Since 1978, surveys of Lake Ontario preyfish communities have provided information on the status and trends of the benthic preyfish community related to Fish Community Objectives that includes understanding preyfish population dynamics and community diversity. Beginning in 2015, the benthic preyfish survey expanded from US-only to incorporate Canadian sites, increasing the survey’s spatial coverage to a lake-wide scale. Additionally, sampling in eastern US embayments (Black River, Chaumont, Guffin, and Henderson Bays), that were historically sampled during a September bottom trawl survey to index Yellow Perch (Perca flavescens; 1978–2007), resumed in 2015. The current survey provides abundance indices for sculpins, Round Goby (Neogobius melanostomus) and Bloater (Coregonus hoyi) with survey techniques, gear and timing comparable to Lake Michigan. This alignment provides a necessary biological reference point for measuring the success of Lake Ontario Bloater reintroduction. In 2022, the collaborative benthic preyfish survey completed 171 bottom trawl tows across main lake and embayment sites at depths from 6 to 222 m. In total, the 2022 survey sampled 141,552 fish from 34 species. Round Goby was the most numerically abundant species comprising 36% of the total catch, followed by Alewife (Alosa pseudoharengus) and Deepwater Sculpin (Myoxocephalus thompsonii), at 20% and 16%, respectively. Alewife accounted for most (623 kg) of the fish biomass sampled during the 2022 survey (total=2,197 kg), followed by Deepwater Sculpin (547 kg), and Round Goby (262 kg). Slimy Sculpin (Cottus cognatus) lake-wide biomass density (0.03 kg/ha) remained low relative to historical observations from US waters during the 1980-1990s and was similar to the average from the previous three survey years (2019-2021 average 0.04 ± 0.02 kg/ha). Lake-wide Deepwater Sculpin biomass density reached a new high (4.4 kg/ha) in 2022. Embayment catches continue to have unique species assemblages compared to main lake habitat. Historically common native benthic preyfish species like Trout-perch (Percopsis omiscomaycus), Spottail Shiner (Notropis hudsonius), and darters (Etheostoma spp.), that are now rare at main lake trawl sites, still occur in some embayment trawl sites.
","language":"English","publisher":"Great Lakes Fishery Commission","usgsCitation":"O’Malley, B., Minihkeim, S.P., McKenna, J., Goretzke, J., and Holden, J.P., 2023, Bottom trawl assessment of Lake Ontario's benthic preyfish community, 2022, 15 p.","productDescription":"15 p.","ipdsId":"IP-151033","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":427241,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":427240,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"http://www.glfc.org/publication-media-search.php","linkFileType":{"id":5,"text":"html"}}],"country":"Canada, United States","otherGeospatial":"Lake Ontario","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.925537109375,\n 43.265206318396025\n ],\n [\n -79.8101806640625,\n 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Center","active":true,"usgs":true}],"preferred":true,"id":872033,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McKenna, James","contributorId":304958,"corporation":false,"usgs":false,"family":"McKenna","given":"James","affiliations":[{"id":38050,"text":"Contractor","active":true,"usgs":false}],"preferred":false,"id":872034,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Goretzke, Jessica A.","contributorId":304959,"corporation":false,"usgs":false,"family":"Goretzke","given":"Jessica A.","affiliations":[{"id":39079,"text":"NYSDEC","active":true,"usgs":false}],"preferred":false,"id":872035,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Holden, Jeremy P.","contributorId":251689,"corporation":false,"usgs":false,"family":"Holden","given":"Jeremy","email":"","middleInitial":"P.","affiliations":[{"id":50374,"text":"Ontario Ministry of Natural Resources and Forests (OMNRF)","active":true,"usgs":false}],"preferred":false,"id":872036,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70254178,"text":"70254178 - 2023 - Rivers of Arctic North America","interactions":[],"lastModifiedDate":"2024-05-13T12:32:56.019427","indexId":"70254178","displayToPublicDate":"2023-05-08T07:30:28","publicationYear":"2023","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"20","title":"Rivers of Arctic North America","docAbstract":"This chapter describes the geomorphology, hydrology, chemistry, biodiversity, and ecology of rivers in the North American Arctic. The history, physiography, climate, and land use of the Arctic regions are also described. The chapter includes details on the Kobuk and Colville rivers in Alaska, the Thelon and Kazan rivers in the central Canadian Arctic, Koroc River and Nakvak Brook in the eastern Canadian low Arctic, Thomsen River on Banks Island in the western Canadian Arctic Archipelago, and Ruggles River on Ellesmere Island in the Canadian high Arctic. The rivers are characteristic of the major ecoregions of the North American Arctic, covering a range of geomorphological and physiographic conditions. The history of use of the rivers by Inuit and Dene First Nations Peoples of the north provides the foundation to understand the social, cultural, and economic importance of the river systems, and potential threats to the rivers from climate change are outlined.
New York City faces accelerating inundation risk from sea level rise, subsidence, and increasing storm intensity from natural and anthropogenic causes. Here we calculate a previously unquantified contribution to subsidence from the cumulative mass and downward pressure exerted by the built environment of the city. We enforce that load distribution in a multiphysics finite element model to calculate expected subsidence. Complex surface geology requires multiple rheological soil models to be applied; clay rich soils and artificial fill are calculated to have the highest post-construction subsidence as compared with more elastic soils. Minimum and maximum calculated building subsidence ranges from 0 to 600 mm depending on soil/rock physical parameters and foundation modes. We compare modeled subsidence and surface geology to observed subsidence rates from satellite data (Interferometric Synthetic Aperture Radar and Global Positioning System). The comparison is complicated because the urban load has accumulated across a much longer period than measured subsidence rates, and there are multiple causes of subsidence. Geodetic measurements show a mean subsidence rate of 1–2 mm/year across the city that is consistent with regional post-glacial deformation, though we find some areas of significantly greater subsidence rates. Some of this deformation is consistent with internal consolidation of artificial fill and other soft sediment that may be exacerbated by recent building loads, though there are many possible causes. New York is emblematic of growing coastal cities all over the world that are observed to be subsiding (Wu et al., 2022, https://doi.org/10.1029/2022GL098477), meaning there is a shared global challenge of mitigation against a growing inundation hazard.
Conservation propagation of pallid sturgeon (Scaphirhynchus albus) upstream of Fort Peck Reservoir, Montana, USA has successfully recruited a new generation of spawning-capable pallid sturgeon where there would otherwise be fewer than 30 remaining wild reproductively mature pallid sturgeon. Successful recovery of pallid sturgeon will now rely on the behavior of pallid sturgeon (e.g., successful spawning in locations that provide adequate drift distance for larvae to recruit). We used location data of pallid sturgeon during four putative spawning seasons to answer the following questions: where do pallid sturgeon spawn; are spawning locations related to discharge; are substrate characteristics at the spawning locations similar to other river reaches; and do spawning-capable females, spawning-capable males, and female pallid sturgeon undergoing mass ovarian follicular atresia use the river similarly? Additionally, we consider if spawning locations are far enough from the river-reservoir transition zone to provide adequate drift distance for larvae to recruit. Spawning-capable pallid sturgeon did explore upstream locations, and four spawning-capable pallid sturgeon were located in the Marias River during the spawning season in 2018 when discharge was at an unprecedented high. Pallid sturgeon exited the Marias River and moved downstream prior to spawning, and when spawning occurred, it was not far enough upstream to prevent larvae from entering the transition zone of Fort Peck Reservoir. Thus, management of discharge and water temperature to mimic 2018 conditions may increase use of the Marias River by pallid sturgeon during the spawning season, which would increase drift distance available to larvae and increase the probability of successful recruitment.
","language":"English","publisher":"MDPI","doi":"10.3390/fishes8050243","usgsCitation":"Cox, T.L., Guy, C.S., Holmquist, L., and Webb, M.A., 2023, Spawning locations of pallid sturgeon in the Missouri River corroborate the mechanism for recruitment failure: Fishes, v. 8, no. 5, 243, 22 p., https://doi.org/10.3390/fishes8050243.","productDescription":"243, 22 p.","ipdsId":"IP-152115","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":443626,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/fishes8050243","text":"Publisher Index Page"},{"id":429783,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, North Dakota","otherGeospatial":"Fort Peck Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.9553366522268,\n 48.58477160833684\n ],\n [\n -106.9553366522268,\n 47.259647654337954\n ],\n [\n -103.53858860535145,\n 47.259647654337954\n ],\n [\n -103.53858860535145,\n 48.58477160833684\n ],\n [\n -106.9553366522268,\n 48.58477160833684\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"8","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-05-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Cox, Tanner L.","contributorId":337434,"corporation":false,"usgs":false,"family":"Cox","given":"Tanner","email":"","middleInitial":"L.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":902419,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guy, Christopher S. 0000-0002-9936-4781 cguy@usgs.gov","orcid":"https://orcid.org/0000-0002-9936-4781","contributorId":2876,"corporation":false,"usgs":true,"family":"Guy","given":"Christopher","email":"cguy@usgs.gov","middleInitial":"S.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":5062,"text":"Office of the Chief Scientist for Ecosystems","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":902420,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holmquist, Luke M.","contributorId":337435,"corporation":false,"usgs":false,"family":"Holmquist","given":"Luke M.","affiliations":[{"id":40948,"text":"Montana Fish Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":902421,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Webb, Molly A. H","contributorId":337436,"corporation":false,"usgs":false,"family":"Webb","given":"Molly","email":"","middleInitial":"A. H","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":902422,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70244040,"text":"70244040 - 2023 - Using eDNA metabarcoding to establish targets for freshwater fish composition following river restoration","interactions":[],"lastModifiedDate":"2026-03-04T14:37:17.3679","indexId":"70244040","displayToPublicDate":"2023-05-06T07:19:55","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3871,"text":"Global Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Using eDNA metabarcoding to establish targets for freshwater fish composition following river restoration","docAbstract":"Establishing realistic targets for fish community composition is needed to assess the effectiveness of river restoration projects. We used environmental DNA (eDNA) metabarcoding with MiFish primers to obtain estimates of fish community composition across 17 sites upstream, downstream and within a restoration mitigation project area (Kaihotsu–Kasumi) located in the Shigenobu River system, Ehime Prefecture, Japan. We evaluate the benefits of using eDNA to quickly, sensitively, and extensively gather data to establish existing fish community composition in the restoration area, as well as potential future short-term, medium-term, and long-term targets of species assemblages that could realistically emerge following dispersal into the project area from upstream and downstream populations. We compare results from eDNA metabarcoding with species lists obtained from contemporaneous capture surveys and historical information. Nonmetric multidimensional scaling plots of community composition obtained from eDNA surveys showed that the Kaihotsu–Kasumi restoration area and surrounding river reaches were divided into three clusters: upper reaches, middle and lower reaches, and estuarine reaches. The Kaihotsu–Kasumi restoration area sites were included in the group containing the middle and lower reaches of the inflow and outflow rivers that were near the restoration area. We detected a total of twenty-six species in this group, twenty-one native species and five non-native species. Therefore, these native species were considered suitable as short-term target species with high potential for dispersal into Kaihotsu–Kasumi restoration area. By comparison, only 14 species would have been selected as target species based on capture surveys and historical literature. One factor increasing the resolution of our eDNA surveys was our ability to identify the presence of intraspecific lineages of Misgurnus anguillicaudatus (Clades A and B), which were missed by the capture surveys. These results indicate that the eDNA metabarcoding method can provide more comprehensive and realistic short-term target species estimates than capture surveys, as well as provide higher resolution monitoring through intraspecific lineage detection.
Garnet’s stability in arc magmas and its influences on their differentiation were explored experimentally in a typical basalt, andesite, and dacite at conditions of 0.9–1.67 GPa, 800–1300 °C, with 2–9 wt.% added H2O, and with oxygen fugacity buffered near Re + O2 = ReO2 (~ Ni-NiO + 1.7 log10 bars). Garnet did not grow at 0.9 GPa in any of the compositions, even with garnet seeds added to facilitate nucleation. At 1.0–1.2 GPa, garnet grew as thin rims (< 5 µm) on introduced garnet seeds coexisting with dacitic to rhyodacitic liquids at temperatures ≤ 1000 °C. At 1.3 GPa, garnet grew readily with no seeds from 900 to 1100 °C coexisting with liquids ranging from peraluminous basaltic andesite to rhyodacite, and at 1.46 GPa, garnet was stable as hot as 1150 °C in metaluminous basaltic liquid. Garnet grew as a liquidus phase only in the dacite, a composition similar to the average upper continental crust. Inverse experiments on the dacite determined a liquidus multiple-saturation point with garnet, plagioclase, orthopyroxene, calcic clinopyroxene, and amphibole at 975 °C, 1.46 GPa, with 7 wt.% dissolved H2O. Such dacitic and more evolved melts can be products of peritectic reactions that with decreasing temperature consume garnet, calcic clinopyroxene, and melt components, producing amphibole and less abundant but more evolved melts. For this reason, experiments on product melts need not produce reactant minerals, accounting for some disparities in published experimental results on the apparent stability of garnet in intermediate-to-evolved arc magmas. Results on more mafic compositions are more reliable guides and show that liquids of arc dacitic composition, and more evolved compositions, would coexist stably with garnet only in the deepest portions of continental-margin arc crust with average thickness and density (~ 43 km, ~ 1.2 GPa) or in the underlying shallow mantle. Metaluminous arc basaltic, basaltic andesitic, and many andesitic liquids would not coexist stably with garnet at pressures ranging from the crust to at least the midpoint of the mantle wedge, but results in the literature allow that some andesitic liquids with higher Fe/Mg than common in arcs may also saturate with garnet in the deeper portions of average-thickness continental arc crust.
","language":"English","publisher":"Springer","doi":"10.1007/s00410-023-02008-w","usgsCitation":"Blatter, D.L., Sisson, T.W., and Hankins, W.B., 2023, Garnet stability in arc basalt, andesite, and dacite—An experimental study: Contributions to Mineralogy and Petrology, v. 178, 33, 40 p., https://doi.org/10.1007/s00410-023-02008-w.","productDescription":"33, 40 p.","ipdsId":"IP-147275","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":435344,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90PTK4B","text":"USGS data release","linkHelpText":"Dataset establishing garnet stability in arc basalt, andesite, and dacite – an experimental study"},{"id":416852,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"178","noUsgsAuthors":false,"publicationDate":"2023-05-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Blatter, Dawnika L. 0000-0002-7161-6844 dblatter@usgs.gov","orcid":"https://orcid.org/0000-0002-7161-6844","contributorId":4899,"corporation":false,"usgs":true,"family":"Blatter","given":"Dawnika","email":"dblatter@usgs.gov","middleInitial":"L.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":872098,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sisson, Thomas W. 0000-0003-3380-6425 tsisson@usgs.gov","orcid":"https://orcid.org/0000-0003-3380-6425","contributorId":2341,"corporation":false,"usgs":true,"family":"Sisson","given":"Thomas","email":"tsisson@usgs.gov","middleInitial":"W.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":872099,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hankins, W. Ben","contributorId":304970,"corporation":false,"usgs":false,"family":"Hankins","given":"W.","email":"","middleInitial":"Ben","affiliations":[{"id":37487,"text":"formerly USGS","active":true,"usgs":false}],"preferred":false,"id":872100,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70243301,"text":"70243301 - 2023 - Aeromagnetic expression of the central Nagssugtoqidian Orogen, South-East Greenland","interactions":[],"lastModifiedDate":"2023-05-08T11:49:07.185166","indexId":"70243301","displayToPublicDate":"2023-05-06T06:45:47","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3112,"text":"Precambrian Research","active":true,"publicationSubtype":{"id":10}},"title":"Aeromagnetic expression of the central Nagssugtoqidian Orogen, South-East Greenland","docAbstract":"The Paleoproterozoic Nagssugtoqidian Orogen is one of the principal tectonic features related to the assembly of Nuna, extending across Greenland from east to west and forming an orogenic belt separating the North Atlantic Craton on the south from the Rae Craton on the north. In South-East Greenland, the Ammassalik Intrusive Complex (AIC) (∼1910 to 1870 Ma) occupies the central part of the orogenic belt, was formed by subduction- and magmatic arc-related processes, and has significant potential for undiscovered deposits of critical minerals. Previous interpretations of aeromagnetic data have been hindered by terrain effects, and we use a novel mix of geophysical analysis tools to develop new tectonomagmatic interpretations of the central Nagssugtoqidian Orogen in South-East Greenland. These interpretations extend into areas covered by ocean and ice. Results show that Archean rocks of the juxtaposed North Atlantic (Isertoq Terrane) and Rae (Kuummiut Terrane) Cratons are relatively weakly magnetized (with the exception of rocks of the Schweizerland Terrane) and have a NW-striking structural fabric that likely formed or was enhanced during the Nagssugtoqidian Orogeny. The AIC is structurally complex, with weakly magnetized metasedimentary rocks, and both weakly and strongly magnetized intrusions, arrayed in a NW-striking tectonic fabric. The strongly magnetized intrusions are largely concealed and distributed in a broader and more spatially complex fashion than previously known, suggesting that additional areas may be considered for mineral exploration. Strongly magnetized NW-striking dikes are imaged within the AIC, where they are spatially closely related to the strongly magnetized intrusions, and extend southward into the North Atlantic Craton (Isertoq Terrane). This spatial pattern of arc magmatism is consistent with previously developed models of SW-directed subduction that preceded collision during the Nagssugtoqidian Orogeny. The strongly magnetized Ammassalik Batholith (∼1670 Ma) and related intrusions form a cluster of plutons within ∼ 50 km of the Nagssugtoqidian suture. Their tectonomagmatic setting is unknown, although are speculatively related to delamination of a lithospheric keel formed during the Nagssugtoqidian Orogeny ∼ 200 m.y. prior. Numerous strongly magnetized NNE-striking Paleogene dikes, related to the opening of the Atlantic Ocean, are imaged cutting most other geologic units.
Blooms of the dinoflagellate Karenia brevis occur almost every year along the southwest Florida Gulf coast. Long-duration blooms with especially high concentrations of K. brevis, known as red tides, destroy marine life through production of neurotoxins. Current hypotheses are that red tides originate in oligotrophic waters far offshore using nitrogen (N) from upwelling bottom water or, alternatively, from blooms of Trichodesmium, followed by advection to nearshore waters. But the amount of N available from terrestrial sources does not appear to be adequate to maintain a nearshore red tide. To explain this discrepancy, we hypothesize that contemporary red tides are associated with release of N from offshore submarine groundwater discharge (SGD) that has accumulated in benthic sediment biomass by dissimilatory nitrate reduction to ammonium (DNRA). The release occurs when sediment labile organic carbon (LOC), used as the electron donor in DNRA, is exhausted. Detritus from the resulting destruction of marine life restores the sediment LOC to continue the cycle of red tides. The severity of individual red tides increases with increased bloom-year precipitation in the geographic region where the SGD originates, while the severity of ordinary blooms is relatively unaffected.
Director, National Geospatial Program
U.S. Geological Survey
12201 Sunrise Valley Drive, Mail Stop 511
Reston, VA 20192
Email: 3DEP@usgs.gov
","tableOfContents":"No abstract available.
","language":"English","publisher":"U.S. Fish and Wildlife Service","usgsCitation":"Skorupa, A.J., Perkins, D., Roy, A.H., and Ryan, J.E., 2023, Watershed selection to support freshwater mussel restoration: An open-loop decision guide: Cooperator Science Series 149-2023, 33 p.","productDescription":"33 p.","ipdsId":"IP-145512","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":432701,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.fws.gov/media/watershed-selection-support-freshwater-mussel-restoration-open-loop-decision-guide"},{"id":433257,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Skorupa, Ayla J.","contributorId":342300,"corporation":false,"usgs":false,"family":"Skorupa","given":"Ayla","email":"","middleInitial":"J.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":909983,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Perkins, David","contributorId":342302,"corporation":false,"usgs":false,"family":"Perkins","given":"David","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":909984,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roy, Allison H. 0000-0002-8080-2729 aroy@usgs.gov","orcid":"https://orcid.org/0000-0002-8080-2729","contributorId":4240,"corporation":false,"usgs":true,"family":"Roy","given":"Allison","email":"aroy@usgs.gov","middleInitial":"H.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":909985,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ryan, Jennifer E.","contributorId":342306,"corporation":false,"usgs":false,"family":"Ryan","given":"Jennifer","email":"","middleInitial":"E.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":909986,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70247397,"text":"70247397 - 2023 - Weak degassing from remote Alaska volcanoes characterized with a new airborne Imaging DOAS instrument and a suite of in situ sensors","interactions":[],"lastModifiedDate":"2023-08-02T15:08:45.825188","indexId":"70247397","displayToPublicDate":"2023-05-05T10:04:25","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5232,"text":"Frontiers in Earth Science","onlineIssn":"2296-6463","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Weak degassing from remote Alaska volcanoes characterized with a new airborne Imaging DOAS instrument and a suite of in situ sensors","title":"Weak degassing from remote Alaska volcanoes characterized with a new airborne Imaging DOAS instrument and a suite of in situ sensors","docAbstract":"Gas emissions from volcanoes occur when volatile species exsolve from magmatic and hydrothermal systems and make their way to the surface. Measurements of emitted gases therefore provide insights into volcanic processes. On 16 July 2021, we made airborne measurements of weak gas plumes emitted from four remote Alaska volcanoes: Iliamna Volcano, Mount Douglas, Mount Martin, and Mount Mageik. Integrated into a small fixed-wing aircraft, a new Imaging Differential Optical Absorption Spectroscopy (DOAS) instrument was used to map the spatial extent of SO2 plumes as they drifted downwind. Contrary to conventional Mobile DOAS instruments, which provide only a single viewing direction, the Imaging DOAS simultaneously measures SO2 column density along 48 individual viewing directions oriented in a swath above or below the aircraft. Each of the individual measurements have a comparable precision and sensitivity to those obtained by conventional instruments. Together, they provide high resolution 2D imagery of the volcanic plumes and allow calculation of limited emission rate time series information. Although zenith-facing DOAS measurements achieve greater accuracy and are performed here, the application of the Imaging DOAS in a nadir-facing setup is also discussed and compared to satellite observations made in similar geometries. Also onboard the aircraft, a suite of electrochemical and optical sensors measured the relative abundances of the six major volcanic volatile species H2O, CO2, SO2, H2S, HCl, and HF as the aircraft passed through the plumes. Mean SO2 emission rates of 90 ± 10, 20 ± 3, and 13 ± 3 t/d were measured at Iliamna Volcano, Mount Douglas, and Mount Martin, respectively. SO2 emissions were below the DOAS detection limit at Mount Mageik but CO2 and H2S could be measured with the in situ sensors. The information gleaned from these measurements was used to assess and compare activity at these volcanoes, all of which were found to be in a state of background degassing but whose emissions pointed to different source conditions ranging from mixed magmatic-hydrothermal to purely hydrothermal in character. Additional measurements at Mount Spurr, Redoubt Volcano, and Augustine Volcano failed to detect the very weak gas concentrations downwind of these persistently degassing vents.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/feart.2023.1088056","usgsCitation":"Kern, C., and Kelly, P.J., 2023, Weak degassing from remote Alaska volcanoes characterized with a new airborne Imaging DOAS instrument and a suite of in situ sensors: Frontiers in Earth Science, v. 11, 1088056, 22 p., https://doi.org/10.3389/feart.2023.1088056.","productDescription":"1088056, 22 p.","ipdsId":"IP-151488","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":443636,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2023.1088056","text":"Publisher Index Page"},{"id":435345,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YTK9PE","text":"USGS data release","linkHelpText":"Airborne Survey of Gas Emissions from Volcanoes in the Cook Inlet and Northern Alaska Peninsula, 2021"},{"id":419503,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Cook Inlet, northern Alaska Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -153.93289670514363,\n 57.10588655261469\n ],\n [\n -148.93900588885816,\n 61.4818937771482\n ],\n [\n -151.74131362830832,\n 61.84262546330652\n ],\n [\n -157.7149279467277,\n 57.30831575931313\n ],\n [\n -153.93289670514363,\n 57.10588655261469\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"11","noUsgsAuthors":false,"publicationDate":"2023-05-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Kern, Christoph 0000-0002-8920-5701 ckern@usgs.gov","orcid":"https://orcid.org/0000-0002-8920-5701","contributorId":3387,"corporation":false,"usgs":true,"family":"Kern","given":"Christoph","email":"ckern@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":879457,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kelly, Peter J. 0000-0002-3868-1046 pkelly@usgs.gov","orcid":"https://orcid.org/0000-0002-3868-1046","contributorId":5931,"corporation":false,"usgs":true,"family":"Kelly","given":"Peter","email":"pkelly@usgs.gov","middleInitial":"J.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":879458,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70243973,"text":"70243973 - 2023 - Creating conservation strategies with value-focused thinking","interactions":[],"lastModifiedDate":"2023-10-11T15:25:58.948879","indexId":"70243973","displayToPublicDate":"2023-05-05T09:52:31","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1321,"text":"Conservation Biology","active":true,"publicationSubtype":{"id":10}},"title":"Creating conservation strategies with value-focused thinking","docAbstract":"Biodiversity and human well-being strategies are only as good as the set of ideas that people think about. This article evaluates value-focused thinking (VFT), a framework that focuses on creating objectives and strategy ideas that are responsive to the objectives. We performed a proof-of-concept study of VFT on six planning teams at a global conservation organization. We developed a package of support materials, including session agendas, a virtual facilitation template, facilitator's guide, and evaluation questionnaires. The study tested whether VFT resulted in a set of quality strategies, resulted in participant satisfaction, and was scalable, meaning that it could be facilitated by someone newly trained in VFT and result in quality strategies and participant satisfaction, as compared to an experienced facilitator. Net response indicated positive quality ratings for the set of strategies per team. Respondents indicated positive satisfaction overall, though it was higher for objectives than for strategies. Among the participants with previous experience, all were at least as satisfied with their VFT strategies compared to previously developed strategies, and none were less satisfied (P = 0.001). Changes in participant satisfaction were not related to facilitator type (P > 0.10). In addition, we found that some participants had a premature sense of shared understanding of important values and interests before entering the study, which VFT strengthened. This study highlights the advantages of structuring the development and evaluation of conservation planning frameworks.
","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/cobi.14109","usgsCitation":"Martin, D.M., Goldstein, J., Smith, D.R., Musengezi, J., Rountree, J.G., Galgamuwe, P.G., Craig, A., Dietz, M., and Kerr, C., 2023, Creating conservation strategies with value-focused thinking: Conservation Biology, v. 37, no. 5, e14109, 14 p., https://doi.org/10.1111/cobi.14109.","productDescription":"e14109, 14 p.","ipdsId":"IP-147268","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":443640,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/cobi.14109","text":"Publisher Index Page"},{"id":417534,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"37","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-07-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Martin, David M. 0000-0002-1514-5734","orcid":"https://orcid.org/0000-0002-1514-5734","contributorId":210575,"corporation":false,"usgs":false,"family":"Martin","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":35215,"text":"Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":873969,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Goldstein, Joshua","contributorId":197267,"corporation":false,"usgs":false,"family":"Goldstein","given":"Joshua","affiliations":[],"preferred":false,"id":873970,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, David R. 0000-0001-6074-9257 drsmith@usgs.gov","orcid":"https://orcid.org/0000-0001-6074-9257","contributorId":168442,"corporation":false,"usgs":true,"family":"Smith","given":"David","email":"drsmith@usgs.gov","middleInitial":"R.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":873971,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Musengezi, Jessica","contributorId":305916,"corporation":false,"usgs":false,"family":"Musengezi","given":"Jessica","email":"","affiliations":[],"preferred":false,"id":874082,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rountree, Jessie G.","contributorId":305917,"corporation":false,"usgs":false,"family":"Rountree","given":"Jessie","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":874083,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Galgamuwe, Pabodha G. A.","contributorId":305918,"corporation":false,"usgs":false,"family":"Galgamuwe","given":"Pabodha","email":"","middleInitial":"G. A.","affiliations":[],"preferred":false,"id":874084,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Craig, Aileen","contributorId":305919,"corporation":false,"usgs":false,"family":"Craig","given":"Aileen","email":"","affiliations":[],"preferred":false,"id":874085,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dietz, Michelle","contributorId":305920,"corporation":false,"usgs":false,"family":"Dietz","given":"Michelle","email":"","affiliations":[],"preferred":false,"id":874086,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kerr, Caitlin","contributorId":305921,"corporation":false,"usgs":false,"family":"Kerr","given":"Caitlin","email":"","affiliations":[],"preferred":false,"id":874087,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70243139,"text":"sir20235014 - 2023 - Magnitude and frequency of floods on Kauaʻi, Oʻahu, Molokaʻi, Maui, and Hawaiʻi, State of Hawaiʻi, based on data through water year 2020","interactions":[],"lastModifiedDate":"2026-03-02T21:59:55.664975","indexId":"sir20235014","displayToPublicDate":"2023-05-05T07:40:10","publicationYear":"2023","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":"2023-5014","displayTitle":"Magnitude and Frequency of Floods on Kauaʻi, Oʻahu, Molokaʻi, Maui, and Hawaiʻi, State of Hawaiʻi, Based on Data through Water Year 2020","title":"Magnitude and frequency of floods on Kauaʻi, Oʻahu, Molokaʻi, Maui, and Hawaiʻi, State of Hawaiʻi, based on data through water year 2020","docAbstract":"Accurate estimates of flood magnitude and frequency are needed to (1) optimize the design and location of infrastructure, including dams, culverts, bridges, industrial buildings, and highways, and (2) inform flood-zoning and flood-insurance studies. The U.S. Geological Survey (USGS), in cooperation with the State of Hawaiʻi Department of Transportation, estimated flood magnitudes for the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities (AEP) for unregulated streamgages in Kauaʻi, Oʻahu, Molokaʻi, Maui, and Hawaiʻi, State of Hawaiʻi, using data through water year 2020. Regression equations were developed to estimate flood magnitude and associated frequency at ungaged streams. This study improves upon a previous USGS flood-frequency report (Oki and others, 2010) by including more peak-flow data, implementing new statistical methods in flood-frequency analysis, and using updated techniques to estimate the regional-skewness coefficient (regional skew).
Flood magnitude and frequency at 238 streamgages were estimated—following national guidelines established in Bulletin 17C (England and others, 2019)—by fitting annual peak-flow data to the Log-Pearson Type III distribution using the expected moments algorithm and the PeakFQ flood-frequency software. Potentially influential low outliers in the data were identified and removed using the Multiple Grubbs-Beck Test. An updated regional skew for Hawaiʻi was estimated using the Bayesian weighted least squares/Bayesian generalized least squares method. The updated regional skew employs a constant model for the five islands in the study area and has a value of −0.157 (mean square error of 0.212).
Multiple linear regression techniques were used to develop regression equations that relate basin and climatic characteristics to peak flows at streamgages. The regression equations can be applied to estimate flood magnitude and frequency at ungaged sites. The study area was split into 10 regions—2 regions per island, generally following a leeward/windward division—containing from 9 to 49 streamgages each. The final regression equations for each region were determined with generalized least-squares analysis using the USGS weighted-multiple-linear regression (WREG) program. The standard error of prediction at the 1-percent AEP for the regression equations ranged from 18 to 164 percent; the pseudo coefficient of determination (pseudo-R2) at the 1-percent AEP ranged from 46 to 100 percent. The regression equations performed well for all regions except leeward Molokaʻi and southern Island of Hawaiʻi; for all other regions, the pseudo-R2 values ranged from about 75 to 100 percent. Compared to the regression equations developed by Oki and others (2010), the regression equations in this study generally showed modest improvements, although the magnitude of differences varied for each region.
Peak-flow estimates at the 238 streamgages included in this study are improved by weighting the at-site statistics computed with PeakFQ and the predicted flows based on the regression equations. Results of this study—including the final peak-flow estimates at streamgages and the regional regression equations—are implemented in the USGS StreamStats web application (U.S. Geological Survey, 2023, StreamStats: https://streamstats.usgs.gov/ss/). StreamStats provides a consistent approach for obtaining peak-flow estimates at streamgages and for applying the regional regression equations for estimating peak flows at ungaged locations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235014","collaboration":"Prepared in cooperation with the State of Hawaiʻi Department of Transportation","usgsCitation":"Mitchell, J.N., Wagner, D.M., and Veilleux, A.G., 2023, Magnitude and frequency of floods on Kauaʻi, Oʻahu, Molokaʻi, Maui, and Hawaiʻi, State of Hawaiʻi, based on data through water year 2020: U.S. Geological Survey Scientific Investigations Report 2023–5014, 66 p. plus 4 appendixes, https://doi.org/10.3133/sir20235014.","productDescription":"Report: vii, ; 8 Tables; 3 Data Releases","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-139812","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":416577,"rank":15,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GGPPV5","text":"USGS data release","description":"USGS data release","linkHelpText":"Data in support of flood-frequency report—Magnitude and frequency of floods on Kauaʻi, Oʻahu, Molokaʻi, Maui, and Hawaiʻi, State of Hawaiʻi, based on data through water year 2020"},{"id":416576,"rank":14,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TOQANM","text":"USGS data release","description":"USGS data release","linkHelpText":"Basin characteristic rasters used in the update of Hawaiʻi StreamStats, 2022"},{"id":416575,"rank":13,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9N61WJ7","text":"USGS data release","description":"USGS data release","linkHelpText":"Geospatial datasets for watershed delineation used in the update of Hawaiʻi StreamStats, 2022"},{"id":416566,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5014/coverthb.jpg"},{"id":416567,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5014/sir20235014.pdf","text":"Report","size":"7.4 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States","state":"Hawaii","otherGeospatial":"Kauaʻi, Oʻahu, Molokaʻi, Maui, Hawaiʻi","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -159.92521972722102,\n 22.39025206306377\n ],\n [\n -159.92521972722102,\n 18.78261358926393\n ],\n [\n -154.69797609100146,\n 18.78261358926393\n ],\n [\n -154.69797609100146,\n 22.39025206306377\n ],\n [\n -159.92521972722102,\n 22.39025206306377\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Pacific Islands Science Center
U.S. Geological Survey
1845 Wasp Blvd., B176
Honolulu, HI 96818
Geodetic velocity data provide first-order constraints on crustal surface strain rates, which in turn are linked to seismic hazard. Estimating the 2-D surface strain tensor everywhere requires knowledge of the surface velocity field everywhere, while geodetic data such as Global Navigation Satellite System (GNSS) only have spatially scattered measurements on the surface of the Earth. To use these data to estimate strain rates, some type of interpolation is required. In this study, we review methodologies for strain rate estimation and compare a suite of methods, including a new implementation based on the geostatistical method of kriging, to compare variation between methods with uncertainty based on one method. We estimate the velocity field and calculate strain rates in southern California using a GNSS velocity field and five different interpolation methods to understand the sources of variability in inferred strain rates. Uncertainty related to data noise and station spacing (aleatoric uncertainty) is minimal where station spacing is dense and maximum far from observations. Differences between methods, related to epistemic uncertainty, are usually highest in areas of high strain rate due to differences in how gradients in the velocity field are handled by different interpolation methods. Parameter choices, unsurprisingly, have a strong influence on strain rate field, and we propose the traditional L-curve approach as one method for quantifying the inherent trade-off between fit to the data and models that are reflective of tectonic strain rates. Doing so, we find total variability between five representative strain rate models to be roughly 40 per cent, a much lower value than roughly 100 per cent that was found in previous studies (Hearn et al.). Using multiple methods to tune parameters and calculate strain rates provides a better understanding of the range of acceptable models for a given velocity field. Finally, we present an open-source Python package (Materna et al.) for calculating strain rates, Strain_2D, which allows for the same data and model grid to be used in multiple strain rate methods, can be extended with other methods from the community, and provides an interface for comparing strain rate models, calculating statistics and estimating strain rate uncertainty for a given GNSS data set.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gji/ggad191","usgsCitation":"Maurer, J., and Materna, K.Z., 2023, Quantification of geodetic strain rate uncertainties and implications for seismic hazard estimates: Geophysical Journal International, v. 234, no. 3, p. 2128-2142, https://doi.org/10.1093/gji/ggad191.","productDescription":"15 p.","startPage":"2128","endPage":"2142","ipdsId":"IP-142818","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":443642,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/gji/ggad191","text":"Publisher Index Page"},{"id":435346,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JJW0DY","text":"USGS data release","linkHelpText":"Strain_2D: a package to compute and compare strain rate maps from geodetic velocities"},{"id":417484,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"234","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-05-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Maurer, Jeremy","contributorId":305786,"corporation":false,"usgs":false,"family":"Maurer","given":"Jeremy","email":"","affiliations":[{"id":37501,"text":"Missouri University of Science and Technology","active":true,"usgs":false}],"preferred":false,"id":873851,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Materna, Kathryn Zerbe 0000-0002-6687-980X","orcid":"https://orcid.org/0000-0002-6687-980X","contributorId":261337,"corporation":false,"usgs":true,"family":"Materna","given":"Kathryn","email":"","middleInitial":"Zerbe","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":873852,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70243951,"text":"70243951 - 2023 - Estimated reduction of nitrogen in streams of the Chesapeake Bay in areas with agricultural conservation practices","interactions":[],"lastModifiedDate":"2023-05-26T12:02:55.072234","indexId":"70243951","displayToPublicDate":"2023-05-05T07:01:09","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":11111,"text":"PLOS Water","active":true,"publicationSubtype":{"id":10}},"title":"Estimated reduction of nitrogen in streams of the Chesapeake Bay in areas with agricultural conservation practices","docAbstract":"Spatial data provided by the U.S. Department of Agriculture National Resource Conservation Service representing implementation at the field-level for a selection of agricultural conservation practices were incorporated within a spatially referenced regression model to estimate their effects on nitrogen loads in streams in the Chesapeake Bay watershed. Conservation practices classified as “high-impact” were estimated to be effective (p = 0.017) at reducing contemporary nitrogen loads to streams of the Chesapeake Bay watershed in areas where groundwater ages are estimated to be less than 14-years old. Watershed-wide, high-impact practices were estimated to reduce nitrogen loads to streams by 1.45%, with up to 60% reductions in areas with shorter groundwater ages and larger amounts of implementation. Effects of “other-impact” practices and practices in areas with groundwater ages of 14 years or more showed less evidence of effectiveness. That the discernable impact of high-impact practices was limited to areas with a median groundwater age of less than 14 years does not imply that conservation practices are not effective in areas with older groundwater ages. A model recalibrated using high-impact agricultural conservation practice data summarized by county suggests effects may also be detectable using implementation data available at such coarser resolution. Despite increasing investment, effects of agricultural conservation practices on regional water quality remain difficult to quantify due to factors such as groundwater travel times, varying modes-of-action, and the general lack of high-quality spatial datasets representing practice implementation.
Convergent adaptation to the same environment by multiple lineages frequently involves rapid evolutionary change at the same genes, implicating these genes as important for environmental adaptation. Such adaptive molecular changes may yield either change or loss of protein function; loss of function can eliminate newly deleterious proteins or reduce energy necessary for protein production. We previously found a striking case of recurrent pseudogenization of the Paraoxonase 1 (Pon1) gene among aquatic mammal lineages—Pon1 became a pseudogene with genetic lesions, such as stop codons and frameshifts, at least four times independently in aquatic and semiaquatic mammals. Here, we assess the landscape and pace of pseudogenization by studying Pon1 sequences, expression levels, and enzymatic activity across four aquatic and semiaquatic mammal lineages: pinnipeds, cetaceans, otters, and beavers. We observe in beavers and pinnipeds an unexpected reduction in expression of Pon3, a paralog with similar expression patterns but different substrate preferences. Ultimately, in all lineages with aquatic/semiaquatic members, we find that preceding any coding-level pseudogenization events in Pon1, there is a drastic decrease in expression, followed by relaxed selection, thus allowing accumulation of disrupting mutations. The recurrent loss of Pon1 function in aquatic/semiaquatic lineages is consistent with a benefit to Pon1 functional loss in aquatic environments. Accordingly, we examine diving and dietary traits across pinniped species as potential driving forces of Pon1 functional loss. We find that loss is best associated with diving activity and likely results from changes in selective pressures associated with hypoxia and hypoxia-induced inflammation.
","language":"English","publisher":"Society for Molecular Biology and Evolution","doi":"10.1093/molbev/msad104","usgsCitation":"Graham, A.M., Jamison, J.M., Bustos, M., Cournoyer, C., Michaels, A., Presnell, J.S., Richter, R., Crocker, D., Fustukjian, A., Hunter, M., Rea, L.D., Marsillach, J., Furlong, C.E., Meyer, W.K., and Clark, N.L., 2023, Reduction of paraoxonase expression followed by inactivation across independent semiaquatic mammals suggests stepwise path to pseudogenization: Molecular Biology and Evolution, v. 40, no. 5, msad104 , 17 p., https://doi.org/10.1093/molbev/msad104.","productDescription":"msad104 , 17 p.","ipdsId":"IP-149663","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":443648,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/molbev/msad104","text":"Publisher Index Page"},{"id":418453,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"40","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-05-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Graham, Allie M.","contributorId":312459,"corporation":false,"usgs":false,"family":"Graham","given":"Allie","email":"","middleInitial":"M.","affiliations":[{"id":67676,"text":"Department of Human Genetics, University of Utah","active":true,"usgs":false}],"preferred":false,"id":876166,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jamison, Jerrica M.","contributorId":312460,"corporation":false,"usgs":false,"family":"Jamison","given":"Jerrica","email":"","middleInitial":"M.","affiliations":[{"id":67678,"text":"Department of Biological Sciences, University of Toronto","active":true,"usgs":false}],"preferred":false,"id":876167,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bustos, Marisol","contributorId":312461,"corporation":false,"usgs":false,"family":"Bustos","given":"Marisol","email":"","affiliations":[{"id":67679,"text":"Department of Biomedical Engineering, University of Texas","active":true,"usgs":false}],"preferred":false,"id":876168,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cournoyer, Charlotte","contributorId":312462,"corporation":false,"usgs":false,"family":"Cournoyer","given":"Charlotte","email":"","affiliations":[{"id":67680,"text":"South Florida Wildlife Center","active":true,"usgs":false}],"preferred":false,"id":876169,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Michaels, Alexa","contributorId":312463,"corporation":false,"usgs":false,"family":"Michaels","given":"Alexa","email":"","affiliations":[{"id":67681,"text":"Tufts University and The Jackson Laboratory","active":true,"usgs":false}],"preferred":false,"id":876170,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Presnell, Jason S.","contributorId":312464,"corporation":false,"usgs":false,"family":"Presnell","given":"Jason","email":"","middleInitial":"S.","affiliations":[{"id":67676,"text":"Department of Human Genetics, University of Utah","active":true,"usgs":false}],"preferred":false,"id":876171,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Richter, Rebecca","contributorId":207464,"corporation":false,"usgs":false,"family":"Richter","given":"Rebecca","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":876172,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Crocker, Daniel E.","contributorId":202543,"corporation":false,"usgs":false,"family":"Crocker","given":"Daniel E.","affiliations":[{"id":36475,"text":"Sonoma State University","active":true,"usgs":false}],"preferred":false,"id":876173,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Fustukjian, Ari","contributorId":312465,"corporation":false,"usgs":false,"family":"Fustukjian","given":"Ari","email":"","affiliations":[{"id":67682,"text":"Loveland Living Planet Aquarium","active":true,"usgs":false}],"preferred":false,"id":876174,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hunter, Margaret 0000-0002-4760-9302","orcid":"https://orcid.org/0000-0002-4760-9302","contributorId":214742,"corporation":false,"usgs":true,"family":"Hunter","given":"Margaret","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":876175,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Rea, Lorrie D.","contributorId":82143,"corporation":false,"usgs":false,"family":"Rea","given":"Lorrie","email":"","middleInitial":"D.","affiliations":[{"id":7058,"text":"Alaska Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":876176,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Marsillach, Judit","contributorId":207472,"corporation":false,"usgs":false,"family":"Marsillach","given":"Judit","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":876177,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Furlong, Clement E.","contributorId":207469,"corporation":false,"usgs":false,"family":"Furlong","given":"Clement","email":"","middleInitial":"E.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":876178,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Meyer, Wynn K.","contributorId":207462,"corporation":false,"usgs":false,"family":"Meyer","given":"Wynn","email":"","middleInitial":"K.","affiliations":[{"id":12465,"text":"University of Pittsburgh","active":true,"usgs":false}],"preferred":false,"id":876179,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Clark, Nathan L.","contributorId":207470,"corporation":false,"usgs":false,"family":"Clark","given":"Nathan","email":"","middleInitial":"L.","affiliations":[{"id":12465,"text":"University of Pittsburgh","active":true,"usgs":false}],"preferred":false,"id":876180,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70243198,"text":"sir20235043 - 2023 - Assessment of conservation management practices on water quality and observed trends in the Plum Creek Basin, 2010–20","interactions":[],"lastModifiedDate":"2026-03-06T21:41:50.935249","indexId":"sir20235043","displayToPublicDate":"2023-05-04T12:49:40","publicationYear":"2023","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":"2023-5043","displayTitle":"Assessment of Conservation Management Practices on Water Quality and Observed Trends in the Plum Creek Basin, 2010–20","title":"Assessment of conservation management practices on water quality and observed trends in the Plum Creek Basin, 2010–20","docAbstract":"The U.S. Geological Survey and University of Wisconsin–Green Bay collected hydrologic and water-quality data to assess the effectiveness of agricultural conservation management practice (CMP) implementation at mainstem Plum Creek and west Plum Creek in northeastern Wisconsin. These two subbasins cover 88 percent of the Plum Creek Basin (Hydrologic Unit Code 12), which is a subbasin of the lower Fox River Basin. A published total maximum daily load report for the lower Fox River Basin rated Plum Creek as one of the greatest contributors of total suspended solids (TSS) and total phosphorus (TP) draining into the lower Fox River. To reduce TSS and TP exports from Plum Creek, additional cropland conservation practices and watercourse protections were applied between 2012 and 2020. To detect water-quality trends, data were collected during 2010 to 2020 at mainstem Plum Creek and 2013 to 2020 at west Plum Creek.
The project used two methods to evaluate CMP effectiveness. The first method focused on evaluating water-quality changes between initial and post-CMP implementation periods during rain- or snowmelt-induced runoff events (hereafter referred to as “events”). In this approach random-forest models were developed to account for environmental factors which influence water quality. Model residuals from the two time periods were compared to determine the significance of water-quality changes associated with CMP implementation for mainstem and west Plum Creek Basins. The second method used a Weighted Regressions on Time, Discharge, and Season time-series approach to examine changes in water quality during the entire study period in mainstem Plum Creek. Results from both methods indicated there were minimal water-quality changes in TSS concentrations and flow-normalized delivery during runoff events during the 10-year period from 2010 to 2020; however, TP concentrations during low streamflow (less than 3 cubic feet per second [ft3/s]) may have decreased. The lack of observed improvement may be attributable to any of the following: variability in weather and hydrologic conditions, insufficient post-treatment data, additional cropland being converted to corn production, above average rainfall, streambank degradation, acute and legacy sources of phosphorus from farm fields, excessive/vulnerable manure applications and spills, and point-source discharges.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235043","collaboration":"Prepared in cooperation with the University of Wisconsin-Green Bay and Outagamie County, Wisconsin","usgsCitation":"Horwatich, J.A., Fermanich, K., Pronschinske, M.A., Robertson, D.M., Kussow, S., Loken, L.C., Reneau, P.C., Freund, J., and Komiskey, M.J., 2023, Assessment of conservation management practices on water quality and observed trends in the Plum Creek Basin, 2010–20: U.S. Geological Survey Scientific Investigations Report 2023–5043, 31 p., https://doi.org/10.3133/sir20235043.","productDescription":"Report: ix, 31 p.; Data Release","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-130579","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":416705,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5043/coverthb.jpg"},{"id":416707,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5043/sir20235043.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":500920,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114718.htm","linkFileType":{"id":5,"text":"html"}},{"id":416709,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92A0H98","text":"USGS data release","linkHelpText":"Water quality and estimated changes in the Plum Creek watershed 2010–2020 (data release and model archive)"},{"id":416708,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5043/images"},{"id":416706,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5043/sir20235043.pdf","text":"Report","size":"8.23 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5043"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Plum Creek Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.30723441433527,\n 44.40323167054055\n ],\n [\n -88.30723441433527,\n 44.12306373303795\n ],\n [\n -87.89405155338416,\n 44.12306373303795\n ],\n [\n -87.89405155338416,\n 44.40323167054055\n ],\n [\n -88.30723441433527,\n 44.40323167054055\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
This poster uses photographs of scientists in action to introduce the principles of critical thinking and curiosity-driven science as they relate to the study of volcanoes. Captions align with educational “Next Generation Science Standards” and include job titles and tasks to increase career awareness among students and their teachers. The poster is available in both English and Spanish.
","language":"English, Spanish","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip212","usgsCitation":"Westby, E.G., and Faust, L.M., 2023, It begins with curiosity—How do scientists learn from volcanoes?: U.S. Geological Survey General Information Product 212, 1 plate, https://doi.org/10.3133/gip212.","productDescription":"2 Plates: 26.00 x 36.00 inches","onlineOnly":"N","ipdsId":"IP-124104","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":416724,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/212/covrthb.jpg"},{"id":416726,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/212/gip212_spanish.pdf","text":"Report","size":"8 MB Spanish","linkFileType":{"id":1,"text":"pdf"},"description":"Spanish"},{"id":416725,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/212/gip212_english.pdf","text":"Report","size":"8 MB English","linkFileType":{"id":1,"text":"pdf"},"description":"English"}],"contact":"Director,
Volcano Science Center
U.S. Geological Survey
1300 SE Cardinal Court
Vancouver, WA 38683
The Alaska Volcano Observatory responded to eruptions, considerable and minor volcanic unrest, and seismic events at 15 volcanic centers in Alaska during 2018. The most notable volcanic activity came from Mount Cleveland, which had continuing intermittent dome growth and ash eruptions, and Mount Veniaminof, Great Sitkin Volcano, and Semisopochnoi Island, the three of which had minor eruptions. This report also documents landslides at Iliamna Volcano; resuspended ash from the 1912 Novarupta-Katmai eruption; anomalous seismicity and heightened degassing at Pavlof Volcano; seismic unrest at Shishaldin Volcano; long-term inflation at Westdahl volcano, Akutan Volcano, and Mount Okmok; steam plumes, anomalous seismicity, and anomalous gas measurements at Makushin Volcano; elevated seismicity at Mount Gareloi; seismic signals possibly related to icequakes at Mount Spurr; and new mud flows at Shrub mud volcano.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235029","programNote":"The Alaska Volcano Observatory is a consortium between the U.S. Geological Survey, the University of Alaska Fairbanks Geophysical Institute, and the Alaska Division of Geological and Geophysical Surveys","usgsCitation":"Cameron, C.E., Orr, T.R., Dixon, J.P., Dietterich, H.R., Waythomas, C.F., Iezzi, A.M., Power, J.A., Searcy, C., Grapenthin, R., Tepp, G., Wallace, K.L., Lopez, T.M., DeGrandpre, K., and Perreault, J.M., 2023, 2018 Volcanic activity in Alaska—Summary of events and response of the Alaska Volcano Observatory: U.S. Geological Survey Scientific Investigations Report 2023–5029, 68 p., https://doi.org/10.3133/sir20235029.","productDescription":"vii, 68 p.","numberOfPages":"68","onlineOnly":"Y","ipdsId":"IP-120153","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":500890,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114715.htm","text":"Mount Okmok; Mount Cleveland; Great Sitkin Volcano","linkFileType":{"id":5,"text":"html"}},{"id":500889,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114714.htm","text":"Westdahl Volcano; Akutan Volcano; Makushin Volcano","linkFileType":{"id":5,"text":"html"}},{"id":500891,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114716.htm","text":"Mount Gareloi; Mount Young; Semisopochnoi Volcano","linkFileType":{"id":5,"text":"html"}},{"id":500888,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114713.htm","text":"Mount Katmai; Mount Veniaminof; Pavlof Volcano","linkFileType":{"id":5,"text":"html"}},{"id":500887,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114712.htm","text":"Copper River Basin mud volcano; Mount Spurr; Iliamna Volcano","linkFileType":{"id":5,"text":"html"}},{"id":416689,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5029/sir20235029.pdf","text":"Report","size":"37 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":416688,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5029/covrthb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -143.20429710112703,\n 61.84436890827459\n ],\n [\n -142.33675036318309,\n 63.49574116221564\n ],\n [\n -146.4768151759031,\n 64.33396122050729\n ],\n [\n -150.9682913255116,\n 63.72196346359824\n ],\n [\n -153.61485795645464,\n 62.14462676432551\n ],\n [\n -160.86271677978394,\n 57.38330531702596\n ],\n [\n -168.6376926084463,\n 54.5268905469634\n ],\n [\n -177.81831378466347,\n 52.19951793188412\n ],\n [\n -178.25757795577434,\n 51.654015133859275\n ],\n [\n -176.93978544244175,\n 51.32665962050979\n ],\n [\n -169.5601473677791,\n 52.146191529316866\n ],\n [\n -156.55792790289746,\n 54.975513752431766\n ],\n [\n -151.28675784956704,\n 57.477711052577945\n ],\n [\n -146.718410470014,\n 60.74058307242174\n ],\n [\n -143.20429710112703,\n 61.84436890827459\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Alaska Volcano Observatory
U.S. Geological Survey
4210 University Drive
Anchorage, AK 99508
The Yellowstone Volcano Observatory (YVO) monitors volcanic and hydrothermal activity associated with the Yellowstone magmatic system, carries out research into magmatic processes occurring beneath Yellowstone Caldera, and issues timely warnings and guidance related to potential future geologic hazards. This report summarizes the activities and findings of YVO during the year 2022, focusing on the Yellowstone volcanic system. Highlights of YVO research and related activities during 2022 include deployments of seismometers in Norris Geyser Basin and Upper Geyser Basin to investigate interactions between hydrothermal features and influences from external influences, geological studies of post-glacial hydrothermal activity, refining the ages of Yellowstone volcanic units and updating existing maps of geologic deposits, new mapping of ash-flow deposits on the Sour Creek dome, installation of a new continuous gas monitoring station near Mud Volcano, sampling of gas emissions and thermal waters around Yellowstone National Park to monitor water chemistry over space and time, research into the age and history of Steamboat Geyser in Norris Geyser Basin, and assessment of thermal output based on satellite imagery and chloride flux in rivers.
The most noteworthy event of the year was not geophysical, but meteorological. Combined runoff from rain and snowmelt caused substantial flooding in Yellowstone National Park, which caused damage to park roads and infrastructure. Steamboat Geyser, in Norris Geyser Basin, continued the pattern of frequent eruptions that began in 2018 with 11 water eruptions in 2022, the lowest number of annual eruptions in the current eruptive sequence. Total seismicity—2,429 located earthquakes—was slightly less than the 2,773 earthquakes located in 2021 and at the upper end of the historical average range of about 1,500–2,500 earthquakes per year. Overall subsidence of the caldera floor, ongoing since late 2015 or early 2016, continued at rates of a few centimeters (1–2 inches) per year. Satellite deformation measurements indicated the possibility of slight uplift amounting to about 1 centimeter (less than 1 inch) along the north caldera rim in 2021, but satellite data spanning 2022 show no uplift in that area. Throughout 2022, the aviation color code for Yellowstone Caldera remained at “green” and the volcano alert level remained at “normal.”
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1508","usgsCitation":"Yellowstone Volcano Observatory, 2023, Yellowstone Volcano Observatory 2022 annual report: U.S. Geological Survey Circular 1508, 49 p., https://doi.org/10.3133/cir1508.","productDescription":"v, 49 p.","numberOfPages":"49","onlineOnly":"N","ipdsId":"IP-149199","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":416682,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1508/covrthb.jpg"},{"id":416683,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1508/cir1508.pdf","text":"Report","size":"28 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":499548,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114711.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.22714973143812,\n 45.10797687707381\n ],\n [\n -111.22714973143812,\n 43.34546446716831\n ],\n [\n -108.61352791332872,\n 43.34546446716831\n ],\n [\n -108.61352791332872,\n 45.10797687707381\n ],\n [\n -111.22714973143812,\n 45.10797687707381\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Yellowstone Volcano Observatory
U.S. Geological Survey
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Email: yvowebteam@usgs.gov
","tableOfContents":"To help support remedial efforts at the former Badger Army Ammunition Plant the U.S. Geological Survey built and calibrated a transient groundwater flow model using the Newton Raphson formulation (MODFLOW–NWT) of the U.S. Geological Survey’s modular three-dimensional finite-difference code. The model simulates the groundwater flow system at the site from 1984 to 2020. The former Badger Army Ammunition Plant is a 7,275-acre site in Sauk County, Wisconsin. The plant produced smokeless gunpower and solid rocket propellent as munitions components. Peak production periods were during World War II, the Korean War, and the Vietnam War. Subsequent groundwater contamination investigations have found four plumes at the site. A health risk assessment identified at least one contaminant of concern for human health risk present in three of the plumes: the propellant burning ground plume, the deterrent burning ground plume, and the central plume. A cooperative study began between the U.S. Army Environmental Command and U.S. Geological Survey to better understand the groundwater flow system at the former Badger Army Ammunition Plant. Field data, including aquifer tests, streamflow measurements, continuous groundwater elevations, and groundwater gradients with the Wisconsin River were collected and used to inform and calibrate the groundwater flow model. The model was used to assess the variability of the groundwater system over the study period, the components of the groundwater budget, and groundwater flow directions from identified source areas towards the Wisconsin River. Model performance assessment focused on using particle tracking to compare groundwater flowpaths that originate in the contaminant source areas to the observed plume footprints. This focus on plume behavior geometry should help constrain the advective component of a future groundwater transport model of the site.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235040","collaboration":"Prepared in cooperation with U.S. Army Environmental Command","usgsCitation":"Haserodt, M.J., Reeves, H.W., Nielsen, M.G., Schachter, L.A., Corson-Dosch, N.T., and Feinstein, D.T., 2023, Simulation of groundwater flow at the former Badger Army Ammunition Plant, Sauk County, Wisconsin: U.S. Geological Survey Scientific Investigations Report 2023–5040, 140 p., https://doi.org/10.3133/sir20235040.","productDescription":"Report: viii, 140 p.; 3 Data Releases; Dataset","numberOfPages":"152","onlineOnly":"Y","ipdsId":"IP-135445","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":416676,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9S2IDV0","text":"USGS data release","linkHelpText":"Soil-Water-Balance (SWB) model archive used to simulate potential annual recharge for the former Badger Army Ammunition Plant study area, Prairie du Sac, Wisconsin, 1980 to 2020"},{"id":416672,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5040/sir20235040.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":416671,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5040/sir20235040.pdf","text":"Report","size":"106 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5040"},{"id":416670,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5040/coverthb.jpg"},{"id":500916,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114707.htm","linkFileType":{"id":5,"text":"html"}},{"id":416678,"rank":8,"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":416675,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95TSI73","text":"USGS data release","linkHelpText":"Slug test analysis results from unconsolidated and bedrock aquifers at Badger Army Ammunition Plant, Sauk County, Wisconsin, 2020"},{"id":416674,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5040/images"},{"id":416677,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LNRILT","text":"USGS data release","linkHelpText":"Groundwater model archive for the former Badger Army Ammunition Plant, Wisconsin"},{"id":416712,"rank":9,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235040/full","text":"Report","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Wisconsin","county":"Sauk County","otherGeospatial":"former Badger Army Ammunition Plant","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.76922975135027,\n 43.38657213852542\n ],\n [\n -89.76922975135027,\n 43.33005054374769\n ],\n [\n -89.70231568538874,\n 43.33005054374769\n ],\n [\n -89.70231568538874,\n 43.38657213852542\n ],\n [\n -89.76922975135027,\n 43.38657213852542\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
Previously glaciated landscapes often share similar surficial characteristics, including large areas of exposed bedrock, blankets of till deposits, and alluvium-floored valleys. These materials play significant roles in geologic and hydrologic resources, geohazards, and landscape evolution; however, the vast extents of many previously glaciated landscapes have rendered comprehensive, detailed field mapping difficult. While recent advances in remote sensing have facilitated mapping of surficial materials and landforms, manual map creation has remained a time-intensive task.
The development of convolutional neural networks (CNNs) for image classification has provided a new opportunity for rapid characterization of digital elevation models, thus enabling efficient mapping of surficial materials and landforms. We have developed a methodology that leverages existing geologic maps and high-resolution (1–3 m) lidar data to train a U-Net CNN to classify alluvium and exposed bedrock in previously glaciated regions. Coupled with U.S. Geological Survey-developed geomorphometry tools capable of approximating stream incision depths, these classifications can be used to estimate the minimum thicknesses of stream-proximal hillslope sediments in areas where streams have undergone minimal incision into bedrock.
We validate this approach in the context of the Neversink River watershed, a subbasin of the Delaware River Basin and significant water source for New York City. Evaluation of deep learning model performance demonstrates substantial agreement with manually drawn maps of alluvium and exposed bedrock. Validation of the minimum sediment thickness map using borehole data and passive seismic measurements shows the greatest performance for shallow materials and decreased performance in deep sediments, as well as in areas where bedrock exposures were too small to be resolved by lidar. To resolve these issues and create more accurate surficial maps, we are training new CNNs with additional geologic data and exploring advanced approaches for estimating depths of stream incision.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.acags.2023.100116","usgsCitation":"Odom, W.E., and Doctor, D.H., 2023, Rapid estimation of minimum depth-to-bedrock from lidar leveraging deep-learning-derived surficial material maps: Applied Computing and Geosciences, v. 18, 100116, 11 p., https://doi.org/10.1016/j.acags.2023.100116.","productDescription":"100116, 11 p.","ipdsId":"IP-146769","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":443651,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.acags.2023.100116","text":"Publisher Index Page"},{"id":417128,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, New Jersey, New York, Pennsylvania","otherGeospatial":"Delaware River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.31774531759645,\n 38.65648371068133\n ],\n [\n -74.83711116001508,\n 39.00784172038104\n ],\n [\n -74.58034795534637,\n 39.29641683375996\n ],\n [\n -74.84560023131485,\n 39.59878756424641\n ],\n [\n -74.57402157309659,\n 39.81435898583538\n ],\n [\n -74.36164212699806,\n 40.42339954973025\n ],\n [\n -74.7398677925508,\n 40.67483290305228\n ],\n [\n -74.20311730788073,\n 41.49081439956416\n ],\n [\n -73.87900349307475,\n 42.134764710063195\n ],\n [\n -74.40404082594178,\n 42.53787114018144\n ],\n [\n -75.19513563542162,\n 42.478156559974536\n ],\n [\n -75.74165548596034,\n 41.860885020202005\n ],\n [\n -76.19243039302005,\n 41.151909429148475\n ],\n [\n -76.53910969789781,\n 40.49637767696336\n ],\n [\n -76.22016361968275,\n 40.00185213900514\n ],\n [\n -75.6922644432239,\n 39.71427259376151\n ],\n [\n -75.61094038798961,\n 39.383028287456796\n ],\n [\n -75.31774531759645,\n 38.65648371068133\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Odom, William E. 0000-0001-8577-5056","orcid":"https://orcid.org/0000-0001-8577-5056","contributorId":292616,"corporation":false,"usgs":true,"family":"Odom","given":"William","middleInitial":"E.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":872922,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Doctor, Daniel H. 0000-0002-8338-9722 dhdoctor@usgs.gov","orcid":"https://orcid.org/0000-0002-8338-9722","contributorId":2037,"corporation":false,"usgs":true,"family":"Doctor","given":"Daniel","email":"dhdoctor@usgs.gov","middleInitial":"H.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":872923,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70244178,"text":"70244178 - 2023 - Bringing the Nature Futures Framework to life: Creating a set of illustrative narratives of nature futures","interactions":[],"lastModifiedDate":"2023-06-06T11:51:36.740586","indexId":"70244178","displayToPublicDate":"2023-05-04T06:49:14","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5318,"text":"Sustainability Science","active":true,"publicationSubtype":{"id":10}},"title":"Bringing the Nature Futures Framework to life: Creating a set of illustrative narratives of nature futures","docAbstract":"To halt further destruction of the biosphere, most people and societies around the globe need to transform their relationships with nature. The internationally agreed vision under the Convention of Biological Diversity—Living in harmony with nature—is that “By 2050, biodiversity is valued, conserved, restored and wisely used, maintaining ecosystem services, sustaining a healthy planet and delivering benefits essential for all people”. In this context, there are a variety of debates between alternative perspectives on how to achieve this vision. Yet, scenarios and models that are able to explore these debates in the context of “living in harmony with nature” have not been widely developed. To address this gap, the Nature Futures Framework has been developed to catalyse the development of new scenarios and models that embrace a plurality of perspectives on desirable futures for nature and people. In this paper, members of the IPBES task force on scenarios and models provide an example of how the Nature Futures Framework can be implemented for the development of illustrative narratives representing a diversity of desirable nature futures: information that can be used to assess and develop scenarios and models whilst acknowledging the underpinning value perspectives on nature. Here, the term illustrative reflects the multiple ways in which desired nature futures can be captured by these narratives. In addition, to explore the interdependence between narratives, and therefore their potential to be translated into scenarios and models, the six narratives developed here were assessed around three areas of the transformative change debate, specifically, (1) land sparing vs. land sharing, (2) Half Earth vs. Whole Earth conservation, and (3) green growth vs. post-growth economic development. The paper concludes with an assessment of how the Nature Futures Framework could be used to assist in developing and articulating transformative pathways towards desirable nature futures.
Water availability is a result of complex interactions between regional water supply and demand and underlying environmental, institutional, and economic determinants. For this study, water availability is defined as “access to a specific quantity and quality of water at a point in time and space, for a specific use, recognizing the social and economic value of water across uses and institutions that facilitate or hinder its equitable and efficient provisioning.” This report identifies the human factors that influence water supply and demand and summarizes (1) the extensive sets of data available to estimate these factors in the agricultural, municipal, and industrial water-use sectors and (2) factors of recreation and ecosystem services that influence water availability in the Upper Colorado River Basin. Lastly, future research needs are identified that can help prioritize collection and refinement of human factors of water use to improve water availability estimation and forecasting.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235015","usgsCitation":"Herman-Mercer, N., Bair, L., Hines, M., Restrepo-Osorio, D., Romero, V., and Lyde, A., 2023, Human factors used to estimate and forecast water supply and demand in the Upper Colorado River Basin: U.S. Geological Survey Scientific Investigations Report 2023–5015, 46 p., https://doi.org/10.3133/sir20235015.","productDescription":"Report: v, 46 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-134554","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true},{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":416657,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99PAIVH","text":"USGS data release","linkHelpText":"Human Factors of Water Availability in the Upper Colorado River Basin"},{"id":416667,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5015/sir20235015.xml"},{"id":416666,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5015/images"},{"id":415708,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5015/coverthb.jpg"},{"id":416656,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5015/sir20235015.pdf","text":"Report","size":"23.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5015"},{"id":416903,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20235015/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5015"},{"id":500709,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114654.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Arizona, Colorado, Nevada, New Mexico, Utah, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.10551489403292,\n 42.12593624912847\n ],\n [\n -112.14876730042165,\n 40.578643397004406\n ],\n [\n -112.4013441988101,\n 38.62958426227543\n ],\n [\n -112.47821542875455,\n 36.49853660965043\n ],\n [\n -111.23729414536629,\n 34.720565339434735\n ],\n [\n -109.59005350370012,\n 33.6922933450013\n ],\n [\n -107.63532794225712,\n 33.832340766660366\n ],\n [\n -106.88857885136851,\n 35.06376781338098\n ],\n [\n -106.31753542892439,\n 37.332120502512296\n ],\n [\n -106.44931468025776,\n 39.33043034353207\n ],\n [\n -107.59140152514598,\n 41.43808608645108\n ],\n [\n -108.03066569625673,\n 42.12593624912847\n ],\n [\n -109.69986954647847,\n 42.935426421776896\n ],\n [\n -110.57839788869994,\n 42.74193975501615\n ],\n [\n -111.10551489403292,\n 42.12593624912847\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Integrated Information Dissemination Division
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1 Gifford Pinchot Drive
Madison, WI 53726