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":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","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":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","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":70230419,"text":"70230419 - 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Although much progress has been made for the topic of ground motion selection and modification (GMSM) in the context of multistory buildings, NLRHA of dams requires at least two additional considerations: (i) accounting for multiple modes of vibration and (ii) including three orthogonal components of GMs. To convey the key ideas in developing an ensemble of multicomponent GMs for this context, the fundamentals of GMSM are first briefly reviewed using a case study. Then, special considerations for concrete dams are highlighted. Finally, a practical method for developing target spectra and selecting multicomponent GMs is presented.
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The 11 locations sampled were long-term monitoring sites that had been annually sampled since 1974. In 2020, the number of species collected at each site ranged from 0 to 13, with a mean of 6.3 and median of six. All comparisons to 2020 results were limited to past collections from Management Unit WI-2. Mean total biomass was 10.5 kg/ha which was similar to the average observed over the past 10 years (10.3 kg/ha), less than averages over the past 20 and 30-years, 15.3 and 19.8 kg/ha respectively, and higher than the average observed from 1974-84 (4.7 kg/ha). Average biomass in 2020 was highest for Bloater (6.2 kg/ha), Lake Whitefish (2.3 kg/ha), and Cisco (0.9 kg/ha). Rainbow Smelt biomass averaged 0.3 kg/ha. Year-class strength, as measured by age-1 densities, was well below the 5, 10, and 25-year averages for Bloater, Cisco, Lake Whitefish and Rainbow Smelt. Bloater averaged 1 age-1 fish/ha, Cisco, 0.2 age-1 fish/ha, Lake Whitefish, 15 age-1 fish/ha, and Rainbow Smelt 6 age-1 fish/ha. Cisco survival to age-1 has been near non-existent since the 2014- and 2015-year classes and the last moderate sized year class was in 2009. 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mvinson@usgs.gov","orcid":"https://orcid.org/0000-0001-5256-9539","contributorId":3800,"corporation":false,"usgs":true,"family":"Vinson","given":"Mark","email":"mvinson@usgs.gov","middleInitial":"R.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":863409,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Evrard, Lori M. 0000-0001-8582-5818 levrard@usgs.gov","orcid":"https://orcid.org/0000-0001-8582-5818","contributorId":2720,"corporation":false,"usgs":true,"family":"Evrard","given":"Lori","email":"levrard@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":863410,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gorman, Owen 0000-0003-0451-110X","orcid":"https://orcid.org/0000-0003-0451-110X","contributorId":216889,"corporation":false,"usgs":true,"family":"Gorman","given":"Owen","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":863411,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yule, Daniel L. 0000-0002-0117-5115","orcid":"https://orcid.org/0000-0002-0117-5115","contributorId":248693,"corporation":false,"usgs":true,"family":"Yule","given":"Daniel","middleInitial":"L.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":863412,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70240292,"text":"70240292 - 2022 - A review of Arctomecon californica (Papaveraceae) with a focus on the species’ potential for propagation and reintroduction and conservation needs","interactions":[],"lastModifiedDate":"2023-02-03T16:39:51.748313","indexId":"70240292","displayToPublicDate":"2022-12-31T10:22:13","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2785,"text":"Monographs of the Western North American Naturalist","active":true,"publicationSubtype":{"id":10}},"displayTitle":"A review of Arctomecon californica (Papaveraceae) with a focus on the species’ potential for propagation and reintroduction and conservation needs","title":"A review of Arctomecon californica (Papaveraceae) with a focus on the species’ potential for propagation and reintroduction and conservation needs","docAbstract":"Las Vegas bearpoppy (Arctomecon californica) occurrences have fluctuated during the past several decades, in part due to interannual variability in rainfall that influences recruitment and mortality events; yet, development in the Las Vegas Valley continues to threaten habitat supporting this species. Arctomecon californica was petitioned for listing under the Endangered Species Act in 2019 and is currently under review to determine whether listing is warranted (USFWS 2020). This review updates species information for A. californica and includes recent insights into the species' seed ecology, habitat requirements and suitability models, propagation and reintroduction, and pollinator biology. We include information from the past 20 years in these areas that supplement conservation and restoration actions for the species. We also identify topics with scarce information and highlight areas for future study, including the following: preservation of genetic diversity through germplasm collections, identification of mechanisms driving the species' soil endemism, maintenance of A. californica–pollinator relationships through understanding pollinator habitat, determination of the viable seed fraction and its longevity in the soil seed reserves, and prediction of population response to regional climate change based on demographic modeling.
","language":"English","publisher":"Monte L. Bean Life Science Museum, Brigham Young University","doi":"10.3398/042.014.0101","usgsCitation":"Stosich, A., DeFalco, L., and Scoles-Sciulla, S.J., 2022, A review of Arctomecon californica (Papaveraceae) with a focus on the species’ potential for propagation and reintroduction and conservation needs: Monographs of the Western North American Naturalist, v. 14, no. 1, p. 1-22, https://doi.org/10.3398/042.014.0101.","productDescription":"22 p.","startPage":"1","endPage":"22","ipdsId":"IP-140238","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":445614,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3398/042.014.0101","text":"Publisher Index Page"},{"id":412688,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Nevada","county":"Clark County, Mohave 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Alexander 0000-0002-4403-1090","orcid":"https://orcid.org/0000-0002-4403-1090","contributorId":301994,"corporation":false,"usgs":true,"family":"Stosich","given":"Alexander","email":"","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863260,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeFalco, Lesley A. 0000-0002-7542-9261","orcid":"https://orcid.org/0000-0002-7542-9261","contributorId":208658,"corporation":false,"usgs":true,"family":"DeFalco","given":"Lesley A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863261,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Scoles-Sciulla, Sara J. 0000-0003-1693-5030 sscoles@usgs.gov","orcid":"https://orcid.org/0000-0003-1693-5030","contributorId":2614,"corporation":false,"usgs":true,"family":"Scoles-Sciulla","given":"Sara","email":"sscoles@usgs.gov","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863262,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70239159,"text":"dr1165 - 2022 - Range-wide population trend analysis for greater sage-grouse (Centrocercus urophasianus)—Updated 1960–2021","interactions":[],"lastModifiedDate":"2023-01-03T11:51:43.361028","indexId":"dr1165","displayToPublicDate":"2022-12-30T09:43:53","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":9318,"text":"Data Report","code":"DR","onlineIssn":"2771-9448","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1165","displayTitle":"Range-wide Population Trend Analysis for Greater Sage-Grouse (Centrocercus urophasianus)—Updated 1960–2021","title":"Range-wide population trend analysis for greater sage-grouse (Centrocercus urophasianus)—Updated 1960–2021","docAbstract":"Greater sage-grouse (Centrocercus urophasianus) are at the center of state and national land use policies largely because of their unique life-history traits as an ecological indicator for health of sagebrush ecosystems. This updated population trend analysis provides state and federal land and wildlife managers with best-available science to help guide current management and conservation plans aimed at benefitting sage-grouse populations. This analysis relied on previously published population trend modeling methodology from Coates and others (2021) and includes the addition of three analytical updates: (1) identification of population nadirs (lowest points within cycles) at the lek (breeding ground) and neighborhood cluster (group of leks) spatial scales, (2) truncation of prior distributions on rate of change in apparent abundance values to more realistic boundaries for leks with missing data, and (3) addition of 2 years of population lek count data (2020 and 2021) to the current dataset (1953–2021). Bayesian state-space models estimated 2.9 percent average annual decline in sage-grouse populations across their geographical range, which varied among subpopulations at the largest scale of analysis, termed climate clusters (2.2–4.6). Cumulative declines were 42.5, 65.6, and 80.1 percent range-wide across short (19 years), medium (35 years), and long (55 years) temporal periods, respectively. These results indicate that range-wide populations continued to decline during 2020 and 2021, although two climate clusters (eastern area and Bi-State area) have shown growth in population abundance in recent years, indicating they have surpassed a recent population abundance nadir.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1165","issn":"2771-9448","collaboration":"Prepared in cooperation with the Western Association of Fish and Wildlife Agencies and the Bureau of Land Management","programNote":"Species Management Research Program","usgsCitation":"Coates, P.S., Prochazka, B.G., Aldridge, C.L., O'Donnell, M.S., Edmunds, D.R., Monroe, A.P., Hanser, S.E., Wiechman, L.A., and Chenaille, M.P., 2022, Range-wide population trend analysis for greater sage-grouse (Centrocercus urophasianus)—Updated 1960–2021: Data Report 1165, 16 p., https://doi.org/10.3133/dr1165.","productDescription":"Report: viii, 16 p.; Data Release","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-144163","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":411225,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OQWGIV","text":"U.S. Geological Survey data release","linkHelpText":"Trends and a targeted annual warning system for greater sage-grouse in the western United States (1960–2021)"},{"id":411222,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1165/dr1165.pdf","text":"Report","size":"8.95 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DR 1165"},{"id":411223,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1165/images"},{"id":411221,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1165/coverthb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.19101705712868,\n 48.89284566335482\n ],\n [\n -122.13131256755497,\n 48.37855671670232\n ],\n [\n -121.63700211579436,\n 47.85380781194411\n ],\n [\n -121.86067071873805,\n 47.51242609246373\n ],\n [\n -121.87668371107034,\n 47.11115078292244\n ],\n [\n -122.4532512741522,\n 46.70567448488106\n ],\n [\n -122.57397075207928,\n 46.22739431053469\n ],\n [\n -122.36606239056442,\n 45.90712886523926\n ],\n [\n -121.91588031540189,\n 45.358384504867246\n ],\n [\n -122.45322729336237,\n 44.953343420279765\n ],\n [\n -122.52983521148596,\n 44.52144457126775\n ],\n [\n -122.54530349941209,\n 44.259263341793\n ],\n [\n -122.85735468588939,\n 43.86827650155766\n ],\n [\n -123.01704364538307,\n 43.46272668287682\n ],\n [\n -123.01026119844099,\n 43.21452795313556\n ],\n [\n -123.10511846238558,\n 42.807780288627384\n ],\n [\n -122.77301525682884,\n 42.07884845828369\n ],\n [\n -122.62454526743153,\n 41.5133850817748\n ],\n [\n -122.14925905063672,\n 40.83036532174481\n ],\n [\n -121.68837858072334,\n 40.49667518946566\n ],\n [\n -121.57750448671058,\n 39.80789795232664\n ],\n [\n -121.16256808423407,\n 39.215880289775015\n ],\n [\n -120.64471039578123,\n 38.57511631320122\n ],\n [\n -119.84212022937817,\n 37.43985090599355\n ],\n [\n -118.83488568782542,\n 36.55782111752346\n ],\n [\n -118.59349323888452,\n 35.83229168807691\n ],\n [\n -117.40450808029976,\n 35.69535862053445\n ],\n [\n -116.43105077565428,\n 35.459239916109155\n ],\n [\n -114.6229823251931,\n 35.413666627683824\n ],\n [\n -96.18756245429773,\n 35.03931170375198\n ],\n [\n -94.57905716272413,\n 37.1225590274522\n ],\n [\n -94.53555587839759,\n 39.07884303955356\n ],\n [\n -95.83917624651616,\n 40.61806593890233\n ],\n [\n -96.37123578579417,\n 42.39205565405334\n ],\n [\n -96.46625839716233,\n 45.31077773932529\n ],\n [\n -97.30155203348903,\n 48.980368873698694\n ],\n [\n -122.19101705712868,\n 48.89284566335482\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
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Sacramento, California 95819
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Stratabound tourmalinites are metallogenically important rocks that locally show a close spatial association with diverse types of mineralization, especially volcanogenic massive sulfides (VMS) and clastic-dominated (CD) Zn-Pb deposits. These tourmalinite occurrences pan the geologic record from Eoarchean to Jurassic. Host lithologies are dominated by clastic metasedimentary rocks but in some areas include metavolcanic rocks, marble, or metaevaporites. Stratabound and stratiform (conformable) tourmalinites commonly display sedimentary structures such as graded beds, cross-beds, and rip-up clasts. In most cases, field and microtextural relationships are consistent with a synsedimentary to the early diagenetic introduction of boron as a precursor to tourmaline formation.
Whole-rock geochemical data for major, trace, and rare earth elements (REE) provide valuable insights into tourmalinite origins. Al-normalized values relative to those for least-altered host metasedimentary rocks suggest that tourmalinites in proximal settings at or near hydrothermal vent sites characterized by high fluid/rock regimes (e.g., Sullivan Pb-Zn-Ag deposit, Canada) have very different signatures than those in low fluid/rock, distal settings (e.g., Broken Hill Pb-Zn-Ag deposit, Australia). The high fluid/rock regimes at Sullivan show large mass changes of +60 % for Mg and +180 % for Mn, as well as large variations in abundances of light and middle REE. In contrast, tourmalinite formation in low fluid/rock regimes yields minimal Al-normalized changes in major elements, trace elements, and REE. Boron isotope values of tourmalinite-hosted tourmaline vary widely from -26.1 to +27.5 ‰, and are attributed mainly to boron sources (e.g., sediments, evaporites) with generally minor influence from processes such as formational temperature, fluid/rock ratio, and secular variation in seawater δ11B values.
Laterally extensive stratiform tourmalinites formed mainly by syngenetic or early diagenetic processes on or beneath the seafloor. The syngenetic process is attributed to the interaction of vented B-rich brines with aluminous minerals in sediments, whereas the diagenetic process involves the selective replacement of aluminous sediments by B-rich fluids. Modern examples of tourmalinites, as yet undiscovered, may exist in metalliferous sediments of the Red Sea and the eastern Pacific Ocean, in altered volcaniclastic sediments within active seafloor-hydrothermal systems of the South Pacific, and in hydrothermal mounds and vents associated with mafic sill complexes in extensional basins as in the North Sea and South China Sea. Stratabound tourmalinites that contain base-metal sulfides, high Mn concentrations (>1 wt. % MnO), or positive Eu anomalies can be valuable exploration guides for base-metal sulfide deposits in sedimentary and volcanic terranes.
Understanding what drives changes in wildlife demography is fundamental to the conservation and management of depleted or declining populations, though making inference about the intrinsic and extrinsic factors that influence survival and reproduction remains challenging. Here we use mark–resight data from 2000 to 2018 to examine the effects of environmental variability on age-specific survival and natality for the endangered western distinct population segment (wDPS) of Steller sea lions (Eumetopias jubatus) in Alaska, USA. Though this population has been studied extensively over the last four decades, the causes of divergent abundance trends that have been observed across the wDPS range remain unknown. We developed a Bayesian multievent mark–resight model that accounts for female reproductive state uncertainty. Annual survival probabilities for male pups (0.44; 0.36–0.53), female yearlings (0.63; 0.49–0.73), and male yearlings (0.62; 0.51–0.71) born in the western portion of the wDPS range, estimated here for the first time, were lower than those in the eastern portion of the wDPS range, estimated as: male pups (0.69; 0.65–0.74), female yearlings (0.76; 0.71–0.81), and male yearlings (0.71; 0.65–0.78). There was a higher proportion of young female breeders in the western portion of the range, but overall natality was lower (0.69; 0.47–0.96) than in the eastern portion of the range (0.80; 0.74–0.84). Additionally, pup mass had a positive effect on pup survival in the eastern portion of the range and a negative effect in the western portion of the range, potentially due to earlier weaning of heavier pups. Local- and basin-scale oceanographic features such as the Aleutian Low, the Arctic Oscillation Index, the North Pacific Gyre Oscillation, chlorophyll concentration, upwelling, and wind in certain seasons were correlated with vital rates. However, drawing strong inferences from these correlations is challenging given that relationships between ocean conditions and an adaptive top predator in a dynamic ecosystem are exceedingly complex. This study provides the first demographic rate estimates for the western portion of the range where abundance estimates continue to decline. These results will advance efforts to identify factors driving regionally divergent abundance trends, with implications for population-level responses to future climate variability.
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.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4321","usgsCitation":"Lawson, A.J., Jodice, P.G., Rainwater, T., Dunham, K.D., Hart, M., Butfiloski, J.W., Wilkinson, P., and Moore, C., 2022, Hidden in plain sight: Integrated population models to resolve partially observable latent population structure: Ecosphere, v. 13, e4321, 22 p., https://doi.org/10.1002/ecs2.4321.","productDescription":"e4321, 22 p.","ipdsId":"IP-137983","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":445620,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4321","text":"Publisher Index Page"},{"id":430217,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Abigail Jean 0000-0002-2799-8750","orcid":"https://orcid.org/0000-0002-2799-8750","contributorId":276319,"corporation":false,"usgs":true,"family":"Lawson","given":"Abigail","email":"","middleInitial":"Jean","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":903449,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jodice, Patrick G.R. 0000-0001-8716-120X","orcid":"https://orcid.org/0000-0001-8716-120X","contributorId":219852,"corporation":false,"usgs":true,"family":"Jodice","given":"Patrick","middleInitial":"G.R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":903450,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rainwater, Thomas R.","contributorId":338672,"corporation":false,"usgs":false,"family":"Rainwater","given":"Thomas R.","affiliations":[{"id":7084,"text":"Clemson 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":70239082,"text":"70239082 - 2022 - Moisture abundance and proximity mediate seasonal use of mesic areas and survival of greater sage-grouse broods","interactions":[],"lastModifiedDate":"2022-12-26T18:10:26.43659","indexId":"70239082","displayToPublicDate":"2022-12-26T11:24:12","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9977,"text":"Ecological Solutions and Evidence","active":true,"publicationSubtype":{"id":10}},"title":"Moisture abundance and proximity mediate seasonal use of mesic areas and survival of greater sage-grouse broods","docAbstract":"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.
Data availability remains a principal factor limiting the use of species distribution models (SDMs) as tools for wildlife conservation and management of rare species. Although data collected in systematic and rigorous fashion are preferable, available data for most species of conservation interest are usually low in both quality and number. Here we show that combining records published in peer-reviewed journals and gray literature sources (e.g., theses, government, and NGO reports) with unpublished records obtained by personal communications from relevant stakeholders affect the predicted distribution of spectacled bears (Tremarctos ornatus) in Peru. We built SDMs using generalized linear models, random forest, and Maxent, first using a dataset that only included published records, and second with a dataset using both published and unpublished records. All models were replicated ten times with random subsets with controlled sample size. Models that combined published and unpublished spectacled bear records had a better performance, irrespective of with SDM method used, increasing the connectivity of the species’ range, and increasing the overall predicted distribution area than models that only included published records. This was because unpublished records added key new localities, reducing spatial sampling biases. Our study shows that the inclusion of commonly disregarded data such as opportunistic records, reports from natural park rangers, student theses, and data-deficient small studies can make an important contribution to the overall ecological knowledge of rare and difficult-to-study species such as the spectacled bear.
","language":"English","publisher":"Springer Link","doi":"10.1007/s13364-022-00664-0","usgsCitation":"Falconi, N., Finn, J.T., Fuller, T., and Organ, J.F., 2022, Do unpublished data help to redraw distributions? The case of the spectacled bear in Peru: Mammal Research, v. 68, p. 143-150, https://doi.org/10.1007/s13364-022-00664-0.","productDescription":"8 p.","startPage":"143","endPage":"150","ipdsId":"IP-119469","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433452,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Peru","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-69.59042,-17.58001],[-69.85844,-18.09269],[-70.37257,-18.34798],[-71.37525,-17.7738],[-71.46204,-17.36349],[-73.44453,-16.35936],[-75.23788,-15.26568],[-76.00921,-14.64929],[-76.42347,-13.82319],[-76.25924,-13.53504],[-77.10619,-12.22272],[-78.09215,-10.37771],[-79.03695,-8.38657],[-79.44592,-7.93083],[-79.76058,-7.19434],[-80.53748,-6.54167],[-81.25,-6.13683],[-80.92635,-5.69056],[-81.41094,-4.73676],[-81.09967,-4.03639],[-80.30256,-3.40486],[-80.18401,-3.82116],[-80.46929,-4.05929],[-80.44224,-4.42572],[-80.02891,-4.34609],[-79.62498,-4.4542],[-79.20529,-4.95913],[-78.6399,-4.54778],[-78.45068,-3.8731],[-77.8379,-3.00302],[-76.63539,-2.60868],[-75.545,-1.56161],[-75.23372,-0.91142],[-75.37322,-0.15203],[-75.10662,-0.05721],[-74.4416,-0.53082],[-74.1224,-1.00283],[-73.6595,-1.26049],[-73.07039,-2.30895],[-72.32579,-2.43422],[-71.77476,-2.16979],[-71.41365,-2.3428],[-70.81348,-2.25686],[-70.04771,-2.72516],[-70.69268,-3.74287],[-70.39404,-3.76659],[-69.89364,-4.29819],[-70.79477,-4.25126],[-70.92884,-4.40159],[-71.74841,-4.59398],[-72.89193,-5.27456],[-72.96451,-5.74125],[-73.21971,-6.08919],[-73.12003,-6.62993],[-73.72449,-6.9186],[-73.7234,-7.341],[-73.98724,-7.52383],[-73.57106,-8.42445],[-73.01538,-9.03283],[-73.22671,-9.46221],[-72.56303,-9.52019],[-72.18489,-10.0536],[-71.30241,-10.07944],[-70.48189,-9.49012],[-70.54869,-11.00915],[-70.09375,-11.12397],[-69.52968,-10.95173],[-68.66508,-12.5613],[-68.88008,-12.89973],[-68.92922,-13.60268],[-68.94889,-14.45364],[-69.33953,-14.9532],[-69.16035,-15.32397],[-69.38976,-15.66013],[-68.95964,-16.5007],[-69.59042,-17.58001]]]},\"properties\":{\"name\":\"Peru\"}}]}","volume":"68","noUsgsAuthors":false,"publicationDate":"2022-12-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Falconi, Nereyda","contributorId":272944,"corporation":false,"usgs":false,"family":"Falconi","given":"Nereyda","email":"","affiliations":[],"preferred":false,"id":909147,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Finn, John T.","contributorId":43398,"corporation":false,"usgs":false,"family":"Finn","given":"John","email":"","middleInitial":"T.","affiliations":[{"id":16720,"text":"Department of Environmental Conservation, University of Massachusetts, Amherst, MA 01003-9485, USA","active":true,"usgs":false}],"preferred":false,"id":909148,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fuller, Todd K.","contributorId":270781,"corporation":false,"usgs":false,"family":"Fuller","given":"Todd K.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":909149,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Organ, John F. 0000-0002-0959-0639 jorgan@usgs.gov","orcid":"https://orcid.org/0000-0002-0959-0639","contributorId":189047,"corporation":false,"usgs":true,"family":"Organ","given":"John","email":"jorgan@usgs.gov","middleInitial":"F.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":909150,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70239113,"text":"70239113 - 2022 - Models combining multiple scales of inference capture hydrologic and climatic drivers of riparian tree distributions","interactions":[],"lastModifiedDate":"2022-12-28T14:04:34.673006","indexId":"70239113","displayToPublicDate":"2022-12-22T08:00:36","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Models combining multiple scales of inference capture hydrologic and climatic drivers of riparian tree distributions","docAbstract":"Predicting species geographic distributions is key to managing invasive species, conserving biodiversity, and understanding species' environmental requirements. Species distribution models (SDMs) commonly focus on climatic predictors, but other environmental factors can also be essential, particularly for species with specialized habitats defined by hydrologic, topographic, or edaphic conditions (e.g., riparian, wetland, alpine, coastal, serpentine). Here, we demonstrate a novel approach for capturing strong effects of both hydrologic and climatic predictors in SDMs for riparian plants, by merging analyses targeted at environmental drivers within riparian ecosystems and across the western USA (3.8 × 106 km2). We developed presence-background SDMs from five algorithms for three invasive riparian trees (Tamarix ramossisima/chinensis [saltcedar], Elaeagnus angustifolia [Russian olive], and Ulmus pumila [Siberian elm]) and three native Populus spp. (cottonwoods). We used separate background datasets to develop models with different spatial scales of inference: (1) spatially filtered random points to represent available habitat across the study area and (2) target-group points from Salix (willow) occurrences to represent available riparian habitat. Random-background models captured hydrologic drivers of riparian tree distributions relative to the largely upland western USA, whereas Salix-background models captured climatic drivers within the context of riparian ecosystems. Combining predictions from the two backgrounds identified hydrologically suitable habitats within climatically suitable regions, resulting in fewer false “absences” than either background alone, improving predictions over previous SDMs, and providing more complete information to guide management decisions. Surprisingly, the predicted habitat for U. pumila, a newly recognized riparian invader, was as or more extensive than Populus deltoides/fremontii, T. ramossisima/chinensis, and E. angustifolia, the most common riparian tree complexes in the western USA. Watersheds constituting 20% of U. pumila predicted habitat contained no occurrence records, indicating high risk of future and unrecognized invasions. Combining models from random and ecosystem-specific target-group backgrounds may improve SDMs for species from many specialized habitats, providing a method to link predicted distributions to localized geographic features while capturing broad-scale climatic requirements.
","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.4305","usgsCitation":"Perry, L.G., Jarnevich, C.S., and Shafroth, P., 2022, Models combining multiple scales of inference capture hydrologic and climatic drivers of riparian tree distributions: Ecosphere, v. 13, no. 12, e4305, 22 p., https://doi.org/10.1002/ecs2.4305.","productDescription":"e4305, 22 p.","ipdsId":"IP-133461","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":445636,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4305","text":"Publisher Index Page"},{"id":435593,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LIB2TF","text":"USGS data release","linkHelpText":"Occurrence data and models for woody riparian native and invasive plant species in the conterminous western USA"},{"id":411118,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -100,\n 49\n ],\n [\n -124,\n 49\n ],\n [\n -124,\n 28\n ],\n [\n -100,\n 28\n ],\n [\n -100,\n 49\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"13","issue":"12","noUsgsAuthors":false,"publicationDate":"2022-12-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Perry, Laura G","contributorId":177873,"corporation":false,"usgs":false,"family":"Perry","given":"Laura","email":"","middleInitial":"G","affiliations":[],"preferred":false,"id":860091,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 jarnevichc@usgs.gov","orcid":"https://orcid.org/0000-0002-9699-2336","contributorId":3424,"corporation":false,"usgs":true,"family":"Jarnevich","given":"Catherine","email":"jarnevichc@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":860092,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shafroth, Patrick B. 0000-0002-6064-871X","orcid":"https://orcid.org/0000-0002-6064-871X","contributorId":225182,"corporation":false,"usgs":true,"family":"Shafroth","given":"Patrick B.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":860093,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70239342,"text":"70239342 - 2022 - Analysis of per capita contributions from a spatial model provides strategies for controlling spread of invasive carp","interactions":[],"lastModifiedDate":"2023-01-10T13:25:00.980147","indexId":"70239342","displayToPublicDate":"2022-12-22T07:22:52","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Analysis of per capita contributions from a spatial model provides strategies for controlling spread of invasive carp","docAbstract":"Metapopulation models may be applied to inform natural resource management to guide actions targeted at location-specific subpopulations. Model insights frequently help to understand which subpopulations to target and highlight the importance of connections among subpopulations. For example, managers often treat aquatic invasive species populations as discrete populations due to hydrological (e.g., lakes, pools formed by dams) or jurisdictional boundaries (e.g., river segments by country or jurisdictional units such as states or provinces). However, aquatic invasive species often have high rates of dispersion and migration among heterogenous locations, which complicates traditional metapopulation models and may not conform to management boundaries. Controlling invasive species requires consideration of spatial dynamics because local management activities (e.g., harvest, movement deterrents) may have important impacts on connected subpopulations. We expand upon previous work to create a spatial linear matrix model for an aquatic invasive species, Bighead Carp, in the Illinois River, USA, to examine the per capita contributions of specific subpopulations and impacts of different management scenarios on these subpopulations. Managers currently seek to prevent Bighead Carp from invading the Great Lakes via a connection between the Illinois Waterway and Lake Michigan by allocating management actions across a series of river pools. We applied the model to highlight how spatial variation in movement rates and recruitment can affect decisions about where management activities might occur. We found that where the model suggested management actions should occur depend crucially on the specific management goal (i.e., limiting the growth rate of the metapopulation vs. limiting the growth rate of the invasion front) and the per capita recruitment rate in downstream pools. Our findings illustrate the importance of linking metapopulation dynamics to management goals for invasive species control.
Cross-correlation waveforms of seismic noise in the Seattle basin, Washington, were analyzed to determine the group velocities of surface waves and constrain the shear-wave velocity (VS) for depths less than about 2 kilometers (km). Twenty broadband seismometers were deployed for about 3 weeks in three dense arrays separated by about 5 km, with minimum intra-array station spacing of about 0.5 km. Cross correlations of only 9 days of noise recordings produced Green’s functions at periods of 2 to 6 seconds (s) for sites about 5 km apart. Usable noise correlations for shorter periods of 0.5 to 1.0 s were found for sites within the arrays separated by 1 to 2 km. We bandpass filtered the inter- and intra-array cross-correlation waveforms to determine Love-wave group velocities at periods of 0.5 to 6 s for paths within the Seattle basin and at 3 to 5 s for paths crossing the southern edge of the basin. We developed a non-linear inversion program to determine VS profiles that fit the observed group velocities for paths in the basin. We found that these group velocities are well fit by a variety of VS profiles, each with a distinct jump in VS at depths ranging from 0.9 to 1.3 km. This jump in VS is inferred to represent the top of bedrock. The observed group velocities are not matched by models with the top of bedrock at 0.7-km depth or shallower. The group velocities are also fit by a model with no large jumps in VS in depths less than 2.4 km. The VS profile for the middle of the basin from Stephenson and others (2017), with a depth to bedrock of 0.9 km, also adequately fits the group velocity observations, if a velocity gradient is added from 0.05- to 0.1-km depth. The results indicate that short (3-week) deployments of seismometers to record seismic noise may provide useful constraints on the VS of sedimentary basins.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221108","collaboration":"Prepared in cooperation with the University of Washington","usgsCitation":"Frankel, A., and Bodin, P., 2022, Using seismic noise correlation to determine the shallow velocity structure of the Seattle basin, Washington: U.S. Geological Survey Open-File Report 2022–1108, 13 p., https://doi.org/10.3133/ofr20221108.","productDescription":"vi, 12 p.","onlineOnly":"Y","ipdsId":"IP-140830","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":501842,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114001.htm","linkFileType":{"id":5,"text":"html"}},{"id":410660,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1108/ofr20221108.XML"},{"id":410656,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1108/coverthb.jpg"},{"id":410657,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1108/ofr20221108.pdf","text":"Report","description":"OFR 2022-1108"},{"id":410658,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221108/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1108"},{"id":410659,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1108/images"}],"country":"United States","state":"Washington","city":"Seattle","otherGeospatial":"Seattle Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.45036951581977,\n 47.693059199440825\n ],\n [\n -122.45036951581977,\n 47.51906296781365\n ],\n [\n -122.22524539503297,\n 47.51906296781365\n ],\n [\n -122.22524539503297,\n 47.693059199440825\n ],\n [\n -122.45036951581977,\n 47.693059199440825\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Earthquake Science Center
U.S. Geological Survey
345 Middlefield Road, MS 977
Menlo Park, California 94025
Jewel Cave National Monument is in the western Black Hills of South Dakota and contains an extensive cave network, including various subterranean water bodies (cave lakes) that are believed to represent the regionally important Madison aquifer. Recent investigations have sought to improve understanding of hydrogeologic characteristics of cave lakes in Jewel Cave. The U.S. Geological Survey, in cooperation with the National Park Service, collected water-level and water-chemistry data within and near Jewel Cave to better understand groundwater interactions in Jewel Cave and to evaluate recharge characteristics of cave lakes. Continuous water-level data were collected at two cave lakes (Hourglass and New Years Lakes) from 2018 to 2021, and discrete measurements were collected by National Park Service staff from 2015 to 2021. Water samples were collected from one stream, one rain collector, three springs, and two cave lakes. The approach for this study included comparing water-level data collected from two cave lakes to historical climate data and using multivariate statistical analyses to evaluate water samples collected during this study and from previous investigations. This study builds on interpretations from previous investigations that collected similar datasets and performed similar analyses.
Hydrographs of Hourglass and News Years Lakes from 2015 to 2021 demonstrated the variability of groundwater levels in Jewel Cave in response to dry and wet climate conditions. Hourglass Lake displayed small (up to 4.8 feet), gradual water-level changes, whereas New Years Lake displayed relatively large (up to at least 27.5 feet) and rapid water-level changes. Hourglass and New Years Lakes are about 0.4 mile apart at the land surface, and the water-level elevation between the lakes varied from 61 to 93.5 feet from 2016 to 2021. The proximity and relatively small elevation difference of Hourglass and New Years Lakes indicated different recharge sources and (or) mechanisms were responsible for hydrograph dissimilarities. Water-level changes at Hourglass Lake were similar to water-level changes at a well completed in the Madison aquifer about 9 miles south of Jewel Cave National Monument, which indicated Hourglass Lake may be recharged similar to the regional Madison aquifer along outcrops north of Jewel Cave. New Years Lake displayed almost no similarities to the well completed in the Madison aquifer—indicating a more direct connection to local recharge rather than solely from outcrops recharging the regional Madison aquifer.
Results from multivariate statistical analyses of water-chemistry data were used to evaluate recharge observations from water-level data. The water chemistry of Hourglass Lake indicated its water was chemically more similar to precipitation than other groundwater sites sampled. A conceptual karst recharge model indicated that the dominant recharge source to Hourglass Lake was diffuse allogenic recharge from vertical movement of infiltrated precipitation through vertical or near-vertical fractures that extend through the Minnelusa Formation and unsaturated zone of the Madison Limestone. The water chemistry of New Years Lake was chemically similar to Hell Canyon Creek about 0.2 mile from New Years Lake at the land surface. Streamflow loss zones (concentrated allogenic recharge) along Hell Canyon Creek have not been mapped, but their presence in the Jewel Cave area has been speculated by previous investigations. A fault observed in the cave ceiling above New Years Lake by National Park Service staff could provide a natural conduit for direct recharge from Hell Canyon Creek to New Years Lake if the fault is extensive. Additional water-chemistry and water-level data, as well as streamflow data upstream and downstream of the potential streamflow loss zone along Hell Canyon Creek, are needed to prove the presence of this loss zone and discern further correlations between streamflow and water levels in New Years Lake. Observations from previous investigations and this study indicated recharge to Jewel Cave is complex and occurs on various timescales that are affected temporally by precipitation patterns and spatially by hydrologic connection with the overlying Minnelusa aquifer of the Minnelusa Formation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225108","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Medler, C.J., 2022, Hydrogeologic characteristics of Hourglass and New Years Cave Lakes at Jewel Cave National Monument, South Dakota, from water-level and water-chemistry data, 2015–21: U.S. Geological Survey Scientific Investigations Report 2022–5108, 47 p., https://doi.org/10.3133/sir20225108.","productDescription":"Report: viii, 47 p.; Dataset","numberOfPages":"60","onlineOnly":"Y","ipdsId":"IP-137086","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":410721,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225108/full","text":"Report"},{"id":410718,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":410717,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5108/images"},{"id":410715,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5108/sir20225108.pdf","text":"Report","size":"8.03 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5108"},{"id":410714,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5108/coverthb.jpg"},{"id":410716,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5108/sir20225108.XML"},{"id":503566,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113998.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"South Dakota","otherGeospatial":"Jewel Cave National Monument","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -103.2,\n 43.738808274610875\n ],\n [\n -104.0,\n 43.738808274610875\n ],\n [\n -104.0,\n 43.35675372367402\n ],\n [\n -103.2,\n 43.35675372367402\n ],\n [\n -103.2,\n 43.738808274610875\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702
Rapid post-earthquake reconnaissance is important for emergency responses and rehabilitation by providing accurate and timely information about secondary hazards and impacts, including landslide, liquefaction, and building damage. Despite the extensive collection of geospatial data and satellite images, existing physics-based and data-driven methods suffer from low estimation performance due to the complex and event-specific causal dependencies underlying the cascading processes of earthquake-triggered hazards and impacts. Herein, we present a rapid seismic multi-hazard and impact estimation system that leverages advanced statistical causal inference and remote sensing techniques. The unique feature of this system is that it provides accurate and high-resolution estimations on a regional scale by jointly inferring multiple hazards and building damage from satellite images through modeling their causal dependencies. We evaluate our system on multiple seismic events from diverse countries around the globe. Our results corroborate that incorporating causal dependencies significantly improves large-scale estimation accuracy for multiple hazards and impacts compared to existing systems. The results also reveal quantitative causal mechanisms among earthquake-triggered multi-hazard and impact for multiple seismic events. Our system establishes a new way to extract and utilize the complex interactions of multiple hazards and impacts for effective disaster responses and advancing understanding of seismic geological processes.
","language":"English","publisher":"Springer","doi":"10.1038/s41467-022-35418-8","usgsCitation":"Xu, S., Dimasaka, J., Wald, D.J., and Noh, H.Y., 2022, Seismic multi-hazard and impact estimation via causal inference from satellite imagery: Nature Communications, v. 13, 7793, 13 p., https://doi.org/10.1038/s41467-022-35418-8.","productDescription":"7793, 13 p.","ipdsId":"IP-131046","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":445655,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-022-35418-8","text":"Publisher Index Page"},{"id":410700,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","noUsgsAuthors":false,"publicationDate":"2022-12-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Xu, Susu","contributorId":300127,"corporation":false,"usgs":false,"family":"Xu","given":"Susu","email":"","affiliations":[{"id":65025,"text":"Stony Brook University, NY, USA","active":true,"usgs":false}],"preferred":false,"id":859455,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dimasaka, Joshua","contributorId":300128,"corporation":false,"usgs":false,"family":"Dimasaka","given":"Joshua","email":"","affiliations":[{"id":65026,"text":"Stanford University, CA, USA","active":true,"usgs":false}],"preferred":false,"id":859456,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wald, David J. 0000-0002-1454-4514 wald@usgs.gov","orcid":"https://orcid.org/0000-0002-1454-4514","contributorId":795,"corporation":false,"usgs":true,"family":"Wald","given":"David","email":"wald@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":859457,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Noh, Hae Young","contributorId":265961,"corporation":false,"usgs":false,"family":"Noh","given":"Hae","email":"","middleInitial":"Young","affiliations":[{"id":54844,"text":"Carnegie Mellon University (now at Stanford University)","active":true,"usgs":false}],"preferred":false,"id":859458,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70238906,"text":"sim3497 - 2022 - Delineating the Pierre Shale from geophysical surveys east and southeast of Ellsworth Air Force Base, South Dakota, 2021","interactions":[],"lastModifiedDate":"2026-04-01T15:30:56.71177","indexId":"sim3497","displayToPublicDate":"2022-12-19T07:51:11","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3497","displayTitle":"Delineating the Pierre Shale from Geophysical Surveys East and Southeast of Ellsworth Air Force Base, South Dakota, 2021","title":"Delineating the Pierre Shale from geophysical surveys east and southeast of Ellsworth Air Force Base, South Dakota, 2021","docAbstract":"The U.S. Geological Survey, in cooperation with the U.S. Air Force Civil Engineer Center, used surface-geophysical methods to delineate the top of Cretaceous Pierre Shale along survey transects in selected areas east and southeast of Ellsworth Air Force Base, South Dakota, from April to September 2021. Two complementary geophysical methods—electrical resistivity and passive seismic—were used along 21 colocated transect surveys east and southeast of Ellsworth Air Force Base for a total of 24.7 line-kilometers. Electrical resistivity results were analyzed using EarthImager2D electrical resistivity tomography processing and inversion software. Two-dimensional earth models showing the electrical properties of the subsurface were evaluated by directly comparing the high and low subsurface resistivity values to a surficial-geologic map and nearby wells with drillers logs. Passive seismic data were analyzed using the horizontal-to-vertical spectral ratio method to determine the depth to the Cretaceous Pierre Shale at each survey point. The depth to the Pierre Shale along the transects ranged from 0.0 to about 19.8 meters, and the mean and median depths were about 6.1 and 5.6 meters, respectively. The elevation of the Pierre Shale and thickness of unconsolidated deposits generally increased with land-surface elevation from south to north; however, some transects displayed topographically high and low areas that did not correlate with land-surface topography.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3497","collaboration":"Prepared in cooperation with the U.S. Air Force Civil Engineer Center","usgsCitation":"Medler, C.J., 2022, Delineating the Pierre Shale from geophysical surveys east and southeast of Ellsworth Air Force Base, South Dakota, 2021: U.S. Geological Survey Scientific Investigations Map 3497, 3 sheets, 15-p. pamphlet, https://doi.org/10.3133/sim3497.","productDescription":"Report: vi, 15 p.; 3 Sheets: 64.00 × 53.33 inches or smaller; Data Release","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-137098","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":501938,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113997.htm","linkFileType":{"id":5,"text":"html"}},{"id":410625,"rank":7,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3497/sim3497_sheet03.pdf","text":"Sheet 3","size":"16.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3497, sheet 3","linkHelpText":"—Depth to Pierre Shale from Electrical Resistivity Tomography Inversion and Horizontal-to-Vertical Spectral Ratio Results for Transects 4A, 4B, 4D, 4E, 4FD3, 4FD4, 4FD5, 4G, 4H, and 5, Ellsworth Air Force Base, South Dakota"},{"id":410609,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3497/sim3497_sheet02.pdf","text":"Sheet 2","size":"14.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3497, sheet 2","linkHelpText":"—Depth to Pierre Shale from Electrical Resistivity Tomography Inversion and Horizontal-to-Vertical Spectral Ratio Results for Transects 2, 3A, 3B, 3D, 3E, and 3F, Ellsworth Air Force Base, South Dakota"},{"id":410608,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3497/sim3497_sheet01.pdf","text":"Sheet 1","size":"16.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3497, sheet 1","linkHelpText":"—Depth to Pierre Shale from Electrical Resistivity Tomography Inversion and Horizontal-to-Vertical Spectral Ratio Results for Transects 1A, 1C, 1D, 4F Alternate 1, and 4F Alternate 2, Ellsworth Air Force Base, South Dakota"},{"id":410607,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sim/3497/images"},{"id":410606,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sim/3497/sim3497.XML"},{"id":410605,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3497/sim3497.pdf","text":"Report","size":"8.72 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3497"},{"id":410604,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3497/coverthb.jpg"},{"id":410698,"rank":9,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sim3497/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":410626,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9X57BS0","text":"USGS data release","linkHelpText":"Electrical resistivity tomography (ERT) and horizontal-to-vertical spectral ratio (HVSR) data collected East and Southeast of Ellsworth Air Force Base, South Dakota, in 2021"}],"country":"United States","state":"South Dakota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -103.08,\n 44.06\n ],\n [\n -102.84286176348763,\n 44.06\n ],\n [\n -102.84286176348763,\n 44.2\n ],\n [\n -103.08,\n 44.2\n ],\n [\n -103.08,\n 44.06\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702
Algal blooms are pervasive in many freshwater environments and can pose risks to the health and safety of humans and other organisms. However, monitoring and tracking of potentially harmful blooms often relies on in-person observations by the public. Remote sensing has proven useful in augmenting in situ observations of algal concentration, but many hurdles hinder efficient application by end users. First, numerous approaches to estimate aquatic chlorophyll-a are available and can produce inconsistent results. Second, lack of quantitative in situ observations limits opportunities to train models for specific waterbodies, such that models developed for other systems must be used instead. We (1) implement univariate and multivariate logistic regression models to estimate the probability that aquatic chlorophyll-a concentrations exceed an accepted threshold beyond which harmful effects become likely and (2) evaluate the use of visually classified bloom/no-bloom satellite imagery to augment in situ training data. Using a binary classification of aquatic chlorophyll-a exceeding 10 μg / L, we found that (1) logistic regression models were ∼80 % accurate, (2) univariate models trained with visually classified data produce nearly the same accuracy (79%) as models trained with in situ observations (80%), and (3) augmenting in situ chlorophyll-a observations with visual classifications outperformed (82% accuracy) models trained on in situ observations alone (80% accuracy). These results provide a framework for evaluating multiple spectral indices in retrieving algal bloom presence or absence and illustrate that training data derived directly from satellite imagery can be useful in augmenting in situ observations.
","language":"English","publisher":"SPIE Digital Library","doi":"10.1117/1.JRS.16.044522","usgsCitation":"King, T.V., Hundt, S., Hafen, K., Stengel, V.G., and Ducar, S.D., 2022, Mapping the probability of freshwater algal blooms with various spectral indices and sources of training data: Journal of Applied Remote Sensing, v. 16, no. 4, 044522, 22 p., https://doi.org/10.1117/1.JRS.16.044522.","productDescription":"044522, 22 p.","ipdsId":"IP-127684","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":445656,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1117/1.jrs.16.044522","text":"Publisher Index Page"},{"id":435594,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GF0CBG","text":"USGS data release","linkHelpText":"Chlorophyll-a concentrations and algal bloom condition paired with Sentinel-2 aquatic reflectance values collected for Brownlee Reservoir, ID from 2015 through 2020"},{"id":410786,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.6105415720396,\n 45.15126198574853\n ],\n [\n -117.6105415720396,\n 43.793442297404255\n ],\n [\n -116.58806901557723,\n 43.793442297404255\n ],\n [\n -116.58806901557723,\n 45.15126198574853\n ],\n [\n -117.6105415720396,\n 45.15126198574853\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"King, Tyler V. 0000-0002-5785-3077","orcid":"https://orcid.org/0000-0002-5785-3077","contributorId":292424,"corporation":false,"usgs":true,"family":"King","given":"Tyler","middleInitial":"V.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":859498,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hundt, Stephen A. 0000-0002-6484-0637","orcid":"https://orcid.org/0000-0002-6484-0637","contributorId":204678,"corporation":false,"usgs":true,"family":"Hundt","given":"Stephen","middleInitial":"A.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":859499,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hafen, Konrad 0000-0002-1451-362X","orcid":"https://orcid.org/0000-0002-1451-362X","contributorId":215959,"corporation":false,"usgs":true,"family":"Hafen","given":"Konrad","email":"","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":859500,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stengel, Victoria G. 0000-0003-0481-3159 vstengel@usgs.gov","orcid":"https://orcid.org/0000-0003-0481-3159","contributorId":5932,"corporation":false,"usgs":true,"family":"Stengel","given":"Victoria","email":"vstengel@usgs.gov","middleInitial":"G.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":859501,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ducar, Scott D. 0000-0003-0781-5598","orcid":"https://orcid.org/0000-0003-0781-5598","contributorId":297547,"corporation":false,"usgs":true,"family":"Ducar","given":"Scott","email":"","middleInitial":"D.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":859502,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70238905,"text":"sir20225114 - 2022 - BFS—A non-linear, state-space model for baseflow separation and prediction","interactions":[],"lastModifiedDate":"2026-04-27T19:12:45.123597","indexId":"sir20225114","displayToPublicDate":"2022-12-16T12:26:01","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":"2022-5114","displayTitle":"BFS—A Non-Linear, State-Space Model for Baseflow Separation and Prediction","title":"BFS—A non-linear, state-space model for baseflow separation and prediction","docAbstract":"Streamflow in rivers can be separated into a relatively steady component, or baseflow, that represents reliably available surface water and more dynamic components of runoff that typically represent a large fraction of total streamflow. A spatially aggregated numerical time-series model was developed to separate the baseflow component of a streamflow time-series using a state-space framework in which baseflow is a non-linear function of upstream storage, an unmeasured state variable. The state-space framework allows forecasting of baseflow for periods with no rainfall or snowmelt and estimation of residence times in contrast to other hydrograph separation models. The use of a non-linear relation between baseflow and storage maintains model performance over a wide range of time scales but will only provide reliable predictions for periods when the rate of streamflow recession as a fraction of streamflow decreases over time.
The baseflow separation model, BFS, is implemented as set of functions in the statistical computing language R. BFS is run using the main function, bf_sep, which reads model input (a time series of streamflow), calculates the baseflow component of streamflow, writes model output to a file, and returns an error to the user to facilitate automated calibration. The function, bf_sep, has six arguments, which a user must enter: a numerical vector with the time series of measured streamflow volume for each time step; a character string, timestep, that has a value of either “daily” or “hourly” indicating the time step; a character string, error_basis, indicating which simulated streamflow components are used for error calculations; a six-element numeric vector, flow, with parameters characterizing streamflow; a six-element vector, basin_char, with parameters characterizing the geometry of stream basin and reservoirs; and a six-element vector, gw_hyd, with hydraulic parameters. The function bf_sep calls a series of other functions to calculate surface and base reservoir storage and fluxes.
Calibration of a non-linear model for baseflow recession must confront three issues. First, baseflow is a component of streamflow, so it is always less than or equal to streamflow but there is no independent standard for the baseflow component of streamflow. Second, optimization routines can converge on a set of model parameters that result in relatively steady but minimal baseflow that does not exceed streamflow, Q, but has a limited dynamic range. Third, the power function used to generate non-linear first-order baseflow recession (dQ/dt)/Q ≠ constant) may only be sensitive to parameters over a limited range of values, which may not be found by optimization routines.
To address these issues, BFS calculates error as the mean of weighted differences between measured streamflow and either simulated baseflow or the sum of simulated baseflow and surface flow as a fraction of measured streamflow. The difference for each time step is weighted by an exponential function of the length of recession for each time step ranging from 0 for periods when streamflow increases and approaching 1 for long recessional periods. The weight is set to 1 for any time step when simulated streamflow exceeds measured streamflow. Error calculation incorporates limited precision of streamflow measurements.
A four-step calibration process was developed to find a set of viable parameters that maximize the baseflow component within the constraints of the conceptual model (a first-order recession rate that decreases during dry periods). BFS was calibrated at 13,208 U.S. Geological Survey streamgages with available daily streamflow records for at least 300 days from water years 1981 to 2020. The total simulated baseflow component as a fraction of streamflow (BFF) was generally less than the baseflow index (BFI) for 8,368 streamgages where BFF and BFI were available. The median difference was BFF–BFI = 0.11. Large differences were most common in the Interior West where streamflow in many rivers is regulated and is generated predominantly by snowmelt. The baseflow separation model generally allocates less streamflow to baseflow than graphical hydrograph separation in snowmelt rivers.
BFS can be used to forecast streamflow during dry periods by using a time series of real-time streamflow with values of Not Available (NA), appended to the time-series to represent missing (future) streamflow values. The forecast skill of BFS was evaluated in terms of difference between simulated baseflow and measured streamflow as a fraction of measured streamflow on the days of the annual maximum recession period at 5,916 of the sites with at least 10 years of record. The median annual error was less than 50 percent at one-half of the sites and generally improved for drier years with longer recession periods.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225114","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency and the Washington State Department of Ecology","usgsCitation":"Konrad, C.P., 2022, BFS—A non-linear, state-space model for baseflow separation and prediction: U.S. Geological Survey Scientific Investigations Report 2022–5114, 24 p., https://doi.org/10.3133/sir20225114.","productDescription":"Report: vii, 24 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-122969","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":503569,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113944.htm","linkFileType":{"id":5,"text":"html"}},{"id":410600,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5114/sir20225114.XML"},{"id":410599,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5114/images"},{"id":410598,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AIPHEP","text":"USGS data release","description":"USGS data release","linkHelpText":"Non-linear baseflow separation model with parameters and results (ver. 2.0, October 2022)"},{"id":410596,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5114/sir20225114.pdf","text":"Report","size":"18.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5114"},{"id":410595,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5114/coverthb.jpg"}],"contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
Green Lake, located in central Wisconsin USA within a watershed with land use dominated by agriculture, is listed as impaired under Sect. 303(d) of the Clean Water Act. The primary tributary, Silver Creek, is also impaired because of high total phosphorus (TP) concentrations. Silver Creek flows through a shallow marsh before reaching the lake. Prior to 2006, the marsh was turbid and free of macrophytes. Efforts to restrict carp (Cyprinus carpio) in the marsh and reduce the primary upstream phosphorus point source, resulted in the marsh becoming a clear-water, macrophyte-dominated system.
The point source reduction and marsh phytoplankton-to-macrophyte shift reduced the export of TP and suspended sediment (SS). These measured reductions at the marsh outlet exceeded the documented reductions in the upstream point source suggesting that the shift to a macrophyte-dominated system drove part of the TP reductions. TP loads at the marsh outlet significantly decreased in all seasons; however, SS loads significantly decreased in all seasons except winter, suggesting the vegetation shift was an important driver for these reductions. During 2012–2017, the marsh served as an overall sink for TP and SS, retaining on average 1.59 kg/day and 0.95 MT/day, respectively. Overall, this study documents benefits of a multi-stakeholder, collaborative ecological effort to restore a marsh from a turbid system to a macrophyte-dominated system, which resulted in significant reductions in downstream TP and SS loading to a major inland lake. This effort may serve as a model for similar restorations in other watersheds with land use dominated by agriculture.
","language":"English","publisher":"Springer","doi":"10.1007/s13157-022-01649-0","usgsCitation":"Fuller, S., Boswell, E.P., Thompson, A., and Robertson, D., 2022, Water-quality improvement of an agricultural watershed marsh after macrophyte establishment and point-source reduction: Wetlands, v. 42, 129, 13 p., https://doi.org/10.1007/s13157-022-01649-0.","productDescription":"129, 13 p.","ipdsId":"IP-139219","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":413530,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Green Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.08144298728385,\n 43.755719784168264\n ],\n [\n -88.8933819839438,\n 43.755719784168264\n ],\n [\n -88.8933819839438,\n 43.85676732584028\n ],\n [\n -89.08144298728385,\n 43.85676732584028\n ],\n [\n -89.08144298728385,\n 43.755719784168264\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"42","noUsgsAuthors":false,"publicationDate":"2022-12-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Fuller, Sarah","contributorId":302730,"corporation":false,"usgs":false,"family":"Fuller","given":"Sarah","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":865263,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boswell, Edward P 0000-0002-2644-4043","orcid":"https://orcid.org/0000-0002-2644-4043","contributorId":302732,"corporation":false,"usgs":false,"family":"Boswell","given":"Edward","email":"","middleInitial":"P","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":865264,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thompson, Anita M.","contributorId":200233,"corporation":false,"usgs":false,"family":"Thompson","given":"Anita M.","affiliations":[{"id":16128,"text":"Department of Biological System Engineering, University of Wisconsin—Madison, Madison, WI, USA","active":true,"usgs":false}],"preferred":false,"id":865265,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Robertson, Dale M. 0000-0001-6799-0596","orcid":"https://orcid.org/0000-0001-6799-0596","contributorId":217258,"corporation":false,"usgs":true,"family":"Robertson","given":"Dale M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":865266,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70270408,"text":"70270408 - 2022 - Examining landowners’ preferences for a chronic wasting disease management program","interactions":[],"lastModifiedDate":"2025-08-19T15:18:27.733358","indexId":"70270408","displayToPublicDate":"2022-12-15T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"Examining landowners’ preferences for a chronic wasting disease management program","docAbstract":"Private landowners are key partners in chronic wasting disease (CWD) management, especially in landscapes where there is limited public ownership. In this study, we evaluated landowners' preferences for alternative hypothetical CWD management programs using a stated choice experiment. We were particularly interested in understanding preferences for the use of financial incentives to motivate white-tailed deer harvest and facilitate hunter access to private lands as potential CWD management tools. We used latent class analysis to characterize preference heterogeneity among landowners stemming from patterns of choice. We compared means and distributions of auxiliary variables related to landowners' perceived risks, trust, attitudes toward management, and sociodemographics across latent classes stemming from choice model results. The pooled model demonstrated that reducing deer population density, providing payments to landowners for CWD-positive deer taken from their property, the form of incentives for public access, and banning recreational deer feeding had a small positive effect on respondents' choice of CWD management program. However, providing financial payments to hunters for harvesting CWD-positive deer and the use of targeted culling had the opposite effect on choice. Latent class models revealed that a majority of respondents exhibited a pattern of preference where all forms of incentives exerted a negative effect on choice, but smaller subsets of landowners positively evaluate the use of some incentives. Post-hoc contrasts revealed relationships between patterns of preferences and trust, risk, and attitudes toward CWD management with small to medium effects. Results demonstrated limited support for the use of financial incentives as a tool to manage access and harvest in the southeast Minnesota CWD management zone
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