In 2016, Colorado Parks and Wildlife identified filamentous algae collected from the main stem White River as Cladophora glomerata, a pervasive nuisance aquatic alga. Excessive levels of filamentous algae can compromise aesthetic quality, limit recreational activities, and have negative effects on aquatic life including strong fluctuations in dissolved oxygen levels and a reduction in overall biodiversity. To increase understanding of the biology of the upper White River Basin in Colorado, identify potential factors promoting or limiting nuisance algal abundance, and outline information to aid in the understanding and protection of water resources, the U.S. Geological Survey (USGS), in cooperation with the White River and Douglas Creek Conservation Districts and the White River Algae Technical Advisory Group, initiated a study to collect and analyze physical, chemical, and biological information for the upper White River Basin. The report describes long-term changes and spatial variations in streamflow and nutrient concentrations and loads in the upper White River Basin and identifies possible nutrient sources in the basin.
Long-term streamflow and nutrient data indicate that conditions in the upper White River Basin have become more favorable to benthic algae over varying timescales. Upward trends in total phosphorus concentrations and loads were found at three sites across the basin from 2000 to 2020. Total phosphorus loads increased around 50 percent, ranging from 18 to 48 pounds per year. Annual estimated concentrations of total phosphorus from 2005 to 2020 were above algal-specific nutrient criteria at the North Fork White River at Buford, Colo., indicating that phosphorus concentrations at this site likely promote algal growth. Discrete concentrations of total phosphorus exceeded algal-specific nutrient criteria on the South Fork and main stem White River during the summer season, though less frequently than samples collected from the North Fork White River. Nitrogen to phosphorus molar ratios collected from July to September indicate movement from colimitation (10–22) to nitrogen limited (less than 13) conditions at the North Fork White River at Buford, Colo. and the South Fork White River at Buford, Colo. starting in 2012. The magnitude of trends in phosphorus loads were generally greater than trends in concentrations across all sites, indicating that the largest changes in concentrations occurred during greater streamflow periods.
At White River above Coal Creek, near Meeker, Colo., significant downward trends in streamflow were found in August and September for mean streamflow (15 and 14 percent per decade, respectively) and 7-day minimum streamflows (23 and 22 percent per decade, respectively). Significant downward trends in annual 7-day minimum streamflows of 24 percent per decade, or 66 percent over the 40-year period of analysis, were also observed. Though not significant based on 90-percent confidence intervals, downward trends in 1-day maximum and mean streamflows in May and June and corresponding increases in April may indicate a shift toward earlier snowmelt runoff, as observed across western North America and the Colorado River Basin. Alteration of the annual hydrograph can influence factors that influence algae including nutrient input and dilution potential, water temperature, dissolved oxygen, light availability, and physical disturbance.
Results from a synoptic-style sampling identified the lower North Fork White River subbasin as a large source of phosphorus to the downstream system. Large increases in phosphorus loads were observed below Marvine Creek. Synoptic samples and samples collected during spring and summer of 2019 and 2020 also show large increases in total nitrogen, orthophosphate, and total phosphorus occurring at the furthest three downstream sites on the White River. To further evaluate sources of nitrogen in the upper White River Basin, the dual isotopic composition of nitrate was compared across four sites. The isotopic compositions of nitrate were all within the expected range of typical soil-derived nitrate, though the same values can also be derived from a mixture of agricultural fertilizer and manure or septic sources.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225112","collaboration":"Prepared in cooperation with the White River and Douglas Creek Conservation Districts","usgsCitation":"Day, N.K., 2023, Characterization of streamflow and nutrient occurrence in the upper White River Basin, Colorado, 1980–2020: U.S. Geological Survey Scientific Investigations Report 2022–5112, 37 p., https://doi.org/10.3133/sir20225112.","productDescription":"Report: vi, 37 p.; 2 Data Release","onlineOnly":"Y","ipdsId":"IP-133327","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":414992,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS data release","linkHelpText":"USGS water data for the Nation: U.S. Geological Survey National Water Information System database"},{"id":415083,"rank":8,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5112/sir20225112.xml"},{"id":415082,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5112/images"},{"id":415567,"rank":9,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225112/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5112"},{"id":414990,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20235009","text":"USGS Scientific Investigations Report 2023-5009—","linkHelpText":"Investigation of Potential Factors Controlling Benthic Algae in the Upper White River Basin, Colorado, 2018–21"},{"id":414991,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9E82RMQ","text":"USGS data release","linkHelpText":"Channel Characteristics, benthic algae, and water quality model data for selected sites in the upper White River Basin, Colorado, 2018-21"},{"id":414989,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/fs20233005","text":"USGS Fact Sheet 2023-3005—","linkHelpText":"Potential Factors Controlling Benthic Algae in the Upper White River Basin, Colorado, 2018–21"},{"id":414988,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5112/sir20225112.pdf","text":"Report","size":"7.55 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5112"},{"id":414987,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5112/coverthb.jpg"},{"id":500459,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114622.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Colorado","otherGeospatial":"Upper White River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108.20175418327835,\n 40.550962714804655\n ],\n [\n -108.20175418327835,\n 39.298023775605145\n ],\n [\n -105.58075670984697,\n 39.298023775605145\n ],\n [\n -105.58075670984697,\n 40.550962714804655\n ],\n [\n -108.20175418327835,\n 40.550962714804655\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
Denver, CO 80225
Nuisance levels of benthic filamentous green algae are becoming increasingly common in surface waters of Colorado and the western United States. In 2018 the U.S. Geological Survey began a study in cooperation with the White River and Douglas Creek Conservation Districts, Colorado River Basin Salinity Control Forum, and the Colorado River Water Conservation District to collect and analyze physical, chemical, and biological information for the upper White River Basin in Colorado and investigate causes of benthic algal blooms in the basin. This report (1) presents site-specific data including water temperature, riparian canopy cover, streambed particle size, and algal biomass and community composition; (2) describes the potential for streambed movement during spring runoff using physical channel characteristics and peak streamflow velocities; and (3) explains the results of a linear mixed-effects model used to test hypotheses about the influence of physical and chemical factors in explaining the occurrence of algal blooms across the basin.
Benthic algal biomass ranged from 0.7 to 309 milligrams per square meter during the summer (July–August) from 2018 through 2021 and exceeded the Colorado Department of Public Health and Environment criteria of 150 milligrams per square meter on four occasions, in 2018. Four genera of filamentous green algae were identified in the upper White River Basin, including Cladophora, Stigeoclonium, Ulothrix, and Spirogyra. Many genera of cyanobacteria were present, including some capable of producing toxins and taste and odor compounds. The nuisance diatom Didymosphenia geminata, commonly referred to as didymo, was found at two sites on the South Fork White River and along the main stem White River.
Hypotheses pertaining to the influence of measured variables on algal biomass were tested with a linear mixed-effects model. Median rock size and mean August water temperature had significant positive effects, meaning that greater bed stability and higher mean August water temperatures result in greater algal biomass. Total nitrogen to total phosphorus ratios had a significant negative effect on algal biomass, meaning that more nitrogen-limiting conditions, or greater phosphorus availability, corresponded to greater algal biomass.
Streamflow and water temperature data at White River above Coal Creek near Meeker, Colo., were used to assess possible causes of bloom conditions across years, including when algal blooms were first studied in the basin during 2016 and 2017. Early or low-magnitude peak streamflow conditions were not prerequisites for algal bloom occurrence. Conversely, relatively large, late, and long-lasting peak streamflows, such as those measured in 2019, may limit algal blooms during the same year and into subsequent years, as evidenced by extremely low algal biomass in 2019 and 2020. The broad spatial extent of bloom conditions indicates that the factors contributing to the occurrence of algal blooms are likely basinwide. Findings from this multiyear study indicate that the effects caused by larger peak streamflow, including movement of the streambed, may be the dominant control on the occurrence of an algal bloom. The findings also indicate that in the absence of disturbance other resources, including substrate size, water temperature, and nutrient availability, moderate algal biomass.
Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
Denver, CO 80225
Bathymetric change analyses document historical patterns of sediment deposition and erosion, providing valuable insight into the sediment dynamics of coastal systems, including pathways of sediment and sediment-bound contaminants. In 2014 and 2015, the Office for Coastal Management, in partnership with the National Oceanic and Atmospheric Administration (NOAA) Office of Coastal Management, provided funding for new bathymetric surveys of large portions of San Francisco Bay. A total of 93 bathymetric surveys were conducted during this 2-year period, using a combination of interferometric sidescan and multibeam sonar systems. These data, along with recent NOAA, U.S. Geological Survey (USGS), U.S. Army Corps of Engineers, and private contractor surveys collected from 1999 to 2020 (hereinafter referred to as 2010s), were used to create the most comprehensive bathymetric digital elevation models (DEMs) of San Francisco Bay since the 1980s. Comparing DEMs created from these 2010s surveys with USGS DEMs created from NOAA’s 1971–1990 (hereinafter referred to as 1980s) surveys provides information on the quantities and patterns of erosion and deposition in San Francisco Bay during the 9 to 47 years between surveys. This analysis reveals that in the areas surveyed in both the 1980s and 2010s, the bay floor lost about 34 million cubic meters of sediment since the 1980s. Results from this study can be used to assess how San Francisco Bay has responded to changes in the system, such as sea-level rise and variation in sediment supply from the Sacramento-San Joaquin Delta and local tributaries, and supports the creation of a new, system-wide sediment budget. This report provides data on the quantities and patterns of sediment volume change in San Francisco Bay for ecosystem managers that are pertinent to various sediment-related issues, including restoration of tidal marshes, exposure of legacy contaminated sediment, and strategies for the beneficial use of dredged sediment.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231031","collaboration":"Prepared in cooperation with the Regional Monitoring Program for Water Quality in San Francisco Bay","usgsCitation":"Fregoso, T.A., Foxgrover, A.C., and Jaffe, B.E., 2023, Sediment deposition, erosion, and bathymetric change in San Francisco Bay, California, 1971–1990 and 1999–2020 (ver. 1.1, June 2024): U.S. Geological Survey Open-File Report 2023–1031, 19 p., https://doi.org/ 10.3133/ ofr20231031.","productDescription":"Report: vi, 19 p.; Data Release","numberOfPages":"19","onlineOnly":"Y","ipdsId":"IP-135389","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":435389,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1332UUW","text":"USGS data release","linkHelpText":"Bathymetric change analysis in San Francisco Bay, California, from 1971 to 2020"},{"id":430608,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2023/1031/versionHist.txt","size":"10.7 KB","linkFileType":{"id":2,"text":"txt"}},{"id":415025,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1031/images"},{"id":415024,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1031/ofr20231031.pdf","text":"Report","size":"15.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":415023,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1031/coverthb2.jpg"},{"id":499186,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114619.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"California","county":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.84417858005195,\n 38.240616044555935\n ],\n [\n -122.84417858005195,\n 37.276937922454465\n ],\n [\n -121.28479077260828,\n 37.276937922454465\n ],\n [\n -121.28479077260828,\n 38.240616044555935\n ],\n [\n -122.84417858005195,\n 38.240616044555935\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: March 31, 2023; Version 1.1: June 28, 2024","contact":"Pacific Coastal and Marine Science Center
U.S. Geological Survey
2885 Mission St.
Santa Cruz, CA 95060
Seismic behavior and performance of the new Self- Anchored Suspension (SAS) Bridge of the San Francisco Bay Bridge System is studied using response data recorded during the October 14, 2019, \uD835\uDC40\uD835\uDC644.6 Pleasant Hill earthquake. The new bridge went into service within the last decade as a replacement for the older truss bridge that spanned between Yerba Buena Island and East Bay. During the October 19, 1989, M6.9 Loma Prieta earthquake, which occurred ∼100 km away from the Bay Bridge, a section of the upper deck of the old truss bridge fell onto the lower deck—thus closing this important lifeline between San Francisco and East Bay. The new SAS Bridge (as well as the rest of the Bay Bridge) is instrumented by the California Strong Motion Instrumentation Program (CSMIP). The unique SAS Bridge is suspended by a single tower that is pivotal in trafficking the cable and hanger system to support the eastbound (E) and westbound (W) decks. At both the west and east ends of the SAS, there is a hinge system that connects the W and E decks to the skyways leading to highways. For the west side, the SAS is led to a tunnel at Yerba Buena Island. The response data analyses highlight the complex and yet identifiable coupled response of the deck, tower, and cable system. Using system identification methods including spectral analyses of both acceleration and displacement time history data, the fundamental frequencies (periods) and critical damping percentages are extracted for the main components (tower, deck, and cables) of the bridge where the sensors are deployed. Frequencies (periods) are then compared with the values computed during the design and analysis process of the bridge. The analyses in this paper showed that there is strong evidence of a beating effect attributed to low critical damping percentages and coupled modes. A possible correlation of fundamental periods of such suspension bridges with their span lengths is discussed. The beating effect and period versus span length can be significant topics for further research.
","language":"English","publisher":"American Society of Civil Engineering","doi":"10.1061/JSENDH.STENG-11725","usgsCitation":"Celebi, M., 2023, The new Self Anchored Suspension (SAS) Bridge of the San Francisco Bay Bridge System: A preliminary study of its response and behavior during a small earthquake: Journal of Structural Engineering, v. 149, no. 6, 05023003, 12 p., https://doi.org/10.1061/JSENDH.STENG-11725.","productDescription":"05023003, 12 p.","ipdsId":"IP-138272","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":488064,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1061/jsendh.steng-11725","text":"Publisher Index Page"},{"id":481928,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay Bridge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.30992911723865,\n 37.83469490117358\n ],\n [\n -122.36848380330268,\n 37.83469490117358\n ],\n [\n -122.36848380330268,\n 37.80847229984835\n ],\n [\n -122.30992911723865,\n 37.80847229984835\n ],\n [\n -122.30992911723865,\n 37.83469490117358\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"149","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Celebi, Mehmet 0000-0002-4769-7357 celebi@usgs.gov","orcid":"https://orcid.org/0000-0002-4769-7357","contributorId":200969,"corporation":false,"usgs":true,"family":"Celebi","given":"Mehmet","email":"celebi@usgs.gov","affiliations":[],"preferred":true,"id":926975,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70263877,"text":"70263877 - 2023 - Drivers and timing of grass carp movement within the Sandusky River, Ohio: Implications to potential spawning barrier response strategy","interactions":[],"lastModifiedDate":"2025-02-27T14:48:12.355469","indexId":"70263877","displayToPublicDate":"2023-03-31T08:41:37","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1018,"text":"Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Drivers and timing of grass carp movement within the Sandusky River, Ohio: Implications to potential spawning barrier response strategy","docAbstract":"Understanding the timing and drivers of migration can be beneficial for improving response efforts aimed at reducing invasive species densities. Efforts by management agencies to remove grass carp (Ctenopharyngodon idella), an invasive species to the Laurentian Great Lakes, have been ongoing in Lake Erie tributaries since 2018. To bolster efforts, deployment of a non-physical barrier has been proposed downstream of a known grass carp spawning location near Brady’s Island (BI) in the Sandusky River, OH, USA to limit recruitment. However, knowledge of grass carp migratory timing, the environmental variables that cue carp migration, and the potential effects the barrier might impose on native fish [e.g., walleye (Sander vitreus)] movements would help inform barrier deployment and scheduling. We used detection data from grass carp (n = 29) and walleye (n = 84) tagged with acoustic transmitters to address four objectives: (1) quantify interannual variation (years = 2015–2021) of grass carp migration timing to BI; (2) evaluate timing of different grass carp movement modalities (residents and migrants); (3) assess overlap in migration timing with native walleye, and (4) evaluate environmental cues of grass carp migration to BI. Median grass carp arrival at BI occurred within a three-week period (148–165 Julian days), suggesting that deploying a barrier immediately prior to this time frame may be effective for deterring grass carp spawning. Temperature, photoperiod, and discharge influenced grass carp migration timing given that most arrival events occurred at daylengths > 14.5 h, temperatures exceeding 18 °C, and low discharge events (< 3,000 cubic feet second−1 [CFS]). Minimal interannual variability in migration timing existed for grass carp and walleye over a six-year period. However, the median departure time of walleye was more than 45 days before the median arrival time of grass carp, suggesting a spawning barrier may minimally affect walleye spawning. No differences in arrival timing at BI were observed between grass carp migratory contingents, indicating that if a barrier were deployed in the spring, it would likely affect all grass carp spatial contingents. This work highlights management implications of barrier control efforts of aquatic invasive species and provides insight into the environmental cues that grass carp use for upstream migration.
","language":"English","publisher":"Springer","doi":"10.1007/s10530-023-03049-9","usgsCitation":"Bopp, J., Brenden, T.O., Faust, M., Vandergoot, C., Kraus, R., Roberts, J., and Nathan, L., 2023, Drivers and timing of grass carp movement within the Sandusky River, Ohio: Implications to potential spawning barrier response strategy: Biological Invasions, v. 25, p. 2439-2459, https://doi.org/10.1007/s10530-023-03049-9.","productDescription":"21 p.","startPage":"2439","endPage":"2459","ipdsId":"IP-140268","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":482553,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Ohio","otherGeospatial":"Sandusky River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.5,\n 41.6\n ],\n [\n -83.5,\n 41.6\n ],\n [\n -83.5,\n 41.3\n ],\n [\n -82.5,\n 41.3\n ],\n [\n -82.5,\n 41.6\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"25","noUsgsAuthors":false,"publicationDate":"2023-03-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Bopp, Justin","contributorId":340933,"corporation":false,"usgs":false,"family":"Bopp","given":"Justin","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":928798,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brenden, Travis O.","contributorId":126759,"corporation":false,"usgs":false,"family":"Brenden","given":"Travis","email":"","middleInitial":"O.","affiliations":[{"id":6596,"text":"Quantitative Fisheries Center, Department of Fisheries and Wildlife Michigan State University","active":true,"usgs":false}],"preferred":false,"id":928799,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Faust, Matthew D.","contributorId":348473,"corporation":false,"usgs":false,"family":"Faust","given":"Matthew D.","affiliations":[{"id":13589,"text":"Ohio DNR","active":true,"usgs":false}],"preferred":false,"id":928800,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vandergoot, Christopher","contributorId":351529,"corporation":false,"usgs":false,"family":"Vandergoot","given":"Christopher","affiliations":[{"id":84005,"text":"Michigan State University/GLATOS","active":true,"usgs":false}],"preferred":false,"id":928801,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kraus, Richard 0000-0003-4494-1841","orcid":"https://orcid.org/0000-0003-4494-1841","contributorId":216548,"corporation":false,"usgs":true,"family":"Kraus","given":"Richard","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":928803,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Roberts, James 0000-0002-4193-610X jroberts@usgs.gov","orcid":"https://orcid.org/0000-0002-4193-610X","contributorId":5453,"corporation":false,"usgs":true,"family":"Roberts","given":"James","email":"jroberts@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":928802,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Nathan, Lucas","contributorId":351530,"corporation":false,"usgs":false,"family":"Nathan","given":"Lucas","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":928804,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70242804,"text":"70242804 - 2023 - Fusing geophysical and remotely sensed data for observing overwash occurrence, frequency, and impact","interactions":[],"lastModifiedDate":"2023-06-08T14:49:26.09138","indexId":"70242804","displayToPublicDate":"2023-03-31T06:59:58","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Fusing geophysical and remotely sensed data for observing overwash occurrence, frequency, and impact","docAbstract":"Overwash is an important process that enables a barrier island to migrate landward to adapt to rising sea levels but can also impact vegetated areas and create coastal hazards for populated barrier islands. Our overall objectives were to hindcast overwash events from September 2008 to November 2009 and assess whether overwash impacts could be detected using moderate-resolution imagery (30 m). Estimates of wave and still water levels can be benchmarked against morphological characteristics from elevation datasets to predict overwash events. These observations can be combined with optical remote sensing data used to monitor for changes in vegetation greenness over time to evaluate potential impacts from overwash. This study highlighted how physical-based overwash data can be paired with observations of greenness. The results from our study highlighted that a discernable drop in greenness can be detected for major hurricanes, such as Hurricane Gustav in 2008, with a weaker signal observed for smaller magnitude events in 2009 like Hurricane Ida. Tracking overwash impacts to vegetation can be helpful for observing impacts to vegetation associated with restoration efforts and advancing our understanding of general overwash impacts and recovery.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The proceedings of the coastal sediments 2023","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"World Scientific","doi":"10.1142/9789811275135_0203","collaboration":"The Water Institute of the Gulf, U.S. Army Corps of Engineers","usgsCitation":"Enwright, N., Dalyander, P., Jenkins, R.L., Godsey, E.S., and Stelly, S.J., 2023, Fusing geophysical and remotely sensed data for observing overwash occurrence, frequency, and impact, in The proceedings of the coastal sediments 2023, p. 2206-2219, https://doi.org/10.1142/9789811275135_0203.","productDescription":"14 p.; Data Release","startPage":"2206","endPage":"2219","ipdsId":"IP-147117","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":415994,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":417818,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9A19Q8J"}],"noUsgsAuthors":false,"publicationDate":"2023-03-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Enwright, Nicholas 0000-0002-7887-3261","orcid":"https://orcid.org/0000-0002-7887-3261","contributorId":217781,"corporation":false,"usgs":true,"family":"Enwright","given":"Nicholas","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":869824,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dalyander, P. Soupy 0000-0001-9583-0872","orcid":"https://orcid.org/0000-0001-9583-0872","contributorId":221891,"corporation":false,"usgs":false,"family":"Dalyander","given":"P. Soupy","affiliations":[{"id":40456,"text":"St. Petersburg Coastal and Marine Science Center (Former Employee)","active":true,"usgs":false}],"preferred":false,"id":869825,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jenkins, Robert L 0000-0002-9163-7773 rljenkins@usgs.gov","orcid":"https://orcid.org/0000-0002-9163-7773","contributorId":304231,"corporation":false,"usgs":true,"family":"Jenkins","given":"Robert","email":"rljenkins@usgs.gov","middleInitial":"L","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":869826,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Godsey, Elizabeth S.","contributorId":304232,"corporation":false,"usgs":false,"family":"Godsey","given":"Elizabeth","email":"","middleInitial":"S.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":869827,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stelly, Spencer J. 0000-0003-1050-1733","orcid":"https://orcid.org/0000-0003-1050-1733","contributorId":215852,"corporation":false,"usgs":false,"family":"Stelly","given":"Spencer","email":"","middleInitial":"J.","affiliations":[{"id":39319,"text":"Student Services Contractor at the U.S. Geological Survey Wetland and Aquatic Research Center","active":true,"usgs":false}],"preferred":false,"id":869828,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70241955,"text":"70241955 - 2023 - Assessing arthropod diversity metrics derived from stream environmental DNA: Spatiotemporal variation and paired comparisons with manual sampling","interactions":[],"lastModifiedDate":"2023-04-03T11:43:32.05906","indexId":"70241955","displayToPublicDate":"2023-03-31T06:40:34","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3840,"text":"PeerJ","active":true,"publicationSubtype":{"id":10}},"title":"Assessing arthropod diversity metrics derived from stream environmental DNA: Spatiotemporal variation and paired comparisons with manual sampling","docAbstract":"Benthic invertebrate (BI) surveys have been widely used to characterize freshwater environmental quality but can be challenging to implement at desired spatial scales and frequency. Environmental DNA (eDNA) allows an alternative BI survey approach, one that can potentially be implemented more rapidly and cheaply than traditional methods.
We evaluated eDNA analogs of BI metrics in the Potomac River watershed of the eastern United States. We first compared arthropod diversity detected with primers targeting mitochondrial 16S (mt16S) and cytochrome c oxidase 1 (cox1 or COI) loci to that detected by manual surveys conducted in parallel. We then evaluated spatial and temporal variation in arthropod diversity metrics with repeated sampling in three focal parks. We also investigated technical factors such as filter type used to capture eDNA and PCR inhibition treatment.
Our results indicate that genus-level assessment of eDNA compositions is achievable at both loci with modest technical noise, although database gaps remain substantial at mt16S for regional taxa. While the specific taxa identified by eDNA did not strongly overlap with paired manual surveys, some metrics derived from eDNA compositions were rank-correlated with previously derived biological indices of environmental quality. Repeated sampling revealed statistical differences between high- and low-quality sites based on taxonomic diversity, functional diversity, and tolerance scores weighted by taxon proportions in transformed counts. We conclude that eDNA compositions are efficient and informative of stream condition. Further development and validation of scoring schemes analogous to commonly used biological indices should allow increased application of the approach to management needs.
","language":"English","publisher":"PeerJ","doi":"10.7717/peerj.15163","usgsCitation":"Aunins, A.W., Mueller, S.J., Fike, J., and Cornman, R.S., 2023, Assessing arthropod diversity metrics derived from stream environmental DNA: Spatiotemporal variation and paired comparisons with manual sampling: PeerJ, v. 11, e15163, 34 p., https://doi.org/10.7717/peerj.15163.","productDescription":"e15163, 34 p.","ipdsId":"IP-146615","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":444004,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.15163","text":"Publisher Index Page"},{"id":435391,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NNZNVH","text":"USGS data release","linkHelpText":"Metabarcode sequencing of aquatic environmental DNA from the Potomac River Watershed, 2015-2020"},{"id":415048,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","noUsgsAuthors":false,"publicationDate":"2023-03-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Aunins, Aaron W. 0000-0001-5240-1453 aaunins@usgs.gov","orcid":"https://orcid.org/0000-0001-5240-1453","contributorId":5863,"corporation":false,"usgs":true,"family":"Aunins","given":"Aaron","email":"aaunins@usgs.gov","middleInitial":"W.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":868369,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mueller, Sara J.","contributorId":303889,"corporation":false,"usgs":false,"family":"Mueller","given":"Sara","email":"","middleInitial":"J.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":868370,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fike, Jennifer A. 0000-0001-8797-7823","orcid":"https://orcid.org/0000-0001-8797-7823","contributorId":207268,"corporation":false,"usgs":true,"family":"Fike","given":"Jennifer A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":868371,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cornman, Robert S. 0000-0001-9511-2192 rcornman@usgs.gov","orcid":"https://orcid.org/0000-0001-9511-2192","contributorId":5356,"corporation":false,"usgs":true,"family":"Cornman","given":"Robert","email":"rcornman@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":868372,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70242847,"text":"70242847 - 2023 - Ediacaran-Ordovician magmatism and REE mineralization in the Wet Mountains, Colorado, USA: Implications for failed continental rifting","interactions":[],"lastModifiedDate":"2023-04-20T12:03:48.647713","indexId":"70242847","displayToPublicDate":"2023-03-30T06:56:07","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3524,"text":"Tectonics","active":true,"publicationSubtype":{"id":10}},"title":"Ediacaran-Ordovician magmatism and REE mineralization in the Wet Mountains, Colorado, USA: Implications for failed continental rifting","docAbstract":"Structures associated with Ediacaran-Ordovician alkaline magmatism and the timing of rare earth element (REE) mineralization in the Wet Mountains, CO, were analyzed using field, geophysical, and U-Th-Pb isotope methods to interpret their tectonic setting in the context of previously proposed rift models. The Wet Mountains are known for thorium and REE mineralization associated with failed rift-related, Ediacaran-Ordovician alkaline intrusions and veins. Structural field data indicate that alkaline dikes and mineralized veins are controlled by a system of northwest-striking, high-angle faults and tension fractures formed in a 040°-directed extensional regime. Magnetic and surface expressions of Democrat Creek and McClure Mountain complexes show tectonic elongation toward ∼045°, consistent with NE-directed extension. Magnetic data also suggest the existence of a fourth, previously unrecognized mafic-ultramafic complex of inferred Cambrian age with a similar elongated orientation. Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS) 208Pb/232Th analysis of low-uranium zircon from carbonatite dikes and in situ 206Pb/238U LA-ICP-MS analysis of monazite in mineralized dikes yielded 465 ± 18 Ma and 489 ± 33 Ma ages, respectively. These ages are consistent with the expected age based on slightly older, cross-cut syenite dikes and the hypothesized Ordovician end to failed rift-related magmatism. The Ediacaran-Ordovician age of alkaline magmatic rocks and the associated northeast-directed extension direction are similar to those of the along-strike, Ediacaran-Cambrian Southern Oklahoma Aulacogen. Therefore, the failed rift system in the Wet Mountains is interpreted to be a northwestern continuation of the Southern Oklahoma Aulacogen with carbonatite magmatism and thorium/REE mineralization representing late intrusive phases.
In the United States, National Park Service Civil War battlefield units are managed for both historical accuracy (i.e., to represent landscape conditions at the time of the conflict for historical interpretation), and for natural resource protection. However, managing for both goals can create conflicts as many battlefields were largely open or in second growth forests historically, but now harbor significant forest resources after more than 100 years of preservation. Managing for historical accuracy therefore may require maintenance of the landscape in a successional stage out of phase with the current landscape. We use historical landscape maps and current high-resolution forest structure data derived from lidar to examine tradeoffs in returning the landscape of a major Civil War battlefield (Wilderness Battlefield) to conditions present at the time of the battle. We demonstrate that National Park battlefield units can harbor significant forest resources in contrast to the surrounding landscape, especially in areas of intense commercial, urban, and suburban development. Managing for or restoring landscapes to historical conditions could have important ecological implications.
","language":"English","publisher":"Wiley","doi":"10.1111/rec.13911","usgsCitation":"Young, J.A., and Mahan, C., 2023, Assessing tradeoffs between current and desired vegetation condition in a National Park using historical maps and high resolution lidar data: Restoration Ecology, v. 31, no. 5, e13911, 8 p., https://doi.org/10.1111/rec.13911.","productDescription":"e13911, 8 p.","ipdsId":"IP-145189","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":444014,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1111/rec.13911","text":"Publisher Index Page"},{"id":415154,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-05-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Young, John A. 0000-0002-4500-3673 jyoung@usgs.gov","orcid":"https://orcid.org/0000-0002-4500-3673","contributorId":3777,"corporation":false,"usgs":true,"family":"Young","given":"John","email":"jyoung@usgs.gov","middleInitial":"A.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":868511,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mahan, Carolyn","contributorId":303907,"corporation":false,"usgs":false,"family":"Mahan","given":"Carolyn","affiliations":[{"id":65925,"text":"Pennsylvania State University - Altoona","active":true,"usgs":false}],"preferred":false,"id":868510,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70242726,"text":"70242726 - 2023 - The invasive plant data landscape: A synthesis of spatial data and applications for research and management in the United States","interactions":[],"lastModifiedDate":"2024-01-04T14:44:29.657111","indexId":"70242726","displayToPublicDate":"2023-03-30T06:35:05","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"The invasive plant data landscape: A synthesis of spatial data and applications for research and management in the United States","docAbstract":"An increase in the number and availability of datasets cataloging invasive plant distributions offers opportunities to expand our understanding, monitoring, and management of invasives across spatial scales. These datasets, created using on-the-ground observations and modeling techniques, are made both for and by researchers and managers.
The large number and variety of data types and associated datasets can be difficult to navigate, require high levels of data literacy, and can overwhelm the intended end-users. By providing a synthesis of available data types and datasets, this work may facilitate data understanding and use among researchers and managers.
We synthesize types of invasive plant distribution data sources, highlighting publicly available datasets and their potential applications and limitations for research and management.
Eight data types and their potential applications for research and management are described. We also describe gaps in current invasive species distribution data usability and outline a path forward for improving the use of invasive plant data in future research and management.
Accessible and usable invasive plant spatial data are needed for developing landscape scale analysis and management plans. By synthesizing the invasive plant data available, with examples and limitations for application, this work will serve as a guide to facilitate appropriate and efficient data choices in current and future research and management.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-023-01623-z","usgsCitation":"Fusco, E.J., Beaury, E.M., Bradley, B., Cox, M., Jarnevich, C.S., Mahood, A.L., Nagy, R.C., Nietupski, T., and Halofsky, J.E., 2023, The invasive plant data landscape: A synthesis of spatial data and applications for research and management in the United States: Landscape Ecology, v. 38, p. 3825-3843, https://doi.org/10.1007/s10980-023-01623-z.","productDescription":"19 p.","startPage":"3825","endPage":"3843","ipdsId":"IP-145662","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":415769,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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L.","contributorId":304165,"corporation":false,"usgs":false,"family":"Mahood","given":"Adam","email":"","middleInitial":"L.","affiliations":[{"id":65988,"text":"Earth Lab, University of Colorado Boulder and U.S Department of Agriculture, Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":869519,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Nagy, R. Chelsea","contributorId":298272,"corporation":false,"usgs":false,"family":"Nagy","given":"R.","email":"","middleInitial":"Chelsea","affiliations":[{"id":13693,"text":"University of Colorado Boulder","active":true,"usgs":false}],"preferred":false,"id":869520,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Nietupski, Ty","contributorId":304166,"corporation":false,"usgs":false,"family":"Nietupski","given":"Ty","email":"","affiliations":[{"id":65989,"text":"ORISE Fellow hosted at the U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station","active":true,"usgs":false}],"preferred":false,"id":869521,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Halofsky, Jessica E.","contributorId":146628,"corporation":false,"usgs":false,"family":"Halofsky","given":"Jessica","email":"","middleInitial":"E.","affiliations":[{"id":13553,"text":"University of Washington-Seattle","active":true,"usgs":false}],"preferred":false,"id":869522,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70241872,"text":"ofr20221115 - 2023 - Geochronologic and geochemical data from metasedimentary and associated rocks in the Lane Mountain area, San Bernardino County, California","interactions":[],"lastModifiedDate":"2026-02-10T21:15:46.852915","indexId":"ofr20221115","displayToPublicDate":"2023-03-29T10:44:23","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1115","displayTitle":"Geochronologic and Geochemical Data from Metasedimentary and Associated Rocks in the Lane Mountain Area, San Bernardino County, California","title":"Geochronologic and geochemical data from metasedimentary and associated rocks in the Lane Mountain area, San Bernardino County, California","docAbstract":"Eugeoclinal metasedimentary and metavolcanic rocks in the Lane Mountain area, California, are considered part of the El Paso terrane, which is commonly thought to have been displaced several hundred kilometers (km) southeastward from its place of origin during late Paleozoic truncation of the North American continental margin. Uranium-lead dating of detrital zircons from this area was undertaken to limit the depositional ages of these nearly non-fossiliferous metamorphic rocks. Analysis of detrital zircons from 17 metasedimentary rock samples yielded a composite age distribution that ranges from Archean to Jurassic and has significant peaks at ~2,800 2,400 mega-annum (Ma), 2,100–1,600 Ma, and ~300–200 Ma. The Proterozoic and Archean ages indicate derivation from continental sources in ancestral North America, whereas the late Paleozoic and Mesozoic ages are interpreted as derived from a magmatic arc that began to develop along the continental margin in Permian to Triassic time.
The 17 detrital zircon samples are from quartzitic and conglomeratic rocks of the Carbide, Williams Well, and Noble Well formations, which were informally named by T.H. McCulloh in 1960. The zircon data indicate that the oldest rocks in the Carbide formation are quartzites likely correlative with the Ordovician Eureka Quartzite of the Cordilleran miogeocline. These rocks lie structurally above the rest of the Carbide formation, different units of which yielded zircons that indicate maximum depositional ages ranging from middle Paleozoic to Late Triassic. Zircons from the Williams Well and Noble Well formations indicate maximum depositional ages of late Paleozoic and Early Jurassic, respectively. The Noble Well formation is interpreted to correlate with the lithologically similar, Early Jurassic, Fairview Valley Formation of the Black and Quartzite Mountain areas some 60 km to the southwest.
The above interpretations depend on the presumption that the detrital zircons in these samples did not undergo extreme, postdepositional lead loss, which would result in misleadingly young ages. Although such lead loss is considered unlikely for these samples, further work could test the validity of this interpretation.
Zircons from six additional samples were also analyzed: (1) a quartzite from which all the zircons are interpreted to have formed by Late Jurassic metamorphism; (2) three samples interpreted as albitized igneous rocks of Middle Permian age; and (3) two samples interpreted as fine-grained monzonite to diorite of Late Jurassic age. Both sets of igneous rocks were initially thought to be metasedimentary but were reinterpreted as igneous largely on the basis of the zircon data.
Based on the interpretations presented here, this study demonstrates that the depositional, magmatic, and deformational history of the El Paso terrane was longer and more complex than previously thought and will require reevaluation of existing tectonic models involving this terrane.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221115","usgsCitation":"Stone, P., Cecil, M.R., Brown, H.J., and Vazquez, J.A., 2023, Geochronologic and geochemical data from metasedimentary and associated rocks in the Lane Mountain area, San Bernardino County, California: U.S. Geological Survey Open-File Report 2022–1115, 34 p., https://doi.org/10.3133/ofr20221115.","productDescription":"Report: vi, 34 p.; Data Release","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-126391","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":414958,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2022/1115/ofr20221115_table5.pdf","text":"Table 5","size":"1 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- SHRIMP-RG U-Pb zircon data for samples analyzed for this report, Lane Mountain area."},{"id":414957,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2022/1115/ofr20221115_table4.pdf","text":"Table 4","size":"3 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- LA-SF-ICPMS U-Pb zircon data for samples analyzed for this report, Lane Mountain area."},{"id":414900,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1115/covrthb.jpg"},{"id":414901,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1115/ofr20221115.pdf","text":"Report","size":"7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2022–1115"},{"id":414902,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20211094","text":"Open-File Report 2021-1094","description":"Stone, P., Brown, H.J., Cecil, M.R., Fleck, R.J., Vazquez, J.A., and Fitzpatrick, J.A., 2021, Geochronologic, isotopic, and geochemical data from pre-Cretaceous plutonic rocks in the Lane Mountain area, San Bernardino County, California: U.S. Geological Survey Open-File Report 2021–1094, 74 p., https://doi.org/10.3133/ofr20211094.","linkHelpText":"- Geochronologic, Isotopic, and Geochemical Data from Pre- Cretaceous Plutonic Rocks in the Lane Mountain Area, San Bernardino County, California"},{"id":414903,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20191070","text":"Open-File Report 2019-1070","description":"Stone, P., Brown, H.J., Cecil, M.R., Fleck, R.J., Vazquez, J.A., Fitzpatrick, J.A., and Rosario, J., 2019, Geochronologic, isotopic, and geochemical data from igneous rocks in the Lane Mountain area, San Bernardino County, California: U.S. Geological Survey Open-File Report 2019–1070, 34 p., https://doi.org/10.3133/ofr20191070.","linkHelpText":"- Geochronologic, Isotopic, and Geochemical Data from Igneous Rocks in the Lane Mountain Area, San Bernardino County, California"},{"id":499724,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114618.htm","linkFileType":{"id":5,"text":"html"}},{"id":414899,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9G6YNEF","text":"Tabular geochronologic and geochemical data from metasedimentary and associated rocks in the Lane Mountain area, San Bernardino County, California","description":"Stone, P., Cecil, M.R., and Vazquez, J.A., 2023, Tabular geochronologic and geochemical data from metasedimentary and associated rocks in the Lane Mountain area, San Bernardino County, California: U.S. Geological Survey data release, https://doi.org/10.5066/P9G6YNEF."}],"country":"United States","state":"California","county":"San Bernardino County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.50673206071878,\n 34.67301889093439\n ],\n [\n -116.50673206071878,\n 35.322960316934655\n ],\n [\n -117.53351206069043,\n 35.322960316934655\n ],\n [\n -117.53351206069043,\n 34.67301889093439\n ],\n [\n -116.50673206071878,\n 34.67301889093439\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Contact Information,
Geology, Minerals, Energy, & Geophysics Science Center
Menlo Park, California
U.S. Geological Survey
Building 19, 350 N. Akron Rd.
P.O. Box 158
Moffett Field, CA 94035
Phytoplankton are an important and limiting food source in the Sacramento-San Joaquin Delta and San Francisco Bay. The decline of phytoplankton biomass is one potential factor in the decline of the protected Hypomesus transpacificus (delta smelt) and other pelagic organisms. The bivalves Corbicula fluminea and Potamocorbula amurensis (hereafter C. fluminea and P. amurensis, respectively) have been shown to control phytoplankton biomass in several locations throughout the San Francisco Bay and the Sacramento-San Joaquin Delta; therefore, knowledge of their distribution and population dynamics are of great interest.
Here, we describe the distribution and dynamics of bivalve biomass using samples collected by the California Department of Water Resources (DWR) as part of the benthic monitoring program in 2019. One element of DWR’s and the Bureau of Reclamation’s Environmental Monitoring Program—the Generalized Random Tessellation Stratified (GRTS) program—examines the spatial and temporal extent of C. fluminea and P. amurensis control on phytoplankton. Historically, the GRTS program sampled 175 benthic stations (50 stations that are monitored every year and 125 randomly selected new stations that are changed yearly) throughout the Sacramento-San Joaquin Delta and northern San Francisco Bay (San Pablo and Suisun Bays) during one week in May and October. In 2019, only the 50 annually replicated stations were sampled.
Corbicula fluminea and P. amurensis biomass and grazing rates had similar trends; therefore, the conclusions regarding biomass are applied to grazing rate data as well. Corbicula fluminea biomass decreased from May to October, whereas P. amurensis average biomass (reported increased from May (1 g ash-free-dry-tissue mass/square meter (g AFDM/m2) to October (2 g AFDM/m2). Although C. fluminea’s average biomass was lower in October (10 gAFDM/m2) than in May (20 gAFDM/m2), the highest single biomass value was also observed in October (300 gAFDM/m2). In both May and October, most stations that recorded high C. fluminea biomass values were located in the deep water (≥3 m of depth between the surface of the water and the surface of the substrate on the bottom) and were sampled in either rivers or sloughs. A relation between depth and biomass was not observed for P. amurensis.
Both C. fluminea and P. amurensis recruitment (recruits are considered animals ≤2.5mm in length in this study and recruitment is the process of recruits successfully settled to the bottom) increased from May to October. The total number of C. fluminea recruits more than doubled from May to October, whereas P. amurensis total recruitment increased by 8-fold during the same period. Most P. amurensis recruits in May can be attributed to one station, whereas the recruits in October were found at 14 stations. A relation between number of recruits and station depth was not evident for either C. fluminea or P. amurensis.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221102","collaboration":"Prepared in cooperation with California Department of Water Resources","usgsCitation":"Zierdt Smith, E.L., Shrader, K.H., Thompson, J.K., Parchaso, F., Gehrts, K., and Wells, E., 2023, Bivalve effects on the food web supporting delta smelt—A spatially intensive study of bivalve recruitment, biomass, and grazing rate patterns with varying freshwater outflow in 2019: U.S. Geological Survey Open-File Report 2022–1102, 15 p., https://doi.org/10.3133/ofr20221102.","productDescription":"Report: vi, 15 p.; Data Release","numberOfPages":"15","onlineOnly":"Y","ipdsId":"IP-120563","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":414835,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93BAY64","description":"Zierdt Smith, E.L., Shrader, K.H., Parchaso, F., and Thompson, J.K., 2021, A spatially and temporally intensive sampling study of benthic community and bivalve metrics in the Sacramento-San Joaquin Delta (ver. 2.0, May 2021): U.S. Geological Survey data release, https://doi.org/10.5066/P93BAY64.","linkHelpText":"A spatially and temporally intensive sampling study of benthic community and bivalve metrics in the Sacramento-San Joaquin Delta (ver. 2.0, May 2021)"},{"id":414837,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1102/covrthb.jpg"},{"id":414838,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1102/ofr20221102.pdf","text":"Report","size":"7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2022–1102"},{"id":415721,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20221101","text":"Open-File Report 2022-1101","linkHelpText":"- Bivalve Effects on the Food Web Supporting Delta Smelt—A One-Year Study of Bivalve Recruitment, Biomass, and Grazing Rate Patterns with Varying Freshwater Outflow"},{"id":499721,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114617.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.32432978338892,\n 37.803391256717845\n ],\n [\n -121.87545670853471,\n 37.791400721304846\n ],\n [\n -121.29205898127825,\n 37.80121221377027\n ],\n [\n -121.31951299197254,\n 38.399657702215876\n ],\n [\n -122.30511197590273,\n 38.40180916920502\n ],\n [\n -122.32432978338892,\n 37.803391256717845\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
Water Resources, Earth System Processes Division
U.S. Geological Survey
411 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
In 2021, the U.S. Geological Survey, in cooperation with the U.S. Department of Energy, drilled and constructed borehole TAN-2336 for stratigraphic framework analyses and long-term groundwater monitoring of the eastern Snake River Plain aquifer at the Idaho National Laboratory in southeastern Idaho. Borehole TAN-2336 initially was cored from the depths of 34.0–255.8 ft below land surface (BLS) to collect continuous geologic data and then redrilled to complete construction as a monitoring well completed to about 255 ft BLS. Three sediment layers are described in geophysical data, but only one was recovered in core and described as fine sand with evidence of ash (pumice) near 203 ft BLS. Basalt texture for borehole TAN-2336 generally was described as aphanitic, phaneritic, diktytaxitic, and porphyritic. Basalt flows varied from highly fractured to dense with high to low vesiculation.
Geophysical data were examined with photographed core material to make lithologic descriptions as well as suggest zones where groundwater flow was anticipated. Primary pathways for groundwater, fractured basalt, occur in two areas with the first occurrence near 232.0 ft BLS and the second occurrence near 248.6 ft BLS in borehole TAN-2336. The first occurrence was identified near the top of the water column (232.0 ft BLS) and is more pronounced than the bottom interval (248.6 ft BLS). The location of these fractures in borehole TAN-2336 appear to impact the aquifer tests that were conducted following final well construction. Single-well aquifer tests were completed July 14, 2021, to provide estimates of transmissivity and hydraulic conductivity. Estimates for transmissivity and hydraulic conductivity during aquifer test 1 were 1.24×103 feet squared per day (ft2/d) and 1.76 feet per day (ft/d), respectively. Estimates for transmissivity and hydraulic conductivity during aquifer test 2 were 1.22×103 ft2/d and 1.75 ft/d, respectively. The transmissivity and hydraulic conductivity estimates for well TAN-2336 were within range of those considered from previous aquifer tests in other wells near Test Area North.
Water-quality samples were analyzed for cations, anions, metals, nutrients, volatile organic compounds, stable isotopes, and radionuclides. Water samples for select inorganic constituents showed concentrations consistent with signatures from regional groundwater. Water-quality samples analyzed for stable isotopes of oxygen and hydrogen are consistent with signatures from irrigation and agricultural recharge inputs to the aquifer. Results for trichloroethene, vinyl chloride, and strontium-90 were all measured above their respective maximum contaminant levels (MCLs) for public drinking water supplies. The nutrient concentration results are likely being impacted by the remediation amendment introduced to the aquifer to address trichloroethylene concentrations from past waste-disposal activities. These waste-disposal activities have resulted in volatile organic compound and radiochemical detections in groundwater samples collected at well TAN-2336.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235020","collaboration":"Prepared in cooperation with the U.S. Department of Energy","programNote":"DOE/ID-22260","usgsCitation":"Twining, B.V., Treinen, K.C., and Trcka, A.R., 2023, Completion summary for Borehole TAN-2336 at Test Area North, Idaho National Laboratory, Idaho: U.S. Geological Survey Scientific Investigations Report 2023–5020, 33 p. plus appendixes, https://doi.org/10.3133/sir20235020.","productDescription":"Report: vii, 33 p.; Appendix: 2","additionalOnlineFiles":"Y","ipdsId":"IP-137450","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":414342,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5020/sir20235020.XML"},{"id":414336,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5020/coverthb.jpg"},{"id":414337,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5020/sir20235020.pdf","text":"Report","size":"3.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5020"},{"id":414340,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235020/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5020"},{"id":500714,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114615.htm","linkFileType":{"id":5,"text":"html"}},{"id":414341,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5020/images"},{"id":414339,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2023/5020/sir20235020_appendix2.pdf","text":"Appendix 2","size":"43.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5020 Appendix 2"},{"id":414338,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2023/5020/sir20235020_appendix1.pdf","text":"Appendix 1","size":"218 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5020 Appendix 1"}],"country":"United States","state":"Idaho","otherGeospatial":"Idaho National Laboratory","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.07738340728746,\n 43.34536223650912\n ],\n [\n -112.07738340728746,\n 44.091416267461994\n ],\n [\n -113.46655634842513,\n 44.091416267461994\n ],\n [\n -113.46655634842513,\n 43.34536223650912\n ],\n [\n -112.07738340728746,\n 43.34536223650912\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Rd
Boise, Idaho 83702-4520
Using social media, we collect evidence for how nearshore fisheries are impacted by the global COVID-19 pandemic in Hawai’i. We later confirm our social media findings and obtain a more complete understanding of the changes in nearshore non-commercial fisheries in Hawai’i through a more conventional approach—speaking directly with fishers. Resource users posted photographs to social media nearly three times as often during the pandemic with nearly double the number of fishes pictured per post. Individuals who fished for subsistence were more likely to increase the amount of time spent fishing and relied more on their catch for food security. Furthermore, individuals fishing exclusively for subsistence were more likely to fish for different species during the pandemic than individuals fishing recreationally. Traditional data collection methods are resource-intensive and this study shows that during times of rapid changes, be it ecological or societal, social media can more quickly identify how near shore marine resource use adapts. As climate change threatens additional economic and societal disturbances, it will be necessary for resource managers to collect reliable data efficiently to better target monitoring and management efforts.
","language":"English","publisher":"PeerJ Inc","doi":"10.7717/peerj.14994","usgsCitation":"Grabowski, T.B., Benedum, M.E., Curley, A., Dill-De, C., and Shuey, M.L., 2023, Pandemic-driven changes in the nearshore non-commercial fishery in Hawai'i: Catch photos posted to social media capture changes in fisher behavior: PeerJ, v. 11, e14994, 18 p., https://doi.org/10.7717/peerj.14994.","productDescription":"e14994, 18 p.","ipdsId":"IP-140778","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":444034,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.14994","text":"Publisher Index Page"},{"id":430275,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Hilo Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -155.113909840586,\n 19.85004375716514\n ],\n [\n -155.113909840586,\n 19.718858550527997\n ],\n [\n -154.9953832185561,\n 19.718858550527997\n ],\n [\n -154.9953832185561,\n 19.85004375716514\n ],\n [\n -155.113909840586,\n 19.85004375716514\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"11","noUsgsAuthors":false,"publicationDate":"2023-03-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Grabowski, Timothy B. 0000-0001-9763-8948 tgrabowski@usgs.gov","orcid":"https://orcid.org/0000-0001-9763-8948","contributorId":4178,"corporation":false,"usgs":true,"family":"Grabowski","given":"Timothy","email":"tgrabowski@usgs.gov","middleInitial":"B.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":904106,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Benedum, Michelle E.","contributorId":339351,"corporation":false,"usgs":false,"family":"Benedum","given":"Michelle","email":"","middleInitial":"E.","affiliations":[{"id":13693,"text":"University of Colorado Boulder","active":true,"usgs":false}],"preferred":false,"id":904107,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Curley, Andrew","contributorId":339352,"corporation":false,"usgs":false,"family":"Curley","given":"Andrew","email":"","affiliations":[{"id":81292,"text":"University of Hawaiʻi at Hilo","active":true,"usgs":false}],"preferred":false,"id":904108,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dill-De, Cole","contributorId":339353,"corporation":false,"usgs":false,"family":"Dill-De","given":"Cole","email":"","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":904109,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shuey, Michelle L.","contributorId":339354,"corporation":false,"usgs":false,"family":"Shuey","given":"Michelle","email":"","middleInitial":"L.","affiliations":[{"id":81292,"text":"University of Hawaiʻi at Hilo","active":true,"usgs":false}],"preferred":false,"id":904110,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70249688,"text":"70249688 - 2023 - Satellite remote sensing of river discharge: A framework for assessing the accuracy of discharge estimates made from satellite remote sensing observations","interactions":[],"lastModifiedDate":"2023-10-25T13:30:03.270824","indexId":"70249688","displayToPublicDate":"2023-03-28T08:24:03","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2172,"text":"Journal of Applied Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Satellite remote sensing of river discharge: A framework for assessing the accuracy of discharge estimates made from satellite remote sensing observations","docAbstract":"This research presents an evaluation of the accuracy and uncertainty of estimates of river discharge made using satellite observed data sources as input to a modified form of Manning’s equation. Conventional U.S. Geological Survey (USGS) streamflow gaging station data and in-situ measurements of width, depth, height, slope, discharge, and velocity from 30 USGS gage sites were used as ground-truth to assess accuracy. This study explores accuracy in relation to the amount of ground truth information available, the number of calibration points available, and the accuracy of the input data. This research indicates that remotely sensed discharge estimates associated with the modified Manning equation may be expected to have an uncertainty in range of 10% overall given a sufficient number of calibration points. The uncertainty associated with the modified Manning algorithm increased markedly for depths <3 meters (m) and for discharges <1000 cubic meters per second (m3 / s) for many rivers after calibration. Rivers that exhibit (1) a wide range of flow conditions, (2) a significant number of dams in the watershed and along the channel, and (3) a high baseflow index are more likely to have relatively large errors overall and particularly at the low end of the streamflow range. Uncertainty in remotely sensed measurements of water-surface elevation (WSE) and width in the expected range (WSE, + / − 10 cm; Width, + / − 15 m) introduces uncertainty in the discharge estimates on the order of 10% and is greatest at the low end of discharge as rivers get shallower and narrower. As WSE and width measurement uncertainty increases, discharge uncertainty increases accordingly. In general, the observation errors are greater than the errors associated with the algorithm for a well-calibrated model (e.g., 20 calibration points). |
While rare species are vulnerable to global change, large declines in common species (i.e., those with large population sizes, large geographic distributions, and/or that are habitat generalists) also are of conservation concern. Understanding if and how commonness mediates species' responses to global change, including land cover change, can help guide conservation strategies. We explored avian population responses to land cover change along a gradient from common to rare species using avian data from the North American Breeding Bird Survey (BBS) and land cover data from the National Land Cover Database for the conterminous United States. Specifically, we used generalized linear mixed effects models to ask if species' commonness affected the relationship between land cover and counts, using the initial amount of and change in land cover surrounding each North American BBS route from 2001 to 2016. We quantified species' commonness as a continuous metric at the national scale using the logarithm (base 10) of each species' total count across all routes in the conterminous United States in 2001. For our focal 15-year period, we found that higher proportions of initial natural land cover favored (i.e., were correlated with higher) counts of rare but not common species. We also found that commonness mediated how change in human land cover, but not natural land cover, was associated with species' counts at the end of the study period. Increases in developed lands did not favor counts of any species. Increases in agriculture and declines in pasture favored counts of common but not rare species. Our findings show a signal of commonness in how species respond to a major dimension of global change. Evaluating how and why commonness mediates species' responses to land cover change can help managers design conservation portfolios that sustain the spectrum of common to rare species.
The Surface Water and Ocean Topography (SWOT) mission will vastly expand measurements of global rivers, providing critical new data sets for both gaged and ungaged basins. SWOT discharge products (available approximately 1 year after launch) will provide discharge for all river that reaches wider than 100 m. In this paper, we describe how SWOT discharge produced and archived by the US and French space agencies will be computed from measurements of river water surface elevation, width, and slope and ancillary data, along with expected discharge accuracy. We present for the first time a complete estimate of the SWOT discharge uncertainty budget, with separate terms for random (standard error) and systematic (bias) uncertainty components in river discharge time series. We expect that discharge uncertainty will be less than 30% for two-thirds of global reaches and will be dominated by bias. Separate river discharge estimates will combine both SWOT and in situ data; these “gage-constrained” discharge estimates can be expected to have lower systematic uncertainty. Temporal variations in river discharge time series will be dominated by random error and are expected to be estimated within 15% for nearly all reaches, allowing accurate inference of event flow dynamics globally, including in ungaged basins. We believe this level of accuracy lays the groundwork for SWOT to enable breakthroughs in global hydrologic science.
Drinking water supplies across the United States have been contaminated by firefighting and fire-training activities that use aqueous film-forming foams (AFFF) containing per- and polyfluoroalkyl substances (PFAS). Much of the AFFF is manufactured using electrochemical fluorination by 3M. Precursors with six perfluorinated carbons (C6) and non-fluorinated amine substituents make up approximately one-third of the PFAS in 3M AFFF. C6 precursors can be transformed through nitrification (microbial oxidation) of amine moieties into perfluorohexane sulfonate (PFHxS), a compound of regulatory concern. Here, we report biotransformation of the most abundant C6 sulfonamido precursors in 3M AFFF with available commercial standards (FHxSA, PFHxSAm, and PFHxSAmS) in microcosms representative of the groundwater/surface water boundary. Results show rapid (<1 day) biosorption to living cells by precursors but slow biotransformation into PFHxS (1–100 pM day–1). The transformation pathway includes one or two nitrification steps and is supported by the detection of key intermediates using high-resolution mass spectrometry. Increasing nitrate concentrations and total abundance of nitrifying taxa occur in parallel with precursor biotransformation. Together, these data provide multiple lines of evidence supporting microbially limited biotransformation of C6 sulfonamido precursors involving ammonia-oxidizing archaea (Nitrososphaeria) and nitrite-oxidizing bacteria (Nitrospina). Further elucidation of interrelationships between precursor biotransformation and nitrogen cycling in ecosystems would help inform site remediation efforts.
Increasingly severe and prolonged droughts are contributing to tree stress and forest mortality across western North America. However, in many cases, we currently have poor information concerning how drought responses in forests vary in relation to competition, climate, and site and tree characteristics. We used annual tree ring evidence of 13C discrimination (Δ13C) and growth metrics to assess drought resistance and resilience for six conifer species at the intersection of several bioregions in northern California. Within each species' range in northern California, we collected competition and tree characteristics from 270 focal trees across sites that varied from wetter to drier habitat conditions (54 sites). Across sites, all six conifer species weathered the severe 2013–2015 drought with reasonably high resistance and post-drought resilience. However, we found important differences in drought responses between coastal and montane species based on annual growth and Δ13C metrics. Broadly, the two coastal species showed consistent declines in drought resistance across successive drought years, whereas the four montane species maintained high drought resistance across drought years. More specifically, we found lower Δ13C and growth during drought years in coastal species, suggesting stomatal closure during drought with the potential for vulnerability to carbon depletion during long-term drought. Conversely, Δ13C and growth were stable in montane species throughout the drought, which may contribute to hydraulic failure under increased drought frequency and/or severity. We also evaluated environmental factors that affect Δ13C using data from before and during the drought. These physiological models were consistent for the two coastal species, with a positive relationship between annual precipitation and Δ13C and a negative relationship between tree density and Δ13C. Conversely, the four montane models illustrated a greater importance of site conditions on drought responses for these species. Our findings show differential risk for drought stress across diverse conifers during severe drought. This work highlights the importance of site and tree characteristics in determining drought responses across cool, annually humid coastal habitats to seasonally dry montane habitats.
The Oligocene Latir magmatic center in northern New Mexico is an exceptionally well-exposed volcanoplutonic complex that hosts a variety of magmatic-hydrothermal deposits, ranging from relatively deep, F-rich porphyry Mo mineralization to shallower epithermal deposits. We present new whole-rock chemical and isotopic data for plutonic rocks from the Latir magmatic center, including extensive sampling of drill core samples of intrusive rocks from the Questa porphyry Mo deposit. These data document temporal chemical trends of porphyry-related mineralization that occurred after caldera-forming magmatism and during postcaldera batholith assembly. Silicic magmas were generated multiple times throughout the history of the Latir magmatic center, but few are associated with the formation of a mineral deposit. Whole-rock trace element ratios and Sr, Nd, and Pb isotope compositions vary throughout the protracted history of silicic magmatism. The caldera-forming ignimbrite and early phase of postcaldera intrusions are unmineralized, more enriched in high field strength elements, and generally contain less radiogenic Sr and Pb and more radiogenic Nd than later intrusions. The Questa porphyry Mo deposit formed immediately after the most isotopically primitive phase of the batholith was assembled, ruling out simple reworking of juvenile mantle-derived crust as the source for mineralizing magmas. Rhyolite dikes associated with polymetallic sulfide deposits intruded ~800 k.y. after Mo mineralization, and Nd isotope data indicate that these dikes are associated with different batches of magma and are unrelated to the Mo-mineralizing intrusions at the Questa mine. Together, these data indicate that the source of magmas changed significantly throughout the 10-m.y. history of the magmatic center. We assess multiple genetic models for porphyry-related magmatism against this data set, favoring models with discrete periods of magma genesis from a deep hybridized zone in the lower crust giving rise to the punctuated periods of mineralization. These observations suggest that the formation of mineral deposits within a central magmatic locus is likely the result of the piecemeal assembly of individual hydrothermal-magmatic systems, and that distal and younger polymetallic mineralization commonly observed near known porphyry deposits represents decoupled processes.
","language":"English","publisher":"Society of Economic Geologists","doi":"10.5382/econgeo.5011","usgsCitation":"Gaynor, S., Rosera, J.M., and Coleman, D.S., 2023, Genesis of the Questa Mo porphyry deposit and nearby polymetallic mineralization, New Mexico, USA: Economic Geology, v. 118, no. 6, p. 1319-1339, https://doi.org/10.5382/econgeo.5011.","productDescription":"21 p.","startPage":"1319","endPage":"1339","ipdsId":"IP-138609","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":502418,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://archive-ouverte.unige.ch/unige:167974","text":"External Repository"},{"id":414955,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105.625,\n 37\n ],\n [\n -105.625,\n 36.5\n ],\n [\n -105.375,\n 36.5\n ],\n [\n -105.375,\n 37\n ],\n [\n -105.625,\n 37\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"118","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gaynor, Sean P.","contributorId":297927,"corporation":false,"usgs":false,"family":"Gaynor","given":"Sean P.","affiliations":[],"preferred":false,"id":868058,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rosera, Joshua Mark 0000-0003-3807-5000","orcid":"https://orcid.org/0000-0003-3807-5000","contributorId":270284,"corporation":false,"usgs":true,"family":"Rosera","given":"Joshua","email":"","middleInitial":"Mark","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":868059,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coleman, Drew S.","contributorId":303771,"corporation":false,"usgs":false,"family":"Coleman","given":"Drew","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":868060,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70241574,"text":"sir20235019 - 2023 - Assessing Escherichia coli and microbial source tracking markers in the Rio Grande in the South Valley, Albuquerque, New Mexico, 2020–21","interactions":[],"lastModifiedDate":"2026-03-02T22:13:04.87586","indexId":"sir20235019","displayToPublicDate":"2023-03-24T08:55:27","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":"2023-5019","displayTitle":"Assessing Escherichia coli and Microbial Source Tracking Markers in the Rio Grande in the South Valley, Albuquerque, New Mexico, 2020–21","title":"Assessing Escherichia coli and microbial source tracking markers in the Rio Grande in the South Valley, Albuquerque, New Mexico, 2020–21","docAbstract":"The Rio Grande, in southern Albuquerque, New Mexico, is a Clean Water Act Section 303(d) Category 5 impaired reach for Escherichia coli (E. coli). The reach is 5 miles in length, extending from Tijeras Arroyo south to the Isleta Pueblo boundary. An evaluation of E. coli and microbial source tracking markers (human-, canine-, and waterfowl-specific sources) was conducted by the U.S. Geological Survey to determine the extent and source of fecal bacteria within the impaired reach of the Rio Grande, primarily during the dry season (November through June) in 2020 and 2021. Samples were collected in the river cross section at three locations within each site and collected during both the dry season and the wet season, thereby providing data over a range of flow conditions to better understand the extent and source of fecal bacteria. Because fecal microorganisms may readily attach to sediments, riverbed material samples were also collected. During the dry season, E. coli concentrations in water were primarily detected below the New Mexico Surface Water Quality Standard of 410 colony forming units per 100 milliliters and mostly human and canine sources were detected. However, approximately 40 percent of the water samples exceeded the Isleta Pueblo water quality standard of 88 colony forming units per 100 milliliters. E. coli concentrations in bed material were detected at low concentrations, and the bed material was a sandy substrate, with little fine-grained material, a suitable habitat that would allow for bacterial growth during the dry season. Significant spatial and temporal differences, where p-values were less than 0.05, were found in water-quality samples for E. coli (seasonal) and the human tracker concentrations (between sites and within a cross section of a site). Given the lack of correlation between discharge and E. coli concentration and the human marker being most prevalent in the study area, the sources of E. coli in the dry season are likely nonpoint sources. The results from this study will help decision makers determine the efficacy of their best management practices and guide new practices to improve water quality in the reach.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235019","issn":"ISSN 2328-0328","collaboration":"Prepared in cooperation with Bernalillo County","usgsCitation":"Travis, R.E., Wilkins, K.L., and Kephart, C.M., 2023, Assessing Escherichia coli and microbial source tracking markers in the Rio Grande in the South Valley, Albuquerque, New Mexico, 2020–21: U.S. Geological Survey Scientific Investigations Report 2023–5019, 48 p., https://doi.org/10.3133/sir20235019.","productDescription":"Report: viii, 48 p.; Data Release","numberOfPages":"60","onlineOnly":"Y","ipdsId":"IP-139896","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":414732,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5019/sir20235019.XML","size":"328 KB","linkFileType":{"id":8,"text":"xml"}},{"id":414632,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Q2ECYV","text":"U.S. Geological Survey data release—Fecal bacteria and microbial source tracking marker data in the Rio Grande, Albuquerque, New Mexico 2017–2020"},{"id":414629,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5019/images"},{"id":414627,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5019/coverthb.jpg"},{"id":414626,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5019/sir20235019.pdf","text":"Report","size":"2.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5019"},{"id":500713,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114616.htm","linkFileType":{"id":5,"text":"html"}},{"id":414733,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20235019/full","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New Mexico","city":"Albuquerque","otherGeospatial":"Rio Grande, South Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107,\n 35.5\n ],\n [\n -107,\n 34.75\n ],\n [\n -106.25,\n 34.75\n ],\n [\n -106.25,\n 35.5\n ],\n [\n -107,\n 35.5\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