This paper presents a conceptual framework to operationalize flow–ecology relationships into decision-support systems of practical use to water-resource managers, who are commonly tasked with balancing multiple competing socioeconomic and environmental priorities. We illustrate this framework with a case study, whereby fish community responses to various water-management scenarios were predicted in a partially regulated river system at a local watershed scale. This case study simulates management scenarios based on interactive effects of dam operation protocols, withdrawals for municipal water supply, effluent discharges from wastewater treatment, and inter-basin water transfers. Modeled streamflow was integrated with flow–ecology relationships relating hydrologic departure from reference conditions to fish species richness, stratified by trophic, reproductive, and habitat characteristics. Adding a hypothetical new water-withdrawal site was predicted to increase the frequency of low-flow conditions with adverse effects for several fish groups. Imposition of new reservoir release requirements was predicted to enhance flow and fish species richness immediately downstream of the reservoir, but these effects were dissipated further downstream. The framework presented here can be used to translate flow–ecology relationships into evidence-based management by developing decision-support systems for conservation of riverine biodiversity while optimizing water availability for human use.
","language":"English","publisher":"MDPI","doi":"10.3390/w9030196","usgsCitation":"Cartwright, J.M., Caldwell, C., Nebiker, S., and Knight, R., 2017, Putting flow-ecology relationships into practice: A decision-support system to assess fish community response to water-management scenarios: Water, v. 9, no. 3, p. 1-18, https://doi.org/10.3390/w9030196.","productDescription":"Article 196; 18 p.","startPage":"1","endPage":"18","ipdsId":"IP-076084","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":470021,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w9030196","text":"Publisher Index Page"},{"id":337171,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"3","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-08","publicationStatus":"PW","scienceBaseUri":"58c277d5e4b014cc3a3e76a9","contributors":{"authors":[{"text":"Cartwright, Jennifer M. 0000-0003-0851-8456 jmcart@usgs.gov","orcid":"https://orcid.org/0000-0003-0851-8456","contributorId":5386,"corporation":false,"usgs":true,"family":"Cartwright","given":"Jennifer","email":"jmcart@usgs.gov","middleInitial":"M.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true}],"preferred":true,"id":681522,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Caldwell, Casey","contributorId":187734,"corporation":false,"usgs":false,"family":"Caldwell","given":"Casey","email":"","affiliations":[],"preferred":false,"id":681523,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nebiker, Steven","contributorId":187735,"corporation":false,"usgs":false,"family":"Nebiker","given":"Steven","email":"","affiliations":[],"preferred":false,"id":681524,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Knight, Rodney 0000-0001-9588-0167 rrknight@usgs.gov","orcid":"https://orcid.org/0000-0001-9588-0167","contributorId":152422,"corporation":false,"usgs":true,"family":"Knight","given":"Rodney","email":"rrknight@usgs.gov","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true}],"preferred":true,"id":681525,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70179269,"text":"sir20165173 - 2017 - Geology and mining history of the Southeast Missouri Barite District and the Valles Mines, Washington, Jefferson, and St. Francois Counties, Missouri","interactions":[],"lastModifiedDate":"2017-03-09T15:14:37","indexId":"sir20165173","displayToPublicDate":"2017-03-09T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-5173","title":"Geology and mining history of the Southeast Missouri Barite District and the Valles Mines, Washington, Jefferson, and St. Francois Counties, Missouri","docAbstract":"The Southeast Missouri Barite District and the Valles Mines are located in Washington, Jefferson, and St. Francois Counties, Missouri, where barite and lead ore are present together in surficial and near-surface deposits. Lead mining in the area began in the early 1700’s and extended into the early 1900’s. Hand mining of lead in the residuum resulted in widespread pits (also called shafts or diggings), and there was some underground mining of lead in bedrock. By the 1860’s barite was recovered from the residuum by hand mining, also resulting in widespread diggings, but generally not underground mines in bedrock. Mechanized open-pit mining of the residuum for barite began in the 1920’s. Barite production slowed by the 1980’s, and there has not been any barite mining since 1998. Mechanized barite mining resulted in large mined areas and tailings ponds containing waste from barite mills.
The U.S. Environmental Protection Agency (EPA) has determined that lead is present in surface soils in Washington and Jefferson Counties at concentrations exceeding health-based screening levels. Also, elevated concentrations of barium, arsenic, and cadmium have been identified in surface soils, and lead concentrations exceeding the Federal drinking-water standard of 15 micrograms per liter have been identified in private drinking-water wells. Potential sources of these contaminants are wastes associated with barite mining, wastes associated with lead mining, or unmined natural deposits of barium, lead, and other metals. As a first step in helping EPA determine the source of soil and groundwater contamination, the U.S. Geological Survey (USGS), in cooperation with the EPA, investigated the geology and mining history of the Southeast Missouri Barite District and the Valles Mines.
Ore minerals are barite (barium sulfate), galena (lead sulfide), cerussite (lead carbonate), anglesite (lead sulfate), sphalerite (zinc sulfide), smithsonite (zinc carbonate), and chalcopyrite (copper-iron sulfide). The Cambrian Potosi Dolomite is the most important formation for the ore deposits, followed by the Eminence Dolomite. Because galena, sphalerite, and barite are less soluble than dolomite, chemical weathering of the ore-bearing dolomite bedrock resulted in the concentration of ore minerals in the residuum. Most of the barite and lead mining was in the residuum, which averages 10 to 15 feet thick.
Lead mining by French explorers may have begun in 1719 along Old Mines Creek at Cabanage de Renaudiere, which was followed shortly by the discovery of lead and the development of lead mines at Mine Renault (also called Forche a Renault Mine), Old Mines, and at other places along the Big River, Mineral Fork, and Forche a Renault Creek. Lead mining began sometime between 1775 and 1780 at Mine a Breton, the name of which was later changed to Potosi. Other mining areas were developed in the early part of the 19th century, including Fourche a Courtois (Palmer Mines), the French Diggings, and the Richwoods Mines. Zinc became a valuable resource after the Civil War, and the Valles Mines was an important supplier of zinc as well as lead, with at least some production up until the 1920’s. Lead mining declined in the early part of the 20th century as mining in the Old Lead Belt, Mine La Motte, and the Tri-State District expanded.
The earliest lead mines were diggings in the residuum and were round holes (shafts) about 4 feet in diameter dug with pick and shovel about 15–20 feet deep, with drifts dug a short distance laterally from the bottom of the shafts. This mining process was repeated a short distance away until a large area was covered with pits. Some mining in bedrock began by about 1800, with shafts as deep as 170 feet and as much as several hundred feet of lateral drifts.
Smelting of the lead ore to elemental lead was first done using a log furnace, which was inefficient; estimates have been made that only about 50 percent of the lead was recovered, and the remainder was lost to the ashes (slags) and to volatilization. Starting in 1798, ash furnaces were used to smelt the ashes from the log furnaces. These two furnaces were worked in tandem for many years but were gradually replaced by other furnaces, including the Scotch hearth. Estimates of lead recovery as high as 80–90 percent have been made for the Scotch hearth. By the mid-1870’s the air furnace was being used, also with estimated lead recovery as high as 80–90 percent. Zinc furnaces were built when zinc became a valuable commodity, but much of the zinc ore was shipped out of the area, either to a smelter in St. Louis, Missouri, or to other smelters.
The total lead and zinc production from the Southeast Missouri Barite District and the Valles Mines is estimated at 180,000 tons of lead and 60,000 tons of zinc. An estimated 97,000 tons of lead and an estimated 120,000 tons of zinc were lost during smelting. The estimated losses do not include losses at the mine site during mining and preparation for smelting, such as the loss of fine-grained galena during hand cleaning or the discarding of zinc ore before its value was known, for which no estimates are available.
Hand mining for barite in the residuum was active by at least the 1860’s and peaked from 1905 to the 1930’s when several thousand people were engaged in barite mining. Hand mining (diggings) and cleaning of the ore was done in much the same way as earlier lead mining, with the additional use of a rattle box to further clean the barite. Mechanized open-pit mining of old barite diggings began in 1924 to recover barite left behind by hand mining, and washing plants were used to clean the clay from the barite. Hand mining, however, continued to thrive, and washer plants began to close temporarily in 1931; nearly all of the barite produced before 1937 was by hand mining. By the 1940’s, however, all barite mining was mechanized.
Mechanized mining used shovels powered by steam, gasoline, or electricity (and by the 1950’s draglines and front-end loaders) to mine the residuum. The ore was loaded onto rail cars (and by the 1940’s, trucks) for shipment to washer plants. Clay was removed from the barite using a log washer, and a jig was used to concentrate the barite. Overflow from the log washers was waste and went to a mud (tailings) pond. The coarse jig tailings went to tailings piles or were used as railroad ballast and, later, to create roads within the mine pit. Some barite was ground, depending on its final use, and some ground barite was bleached using a hot solution of sulfuric acid to remove impurities such as iron minerals and lead sulfide (galena). An earlier bleaching process used lead-lined tanks.
Large quantities of water were required for milling the barite; some was recirculated water and the remainder came from dammed streams or was pumped from wells. Tailings and wastewater were impounded behind dikes that were built across small valleys and were increased in height as necessary using washer waste and any overburden that had been stripped. In some cases, dikes were built across valleys that had already been mined for barite.
The total production of barite from the Southeast Missouri Barite District and the Valles Mines is estimated to have been about 13.1 million tons. Most of the barite production was from Washington County. Hand mining and processing of barite was inefficient. Estimates of barite recovery range from less than one-fourth to about one-half because pillars between the shafts in the residuum needed to be left unmined for stability. With mechanized mining, large amounts of barite were lost during the milling process. It has been estimated that about 30 percent of the barite was lost and that about two-thirds of the lost barite was fine-grained and was discharged to the tailings ponds. Some galena was lost to the tailings ponds.
A 1972 inventory of tailings ponds by the Missouri Geological Survey identified 67 ponds in the Southeast Missouri Barite District (there are more than this currently documented). Results from samples from four ponds that were drilled were used to estimate that the 67 ponds contained almost 39 million tons (or cubic yards) of tailings averaging about 5 percent barite, for a potential reserve of 1.935 million tons of barite.
It is not known how much lead was removed during barite mining, either by hand or mechanized mining and processing, how much lead was recovered, or how much lead went as fines to the tailing ponds or as coarse material to mine roads or was otherwise lost.
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1400 Independence Road
Rolla, MO 65401
The Priority Basin Project (PBP) of the Groundwater Ambient Monitoring and Assessment (GAMA) program was developed in response to the Groundwater Quality Monitoring Act of 2001 and is being conducted by the U.S. Geological Survey in cooperation with the California State Water Resources Control Board. From 2004 through 2012, the GAMA-PBP collected samples and assessed the quality of groundwater resources that supply public drinking water in 35 study units across the State. Selected sites in each study unit were sampled again approximately 3 years after initial sampling as part of an assessment of temporal trends in water quality by the GAMA-PBP. Twelve of the study units, initially sampled during 2006–11 (initial sampling period) and sampled a second time during 2008–13 (trend sampling period) to assess temporal trends, are the subject of this report.
The initial sampling was designed to provide a spatially unbiased assessment of the quality of untreated groundwater used for public water supplies in the 12 study units. In these study units, 550 sampling sites were selected by using a spatially distributed, randomized, grid-based method to provide spatially unbiased representation of the areas assessed (grid sites, also called “status sites”). After the initial sampling period, 76 of the previously sampled status sites (approximately 10 percent in each study unit) were randomly selected for trend sampling (“trend sites”). The 12 study units sampled both during the initial sampling and during the trend sampling period were distributed among 6 hydrogeologic provinces: Coastal (Northern and Southern), Transverse Ranges and Selected Peninsular Ranges, Klamath, Modoc Plateau and Cascades, and Sierra Nevada Hydrogeologic Provinces. For the purposes of this trend report, the six hydrogeologic provinces were grouped into two hydrogeologic regions based on location: Coastal and Mountain.
The groundwater samples were analyzed for a number of synthetic organic constituents (volatile organic compounds, pesticides, and pesticide degradates), constituents of special interest (perchlorate and 1,2,3-trichloropropane), and natural inorganic constituents (nutrients, major and minor ions, and trace elements). Isotopic tracers (tritium, carbon-14, and stable isotopes of hydrogen and oxygen in water) also were measured to help identify processes affecting groundwater quality and the sources and ages of the sampled groundwater. More than 200 constituents and water-quality indicators were measured during the trend sampling period.
Quality-control samples (blanks, replicates, matrix-spikes, and surrogate compounds) were collected at about one-third of the trend sites, and the results for these samples were used to evaluate the quality of the data for the groundwater samples. On the basis of detections in laboratory and field blank samples collected by GAMA-PBP study units, including the 12 study units presented here, reporting levels for some groundwater results were adjusted in this report. Differences between replicate samples were mostly within acceptable ranges, indicating low variability in analytical results. Matrix-spike recoveries were largely within the acceptable range (70 to 130 percent).
This study did not attempt to evaluate the quality of water delivered to consumers. After withdrawal, groundwater used for drinking water typically is treated, disinfected, and blended with other waters to achieve acceptable water quality. The comparison benchmarks used in this report apply to treated water that is served to the consumer, not to untreated groundwater. To provide some context for the results, however, concentrations of constituents measured in these groundwater samples were compared with benchmarks established by the U.S. Environmental Protection Agency and the State of California. Comparisons between data collected for this study and benchmarks for drinking water are for illustrative purposes only and are not indicative of compliance or non-compliance with those benchmarks.
Most organic constituents that were detected in groundwater samples from the trend sites were found at concentrations less than health-based benchmarks. One volatile organic compound—perchloroethene—was detected at a concentration greater than the health-based benchmark in samples from one trend site during the initial and trend sampling periods. Chloroform was detected in at least 10 percent of the samples at trend sites in both sampling periods. Methyl tert-butyl ether was detected in samples from more than 10 percent of the trend sites during the initial sampling period. No pesticide or pesticide degradate was detected in greater than 10 percent of the samples from trend sites or at concentrations greater than their health-based benchmarks during either sampling period. Nutrients were not detected at concentrations greater than their health-based benchmarks during either sampling period.
Most detections of major ions and trace elements in samples from trend sites were less than health-based benchmarks during both sampling periods. Arsenic and boron each were detected at concentrations greater than the health-based benchmark in samples from four trend sites during the initial and trend sampling periods. Molybdenum was detected in samples from four trend sites at concentrations greater than the health-based benchmark during both sampling periods. Samples from two of these trend sites had similar molybdenum concentrations, and two had substantially different concentrations during the initial and trend sampling periods. Uranium was detected at a concentration greater than the health-based benchmark only at two trend sites.
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\"}}]}","contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
http://ca.water.usgs.gov
At least one-third of all amphibian species face the threat of extinction, and current amphibian extinction rates are four orders of magnitude greater than background rates. Preventing extirpation often requires both ex situ (i.e., conservation breeding programs) and in situ strategies (i.e., protecting natural habitats). Flatwoods salamanders (Ambystoma bishopi and A. cingulatum) are protected under the U.S. Endangered Species Act. The two species have decreased from 476 historical locations to 63 recently extant locations (86.8% loss). We suggest that recovery efforts are needed to increase populations and prevent extinction, but uncertainty regarding optimal actions in both ex situ and in situ realms hinders recovery planning. We used structured decision making (SDM) to address key uncertainties regarding both captive breeding and habitat restoration, and we developed short-, medium-, and long-term goals to achieve recovery objectives. By promoting a transparent, logical approach, SDM has proven vital to recovery plan development for flatwoods salamanders. The SDM approach has clear advantages over other previous approaches to recovery efforts, and we suggest that it should be considered for other complex decisions regarding endangered species.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jnc.2017.02.011","usgsCitation":"O’Donnell, K., Messerman, A.F., Barichivich, W.J., Semlitsch, R.D., Gorman, T.A., Mitchell, H.G., Allan, N., Fenolio, D.B., Green, A., Johnson, F.A., Keever, A., Mandica, M., Martin, J., Mott, J., Peacock, T., Reinman, J., Romanach, S.S., Titus, G., McGowan, C.P., and Walls, S.C., 2017, Structured decision making as a conservation tool for recovery planning of two endangered salamanders: Journal for Nature Conservation, v. 37, p. 66-72, https://doi.org/10.1016/j.jnc.2017.02.011.","productDescription":"7 p.","startPage":"66","endPage":"72","ipdsId":"IP-075815","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":470020,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jnc.2017.02.011","text":"Publisher Index 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F","contributorId":187740,"corporation":false,"usgs":false,"family":"Messerman","given":"Arianne","email":"","middleInitial":"F","affiliations":[],"preferred":false,"id":681536,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barichivich, William J. 0000-0003-1103-6861 wbarichivich@usgs.gov","orcid":"https://orcid.org/0000-0003-1103-6861","contributorId":3697,"corporation":false,"usgs":true,"family":"Barichivich","given":"William","email":"wbarichivich@usgs.gov","middleInitial":"J.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":681537,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Semlitsch, Raymond 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B.","contributorId":167680,"corporation":false,"usgs":false,"family":"Fenolio","given":"Dante","email":"","middleInitial":"B.","affiliations":[{"id":24805,"text":"Department of Conservation and Research, San Antonio Zoo","active":true,"usgs":false}],"preferred":false,"id":681542,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Green, Adam","contributorId":150581,"corporation":false,"usgs":false,"family":"Green","given":"Adam","affiliations":[],"preferred":false,"id":681543,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Johnson, Fred A. 0000-0002-5854-3695 fjohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-5854-3695","contributorId":2773,"corporation":false,"usgs":true,"family":"Johnson","given":"Fred","email":"fjohnson@usgs.gov","middleInitial":"A.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science 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Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":681547,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Mott, Jana","contributorId":187745,"corporation":false,"usgs":false,"family":"Mott","given":"Jana","email":"","affiliations":[],"preferred":false,"id":681548,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Peacock, Terry","contributorId":187746,"corporation":false,"usgs":false,"family":"Peacock","given":"Terry","email":"","affiliations":[],"preferred":false,"id":681549,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Reinman, Joseph","contributorId":187747,"corporation":false,"usgs":false,"family":"Reinman","given":"Joseph","email":"","affiliations":[],"preferred":false,"id":681550,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Romanach, Stephanie S. 0000-0003-0271-7825 sromanach@usgs.gov","orcid":"https://orcid.org/0000-0003-0271-7825","contributorId":140419,"corporation":false,"usgs":true,"family":"Romanach","given":"Stephanie","email":"sromanach@usgs.gov","middleInitial":"S.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":681551,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Titus, Greg","contributorId":187748,"corporation":false,"usgs":false,"family":"Titus","given":"Greg","email":"","affiliations":[],"preferred":false,"id":681552,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"McGowan, Conor P. 0000-0002-7330-9581 cmcgowan@usgs.gov","orcid":"https://orcid.org/0000-0002-7330-9581","contributorId":167162,"corporation":false,"usgs":true,"family":"McGowan","given":"Conor","email":"cmcgowan@usgs.gov","middleInitial":"P.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":681553,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Walls, Susan C. 0000-0001-7391-9155 swalls@usgs.gov","orcid":"https://orcid.org/0000-0001-7391-9155","contributorId":138952,"corporation":false,"usgs":true,"family":"Walls","given":"Susan","email":"swalls@usgs.gov","middleInitial":"C.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":681554,"contributorType":{"id":1,"text":"Authors"},"rank":20}]}} ,{"id":70184315,"text":"ds1037 - 2017 - Sediment data collected in 2014 and 2015 from around Breton and Gosier Islands, Breton National Wildlife Refuge, Louisiana","interactions":[],"lastModifiedDate":"2017-03-13T09:54:04","indexId":"ds1037","displayToPublicDate":"2017-03-08T11:30:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1037","title":"Sediment data collected in 2014 and 2015 from around Breton and Gosier Islands, Breton National Wildlife Refuge, Louisiana","docAbstract":"Breton Island, located at the southern end of the Chandeleur Islands, supports one of Louisiana’s largest historical brown pelican (Pelecanus occidentalis) nesting colonies. Although the brown pelican was delisted as an endangered species in 2009, nesting areas are threatened by continued land loss and are extremely vulnerable to storm impacts. The U.S. Fish and Wildlife Service proposed to restore Breton Island to pre-Hurricane Katrina conditions through rebuilding the shoreface, dune, and back-barrier marsh environments. Prior to restoration, scientists from the U.S. Geological Survey’s (USGS) St. Petersburg Coastal and Marine Science Center Geologic and Morphologic Evolution of Coastal Margins project collected high-resolution geophysical (topography, bathymetry, and sub-bottom profiles) and sedimentologic data from around Breton Island to characterize the geologic framework of the island platform, nearshore, and shelf environments. These data will be used to characterize the geologic framework around Breton Island, identify potential borrow areas for restoration efforts, quantify seafloor change, and provide information for sediment transport and morphologic change models to assess island response to restoration and natural processes.
This report, along with the accompanying USGS data release, serves as an archive of sediment data from vibracores, push cores, and submerged grab samples collected from around Breton and Gosier Islands, Louisiana, during two surveys conducted in July 2014 and January 2015 (USGS Field Activity Numbers 2014–314–FA and 2014–336–FA, respectively). Sedimentologic and stratigraphic metrics (for example, sediment texture or unit thicknesses) derived from these data can be used to ground-truth the geophysical data and characterize potential sand resources or can be incorporated into sediment transport or morphologic change models. Data products, including sample location tables, descriptive core logs, core photographs and x-radiographs, results of sediment grain-size analyses, and geographic information system data files with accompanying formal Federal Geographic Data Committee metadata can be downloaded from the accompanying data release.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1037","usgsCitation":"Bernier, J.C., Kelso, K.W., Tuten, T.M., Stalk, C.A., and Flocks, J.G., 2017, Sediment data collected in 2014 and 2015 from around Breton and Gosier Islands, Breton National Wildlife Refuge, Louisiana: U.S. Geological Survey Data Series 1037, https://doi.org/10.3133/ds1037.","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-081095","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":336951,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1037/coverthb.jpg"},{"id":336945,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1037","text":"Report HTML","linkFileType":{"id":5,"text":"html"},"description":"DS 1037"},{"id":336959,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F79C6VKF","text":"USGS data release ","linkHelpText":"Archive of Sediment Data Collected in 2014 and 2015 From Around Breton and Gosier Islands, Breton National Wildlife Refuge, Louisiana"}],"country":"United States","state":"Louisiana","otherGeospatial":"Breton Islands, Breton National Wildlife Refuge, Gosier Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.241943359375,\n 29.450360671054415\n ],\n [\n -88.97140502929688,\n 29.450360671054415\n ],\n [\n -88.97140502929688,\n 29.60987920220905\n ],\n [\n -89.241943359375,\n 29.60987920220905\n ],\n [\n -89.241943359375,\n 29.450360671054415\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
https://coastal.er.usgs.gov/
This scientific investigations map is a product of the U.S. Geological Survey (USGS) National Water-Quality Assessment (NAWQA) project modeling and mapping team. The prediction grids depicted in this map are of continuous pH and are intended to provide an understanding of groundwater-quality conditions at the domestic and public supply drinking water zones in the groundwater of the Central Valley of California. The chemical quality of groundwater and the fate of many contaminants is often influenced by pH in all aquifers. These grids are of interest to water-resource managers, water-quality researchers, and groundwater modelers concerned with the occurrence of natural and anthropogenic contaminants related to pH. In this work, the median well depth categorized as domestic supply was 30 meters below land surface, and the median well depth categorized as public supply is 100 meters below land surface. Prediction grids were created using prediction modeling methods, specifically boosted regression trees (BRT) with a Gaussian error distribution within a statistical learning framework within the computing framework of R (http://www.r-project.org/). The statistical learning framework seeks to maximize the predictive performance of machine learning methods through model tuning by cross validation. The response variable was measured pH from 1,337 wells and was compiled from two sources: USGS National Water Information System (NWIS) database (all data are publicly available from the USGS: http://waterdata.usgs.gov/ca/nwis/nwis) and the California State Water Resources Control Board Division of Drinking Water (SWRCB-DDW) database (water quality data are publicly available from the SWRCB: http://www.waterboards.ca.gov/gama/geotracker_gama.shtml). Only wells with measured pH and well depth data were selected, and for wells with multiple records, only the most recent sample in the period 1993–2014 was used. A total of 1,003 wells (training dataset) were used to train the BRT model, and 334 wells (hold-out dataset) were used to validate the prediction model. The training r-squared was 0.70, and the root-mean-square error (RMSE) in standard pH units was 0.26. The hold-out r-squared was 0.43, and RMSE in standard pH units was 0.37. Predictor variables consisting of more than 60 variables from 7 sources were assembled to develop a model that incorporates regional-scale soil properties, soil chemistry, land use, aquifer textures, and aquifer hydrology. Previously developed Central Valley model outputs of textures (Central Valley Textural Model, CVTM; Faunt and others, 2010) and MODFLOW-simulated vertical water fluxes and predicted depth to water table (Central Valley Hydrologic Model, CVHM; Faunt, 2009) were used to represent aquifer textures and groundwater hydraulics, respectively. In this work, wells were attributed to predictor variable values in ArcGIS using a 500-meter buffer.
Faunt, C.C., ed., 2009, Groundwater availability in the Central Valley aquifer, California: U.S. Geological Survey Professional Paper 1776, 225 p., accessed at https://pubs.usgs.gov/pp/1766/.
Faunt, C.C., Belitz, K., and Hanson, R.T., 2010, Development of a three-dimensional model of sedimentary texture in valley-fill deposits of Central Valley, California, USA: Hydrogeology Journal, v. 18, no. 3, p. 625–649, https://doi.org/10.1007/s10040-009-0539-7.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3377","usgsCitation":"Rosecrans, C.Z., Nolan, B.T., Gronberg, J.M., 2017, Predicted pH at the domestic and public supply drinking water depths, Central Valley, California: U.S. Geological Survey Scientific Investigations Map 3377, 1 sheet, scale 1:2,400,000, https://doi.org/10.3133/sim3377.","productDescription":"Sheet: 19.00 x 21.00 inches; Data Release","onlineOnly":"Y","ipdsId":"IP-079912","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":336887,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7FX77K4","text":"USGS data release","description":"USGS data release","linkHelpText":"Ascii grids of predicted pH in depth zones used by domestic and public drinking water supply depths, Central Valley, California."},{"id":336878,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3377/coverthb.jpg"},{"id":336879,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3377/sim3377.pdf","text":"Report","size":"1.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3377"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.22290039062499,\n 40.75557964275589\n ],\n [\n -122.958984375,\n 40.38839687388361\n ],\n [\n -122.574462890625,\n 39.32579941789298\n ],\n [\n -122.08007812499999,\n 38.07404145941957\n ],\n [\n -120.7177734375,\n 36.77409249464195\n ],\n [\n -119.83886718750001,\n 35.33529320309328\n ],\n [\n -119.267578125,\n 34.912962495216966\n ],\n [\n -118.740234375,\n 35.110921809704756\n ],\n [\n -118.740234375,\n 35.8356283888737\n ],\n [\n -118.91601562499999,\n 36.359374956015856\n ],\n [\n -119.84985351562499,\n 37.32648861334206\n ],\n [\n -120.82763671875,\n 38.24680876017446\n ],\n [\n -121.39892578125,\n 39.2492708462234\n ],\n [\n -122.1240234375,\n 40.53050177574321\n ],\n [\n -122.22290039062499,\n 40.75557964275589\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, California Water Science Center
6000 J Street, Placer Hall
Sacramento, CA 95819
Telephone number: (916) 278-3000
http://ca.water.usgs.gov/
The Lower Colorado River provides critical riparian areas in an otherwise arid region and is an important stopover site for migrating landbirds. In order to reverse ongoing habitat degradation due to drought and human-altered hydrology, a pulse flow was released from Morelos Dam in spring of 2014, which brought surface flow to dry stretches of the Colorado River in Mexico. To assess the potential effects of habitat modification resulting from the pulse flow, we used foraging behavior of spring migrants from past and current studies to assess the relative importance of different riparian habitats. We observed foraging birds in 2000 and 2014 at five riparian sites along the Lower Colorado River in Mexico to quantify prey attack rates, prey attack maneuvers, vegetation use patterns, and degree of preference for fully leafed-out or flowering plants. Prey attack rate was highest in mesquite (Prosopis spp.) in 2000 and in willow (Salix gooddingii) in 2014; correspondingly, migrants predominantly used mesquite in 2000 and willow in 2014 and showed a preference for willows in flower or fruit in 2014. Wilson’s warbler (Cardellina pusilla) used relatively more low-energy foraging maneuvers in willow than in tamarisk (Tamarix spp.) or mesquite. Those patterns in foraging behavior suggest native riparian vegetation, and especially willow, are important resources for spring migrants along the lower Colorado River. Willow is a relatively short-lived tree dependent on spring floods for dispersal and establishment and thus spring migrants are likely to benefit from controlled pulse flows.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecoleng.2016.06.001","usgsCitation":"Darrah, A., Greeney, H.F., and van Riper, C., 2017, Importance of the 2014 Colorado River Delta pulse flow for migratory songbirds: Insights from foraging behavior: Ecological Engineering, v. 106, no. B, p. 784-790, https://doi.org/10.1016/j.ecoleng.2016.06.001.","productDescription":"7 p.","startPage":"784","endPage":"790","ipdsId":"IP-071691","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":470022,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecoleng.2016.06.001","text":"Publisher Index Page"},{"id":337066,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","state":"Arizona, California","otherGeospatial":"Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.1202392578125,\n 31.765537409484374\n ],\n [\n -113.70849609375,\n 31.765537409484374\n ],\n [\n -113.70849609375,\n 32.78265637602964\n ],\n [\n -115.1202392578125,\n 32.78265637602964\n ],\n [\n -115.1202392578125,\n 31.765537409484374\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"106","issue":"B","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58c12633e4b014cc3a3d344c","contributors":{"authors":[{"text":"Darrah, Abigail J.","contributorId":187674,"corporation":false,"usgs":false,"family":"Darrah","given":"Abigail J.","affiliations":[{"id":35720,"text":"Audubon Mississippi, Coastal Bird Stewardship ProgramMoss PointUSA","active":true,"usgs":false},{"id":12625,"text":"School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, 85721, USA","active":true,"usgs":false}],"preferred":false,"id":681275,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Greeney, Harold F.","contributorId":187675,"corporation":false,"usgs":false,"family":"Greeney","given":"Harold","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":681276,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"van Riper, Charles III 0000-0003-1084-5843 charles_van_riper@usgs.gov","orcid":"https://orcid.org/0000-0003-1084-5843","contributorId":169488,"corporation":false,"usgs":true,"family":"van Riper","given":"Charles","suffix":"III","email":"charles_van_riper@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":false,"id":681274,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70184365,"text":"70184365 - 2017 - Effects of carbon dioxide on juveniles of the freshwater mussel (Lampsilis siliquoidea [Unionidae])","interactions":[],"lastModifiedDate":"2017-03-08T12:23:32","indexId":"70184365","displayToPublicDate":"2017-03-08T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Effects of carbon dioxide on juveniles of the freshwater mussel (Lampsilis siliquoidea [Unionidae])","docAbstract":"Carbon dioxide (CO2) has shown promise as a tool to control movements of invasive Asian carp, but its effects on native freshwater biota have not been well studied. The authors evaluated lethal and sublethal responses of juvenile fatmucket (Lampsilis siliquoidea) mussels to CO2 at levels (43–269 mg/L, mean concentration) that bracket concentrations effective for deterring carp movement. The 28-d lethal concentration to 50% of the mussels was 87.0 mg/L (95% confidence interval [CI] 78.4–95.9) and at 16-d postexposure, 76.0 mg/L (95% CI 62.9–90.3). A proportional hazards regression model predicted that juveniles could not survive CO2 concentrations >160 mg/L for more than 2 wk or >100 mg/L CO2 for more than 30 d. Mean shell growth was significantly lower for mussels that survived CO2 treatments. Growth during the postexposure period did not differ among treatments, indicating recovery of the mussels. Also, CO2 caused shell pitting and erosion. Behavioral effects of CO2 included movement of mussels to the substrate surface and narcotization at the highest concentrations. Mussels in the 110 mg/L mean CO2treatment had the most movements in the first 3 d of exposure. If CO2 is infused continuously as a fish deterrent, concentrations <76 mg/L are recommended to prevent juvenile mussel mortality and shell damage. Mussels may survive and recover from brief exposure to higher concentrations.
","language":"English","publisher":"Wiley","doi":"10.1002/etc.3567","usgsCitation":"Waller, D.L., Bartsch, M.R., Fredricks, K.T., Bartsch, L., Schleis, S.M., and Lee, S., 2017, Effects of carbon dioxide on juveniles of the freshwater mussel (Lampsilis siliquoidea [Unionidae]): Environmental Toxicology and Chemistry, v. 36, no. 3, p. 671-681, https://doi.org/10.1002/etc.3567.","productDescription":"11 p.","startPage":"671","endPage":"681","ipdsId":"IP-074542","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":337067,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","issue":"3","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2016-07-28","publicationStatus":"PW","scienceBaseUri":"58c12634e4b014cc3a3d344e","contributors":{"authors":[{"text":"Waller, Diane L. 0000-0002-6104-810X dwaller@usgs.gov","orcid":"https://orcid.org/0000-0002-6104-810X","contributorId":5272,"corporation":false,"usgs":true,"family":"Waller","given":"Diane","email":"dwaller@usgs.gov","middleInitial":"L.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":681182,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bartsch, Michelle R. 0000-0002-9571-5564 mbartsch@usgs.gov","orcid":"https://orcid.org/0000-0002-9571-5564","contributorId":149359,"corporation":false,"usgs":true,"family":"Bartsch","given":"Michelle","email":"mbartsch@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":681183,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fredricks, Kim T. 0000-0003-2363-7891 kfredricks@usgs.gov","orcid":"https://orcid.org/0000-0003-2363-7891","contributorId":173994,"corporation":false,"usgs":true,"family":"Fredricks","given":"Kim","email":"kfredricks@usgs.gov","middleInitial":"T.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":681184,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bartsch, Lynn A. 0000-0002-1483-4845 lbartsch@usgs.gov","orcid":"https://orcid.org/0000-0002-1483-4845","contributorId":149360,"corporation":false,"usgs":true,"family":"Bartsch","given":"Lynn A.","email":"lbartsch@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":681185,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schleis, Susan M. 0000-0002-9396-7856 sschleis@usgs.gov","orcid":"https://orcid.org/0000-0002-9396-7856","contributorId":2858,"corporation":false,"usgs":true,"family":"Schleis","given":"Susan","email":"sschleis@usgs.gov","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":681186,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lee, Sheldon","contributorId":173995,"corporation":false,"usgs":false,"family":"Lee","given":"Sheldon","email":"","affiliations":[{"id":25359,"text":"Viterbo University","active":true,"usgs":false}],"preferred":false,"id":681187,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70184362,"text":"70184362 - 2017 - Restoration versus invasive species: Bigheaded carps’ use of a rehabilitated backwater","interactions":[],"lastModifiedDate":"2017-06-07T10:29:07","indexId":"70184362","displayToPublicDate":"2017-03-08T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Restoration versus invasive species: Bigheaded carps’ use of a rehabilitated backwater","docAbstract":"Knowledge of how invasive species use invaded habitats can aid in developing management practices to exclude them. Swan Lake, a 1100-ha Illinois River (USA) backwater, was rehabilitated to restore ecosystem functions, but may provide valuable habitat for invasive bigheaded carps [bighead carp (Hypophthalmichthys nobilis) and silver carp (H. molitrix)]. Use (residency and passages) of Swan Lake by invasive bigheaded carps was monitored using acoustic telemetry (n = 50 individuals/species) to evaluate the use of a large, restored habitat from 2004 to 2005. Passages (entrances/exits) by bigheaded carps were highest in winter, and residency was highest in the summer. Bighead carp backwater use was associated with the differences in temperature between the main channel and backwater, and passages primarily occurred between 18:00 h and midnight. Silver carp backwater use was positively correlated with water level and main channel discharge, and fewer passages occurred between 12:00 h and 18:00 h than during any other time of day. Harvest occurring during summer or high main channel discharge could reduce backwater abundances while maintenance of low water levels could reduce overall backwater use. Conclusions from this study regarding the timing of bigheaded carps' use of backwater habitats are critical to integrated pest management plans to control invasive species.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.3122","usgsCitation":"Coulter, A.A., Schultz, D., Tristano, E., Brey, M.K., and Garvey, J.E., 2017, Restoration versus invasive species: Bigheaded carps’ use of a rehabilitated backwater: River Research and Applications, v. 33, no. 5, p. 662-669, https://doi.org/10.1002/rra.3122.","productDescription":"8 p.","startPage":"662","endPage":"669","ipdsId":"IP-072124","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":438423,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7HH6J1S","text":"USGS data release","linkHelpText":"Remotely sensed variables analyzed and reported in the paper titled &amp;amp;amp;quot;Multi-year data from satellite- and ground-based sensors show details and scale matter in assessing climate&amp;amp;amp;rsquo;s effects on wetland surface water, amphibians, and landscape conditions&amp;amp;amp;quot;"},{"id":438422,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F76Q1VDT","text":"USGS data release","linkHelpText":"Restoration versus invasive species: bigheaded carps use of a rehabilitated backwater: Data"},{"id":337010,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"33","issue":"5","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-24","publicationStatus":"PW","scienceBaseUri":"58c12634e4b014cc3a3d3450","contributors":{"authors":[{"text":"Coulter, Alison A.","contributorId":187652,"corporation":false,"usgs":false,"family":"Coulter","given":"Alison","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":681168,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schultz, Douglas","contributorId":187653,"corporation":false,"usgs":false,"family":"Schultz","given":"Douglas","affiliations":[],"preferred":false,"id":681169,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tristano, Elizabeth","contributorId":187654,"corporation":false,"usgs":false,"family":"Tristano","given":"Elizabeth","affiliations":[],"preferred":false,"id":681170,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brey, Marybeth K. 0000-0003-4403-9655 mbrey@usgs.gov","orcid":"https://orcid.org/0000-0003-4403-9655","contributorId":187651,"corporation":false,"usgs":true,"family":"Brey","given":"Marybeth","email":"mbrey@usgs.gov","middleInitial":"K.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":681167,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Garvey, James E.","contributorId":178007,"corporation":false,"usgs":false,"family":"Garvey","given":"James","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":681171,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70179144,"text":"sir20165142 - 2017 - The effects of forest cover on base flow of streams in the mountainous interior of Puerto Rico, 2010","interactions":[],"lastModifiedDate":"2017-03-14T09:22:51","indexId":"sir20165142","displayToPublicDate":"2017-03-07T15:45:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-5142","title":"The effects of forest cover on base flow of streams in the mountainous interior of Puerto Rico, 2010","docAbstract":"The U.S. Geological Survey, in cooperation with the Puerto Rico Department of Natural and Environmental Resources, completed a study to determine whether a relation exists between the extent of forest cover and the magnitude of base flow at two sets of paired drainage basins in the highlands of the municipalities of Adjuntas and Utuado within the mountainous interior of Puerto Rico. One set of paired basins includes the Río Guaónica and Río Tanamá, both tributaries of the Río Grande de Arecibo. The other set includes two smaller basins in the drainage basin of the Río Coabey, which is a tributary of the Río Tanamá. The paired basins in each set have similar rainfall patterns, geologic substrate, and aspect; the principal difference identified in the study is the extent of forest cover and related land uses such as the cultivation of shade and sun coffee. Data describing the hydrology, hydrogeology, and streamflow were used in the analysis. The principal objective of the study was to compare base flow per unit area among basins having different areal extents of forest cover and land uses such as shade coffee and sun coffee cultivation.
Within the mountainous interior of Puerto Rico, a substantial amount of the annual rainfall (45 to 39 percent in the Rio Guaónica and Rio Tanamá, respectively) can migrate to the subsurface and later emerge as base flow in streams. The magnitude of base flow within the two sets of paired basins varies seasonally. Minimum base flows occur during the annual dry season (generally from January to March), and maximum base flows occur during the wet season (generally from August to October). During the dry season or periods of below-normal rainfall, base flow is either the primary or the sole component of streamflow. Daily mean base flow ranged from 3.2 to 20.5 cubic feet per second (ft3 /s) at the Rio Guaónica Basin, and from 4.2 to 23.0 ft3 /s at the Rio Tanamá Basin. The daily mean base flows during 2010 ranged from 0.28 to 0.98 ft3 /s at Tributary 1 and from 0.22 to 0.58 ft3 /s at Tributary 2 of the Rio Coabey. The normalized daily base flow at the Río Guaónica and Río Tanamá Basin during 2010 ranged from 1.3 to 8.1 cubic feet per second per square mile (ft3 /s)/mi2 and from 1.1 to 6.1 (ft3 /s)/mi2 , respectively. The normalized daily base flow for the basins of Tributary 1 and Tributary 2 of Río Coabey during 2010 ranged from 1.0 to 3.6 (ft3 /s)/mi2 and from 1.5 to 3.9 (ft3 /s)/mi2 , respectively.
The normalized mean annual base flow is similar within the larger paired basins of Río Tanamá (2.74 [ft3 /s]/mi2 ) and Río Guaónica (3.15 [ft3 /s]/mi2 ). The mean annual base flow per unit area for both of these basins is about 79 percent of the mean annual streamflow. In the large paired basins, the proportion of Type I land use (forest patches, shade and mixed shade/sun coffee with associated cash crops) is substantially higher in Rio Guaónica Basin (81 percent) than in the Rio Tanamá Basin (59 percent), and the base flow per unit area is also higher. In the small paired basins of Rio Coabey, the proportion of Type I land use is much higher at Tributary 1 (52 percent) than at Tributary 2 (15 percent), but, in contrast to the large basins, the mean annual base flow per unit area is lower (2.22 and 2.62 [ft3 /s]/mi2 , respectively). There is no consistent relation between land use and normalized base flow between the two sets of paired basins in the study.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165142","collaboration":"Prepared in cooperation with the Puerto Rico Department of Natural and Environmental Resources","usgsCitation":"Rodríguez-Martínez, Jesús, and Santiago, Marilyn, 2017, The effects of forest cover on base flow of streams in the mountainous interior of Puerto Rico, 2010: U.S. Geological Survey Scientific Investigations Report 2016–5142, 19 p., https://doi.org/10.3133/sir20165142.","productDescription":"Report: vii, 19 p.; Data Release","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-061550","costCenters":[{"id":156,"text":"Caribbean Water Science Center","active":true,"usgs":true}],"links":[{"id":438424,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7N58JG5","text":"USGS data release","linkHelpText":"Hydrologic data for the effects of forest cover on base flow of streams in the mountainous interior of Puerto Rico"},{"id":336264,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7N58JG5","text":"USGS data release ","description":"USGS data release","linkHelpText":"Hydrologic data for the effects of forest cover on base flow of streams in the mountainous interior of Puerto Rico"},{"id":336240,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5142/coverthb.jpg"},{"id":336241,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5142/sir20165142.pdf","text":"Report","size":"15.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5142"}],"otherGeospatial":"Puerto Rico","contact":"Director, Caribbean-Florida Water Science Center
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Lutz, FL 33559
https://pr.water.usgs.gov
Datum conversions from the National Geodetic Vertical Datum of 1929 to the North American Vertical Datum of 1988 among inland and coastal gages throughout the hydrologic regions of New England, the Mid-Atlantic, and the South Atlantic-Gulf have implications among river and storm surge forecasting, general commerce, and water-control operations. The process of data conversions may involve the application of a recovered National Geodetic Vertical Datum of 1929–North American Vertical Datum of 1988 offset, a simplistic datum transformation using VDatum or VERTCON software, or a survey, depending on a gaging network datum evaluation, anticipated uncertainties for data use among the cooperative water community, and methods used to derive the conversion. Datum transformations from National Geodetic Vertical Datum of 1929 to North American Vertical Datum of 1988 using VERTCON purport errors of ± 0.13 foot at the 95 percent confidence level among modeled points, claiming more consistency along the east coast. Survey methods involving differential and trigonometric leveling, along with observations using Global Navigation Satellite System technology, afford a variety of approaches to establish or perpetuate a datum during a survey. Uncertainties among leveling approaches are generally < 0.1 foot, and and Global Navigation Satellite System approaches may be categorized with uncertainties of ≤0.1 foot for a Level I quality category and ≥0.1 foot for Level II or III quality categories (defined by the U.S. Geological Survey) by observation and review of experienced practice. The conversion process is initiated with an evaluation of the inland and coastal gage network datum, beginning with altitude datum components and the history of those components queried through the U.S. Geological Survey Groundwater Site Inventory database. Subsequent edits to the Groundwater Site Inventory database may be required and a consensus reached among the U.S. Geological Survey Water Science Centers to identify the outstanding workload categorized as in-office datum transformations or offset applications versus out-of-office survey efforts. Datum conversions or datum establishment for the inland or coastal gaging network should meet datum uncertainty requirements among other Federal agencies. Datum uncertainty requirements are ±0.25 foot for U.S. Army Corps of Engineers water-control or construction projects and ±0.16 foot for Federal Emergency Management Agency field surveys and checkpoint surveys used for mapping. River level forecasts generally are defined as ± 0.10 foot among the National Oceanic and Atmospheric Administration–National Weather Service. Collaboration and communication among the cooperative water community is necessary during a datum conversion or datum change. Datum notification time-change requirements set by the National Oceanic and Atmospheric Administration–National Weather Service vary from 30 to 120 days, depending on datum conversion or datum-change case scenarios. Notification times associated with these case scenarios may be useful to the National Oceanic and Atmospheric Administration–National Weather Service and U.S. Army Corps of Engineers, because their daily operations are time sensitive, unlike the notification time change requirements of other entities that make up the cooperative water community. At the time of this writing, a future geopotential datum resulting from Gravity for the Redefinition of the American Vertical Datum is anticipated in 2022. A future version of VDatum and VERTCON is anticipated to provide a transformation among North American Vertical Datum of 1988 elevations to the new geopotential datum.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section B: U.S. Geological Survey Standards in Book 11: Collection and delineation of spatial data","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm11B8","usgsCitation":"Rydlund, P.H., Jr., and Noll, M.L., 2017, Vertical datum conversion process for the inland and coastal gage network located in the New England, Mid-Atlantic, and South Atlantic-Gulf hydrologic regions (ver. 1.1, July 2017) U.S. Geological Survey Techniques and Methods, book 11, chap. B8, 29 p., https://doi.org/10.3133/tm11B8.","productDescription":"ix, 29 p.","numberOfPages":"44","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-078726","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":399692,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_105491.htm"},{"id":344212,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/tm/11/b08/versionHist.txt"},{"id":336266,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/11/b08/coverthb2.jpg"},{"id":336267,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/11/b08/tm11B8.pdf","text":"Report","size":"11.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 11-8B"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.6484375,\n 30.183121842195515\n ],\n [\n -89.23095703125,\n 30.278044377800153\n ],\n [\n -88.39599609375,\n 30.221101852485987\n ],\n [\n -87.07763671875,\n 30.35391637229704\n ],\n [\n -86.06689453125,\n 30.164126343161097\n ],\n [\n -85.23193359375,\n 29.49698759653577\n ],\n [\n -84.08935546875,\n 30.012030680358613\n ],\n [\n -83.60595703125,\n 29.630771207229\n ],\n [\n -82.77099609375,\n 28.671310915880834\n ],\n [\n -82.99072265625,\n 27.858503954841247\n ],\n [\n -81.93603515625,\n 26.017297563851745\n ],\n [\n -81.32080078125,\n 25.105497373014686\n ],\n [\n -80.15625,\n 25.045792240303445\n ],\n [\n -79.8486328125,\n 26.70635985763354\n ],\n [\n -80.39794921875,\n 28.013801376380712\n ],\n [\n -81.1669921875,\n 30.939924331023445\n ],\n [\n -80.22216796875,\n 32.194208672875384\n ],\n [\n -77.93701171875,\n 33.687781758439364\n ],\n [\n -75.322265625,\n 35.08395557927643\n ],\n [\n -75.1025390625,\n 35.51434313431818\n ],\n [\n -75.7177734375,\n 36.87962060502676\n ],\n [\n -73.6962890625,\n 40.36328834091583\n ],\n [\n -69.85107421874999,\n 41.178653972331674\n ],\n [\n -69.49951171875,\n 41.83682786072714\n ],\n [\n -70.51025390625,\n 42.24478535602799\n ],\n [\n -70.42236328125,\n 43.197167282501276\n ],\n [\n -69.6533203125,\n 43.644025847699496\n ],\n [\n -66.81884765625,\n 44.74673324024678\n ],\n [\n -67.7197265625,\n 45.72152152227954\n ],\n [\n -67.7197265625,\n 47.100044694025215\n ],\n [\n -68.26904296875,\n 47.35371061951363\n ],\n [\n -69.10400390625,\n 47.517200697839414\n ],\n [\n -69.9609375,\n 46.89023157359399\n ],\n [\n -70.51025390625,\n 45.767522962149876\n ],\n [\n -71.25732421875,\n 45.336701909968134\n ],\n [\n -71.71875,\n 45.02695045318546\n ],\n [\n -73.41064453125,\n 45.01141864227728\n ],\n [\n -74.5751953125,\n 44.63739123445585\n ],\n [\n -76.97021484375,\n 43.13306116240612\n ],\n [\n -78.3544921875,\n 42.114523952464246\n ],\n [\n -79.189453125,\n 39.791654835253425\n ],\n [\n -80.26611328125,\n 37.82280243352756\n ],\n [\n -80.88134765625,\n 36.65079252503471\n ],\n [\n -82.41943359375,\n 35.28150065789119\n ],\n [\n -89.49462890625,\n 35.02999636902566\n ],\n [\n -90.41748046874999,\n 32.47269502206151\n ],\n [\n -90.10986328125,\n 31.034108344903512\n ],\n [\n -89.6484375,\n 30.183121842195515\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: Originally posted March 2017; Version 1.1: July 2017","publicComments":"This report is Chapter 8 in Section B: U.S. Geological Survey Standards in Book 11: Collection and delineation of spatial data.","contact":"Director, Missouri Water Science Center
U.S. Geological Survey
1400 Independence Road, MS 100
Rolla, MO 65401
https://mo.water.usgs.gov/
The quartz monzodiorite of Mount Edith and the concentrically zoned intrusive suite of Boulder Baldy constitute the principal Late Cretaceous igneous intrusions hosted by Mesoproterozoic sedimentary rocks of the Newland Formation in the Big Belt Mountains, Montana. These calc-alkaline plutonic masses are manifestations of subduction-related magmatism that prevailed along the western edge of North America during the Cretaceous. Radiogenic isotope data for neodymium, strontium, and lead indicate that the petrogenesis of the associated magmas involved a combination of (1) sources that were compositionally heterogeneous at the scale of the geographically restricted intrusive rocks in the Big Belt Mountains and (2) variable contamination by crustal assimilants also having diverse isotopic compositions. Altered and mineralized rocks temporally, spatially, and genetically related to these intrusions manifest at least two isotopically distinct mineralizing events, both of which involve major inputs from spatially associated Late Cretaceous igneous rocks. Alteration and mineralization of rock associated with the intrusive suite of Boulder Baldy requires a component characterized by significantly more radiogenic strontium than that characteristic of the associated igneous rocks. However, the source of such a component was not identified in the Big Belt Mountains. Similarly, altered and mineralized rocks associated with the quartz monzodiorite of Mount Edith include a component characterized by significantly more radiogenic strontium and lead, particularly as defined by 207Pb/204Pb values. The source of this component appears to be fluids that equilibrated with proximal Newland Formation rocks. Oxygen isotope data for rocks of the intrusive suite of Boulder Baldy are similar to those of subduction-related magmatism that include mantle-derived components; oxygen isotope data for altered and mineralized equivalents are slightly lighter.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1040","usgsCitation":"du Bray, E.A., Unruh, D.M., and Hofstra, A.H., 2017, Isotopic data for Late Cretaceous intrusions and associated altered and mineralized rocks in the Big Belt Mountains, Montana: U.S. Geological Survey Data Series 1040, 12 p., https://doi.org/10.3133/ds1040.","productDescription":"iii, 12 p.","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-079476","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":336901,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1040/ds1040.pdf","text":"Report","size":"5.99 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1040"},{"id":336899,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1040/coverthb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Big Belt Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.5,\n 46.25\n ],\n [\n -111,\n 46.25\n ],\n [\n -111,\n 46.74\n ],\n [\n -111.5,\n 46.74\n ],\n [\n -111.5,\n 46.25\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Center Director, USGS Central Mineral and Environmental Resources Science Center
Box 25046, Mail Stop 973
Denver, CO 80225
Regional-scale, three-dimensional continuous probability models, were constructed for aspects of redox conditions in the groundwater system of the Central Valley, California. These models yield grids depicting the probability that groundwater in a particular location will have dissolved oxygen (DO) concentrations less than selected threshold values representing anoxic groundwater conditions, or will have dissolved manganese (Mn) concentrations greater than selected threshold values representing secondary drinking water-quality contaminant levels (SMCL) and health-based screening levels (HBSL). The probability models were constrained by the alluvial boundary of the Central Valley to a depth of approximately 300 m. Probability distribution grids can be extracted from the 3-D models at any desired depth, and are of interest to water-resource managers, water-quality researchers, and groundwater modelers concerned with the occurrence of natural and anthropogenic contaminants related to anoxic conditions.
Models were constructed using a Boosted Regression Trees (BRT) machine learning technique that produces many trees as part of an additive model and has the ability to handle many variables, automatically incorporate interactions, and is resistant to collinearity. Machine learning methods for statistical prediction are becoming increasing popular in that they do not require assumptions associated with traditional hypothesis testing. Models were constructed using measured dissolved oxygen and manganese concentrations sampled from 2767 wells within the alluvial boundary of the Central Valley, and over 60 explanatory variables representing regional-scale soil properties, soil chemistry, land use, aquifer textures, and aquifer hydrologic properties. Models were trained on a USGS dataset of 932 wells, and evaluated on an independent hold-out dataset of 1835 wells from the California Division of Drinking Water. We used cross-validation to assess the predictive performance of models of varying complexity, as a basis for selecting final models. Trained models were applied to cross-validation testing data and a separate hold-out dataset to evaluate model predictive performance by emphasizing three model metrics of fit: Kappa; accuracy; and the area under the receiver operator characteristic curve (ROC). The final trained models were used for mapping predictions at discrete depths to a depth of 304.8 m. Trained DO and Mn models had accuracies of 86–100%, Kappa values of 0.69–0.99, and ROC values of 0.92–1.0. Model accuracies for cross-validation testing datasets were 82–95% and ROC values were 0.87–0.91, indicating good predictive performance. Kappas for the cross-validation testing dataset were 0.30–0.69, indicating fair to substantial agreement between testing observations and model predictions. Hold-out data were available for the manganese model only and indicated accuracies of 89–97%, ROC values of 0.73–0.75, and Kappa values of 0.06–0.30. The predictive performance of both the DO and Mn models was reasonable, considering all three of these fit metrics and the low percentages of low-DO and high-Mn events in the data.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2017.01.014","usgsCitation":"Rosecrans, C.Z., Nolan, B.T., and Gronberg, J.M., 2017, Prediction and visualization of redox conditions in the groundwater of Central Valley, California: Journal of Hydrology, v. 546, p. 341-356, https://doi.org/10.1016/j.jhydrol.2017.01.014.","productDescription":"16 p.","startPage":"341","endPage":"356","ipdsId":"IP-075668","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":336939,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","volume":"546","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58bfd4f0e4b014cc3a3ba483","contributors":{"authors":[{"text":"Rosecrans, Celia Z. 0000-0003-1456-4360 crosecrans@usgs.gov","orcid":"https://orcid.org/0000-0003-1456-4360","contributorId":187542,"corporation":false,"usgs":true,"family":"Rosecrans","given":"Celia","email":"crosecrans@usgs.gov","middleInitial":"Z.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":680860,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nolan, Bernard T. 0000-0002-6945-9659 btnolan@usgs.gov","orcid":"https://orcid.org/0000-0002-6945-9659","contributorId":2190,"corporation":false,"usgs":true,"family":"Nolan","given":"Bernard","email":"btnolan@usgs.gov","middleInitial":"T.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":680862,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gronberg, JoAnn M. 0000-0003-4822-7434 jmgronbe@usgs.gov","orcid":"https://orcid.org/0000-0003-4822-7434","contributorId":3548,"corporation":false,"usgs":true,"family":"Gronberg","given":"JoAnn","email":"jmgronbe@usgs.gov","middleInitial":"M.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":680861,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70175427,"text":"70175427 - 2017 - Mineral potential mapping in an accreted island-arc setting using aeromagnetic data: An example from southwest Alaska","interactions":[],"lastModifiedDate":"2021-04-19T17:06:54.450804","indexId":"70175427","displayToPublicDate":"2017-03-07T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1472,"text":"Economic Geology","active":true,"publicationSubtype":{"id":10}},"title":"Mineral potential mapping in an accreted island-arc setting using aeromagnetic data: An example from southwest Alaska","docAbstract":"The distribution of volcanogenic massive sulfide (VMS), porphyry-epithermal, Alaska-type ultramafic-mafic complexes, intrusion-related Au, and granitoid Sn-W ore deposits in southwest Alaska supports current metallogenic models linking the formation of these deposit types to the emplacement of different suites of igneous rocks during the evolution of this convergent plate margin. Regional-scale aeromagnetic data provide a continuous set of observations over the deposits and show contrasting patterns over the igneous rock suites hosting the various deposit types. Combined with surface geologic data and regional metallogenic constraints, aeromagnetic data—filtered to enhance the anomalous magnetic field and map magnetic domains—were used to produce a mineral potential map across this accreted island-arc setting. The reduced-to-pole, upward continuation, and total horizontal gradient transform maps show anomalies that could represent porphyry-epithermal deposits within the intraoceanic- and continental-arc terranes. The tilt derivative transform highlights lineaments within the back arc that may represent zones with potential for VMS deposits. The truncations of tilt derivative lineaments outline a major magnetic domain boundary between the back-arc and craton margin, which is prospective for granitoid Sn-W deposits. Annular tilt derivative highs outline granitoids that could be associated with intrusion-related Au deposits within the craton margin. Shallow, magnetite-rich Alaska-type ultramafic-mafic complexes are mapped by their short-wavelength, high-amplitude anomalies. Successful mineral potential mapping across southwestern Alaska as performed in the present study suggests that filtered aeromagnetic data can be effectively used in mineral exploration in convergent continental margin settings.
","language":"English","publisher":"Society of Economic Geologists","doi":"10.2113/econgeo.112.2.375","usgsCitation":"Anderson, E., Monecke, T., Hitzman, M.W., Zhou, W., and Bedrosian, P.A., 2017, Mineral potential mapping in an accreted island-arc setting using aeromagnetic data: An example from southwest Alaska: Economic Geology, v. 112, no. 2, p. 375-396, https://doi.org/10.2113/econgeo.112.2.375.","productDescription":"22 p.","startPage":"375","endPage":"396","ipdsId":"IP-069250","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":327805,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -157.23632812499997,\n 56.992882804633986\n ],\n [\n -132.275390625,\n 56.992882804633986\n ],\n [\n -132.275390625,\n 62.55285695857292\n ],\n [\n -157.23632812499997,\n 62.55285695857292\n ],\n [\n -157.23632812499997,\n 56.992882804633986\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"112","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-07","publicationStatus":"PW","scienceBaseUri":"57c6b175e4b0f2f0cebe6f32","contributors":{"authors":[{"text":"Anderson, Eric D. 0000-0002-0138-6166 ericanderson@usgs.gov","orcid":"https://orcid.org/0000-0002-0138-6166","contributorId":172766,"corporation":false,"usgs":true,"family":"Anderson","given":"Eric","email":"ericanderson@usgs.gov","middleInitial":"D.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":645153,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Monecke, Thomas","contributorId":50423,"corporation":false,"usgs":true,"family":"Monecke","given":"Thomas","affiliations":[],"preferred":false,"id":645155,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hitzman, Murray W. 0000-0002-3876-0537 mhitzman@usgs.gov","orcid":"https://orcid.org/0000-0002-3876-0537","contributorId":200913,"corporation":false,"usgs":true,"family":"Hitzman","given":"Murray","email":"mhitzman@usgs.gov","middleInitial":"W.","affiliations":[],"preferred":false,"id":645156,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zhou, Wendy","contributorId":205989,"corporation":false,"usgs":false,"family":"Zhou","given":"Wendy","email":"","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":814483,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bedrosian, Paul A. 0000-0002-6786-1038 pbedrosian@usgs.gov","orcid":"https://orcid.org/0000-0002-6786-1038","contributorId":839,"corporation":false,"usgs":true,"family":"Bedrosian","given":"Paul","email":"pbedrosian@usgs.gov","middleInitial":"A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":814484,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70184313,"text":"70184313 - 2017 - Pushing precipitation to the extremes in distributed experiments: Recommendations for simulating wet and dry years","interactions":[],"lastModifiedDate":"2017-04-04T09:10:15","indexId":"70184313","displayToPublicDate":"2017-03-07T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Pushing precipitation to the extremes in distributed experiments: Recommendations for simulating wet and dry years","docAbstract":"Intensification of the global hydrological cycle, ranging from larger individual precipitation events to more extreme multiyear droughts, has the potential to cause widespread alterations in ecosystem structure and function. With evidence that the incidence of extreme precipitation years (defined statistically from historical precipitation records) is increasing, there is a clear need to identify ecosystems that are most vulnerable to these changes and understand why some ecosystems are more sensitive to extremes than others. To date, opportunistic studies of naturally occurring extreme precipitation years, combined with results from a relatively small number of experiments, have provided limited mechanistic understanding of differences in ecosystem sensitivity, suggesting that new approaches are needed. Coordinated distributed experiments (CDEs) arrayed across multiple ecosystem types and focused on water can enhance our understanding of differential ecosystem sensitivity to precipitation extremes, but there are many design challenges to overcome (e.g., cost, comparability, standardization). Here, we evaluate contemporary experimental approaches for manipulating precipitation under field conditions to inform the design of ‘Drought-Net’, a relatively low-cost CDE that simulates extreme precipitation years. A common method for imposing both dry and wet years is to alter each ambient precipitation event. We endorse this approach for imposing extreme precipitation years because it simultaneously alters other precipitation characteristics (i.e., event size) consistent with natural precipitation patterns. However, we do not advocate applying identical treatment levels at all sites – a common approach to standardization in CDEs. This is because precipitation variability varies >fivefold globally resulting in a wide range of ecosystem-specific thresholds for defining extreme precipitation years. For CDEs focused on precipitation extremes, treatments should be based on each site's past climatic characteristics. This approach, though not often used by ecologists, allows ecological responses to be directly compared across disparate ecosystems and climates, facilitating process-level understanding of ecosystem sensitivity to precipitation extremes.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.13504","usgsCitation":"Knapp, A., Avolio, M.L., Beier, C., Carroll, C.J., Collins, S., Dukes, J.S., Fraser, L.H., Griffin-Nolan, R.J., Hoover, D.L., Jentsch, A., Loik, M.E., Phillips, R.P., Post, A.K., Sala, O.E., Slette, I.J., Yahdjian, L., and Smith, M.D., 2017, Pushing precipitation to the extremes in distributed experiments: Recommendations for simulating wet and dry years: Global Change Biology, v. 23, no. 5, p. 1774-1782, https://doi.org/10.1111/gcb.13504.","productDescription":"9 p.","startPage":"1774","endPage":"1782","ipdsId":"IP-079614","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":470023,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/gcb.13504","text":"External Repository"},{"id":336943,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","issue":"5","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58bfd4efe4b014cc3a3ba47a","contributors":{"authors":[{"text":"Knapp, Alan K.","contributorId":139807,"corporation":false,"usgs":false,"family":"Knapp","given":"Alan K.","affiliations":[{"id":13277,"text":"Graduate Degree Program in Ecology and Department of Biology, Colorado State University, Ft. Collins, CO","active":true,"usgs":false}],"preferred":false,"id":680953,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Avolio, Meghan L.","contributorId":187573,"corporation":false,"usgs":false,"family":"Avolio","given":"Meghan","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":680954,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beier, Claus","contributorId":187574,"corporation":false,"usgs":false,"family":"Beier","given":"Claus","email":"","affiliations":[],"preferred":false,"id":680955,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Carroll, Charles J. W.","contributorId":187575,"corporation":false,"usgs":false,"family":"Carroll","given":"Charles","email":"","middleInitial":"J. W.","affiliations":[],"preferred":false,"id":680956,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Collins, Scott L.","contributorId":71307,"corporation":false,"usgs":false,"family":"Collins","given":"Scott L.","affiliations":[{"id":7000,"text":"Department of Biology, University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":680957,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dukes, Jeffrey S.","contributorId":187576,"corporation":false,"usgs":false,"family":"Dukes","given":"Jeffrey","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":680958,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fraser, Lauchlan H.","contributorId":187577,"corporation":false,"usgs":false,"family":"Fraser","given":"Lauchlan","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":680959,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Griffin-Nolan, Robert J.","contributorId":187578,"corporation":false,"usgs":false,"family":"Griffin-Nolan","given":"Robert","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":680960,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hoover, David L. dlhoover@usgs.gov","contributorId":5843,"corporation":false,"usgs":true,"family":"Hoover","given":"David","email":"dlhoover@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":false,"id":680952,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Jentsch, Anke","contributorId":187579,"corporation":false,"usgs":false,"family":"Jentsch","given":"Anke","email":"","affiliations":[],"preferred":false,"id":680961,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Loik, Michael E.","contributorId":187580,"corporation":false,"usgs":false,"family":"Loik","given":"Michael","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":680962,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Phillips, Richard P.","contributorId":187581,"corporation":false,"usgs":false,"family":"Phillips","given":"Richard","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":680963,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Post, Alison K.","contributorId":187582,"corporation":false,"usgs":false,"family":"Post","given":"Alison","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":680964,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Sala, Osvaldo E.","contributorId":139047,"corporation":false,"usgs":false,"family":"Sala","given":"Osvaldo","email":"","middleInitial":"E.","affiliations":[{"id":12629,"text":"Arizona State University, Tempe, AZ (DETAIL TO BE ADDED)","active":true,"usgs":false}],"preferred":false,"id":680965,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Slette, Ingrid J.","contributorId":187583,"corporation":false,"usgs":false,"family":"Slette","given":"Ingrid","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":680966,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Yahdjian, Laura","contributorId":187584,"corporation":false,"usgs":false,"family":"Yahdjian","given":"Laura","email":"","affiliations":[],"preferred":false,"id":680967,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Smith, Melinda D.","contributorId":187585,"corporation":false,"usgs":false,"family":"Smith","given":"Melinda","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":680968,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70182091,"text":"sir20175009 - 2017 - Enhanced and updated spatially referenced statistical assessment of dissolved-solids load sources and transport in streams of the Upper Colorado River Basin","interactions":[],"lastModifiedDate":"2017-03-08T09:08:36","indexId":"sir20175009","displayToPublicDate":"2017-03-07T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5009","title":"Enhanced and updated spatially referenced statistical assessment of dissolved-solids load sources and transport in streams of the Upper Colorado River Basin","docAbstract":"Approximately 6.4 million tons of dissolved solids are discharged from the Upper Colorado River Basin (UCRB) to the Lower Colorado River Basin each year. This results in substantial economic damages, and tens of millions of dollars are spent annually on salinity control projects designed to reduce salinity loads in surface waters of the UCRB. Dissolved solids in surface water and groundwater have been studied extensively over the past century, and these studies have contributed to a conceptual understanding of sources and transport of dissolved solids. This conceptual understanding was incorporated into a Spatially Referenced Regressions on Watershed Attributes (SPARROW) model to examine sources and transport of dissolved solids in the UCRB. The results of this model were published in 2009. The present report documents the methods and data used to develop an updated dissolved-solids SPARROW model for the UCRB, and incorporates data defining current basin attributes not available in the previous model, including delineation of irrigated lands by irrigation type (sprinkler or flood irrigation), and calibration data from additional monitoring sites.
Dissolved-solids loads estimated for 312 monitoring sites were used to calibrate the SPARROW model, which predicted loads for each of 10,789 stream reaches in the UCRB. The calibrated model provided a good fit to the calibration data as evidenced by R2 and yield R2 values of 0.96 and 0.73, respectively, and a root-mean-square error of 0.47. The model included seven geologic sources that have estimated dissolved-solids yields ranging from approximately 1 to 45 tons per square mile (tons/mi2). Yields generated from irrigated agricultural lands are substantially greater than those from geologic sources, with sprinkler irrigated lands generating an average of approximately 150 tons/mi2 and flood irrigated lands generating between 770 and 2,300 tons/mi2 depending on underlying lithology. The coefficients estimated for six landscape transport characteristics that influence the delivery of dissolved solids from sources to streams, are consistent with the process understanding of dissolved-solids loading to streams in the UCRB.
Dissolved-solids loads and the proportion of those loads among sources in the entire UCRB as well as in major tributaries in the basin are reported, as are loads generated from irrigated lands, rangelands, Bureau of Land Management (BLM) lands, and grazing allotments on BLM lands. Model-predicted loads also are compared with load estimates from 1957 and 1991 at selected locations in three divisions of the UCRB. At the basin scale, the model estimates that 32 percent of the dissolved-solids loads are from irrigated agricultural land sources that compose less than 2 percent of the land area in the UCRB. This estimate is less than previously reported estimates of 40 to 45 percent of basin-scale dissolved-solids loads from irrigated agricultural land sources. This discrepancy could be a result of the implementation of salinity control projects in the basin. Notably, results indicate that the conversion of flood irrigated agricultural lands to sprinkler irrigated agricultural lands is a likely process contributing to the temporal decrease in dissolved-solids loads from irrigated lands.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175009","collaboration":"Prepared in cooperation with the Colorado River Basin Salinity Control Forum","usgsCitation":"Miller, M.P., Buto, S.G., Lambert, P.M., and Rumsey, C.A., 2017, Enhanced and updated spatially referenced statistical assessment of dissolved-solids load sources and transport in streams of the Upper Colorado River Basin: U.S. Geological Survey Scientific Investigations Report 2017–5009, 23 p., https://doi.org/10.3133/sir20175009.","productDescription":"vi, 23 p.","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-076357","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":438425,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LO3JV2","text":"USGS data release","linkHelpText":"SPARROW model input datasets and predictions of total dissolved loads in streams of the Upper Colorado River Basin watershed"},{"id":336947,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5009/coverthb.jpg"},{"id":336948,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5009/sir20175009.pdf","text":"Report","size":"5.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5009"},{"id":336949,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F76T0JT4","text":"USGS data release","description":"USGS data release","linkHelpText":"Catchment-flowline network and selected model inputs for an enhanced and updated spatially referenced statistical assessment of dissolved-solids load sources and transport in streams of the Upper Colorado River Basin"}],"country":"United States","otherGeospatial":"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 -111.500244140625,\n 35.53222622770337\n ],\n [\n -106.14990234375,\n 35.53222622770337\n ],\n [\n -106.14990234375,\n 43.27720532212024\n ],\n [\n -111.500244140625,\n 43.27720532212024\n ],\n [\n -111.500244140625,\n 35.53222622770337\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"When performing screening-level and baseline risk assessments, assessors usually compare estimated exposures of wildlife receptor species with toxicity reference values (TRVs). We modeled the exposure of American robins (Turdus migratorius) to 10 elements (As, Cd, Cr, Cu, Hg, Mn, Pb, Se, Zn, and V) in spring and early summer, a time when earthworms are the preferred prey. We calculated soil benchmarks associated with possible toxic effects to these robins from 6 sets of published TRVs. Several of the resulting soil screening-level benchmarks were inconsistent with each other and less than soil background concentrations. Accordingly, we examined the derivations of the TRVs as a possible source of error. In the case of V, a particularly toxic chemical compound (ammonium vanadate) containing V, not normally present in soil, had been used to estimate a TRV. In the cases of Zn and Cu, use of uncertainty values of 10 in estimating TRVs led to implausibly low soil screening values. In the case of Pb, a TRV was calculated from studies demonstrating reductions in egg production in Japanese quail (Coturnix coturnix japonica) exposed to Pb concentrations well below than those causing toxic effects in other species of birds. The results on quail, which were replicated in additional trials, are probably not applicable to other, unrelated species, although we acknowledge that only a small fraction of all species of birds has been tested. These examples underscore the importance of understanding the derivation and relevance of TRVs before selecting them for use in screening or in ecological risk assessment.
","language":"English","publisher":"Wiley","doi":"10.1002/ieam.1792","usgsCitation":"Beyer, W.N., and Sample, B.E., 2017, An evaluation of inorganic toxicity reference values for use in assessing hazards to American robins (Turdus migratorius): Integrated Environmental Assessment and Management, v. 13, no. 2, p. 352-359, https://doi.org/10.1002/ieam.1792.","productDescription":"8 p.","startPage":"352","endPage":"359","ipdsId":"IP-068783","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":336938,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"2","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-07","publicationStatus":"PW","scienceBaseUri":"58bfd4efe4b014cc3a3ba47f","contributors":{"authors":[{"text":"Beyer, W. Nelson 0000-0002-8911-9141 nbeyer@usgs.gov","orcid":"https://orcid.org/0000-0002-8911-9141","contributorId":3301,"corporation":false,"usgs":true,"family":"Beyer","given":"W.","email":"nbeyer@usgs.gov","middleInitial":"Nelson","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":680906,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sample, Bradley E.","contributorId":61135,"corporation":false,"usgs":true,"family":"Sample","given":"Bradley","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":680942,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70184252,"text":"sim3375 - 2017 - Bathymetry of Clear Creek Reservoir, Chaffee County, Colorado, 2016","interactions":[],"lastModifiedDate":"2017-03-07T11:02:19","indexId":"sim3375","displayToPublicDate":"2017-03-06T16:15:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3375","title":"Bathymetry of Clear Creek Reservoir, Chaffee County, Colorado, 2016","docAbstract":"To better characterize the water supply capacity of Clear Creek Reservoir, Chaffee County, Colorado, the U.S. Geological Survey, in cooperation with the Pueblo Board of Water Works and Colorado Mountain College, carried out a bathymetry survey of Clear Creek Reservoir. A bathymetry map of the reservoir is presented here with the elevation-surface area and the elevation-volume relations. The bathymetry survey was carried out June 6–9, 2016, using a man-operated boat-mounted, multibeam echo sounder integrated with a Global Positioning System and a terrestrial survey using real-time kinematic Global Navigation Satellite Systems. The two collected datasets were merged and imported into geographic information system software. The equipment and methods used in this study allowed water-resource managers to maintain typical reservoir operations, eliminating the need to empty the reservoir to carry out the survey.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3375","collaboration":"Prepared in cooperation with the Pueblo Board of Water Works and Colorado Mountain College","usgsCitation":"Kohn, M.S., Kinzel, P.J., and Mohrmann, J.S., 2017, Bathymetry of Clear Creek Reservoir, Chaffee County, Colorado, 2016: U.S. Geological Survey Scientific Investigations Map 3375, 1 sheet, https://doi.org/10.3133/sim3375","productDescription":"Map: 36.01 x 28.00 inches; Data Release, Read Me","onlineOnly":"Y","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":336842,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3375/sim3375.pdf","text":"Map","size":"9.37 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3375"},{"id":336843,"rank":3,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3375/sim3375_readme.txt","size":"8.00 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Denver Federal Center
Box 25046, Mail Stop 415
Denver, CO 80225
The U.S. Geological Survey completed a geology-based assessment of undiscovered, technically recoverable continuous petroleum resources in the Wolfcamp shale in the Midland Basin part of the Permian Basin Province of west Texas. This is the first U.S. Geological Survey evaluation of continuous resources in the Wolfcamp shale in the Midland Basin. Since the 1980s, the Wolfcamp shale in the Midland Basin has been part of the “Wolfberry” play. This play has traditionally been developed using vertical wells that are completed and stimulated in multiple productive stratigraphic intervals that include the Wolfcamp shale and overlying Spraberry Formation. Since the shift to horizontal wells targeting the organic-rich shale of the Wolfcamp, more than 3,000 horizontal wells have been drilled and completed in the Midland Basin Wolfcamp section. The U.S. Geological Survey assessed technically recoverable mean resources of 20 billion barrels of oil and 16 trillion cubic feet of associated gas in the Wolfcamp shale in the Midland Basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171013","usgsCitation":"Gaswirth, S.B., 2017, Assessment of continuous oil resources in the Wolfcamp shale of the Midland Basin, Permian Basin Province, Texas, 2016: U.S. Geological Survey Open File-Report 2017–1013, 14 p., https://doi.org/10.3133/ofr20171013.\n","productDescription":"14 p.","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-081644","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":336741,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1013/ofr20171013.pdf","text":"Report","size":"14.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1013"},{"id":336740,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1013/coverthb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Midland Basin, Permian Basin Province","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103,\n 30.5\n ],\n [\n -103,\n 33.8\n ],\n [\n -100,\n 33.8\n ],\n [\n -100,\n 30.5\n ],\n [\n -103,\n 30.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Center Director, USGS Central Energy Resources Science Center
Box 25046, Mail Stop 939
Denver, CO 80225
Groundwater is a major source of drinking water in Lycoming County and adjacent counties in north-central and northeastern Pennsylvania, which are largely forested and rural and are currently undergoing development for hydrocarbon gases. Water-quality data are needed for assessing the natural characteristics of the groundwater resource and the potential effects from energy and mineral extraction, timber harvesting, agriculture, sewage and septic systems, and other human influences.
This report, prepared in cooperation with Lycoming County, presents analytical data for groundwater samples from 75 domestic wells sampled throughout Lycoming County in June, July, and August 2014. The samples were collected using existing pumps and plumbing prior to any treatment and analyzed for physical and chemical characteristics, including nutrients, major ions, metals and trace elements, volatile organic compounds, gross-alpha particle and gross beta-particle activity, uranium, and dissolved gases, including methane and radon-222.
Results indicate groundwater quality generally met most drinking-water standards, but that some samples exceeded primary or secondary maximum contaminant levels (MCLs) for arsenic, iron, manganese, total dissolved solids (TDS), chloride, pH, bacteria, or radon-222. Arsenic concentrations were higher than the MCL of 10 micrograms per liter (µg/L) in 9 of the 75 (12 percent) well-water samples, with concentrations as high as 23.6 μg/L; arsenic concentrations were higher than the health advisory level (HAL) of 2 μg/L in 23 samples (31 percent). Total iron concentrations exceeded the secondary maximum contaminant level (SMCL) of 300 μg/L in 20 of the 75 samples. Total manganese concentrations exceeded the SMCL of 50 μg/L in 20 samples and the HAL of 300 μg/L in 2 of those samples. Three samples had chloride concentrations that exceeded the SMCL of 250 milligrams per liter (mg/L); two of those samples exceeded the SMCL of 500 mg/L for TDS. The pH ranged from 5.3 to 9.15 and did not meet the SMCL range of 6.5 to 8.5 in 22 samples, with 17 samples having a pH less than 6.5 and 8 samples having pH greater than 8.5. Generally, the samples that had elevated TDS, chloride, or arsenic concentrations had high pH.
Total coliform bacteria were detected in 39 of 75 samples (52 percent), with Escherichia coli detected in 10 of those 39 samples. Radon-222 activities ranged from non-detect to 7,420 picocuries per liter (pCi/L), with a median of 863 pCi/L, and exceeded the proposed drinking-water standard of 300 pCi/L in 50 (67 percent) of the 75 samples; radon-222 activities were higher than the alternative proposed standard of 4,000 pCi/L in 3 samples.
Water from 15 of 75 (20 percent) wells had concentrations of methane greater than the reporting level of 0.01 mg/L; detectable methane concentrations ranged from 0.04 to 16.8 mg/L. Two samples had methane concentrations (13.1 and 16.8 mg/L) exceeding the action level of 7 mg/L. Low levels of ethane (up to 0.12 mg/L) were present in the five samples with the highest methane concentrations (near or above 1 mg/L) that were analyzed for hydrocarbon compounds and isotopic composition. The isotopic composition of methane in four of these groundwater samples, from the Catskill and Lock Haven Formations and the Hamilton Group, have sample carbon isotopic ratio delta values (carbon-13/carbon-12) ranging from –42.36 to –36.08 parts per thousand (‰) and hydrogen isotopic ratio delta values (deuterium/protium) ranging from –212.0 to –188.4 ‰, which are consistent with the isotopic compositions reported for mud-gas logging samples from these geologic units and a thermogenic source of the methane. However, the isotopic composition and ratios of methane to ethane in a fifth sample indicate the methane in that sample may be of microbial origin that subsequently underwent oxidation. The fifth sample had the highest concentration of methane, 16.8 mg/L, with an carbon isotopic ratio delta values of -50.59 ‰ and a hydrogen isotopic ratio delta values of -209.7 ‰.
The six well-water samples with the highest methane concentrations also had among the highest pH values (8.25 to 9.15) and elevated concentrations of sodium, lithium, boron, fluoride, arsenic, and bromide. Relatively elevated concentrations of some other constituents, such as barium, strontium, and chloride, commonly were present in, but not limited to, those well-water samples with elevated methane.
Three of the six groundwater samples with the highest methane concentrations had chloride/bromide ratios that indicate mixing with a small amount of brine (0.02 percent or less) similar in composition to those reported at undetermined depth below the freshwater aquifer and for gas and oil well brines in Pennsylvania. The sample with the highest methane concentration and most other samples with low methane concentrations (less than about 1 mg/L) have chloride/bromide ratios that indicate predominantly anthropogenic sources of chloride, such as road-deicing salt, septic systems, and (or) animal waste. Brines that are naturally present may originate from deeper parts of the aquifer system, while anthropogenic sources are more likely to affect shallow groundwater because they occur on or near the land-surface.
The spatial distribution of groundwater compositions generally indicate that (1) uplands along the western border of Lycoming County usually have dilute, slightly acidic oxygenated, calcium-bicarbonate type waters; (2) intermediate altitudes or areas of carbonate bedrock usually have water of near neutral pH, with highest amounts of hardness (calcium and magnesium); (3) stream valleys, low elevations where groundwater may be discharging, and deep wells in uplands usually have water with pH values greater than 8 and highest arsenic, sodium, lithium, bromide concentrations. Geochemical modeling indicated that for samples with elevated pH, sodium, lithium, bromide, and alkalinity, the water chemistry could have resulted by dissolution of calcite (calcium carbonate) combined with cation-exchange and mixing with a small amount of brine. Through cation-exchange reactions between water and bedrock, which are equivalent to processes in a water softener, calcium ions released by calcite dissolution are exchanged for sodium ions on clay minerals. Thus, the assessment of groundwater quality in Lycoming County indicates groundwater is generally of good quality, but various parts of Lycoming County can have groundwater with low to moderate concentrations of methane and other constituents that appear in naturally present brine and produced waters from gas and oil wells at high concentrations.\"
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165143","collaboration":"Prepared in cooperation with the County of Lycoming, Pennsylvania","usgsCitation":"Gross, E.L., and Cravotta, C.A., III, 2017, Groundwater quality for 75 domestic wells in Lycoming County, Pennsylvania, 2014: U.S. Geological Survey Scientific Investigations Report 2016–5143, 74 p., https://doi.org/10.3133/sir20165143.","productDescription":"Report: xi, 74 p.; Appendixes 1-2","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-076071","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":336804,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5143/sir20165143_appendix2.xlsx","text":"Appendix 2 - Table 2-1 - ","size":"27.3 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016-5143","linkHelpText":"Spearman rank correlation coefficient (r) matrix for groundwater chemical data Lycoming County, 2014"},{"id":336802,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5143/coverthb.jpg"},{"id":336803,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5143/sir20165143_appendix1.xlsx","text":"Appendix 1 - Table 1-1 - ","size":"15.3 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016-5143","linkHelpText":" Compilation of data not available in the National Water Information System"},{"id":336722,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5143/sir20165143.pdf","text":"Report","size":"8.63 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5143"}],"country":"United States","state":"Pennsylvania","county":"Lycoming 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Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070
http://pa.water.usgs.gov/
In 2017, the U.S. Geological Survey (USGS) National Water-Quality Assessment (NAWQA) project is assessing stream quality in coastal California, United States. The USGS California Stream Quality Assessment (CSQA) will sample streams over most of the Central California Foothills and Coastal Mountains ecoregion (modified from Griffith and others, 2016), where rapid urban growth and intensive agriculture in the larger river valleys are raising concerns that stream health is being degraded. Findings will provide the public and policy-makers with information regarding which human and natural factors are the most critical in affecting stream quality and, thus, provide insights about possible approaches to protect the health of streams in the region.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173014","issn":"2327-6916 (print)","usgsCitation":"Van Metre, P.C., Egler, A.L., and May, Jason, 2017, The California Stream Quality Assessment: U.S. Geological Survey Fact Sheet 2017–3014, 2 p., https://doi.org/10.3133/fs20173014.","productDescription":"2 p.","additionalOnlineFiles":"N","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":336797,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3014/cover/coverthb.jpg"},{"id":336798,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3014/fs20173014.pdf","text":"Report","size":"733 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Fact Sheet 2017-3014"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.45312499999999,\n 42.00032514831621\n ],\n [\n -124.365234375,\n 41.86956082699455\n ],\n [\n -124.23339843749999,\n 41.623655390686395\n ],\n [\n -124.18945312500001,\n 41.27780646738183\n ],\n [\n -124.27734374999999,\n 40.896905775860006\n ],\n [\n -124.365234375,\n 40.697299008636755\n ],\n [\n -124.56298828125001,\n 40.329795743702064\n ],\n [\n -123.90380859374999,\n 39.740986355883564\n ],\n [\n -123.92578125,\n 39.30029918615029\n ],\n [\n -123.662109375,\n 38.788345355085625\n ],\n [\n -123.1787109375,\n 38.151837403006766\n ],\n [\n -123.02490234375,\n 37.85750715625203\n ],\n [\n -122.67333984374999,\n 37.666429212090605\n ],\n [\n -122.4755859375,\n 37.28279464911045\n ],\n [\n -122.32177734375,\n 37.020098201368114\n ],\n [\n -121.904296875,\n 36.82687474287728\n ],\n [\n -122.2119140625,\n 36.59788913307022\n ],\n [\n -121.86035156249999,\n 36.03133177633187\n ],\n [\n -121.33300781249999,\n 35.53222622770337\n ],\n [\n -121.00341796874999,\n 35.29943548054545\n ],\n [\n -120.80566406250001,\n 35.08395557927643\n ],\n [\n -120.7177734375,\n 34.52466147177172\n ],\n [\n -119.44335937499999,\n 34.161818161230386\n ],\n [\n -118.828125,\n 33.90689555128866\n ],\n [\n -118.47656249999999,\n 33.706062655101206\n ],\n [\n -118.0810546875,\n 33.65120829920497\n ],\n [\n -117.42187500000001,\n 33.15594830078649\n ],\n [\n -117.20214843749999,\n 32.45415593941475\n ],\n [\n -116.96044921875,\n 32.39851580247402\n ],\n [\n -116.08154296875001,\n 32.43561304116276\n ],\n [\n -115.4443359375,\n 32.54681317351514\n ],\n [\n -114.7412109375,\n 32.47269502206151\n ],\n [\n -114.54345703125,\n 33.04550781490999\n ],\n [\n -114.6533203125,\n 33.33970700424026\n ],\n [\n -114.49951171875,\n 33.54139466898275\n ],\n [\n -114.47753906249999,\n 33.797408767572485\n ],\n [\n -114.14794921875,\n 34.07086232376631\n ],\n [\n -113.92822265625,\n 34.34343606848294\n ],\n [\n -114.345703125,\n 34.470335121217474\n ],\n [\n -114.43359375,\n 34.77771580360469\n ],\n [\n -120.0146484375,\n 38.89103282648846\n ],\n [\n -120.1904296875,\n 42.09822241118974\n ],\n [\n -124.45312499999999,\n 42.00032514831621\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"National Water-Quality Assessment (NAWQA) Program
U.S. Geological Survey
413 National Center
12201 Sunrise Valley Drive
Reston, Virginia 20192
http://water.usgs.gov/nawqa/
Ontogenetic niche shifts (ONS) are important drivers of population and community dynamics, but they can be difficult to identify for species with prolonged larval or juvenile stages, or for species that inhabit continuous habitats. Most studies of ONS focus on single transitions among discrete habitat patches at local scales. However, for species with long larval or juvenile periods, affinity for particular locations within connected habitat networks may differ among cohorts. The resulting spatial patterns of distribution can result from a combination of landscape-scale habitat structure, position of a habitat patch within a network, and local habitat characteristics—all of which may interact and change as individuals grow. We estimated such spatial ONS for spring salamanders (Gyrinophilus porphyriticus), which have a larval period that can last 4 years or more. Using mixture models to identify larval cohorts from size frequency data, we fit occupancy models for each age class using two measures of the branching structure of stream networks and three measures of stream network position. Larval salamander cohorts showed different preferences for the position of a site within the stream network, and the strength of these responses depended on the basin-wide spatial structure of the stream network. The isolation of a site had a stronger effect on occupancy in watersheds with more isolated headwater streams, while the catchment area, which is associated with gradients in stream habitat, had a stronger effect on occupancy in watersheds with more paired headwater streams. Our results show that considering the spatial structure of habitat networks can provide new insights on ONS in long-lived species.
","language":"English","publisher":"Ecological Society of America","publisherLocation":"Washington, D.C.","doi":"10.1002/ecs2.1662","usgsCitation":"Fields, W.R., Grant, E., and Lowe, W.H., 2017, Detecting spatial ontogenetic niche shifts in complex dendritic ecological networks: Ecosphere, v. 8, no. 2, e01662: 10 p., https://doi.org/10.1002/ecs2.1662.","productDescription":"e01662: 10 p.","ipdsId":"IP-069867","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":470024,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1662","text":"Publisher Index Page"},{"id":336846,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","otherGeospatial":"Shenandoah National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n 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Thelebolales) are closely related groups of globally occurring soil-associated fungi. Recently, these genera of fungi have received attention because a newly identified species, Pseudogymnoascus (initially classified as Geomyces) destructans, was discovered in association with significant and unusual mortality of hibernating bats in North America (Blehert et al. 2009; Gargas et al. 2009; Minnis and Linder 2013). This emergent disease called bat white-nose syndrome (WNS), has since caused drastic declines in populations of hibernating bats in the United States and Canada (Turner, Reeder, and Coleman 2011; Thogmartin et al. 2012) and threatens some species with regional extinction (Frick et al. 2010; Langwig et al. 2012; Thogmartin et al. 2013). As primary predators of insects and keystone species for cave ecosystems, the loss of bats due to WNS has important economic and ecological implications.
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