Tidal wetlands are greatly impacted by climate change, and by the invasion of alien plant species that are being exposed to salinity changes and longer inundation periods resulting from sea level rise. To explore the capacity for the invasion of Iris pseudacorus to persist with sea level rise, we initiated an intercontinental study along estuarine gradients in the invaded North American range and the native European range.
San Francisco Bay-Delta Estuary; California, USA and Guadalquivir River Estuary; Andalusia, Spain.
We compared 15 morphological, biochemical, and reproductive plant traits within populations in both ranges to determine if specific functional traits can predict invasion success and if environmental factors explain observed phenotypic differences.
Alien I. pseudacorus plants in the introduced range had more robust growth than plants in the native range. The vigour of the alien plants was reflected by expression of higher leaf water content, fewer senescent leaves per leaf fan, and more carbohydrate storage reserves in rhizomes than plants in the native range. Moreover, alien plants tended to show higher specific leaf area and seed production than native plants. I. pseudacorus plants in the introduced range were less affected by increasing salinity and were exposed to deeper inundation water along the estuarine gradient than those in the native range.
Functional trait differences suggest mature populations of I. pseudacorus in the introduced range have greater adapted capacity to adjust to environmental stresses induced by rising sea level than those in the native range. Knowledge of these trait responses can be applied to improve risk assessments in invaded estuaries and to achieve climate-adapted conservation goals for conservation of the species in its native range.
","language":"English","publisher":"Wiley","doi":"10.1111/ddi.13694","usgsCitation":"Grewell, B.J., Gallego-Tevar, B., Barcenas-Moreno, G., Whitcraft, C.R., Thorne, K., Buffington, K., and Castillo, J.M., 2023, Phenotypic trait differences between Iris pseudacorus in native and introduced ranges support greater capacity of invasive populations to withstand sea level rise: Diversity and Distributions, v. 29, no. 7, p. 834-848, https://doi.org/10.1111/ddi.13694.","productDescription":"15 p.","startPage":"834","endPage":"848","ipdsId":"IP-151016","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":443610,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ddi.13694","text":"Publisher Index Page"},{"id":417131,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Spain, United States","state":"Andalusia, California","otherGeospatial":"Guadalquivir River Estuary, San Francisco Bay-Delta Estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.17511083170808,\n 38.31712828438441\n ],\n [\n -123.17511083170808,\n 37.205650879413625\n ],\n [\n -120.22951725697519,\n 37.205650879413625\n ],\n [\n -120.22951725697519,\n 38.31712828438441\n ],\n [\n -123.17511083170808,\n 38.31712828438441\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -7.015998637877459,\n 37.34832618116711\n ],\n [\n -7.015998637877459,\n 36.72067723528794\n ],\n [\n -5.810620224229581,\n 36.72067723528794\n ],\n [\n -5.810620224229581,\n 37.34832618116711\n ],\n [\n -7.015998637877459,\n 37.34832618116711\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"29","issue":"7","noUsgsAuthors":false,"publicationDate":"2023-05-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Grewell, Brenda J.","contributorId":305471,"corporation":false,"usgs":false,"family":"Grewell","given":"Brenda","email":"","middleInitial":"J.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":872885,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gallego-Tevar, Blanca","contributorId":305472,"corporation":false,"usgs":false,"family":"Gallego-Tevar","given":"Blanca","email":"","affiliations":[{"id":66227,"text":"Universidad de Sevilla","active":true,"usgs":false}],"preferred":false,"id":872886,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barcenas-Moreno, Gael","contributorId":305474,"corporation":false,"usgs":false,"family":"Barcenas-Moreno","given":"Gael","email":"","affiliations":[{"id":66227,"text":"Universidad de Sevilla","active":true,"usgs":false}],"preferred":false,"id":872887,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Whitcraft, Christine R.","contributorId":305476,"corporation":false,"usgs":false,"family":"Whitcraft","given":"Christine","email":"","middleInitial":"R.","affiliations":[{"id":66229,"text":"CSU Long Beach","active":true,"usgs":false}],"preferred":false,"id":872888,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thorne, Karen M. 0000-0002-1381-0657","orcid":"https://orcid.org/0000-0002-1381-0657","contributorId":204579,"corporation":false,"usgs":true,"family":"Thorne","given":"Karen M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":872889,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Buffington, Kevin 0000-0001-9741-1241 kbuffington@usgs.gov","orcid":"https://orcid.org/0000-0001-9741-1241","contributorId":4775,"corporation":false,"usgs":true,"family":"Buffington","given":"Kevin","email":"kbuffington@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":872890,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Castillo, Jesus M.","contributorId":305477,"corporation":false,"usgs":false,"family":"Castillo","given":"Jesus","email":"","middleInitial":"M.","affiliations":[{"id":66227,"text":"Universidad de Sevilla","active":true,"usgs":false}],"preferred":false,"id":872891,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70249368,"text":"70249368 - 2023 - The Toolbox for River Velocimetry using Images from Aircraft (TRiVIA)","interactions":[],"lastModifiedDate":"2023-10-05T12:06:20.98252","indexId":"70249368","displayToPublicDate":"2023-05-09T07:05:04","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"The Toolbox for River Velocimetry using Images from Aircraft (TRiVIA)","docAbstract":"Accurate knowledge of the speed at which water moves along a river is essential for understanding ecohydraulic processes and managing natural resources. Measuring flow velocity via remote sensing can be more efficient than conventional field methods, and powerful computational techniques for inferring velocity fields from videos or image time series have been developed. The development of dedicated software tools for particle image velocimetry (PIV) could facilitate greater use of these methods by the river community. This paper introduces a standalone app designed for this exact purpose: the Toolbox for River Velocimetry using Images from Aircraft, or TRiVIA. The program provides a complete workflow for producing spatially distributed velocity vectors from a video or sequence of images, all within an accessible graphical user interface. TRiVIA includes modules for extracting and resampling frames, stabilization and geo-referencing images, defining a region of interest, enhancing images, performing PIV with an efficient ensemble correlation algorithm, visualizing results, assessing accuracy assessment, and exporting PIV output. We illustrate the software's capabilities using an example data set from a large river in Alaska. The initial release of the toolbox is now freely available. Augmenting TRiVIA to incorporate bathymetric information could enable discharge calculation functionality.
Lakes provide important habitat for salmonids that may use them as a primary feeding area between periods of reproduction. The seasonal changes in vertical thermal structure in lakes can affect the distribution of salmonids on seasonal and diel time scales as they search for, consume, and digest prey that also exploits the water column's distribution of food, temperature and light. Our goal was to analyse the vertical distribution of wild, native coastal cutthroat trout (Oncorhynchus clarkii clarkii) in Lake Washington on daily and seasonal time scales. This lake is stratified in the summer and isothermal in winter, allowing us to compare vertical movements between periods with and without thermal structure in water 50 m deep. We predicted that trout would be deeper in the water column during stratified months and shallower during isothermal months, and shallower at night than in the day. Overall, the trout showed these patterns in the depths and temperatures they occupied, tending to be within or below the thermocline in the summer but not in the coolest water available, and closer to the surface when the lake was isothermal. The trout were also closer to the surface at night and deeper during the day. The vertical range of these diel movements shifted with the seasons–deepest in October, as the thermocline deepened and weakened, and shallowest in January when the lake was isothermal. These seasonal and diel vertical distribution patterns by the trout optimise metabolism for growth, and facilitate feeding on planktivorous fishes that also show seasonal and diel vertical distribution changes.
The Rio San Jose Integrated Hydrologic Model (RSJIHM) was developed to provide a tool for analyzing the hydrologic system response to historical water use and potential changes in water supplies and demands in the Rio San Jose Basin. The study area encompasses about 6,300 square miles in west-central New Mexico and includes the communities of Grants, Bluewater, and San Rafael and three Native American Tribal lands: the Acoma and Laguna Pueblos and the Navajo Nation. Perennial surface water features are sparse in the study area and most water resources consist of groundwater pumped from sedimentary and basalt aquifers.
Calibration of the RSJIHM was performed using PEST++ (version 4.3.20) and BeoPEST (version 13.6). Model parameter values were adjusted during calibration to fit model simulated values to the measured or estimated values for several observation groups: (1) solar radiation, (2) potential evapotranspiration, (3) actual evapotranspiration, (4) precipitation and minimum and maximum air temperature, (5) snow water equivalent, (6) snow-covered area, (7) streamflow, (8) hydraulic head, (9) springflow at Ojo del Gallo, (10) springflow at Horace Springs, (11) surface-water releases from Bluewater Lake, and (12) surface-water diversions for irrigation within the Bluewater-Toltec Irrigation District.
The simulated average annual hydrologic budget from 1950 through 2018 indicated that the majority (greater than 98 percent) of precipitation within the basin was consumed by evapotranspiration, leaving 1.2 percent to recharge the groundwater system, 0.47 percent to direct runoff to streams, and 0.20 percent to infiltrate the soil zone and interflow to streams. The average annual recharge to the groundwater system and runoff to streams simulated by the RSJIHM was about 28,000 and 11,000 acre-feet, respectively. The RSJIHM simulated about 590,000 acre-feet of cumulative aquifer storage depletion from 1950 through 2018.
Additional work that could improve the simulation capability of the RSJIHM includes (1) further data collection (streamflow, head, springflow) in the southwestern subbasin that includes the El Malpais National Monument, (2) incorporating temporally variable vegetation parameters, (3) spatial downscaling of the hydrometeorological input datasets, (4) incorporating additional spatial variability to hydraulic property parameters on the basis of new data collection, and (5) using environmental tracers to verify and calibrate model parameters.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235028","issn":"2328-0328","collaboration":"Prepared in cooperation with the Bureau of Reclamation, Pueblo of Acoma, and Pueblo of Laguna","usgsCitation":"Ritchie, A.B., Chavarria, S.B., Galanter, A.E., Flickinger, A.K., Robertson, A.J., and Sweetkind, D.S., 2023, Development of an integrated hydrologic flow model of the Rio San Jose Basin and surrounding areas, New Mexico: U.S. Geological Survey Scientific Investigations Report 2023–5028, 76 p., 1 pl., https://doi.org/10.3133/sir20235028.","productDescription":"Report: x, 76 p.; 1 Plate: 25.37 x 40.38 inches; Data Release","numberOfPages":"90","onlineOnly":"Y","ipdsId":"IP-111893","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":416632,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5028/coverthb.jpg"},{"id":416635,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20235028/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5028 HTML"},{"id":416634,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5028/sir20235028.XML","size":"482 KB","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2023-5028 XML"},{"id":416638,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2023/5028/sir20235028_plate1.pdf","size":"550 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5028 plate 1"},{"id":416633,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5028/sir20235028.pdf","size":"5.62 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5028"},{"id":416637,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YRTKTM","text":"USGS data release—GSFLOW, used to run PRMS and MODFLOW-NWT models, to simulate the effects of natural and anthropogenic impacts on water resources in the Rio San Jose Basin and surrounding areas, New Mexico"},{"id":416636,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5028/Images/"},{"id":500886,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114717.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New Mexico","otherGeospatial":"Rio San Jose Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.5,\n 36\n ],\n [\n -108.5,\n 36\n ],\n [\n -108.5,\n 34\n ],\n [\n -106.5,\n 34\n ],\n [\n -106.5,\n 36\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
Discussed in this chapter are seven significant tributaries of the Lower Mississippi River and its major distributary. As a group, these eight rivers and their basins encompass substantial variation in physical form, hydrology, biota, ecology, and human impacts. The Current River, Ouachita River, and Saline River, flow to the Mississippi out of the U.S. Interior Highlands. The Cache River basin, centered in Arkansas, contains a vast expanse of bottomland hardwood forest and is famous for wintering waterfowl. The Hatchie River, flowing west out of Tennessee, is the longest free-flowing tributary of the Lower Mississippi River and famous for its rich diversity of mussels and fishes. The Wolf River of Tennessee supports a magnificent bald cypress-tupelo swamp and is notable as a protected urban river that flows through Memphis. The Big Sunflower River begins and ends in the alluvial floodplain of the Mississippi River and flows through a basin of intense agriculture and historical human conflict. The Atchafalaya River, the primary distributary of the Mississippi River, supports the nation's largest expanse of bottomland hardwood forest and swamp wetlands. In this chapter, we review the physiography, geomorphology, hydrology, water chemistry, land use, biological diversity, ecological processes, human impacts, and areas of need for research and management of each of these eight rivers and their basins.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Rivers of North America (second edition)","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Elsevier","doi":"10.1016/B978-0-12-818847-7.00002-1","usgsCitation":"Ochs, C., Baustian, J., Harrison, A., Hartfield, P., Johnston, C., Justis, C.A., Larsen, D., Mickelson, A., Piazza, B., and Spurgeon, J.J., 2023, Rivers of the Lower Mississippi Basin, chap. 6 of Rivers of North America (second edition), p. 226-271, https://doi.org/10.1016/B978-0-12-818847-7.00002-1.","productDescription":"46 p.","startPage":"226","endPage":"271","ipdsId":"IP-125987","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433453,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lower Mississippi River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.93450410030157,\n 29.382748469377404\n ],\n [\n -89.33548846730534,\n 30.779284874646976\n ],\n [\n -82.62880013103909,\n 36.5183105334489\n ],\n [\n -99.00599352782979,\n 36.52450752669955\n ],\n [\n -95.77205257921551,\n 32.53534478594656\n ],\n [\n -92.1616938289437,\n 30.17414276402424\n ],\n [\n -91.08124857898872,\n 29.329443449275175\n ],\n [\n -89.11222202428036,\n 29.116282171426377\n ],\n [\n -88.93450410030157,\n 29.382748469377404\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ochs, C.","contributorId":341753,"corporation":false,"usgs":false,"family":"Ochs","given":"C.","email":"","affiliations":[{"id":36508,"text":"University of Mississippi","active":true,"usgs":false}],"preferred":false,"id":908859,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baustian, J.J.","contributorId":196475,"corporation":false,"usgs":false,"family":"Baustian","given":"J.J.","email":"","affiliations":[],"preferred":false,"id":908863,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harrison, A.","contributorId":341754,"corporation":false,"usgs":false,"family":"Harrison","given":"A.","affiliations":[{"id":81780,"text":"U.S. Army COE","active":true,"usgs":false}],"preferred":false,"id":908862,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hartfield, P.","contributorId":189996,"corporation":false,"usgs":false,"family":"Hartfield","given":"P.","affiliations":[],"preferred":false,"id":908861,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnston, C.S.","contributorId":317794,"corporation":false,"usgs":false,"family":"Johnston","given":"C.S.","email":"","affiliations":[{"id":39883,"text":"Univ of Oklahoma","active":true,"usgs":false}],"preferred":false,"id":908860,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Justis, Catherine A.","contributorId":343866,"corporation":false,"usgs":false,"family":"Justis","given":"Catherine","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":908865,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Larsen, D.","contributorId":341758,"corporation":false,"usgs":false,"family":"Larsen","given":"D.","affiliations":[{"id":17864,"text":"University of Memphis","active":true,"usgs":false}],"preferred":false,"id":908866,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mickelson, A.","contributorId":341759,"corporation":false,"usgs":false,"family":"Mickelson","given":"A.","email":"","affiliations":[{"id":17864,"text":"University of Memphis","active":true,"usgs":false}],"preferred":false,"id":908867,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Piazza, B.","contributorId":341756,"corporation":false,"usgs":false,"family":"Piazza","given":"B.","email":"","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":908864,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Spurgeon, Jonathan J. 0000-0002-6888-5867","orcid":"https://orcid.org/0000-0002-6888-5867","contributorId":270349,"corporation":false,"usgs":true,"family":"Spurgeon","given":"Jonathan","email":"","middleInitial":"J.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":16610,"text":"University of Nebraska-Lincoln","active":true,"usgs":false}],"preferred":false,"id":908868,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70243322,"text":"70243322 - 2023 - Bottom trawl assessment of Lake Ontario's benthic preyfish community, 2022","interactions":[],"lastModifiedDate":"2024-03-29T15:16:28.215504","indexId":"70243322","displayToPublicDate":"2023-05-08T10:10:23","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"title":"Bottom trawl assessment of Lake Ontario's benthic preyfish community, 2022","docAbstract":"Since 1978, surveys of Lake Ontario preyfish communities have provided information on the status and trends of the benthic preyfish community related to Fish Community Objectives that includes understanding preyfish population dynamics and community diversity. Beginning in 2015, the benthic preyfish survey expanded from US-only to incorporate Canadian sites, increasing the survey’s spatial coverage to a lake-wide scale. Additionally, sampling in eastern US embayments (Black River, Chaumont, Guffin, and Henderson Bays), that were historically sampled during a September bottom trawl survey to index Yellow Perch (Perca flavescens; 1978–2007), resumed in 2015. The current survey provides abundance indices for sculpins, Round Goby (Neogobius melanostomus) and Bloater (Coregonus hoyi) with survey techniques, gear and timing comparable to Lake Michigan. This alignment provides a necessary biological reference point for measuring the success of Lake Ontario Bloater reintroduction. In 2022, the collaborative benthic preyfish survey completed 171 bottom trawl tows across main lake and embayment sites at depths from 6 to 222 m. In total, the 2022 survey sampled 141,552 fish from 34 species. Round Goby was the most numerically abundant species comprising 36% of the total catch, followed by Alewife (Alosa pseudoharengus) and Deepwater Sculpin (Myoxocephalus thompsonii), at 20% and 16%, respectively. Alewife accounted for most (623 kg) of the fish biomass sampled during the 2022 survey (total=2,197 kg), followed by Deepwater Sculpin (547 kg), and Round Goby (262 kg). Slimy Sculpin (Cottus cognatus) lake-wide biomass density (0.03 kg/ha) remained low relative to historical observations from US waters during the 1980-1990s and was similar to the average from the previous three survey years (2019-2021 average 0.04 ± 0.02 kg/ha). Lake-wide Deepwater Sculpin biomass density reached a new high (4.4 kg/ha) in 2022. Embayment catches continue to have unique species assemblages compared to main lake habitat. Historically common native benthic preyfish species like Trout-perch (Percopsis omiscomaycus), Spottail Shiner (Notropis hudsonius), and darters (Etheostoma spp.), that are now rare at main lake trawl sites, still occur in some embayment trawl sites.
","language":"English","publisher":"Great Lakes Fishery Commission","usgsCitation":"O’Malley, B., Minihkeim, S.P., McKenna, J., Goretzke, J., and Holden, J.P., 2023, Bottom trawl assessment of Lake Ontario's benthic preyfish community, 2022, 15 p.","productDescription":"15 p.","ipdsId":"IP-151033","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":427241,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":427240,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"http://www.glfc.org/publication-media-search.php","linkFileType":{"id":5,"text":"html"}}],"country":"Canada, United States","otherGeospatial":"Lake Ontario","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.925537109375,\n 43.265206318396025\n ],\n [\n -79.8101806640625,\n 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Center","active":true,"usgs":true}],"preferred":true,"id":872033,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McKenna, James","contributorId":304958,"corporation":false,"usgs":false,"family":"McKenna","given":"James","affiliations":[{"id":38050,"text":"Contractor","active":true,"usgs":false}],"preferred":false,"id":872034,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Goretzke, Jessica A.","contributorId":304959,"corporation":false,"usgs":false,"family":"Goretzke","given":"Jessica A.","affiliations":[{"id":39079,"text":"NYSDEC","active":true,"usgs":false}],"preferred":false,"id":872035,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Holden, Jeremy P.","contributorId":251689,"corporation":false,"usgs":false,"family":"Holden","given":"Jeremy","email":"","middleInitial":"P.","affiliations":[{"id":50374,"text":"Ontario Ministry of Natural Resources and Forests (OMNRF)","active":true,"usgs":false}],"preferred":false,"id":872036,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70254178,"text":"70254178 - 2023 - Rivers of Arctic North America","interactions":[],"lastModifiedDate":"2024-05-13T12:32:56.019427","indexId":"70254178","displayToPublicDate":"2023-05-08T07:30:28","publicationYear":"2023","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"20","title":"Rivers of Arctic North America","docAbstract":"This chapter describes the geomorphology, hydrology, chemistry, biodiversity, and ecology of rivers in the North American Arctic. The history, physiography, climate, and land use of the Arctic regions are also described. The chapter includes details on the Kobuk and Colville rivers in Alaska, the Thelon and Kazan rivers in the central Canadian Arctic, Koroc River and Nakvak Brook in the eastern Canadian low Arctic, Thomsen River on Banks Island in the western Canadian Arctic Archipelago, and Ruggles River on Ellesmere Island in the Canadian high Arctic. The rivers are characteristic of the major ecoregions of the North American Arctic, covering a range of geomorphological and physiographic conditions. The history of use of the rivers by Inuit and Dene First Nations Peoples of the north provides the foundation to understand the social, cultural, and economic importance of the river systems, and potential threats to the rivers from climate change are outlined.
Conservation propagation of pallid sturgeon (Scaphirhynchus albus) upstream of Fort Peck Reservoir, Montana, USA has successfully recruited a new generation of spawning-capable pallid sturgeon where there would otherwise be fewer than 30 remaining wild reproductively mature pallid sturgeon. Successful recovery of pallid sturgeon will now rely on the behavior of pallid sturgeon (e.g., successful spawning in locations that provide adequate drift distance for larvae to recruit). We used location data of pallid sturgeon during four putative spawning seasons to answer the following questions: where do pallid sturgeon spawn; are spawning locations related to discharge; are substrate characteristics at the spawning locations similar to other river reaches; and do spawning-capable females, spawning-capable males, and female pallid sturgeon undergoing mass ovarian follicular atresia use the river similarly? Additionally, we consider if spawning locations are far enough from the river-reservoir transition zone to provide adequate drift distance for larvae to recruit. Spawning-capable pallid sturgeon did explore upstream locations, and four spawning-capable pallid sturgeon were located in the Marias River during the spawning season in 2018 when discharge was at an unprecedented high. Pallid sturgeon exited the Marias River and moved downstream prior to spawning, and when spawning occurred, it was not far enough upstream to prevent larvae from entering the transition zone of Fort Peck Reservoir. Thus, management of discharge and water temperature to mimic 2018 conditions may increase use of the Marias River by pallid sturgeon during the spawning season, which would increase drift distance available to larvae and increase the probability of successful recruitment.
","language":"English","publisher":"MDPI","doi":"10.3390/fishes8050243","usgsCitation":"Cox, T.L., Guy, C.S., Holmquist, L., and Webb, M.A., 2023, Spawning locations of pallid sturgeon in the Missouri River corroborate the mechanism for recruitment failure: Fishes, v. 8, no. 5, 243, 22 p., https://doi.org/10.3390/fishes8050243.","productDescription":"243, 22 p.","ipdsId":"IP-152115","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":443626,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/fishes8050243","text":"Publisher Index Page"},{"id":429783,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, North Dakota","otherGeospatial":"Fort Peck Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.9553366522268,\n 48.58477160833684\n ],\n [\n -106.9553366522268,\n 47.259647654337954\n ],\n [\n -103.53858860535145,\n 47.259647654337954\n ],\n [\n -103.53858860535145,\n 48.58477160833684\n ],\n [\n -106.9553366522268,\n 48.58477160833684\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"8","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-05-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Cox, Tanner L.","contributorId":337434,"corporation":false,"usgs":false,"family":"Cox","given":"Tanner","email":"","middleInitial":"L.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":902419,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guy, Christopher S. 0000-0002-9936-4781 cguy@usgs.gov","orcid":"https://orcid.org/0000-0002-9936-4781","contributorId":2876,"corporation":false,"usgs":true,"family":"Guy","given":"Christopher","email":"cguy@usgs.gov","middleInitial":"S.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":5062,"text":"Office of the Chief Scientist for Ecosystems","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":902420,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holmquist, Luke M.","contributorId":337435,"corporation":false,"usgs":false,"family":"Holmquist","given":"Luke M.","affiliations":[{"id":40948,"text":"Montana Fish Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":902421,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Webb, Molly A. H","contributorId":337436,"corporation":false,"usgs":false,"family":"Webb","given":"Molly","email":"","middleInitial":"A. H","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":902422,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70243326,"text":"70243326 - 2023 - Precipitation, submarine groundwater discharge of nitrogen, and red tides along the southwest Florida Gulf coast","interactions":[],"lastModifiedDate":"2023-05-12T15:08:20.800239","indexId":"70243326","displayToPublicDate":"2023-05-06T06:36:44","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5211,"text":"Heliyon","active":true,"publicationSubtype":{"id":10}},"title":"Precipitation, submarine groundwater discharge of nitrogen, and red tides along the southwest Florida Gulf coast","docAbstract":"Blooms of the dinoflagellate Karenia brevis occur almost every year along the southwest Florida Gulf coast. Long-duration blooms with especially high concentrations of K. brevis, known as red tides, destroy marine life through production of neurotoxins. Current hypotheses are that red tides originate in oligotrophic waters far offshore using nitrogen (N) from upwelling bottom water or, alternatively, from blooms of Trichodesmium, followed by advection to nearshore waters. But the amount of N available from terrestrial sources does not appear to be adequate to maintain a nearshore red tide. To explain this discrepancy, we hypothesize that contemporary red tides are associated with release of N from offshore submarine groundwater discharge (SGD) that has accumulated in benthic sediment biomass by dissimilatory nitrate reduction to ammonium (DNRA). The release occurs when sediment labile organic carbon (LOC), used as the electron donor in DNRA, is exhausted. Detritus from the resulting destruction of marine life restores the sediment LOC to continue the cycle of red tides. The severity of individual red tides increases with increased bloom-year precipitation in the geographic region where the SGD originates, while the severity of ordinary blooms is relatively unaffected.
No abstract available.
","language":"English","publisher":"U.S. Fish and Wildlife Service","usgsCitation":"Skorupa, A.J., Perkins, D., Roy, A.H., and Ryan, J.E., 2023, Watershed selection to support freshwater mussel restoration: An open-loop decision guide: Cooperator Science Series 149-2023, 33 p.","productDescription":"33 p.","ipdsId":"IP-145512","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":432701,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.fws.gov/media/watershed-selection-support-freshwater-mussel-restoration-open-loop-decision-guide"},{"id":433257,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Skorupa, Ayla J.","contributorId":342300,"corporation":false,"usgs":false,"family":"Skorupa","given":"Ayla","email":"","middleInitial":"J.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":909983,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Perkins, David","contributorId":342302,"corporation":false,"usgs":false,"family":"Perkins","given":"David","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":909984,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roy, Allison H. 0000-0002-8080-2729 aroy@usgs.gov","orcid":"https://orcid.org/0000-0002-8080-2729","contributorId":4240,"corporation":false,"usgs":true,"family":"Roy","given":"Allison","email":"aroy@usgs.gov","middleInitial":"H.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":909985,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ryan, Jennifer E.","contributorId":342306,"corporation":false,"usgs":false,"family":"Ryan","given":"Jennifer","email":"","middleInitial":"E.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":909986,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70243139,"text":"sir20235014 - 2023 - Magnitude and frequency of floods on Kauaʻi, Oʻahu, Molokaʻi, Maui, and Hawaiʻi, State of Hawaiʻi, based on data through water year 2020","interactions":[],"lastModifiedDate":"2026-03-02T21:59:55.664975","indexId":"sir20235014","displayToPublicDate":"2023-05-05T07:40:10","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-5014","displayTitle":"Magnitude and Frequency of Floods on Kauaʻi, Oʻahu, Molokaʻi, Maui, and Hawaiʻi, State of Hawaiʻi, Based on Data through Water Year 2020","title":"Magnitude and frequency of floods on Kauaʻi, Oʻahu, Molokaʻi, Maui, and Hawaiʻi, State of Hawaiʻi, based on data through water year 2020","docAbstract":"Accurate estimates of flood magnitude and frequency are needed to (1) optimize the design and location of infrastructure, including dams, culverts, bridges, industrial buildings, and highways, and (2) inform flood-zoning and flood-insurance studies. The U.S. Geological Survey (USGS), in cooperation with the State of Hawaiʻi Department of Transportation, estimated flood magnitudes for the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities (AEP) for unregulated streamgages in Kauaʻi, Oʻahu, Molokaʻi, Maui, and Hawaiʻi, State of Hawaiʻi, using data through water year 2020. Regression equations were developed to estimate flood magnitude and associated frequency at ungaged streams. This study improves upon a previous USGS flood-frequency report (Oki and others, 2010) by including more peak-flow data, implementing new statistical methods in flood-frequency analysis, and using updated techniques to estimate the regional-skewness coefficient (regional skew).
Flood magnitude and frequency at 238 streamgages were estimated—following national guidelines established in Bulletin 17C (England and others, 2019)—by fitting annual peak-flow data to the Log-Pearson Type III distribution using the expected moments algorithm and the PeakFQ flood-frequency software. Potentially influential low outliers in the data were identified and removed using the Multiple Grubbs-Beck Test. An updated regional skew for Hawaiʻi was estimated using the Bayesian weighted least squares/Bayesian generalized least squares method. The updated regional skew employs a constant model for the five islands in the study area and has a value of −0.157 (mean square error of 0.212).
Multiple linear regression techniques were used to develop regression equations that relate basin and climatic characteristics to peak flows at streamgages. The regression equations can be applied to estimate flood magnitude and frequency at ungaged sites. The study area was split into 10 regions—2 regions per island, generally following a leeward/windward division—containing from 9 to 49 streamgages each. The final regression equations for each region were determined with generalized least-squares analysis using the USGS weighted-multiple-linear regression (WREG) program. The standard error of prediction at the 1-percent AEP for the regression equations ranged from 18 to 164 percent; the pseudo coefficient of determination (pseudo-R2) at the 1-percent AEP ranged from 46 to 100 percent. The regression equations performed well for all regions except leeward Molokaʻi and southern Island of Hawaiʻi; for all other regions, the pseudo-R2 values ranged from about 75 to 100 percent. Compared to the regression equations developed by Oki and others (2010), the regression equations in this study generally showed modest improvements, although the magnitude of differences varied for each region.
Peak-flow estimates at the 238 streamgages included in this study are improved by weighting the at-site statistics computed with PeakFQ and the predicted flows based on the regression equations. Results of this study—including the final peak-flow estimates at streamgages and the regional regression equations—are implemented in the USGS StreamStats web application (U.S. Geological Survey, 2023, StreamStats: https://streamstats.usgs.gov/ss/). StreamStats provides a consistent approach for obtaining peak-flow estimates at streamgages and for applying the regional regression equations for estimating peak flows at ungaged locations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235014","collaboration":"Prepared in cooperation with the State of Hawaiʻi Department of Transportation","usgsCitation":"Mitchell, J.N., Wagner, D.M., and Veilleux, A.G., 2023, Magnitude and frequency of floods on Kauaʻi, Oʻahu, Molokaʻi, Maui, and Hawaiʻi, State of Hawaiʻi, based on data through water year 2020: U.S. Geological Survey Scientific Investigations Report 2023–5014, 66 p. plus 4 appendixes, https://doi.org/10.3133/sir20235014.","productDescription":"Report: vii, ; 8 Tables; 3 Data Releases","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-139812","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":416577,"rank":15,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GGPPV5","text":"USGS data release","description":"USGS data release","linkHelpText":"Data in support of flood-frequency report—Magnitude and frequency of floods on Kauaʻi, Oʻahu, Molokaʻi, Maui, and Hawaiʻi, State of Hawaiʻi, based on data through water year 2020"},{"id":416576,"rank":14,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TOQANM","text":"USGS data release","description":"USGS data release","linkHelpText":"Basin characteristic rasters used in the update of Hawaiʻi StreamStats, 2022"},{"id":416575,"rank":13,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9N61WJ7","text":"USGS data release","description":"USGS data release","linkHelpText":"Geospatial datasets for watershed delineation used in the update of Hawaiʻi StreamStats, 2022"},{"id":416566,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5014/coverthb.jpg"},{"id":416567,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5014/sir20235014.pdf","text":"Report","size":"7.4 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States","state":"Hawaii","otherGeospatial":"Kauaʻi, Oʻahu, Molokaʻi, Maui, Hawaiʻi","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -159.92521972722102,\n 22.39025206306377\n ],\n [\n -159.92521972722102,\n 18.78261358926393\n ],\n [\n -154.69797609100146,\n 18.78261358926393\n ],\n [\n -154.69797609100146,\n 22.39025206306377\n ],\n [\n -159.92521972722102,\n 22.39025206306377\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Pacific Islands Science Center
U.S. Geological Survey
1845 Wasp Blvd., B176
Honolulu, HI 96818
Spatial data provided by the U.S. Department of Agriculture National Resource Conservation Service representing implementation at the field-level for a selection of agricultural conservation practices were incorporated within a spatially referenced regression model to estimate their effects on nitrogen loads in streams in the Chesapeake Bay watershed. Conservation practices classified as “high-impact” were estimated to be effective (p = 0.017) at reducing contemporary nitrogen loads to streams of the Chesapeake Bay watershed in areas where groundwater ages are estimated to be less than 14-years old. Watershed-wide, high-impact practices were estimated to reduce nitrogen loads to streams by 1.45%, with up to 60% reductions in areas with shorter groundwater ages and larger amounts of implementation. Effects of “other-impact” practices and practices in areas with groundwater ages of 14 years or more showed less evidence of effectiveness. That the discernable impact of high-impact practices was limited to areas with a median groundwater age of less than 14 years does not imply that conservation practices are not effective in areas with older groundwater ages. A model recalibrated using high-impact agricultural conservation practice data summarized by county suggests effects may also be detectable using implementation data available at such coarser resolution. Despite increasing investment, effects of agricultural conservation practices on regional water quality remain difficult to quantify due to factors such as groundwater travel times, varying modes-of-action, and the general lack of high-quality spatial datasets representing practice implementation.
The U.S. Geological Survey and University of Wisconsin–Green Bay collected hydrologic and water-quality data to assess the effectiveness of agricultural conservation management practice (CMP) implementation at mainstem Plum Creek and west Plum Creek in northeastern Wisconsin. These two subbasins cover 88 percent of the Plum Creek Basin (Hydrologic Unit Code 12), which is a subbasin of the lower Fox River Basin. A published total maximum daily load report for the lower Fox River Basin rated Plum Creek as one of the greatest contributors of total suspended solids (TSS) and total phosphorus (TP) draining into the lower Fox River. To reduce TSS and TP exports from Plum Creek, additional cropland conservation practices and watercourse protections were applied between 2012 and 2020. To detect water-quality trends, data were collected during 2010 to 2020 at mainstem Plum Creek and 2013 to 2020 at west Plum Creek.
The project used two methods to evaluate CMP effectiveness. The first method focused on evaluating water-quality changes between initial and post-CMP implementation periods during rain- or snowmelt-induced runoff events (hereafter referred to as “events”). In this approach random-forest models were developed to account for environmental factors which influence water quality. Model residuals from the two time periods were compared to determine the significance of water-quality changes associated with CMP implementation for mainstem and west Plum Creek Basins. The second method used a Weighted Regressions on Time, Discharge, and Season time-series approach to examine changes in water quality during the entire study period in mainstem Plum Creek. Results from both methods indicated there were minimal water-quality changes in TSS concentrations and flow-normalized delivery during runoff events during the 10-year period from 2010 to 2020; however, TP concentrations during low streamflow (less than 3 cubic feet per second [ft3/s]) may have decreased. The lack of observed improvement may be attributable to any of the following: variability in weather and hydrologic conditions, insufficient post-treatment data, additional cropland being converted to corn production, above average rainfall, streambank degradation, acute and legacy sources of phosphorus from farm fields, excessive/vulnerable manure applications and spills, and point-source discharges.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235043","collaboration":"Prepared in cooperation with the University of Wisconsin-Green Bay and Outagamie County, Wisconsin","usgsCitation":"Horwatich, J.A., Fermanich, K., Pronschinske, M.A., Robertson, D.M., Kussow, S., Loken, L.C., Reneau, P.C., Freund, J., and Komiskey, M.J., 2023, Assessment of conservation management practices on water quality and observed trends in the Plum Creek Basin, 2010–20: U.S. Geological Survey Scientific Investigations Report 2023–5043, 31 p., https://doi.org/10.3133/sir20235043.","productDescription":"Report: ix, 31 p.; Data Release","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-130579","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":416705,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5043/coverthb.jpg"},{"id":416707,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5043/sir20235043.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":500920,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114718.htm","linkFileType":{"id":5,"text":"html"}},{"id":416709,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92A0H98","text":"USGS data release","linkHelpText":"Water quality and estimated changes in the Plum Creek watershed 2010–2020 (data release and model archive)"},{"id":416708,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5043/images"},{"id":416706,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5043/sir20235043.pdf","text":"Report","size":"8.23 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5043"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Plum Creek Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.30723441433527,\n 44.40323167054055\n ],\n [\n -88.30723441433527,\n 44.12306373303795\n ],\n [\n -87.89405155338416,\n 44.12306373303795\n ],\n [\n -87.89405155338416,\n 44.40323167054055\n ],\n [\n -88.30723441433527,\n 44.40323167054055\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
The Yellowstone Volcano Observatory (YVO) monitors volcanic and hydrothermal activity associated with the Yellowstone magmatic system, carries out research into magmatic processes occurring beneath Yellowstone Caldera, and issues timely warnings and guidance related to potential future geologic hazards. This report summarizes the activities and findings of YVO during the year 2022, focusing on the Yellowstone volcanic system. Highlights of YVO research and related activities during 2022 include deployments of seismometers in Norris Geyser Basin and Upper Geyser Basin to investigate interactions between hydrothermal features and influences from external influences, geological studies of post-glacial hydrothermal activity, refining the ages of Yellowstone volcanic units and updating existing maps of geologic deposits, new mapping of ash-flow deposits on the Sour Creek dome, installation of a new continuous gas monitoring station near Mud Volcano, sampling of gas emissions and thermal waters around Yellowstone National Park to monitor water chemistry over space and time, research into the age and history of Steamboat Geyser in Norris Geyser Basin, and assessment of thermal output based on satellite imagery and chloride flux in rivers.
The most noteworthy event of the year was not geophysical, but meteorological. Combined runoff from rain and snowmelt caused substantial flooding in Yellowstone National Park, which caused damage to park roads and infrastructure. Steamboat Geyser, in Norris Geyser Basin, continued the pattern of frequent eruptions that began in 2018 with 11 water eruptions in 2022, the lowest number of annual eruptions in the current eruptive sequence. Total seismicity—2,429 located earthquakes—was slightly less than the 2,773 earthquakes located in 2021 and at the upper end of the historical average range of about 1,500–2,500 earthquakes per year. Overall subsidence of the caldera floor, ongoing since late 2015 or early 2016, continued at rates of a few centimeters (1–2 inches) per year. Satellite deformation measurements indicated the possibility of slight uplift amounting to about 1 centimeter (less than 1 inch) along the north caldera rim in 2021, but satellite data spanning 2022 show no uplift in that area. Throughout 2022, the aviation color code for Yellowstone Caldera remained at “green” and the volcano alert level remained at “normal.”
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1508","usgsCitation":"Yellowstone Volcano Observatory, 2023, Yellowstone Volcano Observatory 2022 annual report: U.S. Geological Survey Circular 1508, 49 p., https://doi.org/10.3133/cir1508.","productDescription":"v, 49 p.","numberOfPages":"49","onlineOnly":"N","ipdsId":"IP-149199","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":416682,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1508/covrthb.jpg"},{"id":416683,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1508/cir1508.pdf","text":"Report","size":"28 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":499548,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114711.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.22714973143812,\n 45.10797687707381\n ],\n [\n -111.22714973143812,\n 43.34546446716831\n ],\n [\n -108.61352791332872,\n 43.34546446716831\n ],\n [\n -108.61352791332872,\n 45.10797687707381\n ],\n [\n -111.22714973143812,\n 45.10797687707381\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Yellowstone Volcano Observatory
U.S. Geological Survey
1300 SE Cardinal Court, Suite 100
Vancouver, WA 98683
Email: yvowebteam@usgs.gov
","tableOfContents":"To help support remedial efforts at the former Badger Army Ammunition Plant the U.S. Geological Survey built and calibrated a transient groundwater flow model using the Newton Raphson formulation (MODFLOW–NWT) of the U.S. Geological Survey’s modular three-dimensional finite-difference code. The model simulates the groundwater flow system at the site from 1984 to 2020. The former Badger Army Ammunition Plant is a 7,275-acre site in Sauk County, Wisconsin. The plant produced smokeless gunpower and solid rocket propellent as munitions components. Peak production periods were during World War II, the Korean War, and the Vietnam War. Subsequent groundwater contamination investigations have found four plumes at the site. A health risk assessment identified at least one contaminant of concern for human health risk present in three of the plumes: the propellant burning ground plume, the deterrent burning ground plume, and the central plume. A cooperative study began between the U.S. Army Environmental Command and U.S. Geological Survey to better understand the groundwater flow system at the former Badger Army Ammunition Plant. Field data, including aquifer tests, streamflow measurements, continuous groundwater elevations, and groundwater gradients with the Wisconsin River were collected and used to inform and calibrate the groundwater flow model. The model was used to assess the variability of the groundwater system over the study period, the components of the groundwater budget, and groundwater flow directions from identified source areas towards the Wisconsin River. Model performance assessment focused on using particle tracking to compare groundwater flowpaths that originate in the contaminant source areas to the observed plume footprints. This focus on plume behavior geometry should help constrain the advective component of a future groundwater transport model of the site.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235040","collaboration":"Prepared in cooperation with U.S. Army Environmental Command","usgsCitation":"Haserodt, M.J., Reeves, H.W., Nielsen, M.G., Schachter, L.A., Corson-Dosch, N.T., and Feinstein, D.T., 2023, Simulation of groundwater flow at the former Badger Army Ammunition Plant, Sauk County, Wisconsin: U.S. Geological Survey Scientific Investigations Report 2023–5040, 140 p., https://doi.org/10.3133/sir20235040.","productDescription":"Report: viii, 140 p.; 3 Data Releases; Dataset","numberOfPages":"152","onlineOnly":"Y","ipdsId":"IP-135445","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":416676,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9S2IDV0","text":"USGS data release","linkHelpText":"Soil-Water-Balance (SWB) model archive used to simulate potential annual recharge for the former Badger Army Ammunition Plant study area, Prairie du Sac, Wisconsin, 1980 to 2020"},{"id":416672,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5040/sir20235040.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":416671,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5040/sir20235040.pdf","text":"Report","size":"106 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5040"},{"id":416670,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5040/coverthb.jpg"},{"id":500916,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114707.htm","linkFileType":{"id":5,"text":"html"}},{"id":416678,"rank":8,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":416675,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95TSI73","text":"USGS data release","linkHelpText":"Slug test analysis results from unconsolidated and bedrock aquifers at Badger Army Ammunition Plant, Sauk County, Wisconsin, 2020"},{"id":416674,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5040/images"},{"id":416677,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LNRILT","text":"USGS data release","linkHelpText":"Groundwater model archive for the former Badger Army Ammunition Plant, Wisconsin"},{"id":416712,"rank":9,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235040/full","text":"Report","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Wisconsin","county":"Sauk County","otherGeospatial":"former Badger Army Ammunition Plant","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.76922975135027,\n 43.38657213852542\n ],\n [\n -89.76922975135027,\n 43.33005054374769\n ],\n [\n -89.70231568538874,\n 43.33005054374769\n ],\n [\n -89.70231568538874,\n 43.38657213852542\n ],\n [\n -89.76922975135027,\n 43.38657213852542\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
Previously glaciated landscapes often share similar surficial characteristics, including large areas of exposed bedrock, blankets of till deposits, and alluvium-floored valleys. These materials play significant roles in geologic and hydrologic resources, geohazards, and landscape evolution; however, the vast extents of many previously glaciated landscapes have rendered comprehensive, detailed field mapping difficult. While recent advances in remote sensing have facilitated mapping of surficial materials and landforms, manual map creation has remained a time-intensive task.
The development of convolutional neural networks (CNNs) for image classification has provided a new opportunity for rapid characterization of digital elevation models, thus enabling efficient mapping of surficial materials and landforms. We have developed a methodology that leverages existing geologic maps and high-resolution (1–3 m) lidar data to train a U-Net CNN to classify alluvium and exposed bedrock in previously glaciated regions. Coupled with U.S. Geological Survey-developed geomorphometry tools capable of approximating stream incision depths, these classifications can be used to estimate the minimum thicknesses of stream-proximal hillslope sediments in areas where streams have undergone minimal incision into bedrock.
We validate this approach in the context of the Neversink River watershed, a subbasin of the Delaware River Basin and significant water source for New York City. Evaluation of deep learning model performance demonstrates substantial agreement with manually drawn maps of alluvium and exposed bedrock. Validation of the minimum sediment thickness map using borehole data and passive seismic measurements shows the greatest performance for shallow materials and decreased performance in deep sediments, as well as in areas where bedrock exposures were too small to be resolved by lidar. To resolve these issues and create more accurate surficial maps, we are training new CNNs with additional geologic data and exploring advanced approaches for estimating depths of stream incision.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.acags.2023.100116","usgsCitation":"Odom, W.E., and Doctor, D.H., 2023, Rapid estimation of minimum depth-to-bedrock from lidar leveraging deep-learning-derived surficial material maps: Applied Computing and Geosciences, v. 18, 100116, 11 p., https://doi.org/10.1016/j.acags.2023.100116.","productDescription":"100116, 11 p.","ipdsId":"IP-146769","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":443651,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.acags.2023.100116","text":"Publisher Index Page"},{"id":417128,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, New Jersey, New York, Pennsylvania","otherGeospatial":"Delaware River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.31774531759645,\n 38.65648371068133\n ],\n [\n -74.83711116001508,\n 39.00784172038104\n ],\n [\n -74.58034795534637,\n 39.29641683375996\n ],\n [\n -74.84560023131485,\n 39.59878756424641\n ],\n [\n -74.57402157309659,\n 39.81435898583538\n ],\n [\n -74.36164212699806,\n 40.42339954973025\n ],\n [\n -74.7398677925508,\n 40.67483290305228\n ],\n [\n -74.20311730788073,\n 41.49081439956416\n ],\n [\n -73.87900349307475,\n 42.134764710063195\n ],\n [\n -74.40404082594178,\n 42.53787114018144\n ],\n [\n -75.19513563542162,\n 42.478156559974536\n ],\n [\n -75.74165548596034,\n 41.860885020202005\n ],\n [\n -76.19243039302005,\n 41.151909429148475\n ],\n [\n -76.53910969789781,\n 40.49637767696336\n ],\n [\n -76.22016361968275,\n 40.00185213900514\n ],\n [\n -75.6922644432239,\n 39.71427259376151\n ],\n [\n -75.61094038798961,\n 39.383028287456796\n ],\n [\n -75.31774531759645,\n 38.65648371068133\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Odom, William Elijah 0000-0001-8577-5056","orcid":"https://orcid.org/0000-0001-8577-5056","contributorId":292616,"corporation":false,"usgs":true,"family":"Odom","given":"William","email":"","middleInitial":"Elijah","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":872922,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Doctor, Daniel H. 0000-0002-8338-9722 dhdoctor@usgs.gov","orcid":"https://orcid.org/0000-0002-8338-9722","contributorId":2037,"corporation":false,"usgs":true,"family":"Doctor","given":"Daniel","email":"dhdoctor@usgs.gov","middleInitial":"H.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":872923,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70242634,"text":"sir20235015 - 2023 - Human factors used to estimate and forecast water supply and demand in the Upper Colorado River Basin","interactions":[],"lastModifiedDate":"2026-03-02T22:01:38.477103","indexId":"sir20235015","displayToPublicDate":"2023-05-03T14:00:00","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":"2023-5015","displayTitle":"Human Factors Used to Estimate and Forecast Water Supply and Demand in the Upper Colorado River Basin","title":"Human factors used to estimate and forecast water supply and demand in the Upper Colorado River Basin","docAbstract":"Water availability is a result of complex interactions between regional water supply and demand and underlying environmental, institutional, and economic determinants. For this study, water availability is defined as “access to a specific quantity and quality of water at a point in time and space, for a specific use, recognizing the social and economic value of water across uses and institutions that facilitate or hinder its equitable and efficient provisioning.” This report identifies the human factors that influence water supply and demand and summarizes (1) the extensive sets of data available to estimate these factors in the agricultural, municipal, and industrial water-use sectors and (2) factors of recreation and ecosystem services that influence water availability in the Upper Colorado River Basin. Lastly, future research needs are identified that can help prioritize collection and refinement of human factors of water use to improve water availability estimation and forecasting.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235015","usgsCitation":"Herman-Mercer, N., Bair, L., Hines, M., Restrepo-Osorio, D., Romero, V., and Lyde, A., 2023, Human factors used to estimate and forecast water supply and demand in the Upper Colorado River Basin: U.S. Geological Survey Scientific Investigations Report 2023–5015, 46 p., https://doi.org/10.3133/sir20235015.","productDescription":"Report: v, 46 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-134554","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true},{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":416657,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99PAIVH","text":"USGS data release","linkHelpText":"Human Factors of Water Availability in the Upper Colorado River Basin"},{"id":416667,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5015/sir20235015.xml"},{"id":416666,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5015/images"},{"id":415708,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5015/coverthb.jpg"},{"id":416656,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5015/sir20235015.pdf","text":"Report","size":"23.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5015"},{"id":416903,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20235015/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5015"},{"id":500709,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114654.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Arizona, Colorado, Nevada, New Mexico, Utah, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.10551489403292,\n 42.12593624912847\n ],\n [\n -112.14876730042165,\n 40.578643397004406\n ],\n [\n -112.4013441988101,\n 38.62958426227543\n ],\n [\n -112.47821542875455,\n 36.49853660965043\n ],\n [\n -111.23729414536629,\n 34.720565339434735\n ],\n [\n -109.59005350370012,\n 33.6922933450013\n ],\n [\n -107.63532794225712,\n 33.832340766660366\n ],\n [\n -106.88857885136851,\n 35.06376781338098\n ],\n [\n -106.31753542892439,\n 37.332120502512296\n ],\n [\n -106.44931468025776,\n 39.33043034353207\n ],\n [\n -107.59140152514598,\n 41.43808608645108\n ],\n [\n -108.03066569625673,\n 42.12593624912847\n ],\n [\n -109.69986954647847,\n 42.935426421776896\n ],\n [\n -110.57839788869994,\n 42.74193975501615\n ],\n [\n -111.10551489403292,\n 42.12593624912847\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Integrated Information Dissemination Division
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
Iron-sulfide minerals found in shale formations are stable under anaerobic conditions. However, in the presence of oxygen and water, acid-loving chemolithotrophic bacteria can transform the iron-sulfide minerals into a toxic solution of sulfuric acid and dissolved iron and minerals known as acid rock drainage (ARD). The objective of this study was to disrupt chemolithotrophic bacteria responsible for ARD using chemical treatments and to foster an environment favorable for competing microorganisms to attenuate the biologically induced ARD. Chemical treatments were injected into flow-through microcosms consisting of 501 g of pyrite-rich shale pieces inoculated with ARD bacteria. Three treatments were tested in the microcosms: (1) a sodium hydroxide-bleach mix, (2) a sodium lactate solution, and (3) a sodium lactate-soy infant formula mix. The effectiveness of the treatments was assessed by monitoring pH, dissolved iron, and other geochemical constituents in the discharge waters. The optimal treatment was a sequential injection of 1.5 g sodium hydroxide, followed by 0.75 g lactate and 1.5 g soy formula dissolved in 20 mL water. The pH of the discharge water rose to 6.0 within 10 days, dissolved iron concentrations dropped below 1 mg/L, the median alkalinity increased to 98 mg/L CaCO3, and sulfur-reducing and slime-producing bacteria populations were stimulated. The ARD attenuating benefits of this treatment were still evident after 231 days. Other treatments provided a number of ARD attenuating effects but were tempered by problems such as high phosphate concentrations, short longevity, or other shortcomings. The results of these laboratory microcosm experiments were promising for the attenuation of ARD. Additional investigations and careful selection of treatment methods will be needed for field application.
Groundwater in the karst groundwater system at Area B of Fort Detrick in Frederick County, Maryland, is contaminated with chlorinated solvents from the past disposal of laboratory wastes. In cooperation with U.S. Army Environmental Command and U.S. Army Garrison Fort Detrick, the U.S. Geological Survey performed a 3-year study to refine the conceptual model of groundwater flow in and around Area B of Fort Detrick at the site- to regional-scale. The investigation was designed to review the geologic setting, assess the temporal variability of the hydrologic system, evaluate the potential for interbasin groundwater flow, determine the degree of vertical connectivity of the aquifer, characterize the sources and timing of groundwater recharge, and identify if dyes from previous tracer tests continue to drain from the aquifer. This study established a continuous hydrologic monitoring network of 12 water level gages, 2 streamgages, a precipitation gage, and in situ fluorometric monitoring. A water budget analysis was performed using hydrologic monitoring data and a soil-water balance model constructed for the study. In this study each individual water budget term is calculated using available data or through modeling, and a water budget residual term is calculated. If the water budget residual term is small relative to the uncertainty of the underlying data, then an additional import or export of water (in other words, interbasin transfer) is not needed to fully describe the hydrologic system. Groundwater and spring samples from 20 locations were collected in a 2019 synoptic geochemical sampling event and analyzed for a suite of analytes that included groundwater age tracer constituents.
The karst groundwater system was found to be highly responsive to hydrologic events, with strong water level and stream base flow responses to individual storm events and a historic wet period in 2017 and 2018. The water budget analysis included historic flooding in May 2018, though more typical hydrologic patterns were observed in 2019 and 2020. During most evaluated intervals, the water budget residual was less than the estimated uncertainty on the residual for the two Carroll Creek watersheds, which suggested no substantial net interbasin flow occurs from these watersheds. The watershed difference area, a region that includes Area B, had a significant negative water budget residual, which may be the result of a net interbasin import of groundwater or the result of focused groundwater recharge not simulated by the soil-water balance model. Geochemical analysis and groundwater age dating reveals shallow groundwater (approximately less than [<] 150 feet deep) appears to be relatively young (approximately <30 years) and to be recharged in the vicinity of Area B. In the deep groundwater sampled in this study (approximately greater than [>] 150 feet deep), older groundwater from a differing recharge source, based on stable isotopes and noble gas analyses, is observed and interpreted to represent less direct connectivity to the surface and increased proportions of water recharged to the north and (or) west of Area B. A clustering analysis to reveal groupings within the suite of geochemical data was used to define seven groups. The groupings generally show that wells in similar depths and lateral aquifer positions generally cluster together, with some exceptions. Although limited by suspended sediments, the in situ fluorometric monitoring at springs did not detect any dye leaving the system above the limit of detection for the method. Dye was only detected above the limit of detection in one well, which was used as an injection well during a previous dye tracer test.
The results of this study support and refine the conceptual site model of groundwater hydrology at Area B. The geologic and geophysical log review in this study agrees with prior assessments of physical controls on groundwater flow. A literature review of mid-Atlantic karst studies identified similar controls reported in these environments. The additional characterization of hydrologic responsiveness in this study suggests that hydrologic conditions and events are important considerations when interpreting potentiometric surfaces and contaminant trends over time and highlights the importance of continuous hydrologic monitoring. There is evidence to suggest that either intense focused groundwater recharge occurs in the vicinity of Area B or net along-valley groundwater interbasin flow from the upper study watershed enters the lower watershed and discharges to Carroll Creek. Geochemical analyses also suggest that water recharged from Catoctin Mountain and the elevated areas to the north and (or) west of the site may be present in the older and deeper Area B groundwater.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225054","collaboration":"Prepared in cooperation with U.S. Army Environmental Command and U.S. Army Garrison, Fort Detrick","usgsCitation":"Goodling, P.J., Fleming, B.J., Solder, J., Soroka, A., and Raffensperger, J., 2023, Hydrogeologic characterization of Area B, Fort Detrick, Maryland: U.S. Geological Survey Scientific Investigations Report 2022–5054, 128 p., https://doi.org/10.3133/sir20225054.","productDescription":"Report: xiv, 128 p.; 2 Data Releases","numberOfPages":"128","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-124092","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":435349,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DUFZY7","text":"USGS data release","linkHelpText":"Supporting Datasets for Hydrogeological Characterization of Ft. Detrick Area B, Maryland"},{"id":500936,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114709.htm","linkFileType":{"id":5,"text":"html"}},{"id":416517,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GTTX8Q","text":"USGS data release","linkHelpText":"Soil water balance model developed for Maryland and Pennsylvania"},{"id":416516,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AYWBXU","text":"USGS data release","linkHelpText":"Supporting datasets for hydrogeological characterization of Area B, Fort Detrick, Maryland"},{"id":416515,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5054/sir20225054.pdf","text":"Report","size":"51.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5054"},{"id":416514,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5054/coverthb.jpg"},{"id":416562,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225054/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5054"},{"id":416564,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5054/images/"},{"id":416563,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5054/sir20225054.XML"}],"country":"United States","state":"Maryland","otherGeospatial":"Fort Detrick","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -77.4693386578843,\n 39.458154924593\n ],\n [\n -77.4693386578843,\n 39.41628758896462\n ],\n [\n -77.38630852298522,\n 39.41628758896462\n ],\n [\n -77.38630852298522,\n 39.458154924593\n ],\n [\n -77.4693386578843,\n 39.458154924593\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Maryland-Delaware-D.C. Water Science Center
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A 2,000 year-long oceanographic history, in sub-centennial resolution, from a Canadian Beaufort Sea continental shelf site (60meters water depth) near the Mackenzie River outlet is reconstructed from ostracode and foraminifera faunal assemblages, shell stable isotopes (delta 18O, delta 13C) and sediment biogenic silica. The chronology of three sediment cores making up the composite section was established using 137Cs and 210Pb dating for the most recent 150 years and combined with linear interpolation of radiocarbon dates from bivalve shells and foraminifera tests.Continuous centimeter-sampling of the multicore and high-resolution sampling of a gravity and piston core yielded a time-averaged faunal record of every approximately 40 years from 0 to 1850 CE and every approximately 24 years from 1850 to 2013 CE. Proxy records were consistent with temperature oscillations and related changes in organic carbon cycling associated with the Medieval Climate Anomaly (MCA) and the Little Ice Age (LIA). Abundance changes in dominant microfossil species, such as the ostracode Paracyprideis pseudopunctillata and agglutinated foraminifers Spiroplectammina biformis and S. earlandi, are used as indicators of less saline, and possibly corrosive/turbid bottom conditions associated with the MCA (approximately 800 to 1200 CE) and the most recent approximately 60 years (1950–2013). During these periods, pronounced fluctuations in these species suggest that prolonged seasonal sea-ice melting, changes in riverine inputs and sediment dynamics affected the benthic environment. Taxa analyzed for stable oxygen isotope composition of carbonates show the lowest delta 18O values during intervals within the MCA and the highest during the late LIA, which is consistent with a 1 degree to 2 degree C cooling of bottom waters. Faunal and isotopic changes during the cooler LIA (1300 to 1850 CE) are most apparent at approximately 1500 to 1850 CE and are particularly pronounced during 1850 to approximately 1900 CE, with an approximate 0.5 per mil increase in delta 18O values of carbonates from median values in the analyzed taxa. This very cold 50-year period suggests that enhanced summer sea ice suppressed productivity,which is indicated by low sediment biogenic silica values and lower delta 13C values in analyzed species. From 1900CE to present, declines in calcareous faunal assemblages and changes in dominant species (Cassidulina reniforme and P. pseudopunctillata) are associated with less hospitable bottom waters, indicated by a peak in agglutinated foraminifera from 1950 to 1990 CE.
","language":"English","publisher":"Micropaleontology Press","doi":"10.47894/mpal.69.3.04","usgsCitation":"Gemery, L., Cronin, T.M., Cooper, L.W., Roberts, L., Keigwin, L., Addison, J.A., Leng, M., Lin, P., Magen, C., Marot, M.E., and Schwartz, V., 2023, Multi-proxy record of ocean-climate variability during the last 2 millennia on the Mackenzie Shelf, Beaufort Sea: Micropaleontology, v. 69, no. 3, p. 345-366, https://doi.org/10.47894/mpal.69.3.04.","productDescription":"22 p.","startPage":"345","endPage":"366","ipdsId":"IP-134578","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":443665,"rank":3,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://nora.nerc.ac.uk/id/eprint/535369/1/33670_articles_article_file_2322.pdf","text":"External Repository"},{"id":435351,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SRRW6T","text":"USGS data release","linkHelpText":"Data Release to Multi-proxy record of ocean-climate variability during the last 2 millennia on the Mackenzie Shelf, Beaufort Sea (2013)"},{"id":416619,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Beaufort Sea, Mackenzie Shelf","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -130.57152373008722,\n 71.44885475594037\n ],\n [\n -144.9141394593442,\n 71.44885475594037\n ],\n [\n -144.9141394593442,\n 68.59098357271469\n ],\n [\n -130.57152373008722,\n 68.59098357271469\n ],\n [\n -130.57152373008722,\n 71.44885475594037\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"69","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-05-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Gemery, Laura 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Oceanographic Institution","active":true,"usgs":false}],"preferred":false,"id":871360,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Magen, Cedric","contributorId":265132,"corporation":false,"usgs":false,"family":"Magen","given":"Cedric","email":"","affiliations":[{"id":54603,"text":"University of Maryland Center for Environmental Science, Chesapeake Biological Lab, Solomons MD","active":true,"usgs":false}],"preferred":false,"id":871361,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Marot, Marci E. 0000-0003-0504-315X mmarot@usgs.gov","orcid":"https://orcid.org/0000-0003-0504-315X","contributorId":2078,"corporation":false,"usgs":true,"family":"Marot","given":"Marci","email":"mmarot@usgs.gov","middleInitial":"E.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":871362,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Schwartz, Valerie 0000-0003-2874-8435","orcid":"https://orcid.org/0000-0003-2874-8435","contributorId":279845,"corporation":false,"usgs":false,"family":"Schwartz","given":"Valerie","affiliations":[{"id":57375,"text":"Juul","active":true,"usgs":false}],"preferred":false,"id":871363,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70243022,"text":"sir20235023 - 2023 - Sediment transport in two tributaries to the San Joaquin River immediately below Friant Dam—Cottonwood Creek and Little Dry Creek, California","interactions":[],"lastModifiedDate":"2026-03-06T20:43:31.136709","indexId":"sir20235023","displayToPublicDate":"2023-05-02T08:31:13","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-5023","displayTitle":"Sediment Transport in Two Tributaries to the San Joaquin River Immediately Below Friant Dam—Cottonwood Creek and Little Dry Creek, California","title":"Sediment transport in two tributaries to the San Joaquin River immediately below Friant Dam—Cottonwood Creek and Little Dry Creek, California","docAbstract":"Two tributaries to the greater San Joaquin River watershed, Cottonwood and Little Dry Creeks, in California’s Central Valley, were assessed for sediment and streamflow dynamics between October 1, 2011, and September 30, 2019. The two systems deliver sediment to the San Joaquin River below Friant Dam, California. Dams create downstream discontinuities in streamflow and sediment transport and therefore influence fish habitat and sediment dynamics. Because these two creeks are directly downriver from Friant Dam, they become the most upstream source of sediment to the San Joaquin River below Friant Dam.
The quality and quantity of spawning habitat for fish in the gravel-bedded reach of the San Joaquin River relies on a range of bed material particle size suitable for redd structure. The effects of coarse-sand to fine-gravel supply on salmonid habitat depends primarily on the size of the sediment and the timing of its addition from tributaries to the San Joaquin River; thus, understanding the timing, quantity, and size of sediment supplied from these two tributaries is critical to the management of ecological and biological sustainability.
Streamflow from Cottonwood and Little Dry Creeks, along with streamflow from the San Joaquin River below Friant Dam, were compared to continuously measured water-surface elevations to quantify the timing and direction of streamflow. Suspended-sediment samples were collected with multiple automatic samplers and analyzed for concentration and grain-size distribution. Measured suspended-sediment concentrations and streamflows were used to develop sediment rating curves and compute continuous estimates of suspended-sediment load for each tributary. Satellite imagery was used to qualify spatial and temporal dynamics through the lower watersheds and support more quantitative sediment-load estimates.
Computed annual sediment loads ranged from 1.32x101 to 2.68x104 metric tons for Little Dry Creek and 9.82 to 1.98x103 metric tons for Cottonwood Creek. Sediment loads computed during the study period for both watersheds show that annual loads were highest during water year 2017 (October 1, 2016, to September 30, 2017). Sediment transport primarily occurred between the months of January and March. In both tributaries, grain-size distributions of suspended sediment were predominantly coarse-sized sand and were finer than the remnant bed material.
Both creeks demonstrate backwater effects from the San Joaquin River, but the more tortuous stream channel and historical mining pits within Little Dry Creek provide more capacity for sediment storage compared to the less complex stream network of Cottonwood Creek. Because loads were computed based on upstream streamgages and not at the confluence of each tributary to the San Joaquin River, annual load estimates do not represent direct flux into the San Joaquin River; instead, these results indicated that in Little Dry Creek, particularly, the lowest portion of the watershed stores sediment before it reaches the San Joaquin River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235023","collaboration":"Prepared in cooperation with the Bureau of Reclamation San Joaquin River Restoration Program","programNote":"National Water Quality Program and Water Availability and Use Science Program","usgsCitation":"Haught, D.R.W., Marineau, M.D., Minear, J.T., Wright, S.A., and Lopez, J.V., 2023, Sediment transport in two tributaries to the San Joaquin River immediately below Friant Dam—Cottonwood Creek and Little Dry Creek, California: U.S. Geological Survey Scientific Investigations Report 2023–5023, 34 p., https://doi.org/10.3133/sir20235023.","productDescription":"Report: ix, 34 p.; Data Release","numberOfPages":"34","ipdsId":"IP-120407","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":416394,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5023/images"},{"id":416392,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5023/sir20235023.pdf","text":"Report","size":"20 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":416391,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5023/covrthb.jpg"},{"id":500875,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114705.htm","linkFileType":{"id":5,"text":"html"}},{"id":416396,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9E1OYNM","text":"Little Dry Creek and Cottonwood Creek sediment transport data, 2012–2018, San Joaquin Watershed in the California Central Valley","description":"Haught, D.R.W., and Marineau, M.D., 2023, Little Dry Creek and Cottonwood Creek sediment transport data, 2012–2018, San Joaquin Watershed in the California Central Valley: U.S. Geological Survey data release, https://doi.org/10.5066/P9E1OYNM."}],"country":"United States","state":"California","otherGeospatial":"Little Dry Creek and Cottonwood Creek watersheds, San Joaquin Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.375,\n 37.1667\n ],\n [\n -119.833333,\n 37.1667\n ],\n [\n -119.833333,\n 36.75\n ],\n [\n -119.375,\n 36.75\n ],\n [\n -119.375,\n 37.1667\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
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U.S. Geological Survey
6000 J Street, Placer Hall
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Over the past two decades, the U.S. Geological Survey (USGS) and other agencies have pioneered the use of active acoustic sensors to monitor suspended-sediment concentrations and particle sizes in rivers and streams at the subdaily time scale. The LISST-ABS submersible acoustic backscatter sediment sensor (or “ABS sensor”) was developed by Sequoia Scientific, Inc., as an alternative to turbidity sensors for monitoring suspended-sediment concentrations in surface waters. The ABS sensor is different than traditional active acoustic instruments because it is small, lower in cost, lightweight, and requires less power; and the sampling volume is within the first 15 centimeters of the transducer face. Initial testing by the USGS indicated the ABS sensor had utility as a novel, cost-effective, off-the-shelf tool for monitoring suspended-sediment concentration in surface waters, and its use within the agency has increased in since its introduction around 2016. However, initial testing did not account for the potential of transducer calibration drift over longer deployments.
As part of its mission to unify and standardize research and development activities of Federal agencies involved in fluvial sediment studies, the Federal Interagency Sedimentation Project partnered with the USGS Wyoming-Montana and New Mexico Water Science Centers to examine the potential for use of standard, low-tech laboratory equipment to perform calibration checks on ABS sensors on long-term deployments. The experiments were intended to provide USGS scientists and the public with interim guidance to assist in operating and maintaining the ABS sensor.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231039","collaboration":"Prepared in cooperation with the Federal Interagency Sedimentation Project","usgsCitation":"Alexander, J.S., O’Connell, J.P., and Brown, J.E., 2023, Interim guidance for calibration checks on a submersible acoustic backscatter sediment sensor (ver. 1.1, November 2023): U.S. Geological Survey Open-File Report 2023–1039, 23 p., https://doi.org/10.3133/ofr20231039.","productDescription":"Report: v, 23 p.; Data Release; 2 Datasets","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-147547","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true},{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"links":[{"id":422961,"rank":8,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2023/1039/versionHist.txt","text":"Version History","size":"1 kB","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2023-1039 Version History"},{"id":416561,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://waterdata.usgs.gov/nwis/inventory/?site_no=09363500&agency_cd=USGS&","text":"USGS National Water Information System database","linkHelpText":"—USGS 09363500 Animas River near Cedar Hill, NM, in USGS water data for the Nation"},{"id":416559,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9M41G3W","text":"USGS data release","linkHelpText":"Data from lab experiments to support interim guidance for performing calibration checks on the Sequoia Scientific LISST-ABS acoustic backscatter sensor"},{"id":416557,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1039/ofr20231039.XML","size":"149 kB","description":"OFR 2023-1039 XML"},{"id":416555,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1039/coverthb2.jpg"},{"id":422960,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1039/ofr20231039.pdf","text":"Report","size":"2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1039"},{"id":416558,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1039/images"},{"id":416560,"rank":6,"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"}],"edition":"Version 1.0: May 1, 2023; Version 1.1: November 27, 2023","contact":"Director, Wyoming-Montana Water Science Center
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3162 Bozeman Avenue
Helena, MT 59601
Many microorganisms secrete secondary metabolites with antibiotic properties; however, there is debate whether the secretions evolved as a means to gain a competitive edge or as a chemical signal to coordinate community growth. The objective of this research was to investigate if select antibiotics acted as a weapon or as a chemical signal by exposing communities of aquatic cave bacteria to increasing concentrations of antibiotics. Water samples were collected from six cave locations where actinobacterial mats appeared to be plentiful. Bacterial growth was measured using colony counts on 10 % tryptic soy agar augmented with increasing concentrations of erythromycin, tetracycline, kanamycin, gentamicin, or quaternary ammonia compounds (QAC). Colony counts generally decreased as the gentamicin, kanamycin and QAC dose increased. In contrast, the colony numbers increased on agar plates supplemented with 0.01 mg L−1, 0.10 mg L−1 and 1.00 mg L−1 erythromycin or tetracycline. A 10.00 mg L−1 dose of each antibiotic treatment reduced bacteria colonies by 98 % or more. Community-level physiological capabilities were evaluated using Ecolog plates inoculated with cave water dosed with either 0.00 mg L−1 or 0.10 mg L−1 of erythromycin. Incubation with the antibiotic almost doubled the number of food substrates used in the first 24 hours. There was a significant increase in the use of acetyl glucosamine, arginine, and putrescine when bacteria were exposed to 0.10 mg L−1 erythromycin triggered by the antibiotic acting as a chemical messenger. Principal component analysis confirmed a shift in substrate preferences when erythromycin was added. A conceptual ecological model is proposed based on the response of aquatic cave bacteria to sublethal antibiotics.
","language":"English","publisher":"National Speleological Society","doi":"10.4311/2022MB0106","usgsCitation":"Byl, T.D., Byl, P.K., Byl, J.P., and Toomey, R., 2023, Stimulation of aquatic bacteria from Mammoth Cave, Kentucky, by sublethal concentrations of antibiotics: Journal of Cave and Karst Studies, v. 85, no. 1, p. 16-27, https://doi.org/10.4311/2022MB0106.","productDescription":"12 p.","startPage":"16","endPage":"27","ipdsId":"IP-065135","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":443673,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://doi.org/10.4311/2022mb0106","text":"Publisher Index Page"},{"id":435353,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7J1018X","text":"USGS data release","linkHelpText":"Average well color development data for water samples from six locations within the historic section of Mammoth Cave National Park, Kentucky"},{"id":418004,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kentucky","otherGeospatial":"Mammoth Cave","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -86.2828185616811,\n 37.28844220519747\n ],\n [\n -86.2828185616811,\n 37.09470580139313\n ],\n [\n -85.96018319973803,\n 37.09470580139313\n ],\n [\n -85.96018319973803,\n 37.28844220519747\n ],\n [\n -86.2828185616811,\n 37.28844220519747\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"85","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Byl, Thomas D. 0000-0001-6907-9149 tdbyl@usgs.gov","orcid":"https://orcid.org/0000-0001-6907-9149","contributorId":583,"corporation":false,"usgs":true,"family":"Byl","given":"Thomas","email":"tdbyl@usgs.gov","middleInitial":"D.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":871489,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Byl, Petra Kim 0000-0002-9168-2603","orcid":"https://orcid.org/0000-0002-9168-2603","contributorId":304716,"corporation":false,"usgs":false,"family":"Byl","given":"Petra","email":"","middleInitial":"Kim","affiliations":[{"id":66150,"text":"Biological Oceanography University of Hawaii at Mānoa School of Ocean and Earth Science and Technology Department of Oceanography","active":true,"usgs":false}],"preferred":false,"id":875145,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Byl, Jacob P. 0000-0001-7998-9795","orcid":"https://orcid.org/0000-0001-7998-9795","contributorId":304724,"corporation":false,"usgs":false,"family":"Byl","given":"Jacob","email":"","middleInitial":"P.","affiliations":[{"id":36656,"text":"Vanderbilt University","active":true,"usgs":false}],"preferred":false,"id":875146,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Toomey, Rickard III","contributorId":306230,"corporation":false,"usgs":false,"family":"Toomey","given":"Rickard","suffix":"III","email":"","affiliations":[],"preferred":false,"id":875147,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}