Salt marsh ponds expand and deepen over time, potentially reducing ecosystem carbon storage and resilience. The water filled volumes of ponds represent missing carbon due to prevented soil accumulation and removal by erosion and decomposition. Removal mechanisms have different implications as eroded carbon can be redistributed while decomposition results in loss. We constrained ponding effects on carbon dynamics in a New England marsh and determined whether expansion and deepening impact nearby soils by conducting geochemical characterizations of cores from three ponds and surrounding high marshes and models of wind-driven erosion. Radioisotope profiles demonstrate that ponds are not depositional environments and that contemporaneous marsh accretion represents prevented accumulation accounting for 32%–42% of the missing carbon. Erosion accounted for 0%–38% and was bracketed using radioisotope inventories and wind-driven resuspension models. Decomposition, calculated by difference, removes 22%–68%, and when normalized over pond lifespans, produces rates that agree with previous metabolism measurements. Pond surface soils contain new contributions from submerged primary producers and evidence of microbial alteration of underlying peat, as higher levels of detrital biomarkers and thermal stability indices, compared to the marsh. Below pond surface horizons, soil properties and organic matter composition were similar to the marsh, indicating that ponding effects are shallow. Soil bulk density, elemental content, and accretion rates were similar between marsh sites but different from ponds, suggesting that lateral effects are spatially confined. Consequently, ponds negatively impact ecosystem carbon storage but at current densities are not causing pervasive degradation of marshes in this system.
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
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
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.
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.
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
While recent efforts to catalogue Earth’s microbial diversity have focused upon surface and marine habitats, 12–20 % of Earth’s biomass is suggested to exist in the terrestrial deep subsurface, compared to ~1.8 % in the deep subseafloor. Metagenomic studies of the terrestrial deep subsurface have yielded a trove of divergent and functionally important microbiomes from a range of localities. However, a wider perspective of microbial diversity and its relationship to environmental conditions within the terrestrial deep subsurface is still required. Our meta-analysis reveals that terrestrial deep subsurface microbiota are dominated by Betaproteobacteria, Gammaproteobacteria and Firmicutes, probably as a function of the diverse metabolic strategies of these taxa. Evidence was also found for a common small consortium of prevalent Betaproteobacteria and Gammaproteobacteria operational taxonomic units across the localities. This implies a core terrestrial deep subsurface community, irrespective of aquifer lithology, depth and other variables, that may play an important role in colonizing and sustaining microbial habitats in the deep terrestrial subsurface. An in silico contamination-aware approach to analysing this dataset underscores the importance of downstream methods for assuring that robust conclusions can be reached from deep subsurface-derived sequencing data. Understanding the global panorama of microbial diversity and ecological dynamics in the deep terrestrial subsurface provides a first step towards understanding the role of microbes in global subsurface element and nutrient cycling.
","language":"English","publisher":"Microbiology Society","doi":"10.1099/mic.0.001172","usgsCitation":"Soares, A., Edwards, A.L., Bagnoud, A., Bradley, J., Barnhart, E.P., Bomberger Brown, M., Budwill, K., Caffrey, S.M., Fields, M., Gralnick., J., Kadnikov, V., Momper, L., Osburn, M., Mu, A., Moreau, J., Moser, D., Purkamo, L., Rassner, S.M., Sheik, C.S., Lollar, B.S., Toner, B., Voordouw, G., Wouters, K., and Mitchell, A.C., 2023, A global perspective on bacterial diversity in the terrestrial deep subsurface: Microbiology, v. 169, no. 1, 001172, 10 p., https://doi.org/10.1099/mic.0.001172.","productDescription":"001172, 10 p.","ipdsId":"IP-106711","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":444736,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1099/mic.0.001172","text":"External Repository"},{"id":412067,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"169","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Soares, A.","contributorId":301036,"corporation":false,"usgs":false,"family":"Soares","given":"A.","email":"","affiliations":[{"id":65275,"text":"1. Department of Geography and Earth Sciences (DGES), Aberystwyth University (AU), Wales, UK","active":true,"usgs":false}],"preferred":false,"id":861819,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Edwards, A. L.","contributorId":43551,"corporation":false,"usgs":false,"family":"Edwards","given":"A.","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":861820,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bagnoud, A.","contributorId":301037,"corporation":false,"usgs":false,"family":"Bagnoud","given":"A.","email":"","affiliations":[{"id":65276,"text":"Institut de Génie Thermique (IGT), Haute École d'Ingénierie et de Gestion du Canton de Vaud (HEIG-VD), Yverdon-les-Bains, Switzerland","active":true,"usgs":false}],"preferred":false,"id":861822,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bradley, J.","contributorId":301038,"corporation":false,"usgs":false,"family":"Bradley","given":"J.","affiliations":[{"id":65277,"text":"6. School of Geography, Queen Mary University of London, London, UK.","active":true,"usgs":false}],"preferred":false,"id":861823,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barnhart, Elliott P. 0000-0002-8788-8393","orcid":"https://orcid.org/0000-0002-8788-8393","contributorId":203225,"corporation":false,"usgs":true,"family":"Barnhart","given":"Elliott","middleInitial":"P.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":861824,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bomberger Brown, M.","contributorId":169602,"corporation":false,"usgs":false,"family":"Bomberger Brown","given":"M.","email":"","affiliations":[{"id":25563,"text":"School of Natural Resources, University of Nebraska, Lincoln, NE 68583","active":true,"usgs":false}],"preferred":false,"id":861825,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Budwill, K.","contributorId":301039,"corporation":false,"usgs":false,"family":"Budwill","given":"K.","email":"","affiliations":[{"id":17795,"text":"Alberta Innovates, Canada","active":true,"usgs":false}],"preferred":false,"id":861826,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Caffrey, S. M.","contributorId":301040,"corporation":false,"usgs":false,"family":"Caffrey","given":"S.","email":"","middleInitial":"M.","affiliations":[{"id":65278,"text":"University of Toronto, Canada (UT)","active":true,"usgs":false}],"preferred":false,"id":861827,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Fields, M.","contributorId":301041,"corporation":false,"usgs":false,"family":"Fields","given":"M.","email":"","affiliations":[{"id":65279,"text":"Center for Biofilm Engineering (CBE), Montana State University (MSU), USA","active":true,"usgs":false}],"preferred":false,"id":861828,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Gralnick., J.","contributorId":301042,"corporation":false,"usgs":false,"family":"Gralnick.","given":"J.","email":"","affiliations":[{"id":65280,"text":"Department of Plant and Microbial Biology, UM, USA","active":true,"usgs":false}],"preferred":false,"id":861829,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Kadnikov, V.","contributorId":301043,"corporation":false,"usgs":false,"family":"Kadnikov","given":"V.","email":"","affiliations":[{"id":65281,"text":"14. Faculty of Biology, Moscow State University (MoSU), Russia","active":true,"usgs":false}],"preferred":false,"id":861830,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Momper, L.","contributorId":301044,"corporation":false,"usgs":false,"family":"Momper","given":"L.","email":"","affiliations":[{"id":65282,"text":"16. Department of Earth, Atmospheric and Planetary Sciences (DEAPS), The Massachusetts Institute of Technology (MIT), United States of America (USA)","active":true,"usgs":false}],"preferred":false,"id":861831,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Osburn, M.","contributorId":301045,"corporation":false,"usgs":false,"family":"Osburn","given":"M.","email":"","affiliations":[{"id":65283,"text":"Department of Earth and Planetary Sciences (DEPS), Northwestern University (NWU), USA","active":true,"usgs":false}],"preferred":false,"id":861832,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Mu, A.","contributorId":301073,"corporation":false,"usgs":false,"family":"Mu","given":"A.","email":"","affiliations":[],"preferred":false,"id":861833,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Moreau, J.W.","contributorId":301046,"corporation":false,"usgs":false,"family":"Moreau","given":"J.W.","affiliations":[{"id":65284,"text":"21. School of Earth Sciences, The University of Melbourne (UM), Parkville, Australia","active":true,"usgs":false}],"preferred":false,"id":861834,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Moser, D.","contributorId":301047,"corporation":false,"usgs":false,"family":"Moser","given":"D.","email":"","affiliations":[{"id":65285,"text":"Division of Hydrologic Sciences, Desert Research Institute (DRI), Las Vegas, NV, USA","active":true,"usgs":false}],"preferred":false,"id":861835,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Purkamo, L.","contributorId":301048,"corporation":false,"usgs":false,"family":"Purkamo","given":"L.","email":"","affiliations":[{"id":65286,"text":"VTT Technical Research Centre of Finland, Finland","active":true,"usgs":false}],"preferred":false,"id":861836,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Rassner, S. M.","contributorId":301049,"corporation":false,"usgs":false,"family":"Rassner","given":"S.","email":"","middleInitial":"M.","affiliations":[{"id":65287,"text":"Department of Geography and Earth Sciences (DGES), Aberystwyth University (AU), Wales, UK","active":true,"usgs":false}],"preferred":false,"id":861837,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Sheik, C. S.","contributorId":301050,"corporation":false,"usgs":false,"family":"Sheik","given":"C.","email":"","middleInitial":"S.","affiliations":[{"id":65288,"text":"Large Lakes Observatory, University of Minnesota – Duluth (UMD)","active":true,"usgs":false}],"preferred":false,"id":861838,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Lollar, B. Sherwood","contributorId":106719,"corporation":false,"usgs":true,"family":"Lollar","given":"B.","email":"","middleInitial":"Sherwood","affiliations":[],"preferred":false,"id":861905,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Toner, B. M.","contributorId":301051,"corporation":false,"usgs":false,"family":"Toner","given":"B. M.","affiliations":[{"id":65289,"text":"Department of Soil, Water & Climate, University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":861839,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Voordouw, G.","contributorId":301052,"corporation":false,"usgs":false,"family":"Voordouw","given":"G.","email":"","affiliations":[{"id":65291,"text":"Department of Biological Sciences, University of Calgary, Canada","active":true,"usgs":false}],"preferred":false,"id":861840,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Wouters, K.","contributorId":301053,"corporation":false,"usgs":false,"family":"Wouters","given":"K.","email":"","affiliations":[{"id":65292,"text":"Institute for Environment, Health and Safety (EHS), Belgian Nuclear Research Centre SCK•CEN, Mol, Belgium","active":true,"usgs":false}],"preferred":false,"id":861841,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Mitchell, A. C.","contributorId":301054,"corporation":false,"usgs":false,"family":"Mitchell","given":"A.","email":"","middleInitial":"C.","affiliations":[{"id":65287,"text":"Department of Geography and Earth Sciences (DGES), Aberystwyth University (AU), Wales, UK","active":true,"usgs":false}],"preferred":false,"id":861842,"contributorType":{"id":1,"text":"Authors"},"rank":24}]}} ,{"id":70239922,"text":"70239922 - 2023 - Nitrogen-15 NMR study on the incorporation of nitrogen into aquatic NOM upon chloramination","interactions":[],"lastModifiedDate":"2023-01-25T12:42:43.226364","indexId":"70239922","displayToPublicDate":"2023-01-23T06:42:02","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":873,"text":"Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Nitrogen-15 NMR study on the incorporation of nitrogen into aquatic NOM upon chloramination","docAbstract":"Chloramination is being used increasingly in water treatment to lower the formation of regulated disinfection byproducts (DBPs). How monochloramine nitrogen becomes incorporated into aquatic natural organic matter (NOM) and potentially affects the formation of nitrogenous DBPs is an unresolved question in the chemistry of humic substances. To address the problem, Suwannee River NOM and Suwannee River fulvic acid were reacted with preformed 15NH2Cl and analyzed by solid and liquid state 15N NMR spectrometry. Both samples were also reacted with 15NH4Cl as a control. A majority of the monochloramine nitrogen incorporated into the samples matched the structural forms resulting from the control reaction with ammonia, indicating that condensation reactions of ammonia with the carbonyl functionality can partly explain the transformation of the 15NH2Cl nitrogen into the NOM. These structural forms include aminohydroquinone, 1° amide, indole, and pyridine-like nitrogens. Spectra of the samples reacted with 15NH2Cl also showed possible evidence for nitrosophenol nitrogens, which would arise from the reaction of hydroxylamine or nitrite, intermediates in the chemical oxidation of the inorganic nitrogen to nitrate.
The U.S. Geological Survey’s online Water Science School is a one-stop shop for water education resources. In addition to sharing images, data, and diagrams, the Water Science School provides lesson plans for teachers as well as multiple interactive activities for students, such as questionnaires, calculators, and quizzes. This bookmark introduces Drippy, the Water Science School mascot, and shares fun facts about water that can also be found on our website at https://www.usgs.gov/water-science-school.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip219","usgsCitation":"Gross, T.A., 2023, Water Science School [bookmark]: U.S. Geological Survey General Information Product 219, https://doi.org/10.3133/gip219.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-142449","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":411628,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/219/gip219.pdf","text":"Report","size":"135 KB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 219"},{"id":411627,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/219/coverthb5.jpg"}],"contact":"Integrated Information Dissemination Division
Water Resource Mission Area
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
water-science-school@usgs.gov
Redside Shiner Richardsonius balteatus has expanded from its native range in the Pacific Northwest region of North America to establish populations in six other western states. This expansion has fueled concerns regarding competition between Redside Shiner and native species, including salmonids. We developed a bioenergetic model for Redside Shiner, providing a powerful tool to quantify its trophic role in invaded ecosystems and evaluate potential impacts on native species.
Mass- and temperature-dependent functions for consumption and respiration were fit based on controlled laboratory experiments of maximum consumption rates and routine metabolic rates using intermittent-flow respirometry, across a range of fish sizes (0.6–27.3 g) and temperatures (5–31°C). Laboratory growth experiments were conducted to corroborate model performance across different temperatures and feeding rates.
Initial bioenergetic simulations of long-term growth experiments indicated large model error for predicted consumption and growth, and deviations from observed responses varied systematically as a function of daily consumption rate (J·g−1·d−1) and water temperature. A growth rate error correction function was developed and included in the bioenergetics model framework on a daily time step, resulting in decreased absolute model error in all experimental groups. Predicted values from the corrected model were highly correlated with observed values (�2; consumption = 0.97, final weight = 0.99) and unbiased. These results show that the optimal temperature for Redside Shiner growth (18°C) exceeds that of Pacific salmon Oncorhynchus spp. by 2–6°C under a scenario of high food availability and moderate food quality.
Consequently, increases in water temperature associated with climate change may favor growth and expansion of Redside Shiner populations, while negatively affecting some salmonids. The bioenergetics model presented here provides the necessary first step in quantifying trophic impacts in sensitive ecosystems where Redside Shiner have invaded or in ecosystems where anadromous salmonid reintroductions are being considered.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/tafs.10392","usgsCitation":"Johnson, R.C., Beauchamp, D., and Olden, J., 2023, Bioenergetics model for the nonnative Redside Shiner: Transactions of the American Fisheries Society, v. 152, no. 1, p. 94-113, https://doi.org/10.1002/tafs.10392.","productDescription":"20 p.","startPage":"94","endPage":"113","ipdsId":"IP-140159","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":444746,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/tafs.10392","text":"Publisher Index Page"},{"id":435494,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NAIACL","text":"USGS data release","linkHelpText":"Data used to parameterize and evaluate a bioenergetics model for Redside Shiner (Richardsonius balteatus)"},{"id":412271,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"152","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-01-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Johnson, Rachelle Carina 0000-0003-1480-4088","orcid":"https://orcid.org/0000-0003-1480-4088","contributorId":241962,"corporation":false,"usgs":true,"family":"Johnson","given":"Rachelle","email":"","middleInitial":"Carina","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":862274,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beauchamp, David 0000-0002-3592-8381","orcid":"https://orcid.org/0000-0002-3592-8381","contributorId":217816,"corporation":false,"usgs":true,"family":"Beauchamp","given":"David","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":862275,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Olden, Julian D.","contributorId":202893,"corporation":false,"usgs":false,"family":"Olden","given":"Julian D.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":862276,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70266472,"text":"70266472 - 2023 - Habitat selection of a migratory freshwater fish in response to seasonal hypoxia as revealed by acoustic telemetry","interactions":[],"lastModifiedDate":"2025-05-07T18:10:32.226189","indexId":"70266472","displayToPublicDate":"2023-01-21T00:00:00","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Habitat selection of a migratory freshwater fish in response to seasonal hypoxia as revealed by acoustic telemetry","docAbstract":"Adaptive efforts to achieve water quality objectives by modifying nutrient loading can have attendant impacts on fish habitats and fisheries. Thus, coordinating fishery and water quality management depends on knowledge of fish behavioral responses to habitat change. This study combined acoustic telemetry of fish with water quality modeling to understand how water quality management might impact fishery management. We examined habitat use of a native demersal fish, lake whitefish Coregonus clupeaformis, in Lake Erie. We focused on the summer stratified period when habitat was expected to be most limiting and used a forecast model to predict temperature and oxygen in the hypolimnion when fish were detected. As hypothesized, lake whitefish occupied a subset of available conditions with occupied habitats characterized by a cool, normoxic, hypolimnion. On some occasions fish were detected when the hypolimnion was predicted to be hypoxic, suggesting that fish were either displaced vertically or horizontally into marginal habitats or uncertainty in model predictions was high. Still, when hypolimnetic conditions were hypoxic, fish tended to move toward normoxia as expected, but when initial conditions were cold with high dissolved oxygen, fish movements were toward lower oxygen (but still normoxic) conditions. We also observed a high affinity for fish to remain near the southern shore in eastern Ohio, Pennsylvania, and New York. If current nutrient reduction objectives are achieved and the extent and severity of hypoxia is reduced, an expansion of lake whitefish habitat and distribution may have significance to the spatial regulation of fishing effort in Lake Erie.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2023.01.004","usgsCitation":"Kraus, R., Cook, H., Faust, M., Schmitt, J., Rowe, M., and Vandergoot, C., 2023, Habitat selection of a migratory freshwater fish in response to seasonal hypoxia as revealed by acoustic telemetry: Journal of Great Lakes Research, v. 49, no. 5, p. 1004-1014, https://doi.org/10.1016/j.jglr.2023.01.004.","productDescription":"11 p.","startPage":"1004","endPage":"1014","ipdsId":"IP-144704","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":485514,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan, New York, Ohio, Pennsylvania","otherGeospatial":"Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.45746247368997,\n 42.19332204270543\n ],\n [\n -83.58088952321158,\n 41.37751990998936\n ],\n [\n -81.36793483137687,\n 41.36610477953545\n ],\n [\n -79.12723694634781,\n 42.41574902379864\n ],\n [\n -78.7500162167698,\n 43.007471194229566\n ],\n [\n -81.13424345938826,\n 42.76252432461877\n ],\n [\n -82.2940150129951,\n 42.35073159163453\n ],\n [\n -83.45746247368997,\n 42.19332204270543\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"49","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kraus, Richard 0000-0003-4494-1841","orcid":"https://orcid.org/0000-0003-4494-1841","contributorId":216548,"corporation":false,"usgs":true,"family":"Kraus","given":"Richard","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":936069,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cook, H. Andrew","contributorId":354648,"corporation":false,"usgs":false,"family":"Cook","given":"H. Andrew","affiliations":[{"id":65742,"text":"Ontario Ministry of Northern Development, Mines, Natural Resources and Forestry","active":true,"usgs":false}],"preferred":false,"id":936070,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Faust, Matthew D.","contributorId":354649,"corporation":false,"usgs":false,"family":"Faust","given":"Matthew D.","affiliations":[{"id":16232,"text":"Ohio Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":936071,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schmitt, Joseph 0000-0002-8354-4067","orcid":"https://orcid.org/0000-0002-8354-4067","contributorId":221020,"corporation":false,"usgs":true,"family":"Schmitt","given":"Joseph","email":"","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":936072,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rowe, Mark D.","contributorId":354650,"corporation":false,"usgs":false,"family":"Rowe","given":"Mark D.","affiliations":[{"id":34438,"text":"NOAA-GLERL","active":true,"usgs":false}],"preferred":false,"id":936073,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vandergoot, Christopher S.","contributorId":354651,"corporation":false,"usgs":false,"family":"Vandergoot","given":"Christopher S.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":936074,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70239845,"text":"70239845 - 2023 - Appendix D: Synthesis element 1 (revised): Water temperature effects on fisheries and stream health in nontidal waters","interactions":[],"lastModifiedDate":"2026-03-18T16:06:37.364881","indexId":"70239845","displayToPublicDate":"2023-01-20T11:03:04","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"seriesNumber":"23-001","title":"Appendix D: Synthesis element 1 (revised): Water temperature effects on fisheries and stream health in nontidal waters","docAbstract":"A limited review of relevant scientific literature related to temperature sensitivities of fish species, stream health indicators, and any related geospatial information was conducted. Based on this review, we provide a syntheses of information related to nontidal waters in the Chesapeake Bay Rising stream temperatures will have a range of impacts on nontidal aquatic ecosystems. Cold headwaters and associated species like brook trout and sculpin are especially vulnerable to higher stream temperatures. Efforts could be taken to identify and protect high quality resilient cold headwater brook trout (Salvelinus fontinalis) habitat. More information on groundwater impacts on stream temperatures and ecologically relevant temperature thresholds for species of concern could help resource managers identify temperature resilient habitats and populations. A vulnerability assessment could be valuable to better understand the drivers and stressors of rising stream temperatures, their effects on aquatic resources, and the risk to fish and other aquatic species. Further research could help in developing and fully vetting a complete list of cold/cool water benthic macroinvertebrate taxa and freshwater mussel taxa that are vulnerable to temperature change in the Chesapeake watershed.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Rising watershed and bay water temperatures— Ecological implications and management responses","largerWorkSubtype":{"id":3,"text":"Organization Series"},"language":"English","publisher":"Chesapeake Bay Program STAC","usgsCitation":"Faulkner, S., Borsuk, F., Pond, G., Krause, K., Fanelli, R.M., Cashman, M.J., Hitt, N.P., and Letcher, B., 2023, Appendix D: Synthesis element 1 (revised): Water temperature effects on fisheries and stream health in nontidal waters.","startPage":"D-1","endPage":"D-25","ipdsId":"IP-145529","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":501259,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":501258,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.chesapeake.org/stac/document-library/rising-watershed-and-bay-water-temperatures-ecological-implications-and-management-responses/"}],"country":"25 p.","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Faulkner, Stephen 0000-0001-5295-1383 faulkners@usgs.gov","orcid":"https://orcid.org/0000-0001-5295-1383","contributorId":146152,"corporation":false,"usgs":true,"family":"Faulkner","given":"Stephen","email":"faulkners@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":862116,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Borsuk, Frank","contributorId":301126,"corporation":false,"usgs":false,"family":"Borsuk","given":"Frank","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":862117,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pond, Greg","contributorId":238186,"corporation":false,"usgs":false,"family":"Pond","given":"Greg","affiliations":[{"id":13529,"text":"US Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":862118,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Krause, Kevin","contributorId":301127,"corporation":false,"usgs":false,"family":"Krause","given":"Kevin","affiliations":[{"id":65315,"text":"MN DNR","active":true,"usgs":false}],"preferred":false,"id":862119,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fanelli, Rosemary M. 0000-0002-0874-1925 rfanelli@usgs.gov","orcid":"https://orcid.org/0000-0002-0874-1925","contributorId":199822,"corporation":false,"usgs":true,"family":"Fanelli","given":"Rosemary","email":"rfanelli@usgs.gov","middleInitial":"M.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":862120,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cashman, Matthew J. 0000-0002-6635-4309","orcid":"https://orcid.org/0000-0002-6635-4309","contributorId":203315,"corporation":false,"usgs":true,"family":"Cashman","given":"Matthew","middleInitial":"J.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":862121,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hitt, Nathaniel P. 0000-0002-1046-4568 nhitt@usgs.gov","orcid":"https://orcid.org/0000-0002-1046-4568","contributorId":4435,"corporation":false,"usgs":true,"family":"Hitt","given":"Nathaniel","email":"nhitt@usgs.gov","middleInitial":"P.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":862122,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Letcher, Benjamin 0000-0003-0191-5678 bletcher@usgs.gov","orcid":"https://orcid.org/0000-0003-0191-5678","contributorId":169305,"corporation":false,"usgs":true,"family":"Letcher","given":"Benjamin","email":"bletcher@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":862123,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70253916,"text":"70253916 - 2023 - Product specification document for dynamic surface water extent from Harmonized Landsat and Sentinel-2","interactions":[],"lastModifiedDate":"2024-05-03T15:37:18.030859","indexId":"70253916","displayToPublicDate":"2023-01-20T10:34:26","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesNumber":"JPL D-107395, Rev - Preliminary","title":"Product specification document for dynamic surface water extent from Harmonized Landsat and Sentinel-2","docAbstract":"The primary purpose of this document is to convey product specifications of the OPERA (Observational Products for End-users from Remote-sensing Analysis) Level-3 Dynamic Surface Water Extent (DSWx) product that uses Harmonized Landsat-8 and Sentinel-2A/B (HLS) as the primary image-based inputs. This product, referred to by the short name DSWx-HLS, will be generated by the OPERA Data System (SDS). It will be openly distributed by NASA’s Physical Oceanography Distributed Active Archive Center (PO.DAAC).
","language":"English","publisher":"NASA","usgsCitation":"Jones, J., and Shiroma, G., 2023, Product specification document for dynamic surface water extent from Harmonized Landsat and Sentinel-2, 28 p.","productDescription":"28 p.","ipdsId":"IP-141277","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":428344,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://podaac.jpl.nasa.gov/dataset/OPERA_L3_DSWX-HLS_PROVISIONAL_V0","linkFileType":{"id":5,"text":"html"}},{"id":428362,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jones, John 0000-0001-6117-3691 jwjones@usgs.gov","orcid":"https://orcid.org/0000-0001-6117-3691","contributorId":2220,"corporation":false,"usgs":true,"family":"Jones","given":"John","email":"jwjones@usgs.gov","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":900099,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shiroma, G. 0000-0002-7753-1876","orcid":"https://orcid.org/0000-0002-7753-1876","contributorId":336189,"corporation":false,"usgs":false,"family":"Shiroma","given":"G.","affiliations":[{"id":27365,"text":"NASA Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":900100,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70240774,"text":"70240774 - 2023 - A 1.2 billion pixel human-labeled dataset for data-driven classification of coastal environments","interactions":[],"lastModifiedDate":"2023-02-22T13:23:47.409683","indexId":"70240774","displayToPublicDate":"2023-01-20T07:21:18","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3907,"text":"Scientific Data","active":true,"publicationSubtype":{"id":10}},"title":"A 1.2 billion pixel human-labeled dataset for data-driven classification of coastal environments","docAbstract":"The world’s coastlines are spatially highly variable, coupled-human-natural systems that comprise a nested hierarchy of component landforms, ecosystems, and human interventions, each interacting over a range of space and time scales. Understanding and predicting coastline dynamics necessitates frequent observation from imaging sensors on remote sensing platforms. Machine Learning models that carry out supervised (i.e., human-guided) pixel-based classification, or image segmentation, have transformative applications in spatio-temporal mapping of dynamic environments, including transient coastal landforms, sediments, habitats, waterbodies, and water flows. However, these models require large and well-documented training and testing datasets consisting of labeled imagery. We describe “Coast Train,” a multi-labeler dataset of orthomosaic and satellite images of coastal environments and corresponding labels. These data include imagery that are diverse in space and time, and contain 1.2 billion labeled pixels, representing over 3.6 million hectares. We use a human-in-the-loop tool especially designed for rapid and reproducible Earth surface image segmentation. Our approach permits image labeling by multiple labelers, in turn enabling quantification of pixel-level agreement over individual and collections of images.
For nearly 40 years, the Conservation Reserve Program (CRP) has implemented practices to reduce soil erosion, improve water quality, and provide habitat for wildlife and pollinators on highly erodible cropland in the United States. However, an approximately 40,470 ha (10 million acres) decline in enrolled CRP land over the last decade has greatly reduced the program's environmental benefits. We sought to assess the program's enduring benefits in the central and western United States by (1) determining the proportion of fields that persist in CRP cover after contracts expired, (2) identifying the type of agricultural production that CRP fields shift to after contract expiration, (3) comparing the vegetation characteristics of expired CRP fields that are persisting in CRP-type cover with enrolled CRP fields, and (4) identifying differences in management activities (e.g., haying, grazing) between expired and enrolled CRP fields. We conducted edge-of-field vegetation cover surveys in 1092 CRP fields with contracts that expired ≥3 years prior and 1786 currently enrolled CRP fields in 14 states. We found that 41% of expired CRP fields retained at least half of their area in CRP-type cover, with significant variation in persistence among regions ranging from 19% to 84%. When expired fields retained CRP vegetation, bare ground was low in all regions and grass cover was somewhat greater than in fields with current CRP contracts, but at the expense of forb cover in some regions. Evidence of more frequent management in expired CRP fields may explain differences between active and expired CRP fields. Overall, there is clear evidence that CRP-type cover frequently persists and provides benefits for more than three years after contract expiration. Retaining CRP-type cover, post-contract, is an under-recognized program benefit that persists across the central and western United States long after the initial retirement from cropland.
Concurrent, distribution-wide abundance declines of some Pacific salmon species, including Chinook salmon (Oncorhynchus tshawytscha), highlights the need to understand how vulnerability at different life stages to climate stressors affects population dynamics and fisheries sustainability. Yukon River Chinook salmon stocks are among the largest subarctic populations, near the northernmost extent of the species range. Existing research suggests that Yukon River Chinook salmon population dynamics are largely driven by factors occurring between the adult spawner life stage and their offspring's first summer at sea (second year post-hatching). However, specific mechanisms sustaining chronic poor productivity are unknown, and there is a tremendous sense of urgency to understand causes, as declines of these stocks have taken a serious toll on commercial, recreational, and indigenous subsistence fisheries. Therefore, we leveraged multiple existing datasets spanning parent and juvenile stages of life history in freshwater and marine habitats. We analyzed environmental data in association with the production of offspring that survive to the marine juvenile stage (juveniles per spawner). These analyses suggest more than 45% of the variability in the production of juvenile Chinook salmon is associated with river temperatures or water discharge levels during the parent spawning migration. Over the past two decades, parents that experienced warmer water temperatures and lower discharge in the mainstem Yukon River produced fewer juveniles per spawning adult. We propose the adult spawner life stage as a critical period regulating population dynamics. We also propose a conceptual model that can explain associations between population dynamics and climate stressors using independent data focused on marine nutrition and freshwater heat stress. It is sobering to consider that some of the northernmost Pacific salmon habitats may already be unfavorable to these cold-water species. Our findings have immediate implications, given the common assumption that northern ranges of Pacific salmon offer refugia from climate stressors.
Microfaunal assemblages of benthic foraminifera, ostracods, and tintinnids from two marine sediment cores retrieved from the Herschel Basin of the Canadian Beaufort Sea shelf document relationships with environmental parameters such as salinity, sea-ice cover, and turbulence. Cores YC18-HB-GC01 and PG2303-1 were collected at 18 and 32 m water depth, respectively. At these sites, sediment accumulation rates range between 0.6 and 1.7 cm yr–1 allowing a near-annual temporal resolution over the last 50 years. Multivariate analyses indicate that benthic foraminiferal assemblages respond primarily to food supply. Dissimilarities between the microfaunal assemblages of the two cores are mainly the result of bottom water salinity levels linked to water depth. High abundance of the benthic foraminiferal species Elphidium clavatum and occurrences of Elphidium bartletti point to varying, but relatively low, salinities at the shallow core site YC18-HB-GC01, which may be affected by variations in the summer halocline depth. Higher species diversity and more abundant Cassidulina reniforme and Stainforthia feylingi characterize the deeper core PG2303-1, which might reflect more stable conditions and higher bottom-water salinities throughout the studied time interval. The most important microfaunal shift of the last 50 years, observed in the shallower longer core YC18-HB-GC01, occurred at the turn of the 21st century. Prior to ∼2000 CE, the presence of Islandiella norcrossi indicates more stable and saline conditions. Since ∼2000 CE, increased abundances of Haynesina nivea and of the ciliate Tintinnopsis fimbriata suggest decreased salinity and increased turbidity. An increased abundance of Eoeponidella pulchella after ∼2000 CE suggests a concurrent increase in productivity in the last two decades. This shift is nearly synchronous with a decrease in mean summer sea-ice concentration, which can play an important role in bottom water stability on the shelf. Easterly winds can induce a reduction in the sea-ice cover, but also foster a westward spreading of the Mackenzie River plume and the upwelling of nutrient-rich Pacific waters onto the shelf. Both factors would explain the increased freshening and productivity of the Herschel Basin. The last two decades were also marked by a decrease in ostracod abundance that may relate to higher water turbidity. This study shows that combining information from benthic foraminifera, ostracods, and tintinnids provides a comprehensive insight into recent hydrographic/climatic changes in nearshore Arctic habitats, where productivity is critical for the food security of local communities.
A global network of subsea telecommunications cables underpins our daily lives, enabling >95% of global digital data transfer, $trillions/day in financial trading, and providing critical communications links, particularly to remote, low-income countries. Despite their importance, subsea cables and their landing stations are vulnerable to damage by natural hazards, including storm surges, waves, cyclones, earthquakes, floods, volcanic eruptions, submarine landslides and ice scour. However, the likelihood or recurrence interval of many of these types of events will likely change under future projected climate change scenarios, compounded by sea-level rise; potentially increasing hazard severity, creating previously unanticipated hazards, or hazards may shift to new locations during the 20–30-year operational life of cable systems. To date, no study has assessed the wide-reaching impacts of future climate change on subsea cables and landing stations on a global scale. Here, for the first time we synthesize the current evidence base, based on published peer-reviewed datasets, to fill this crucial knowledge gap, specifically to assess how and where future climate change is likely to impact subsea cables and their shore-based infrastructure. We find that ocean conditions are highly likely to change on a global basis as a result of climate change, but the feedbacks and links between climate change, natural processes and human activities are often complicated, resulting in a high degree of geographic variability. We identify climate change ‘hotspots’ (regions and locations likely to experience the greatest impacts) but find that not all areas will be affected in the same manner, nor synchronously by the same processes. We conclude that cable routes should carefully consider locally-variable drivers of hazard frequency and magnitude. Consideration should be given both to instantaneous events (e.g. landslides, tropical cyclones) as well as longer-term, sustained impacts (e.g. seabed currents that circulate even in deep water). Multiple factors can combine to increase the risk posed to subsea cables, hence a holistic approach is essential to assess the compounded effects of both natural processes and human activities in the future.