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.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2020.104692","usgsCitation":"Stanley, T.A., Kirschbaum, D.B., Sobieszczyk, S., Jasinski, M.F., Borak, J.S., and Slaughter, S.L., 2020, Building a landslide hazard indicator with machine learning and land surface models: Environmental Modelling & Software, v. 129, 104692, 15 p., https://doi.org/10.1016/j.envsoft.2020.104692.","productDescription":"104692, 15 p.","ipdsId":"IP-114297","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":457392,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2020.104692","text":"Publisher Index Page"},{"id":410787,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon, 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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":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","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":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","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":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true},{"id":5046,"text":"Branch of Analytical Serv (NWQL)","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":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","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":70210150,"text":"70210150 - 2020 - Temporal evolution of measured and simulated infiltration following wildfire in the Colorado Front Range, USA: Shifting thresholds of runoff generation and hydrologic hazards","interactions":[],"lastModifiedDate":"2020-05-18T12:08:31.572534","indexId":"70210150","displayToPublicDate":"2020-03-13T07:02:48","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Temporal evolution of measured and simulated infiltration following wildfire in the Colorado Front Range, USA: Shifting thresholds of runoff generation and hydrologic hazards","docAbstract":"Destructive flash floods and debris flows are a common menace following wildfire. The restoration of protection provided by forests from post-fire floods and debris flows depends on the recovery of infiltration and attendant reduction of infiltration-excess surface runoff generation. This work examines seven years of post-fire infiltration measurements and temporal relations fit to soil-hydraulic properties from the Colorado Front Range, USA to assess infiltration recovery with increasing time since fire. Point-scale Green-Ampt simulations of infiltration across a full spectrum of rainfall events are used to evaluate infiltration changes and shifts in surface runoff generation thresholds with post-fire temporal recovery. Measured and simulated infiltration generally recovered monotonically with increasing time since fire. This indicates a reduced vulnerability to infiltration-excess runoff generation as time elapses, with the greatest risk in the first two years after the fire. The threshold for infiltration-excess runoff advances with increasing time to rainfall events with higher intensity and greater return intervals; by the third year after wildfire only extreme events (30-100 year recurrence) generate surface runoff and by the fifth and seventh year even extreme rainfall events typically fail to generate surface runoff. Remotely-sensed vegetation indices suggest linked, or at least contemporaneous, recovery of understory vegetation and field-saturated hydraulic conductivity at this field site, suggesting coincident recovery of multiple hillslope properties impacting surface runoff generation. This work indicates that the closing of the window of disturbance after wildfire, relative to infiltration-excess runoff generation and corresponding flash flood and debris flow hazards, relies on coupled assessments of hillslope property recovery and stochasticity of high-intensity rainfall. Post-fire hazard assessments using static hillslope properties could fail to predict flash floods and debris flows associated with infrequent extreme rainfall events that strike during the post-fire recovery period when hillslope properties are partially recovered.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2020.124765","collaboration":"","usgsCitation":"Ebel, B., 2020, Temporal evolution of measured and simulated infiltration following wildfire in the Colorado Front Range, USA: Shifting thresholds of runoff generation and hydrologic hazards: Journal of Hydrology, v. 585, https://doi.org/10.1016/j.jhydrol.2020.124765.","productDescription":"124765, 16 p.","startPage":"124765","ipdsId":"IP-111116","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":457401,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2020.124765","text":"Publisher Index Page"},{"id":437057,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RCNFD9","text":"USGS data release","linkHelpText":"Green-Ampt infiltration modeling following wildfire in the Colorado Front Range, USA"},{"id":374881,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Colorado Front Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.270751953125,\n 39.52522954427751\n ],\n [\n -104.52392578125,\n 39.52522954427751\n ],\n [\n -104.52392578125,\n 40.901057866884024\n ],\n [\n -106.270751953125,\n 40.901057866884024\n ],\n [\n -106.270751953125,\n 39.52522954427751\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"585","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ebel, Brian A. 0000-0002-5413-3963","orcid":"https://orcid.org/0000-0002-5413-3963","contributorId":211845,"corporation":false,"usgs":true,"family":"Ebel","given":"Brian A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":789317,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"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.
This chapter introduces background and historical information on how structural equation modeling (SEM) came to be developed. Then, the main differences between SEM and earlier multivariate methods are explained. The chapter describes three main applications of SEM: path analysis, factor analysis, and hybrid models. Some computer programs are recommended for these applications. The step-by-step section goes over how to estimate structural models with AMOS and R. The chapter concludes with two example applications of SEM in the planning field.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Advanced Quantitative Research Methods for Urban Planners","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Taylor and Francis","doi":"10.4324/9780429325038-8","collaboration":"","usgsCitation":"Miller, M., Tasic, I., Lyons, T., Ewing, R., and Grace, J., 2020, Structural equation modeling, chap. 8 of Advanced Quantitative Research Methods for Urban Planners, p. 185-215, https://doi.org/10.4324/9780429325038-8.","productDescription":"31 p.","startPage":"185","endPage":"215","ipdsId":"IP-101484","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":374194,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Miller, Matt","contributorId":224278,"corporation":false,"usgs":false,"family":"Miller","given":"Matt","affiliations":[{"id":40845,"text":"Department of City and Metropolitan Planning, University of Utah, Salt Lake City","active":true,"usgs":false}],"preferred":false,"id":787615,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tasic, Ivana","contributorId":224279,"corporation":false,"usgs":false,"family":"Tasic","given":"Ivana","email":"","affiliations":[],"preferred":false,"id":787616,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lyons, Torrey","contributorId":224280,"corporation":false,"usgs":false,"family":"Lyons","given":"Torrey","email":"","affiliations":[{"id":40845,"text":"Department of City and Metropolitan Planning, University of Utah, Salt Lake City","active":true,"usgs":false}],"preferred":false,"id":787617,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ewing, Reid","contributorId":204537,"corporation":false,"usgs":false,"family":"Ewing","given":"Reid","email":"","affiliations":[{"id":13252,"text":"University of Utah","active":true,"usgs":false}],"preferred":false,"id":787618,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Grace, James B. 0000-0001-6374-4726","orcid":"https://orcid.org/0000-0001-6374-4726","contributorId":221554,"corporation":false,"usgs":true,"family":"Grace","given":"James B.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":787619,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70210368,"text":"70210368 - 2020 - Dynamic rupture simulations of the M6.4 and M7.1 July 2019 Ridgecrest, California earthquakes","interactions":[],"lastModifiedDate":"2020-06-02T14:16:37.468532","indexId":"70210368","displayToPublicDate":"2020-03-12T09:11:43","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Dynamic rupture simulations of the M6.4 and M7.1 July 2019 Ridgecrest, California earthquakes","docAbstract":"The largest earthquakes of the 2019 Ridgecrest, California, sequence were a M 6.4 left‐lateral rupture followed 34 hr later by a M 7.1 on a perpendicular right‐lateral fault. We use dynamic rupture modeling to address the questions of why the first earthquake did not propagate through the right‐lateral fault in one larger event, whether stress changes from the M 6.4 were necessary for the M 7.1 to occur, and how the Ridgecrest earthquakes affected the nearby Garlock Fault. We find that dynamic clamping and shear stress reduction confined surface rupture in the M 6.4 to the left‐lateral fault. We also find that stress changes from the M 6.4 were not necessary to allow a M 7.1 on the right‐lateral fault but that they affected the slip and likely accelerated the timing of the M 7.1. Lastly, we find that the Ridgecrest earthquakes may have brought the central Garlock Fault closer to failure.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019GL086020","usgsCitation":"Lozos, J.C., and Harris, R.A., 2020, Dynamic rupture simulations of the M6.4 and M7.1 July 2019 Ridgecrest, California earthquakes: Geophysical Research Letters, v. 47, no. 7, e2019GL086020, 9 p., https://doi.org/10.1029/2019GL086020.","productDescription":"e2019GL086020, 9 p.","ipdsId":"IP-112946","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":457415,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019gl086020","text":"Publisher Index Page"},{"id":375246,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"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 -118.95996093749999,\n 34.37064492478658\n ],\n [\n -115.34545898437499,\n 34.37064492478658\n ],\n [\n -115.34545898437499,\n 36.84446074079564\n ],\n [\n -118.95996093749999,\n 36.84446074079564\n ],\n [\n -118.95996093749999,\n 34.37064492478658\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"7","noUsgsAuthors":false,"publicationDate":"2020-04-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Lozos, Julian C.","contributorId":146525,"corporation":false,"usgs":false,"family":"Lozos","given":"Julian","email":"","middleInitial":"C.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":790058,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harris, Ruth A. 0000-0002-9247-0768 harris@usgs.gov","orcid":"https://orcid.org/0000-0002-9247-0768","contributorId":786,"corporation":false,"usgs":true,"family":"Harris","given":"Ruth","email":"harris@usgs.gov","middleInitial":"A.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":790059,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70209341,"text":"70209341 - 2020 - A comparison of groundwater sampling technologies, including passive diffusion sampling, for radionuclide contamination","interactions":[],"lastModifiedDate":"2020-05-04T13:43:06.273109","indexId":"70209341","displayToPublicDate":"2020-03-12T07:54:42","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"A comparison of groundwater sampling technologies, including passive diffusion sampling, for radionuclide contamination","docAbstract":"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":70209337,"text":"70209337 - 2020 - Louisiana Adaptive Management Status and Improvement Report: Vision and Recommendations","interactions":[],"lastModifiedDate":"2020-04-06T20:46:39.417241","indexId":"70209337","displayToPublicDate":"2020-03-12T06:13:33","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Louisiana Adaptive Management Status and Improvement Report: Vision and Recommendations","docAbstract":"As part of the process to increase implementation of adaptive management for ecosystem-based coastal restoration within Louisiana, we aim for this report to be broadly applicable across planning processes as well as funding and implementing entities. It compiles technical knowledge and guidance summarized as key findings through the text which lead to eight priority recommendations to improve application of adaptive management in Louisiana. This report identifies critical linkage points and opportunities for knowledge and data transfers within, and among, agencies in Louisiana. The primary focus is on CPRA and others affiliated with the LA TIG.\nThis report presents a consensus based common vision for adaptive management of coastal restoration implementation in Louisiana. As CPRA is the primary agency responsible for coordinating and facilitating coastal restoration projects within the state of Louisiana, the primary source of information and experience was collated from personnel and processes carried out at CPRA. To capture needs and mechanisms ,across agencies, extensive input was also received from the LA TIG, representing the other state and federal Trustees.","language":"English","publisher":"Deepwater Horizon Natural Resource Damages Trustees ","doi":"","collaboration":"","usgsCitation":"Boshart, B., Crutcher, M., Freeman, A., Haywood, E., Khalil, S.M., Langlois, S., Lee, D., Lindquist, D., McGinnis, T., Pahl, J., Parsons-Richards, C., Plitsch, E., Raynie, R., Routon, R., Sharp, L.A., Troutman, J., Villarrubia, C., Folse, T., Graugnard, A., Hawes, A., Joffrion, R., Leblanc, W., Lezina, B., Pahl, J., White, J., Conzelmann, C., Hijuelos, A., Piazza, S., Spear, K.A., Steyer, G.D., Regalado, N., Tirpak, J.M., Schupp, C., Carle, M., Daly, J., Eckhardt, N., Fellas, C., Fougeres, E.M., Heverly, S., Horstman, S., Kroll, J., Landry, M., Schroeder, B.A., Weissberger, E., Wissman, S., Grayson, T., Taylor, P., Wiegand, D., Defley, M., Kolic, P., Bienn, H., Carruthers, T., Dausman, A., Grace, A., Hemmerling, S., Jerabek, A., White, E., Clark, R., McHugh, C., Moss, L., Ramatchandirane, C., and Wold, A., 2020, Louisiana Adaptive Management Status and Improvement Report: Vision and Recommendations, 205 p., https://doi.org/.","productDescription":"205 p.","startPage":"","ipdsId":"IP-114100","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":373758,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":373702,"type":{"id":15,"text":"Index Page"},"url":"https://cims.coastal.louisiana.gov/RecordDetail.aspx?Root=0&sid=23922"}],"country":"United 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B.","affiliations":[],"preferred":false,"id":786197,"contributorType":{"id":1,"text":"Authors"},"rank":52},{"text":"Dausman, Alyssa","contributorId":223766,"corporation":false,"usgs":false,"family":"Dausman","given":"Alyssa","affiliations":[{"id":13499,"text":"The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":786198,"contributorType":{"id":1,"text":"Authors"},"rank":53},{"text":"Grace, Alaina","contributorId":221276,"corporation":false,"usgs":false,"family":"Grace","given":"Alaina","email":"","affiliations":[],"preferred":false,"id":786199,"contributorType":{"id":1,"text":"Authors"},"rank":54},{"text":"Hemmerling, Scott","contributorId":221274,"corporation":false,"usgs":false,"family":"Hemmerling","given":"Scott","affiliations":[],"preferred":false,"id":786200,"contributorType":{"id":1,"text":"Authors"},"rank":55},{"text":"Jerabek, Andrea","contributorId":223767,"corporation":false,"usgs":false,"family":"Jerabek","given":"Andrea","email":"","affiliations":[{"id":40765,"text":"formerly with The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":786201,"contributorType":{"id":1,"text":"Authors"},"rank":56},{"text":"White, Eric","contributorId":194507,"corporation":false,"usgs":false,"family":"White","given":"Eric","affiliations":[],"preferred":false,"id":786202,"contributorType":{"id":1,"text":"Authors"},"rank":57},{"text":"Clark, Ryan","contributorId":193538,"corporation":false,"usgs":false,"family":"Clark","given":"Ryan","email":"","affiliations":[],"preferred":false,"id":786409,"contributorType":{"id":1,"text":"Authors"},"rank":58},{"text":"McHugh, Coleen","contributorId":223850,"corporation":false,"usgs":false,"family":"McHugh","given":"Coleen","email":"","affiliations":[],"preferred":false,"id":786410,"contributorType":{"id":1,"text":"Authors"},"rank":59},{"text":"Moss, Leland C","contributorId":189571,"corporation":false,"usgs":false,"family":"Moss","given":"Leland C","affiliations":[],"preferred":false,"id":786411,"contributorType":{"id":1,"text":"Authors"},"rank":60},{"text":"Ramatchandirane, Cyndhia","contributorId":223851,"corporation":false,"usgs":false,"family":"Ramatchandirane","given":"Cyndhia","email":"","affiliations":[],"preferred":false,"id":786412,"contributorType":{"id":1,"text":"Authors"},"rank":61},{"text":"Wold, Amy","contributorId":223852,"corporation":false,"usgs":false,"family":"Wold","given":"Amy","email":"","affiliations":[],"preferred":false,"id":786413,"contributorType":{"id":1,"text":"Authors"},"rank":62}]}} ,{"id":70228345,"text":"70228345 - 2020 - Ecological prediction at macroscales using big data: Does sampling design matter?","interactions":[],"lastModifiedDate":"2022-02-09T23:31:04.189125","indexId":"70228345","displayToPublicDate":"2020-03-11T17:23:23","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Ecological prediction at macroscales using big data: Does sampling design matter?","docAbstract":"Although ecosystems respond to global change at regional to continental scales (i.e., macroscales), model predictions of ecosystem responses often rely on data from targeted monitoring of a small proportion of sampled ecosystems within a particular geographic area. In this study, we examined how the sampling strategy used to collect data for such models influences predictive performance. We subsampled a large and spatially-extensive dataset to investigate how macroscale sampling strategy affects prediction of ecosystem characteristics in 6,784 lakes across a 1.8 million km2 area. We estimated model predictive performance for different subsets of the dataset to mimic three common sampling strategies for collecting observations of ecosystem characteristics: random sampling design, stratified random sampling design, and targeted sampling. We found that sampling strategy influenced model predictive performance such that (1) stratified random sampling designs did not improve predictive performance compared to simple random sampling designs and (2) although one of the scenarios that mimicked targeted (non-random) sampling had the poorest performing predictive models, the other targeted sampling scenarios resulted in models with similar predictive performance to that of the random sampling scenarios. Our results suggest that although potential biases in datasets from some forms of targeted sampling may limit predictive performance, compiling existing spatially-extensive datasets can result in models with good predictive performance that may inform a wide range of science questions and policy goals related to global change.","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2123","usgsCitation":"Patricia A. Soranno, Cheruvelil, K.S., Boyang Liu, Wang, Q., Pang-Ning Tan, Jiayu Zhou, King, K.B., Ian M. McCullough, Joseph Stachelek, Bartley, M., Filstrup, C.T., Hanks, E., Lapierre, J., Lottig, N.R., Schliep, E., Wagner, T., and Webster, K.E., 2020, Ecological prediction at macroscales using big data: Does sampling design matter?: Ecological Applications, v. 30, no. 6, e02123, 13 p., https://doi.org/10.1002/eap.2123.","productDescription":"e02123, 13 p.","ipdsId":"IP-110739","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":395750,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"30","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-04-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Patricia A. Soranno","contributorId":275249,"corporation":false,"usgs":false,"family":"Patricia A. Soranno","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":833879,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cheruvelil, Kendra Spence","contributorId":275250,"corporation":false,"usgs":false,"family":"Cheruvelil","given":"Kendra","email":"","middleInitial":"Spence","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":833880,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boyang Liu","contributorId":275251,"corporation":false,"usgs":false,"family":"Boyang Liu","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":833881,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wang, Qi","contributorId":275252,"corporation":false,"usgs":false,"family":"Wang","given":"Qi","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":833882,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pang-Ning Tan","contributorId":275253,"corporation":false,"usgs":false,"family":"Pang-Ning Tan","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":833883,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jiayu Zhou","contributorId":275254,"corporation":false,"usgs":false,"family":"Jiayu Zhou","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":833884,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"King, Katelyn B.S.","contributorId":275255,"corporation":false,"usgs":false,"family":"King","given":"Katelyn","email":"","middleInitial":"B.S.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":833885,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ian M. McCullough","contributorId":275256,"corporation":false,"usgs":false,"family":"Ian M. McCullough","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":833886,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Joseph Stachelek","contributorId":275257,"corporation":false,"usgs":false,"family":"Joseph Stachelek","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":833887,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Bartley, Meridith","contributorId":275258,"corporation":false,"usgs":false,"family":"Bartley","given":"Meridith","email":"","affiliations":[{"id":56753,"text":"PennState University","active":true,"usgs":false}],"preferred":false,"id":833888,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Filstrup, Christopher T.","contributorId":169032,"corporation":false,"usgs":false,"family":"Filstrup","given":"Christopher","email":"","middleInitial":"T.","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":834081,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Hanks, Ephraim M.","contributorId":270432,"corporation":false,"usgs":false,"family":"Hanks","given":"Ephraim M.","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":834082,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Lapierre, Jean-Francois","contributorId":172182,"corporation":false,"usgs":false,"family":"Lapierre","given":"Jean-Francois","email":"","affiliations":[],"preferred":false,"id":834083,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Lottig, Noah R.","contributorId":172031,"corporation":false,"usgs":false,"family":"Lottig","given":"Noah","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":834084,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Schliep, Erin M.","contributorId":270915,"corporation":false,"usgs":false,"family":"Schliep","given":"Erin M.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":834085,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":833878,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Webster, Katherine E.","contributorId":147903,"corporation":false,"usgs":false,"family":"Webster","given":"Katherine","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":834086,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70228393,"text":"70228393 - 2020 - An interactive decision-making tool for evaluating biological and statistical standards of migrating fish survival past hydroelectric dams","interactions":[],"lastModifiedDate":"2022-02-10T17:06:48.440966","indexId":"70228393","displayToPublicDate":"2020-03-11T11:04:34","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"An interactive decision-making tool for evaluating biological and statistical standards of migrating fish survival past hydroelectric dams","docAbstract":"Quantifying the downstream survival of migrating fish past dams is critical for conservation efforts. Regulators require assessments of survival as a condition of operation. Failure to meet an established survival standard may result in required operational or costly structural changes at a facility. Establishing the survival standard, as well as the rules of assessment, is a point of contention between regulators and operators. Management goals are based on biological criteria, but there are inherent statistical and probabilistic trade-offs when choosing a standard value and the method for assessment. We make a distinction between a “biological” goal (the conservation goal) and a “statistical” standard (a function of the biological goal, sample size, assessment method, and years of consecutive evaluation). An effective statistical standard maximizes true positives (passing the standard when the biological goal is being met) and true negatives (failing the standard when the goal is not being met), while minimizing false negatives and false positives. We explored the effects of sample size, true survival, and assessment methods on the probability of passing different statistical standards by simulating survival studies (simulating mark-recapture experiments). We observed a strong influence of assessment methods on the probability of making the right decision (true positive or true negative), especially when sample size, and recapture probability was low. As a support tool, we developed an interactive user interface to explore specific scenarios, and to aid communication among decision-makers.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.3616","usgsCitation":"Molina-Moctezuma, A., and Zydlewski, J.D., 2020, An interactive decision-making tool for evaluating biological and statistical standards of migrating fish survival past hydroelectric dams: River Research and Applications, v. 36, no. 7, p. 1024-1032, https://doi.org/10.1002/rra.3616.","productDescription":"9 p.","startPage":"1024","endPage":"1032","ipdsId":"IP-111248","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":395782,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","issue":"7","noUsgsAuthors":false,"publicationDate":"2020-03-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Molina-Moctezuma, Alejandro","contributorId":275649,"corporation":false,"usgs":false,"family":"Molina-Moctezuma","given":"Alejandro","email":"","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":834191,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zydlewski, Joseph D. 0000-0002-2255-2303 jzydlewski@usgs.gov","orcid":"https://orcid.org/0000-0002-2255-2303","contributorId":2004,"corporation":false,"usgs":true,"family":"Zydlewski","given":"Joseph","email":"jzydlewski@usgs.gov","middleInitial":"D.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":834190,"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.
Large-scale terrestrial carbon (C) estimating studies using methods such as atmospheric inversion, biogeochemical modeling, and field inventories have produced different results. The goal of this study was to integrate fine-scale processes including land use and land cover change into a large-scale ecosystem framework. We analyzed the terrestrial C budget of the conterminous United States from 1971 to 2015 at 1-km resolution using an enhanced dynamic global vegetation model and comprehensive land cover change data. Effects of atmospheric CO2 fertilization, nitrogen deposition, climate, wildland fire, harvest, and land use/land cover change (LUCC) were considered. We estimate annual C losses from cropland harvest, forest clearcut and thinning, fire, and LUCC were 436.8, 117.9, 10.5, and 10.4 TgC/year, respectively. C stored in ecosystems increased from 119,494 to 127,157 TgC between 1971 and 2015, indicating a mean annual net C sink of 170.3 TgC/year. Although ecosystem net primary production increased by approximately 12.3 TgC/year, most of it was offset by increased C loss from harvest and natural disturbance and increased ecosystem respiration related to forest aging. As a result, the strength of the overall ecosystem C sink did not increase over time. Our modeled results indicate the conterminous US C sink was about 30% smaller than previous modeling studies, but converged more closely with inventory data.
Erosion related to glacial activity produces enormous amounts of sediment. However, sediment mobilization in glacial systems is extremely complex. Sediment is derived from headwalls, slopes along the margins of glaciers, and basal erosion; however, the rates and relative contributions of each are unknown. To test and quantify conceptual models for sediment generation and transport in a simple valley glacier system, we collected samples for 10Be analysis from the Kahiltna Glacier, which flows off Denali, the tallest mountain in North America. We collected angular quartz clasts on bedrock ledges from a high mountainside above the equilibrium line altitude (ELA), amalgamated clast samples from medial moraines, and sand samples from the river below the glacier. We also collected sand from nine other rivers along the south flank of the Alaska Range. In the upper catchment of the Kahiltna drainage system, toppling, rockfall, and slab collapse are significant erosional processes. Erosion rates of hundreds of millimeters per thousand years were calculated from 10Be concentrations. The 10Be concentrations in amalgamated samples from medial moraines showed concentrations much lower than those measured from the high mountainside, a result of the incorporation of thick, and effectively unexposed, blocks into the moraine, as well as the incorporation of material from lower-elevation nearby slopes above the moraines. The 10Be sediment samples from downstream of the Kahiltna Glacier terminus showed decreasing concentrations with increasing distance from the moraine, indicating the incorporation of material that was less exposed to cosmic rays, most likely from the glacier base as well as from slopes downstream of the glacier. Taken together, 10Be concentrations in various samples from the Kahiltna drainage system indicated erosion rates of hundreds of millimeters per thousand years, which is typical of tectonically active terrains. We also measured 10Be concentrations from river sediment samples collected from across the south flank of the Alaska Range. Calculation of basinwide weighted erosion rates that incorporated hypsometric curves produced unrealistically high erosion rates, which indicates that the major source of sediment was not exposed to cosmic rays and was primarily derived from the base of glaciers. Moreover, the apparently high erosion rates suggest that parts of each drainage system are not in erosional steady state with respect to cosmogenic isotope accumulation.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02190.1","usgsCitation":"Matmon, A., and Haeussler, P., 2020, Sediment sources and transport by the Kahiltna Glacier and other catchments along the south side of the Alaska Range, Alaska: Geosphere, v. 16, no. 3, p. 787-805, https://doi.org/10.1130/GES02190.1.","productDescription":"19 p.","startPage":"787","endPage":"805","ipdsId":"IP-113858","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":457429,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02190.1","text":"Publisher Index Page"},{"id":382463,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Alaska Range, Kahiltna Glacier","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -154,\n 64\n ],\n [\n -154,\n 61\n ],\n [\n -146,\n 61\n ],\n [\n -146,\n 64\n ],\n [\n -154,\n 64\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-03-10","publicationStatus":"PW","contributors":{"editors":[{"text":"Team, ASTER","contributorId":248231,"corporation":false,"usgs":false,"family":"Team","given":"ASTER","email":"","affiliations":[{"id":49834,"text":"CNRS-Aix-Marseille University, Aix en Provence, France","active":true,"usgs":false}],"preferred":false,"id":808640,"contributorType":{"id":2,"text":"Editors"},"rank":3}],"authors":[{"text":"Matmon, Ari","contributorId":196405,"corporation":false,"usgs":false,"family":"Matmon","given":"Ari","email":"","affiliations":[],"preferred":false,"id":808638,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haeussler, Peter J. 0000-0002-1503-6247","orcid":"https://orcid.org/0000-0002-1503-6247","contributorId":219956,"corporation":false,"usgs":true,"family":"Haeussler","given":"Peter J.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":808639,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70209237,"text":"70209237 - 2020 - Uptake and toxicity of clothianidin to monarch butterflies from milkweed consumption","interactions":[],"lastModifiedDate":"2020-03-26T06:40:13","indexId":"70209237","displayToPublicDate":"2020-03-10T14:14:40","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3840,"text":"PeerJ","active":true,"publicationSubtype":{"id":10}},"title":"Uptake and toxicity of clothianidin to monarch butterflies from milkweed consumption","docAbstract":"Recent concern for the adverse effects from neonicotinoid insecticides has centered on risk for insect pollinators in general and bees specifically. However, natural resource managers are also concerned about the risk of neonicotinoids to conservation efforts for the monarch butterfly (Danaus plexippus) and need additional data to help estimate risk for wild monarch butterflies exposed to those insecticides. In the present study, monarch butterfly larvae were exposed in the laboratory to clothianidin via contaminated milkweed plants from hatch until pupation, and the effects upon larval survival, larval growth, pupation success, and adult size were measured. Soils dosed with a granular insecticide product led to mean clothianidin concentrations of 10.8–2,193 ng/g in milkweed leaves and 5.8–58.0 ng/g in larvae. Treatment of soils also led to clothianidin concentrations of 2.6–5.1 ng/g in adult butterflies indicating potential for transfer of systemic insecticides from the soil through plants and larvae to adult butterflies. Estimated LC50s for total mortality (combined mortality of larvae and pupae) and EC50 for larval growth were variable but higher than the majority of concentrations reported in the literature for clothianidin contamination of leaves.","language":"English","publisher":"PeerJ","doi":"10.7717/peerj.8669","usgsCitation":"Bargar, T.A., Hladik, M., and Daniels, J.C., 2020, Uptake and toxicity of clothianidin to monarch butterflies from milkweed consumption: PeerJ, v. 8, e8669, https://doi.org/10.7717/peerj.8669.","productDescription":"e8669","ipdsId":"IP-108502","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":457431,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.8669","text":"Publisher Index Page"},{"id":373526,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2020-03-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Bargar, Timothy A. 0000-0001-8588-3436 tbargar@usgs.gov","orcid":"https://orcid.org/0000-0001-8588-3436","contributorId":2450,"corporation":false,"usgs":true,"family":"Bargar","given":"Timothy","email":"tbargar@usgs.gov","middleInitial":"A.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":785504,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hladik, Michelle L. 0000-0002-0891-2712 mhladik@usgs.gov","orcid":"https://orcid.org/0000-0002-0891-2712","contributorId":201293,"corporation":false,"usgs":true,"family":"Hladik","given":"Michelle L.","email":"mhladik@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":785505,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Daniels, Jaret C.","contributorId":223585,"corporation":false,"usgs":false,"family":"Daniels","given":"Jaret","email":"","middleInitial":"C.","affiliations":[{"id":40743,"text":"Florida Museum of Natural History and University of Florida","active":true,"usgs":false}],"preferred":false,"id":785506,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70209193,"text":"70209193 - 2020 - Climate change effects on biodiversity, ecosystems, ecosystem services, and natural resource management in the United States","interactions":[],"lastModifiedDate":"2020-12-08T18:12:20.831907","indexId":"70209193","displayToPublicDate":"2020-03-10T11:49:40","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Climate change effects on biodiversity, ecosystems, ecosystem services, and natural resource management in the United States","docAbstract":"Climate change is a pervasive and growing global threat to biodiversity and ecosystems. Here, we present the most up-to-date assessment of climate change impacts on biodiversity, ecosystems, and ecosystem services in the U.S. and implications for natural resource management. We draw from the 4th National Climate Assessment to summarize observed and projected changes to ecosystems and biodiversity, explore linkages to important ecosystem services, and discuss associated challenges and opportunities for natural resource management. We find that species are responding to climate change through changes in morphology and behavior, phenology, and geographic range shifts, and these changes are mediated by plastic and evolutionary responses. Responses by species and populations, combined with direct effects of climate change on ecosystems (including more extreme events), are resulting in widespread changes in productivity, species interactions, vulnerability to biological invasions, and other emergent properties. Collectively, these impacts alter the benefits and services that natural ecosystems can provide to society. Although not all impacts are negative, even positive changes can require costly societal adjustments. Natural resource managers need proactive, flexible adaptation strategies that consider historical and future outlooks to minimize costs over the long term. Many organizations are beginning to explore these approaches, but implementation is not yet prevalent or systematic across the nation.","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2020.137782","usgsCitation":"Weiskopf, S.R., Rubenstein, M.A., Crozier, L., Gaichas, S., Griffis, R., Halofsky, J.E., Hyde, K.J., Morelli, T.L., Morisette, J.T., Munoz, R.C., Pershing, A.J., Peterson, D.L., Poudel, R., Staudinger, M., Sutton-Grier, A.E., Thompson, L., Vose, J., Weltzin, J., and Whyte, K.P., 2020, Climate change effects on biodiversity, ecosystems, ecosystem services, and natural resource management in the United States: Science of the Total Environment, v. 733, 137782, 18 p., https://doi.org/10.1016/j.scitotenv.2020.137782.","productDescription":"137782, 18 p.","ipdsId":"IP-111339","costCenters":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":36940,"text":"National Climate Adaptation Science 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