Sediment from urban impervious surfaces has the potential to be an important vector for contaminants, particularly where stormwater culverts and other buried channels draining large impervious areas exit from underground pipes into open channels. To better understand urban sediment sources and their relation to fallout radionuclides, we collected samples of rainfall, urban sediment (pavement sediment, topsoil), streambank sediment, and fluvial sediment (suspended sediment and bed sediment) for 7Be, 210Pbex, and 137Cs analysis. The results indicate that each rainfall event tags pavement sediment with elevated activities of 7Be and 210Pbex such that runoff from impervious surfaces in the buried channel part of the stream network contains the highest activities. Pavement sediment, because it is characteristically a thin veneer, has a small mass to rainwater ratio resulting in a greater tagging of 7Be and 210Pbex activity than does topsoil on a per gram basis. An unmixing model indicated that suspended-sediment samples collected at the culvert outlet from the buried-channel network are from pavement sediment sources (45 ± 25%) with a smaller component of topsoil (22 ± 19%), and a component from streambanks (32 ± 35%) that we infer to be older channel material and subsoil eroded from within the culvert system. Downstream from the culvert, suspended sediment collected from the open-channel parts of the stream had 7Be and 210Pbex activities that were substantially reduced by the contribution of sediment from streambanks (57 ± 15%), with pavement contributions decreasing to 15 (±9%) and topsoil contributing 28 (±7%). The results highlight the utility of 7Be, 210Pbex, and 137Cs as tracers of urban sediment sources, resulting in a unique radionuclide signature for urban watersheds compared to other sediment-source settings.
Mercury (Hg) is a global pollutant that affects biota in remote settings due to atmospheric deposition of inorganic Hg, and its conversion to methylmercury (MeHg), the bioaccumulating and toxic form. Characterizing biotic MeHg is important for evaluating aquatic ecosystem responses to changes in Hg inputs. Aquatic insects possess many qualities desired for MeHg biomonitoring, but are not widely used, largely because of limited information regarding percentages of total mercury (THg) composed of MeHg (i.e., MeHg%) in various taxa. Here, we examine taxonomic, spatial, and seasonal variation in MeHg% of stream-dwelling predator and primary-consumer insects from nine streams in the Adirondack region (NY, USA). Predator MeHg% was high (median 94%) and did not differ significantly among five taxa. MeHg% in selected dragonflies (the most abundant predators, Odonata: Aeshnidae and Libellulidae) exhibited little seasonal and spatial variation, and THg concentration was strongly correlated with aqueous (filtered) MeHg (FMeHg; rs = 0.76). In contrast, MeHg% in primary consumers—shredders (northern caddisflies [Trichoptera: Limnephilidae]) and scrapers (flathead mayflies [Ephemeroptera: Heptageniidae]), were lower (medians 52% and 35%, respectively), and differed significantly between taxa, among sites, and seasonally. Correlations of THg with FMeHg were weak (shredders, rs = 0.45, p = 0.09) or not significant (scrapers, p = 0.89). The higher MeHg% of predators corresponded with their higher trophic positions (indicated by nitrogen stable isotopes). Results suggest obligate predators hold the most promise for the use of THg as a surrogate for MeHg biomonitoring with aquatic insects within the Adirondack region.
","language":"English","publisher":"Springer","doi":"10.1007/s10646-020-02191-7","usgsCitation":"Riva-Murray, K., Bradley, P., and Brigham, M.E., 2020, Methylmercury-Total mercury ratios in predator and primary consumer insects from Adirondack streams (New York, USA): Ecotoxicology, v. 29, https://doi.org/10.1007/s10646-020-02191-7.","productDescription":"15 p.","startPage":"1658","ipdsId":"IP-086907","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":373331,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Adirondack Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.35498046875,\n 42.827638636242284\n ],\n [\n -73.2568359375,\n 42.827638636242284\n ],\n [\n -73.2568359375,\n 45.24395342262324\n ],\n [\n -76.35498046875,\n 45.24395342262324\n ],\n [\n -76.35498046875,\n 42.827638636242284\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"29","edition":"1644","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2020-03-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Riva-Murray, Karen 0000-0001-6683-2238 krmurray@usgs.gov","orcid":"https://orcid.org/0000-0001-6683-2238","contributorId":168876,"corporation":false,"usgs":true,"family":"Riva-Murray","given":"Karen","email":"krmurray@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":785016,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bradley, Paul M. 0000-0001-7522-8606","orcid":"https://orcid.org/0000-0001-7522-8606","contributorId":221226,"corporation":false,"usgs":true,"family":"Bradley","given":"Paul M.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true}],"preferred":true,"id":785018,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brigham, Mark E. 0000-0001-7412-6800 mbrigham@usgs.gov","orcid":"https://orcid.org/0000-0001-7412-6800","contributorId":1840,"corporation":false,"usgs":true,"family":"Brigham","given":"Mark","email":"mbrigham@usgs.gov","middleInitial":"E.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":785017,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70209076,"text":"70209076 - 2020 - Colorado River flow dwindles as warming-driven loss of reflective snow energizes evaporation","interactions":[],"lastModifiedDate":"2020-03-20T11:05:00","indexId":"70209076","displayToPublicDate":"2020-03-13T10:11:03","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3338,"text":"Science","active":true,"publicationSubtype":{"id":10}},"title":"Colorado River flow dwindles as warming-driven loss of reflective snow energizes evaporation","docAbstract":"The sensitivity of river discharge to climate-system warming is highly uncertain, and the processes that govern river discharge are poorly understood, which impedes climate-change adaptation. A prominent exemplar is the Colorado River, where meteorological drought and warming are shrinking a water resource that supports more than 1 trillion dollars of economic activity per year. A Monte Carlo simulation with a radiation-aware hydrologic model resolves the longstanding, wide disparity in sensitivity estimates and reveals the controlling physical processes. We estimate that annual mean discharge has been decreasing by 9.3% per degree Celsius of warming because of increased evapotranspiration, mainly driven by snow loss and a consequent decrease in reflection of solar radiation. Projected precipitation increases likely will not suffice to fully counter the robust, thermodynamically induced drying. Thus, an increasing risk of severe water shortages is expected.","language":"English","publisher":"American Association for the Advancement of Science","doi":"10.1126/science.aay9187","usgsCitation":"Milly, P.C., and Dunne, K.A., 2020, Colorado River flow dwindles as warming-driven loss of reflective snow energizes evaporation: Science, v. 367, no. 6483, p. 1252-1255, https://doi.org/10.1126/science.aay9187.","productDescription":"4 p.","startPage":"1252","endPage":"1255","ipdsId":"IP-110304","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":457386,"rank":6,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1126/science.aay9187","text":"Publisher Index Page"},{"id":437055,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PEXXLB","text":"USGS data release","linkHelpText":"Model-Estimated, Spatially Distributed Monthly Water Balance of the Upper Colorado River Basin, Water Years 1913-2017"},{"id":373250,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":373383,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://science.sciencemag.org/cgi/content/full/science.aay9187?ijkey=xqtlOT7tqrPa.&keytype=ref&siteid=sci","text":"Publisher-provided full text access","linkFileType":{"id":5,"text":"html"},"linkHelpText":"Web page"},{"id":373384,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://science.sciencemag.org/content/sci/367/6483/1252.full.pdf?ijkey=xqtlOT7tqrPa.&keytype=ref&siteid=sci","text":"Publisher-provided full text access","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"Reprint"},{"id":373410,"rank":4,"type":{"id":1,"text":"Abstract"},"url":"https://science.sciencemag.org/cgi/content/abstract/science.aay9187?ijkey=xqtlOT7tqrPa.&keytype=ref&siteid=sci","text":"Publisher-provided abstract","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Arizona, Colorado, Idaho, New Mexico, Utah, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.2,\n 35.75\n ],\n [\n -105.9,\n 35.75\n ],\n [\n -105.9,\n 42.5\n ],\n [\n -112.2,\n 42.5\n ],\n [\n -112.2,\n 35.75\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"367","issue":"6483","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Milly, Paul C. D. 0000-0003-4389-3139 cmilly@usgs.gov","orcid":"https://orcid.org/0000-0003-4389-3139","contributorId":176836,"corporation":false,"usgs":true,"family":"Milly","given":"Paul","email":"cmilly@usgs.gov","middleInitial":"C. D.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":false,"id":784807,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dunne, Krista A. 0000-0002-1220-6140 kadunne@usgs.gov","orcid":"https://orcid.org/0000-0002-1220-6140","contributorId":203816,"corporation":false,"usgs":true,"family":"Dunne","given":"Krista","email":"kadunne@usgs.gov","middleInitial":"A.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":784808,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70208470,"text":"sir20205010 - 2020 - Bathymetry of Morris Lake (Newton Reservoir), New Jersey, 2018","interactions":[],"lastModifiedDate":"2022-04-25T21:35:14.628865","indexId":"sir20205010","displayToPublicDate":"2020-03-13T09:15:00","publicationYear":"2020","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":"2020-5010","displayTitle":"Bathymetry of Morris Lake (Newton Reservoir), New Jersey, 2018","title":"Bathymetry of Morris Lake (Newton Reservoir), New Jersey, 2018","docAbstract":"Morris Lake, also known as Newton Reservoir, has been the source of drinking water for the Town of Newton, New Jersey, since the early 1900s. Although Morris Lake has been used as a source of drinking water for many years, its capacity was previously uncertain. In April 2018, the U.S. Geological Survey and the New Jersey Department of Environmental Protection conducted a bathymetric survey of Morris Lake using a multibeam echosounder to map the reservoir. The points measured with the multibeam echosounder were combined with light detection and ranging data above the water surface and processed to create a 3.3-foot (1 meter) raster grid of the bathymetric surface, bathymetric contours at 2-foot intervals of depth and elevation, and an elevation-area-capacity table.
The results of the bathymetric survey show that Morris Lake has a maximum depth of just over 119 feet with an average depth of 42 feet. Like the surrounding topography, parts of the reservoir are extremely steep. The capacity of the reservoir at full spillway level is 1,980 million gallons, with a corresponding surface area of 145 acres. The accuracy of the mapped multibeam echosounder bathymetric data was evaluated using a quality assurance dataset collected with a single-beam echosounder; 9,386 quality assurance points were spatially joined with the mapped raster surface to compute measurement errors. The calculated median point error for Morris Lake was 0.23 foot, the median absolute error was 0.35 foot, and the 95-percent accuracy was 2.68 feet. The largest errors occurred in the steepest areas of the reservoir and in unmeasured areas. Geospatial files of the bathymetry data, including the mapped bathymetric surface, contours, and capacity tables, quality assurance points, and associated metadata are available for download as part of an accompanying U.S. Geological Survey data release.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205010","collaboration":"Prepared in cooperation with the New Jersey Department of Environmental Protection","usgsCitation":"Nystrom, E.A., and Collenburg, J.V., 2020, Bathymetry of Morris Lake (Newton Reservoir), New Jersey, 2018: U.S. Geological Survey Scientific Investigations Report 2020–5010, 14 p., https://doi.org/10.3133/sir20205010.","productDescription":"Report: vii, 14 p.; Data Release","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-103879","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":399631,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109786.htm"},{"id":373089,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P977GO3J","text":"USGS data release","linkHelpText":"Geospatial Bathymetry Dataset and Elevation-Area-Capacity Table for Morris Lake (Newton Reservoir), New Jersey"},{"id":373091,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5010/sir20205010.pdf","text":"Report","size":"4.66 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5010"},{"id":373090,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5010/coverthb.jpg"}],"country":"United States","state":"New Jersey","otherGeospatial":"Morris Lake (Newton Reservoir)","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.62128639221191,\n 41.0387074972886\n ],\n [\n -74.59296226501463,\n 41.0387074972886\n ],\n [\n -74.59296226501463,\n 41.05366055046841\n ],\n [\n -74.62128639221191,\n 41.05366055046841\n ],\n [\n -74.62128639221191,\n 41.0387074972886\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Streamflow generation in mountain watersheds is strongly influenced by snow accumulation and melt, and multiple studies have found that snow loss leads to earlier snowmelt timing and declines in annual streamflow. However, hydrologic responses to snow loss are heterogeneous, and not all areas experience streamflow declines. This research examines whether streamflow generation is different for rainfall versus snowmelt inputs. We compiled a sample of 57 small U.S. Geological Survey watersheds in the western United States containing a Natural Resource Conservation Service Snow Telemetry site and having ratios of mean annual peak snow water equivalent to precipitation ratios >0.25. Daily streamflow was separated into quickflow and baseflow using a digital filter, and quickflow was then divided into quickflow response intervals using thresholds in quickflow slope. Each quickflow response interval was categorized by its fraction of input from snowmelt. Most sites exhibited two streamflow generation peaks each year, with one peak in the winter when runoff efficiency is greatest, and the second in the spring during peak snowmelt input. On average, study watersheds were dominated by snowmelt inputs (70%), and snowmelt and mixed inputs usually generated greater streamflow than rainfall because of higher inputs and longer durations. However, rainfall produced high streamflow generation in winter, when watersheds have their highest runoff efficiency (81%) across all input types. We demonstrate that while snowmelt is important for streamflow generation due to high input over long periods, increases in rain and mixed input during wet winter periods can countervail tendencies for reduced streamflow with declining snowpacks.
The U.S. Pacific Northwest has a history of frequent and occasionally deadly landslides caused by various factors. Using a multivariate, machine-learning approach, we combined a Pacific Northwest Landslide Inventory with a 36-year gridded hydrologic dataset from the National Climate Assessment – Land Data Assimilation System to produce a landslide hazard indicator (LHI) on a daily 0.125-degree grid. The LHI identified where and when landslides were most probable over the years 1979–2016, addressing issues of bias and completeness that muddy the analysis of multi-decadal landslide inventories. The seasonal cycle was strong along the west coast, with a peak in the winter, but weaker east of the Cascade Range. This lagging indicator can fill gaps in the observational record to identify the seasonality of landslides over a large spatiotemporal domain and show how landslide hazard has responded to a changing climate.
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F.","contributorId":300152,"corporation":false,"usgs":false,"family":"Jasinski","given":"M.","email":"","middleInitial":"F.","affiliations":[{"id":40052,"text":"NASA Goddard","active":true,"usgs":false}],"preferred":false,"id":859495,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Borak, J. S.","contributorId":300155,"corporation":false,"usgs":false,"family":"Borak","given":"J.","email":"","middleInitial":"S.","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":859496,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Slaughter, Stephen L. 0000-0002-4322-3330","orcid":"https://orcid.org/0000-0002-4322-3330","contributorId":224686,"corporation":false,"usgs":true,"family":"Slaughter","given":"Stephen","email":"","middleInitial":"L.","affiliations":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true}],"preferred":true,"id":859497,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70209137,"text":"70209137 - 2020 - Landfill leachate contributes per-/poly-fluoroalkyl substances (PFAS) and pharmaceuticals to municipal wastewater","interactions":[],"lastModifiedDate":"2021-05-28T14:10:48.45113","indexId":"70209137","displayToPublicDate":"2020-03-13T07:10:51","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5112,"text":"Environmental Science: Water Research & Technology","active":true,"publicationSubtype":{"id":10}},"title":"Landfill leachate contributes per-/poly-fluoroalkyl substances (PFAS) and pharmaceuticals to municipal wastewater","docAbstract":"Widespread disposal of landfill leachate to municipal sewer infrastructure in the United States calls for an improved understanding of the relative organic-chemical contributions to the wastewater treatment plant (WWTP) waste stream and associated surface-water discharge to receptors in the environment. Landfill leachate, WWTP influent, and WWTP effluent samples were collected from three landfill-WWTP systems and compared with analogous influent and effluent samples from two WWTPs that did not receive leachate. Samples were analyzed for 73 per-/poly-fluoroalkyl substances (PFAS), 109 pharmaceuticals, and 21 hormones and related compounds. PFAS were detected more frequently in leachate (92%) than in influent (55%). Total PFAS concentrations in leachate (93,100 ng/L) were more than ten times higher than in influent (6,950 ng/L), and effluent samples (3,730 ng/L). Concentrations of bisphenol A; the nonprescription pharmaceuticals cotinine, lidocaine, nicotine; and the prescription pharmaceuticals amphetamine, carisoprodol, pentoxifylline, and thiabendazole were an order of magnitude higher in landfill leachate than WWTP influent. Leachate load contributions for PFAS (0.78 to 31 g/d), bisphenol A (0.97 to 8.3 g/d), and nonprescription (2.0 to 3.1 g/d) and prescription (0.48 to 2.5 g/d) pharmaceuticals to WWTP influent were generally low (<10 g/d) for most compounds because of high influent-to-leachate volumetric ratios (0.983). No clear differences in concentrations were apparent between effluents from WWTPs receiving landfill leachate and those that did not receive landfill leachate.","language":"English","publisher":"Royal Society of Chemistry","doi":"10.1039/D0EW00045K","usgsCitation":"Masoner, J.R., Kolpin, D.W., Cozzarelli, I.M., Smalling, K.L., Bolyard, S., Field, J., Furlong, E.T., Gray, J.L., Lozinski, D., Reinhart, D., Rodowa, A., and Bradley, P.M., 2020, Landfill leachate contributes per-/poly-fluoroalkyl substances (PFAS) and pharmaceuticals to municipal wastewater: Environmental Science: Water Research & Technology, v. 6, p. 1300-1311, https://doi.org/10.1039/D0EW00045K.","productDescription":"12 p.","startPage":"1300","endPage":"1311","ipdsId":"IP-116926","costCenters":[{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true},{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"links":[{"id":457394,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1039/d0ew00045k","text":"Publisher Index Page"},{"id":437056,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97LMTKZ","text":"USGS data release","linkHelpText":"Target-Chemical Concentrations in Landfill Leachate and Wastewater Treatment Influent and Effluent"},{"id":373360,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Masoner, Jason R. 0000-0002-4829-6379 jmasoner@usgs.gov","orcid":"https://orcid.org/0000-0002-4829-6379","contributorId":3193,"corporation":false,"usgs":true,"family":"Masoner","given":"Jason","email":"jmasoner@usgs.gov","middleInitial":"R.","affiliations":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":785068,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kolpin, Dana W. 0000-0002-3529-6505 dwkolpin@usgs.gov","orcid":"https://orcid.org/0000-0002-3529-6505","contributorId":1239,"corporation":false,"usgs":true,"family":"Kolpin","given":"Dana","email":"dwkolpin@usgs.gov","middleInitial":"W.","affiliations":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"preferred":true,"id":785069,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cozzarelli, Isabelle M. 0000-0002-5123-1007 icozzare@usgs.gov","orcid":"https://orcid.org/0000-0002-5123-1007","contributorId":1693,"corporation":false,"usgs":true,"family":"Cozzarelli","given":"Isabelle","email":"icozzare@usgs.gov","middleInitial":"M.","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":785070,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smalling, Kelly L. 0000-0002-1214-4920 ksmall@usgs.gov","orcid":"https://orcid.org/0000-0002-1214-4920","contributorId":190789,"corporation":false,"usgs":true,"family":"Smalling","given":"Kelly","email":"ksmall@usgs.gov","middleInitial":"L.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":785071,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bolyard, Stephanie 0000-0001-5590-0776","orcid":"https://orcid.org/0000-0001-5590-0776","contributorId":223446,"corporation":false,"usgs":false,"family":"Bolyard","given":"Stephanie","email":"","affiliations":[{"id":18879,"text":"University of Central Florida","active":true,"usgs":false}],"preferred":false,"id":785072,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Field, Jennifer 0000-0002-9346-4693","orcid":"https://orcid.org/0000-0002-9346-4693","contributorId":223447,"corporation":false,"usgs":false,"family":"Field","given":"Jennifer","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":785073,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Furlong, Edward T. 0000-0002-7305-4603 efurlong@usgs.gov","orcid":"https://orcid.org/0000-0002-7305-4603","contributorId":740,"corporation":false,"usgs":true,"family":"Furlong","given":"Edward","email":"efurlong@usgs.gov","middleInitial":"T.","affiliations":[{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":785074,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gray, James L. 0000-0002-0807-5635 jlgray@usgs.gov","orcid":"https://orcid.org/0000-0002-0807-5635","contributorId":1253,"corporation":false,"usgs":true,"family":"Gray","given":"James","email":"jlgray@usgs.gov","middleInitial":"L.","affiliations":[{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true}],"preferred":true,"id":785075,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lozinski, Duncan 0000-0001-7646-466X","orcid":"https://orcid.org/0000-0001-7646-466X","contributorId":223450,"corporation":false,"usgs":false,"family":"Lozinski","given":"Duncan","email":"","affiliations":[{"id":40716,"text":"Brown and Caldwell","active":true,"usgs":false}],"preferred":false,"id":785076,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Reinhart, Debra","contributorId":223451,"corporation":false,"usgs":false,"family":"Reinhart","given":"Debra","email":"","affiliations":[{"id":18879,"text":"University of Central Florida","active":true,"usgs":false}],"preferred":false,"id":785077,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Rodowa, Alix 0000-0002-3990-2111","orcid":"https://orcid.org/0000-0002-3990-2111","contributorId":223452,"corporation":false,"usgs":false,"family":"Rodowa","given":"Alix","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":785078,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Bradley, Paul M. 0000-0001-7522-8606 pbradley@usgs.gov","orcid":"https://orcid.org/0000-0001-7522-8606","contributorId":361,"corporation":false,"usgs":true,"family":"Bradley","given":"Paul","email":"pbradley@usgs.gov","middleInitial":"M.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":785079,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70209825,"text":"70209825 - 2020 - A post-eruption study of gases and thermal waters at Okmok Volcano, Alaska","interactions":[],"lastModifiedDate":"2020-04-30T12:12:19.796285","indexId":"70209825","displayToPublicDate":"2020-03-13T07:05:03","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"A post-eruption study of gases and thermal waters at Okmok Volcano, Alaska","docAbstract":"We report here on the first focused study of gas discharges and thermal spring waters at Okmok Volcano since the 2008 phreatomagmatic eruptions. Results include the first compositional gas data from Okmok with minimal air contamination and the first data on magmatic carbon in Okmok spring waters. Chemical and isotopic analyses of the waters and gases are used to assess the character of Okmok fluids eight years after the eruptions ceased. \n\nGases from vents on intracaldera Cone C have high concentrations of H2 and contain H2S rather than SO2, demonstrating the influence of a hydrothermal system, while isotope values of carbon ( 10.2 to 8.9‰) and helium (~8 RA) confirm the presence of magma-derived volatiles. Estimates of equilibrium temperatures for the Cone C gas are ~230 ± 30 ºC. A much cooler reservoir with a maximum temperature of ~55 ºC feeds the intracaldera warm springs. Based on discharge measurements of creeks draining the caldera, the total heat output of the warm springs is estimated to be about 32 MW.\n\nGas data from a single location of steaming ground at the Geyser Bight geothermal area southwest of the Okmok Caldera are given. The gas is typical of geothermal gases with high concentrations of H2S and an air-corrected helium isotope ratio of 7.15 RA.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2020.106853","collaboration":"","usgsCitation":"Bergfeld, D., Evans, W.C., Hunt, A., Lopez, T., and Schaefer, J., 2020, A post-eruption study of gases and thermal waters at Okmok Volcano, Alaska: Journal of Volcanology and Geothermal Research, v. 396, https://doi.org/10.1016/j.jvolgeores.2020.106853.","productDescription":"106853, 16 p.","startPage":"","ipdsId":"IP-115008","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":457400,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jvolgeores.2020.106853","text":"Publisher Index Page"},{"id":374393,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Okmok Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -168.5687255859375,\n 53.212612189941574\n ],\n [\n -167.6898193359375,\n 53.212612189941574\n ],\n [\n -167.6898193359375,\n 53.589244357588655\n ],\n [\n -168.5687255859375,\n 53.589244357588655\n ],\n [\n -168.5687255859375,\n 53.212612189941574\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"396","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bergfeld, Deborah 0000-0003-4570-7627 dbergfel@usgs.gov","orcid":"https://orcid.org/0000-0003-4570-7627","contributorId":152531,"corporation":false,"usgs":true,"family":"Bergfeld","given":"Deborah","email":"dbergfel@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":788182,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Evans, William C. 0000-0001-5942-3102 wcevans@usgs.gov","orcid":"https://orcid.org/0000-0001-5942-3102","contributorId":2353,"corporation":false,"usgs":true,"family":"Evans","given":"William","email":"wcevans@usgs.gov","middleInitial":"C.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":788183,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hunt, Andrew G. 0000-0002-3810-8610","orcid":"https://orcid.org/0000-0002-3810-8610","contributorId":206197,"corporation":false,"usgs":true,"family":"Hunt","given":"Andrew G.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":788186,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lopez, Taryn","contributorId":146828,"corporation":false,"usgs":false,"family":"Lopez","given":"Taryn","affiliations":[{"id":16753,"text":"University of Alaska Geophysical Institute","active":true,"usgs":false}],"preferred":false,"id":788184,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schaefer, Janet","contributorId":199547,"corporation":false,"usgs":false,"family":"Schaefer","given":"Janet","affiliations":[],"preferred":false,"id":788185,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70219483,"text":"70219483 - 2020 - Small-scale water deficits after wildfires create long-lasting ecological impacts","interactions":[],"lastModifiedDate":"2021-04-12T11:58:12.438476","indexId":"70219483","displayToPublicDate":"2020-03-13T07:01:10","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Small-scale water deficits after wildfires create long-lasting ecological impacts","docAbstract":"Ecological droughts are deficits in soil–water availability that induce threshold-like ecosystem responses, such as causing altered or degraded plant-community conditions, which can be exceedingly difficult to reverse. However, 'ecological drought' can be difficult to define, let alone to quantify, especially at spatial and temporal scales relevant to land managers. This is despite a growing need to integrate drought-related factors into management decisions as climate changes result in precipitation instability in many semi-arid ecosystems. We asked whether success in restoration seedings of the foundational species big sagebrush (Artemisia tridentata) was related to estimated water deficit, using the SoilWat2 model and data from >600 plots located in previously burned areas in the western United States. Water deficit was characterized by: (1) the standardized precipitation-evapotranspiration index (SPEI), a coarse-scale drought index, and (2) the number of days with wet and warm conditions in the near-surface soil, where seeds and seedlings germinate and emerge (i.e. days with 0–5 cm deep soil water potential >−2.5 MPa and temperature above 0 °C). SPEI, a widely used drought index, was not predictive of whether sagebrush had reestablished. In contrast, wet-warm days elicited a critical drought threshold response, with successfully reestablished sites having experienced seven more wet-warm days than unsuccessful sites during the first March following summer wildfire and restoration. Thus, seemingly small-scale and short-term changes in water availability and temperature can contribute to major ecosystem shifts, as many of these sites remained shrubless two decades later. These findings help clarify the definition of ecological drought for a foundational species and its imperiled semi-arid ecosystem. Drought is well known to affect the occurrence of wildfires, but drought in the year(s) after fire can determine whether fire causes long-lasting, negative impacts on ecosystems.
","language":"English","publisher":"IOP Science","doi":"10.1088/1748-9326/ab79e4","usgsCitation":"O’Connor, R., Germino, M., Barnard, D.M., Andrews, C.M., Bradford, J., Pilliod, D., Arkle, R.S., and Shriver, R.K., 2020, Small-scale water deficits after wildfires create long-lasting ecological impacts: Environmental Research Letters, v. 15, no. 4, 044001, 11 p., https://doi.org/10.1088/1748-9326/ab79e4.","productDescription":"044001, 11 p.","ipdsId":"IP-114720","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":457404,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ab79e4","text":"Publisher Index Page"},{"id":437058,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LDKQE2","text":"USGS data release","linkHelpText":"Ecological drought for sagebrush seedings in the Great Basin"},{"id":384960,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon, Idaho, Nevada, Utah","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.0810546875,\n 40.84706035607122\n ],\n [\n -113.0712890625,\n 40.84706035607122\n ],\n [\n -113.0712890625,\n 43.32517767999296\n ],\n [\n -118.0810546875,\n 43.32517767999296\n ],\n [\n -118.0810546875,\n 40.84706035607122\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"15","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-03-13","publicationStatus":"PW","contributors":{"authors":[{"text":"O’Connor, Rory 0000-0002-6473-0032","orcid":"https://orcid.org/0000-0002-6473-0032","contributorId":222832,"corporation":false,"usgs":true,"family":"O’Connor","given":"Rory","email":"","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":813764,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Germino, Matthew J. 0000-0001-6326-7579","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":251901,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":813765,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barnard, David M 0000-0003-1877-3151","orcid":"https://orcid.org/0000-0003-1877-3151","contributorId":222833,"corporation":false,"usgs":false,"family":"Barnard","given":"David","email":"","middleInitial":"M","affiliations":[{"id":18168,"text":"USDA ARS","active":true,"usgs":false}],"preferred":false,"id":813766,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Andrews, Caitlin M. 0000-0003-4593-1071 candrews@usgs.gov","orcid":"https://orcid.org/0000-0003-4593-1071","contributorId":192985,"corporation":false,"usgs":true,"family":"Andrews","given":"Caitlin","email":"candrews@usgs.gov","middleInitial":"M.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":813767,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bradford, John B. 0000-0001-9257-6303","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":219257,"corporation":false,"usgs":true,"family":"Bradford","given":"John B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":813768,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pilliod, David S. 0000-0003-4207-3518","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":229349,"corporation":false,"usgs":true,"family":"Pilliod","given":"David S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":813769,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Arkle, Robert S. 0000-0003-3021-1389","orcid":"https://orcid.org/0000-0003-3021-1389","contributorId":218006,"corporation":false,"usgs":true,"family":"Arkle","given":"Robert","middleInitial":"S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":813770,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Shriver, Robert K 0000-0002-4590-4834","orcid":"https://orcid.org/0000-0002-4590-4834","contributorId":222834,"corporation":false,"usgs":false,"family":"Shriver","given":"Robert","email":"","middleInitial":"K","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":813771,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70208384,"text":"fs20203006 - 2020 - Pooling resources across organizations — Multisource water-quality data for the Delaware River Basin","interactions":[],"lastModifiedDate":"2022-04-20T18:14:13.866211","indexId":"fs20203006","displayToPublicDate":"2020-03-12T16:33:50","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-3006","displayTitle":"Pooling Resources Across Organizations — Multisource Water-Quality Data for the Delaware River Basin","title":"Pooling resources across organizations — Multisource water-quality data for the Delaware River Basin","docAbstract":"The U.S. Geological Survey (USGS) recently launched a pilot Integrated Water Availability Assessment (IWAA) in the Delaware River Basin to explore, test, and refine systems and processes for assessing water availability for human and ecological uses based on water monitoring data. Water-quality monitoring provides citizens, managers, and scientists with the information needed to evaluate the health of aquatic ecosystems and the safety and availability of water for drinking, agriculture, recreation, and other uses. Many organizations collect water-quality data at various sites and sampling frequencies to meet their assessment needs. The result is multiple individual datasets suitable for the specific organization’s needs that also hold great potential if pooled into a much larger dataset sourced from multiple organizations (multisource data). A multisource dataset increases the value and power of multiple single datasets and expands the breadth and depth of available water-quality data to ultimately increase the number and types of questions that can be answered. This fact sheet describes the process of “harmonizing” water-quality data from multiple organizations and presents a recently developed dataset for surface-water quality in the Delaware River Basin. This harmonized multisource surface-water-quality dataset will serve as a resource for analysis and modeling of surface-water quality to support IWAA efforts in the basin. Furthermore, this harmonization process can be expanded and applied to other regional IWAA basins or applied nationally.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203006","collaboration":"Integrated Water Availability Assessments Program","usgsCitation":"Murphy, J.C., and Shoda, M.E., 2020, Pooling resources across organizations — Multisource water-quality data for the Delaware River Basin: U.S. Geological Survey Fact Sheet 2020–3006, 2 p., https://doi.org/10.3133/fs20203006.","productDescription":"Report: 2 p.; Data Release","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-113620","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":373170,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PX8LZO","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Multisource surface-water-quality data and U.S. Geological Survey streamgage match for the Delaware River Basin"},{"id":373169,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3006/fs20203006.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020–3006"},{"id":373168,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3006/coverthb.jpg"},{"id":399198,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109784.htm"}],"country":"United States","state":"Delaware, Maryland, New York, New Jersey, Pennsylvania","otherGeospatial":"Delaware River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.5,\n 38.6\n ],\n [\n -74.333,\n 38.6\n ],\n [\n -74.333,\n 42.5\n ],\n [\n -76.5,\n 42.5\n ],\n [\n -76.5,\n 38.6\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Program Coordinator, Water Availability and Use Science Program
U.S. Geological Survey
Water Resources Mission Area
Email: wausp-info@usgs.gov
","tableOfContents":"Marbled murrelets (Brachyramphus marmoratus) have been listed as “endangered” by the State of California and “threatened” by the U.S. Fish and Wildlife Service since 1992 in California, Oregon, and Washington. Information regarding marbled murrelet abundance, distribution, population trends, and habitat associations is critical for risk assessment, effective management, evaluation of conservation efficacy, and ultimately, to meet Federal and State recovery efforts for this species. During June–August 2019, the U.S. Geological Survey Western Ecological Research Center continued previously established, long-term (1996–2019), at-sea surveys to estimate abundance and productivity of marbled murrelets in U.S. Fish and Wildlife Service Conservation Zone 6 (San Francisco Bay to Point Sur in central California). Using conventional distance sampling methods, we estimated marbled murrelet abundance using 125 detections of 216 murrelets (mean group size, 1.72) observed on 8 surveys. The abundance estimated for the entire study area using all surveys in 2019 was 404 birds (95-percent confidence interval, 272–601 birds). Estimated abundance from 2019 is comparable to most prior years of study. In 2019, we estimated reproductive productivity (calculated as the hatch-year [HY] to after-hatch-year [AHY] ratio) using three detections of three HY murrelets observed on six surveys. After date-correcting HY and AHY counts to account for birds expected to be absent from the water while inland at nests, the date-corrected juvenile ratio was 0.025±0.020 standard error. We discuss changes in methodologies during 1996–2019 that could be addressed in re-analysis of this long-term dataset. We updated a synthesized database of all Zone 6 marbled murrelet survey data since 1999 with 2019 data to allow scientists and managers to evaluate established survey methods and assess trends in abundance and productivity estimates.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1123","usgsCitation":"Felis, J.J., Kelsey, E.C., Adams, J., Horton, C., and White, L., 2020, Abundance and productivity of marbled murrelets (Brachyramphus marmoratus) off central California during the 2019 breeding season: U.S. Geological Survey Data Series 1123, 13 p., https://doi.org/10.3133/ds 1123.","productDescription":"Report: vi, 13 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-114914","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":373097,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1123/coverthb.jpg"},{"id":373098,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1123/ds1123.pdf","text":"Report","size":"2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Data Series 1123"},{"id":373220,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F75B01RW","linkHelpText":"Annual Marbled Murrelet Abundance and Productivity Surveys Off Central California (Zone 6), 1999-2018 (ver. 2.0, March 2019)"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.991943359375,\n 36.89719446989036\n ],\n [\n -121.92626953124999,\n 36.89719446989036\n ],\n [\n -121.92626953124999,\n 37.68382032669382\n ],\n [\n -122.991943359375,\n 37.68382032669382\n ],\n [\n -122.991943359375,\n 36.89719446989036\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Coal mining has been the dominant industry and land use in West Virginia’s southern coal fields since the mid-1800s. Mortality rates for a variety of serious chronic conditions, such as diabetes, heart disease, and some forms of cancer in Appalachian coal mining regions, are higher than in areas lacking substantial coal mining activity within the Appalachian Region or elsewhere in the United States. Causes of the increased mortality and morbidity are not clear, but poor diet, high rates of smoking, socioeconomic factors, and the quality of groundwater used by area residents are all possible contributing factors. This study was conducted by the U.S. Geological Survey in cooperation with the West Virginia Department of Health and Human Resources and the West Virginia Department of Environmental Protection, with grant support from the Centers for Disease Control and Prevention (CDC) to assess the quality of groundwater in southern West Virginia. The data from this assessment of groundwater quality may be used by the CDC and other agencies to potentially investigate the role or lack thereof of groundwater quality with respect to mortality and morbidity rates in the region. The study was conducted in a region where a high density of current or past coal mining combined with a lack of advanced sewage treatment could affect concentrations of commonly occurring constituents plus contaminants, including nitrate, trace metals, major ions, indicator bacteria, radon, hydrogen sulfide, and dissolved hydrocarbons.
Because rural residential wells and mine outfalls are considered private sources of water in the region, and are therefore unregulated and unmonitored, water-quality data are sparse. To fill the data gap and assess the groundwater quality in the region, water-quality samples were collected from 60 sites in a 10-county area. The 60 sites sampled included 46 rural residential homeowner wells and 14 mine outfall discharges used for residential supply. For this study, all samples were collected prior to any filtration or other treatments, typically at the pressure tank, and are indicative of total and dissolved constituents in the untreated water.
Generally, data for the 60 sites indicate that most waters sampled do not exceed thresholds for most U.S. Environmental Protection Agency (EPA) drinking-water standards and U.S. Geological Survey (USGS) drinking-water screening criteria. However, there were several notable exceptions. Turbidity exceeded the 5-Nephelometric Turbidity Unit (NTU) EPA treatment technique (TT) drinking-water standard in 14 of 60 (23 percent) sites sampled and exceeded the 1-NTU TT standard in 51 of 60 (85 percent) sites sampled. Turbidity is common in many wells in southern West Virginia and may be attributed to iron oxyhydroxide precipitates, sediment carried into the aquifers from the shallow soil zone due to improperly constructed or cased wells or transported to the aquifer in shallow stress-relief fracture zones or through permeable bedding-plane partings. For the sites sampled, 31 of 60 (52 percent) had pH values at, above, or below the upper and lower range of the EPA Secondary Maximum Contaminant Level (SMCL, 6.5–8.5 standard units). Of those 31 sites, 28 (90 percent) were indicative of acidic corrosive water and 3 (10 percent) were indicative of alkaline water.
The Langelier Saturation Index (LSI), which is a measure of the corrosivity of the water, was computed for all sites sampled for the study. Eighty-two percent of the sites sampled had waters that were classified as corrosive, based on a LSI less than −0.5. Corrosive water has the potential to leach lead, copper, and other metals from lead, copper, galvanized, or lead-tin soldered connections in water lines. The chloride to sulfate mass ratio also was assessed with the alkalinity to indicate the potential to promote galvanic corrosion (PPGC) of water lines and plumbing fixtures. Only one of the sites (1.7 percent) classified as a corrosive water site, had a PPGC considered high; the remaining sites were classified as having either a moderate (53.3 percent) or low (45 percent) PPGC. Therefore, the type of plumbing systems sampled for this study may be affected by corrosive water, but the potential for leaching trace metals and other constituents from residential plumbing systems containing older galvanized pipes or lead-tin soldered copper pipes is moderate to low.
The indicator bacteria total coliform and Escherichia coli (E. coli) also were detected in groundwater samples to varying degrees. Total coliforms, which are a broad class of indicator bacteria, are common in groundwater in southern West Virginia and were detected in 39 of the 60 sites (65 percent) sampled. The presence of total coliform bacteria is a potential indicator of surface contamination, due to improperly constructed or cased wells, or infiltration of soil or other surface contaminants into the aquifer or well bore. E. coli bacteria, however, are much more indicative of fecal contamination of groundwater from either human or animal sources, and 14 of the 60 (23 percent) sites sampled had detections of E. coli. Although only a few strains of E. coli are known pathogens, their presence in groundwater may be an indicator of other related pathogens such as viruses and should be regarded as a serious potential issue. Water treatment such as chlorination, ozonation, or ultraviolet light may be appropriate to kill potential pathogenic bacteria or viruses in the source water.
Manganese and iron were prevalent contaminants in the groundwater samples collected for this study, with 30 of 60 (50 percent) sites analyzed for manganese and 25 of 60 (42 percent) sites analyzed for iron exceeding the proposed 50- and 300-micrograms per liter (µg/L) SMCL drinking-water standards, respectively, for aesthetic criteria such as taste, odor, or staining of plumbing fixtures. Fourteen of the 60 sites sampled (23 percent) had concentrations of manganese that exceeded the 300-µg/L USGS health-based screening level, and 1 site exceeded the 1,600-µg/L EPA drinking-water equivalent level, which is based on a lifetime exposure level. Sodium is another common constituent in groundwater within the study area. Sodium has an EPA health-based value (HBV) of 20 milligrams per liter (mg/L) for individuals who are on a sodium-restricted diet for blood pressure or other health reasons. Sodium concentrations exceeded the 20-mg/L EPA HBV in 27 of 60 (45 percent) samples.
Radon, a naturally occurring carcinogenic radioactive gas known to cause lung cancer, was detected at concentrations at or exceeding the proposed 300-picocuries per liter (pCi/L) EPA Maximum Contaminant Level (MCL) in 12 of the 60 (20 percent) sites sampled. Sites with radon gas concentrations exceeding the 300-pCi/L proposed MCL have the potential for airborne concentrations of radon to exceed the 4-pCi/L indoor air standard. Inhalation of radon can cause lung cancer, and the 4-pCi/L indoor air standard is based on an inhalation standard. Therefore, homeowners whose wells have radon gas concentrations exceeding 300 pCi/L may be advised to have their indoor air tested to determine if indoor air concentrations exceed the 4-pCi/L indoor air standard established by the EPA.
Various factors were analyzed statistically and graphically to determine whether they have an influence on groundwater quality within the study area, including topographic setting, well depth, type of mining (surface or underground), type of site (well or mine outfall), and geologic formation. Only geologic formation and the type of site sampled had strong statistical correlations with one or more of the constituents of concern for this study. The overall chemistry of outfalls (mine outfalls) and wells was significantly different, with a much higher dissolved oxygen content in outfalls than in wells. The dissolved oxygen content is the primary component driving the oxidation and reduction of minerals, and the precipitation of minerals that are saturated or super saturated with respect to various cations and anions. Median dissolved oxygen concentrations for the outfalls sampled was 8.75 mg/L, and only 0.4 mg/L for the wells sampled.
Median concentrations of sulfate and selenium were much higher in waters from the outfalls sampled, with median concentrations of 73.75 mg/L and 2.35 µg/L, respectively, compared to the wells sampled, which had median concentrations of 18.3 mg/L and less than (<) the 0.05-µg/L method detection limit, respectively. The maximum selenium concentration was for a well, with a concentration of 16.6 µg/L. The geochemical processes that control sulfate and selenium concentrations in groundwater are similar and are the result of the oxidation of sulfide minerals such as pyrite and ferroselite. Iron and manganese concentrations were elevated in most of the wells sampled, with median concentrations of 269.5 and 124.5 µg/L, respectively, but were rarely detected in the outfalls sampled, with median concentrations of < 4.0 and < 0.4 µg/L, respectively. The difference in iron and manganese between wells and outfalls is indicative of the role of dissolved oxygen on processes controlling groundwater chemistry in the region.
Three principal geologic formations were assessed for the study, and the overall chemistry for the Pocahontas, New River, and Kanawha Formations varied substantially with respect to several constituents. Concentrations of calcium, magnesium, and total dissolved solids were highest for sites sampled in the Pocahontas Formation, with median concentrations of 41.9, 18.6, and 312 mg/L, respectively. For constituents that are commonly associated with mining activity, the highest concentrations were for sites sampled in the New River Formation, with median concentrations of iron and manganese of 2,450 µg/L and 482 µg/L, respectively, and a median pH of 6.35 standard units. Concentrations of barium also were elevated in samples collected from sites in the New River Formation, with a median barium concentration of 184 µg/L. The source of the barium is not fully known but may be associated with commingling of shallow groundwater with deeper brines or dissolution of the mineral barite. The highest median sulfate concentrations were from sites sampled in the Pocahontas Formation, with a median concentration of 64.0 mg/L. Of the 12 sites at or exceeding the 300-pCi/L proposed drinking-water standard for radon, 8 (67 percent of MCL exceedances) were for sites deriving water from the Kanawha Formation, 3 (25 percent of MCL exceedances) were for sites deriving water from the New River Formation, and only 1 site was for water from the Pocahontas Formation (8 percent of proposed MCL exceedances).
Dissolved hydrocarbons, including methane, ethane, propane, propene, n- and i-butane, 1-butene, n- and i-pentane, pentane, 2- and 3-ethyl pentane, hexane, and benzene were analyzed in samples collected from 59 of the 60 sites to assess the potential occurrence and sources of these trace gases in groundwater within the study area. Results of the analysis indicate that most of the gas is of shallow biogenic origin, possibly associated with coal-bed methane, but a subset of samples has a gas signature and a chloride to bromide ratio indicative of potential mixing with deeper thermogenic gases. Only 2 of the 59 (3.3 percent) sites sampled had concentrations of methane gas, which is a highly combustible and explosive gas, exceeding the 10 milligrams per kilogram level of concern established by the U.S. Office of Surface Mining Reclamation and Enforcement.
Principal components analysis was used to assess the primary geochemical processes occurring in the aquifers sampled. The first principal component had significant positive loadings for bromide, chloride, silica, ammonia, barium, iron, manganese, and arsenic, and significant negative loadings for dissolved oxygen, potassium, nitrate, and uranium, and reflects reduction and oxidation (redox) processes occurring in deeper anoxic groundwater or shallow oxic groundwater. The strong positive loadings for iron, manganese, barium, and arsenic are correlated with reducing conditions often found deeper in the aquifer. More oxic water is correlated with oxidation of nitrogen species to nitrate and environmental mobilization of uranium and sulfate in shallow wells and mine outfalls.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195059","collaboration":"Prepared in cooperation with the West Virginia Department of Health and Human Resources, Office of Environmental Health Services and the West Virginia Department of Environmental Protection, Division of Water and Waste Management","usgsCitation":"Kozar, M.D., McAdoo, M.A., and Haase, K.B., 2020, Groundwater quality and geochemistry of West Virginia’s southern coal fields (ver. 1.1, March 2020): U.S. Geological Survey Scientific Investigations Report 2019−5059, 78 p., https://doi.org/10.3133/sir20195059.","productDescription":"x, 78 p.","numberOfPages":"92","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-103597","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":399535,"rank":4,"type":{"id":36,"text":"NGMDB Index 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1.1: March 2020; Version 1.0: February 2020","contact":"Director, Virginia/West Virginia Science Center
U.S. Geological Survey
11 Dunbar Street
Charleston, WV 25301
Hurricane Katrina made landfall in New Orleans, Louisiana as a Category 3 storm in August 2005. Storm surges, levee failures, and the low-lying nature of New Orleans led to widespread flooding, damage to over 70% of occupied housing, and evacuation of 80–90% of city residents. Only 57% of the city's black population has returned. Many residents complain of gentrification following rebuilding efforts. Climate gentrification is a recently described phenomenon whereby the effects of climate change, most notably rising sea levels and more frequent flooding and storm surges, alter housing values in a way that leads to gentrification.
To examine the climate gentrification following hurricane Katrina by (1) estimating the associations between flooding severity, ground elevation, and gentrification and (2) whether these relationships are modified by neighborhood level pre- and post-storm sociodemographic factors.
Lidar data collected in 2002 were used to determine elevation. Water gauge height of Lake Ponchartrain was used to estimate flood depth. Using census tracts as a proxy for neighborhoods, demographic, housing, and economic data from the 2000 decennial census and the 2010 and 2015 American Community Survey 5-year estimates US Census records were used to determine census tracts considered eligible for gentrification (median income < 2000 Orleans Parish median income). A gentrification index was created using tract changes in education level, population above the poverty limit, and median household income. Proportional odds ordinal logistic regression was used with product terms to test for effect measure modification by sociodemographic factors.
Census tracts eligible for gentrification in 2000 were 80.2% black. Median census tract flood depth was significantly lower in areas eligible to undergo gentrification (0.70 m vs. 1.03 m). Residents of gentrification-eligible tracts in 2000 were significantly more likely to be black, less educated, lower income, unemployed, and rent their home rather than own. In 2015 in these same eligible tracts, areas that underwent gentrification became significantly whiter, more educated, higher income, less unemployed, and more likely to live in a multi-unit dwelling. Gentrification was inversely associated with flood depth and directly associated with ground elevation in eligible tracts. Marginal effect modification was detected by the effect of pre-storm black race on the relationships of flood depth and elevation with gentrification.
Gentrification was strongly associated with higher ground elevation in New Orleans. These results provide evidence to support the idea of climate gentrification described in other low-elevation major metropolitan areas like Miami, FL. High elevation, low-income, demographically transitional areas in particular – that is areas that more closely resemble high-income area demographics, may be vulnerable to future climate gentrification.
Using traditional high-flow purge methods for long-term water quality monitoring of deep groundwater wells can be expensive, affect contaminant migration, and produce excessive volumes of discharge water that can be difficult to manage. The use of low-flow pumping methods and depth discrete bailers (DDBs) can reduce the cost of sampling deep groundwater wells. In general, using different pumping methods to obtain reproducible and representative groundwater can be challenging. Passive diffusion samplers (PDSs) have successfully been used in long-term monitoring for volatile organic compounds, major ions, and trace-elements, but application has been limited for stable and radioactive isotopes. Beginning in 2018, the United States Geological Survey (USGS) conducted three sampling events to test the ability of PDSs and DDBs to obtain reproducible and representative groundwater samples. The sampled well is completed in a regional, permeable carbonate aquifer and is one in a set of wells that have historically been used for tritium tracer testing. All samples were obtained at 180 m (590 ft) below land surface (bls) in a 13.97 cm (5.5 inch) uncased well that has a total depth of 202 m (662 ft) bls. The first sampling event deployed a regenerated cellulose dialysis membrane (RCDM) PDS for 14 days with deionized water as the blank. The second sampling event deployed a RCDM for 27 days also with deionized water as the blank. The third sampling event deployed a Dual Membrane (DM) PDS for 65 days using a blank of tritium-dead carbonate water. The DM PDS was used to assess the effect of longer-term deployment on tritium concentrations and address whether or not the PDSs reached equilibrium with ambient groundwater. The day after each of the three passive samplers were retrieved a DDB was used to obtain discrete non-integrated groundwater samples. For each DDB sampling day, the bailer was lowered into the well 10 consecutive times to determine if the water chemistry changed from the first to the last bailed sample. Quality assurance samples including blanks and duplicates were obtained during all three sampling events. All blank waters had tritium concentrations less than 21±33 pCi/L. Major ion (e.g. calcium, chloride, sodium, and sulfate) results were compared between all samples obtained with RCDM and DDB. Major ion concentrations showed a coefficient of variation of less than 6% between all RCDM and DDB samples; however, the coefficient of variation between the different deployment times and the two different methods for trace-element concentrations was much larger, particularly for manganese (82%), lead (41%), and zinc (33%). Stable isotope values were compared between the RCDM and DDB samples. The DDB sample results all fell within analytical uncertainty and were considered representative of the formation groundwater. The stable isotope values from the RCDM samples indicated that a longer deployment time was necessary to gain equilibrium and to obtain representative groundwater samples. Tritium results from groundwater samples obtained from the RCDM, DM, and DDB indicate that groundwaters obtained with PDSs produced tritium concentrations 2 to 3 times higher (between 1,426 and 3,060±87 pCi/L) than groundwaters obtained with a DDB (479 to 1,219±51 pCi/L). The longer the passive diffusion samplers were deployed, the higher the tritium concentration, suggesting that equilibrium with tritium was not reached within a 27-day deployment. DDB samples showed tritium results declining from the first to the last bailed sample for all three sampling events. This research suggests that tritium results from groundwater samples obtained from PDSs are more reproducible than samples obtained from DDBs. Also, PDSs likely do not accumulate isotopes of water but rather equilibrate with the ambient groundwater. On the other hand, both PDSs and DDBs were able to provide representative groundwater samples for major ions and have the potential to produce.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Waste Management Symposium proceedings","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Waste Management Symposia","collaboration":"Department of Energy","usgsCitation":"Frus, R.J., and Imbrigiotta, T., 2020, A comparison of groundwater sampling technologies, including passive diffusion sampling, for radionuclide contamination, in Waste Management Symposium proceedings, p. 15-15.","productDescription":"1 p.","startPage":"15","endPage":"15","ipdsId":"IP-113326","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":374440,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":373709,"type":{"id":15,"text":"Index Page"},"url":"https://www.wmsym.org/technical-program/proceedings/"}],"publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Frus, Rebecca J. 0000-0002-2435-7202","orcid":"https://orcid.org/0000-0002-2435-7202","contributorId":206261,"corporation":false,"usgs":true,"family":"Frus","given":"Rebecca","email":"","middleInitial":"J.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":786212,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Imbrigiotta, Thomas 0000-0003-1716-4768","orcid":"https://orcid.org/0000-0003-1716-4768","contributorId":216749,"corporation":false,"usgs":true,"family":"Imbrigiotta","given":"Thomas","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":false,"id":786213,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70236519,"text":"70236519 - 2020 - Antibiotic resistance in marine microbial communities proximal to a Florida sewage outfall system","interactions":[],"lastModifiedDate":"2022-09-09T12:20:54.806699","indexId":"70236519","displayToPublicDate":"2020-03-11T07:18:19","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12582,"text":"Antibiotics","active":true,"publicationSubtype":{"id":10}},"title":"Antibiotic resistance in marine microbial communities proximal to a Florida sewage outfall system","docAbstract":"Water samples were collected at several wastewater treatment plants in southeast Florida, and water and sediment samples were collected along and around one outfall pipe, as well as along several transects extending both north and south of the respective outfall outlet. Two sets of samples were collected to address potential seasonal differences, including 38 in the wet season (June 2018) and 42 in the dry season (March 2019). Samples were screened for the presence/absence of 15 select antibiotic resistance gene targets using the polymerase chain reaction. A contrast between seasons was found, with a higher frequency of detections occurring in the wet season and fewer during the dry season. These data illustrate an anthropogenic influence on offshore microbial genetics and seasonal flux regarding associated health risks to recreational users and the regional ecosystem.
The National Hydrography Dataset Plus, Version 2.1 (NHDPlusV2.1) is an attribute-rich digital stream network for the conterminous United States, serving as a foundational infrastructure for reporting hydrologic information at both regional and national scales. SPAtially Referenced Regressions On Watershed attributes (SPARROW) is a process-based statistical model that relies on a digital hydrologic network like NHDPlusV2.1 to establish spatial relations between quantities of monitored contaminant loads and contaminant sources, accounting for the physical characteristics along flow paths affecting contaminant transport. The U.S. Geological Survey National Water Quality Assessment project adopted and modified the medium-resolution NHDPlusV2.1 network for use as the primary framework supporting SPARROW modeling. This report describes the enhancements made to improve the routing capabilities and the value-added attributes of NHDPlusV2.1 to support modeling and other hydrologic analyses. These enhancements include corrections to inconsistencies in network/routing information, filling in missing attribute values of associated characteristics, accounting of water use affecting flow, new variables useful for interpreting network data, revised flowline attributes such as slope and flow, and incorporation of ancillary spatial data into the network. The resulting dataset containing the enhancements to the network is named E2NHDPlusV2_US. Although the enhancements described in the report were developed for use in SPARROW modeling, the enhancements are expected to be useful for a wide variety of hydrologic studies within the United States.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195127","usgsCitation":"Brakebill, J.W., Schwarz, G.E., and Wieczorek, M.E., 2020, An enhanced hydrologic stream network based on the NHDPlus medium resolution dataset: U.S. Geological Survey Scientific Investigations Report 2019–5127, 49 p., https://doi.org/10.3133/sir20195127.","productDescription":"Report: vii, 49 p.; Data Release","numberOfPages":"62","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-098180","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":372768,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P986KZEM","text":"USGS data release","linkHelpText":"E2NHDPlusV2_us: Database of Ancillary Hydrologic Attributes and Modified Routing for NHDPlus Version 2.1 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National Water Quality Program
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 413
Reston, VA 20191-0002
Ecological communities are controlled by multiple, interacting abiotic and biotic factors that influence the distribution, abundance, and diversity of species. These processes jointly determine resource availability, resource competition, and ultimately species richness. For many terrestrial ecosystems in dryland climates, soil water availability is the most frequent limiting resource for plant species. We used field sampling coupled with process‐based soil water balance modeling to explore the relative importance of multiple macroclimatic, ecohydrological, and biotic variables on plant species and functional type richness at the landscape scale in dryland plant communities.
Dryland plant communities dominated by big sagebrush (Artemisia tridentata ) that span climatic and elevational gradients in Wyoming, USA.
We quantified species richness at 1,000 m2 and used multiple regression to determine whether mean climatic conditions, multiple metrics of soil moisture from a soil water balance model (SOILWAT2), soil physical and chemical properties, and shrub stand structure (biotic) variables were related to species and functional type richness.
Species richness varied between 16 and 54 across sites. We found that species and functional type richness were related to both macroclimate and ecohydrology, but ecohydrology explained slightly more variation than climate. Biotic variables were always secondary to macroclimate and ecohydrology in our models. Variance partitioning revealed that large portions of variability in species (~54%), forb (~47%), and grass (~40%) richness were explained by ecohydrological variables.
Our results highlight the importance of the spatial and temporal distribution of soil water for dryland plant species richness and suggest that documenting the ways in which climate, vegetation, and soil properties interact to determine soil water availability is critical for understanding biodiversity patterns in dryland plant communities. This work has relevance for other mid‐latitude, shrub‐dominated dryland plant communities where soil water availability strongly influences ecosystem structure and function.
","language":"English","publisher":"Wiley","doi":"10.1111/jvs.12874","usgsCitation":"Jordan, S., Palmquist, K.A., Bradford, J., and Lauenroth, W.K., 2020, Soil water availability shapes species richness in mid-latitude shrub steppe plant communities: Journal of Vegetation Science, v. 31, no. 4, p. 646-657, https://doi.org/10.1111/jvs.12874.","productDescription":"12 p.","startPage":"646","endPage":"657","ipdsId":"IP-101810","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":376122,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"31","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-05-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Jordan, Samuel E. 0000-0001-6074-3330","orcid":"https://orcid.org/0000-0001-6074-3330","contributorId":228826,"corporation":false,"usgs":false,"family":"Jordan","given":"Samuel E.","affiliations":[],"preferred":false,"id":792148,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Palmquist, Kyle A.","contributorId":169517,"corporation":false,"usgs":false,"family":"Palmquist","given":"Kyle","email":"","middleInitial":"A.","affiliations":[{"id":7098,"text":"University of Wyoming, Department of Botany, 1000 E. University Avenue, Laramie, WY 82071, USA","active":true,"usgs":false}],"preferred":false,"id":792149,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bradford, John B. 0000-0001-9257-6303","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":219257,"corporation":false,"usgs":true,"family":"Bradford","given":"John B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":792150,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lauenroth, William K.","contributorId":80982,"corporation":false,"usgs":false,"family":"Lauenroth","given":"William","email":"","middleInitial":"K.","affiliations":[{"id":7098,"text":"University of Wyoming, Department of Botany, 1000 E. University Avenue, Laramie, WY 82071, USA","active":true,"usgs":false}],"preferred":false,"id":792151,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70215559,"text":"70215559 - 2020 - Probabilistic categorical groundwater salinity mapping from airborne electromagnetic data adjacent to California’s Lost Hills and Belridge oil fields","interactions":[],"lastModifiedDate":"2020-10-23T14:06:56.145727","indexId":"70215559","displayToPublicDate":"2020-03-10T09:01:37","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Probabilistic categorical groundwater salinity mapping from airborne electromagnetic data adjacent to California’s Lost Hills and Belridge oil fields","docAbstract":"Growing water stress has led to emerging interest in protecting fresh and brackish groundwater as a potential supplement to water supplies and raised questions about factors that could affect the future quality of fresh and brackish aquifers. Limited well infrastructure, particularly in regions where elevated salinity has led to limited historical groundwater development, hinders traditional mapping of salinity distributions through groundwater sampling. This paper presents a quantitative salinity mapping approach of the upper 300 m using high‐resolution, regionally comprehensive resistivity models derived from Bayesian inversion of an airborne electromagnetic survey adjacent to the Lost Hills and Belridge oil fields in the southwestern San Joaquin Valley of California. Using local water quality observations as an interpretational foundation, a probabilistic approach yields maps of fresh, saline, and brackish groundwater while quantifying joint uncertainty inherited from the geophysical data and interpretational relations. Saline and fresh regions are mapped with relatively high confidence in many locations, while areas of lower confidence, particularly at depth, can be mapped as their most probable salinity category while reflecting the relative uncertainty in the interpretation. These maps identify a stratified salinity structure, where saline water commonly occurs in the surficial aquifer overlying fresher groundwater in the Tulare aquifer, separated by regional confining clay layers. Downgradient of unlined surface water diversions, recharge of imported surface water results in relatively fresh groundwater throughout the depth of investigation.
Water limitation is a primary driver of plant geographic distributions and individual plant fitness. Drought resistance is the ability to survive and reproduce despite limited water, and numerous studies have explored its physiological basis in plants. However, it is unclear how drought resistance and trade-offs associated with drought resistance evolve within plant clades. We quantified the relationship between water availability and fitness for 13 short-lived plant taxa in the Streptanthus clade that vary in their phenology and the availability of water in the environments where they occur. We derived two parameters from these relationships: plant fitness when water is not limiting and the water inflection point (WIF), the watering level at which additional water is most efficiently turned into fitness. We used phylogenetic comparative methods to explore trade-offs related to drought resistance and trait plasticity and the degree to which water relationship parameters are conserved. Taxa from drier climates produced fruits at the lowest water levels, had a lower WIF, flowered earlier, had shorter life spans, had greater plastic water-use efficiency (WUE), and had lower fitness at nonlimiting water. In contrast, later-flowering Streptanthus taxa from less xeric climates experienced high fitness at nonlimiting water but had no fitness at the lowest water levels. Across the clade, we found a trade-off between drought resistance and fitness at high water, though a single ruderal species was an outlier in this relationship. Our results suggest that drought escape trades off with maximal fitness under nonlimiting water, and both are tied to phenology. We also found that variation in trait plasticity determines how different plant species produce fitness over a water gradient.
","language":"English","publisher":"University of Chicago Press","doi":"10.1086/707371","usgsCitation":"Pearse, I.S., Aguilar, J., and Strauss, S., 2020, Life-history plasticity and water-use trade-offs associated with drought resistance in a clade of California jewelflowers: The American Naturalist, v. 195, p. 691-704, https://doi.org/10.1086/707371.","productDescription":"14 p.","startPage":"691","endPage":"704","ipdsId":"IP-106442","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":376486,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Rimini, Montana, September 2011","docAbstract":"The principle sources of trace elements entering upper Tenmile Creek, Montana, during September 2011, four trace metals and the metalloid arsenic, were identified and quantified by combining and analyzing streamflow data determined from tracer injection with trace-element concentrations and related water-quality data determined from synoptic sampling. The study reach was along upper Tenmile Creek, beginning downstream from the city of Helena’s diversion and extending 5,020 feet downstream. Results from the 2011 study, completed by the U.S. Geological Survey in cooperation with the Montana Department of Environmental Quality, were compared to results from a similar study conducted in 1998 to assess the effectiveness of mine reclamation and remediation work to reduce trace-element loading to upper Tenmile Creek, which has been ongoing throughout the drainage basin.
Main-stem concentrations of most trace elements analyzed were generally greater in 1998 than in 2011. However, the State of Montana human-health criteria for total-recoverable cadmium and arsenic were exceeded in parts of upper Tenmile Creek, and concentrations of cadmium and zinc exceeded the acute aquatic-life criteria at all main-stem sites during both studies. Total-recoverable copper concentrations observed in 2011 exceeded the chronic aquatic-life criterion upstream from the Lee Mountain adit, whereas, in 1998, all sites exceeded the acute aquatic-life criteria.
Direct comparison of loads from the 1998 and 2011 tracer studies were complicated by the differences in hydrologic conditions. Streamflow in 1998 was about 10 percent of the 2011 streamflow. The Lee Mountain Mine and Susie Lode adit were identified as major contributors of trace elements to upper Tenmile Creek in both studies. However, trace-element loading from the Lee Mountain Mine area was substantially reduced between 1998 and 2011. Total-recoverable loads of all trace elements showed substantial loss in 1998 but increased in 2011 downstream from the Susie Lode adit to the end of the study reach. This reach was one of the primary sources of trace-element loading to upper Tenmile Creek in 2011. This difference indicated that the streambed may act as a sink or a source for trace elements, depending on hydrologic conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195126","collaboration":"Prepared in cooperation with the Montana Department of Environmental Quality","usgsCitation":"Cleasby, T., and Eldridge, S.L.C., 2020, Quantification of trace element loading in the upper Tenmile Creek drainage basin near Rimini, Montana, September 2011: U.S. Geological Survey Scientific Investigations Report 2019–5126, 40 p., https://doi.org/10.3133/sir20195126.","productDescription":"Report: vii, 40 p.; Dataset","numberOfPages":"52","onlineOnly":"Y","ipdsId":"IP-043897","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":399607,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109755.htm"},{"id":372808,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"National Water Information System database","linkHelpText":"– USGS water data for the Nation"},{"id":372807,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5126/sir20195126.pdf","text":"Report","size":"6.00 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5126"},{"id":372806,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5126/coverthb.jpg"}],"country":"United States","state":"Montana","county":"Lewis and Clark County","city":"Rimini","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.2533,\n 46.4808\n ],\n [\n -112.2444,\n 46.4808\n ],\n [\n -112.2444,\n 46.5008\n ],\n [\n -112.2533,\n 46.5008\n ],\n [\n -112.2533,\n 46.4808\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601