Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Ecosystem transformations to altered or novel ecological states are accelerating across the globe. Indicators of ecological resilience to disturbance and resistance to invasion can aid in assessing risks and prioritizing areas for conservation and restoration. The sagebrush biome encompasses parts of 11 western states and is experiencing rapid transformations due to human population growth, invasive species, altered disturbance regimes, and climate change. We built on prior use of static soil moisture and temperature regimes to develop new, ecologically relevant and climate responsive indicators of both resilience and resistance. Our new indicators were based on climate and soil water availability variables derived from process-based ecohydrological models that allow predictions of future conditions. We asked: (1) Which variables best indicate resilience and resistance? (2) What are the relationships among the indicator variables and resilience and resistance categories? (3) How do patterns of resilience and resistance vary across the area? We assembled a large database (n = 24,045) of vegetation sample plots from regional monitoring programs and derived multiple climate and soil water availability variables for each plot from ecohydrological simulations. We used USDA Natural Resources Conservation Service National Soils Survey Information, Ecological Site Descriptions, and expert knowledge to develop and assign ecological types and resilience and resistance categories to each plot. We used random forest models to derive a set of 19 climate and water availability variables that best predicted resilience and resistance categories. Our models had relatively high multiclass accuracy (80% for resilience; 75% for resistance). Top indicator variables for both resilience and resistance included mean temperature, coldest month temperature, climatic water deficit, and summer and driest month precipitation. Variable relationships and patterns differed among ecoregions but reflected environmental gradients; low resilience and resistance were indicated by warm and dry conditions with high climatic water deficits, and moderately high to high resilience and resistance were characterized by cooler and moister conditions with low climatic water deficits. The new, ecologically-relevant indicators provide information on the vulnerability of resources and likely success of management actions, and can be used to develop new approaches and tools for prioritizing areas for conservation and restoration actions.
The diffusion of molecular water (H2Om) from the environment into volcanic glass can hydrate the glass up to several wt% at low temperature over long timescales. During this process, the water imprints its hydrogen isotope composition (δDH2O) to the glass (δDgl) offset by a glass-H2O fractionation factor (ΔDgl-H2O = δDgl – δDH2O) which is approximately −33‰ at Earth surface temperatures. Glasses hydrate much more rapidly at higher, sub-magmatic temperatures as they interact with H2O during eruption, transport, and emplacement. To aid in the interpretation of δDgl in natural samples, we present hydrogen isotope results from vapor hydration experiments conducted at 175–375 °C for durations of hours to months using natural volcanic glasses. The results can be divided into two thermal regimes: above 250 °C and below 250 °C. Lower temperature experiments yield raw ΔDgl-H2O values in the range of −33 ± 11‰. Experiments at 225 °C using both positive and negative initial ΔDgl-H2O values converge on this range of values, suggesting this range represents the approximate equilibrium fractionation for H isotopes between glass and H2O vapor (103lnαgl-H2O) below 250 °C. Variation in ΔDgl-H2O (−33 ± 11‰) between different experiments and glasses may arise from incomplete hydration, analytical uncertainty, differences in glass chemistry, and/or subordinate kinetic isotope effects. Experiments above 250 °C yield unexpectedly low δDgl values with ΔDgl-H2O values of ≤–85‰. While alteration alone is incapable of explaining the data, these run products have more extensive surface alteration and are not interpreted to reflect equilibrium fractionation between glass and H2O vapor. Fourier transform infrared spectroscopy (FTIR) shows that glass can hydrate with as much as 5.9 wt% H2Om and 1.0 wt% hydroxl (OH−) in the highest P-T experiment at 375 °C and 21.1 MPa. Therefore, we employ a 1D isotope diffusion–reaction model of glass hydration to evaluate the roles of equilibrium fractionation, isotope diffusion, water speciation reactions internal to the glass, and changing boundary conditions (e.g. alteration and dissolution). At lower temperatures, the best fitting model results to experimental data for low silica rhyolite (LSR) glasses require only an equilibrium fractionation factor and yield 103lnαgl-H2O values of −33‰ ± 5‰ and −25‰ ± 5‰ at 175 °C and 225 °C, respectively. At higher temperatures, ΔDgl-H2O is dominated by boundary layer effects during glass hydration and glass surface alteration. The modeled bulk δDgl value is highly responsive to changes in the δDgl boundary condition regardless of the magnitude of other kinetic effects. Observed glass dissolution and surficial secondary mineral formation are likely to impose a disequilibrium boundary layer that drives extreme δDgl fractionation with progressive glass hydration. These results indicate that the observed ΔDgl-H2O of ∼−33 ± 11‰ can be cautiously applied as an equilibrium 103lnαgl-H2O value to natural silicic glasses hydrated below 250 °C to identify hydration sources. This approximate ΔDgl-H2O may be applicable to even higher temperature glasses hydrated on short timescales (of seconds to minutes) in phreatomagmatic or submarine eruptions before H2O in the glass is primarily affected by boundary layer effects associated with alteration on the glass surface.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gca.2022.09.032","usgsCitation":"Hudak, M.R., Bindeman, I.N., Watkins, J.M., and Lowenstern, J.B., 2022, Hydrogen isotope behavior during rhyolite glass hydration under hydrothermal conditions: Geochimica et Cosmochimica Acta, v. 337, p. 33-48, https://doi.org/10.1016/j.gca.2022.09.032.","productDescription":"16 p.","startPage":"33","endPage":"48","ipdsId":"IP-125992","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":445596,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gca.2022.09.032","text":"Publisher Index Page"},{"id":411968,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"337","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hudak, Michael R. 0000-0002-0583-5424","orcid":"https://orcid.org/0000-0002-0583-5424","contributorId":287589,"corporation":false,"usgs":false,"family":"Hudak","given":"Michael","email":"","middleInitial":"R.","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":861684,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bindeman, Ilya N.","contributorId":175500,"corporation":false,"usgs":false,"family":"Bindeman","given":"Ilya","email":"","middleInitial":"N.","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":861685,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Watkins, James M.","contributorId":189286,"corporation":false,"usgs":false,"family":"Watkins","given":"James","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":861686,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lowenstern, Jacob B. 0000-0003-0464-7779 jlwnstrn@usgs.gov","orcid":"https://orcid.org/0000-0003-0464-7779","contributorId":2755,"corporation":false,"usgs":true,"family":"Lowenstern","given":"Jacob","email":"jlwnstrn@usgs.gov","middleInitial":"B.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":861687,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70238786,"text":"70238786 - 2022 - The source, fate, and transport of arsenic in the Yellowstone hydrothermal system - An overview","interactions":[],"lastModifiedDate":"2022-12-12T14:28:56.322416","indexId":"70238786","displayToPublicDate":"2023-01-09T08:21:52","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"The source, fate, and transport of arsenic in the Yellowstone hydrothermal system - An overview","docAbstract":"The Yellowstone Plateau Volcanic Field (YPVF) contains >10,000 thermal features including hot springs, pools, geysers, mud pots, and fumaroles with diverse chemical compositions. Arsenic (As) concentrations in YPVF thermal waters typically range from 0.005 to 4 mg/L, but an As concentration of 17 mg/L has been reported. Arsenic data from thermal springs, outflow drainages, rivers, and from volcanic rocks and silica sinter were used to identify the sources, characterize geochemical and microbial processes affecting As, and quantify As fluvial transport. Arsenic in YPVF thermal waters is mainly derived from high temperature leaching of rhyolites. Arsenic concentrations in thermal waters primarily depend on water type, which is controlled by boiling, evaporation, mixing, and mineral precipitation and dissolution. Springs with low As concentrations include acid-SO4 (0.1 ± 0.1 mg/L), NH4-SO4 rich (0.003 ± 0.007 mg/L), and dilute thermal waters (0.1 ± 0.1 mg/L); travertine-forming waters have moderate As concentrations (0.4 ± 0.2 mg/L); and neutral- Cl waters (1.2 ± 0.8 mg/L) common in the western portion of the Yellowstone Caldera and Cl-rich waters (1.9 ± 1.2 mg/L) primarily from Basins near the Caldera boundary have elevated As concentrations. Reduced As species (arsenite and thiolated-As species) are most prevalent near the orifice of hot springs, and then As rapidly oxidizes to arsenate along drainages. Previously published cultivation-based studies and metagenomic data from microbial communities inhabiting a variety of hot springs indicate a widespread distribution of arsenite oxidation and arsenate reduction capabilities among the hot springs. Widespread use and transformation of As by thermophilic microorganisms promotes more soluble and toxic forms. Most of the water discharged from thermal springs eventually ends up in a nearby river where As remains soluble and exhibits little attenuation during downstream transport. Since 2010, 183 ± 10 metric tons/year of As were transported from Yellowstone National Park (YNP) via rivers. The discharge from YPVF thermal features impairs river water quality whereby As concentrations exceed 10 μg/L for many rivers reaches within and downstream from YNP.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2022.107709","usgsCitation":"McCleskey, R., Nordstrom, D.K., Hurwitz, S., Colman, D.R., Roth, D.A., Johnson, M.O., and Boyd, E., 2022, The source, fate, and transport of arsenic in the Yellowstone hydrothermal system - An overview: Journal of Volcanology and Geothermal Research, v. 432, 107709, 20 p., https://doi.org/10.1016/j.jvolgeores.2022.107709.","productDescription":"107709, 20 p.","ipdsId":"IP-143378","costCenters":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"links":[{"id":467136,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jvolgeores.2022.107709","text":"Publisher Index Page"},{"id":410276,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Montana, Wyoming","otherGeospatial":"Yellowstone Plateau Volcanic Field","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.94,\n 45.84\n ],\n [\n -110.94,\n 45.83\n ],\n [\n -110.93,\n 45.83\n ],\n [\n -110.93,\n 45.84\n ],\n [\n -110.94,\n 45.84\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.00219450142964,\n 44.1359427252448\n ],\n [\n -109.9963211835457,\n 44.32742371136524\n ],\n [\n -110.01687779613967,\n 44.32742371136524\n ],\n [\n -110.06973765709618,\n 44.352627456744756\n ],\n [\n -110.06386433921189,\n 44.40719840334816\n ],\n [\n -110.052117703444,\n 44.42397923099992\n ],\n [\n -110.0286244319079,\n 44.43236783899505\n ],\n [\n -110.00513116037179,\n 44.46800599192275\n ],\n [\n -110.00513116037179,\n 44.50781113480417\n ],\n [\n -110.08735761074799,\n 44.491054385481874\n ],\n [\n -110.14021747170415,\n 44.49314924251027\n ],\n [\n -110.15490076641417,\n 44.52037553641682\n ],\n [\n -110.16077408429847,\n 44.54968193879381\n ],\n [\n -110.12259751805233,\n 44.57060605348323\n ],\n [\n -110.07267431603796,\n 44.58733992725041\n ],\n [\n -110.03743440873397,\n 44.60825049691036\n ],\n [\n -110.0198144550818,\n 44.624973534551\n ],\n [\n -109.90234809740163,\n 44.64169175607395\n ],\n [\n -109.83186828279328,\n 44.70225493592861\n ],\n [\n -109.83480494173509,\n 44.721037537133384\n ],\n [\n -109.87004484903945,\n 44.74607152330452\n ],\n [\n -109.94933464047348,\n 44.80860907769713\n ],\n [\n -109.92584136893738,\n 44.83777009077582\n ],\n [\n -109.92290470999559,\n 44.91268782141975\n ],\n [\n -109.95520795835778,\n 44.956344789149\n ],\n [\n -109.96695459412565,\n 44.97504475832312\n ],\n [\n -109.99925784248785,\n 44.972967285003364\n ],\n [\n -110.00513116037179,\n 45.04563407251294\n ],\n [\n -110.01687779613967,\n 45.05393297808422\n ],\n [\n -110.04624438556007,\n 45.03940910312352\n ],\n [\n -110.08148429286405,\n 45.02903264897614\n ],\n [\n -110.7011193296279,\n 45.02903264897614\n ],\n [\n -110.77453580317838,\n 45.064304916740326\n ],\n [\n -110.82152234625059,\n 45.05393297808422\n ],\n [\n -110.8391422999024,\n 45.04148416817836\n ],\n [\n -110.91255877345289,\n 45.02903264897614\n ],\n [\n -110.936052044989,\n 45.049783675815206\n ],\n [\n -110.93311538604685,\n 45.068453165395425\n ],\n [\n -111.01534183642306,\n 45.095409443997795\n ],\n [\n -111.05645506161133,\n 45.066379078696684\n ],\n [\n -111.10931492256749,\n 45.10577385685349\n ],\n [\n -111.14455482987147,\n 45.08711655907109\n ],\n [\n -111.15336480669755,\n 45.04563407251294\n ],\n [\n -111.17979473717546,\n 45.016578420476634\n ],\n [\n -111.20328800871158,\n 45.00204506297516\n ],\n [\n -111.23852791601591,\n 45.00204506297516\n ],\n [\n -111.25614786966773,\n 44.968812112589006\n ],\n [\n -111.25908452860988,\n 44.94387475641295\n ],\n [\n -111.21209798553765,\n 44.910608090355964\n ],\n [\n -111.1827313961176,\n 44.91268782141975\n ],\n [\n -111.1239982172775,\n 44.90644840245457\n ],\n [\n -111.09756828679926,\n 44.88356473806243\n ],\n [\n -111.09756828679926,\n 44.12961955801748\n ],\n [\n -110.00513116037179,\n 44.13383507805938\n ],\n [\n -110.00219450142964,\n 44.1359427252448\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"432","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McCleskey, R. 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Kirk 0000-0003-3283-5136 dkn@usgs.gov","orcid":"https://orcid.org/0000-0003-3283-5136","contributorId":749,"corporation":false,"usgs":true,"family":"Nordstrom","given":"D.","email":"dkn@usgs.gov","middleInitial":"Kirk","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":false,"id":858703,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hurwitz, Shaul 0000-0001-5142-6886 shaulh@usgs.gov","orcid":"https://orcid.org/0000-0001-5142-6886","contributorId":2169,"corporation":false,"usgs":true,"family":"Hurwitz","given":"Shaul","email":"shaulh@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":858704,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Colman, Daniel R. 0000-0002-3253-6833","orcid":"https://orcid.org/0000-0002-3253-6833","contributorId":299802,"corporation":false,"usgs":false,"family":"Colman","given":"Daniel","email":"","middleInitial":"R.","affiliations":[{"id":64955,"text":"Department of Microbiology and Cell Biology, Montana State University","active":true,"usgs":false}],"preferred":false,"id":858705,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Roth, David A. 0000-0002-7515-3533 daroth@usgs.gov","orcid":"https://orcid.org/0000-0002-7515-3533","contributorId":2340,"corporation":false,"usgs":true,"family":"Roth","given":"David","email":"daroth@usgs.gov","middleInitial":"A.","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":858706,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Johnson, Madeline Oxner 0000-0001-7661-9748","orcid":"https://orcid.org/0000-0001-7661-9748","contributorId":299803,"corporation":false,"usgs":true,"family":"Johnson","given":"Madeline","email":"","middleInitial":"Oxner","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":858707,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Boyd, Eric S. 0000-0003-4436-5856","orcid":"https://orcid.org/0000-0003-4436-5856","contributorId":299804,"corporation":false,"usgs":false,"family":"Boyd","given":"Eric S.","affiliations":[{"id":64955,"text":"Department of Microbiology and Cell Biology, Montana State University","active":true,"usgs":false}],"preferred":false,"id":858708,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70239344,"text":"70239344 - 2022 - Water and endangered fish in the Klamath River Basin: Do Upper Klamath Lake surface elevation and water quality affect adult Lost River and Shortnose Sucker survival?","interactions":[],"lastModifiedDate":"2023-01-10T13:02:51.552487","indexId":"70239344","displayToPublicDate":"2023-01-06T07:00:27","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Water and endangered fish in the Klamath River Basin: Do Upper Klamath Lake surface elevation and water quality affect adult Lost River and Shortnose Sucker survival?","docAbstract":"In the western United States, water allocation decisions often incorporate the needs of endangered fish. In the Klamath River basin, an understanding of temporal variation in annual survival rates of Shortnose Suckers Chasmistes brevirostris and Lost River Suckers Deltistes luxatus and their relation to environmental drivers is critical to water management and sucker recovery. Extinction risk is high for these fish because most individuals in the populations are approaching their maximum life span and recruitment of new fish into the adult populations has never exceeded mortality losses in the past 22 years. We used a time series of mark–recapture data from the years 1999–2021 to analyze the relationship between lake level, water quality covariates, and survival of adult Shortnose Suckers and two spawning populations of Lost River Suckers in Upper Klamath Lake, Oregon. We compared competing model hypotheses in a maximum likelihood framework using Akaike's information criterion and then ran the top environmental covariates in a Bayesian framework to estimate how much of the variation in survival was explained by these covariates as compared to random variation. The complementary analyses found almost unequivocal support for our base model without environmental covariates. Estimated adult sucker survival was high across the time series and consistent with sucker life history (mean annual survival = 0.82–0.91). This suggests that adult suckers were generally robust to interannual variation in lake levels as well as consistently poor water quality within the years of our data set. Recovery time is limited, as a declining survival trend for adult suckers in recent years may be due to the onset of senescence. The successful recovery of suckers in Upper Klamath Lake may rely on shifting research from the causes of adult mortality and its relationship with lake surface elevation to the causes of poor recruitment into adult populations.
This chapter provides an overview of machine learning models and their applications to the science of inland waters. Such models serve a wide range of purposes for science and management: predicting water quality, quantity, or ecological dynamics across space, time, or hypothetical scenarios; vetting and distilling raw data for further modeling or analysis; generating and exploring hypotheses; estimating physically or biologically meaningful parameters for use in further modeling; and revealing patterns in complex, multidimensional data or model outputs. An important research frontier is the injection of limnological knowledge into machine-learning models, which has shown great promise for increasing such models’ accuracy, trustworthiness, and interpretability. Here we describe a few of the most powerful machine learning tools, describe best practices for employing these tools and injecting knowledge guidance, and give examples of their applications to advance understanding of inland waters.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Encyclopedia of inland waters","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Elsevier","doi":"10.1016/B978-0-12-819166-8.00121-3","usgsCitation":"Appling, A.P., Oliver, S.K., Read, J., Sadler, J.M., and Zwart, J.A., 2022, Machine learning for understanding inland water quantity, quality, and ecology, chap. of Encyclopedia of inland waters, v. 4, p. 585-606, https://doi.org/10.1016/B978-0-12-819166-8.00121-3.","productDescription":"22 p.","startPage":"585","endPage":"606","ipdsId":"IP-122850","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":5054,"text":"Office of Water Information","active":true,"usgs":true},{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":445607,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.31223/x5964s","text":"External Repository"},{"id":411277,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","edition":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Mehner, Thomas","contributorId":272917,"corporation":false,"usgs":false,"family":"Mehner","given":"Thomas","email":"","affiliations":[{"id":38332,"text":"Leibniz-Institute of Freshwater Ecology and Inland Fisheries","active":true,"usgs":false}],"preferred":false,"id":860710,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Tockner, Klement","contributorId":224174,"corporation":false,"usgs":false,"family":"Tockner","given":"Klement","email":"","affiliations":[{"id":40838,"text":"FWF Austrian Science Fund","active":true,"usgs":false}],"preferred":false,"id":860711,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"Appling, Alison P. 0000-0003-3638-8572 aappling@usgs.gov","orcid":"https://orcid.org/0000-0003-3638-8572","contributorId":150595,"corporation":false,"usgs":true,"family":"Appling","given":"Alison","email":"aappling@usgs.gov","middleInitial":"P.","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":860690,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Oliver, Samantha K. 0000-0001-5668-1165","orcid":"https://orcid.org/0000-0001-5668-1165","contributorId":211886,"corporation":false,"usgs":true,"family":"Oliver","given":"Samantha","email":"","middleInitial":"K.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":860691,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Read, Jordan 0000-0002-3888-6631","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":221385,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":860692,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sadler, Jeffrey Michael 0000-0001-8776-4844","orcid":"https://orcid.org/0000-0001-8776-4844","contributorId":260092,"corporation":false,"usgs":true,"family":"Sadler","given":"Jeffrey","email":"","middleInitial":"Michael","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":860693,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zwart, Jacob Aaron 0000-0002-3870-405X","orcid":"https://orcid.org/0000-0002-3870-405X","contributorId":237809,"corporation":false,"usgs":true,"family":"Zwart","given":"Jacob","email":"","middleInitial":"Aaron","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":860694,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70239182,"text":"70239182 - 2022 - Modeling reservoir release using pseudo-prospective learning and physical simulations to predict water temperature","interactions":[],"lastModifiedDate":"2023-01-02T19:15:38.169222","indexId":"70239182","displayToPublicDate":"2023-01-02T13:08:22","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Modeling reservoir release using pseudo-prospective learning and physical simulations to predict water temperature","docAbstract":"This paper proposes a new data-driven method for predicting water temperature in stream networks with reservoirs. The water flows released from reservoirs greatly affect the water temperature of downstream river segments. However, the information of released water flow is often not available for many reservoirs, which makes it difficult for data-driven models to capture the impact to downstream river segments. In this paper, we first build a state-aware graph model to represent the interactions amongst streams and reservoirs, and then propose a parallel learning structure to extract the reservoir release information and use it to improve the prediction. In particular, for reservoirs with no available release information, we mimic the water managers' release decision process through a pseudo-prospective learning method, which infers the release information from anticipated water temperature dynamics. For reservoirs with the release information, we leverage a physics-based model to simulate the water release temperature and transfer such information to guide the learning process for other reservoirs. The evaluation for the Delaware River Basin shows that the proposed method brings over 10% accuracy improvement over existing data-driven models for stream temperature prediction when the release data is not available for any reservoirs. The performance is further improved after we incorporate the release data and physical simulations for a subset of reservoirs.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 2022 SIAM International Conference on Data Mining (SDM)","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"2022 SIAM International Conference on Data Mining (SDM)","conferenceDate":"April 28-30, 2022","conferenceLocation":"Alexandria, Virginia, United States","language":"English","publisher":"Society for Industrial and Applied Mathematics","doi":"10.1137/1.9781611977172.11","usgsCitation":"Jia, X., Chen, S., Xie, Y., Yang, H., Appling, A.P., Oliver, S.K., and Jiang, Z., 2022, Modeling reservoir release using pseudo-prospective learning and physical simulations to predict water temperature, in Proceedings of the 2022 SIAM International Conference on Data Mining (SDM), Alexandria, Virginia, United States, April 28-30, 2022, p. 91-99, https://doi.org/10.1137/1.9781611977172.11.","productDescription":"9 p.","startPage":"91","endPage":"99","ipdsId":"IP-134356","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":445610,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://arxiv.org/abs/2202.05714","text":"External Repository"},{"id":411275,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2022-04-20","publicationStatus":"PW","contributors":{"editors":[{"text":"Banerjee, Arindam","contributorId":300535,"corporation":false,"usgs":false,"family":"Banerjee","given":"Arindam","email":"","affiliations":[],"preferred":false,"id":860702,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Zhou, Zhi-Hua","contributorId":300536,"corporation":false,"usgs":false,"family":"Zhou","given":"Zhi-Hua","email":"","affiliations":[],"preferred":false,"id":860703,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Papalexakis, Evangelos E.","contributorId":300537,"corporation":false,"usgs":false,"family":"Papalexakis","given":"Evangelos","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":860704,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Riondato, Matteo","contributorId":300538,"corporation":false,"usgs":false,"family":"Riondato","given":"Matteo","email":"","affiliations":[],"preferred":false,"id":860705,"contributorType":{"id":2,"text":"Editors"},"rank":4}],"authors":[{"text":"Jia, Xiaowei 0000-0001-8544-5233","orcid":"https://orcid.org/0000-0001-8544-5233","contributorId":237807,"corporation":false,"usgs":false,"family":"Jia","given":"Xiaowei","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":860695,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chen, Shengyu","contributorId":297452,"corporation":false,"usgs":false,"family":"Chen","given":"Shengyu","email":"","affiliations":[{"id":12465,"text":"University of Pittsburgh","active":true,"usgs":false}],"preferred":false,"id":860696,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Xie, Yiqun","contributorId":297447,"corporation":false,"usgs":false,"family":"Xie","given":"Yiqun","email":"","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":860697,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yang, Haoyu","contributorId":298611,"corporation":false,"usgs":false,"family":"Yang","given":"Haoyu","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":860698,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Appling, Alison P. 0000-0003-3638-8572 aappling@usgs.gov","orcid":"https://orcid.org/0000-0003-3638-8572","contributorId":150595,"corporation":false,"usgs":true,"family":"Appling","given":"Alison","email":"aappling@usgs.gov","middleInitial":"P.","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":860699,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Oliver, Samantha K. 0000-0001-5668-1165","orcid":"https://orcid.org/0000-0001-5668-1165","contributorId":211886,"corporation":false,"usgs":true,"family":"Oliver","given":"Samantha","email":"","middleInitial":"K.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":860700,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jiang, Zhe","contributorId":267317,"corporation":false,"usgs":false,"family":"Jiang","given":"Zhe","email":"","affiliations":[{"id":36730,"text":"University of Alabama","active":true,"usgs":false}],"preferred":false,"id":860701,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70236479,"text":"70236479 - 2022 - Assessing the efficacy of oblique bubble screens for control of aquatic invasive species","interactions":[],"lastModifiedDate":"2024-02-22T17:08:42.891975","indexId":"70236479","displayToPublicDate":"2022-12-31T11:06:29","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Assessing the efficacy of oblique bubble screens for control of aquatic invasive species","docAbstract":"Non-physical barriers, such as bubble screens (or curtains), are promising low-impact strategies to deter the spread of Aquatic Invasive Species (AIS) in streams. Bubble screens have been successfully implemented to redirect and/or deter adult fish and to capture plastics in some rivers, but their efficacy on invasive fish at multiple life stages (eggs, larvae, and adult fish) is not yet known. Air bubbles rising from a diffuser placed at the bottom of a stream generate counterrotating eddies that interact with the mean flow. Understanding such interactions allows us to design an Oblique Bubble Screen (OBS), a system able to direct drifting particles (i.e., eggs and larvae) towards the banks of a stream for potential capture, based on the water velocity, river morphology, orientation of the OBS, diffuser material, and air flow rate. We present the results from a series of laboratory experiments at the Ecohydraulics and Ecomorphodynamics Laboratory at the University of Illinois at Urbana-Champaign, using positively buoyant (specific gravity SG=0.9, and diameter d=7.09mm) and negatively buoyant (SG=1.04, d=5.9mm) spherical particles to represent the range of size and density of developing Grass carp eggs, an invasive species in North America (Ctenopharyngodon idella). An air compressor was connected to a porous tube laid at the bottom of a recirculating flume, with a regulator and a flow meter to control air pressure and air flow rate. Acoustic Doppler Velocimeters (ADV) and Surface Particle Image Velocimetry (PIV) were used to measure the effect of the OBS on the velocity field. Our collected data showed that: (1) a single OBS is able to direct drifting particles towards the bank at the downstream end of the OBS, (2) adjusting orientation angle and air flow rate of the diffuser can increase capture efficacy under different flow conditions, reaching up to a 100% of capture for buoyant particles, and (3) the ratio between lateral velocity generated by the OBS (as a function of air flow rate) and the mean longitudinal flow velocities, can be used as an indicator to predict whether the OBS will be able to carry the particles all along the length of the diffuser in wider and deeper streams. The optimal configurations from our study will be tested with live Grass carp eggs and larvae, as well as with upstream-swimming adult carp to assess its potential as a two-way barrier, and to provide design parameters to set the air-flow rate and diffuser type needed for field deployments, according to width-to-depth ratios and stream morphology.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 39th IAHR World Congress, Granada, Spain","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"International Association for Hydro-Environment Engineering and Research","doi":"10.3850/IAHR-39WC2521711920221833","usgsCitation":"Prasad, V., Suski, C., Jackson, P.R., George, A.E., Chapman, D., Fischer, J., and Tinoco, R.O., 2022, Assessing the efficacy of oblique bubble screens for control of aquatic invasive species, in Proceedings of the 39th IAHR World Congress, Granada, Spain, v. 39, p. 1565-1570, https://doi.org/10.3850/IAHR-39WC2521711920221833.","productDescription":"6 p.","startPage":"1565","endPage":"1570","ipdsId":"IP-135122","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":445612,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3850/iahr-39wc2521711920221833","text":"Publisher Index Page"},{"id":425879,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"39","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Prasad, Vindhyawasini 0000-0003-0585-7217","orcid":"https://orcid.org/0000-0003-0585-7217","contributorId":296287,"corporation":false,"usgs":false,"family":"Prasad","given":"Vindhyawasini","email":"","affiliations":[{"id":16984,"text":"University of Illinois at Urbana-Champaign","active":true,"usgs":false}],"preferred":false,"id":851177,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Suski, C. D.","contributorId":190151,"corporation":false,"usgs":false,"family":"Suski","given":"C.","middleInitial":"D.","affiliations":[],"preferred":false,"id":851178,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jackson, P. Ryan 0000-0002-3154-6108 pjackson@usgs.gov","orcid":"https://orcid.org/0000-0002-3154-6108","contributorId":194529,"corporation":false,"usgs":true,"family":"Jackson","given":"P.","email":"pjackson@usgs.gov","middleInitial":"Ryan","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":851179,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"George, Amy E. 0000-0003-1150-8646 ageorge@usgs.gov","orcid":"https://orcid.org/0000-0003-1150-8646","contributorId":3950,"corporation":false,"usgs":true,"family":"George","given":"Amy","email":"ageorge@usgs.gov","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":851180,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chapman, Duane 0000-0002-1086-8853 dchapman@usgs.gov","orcid":"https://orcid.org/0000-0002-1086-8853","contributorId":1291,"corporation":false,"usgs":true,"family":"Chapman","given":"Duane","email":"dchapman@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":851181,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fischer, Jesse Robert","contributorId":296288,"corporation":false,"usgs":true,"family":"Fischer","given":"Jesse Robert","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":851182,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Tinoco, Rafael O.","contributorId":211779,"corporation":false,"usgs":false,"family":"Tinoco","given":"Rafael","email":"","middleInitial":"O.","affiliations":[{"id":38317,"text":"Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL","active":true,"usgs":false}],"preferred":false,"id":851183,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70240326,"text":"70240326 - 2022 - Status and trends in the Lake Superior fish community, 2021","interactions":[],"lastModifiedDate":"2023-03-30T16:33:53.138792","indexId":"70240326","displayToPublicDate":"2022-12-31T10:51:30","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"title":"Status and trends in the Lake Superior fish community, 2021","docAbstract":"The Lake Superior nearshore fish community was sampled in May-June 2021 with daytime bottom trawl tows at 45 stations located in USA waters. The 45 locations sampled were long-term monitoring sites that had been annually sampled since 1978. All comparisons to 2021 results were limited to past collections from USA waters, as compared to previous years, where comparisons included USA and Canadian sites. In 2021, the number of species collected at each site ranged from 0 to 15, with a median of 5 species. Average fish biomass was 6.3 kg/ha, which was higher than the average observed over the past 10 years (4.7 kg/ha), similar to the average observed from 2001-10 (6.7 kg/ha), and less than the averages observed in 1991-2000 (14.8 kg/ha), and 1981-1990 (11.9 kg/ha), but higher than the average from 1978-1980 (5.2 kg/ha). Average biomass in 2021 was highest for Lake Whitefish (Coregonus clupeaformis, 3.2 kg/ha), Bloater (C. hoyi, 1.4 kg/ha), Rainbow Smelt (Osmerus mordax, 0.5 kg/ha), and Cisco (C. artedi, 0.3 kg/ha). Coregonus spp. year-class strength, as measured by densities of age-1 fish, was 8 fish/ha for Bloater, 11 fish/ha for Cisco, and 41 fish/ha for Lake Whitefish. The age-1 Bloater estimate was in the range observed for the 2014, 2015, and 2016 year-classes (7-9 age-1 fish/ha) and greater than that observed in other years over the past decade (<1 age-1 fish/ha). The age-1 Cisco estimate was the highest estimate since the 2009 year-class. Average Lake Whitefish age-1 density was the second highest estimate observed over the past 44-years. Cisco survival to age-1 has been low since 2009 and near zero since the 2014- and 2015-year classes. This lack of survival has yet to be adequately explained and continues to be a major concern of fishery managers due to Cisco’s importance in ecosystem dynamics and value to the commercial fishery.
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0000-0003-0451-110X","orcid":"https://orcid.org/0000-0003-0451-110X","contributorId":302070,"corporation":false,"usgs":true,"family":"Gorman","given":"Owen","email":"","middleInitial":"T.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":863416,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Phillips, Sydney B 0000-0003-0179-6533","orcid":"https://orcid.org/0000-0003-0179-6533","contributorId":302071,"corporation":false,"usgs":true,"family":"Phillips","given":"Sydney","email":"","middleInitial":"B","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":863417,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70245367,"text":"70245367 - 2022 - Nuclear magnetic resonance logging of a deep test well for estimation of aquifer and confining-unit hydraulic properties, Long Island, New York","interactions":[],"lastModifiedDate":"2024-02-27T17:08:46.461733","indexId":"70245367","displayToPublicDate":"2022-12-31T10:44:22","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Nuclear magnetic resonance logging of a deep test well for estimation of aquifer and confining-unit hydraulic properties, Long Island, New York","docAbstract":"A 1,200-foot deep well in southwestern Nassau County, Long Island, N.Y. was selected to evaluate the application of a nuclear magnetic resonance (NMR) logging tool. Technological advances in NMR borehole systems have allowed for reduced probe length and diameter, and focused measurement at specific diameters beyond the disturbed zone surrounding a well. This 3-inch-diameter NMR tool was specifically developed for use in deep 4-inch-diameter polyvinyl chloride cased wells common to Long Island. Selected intervals of the Magothy and Lloyd aquifers and the Raritan confining unit were logged for the evaluation.
Unlike other petrophysical logs that respond to the rock matrix and fluid properties and are strongly dependent on mineralogy, NMR logs respond to the presence of hydrogen protons in the formation fluid to determine water fraction and pore-size distribution. NMR log analysis provided estimates of the clay-bound, capillary-bound, and mobile water fraction and hydraulic conductivity of aquifers and confining units penetrated by the well. NMR-estimated porosity and mobile water fraction for the Magothy aquifer (0.34 and 0.22 respectively), Magothy/Raritan(?) (0.35 and 0.30), Raritan confining unit (0.30 and 0.13), Raritan clay and silt (0.23 and 0.01), and the Lloyd aquifer (0.27 and 0.19) was determined from the NMR log.
Hydraulic conductivity was estimated from the NMR-log data using the Schlumberger- Doll Research and sum of squared echoes equations with empirically derived constants for unconsolidated aquifers. Average hydraulic conductivity of the Magothy aquifer was 70 ft/d, the Raritan confining unit was 9.0 ft/d overall, the clay-rich lower part 0.24 ft/d, and the Lloyd aquifer was 56 ft/d. The coarse sandy Magothy/Raritan(?) unit between the Magothy aquifer and the top of the Raritan confining unit had the highest hydraulic conductivity of 345 ft/d. The hydraulic-conductivity estimates from the NMR log analysis for the Magothy and Lloyd aquifers were consistent with published values and that estimated for the Lloyd aquifer from a specific-capacity test at the well site.
","conferenceTitle":"29th Conference on Geology of Long Island and Metropolitan New York","conferenceDate":"April 9, 2022","language":"English","publisher":"Long Island Geologists","usgsCitation":"Stumm, F., and Williams, J., 2022, Nuclear magnetic resonance logging of a deep test well for estimation of aquifer and confining-unit hydraulic properties, Long Island, New York, 29th Conference on Geology of Long Island and Metropolitan New York, v. 29, April 9, 2022, 11 p.","productDescription":"11 p.","ipdsId":"IP-138611","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":426032,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":426031,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.stonybrook.edu/commcms/geosciences/about/_LIG-Past-Conferences/2022Conference.php","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New York","county":"Nassau County","otherGeospatial":"Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.695,\n 40.65\n ],\n [\n -73.695,\n 40.64\n ],\n [\n -73.685,\n 40.64\n ],\n [\n -73.685,\n 40.65\n ],\n [\n -73.695,\n 40.65\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"29","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stumm, Frederick 0000-0002-5388-8811 fstumm@usgs.gov","orcid":"https://orcid.org/0000-0002-5388-8811","contributorId":1077,"corporation":false,"usgs":true,"family":"Stumm","given":"Frederick","email":"fstumm@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":875903,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Williams, John 0000-0002-6054-6908 jhwillia@usgs.gov","orcid":"https://orcid.org/0000-0002-6054-6908","contributorId":1553,"corporation":false,"usgs":true,"family":"Williams","given":"John","email":"jhwillia@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":875904,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70235860,"text":"70235860 - 2022 - Environmental geochemistry of an epigenetic Pb-Zn-Ag deposit at the abandoned Cecilia mine, Puno region, Peru","interactions":[],"lastModifiedDate":"2024-02-22T16:58:58.60074","indexId":"70235860","displayToPublicDate":"2022-12-31T10:32:03","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Environmental geochemistry of an epigenetic Pb-Zn-Ag deposit at the abandoned Cecilia mine, Puno region, Peru","docAbstract":"The abandoned Cecilia Pb-Zn-Ag mine is located at the headwaters of the Lake Titicaca watershed in the Altiplano of Peru. The site is characterized by three months of high precipitation and nine months of limited precipitation. The environmental geochemical characterization of the abandoned mine was done to evaluate environmental risks at the site from mine wastes and mine drainage, and their potential for downstream impacts on water quality in Lake Titicaca.
The approach included sampling of mine waste, water, and sediment. Composite mine waste samples were collected from six main piles (four tailings, two waste rock). The surface water was collected from (1) drainage from mine portals; (2) the Cecilia and Crucero rivers upstream of mine influences; (3) the impacted reach of the Cecilia River down to its confluence with the Crucero River; and (4) the Crucero River, which receives drainage from the Cecilia River. Sampling in the dry season did not identify seeps from the waste rock or tailings piles.
This study documents the capacity of the site to generate acid mine drainage from the mine waste and underground workings. Mine waste has elevated concentrations of As (up to 883 mg/kg), Cu (up to 20,106 mg/kg), Pb (up to 16,716 mg/kg), and Zn (up to 11,937 mg/kg). Results for dissolved concentrations from leaching experiments on mine waste samples showed high leachability for As (0.001 to 0.95 mg/L), Cu (0.01 to 57.34 mg/L), Cd (0.001 to 1.13 mg/L), Fe (0.42 to 785 mg/L), Zn (0.01 to 91 mg/L), and Mn (0.05 to 279 mg/L) with an acidic pH (2.5 to 6.0). Water chemistry at the site varied on the basis of water type. The Cecilia and Crucero rivers had neutral pH and low concentrations of metals upstream of mine influences. In contrast, samples collected at the mine portal were highly acidic (pH 1.4 to 3.7) with high dissolved concentrations of Fe (up to 4,720 mg/L), Al (up to 400 mg/L), sulfate (up to 19,428 mg/L), As (up to 5.92 mg/L), Cd (up to 9.84 mg/L), Cu (up to 1.56 mg/L), Pb (up to 1.95 mg/L), and Zn (up to 4,065 mg/L), causing an increase in metal concentrations in the river downstream after mixing. Carbonate rocks in the watershed produce alkaline waters that neutralize acid drainage prior to its confluence with the Crucero River. Proposed remediation methods include capping mine waste to limit contact with rainwater and passive treatment of mine portal drainage.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 12th international conference on acid rock drainage","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"12th International Conference on Acid Rock Drainage (ICARD)","conferenceDate":"September 18-24, 2022","language":"English","publisher":"University of Queensland","usgsCitation":"Palomino, S., Seal,, R., Garcia, F., Ochoa, M., Machaca, D., Condorhuaman, A., and Valencia, M., 2022, Environmental geochemistry of an epigenetic Pb-Zn-Ag deposit at the abandoned Cecilia mine, Puno region, Peru, in Proceedings of the 12th international conference on acid rock drainage, September 18-24, 2022, p. 126-137.","productDescription":"12 p.","startPage":"126","endPage":"137","ipdsId":"IP-140549","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":425878,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":425877,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://smi.uq.edu.au/conferences/international-conference-acid-rock-drainage-2022#"}],"country":"Peru","otherGeospatial":"Cecilia mine","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -69.86739066896408,\n -14.47810004873395\n ],\n [\n -69.86739066896408,\n -14.518553485612628\n ],\n [\n -69.8006182214147,\n -14.518553485612628\n ],\n [\n -69.8006182214147,\n -14.47810004873395\n ],\n [\n -69.86739066896408,\n -14.47810004873395\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Palomino, S.","contributorId":295480,"corporation":false,"usgs":false,"family":"Palomino","given":"S.","email":"","affiliations":[{"id":63892,"text":"Instituto Geológico Minero y Metalúrgico (Peru)","active":true,"usgs":false}],"preferred":false,"id":849540,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Seal,, Robert R. II 0000-0003-0901-2529 rseal@usgs.gov","orcid":"https://orcid.org/0000-0003-0901-2529","contributorId":141204,"corporation":false,"usgs":true,"family":"Seal,","given":"Robert R.","suffix":"II","email":"rseal@usgs.gov","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":849541,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Garcia, F.","contributorId":334298,"corporation":false,"usgs":false,"family":"Garcia","given":"F.","email":"","affiliations":[],"preferred":false,"id":849542,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ochoa, M.","contributorId":334299,"corporation":false,"usgs":false,"family":"Ochoa","given":"M.","email":"","affiliations":[],"preferred":false,"id":895246,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Machaca, D.","contributorId":334300,"corporation":false,"usgs":false,"family":"Machaca","given":"D.","email":"","affiliations":[],"preferred":false,"id":849543,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Condorhuaman, A.","contributorId":295482,"corporation":false,"usgs":false,"family":"Condorhuaman","given":"A.","email":"","affiliations":[{"id":63892,"text":"Instituto Geológico Minero y Metalúrgico (Peru)","active":true,"usgs":false}],"preferred":false,"id":849544,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Valencia, M.","contributorId":334301,"corporation":false,"usgs":false,"family":"Valencia","given":"M.","email":"","affiliations":[],"preferred":false,"id":895247,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70239166,"text":"ofr20221118 - 2022 - Characterization of subsurface conditions and recharge at the irrigated four-plex baseball field, Fort Irwin National Training Center, California, 2018–20","interactions":[],"lastModifiedDate":"2026-02-10T21:20:37.248913","indexId":"ofr20221118","displayToPublicDate":"2022-12-30T13:25:58","publicationYear":"2022","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":"2022-1118","displayTitle":"Characterization of Subsurface Conditions and Recharge at the Irrigated Four-Plex Baseball Field, Fort Irwin National Training Center, California, 2018-20","title":"Characterization of subsurface conditions and recharge at the irrigated four-plex baseball field, Fort Irwin National Training Center, California, 2018–20","docAbstract":"The U.S. Geological Survey performed subsurface and geophysical site characterization of the irrigated four-plex baseball field in the Langford Valley–Irwin Groundwater Subbasin, as part of a research study in cooperation with the U.S. Environmental Protection Agency, the Agricultural Research Service, and the Fort Irwin National Training Center, California. To help meet future demands, the Fort Irwin National Training Center is evaluating the efficacy of gravity-fed drywells to enhance storm-water recharge into the Langford Valley–Irwin Groundwater Subbasin by bypassing fine-grained, less permeable deposits between land surface and the water table. The amount, rate, and location of recharge beneath an irrigated baseball field in the groundwater basin at the Fort Irwin National Training Center is not well understood, so data were collected using physical and geophysical techniques to characterize subsurface materials, geologic controls, and the vertical movement of water through the unsaturated zone to the water table near the drywell at the Fort Irwin National Training Center. Based on the data collected and interpreted from these techniques, several fine-grained deposits were identified. Although these deposits appear to impede the downward movement of water through the unsaturated zone locally, they are not laterally continuous, and water appears to continue to move downward when it reaches the edges of the deposits. These data will help managers evaluate recharge at the site and determine if the use of gravity-fed drywells enhances recharge from surface runoff.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221118","issn":"2331-1258","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","programNote":"U.S. Environmental Protection Agency","usgsCitation":"Densmore, J.N., Dick, M.C., Groover, K.D., Ely, C.P., and Brown, A., 2022, Characterization of subsurface conditions and recharge at the irrigated four-plex baseball field, Fort Irwin National Training Center, California, 2018–20: U.S. Geological Survey Open File Report 2022-1118, 13 p., https://doi.org/10.3133/ofr20221118","productDescription":"13 p.","numberOfPages":"13","onlineOnly":"Y","ipdsId":"IP-129107","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":499727,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114180.htm","linkFileType":{"id":5,"text":"html"}},{"id":411259,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1118/ofr20221118.XML"},{"id":411257,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1118/images"},{"id":411255,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1118/coverthb.jpg"},{"id":411256,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1118/ofr20221118.pdf","text":"Report","size":"3.61 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"California","city":"Fort Irwin","otherGeospatial":"Fort Irwin National Training Center","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.69261791592291,\n 35.26487666931442\n ],\n [\n -116.69251062756216,\n 35.26470146901906\n ],\n [\n -116.69238188152951,\n 35.26459634865991\n ],\n [\n -116.69212438946424,\n 35.264447427917574\n ],\n [\n -116.69163086300526,\n 35.264359827352905\n ],\n [\n -116.69129826908738,\n 35.26422842632836\n ],\n [\n -116.69092275982544,\n 35.263983143846076\n ],\n [\n -116.68835856800732,\n 35.2661468601287\n ],\n [\n -116.6903755891864,\n 35.26772362102348\n ],\n [\n -116.69260718708664,\n 35.265822744364684\n ],\n [\n -116.69281103497185,\n 35.265752665110384\n ],\n [\n -116.69268228893922,\n 35.26531466839623\n ],\n [\n -116.69261791592291,\n 35.26487666931442\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
https://www.usgs.gov/centers/ca-water/
Contact Pubs Warehouse
https://pubs.er.usgs.gov/contact
The protection of all sea turtles globally is a high priority, and research projects on these imperiled species are focused on those that are likely to result in improvements in monitoring and management for population recovery. Determining distribution, seasonal movements, vital rates and habitat use for all life-stages of sea turtles has been identified by the US Fish and Wildlife Service (USFWS) and US National Marine Fisheries Service (NMFS) as important for achieving recovery. This study provides information on in-water aggregations of sea turtles in the northern Gulf of Mexico. Data collected includes individual dive profiles, movements, seasonal site fidelity, genetic population structure, and isotopic signatures.
","language":"English","publisher":"Bureau of Ocean Energy Management","usgsCitation":"Hart, K., and Lamont, M., 2022, Discerning behavioral patterns of sea turtles in the Gulf of Mexico to inform management decisions, iv, 76 p.","productDescription":"iv, 76 p.","ipdsId":"IP-133772","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":411563,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":411550,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://espis.boem.gov/final%20reports/BOEM_2021-088.pdf"}],"country":"United States","otherGeospatial":"Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.65793958565853,\n 25.620964087484467\n ],\n [\n -80.65793958565853,\n 31.33180053527252\n ],\n [\n -99.2828424566061,\n 31.33180053527252\n ],\n [\n -99.2828424566061,\n 25.620964087484467\n ],\n [\n -80.65793958565853,\n 25.620964087484467\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hart, Kristen 0000-0002-5257-7974","orcid":"https://orcid.org/0000-0002-5257-7974","contributorId":222407,"corporation":false,"usgs":true,"family":"Hart","given":"Kristen","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":861127,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lamont, Margaret 0000-0001-7520-6669","orcid":"https://orcid.org/0000-0001-7520-6669","contributorId":222403,"corporation":false,"usgs":true,"family":"Lamont","given":"Margaret","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":861128,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70241819,"text":"70241819 - 2022 - Opportunities to improve water quality during abandoned mine-tunnel reclamation","interactions":[],"lastModifiedDate":"2023-03-28T11:59:41.608745","indexId":"70241819","displayToPublicDate":"2022-12-30T06:58:27","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Opportunities to improve water quality during abandoned mine-tunnel reclamation","docAbstract":"In the western United States, bulkheads are constructed to limit drainage from abandoned, draining mine adits and to protect downstream resources from uncontrolled releases of degraded adit water. Although bulkheads improve safety and water-quality conditions at the mouth of the adit, elevated hydraulic pressure behind the bulkhead often causes continuing water-quality problems in new locations. Solutions to improve water-quality outcomes from bulkheads might include in situ or ex situ passive or active treatment of mine-pool water or continuing tunnel drainage, in situ treatment of groundwater plumes resulting from bulkhead emplacement, direct extraction of metals from mine water, or bactericide application.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"IMWA – Reconnect","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"International Mine Water Association","collaboration":"Environmental Protection Agency","usgsCitation":"Walton-Day, K., Gusek, J.J., and Newman, C.P., 2022, Opportunities to improve water quality during abandoned mine-tunnel reclamation, in IMWA – Reconnect, p. 551-551.","productDescription":"7 p.","startPage":"551","endPage":"551","ipdsId":"IP-144257","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":414810,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":414804,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.imwa.info/docs/imwa_2022/IMWA_2022_proceedings.pdf"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Walton-Day, Katherine 0000-0002-9146-6193 kwaltond@usgs.gov","orcid":"https://orcid.org/0000-0002-9146-6193","contributorId":184043,"corporation":false,"usgs":true,"family":"Walton-Day","given":"Katherine","email":"kwaltond@usgs.gov","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":867823,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gusek, James J.","contributorId":303700,"corporation":false,"usgs":false,"family":"Gusek","given":"James","email":"","middleInitial":"J.","affiliations":[{"id":65881,"text":"Linkan Engineering","active":true,"usgs":false}],"preferred":false,"id":867824,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Newman, Connor P. 0000-0002-6978-3440","orcid":"https://orcid.org/0000-0002-6978-3440","contributorId":222596,"corporation":false,"usgs":true,"family":"Newman","given":"Connor","email":"","middleInitial":"P.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":867825,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70239137,"text":"fs20223087 - 2022 - Continuous water-quality and suspended-sediment transport monitoring in San Francisco Bay, California, water years 2020–21","interactions":[],"lastModifiedDate":"2026-03-25T16:44:46.088797","indexId":"fs20223087","displayToPublicDate":"2022-12-29T12:04:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-3087","displayTitle":"Continuous Water-Quality and Suspended-Sediment Transport Monitoring in San Francisco Bay, California, Water Years 2020–21","title":"Continuous water-quality and suspended-sediment transport monitoring in San Francisco Bay, California, water years 2020–21","docAbstract":"The U.S. Geological Survey (USGS) has continuously monitored real-time water quality and suspended-sediment transport in San Francisco Bay (the Bay) since 1989 as part of a multi-agency effort (see “Acknowledgments” section) to address estuary management, water supply, and ecological concerns. The San Francisco Bay area is home to millions of people and biologically diverse marine and terrestrial flora and fauna. Freshwater mixes with saltwater in the Bay and is subject to riverine influences (floods, droughts, managed reservoir releases, and freshwater diversions) and marine influences (tides, waves, and effects of saltwater).
Water temperature, salinity, suspended-sediment concentration (SSC), and turbidity, are used by State and Federal resources managers and are monitored at eight key locations throughout the Bay (fig. 1). Water temperature and salinity affect the density of water, which controls gravity-driven circulation patterns and stratification in the water column. Salinity indicates the relative mixing of fresh and ocean waters in the Bay and is derived from specific conductance measurements. Turbidity is a measure of light scattered from suspended particles in the water that is used to estimate suspended-sediment concentration. Suspended-sediment concentrations also are directly measured through depth-integrated water sampling.
Suspended sediment affects Bay water quality in multiple ways. Suspended sediment affects phytoplankton growth by attenuating sunlight in the water column. Suspended sediment deposition on tidal marshes and intertidal mudflats helps to restore and sustain these habitats as sea level rises. Settling of suspended sediment in ports and shipping channels creates the need for more dredging. In addition, suspended sediment often carries adsorbed contaminants as it is transported in the water column, which affects the distributions and concentrations of adsorbed contaminants in the environment. Excessive concentrations of sediment-adsorbed contaminants in deposits on the bottom of the Bay can affect ecosystem health.
External factors, such as tidal currents, waves, and wind can also affect water quality in the Bay. Tidal currents in the Bay change direction four times daily, and wind direction and intensity typically fluctuate on a daily cycle. Consequently, salinity, water temperature, and suspended-sediment concentration differ spatially and temporally throughout the Bay. Therefore, high-frequency measurements at multiple locations are needed to monitor these changes. Data collected at eight stations throughout the Bay are transmitted in near real-time using cellular telemetry and posted to the USGS National Water Information System (NWIS; https://waterdata.usgs.gov/usa/nwis). The purposes of this fact sheet are to (1) provide information about the USGS San Francisco Bay water-quality monitoring network; (2) highlight various applications in which these data can be used; and (3) provide internet links to access the resulting continuous water-quality data collected by the USGS.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223087","usgsCitation":"Palm, D.L., Einhell, D.C., Davila Olivera, S.M., 2022, Continuous water-quality and suspended-sediment transport monitoring in San Francisco Bay, California, water years 2020–21: U.S. Geological Survey Fact Sheet 2022–3087, 4 p., https://doi.org/10.3133/fs20223087.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-138273","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":411162,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3087/coverthb.jpg"},{"id":411163,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3087/fs20223087.pdf","text":"Report","size":"2.03 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022-3087"},{"id":411164,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/fs20223087/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2022-3087"},{"id":411165,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3087/images/"},{"id":411166,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3087/fs20223087.XML"},{"id":501514,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114175.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.7032269260365,\n 38.2575437837898\n ],\n [\n -122.64478458511374,\n 38.2575437837898\n ],\n [\n -122.64478458511374,\n 37.373118003359465\n ],\n [\n -121.7032269260365,\n 37.373118003359465\n ],\n [\n -121.7032269260365,\n 38.2575437837898\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, CA 95819
Population models often require detailed information on sex-, age-, or size-specific abundances, but population monitoring programs cannot always acquire data at the desired resolution. Thus, state uncertainty in monitoring data can potentially limit the demographic resolution of management decisions, which may be particularly problematic for stage- or size-structured species subject to consumptive use. American alligators (Alligator mississippiensis; hereafter alligator) have a complex life history characterized by delayed maturity and slow somatic growth, which makes the species particularly sensitive to overharvest. Though alligator populations are subject to recreational harvest throughout their range, the most widely used monitoring method (nightlight surveys) is often unable to obtain size class-specific counts, which limits the ability of managers to evaluate the effects of harvest policies. We constructed a Bayesian integrated population model (IPM) for alligators in Georgetown County, SC, USA, using records of mark–recapture–recovery, clutch size, harvest, and nightlight survey counts collected locally, and auxiliary information on fecundity, sex ratio, and somatic growth from other studies. We created a multistate mark–recapture–recovery model with six size classes to estimate survival probability, and we linked it to a state-space count model to derive estimates of size class-specific detection probability and abundance. Because we worked from a count dataset in which 60% of the original observations were of unknown size, we treated size class as a latent property of detections and developed a novel observation model to make use of information where size could be partly observed. Detection probability was positively associated with alligator size and water temperature, and negatively influenced by water level. Survival probability was lowest in the smallest size class but was relatively similar among the other five size classes (>0.90 for each). While the two nightlight survey count sites exhibited relatively stable population trends, we detected substantially different patterns in size class-specific abundance and trends between each site, including 30%–50% declines in the largest size classes at the site with greater harvest pressure. Here, we illustrate the use of IPMs to produce high-resolution output of latent population structure that is partially observed during the monitoring process.
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University","active":true,"usgs":false}],"preferred":false,"id":903451,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dunham, Kylee Denise 0000-0002-9249-0590","orcid":"https://orcid.org/0000-0002-9249-0590","contributorId":296991,"corporation":false,"usgs":true,"family":"Dunham","given":"Kylee","email":"","middleInitial":"Denise","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":903452,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hart, Morgan","contributorId":338673,"corporation":false,"usgs":false,"family":"Hart","given":"Morgan","email":"","affiliations":[{"id":35670,"text":"South Carolina Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":903453,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Butfiloski, Joseph W.","contributorId":338675,"corporation":false,"usgs":false,"family":"Butfiloski","given":"Joseph","email":"","middleInitial":"W.","affiliations":[{"id":35670,"text":"South Carolina Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":903454,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wilkinson, Philip M.","contributorId":338676,"corporation":false,"usgs":false,"family":"Wilkinson","given":"Philip M.","affiliations":[{"id":54598,"text":"Tom Yawkey Wildlife Center","active":true,"usgs":false}],"preferred":false,"id":903455,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Moore, Clinton 0000-0001-7782-3994 cmoore@usgs.gov","orcid":"https://orcid.org/0000-0001-7782-3994","contributorId":338679,"corporation":false,"usgs":true,"family":"Moore","given":"Clinton","email":"cmoore@usgs.gov","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903456,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70239065,"text":"ofr20221119 - 2022 - Hydrologic effects of leakage from the Catskill Aqueduct on the bedrock-aquifer system near High Falls, New York, November 2019–January 2020","interactions":[],"lastModifiedDate":"2026-03-30T20:55:17.842923","indexId":"ofr20221119","displayToPublicDate":"2022-12-27T14:00:00","publicationYear":"2022","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":"2022-1119","displayTitle":"Hydrologic Effects of Leakage from the Catskill Aqueduct on the Bedrock-Aquifer System near High Falls, New York, November 2019–January 2020","title":"Hydrologic effects of leakage from the Catskill Aqueduct on the bedrock-aquifer system near High Falls, New York, November 2019–January 2020","docAbstract":"Historical observations by the New York City Department of Environmental Protection (NYCDEP) indicate that the Rondout pressure tunnel has been leaking in the vicinity of the hamlet of High Falls, New York. In the 74 days from November 11, 2019, to January 23, 2020, NYCDEP shut down and partially dewatered the pressure tunnel for inspection and repairs. On November 5–7, 2019 (during normal tunnel operations), and on January 21–22, 2020 (when the tunnel was shut down), the U.S. Geological Survey used a network of 31 groundwater wells to collect water-level elevations and determine the potentiometric surface of the bedrock aquifer adjacent to the Rondout pressure tunnel. When the tunnel was fully pressurized during normal operations, water levels indicated a two-mile-long groundwater mound which trended northeastward, approximately along the regional strike of the bedrock units. The mound ranged in elevation from 250 to 300 feet (ft) above the North American Vertical Datum of 1988 and extended from 1,500 ft southwest of a suspected leak at the Rondout pressure tunnel to about 8,500 ft northeast of the possible leak. During the 74-day shutdown, during which the aqueduct was nonoperational, this groundwater mound decreased in magnitude and extent as it reverted to equilibrium conditions. This resulted in a flattening of the potentiometric surface, represented by two remnant groundwater plateaus.
Water-level differences were calculated for wells that may be affected by potential tunnel leakage to determine the influence on the local bedrock aquifer. The five largest water-level differences (77, 61, 49, 42, and 41 ft) occurred in wells that were generally aligned with the northeastward trend of regional bedrock strike; these wells may penetrate the karstic Helderberg Group bedrock unit. Near the suspected tunnel leak, the Helderberg Group overlies the Binnewater Sandstone and the High Falls Shale, both of which produced substantial groundwater inflows during the construction of the Rondout pressure tunnel. Water levels in wells penetrating the Shawangunk Formation just east of Rondout Creek, where the unit is in contact with the High Falls Shale, and in wells penetrating the Esopus Shale, which is adjacent to the Helderberg Group and northwest of the tunnel leak, may be affected by tunnel leakage. It is unclear if water levels in a well 9,000 ft northwest of the suspected tunnel leak are influenced by the tunnel leakage, by another source of artificial recharge, or by both. This well penetrates the Onondaga Limestone in the northwestern part of the study area. An unconsolidated aquifer composed of stratified gravel, sand, silt, and clay overlies the limestone bedrock in this part of study area―additional study is required to determine if this unconsolidated aquifer is affected by tunnel leakage.
Robert Francis Breault, Center Director
New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180-8349
Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
In 2019, the U.S. Geological Survey evaluated the performance of the Turner Designs, Inc. PhytoFind, an in-place phytoplankton classification tool. The sensor was tested with sample blanks, monoculture and mixed phytoplankton cultures, and turbidity challenges in a laboratory, and was tested on a 120-mile survey of the Caloosahatchee and St. Lucie Rivers in Florida, including Lake Okeechobee. Results include the following:
Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
The Columbia River provides important cultural, economic, and ecological services to a significant portion of the United States. Anadromous and resident fish species and other wildlife are integrated into the cultural traditions of all Tribes in the Columbia River Basin. Salmon, lamprey, sturgeon, and resident fish are an integral part of Tribal religion, culture, and physical sustenance. Despite concerns about the effect of contaminants on the aquatic ecosystem, the disproportionate effects of contaminants on members of Tribal sovereignties, and the known effects of contaminants on species protected under the Endangered Species Act, efforts to address toxic chemical pollution in the Columbia River have been limited. The lack of a dedicated contaminant monitoring program impedes evaluation and decision making regarding the health of the Columbia River ecosystem, as well as human health for Tribal members and others that consume fish and other biota from the Columbia River.
The purpose of this framework is to provide guidance for the development of a long-term program (Program) that provides the basis for assessing the status and trends of contaminants in fish, sediment, water, and invertebrates along the 962-kilometer length of the Columbia River from the Bonneville Dam upriver to the Canadian Border (Figure ES1).
This framework will focus on four persistent and bioaccumulative classes of toxic contaminants:
• Mercury
• Polychlorinated biphenyls (PCBs)
• Dichlorodiphenyltrichloroethane (DDT)
• Polybrominated diphenyl ethers (PBDEs)
Media of interest in this framework include anadromous and resident fish, sediment, invertebrates, biofilm, and surface water.
Future consideration of additional contaminants could include pesticides, per or poly-fluoroalkyl substances, 6PPD-quinone, and contaminants of emerging concern (CECs), which comprises a diverse group of anthropogenic chemicals that include thousands of pharmaceuticals, hormones, illicit drugs, new pesticides, personal care products, flame retardants, artificial sweeteners, perfluorinated compounds, disinfection byproducts, ultraviolet filters, and other industrial chemicals.
This framework includes the vision, goals, and objectives for the Program. The vision for the Program is that it will provide the basis for assessing the status and trends of contaminants in the Columbia River to guide ecosystem recovery resulting in clean, healthy fish for current and future generations. The goals of the Program are to 1) conduct long-term monitoring to assess the spatial and temporal status and trends of toxics in fish, water, sediment, and other potential media in the Columbia River mainstem, from Bonneville Dam to the Canadian Border in perpetuity, 2) stimulate conversion of science into action by providing information to facilitate future decision making that improves ecosystem function and reduces contaminants in all levels of the food chain, and 3) adaptively manage the Program to address new key questions, incorporate new and emerging science advancements, and respond to community information needs.
To facilitate achieving these goals, this framework provides details on technical planning; community outreach and engagement; and adaptive management to promote understanding and improve future decision making over the long-term, including updating the Program with new and emerging science and community needs. Additionally, data associated with the Program will be made available to the public through the EPA Water Quality Exchange (https://www.epa.gov/waterdata/water-quality-data). Documents and other materials associated with the Program can be accessed via a website hosted by Yakama Nation Fisheries (https://yakamafish-nsn.gov/restore/projects/columbia-river-mainstem- water-quality-monitoring-program).
Although the Program is limited to the Columbia River upstream of the Bonneville Dam, collaboration with other entities that monitor contaminants in the Columbia River Basin, including the Columbia River estuary below Bonneville Dam, are also an important component of outreach. Our goal is to encourage efforts to ensure data comparability across programs and recognize that the growth and adaptive management of the Program considers basin-wide monitoring developments.
","language":"English","publisher":"Yakama Nation Fisheries","usgsCitation":"Counihan, T., Moran, P.W., Waite, I.R., Duncan, S., and Shira, L., 2022, Framework for the development of the Columbia River mainstem fish tissue and water quality monitoring program - Bonneville Dam to Canadian border, vii, 54 p.","productDescription":"vii, 54 p.","ipdsId":"IP-144898","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true},{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":417648,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":417624,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://yakamafish-nsn.gov/restore/projects/columbia-river-mainstem-water-quality-monitoring-program","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Oregon, Washington","otherGeospatial":"Columbia River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.98709633523731,\n 45.5\n ],\n [\n -117.35655823426674,\n 45.5\n ],\n [\n -117.35655823426674,\n 49\n ],\n [\n -121.98709633523731,\n 49\n ],\n [\n -121.98709633523731,\n 45.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Counihan, Timothy D. 0000-0003-4967-6514","orcid":"https://orcid.org/0000-0003-4967-6514","contributorId":207532,"corporation":false,"usgs":true,"family":"Counihan","given":"Timothy D.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":874394,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moran, Patrick W. 0000-0002-2002-3539 pwmoran@usgs.gov","orcid":"https://orcid.org/0000-0002-2002-3539","contributorId":489,"corporation":false,"usgs":true,"family":"Moran","given":"Patrick","email":"pwmoran@usgs.gov","middleInitial":"W.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":874395,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Waite, Ian R. 0000-0003-1681-6955 iwaite@usgs.gov","orcid":"https://orcid.org/0000-0003-1681-6955","contributorId":616,"corporation":false,"usgs":true,"family":"Waite","given":"Ian","email":"iwaite@usgs.gov","middleInitial":"R.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":874396,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Duncan, Sherrie","contributorId":306011,"corporation":false,"usgs":false,"family":"Duncan","given":"Sherrie","email":"","affiliations":[{"id":66344,"text":"Sky Environmental, Tacoma, Washington","active":true,"usgs":false}],"preferred":false,"id":874397,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shira, Laura","contributorId":306012,"corporation":false,"usgs":false,"family":"Shira","given":"Laura","email":"","affiliations":[{"id":66345,"text":"Yakama Nation Fisheries, Yakima, Washington","active":true,"usgs":false}],"preferred":false,"id":874398,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70239066,"text":"sir20225123 - 2022 - Estimated effects of pumping on groundwater storage and Walker River stream efficiencies in Smith and Mason Valleys, west-central Nevada","interactions":[],"lastModifiedDate":"2022-12-28T13:01:23.048714","indexId":"sir20225123","displayToPublicDate":"2022-12-27T07:56:08","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5123","displayTitle":"Estimated Effects of Pumping on Groundwater Storage and Walker River Stream Efficiencies in Smith and Mason Valleys, West-Central Nevada","title":"Estimated effects of pumping on groundwater storage and Walker River stream efficiencies in Smith and Mason Valleys, west-central Nevada","docAbstract":"The Walker River originates in the Sierra Nevada Mountains and flows nearly 160 miles to its terminus at Walker Lake in west-central Nevada. The river provides a source of irrigation water for tens of thousands of acres of agricultural lands in California and Nevada and is the principal source of inflow to Walker Lake. Extraction of groundwater for agricultural use became prevalent in the late 1950s and early 1960s to supplement irrigation demands not met by surface-water diversions during times of drought. There is growing concern that continued groundwater withdrawals within the Walker River Basin are likely contributing to depleted streamflow of the Walker River and the long-term depletion of groundwater storage in the basin. This report documents changes in groundwater storage-volume and trends in Walker River stream efficiency, a measure of change in flow due to gaining or losing conditions, in the two largest agricultural valleys in the Walker River Basin, Smith and Mason Valleys, for a multi-decade period. Groundwater-level maps from previous studies were used for the beginning (1970) and middle (2006) points of this study. Groundwater levels measured from 1991–95 and 2016–20 were used to construct median groundwater-level maps that represented conditions in 1995 and 2020. Valley wide groundwater-level change was calculated by comparing groundwater-level maps for the periods 1970–95, 1996–2006, and 2007–20 and by observing the overall change from 1970 to 2020. Groundwater storage-volume change was calculated using groundwater-level change and previously defined specific yield values. Between 1970 and 2020, groundwater storage-volume declined 287,600 acre-feet in Smith Valley and 269,000 acre-feet in Mason Valley. Using groundwater storage-volume decline and annual groundwater pumpage rates, a maximum groundwater pumpage rate can be computed to support management of water resources. In Smith Valley, groundwater pumping in excess of 22,300 acre-feet per year would likely result in groundwater storage decline. In Mason Valley, groundwater pumping in excess of 75,200 acre-feet per year would likely result in groundwater storage decline. Stream efficiency was calculated using continuous streamflow data and monthly diversion volumes on two reaches: (1) the West Walker River in Smith Valley, from 1948 to 2020 and (2) the Walker River in Mason Valley, from 1958 to 2020. Stream efficiency during non-irrigation season in Smith and Mason Valleys declined at a statistically significant rate of 1.1 and 0.6 percent per year, respectively. Trends in stream efficiency corresponded to occurrence of prolonged drought, deviation from average annual streamflows, and total groundwater pumpage. Long-term declines in groundwater storage-volume and stream efficiency demonstrate that the alluvial aquifer system is becoming increasingly depleted, such that the river can no longer replenish groundwater storage while simultaneously balancing groundwater and surface-water withdrawals. The introduction of supplemental groundwater pumpage was intended to offset surface-water deficits during dry years; however, pumpage occurs even in years when average or above average streamflows meet surface-water demands. Reliance on supplemental groundwater pumpage has resulted in widespread groundwater storage-volume decline and decreased stream efficiency. With each successive drought cycle, the ability of Walker River to sustain streamflows and convey water downstream has diminished. Above average wet periods have a marginal and short-lived effect on rebounding the groundwater levels outside of the river corridor. Moreover, if the trend continues, each future drought cycle may further reduce groundwater supplies and that may further decrease streamflow reliability.
Director, Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road, Suite 3
Carson City, Nevada 89701
Water is a critical and limited resource, particularly in the arid West, but water availability is projected to decline even while demand increases due to growing human populations and increases in duration and severity of drought. Mesic areas provide important water resources for numerous wildlife species, including the greater sage-grouse (Centrocercus urophasianus; hereafter, sage-grouse), an indicator for the health of sagebrush ecosystems. Understanding how wildlife use these crucial areas is necessary to inform management and conservation of sensitive species. Specifically, the influence of anthropogenic water subsidies such as irrigated pastures is not well-studied.
We evaluated brood-rearing habitat selection and brood survival of sage-grouse in Long Valley, California, an area where the water rights are primarily owned by the city of Los Angeles and water is used locally to irrigate for livestock. This area thus represents a unique balance between the needs of wildlife and people that could increasingly define future water management.
In this study, sage-grouse broods moved closer to the edge of mesic areas and used more interior areas during the late brood-rearing period, selecting for greener areas after 1 July. Mesic areas were particularly important during dry years, with broods using areas farther interior than in wet years. Brood survival was also positively influenced by the availability and condition of mesic resources, as indicated by variation in values of normalized difference vegetation index (NDVI), with survival peaking at moderate values of NDVI and just outside the edge but decreasing inside the mesic areas.
Our results highlight the importance of quality edge habitat of large mesic areas for sage-grouse to balance habitat selection and survival, particularly during drier years and during the late brood-rearing period, which is a critical period because chick survival has been shown to influence population growth.
This study highlights the implications of large-scale anthropogenic water manipulation, and the balance between local irrigation and water distribution to benefit other regions, from the context of a species of high conservation concern in North American sagebrush ecosystems.
Estimates of riparian vegetation water use are important for hydromorphological assessment, partitioning within human and natural environments, and informing environmental policy decisions. The objectives of this study were to calculate the actual evapotranspiration (ETa) (mm/day and mm/year) and derive riparian vegetation annual consumptive use (CU) in acre-feet (AF) for select riparian areas of the Little Colorado River watershed within the Navajo Nation, in northeastern Arizona, USA. This was accomplished by first estimating the riparian land cover area for trees and shrubs using a 2019 summer scene from National Agricultural Imagery Program (NAIP) (1 m resolution), and then fusing the riparian delineation with Landsat-8 OLI (30-m) to estimate ETa for 2014–2020. We used indirect remote sensing methods based on gridded weather data, Daymet (1 km) and PRISM (4 km), and Landsat measurements of vegetation activity using the two-band Enhanced Vegetation Index (EVI2). Estimates of potential ET were calculated using Blaney-Criddle. Riparian ETa was quantified using the Nagler ET(EVI2) approach. Using both vector and raster estimates of tree, shrub, and total riparian area, we produced the first CU measurements for this region. Our best estimate of annual CU is 36,983 AF with a range between 31,648–41,585 AF and refines earlier projections of 25,387–46,397 AF.
","language":"English","publisher":"MDPI","doi":"10.3390/rs15010052","usgsCitation":"Nagler, P.L., Barreto-Muñoz, A., Sall, I., Lurtz, M.R., and Didan, K., 2022, Riparian plant evapotranspiration and consumptive use for selected areas of the Little Colorado River watershed on the Navajo Nation: Remote Sensing, v. 15, no. 1, 52, 37 p.; Data Release, https://doi.org/10.3390/rs15010052.","productDescription":"52, 37 p.; Data Release","ipdsId":"IP-143742","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":445627,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs15010052","text":"Publisher Index Page"},{"id":435592,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EFZWPP","text":"USGS data release","linkHelpText":"Uncultivated plant water use (riparian evapotranspiration) and consumptive use data for selected areas of the Little Colorado River watershed on the Navajo Nation, Arizona"},{"id":411050,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, New Mexico, Utah","otherGeospatial":"Hopi Reservation, Little Colorado River Watershed, Navajo Nation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.27451835472442,\n 37.936722934098526\n ],\n [\n -112.27451835472442,\n 33.63417184178236\n ],\n [\n -108.7808660109742,\n 33.63417184178236\n ],\n [\n -108.7808660109742,\n 37.936722934098526\n ],\n [\n -112.27451835472442,\n 37.936722934098526\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-12-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Nagler, Pamela L. 0000-0003-0674-103X pnagler@usgs.gov","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":1398,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","email":"pnagler@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":859997,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barreto-Muñoz, Armando","contributorId":239891,"corporation":false,"usgs":false,"family":"Barreto-Muñoz","given":"Armando","affiliations":[{"id":48028,"text":"University of Arizona, Biosystems Engineering, Tucson, AZ, 85721 USA","active":true,"usgs":false}],"preferred":false,"id":859998,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sall, Ibrahima 0000-0002-7526-636X","orcid":"https://orcid.org/0000-0002-7526-636X","contributorId":251750,"corporation":false,"usgs":false,"family":"Sall","given":"Ibrahima","email":"","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":859999,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lurtz, Matthew R.","contributorId":300337,"corporation":false,"usgs":false,"family":"Lurtz","given":"Matthew","email":"","middleInitial":"R.","affiliations":[{"id":65088,"text":"Civil and Environmental Engineering, Colorado State University, Fort Collins, CO, 80523 USA","active":true,"usgs":false}],"preferred":false,"id":860000,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Didan, Kamel","contributorId":292780,"corporation":false,"usgs":false,"family":"Didan","given":"Kamel","affiliations":[{"id":62999,"text":"Biosystems Engineering, University of Arizona, Tucson, AZ, 85721 USA","active":true,"usgs":false}],"preferred":false,"id":860001,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70238957,"text":"70238957 - 2022 - New larger benthic foraminifera from the subsurface Lower to Middle Eocene Oldsmar Formation of southeastern Florida (USA)","interactions":[],"lastModifiedDate":"2022-12-28T15:09:23.794846","indexId":"70238957","displayToPublicDate":"2022-12-25T09:02:57","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12981,"text":"Carnets Geol.","active":true,"publicationSubtype":{"id":10}},"title":"New larger benthic foraminifera from the subsurface Lower to Middle Eocene Oldsmar Formation of southeastern Florida (USA)","docAbstract":"We describe two larger benthic foraminiferal taxa collected from wells drilled in the subsurface Eocene rocks of southeastern Florida that are new to peninsular Florida and the Caribbean region. Saudia floridana n.sp. is characteristic of a foraminiferal assemblage, along with Helicostegina gyralis, wide forms of the Cushmania americana group, and Gunteria floridana, in an upper part of the Oldsmar Formation. Globogypsinoides browardensis n.gen. n.sp. occurs in a second foraminiferal assemblage, along with Borelis cf. floridanus, Coskinolina cf. yucatanensis, and as-yet undescribed large rotaliids, in a middle part of the Oldsmar Formation. The foraminiferal assemblage of the middle Oldsmar unit is ascribed an Ypresian age and the assemblage of the upper Oldsmar unit a Lutetian age. These two assemblages indicate inner shelf water depths of 40 m or less on the Florida Platform during the Early to Middle Eocene deposition of the middle to upper part of the Oldsmar Formation.
","language":"English","publisher":"Carnet Geol.","doi":"10.2110/carnets.2022.2221","usgsCitation":"Robinson, E., and Cunningham, K., 2022, New larger benthic foraminifera from the subsurface Lower to Middle Eocene Oldsmar Formation of southeastern Florida (USA): Carnets Geol., v. 22, no. 1, p. 857-865, https://doi.org/10.2110/carnets.2022.2221.","productDescription":"9 p.","startPage":"857","endPage":"865","ipdsId":"IP-136586","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":489218,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2110/carnets.2022.2221","text":"Publisher Index Page"},{"id":411120,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80,\n 26.25\n ],\n [\n -80.5,\n 26.25\n ],\n [\n -80.5,\n 25.4\n ],\n [\n -80,\n 25.4\n ],\n [\n -80,\n 26.25\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"22","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-12-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Robinson, Edward 0000-0002-5377-3248","orcid":"https://orcid.org/0000-0002-5377-3248","contributorId":300068,"corporation":false,"usgs":false,"family":"Robinson","given":"Edward","email":"","affiliations":[{"id":52507,"text":"University of West Indies","active":true,"usgs":false}],"preferred":false,"id":859368,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cunningham, Kevin J. 0000-0002-2179-8686","orcid":"https://orcid.org/0000-0002-2179-8686","contributorId":214677,"corporation":false,"usgs":true,"family":"Cunningham","given":"Kevin J.","affiliations":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"preferred":true,"id":859369,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}