.802001953125,\n 41.1290213474951\n ],\n [\n -90.06591796875,\n 41.88592102814744\n ],\n [\n -90.04394531249999,\n 42.13082130188811\n ],\n [\n -90.889892578125,\n 43.052833917627936\n ],\n [\n -91.14257812499999,\n 43.97700467496408\n ],\n [\n -91.900634765625,\n 44.47299117260252\n ],\n [\n -92.757568359375,\n 44.809121700077355\n ],\n [\n -93.71337890625,\n 45.390735154248894\n ],\n [\n -94.141845703125,\n 45.69083283645816\n ],\n [\n -94.15283203125,\n 46.37725420510028\n ],\n [\n -94.7021484375,\n 46.38483322349276\n ],\n [\n -94.39453125,\n 45.583289756006316\n ],\n [\n -93.44970703125,\n 44.92591837128866\n ],\n [\n -92.186279296875,\n 44.37098696297173\n ],\n [\n -91.64794921875,\n 43.98491011404692\n ],\n [\n -91.38427734374999,\n 43.74728909225908\n ],\n [\n -91.34033203125,\n 43.33316939281732\n ],\n [\n -91.2744140625,\n 42.64204079304426\n ],\n [\n -90.582275390625,\n 42.285437007491545\n ],\n [\n -90.3515625,\n 42.00032514831621\n ],\n [\n -91.219482421875,\n 41.36856413680967\n ],\n [\n -91.669921875,\n 40.622291783092706\n ],\n [\n -91.7138671875,\n 39.93501296038254\n ],\n [\n -90.8349609375,\n 38.75408327579141\n ],\n [\n -90.384521484375,\n 38.762650338334154\n ],\n [\n -90.494384765625,\n 38.298559092254344\n ],\n [\n -89.857177734375,\n 37.60552821745789\n ],\n [\n -89.58251953125,\n 37.02886944696474\n ],\n [\n -89.53857421875,\n 36.82687474287728\n ],\n [\n -89.80224609374999,\n 36.30627216957992\n ],\n [\n -89.384765625,\n 36.32397712011264\n ],\n [\n -89.02221679687499,\n 36.76529191711624\n ],\n [\n -89.110107421875,\n 37.28279464911045\n ],\n [\n -89.49462890625,\n 37.92686760148135\n ],\n [\n -90.263671875,\n 38.35027253825765\n ],\n [\n -89.912109375,\n 39.01064750994083\n ],\n [\n -90.50537109375,\n 39.18969082109678\n ],\n [\n -90.362548828125,\n 39.85915479295669\n ],\n [\n -89.593505859375,\n 40.51379915504413\n ],\n [\n -89.154052734375,\n 41.16211393939692\n ],\n [\n -88.4619140625,\n 41.253032440653186\n ],\n [\n -88.05541992187499,\n 41.3850519497068\n ],\n [\n -88.2861328125,\n 41.590796851056005\n ],\n [\n -89.549560546875,\n 41.42625319507269\n ],\n [\n -90.208740234375,\n 40.613952441166596\n ],\n [\n -90.867919921875,\n 39.90130858574735\n ],\n [\n -91.219482421875,\n 39.8928799002948\n ],\n [\n -91.285400390625,\n 40.329795743702064\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ickes, Brian 0000-0001-5622-3842 bickes@usgs.gov","orcid":"https://orcid.org/0000-0001-5622-3842","contributorId":2925,"corporation":false,"usgs":true,"family":"Ickes","given":"Brian","email":"bickes@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":801849,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70216569,"text":"70216569 - 2020 - An analysis of Twitter responses to the 2019 Ridgecrest Earthquake sequence","interactions":[],"lastModifiedDate":"2021-10-04T11:52:22.142345","indexId":"70216569","displayToPublicDate":"2020-12-31T10:03:26","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"An analysis of Twitter responses to the 2019 Ridgecrest Earthquake sequence","docAbstract":"Previous research has shown that online social networks can provide valuable insights regarding collective human responses to extreme natural events, such as earthquakes. Most previous studies focused on one large earthquake, while the 2019 Ridgecrest earthquakes involved two significant earthquakes occurring within a short period of time (a M6.4 foreshock on July 4 and a M7.1 mainshock on July 5 in southern California). These earthquakes were the first time in more than a decade that the southern California region, with an estimated population of 15 million, felt light to moderate shaking over an extended period of time. This valuable opportunity allows us to study how people respond dynamically to such sequences of extreme events. We collected 510,579 tweets about the 2019 Ridgecrest earthquakes to answer the following research questions: (1) Which Twitter accounts were the major players? Did they behave differently and get different responses? (2) How did the publics' response change during these sequential earthquakes? and (3) Which earthquake-related rumors were disseminated on Twitter during the earthquake sequence, by whom, and at what time?
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"2020 IEEE International Conference on Parallel and Distributed Processing with Applications, Big Data & Cloud Computing","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"2020 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)","conferenceDate":"December 17-19, 2020","conferenceLocation":"Exeter, United Kingdom","language":"English","publisher":"International Conference on Social Computing and Networking","doi":"10.1109/ISPA-BDCloud-SocialCom-SustainCom51426.2020.00127","usgsCitation":"Ruan, T., Kong, Q., Zhang, Y., McBride, S., and Lv, Q., 2020, An analysis of Twitter responses to the 2019 Ridgecrest Earthquake sequence, in 2020 IEEE International Conference on Parallel and Distributed Processing with Applications, Big Data & Cloud Computing, Exeter, United Kingdom, December 17-19, 2020, p. 810-818, https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom51426.2020.00127.","productDescription":"9 p.","startPage":"810","endPage":"818","ipdsId":"IP-118686","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":390122,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Ridgecrest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.157958984375,\n 35.42486791930558\n ],\n [\n -117.3614501953125,\n 35.42486791930558\n ],\n [\n -117.3614501953125,\n 35.92464453144099\n ],\n [\n -118.157958984375,\n 35.92464453144099\n ],\n [\n -118.157958984375,\n 35.42486791930558\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ruan, Tao 0000-0002-6718-7223","orcid":"https://orcid.org/0000-0002-6718-7223","contributorId":245222,"corporation":false,"usgs":false,"family":"Ruan","given":"Tao","email":"","affiliations":[{"id":12502,"text":"University of Colorado - Boulder","active":true,"usgs":false}],"preferred":false,"id":805648,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kong, Qingkai 0000-0002-7399-0661","orcid":"https://orcid.org/0000-0002-7399-0661","contributorId":245223,"corporation":false,"usgs":false,"family":"Kong","given":"Qingkai","email":"","affiliations":[{"id":6643,"text":"University of California - Berkeley","active":true,"usgs":false}],"preferred":false,"id":805649,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhang, Yawen 0000-0002-6867-0399","orcid":"https://orcid.org/0000-0002-6867-0399","contributorId":245225,"corporation":false,"usgs":false,"family":"Zhang","given":"Yawen","email":"","affiliations":[{"id":12502,"text":"University of Colorado - Boulder","active":true,"usgs":false}],"preferred":false,"id":805650,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McBride, Sara K. 0000-0002-8062-6542","orcid":"https://orcid.org/0000-0002-8062-6542","contributorId":206933,"corporation":false,"usgs":true,"family":"McBride","given":"Sara K.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":805651,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lv, Qin","contributorId":245227,"corporation":false,"usgs":false,"family":"Lv","given":"Qin","email":"","affiliations":[{"id":12502,"text":"University of Colorado - Boulder","active":true,"usgs":false}],"preferred":false,"id":805652,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70240295,"text":"70240295 - 2020 - Evaluating and optimizing the use of logistic regression for tree mortality models in the First Order Fire Effects Model (FOFEM)","interactions":[],"lastModifiedDate":"2023-02-03T15:25:05.570097","indexId":"70240295","displayToPublicDate":"2020-12-31T09:24:33","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Evaluating and optimizing the use of logistic regression for tree mortality models in the First Order Fire Effects Model (FOFEM)","docAbstract":"Wildland fires burn millions of forested hectares annually around the world, affecting biodiversity, carbon storage, hydrologic processes, and ecosystem services largely through fire-induced tree mortality (Bond-Lamberty et al. 2007; Dantas et al. 2016). In spite of this widespread importance, the underlying mechanisms of fire-caused tree mortality remain poorly understood, (Hood et al. 2018). Post-fire tree mortality has been traditionally modeled as an empirical function of tree defenses (bark thickness) and fire injury (crown scorch, stem char) (Ryan and Amman 1996; Woolley et al. 2012). Empirical models are commonly used in fire management to predict fire effects (Reinhardt et al. 1997), from the finescale software tools for fire management planning, to process-based succession models (Keane et al. 2011), and global models of the terrestrial carbon cycle (Hantson et al. 2016). Nevertheless, many fire-caused tree mortality models have undergone little evaluation.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the Fire Continuum-Preparing for the future of wildland fire","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"U.S. Forest Service","usgsCitation":"Cansler, C.A., Hood, S., Varner, J., and van Mantgem, P., 2020, Evaluating and optimizing the use of logistic regression for tree mortality models in the First Order Fire Effects Model (FOFEM), in Proceedings of the Fire Continuum-Preparing for the future of wildland fire, p. 239-246.","productDescription":"8 p.","startPage":"239","endPage":"246","ipdsId":"IP-106540","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":412676,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":412660,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.fs.usda.gov/research/treesearch/63223"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Hood, Sharon M.","contributorId":221183,"corporation":false,"usgs":false,"family":"Hood","given":"Sharon","email":"","middleInitial":"M.","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":863362,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Drury, Stacy","contributorId":302054,"corporation":false,"usgs":false,"family":"Drury","given":"Stacy","email":"","affiliations":[],"preferred":false,"id":863363,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Steelman, Toddi A","contributorId":169893,"corporation":false,"usgs":false,"family":"Steelman","given":"Toddi","email":"","middleInitial":"A","affiliations":[{"id":18060,"text":"School of Environment and Sustainability, University of Saskatchewan, Canada","active":true,"usgs":false}],"preferred":false,"id":863364,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Steffens, Ron","contributorId":302055,"corporation":false,"usgs":false,"family":"Steffens","given":"Ron","email":"","affiliations":[],"preferred":false,"id":863365,"contributorType":{"id":2,"text":"Editors"},"rank":4}],"authors":[{"text":"Cansler, C. Alina 0000-0002-2155-4438","orcid":"https://orcid.org/0000-0002-2155-4438","contributorId":225029,"corporation":false,"usgs":false,"family":"Cansler","given":"C.","email":"","middleInitial":"Alina","affiliations":[{"id":41022,"text":"Missoula Fire Science Lab","active":true,"usgs":false}],"preferred":false,"id":863286,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hood, Sharon","contributorId":147091,"corporation":false,"usgs":false,"family":"Hood","given":"Sharon","affiliations":[{"id":16786,"text":"U of Montana, Missoula, MT","active":true,"usgs":false}],"preferred":false,"id":863287,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Varner, J. Morgan","contributorId":265933,"corporation":false,"usgs":false,"family":"Varner","given":"J. Morgan","affiliations":[{"id":36874,"text":"Tall Timbers Research Station","active":true,"usgs":false}],"preferred":false,"id":863288,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"van Mantgem, Phillip J. 0000-0002-3068-9422","orcid":"https://orcid.org/0000-0002-3068-9422","contributorId":204320,"corporation":false,"usgs":true,"family":"van Mantgem","given":"Phillip J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863289,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70217284,"text":"70217284 - 2020 - Hatchling emergence ecology of Ouachita map turtles (Graptemys ouachitensis) on the lower Wisconsin River, Wisconsin, USA","interactions":[],"lastModifiedDate":"2021-01-19T12:40:19.075023","indexId":"70217284","displayToPublicDate":"2020-12-31T08:06:11","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1210,"text":"Chelonian Conservation and Biology","active":true,"publicationSubtype":{"id":10}},"title":"Hatchling emergence ecology of Ouachita map turtles (Graptemys ouachitensis) on the lower Wisconsin River, Wisconsin, USA","docAbstract":"Despite its biological importance in shaping both individual fitness and population structure, much remains to be learned about the hatchling emergence ecology of most freshwater turtles. Here, we provide some of the first details on these early life stages for the Ouachita map turtle (Graptemys ouachitensis) obtained during 2015–2017 along the lower Wisconsin River, Iowa County, Wisconsin, and integrate our results into related research within the genus Graptemys. Dedicated trail cameras over in situ turtle nests provided otherwise difficult to obtain observational data relevant to natural hatchling emergence without disturbing nests or hatchlings. In contrast to some earlier reports for Graptemys, hatchling emergence was mostly diurnal and synchronous, primarily in the morning soon after soil temperatures began to rise from overnight low values. Data suggest a temperature change model of cueing hatchling emergence, which may represent a local or regional adaptation to reduce nocturnal predation risks, mostly from raccoons (Procyon lotor), or may simply reflect default diurnal hatchling activity patterns when not affected by thermal constraints. Aside from predation, hatchlings on this small study site are affected by vegetative shading, leading to relatively long times to first emergence periods (mean, 82.3 d), low mean nest temperatures (25.9°C), and a likely male-biased sex ratio. These findings highlight the value of hatchling emergence studies in revealing important influences on population viability and in guiding appropriate habitat management in conservation efforts.
The Washita River alluvial aquifer is a valley-fill and terrace alluvial aquifer along the valley of the Washita River in western Oklahoma that provides a productive source of groundwater for agricultural irrigation and water supply. The Oklahoma Water Resources Board (OWRB) has designated the westernmost section of the aquifer in Roger Mills and Custer Counties, Okla., as reach 1 of the Washita River alluvial aquifer; reach 1 is the focus of this report. The OWRB issued an order on November 13, 1990, that established the maximum annual yield (MAY; 120,320 acre-feet per year [acre-ft/yr]) and equal-proportionate-share (EPS) pumping rate (2.0 acre-feet per acre per year [(acre-ft/acre)/yr]) for reach 1 of the Washita River alluvial aquifer. The MAY and EPS were based on hydrologic investigations that evaluated the effects of potential groundwater withdrawals on groundwater availability in the Washita River alluvial aquifer. Every 20 years, the OWRB is statutorily required to update the hydrologic investigation on which the MAY and EPS were based. Because 30 years have elapsed since the last order was issued, the U.S. Geological Survey, in cooperation with the OWRB, conducted a new hydrologic investigation and evaluated the effects of potential groundwater withdrawals on groundwater flow and availability in the Washita River alluvial aquifer.
The Washita River is the primary source of inflow to Foss Reservoir, a Bureau of Reclamation reservoir constructed in 1961 for flood control, water supply, and recreation. Foss Reservoir provides water for Bessie, Clinton, New Cordell, and Hobart, Okla. Nearly 98 percent of the total groundwater use from the Washita River alluvial aquifer during 1967 to 2015 was for irrigation; other uses of groundwater in the study area include public supply, mining, and agriculture.
A hydrogeologic framework was developed for the Washita River alluvial aquifer and included the physical characteristics of the aquifer, the geologic setting, the hydraulic properties of hydrogeologic units, the potentiometric surface (water table), and groundwater-flow directions at a scale that captures the regional controls on groundwater flow. The Washita River alluvial aquifer consists of alluvium and terrace deposits that were transported primarily by water and range from clay to gravel in size. The terrace includes windblown deposits of silt size and, in some cases, contains gravel laid down at several levels along former courses of present-day rivers.
A conceptual flow model is a simplified description of the aquifer system that includes hydrologic boundaries, major inflow and outflow sources of the groundwater-flow system, and a conceptual water budget with the estimated mean flows between those hydrologic boundaries. During the study period 1980–2015, mean annual groundwater withdrawals, predominantly used for agricultural irrigation, totaled 5,502 acre-ft/yr, or 14 percent of aquifer outflows. When applied across the 132-square-mile aquifer area used for modeling purposes (84,366 acres), mean annual recharge of 3.15 inches per year corresponds to a mean annual recharge volume of 22,169 acre-ft/yr, or 56 percent of aquifer inflows. The annual saturated-zone evapotranspiration outflow was 11,828 acre-ft/yr for the Washita River alluvial aquifer, or about 30 percent of aquifer outflows. For the Washita River alluvial aquifer, lateral flow was 17,157 acre-ft/yr, or 44 percent of the aquifer inflows. The conceptual flow model and hydrogeologic framework were used to conceptualize, design, and build the numerical groundwater-flow model.
A numerical groundwater-flow model of the Washita River alluvial aquifer was constructed by using MODFLOW-2005. The Washita River alluvial aquifer groundwater-model grid was spatially discretized into 350-foot (ft) cells and two layers. Layer 1 represented the undifferentiated alluvium and terrace deposits of Quaternary age, and layer 2 represented the bedrock of Permian age, which was given a uniform nominal thickness of 100 ft. The groundwater-simulation period was temporally discretized into 433 monthly transient stress periods, representing January 1980 to December 2015. An initial 365-day steady-state stress period was configured to represent mean annual inflows and outflows from the Washita River alluvial aquifer for the study period. The groundwater-flow model was calibrated manually and by automated adjustment of model inputs by using PEST++. Calibration targets for the Washita River alluvial aquifer model included groundwater-level observations and reservoir-stage observations, as well as base-flow and stream-seepage estimates.
Three groundwater-availability scenarios were used in the calibrated groundwater model to (1) estimate the EPS pumping rate that retains the saturated thickness that meets the minimum 20-year life of the aquifer, (2) quantify the effects of projected pumping rates on groundwater storage over a 50-year period, and (3) evaluate how projected pumping rates extended 50 years into the future and sustained hypothetical drought conditions over a 10-year period affect base flow and groundwater in storage. The results of the groundwater-availability scenarios could be used by the OWRB to reevaluate the established MAY of groundwater from the Washita River alluvial aquifer.
EPS scenarios for the Washita River alluvial aquifer were run for periods of 20, 40, and 50 years. The 20-, 40-, and 50-year EPS pumping rates under normal recharge conditions were 1.7, 1.6, and 1.6 (acre-ft/acre)/yr, respectively. Given the aquifer area used for modeling purposes (84,366 acres), these rates correspond to annual yields of 142,579, 134,986, and 134,986 acre-ft/yr, respectively. Groundwater storage at the end of the 20-year EPS scenario was about 281,000 acre-feet (acre-ft), or about 306,000 acre-ft (52 percent) less than the starting storage. Considering the land-surface area of the Washita River alluvial aquifer and using a specific yield of 0.12, this decrease in storage was equivalent to a mean groundwater-level decline of about 30 ft. The Washita River downstream from Foss Reservoir and most of the streams in the study area were dry at the end of the 20-year EPS scenario. Foss Reservoir stage was below the dead-pool stage of 1,597 ft after about 7 years of pumping in the 20-year EPS scenario.
Four projected 50-year groundwater-use scenarios were used to simulate the effects of selected well withdrawal rates on groundwater storage in the Washita River alluvial aquifer. These four scenarios used (1) no groundwater use, (2) groundwater use at the 2015 pumping rate, (3) mean groundwater use for the simulation period, and (4) increasing groundwater use. Groundwater storage after 50 years with no groundwater use was 545,249 acre-ft, or 693 acre-ft (0.1 percent) greater than the initial groundwater storage; this groundwater storage increase is equivalent to a mean groundwater-level increase of 0.1 ft. Groundwater storage at the end of the 50-year period with 2015 pumping rates was 543,831 acre-ft, or 723 acre-ft (0.1 percent) less than the initial storage; this groundwater storage decrease is equivalent to a mean groundwater-level decrease of 0.1 ft. Groundwater storage after 50 years with the mean pumping rate for the study period was 543,202 acre-ft, or 1,349 acre-ft (0.2 percent) less than the initial groundwater storage; this groundwater storage decrease is equivalent to a mean groundwater-level decrease of 0.1 ft. Groundwater storage at the end of the 50-year period with an increasing demand groundwater-pumping rate, which was 38 percent greater than the 2015 groundwater-pumping rate, was 542,584 acre-ft, or 1,967 acre-ft (0.4 percent) less than the initial storage; this groundwater storage decrease is equivalent to a mean groundwater-level decrease of 0.2 ft.
A hypothetical 10-year-drought scenario was used to simulate the effects of a prolonged period of reduced recharge on groundwater storage in the Washita River alluvial aquifer and Foss Reservoir stage and storage. To simulate the hypothetical drought, recharge in the calibrated model was reduced by 50 percent during the simulated drought period (1983–1992). Groundwater storage at the end of the drought period in December 1992 was 562,000 acre-ft, or 36,000 acre-ft (6 percent) less than the groundwater storage of the calibrated groundwater model (598,000 acre-ft). At the end of the hypothetical drought, the largest changes in saturated thickness (as great as 43.5 ft) were in the area upgradient from Foss Reservoir, particularly in the terrace at the model boundary. Substantial decreases in the Foss Reservoir stage began during the fall of 1985 in conjunction with base-flow decreases of up to 100 percent at U.S. Geological Survey streamgage 07324200 Washita River near Hammon, Okla. These lake-stage declines outpaced groundwater-level declines in the surrounding aquifer. The minimum Foss Reservoir storage simulated during the drought period was 77,954 acre-ft, which was a decrease of 46 percent from the nondrought storage.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205118","collaboration":"Prepared in cooperation with the Oklahoma Water Resources Board","usgsCitation":"Ellis, J.H., Ryter, D.W., Fuhrig, L.T., Spears, K.W., Mashburn, S.L., and Rogers, I.M.J., 2020, Hydrogeology, numerical simulation of groundwater flow, and effects of future water use and drought for reach 1 of the Washita River alluvial aquifer, Roger Mills and Custer Counties, western Oklahoma, 1980–2015: U.S. Geological Survey Scientific Investigations Report 2020–5118, 81 p., https://doi.org/10.3133/sir20205118.","productDescription":"Report: xi, 81 p.; Data Release","numberOfPages":"98","onlineOnly":"Y","ipdsId":"IP-116035","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":381399,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PKMG6U","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW-NWT model used in simulation of groundwater flow, and analysis of projected water use for the Washita River alluvial aquifer, western Oklahoma"},{"id":381398,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5118/sir20205118.pdf","text":"Report","size":"18.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5118"},{"id":381397,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5118/coverthb.jpg"}],"country":"United States","state":"Oklahoma","county":"Roger Mills County, Custer County","otherGeospatial":"Washita River alluvial aquifer","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-98.6305,35.812],[-98.6308,35.6387],[-98.6307,35.552],[-98.6199,35.552],[-98.6209,35.4639],[-98.8338,35.4653],[-98.9399,35.4659],[-99.0455,35.4654],[-99.1517,35.4658],[-99.3629,35.4649],[-99.3631,35.508],[-99.5755,35.5085],[-99.576,35.42],[-100.0009,35.4223],[-100.0014,35.4558],[-100.0011,35.6197],[-100.001,35.64],[-100.0015,35.8008],[-100.0015,35.8782],[-99.9742,35.8921],[-99.9566,35.8959],[-99.947,35.9009],[-99.938,35.9037],[-99.9272,35.9074],[-99.9228,35.9115],[-99.9177,35.9175],[-99.9132,35.9234],[-99.911,35.928],[-99.9082,35.9325],[-99.9049,35.9371],[-99.9038,35.9462],[-99.9045,35.9562],[-99.9051,35.9589],[-99.899,35.9698],[-99.894,35.9748],[-99.8522,36.0051],[-99.8398,36.0115],[-99.829,36.0107],[-99.8227,36.0089],[-99.8152,36.0026],[-99.8078,35.9949],[-99.8019,35.9827],[-99.8019,35.9737],[-99.8051,35.9618],[-99.809,35.9518],[-99.8111,35.9364],[-99.8099,35.9287],[-99.8087,35.9246],[-99.8007,35.9174],[-99.7938,35.9102],[-99.788,35.8962],[-99.784,35.8921],[-99.7725,35.8867],[-99.76,35.885],[-99.7521,35.8824],[-99.7372,35.8738],[-99.7258,35.8653],[-99.7189,35.8626],[-99.7149,35.854],[-99.6979,35.855],[-99.6774,35.847],[-99.6615,35.847],[-99.6558,35.8457],[-99.6416,35.8444],[-99.6291,35.84],[-99.6149,35.84],[-99.6042,35.8478],[-99.6002,35.8519],[-99.5929,35.8551],[-99.5855,35.8574],[-99.577,35.8588],[-99.5623,35.8621],[-99.5578,35.8675],[-99.5562,35.8825],[-99.5416,35.903],[-99.532,35.9076],[-99.5241,35.9185],[-99.5156,35.9281],[-99.5067,35.9481],[-99.5061,35.9535],[-99.5085,35.9608],[-99.5085,35.9649],[-99.5045,35.9703],[-99.4995,35.974],[-99.4876,35.9795],[-99.4785,35.9899],[-99.4638,35.9995],[-99.4445,36.01],[-99.4303,36.016],[-99.4144,36.0169],[-99.3928,36.017],[-99.3809,36.017],[-99.3808,35.8991],[-99.374,35.8991],[-99.3736,35.8111],[-99.0571,35.8112],[-98.7366,35.8118],[-98.6305,35.812]]]},\"properties\":{\"name\":\"Custer\",\"state\":\"OK\"}}]}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, Texas 78754–4501
Outbursts from impounded water bodies produce large, hazardous, and geomorphically significant floods affecting the Earth as well as other planetary surfaces. Two broad classes of impoundments are: (1) valleys blocked by ice, landslides, constructed dams, and volcanic materials; and (2) closed basins such as tectonic depressions, calderas, meteor craters, and those rimmed by glaciers and moraines. In some environments, floods emanate from subglacial and subterranean sources. Outburst floods are geomorphically important over geologic time because large flows achieve exceptional shear stress and stream power values, thus forming some of the most spectacular landscapes in the solar system.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Reference Module in Earth Systems and Environmental Sciences","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Elsevier","doi":"10.1016/B978-0-12-818234-5.00007-9","usgsCitation":"O'Connor, J., Clague, J.J., Walder, J.S., Manville, V., and Beebee, R.A., 2020, Outburst floods, chap. of Reference Module in Earth Systems and Environmental Sciences, HTML Document, https://doi.org/10.1016/B978-0-12-818234-5.00007-9.","productDescription":"HTML Document","ipdsId":"IP-120078","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":382557,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"O'Connor, Jim E. 0000-0002-7928-5883 oconnor@usgs.gov","orcid":"https://orcid.org/0000-0002-7928-5883","contributorId":140771,"corporation":false,"usgs":true,"family":"O'Connor","given":"Jim E.","email":"oconnor@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":false,"id":808096,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Clague, John J.","contributorId":191448,"corporation":false,"usgs":false,"family":"Clague","given":"John","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":808097,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walder, Joseph S. 0000-0003-3523-2998 jswalder@usgs.gov","orcid":"https://orcid.org/0000-0003-3523-2998","contributorId":247681,"corporation":false,"usgs":true,"family":"Walder","given":"Joseph","email":"jswalder@usgs.gov","middleInitial":"S.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":808098,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Manville, Vernon","contributorId":247682,"corporation":false,"usgs":false,"family":"Manville","given":"Vernon","affiliations":[{"id":49608,"text":"University of Leeds, Leeds, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":808099,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Beebee, Robin A. 0000-0002-2976-7294 rbeebee@usgs.gov","orcid":"https://orcid.org/0000-0002-2976-7294","contributorId":5778,"corporation":false,"usgs":true,"family":"Beebee","given":"Robin","email":"rbeebee@usgs.gov","middleInitial":"A.","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":808100,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70217046,"text":"ofr20201142 - 2020 - Changing storm conditions in response to projected 21st century climate change and the potential impact on an arctic barrier island–lagoon system—A pilot study for Arey Island and Lagoon, eastern Arctic Alaska","interactions":[],"lastModifiedDate":"2020-12-30T12:49:16.90443","indexId":"ofr20201142","displayToPublicDate":"2020-12-29T16:50:00","publicationYear":"2020","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":"2020-1142","displayTitle":"Changing Storm Conditions in Response to Projected 21st Century Climate Change and the Potential Impact on an Arctic Barrier Island–Lagoon System—A Pilot Study for Arey Island and Lagoon, Eastern Arctic Alaska","title":"Changing storm conditions in response to projected 21st century climate change and the potential impact on an arctic barrier island–lagoon system—A pilot study for Arey Island and Lagoon, eastern Arctic Alaska","docAbstract":"Arey Lagoon, located in eastern Arctic Alaska, supports a highly productive ecosystem, where soft substrate and coastal wet sedge fringing the shores are feeding grounds and nurseries for a variety of marine fish and waterfowl. The lagoon is partially protected from the direct onslaught of Arctic Ocean waves by a barrier island chain (Arey Island) which in itself provides important habitat for migratory shorebirds and waterfowl. In this work, numerically modeled waves and water levels are computed under the provision of sea-level rise and changing conditions brought about by 21st century climate variability. Model results, supported by observations, are used to assess the stability of the barrier chain and spatiotemporal changes in flood patterns across fringing coastal wet sedge areas. The results aim to support studies that investigate the possibility of new biological succession trajectories and loss or increase of habitat areas.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201142","collaboration":"Prepared in cooperation with and funded in part by the Arctic Landscape Conservation Cooperation (ALCC)","usgsCitation":"Erikson, L.H., Gibbs, A.E., Richmond, B.M., Storlazzi, C.D., Jones, B.M., and Ohman, K.A., 2020, Changing storm conditions in response to projected 21st century climate change and the potential impact on an arctic barrier island–lagoon system—A pilot study for Arey Island and Lagoon, eastern Arctic Alaska: U.S. Geological Survey Open-File Report 2020–1142, 68, p., https://doi.org/10.3133/ofr20201142.","productDescription":"Report: x, 68 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-079323","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":381735,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LGYO2Q","text":"USGS data release","linkHelpText":"Modeled 21st century storm surge, waves, and coastal flood hazards and supporting oceanographic and geological field data (2010 and 2011) for Arey and Barter Islands, Alaska and vicinity"},{"id":381739,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1142/coverthb.jpg"},{"id":381740,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1142/ofr20201142.pdf","text":"Report","size":"8.98 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1142"}],"country":"United States","state":"Alaska","otherGeospatial":"Arey Island and Lagoon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -144.09805297851562,\n 70.03559723423488\n ],\n [\n -143.6407470703125,\n 70.03559723423488\n ],\n [\n -143.6407470703125,\n 70.13476515043729\n ],\n [\n -144.09805297851562,\n 70.13476515043729\n ],\n [\n -144.09805297851562,\n 70.03559723423488\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Pacific Coastal and Marine Science Center
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We developed a blended (or hybrid) interactive course—Managing for a Changing Climate—that provides a holistic view of climate change. The course results from communication with university students and natural and cultural resource managers as well as the need for educational efforts aimed at the public, legislators, and decision-makers. Content includes the components of the physical climate system, natural climate variability, anthropogenic drivers of climate change, climate models and projections, climate assessments, energy economics, environmental policy, vulnerabilities to climate hazards, impacts of climate change, and decision-making related to climate adaptation and mitigation efforts. To convey most of the content, the course-development team created over 50 short videos (3–10 min each) in partnership with experts from a variety of academic, government, and industry institutions. The blended course has been offered as an upper-division, undergraduate course in the Department of Geography and Environmental Sustainability and School of Meteorology (four times) and College of International Studies (in Italy, once) at the University of Oklahoma with over 100 total students. The course has also been presented online-only at no cost to the participants in four fall semesters with over 1,000 total registrations. Videos created for this course are freely available on the YouTube page of the South Central Climate Adaptation Science Center. This course and its associated materials comprise high-quality, formal climate training and education that can be adapted to other formal and informal education settings beyond the walls of the university.
Environmental DNA (eDNA) can be used for early detection, population estimations, and assessment of potential spread of invasive species, but questions remain about factors that influence eDNA detection results. Efforts are being made to understand how physical, chemical, and biological factors—settling, resuspension, dispersion, eDNA stability/decay—influence eDNA estimations and potentially population abundance. In a series of field and controlled mesocosm experiments, we examined the detection and accumulation of eDNA in sediment and water and the transport of eDNA in a small stream in the Lake Michigan watershed, using the invasive round goby fish (Neogobius melanostomus) as a DNA source. Experiment 1: caged fish (average n = 44) were placed in a stream devoid of round goby; water was collected over 24 hours along 120-m of stream, including a simultaneous sampling event at 7 distances from DNA source; stream monitoring continued for 24 hours after fish were removed. Experiment 2: round goby were placed in laboratory tanks; water and sediment were collected over 14 days and for another 150 days post-fish removal to calculate eDNA shedding and decay rates for water and sediment. For samples from both experiments, DNA was extracted, and qPCR targeted a cytochrome oxidase I gene (COI) fragment specific to round goby. Results indicated that eDNA accumulated and decayed more slowly in sediment than water. In the stream, DNA shedding was markedly lower than calculated in the laboratory, but models indicate eDNA could potentially travel long distances (up to 50 km) under certain circumstances. Collectively, these findings show that the interactive effects of ambient conditions (e.g., eDNA stability and decay, hydrology, settling-resuspension) are important to consider when developing comprehensive models. Results of this study can help resource managers target representative sites downstream of potential invasion sites, thereby maximizing resource use.
","language":"English","publisher":"Public Library of Science (PLoS)","doi":"10.1371/journal.pone.0244086","usgsCitation":"Nevers, M., Przybyla-Kelly, K., Shively, D., Morris, C.C., Dickey, J., and Byappanahalli, M., 2020, Influence of sediment and stream transport on detecting a source of environmental DNA: PLoS ONE, v. 15, no. 12, e0244086, 21 p., https://doi.org/10.1371/journal.pone.0244086.","productDescription":"e0244086, 21 p.","ipdsId":"IP-120509","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":454617,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0244086","text":"Publisher Index Page"},{"id":436691,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HI425V","text":"USGS data release","linkHelpText":"Environmental DNA detection and survival, influence of sediment, and stream transport in a Lake Michigan watershed, 2018"},{"id":381935,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"12","noUsgsAuthors":false,"publicationDate":"2020-12-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Nevers, Meredith B. 0000-0001-6963-6734","orcid":"https://orcid.org/0000-0001-6963-6734","contributorId":201531,"corporation":false,"usgs":true,"family":"Nevers","given":"Meredith B.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":807644,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Przybyla-Kelly, Katarzyna 0000-0001-9168-3545 kprzybyla-kelly@usgs.gov","orcid":"https://orcid.org/0000-0001-9168-3545","contributorId":201534,"corporation":false,"usgs":true,"family":"Przybyla-Kelly","given":"Katarzyna","email":"kprzybyla-kelly@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":807645,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shively, Dawn A.","contributorId":247309,"corporation":false,"usgs":false,"family":"Shively","given":"Dawn A.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":807646,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Morris, Charles C.","contributorId":201532,"corporation":false,"usgs":false,"family":"Morris","given":"Charles","email":"","middleInitial":"C.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":807647,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dickey, Joshua","contributorId":201536,"corporation":false,"usgs":false,"family":"Dickey","given":"Joshua","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":807648,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Byappanahalli, Muruleedhara 0000-0001-5376-597X byappan@usgs.gov","orcid":"https://orcid.org/0000-0001-5376-597X","contributorId":147923,"corporation":false,"usgs":true,"family":"Byappanahalli","given":"Muruleedhara","email":"byappan@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":807649,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}