The Permian Basin has a long history of induced earthquakes, but the seismicity rates have increased dramatically over the past two decades and included a MW 5.0 likely induced by wastewater disposal (WD) in March 2020. A detailed characterization of the proliferation of seismicity in the Permian Basin throughout this time period is needed for improving the scientific understanding of the mechanisms responsible and for mitigating future seismic hazard. Due to a sparse regional seismic network before the advent of Texas Seismological Network in 2017, we characterize seismicity using the 10-station TXAR array that is 100s of km away from most of the seismicity, with the objective of improving upon the substantial contributions from previous work. By exploiting the nature of waveform similarity, we detect events with template matching, performing a quantitative analysis of spatially varying detection capabilities throughout the study area. From an initial catalog of 10,753 events, we identify 45,009 earthquakes and 10,208 quarry blasts. Using our catalog of earthquakes, we improve epicentral locations, compare relative magnitude techniques, and associate earthquakes to WD or hydraulic stimulations. We further use our earthquake catalog to investigate the relationship between seismicity and human activities near the city of Pecos, Texas. Through a comparison of our earthquake catalog with industrial records, we determine that the vast majority seismicity near Pecos, Texas, since 2000 is likely induced by an increase of WD at wells injecting at depths greater than 1.5 km.
The unglaciated southeastern United States is a biodiversity hotspot, with a disproportionate amount of this biodiversity concentrated in grasslands. Like most hotspots, the Southeast is also threatened by human activities, with the total reduction of southeastern grasslands estimated as 90 percent (upwards to 100 percent for some types) and with many threats escalating today. This report summarizes the results of a multistakeholder workshop organized by the Southeastern Grasslands Initiative and the U.S. Geological Survey, held in January 2020 to provide a scientific needs assessment to help inform the Species Status Assessment (SSA) process under the U.S. Endangered Species Act, with a focus on grassland species and communities of conservation concern in the southeastern United States. This report reviews the ecology of southeastern grasslands, including influences on their origin, maintenance, and high species richness and endemism; presents findings from the workshop; and discusses science questions, hypotheses, and possibilities for future research projects to help fill key knowledge gaps.
Participants in the January 2020 workshop, representing diverse expertise in various topics in southeastern grassland ecology, were tasked with identifying major threats to grassland species in the Southeast as well as potential ways to make the SSA process more efficient and effective. An underlying assumption and starting place for workshop discussion was that an ecosystem-based approach to the SSA process is more cost-efficient than a species-by-species approach, in large part because many species with similar biological requirements can be addressed by the same actions. Nevertheless, one partner in this effort, the U.S. Fish and Wildlife Service, does require specific attention be given to taxa that have been petitioned for Federal listing, though as often as possible these taxa are considered alongside a larger group of priority taxa with an ecosystem approach.
For group discussions, workshop participants followed a modified “World Café” method, a structured conversational approach for knowledge sharing. Group discussions focused on five categories of threats to grassland communities and species: (1) habitat loss, fragmentation, and disruption of functional population connectivity; (2) climate change, especially changes in temperature and precipitation, including intensity and seasonality, and impacts on soil moisture, groundwater levels, and other ecosystem parameters; (3) changes to disturbance regimes, as influenced by climate and land-use change, extinctions, and human attitudes and behaviors; (4) invasive species (not limited to nonnative species); and (5) localized or subregional impacts such as sea-level rise. In addition to group discussions, workshop participants—as well as other grassland experts who were unable to attend the workshop—completed a preworkshop survey concerning challenges and opportunities for grassland conservation. Findings reported here under each of these topics represent ideas, problems, hypotheses, and questions identified by a diverse community of grassland managers and researchers which may be addressed by future research and monitoring in southeastern grassland ecosystems to help guide science-based conservation of grassland-dependent species.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211047","collaboration":"Prepared in cooperation with the Department of the Interior Southeast Climate Adaptation Science Center","usgsCitation":"Noss, R.F., Cartwright, J.M., Estes, D., Witsell, T., Elliott, K.G., Adams, D.S., Albrecht, M.A., Boyles, R., Comer, P.J., Doffitt, C., Faber-Langendoen, D., Hill, J.G., Hunter, W.C., Knapp, W.M., Marshall, M., Pyne, M., Singhurst, J.R., Tracey, C., Walck, J.L., and Weakley, A., 2021, Science needs of southeastern grassland species of conservation concern—A framework for species status assessments: U.S. Geological Survey Open-File Report 2021–1047, 58 p., https://doi.org/10.3133/ofr20211047.","productDescription":"ix, 58 p.","numberOfPages":"72","onlineOnly":"Y","ipdsId":"IP-122270","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":385785,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1047/images"},{"id":385732,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1047/ofr20211047.pdf","text":"Report","size":"11.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1047"},{"id":385731,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1047/coverthb.jpg"}],"country":"United States","state":"Alabama, Arkansas, Florida, Georgia, Kentucky, Louisiana, Mississippi, North Carolina, Tennessee, Virginia, South 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\"}}]}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park Drive
Nashville, TN 37211
U.S. Geological Survey (USGS) scientists and technical staff deployed instrumented underwater platforms and buoys to collect oceanographic and atmospheric data at two sites near Matanzas Inlet, Florida, on January 24, 2018, and recovered them on April 13, 2018. Matanzas Inlet is a natural, unmaintained inlet on the Florida Atlantic coast that is well suited to study inlet and cross-shore processes. The two study sites were located offshore of the surf zone, in 9 and 15 meters of water depth, in a line perpendicular to the coast. A sea-floor platform was deployed at each site to measure ocean currents, wave motions, acoustic and optical backscatter, temperature, salinity, and pressure. The objective was to quantify the hydrodynamic forcing for sediment transport and the response to such forcing near the seabed in the vicinity of an unmaintained inlet.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211014","usgsCitation":"Martini, M.A., Montgomery, E.T., Suttles, S.E., and Warner, J.C., 2021, Summary of oceanographic and water-quality measurements offshore of Matanzas Inlet, Florida, 2018: U.S. Geological Survey Open-File Report 2021–1014, 21 p., https://doi.org/10.3133/ofr20211014.","productDescription":"Report: viii, 21 p.; 2 Data Releases","numberOfPages":"21","onlineOnly":"Y","ipdsId":"IP-117529","costCenters":[{"id":680,"text":"Woods Hole Science Center","active":false,"usgs":true}],"links":[{"id":385610,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1014/coverthb.jpg"},{"id":385611,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1014/ofr20211014.pdf","text":"Report","size":"12.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1014"},{"id":385612,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GKB537","text":"USGS Data Release","linkHelpText":"Oceanographic and water quality measurements in the nearshore zone at Matanzas Inlet, Florida, January–April, 2018"},{"id":385613,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FKARIZ","text":"USGS Data Release","linkHelpText":"Grain-size analysis data from sediment samples in support of oceanographic and water-quality measurements in the nearshore zone of Matanzas Inlet, Florida, 2018"}],"country":"United States","state":"Florida","otherGeospatial":"Matanzas Inlet","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.28509521484375,\n 29.666277672570676\n ],\n [\n -81.17420196533203,\n 29.666277672570676\n ],\n [\n -81.17420196533203,\n 29.79298413547051\n ],\n [\n -81.28509521484375,\n 29.79298413547051\n ],\n [\n -81.28509521484375,\n 29.666277672570676\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Woods Hole Coastal and Marine Science Center
U.S. Geological Survey
384 Woods Hole Road
Quissett Campus
Woods Hole, MA 02543
The U.S. Geological Survey (USGS), in cooperation with the Sisseton Wahpeton Oyate, completed a study to characterize water-level fluctuations in observation wells relative to driving factors that affect water levels in and near the historical 1867 boundary of the Lake Traverse Indian Reservation. The study investigated concerns regarding potential effects of groundwater withdrawals and climate conditions on groundwater levels within an area that includes the historical boundary of the reservation and a surrounding area that extends 10 miles in all directions within South Dakota. Characterization of water-level fluctuations in observation wells and relative driving factors was accomplished by statistical trend analysis.
Monthly data from the Parameter-elevation Regressions on Independent Slopes Model (PRISM) were aggregated to obtain annual and seasonal datasets for total precipitation, minimum air temperature (Tmin), and maximum air temperature (Tmax) for the study area and a surrounding buffer area. Trend tests for gridded data for total precipitation, Tmin, and Tmax were completed for annual and seasonal time series for water years 1956–2017, which is about 2 years before the earliest available water-level measurements. A 2-year offset was arbitrarily selected because scrutiny of water-level and precipitation data indicated that responses of groundwater levels for many of the observation wells lagged major changes in precipitation patterns by about 2 years. Statistically significant upward trends were detected for annual precipitation and annual Tmin for most of the study area and the surrounding buffer area. Statistically significant downward trends in Tmax were detected for only a few 2.5 arc-minute grid cells; however, the sparsity of the spatial coverage reduces confidence that these are true trends, in contrast to the near completeness of the spatial coverage in upward trends for Tmin. Spatial distributions of statistically significant trends in seasonal climate data were generally similar to the annual trends, but with substantial differences in the spatial density of the trends.
Potential interactions among water levels in observation wells and streamflow were examined through correlation analyses of the annual median water level for each of 76 observation wells versus the annual mean streamflow for each of four area streamgages. Potential interactions among water levels in observation wells and lake levels were examined through correlation analyses involving 25 area lakes. Resulting correlation coefficients were used as part of an approach for selecting a lake to be plotted in conjunction with water-level and precipitation data for each observation well.
Groundwater trends for 76 observation wells were analyzed for three separate water-level parameters (minimum, median, and maximum) because wells are measured sporadically, and data are biased towards more frequent measurements during periods of heaviest irrigation demand. Trends in the time series of annual precipitation (from PRISM) starting 2 years earlier than the associated water-level trend also were analyzed for the location of each individual observation well. Sen’s slope and Mann-Kendall p-values were computed for the three water-level parameters and for the annual precipitation time series. Graphs showing results of trend analyses for each observation well also showed changes with time in the sum of licensed groundwater withdrawals within six specified radii (0.5, 1.0, 2.0, 3.0, 4.0, and 5.0 miles) of each well as a qualitative indicator of proximal groundwater demand.
Trends in groundwater levels in observation wells in the study area are predominantly upward, with 43 of 76 wells having significant upward trends for at least one of the three water-level parameters and only 8 wells having significant downward trends for at least one water-level parameter. The upward groundwater trends are driven by predominantly upward precipitation trends, with 43 wells (not all the same wells) also having significant upward trends and no wells having significant downward trends. Significant upward precipitation trends were detected for only two of the eight wells with significant downward groundwater trends. Groundwater levels in some observation wells likely are also substantially affected by interactions with surface water, especially with lakes. Water levels in many area lakes increased in response to wet conditions of the early 1990s and have maintained high water levels ever since. It is recognized that in many cases lakes that were selected for plotting with groundwater hydrographs likely are not hydraulically connected with a groundwater system or aquifer associated with an individual well; however, interactions also are plausible for numerous other lakes for which water-level records are not available.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205151","collaboration":"Prepared in cooperation with the Sisseton Wahpeton Oyate","usgsCitation":"Valseth, K.J., and Driscoll, D.G., 2021, Characterization of factors affecting groundwater levels in and near the former Lake Traverse Indian Reservation, South Dakota, water years 1956–2017: U.S. Geological Survey Scientific Investigations Report 2020–5151, 64 p., https://doi.org/10.3133/sir20205151.","productDescription":"Report: vi, 64 p.; 2 Appendixes; Dataset","numberOfPages":"74","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-114147","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":385692,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5151/sir20205151_appendix1.pdf","text":"Appendix 1","size":"957 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5151 Appendix 1","linkHelpText":"— Figure 1.1 Graphs showing trends in annual precipitation totals, trends in measured groundwater levels, lake levels for a selected lake, and proximal groundwater withdrawals"},{"id":385685,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5151/coverthb.jpg"},{"id":385686,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5151/sir20205151.pdf","text":"Report","size":"4.79 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5151"},{"id":385689,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"— USGS water data for the Nation"},{"id":385693,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5151/sir20205151_appendix2.pdf","text":"Appendix 2","size":"165 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5151 Appendix 2","linkHelpText":"— Figure 2.1 Graphs showing autocorrelation function values for annual total precipitation, annual mean maximum temperature, and annual mean minimum temperature for the study area from 1956 to 2017"}],"country":"United States","state":"South Dakota","otherGeospatial":"Lake Traverse Indian Reservation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.72338867187499,\n 45.034714778688624\n ],\n [\n -96.43798828125,\n 45.034714778688624\n ],\n [\n -96.43798828125,\n 45.9511496866914\n ],\n [\n -97.72338867187499,\n 45.9511496866914\n ],\n [\n -97.72338867187499,\n 45.034714778688624\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702
The lampricide 3-trifluoromethyl-4-nitrophenol (TFM) has been used in liquid form to control larval sea lamprey (Petromyzon marinus) in Great Lakes tributaries since the late 1950s. In the 1980s a dissolvable TFM bar was developed as a supplemental tool for application to small tributaries as a deterrent to larvae seeking water not activated with TFM. The size, mass, and number of bars needed in some streams, as well as the location of the streams, limit the utility of a TFM bar. The development and use of an alternative niclosamide bar has the potential to use fewer bars to achieve similar results. However, the use of a niclosamide bar is dependent upon its larval deterrent capability compared to the TFM bar. In this study, we developed a laboratory-scale, simulated stream fluvarium with several avoidance areas including two side channels and a seep. The objective was to evaluate the deterrent capabilities of TFM and niclosamide. We found similar behavioral responses, with TFM and niclosamide having similar capabilities to prevent sea lamprey from seeking refuge in side channels and seep avoidance areas. TFM-treated side channels and seep increased sea lamprey occupancy in the main channel 2.56 times more than the untreated-controls (95% CI 1.63–4.14) whereas niclosamide-treated side channels and seep increased sea lamprey occupancy of the main channel 2.68 times more than the untreated-controls (95% CI 1.72–4.32). These responses indicate a niclosamide bar would effectively prevent sea lamprey escapement into freshwater during a lampricide treatment at concentrations unlikely to harm aquatic organisms.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2021.04.015","usgsCitation":"Schloesser, N., Boogaard, M.A., Johnson, T., Kirkeeng, C., Schueller, J., and Erickson, R.A., 2021, Use of an artificial stream to monitor avoidance behavior of larval sea lamprey in response to TFM and niclosamide: Journal of Great Lakes Research, v. 47, no. 4, p. 1192-1199, https://doi.org/10.1016/j.jglr.2021.04.015.","productDescription":"8 p.","startPage":"1192","endPage":"1199","ipdsId":"IP-111329","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":436357,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CNU24G","text":"USGS data release","linkHelpText":"Use of an artificial stream to monitor avoidancebehavior of larval sea lamprey in response to TFM and Niclosamide"},{"id":387624,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"47","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Schloesser, Nicholas 0000-0002-3815-5302","orcid":"https://orcid.org/0000-0002-3815-5302","contributorId":237025,"corporation":false,"usgs":true,"family":"Schloesser","given":"Nicholas","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":820394,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boogaard, Michael A. 0000-0002-5192-8437 mboogaard@usgs.gov","orcid":"https://orcid.org/0000-0002-5192-8437","contributorId":865,"corporation":false,"usgs":true,"family":"Boogaard","given":"Michael","email":"mboogaard@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":820395,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Todd 0000-0003-2152-8528","orcid":"https://orcid.org/0000-0003-2152-8528","contributorId":261519,"corporation":false,"usgs":true,"family":"Johnson","given":"Todd","email":"","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":820396,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kirkeeng, Courtney A. 0000-0002-7141-1216","orcid":"https://orcid.org/0000-0002-7141-1216","contributorId":237026,"corporation":false,"usgs":true,"family":"Kirkeeng","given":"Courtney","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":820397,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schueller, Justin R. 0000-0002-7102-3889","orcid":"https://orcid.org/0000-0002-7102-3889","contributorId":213527,"corporation":false,"usgs":true,"family":"Schueller","given":"Justin","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":820398,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":820399,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70221155,"text":"70221155 - 2021 - Prototyping a methodology for long-term (1680-2100) historical-to-future landscape modeling for the conterminous United States","interactions":[],"lastModifiedDate":"2022-04-01T22:14:57.190942","indexId":"70221155","displayToPublicDate":"2021-05-19T08:12:42","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2596,"text":"Land","active":true,"publicationSubtype":{"id":10}},"title":"Prototyping a methodology for long-term (1680-2100) historical-to-future landscape modeling for the conterminous United States","docAbstract":"Land system change has been identified as one of four major Earth system processes where change has passed a destabilizing threshold. A historical record of landscape change is required to understand the impacts change has had on human and natural systems, while scenarios of future landscape change are required to facilitate planning and mitigation efforts. A methodology for modeling long-term historical and future landscape change was applied in the Delaware River Basin of the United States. A parcel-based modeling framework was used to reconstruct historical landscapes back to 1680, parameterized with a variety of spatial and nonspatial historical datasets. Similarly, scenarios of future landscape change were modeled for multiple scenarios out to 2100. Results demonstrate the ability to represent historical land cover proportions and general patterns at broad spatial scales and model multiple potential future landscape trajectories. The resulting land cover collection provides consistent data from 1680 through 2100, at a 30-m spatial resolution, 10-year intervals, and high thematic resolution. The data are consistent with the spatial and thematic characteristics of widely used national-scale land cover datasets, facilitating use within existing land management and research workflows. The methodology demonstrated in the Delaware River Basin is extensible and scalable, with potential applications at national scales for the United States.
","language":"English","publisher":"MDPI","doi":"10.3390/land10050536","usgsCitation":"Dornbierer, J., Wika, S., Robison, C., Rouze, G., and Sohl, T.L., 2021, Prototyping a methodology for long-term (1680-2100) historical-to-future landscape modeling for the conterminous United States: Land, v. 10, no. 5, 536, 31 p.; Data Release, https://doi.org/10.3390/land10050536.","productDescription":"536, 31 p.; Data Release","ipdsId":"IP-127950","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":452199,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/land10050536","text":"Publisher Index Page"},{"id":386174,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":397938,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93J4Z2W"}],"country":"United States","state":"Delaware, Maryland, New Jersey, New York, Pennsylvania","otherGeospatial":"Delaware River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.728759765625,\n 38.676933444637925\n ],\n [\n -75.333251953125,\n 38.46219172306828\n ],\n [\n -74.827880859375,\n 39.06184913429154\n ],\n [\n -75.025634765625,\n 39.38526381099774\n ],\n [\n -74.2236328125,\n 40.212440718286466\n ],\n [\n -74.696044921875,\n 40.78885994449482\n ],\n [\n -73.58642578125,\n 41.5579215778042\n ],\n [\n -74.278564453125,\n 42.27730877423709\n ],\n [\n -76.83837890625,\n 40.538851525354666\n ],\n [\n -75.728759765625,\n 38.676933444637925\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-05-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Dornbierer, Jordan 0000-0003-2099-5095","orcid":"https://orcid.org/0000-0003-2099-5095","contributorId":213067,"corporation":false,"usgs":false,"family":"Dornbierer","given":"Jordan","affiliations":[{"id":38270,"text":"SGT Inc., contractor to USGS EROS","active":true,"usgs":false}],"preferred":false,"id":816876,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wika, Steve 0000-0001-9992-8973","orcid":"https://orcid.org/0000-0001-9992-8973","contributorId":213068,"corporation":false,"usgs":false,"family":"Wika","given":"Steve","affiliations":[{"id":38700,"text":"SGT Inc.","active":true,"usgs":false}],"preferred":false,"id":816877,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Robison, Charles 0000-0002-7623-2380","orcid":"https://orcid.org/0000-0002-7623-2380","contributorId":217916,"corporation":false,"usgs":false,"family":"Robison","given":"Charles","email":"","affiliations":[{"id":39714,"text":"SGT Inc. (USGS Contractor)","active":true,"usgs":false}],"preferred":false,"id":816878,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rouze, Gregory 0000-0002-3344-2708","orcid":"https://orcid.org/0000-0002-3344-2708","contributorId":259239,"corporation":false,"usgs":false,"family":"Rouze","given":"Gregory","email":"","affiliations":[{"id":52337,"text":"TSSC contractor to USGS EROS","active":true,"usgs":false}],"preferred":false,"id":816879,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sohl, Terry L. 0000-0002-9771-4231 sohl@usgs.gov","orcid":"https://orcid.org/0000-0002-9771-4231","contributorId":648,"corporation":false,"usgs":true,"family":"Sohl","given":"Terry","email":"sohl@usgs.gov","middleInitial":"L.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":816880,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70220861,"text":"70220861 - 2021 - Incorporating climate change in a harvest risk assessment for polar bears Ursus maritimus in Southern Hudson Bay","interactions":[],"lastModifiedDate":"2021-05-26T12:28:44.821314","indexId":"70220861","displayToPublicDate":"2021-05-19T07:26:46","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Incorporating climate change in a harvest risk assessment for polar bears Ursus maritimus in Southern Hudson Bay","docAbstract":"Arctic marine mammals are harvested by Indigenous people for subsistence and are socially and culturally important. For ice-dependent species like the polar bear Ursus maritimus, management and conservation require understanding interactions between harvest and sea-ice loss due to climate change. We developed a demographic model to evaluate harvest risk for polar bears in Southern Hudson Bay, Canada, where the annual ice-free season has increased by approximately one month in recent decades. The model was based on the theta-logistic equation and allowed for density-dependent changes (through carrying capacity [K]) and density-independent changes (through population growth rate [r]). Model parameters were estimated using a Bayesian Monte Carlo method that included capture-recapture, aerial survey, and harvest data. Harvest management followed a state-dependent approach under which new estimates of abundance were used to update the harvest level every five years. Under a middle-of-the-road environmental scenario that assumed K and r would decline in proportion to projected sea-ice declines, annual removal of 0.02–0.03 of females resulted in a 0.8 probability of maintaining subpopulation abundance above maximum net productivity level for three polar bear generations (~34 years), our primary criterion for sustainability. Under more pessimistic and optimistic environmental scenarios, comparable female harvest rates were 0.01 and 0.055, respectively. Our coupled modeling-management framework can be used to inform tradeoffs between protection and sustainable use for wildlife populations experiencing habitat loss.
Major flooding occurred June 30–July 1, 2018, in the Fourmile Creek Basin in central Iowa after thunderstorm activity over the region. The largest recorded 24-hour precipitation total at a National Oceanic and Atmospheric Administration weather station was 8.72 inches in Ankeny, Iowa, and 7.54 inches in Des Moines, Iowa. A maximum peak-of-record discharge of 10,000 cubic feet per second was recorded at U.S. Geological Survey streamgage 05485605, Fourmile Creek near Ankeny, Iowa, on July 1, 2018, with an annual exceedance probability of less than 0.2 percent. A maximum peak-of-record discharge of 12,000 cubic feet per second also was recorded at U.S. Geological Survey streamgage 05485640, Fourmile Creek at Des Moines, Iowa, on July 1, 2018, with an annual exceedance-probability range of 0.5–1 percent. High-water mark elevations were surveyed at 11 locations along Fourmile Creek between State Highway 163 in Pleasant Hill, Iowa, and U.S. Route 69 near Alleman, Iowa, a distance of 21.0 river miles. The high-water marks were used to develop a flood profile for Fourmile Creek.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211044","collaboration":"Prepared in cooperation with the Iowa Department of Transportation and the Iowa Highway Research Board (Project HR–140)","usgsCitation":"O’Shea, P.S., Vegrzyn, J.C., and Barnes, K.K., 2021, Flood of June 30–July 1, 2018, in the Fourmile Creek Basin, near Ankeny, Iowa: U.S. Geological Survey Open-File Report 2021–1044, 18 p., https://doi.org/10.3133/ofr20211044.","productDescription":"Report: vi, 18 p.; Data Release; Dataset","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-111452","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":385727,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1044/coverthb.jpg"},{"id":385791,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1044/images"},{"id":385790,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1044/ofr20211044.xml"},{"id":385730,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"},{"id":385729,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XZGOG3","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Peak-flow frequency analysis for two selected streamgages in the Fourmile Creek Basin in central Iowa, based on data through water year 2018"},{"id":385728,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1044/ofr20211044.pdf","text":"Report","size":"1.53 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1044"}],"country":"United States","state":"Iowa","city":"Ankeny","otherGeospatial":"Fourmile Creek basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.548583984375,\n 41.86956082699455\n ],\n [\n -93.75457763671874,\n 41.933954896061636\n ],\n [\n -93.63784790039061,\n 41.71187978193456\n ],\n [\n -93.51699829101562,\n 41.54867239252432\n ],\n [\n -93.4002685546875,\n 41.57641597789266\n ],\n [\n -93.548583984375,\n 41.86956082699455\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240
The federally threatened American crocodile (Crocodylus acutus) is a flagship species and ecological indicator of hydrologic restoration in the Florida Everglades. We conducted a long-term capture-recapture study on the South Florida population of American crocodiles from 1978 to 2015 to evaluate the effects of restoration efforts to more historic hydrologic conditions. The study produced 10,040 crocodile capture events of 9,865 individuals and more than 90% of captures were of hatchlings. Body condition and growth rates of crocodiles were highly age-structured with younger crocodiles presenting with the poorest body condition and highest growth rates. Mean crocodile body condition in this study was 2.14±0.35 SD across the South Florida population. Crocodiles exposed to hypersaline conditions (> 40 psu) during the dry season maintained lower body condition scores and reduced growth rate by 13% after one year, by 24% after five years, and by 29% after ten years. Estimated hatchling survival for the South Florida population was 25% increasing with ontogeny and reaching near 90% survival at year six. Hatchling survival was 34% in NE Florida Bay relative to a 69% hatchling survival at Crocodile Lake National Wildlife Refuge and 53% in Flamingo area of Everglades National Park. Hypersaline conditions negatively affected survival, growth and body condition and was most pronounced in NE Florida Bay, where the hydrologic conditions have been most disturbed. The American crocodile, a long-lived animal, with relatively slow growth rate provides an excellent model system to measure the effects of altered hydropatterns in the Everglades landscape. These results illustrate the need for continued long-term monitoring to assess system-wide restoration outcomes and inform resource managers.
Ongoing climate change and human conversion of forests to other land uses alter regional evapotranspiration dynamics and, consequently, impact associated hydrological systems in Amazonia. We studied the effects of drought and fragmentation on forest evapotranspiration using the surface energy balance-based model METRIC (Mapping Evapotranspiration at high Resolution with Internalized Calibration) for a fragmented forest landscape in Brazil's Amazonian state of Rondônia.
Dry season (June-August) forest evapotranspiration estimates were produced for the 2009-2011 period that encompassed the 2010 drought event, one of the extreme droughts in the Amazon. METRIC evapotranspiration data were analyzed in relation to climate (monthly precipitation and cumulative water deficit) and forest fragmentation (edge distance from 100m to 1000m from forest edge and edge density). During the dry season of 2009, pre-drought, forest evapotranspiration did not fall below 110mm/month. However, the 2010 drought year showed a drastic decline in evapotranspiration by 32%, to 75mm/month, from July to August. In 2011, evapotranspiration rates were still depressed with August rates dropping as low as 85mm/month. Forest evapotranspiration dynamics were driven mainly by precipitation and corresponding water deficits in the drier years (2010 and 2011), although evapotranspiration deficits along the edges of forest fragments were locally significant, at the landscape scale. The forests near edges (to 100m) had progressively lower evapotranspiration levels than interior forests as dry seasons progressed and these differences were greatest in the 2010 drought year, reaching almost 5%.
Our results suggest that during the driest months, fragmentation exacerbated both the rate and extent of evapotranspiration reductions over forest areas up to 100m from edges, equivalent to ~20% of the forested landscape in our study area.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.agrformet.2021.108446","usgsCitation":"Numata, I., Khand, K.B., Kjaersgaard, J., Cochrane, M.A., and Silva, S.S., 2021, Forest evapotranspiration dynamics over a fragmented forest landscape under drought in southwestern Amazonia: Agricultural and Forest Meteorology, v. 306, 108446, 9 p., https://doi.org/10.1016/j.agrformet.2021.108446.","productDescription":"108446, 9 p.","ipdsId":"IP-122348","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":452208,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.agrformet.2021.108446","text":"Publisher Index Page"},{"id":385833,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Brazil","state":"Rondonia","otherGeospatial":"Amazon Rain Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -66.97265625,\n -2.4162756547063857\n ],\n [\n -56.6455078125,\n -2.4162756547063857\n ],\n [\n -56.6455078125,\n 6.18424616128059\n ],\n [\n -66.97265625,\n 6.18424616128059\n ],\n [\n -66.97265625,\n -2.4162756547063857\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"306","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Numata, Izaya","contributorId":219508,"corporation":false,"usgs":false,"family":"Numata","given":"Izaya","email":"","affiliations":[],"preferred":false,"id":816235,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Khand, Kul Bikram 0000-0002-1593-1508","orcid":"https://orcid.org/0000-0002-1593-1508","contributorId":242921,"corporation":false,"usgs":true,"family":"Khand","given":"Kul","email":"","middleInitial":"Bikram","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":816236,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kjaersgaard, Jeppe","contributorId":258261,"corporation":false,"usgs":false,"family":"Kjaersgaard","given":"Jeppe","email":"","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":816237,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cochrane, Mark A.","contributorId":20884,"corporation":false,"usgs":false,"family":"Cochrane","given":"Mark","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":816238,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Silva, Sonaira S.","contributorId":258262,"corporation":false,"usgs":false,"family":"Silva","given":"Sonaira","email":"","middleInitial":"S.","affiliations":[{"id":52266,"text":"Federal University of Acre","active":true,"usgs":false}],"preferred":false,"id":816239,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70220617,"text":"70220617 - 2021 - Dissolved Fe supply to the central Gulf of Alaska is inferred to be derived from Alaskan glacial dust that is not resolved by dust transport models","interactions":[],"lastModifiedDate":"2021-06-30T18:58:48.8679","indexId":"70220617","displayToPublicDate":"2021-05-19T06:41:56","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8605,"text":"JGR-Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Dissolved Fe supply to the central Gulf of Alaska is inferred to be derived from Alaskan glacial dust that is not resolved by dust transport models","docAbstract":"Re-examination of previously published dissolved iron time-series data from Ocean Station Papa in the central Gulf of Alaska (GoA) reveals 33-70% increases in the dissolved iron inventories occurring between September and February of successive years, implying a source of Fe to this region during autumn or early winter. Because I can virtually rule out many possible iron sources at this time of year, I suggest Alaskan glacial dust is the likely iron source. Large plumes of such dust are known to be generated regularly in the autumn by anomalous offshore winds and channelled through mountain gaps, simultaneously from several locations spanning ∼1000 km of the northern Gulf of Alaska coastline. Large dust flux events occur when below-freezing, low-humidity air temperatures persist for many days during the autumn. I suggest that existing state-of-the-art global dust models fail to reproduce this Alaskan dust flux because the model spatial resolution is too coarse to resolve the high winds through the narrow mountain gaps that generate the dust. Future work that could help to confirm this Fe source to the central GoA includes time-series profiles of iron concentrations, and ancillary information from sensor-equipped profiling floats. If this mechanism of Fe supply to the central GoA were confirmed, it would imply this Alaskan dust is transported ≥ 1100 km from the coast, more than twice as far as has been visually documented from satellite observations.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021JG006323","usgsCitation":"Crusius, J., 2021, Dissolved Fe supply to the central Gulf of Alaska is inferred to be derived from Alaskan glacial dust that is not resolved by dust transport models: JGR-Biogeosciences, v. 126, e2021JG006323, 13 p., https://doi.org/10.1029/2021JG006323.","productDescription":"e2021JG006323, 13 p.","ipdsId":"IP-102176","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":385832,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Gulf of Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -153.28125,\n 51.39920565355378\n ],\n [\n -131.1328125,\n 51.39920565355378\n ],\n [\n -131.1328125,\n 59.5343180010956\n ],\n [\n -153.28125,\n 59.5343180010956\n ],\n [\n -153.28125,\n 51.39920565355378\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"126","noUsgsAuthors":false,"publicationDate":"2021-06-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Crusius, John 0000-0003-2554-0831 jcrusius@usgs.gov","orcid":"https://orcid.org/0000-0003-2554-0831","contributorId":2155,"corporation":false,"usgs":true,"family":"Crusius","given":"John","email":"jcrusius@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":816240,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70220525,"text":"ofr20211043 - 2021 - Dynamics of endangered sucker populations in Clear Lake Reservoir, California","interactions":[],"lastModifiedDate":"2021-05-19T12:01:59.893466","indexId":"ofr20211043","displayToPublicDate":"2021-05-18T16:18:22","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1043","displayTitle":"Dynamics of Endangered Sucker Populations in Clear Lake Reservoir, California","title":"Dynamics of endangered sucker populations in Clear Lake Reservoir, California","docAbstract":"In collaboration with the Bureau of Reclamation, the U.S. Geological Survey began a consistent monitoring program for endangered Lost River suckers (Deltistes luxatus) and shortnose suckers (Chasmistes brevirostris) in Clear Lake Reservoir, California, in fall 2004. The program was intended to improve understanding of the Clear Lake Reservoir populations because they are important to recovery efforts for these species. We report results from the ongoing program and include sampling efforts through fall 2019. We summarize catches and passive integrated transponder (PIT) tagging efforts from trammel net sampling in the fall seasons (September–October each year) and detections of PIT-tagged suckers on remote antennas in the spring in each year from 2006 to 2019. We also combine the data from physical captures and remote detections in capture-recapture models to provide estimates of annual survival for suckers in the reservoir.
A lack of genetic distinctiveness between shortnose suckers and Klamath largescale suckers (Catostomus snyderi) in the Lost River subbasin, including Clear Lake Reservoir, is a likely cause of past difficulty in identification of these species. Field identification can be subjective for many captured individuals, and very few individuals were identified as Klamath largescale suckers in the most recent years of our monitoring program. For this report, we combine individuals that were identified as either shortnose sucker (SNS) or Klamath largescale sucker (KLS) into a single “SNS-KLS” group for most analyses. Identification of Lost River suckers (LRS) is based on external morphological characteristics.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211043","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Hewitt, D.A., Hayes, B.S., Harris, A.C., Janney, E.C., Kelsey, C.M., Perry, R.W., and Burdick, S.M., 2021, Dynamics of endangered sucker populations in Clear Lake Reservoir, California: U.S. Geological Survey Open-File Report 2021–1043, 59 p., https://doi.org/10.3133/ofr20211043.","productDescription":"v, 59 p.","onlineOnly":"Y","ipdsId":"IP-108970","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":385709,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1043/coverthb.jpg"},{"id":385710,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1043/ofr20211043.pdf","text":"Report","size":"12.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1043"}],"country":"United States","state":"California","otherGeospatial":"Clear Lake Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.25747680664064,\n 41.78360106648078\n ],\n [\n -121.01852416992186,\n 41.78360106648078\n ],\n [\n -121.01852416992186,\n 41.96663812286332\n ],\n [\n -121.25747680664064,\n 41.96663812286332\n ],\n [\n -121.25747680664064,\n 41.78360106648078\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
In 2015–16, a comparison study of stream sediment collection techniques was done for a U.S. Geological Survey streamgage on the Nemadji River near South Superior, Wisconsin (U.S. Geological Survey station number 04024430) to provide an adjustment factor for comparing suspended-sediment rating curves for two historical periods 1973–86 and 2006–16. During 1973–1986, the U.S. Geological Survey used the equal-width-increment technique to collect suspended-sediment concentration data (EWI SSC). The Wisconsin Department of Natural Resources and Minnesota Pollution Control Agency collected grab samples for total suspended solids (grab TSS) concentration starting in 2006 and continuing beyond 2016. In addition to the comparison study of suspended-sediment concentrations, bedload and bed material samples were collected in 2015–16, and the modified Einstein procedure was run to further characterize total sediment loads. The 2015–16 study indicated that the EWI SSC and grab TSS concentrations were different, but not as much as expected, especially on the high end where grab TSS concentrations were sometimes higher than EWI SSC concentrations, possibly due to a combination of a high percentage of fines in suspension and higher concentrations in the center of the channel than the margins. The 2015–16 measured bedload made up a small percentage of total sediment load, and bedload and streambed particle sizes are 90 to 100 percent sand sized or smaller. The relative proportion of measured bedload to total load decreased with increased streamflow, and for streamflows greater than 1,800 cubic feet per second, the suspended load made up 98 percent of the total load. Calculated 2015–16 instantaneous total sediment loads from the modified Einstein procedure were up to 70 percent of the measured loads for flows less than 1,000 cubic feet per second and near or more than 100 percent for flows greater than 1,000 cubic feet per second. The sediment rating curve developed for the 2006–16 adjusted grab TSS data had a similar slope but a lower intercept than its 1973–86 EWI SSC counterpart, indicating that for a given streamflow, suspended-sediment concentrations were lower for 2006–16 compared to 1973–86. The negative offset equates to estimates of annual suspended-sediment loads in 2006–16 being on average 87 percent of the 1973–86 loads. Over the period 2009–16, annual suspended-sediment loads ranged from a low of about 21,000 tons per year in 2015 to a high of 167,000 tons per year in 2012 with a mean of 85,000 tons per year. However, reductions in suspended-sediment concentrations are likely obscured by large loads during years with flooding.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211003","collaboration":"Prepared in cooperation with the Wisconsin Department of Natural Resources","usgsCitation":"Fitzpatrick, F.A., 2021, Sediment characteristics of northwestern Wisconsin’s Nemadji River, 1973–2016: U.S. Geological Survey Open-File Report 2021–1003, 27 p., https://doi.org/10.3133/ofr20211003.","productDescription":"Report: viii, 27 p.; Data Release","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-085024","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":385361,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FX0X6Y","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Selected sediment data and results from regression models, modified Einstein Procedure, and loads estimation for the Nemadji River, 1973–2016"},{"id":385360,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1003/ofr20211003.pdf","text":"Report","size":"5.16 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1003"},{"id":385359,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1003/coverthb.jpg"}],"country":"United States","state":"Minnesota, Wisconsin","otherGeospatial":"Nemadji River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.55157470703125,\n 46.38672781370433\n ],\n [\n -92.01599121093749,\n 46.38672781370433\n ],\n [\n -92.01599121093749,\n 46.65697731621612\n ],\n [\n -92.55157470703125,\n 46.65697731621612\n ],\n [\n -92.55157470703125,\n 46.38672781370433\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
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Middleton, WI 53562
Baker River (Washington, USA) sockeye salmon (Oncorhynchus nerka) are a recovering Puget Sound stock that are aided by trap-and-haul and hatchery programs to mitigate for the presence of a high head dam. The relative contribution of hatchery and natural adults to overall production of smolts and recruits is unknown. The ability to identify three different sockeye production groups (natural production, artificial incubation, and artificial spawning beach) within the Baker system is crucial to moving forward with management goals. The examination of otoliths was proposed as a technical tool for improved understanding and management of Baker sockeye rebuilding efforts. Otoliths were chosen as they provide a chronological record on an individual fish basis and have been shown to identify fish origin through both otolith microstructure and chemistry.
The goal of this pilot project was to determine the feasibility of assigning sockeye to their production source based on otolith analysis. A variety of methods were employed and compared for accuracy of group assignment. The maximum overall accuracy capable of attainment was 88.57 percent, however complete confidence (100 percent) in the separation of natural production from artificial production was reached through the analysis of trace elements alone. Some segregation of the two artificial production groups was reached through analysis of a few specific trace elements (magnesium, manganese, and zinc). This confidence in assignment for the artificial production groups was aided by a two-step process of combining trace elements with microstructure. The Sr isotope ratios supported the trace element findings but did not help to boost the overall level of confidence in the separation of production groups. Based upon the results from this preliminary investigation, one could choose a statistically sound, efficient, and cost-effective use of otoliths as a tool for discriminating between the sockeye production groups of the Baker Lake system.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211032","collaboration":"A Report to the Upper Skagit Indian Tribe per agreement # 17WNYD00SIT5545","usgsCitation":"Larsen, K.A., Wetzel, L.A., Stenberg, K.D., and Lind-Null, A.M., 2021, Investigation of otolith microstructure and composition for identification of rearing strategies and associated Baker Lake sockeye salmon (Oncorhynchus nerka) smolt production, Washington, 2016–17: U.S. Geological Survey Open-File Report 2021–1032, 15 p., https://doi.org/10.3133/ofr20211032.","productDescription":"vii, 16 p.","onlineOnly":"Y","ipdsId":"IP-091287","costCenters":[{"id":654,"text":"Western Fisheries Research 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\"}}]}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
Integrating remote sensing into assessments of carbon stocks and fluxes has advanced our understanding of how global change affects landscapes and our capacity to support decision making about forest management. However, there remains a lack of detailed and actionable analyses conducted across widely ranging environmental conditions that are appropriate for tactical planning. We used airborne laser scanning data and multi-source satellite imagery to estimate forest aboveground carbon density and gross primary production, and to map forest cover across the main Hawaiian Islands. We used these measures to develop the Carbon Sequestration Potential Index (CSPI), which identifies where the potential for carbon sequestration following afforestation would be highest within a complex landscape of 304 management units. Variation in CSPI was high across islands and between ecosystems, with low values for cool, dry and largely intact forest systems and high values for warm, wet and largely non-forested systems. The CSPI provided a rapid, spatially-explicit and actionable assessment of Hawaiian forest reserves, which can help stewardship agencies contribute to state carbon neutrality goals through climate-smart and science-driven prescriptions that encompass conservation to restoration.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2021.119343","usgsCitation":"Pascual, A., Giardina, C.P., Selmants, P., Laramee, L.J., and Asner, G.P., 2021, A new remote sensing-based Carbon Sequestration Potential Index (CSPI): A tool to support land carbon management: Forest Ecology and Management, v. 494, 119343, 10 p., https://doi.org/10.1016/j.foreco.2021.119343.","productDescription":"119343, 10 p.","ipdsId":"IP-125868","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":452211,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.foreco.2021.119343","text":"Publisher Index Page"},{"id":386868,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.45654296875,\n 18.8335153964335\n ],\n [\n -154.632568359375,\n 19.580493479202527\n ],\n [\n -155.928955078125,\n 20.910134481692683\n ],\n [\n -156.895751953125,\n 21.320080964008206\n ],\n [\n -157.96142578124997,\n 21.790107059807873\n ],\n [\n -159.59838867187497,\n 22.370396344320053\n ],\n [\n -159.9169921875,\n 22.085639901650328\n ],\n [\n -158.192138671875,\n 21.135745255030603\n ],\n [\n -157.005615234375,\n 20.64306554672647\n ],\n [\n -156.478271484375,\n 19.921712747556207\n ],\n [\n -156.07177734375,\n 18.8335153964335\n ],\n [\n -155.45654296875,\n 18.8335153964335\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"494","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Pascual, Adrian","contributorId":260677,"corporation":false,"usgs":false,"family":"Pascual","given":"Adrian","email":"","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":818464,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Giardina, Christian P. 0000-0002-3431-5073","orcid":"https://orcid.org/0000-0002-3431-5073","contributorId":182695,"corporation":false,"usgs":false,"family":"Giardina","given":"Christian","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":818465,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Selmants, Paul 0000-0001-6211-3957 pselmants@usgs.gov","orcid":"https://orcid.org/0000-0001-6211-3957","contributorId":192591,"corporation":false,"usgs":true,"family":"Selmants","given":"Paul","email":"pselmants@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":818466,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Laramee, Leah J","contributorId":260678,"corporation":false,"usgs":false,"family":"Laramee","given":"Leah","email":"","middleInitial":"J","affiliations":[{"id":52640,"text":"Dept. of Land and Natural Resources, State of Hawaii","active":true,"usgs":false}],"preferred":false,"id":818467,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Asner, Gregory P.","contributorId":25393,"corporation":false,"usgs":false,"family":"Asner","given":"Gregory","email":"","middleInitial":"P.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":818468,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70263925,"text":"70263925 - 2021 - Rupture passing probabilities at fault bends and steps, with application to rupture length probabilities for earthquake early warning","interactions":[],"lastModifiedDate":"2025-02-28T16:13:52.073178","indexId":"70263925","displayToPublicDate":"2021-05-18T10:10:47","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Rupture passing probabilities at fault bends and steps, with application to rupture length probabilities for earthquake early warning","docAbstract":"Earthquake early warning (EEW) systems can quickly identify the beginning of a significant earthquake rupture, but the first seconds of seismic data have not been found to predict the final rupture length. We present two approaches for estimating probabilities of rupture length given the rupture initiation from an EEW system. In the first approach, bends and steps on the fault are interpreted as physical mechanisms for rupture arrest. Arrest probability relations are developed from empirical observations and depend on bend angle and step size. Probability of arrest compounds serially with increasing rupture length as bends or steps are encountered. In the second approach, time‐independent rates among ruptures from the Uniform California Earthquake Rupture Forecast, Version 3 (UCERF3), are interpreted to apply to the time‐dependent condition in which rupture grows from a known starting point. Length probabilities from a Gutenberg–Richter magnitude–frequency relation provide a reference of comparison. We illustrate the new approach using the discretized fault model for California developed for UCERF3. For the case of rupture initiating on the southeast end of the San Andreas fault we find the geometric complexity of the Mill Creek section impedes most ruptures, and only ∼5% are predicted to reach to San Bernardino on the eastern edge of the greater Los Angeles region. Conditional probabilities of length can be precompiled in this manner for any initiation point on the fault system and thus are of potential value in seismic hazard and EEW applications.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120200370","usgsCitation":"Biasi, G., and Wesnousky, S.G., 2021, Rupture passing probabilities at fault bends and steps, with application to rupture length probabilities for earthquake early warning: Bulletin of the Seismological Society of America, v. 111, no. 4, p. 2235-2247, https://doi.org/10.1785/0120200370.","productDescription":"13 p.","startPage":"2235","endPage":"2247","ipdsId":"IP-116890","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482646,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"111","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-05-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Biasi, Glenn 0000-0003-0940-5488 gbiasi@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-5488","contributorId":195946,"corporation":false,"usgs":true,"family":"Biasi","given":"Glenn","email":"gbiasi@usgs.gov","affiliations":[],"preferred":true,"id":929127,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wesnousky, Steven G.","contributorId":193416,"corporation":false,"usgs":false,"family":"Wesnousky","given":"Steven","email":"","middleInitial":"G.","affiliations":[{"id":33746,"text":"Center for Neotectonic Studies, Reno, NV","active":true,"usgs":false}],"preferred":false,"id":929128,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70229234,"text":"70229234 - 2021 - Rediscovery and genetic confirmation of the Threeridge Mussel, Amblema plicata (Say, 1817) (Bivalvia, Unionidae), in the Choctawhatchee River, Florida, USA","interactions":[],"lastModifiedDate":"2022-03-03T16:08:43.422797","indexId":"70229234","displayToPublicDate":"2021-05-18T10:04:07","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1208,"text":"Check List","active":true,"publicationSubtype":{"id":10}},"title":"Rediscovery and genetic confirmation of the Threeridge Mussel, Amblema plicata (Say, 1817) (Bivalvia, Unionidae), in the Choctawhatchee River, Florida, USA","docAbstract":"Recent freshwater mussel research has resulted in rediscovery of several species presumed extinct. We report the rediscovery of Amblema plicata (Say, 1817) in 2019 from the Choctawhatchee River, Florida, USA. Amblema plicata has not been reported in the Choctawhatchee river basin since 1958, more than 61 years ago. This species was collected during the long-term monitoring of freshwater mussels in Florida streams. We provide genetic confirmation of our voucher identification using a DNA barcoding approach and discuss potential risks to A. plicata populations in the Choctawhatchee river basin.
","language":"English","publisher":"Pensoft","doi":"10.15560/17.3.783","usgsCitation":"Patterson, L.N., Geda, S.R., and Johnson, N., 2021, Rediscovery and genetic confirmation of the Threeridge Mussel, Amblema plicata (Say, 1817) (Bivalvia, Unionidae), in the Choctawhatchee River, Florida, USA: Check List, v. 17, no. 3, p. 783-790, https://doi.org/10.15560/17.3.783.","productDescription":"8 p.","startPage":"783","endPage":"790","ipdsId":"IP-124667","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":452215,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.15560/17.3.783","text":"Publisher Index Page"},{"id":396705,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Choctawhatchee River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.10671997070311,\n 30.359841397025537\n ],\n [\n -85.79635620117188,\n 30.359841397025537\n ],\n [\n -85.79635620117188,\n 30.782547981939047\n ],\n [\n -86.10671997070311,\n 30.782547981939047\n ],\n [\n -86.10671997070311,\n 30.359841397025537\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"17","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-05-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Patterson, Lauren N.","contributorId":287676,"corporation":false,"usgs":false,"family":"Patterson","given":"Lauren","email":"","middleInitial":"N.","affiliations":[{"id":12556,"text":"Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":837003,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Geda, Susan R.","contributorId":287678,"corporation":false,"usgs":false,"family":"Geda","given":"Susan","email":"","middleInitial":"R.","affiliations":[{"id":12556,"text":"Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":837004,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Nathan A. 0000-0001-5167-1988","orcid":"https://orcid.org/0000-0001-5167-1988","contributorId":218986,"corporation":false,"usgs":true,"family":"Johnson","given":"Nathan A.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":837005,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70224285,"text":"70224285 - 2021 - Identifying chemicals and mixtures of potential biological concern detected in passive samplers from Great Lakes tributaries using high-throughput data and biological pathways","interactions":[],"lastModifiedDate":"2021-09-20T12:59:52.651404","indexId":"70224285","displayToPublicDate":"2021-05-18T07:57:20","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Identifying chemicals and mixtures of potential biological concern detected in passive samplers from Great Lakes tributaries using high-throughput data and biological pathways","docAbstract":"Waterborne contaminants were monitored in 69 tributaries of the Laurentian Great Lakes in 2010 and 2014 using semipermeable membrane devices (SPMDs) and polar organic chemical integrative samplers (POCIS). A risk-based screening approach was used to prioritize chemicals and chemical mixtures, identify sites at greatest risk for biological impacts, and identify potential hazards to monitor at those sites. Analyses included 185 chemicals (143 detected) including polycyclic aromatic hydrocarbons (PAHs), legacy and current-use pesticides, fire retardants, pharmaceuticals, and fragrances. Hazard quotients were calculated by dividing detected concentrations by biological effect concentrations reported in the ECOTOX Knowledgebase (toxicity quotients) or ToxCast database (exposure–activity ratios [EARs]). Mixture effects were estimated by summation of EAR values for chemicals that influence ToxCast assays with common gene targets. Nineteen chemicals—atrazine, N,N-diethyltoluamide, di(2-ethylhexyl)phthalate, dl-menthol, galaxolide, p-tert-octylphenol, 3 organochlorine pesticides, 3 PAHs, 4 pharmaceuticals, and 3 phosphate flame retardants—had toxicity quotients >0.1 or EARs for individual chemicals >10–3 at 10% or more of the sites monitored. An additional 4 chemicals (tributyl phosphate, triethyl citrate, benz[a]anthracene, and benzo[b]fluoranthene) were present in mixtures with EARs >10–3. To evaluate potential apical effects and biological endpoints to monitor in exposed wildlife, in vitro bioactivity data were compared to adverse outcome pathway gene ontology information. Endpoints and effects associated with endocrine disruption, alterations in xenobiotic metabolism, and potentially neuronal development would be relevant to monitor at the priority sites. The EAR threshold exceedance for many chemical classes was correlated with urban land cover and wastewater effluent influence, whereas herbicides and fire retardants were also correlated to agricultural land cover. Environ Toxicol Chem 2021;40:2165–2182. Published 2021. This article is a U.S. Government work and is in the public domain in the USA. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
Increasing the resilience of coastal communities while decreasing the risk to them are key to the continued inhabitance and sustainability of these areas. Low-lying coral reef-lined islands are experiencing storm wave-driven flood events that currently strike with little to no warning. These events are occurring more frequently and with increasing severity. There is a need along the world’s coral reef-lined coasts for a tool that can forecast the timing and severity of wave-driven flooding events. Without this tool, coastal communities are vulnerable to:
loss of life from drowning • loss of, and damage to, property and infrastructure • decreasing viability of communities via loss of, and damage to crops, fishing (via decreased water quality and wave-damaged reefs), and freshwater resources • reduction of livable land due to increased erosion and salt intrusion. The currently available tools were developed for sandy shorelines and do not accurately predict wave-driven flooding on reef-lined coasts, leaving inhabitants without accurate and timely warnings. In addition, the flood models that do exist for reef-lined coasts have only been implemented on a small number of areas throughout the world because running these models is costly and requires a high level of computing power. Using these existing models and techniques to generate high-resolution forecasts for wave-driven flooding for all reef-lined coasts would cost approximately US$1 billion. To remedy this issue, an international team associated with the GEO Blue Planet initiative is working to develop a wave-driven flood-forecasting early-warning system (EWS) for coral reef-lined coasts known as WaveFoRCE. The system aims to provide all nations and people living on a coral reef-lined coast anywhere in the world with an up to 7.5-day forecast of storm wave-driven flood events.
","largerWorkType":{"id":25,"text":"Newsletter"},"largerWorkTitle":"Environment Coastal & Offshore (ECO)","language":"English","publisher":"United Nations","usgsCitation":"Skirving, W., Storlazzi, C.D., and Smail, E.A., 2021, Wave-driven flood-forecasting on reef-lined coasts early warning system (WaveFoRCE), p. 144-147.","productDescription":"4 p.","startPage":"144","endPage":"147","ipdsId":"IP-127710","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":386985,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":386977,"type":{"id":15,"text":"Index Page"},"url":"https://www.oceandecade.org/news/128/ECO-Magazine--special-digital-issue-on-the-Ocean-Decade-May-2021"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Skirving, William","contributorId":224303,"corporation":false,"usgs":false,"family":"Skirving","given":"William","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":818745,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Storlazzi, Curt D. 0000-0001-8057-4490","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":213610,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","middleInitial":"D.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":818746,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smail, Emily A","contributorId":217219,"corporation":false,"usgs":false,"family":"Smail","given":"Emily","email":"","middleInitial":"A","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":818747,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70220611,"text":"70220611 - 2021 - Pilot-scale expanded assessment of inorganic and organic tapwater exposures and predicted effects in Puerto Rico, USA","interactions":[],"lastModifiedDate":"2021-06-01T17:49:41.562885","indexId":"70220611","displayToPublicDate":"2021-05-18T06:57:25","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1523,"text":"Environment International","active":true,"publicationSubtype":{"id":10}},"title":"Pilot-scale expanded assessment of inorganic and organic tapwater exposures and predicted effects in Puerto Rico, USA","docAbstract":"A pilot-scale expanded target assessment of mixtures of inorganic and organic contaminants in point-of-consumption drinking water (tapwater, TW) was conducted in Puerto Rico (PR) to continue to inform TW exposures and corresponding estimations of cumulative human-health risks across the US. In August 2018, a spatial synoptic pilot assessment of than 524 organic, 37 inorganic, and select microbiological contaminant indicators was conducted in 14 locations (7 home; 7 commercial) across PR. A follow-up 3-day temporal assessment of TW variability was conducted in December 2018 at two of the synoptic locations (1 home, 1 commercial) and included daily pre- and post-flush samples. Concentrations of regulated and unregulated TW contaminants were used to calculate cumulative in vitro bioactivity ratios and Hazard Indices (HI) based on existing human-health benchmarks. Synoptic results confirmed that human exposures to inorganic and organic contaminant mixtures, which are rarely monitored together in drinking water at the point of consumption, occurred across PR and consisted of elevated concentrations of inorganic contaminants (e.g., lead, copper), disinfection byproducts (DBP), and to a lesser extent per/polyfluoroalkyl substances (PFAS) and phthalates. Exceedances of human-health benchmarks in every synoptic TW sample support further investigation of the potential cumulative risk to vulnerable populations in PR and emphasize the importance of continued broad characterization of drinking-water exposures at the tap with analytical capabilities that better represent the complexity of both inorganic and organic contaminant mixtures known to occur in ambient source waters. Such health-based monitoring data are essential to support public engagement in source water sustainability and treatment and to inform consumer point-of-use treatment decision making in PR and throughout the US.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2021.147721","usgsCitation":"Bradley, P., Padilla, I.Y., Romanok, K., Smalling, K., Focazio, M.J., Breitmeyer, S.E., Cardon, M.C., Conley, J.M., Evans, N., Givens, C.E., Gray, J., Gray, L., Hartig, P.C., Hladik, M.L., Higgins, C.P., Iwanowicz, L., Lane, R.F., Loftin, K.A., McCleskey, R., McDonough, C.A., Medlock-Kakaley, E., Meppelink, S.M., Weis, C.P., and Wilson, V.S., 2021, Pilot-scale expanded assessment of inorganic and organic tapwater exposures and predicted effects in Puerto Rico, USA: Environment International, v. 788, 147721, 14 p., https://doi.org/10.1016/j.scitotenv.2021.147721.","productDescription":"147721, 14 p.","ipdsId":"IP-110491","costCenters":[{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":452219,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://europepmc.org/pmc/articles/PMC8504685","text":"Publisher Index Page"},{"id":436359,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EQS5CS","text":"USGS data release","linkHelpText":"Target-Chemical Concentration Results of Mixed-Organic/Inorganic Chemical Exposures in Puerto Rico Tapwater, 2017 to 2018"},{"id":385835,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.3681640625,\n 17.727758609852284\n ],\n [\n -65.5224609375,\n 17.727758609852284\n ],\n [\n -65.5224609375,\n 18.625424540701264\n ],\n [\n -67.3681640625,\n 18.625424540701264\n ],\n [\n -67.3681640625,\n 17.727758609852284\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"788","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bradley, Paul M. 0000-0001-7522-8606","orcid":"https://orcid.org/0000-0001-7522-8606","contributorId":221226,"corporation":false,"usgs":true,"family":"Bradley","given":"Paul M.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816175,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Padilla, Ingrid Y. 0000-0001-8460-1679","orcid":"https://orcid.org/0000-0001-8460-1679","contributorId":258259,"corporation":false,"usgs":false,"family":"Padilla","given":"Ingrid","email":"","middleInitial":"Y.","affiliations":[{"id":52264,"text":"University of Puerto Rico-Mayaguez","active":true,"usgs":false}],"preferred":false,"id":816178,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Romanok, Kristin M. 0000-0002-8472-8765","orcid":"https://orcid.org/0000-0002-8472-8765","contributorId":221227,"corporation":false,"usgs":true,"family":"Romanok","given":"Kristin M.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816176,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smalling, Kelly 0000-0002-1214-4920","orcid":"https://orcid.org/0000-0002-1214-4920","contributorId":221234,"corporation":false,"usgs":true,"family":"Smalling","given":"Kelly","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816177,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Focazio, Michael J. 0000-0003-0967-5576 mfocazio@usgs.gov","orcid":"https://orcid.org/0000-0003-0967-5576","contributorId":1276,"corporation":false,"usgs":true,"family":"Focazio","given":"Michael","email":"mfocazio@usgs.gov","middleInitial":"J.","affiliations":[{"id":5056,"text":"Office of the AD Energy and Minerals, and Environmental Health","active":true,"usgs":true},{"id":38175,"text":"Toxics Substances Hydrology Program","active":true,"usgs":true}],"preferred":true,"id":816179,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Breitmeyer, Sara E. 0000-0003-0609-1559 sbreitmeyer@usgs.gov","orcid":"https://orcid.org/0000-0003-0609-1559","contributorId":172622,"corporation":false,"usgs":true,"family":"Breitmeyer","given":"Sara","email":"sbreitmeyer@usgs.gov","middleInitial":"E.","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":816180,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cardon, Mary C.","contributorId":190792,"corporation":false,"usgs":false,"family":"Cardon","given":"Mary","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":816181,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Conley, Justin M.","contributorId":184086,"corporation":false,"usgs":false,"family":"Conley","given":"Justin","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":816182,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Evans, Nicola","contributorId":184087,"corporation":false,"usgs":false,"family":"Evans","given":"Nicola","email":"","affiliations":[],"preferred":false,"id":816183,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Givens, Carrie E. 0000-0003-2543-9610","orcid":"https://orcid.org/0000-0003-2543-9610","contributorId":247691,"corporation":false,"usgs":true,"family":"Givens","given":"Carrie","middleInitial":"E.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816184,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Gray, James L. 0000-0002-0807-5635","orcid":"https://orcid.org/0000-0002-0807-5635","contributorId":202726,"corporation":false,"usgs":true,"family":"Gray","given":"James L.","affiliations":[{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":816185,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Gray, L. Earl","contributorId":193147,"corporation":false,"usgs":false,"family":"Gray","given":"L. 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Blaine 0000-0002-2521-8052","orcid":"https://orcid.org/0000-0002-2521-8052","contributorId":205663,"corporation":false,"usgs":true,"family":"McCleskey","given":"R. 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The geochronology of this coastal system is based on uranium series, radiocarbon, amino acid racemization (AAR), and optically stimulated luminescence (OSL) methods. We report over 600 mollusk AAR results from 93 sites between northeastern North Carolina and the central New Jersey shelf, representing samples from both onshore cores or outcrops, sub-barrier and offshore cores, and transported shells from barrier island beaches. AAR age estimates are constrained by paired 14C analyses on specific shells and associated U-series coral ages from onshore sites. AAR data from offshore cores are interpreted in the context of detailed seismic stratigraphy. The distribution of Pleistocene-age shells on the island beaches is linked to the distribution of inner shelf or sub-barrier source units. Age mixing over a range of time-scales (~1 ka to ~100 ka) is identified by AAR results from onshore, beach, and shelf collections, often contributing insights into the processes forming individual barrier islands. The regional aminostratigraphic framework identifies a widespread late Pleistocene (Marine Isotope Stage 5) aminozone, with isolated records of middle and early Pleistocene deposition. AAR results provide age estimates for the timing of formation of the three major paleochannels that underlie the Delmarva Peninsula: Persimmon Point paleochannel ≥800 ka; Exmore paleochannel ~400–500 ka (MIS 12); and Eastville paleochannel > 125 ka (MIS 6). The results demonstrate the value of synthesizing abundant AAR chronologic data across various coastal environments, integrating multiple distinct geologic studies. The ages and elevations of the Quaternary units are important for current hypotheses about relative sea-level history and crustal dynamics in the region, which was likely influenced by the Laurentide ice sheet, the margin just ~400 km to the north.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.quageo.2021.101177","usgsCitation":"Wehmiller, J., Brothers, L.L., Ramsey, K., Foster, D.S., Mattheus, C., Hein, C., and Shawler, J.L., 2021, Molluscan aminostratigraphy of the US Mid-Atlantic Quaternary coastal system: Implications for onshore-offshore correlation, paleochannel and barrier island evolution, and local late Quaternary sea-level history: Quaternary Geochronology, v. 66, 101177, 34 p., https://doi.org/10.1016/j.quageo.2021.101177.","productDescription":"101177, 34 p.","ipdsId":"IP-122893","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":452222,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.quageo.2021.101177","text":"Publisher Index Page"},{"id":386336,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, Maryland, Virginia","otherGeospatial":"Delmarva Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.365966796875,\n 36.914764288955936\n ],\n [\n -74.8828125,\n 36.914764288955936\n ],\n [\n -74.8828125,\n 39.791654835253425\n ],\n [\n -76.365966796875,\n 39.791654835253425\n ],\n [\n -76.365966796875,\n 36.914764288955936\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"66","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wehmiller, John","contributorId":260088,"corporation":false,"usgs":false,"family":"Wehmiller","given":"John","affiliations":[{"id":52500,"text":"University of Delaware, Newark DE","active":true,"usgs":false}],"preferred":false,"id":817215,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brothers, Laura L. 0000-0003-2986-5166 lbrothers@usgs.gov","orcid":"https://orcid.org/0000-0003-2986-5166","contributorId":176698,"corporation":false,"usgs":true,"family":"Brothers","given":"Laura","email":"lbrothers@usgs.gov","middleInitial":"L.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":817216,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ramsey, Kelvin","contributorId":260089,"corporation":false,"usgs":false,"family":"Ramsey","given":"Kelvin","email":"","affiliations":[{"id":52502,"text":"Geological Survey, University of Delaware, Newark DE","active":true,"usgs":false}],"preferred":false,"id":817217,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Foster, David S. 0000-0003-1205-0884 dfoster@usgs.gov","orcid":"https://orcid.org/0000-0003-1205-0884","contributorId":1320,"corporation":false,"usgs":true,"family":"Foster","given":"David","email":"dfoster@usgs.gov","middleInitial":"S.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":817218,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mattheus, C.R.","contributorId":260090,"corporation":false,"usgs":false,"family":"Mattheus","given":"C.R.","email":"","affiliations":[{"id":52504,"text":"Illinois Geological Survey, DGS","active":true,"usgs":false}],"preferred":false,"id":817219,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hein, Christopher","contributorId":214093,"corporation":false,"usgs":false,"family":"Hein","given":"Christopher","affiliations":[{"id":18865,"text":"VIMS","active":true,"usgs":false}],"preferred":false,"id":817220,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Shawler, Justin L.","contributorId":256701,"corporation":false,"usgs":false,"family":"Shawler","given":"Justin","email":"","middleInitial":"L.","affiliations":[{"id":6708,"text":"Virginia Institute of Marine Science","active":true,"usgs":false}],"preferred":false,"id":817221,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70230080,"text":"70230080 - 2021 - Quantifying eruptive and background seismicity, deformation, degassing, and thermal emissions at volcanoes in the United States during 1978–2020","interactions":[],"lastModifiedDate":"2022-03-28T11:44:20.391333","indexId":"70230080","displayToPublicDate":"2021-05-18T06:40:42","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2312,"text":"Journal of Geophysical Research","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying eruptive and background seismicity, deformation, degassing, and thermal emissions at volcanoes in the United States during 1978–2020","docAbstract":"An important aspect of volcanic hazard assessment is determination of the level and character of background activity at a volcano so that deviations from background (called unrest) can be identified. Here, we compile the instrumentally recorded eruptive and noneruptive activity for 161 US volcanoes between 1978 and 2020. We combine monitoring data from four techniques: seismicity, ground deformation, degassing, and thermal emissions. To previous work, we add the first comprehensive survey of US volcanoes using medium-spatial resolution satellite thermal observations, newly available field surveys of degassing, and new compilations of seismic and deformation data. We report previously undocumented thermal activity at 30 volcanoes using data from the spaceborne ASTER sensor during 2000–2020. To facilitate comparison of activity levels for all US volcanoes, we assign a numerical classification of the Activity Intensity Level for each monitoring technique, with the highest ranking corresponding to an eruption. There are 96 US volcanoes (59%) with at least one type of detected activity, but this represents a lower bound: For example, there are 12 volcanoes where degassing has been observed but has not yet been quantified. We identify dozens of volcanoes where volcanic activity is only measured by satellite (45% of all thermal observations), and other volcanoes where only ground-based sensors have detected activity (e.g., all seismic and 62% of measured degassing observations). Our compilation provides a baseline against which future measurements can be compared, demonstrates the need for both ground-based and remote observations, and serves as a guide for prioritizing future monitoring efforts.
The St. Clair and Detroit rivers historically supported abundant fish populations. However, like many river systems, these rivers have been greatly altered through the creation of navigation channels and other anthropogenic disturbances, resulting in the loss of fish and wildlife habitat and declines in native fish populations. To ameliorate this environmental degradation, artificial fish spawning reefs were constructed in the St. Clair and Detroit rivers. One native species to potentially benefit from artificial reefs is the Northern Madtom Noturus stigmosus, a small ictalurid that is listed as endangered in the state of Michigan and the province of Ontario. Between 2016 and 2018, artificial reefs and nearby control sites were sampled in the St. Clair and Detroit rivers to compare the number of Northern Madtoms. In total, 171 Northern Madtoms were captured in 1,848 minnow traps with one of four bait types: cheese, dog food, worms, or control (no bait). Baited minnow traps successfully captured Northern Madtoms in the fast-flowing, deep water of the St. Clair–Detroit River system, and catch rates were significantly higher when traps were baited with worms. The number of Northern Madtoms captured was lower in the Detroit River than in the St. Clair River and increased with increasing water temperature and turbidity. Artificial reefs constructed in the St. Clair–Detroit River system are providing habitat for Northern Madtoms; however, use did not differ between reef sites and nearby control sites. This work provides insight regarding sampling strategies to target Northern Madtoms in large-river systems and highlights the importance of incorporating a temporal sampling strategy into survey design.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10614","usgsCitation":"Johnson, J., Chiotti, J., Briggs, A.S., Boase, J., Hessenauer, J., and Roseman, E., 2021, Northern Madtom use of artificial reefs in the St. Clair–Detroit River System: North American Journal of Fisheries Management, v. 41, no. S1, p. S42-S53, https://doi.org/10.1002/nafm.10614.","productDescription":"12 p.","startPage":"S42","endPage":"S53","ipdsId":"IP-119659","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":452226,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/nafm.10614","text":"Publisher Index Page"},{"id":386094,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Michigan, Ontario","otherGeospatial":"Detroit River, St Clair River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.07861328125,\n 42.037054301883806\n ],\n [\n -82.94677734375,\n 42.28950073090457\n ],\n [\n -82.4359130859375,\n 42.27730877423709\n ],\n [\n -82.3590087890625,\n 42.532844281713125\n ],\n [\n -82.4853515625,\n 42.56926437219384\n ],\n [\n -82.353515625,\n 43.01669737169671\n ],\n [\n -82.4359130859375,\n 43.04881979669318\n ],\n [\n -82.5677490234375,\n 42.70665956351041\n ],\n [\n -82.7764892578125,\n 42.73894375124377\n ],\n [\n -82.9742431640625,\n 42.55712670332118\n ],\n [\n -82.9742431640625,\n 42.382894009614034\n ],\n [\n -83.177490234375,\n 42.3016903282445\n ],\n [\n -83.2928466796875,\n 42.08191667830631\n ],\n [\n -83.22143554687499,\n 41.97582726102573\n ],\n [\n -83.07861328125,\n 42.037054301883806\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"41","issue":"S1","noUsgsAuthors":false,"publicationDate":"2021-05-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Johnson, Jennifer","contributorId":258148,"corporation":false,"usgs":false,"family":"Johnson","given":"Jennifer","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":815839,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chiotti, Justin A.","contributorId":26629,"corporation":false,"usgs":false,"family":"Chiotti","given":"Justin A.","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":815840,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Briggs, Andrew S 0000-0002-0268-9310","orcid":"https://orcid.org/0000-0002-0268-9310","contributorId":215596,"corporation":false,"usgs":false,"family":"Briggs","given":"Andrew","email":"","middleInitial":"S","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":815841,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boase, James C.","contributorId":38077,"corporation":false,"usgs":false,"family":"Boase","given":"James C.","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":815842,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hessenauer, Jan-Michael","contributorId":257795,"corporation":false,"usgs":false,"family":"Hessenauer","given":"Jan-Michael","email":"","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":815843,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Roseman, Edward F. 0000-0002-5315-9838","orcid":"https://orcid.org/0000-0002-5315-9838","contributorId":217909,"corporation":false,"usgs":true,"family":"Roseman","given":"Edward F.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":815844,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70221150,"text":"70221150 - 2021 - Aeolian sediments in paleowetland deposits of the Las Vegas Formation","interactions":[],"lastModifiedDate":"2022-01-06T17:13:04.657899","indexId":"70221150","displayToPublicDate":"2021-05-17T08:21:24","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3218,"text":"Quaternary Research","active":true,"publicationSubtype":{"id":10}},"title":"Aeolian sediments in paleowetland deposits of the Las Vegas Formation","docAbstract":"The Las Vegas Formation (LVF) is a well-characterized sequence of groundwater discharge (GWD) deposits exposed in and around the Las Vegas Valley in southern Nevada. Nearly monolithologic bedrock surrounds the valley, which provides an excellent opportunity to test the hypothesis that GWD deposits include an aeolian component. Mineralogical data indicate that the LVF sediments are dominated by carbonate minerals, similar to the local bedrock, but silicate minerals are also present. The median particle size is ~35 μm, consistent with modern dust in the region, and magnetic properties contrast strongly with local bedrock, implying an extralocal origin. By combining geochemical data from the LVF sediments and modern dust, we found that an average of ~25% of the LVF deposits were introduced by aeolian processes. The remainder consists primarily of authigenic groundwater carbonate as well as minor amounts of alluvial material and soil carbonate. Our data also show that the aeolian sediments accumulated in spring ecosystems in the Las Vegas Valley in a manner that was independent of both time and the specific hydrologic environment. These results have broad implications for investigations of GWD deposits located elsewhere in the southwestern U.S. and worldwide.