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":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","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":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","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":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic 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
U.S. Geological Survey
Pacific Science Center
2885 Mission St.
Santa Cruz, CA 95060
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}]}} ,{"id":70217713,"text":"70217713 - 2020 - Effects of fish populations on Pacific Loon (Gavia pacifica) and Yellow-billed Loon (G. adamsii) lake occupancy and chick production in northern Alaska","interactions":[],"lastModifiedDate":"2021-02-01T14:24:20.683608","indexId":"70217713","displayToPublicDate":"2020-12-27T07:50:51","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":894,"text":"Arctic","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Effects of fish populations on Pacific Loon (Gavia pacifica) and Yellow-billed Loon (G. adamsii) lake occupancy and chick production in northern Alaska","title":"Effects of fish populations on Pacific Loon (Gavia pacifica) and Yellow-billed Loon (G. adamsii) lake occupancy and chick production in northern Alaska","docAbstract":"Predator populations are vulnerable to changes in prey distribution or availability. With warming temperatures, lake ecosystems in the Arctic are predicted to change in terms of hydrologic flow, water levels, and connectivity with other lakes. We surveyed lakes in northern Alaska to understand how shifts in the distribution or availability of fish may affect the occupancy and breeding success of Pacific (Gavia pacifica) and Yellow-billed Loons (G. adamsii). We then modeled the influence of the presence and abundance of five fish species and the physical characteristics of lakes (e.g., hydrologic connectivity) on loon lake occupancy and chick production. The presence of Alaska blackfish (Dallia pectoralis) had a positive influence on Pacific Loon occupancy and chick production, which suggests that small-bodied fish species provide important prey for loon chicks. No characteristics of fish species abundance affected Yellow-billed Loon lake occupancy. Instead, Yellow-billed Loon occupancy was influenced by the physical characteristics of lakes that contribute to persistent fish populations, such as the size of the lake and the proportion of the lake that remained unfrozen over winter. Neither of these variables, however, influenced chick production. The probability of an unoccupied territory becoming occupied in a subsequent year by Yellow-billed Loons was low, and no loon chicks were successfully raised in territories that were previously unoccupied. In contrast, unoccupied territories had a much higher probability of becoming occupied by Pacific Loons, which suggests that Yellow-billed Loons have strict habitat requirements and suitable breeding lakes may be limited. Territories that were occupied had high probabilities of remaining occupied for both loon species.
With the advent of highly precise total stations and modern surveying instrumentation, trigonometric leveling has become a compelling alternative to conventional leveling methods for establishing vertical-control networks and for perpetuating a datum to field sites. Previous studies of trigonometric-leveling measurement uncertainty proclaim that first-, second-, and third-order accuracies may be achieved if strict leveling protocols are rigorously observed. Common field techniques to obtain quality results include averaging zenith angles and slope distances observed in direct and reverse instrument orientation (F1 and F2, respectively), multiple sets of reciprocal observations, quality meteorological observations to correct for the effects of atmospheric refraction, and electronic distance measurements that generally do not exceed 500 feet. In general, third-order specifications are required for differences between F1 and F2 zenith angles and slope distances; differences between redundant instrument-height measurements; section misclosure determined from reciprocal observations; and closure error for closed traverse. For F1 observations such as backsight check and check shots, the construction-grade specification is required for elevation differences between known and observed values.
Recommended specifications for trigonometric-leveling equipment include a total station instrument with an angular uncertainty specification less than or equal to plus or minus 5 arc-seconds equipped with an integrated electronic distance measurement device with an uncertainty specification of less than or equal to plus or minus 3 millimeters plus 3 parts per million. A paired data collector or integrated microprocessor should have the capability to average multiple sets of measurements in direct and reverse instrument orientation. Redundant and independent measurements by the survey crew and automated or manual reduction of slant heights to the vertical equivalent are recommended to obtain quality instrument heights. Horizontal and vertical collimation tests should be conducted daily during trigonometric-leveling surveys, and electronic distance-measurement instruments should be tested annually on calibrated baselines maintained by the National Geodetic Survey. Specifications that were developed by the National Geodetic Survey for geodetic leveling have been adapted by the U.S. Geological Survey (USGS) for the purpose of developing standards for trigonometric leveling, which are identified as USGS Trigonometric Level I (TL I), USGS Trigonometric Level II (TL II), USGS Trigonometric Level III (TL III), and USGS Trigonometric Level IV (TL IV). TL I, TL II, and TL III surveys have a combination of first, second, and third geodetic leveling specifications that have been modified for plane leveling. The TL III category also has specifications that are adapted from construction-grade standards, which are not recognized by the National Geodetic Survey for geodetic leveling. A TL IV survey represents a leveling approach that does not generally meet criteria of a TL I, TL II, or TL III survey.
Site conditions, such as highly variable topography, and the need for cost-effective, rapid, and accurate data collection in response to coastal or inland flooding have emphasized the need for an alternative approach to conventional leveling methods. Trigonometric leveling and the quality-assurance methods described in this manual will accommodate most site and environmental conditions, but measurement uncertainty is potentially variable and dependent on the survey method. Two types of closed traverse surveys have been identified as reliable methods to establish and perpetuate vertical control: the single-run loop traverse and double-run spur traverse. Leveling measurements for a double-run spur traverse are made in the forward direction from the origin to the destination and are then retraced along the same leveling route in the backward direction, from the destination to the origin. Every control point in a double-run spur traverse is occupied twice. Leveling measurements for a single-run loop traverse are made in the forward direction from the origin point to the destination, and then from the destination to the origin point, along a different leveling route. The only point that is redundantly occupied for the single-run loop traverse is the origin. An open traverse method is also considered an acceptable approach to establish and perpetuate vertical control if the foresight prism height is changed between measurement sets to ensure at least two independent observations. A modified version of leap-frog leveling is recommended for all traverse surveys because it reduces measurement uncertainty by forcing the surveying instrumentation into a level and centered condition over the ground point as the instrumentation is advanced to the objective. Sideshots are considered any radial measurement made from the total station that is not part of a traverse survey. F1 and F2 observations are recommended for sideshots measurements for projects that require precise elevations. Quality-assurance measurements made in F1 from the station to network-control points should be considered for surveys that require a high quantity of sideshots.
The accuracy of a trigonometric-leveling survey essentially depends on four components (1) the skill and experience of the surveyor, (2) the environmental or site conditions, (3) the surveying method, and (4) the quality of the surveying instrumentation. Although components one and two can sometimes be difficult to evaluate and be highly variable, the objective of this manual is to disseminate information needed to identify, maintain, and operate quality land-surveying instrumentation, and to document procedures and best practices for preparing and executing precision trigonometric-leveling surveys in the USGS.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm11D3","usgsCitation":"Noll, M.L., and Rydlund, P.H., 2020, Procedures and best practices for trigonometric leveling in the U.S. Geological Survey: U.S. Geological Survey Techniques and Methods, book 11, chap. D3, 94 p., https://doi.org/10.3133/tm11D3.","productDescription":"Report: vii, 93 p.; Appendix","numberOfPages":"94","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-108800","costCenters":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":381587,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/11d3/coverthb.jpg"},{"id":381588,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/11d3/tm11d3.pdf","text":"Report","size":"6.25 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 11-D3"},{"id":381589,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/11d3/tm11d3_appendix1.pdf","text":"Appendix 1","size":"207 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Standard Field Form for Running Trigonometric Levels"}],"contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Confined (or buried) aquifers of glacial origin overlain by till confining units provide drinking water to hundreds of thousands of Minnesota residents. The sustainability of these groundwater resources is not well understood because hydraulic properties of till that control vertical groundwater fluxes (leakage) to underlying aquifers are largely unknown. The U.S. Geological Survey, Iowa State University, Minnesota Geological Survey, and Minnesota Department of Health investigated hydraulic properties and groundwater flow through till confining units using field studies and heuristic MODFLOW simulations. Till confining units in the following late-Wisconsinan stratigraphic units (with locations in parentheses) were characterized: Des Moines lobe till of the New Ulm Formation (Litchfield, Minnesota), Superior lobe till of the Cromwell and Aitkin Formations (Cromwell, Minn.), and Wadena lobe till of the Hewitt Formation (hydrogeology field camp [HFC] near Akeley, Minn.). Pre-Illinoian till of the Good Thunder formation (Olivia, Minn.) was also characterized.
Hydraulic and geochemical field data were collected from sediment cores and a series of five piezometer nests. Each nest consisted of five to eight piezometers screened at short vertical intervals in hydrostratigraphic units including (if present) surficial aquifers, till confining units, confined/buried aquifers, and underlying bedrock. Till hydraulic conductivity was estimated from slug tests (horizontal [Kh]) and constant-rate aquifer tests in the confined aquifer (vertical [Kv]). Travel times through the till were evaluated with Darcy’s law and stable isotope concentrations. A series of heuristic MODFLOW simulations were used to evaluate groundwater fluxes through till across the range of till hydraulic properties and pumping rates observed at the field sites.
The field data demonstrated variability in hydraulic properties between and within till stratigraphic units horizontally and vertically. The variability in hydraulic properties within and between sites resulted in substantial differences in groundwater flux through till. A conceptual understanding that emerges from the vertical till profiles is that they are not homogeneous hydrostratigraphic units with uniform properties; rather, each vertical sequence is a heterogeneous mixture of glacial sediment with differing abilities to transmit water.
Till thicknesses varied from 60 to 166 feet, and till textures ranged from a sandy loam (Hewitt Formation, HFC site) to a silt loam/clay loam (Good Thunder formation, Olivia site). Till Kh varied by one to three orders of magnitude within each piezometer nest. Four piezometer nests had downward hydraulic gradients ranging from 0.04 to 0.56, and one nest had a slight upward hydraulic gradient of 0.02. The Cromwell, HFC, and Litchfield 1 sites were examples of “leaky” tills with high Kv (0.001 to 1.1 feet per day [ft/d]) and geometric mean Kh (0.03 to 0.07 ft/d) and extensive vertical hydraulic connectivity between the confined aquifer and the overlying till. Estimated groundwater travel times through these sites ranged from 1 to 81 years, and two of these sites had tritium throughout their till profiles. The tills at the other two sites, Olivia and Litchfield 2, were effective confining units that had low Kv (0.001 to 0.0005 ft/d) and geometric mean Kh (0.0002 to 0.004 ft/d). The till piezometers at these sites had no drawdown response to short-term (up to 10 hours for Olivia and up to 5 days for Litchfield) high-capacity pumping from the confined aquifer. Estimated groundwater travel times through the tills at these sites ranged from 165 to nearly 1,800 years, and tritium was only detected in the upper one-third of these till profiles. Across all sites, the till vertical anisotropy (ratio of Kh to Kv) ranged by four orders of magnitude from 0.05 at the Cromwell nest to 70 at the Litchfield 1 nest. Stable isotopes of oxygen and hydrogen indicate that groundwater throughout all five till profiles is younger than the last glacial advance into Minnesota at about 11,000 years ago.
The heuristic modeling demonstrated that, for understanding sustainability of groundwater pumping from confined aquifers, knowledge of till hydraulic properties is just as important as knowledge of aquifer hydraulic properties. Substantial differences in groundwater fluxes into and through till were observed across hydrogeologic settings representative of the field sites. Over long periods of time (hundreds of years), pumping-induced hydraulic gradients are established in confined aquifer systems and, even in low hydraulic conductivity tills, these pumping-induced hydraulic gradients increase leakage into and through till compared to ambient conditions.
In conclusion, groundwater flowing vertically downward through till confining units (leakage) replenishes water pumped from confined aquifers. Till hydraulic properties, such as those presented in this report, provide important information that can be used to quantify leakage rates through till. Till hydraulic properties are variable over short distances and profoundly affect leakage rates, demonstrating the importance of site-specific till hydraulic data for evaluating the sustainability of groundwater withdrawals from confined aquifers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205127","collaboration":"Prepared in cooperation with the Legislative-Citizen Commission on Minnesota Resources and in collaboration with Iowa State University and the Minnesota Department of Health","usgsCitation":"Trost, J.J., Maher, A., Simpkins, W.W., Witt, A.N., Stark, J.R., Blum, J., and Berg, A.M., 2020, Hydrogeology and groundwater geochemistry of till confining units and confined aquifers in glacial deposits near Litchfield, Cromwell, Akeley, and Olivia, Minnesota, 2014–18: U.S. Geological Survey Scientific Investigations Report 2020–5127, 80 p., https://doi.org/10.3133/sir20205127.","productDescription":"Report: ix, 80 p.; 2 Data Releases; Dataset","numberOfPages":"94","onlineOnly":"Y","ipdsId":"IP-103595","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":381538,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS dataset","linkHelpText":"— USGS water data for the Nation"},{"id":381534,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5127/coverthb.jpg"},{"id":381535,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5127/sir20205127.pdf","text":"Report","size":"4.21 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5127"},{"id":381536,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IXC7D3","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Geochemical data, water-level data, and slug test analysis results from till confining units and confined aquifers in glacial deposits near Akeley, Cromwell, Litchfield, and Olivia, Minnesota, 2015–2018"},{"id":381537,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KOI6T3","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Heuristic MODFLOW models used to evaluate the effects of pumping groundwater from confined aquifers overlain by till confining units"}],"country":"United States","state":"Minnesota","city":"Akeley, Cromwell, Litchfield, Olivia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.5758056640625,\n 45.084672408703945\n ],\n [\n -94.48173522949219,\n 45.084672408703945\n ],\n [\n -94.48173522949219,\n 45.16364237397378\n ],\n [\n -94.5758056640625,\n 45.16364237397378\n ],\n [\n -94.5758056640625,\n 45.084672408703945\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.91549682617188,\n 46.65320687122665\n ],\n [\n -92.84614562988281,\n 46.65320687122665\n ],\n [\n -92.84614562988281,\n 46.6965511173143\n ],\n [\n -92.91549682617188,\n 46.6965511173143\n ],\n [\n -92.91549682617188,\n 46.65320687122665\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.74918365478516,\n 46.987044654111116\n ],\n [\n -94.70970153808594,\n 46.987044654111116\n ],\n [\n -94.70970153808594,\n 47.01678007415223\n ],\n [\n -94.74918365478516,\n 47.01678007415223\n ],\n [\n -94.74918365478516,\n 46.987044654111116\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.02418518066406,\n 44.74185630294231\n ],\n [\n -94.95758056640625,\n 44.74185630294231\n ],\n [\n -94.95758056640625,\n 44.79986517347138\n ],\n [\n -95.02418518066406,\n 44.79986517347138\n ],\n [\n -95.02418518066406,\n 44.74185630294231\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, MN 55112
PEST++ Version 5 extends and enhances the functionality of the PEST++ Version 3 software suite, providing environmental modeling practitioners access to updated Version 3 tools as well as new tools to support decision making with environmental models. Version 5 of PEST++ includes tools for global sensitivity analysis (PESTPP-SEN); least-squares parameter estimation with integrated first-order, second-moment parameter and forecast uncertainty estimation (PESTPP-GLM); an iterative, localized ensemble smoother (PESTPP-IES); and a tool for management optimization under uncertainty (PESTPP-OPT). Additionally, all PEST++ Version 5 tools have a built-in fault-tolerant, multithreaded parallel run manager and are model independent, using the same protocol as the widely used PEST software suite.
PEST++ Version 5 is consistent with PEST++ Version 3 conventions and design philosophy. The software’s emphasis continues to target efficient and optimized algorithms that have proven beneficial in decision-support settings and can accommodate large, highly parameterized problems. Expanded and new capabilities are now available to express uncertainty using Monte Carlo and analytical uncertainty approaches and allow evaluation of thousands to millions of parameters. New management optimization capabilities in Version 5 also allow environmental models to be used to answer management questions using multiple societal constraints in a risk-based framework.
The PEST++ Version 5 software suite can be compiled for Microsoft Windows® and Unix-based operating systems such as Apple and Linux®; the source code is available with a Microsoft Visual Studio® 2019 solution; and CMake support for all three operating system is also provided. PEST++ Version 5 continues to build a foundation for an open-source framework capable of producing model-independent, robust, and efficient decision-support tools for large environmental models. The functionality of each of the PEST++ tools are demonstrated on a simple example problem. Implications of decisions used when using the PEST++ suite tools are also discussed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm7C26","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency Great Lakes Restoration Initiative","usgsCitation":"White, J.T., Hunt, R.J., Fienen, M.N., and Doherty, J.E., 2020, Approaches to Highly Parameterized Inversion: PEST++ Version 5, a Software Suite for Parameter Estimation, Uncertainty Analysis, Management Optimization and Sensitivity Analysis: U.S. Geological Survey Techniques and Methods 7C26, 52 p., https://doi.org/10.3133/tm7C26.","productDescription":"Report: viii, 52 p.; Software Release","numberOfPages":"64","onlineOnly":"Y","ipdsId":"IP-119615","costCenters":[],"links":[{"id":436694,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YTQ5PY","text":"USGS data release","linkHelpText":"PEST++ Version 5.0 source code, pre-compiled binaries and example problem"},{"id":381481,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/07/c26/coverthb.jpg"},{"id":381482,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/07/c26/tm7c26.pdf","text":"Report","size":"2.95 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T&M 7 C–26"},{"id":381483,"rank":3,"type":{"id":35,"text":"Software Release"},"url":"https://www.usgs.gov/software/pest-software-suite-parameter-estimation-uncertainty-analysis-management-optimization-and","text":"USGS software release","linkHelpText":"— PEST++, a Software Suite for Parameter Estimation, Uncertainty Analysis, Management Optimization and Sensitivity Analysis"}],"contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
Hanford, Washington (USA) is the construction site of a multi-billion-dollar high-level nuclear waste treatment facility. This site lies 200 kilometers (km) east of Mount St. Helens (MSH), the most active volcano in the contiguous United States. Tephra from a future MSH eruption could pose a hazard to the air intake and filtration systems at this plant. In this report, we present a probabilistic estimate of the amount of tephra that could fall, and the concentrations of airborne ash that could occur at the Hanford Site during a future eruption. Mount St. Helens has produced four large explosive eruptions in approximately the past 500 years, suggesting that its annual probability of eruption (P1) is roughly 4/500=0.008. Assuming that a large eruption occurs, we calculate the probability (P3|1) of a given fall deposit thickness or airborne concentration at Hanford by running about 10,000 simulations of ash-producing eruptions using the atmospheric transport model Ash3d. In each simulation, we calculate the pattern of tephra dispersal, deposit thickness at Hanford, and airborne ash concentration at ground level. As input for each simulation, we choose meteorological conditions from a randomly chosen time in the historical record between 1980 and 2010, using data from the European Centre for Medium-Range Weather Forecasting (ECMWF) Reanalysis (ERA) Interim model. The volume (dense-rock equivalent) of each simulated eruption is randomly chosen from a uniform probability distribution on a log scale from the range of magma volumes (0.008–2.3 cubic kilometers [km3]) estimated for late Holocene eruptions at MSH. Plume heights and durations of each eruption are chosen using empirical correlations between volume, height, and eruption rate, which account for the fact that larger eruptions have higher plumes and last longer. We construct summary tables of final deposit thickness (T), maximum ground-level airborne concentration (Cmax), and average ground-level airborne concentration (Cavg) during tephra-fall for each run. Each table is sorted and ranked by decreasing value of T, Cmax, or Cavg. Conditional probabilities (P3|1) are derived by dividing rank by n+1, where n is the total number of successful runs. For example, a deposit thickness of 5.10 centimeters (cm) from run 446 is ranked 123 of 9,785 successful runs, yielding P3|1=123/9,786=0.01257. Its annual probability is P=P1·P3|1=0.008×0.01257=0.000101. By interpolation, the deposit thickness (T10k) having an annual probability of 1 in 10,000 (P= 0.0001) is 5.11 cm. Analogous concentration values are Cmax,10k=3,819 and Cavg,10k=1,513 milligrams per cubic meter (mg/m3), respectively. Independent calculations using the known mass accumulation rate of the deposit (=0.001–0.006 kilograms per square meter per second [kg/m2/s]), aggregate fall velocities (u=0.3–0.8 meters per second [m/s]), and the simple formula , yield similar results, although highly variable fall velocities add significant uncertainty. This formula implies that deposit accumulation rates of millimeters (mm) to greater than 1 cm per hour, which are not uncommon during heavy ash fall, are associated with airborne concentrations of 102–103 milligrams per cubic meter (mg/m3). These concentrations are much higher than published measurements (10-3–101 mg/m3), which record only suspended particles sampled in sheltered areas. During heavy ashfall, most fine ash falls as aggregates. Whether such aggregates will be ingested into air ducts will depend on the aggregate size and fall rate, the fragility of the aggregates, the air duct geometry, intake velocity, and other factors.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201133","collaboration":"Prepared in cooperation with the U.S. Department of Energy, Office of River Protection","usgsCitation":"Mastin, L.G., Van Eaton, A., and Schwaiger, H.F., 2020, A probabilistic assessment of tephra-fall hazards at Hanford, Washington, from a future eruption of Mount St. Helens: U.S. Geological Survey Open-File Report 2020–1133, 54 p., https://doi.org/10.3133/ofr20201133.","productDescription":"Report: ix, 54 p.; Data Release","numberOfPages":"54","onlineOnly":"Y","ipdsId":"IP-112179","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":381546,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1133/covrthb.jpg"},{"id":381547,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1133/ofr20201133.pdf","text":"Report","size":"9.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":381548,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VPFXQR","linkHelpText":"Data Used to Develop A Probabilistic Assessment of Tephra-Fall Hazards at Hanford, Washington"}],"country":"United States","state":"Washington","otherGeospatial":"Hanford","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.88281249999999,\n 46.33175800051563\n ],\n [\n -119.2950439453125,\n 46.33175800051563\n ],\n [\n -119.2950439453125,\n 46.81509864599243\n ],\n [\n -119.88281249999999,\n 46.81509864599243\n ],\n [\n -119.88281249999999,\n 46.33175800051563\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Volcano Science Center
Cascades Volcano Observatory
U.S. Geological Survey
1300 SE Cardinal Court
Vancouver, WA, 98683