The frequency of large supraglacial landslides (rock avalanches) occurring in glacial environments is thought to be increasing due to feedbacks with climate warming and permafrost degradation. However, it is difficult to (i) test this; (ii) establish cause–effect relationships; and (iii) determine associated lag-times, due to both temporal and spatial biases in detection rates. Here we applied the Google Earth Engine supraglacial debris input detector (GERALDINE) to Glacier Bay National Park & Preserve (GLBA), Alaska. We find that the number of rock avalanches (RAs) has previously been underestimated by 53 %, with a bias in past detections towards large area RAs. In total, GLBA experienced 69 RAs during 1984–2020, with the highest frequency in the last three years. Of these, 58 % were deposited into the accumulation zone and then sequestered into the ice within two years. RA sources clustered spatially at high elevations and around certain peaks and ridges, predominantly at the boundary of modelled permafrost likelihood. They also clustered temporally, occurring mainly between May and September when air temperatures were high enough to initiate rock-permafrost degradation mechanisms. There was a chronic background debris supply from RAs, with at least one RA occurring in all but nine years; however, a debris rich period during 2012–2016 was driven by three large RAs delivering 44 % of all (1984–2020) debris (by area). Comparable investigation of slope-failures in other remote currently glaciated regions is lacking. If RA rates are similar elsewhere, especially the bias towards emplacement onto/into accumulation zones, their contribution to glacial sediment budgets has been globally underestimated.
The U.S. Army Corps of Engineers, Jacksonville District, is deepening the St. Johns River channel in Jacksonville, Florida, by 7 feet along 13 miles of the river channel beginning at the mouth of the river at the Atlantic Ocean, in order to accommodate larger, fully loaded cargo vessels. The U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, monitored stage, discharge, and (or) water temperature and salinity at 26 continuous data collection stations in the St. Johns River and its tributaries.
This is the sixth annual report by the U.S. Geological Survey on data collection for the Jacksonville Harbor deepening project. Prior reports in this series documented data collected from October 2015 to September 2020. This report contains information pertinent to data collection during the 2021 water year, from October 2020 to September 2021. There were no modifications this year to the previously installed monitoring network. Data at each station were compared for the length of the project and on a yearly basis to show the annual variability of discharge and salinity in the project area.
Discharge and salinity varied widely during the 2021 water year data collection period, which included above-average rainfall for four of the five counties in the study area. Total annual rainfall for all counties ranked third among the annual totals computed for the 6 years considered for this study. Annual mean discharge at Durbin Creek was highest among the tributaries, followed by Trout River, Clapboard Creek, Ortega River, Pottsburg Creek at U.S. 90, Julington Creek, Pottsburg Creek near South Jacksonville, Dunn Creek, Cedar River, and Broward River, whose annual mean discharge was lowest. Annual mean discharge at 7 of the 10 tributary monitoring sites was higher for the 2021 water year than for the 2020 water year, and the computed annual mean flow at Clapboard Creek was the highest over the 6 years considered for this study. The annual mean discharge for each of the main-stem sites was higher for the 2021 water year than for the 2020 water year and ranked second among the annual totals computed for the 6 years considered for this study.
Among the tributary sites, annual mean salinity was highest at Clapboard Creek, the site closest to the Atlantic Ocean, and was lowest at Durbin Creek, the site farthest from the ocean. Annual mean salinity data from the main-stem sites on the St. Johns River indicate that salinity decreased with distance upstream from the ocean, which was expected. Relative to annual mean salinity calculated for the 2020 water year, annual mean salinity at all monitoring locations was lower for the 2021 water year except at the tributary site of Durbin Creek, which remained the same. The 2021 annual mean salinity at all sites ranked second lowest since the beginning of the study in 2016 except at Julington Creek and Racy Point, which tied for lowest, and Durbin Creek, which had the same value for each year.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221111","issn":"ISSN 2331-1258","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Ryan, P.J., 2023, Continuous stream discharge, salinity, and associated data collected in the lower St. Johns River and its tributaries, Florida, 2021: U.S. Geological Survey Open-File Report 2022–1111, 48 p., https://doi.org/10.3133/ofr20221111.","productDescription":"Report: x, 48 p.; Dataset","numberOfPages":"62","onlineOnly":"Y","ipdsId":"IP-139675","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":413532,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221111/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":412288,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1111/ofr20221111.XML","linkFileType":{"id":8,"text":"xml"}},{"id":412285,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1111/coverthb.jpg"},{"id":412286,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1111/ofr20221111.pdf","text":"Report","size":"16.3 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":412287,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1111/images"},{"id":412289,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS water data for the Nation—U.S. Geological Survey National Water Information System database"}],"country":"United States","state":"Florida","otherGeospatial":"St. Johns River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.31115628870195,\n 30.583300030597925\n ],\n [\n -82.31115628870195,\n 29.490035998849976\n ],\n [\n -81.03179238276725,\n 29.490035998849976\n ],\n [\n -81.03179238276725,\n 30.583300030597925\n ],\n [\n -82.31115628870195,\n 30.583300030597925\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
The Riverton Processing site was a uranium mill 4 kilometers southwest of Riverton, Wyoming, that prepared uranium ore for nuclear reactors and weapons from 1958 to 1963. The U.S. Department of Energy completed surface remediation of the uranium tailings in 1989; however, groundwater below and downgradient from the tailings site and nearby Little Wind River was not remediated. Beginning in 2010, a series of floods along the Little Wind River began to mobilize contaminants in the unsaturated zone, resulting in substantial increases of uranium and other contaminants of concern in monitoring wells completed inside the contaminant plume. In 2011, the U.S. Department of Energy started a series of university and Government agency retrospective and field investigations to understand the processes controlling contaminant increases in the groundwater plume. The goals of the field investigations described in this report were to (1) identify and quantify the contaminant flux and potential associated biological effects from groundwater associated with the legacy plume as it enters a perennial stream reach, and (2) assess chemical exposure and potential effects to biological receptors from the interaction of the contaminant plume and the river.
Field investigations along the Little Wind River were completed by the U.S. Geological Survey during 2015–17 in cooperation with the U.S. Department of Energy Office of Legacy Management to characterize: (1) seepage areas and seepage rates; (2) pore-water and bed sediment chemistry and hyporheic exchange and reactive loss; and (3) exposure pathways and biological receptors. All data collected during the study are contained in two U.S. Geological Survey data releases, available at https://doi.org/10.5066/F7BR8QX4 and https://doi.org/10.5066/P9J9VJBR. A variety of tools and methods were used during the field characterizations. Streambed temperature mapping, electrical resistivity tomography, electromagnetic induction, fiber-optic distributed temperature sensing, tube seepage meters, vertical thermal sensor arrays, and an environmental tracer (radon) were used to identify areas of groundwater seepage and associated seepage rates along specific sections of the study reach of the river. Drive points, minipiezometers, diffusive equilibrium in thin-film/diffusive gradients in thin-film probes, bed-sediment samples, and equal discharge increment sampling methods were used to characterize pore-water chemistry, estimate hyporheic exchange and reactive loss of selected chemical constituents, and quantify contaminant loadings entering the study reach. Sampling and analysis of surface sediments, filamentous algae, periphytic algae, and macroinvertebrates were used to characterize biological exposure pathways, metal uptake, and receptors.
Areas of focused groundwater discharge identified by the fiber-optic distributed temperature sensing surveys corresponded closely with areas of elevated electrical conductivity identified by the electromagnetic induction survey results in the top 5 meters of sediment. During three monitoring periods in 2016, the mean vertical seepage rate measured with tube seepage meters was 0.45 meter per day, ranging from −0.02 to 1.55 meters per day. Five of the 11 locations where vertical thermal profile data were collected along the study reach during August 2017 indicated mean upwelling values ranging from 0.11 to 0.23 meter per day. Radon data collected from the Little Wind River during June, July, and August 2016 indicated a consistent inflow of groundwater to the central part of the study reach, in the area congruous with the center of the previously mapped groundwater plume discharge zone. During August 2017, the greatest attenuation of uranium from reactive loss in pore-water samples was observed at three locations along the study reach, at depths between 6 and 15 centimeters, and similar trends in molybdenum attenuation were also observed. Bed-sediment concentration profiles collected during 2017 also indicated attenuation of uranium and molybdenum from groundwater during hyporheic mixing of surface water with the legacy plume during groundwater upwelling into the river. Streamflow measurements combined with equal discharge increment water sampling along the study reach indicated an increase in dissolved uranium concentrations in the downstream direction during 2016 and 2017. Net uranium load entering the Little Wind River study reach was about 290 and 435 grams per day during 2016 and 2017, respectively. Biological samples indicated that low levels of uranium and molybdenum exposure were confined to the benthos in the Little Wind River within and immediately downstream from the perimeter of the groundwater plume. Concentrations of molybdenum and uranium in filamentous algae were consistently low at all sites in the study reach with no indication of increased exposure of dissolved bioavailable molybdenum or uranium at sites next to or downstream from the groundwater plume.
Comparison of the August 2017 results from electromagnetic induction, tube seepage meters, vertical thermal profiling, and pore-water chemistry surveys were in general agreement in identifying areas with upwelling groundwater conditions along the study reach. However, the electroconductivity values measured with electromagnetic induction in the top 100 centimeters of sediment did not agree with sodium concentrations measured in pore-water samples collected at similar streambed depths. Differences and similarities between multiple methods can result in additional insights into hydrologic and biogeochemical processes that may be occurring along a reach of a river system interacting with shallow groundwater inputs. It may be advantageous to apply a variety of geophysical, geochemical, hydrologic, and biological tools at other Uranium Mill Tailings Remedial Action (https://www.energy.gov/sites/prod/files/2014/10/f19/UMTRCA.pdf) sites during the investigation of legacy contaminant plume interactions with surface-water systems.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, Va.","doi":"10.3133/sir20225089","collaboration":"Prepared in cooperation with the U.S. Department of Energy","usgsCitation":"Naftz, D.L., Fuller, C.C., Runkel, R.L., Solder, J., Gardner, W.P., Terry, N., Briggs, M.A., Short, T.M., Cain, D.J., Dam, W.L., Byrne, P.A., and Campbell, J.R., 2023, Interaction of a legacy groundwater contaminant plume with the Little Wind River from 2015 through 2017, Riverton Processing site, Wyoming: U.S. Geological Survey Scientific Investigations Report 2022–5089, 66 p., https://doi.org/10.3133/sir20225089.","productDescription":"Report: xi, 66 p.; 3 Datasets; 2 Data Releases","numberOfPages":"82","onlineOnly":"Y","ipdsId":"IP-123760","costCenters":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"links":[{"id":412328,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7BR8QX4","text":"USGS data release","linkHelpText":"Hydrologic, biogeochemical, and radon data collected within and adjacent to the Little Wind River near Riverton, Wyoming (ver. 1.1, January 2019)"},{"id":412329,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9J9VJBR","text":"USGS data release","linkHelpText":"Geophysical data collected within and adjacent to the Little Wind River near Riverton, Wyoming"},{"id":412324,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5089/coverthb.jpg"},{"id":412325,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5089/sir20225089.pdf","text":"Report","size":"16.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5089"},{"id":412330,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://gems.lm.doe.gov/","text":"U.S. Department of Energy Office of Legacy Management Geospatial Environmental Mapping System database","linkHelpText":"—Riverton, WY, Processing site"},{"id":412331,"rank":8,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":412332,"rank":9,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS database","linkHelpText":"—EarthExplorer"},{"id":500452,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114285.htm","linkFileType":{"id":5,"text":"html"}},{"id":412327,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5089/images"},{"id":412326,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5089/sir20225089.XML"}],"country":"United States","state":"Wyoming","city":"Riverton","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -109,\n 43.5\n ],\n [\n -109,\n 42.5\n ],\n [\n -107.5,\n 42.5\n ],\n [\n -107.5,\n 43.5\n ],\n [\n -109,\n 43.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
We conducted a field study during 2020–21 to describe habitat use patterns of juvenile Chinook salmon (Oncorhynchus tshawytscha) in the mainstem Willamette, McKenzie, and Santiam Rivers and to evaluate how habitat suitability criteria affected the predictive accuracy of a hydraulic habitat model. Two approaches were used to collect habitat use data: a stratified sampling design was used to ensure that a representative sample of available habitats was included in our sampling; and a targeted sampling design was used to collect additional data in habitat cells where juvenile Chinook salmon were observed. Habitat attributes and fish presence data were collected in habitat cells that were approximately 2 square meters during April, June, and July. A total of 632 cells were sampled during the study and included habitat located in the main channel (373 cells), side channels (228 cells), and in alcoves (31 cells). Juvenile Chinook salmon were observed in 42 percent of the cells located in the main channel, 38 percent of the cells located in side channels, and 7 percent of the cells located in alcoves. We used logistic regression to develop resource selection functions for April, June, and July, which produced probability-based predictions of habitat use for juvenile Chinook salmon based on water velocity and water depth. The resource selection functions revealed a habitat shift by juvenile Chinook salmon to locations with higher water velocities and greater water depths from April to July as juvenile Chinook salmon size increased. The resource selection functions that we developed are an important addition to habitat modeling in the Willamette River basin because they were developed from in-basin data, capture seasonal differences in habitat use, and facilitate probability-based estimates of habitat use for juvenile Chinook salmon. These advancements will improve habitat modeling efforts for juvenile Chinook salmon during spring and summer months within the Willamette River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231001","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Hansen, G.S., Perry, R.W., Kock, T.J., White, J.S., Haner, P.V., Plumb, J.M., and Wallick, J.R., 2023, Assessment of habitat use by juvenile Chinook salmon (Oncorhynchus tshawytscha) in the Willamette River Basin, 2020–21: U.S. Geological Survey Open-File Report 2023–1001, 20 p., https://doi.org/10.3133/ofr20231001.","productDescription":"vii, 20 p.","onlineOnly":"Y","ipdsId":"IP-141847","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":412251,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1001/coverthb.jpg"},{"id":412252,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1001/ofr20231001.pdf","text":"Report","size":"5.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1001"},{"id":412254,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1001/images"},{"id":412255,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1001/ofr20231001.XML"}],"country":"United States","state":"Oregon","otherGeospatial":"Willamette River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -124.70681047535611,\n 46.26773381073258\n ],\n [\n -124.70681047535611,\n 42.583539358952294\n ],\n [\n -121.08286121390995,\n 42.583539358952294\n ],\n [\n -121.08286121390995,\n 46.26773381073258\n ],\n [\n -124.70681047535611,\n 46.26773381073258\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
A three-dimensional numerical model of groundwater flow was developed and calibrated for the unconsolidated New Jersey Coastal Plain aquifers underlying Joint Base McGuire-Dix-Lakehurst (JBMDL) and vicinity, New Jersey, to evaluate groundwater flow pathways of per- and polyfluoroalkyl substances (PFAS) contamination associated with use of aqueous film forming foam (AFFF) at the base. The regional subsurface flow model spans an area of approximately 518 square miles around JBMDL and is based on a previously developed hydrogeologic framework of the area. Steady-state flow in the unconsolidated aquifers was simulated using the MODFLOW 6 groundwater flow model, which is able to account for hydrostratigraphic pinchouts and discontinuities in the Coastal Plain aquifers underlying JBMDL. To account for local patterns of fluid flow driving advective subsurface migration of PFAS, the grid was refined using quadtree meshes spanning 21 areas where historical AFFF use was identified, five off-site reconnaissance areas identified by AFCEC as areas in which the occurrence of PFAS is most likely to pose a potential danger to local drinking water supplies, and along streams that behave as drains in the base-flow-dominated Coastal Plain.
Following grid refinement, four physical processes known to govern subsurface flow were introduced to the model. These included effective precipitation recharge, discharge to streams and stream-connected wetlands, regional inflows and outflows along the model bottom, and withdrawals from wells, each of which were incorporated into the model as either external or internal boundary conditions. To account for effective precipitation recharge, a specified-flow boundary was assigned along the top of the model. Similarly, regional flows predicted using the modified U.S Geological Survey’s New Jersey Coastal Plain Regional Aquifer System Analysis model were treated as specified-flow boundary conditions along the bottom of the model. Base-flow losses were treated as drains along streams delineated using a 10-foot LiDAR dataset. Drains were also assigned to cells falling within stream-connected National Hydrologic Database wetlands. Finally, well-pumpage data mined from the New Jersey Water Transfer database were added to the model to account for extraction of groundwater through pumping from industrial-supply and drinking-water-supply wells. Along model edges established at groundwater divides, where the net flux of water across the boundary is equal to zero, natural no-flow boundary conditions were imposed.
The refined flow model was calibrated using the parameter-estimation (PEST) program, which adjusts model parameters by performing a gradient search over the sum-of-squared-error objective function until the parameter set that produces simulated water levels and base flows most closely matches 544 water levels and 20 estimated base flows and closely adheres to initial parameter estimates. Based on the analysis of calibration residuals, the model did not appear to be affected by significant model structural error.
The MODPATH particle-tracking algorithm was used to estimate advective transport paths of PFAS in the vicinity of JBMDL. Forward tracking was used to determine paths of PFAS away from AFFF source areas to streams, wetlands, pumping wells, and geographic areas that PFAS may contaminate. Additionally, reverse tracking was used to determine particle pathlines away from off-site PFAS reconnaissance areas, or areas within which all sources of PFAS might be advectively transported into subsurface drinking-water supplies, to locations at land surface that may indicate a source of PFAS.
The coupled and calibrated groundwater flow and particle-tracking transport model provide valuable tools for predicting the relative extent of PFAS contamination from onsite legacy source areas. The calibrated model also provides measures of water-level and base-flow observation influence that can help guide future data-collection efforts related to groundwater and surface water sampling for PFAS.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221112","collaboration":"Prepared in cooperation with the U.S. Air Force","usgsCitation":"Fiore, A.R., and Colarullo, S.J., 2023, Simulation of regional groundwater flow and advective transport of per- and polyfluoroalkyl substances, Joint Base McGuire-Dix-Lakehurst and vicinity, New Jersey, 2018: U.S. Geological Survey Open-File Report 2022–1112, 41 p., 2 pls., https://doi.org/10.3133/ofr20221112.","productDescription":"Report: ix, 41 p.; 2 Plates: 35.00 x 45.00 inches and 45.00 x 30.00 inches; Data Release","numberOfPages":"41","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-129806","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":412124,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EK4CZS","text":"USGS data release","linkHelpText":"MODFLOW6 and MODPATH7 used to simulate regional groundwater flow and advective transport of per- and polyfluoroalkyl substances, Joint Base McGuire-Dix-Lakehurst and vicinity, New Jersey, 2018"},{"id":412125,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1112/ofr20221112.XML"},{"id":412123,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20221112/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1112"},{"id":412121,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1112/coverthb.jpg"},{"id":412126,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1112/images/"},{"id":412129,"rank":7,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2022/1112/ofr20221112_plate1.pdf","text":"Plate 1","size":"212 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Forward particle tracks from aqueous film-forming foam source areas 1 to 15 and reverse particle tracks from per- and polyfluoroalkyl substances reconnaissance areas 4 and 14, Joint Base McGuire-Dix-Lakehurst and vicinity, New Jersey, 2018"},{"id":412122,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1112/ofr20221112.pdf","text":"Report","size":"7.96 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1112"},{"id":412130,"rank":8,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2022/1112/ofr20221112_plate2.pdf","text":"Plate 2","size":"200 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Forward particle tracks from aqueous film-forming foam source areas 16 to 21 and reverse particle tracks from per- and polyfluoroalkyl substances reconnaissance areas 16 to 19, Joint Base McGuire-Dix-Lakehurst and vicinity, New Jersey, 2018"},{"id":499723,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114286.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New Jersey","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.77016941849112,\n 40.156458843115274\n ],\n [\n -74.77016941849112,\n 39.93505011875061\n ],\n [\n -74.17559168378837,\n 39.93505011875061\n ],\n [\n -74.17559168378837,\n 40.156458843115274\n ],\n [\n -74.77016941849112,\n 40.156458843115274\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville, NJ 08648
The Rangeland Condition Monitoring Assessment and Projection (RCMAP) project quantifies the percentage cover of rangeland components across the western United States using Landsat imagery from 1985 to 2021. The RCMAP product suite consists of nine fractional components: annual herbaceous, bare ground, herbaceous, litter, nonsagebrush shrub, perennial herbaceous, sagebrush, shrub, and tree, in addition to the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. First, we have trained time-series predictions directly from 331 high-resolution sites collected from 2013 to 2018 and additional field data; for example, Bureau of Land Management Assessment, Inventory, and Monitoring instead of using the 2016 “base” map as an intermediary. This removes one level of model error and allows the direct association of high-resolution derived training data to the corresponding year of Landsat imagery. Neural network models have replaced Cubist models as our classifier. Continuous Change Detection and Classification synthetic Landsat images were obtained for six monthly periods for each region and were added as predictors. These data enhance the phenologic detail of imagery, improving discrimination among components. Postprocessing has been improved with updated fire recovery equations stratified by ecosystem resistance and resilience classes. Additionally, postprocessing has been enhanced through a revised noise detection model, based on third order polynomial models for each component and each pixel. These data can be used to answer critical questions regarding the effect of climate change and the suitability of management practices. Component products can be downloaded from the Multi-Resolution Land Characteristics Consortium website at https://www.mrlc.gov/data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, Va.","doi":"10.3133/fs20233004","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Rigge, M.B., 2023, Rangeland Condition Monitoring Assessment and Projection, 1985–2021: U.S. Geological Survey Fact Sheet 2023–3004, 6 p., https://doi.org/10.3133/fs20233004.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"Y","ipdsId":"IP-148071","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":499564,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114284.htm","linkFileType":{"id":5,"text":"html"}},{"id":412363,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/fs20233004/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":412361,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2023/3004/images"},{"id":412360,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2023/3004/fs20233004.XML"},{"id":412356,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2023/3004/coverthb.jpg"},{"id":412359,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2023/3004/fs20233004.pdf","text":"Report","size":"2.14 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2023~3004"}],"country":"United States","otherGeospatial":"western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -102.89909633393279,\n 29.0682826215172\n ],\n [\n -102.29956963626972,\n 29.8090966774663\n ],\n [\n -100.87258967244983,\n 29.458733387985006\n ],\n [\n -100.13050111858448,\n 28.240769760178893\n ],\n [\n -100.11959574261397,\n 30.292475426842415\n ],\n [\n -101.92842251021852,\n 31.470932982781036\n ],\n [\n -103.36510762980765,\n 32.48047296916185\n ],\n [\n -100.01803379637153,\n 36.1490156792113\n ],\n [\n -99.27913819165613,\n 37.572777501564005\n ],\n [\n -103.20469410438153,\n 39.32750900585228\n ],\n [\n -104.0623305315396,\n 39.89802382204118\n ],\n [\n -101.43686566661609,\n 42.267519647308234\n ],\n [\n -100.61853110579801,\n 49.04539216233715\n ],\n [\n -123.36052981830295,\n 48.977345645871026\n ],\n [\n -123.7205891173356,\n 48.25142184919315\n ],\n [\n -124.9072340092169,\n 48.68713021246529\n ],\n [\n -124.91684175179148,\n 42.98584722268464\n ],\n [\n -124.79221124944036,\n 40.3836585578014\n ],\n [\n -122.5469831787114,\n 35.7301435211089\n ],\n [\n -120.64609875158641,\n 34.26400897631564\n ],\n [\n -117.38696400421276,\n 32.43463720185768\n ],\n [\n -114.95825417619707,\n 32.70325877979326\n ],\n [\n -110.57738226870978,\n 31.261610018918134\n ],\n [\n -108.12199560515279,\n 31.139851729510895\n ],\n [\n -108.13958017851652,\n 31.827219447629645\n ],\n [\n -106.284256297648,\n 31.694405166866275\n ],\n [\n -104.3199988686059,\n 29.50364197108891\n ],\n [\n -103.16152984843166,\n 28.840396342272896\n ],\n [\n -102.89909633393279,\n 29.0682826215172\n ],\n [\n -102.89909633393279,\n 29.0682826215172\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
This study developed and evaluated DNA metabarcoding to identify the presence of pinniped and cetacean prey DNA in fecal samples of East Greenland (EG) and Southern Beaufort Sea (SB) polar bears Ursus maritimus sampled in the spring of 2015-2019. Prey DNA was detected in half (49/92) of all samples, and when detected, ringed seal Pusa hispida was the predominant prey species, identified in 100% (22/22) of EG and 81% (22/27) of SB polar bear samples with prey DNA detected. Bearded seal Erignathus barbatus DNA was found in 19% (5/27) of SB polar bear samples for which prey DNA was detected. Prey DNA detection frequencies and relative abundances were compared to estimates from quantitative fatty acid signature analysis (QFASA) for a subset of SB polar bears. Ringed seal and bearded seal were the main prey identified by both methods, but QFASA also identified 2 cetacean prey species not found by prey DNA. Differences in DNA metabarcoding vs. QFASA results were likely related to the different dietary timescales captured by each approach, i.e. short-term vs. long-term diet, respectively. Prey DNA detection, sex/age class, and subpopulation significantly explained variation in polar bear gut bacterial composition. Polar bear samples with prey DNA detected were associated with higher abundances of the bacterial classes Clostridia and Bacilli and lower abundances of Negativicutes. Fecal DNA metabarcoding is thus useful for identifying recent prey of polar bears, complementing quantitative and likely longer-term QFASA estimates, and may help understand variation in the polar bear gut microbiome.
","language":"English","doi":"10.3354/meps14228","usgsCitation":"Franz, M., Whyte, L., Atwood, T.C., Menning, D.M., Sonsthagen, S.A., Talbot, S., Laidre, K.L., Gonzalez, E., and McKinney, M., 2023, Fecal DNA metabarcoding shows credible short-term prey detections and explains variation in the gut microbiome of two polar bear subpopulations: Marine Ecology Progress Series (MEPS), v. 704, p. 131-147, https://doi.org/10.3354/meps14228.","productDescription":"17 p.","startPage":"131","endPage":"147","ipdsId":"IP-140646","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":417914,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, Greenland, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -146.46935331781273,\n 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University","active":true,"usgs":false}],"preferred":false,"id":874824,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Whyte, L","contributorId":306134,"corporation":false,"usgs":false,"family":"Whyte","given":"L","email":"","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":874825,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Atwood, Todd C. 0000-0002-1971-3110 tatwood@usgs.gov","orcid":"https://orcid.org/0000-0002-1971-3110","contributorId":4368,"corporation":false,"usgs":true,"family":"Atwood","given":"Todd","email":"tatwood@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":874826,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Menning, Damian M. 0000-0003-3547-3062 dmenning@usgs.gov","orcid":"https://orcid.org/0000-0003-3547-3062","contributorId":205131,"corporation":false,"usgs":true,"family":"Menning","given":"Damian","email":"dmenning@usgs.gov","middleInitial":"M.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":874827,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sonsthagen, Sarah A. 0000-0001-6215-5874 ssonsthagen@usgs.gov","orcid":"https://orcid.org/0000-0001-6215-5874","contributorId":3711,"corporation":false,"usgs":true,"family":"Sonsthagen","given":"Sarah","email":"ssonsthagen@usgs.gov","middleInitial":"A.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":874828,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Talbot, Sandra","contributorId":291357,"corporation":false,"usgs":false,"family":"Talbot","given":"Sandra","affiliations":[{"id":40349,"text":"USGS Alaska Science Center (former employee)","active":true,"usgs":false}],"preferred":false,"id":874829,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Laidre, Kristin L.","contributorId":191798,"corporation":false,"usgs":false,"family":"Laidre","given":"Kristin","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":874830,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gonzalez, Emmanuel","contributorId":306136,"corporation":false,"usgs":false,"family":"Gonzalez","given":"Emmanuel","email":"","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":874831,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"McKinney, Melissa","contributorId":222146,"corporation":false,"usgs":false,"family":"McKinney","given":"Melissa","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":874832,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70241463,"text":"70241463 - 2023 - Using corrected and imputed polarity measurements to improve focal mechanisms in a regional earthquake catalog near the Mt. Lewis Fault Zone, California","interactions":[],"lastModifiedDate":"2024-06-27T16:56:34.358189","indexId":"70241463","displayToPublicDate":"2023-01-26T07:05:31","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6453,"text":"Journal of Geophysical Research Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Using corrected and imputed polarity measurements to improve focal mechanisms in a regional earthquake catalog near the Mt. Lewis Fault Zone, California","docAbstract":"We utilized relative polarity measurements and machine learning techniques to better resolve focal mechanisms and stress orientations considering a catalog of ∼29,000 relocated earthquakes that occurred during 1984–2021 in the southeastern San Francisco Bay Area. Earthquake focal mechanisms are commonly produced using P wave first motion polarities, which traditionally requires events to be well-recorded across a seismic network with good focal sphere coverage. We adapted recently developed approaches that are less dependent on high signal-to-noise records and exploit similar waveforms to produce relative polarity and amplitude measurements between earthquake pairs. These techniques were previously only applied on localized earthquake sequences, and we further developed these approaches so that they can be utilized for regional catalogs. We validated or corrected manually identified polarities by performing polarity consensuses using earthquake pairs. Missing and unreliable polarity measurements were imputed using iterative random forests, an unsupervised ensemble machine learning method. Relative P and S wave amplitude measurements were made between earthquakes, constraining S/P ratios for low signal-to-noise waveforms. Using these techniques, we were able to reduce focal mechanism uncertainties by an average of ∼13° and produced well-constrained focal mechanisms for ∼6 times as many earthquakes than those produced using only the traditionally derived polarities. We performed stress inversions using the focal mechanisms by grouping the focal mechanism results into a quadtree structure. Our stress results are consistent with previous work, albeit at a higher spatial resolution, and demonstrate these techniques can aid our understanding of fault structures and kinematics in more detail than was previously possible.
The U.S. Geological Survey (USGS) National Civil Applications Center (NCAC) analyzes remote-sensing data from the Intelligence Community (IC) and the U.S. Department of Defense (DOD) to support public safety missions and to study land-surface and environmental changes. The NCAC provides remotely sensed images to USGS scientists and other civilian Federal agencies; the images come from intelligence and military sensors, referred to as U.S. National Imagery Systems (USNIS), and unclassified commercial satellite data purchased by the DOD. Often these data are referred to as Geospatial Intelligence (GEOINT), which is defined in U.S. Code, title 10, section 467 as “the exploitation and analysis of imagery and geospatial information to describe, assess, and visually depict physical features and geographically referenced activities on or about the earth. Geospatial intelligence consists of imagery, imagery intelligence, and geospatial information.” The NCAC also provides the secretariat with staff and manages the U.S. interagency Civil Applications Committee (CAC), which oversees and facilitates the appropriate civilian uses of overhead remote-sensing technology and data collected by military and intelligence systems and commercial sources. Funded by the USGS National Land Imaging Program, the NCAC operates facilities in Reston, Virginia, and Lakewood, Colorado.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223085","programNote":"National Land Imaging Program","usgsCitation":"Young, P.M., 2023, National Civil Applications Center (ver. 1.1, March 2023): U.S. Geological Survey Fact Sheet 2022–3085, 4 p., https://doi.org/10.3133/fs20223085.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-129954","costCenters":[{"id":36171,"text":"National Civil Applications Center","active":true,"usgs":true}],"links":[{"id":412228,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3085/fs20223085.pdf","text":"Report","size":"1.92 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022-3085"},{"id":412227,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3085/coverthb3.jpg"},{"id":414012,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2022/3085/versionHist.txt","size":"653 B"}],"edition":"Version 1.0: January 2023; Version 1.1: March 2023","contact":"Director, National Civil Applications Center
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 562
Reston, VA 20192
Email: cac@usgs.gov
In 2021, the U.S. Geological Survey will collect water-quality samples and environmental data from 3 sites in Koocanusa Reservoir and from 1 site in the Kootenai River. The transboundary Koocanusa Reservoir is in southeastern British Columbia, Canada, and northwestern Montana, United States, and was formed with the construction of Libby Dam on the Kootenai River 26 kilometers upstream from Libby, Montana. Two of the reservoir sites and the Kootenai River site, in the Libby Dam tailwater (the outflow of the reservoir flow into the Kootenai River), are equipped with automated, high-frequency ServoSipper water samplers. At the two reservoir sites, these samplers are mounted to pontoon platforms and automatically collect samples from multiple depths; a ServoSipper sampler was deployed at one site in 2019, and another ServoSipper sampler will be deployed at a second site in 2021. Discrete water-quality samples will be collected monthly at two depths at the river site and at two of the reservoir sites. The goal of this project is to collect multidepth, high-frequency vertical and temporal water-quality samples and data to understand the limnological and biological processes that control variations and trends in selenium concentrations and loads throughout Koocanusa Reservoir and in the Libby Dam tailwater at the southern end of the reservoir. This sampling and analysis plan documents the organization, sampling and data-collection scheme and design, pre- and post-collection processes, and quality-assurance and quality-control procedures.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221113","usgsCitation":"Caldwell Eldridge, S.L., Schaar, M.A., Reese, C.B., Bussell, A.M., and Chapin, T., 2023, Sampling and analysis plan for the Koocanusa Reservoir and upper Kootenai River, Montana, water-quality monitoring program, 2021: U.S. Geological Survey Open-File Report 2022–1113, 32 p., https://doi.org/10.3133/ofr20221113.","productDescription":"ix, 32 p.","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-137190","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"links":[{"id":412312,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1113/ofr20221113.XML"},{"id":412310,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1113/coverthb.jpg"},{"id":412311,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1113/ofr20221113.pdf","text":"Report","size":"1.39 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022–1113"},{"id":412313,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1113/images"},{"id":412323,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221113/full","text":"Report","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Montana","otherGeospatial":"Koocanusa Reservoir, Upper Kootenai River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.10472590374278,\n 49.02558777092872\n ],\n [\n -116.10472590374278,\n 47.62376452411149\n ],\n [\n -113.60090641401644,\n 47.62376452411149\n ],\n [\n -113.60090641401644,\n 49.02558777092872\n ],\n [\n -116.10472590374278,\n 49.02558777092872\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
Keys to Clay-colored Sparrow (Spizella pallida) management include providing grasslands with a shrub or forb component or shrub-dominated edge habitat, which includes dense grass and moderately high litter cover, and avoiding disturbances that completely eliminate woody vegetation. Clay-colored Sparrows have been reported to use habitats with 20–186 centimeters (cm) average vegetation height, 3–50 cm visual obstruction reading, 15–74 percent grass cover, 5–23 percent forb cover, less than 30 percent shrub cover, 1–20 percent bare ground, 10–63 percent litter cover, and less than or equal to 5 cm litter depth.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, Va.","doi":"10.3133/pp1842Z","usgsCitation":"Shaffer, J.A., Igl, L.D., Johnson, D.H., Sondreal, M.L., Goldade, C.M., Nenneman, M.P., and Euliss, B.R., 2023, The effects of management practices on grassland birds—Clay-colored Sparrow (Spizella pallida), chap. Z of Johnson, D.H., Igl, L.D., Shaffer, J.A., and DeLong, J.P., eds., The effects of management practices on grassland birds: U.S. Geological Survey Professional Paper 1842, 27 p., https://doi.org/10.3133/pp1842Z.","productDescription":"v, 27 p.","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-097128","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":412290,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1842/z/coverthb.jpg"},{"id":412291,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1842/z/pp1842z.pdf","text":"Report","size":"2.12 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1842Z"}],"contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
Acoustic communication in the form of songs is a learned behavior in oscine that can be passed down from one generation to the next through cultural transmission. Over time songs can change when populations become isolated from one another, creating dialects that are distinct to a population. Habitat fragmentation is an isolating mechanism that can influence differences in songs between populations when there is little to no connectivity between fragments and fragment size can influence diversity of song traits. We characterized and analyzed songs of the ‘ōma‘o (Myadestes obscurus) in a naturally fragmented forest to determine how landscape variables influenced song differences between populations. We chose five fragments of different sizes and isolation to record songs of the ‘ōma‘o. We performed a correlation test to evaluate whether there was a relationship between fragment size and total syllables, and between unique syllables and degree of isolation. We also conducted a Mantel test to determine if geographic distance had an influence on song similarity. Our results indicated that songs from larger fragments tended to have higher syllable diversity, but neither connectivity nor distance was related to the number of unique or shared syllables found within a fragment, respectively. Overall, the results indicated that ‘ōma‘o songs are highly variable at the individual level and that there may be little to no syllable sharing within and among populations.
","language":"English","publisher":"University of Hawaii Press","doi":"10.2984/76.3.6","usgsCitation":"Fernandez, N., Paxton, K.L., Paxton, E.H., Pack, A.A., and Hart, P.J., 2023, Landscape configuration influences Oma‘o (Myadestes obscurus) song diversity: Pacific Science, v. 76, p. 325-335, https://doi.org/10.2984/76.3.6.","productDescription":"11 p.","startPage":"325","endPage":"335","ipdsId":"IP-132856","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":420014,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Island of Hawai'i, Mauna Loa, Waiakea Forest Reserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -155.40,\n 19.70\n ],\n [\n -155.40,\n 19.60\n ],\n [\n -155.30,\n 19.60\n ],\n [\n -155.30,\n 19.70\n ],\n [\n -155.40,\n 19.70\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"76","edition":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fernandez, Nicole","contributorId":328627,"corporation":false,"usgs":false,"family":"Fernandez","given":"Nicole","email":"","affiliations":[{"id":37485,"text":"University of Hawai‘i - Hilo","active":true,"usgs":false}],"preferred":false,"id":880811,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paxton, Kristina L. 0000-0003-2321-5090","orcid":"https://orcid.org/0000-0003-2321-5090","contributorId":41917,"corporation":false,"usgs":false,"family":"Paxton","given":"Kristina","email":"","middleInitial":"L.","affiliations":[{"id":6977,"text":"University of Hawai`i at Hilo","active":true,"usgs":false},{"id":12981,"text":"Department of Biological Sciences, University of Southern Mississippi","active":true,"usgs":false}],"preferred":false,"id":880812,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Paxton, Eben H. 0000-0001-5578-7689","orcid":"https://orcid.org/0000-0001-5578-7689","contributorId":19640,"corporation":false,"usgs":true,"family":"Paxton","given":"Eben","email":"","middleInitial":"H.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":880813,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pack, Adam A.","contributorId":204176,"corporation":false,"usgs":false,"family":"Pack","given":"Adam","email":"","middleInitial":"A.","affiliations":[{"id":36870,"text":"University of Hawai‘i Hilo","active":true,"usgs":false}],"preferred":false,"id":880814,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hart, Patrick J.","contributorId":147728,"corporation":false,"usgs":false,"family":"Hart","given":"Patrick","email":"","middleInitial":"J.","affiliations":[{"id":6977,"text":"University of Hawai`i at Hilo","active":true,"usgs":false}],"preferred":false,"id":880815,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70240951,"text":"70240951 - 2023 - Genetic diversity, structure, and effective population size of an endangered, endemic hoary bat, ʻōpeʻapeʻa, across the Hawaiian Islands","interactions":[],"lastModifiedDate":"2023-03-02T13:16:02.493245","indexId":"70240951","displayToPublicDate":"2023-01-25T07:13:20","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":13441,"text":"Peer J","active":true,"publicationSubtype":{"id":10}},"title":"Genetic diversity, structure, and effective population size of an endangered, endemic hoary bat, ʻōpeʻapeʻa, across the Hawaiian Islands","docAbstract":"Island bat species are disproportionately at risk of extinction, and Hawaiʻi’s only native terrestrial land mammal, the Hawaiian hoary bat (Lasiurus semotus) locally known as ʻōpeʻapeʻa, is no exception. To effectively manage this bat species with an archipelago-wide distribution, it is important to determine the population size on each island and connectivity between islands. We used 18 nuclear microsatellite loci and one mitochondrial gene from 339 individuals collected from 1988–2020 to evaluate genetic diversity, population structure and estimate effective population size on the Islands of Hawaiʻi, Maui, Oʻahu, and Kauaʻi. Genetic differentiation occurred between Hawaiʻi and Maui, both of which were differentiated from Oʻahu and Kauaʻi. The population on Maui presents the greatest per-island genetic diversity, consistent with their hypothesized status as the original founding population. A signature of isolation by distance was detected between islands, with contemporary migration analyses indicating limited gene flow in recent generations, and male-biased sex dispersal within Maui. Historical and long-term estimates of genetic effective population sizes were generally larger than contemporary estimates, although estimates of contemporary genetic effective population size lacked upper bounds in confidence intervals for Hawaiʻi and Kauaʻi. Contemporary genetic effective population sizes were smaller on Oʻahu and Maui. We also detected evidence of past bottlenecks on all islands with the exception of Hawaiʻi. Our study provides population-level estimates for the genetic diversity and geographic structure of ‘ōpeʻapeʻa, that could be used by agencies tasked with wildlife conservation in Hawaiʻi.
Probabilistic predictions support public health planning and decision making, especially in infectious disease emergencies. Aggregating outputs from multiple models yields more robust predictions of outcomes and associated uncertainty. While the selection of an aggregation method can be guided by retrospective performance evaluations, this is not always possible. For example, if predictions are conditional on assumptions about how the future will unfold (e.g. possible interventions), these assumptions may never materialize, precluding any direct comparison between predictions and observations. Here, we summarize literature on aggregating probabilistic predictions, illustrate various methods for infectious disease predictions via simulation, and present a strategy for choosing an aggregation method when empirical validation cannot be used. We focus on the linear opinion pool (LOP) and Vincent average, common methods that make different assumptions about between-prediction uncertainty. We contend that assumptions of the aggregation method should align with a hypothesis about how uncertainty is expressed within and between predictions from different sources. The LOP assumes that between-prediction uncertainty is meaningful and should be retained, while the Vincent average assumes that between-prediction uncertainty is akin to sampling error and should not be preserved. We provide an R package for implementation. Given the rising importance of multi-model infectious disease hubs, our work provides useful guidance on aggregation and a deeper understanding of the benefits and risks of different approaches.
The use of moving boat ADCPs (Acoustic Doppler Current Profilers) for discharge measurements requires identification of the sources and magnitude of uncertainty to ensure accurate measurements. Recently, a tool known as QUant was developed to estimate the contribution to the uncertainty estimates for each transect of moving-boat ADCP discharge measurements, by varying different sampling configurations parameters through the use of Monte Carlo simulations. QUant is not only useful for estimating ADCP discharge measurement uncertainty, but also for identifying contributions of the various sources of uncertainty.
However, the software requires long computational times, and the method to estimate the uncertainty of multiple-transect measurements does not consider the correlation of the variables between transects. Therefore, improvements in QUant are needed to optimize its application for practical purposes by hydrographers immediately after discharge measurements.
This work presents four approaches for optimizing the performance of QUant to estimate the contribution to the uncertainty of different selected variables on ADCP discharge measurements and describes a new method of estimating multi-transect uncertainty with the QUant model that considers the correlation of errors in selected variables between transects. The approaches for optimization and the new multi-transect uncertainty method are evaluated using a dataset of 38 field measurements from a variety of riverine settings.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.flowmeasinst.2023.102311","usgsCitation":"Diaz Lozada, J.M., Garcia, C.M., Oberg, K., Over, T.M., and Flores Nieto, F., 2023, Improvements to estimate ADCP uncertainty sources for discharge measurements: Flow Measurement and Instrumentation, v. 90, 102311, 12 p., https://doi.org/10.1016/j.flowmeasinst.2023.102311.","productDescription":"102311, 12 p.","ipdsId":"IP-122869","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":419175,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"90","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Diaz Lozada, Jose M. 0000-0002-6735-0916","orcid":"https://orcid.org/0000-0002-6735-0916","contributorId":287571,"corporation":false,"usgs":false,"family":"Diaz Lozada","given":"Jose","email":"","middleInitial":"M.","affiliations":[{"id":61615,"text":"Institute for Advanced Studies for Engineering and Technology (IDIT CONICET/UNC) – FCEFyN, National University of Córdoba","active":true,"usgs":false}],"preferred":false,"id":878354,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Garcia, Carlos M. 0000-0002-4091-6756","orcid":"https://orcid.org/0000-0002-4091-6756","contributorId":287572,"corporation":false,"usgs":false,"family":"Garcia","given":"Carlos","email":"","middleInitial":"M.","affiliations":[{"id":61615,"text":"Institute for Advanced Studies for Engineering and Technology (IDIT CONICET/UNC) – FCEFyN, National University of Córdoba","active":true,"usgs":false}],"preferred":false,"id":878355,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Oberg, Kevin 0000-0002-7024-3361 kaoberg@usgs.gov","orcid":"https://orcid.org/0000-0002-7024-3361","contributorId":175229,"corporation":false,"usgs":true,"family":"Oberg","given":"Kevin","email":"kaoberg@usgs.gov","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":true,"id":878356,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Over, Thomas M. 0000-0001-8280-4368","orcid":"https://orcid.org/0000-0001-8280-4368","contributorId":204650,"corporation":false,"usgs":true,"family":"Over","given":"Thomas","email":"","middleInitial":"M.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"preferred":true,"id":878357,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Flores Nieto, Federico","contributorId":316794,"corporation":false,"usgs":false,"family":"Flores Nieto","given":"Federico","email":"","affiliations":[{"id":68697,"text":"Universidad Nacional de Córdoba, Argentina","active":true,"usgs":false}],"preferred":false,"id":878358,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70239820,"text":"sir20225121 - 2023 - Survey of fish communities in tributaries to the Mohawk River, New York, 2019","interactions":[],"lastModifiedDate":"2026-02-23T20:44:15.554802","indexId":"sir20225121","displayToPublicDate":"2023-01-24T09:40:00","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5121","displayTitle":"Survey of Fish Communities in Tributaries to the Mohawk River, New York, 2019","title":"Survey of fish communities in tributaries to the Mohawk River, New York, 2019","docAbstract":"Fish communities of the Mohawk River and associated sections of the New York State Canal System have been well documented but little information is available regarding the status of fish communities in the extensive network of tributaries that feed the Mohawk River. This lack of information is problematic because changes in species distributions or general ecosystem health may go unnoticed in the absence of baseline data. The need for baseline information has been made particularly urgent by the recent establishment of a high-profile invasive fish species in the mainstem of the Mohawk River, the round goby (Neogobius melanostomus). Round goby can adversely affect aquatic ecosystems in numerous ways and are able to colonize streams in addition to large rivers and lakes. This potential threat to the aquatic ecosystem, therefore, has created an urgent need to quantify the distribution and abundance of fish species inhabiting tributaries to the Mohawk River before round goby can begin colonizing these habitats. In response, the U.S. Geological Survey and the Mohawk River Basin Program of the New York State Department of Environmental Conservation initiated a study in 2019 to collect quantitative information on fish communities and stream habitats in tributaries to the Mohawk River that could be used in the future to determine the effects of round goby on local fish assemblages and identify substrate and other habitat characteristics that facilitate or inhibit colonization by round goby.
Fish communities were surveyed at 20 sites on tributaries to the Mohawk River during summer 2019, using three-pass depletion backpack electrofishing surveys. The resulting data were used to produce quantitative estimates of fish population density and biomass for all species at each site. A total of 11,794 individual fish and 37 species were captured during the 20 surveys. Longnose dace (Rhinichthys cataractae), white sucker (Catostomus commersonii), blacknose dace (Rhinichthys atratulus), fantail darter (Etheostoma flabellare), and creek chub (Semotilus atromaculatus) were the most frequently encountered species, occurring at 18, 18, 17, 17, and 16 of the 20 sites, respectively. Six darter species, small bottom-dwelling fish that are highly vulnerable to displacement by round goby, were captured during the surveys, and at least one darter species was captured at all but one of the sites. Round goby were only captured at one site, Ninemile Creek near Rome, New York, where they occurred at a low density. Overall, the results indicated that round goby had not extensively colonized tributaries to the Mohawk River as of 2019, and the suite of data collected in this project should serve as a valuable baseline for future assessments of the effects of round goby and other stressors on aquatic ecosystems.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225121","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"George, S.D., Winterhalter, D.R., and Baldigo, B.P., 2023, Survey of fish communities in tributaries to the Mohawk River, New York, 2019: U.S. Geological Survey Scientific Investigations Report 2022–5121, 37 p., https://doi.org/10.3133/sir20225121.","productDescription":"Report: vii, 37 p.; Data Release","numberOfPages":"37","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-135887","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":412151,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225121/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5121"},{"id":412152,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5121/sir20225121.XML"},{"id":412153,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5121/images/"},{"id":412131,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5121/coverthb.jpg"},{"id":412132,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5121/sir20225121.pdf","text":"Report","size":"5.87 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5121"},{"id":412133,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZRRG3T","text":"USGS data release","linkHelpText":"Fish community and substrate data from tributaries to the Mohawk River"},{"id":500464,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114280.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New York","otherGeospatial":"Mohawk River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.48132611302138,\n 43.63870971245336\n ],\n [\n -75.61112592880968,\n 43.63870971245336\n ],\n [\n -75.61112592880968,\n 42.00327529599426\n ],\n [\n -73.48132611302138,\n 42.00327529599426\n ],\n [\n -73.48132611302138,\n 43.63870971245336\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Flood frequency characteristics and estimated flood discharges for the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities were computed at 299 streamgaged locations in Wisconsin. The State was divided into four flood frequency regions using a cluster analysis to produce regions which are homogeneous with respect to physical basin characteristics. Regression equations relating flood discharges to basin characteristics within each region were developed and can be used to estimate flood discharges at ungaged locations in Wisconsin. Basin characteristics included in the final regression equations include drainage area, saturated hydraulic conductivity, percent forest, percent herbaceous upland, percent open water, and the maximum 24-hour precipitation with a 10-year recurrence interval. The standard error of prediction for regression equations ranges between 40 and 71 percent, and the pseudo coefficient of determination ranges between 0.8 and 0.95. Nonmonotonic trends in the annual peak flow time series in the southwest part of the State are producing bias in some flood discharge estimates at streamgages with shorter (less than 20 years) periods of record. This bias increases the uncertainty in regression equations in this flood frequency region.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, Va.","doi":"10.3133/sir20225118","collaboration":"Prepared in cooperation with Wisconsin Department of Transportation","usgsCitation":"Levin, S.B., and Sanocki, C.A., 2023, Estimating flood magnitude and frequency for unregulated streams in Wisconsin: U.S. Geological Survey Scientific Investigations Report 2022–5118, 25 p., https://doi.org/10.3133/sir20225118. 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\"}}]}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
Lake Powell is a large water storage reservoir in the arid southwestern United States. Here, we present a 58-yr limnology dataset that captures water quality parameters from reservoir filling to present day (temperature, salinity, major ions, total suspended solids), as well as a 38-yr record of Secchi depth, and a ~ 30-yr record of nutrients, phytoplankton, and zooplankton assemblages. The dataset includes 5208 unique site visits spanning 258 unique sites of which 9 have been consistently visited. It also spans the establishment of an invasive bivalve (Dreissena bugensis, i.e. Quagga mussel) which was first detected in 2012. Given the general lack of long-term data from lakes or reservoirs in arid regions, this dataset represents a unique contribution to regional, continental, and global-scale limnology studies. As the hot drought in the desert southwest continues, we expect this dataset will inform water management decision-making for this largest reservoir in the Upper Colorado River Basin.
A suite of complementary techniques was used to examine germanium (Ge), a byproduct critical element, and co-substituent trace elements in ZnS and mine wastes from four mineral districts where germanium is, or has been, produced within the United States. This contribution establishes a comprehensive workflow for characterizing Ge and other trace elements, which captures the full heterogeneity of samples through extensive pre-characterization. This process proceeded from optical microscopy, to scanning electron microscopy and cathodoluminescence (CL) imaging, to electron microprobe analysis, prior to synchrotron-based investigations. Utilizing non-destructive techniques enabled reanalysis, which proved essential for verifying observations and validating unexpected results. In cases where the Fe content was <0.3 wt% in ZnS, cathodoluminescence imaging proved to be an efficient means to qualitatively identify trace element zonation that could then be further explored by other micro-focused techniques. Micro-focused X-ray diffraction was used to map the distribution of the non-cubic ZnS polymorph, whereas micro-focused X-ray fluorescence (μ-XRF) phase mapping distinguished between Ge4+ hosted in primary ZnS and a weathering product, hemimorphite [Zn4Si2O7(OH)2·H2O]. Microprobe data and μ-XRF maps identified spatial relationships among trace elements in ZnS and implied substitutional mechanisms, which were further explored using Ge and copper (Cu) X-ray absorption near-edge spectroscopy (XANES). Both oxidation states of Ge (4+ and 2+) were identified in ZnS along with, almost exclusively, monovalent Cu. However, the relative abundance of Ge oxidation states varied among mineral districts and, sometimes, within samples. Further, bulk XANES measurements typically agreed with micro-focused XANES (μ-XANES) spectra, but unique micro-environments were detected, highlighting the importance of complementary bulk and micro-focused measurements. Some Ge μ-XANES utilized a high energy resolution fluorescence detector, which improved spectral resolution and spectral signal-to-noise ratio. This detector opens new opportunities for exploring byproduct critical elements in complex matrices. Overall, the non-destructive workflow employed here can be extended to other byproduct critical elements to more fully understand fundamental ore enrichment processes, which have practical implications for critical element exploration, resource quantification, and extraction.
Deterministic watershed models (DWMs) are used in nearly all hydrologic planning, design, and management activities, yet they cannot generate streamflow ensembles needed for hydrologic risk management (HRM). The stochastic component of DWMs is often ignored in practice, leading to a systematic bias in extreme events. Since traditional stochastic streamflow models used in HRM struggle to account for anthropogenic change, there is a need to convert DWMs into stochastic watershed models (SWMs) to generate ensembles for use in HRM. A DWM can be converted to an SWM using a post-processing (pp) approach to add error to the DWM predictions. Many pp methods advanced in the area of flood forecasting are useful in HRM and for correcting extreme event biases. Selecting a suitable error model for pp is challenging due to nonnormality, skewness, heteroscedasticity, and autocorrelation. We develop a parsimonious pp method based on an autoregressive (AR) model of the logarithm of the ratio of the observations and simulations, which leads to AR model residuals that are approximately symmetric and independent. We document the value of pp for improving flood and low flow frequency analysis and we reintroduce the concepts of verification and validation of stochastic streamflow ensembles to ensure that the SWM can reproduce both statistics it was and was not designed to reproduce, respectively. These concepts are illustrated on a Massachusetts basin using the USGS Precipitation Runoff Modeling System, with an additional analysis indicating the approach may be applicable to 1,225 other sites across the United States.
Wastewater injection has induced earthquakes in Northeastern Colorado since 2014. We apply ambient noise correlation techniques to determine temporal changes in seismic velocities in the region. We find no clear correlation between seismic velocity fluctuations and either injection volumes or seismicity patterns. We do observe apparent annual variations in velocity that may be associated with hydrologic loading or thermoelastic strain. In addition, we model uniform and vertically localized velocity perturbations, and measure the velocity change with 1D synthetic seismograms. Our results indicate that our methods underestimate the known velocity change, especially at shorter station distances and when variations are restricted to a horizontal layer. If injection does cause measurable velocity changes, its effect is likely diluted in cross correlations due to its localized spatial extent around injection wells.
The U.S. Geological Survey, in cooperation with the U.S. Air Force Civil Engineer Center, collected borehole geophysical data and completed simple aquifer tests to estimate the thickness and hydraulic properties of surficial deposits. The purpose of data collection was to create generalized contour maps of Pierre Shale elevation and surficial deposit thickness within and near Ellsworth Air Force Base (study area). Natural gamma and electromagnetic induction data were collected to refine or determine surficial deposit thickness at selected wells. Additionally, data from previous geophysical studies and driller logs were compiled and combined with results from natural gamma and electromagnetic induction data to provide a more spatially complete image of the subsurface. Borehole nuclear magnetic resonance (bNMR) data were collected to estimate hydraulic conductivity and water content of surficial deposits overlying Pierre Shale. Simple aquifer tests using water slugs (slug tests) were completed to estimate hydraulic conductivity of surficial deposits, and results were compared to hydraulic conductivity estimates from bNMR data. All data used to construct maps and estimate hydraulic properties are provided in an accompanying U.S. Geological Survey data release (available at https://doi.org/10.5066/P9FLR79F).
Generalized contour maps were constructed using results from 26 borehole geophysical logs, 35 geophysical transects from previous studies, and 304 wells with driller logs. Pierre Shale elevation generally followed land-surface topography, sloping from high elevations in the north to lower elevations in the south. Topographic highs of Pierre Shale, where present, could act as groundwater divides that potentially affect groundwater flow direction. Surficial deposit thickness varied spatially and ranged from 0 to 86 feet. Surficial deposits generally were thickest in higher elevation areas near ephemeral streams in the northern part of the study area. Hydraulic conductivity estimated from bNMR results using two analytical methods ranged from 0.1 to 2,314 feet per day, whereas hydraulic conductivity estimated from slug tests ranged from 0.001 to 193 feet per day. Hydraulic conductivity estimates from slug tests were plotted with surficial deposit thickness contours instead of bNMR estimates because bNMR estimates were determined to overestimate hydraulic conductivity. Hydraulic conductivity values generally were greater in the southwestern part of study area than the northeastern part.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3502","collaboration":"Prepared in cooperation with the U.S. Air Force Civil Engineer Center","usgsCitation":"Medler, C.J., Eldridge, W.G., Anderson, T.M., and Phillips, S.N., 2023, Maps of elevation of top of Pierre Shale and surficial deposit thickness with hydraulic properties from borehole geophysics and aquifers tests within and near Ellsworth Air Force Base, South Dakota, 2020–21: U.S. Geological Survey Scientific Investigations Map 3502, 25-p. pamphlet, 2 sheets, https://doi.org/10.3133/sim3502.","productDescription":"Report: vii, 25 p.; 2 Sheets: 36.00 x 36.00 inches; 2 Data Releases; Dataset","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-138395","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":500208,"rank":11,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114281.htm","linkFileType":{"id":5,"text":"html"}},{"id":412159,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sim/3502/images"},{"id":412158,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sim/3502/sim3502.XML"},{"id":412155,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3502/sim3502.pdf","text":"Pamphlet","size":"2.12 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3502"},{"id":412250,"rank":10,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sim3502/full","text":"Pamphlet","linkFileType":{"id":5,"text":"html"}},{"id":412161,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XSJH17","text":"USGS data release","linkHelpText":"Electrical resistivity tomography (ERT) and horizontal-to-vertical spectral ratio (HVSR) data collected within and near Ellsworth Air Force Base, South Dakota, from 2014 to 2019"},{"id":412157,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3502/sim3502_sheet02.pdf","text":"Sheet 2","size":"7.83 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3502, sheet 2","linkHelpText":"—Map showing contours of depth to Pierre Shale, hydraulic conductivity, and groundwater velocity of surficial deposits"},{"id":412156,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3502/sim3502_sheet01.pdf","text":"Sheet 1","size":"10.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3502, sheet 1","linkHelpText":"—Map showing elevation contours of the top of Pierre Shale from well logs and electrical resistivity tomography data"},{"id":412162,"rank":9,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":412154,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3502/coverthb.jpg"},{"id":412160,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FLR79F","text":"USGS data release","linkHelpText":"Datasets used to create maps of Pierre Shale elevation and surficial deposit thickness within and near Ellsworth Air Force Base, South Dakota, 2021"}],"country":"United States","state":"South Dakota","otherGeospatial":"Ellsworth Air Force Base","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -103.17044722999105,\n 44.19638858668296\n ],\n [\n -103.17044722999105,\n 44.0880305228894\n ],\n [\n -103.00984038772282,\n 44.0880305228894\n ],\n [\n -103.00984038772282,\n 44.19638858668296\n ],\n [\n -103.17044722999105,\n 44.19638858668296\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702
Knowledge of the breeding ecology of Endangered Nordmann’s Greenshank Tringa guttifer is necessary to develop a comprehensive species-specific conservation plan. We found nine greenshank nests in Schaste Bay, Russian Far East during the summers of 2019–2021. These are the first nests found in over 40 years and the only discovered to date on mainland Russia. In contrast to previous nest descriptions, we found greenshanks do not exclusively nest in trees, but also place nests on the ground at the base of mature or sapling larches. Our results indicate greenshanks may be larch obligates during the breeding season, and protecting coastal larch forest ecosystems near bogs, meadows, and mudflats throughout the Russian Far East may be critical to the species’ conservation.