In 2010, the U.S. Geological Survey (USGS) assessed Lower Cretaceous Albian to Upper Cretaceous Cenomanian carbonate rocks of the Fredericksburg and Washita Groups and their equivalent units for technically recoverable, undiscovered hydrocarbon resources underlying onshore lands and State Waters of the Gulf Coast region of the United States. This assessment was based on a geologic model that incorporates the Upper Jurassic-Cretaceous-Tertiary Composite Total Petroleum System (TPS) of the Gulf of Mexico basin; the TPS was defined previously by the USGS assessment team in the assessment of undiscovered hydrocarbon resources in Tertiary strata of the Gulf Coast region in 2007. One conventional assessment unit (AU), which extends from south Texas to the Florida panhandle, was defined: the Fredericksburg-Buda Carbonate Platform-Reef Gas and Oil AU. The assessed stratigraphic interval includes the Edwards Limestone of the Fredericksburg Group and the Georgetown and Buda Limestones of the Washita Group. The following factors were evaluated to define the AU and estimate oil and gas resources: potential source rocks, hydrocarbon migration, reservoir porosity and permeability, traps and seals, structural features, paleoenvironments (back-reef lagoon, reef, and fore-reef environments), and the potential for water washing of hydrocarbons near outcrop areas.
In Texas and Louisiana, the downdip boundary of the AU was defined as a line that extends 10 miles downdip of the Lower Cretaceous shelf margin to include potential reef-talus hydrocarbon reservoirs. In Mississippi, Alabama, and the panhandle area of Florida, where the Lower Cretaceous shelf margin extends offshore, the downdip boundary was defined by the offshore boundary of State Waters. Updip boundaries of the AU were drawn based on the updip extent of carbonate rocks within the assessed interval, the presence of basin-margin fault zones, and the presence of producing wells. Other factors evaluated were the middle Cenomanian sea-level fall and erosion that removed large portions of platform and platform-margin carbonate sediments in the Washita Group of central Louisiana. The production history of discovered reservoirs and well data within the AU were examined to estimate the number and size of undiscovered oil and gas reservoirs within the AU. Using the USGS National Oil and Gas Assessment resource assessment methodology, mean volumes of 40 million barrels of oil, 622 billion cubic feet of gas, and 14 million barrels of natural gas liquids are the estimated technically recoverable undiscovered resources for the Fredericksburg-Buda Carbonate Platform-Reef Gas and Oil AU.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161199","usgsCitation":"Swanson, S.M., Enomoto, C.B., Dennen, K.O., Valentine, B.J., and Cahan, S.M., 2017, Geologic assessment of undiscovered oil and gas resources—Lower Cretaceous Albian to Upper Cretaceous Cenomanian carbonate rocks of the Fredericksburg and Washita Groups, United States Gulf of Mexico Coastal Plain and State Waters: U.S. Geological Survey Open-File Report 2016–1199, 69 p., https://doi.org/10.3133/ofr20161199.","productDescription":"Report: vii, 68 p.; Appendix 1: 2 p.","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-064618","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":335075,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1199/ofr20161199.pdf","text":"Report","size":"10.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1199"},{"id":335074,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1199/coverthb.jpg"},{"id":335076,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1199/ofr20161199_appendix1.pdf","text":"Appendix 1 - ","size":"446 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"Input data form for the Fredericksburg-Buda Carbonate Platform-Reef Gas and Oil Assessment Unit (50490127)"}],"country":"United States","state":"Alabama, Arkansas, Florida, Louisiana, Mississippi, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.20898437499999,\n 37.996162679728116\n ],\n [\n -90.3515625,\n 37.75334401310656\n ],\n [\n -90.9228515625,\n 36.491973470593685\n ],\n [\n -92.021484375,\n 35.746512259918504\n ],\n [\n -93.1201171875,\n 35.209721645221386\n ],\n [\n -94.74609375,\n 34.994003757575776\n ],\n [\n -96.6357421875,\n 34.125447565116126\n ],\n [\n -97.55859375,\n 33.90689555128866\n ],\n [\n -98.9208984375,\n 33.687781758439364\n ],\n [\n -100.1513671875,\n 33.211116472416855\n ],\n [\n -101.4697265625,\n 32.24997445586331\n ],\n [\n -102.1728515625,\n 31.27855085894653\n ],\n [\n -101.953125,\n 30.221101852485987\n ],\n [\n -101.42578124999999,\n 29.611670115197377\n ],\n [\n -100.5908203125,\n 28.729130483430154\n ],\n [\n -99.84374999999999,\n 27.605670826465445\n ],\n [\n -99.404296875,\n 26.941659545381516\n ],\n [\n -99.00878906249999,\n 26.31311263768267\n ],\n [\n -98.7451171875,\n 26.194876675795218\n ],\n [\n -97.822265625,\n 25.878994400196202\n ],\n [\n -97.119140625,\n 25.878994400196202\n ],\n [\n -97.03125,\n 26.27371402440643\n ],\n [\n -97.03125,\n 27.0982539061379\n ],\n [\n -96.6796875,\n 27.916766641249065\n ],\n [\n -95.49316406249999,\n 28.459033019728043\n ],\n [\n -94.306640625,\n 29.075375179558346\n ],\n [\n -92.98828125,\n 29.11377539511439\n ],\n [\n -91.7138671875,\n 29.267232865200878\n ],\n [\n -90.87890625,\n 28.92163128242129\n ],\n [\n -89.8681640625,\n 28.806173508854776\n ],\n [\n -89.07714843749999,\n 28.806173508854776\n ],\n [\n -88.8134765625,\n 29.19053283229458\n ],\n [\n -88.9453125,\n 29.80251790576445\n ],\n [\n -88.5498046875,\n 30.14512718337613\n ],\n [\n -87.6708984375,\n 30.031055426540206\n ],\n [\n -86.8798828125,\n 30.031055426540206\n ],\n [\n -85.7373046875,\n 29.76437737516313\n ],\n [\n -85.1220703125,\n 29.38217507514529\n ],\n [\n -84.55078125,\n 29.57345707301757\n ],\n [\n -84.111328125,\n 29.57345707301757\n ],\n [\n -83.6279296875,\n 29.305561325527698\n ],\n [\n -83.27636718749999,\n 29.036960648558267\n ],\n [\n -82.8369140625,\n 29.38217507514529\n ],\n [\n -82.6171875,\n 30.44867367928756\n ],\n [\n -82.705078125,\n 31.690781806136822\n ],\n [\n -83.8037109375,\n 32.287132632616384\n ],\n [\n -85.1220703125,\n 33.211116472416855\n ],\n [\n -86.0009765625,\n 34.415973384481866\n ],\n [\n -87.099609375,\n 35.60371874069731\n ],\n [\n -87.6708984375,\n 37.26530995561875\n ],\n [\n -88.72558593749999,\n 37.78808138412046\n ],\n [\n -89.20898437499999,\n 37.996162679728116\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Energy Resources Science Center
U.S. Geological Survey
Mail Stop 956
12201 Sunrise Valley Drive
Reston, VA 20192
http://energy.usgs.gov/
The characteristics of bed material at selected sites within the Sacramento–San Joaquin Delta, California, during 2010–13 are described in a study conducted by the U.S. Geological Survey in cooperation with the Bureau of Reclamation. During 2010‒13, six complete sets of samples were collected. Samples were initially collected at 30 sites; however, starting in 2012, samples were collected at 7 additional sites. These sites are generally collocated with an active streamgage. At all but one site, a separate bed-material sample was collected at three locations within the channel (left, right, and center). Bed-material samples were collected using either a US BMH–60 or a US BM–54 (for sites with higher stream velocity) cable-suspended, scoop sampler. Samples from each location were oven-dried and sieved. Bed material finer than 2 millimeters was subsampled using a sieving riffler and processed using a Beckman Coulter LS 13–320 laser diffraction particle-size analyzer. To determine the organic content of the bed material, the loss on ignition method was used for one subsample from each location. Particle-size distributions are presented as cumulative percent finer than a given size. Median and 90th-percentile particle size, and the percentage of subsample mass lost using the loss on ignition method for each sample are also presented in this report.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1026","issn":"2327-638X (online)","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Marineau, M.D., and Wright, S.A., 2017, Bed-material characteristics of the Sacramento–San Joaquin Delta, California, 2010–13: U.S. Geological Survey Data Series 1026, 55 p., \nhttps://doi.org/10.3133/ds1026.","productDescription":"vi, 55 p.","numberOfPages":"66","onlineOnly":"Y","ipdsId":"IP-078870","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":334907,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1026/ds1026.pdf","text":"Report","size":"2.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1026"},{"id":334906,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1026/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento–San Joaquin Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122,\n 38.5\n ],\n [\n -122,\n 37.66667\n ],\n [\n -121.1667,\n 37.66667\n ],\n [\n -121.1667,\n 38.5\n ],\n [\n -122,\n 38.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director
U.S. Geological Survey
California Water Science Center
6000 J Street, Placer Hall
Sacramento, CA 95819
The Water, Energy, and Biogeochemical Model (WEBMOD) uses the framework of the U.S. Geological Survey (USGS) Modular Modeling System to simulate fluxes of water and solutes through watersheds. WEBMOD divides watersheds into model response units (MRU) where fluxes and reactions are simulated for the following eight hillslope reservoir types: canopy; snowpack; ponding on impervious surfaces; O-horizon; two reservoirs in the unsaturated zone, which represent preferential flow and matrix flow; and two reservoirs in the saturated zone, which also represent preferential flow and matrix flow. The reservoir representing ponding on impervious surfaces, currently not functional (2016), will be implemented once the model is applied to urban areas. MRUs discharge to one or more stream reservoirs that flow to the outlet of the watershed. Hydrologic fluxes in the watershed are simulated by modules derived from the USGS Precipitation Runoff Modeling System; the National Weather Service Hydro-17 snow model; and a topography-driven hydrologic model (TOPMODEL). Modifications to the standard TOPMODEL include the addition of heterogeneous vertical infiltration rates; irrigation; lateral and vertical preferential flows through the unsaturated zone; pipe flow draining the saturated zone; gains and losses to regional aquifer systems; and the option to simulate baseflow discharge by using an exponential, parabolic, or linear decrease in transmissivity. PHREEQC, an aqueous geochemical model, is incorporated to simulate chemical reactions as waters evaporate, mix, and react within the various reservoirs of the model. The reactions that can be specified for a reservoir include equilibrium reactions among water; minerals; surfaces; exchangers; and kinetic reactions such as kinetic mineral dissolution or precipitation, biologically mediated reactions, and radioactive decay. WEBMOD also simulates variations in the concentrations of the stable isotopes deuterium and oxygen-18 as a result of varying inputs, mixing, and evaporation. This manual describes the WEBMOD input and output files, along with the algorithms and procedures used to simulate the hydrology and water quality in a watershed. Examples are presented that demonstrate hydrologic processes, weathering reactions, and isotopic evolution in an alpine watershed and the effect of irrigation on water flows and salinity in an intensively farmed agricultural area.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section B: Surface Water in Book 6: Modeling Techniques","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm6B35","issn":"2328-7055","usgsCitation":"Webb, R.M.T., and Parkhurst, D.L., 2017, Water, Energy, and Biogeochemical Model (WEBMOD), user’s manual, version 1: U.S. Geological Survey Techniques and Methods, book 6, chap. B35, 171 p., https://doi.org/10.3133/tm6B35.","productDescription":"xiv, 171 p.","numberOfPages":"190","onlineOnly":"Y","costCenters":[{"id":144,"text":"Branch of Regional Research","active":false,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":438440,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P26W9K","text":"USGS data release","linkHelpText":"Water, Energy, and Biogeochemical Model"},{"id":334918,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/06/b35/tm6b35.pdf","text":"Report","size":"8.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T&M 6-B35"},{"id":334917,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/06/b35/coverthb.jpg"},{"id":334980,"rank":3,"type":{"id":4,"text":"Application Site"},"url":"https://doi.org/10.5066/F7P26W9K","text":"Water, Energy, and Biogeochemical Model (WEBMOD)"}],"publicComments":"This report is Chapter 35 of Section B: Surface Water in Book 6 Modeling Techniques","contact":"Chief, National Research Program, Central Branch
U.S. Geological Survey
Box 25585, Mail Stop 418
Denver, CO 80225-0585
Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
https://ca.water.usgs.gov
The U.S. Geological Survey, in cooperation with the Coastal Protection and Restoration Authority of Louisiana and the Coastal Wetlands Planning, Protection and Restoration Act, developed the Forested Floristic Quality Index (FFQI) for the Coastwide Reference Monitoring System (CRMS). The FFQI will help evaluate forested wetland sites on a continuum from severely degraded to healthy and will assist in defining areas where forested wetland restoration can be successful by projecting the trajectories of change. At each CRMS forested wetland site there are stations for quantifying the overstory, understory, and herbaceous vegetation layers. Rapidly responding overstory canopy cover and herbaceous layer composition are measured annually, while gradually changing overstory basal area and species composition are collected on a 3-year cycle.
A CRMS analytical team has tailored these data into an index much like the Floristic Quality Index (FQI) currently used for herbaceous marsh and for the herbaceous layer of the swamp vegetation. The core of the FFQI uses basal area by species to assess the quality and quantity of the overstory at each of three stations within each CRMS forested wetland site. Trees that are considered by experts to be higher quality swamp species like Taxodium distichum (bald cypress) and Nyssa aquatica (water tupelo) are scored higher than tree species like Triadica sebifera (Chinese tallow) and Salix nigra (black willow) that are indicators of recent disturbance. This base FFQI is further enhanced by the percent canopy cover in the overstory and the presence of indicator species at the forest floor. This systemic approach attempts to differentiate between locations with similar basal areas that are on different ecosystem trajectories. Because of these varying states of habitat degradation, paired use of the FQI and the FFQI is useful to interpret the vegetative data in transitional locations. There is often an inverse relation between the health of the overstory and health of the herbaceous community beneath it because of resource competition (for example, light) and differing environmental preferences between the two communities. The herbaceous layer vegetation responds rapidly to basic environmental factors such as flooding, salinity, and nutrients and can offer insight into the sustainability of swamps on a temporal scale shorter than tha of the slowly growing woody vegetation.
The FFQI will be available via the CRMS spatial viewer (http://lacoast.gov/crms2/home.aspx), and a new score will be calculated annually for each CRMS forested wetland site as data are collected to establish trends, to compare among sites, and to evaluate specific restoration projects when applicable. The FFQI will identify forested wetland areas in need of restoration and conservation and will help define targets and trajectories for restoration planning.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171002","collaboration":"Prepared in cooperation with the Coastal Protection and Restoration Authority of Louisiana and the Coastal Wetlands Planning, Protection and Restoration Act","usgsCitation":"Wood, W.B., Shaffer, G.P., Visser, J.M., Krauss, K.W., Piazza, S.C., Sharp, L.A., and Cretini, K.F., 2017, Forested Floristic Quality Index—An assessment tool for forested wetland habitats using the quality and quantity of woody vegetation at Coastwide Reference Monitoring System (CRMS) vegetation monitoring stations: U.S. Geological Survey Open-File Report 2017–1002, 15 p., https://doi.org/10.3133/ofr20171002.","productDescription":"iv, 15 p.","numberOfPages":"24","onlineOnly":"Y","ipdsId":"IP-059586","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":334901,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1002/ofr20171002.pdf","text":"Report","size":"2.45 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017–1002"},{"id":334900,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1002/coverthb.jpg"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.691650390625,\n 28.603814407841327\n ],\n [\n -92.691650390625,\n 30.779598396611537\n ],\n [\n -88.52783203125,\n 30.779598396611537\n ],\n [\n -88.52783203125,\n 28.603814407841327\n ],\n [\n -92.691650390625,\n 28.603814407841327\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wetland and Aquatic Research Center
U.S. Geological Survey
700 Cajundome Blvd.
Lafayette, LA 70506
https://www.usgs.gov/centers/wetland-and-aquatic-research-center-warc
","tableOfContents":"Heavy rainfall occurred across Louisiana and southwestern Mississippi in August 2016 as a result of a slow-moving area of low pressure and a high amount of atmospheric moisture. The storm caused major flooding in the southern portions of Louisiana including areas surrounding Baton Rouge and Lafayette. Flooding occurred along the rivers such as the Amite, Comite, Tangipahoa, Tickfaw, Vermilion, and Mermentau Rivers. Over 31 inches of rain was reported in the city of Watson, 20 miles northeast of Baton Rouge, La., over the duration of the event. Streamflow-gaging stations operated by the U.S. Geological Survey (USGS) recorded peak streamflows of record at 10 locations, and 7 other locations experienced peak streamflows ranking in the top five for the duration of the period of record. In August 2016, USGS hydrographers made 50 discharge measurements at 21 locations on streams in Louisiana. Many of those discharge measurements were made for the purpose of verifying the accuracy of stage-streamflow relations at gaging stations operated by the USGS. Following the storm event, USGS hydrographers recovered and documented 590 high-water marks, noting location and height of the water above land surface. Many of these high-water marks were used to create 12 flood-inundation maps for selected communities of Louisiana that experienced flooding in August 2016. Digital datasets of the inundation area, modeling boundary, water depth rasters, and final map products are available online.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175005","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Watson, K.M., Storm, J.B., Breaker, B.K., and Rose, C.E., 2017, Characterization of peak streamflows and flood inundation of selected areas in Louisiana from the August 2016 flood: U.S. Geological Survey Scientific Investigations Report 2017–5005, 26 p., https://doi.org/10.3133/sir20175005.","productDescription":"Report: v, 26 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-081535","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":334119,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5005/sir20175005.pdf","text":"Report","size":"7.98 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5005"},{"id":334118,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5005/coverthb2.jpg"},{"id":334120,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F79K48C1","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Flood Inundation Extent and Depth in Selected Areas of Louisiana in August 2016"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.3123779296875,\n 28.859107573773\n ],\n [\n -93.3123779296875,\n 31.475524020001806\n ],\n [\n -89.879150390625,\n 31.475524020001806\n ],\n [\n -89.879150390625,\n 28.859107573773\n ],\n [\n -93.3123779296875,\n 28.859107573773\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
401 Hardin Road
Little Rock, AR 72211
Groundwater quality in the 2,390-square-mile Madera/Chowchilla–Kings Shallow Aquifer study unit was investigated by the U.S. Geological Survey from August 2013 to April 2014 as part of the California State Water Resources Control Board Groundwater Ambient Monitoring and Assessment Program’s Priority Basin Project. The study was designed to provide a statistically unbiased, spatially distributed assessment of untreated groundwater quality in the shallow aquifer systems of the Madera, Chowchilla, and Kings subbasins of the San Joaquin Valley groundwater basin. The shallow aquifer system corresponds to the part of the aquifer system generally used by domestic wells and is shallower than the part of the aquifer system generally used by public-supply wells. This report presents the data collected for the study and a brief preliminary description of the results.
Groundwater samples were collected from 77 wells and were analyzed for organic constituents, inorganic constituents, selected isotopic and age-dating tracers, and microbial indicators. Most of the wells sampled for this study were private domestic wells. Unlike groundwater from public-supply wells, the groundwater from private domestic wells is not regulated for quality in California and is rarely analyzed for water-quality constituents. To provide context for the sampling results, however, concentrations of constituents measured in the untreated groundwater were compared with regulatory and non-regulatory benchmarks established for drinking-water quality by the U.S. Environmental Protection Agency, the State of California, and the U.S. Geological Survey.
Of the 319 organic constituents assessed in this study (90 volatile organic compounds and 229 pesticides and pesticide degradates), 17 volatile organic compounds and 23 pesticides and pesticide degradates were detected in groundwater samples; concentrations of all but 2 were less than the respective benchmarks. The fumigants 1,2-dibromo-3-chloropropane (DBCP) and 1,2-dibromoethane (EDB) were detected at concentrations above their respective regulatory benchmarks in samples from a total of four wells.
Most detections of inorganic constituents were at concentrations or activities less than the respective benchmark levels. Five inorganic constituents were detected in groundwater samples from one or more wells at concentrations or activities greater than their respective regulatory, health-based benchmarks: arsenic, uranium, nitrate, adjusted gross alpha particle activity, and gross beta particle activity. Four inorganic constituents were detected in samples from one or more wells at concentrations or activities greater than their respective non-regulatory, health-based benchmarks: manganese, molybdenum, vanadium, and radon-222. Three inorganic constituents were detected in groundwater samples from one or more wells at concentrations greater than their respective non-regulatory, aesthetic-based benchmarks: iron, sulfate, and total dissolved solids.
Microbial indicators (Escherichia coli, total coliform, and enterococci) were analyzed for presence or absence. The presence of Escherichia coli (E. coli) was not detected; the presence of total coliform was detected in samples from 10 of the 72 grid wells for which it was analyzed, and the presence of enterococci was detected in samples from 5 of the 73 grid wells analyzed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1019","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Shelton, J.L., and Fram, M.S., 2017, Groundwater-quality data for the Madera/Chowchilla–Kings shallow aquifer study unit, 2013–14: Results from the California GAMA Program: U.S. Geological Survey Data Series 1019, 115 p., https://doi.org/10.3133/ds1019.","productDescription":"Report: viii, 115 p.","numberOfPages":"128","onlineOnly":"N","ipdsId":"IP-056132","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":334554,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1019/ds1019.pdf","text":"Report","size":"3.67 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1019"},{"id":334553,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1019/coverthb2.jpg"}],"country":"United States","state":"California","otherGeospatial":"Madera/Chowchilla-Kings Shallow Aquifer study unit","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.666667,\n 37.416667\n ],\n [\n -120.666667,\n 36\n ],\n [\n -119.166667,\n 36\n ],\n [\n -119.166667,\n 37.416667\n ],\n [\n -120.666667,\n 37.416667\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
A cross-site analysis was conducted on seven diverse, forested watersheds in the northeastern United States to evaluate hydrological responses (evapotranspiration, soil moisture, seasonal and annual streamflow, and water stress) to projections of future climate. We used output from four atmosphere–ocean general circulation models (AOGCMs; CCSM4, HadGEM2-CC, MIROC5, and MRI-CGCM3) included in Phase 5 of the Coupled Model Intercomparison Project, coupled with two Representative Concentration Pathways (RCP 8.5 and 4.5). The coarse resolution AOGCMs outputs were statistically downscaled using an asynchronous regional regression model to provide finer resolution future climate projections as inputs to the deterministic dynamic ecosystem model PnET-BGC. Simulation results indicated that projected warmer temperatures and longer growing seasons in the northeastern United States are anticipated to increase evapotranspiration across all sites, although invoking CO2 effects on vegetation (growth enhancement and increases in water use efficiency (WUE)) diminish this response. The model showed enhanced evapotranspiration resulted in drier growing season conditions across all sites and all scenarios in the future. Spruce-fir conifer forests have a lower optimum temperature for photosynthesis, making them more susceptible to temperature stress than more tolerant hardwood species, potentially giving hardwoods a competitive advantage in the future. However, some hardwood forests are projected to experience seasonal water stress, despite anticipated increases in precipitation, due to the higher temperatures, earlier loss of snow packs, longer growing seasons, and associated water deficits. Considering future CO2effects on WUE in the model alleviated water stress across all sites. Modeled streamflow responses were highly variable, with some sites showing significant increases in annual water yield, while others showed decreases. This variability in streamflow responses poses a challenge to water resource management in the northeastern United States. Our analyses suggest that dominant vegetation type and soil type are important attributes in determining future hydrological responses to climate change.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.13444","usgsCitation":"Pourmokhtarian, A., Driscoll, C.T., Campbell, J.L., Hayhoe, K., Stoner, A., Adams, M.B., Burns, D., Fernandez, I., Mitchell, M.J., and Shanley, J.B., 2017, Modeled ecohydrological responses to climate change at seven small watersheds in the northeastern United States: Global Change Biology, v. 23, no. 2, p. 840-856, https://doi.org/10.1111/gcb.13444.","productDescription":"17 p.","startPage":"840","endPage":"856","ipdsId":"IP-077080","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":352254,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","issue":"2","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2016-08-25","publicationStatus":"PW","scienceBaseUri":"5afee8d3e4b0da30c1bfc4ba","contributors":{"authors":[{"text":"Pourmokhtarian, Afshin","contributorId":202944,"corporation":false,"usgs":false,"family":"Pourmokhtarian","given":"Afshin","email":"","affiliations":[{"id":5082,"text":"Syracuse University","active":true,"usgs":false}],"preferred":false,"id":730243,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Driscoll, Charles T.","contributorId":167460,"corporation":false,"usgs":false,"family":"Driscoll","given":"Charles","email":"","middleInitial":"T.","affiliations":[{"id":5082,"text":"Syracuse University","active":true,"usgs":false}],"preferred":false,"id":730244,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Campbell, John L.","contributorId":178410,"corporation":false,"usgs":false,"family":"Campbell","given":"John","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":730245,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hayhoe, Katharine","contributorId":149192,"corporation":false,"usgs":false,"family":"Hayhoe","given":"Katharine","email":"","affiliations":[{"id":17667,"text":"Climate Science Center, Texas Tech University, Lubbock, Texas, United States","active":true,"usgs":false}],"preferred":false,"id":730246,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stoner, Anne M. K.","contributorId":202945,"corporation":false,"usgs":false,"family":"Stoner","given":"Anne M. K.","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":730247,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Adams, Mary Beth","contributorId":150354,"corporation":false,"usgs":false,"family":"Adams","given":"Mary","email":"","middleInitial":"Beth","affiliations":[],"preferred":false,"id":730248,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Burns, Douglas A. 0000-0001-6516-2869","orcid":"https://orcid.org/0000-0001-6516-2869","contributorId":202943,"corporation":false,"usgs":true,"family":"Burns","given":"Douglas A.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":730242,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fernandez, Ivan","contributorId":178215,"corporation":false,"usgs":false,"family":"Fernandez","given":"Ivan","affiliations":[],"preferred":false,"id":730249,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Mitchell, Myron J.","contributorId":73734,"corporation":false,"usgs":true,"family":"Mitchell","given":"Myron","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":730250,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Shanley, James B. 0000-0002-4234-3437 jshanley@usgs.gov","orcid":"https://orcid.org/0000-0002-4234-3437","contributorId":1953,"corporation":false,"usgs":true,"family":"Shanley","given":"James","email":"jshanley@usgs.gov","middleInitial":"B.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":730241,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70192059,"text":"70192059 - 2017 - Assessing conditions influencing the longitudinal distribution of exotic brown trout (Salmo trutta) in a mountain stream: a spatially-explicit modeling approach","interactions":[],"lastModifiedDate":"2017-10-19T14:44:32","indexId":"70192059","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1018,"text":"Biological Invasions","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Assessing conditions influencing the longitudinal distribution of exotic brown trout (Salmo trutta) in a mountain stream: a spatially-explicit modeling approach","title":"Assessing conditions influencing the longitudinal distribution of exotic brown trout (Salmo trutta) in a mountain stream: a spatially-explicit modeling approach","docAbstract":"Trout species often segregate along elevational gradients, yet the mechanisms driving this pattern are not fully understood. On the Logan River, Utah, USA, exotic brown trout (Salmo trutta) dominate at low elevations but are near-absent from high elevations with native Bonneville cutthroat trout (Onchorhynchus clarkii utah). We used a spatially-explicit Bayesian modeling approach to evaluate how abiotic conditions (describing mechanisms related to temperature and physical habitat) as well as propagule pressure explained the distribution of brown trout in this system. Many covariates strongly explained redd abundance based on model performance and coefficient strength, including average annual temperature, average summer temperature, gravel availability, distance from a concentrated stocking area, and anchor ice-impeded distance from a concentrated stocking area. In contrast, covariates that exhibited low performance in models and/or a weak relationship to redd abundance included reach-average water depth, stocking intensity to the reach, average winter temperature, and number of days with anchor ice. Even if climate change creates more suitable summer temperature conditions for brown trout at high elevations, our findings suggest their success may be limited by other conditions. The potential role of anchor ice in limiting movement upstream is compelling considering evidence suggesting anchor ice prevalence on the Logan River has decreased significantly over the last several decades, likely in response to climatic changes. Further experimental and field research is needed to explore the role of anchor ice, spawning gravel availability, and locations of historical stocking in structuring brown trout distributions on the Logan River and elsewhere.
","language":"English","publisher":"Springer","doi":"10.1007/s10530-016-1322-z","usgsCitation":"Meredith, C.S., Budy, P., Hooten, M., and Oliveira Prates, M., 2017, Assessing conditions influencing the longitudinal distribution of exotic brown trout (Salmo trutta) in a mountain stream: a spatially-explicit modeling approach: Biological Invasions, v. 19, no. 2, p. 503-519, https://doi.org/10.1007/s10530-016-1322-z.","productDescription":"17 p.","startPage":"503","endPage":"519","ipdsId":"IP-069503","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":346993,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Logan River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.8023681640625,\n 41.734941789858006\n ],\n [\n -111.53594970703125,\n 41.734941789858006\n ],\n [\n -111.53594970703125,\n 41.95336258301847\n ],\n [\n -111.8023681640625,\n 41.95336258301847\n ],\n [\n -111.8023681640625,\n 41.734941789858006\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"19","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-16","publicationStatus":"PW","scienceBaseUri":"59e9b996e4b05fe04cd65cac","contributors":{"authors":[{"text":"Meredith, Christy S.","contributorId":197695,"corporation":false,"usgs":false,"family":"Meredith","given":"Christy","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":714105,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Budy, Phaedra E. 0000-0002-9918-1678 pbudy@usgs.gov","orcid":"https://orcid.org/0000-0002-9918-1678","contributorId":140028,"corporation":false,"usgs":true,"family":"Budy","given":"Phaedra","email":"pbudy@usgs.gov","middleInitial":"E.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":714037,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":714038,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Oliveira Prates, Marcos","contributorId":197696,"corporation":false,"usgs":false,"family":"Oliveira Prates","given":"Marcos","email":"","affiliations":[],"preferred":false,"id":714106,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70195840,"text":"70195840 - 2017 - Using diurnal temperature signals to infer vertical groundwater-surface water exchange","interactions":[],"lastModifiedDate":"2018-03-06T11:07:46","indexId":"70195840","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Using diurnal temperature signals to infer vertical groundwater-surface water exchange","docAbstract":"Heat is a powerful tracer to quantify fluid exchange between surface water and groundwater. Temperature time series can be used to estimate pore water fluid flux, and techniques can be employed to extend these estimates to produce detailed plan-view flux maps. Key advantages of heat tracing include cost-effective sensors and ease of data collection and interpretation, without the need for expensive and time-consuming laboratory analyses or induced tracers. While the collection of temperature data in saturated sediments is relatively straightforward, several factors influence the reliability of flux estimates that are based on time series analysis (diurnal signals) of recorded temperatures. Sensor resolution and deployment are particularly important in obtaining robust flux estimates in upwelling conditions. Also, processing temperature time series data involves a sequence of complex steps, including filtering temperature signals, selection of appropriate thermal parameters, and selection of the optimal analytical solution for modeling. This review provides a synthesis of heat tracing using diurnal temperature oscillations, including details on optimal sensor selection and deployment, data processing, model parameterization, and an overview of computing tools available. Recent advances in diurnal temperature methods also provide the opportunity to determine local saturated thermal diffusivity, which can improve the accuracy of fluid flux modeling and sensor spacing, which is related to streambed scour and deposition. These parameters can also be used to determine the reliability of flux estimates from the use of heat as a tracer.
","language":"English","publisher":"Wiley","doi":"10.1111/gwat.12459","usgsCitation":"Irvine, D.J., Briggs, M.A., Lautz, L.K., Gordon, R.P., McKenzie, J.M., and Cartwright, I., 2017, Using diurnal temperature signals to infer vertical groundwater-surface water exchange: Groundwater, v. 55, no. 1, p. 10-26, https://doi.org/10.1111/gwat.12459.","productDescription":"17 p.","startPage":"10","endPage":"26","ipdsId":"IP-077274","costCenters":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"links":[{"id":470089,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gwat.12459","text":"Publisher Index Page"},{"id":352253,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"55","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-10-03","publicationStatus":"PW","scienceBaseUri":"5afee8d3e4b0da30c1bfc4b8","contributors":{"authors":[{"text":"Irvine, Dylan J.","contributorId":190404,"corporation":false,"usgs":false,"family":"Irvine","given":"Dylan","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":730252,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Briggs, Martin A. 0000-0003-3206-4132 mbriggs@usgs.gov","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":4114,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin","email":"mbriggs@usgs.gov","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":730251,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lautz, Laura K.","contributorId":124523,"corporation":false,"usgs":false,"family":"Lautz","given":"Laura","email":"","middleInitial":"K.","affiliations":[{"id":5082,"text":"Syracuse University","active":true,"usgs":false}],"preferred":false,"id":730253,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gordon, Ryan P.","contributorId":202947,"corporation":false,"usgs":false,"family":"Gordon","given":"Ryan","email":"","middleInitial":"P.","affiliations":[{"id":7257,"text":"Maine Geological Survey","active":true,"usgs":false}],"preferred":false,"id":730254,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McKenzie, Jeffrey M.","contributorId":176299,"corporation":false,"usgs":false,"family":"McKenzie","given":"Jeffrey","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":730255,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cartwright, Ian","contributorId":190405,"corporation":false,"usgs":false,"family":"Cartwright","given":"Ian","affiliations":[],"preferred":false,"id":730256,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70193924,"text":"70193924 - 2017 - Prior knowledge-based approach for associating contaminants with biological effects: A case study in the St. Croix River basin, MN, WI, USA","interactions":[],"lastModifiedDate":"2017-11-10T10:14:48","indexId":"70193924","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1555,"text":"Environmental Pollution","active":true,"publicationSubtype":{"id":10}},"title":"Prior knowledge-based approach for associating contaminants with biological effects: A case study in the St. Croix River basin, MN, WI, USA","docAbstract":"Evaluating potential adverse effects of complex chemical mixtures in the environment is challenging. One way to address that challenge is through more integrated analysis of chemical monitoring and biological effects data. In the present study, water samples from five locations near two municipal wastewater treatment plants in the St. Croix River basin, on the border of MN and WI, USA, were analyzed for 127 organic contaminants. Known chemical-gene interactions were used to develop site-specific knowledge assembly models (KAMs) and formulate hypotheses concerning possible biological effects associated with chemicals detected in water samples from each location. Additionally, hepatic gene expression data were collected for fathead minnows (Pimephales promelas) exposed in situ, for 12 d, at each location. Expression data from oligonucleotide microarrays were analyzed to identify functional annotation terms enriched among the differentially-expressed probes. The general nature of many of the terms made hypothesis formulation on the basis of the transcriptome-level response alone difficult. However, integrated analysis of the transcriptome data in the context of the site-specific KAMs allowed for evaluation of the likelihood of specific chemicals contributing to observed biological responses. Thirteen chemicals (atrazine, carbamazepine, metformin, thiabendazole, diazepam, cholesterol, p-cresol, phenytoin, omeprazole, ethyromycin, 17β-estradiol, cimetidine, and estrone), for which there was statistically significant concordance between occurrence at a site and expected biological response as represented in the KAM, were identified. While not definitive, the approach provides a line of evidence for evaluating potential cause-effect relationships between components of a complex mixture of contaminants and biological effects data, which can inform subsequent monitoring and investigation.
","language":"English","publisher":"Environmental Pollution","doi":"10.1016/j.envpol.2016.12.005","usgsCitation":"Schroeder, A.L., Martinovic-Weigelt, D., Ankley, G., Lee, K., Garcia-Reyero, N., Perkins, E.J., Schoenfuss, H.L., and Villeneuve, D.L., 2017, Prior knowledge-based approach for associating contaminants with biological effects: A case study in the St. Croix River basin, MN, WI, USA: Environmental Pollution, v. 221, p. 427-436, https://doi.org/10.1016/j.envpol.2016.12.005.","productDescription":"10 p.","startPage":"427","endPage":"436","ipdsId":"IP-065526","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"links":[{"id":470101,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6139436","text":"Publisher Index Page"},{"id":348551,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota, Wisconsin","otherGeospatial":"St. Croix River Basin","volume":"221","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a06c8d0e4b09af898c8613c","contributors":{"authors":[{"text":"Schroeder, Anthony L.","contributorId":173596,"corporation":false,"usgs":false,"family":"Schroeder","given":"Anthony","email":"","middleInitial":"L.","affiliations":[{"id":12503,"text":"University of Minnesota - Saint Paul","active":true,"usgs":false},{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":721514,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Martinovic-Weigelt, Dalma","contributorId":173655,"corporation":false,"usgs":false,"family":"Martinovic-Weigelt","given":"Dalma","affiliations":[{"id":6748,"text":"University of St. Thomas","active":true,"usgs":false}],"preferred":false,"id":721515,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ankley, Gerald T.","contributorId":177970,"corporation":false,"usgs":false,"family":"Ankley","given":"Gerald T.","affiliations":[{"id":13485,"text":"U.S. Environmental Protection Agency, Duluth, MN","active":true,"usgs":false}],"preferred":false,"id":721516,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lee, Kathy 0000-0002-7683-1367 klee@usgs.gov","orcid":"https://orcid.org/0000-0002-7683-1367","contributorId":2538,"corporation":false,"usgs":true,"family":"Lee","given":"Kathy","email":"klee@usgs.gov","affiliations":[{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":721517,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Garcia-Reyero, Natalia","contributorId":43961,"corporation":false,"usgs":false,"family":"Garcia-Reyero","given":"Natalia","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false},{"id":26924,"text":"USArmy Engineer Research and Development Center, Vicksburg, MS","active":true,"usgs":false}],"preferred":false,"id":721518,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Perkins, Edward J.","contributorId":89063,"corporation":false,"usgs":false,"family":"Perkins","given":"Edward","email":"","middleInitial":"J.","affiliations":[{"id":26924,"text":"USArmy Engineer Research and Development Center, Vicksburg, MS","active":true,"usgs":false}],"preferred":false,"id":721519,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schoenfuss, Heiko L.","contributorId":76409,"corporation":false,"usgs":false,"family":"Schoenfuss","given":"Heiko","email":"","middleInitial":"L.","affiliations":[{"id":13317,"text":"Saint Cloud State University","active":true,"usgs":false}],"preferred":false,"id":721520,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Villeneuve, Daniel L.","contributorId":32091,"corporation":false,"usgs":false,"family":"Villeneuve","given":"Daniel","email":"","middleInitial":"L.","affiliations":[{"id":13485,"text":"U.S. Environmental Protection Agency, Duluth, MN","active":true,"usgs":false}],"preferred":false,"id":721521,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70182184,"text":"70182184 - 2017 - Critical zone properties control the fate of nitrogen during experimental rainfall in montane forests of the Colorado Front Range","interactions":[],"lastModifiedDate":"2017-02-20T11:34:12","indexId":"70182184","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1007,"text":"Biogeochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Critical zone properties control the fate of nitrogen during experimental rainfall in montane forests of the Colorado Front Range","docAbstract":"Several decades of research in alpine ecosystems have demonstrated links among the critical zone, hydrologic response, and the fate of elevated atmospheric nitrogen (N) deposition. Less research has occurred in mid-elevation forests, which may be important for retaining atmospheric N deposition. To explore the fate of N in the montane zone, we conducted plot-scale experimental rainfall events across a north–south transect within a catchment of the Boulder Creek Critical Zone Observatory. Rainfall events mimicked relatively common storms (20–50% annual exceedance probability) and were labeled with 15N-nitrate (
The McMurdo Dry Valleys, Antarctica, are a polar desert ecosystem. The hydrologic system of the dry valleys is linked to climate with ephemeral streams that flow from glacial melt during the austral summer. Past climate variations have strongly influenced the closed-basin, chemically stratified lakes on the valley floor. Results of previous work point to important roles for both in-stream processes (e.g., mineral weathering, precipitation and dissolution of salts) and in-lake processes (e.g., mixing with paleo-seawater and calcite precipitation) in determining the geochemistry of these lakes. These processes have a significant influence on calcium (Ca) biogeochemistry in this aquatic ecosystem, and thus variations in Ca stable isotope compositions of the waters can aid in validating the importance of these processes. We have analyzed the Ca stable isotope compositions of streams and lakes in the McMurdo Dry Valleys. The results validate the important roles of weathering of aluminosilicate minerals and/or CaCO3 in the hyporheic zone of the streams, and mixing of lake surface water with paleo-seawater and precipitation of Ca-salts during cryo-concentration events to form the deep lake waters. The lakes in the McMurdo Dry Valleys evolved following different geochemical pathways, evidenced by their unique, nonsystematic Ca isotope signatures.
","language":"English","publisher":"AGU Publications","doi":"10.1002/2016GL071169","usgsCitation":"Lyons, W.B., Bullen, T.D., and Welch, K.A., 2017, Ca isotopic geochemistry of an Antarctic aquatic system: Geophysical Research Letters, v. 44, no. 2, p. 882-891, https://doi.org/10.1002/2016GL071169.","productDescription":"10 p.","startPage":"882","endPage":"891","ipdsId":"IP-082104","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":470087,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016gl071169","text":"Publisher Index Page"},{"id":348277,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Antarctica, McMurdo Dry Valleys","volume":"44","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-16","publicationStatus":"PW","scienceBaseUri":"5a07e93ee4b09af898c8cc09","contributors":{"authors":[{"text":"Lyons, W. Berry","contributorId":193456,"corporation":false,"usgs":false,"family":"Lyons","given":"W.","email":"","middleInitial":"Berry","affiliations":[],"preferred":false,"id":714524,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bullen, Thomas D. 0000-0003-2281-1691 tdbullen@usgs.gov","orcid":"https://orcid.org/0000-0003-2281-1691","contributorId":1969,"corporation":false,"usgs":true,"family":"Bullen","given":"Thomas","email":"tdbullen@usgs.gov","middleInitial":"D.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":714523,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Welch, Kathleen A.","contributorId":197891,"corporation":false,"usgs":false,"family":"Welch","given":"Kathleen","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":714525,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70186151,"text":"70186151 - 2017 - Managing American Oystercatcher (Haematopus palliatus) population qrowth by targeting nesting season vital rates","interactions":[],"lastModifiedDate":"2017-03-30T11:16:32","indexId":"70186151","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3731,"text":"Waterbirds","onlineIssn":"19385390","printIssn":"15244695","active":true,"publicationSubtype":{"id":10}},"title":"Managing American Oystercatcher (Haematopus palliatus) population qrowth by targeting nesting season vital rates","docAbstract":"In populations of long-lived species, adult survival typically has a relatively high influence on population growth. From a management perspective, however, adult survival can be difficult to increase in some instances, so other component rates must be considered to reverse population declines. In North Carolina, USA, management to conserve the American Oystercatcher (Haematopus palliatus) targets component vital rates related to fecundity, specifically nest and chick survival. The effectiveness of such a management approach in North Carolina was assessed by creating a three-stage female-based deterministic matrix model. Isoclines were produced from the matrix model to evaluate minimum nest and chick survival rates necessary to reverse population decline, assuming all other vital rates remained stable at mean values. Assuming accurate vital rates, breeding populations within North Carolina appear to be declining. To reverse this decline, combined nest and chick survival would need to increase from 0.14 to ≤ 0.27, a rate that appears to be attainable based on historical estimates. Results are heavily dependent on assumptions of other vital rates, most notably adult survival, revealing the need for accurate estimates of all vital rates to inform management actions. This approach provides valuable insights for evaluating conservation goals for species of concern.
","language":"English","publisher":"The Waterbird Society","doi":"10.1675/063.040.sp106","usgsCitation":"Felton, S.K., Hostetter, N.J., Pollock, K.H., and Simons, T.R., 2017, Managing American Oystercatcher (Haematopus palliatus) population qrowth by targeting nesting season vital rates: Waterbirds, v. 40, no. sp1, p. 44-54, https://doi.org/10.1675/063.040.sp106.","productDescription":"11 p.","startPage":"44","endPage":"54","ipdsId":"IP-071195","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":461763,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1675/063.040.sp106","text":"Publisher Index Page"},{"id":338800,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"40","issue":"sp1","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58de194ee4b02ff32c699c95","contributors":{"authors":[{"text":"Felton, Shilo K.","contributorId":190179,"corporation":false,"usgs":false,"family":"Felton","given":"Shilo","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":687694,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hostetter, Nathan J.","contributorId":171690,"corporation":false,"usgs":false,"family":"Hostetter","given":"Nathan","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":687695,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pollock, Kenneth H.","contributorId":8590,"corporation":false,"usgs":false,"family":"Pollock","given":"Kenneth","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":687696,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Simons, Theodore R. 0000-0002-1884-6229 tsimons@usgs.gov","orcid":"https://orcid.org/0000-0002-1884-6229","contributorId":2623,"corporation":false,"usgs":true,"family":"Simons","given":"Theodore","email":"tsimons@usgs.gov","middleInitial":"R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":687677,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70182759,"text":"70182759 - 2017 - Nationwide reconnaissance of contaminants of emerging concern in source and treated drinking waters of the United States: Pharmaceuticals","interactions":[],"lastModifiedDate":"2019-08-20T08:46:57","indexId":"70182759","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","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":"Nationwide reconnaissance of contaminants of emerging concern in source and treated drinking waters of the United States: Pharmaceuticals","docAbstract":"Mobile and persistent chemicals that are present in urban wastewater, such as pharmaceuticals, may survive\non-site ormunicipal wastewater treatment and post-discharge environmental processes. These pharmaceuticals\nhave the potential to reach surface and groundwaters, essential drinking-water sources. A joint, two-phase U.S.\nGeological Survey-U.S. Environmental Protection Agency study examined source and treated waters from 25\ndrinking-water treatment plants from across the United States. Treatment plants that had probable wastewater\ninputs to their source waters were selected to assess the prevalence of pharmaceuticals in such source waters,\nand to identify which pharmaceuticals persist through drinking-water treatment. All samples were analyzed\nfor 24 pharmaceuticals in Phase I and for 118 in Phase II.\nIn Phase I, 11 pharmaceuticals were detected in all source-water samples, with amaximumof nine pharmaceuticals\ndetected in any one sample. The median number of pharmaceuticals for all 25 samples was five.\nQuantifiable pharmaceutical detections were fewer, with a maximum of five pharmaceuticals in any one\nsample and a median for all samples of two. In Phase II, 47 different pharmaceuticals were detected in all\nsource-water samples, with a maximum of 41 pharmaceuticals detected in any one sample. The median\nnumber of pharmaceuticals for all 25 samples was eight. For 37 quantifiable pharmaceuticals in Phase II,\nmedian concentrations in source water were below 113 ng/L.","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2016.03.128","usgsCitation":"Furlong, E.T., Batt, A.L., Glassmeyer, S.T., Noriega, M.C., Kolpin, D.W., Mash, H., and Schenck, K.M., 2017, Nationwide reconnaissance of contaminants of emerging concern in source and treated drinking waters of the United States: Pharmaceuticals: Science of the Total Environment, v. 579, p. 1629-1642, https://doi.org/10.1016/j.scitotenv.2016.03.128.","productDescription":"14 p. ","startPage":"1629","endPage":"1642","ipdsId":"IP-066272","costCenters":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true},{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true}],"links":[{"id":470097,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://europepmc.org/articles/pmc7017633","text":"External Repository"},{"id":336327,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"579","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58b69a3ee4b01ccd54ff3f7e","contributors":{"authors":[{"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":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","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":673614,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Batt, Angela L.","contributorId":184134,"corporation":false,"usgs":false,"family":"Batt","given":"Angela","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":673615,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Glassmeyer, Susan T.","contributorId":184135,"corporation":false,"usgs":false,"family":"Glassmeyer","given":"Susan","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":673616,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Noriega, Mary C. mnoriega@usgs.gov","contributorId":2553,"corporation":false,"usgs":true,"family":"Noriega","given":"Mary","email":"mnoriega@usgs.gov","middleInitial":"C.","affiliations":[{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true}],"preferred":true,"id":673620,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"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":673619,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mash, Heath","contributorId":184088,"corporation":false,"usgs":false,"family":"Mash","given":"Heath","affiliations":[],"preferred":false,"id":673617,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schenck, Kathleen M.","contributorId":184136,"corporation":false,"usgs":false,"family":"Schenck","given":"Kathleen","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":673618,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70193455,"text":"70193455 - 2017 - Annual changes in seasonal river water temperatures in the eastern and western United States","interactions":[],"lastModifiedDate":"2021-06-04T15:52:04.210872","indexId":"70193455","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Annual changes in seasonal river water temperatures in the eastern and western United States","docAbstract":"Changes in river water temperatures are anticipated to have direct effects on thermal habitat and fish population vital rates, and therefore, understanding temporal trends in water temperatures may be necessary for predicting changes in thermal habitat and how species might respond to such changes. However, many investigations into trends in water temperatures use regression methods that assume long-term monotonic changes in temperature, when in fact changes are likely to be nonmonotonic. Therefore, our objective was to highlight the need and provide an example of an analytical method to better quantify the short-term, nonmonotonic temporal changes in thermal habitat that are likely necessary to determine the effects of changing thermal conditions on fish populations and communities. To achieve this objective, this study uses Bayesian dynamic linear models (DLMs) to examine seasonal trends in river water temperatures from sites located in the eastern and western United States, regions that have dramatically different riverine habitats and fish communities. We estimated the annual rate of change in water temperature and found little evidence of seasonal changes in water temperatures in the eastern U.S. We found more evidence of warming for river sites located in the western U.S., particularly during the fall and winter seasons. Use of DLMs provided a more detailed view of temporal dynamics in river thermal habitat compared to more traditional methods by quantifying year-to-year changes and associated uncertainty, providing managers with the information needed to adapt decision making to short-term changes in habitat conditions that may be necessary for conserving aquatic resources in the face of a changing climate.
","language":"English","publisher":"MDPI","doi":"10.3390/w9020090","usgsCitation":"Wagner, T., Midway, S.R., Whittier, J.B., DeWeber, J.T., and Paukert, C.P., 2017, Annual changes in seasonal river water temperatures in the eastern and western United States: Water, v. 9, no. 2, 90; 13 p., https://doi.org/10.3390/w9020090.","productDescription":"90; 13 p.","ipdsId":"IP-071167","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":470084,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w9020090","text":"Publisher Index Page"},{"id":348596,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Upper Colorado River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.6650390625,\n 36.914764288955936\n ],\n [\n -106.58935546875,\n 36.914764288955936\n ],\n [\n -106.58935546875,\n 40.97989806962013\n ],\n [\n -111.6650390625,\n 40.97989806962013\n ],\n [\n -111.6650390625,\n 36.914764288955936\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.4638671875,\n 37.33522435930639\n ],\n [\n -74.37744140625,\n 37.33522435930639\n ],\n [\n -74.37744140625,\n 42.391008609205045\n ],\n [\n -80.4638671875,\n 42.391008609205045\n ],\n [\n -80.4638671875,\n 37.33522435930639\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-04","publicationStatus":"PW","scienceBaseUri":"5a06c8d1e4b09af898c8614a","contributors":{"authors":[{"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":719126,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Midway, Stephen R.","contributorId":172159,"corporation":false,"usgs":false,"family":"Midway","given":"Stephen","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":721645,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Whittier, Joanna B.","contributorId":53151,"corporation":false,"usgs":false,"family":"Whittier","given":"Joanna","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":721646,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DeWeber, Jefferson T.","contributorId":199675,"corporation":false,"usgs":false,"family":"DeWeber","given":"Jefferson","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":721647,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Paukert, Craig P. 0000-0002-9369-8545 cpaukert@usgs.gov","orcid":"https://orcid.org/0000-0002-9369-8545","contributorId":147821,"corporation":false,"usgs":true,"family":"Paukert","given":"Craig","email":"cpaukert@usgs.gov","middleInitial":"P.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":719127,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70193686,"text":"70193686 - 2017 - Generation of 3-D hydrostratigraphic zones from dense airborne electromagnetic data to assess groundwater model prediction error","interactions":[],"lastModifiedDate":"2017-11-02T16:32:12","indexId":"70193686","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Generation of 3-D hydrostratigraphic zones from dense airborne electromagnetic data to assess groundwater model prediction error","docAbstract":"We present a new methodology to combine spatially dense high-resolution airborne electromagnetic (AEM) data and sparse borehole information to construct multiple plausible geological structures using a stochastic approach. The method developed allows for quantification of the performance of groundwater models built from different geological realizations of structure. Multiple structural realizations are generated using geostatistical Monte Carlo simulations that treat sparse borehole lithological observations as hard data and dense geophysically derived structural probabilities as soft data. Each structural model is used to define 3-D hydrostratigraphical zones of a groundwater model, and the hydraulic parameter values of the zones are estimated by using nonlinear regression to fit hydrological data (hydraulic head and river discharge measurements). Use of the methodology is demonstrated for a synthetic domain having structures of categorical deposits consisting of sand, silt, or clay. It is shown that using dense AEM data with the methodology can significantly improve the estimated accuracy of the sediment distribution as compared to when borehole data are used alone. It is also shown that this use of AEM data can improve the predictive capability of a calibrated groundwater model that uses the geological structures as zones. However, such structural models will always contain errors because even with dense AEM data it is not possible to perfectly resolve the structures of a groundwater system. It is shown that when using such erroneous structures in a groundwater model, they can lead to biased parameter estimates and biased model predictions, therefore impairing the model's predictive capability.
","language":"English","publisher":"AGU","doi":"10.1002/2016WR019141","usgsCitation":"Christensen, N.K., Minsley, B.J., and Christensen, S., 2017, Generation of 3-D hydrostratigraphic zones from dense airborne electromagnetic data to assess groundwater model prediction error: Water Resources Research, v. 53, no. 2, p. 1019-1038, https://doi.org/10.1002/2016WR019141.","productDescription":"20 p.","startPage":"1019","endPage":"1038","ipdsId":"IP-081403","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":488731,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://pure.au.dk/portal/en/publications/dcdb9b5e-bf3c-4826-83aa-0fb5cd606845","text":"External Repository"},{"id":348146,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"53","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59fc2ea5e4b0531197b27f85","contributors":{"authors":[{"text":"Christensen, Nikolaj K","contributorId":199736,"corporation":false,"usgs":false,"family":"Christensen","given":"Nikolaj","email":"","middleInitial":"K","affiliations":[{"id":13419,"text":"Aarhus University, Denmark","active":true,"usgs":false}],"preferred":false,"id":719889,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Minsley, Burke J. 0000-0003-1689-1306 bminsley@usgs.gov","orcid":"https://orcid.org/0000-0003-1689-1306","contributorId":697,"corporation":false,"usgs":true,"family":"Minsley","given":"Burke","email":"bminsley@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":719888,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Christensen, Steen","contributorId":199737,"corporation":false,"usgs":false,"family":"Christensen","given":"Steen","email":"","affiliations":[{"id":13419,"text":"Aarhus University, Denmark","active":true,"usgs":false}],"preferred":false,"id":719890,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70189348,"text":"70189348 - 2017 - Variability of runoff-based drought conditions in the conterminous United States","interactions":[],"lastModifiedDate":"2017-08-29T09:35:36","indexId":"70189348","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2032,"text":"International Journal of Climatology","active":true,"publicationSubtype":{"id":10}},"title":"Variability of runoff-based drought conditions in the conterminous United States","docAbstract":"In this study, a monthly water-balance model is used to simulate monthly runoff for 2109 hydrologic units (HUs) in the conterminous United States (CONUS) for water-years 1901 through 2014. The monthly runoff time series for each HU were smoothed with a 3-month moving average, and then the 3-month moving-average runoff values were converted to percentiles. For each HU, a drought was considered to occur when the HU runoff percentile dropped to the 20th percentile or lower. A drought was considered to end when the HU runoff percentile exceeded the 20th percentile. After identifying drought events for each HU, the frequency and length of drought events were examined. Results indicated that (1) the longest mean drought lengths occur in the eastern CONUS and parts of the Rocky Mountain region and the northwestern CONUS, (2) the frequency of drought is highest in the southwestern and central CONUS, and lowest in the eastern CONUS, the Rocky Mountain region, and the northwestern CONUS, (3) droughts have occurred during all months of the year and there does not appear to be a seasonal pattern to drought occurrence, (4) the variability of precipitation appears to have been the principal climatic factor determining drought, and (5) for most of the CONUS, drought frequency appears to have decreased during the 1901 through 2014 period.
","language":"English","publisher":"Royal Meteorological Society","doi":"10.1002/joc.4756","usgsCitation":"McCabe, G., Wolock, D.M., and Austin, S.H., 2017, Variability of runoff-based drought conditions in the conterminous United States: International Journal of Climatology, v. 37, no. 2, p. 1014-1021, https://doi.org/10.1002/joc.4756.","productDescription":"8 p.","startPage":"1014","endPage":"1021","ipdsId":"IP-073812","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":343608,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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These systems require the best available weather inputs, as well as a long-term historical record to contextualize current observations. This article introduces the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), a custom instance of the NASA Land Information System (LIS) framework. The FLDAS is routinely used to produce multi-model and multi-forcing estimates of hydro-climate states and fluxes over semi-arid, food insecure regions of Africa. These modeled data and derived products, like soil moisture percentiles and water availability, were designed and are currently used to complement FEWS NET’s operational remotely sensed rainfall, evapotranspiration, and vegetation observations. The 30+ years of monthly outputs from the FLDAS simulations are publicly available from the NASA Goddard Earth Science Data and Information Services Center (GES DISC) and recommended for use in hydroclimate studies, early warning applications, and by agro-meteorological scientists in Eastern, Southern, and Western Africa.
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Multi-component Reactive Transport Models (RTMs), initially developed more than three decades ago, have been used extensively to explore the interactions of geothermal, hydrologic, geochemical, and geobiological processes in subsurface systems. Driven by extensive data sets now available from intensive measurement efforts, there is a pressing need to couple RTMs with other community models to explore non-linear interactions among the atmosphere, hydrosphere, biosphere, and geosphere. Here we briefly review the history of RTM development, summarize the current state of RTM approaches, and identify new research directions, opportunities, and infrastructure needs to broaden the use of RTMs. In particular, we envision the expanded use of RTMs in advancing process understanding in the Critical Zone, the veneer of the Earth that extends from the top of vegetation to the bottom of groundwater. We argue that, although parsimonious models are essential at larger scales, process-based models offer tools to explore the highly nonlinear coupling that characterizes natural systems. We present seven testable hypotheses that emphasize the unique capabilities of process-based RTMs for (1) elucidating chemical weathering and its physical and biogeochemical drivers; (2) understanding the interactions among roots, micro-organisms, carbon, water, and minerals in the rhizosphere; (3) assessing the effects of heterogeneity across spatial and temporal scales; and (4) integrating the vast quantity of novel data, including “omics” data (genomics, transcriptomics, proteomics, metabolomics), elemental concentration and speciation data, and isotope data into our understanding of complex earth surface systems. With strong support from data-driven sciences, we are now in an exciting era where integration of RTM framework into other community models will facilitate process understanding across disciplines and across scales.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.earscirev.2016.09.001","usgsCitation":"Li, L., Maher, K., Navarre-Sitchler, A., Druhan, J., Meile, C., Lawrence, C., Moore, J., Perdrial, J., Sullivan, P., Thompson, A., Jin, L., Bolton, E.W., Brantley, S.L., Dietrich, W., Mayer, K.U., Steefel, C., Valocchi, A.J., Zachara, J.M., Kocar, B.D., McIntosh, J., Tutolo, B.M., Kumar, M., Sonnenthal, E., Bao, C., and Beisman, J., 2017, Expanding the role of reactive transport models in critical zone processes: Earth-Science Reviews, v. 165, p. 280-301, https://doi.org/10.1016/j.earscirev.2016.09.001.","productDescription":"22 p.","startPage":"280","endPage":"301","ipdsId":"IP-070272","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":461771,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://escholarship.org/uc/item/81f302jz","text":"Publisher Index Page"},{"id":339191,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"165","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58e60272e4b09da6799ac681","contributors":{"authors":[{"text":"Li, Li","contributorId":190439,"corporation":false,"usgs":false,"family":"Li","given":"Li","affiliations":[],"preferred":false,"id":688432,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Maher, Kate","contributorId":190440,"corporation":false,"usgs":false,"family":"Maher","given":"Kate","email":"","affiliations":[],"preferred":false,"id":688433,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Navarre-Sitchler, Alexis","contributorId":190441,"corporation":false,"usgs":false,"family":"Navarre-Sitchler","given":"Alexis","email":"","affiliations":[],"preferred":false,"id":688434,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Druhan, Jennifer","contributorId":190442,"corporation":false,"usgs":false,"family":"Druhan","given":"Jennifer","affiliations":[],"preferred":false,"id":688435,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Meile, Christof","contributorId":190443,"corporation":false,"usgs":false,"family":"Meile","given":"Christof","email":"","affiliations":[],"preferred":false,"id":688436,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lawrence, Corey 0000-0002-2179-2436 clawrence@usgs.gov","orcid":"https://orcid.org/0000-0002-2179-2436","contributorId":190438,"corporation":false,"usgs":true,"family":"Lawrence","given":"Corey","email":"clawrence@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":688431,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Moore, Joel","contributorId":190444,"corporation":false,"usgs":false,"family":"Moore","given":"Joel","email":"","affiliations":[],"preferred":false,"id":688437,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Perdrial, Julia","contributorId":190445,"corporation":false,"usgs":false,"family":"Perdrial","given":"Julia","affiliations":[],"preferred":false,"id":688438,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sullivan, Pamela","contributorId":190446,"corporation":false,"usgs":false,"family":"Sullivan","given":"Pamela","affiliations":[],"preferred":false,"id":688439,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Thompson, Aaron","contributorId":190447,"corporation":false,"usgs":false,"family":"Thompson","given":"Aaron","affiliations":[],"preferred":false,"id":688440,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Jin, Lixin","contributorId":190448,"corporation":false,"usgs":false,"family":"Jin","given":"Lixin","email":"","affiliations":[],"preferred":false,"id":688441,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Bolton, Edward W.","contributorId":190449,"corporation":false,"usgs":false,"family":"Bolton","given":"Edward","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":688442,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Brantley, Susan L. 0000-0003-4320-2342","orcid":"https://orcid.org/0000-0003-4320-2342","contributorId":184201,"corporation":false,"usgs":false,"family":"Brantley","given":"Susan","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":688443,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Dietrich, William E.","contributorId":115128,"corporation":false,"usgs":true,"family":"Dietrich","given":"William E.","affiliations":[],"preferred":false,"id":688444,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Mayer, K. 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Since 2012, California has experienced a record-setting drought, and multiple studies predict drought conditions currently underway will persist and even increase in severity. Recent declines and local extinctions of California newt populations in Santa Monica Mountain streams motivate our study of the impact of drought on newt population sizes. Although newts are terrestrial salamanders, they migrate to streams each spring to breed and lay eggs. Since egg and larval stages occur in water, a precipitation deficit due to drought conditions reduces the space for newt egg-laying and the necessary habitat for larval development. To mathematically forecast newt population dynamics, we develop a nonlinear system of discrete equations that includes demographic parameters such as survival rates for newt life stages and egg production, which depend on habitat availability and rainfall. We estimate these demographic parameters using 15 years of stream survey data collected from Cold Creek in Los Angeles County, California, and our model captures the observed decline of the parameterized Cold Creek newt population. Based upon data analysis, we predict how the number of available newt egg-laying sites varies with annual precipitation. Our model allows us to make predictions about how the length and severity of drought can affect the likelihood of persistence and the time to critical endangerment of a local newt population. We predict that sustained severe drought will critically endanger the newt population but that the newt population can rebound if a drought is sufficiently short.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jtbi.2016.11.011","usgsCitation":"Jones, M.T., Milligan, W.R., Kats, L.B., Vandergon, T.L., Honeycutt, R.L., Fisher, R.N., Davis, C.L., and Lucas, T.A., 2017, A discrete stage-structured model of California newt population dynamics during a period of drought: Journal of Theoretical Biology, v. 414, p. 245-253, https://doi.org/10.1016/j.jtbi.2016.11.011.","productDescription":"9 p.","startPage":"245","endPage":"253","ipdsId":"IP-081597","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":343986,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"414","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"596f1e25e4b0d1f9f064075b","contributors":{"authors":[{"text":"Jones, Marjorie T.","contributorId":194782,"corporation":false,"usgs":false,"family":"Jones","given":"Marjorie","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":705333,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Milligan, William R.","contributorId":194783,"corporation":false,"usgs":false,"family":"Milligan","given":"William","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":705334,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kats, Lee B.","contributorId":106034,"corporation":false,"usgs":true,"family":"Kats","given":"Lee","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":705335,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vandergon, Thomas L.","contributorId":38489,"corporation":false,"usgs":true,"family":"Vandergon","given":"Thomas","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":705336,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Honeycutt, Rodney L.","contributorId":106426,"corporation":false,"usgs":true,"family":"Honeycutt","given":"Rodney","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":705337,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fisher, Robert N. 0000-0002-2956-3240 rfisher@usgs.gov","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":1529,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert","email":"rfisher@usgs.gov","middleInitial":"N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":705338,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Davis, Courtney L.","contributorId":181922,"corporation":false,"usgs":false,"family":"Davis","given":"Courtney","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":705339,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lucas, Timothy A.","contributorId":194784,"corporation":false,"usgs":false,"family":"Lucas","given":"Timothy","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":705340,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70187165,"text":"70187165 - 2017 - Tracer-based characterization of hyporheic exchange and benthic biolayers in streams","interactions":[],"lastModifiedDate":"2017-04-25T15:20:57","indexId":"70187165","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Tracer-based characterization of hyporheic exchange and benthic biolayers in streams","docAbstract":"Shallow benthic biolayers at the top of the streambed are believed to be places of enhanced biogeochemical turnover within the hyporheic zone. They can be investigated by reactive stream tracer tests with tracer recordings in the streambed and in the stream channel. Common in-stream measurements of such reactive tracers cannot localize where the processing primarily takes place, whereas isolated vertical depth profiles of solutes within the hyporheic zone are usually not representative of the entire stream. We present results of a tracer test where we injected the conservative tracer bromide together with the reactive tracer resazurin into a third-order stream and combined the recording of in-stream breakthrough curves with multidepth sampling of the hyporheic zone at several locations. The transformation of resazurin was used as an indicator of metabolism, and high-reactivity zones were identified from depth profiles. The results from our subsurface analysis indicate that the potential for tracer transformation (i.e., the reaction rate constant) varied with depth in the hyporheic zone. This highlights the importance of the benthic biolayer, which we found to be on average 2 cm thick in this study, ranging from one third to one half of the full depth of the hyporheic zone. The reach-scale approach integrated the effects of processes along the reach length, isolating hyporheic processes relevant for whole-stream chemistry and estimating effective reaction rates.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2016WR019393","usgsCitation":"Knapp, J., Gonzalez-Pinzon, R., Drummond, J.D., Larsen, L., Cirpka, O.A., and Harvey, J.W., 2017, Tracer-based characterization of hyporheic exchange and benthic biolayers in streams: Water Resources Research, v. 53, no. 2, p. 1575-1594, https://doi.org/10.1002/2016WR019393.","productDescription":"20 p.","startPage":"1575","endPage":"1594","ipdsId":"IP-080169","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":470095,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016wr019393","text":"Publisher Index Page"},{"id":340374,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"53","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-21","publicationStatus":"PW","scienceBaseUri":"59006062e4b0e85db3a5ddd1","contributors":{"authors":[{"text":"Knapp, Julia L.A.","contributorId":191389,"corporation":false,"usgs":false,"family":"Knapp","given":"Julia L.A.","affiliations":[],"preferred":false,"id":692887,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gonzalez-Pinzon, Ricardo","contributorId":191362,"corporation":false,"usgs":false,"family":"Gonzalez-Pinzon","given":"Ricardo","email":"","affiliations":[],"preferred":false,"id":692888,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Drummond, Jennifer D.","contributorId":191390,"corporation":false,"usgs":false,"family":"Drummond","given":"Jennifer","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":692889,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Larsen, Laurel G.","contributorId":191391,"corporation":false,"usgs":false,"family":"Larsen","given":"Laurel G.","affiliations":[],"preferred":false,"id":692890,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cirpka, Olaf A.","contributorId":191392,"corporation":false,"usgs":false,"family":"Cirpka","given":"Olaf","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":692891,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Harvey, Judson W. 0000-0002-2654-9873 jwharvey@usgs.gov","orcid":"https://orcid.org/0000-0002-2654-9873","contributorId":1796,"corporation":false,"usgs":true,"family":"Harvey","given":"Judson","email":"jwharvey@usgs.gov","middleInitial":"W.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":692886,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70182740,"text":"70182740 - 2017 - Comparison of in vitro estrogenic activity and estrogen concentrations insource and treated waters from 25 U.S. drinking water treatment plants","interactions":[],"lastModifiedDate":"2017-02-28T11:28:47","indexId":"70182740","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","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":"Comparison of in vitro estrogenic activity and estrogen concentrations insource and treated waters from 25 U.S. drinking water treatment plants","docAbstract":"In vitro bioassays have been successfully used to screen for estrogenic activity in wastewater and surface water,\nhowever, few have been applied to treated drinking water. Here, extracts of source and treated water samples\nwere assayed for estrogenic activity using T47D-KBluc cells and analyzed by liquid chromatography-Fourier transform\nmass spectrometry (LC-FTMS) for natural and synthetic estrogens (including estrone, 17β-estradiol, estriol,\nand ethinyl estradiol). None of the estrogenswere detected above the LC-FTMS quantification limits in treated samples\nand only 5 source waters had quantifiable concentrations of estrone, whereas 3 treated samples and 16 source\nsamples displayed in vitro estrogenicity. Estrone accounted for themajority of estrogenic activity in respective samples,\nhowever the remaining samples that displayed estrogenic activity had no quantitative detections of known estrogenic\ncompounds by chemical analyses. Source water estrogenicity (max, 0.47 ng 17β-estradiol equivalents\n(E2Eq) L−1) was below levels that have been linked to adverse effects in fish and other aquatic organisms. Treated\nwater estrogenicity (max, 0.078 ng E2Eq L−1) was considerably below levels that are expected to be biologically\nrelevant to human consumers. Overall, the advantage of using in vitro techniques in addition to analytical chemical\ndeterminations was displayed by the sensitivity of the T47D-KBluc bioassay, coupled with the ability tomeasure cumulative\neffects of mixtures, specifically when unknown chemicals may be present.","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2016.02.093","collaboration":"U.S. Environmental Protection Agency","usgsCitation":"Conley, J.M., Evans, N., Mash, H., Rosenblum, L., Schenck, K., Glassmeyer, S., Furlong, E.T., Kolpin, D.W., and Wilson, V.S., 2017, Comparison of in vitro estrogenic activity and estrogen concentrations insource and treated waters from 25 U.S. drinking water treatment plants: Science of the Total Environment, v. 579, p. 1610-1617, https://doi.org/10.1016/j.scitotenv.2016.02.093.","productDescription":"8 p. ","startPage":"1610","endPage":"1617","ipdsId":"IP-072842","costCenters":[{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true}],"links":[{"id":336329,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":336298,"type":{"id":15,"text":"Index Page"},"url":"https://www.sciencedirect.com/science/article/pii/S0048969716303035"}],"volume":"579","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58b69a3fe4b01ccd54ff3f80","contributors":{"authors":[{"text":"Conley, Justin M.","contributorId":184086,"corporation":false,"usgs":false,"family":"Conley","given":"Justin","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":673522,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Evans, Nicola","contributorId":184087,"corporation":false,"usgs":false,"family":"Evans","given":"Nicola","email":"","affiliations":[],"preferred":false,"id":673523,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mash, Heath","contributorId":184088,"corporation":false,"usgs":false,"family":"Mash","given":"Heath","affiliations":[],"preferred":false,"id":673524,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rosenblum, Laura","contributorId":184089,"corporation":false,"usgs":false,"family":"Rosenblum","given":"Laura","email":"","affiliations":[],"preferred":false,"id":673525,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schenck, Kathleen","contributorId":184090,"corporation":false,"usgs":false,"family":"Schenck","given":"Kathleen","affiliations":[],"preferred":false,"id":673526,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Glassmeyer, Susan","contributorId":184091,"corporation":false,"usgs":false,"family":"Glassmeyer","given":"Susan","affiliations":[],"preferred":false,"id":673527,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Furlong, Edward T. 0000-0002-7305-4603 efurlong@usgs.gov","orcid":"https://orcid.org/0000-0002-7305-4603","contributorId":740,"corporation":false,"usgs":true,"family":"Furlong","given":"Edward","email":"efurlong@usgs.gov","middleInitial":"T.","affiliations":[{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":673521,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"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":673528,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Wilson, Vickie S. 0000-0003-1661-8481","orcid":"https://orcid.org/0000-0003-1661-8481","contributorId":184092,"corporation":false,"usgs":false,"family":"Wilson","given":"Vickie","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":673529,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ]}