The U.S. Geological Survey began collecting streamflow data, in cooperation with the North Dakota State Water Commission, on the Souris River in and near Minot, North Dakota, in April 1903. The gage was started up to better understand the water resources available in North Dakota. Currently (2016), water availability is still important as well as the flood monitoring and forecasting that has become an important component of this gage. Gage-height and streamflow data for the Souris River in and near Minot have been collected at five different streamgage locations during the years. This fact sheet describes the history of streamgaging (locations, gage-height data, and streamflow data) and flooding on the Souris River in and near Minot since 1903.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163061","usgsCitation":"Baker, K.K., and Robinson, S.M., History of U.S. Geological Survey streamgaging on the Souris River in and near Minot, North Dakota, 1903–2016: U.S. Geological Survey Fact Sheet 2016–3061, 6 p., https://dx.doi.org/10.3133/fs20163061.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"Y","ipdsId":"IP-076990","costCenters":[{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":328296,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3061/fs20163061.pdf","text":"Fact Sheet","size":"7.85 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016–3061"},{"id":328295,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3061/coverthb.jpg"}],"country":"United States","state":"North Dakota","city":"Minot","otherGeospatial":"Souris River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -101.42475128173828,\n 48.16104745279183\n ],\n [\n -101.42475128173828,\n 48.27908037971334\n ],\n [\n -101.17755889892578,\n 48.27908037971334\n ],\n [\n -101.17755889892578,\n 48.16104745279183\n ],\n [\n -101.42475128173828,\n 48.16104745279183\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, North Dakota Water Science Center
U.S. Geological Survey
821 E Interstate Ave
Bismarck, ND 58503
Michigan faces many challenges related to water resources, including flooding, drought, water-quality degradation and impairment, varying water availability, watershed-management issues, stormwater management, aquatic-ecosystem impairment, and invasive species. Michigan’s water resources include approximately 36,000 miles of streams, over 11,000 inland lakes, 3,000 miles of shoreline along the Great Lakes (MDEQ, 2016), and groundwater aquifers throughout the State.
The U.S. Geological Survey (USGS) works in cooperation with local, State, and other Federal agencies, as well as tribes and universities, to provide scientific information used to manage the water resources of Michigan. To effectively assess water resources, the USGS uses standardized methods to operate streamgages, water-quality stations, and groundwater stations. The USGS also monitors water quality in lakes and reservoirs, makes periodic measurements along rivers and streams, and maintains all monitoring data in a national, quality-assured, hydrologic database.
The USGS in Michigan investigates the occurrence, distribution, quantity, movement, and chemical and biological quality of surface water and groundwater statewide. Water-resource monitoring and scientific investigations are conducted statewide by USGS hydrologists, hydrologic technicians, biologists, and microbiologists who have expertise in data collection as well as various scientific specialties. A support staff consisting of computer-operations and administrative personnel provides the USGS the functionality to move science forward. Funding for USGS activities in Michigan comes from local and State agencies, other Federal agencies, direct Federal appropriations, and through the USGS Cooperative Matching Funds, which allows the USGS to partially match funding provided by local and State partners.
This fact sheet provides an overview of the USGS current (2016) capabilities to monitor and study Michigan’s vast water resources. More information regarding projects by the Michigan Water Science Center (MI WSC) is available at http://mi.water.usgs.gov/.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163064","usgsCitation":"Giesen, J.A., and Givens, C.E., 2016, Science center capabilities to monitor and investigate Michigan's Water Resources, 2016: U.S. Geological Survey Fact Sheet 2016-3064, 6 p., https://dx.doi.org/10.3133/fs20163064.","productDescription":"6 p.","onlineOnly":"Y","ipdsId":"IP-070531","costCenters":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"links":[{"id":328222,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3064/coverthb.jpg"},{"id":328223,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3064/fs20163064.pdf","text":"Report","size":"13 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016-3064"}],"country":"United 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\"}}]}","contact":"Director, Michigan-Ohio Water Science Center
U.S. Geological Survey
6520 Mercantile Way
Suite 5
Lansing, MI 48911
http://mi.water.usgs.gov/
Or
Director, Ohio Water Science Center
U.S. Geological Survey
6480 Doubletree Ave
Columbus, OH 43229
http://oh.water.usgs.gov/
Marked uncertainties persist regarding the climatic evolution of the Mediterranean region during the Holocene. For instance, whether moisture availability gradually decreased, remained relatively constant, or increased during the last 7000 years remains a matter of debate. To assess Holocene limnology, hydrology and moisture dynamics, the coastal lakes Lago Preola and Gorgo Basso, located in southwestern Sicily, were investigated through several stratigraphic analyses of ostracodes, including multivariate analyses of assemblages, transfer functions of salinity, and biochemical analyses of valves (Sr/Ca, δ18O and δ13C). During the early Holocene, the Gorgo Basso and Lago Preola ostracode records are similar. After an initial period of moderate salinity (1690–6100 mg/l from ca. 10,000–8190 cal yr BP), syndepositional or diagenetic dissolution of ostracode valves suggests that salinity declined to <250 mg/L from ca. 8190 to 7000 cal yr BP at both sites. After ca. 6250 cal yr BP, the ostracode records are strikingly different. Lago Preola became much more saline, with paleosalinity values that ranged from 2270 to about 24,420 mg/L. We suggest that Lago Preola's change from a freshwater to mesosaline lake at about 6250 cal yr BP was related to sea level rise and resulting intrusion of seawater-influenced groundwater. In contrast, Gorgo Basso remained a freshwater lake. The salinity of Gorgo Basso declined somewhat after 6250 cal yr BP, in comparison to the early Holocene, ranging from about 550 to 1680 mg/L. Cypria ophtalmica, a species capable of rapid swimming and flourishing in waters with low dissolved oxygen levels, became dominant at approximately the time when Greek civilization took root in Sicily (2600 cal yr BP), and it completely dominates the record during Roman occupation (roughly 2100 to 1700 cal yr BP). These freshwater conditions at Gorgo Basso suggest high effective moisture when evergreen olive-oak forests collapsed in response to increased Greco-Roman land use and fire. Ostracode valve geochemistry (Sr/Ca, δ18O) suggests significant changes in early vs. late Holocene hydrochemistry, either as changes in salinity or in the seasonality of precipitation. Harmonizing the autecological and geochemical data from Gorgo Basso suggests the latter was more likely, with relatively more late Holocene precipitation falling during the spring, summer, and fall, than winter compared to the early Holocene. Our ostracode-inferred paleosalinity data indicate that moisture availability did not decline during the late Holocene in the central Mediterranean region. Instead, moisture availability was lowest during the early Holocene, and most abundant during the late Holocene.
","language":"English","publisher":"Pergamon Press","doi":"10.1016/j.quascirev.2016.08.013","usgsCitation":"Curry, B., Henne, P., Mezquita-Joanes, F., Marrone, F., Pieri, V., La Mantia, T., Calo, C., and Tinner, W., 2016, Holocene paleoclimate inferred from salinity histories of adjacent lakes in southwestern Sicily (Italy): Quaternary Science Reviews, v. 150, p. 67-83, https://doi.org/10.1016/j.quascirev.2016.08.013.","productDescription":"17 p.","startPage":"67","endPage":"83","ipdsId":"IP-070996","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":328313,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Italy","state":"Trapani Province","otherGeospatial":"Riserva Naturale Integrale Lago Preola e Gorghi Tondi, Sicily","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 12.6,\n 37.6\n ],\n [\n 12.6,\n 37.64\n ],\n [\n 12.66,\n 37.64\n ],\n [\n 12.66,\n 37.6\n ],\n [\n 12.6,\n 37.6\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"150","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57d13a3de4b0571647cf8ddc","contributors":{"authors":[{"text":"Curry, B Brandon","contributorId":174032,"corporation":false,"usgs":false,"family":"Curry","given":"B Brandon","affiliations":[{"id":27342,"text":"Illinois State Geological Survey, Prairie Research Institute, University of Illinois at Urbana-Champaign, IL, USA","active":true,"usgs":false}],"preferred":false,"id":647000,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Henne, Paul D. 0000-0003-1211-5545 phenne@usgs.gov","orcid":"https://orcid.org/0000-0003-1211-5545","contributorId":169166,"corporation":false,"usgs":true,"family":"Henne","given":"Paul D.","email":"phenne@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":646999,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mezquita-Joanes, Francesc","contributorId":174033,"corporation":false,"usgs":false,"family":"Mezquita-Joanes","given":"Francesc","email":"","affiliations":[{"id":27343,"text":"Department of Microbiology and Ecology/ICBiBE, University of Valencia, Spain","active":true,"usgs":false}],"preferred":false,"id":647001,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Marrone, Federico","contributorId":174034,"corporation":false,"usgs":false,"family":"Marrone","given":"Federico","email":"","affiliations":[{"id":27344,"text":"Universita' degli Studi di Palermo, Sicily, Italy","active":true,"usgs":false}],"preferred":false,"id":647002,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pieri, Valentina","contributorId":174035,"corporation":false,"usgs":false,"family":"Pieri","given":"Valentina","email":"","affiliations":[{"id":27345,"text":"Royal Belgian Institure of Natural Sciences, Brussels, Belgium","active":true,"usgs":false}],"preferred":false,"id":647005,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"La Mantia, Tommaso","contributorId":169175,"corporation":false,"usgs":false,"family":"La Mantia","given":"Tommaso","email":"","affiliations":[{"id":25431,"text":"University of Palermo","active":true,"usgs":false}],"preferred":false,"id":647003,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Calo, Camilla","contributorId":174036,"corporation":false,"usgs":false,"family":"Calo","given":"Camilla","email":"","affiliations":[{"id":27346,"text":"Institute of Plant Sciences & Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland","active":true,"usgs":false}],"preferred":false,"id":647006,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Tinner, Willy 0000-0001-7352-0144","orcid":"https://orcid.org/0000-0001-7352-0144","contributorId":169167,"corporation":false,"usgs":false,"family":"Tinner","given":"Willy","email":"","affiliations":[{"id":25430,"text":"University of Bern","active":true,"usgs":false}],"preferred":false,"id":647004,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70176625,"text":"70176625 - 2016 - Submarine landslides in Arctic sedimentation: Canada Basin","interactions":[],"lastModifiedDate":"2017-06-29T11:56:00","indexId":"70176625","displayToPublicDate":"2016-09-06T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Submarine landslides in Arctic sedimentation: Canada Basin","docAbstract":"Canada Basin of the Arctic Ocean is the least studied ocean basin in the World. Marine seismic field \nprograms were conducted over the past 6 years using Canadian and American icebreakers. These expeditions \nacquired more than 14,000 line-km of multibeam bathymetric and multi-channel seismic reflection data \nover abyssal plain, continental rise and slope regions of Canada Basin; areas where little or no \nseismic reflection data existed previously. Canada Basin is a turbidite-filled basin with flat-lying \nreflections correlateable over 100s of km. For the upper half of the sedimentary succession, evidence \nof sedimentary processes other than turbidity current deposition is rare. The Canadian Archipelago \nand Beaufort Sea margins host stacked mass transport deposits from which many of these turbidites \nappear to derive. The stratigraphic succession of the MacKenzie River fan is dominated by mass \ntransport deposits; one such complex is in excess of 132,000 km2 in area and underlies much of \nthe southern abyssal plain. The modern seafloor is also scarred with escarpments and mass failure \ndeposits; evidence that submarine landsliding is an ongoing process. In its latest phase of \ndevelopment, Canada Basin is geomorphologically confined with stable oceanographic structure, \nresulting in restricted depositional/reworking processes. The sedimentary record, therefore, \nunderscores the significance of mass-transport processes in providing sediments to oceanic abyssal \nplains as few other basins are able to do.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Submarine mass movements and their consequences","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Springer","publisherLocation":"Amsterdam, Netherlands","doi":"10.1007/978-94-007-2162-3_13","usgsCitation":"Mosher, D.C., Shimeld, J., Hutchinson, D.R., Lebedova-Ivanova, N., and Chapman, C., 2016, Submarine landslides in Arctic sedimentation: Canada Basin, chap. of Submarine mass movements and their consequences, v. 31, p. 147-157, https://doi.org/10.1007/978-94-007-2162-3_13.","productDescription":"11 p.","startPage":"147","endPage":"157","ipdsId":"IP-029492","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":329010,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alaska","otherGeospatial":"Canada Basin of the Arctic Ocean","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -132.451171875,\n 70.11048478105927\n ],\n [\n -136.40625,\n 69.56522590149099\n ],\n [\n -139.74609375,\n 69.80930869552193\n ],\n [\n -151.083984375,\n 70.9883492241249\n ],\n [\n -153.544921875,\n 73.84928645675248\n ],\n [\n -155.390625,\n 76.28954161916205\n ],\n [\n -150.46875,\n 77.44694030325893\n ],\n [\n -146.95312499999997,\n 78.2960438968259\n ],\n [\n -140.537109375,\n 78.59529919212493\n ],\n [\n -131.8359375,\n 78.69910592550542\n ],\n [\n -123.48632812499999,\n 78.260332194717\n ],\n [\n -118.564453125,\n 77.78619050110466\n ],\n [\n -124.365234375,\n 76.16399261609192\n ],\n [\n -124.98046874999999,\n 74.68325030051861\n ],\n [\n -125.68359374999999,\n 73.25204504887357\n ],\n [\n -127.001953125,\n 71.38514208411495\n ],\n [\n -127.61718749999999,\n 70.78690984117928\n ],\n [\n -132.451171875,\n 70.11048478105927\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"31","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2011-09-15","publicationStatus":"PW","scienceBaseUri":"57f7c657e4b0bc0bec09c90b","contributors":{"authors":[{"text":"Mosher, David C.","contributorId":66118,"corporation":false,"usgs":false,"family":"Mosher","given":"David","email":"","middleInitial":"C.","affiliations":[{"id":18105,"text":"University of New Hampshire, Durham","active":true,"usgs":false}],"preferred":false,"id":649705,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shimeld, John","contributorId":146869,"corporation":false,"usgs":false,"family":"Shimeld","given":"John","affiliations":[],"preferred":false,"id":649706,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hutchinson, Deborah R. 0000-0002-2544-5466 dhutchinson@usgs.gov","orcid":"https://orcid.org/0000-0002-2544-5466","contributorId":521,"corporation":false,"usgs":true,"family":"Hutchinson","given":"Deborah","email":"dhutchinson@usgs.gov","middleInitial":"R.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":649707,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lebedova-Ivanova, N","contributorId":120457,"corporation":false,"usgs":true,"family":"Lebedova-Ivanova","given":"N","email":"","affiliations":[],"preferred":false,"id":649708,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chapman, C.","contributorId":16951,"corporation":false,"usgs":true,"family":"Chapman","given":"C.","affiliations":[],"preferred":false,"id":649709,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70178585,"text":"70178585 - 2016 - A python framework for environmental model uncertainty analysis","interactions":[],"lastModifiedDate":"2016-11-30T11:44:14","indexId":"70178585","displayToPublicDate":"2016-09-03T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1551,"text":"Environmental Modelling and Software","active":true,"publicationSubtype":{"id":10}},"title":"A python framework for environmental model uncertainty analysis","docAbstract":"We have developed pyEMU, a python framework for Environmental Modeling Uncertainty analyses, open-source tool that is non-intrusive, easy-to-use, computationally efficient, and scalable to highly-parameterized inverse problems. The framework implements several types of linear (first-order, second-moment (FOSM)) and non-linear uncertainty analyses. The FOSM-based analyses can also be completed prior to parameter estimation to help inform important modeling decisions, such as parameterization and objective function formulation. Complete workflows for several types of FOSM-based and non-linear analyses are documented in example notebooks implemented using Jupyter that are available in the online pyEMU repository. Example workflows include basic parameter and forecast analyses, data worth analyses, and error-variance analyses, as well as usage of parameter ensemble generation and management capabilities. These workflows document the necessary steps and provides insights into the results, with the goal of educating users not only in how to apply pyEMU, but also in the underlying theory of applied uncertainty quantification.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2016.08.017","usgsCitation":"White, J.T., Fienen, M., and Doherty, J.E., 2016, A python framework for environmental model uncertainty analysis: Environmental Modelling and Software, v. 85, p. 217-228, https://doi.org/10.1016/j.envsoft.2016.08.017.","productDescription":"12 p.","startPage":"217","endPage":"228","ipdsId":"IP-077464","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":331312,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"85","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"583ff34ee4b04fc80e437264","contributors":{"authors":[{"text":"White, Jeremy T. 0000-0002-4950-1469 jwhite@usgs.gov","orcid":"https://orcid.org/0000-0002-4950-1469","contributorId":167708,"corporation":false,"usgs":true,"family":"White","given":"Jeremy","email":"jwhite@usgs.gov","middleInitial":"T.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":654466,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fienen, Michael N. 0000-0002-7756-4651 mnfienen@usgs.gov","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":177065,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael N.","email":"mnfienen@usgs.gov","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":false,"id":654467,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Doherty, John E.","contributorId":8817,"corporation":false,"usgs":false,"family":"Doherty","given":"John","email":"","middleInitial":"E.","affiliations":[{"id":7046,"text":"Watermark Numerical Computing","active":true,"usgs":false}],"preferred":false,"id":654468,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70176247,"text":"70176247 - 2016 - Sex differences in contaminant concentrations of fish: a synthesis","interactions":[],"lastModifiedDate":"2018-08-08T10:18:06","indexId":"70176247","displayToPublicDate":"2016-09-03T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5200,"text":"Biology of Sex Differences","active":true,"publicationSubtype":{"id":10}},"title":"Sex differences in contaminant concentrations of fish: a synthesis","docAbstract":"Comparison of whole-fish polychlorinated biphenyl (PCB) and total mercury (Hg) concentrations in mature males with those in mature females may provide insights into sex differences in behavior, metabolism, and other physiological processes. In eight species of fish, we observed that males exceeded females in whole-fish PCB concentration by 17 to 43%. Based on results from hypothesis testing, we concluded that these sex differences were most likely primarily driven by a higher rate of energy expenditure, stemming from higher resting metabolic rate (or standard metabolic rate (SMR)) and higher swimming activity, in males compared with females. A higher rate of energy expenditure led to a higher rate of food consumption, which, in turn, resulted in a higher rate of PCB accumulation. For two fish species, the growth dilution effect also made a substantial contribution to the sex difference in PCB concentrations, although the higher energy expenditure rate for males was still the primary driver. Hg concentration data were available for five of the eight species. For four of these five species, the ratio of PCB concentration in males to PCB concentration in females was substantially greater than the ratio of Hg concentration in males to Hg concentration in females. In sea lamprey (Petromyzon marinus), a very primitive fish, the two ratios were nearly identical. The most plausible explanation for this pattern was that certain androgens, such as testosterone and 11-ketotestosterone, enhanced Hg-elimination rate in males. In contrast, long-term elimination of PCBs is negligible for both sexes. According to this explanation, males ingest Hg at a higher rate than females, but also eliminate Hg at a higher rate than females, in fish species other than sea lamprey. Male sea lamprey do not possess either of the above-specified androgens. These apparent sex differences in SMRs, activities, and Hg-elimination rates in teleost fishes may also apply, to some degree, to higher vertebrates including humans. Our synthesis findings will be useful in: (1) developing sex-specific bioenergetics models for fish, (2) developing sex-specific risk assessment models for exposure of humans and wildlife to contaminants, and (3) refining Hg mass balance models for fish and higher vertebrates.","language":"English","publisher":"BioMed Central","doi":"10.1186/s13293-016-0090-x","usgsCitation":"Madenjian, C.P., Rediske, R.R., Krabbenhoft, D.P., Stapanian, M.A., Chernyak, S.M., and O'Keefe, J., 2016, Sex differences in contaminant concentrations of fish: a synthesis: Biology of Sex Differences, v. 7, p. 1-16, https://doi.org/10.1186/s13293-016-0090-x.","productDescription":"Article 42; 16 p.","startPage":"1","endPage":"16","ipdsId":"IP-076759","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":470590,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s13293-016-0090-x","text":"Publisher Index Page"},{"id":328230,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-02","publicationStatus":"PW","scienceBaseUri":"57cbe61be4b0f2f0cec372ad","contributors":{"authors":[{"text":"Madenjian, Charles P. 0000-0002-0326-164X cmadenjian@usgs.gov","orcid":"https://orcid.org/0000-0002-0326-164X","contributorId":2200,"corporation":false,"usgs":true,"family":"Madenjian","given":"Charles","email":"cmadenjian@usgs.gov","middleInitial":"P.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":648066,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rediske, Richard R.","contributorId":79053,"corporation":false,"usgs":true,"family":"Rediske","given":"Richard","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":648067,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Krabbenhoft, David P. 0000-0003-1964-5020 dpkrabbe@usgs.gov","orcid":"https://orcid.org/0000-0003-1964-5020","contributorId":1658,"corporation":false,"usgs":true,"family":"Krabbenhoft","given":"David","email":"dpkrabbe@usgs.gov","middleInitial":"P.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":648068,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stapanian, Martin A. 0000-0001-8173-4273 mstapanian@usgs.gov","orcid":"https://orcid.org/0000-0001-8173-4273","contributorId":3425,"corporation":false,"usgs":true,"family":"Stapanian","given":"Martin","email":"mstapanian@usgs.gov","middleInitial":"A.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":648069,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chernyak, Sergei M.","contributorId":98668,"corporation":false,"usgs":true,"family":"Chernyak","given":"Sergei","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":648070,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"O'Keefe, James P.","contributorId":99499,"corporation":false,"usgs":true,"family":"O'Keefe","given":"James P.","affiliations":[],"preferred":false,"id":648071,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70174994,"text":"sir20165107 - 2016 - Flood-inundation maps for the Green River in Colrain, Leyden, and Greenfield, Massachusetts, from U.S. Geological Survey streamgage 01170100 Green River near Colrain to the confluence with the Deerfield River","interactions":[],"lastModifiedDate":"2016-12-05T09:45:08","indexId":"sir20165107","displayToPublicDate":"2016-09-02T11:45:00","publicationYear":"2016","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":"2016-5107","title":"Flood-inundation maps for the Green River in Colrain, Leyden, and Greenfield, Massachusetts, from U.S. Geological Survey streamgage 01170100 Green River near Colrain to the confluence with the Deerfield River","docAbstract":"The U.S. Geological Survey developed flood elevations in cooperation with the Federal Emergency Management Agency for a 14.3-mile reach of the Green River in Colrain, Leyden, and Greenfield, Massachusetts, to assist landowners and emergency management workers to prepare for and recover from floods. The river reach extends from the U.S. Geological Survey Green River near Colrain, MA (01170100) streamgage downstream to the confluence with the Deerfield River. A series of seven digital flood inundation maps were developed for the upper 4.4 miles of the river reach downstream from the stream. Flood discharges corresponding to the 50-, 10-, 1-, and 0.2-percent annual exceedance probabilities were computed for the reach from updated flood-frequency analyses. These peak flows and the flood flows associated with the stages of 10.2, 12.4, and 14.4 feet (ft) at the Green River streamgage were routed through a one-dimensional step-backwater hydraulic model to obtain the corresponding peak water-surface elevations and to place the Tropical Storm Irene flood of August 28, 2011 (stage 13.97 ft), into historical context. The hydraulic model was calibrated by using the current (2015) stage-discharge relation at the U.S. Geological Survey Green River near Colrain, MA (01170100) streamgage and from documented high-water marks from the Tropical Storm Irene flood, which had a flow higher than a 0.2-percent annual exceedance probability flood discharge.
The hydraulic model was used to compute water-surface profiles for flood stages referenced to the streamgage and ranging from the 50-percent annual exceedance probability (bankfull flow) at 7.6 ft (439.8 ft above the North American Vertical Datum of 1988 [NAVD 88]) to 14.4 ft (446.7 ft NAVD 88), which exceeds the maximum recorded water level of 13.97 ft (Tropical Storm Irene) at the streamgage. The mapped stages of 7.6 to 14.4 ft were selected to match the stages for bankfull; the 50-, 10-, 1-, and 0.2-percent annual exceedance probabilities; incremental stages of 10.2 and 12.4 ft; and the maximum stage of the stage-discharge rating curve. The simulated water-surface profiles were combined with a geographic information system digital elevation model derived from light detection and ranging (lidar) data having a 0.5-ft vertical accuracy to create a set of flood-inundation maps.
The availability of the flood-inundation maps, combined with information regarding near real-time stage from U.S. Geological Survey Green River near Colrain, MA (01170100) streamgage, can provide emergency management personnel and residents with information that is critical for flood response activities, such as evacuations and road closures, and postflood recovery efforts. The flood-inundation maps are nonregulatory but provide Federal, State, and local agencies and the public with estimates of the potential extent of flooding during selected peak-flow events.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165107","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Flynn, R.H., Bent, G.C., and Lombard, P.J., 2016, Flood-inundation maps for the Green River in Colrain, Leyden, and Greenfield, Massachusetts, from U.S. Geological Survey streamgage 01170100 Green River near Colrain to the confluence with the Deerfield River (ver. 1.1, November 2016): U.S. Geological Survey Scientific Investigations Report 2016–5107, 18 p., appendixes, https://doi.org/10.3133/sir20165107.","productDescription":"Report: vi, 18 p.; Appendix 2; Application Site; Metadata; Spatial Data","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-062774","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":331081,"rank":8,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2016/5107/versionHist.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2016-5107"},{"id":331083,"rank":7,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sir/2016/5107/sir20165107_flood-inundation_gis.zip","text":"Flood Inundation GIS","size":"4.59 MB"},{"id":327919,"rank":6,"type":{"id":4,"text":"Application Site"},"url":"https://wimcloud.usgs.gov/apps/FIM/FloodInundationMapper.html","text":"Flood Inundation Mapper ","linkFileType":{"id":5,"text":"html"},"description":"SIR 2016-5107"},{"id":327920,"rank":4,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sir/2016/5107/sir20165107_appendix2_metadata.xml ","text":"Appendix 2 - ","size":"13.6 KB xml","description":"SIR 2016-5107","linkHelpText":"metadata"},{"id":327917,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5107/sir20165107.pdf","text":"Report","size":"1.21 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5107"},{"id":327918,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5107/sir20165107_appendix2_gis.zip","text":"Appendix 2 - ","size":"160 KB","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2016-5107 - Spatial Data","linkHelpText":"GIS"},{"id":327916,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5107/coverthb2.jpg"},{"id":331082,"rank":5,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sir/2016/5107/sir20165107_flood-inundation_metadata.xml","text":"Flood Inundation GIS ","size":"26 KB xml","linkHelpText":"metadata"}],"country":"United States","state":"Massachusetts","city":"Colrain, Greenfield, Leyden","otherGeospatial":"Green River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.55,\n 42.55\n ],\n [\n -72.55,\n 42.72\n ],\n [\n -72.7,\n 42.72\n ],\n [\n -72.7,\n 42.55\n ],\n [\n -72.55,\n 42.55\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: Originally posted September 2, 2016; Version 1.1: November 23, 2016","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Or visit our Web site at:
http://newengland.water.usgs.gov
A Presidential disaster was declared in northwestern Massachusetts, following flooding from tropical storm Irene on August 28, 2011. During the storm, 3 to 10 inches of rain fell on soils that were susceptible to flash flooding because of wet antecedent conditions. The gage height at one U.S. Geological Survey streamgage rose nearly 20 feet in less than 4 hours because of the combination of saturated soils and intense rainfall. On August 28, 2011, in the Deerfield and Hoosic River Basins in northwestern Massachusetts, new peaks of record were set at six of eight U.S. Geological Survey long-term streamgages with 46 to 100 years of record. Additionally, high-water marks were surveyed and indirect measurements of peak discharge were calculated at two discontinued streamgages in the Deerfield and Hoosic River Basins with 24 and 61 years of record, respectively. This data resulted in new historic peaks of record at the two discontinued streamgages from tropical storm Irene.
\nPeak flows that resulted from tropical storm Irene (August 28, 2011) were determined at the U.S. Geological Survey streamgages by using stage-discharge rating curves and indirect computation methods. For six streamgages, indirect computation methods were used to compute the peak flows. Peak flows from tropical storm Irene had annual exceedance probabilities (AEPs) that ranged from 5.4 percent to less than 0.2 percent at 10 streamgages in northwestern Massachusetts.
\nDischarges calculated for select AEPs as a part of this study were compared with discharges published for the same AEPs in the effective Federal Emergency Management Agency flood insurance studies (FISs) for communities in the study area. Discharges estimated for the 10-, 2-, 1-, and 0.2-percent AEPs at two streamgages on the main stem of the Deerfield River ranged from about 3 percent lower to 14 percent higher than discharges in the FISs. AEP discharges calculated for two streamgages on tributaries to the Deerfield River were 27 to 89 percent higher than the FISs. For the four streamgages in the Hoosic River Basin, the 10-, 2-, 1-, and 0.2-percent AEP discharges calculated ranged from about 33 percent lower to 5 percent higher than the FISs.
\nThe simulated 1-percent AEP discharge water-surface elevations (nonregulatory) from recent (2015–16) hydraulic models for river reaches in the study area, which include the Deerfield, Green, and North Rivers in the Deerfield River Basin and the Hoosic River in the Hoosic River Basin, were compared with water-surface profiles in the FISs. The water-surface elevation comparisons were generally done downstream and upstream from bridges, dams, and major tributaries. The simulated 1-percent AEP discharge water-surface elevations of the recent hydraulic studies averaged 2.2, 2.3, 0.3, and 0.7 ft higher than water-surface elevations in the FISs for the Deerfield, Green, North, and Hoosic Rivers, respectively. The differences in water-surface elevations between the recent (2015–16) hydraulic studies and the FISs likely are because of (1) improved land elevation data from light detection and ranging (lidar) data collected in 2012, (2) detailed surveying of hydraulic structures and cross sections throughout the river reaches in 2012–13 (reflecting structure and cross section changes during the last 30–35 years), (3) updated hydrology analyses (30–35 water years of additional peak flow data at streamgages), and (4) high-water marks from the 2011 tropical storm Irene flood being used for model calibration.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165027","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Bent, G.C., Olson, S.A., and Massey, A.J., 2016, Tropical storm Irene flood of August 2011 in northwestern Massachusetts: U.S. Geological Survey Scientific Investigations Report 2016–5027, 28 p., https://dx.doi.org/10.3133/sir20165027.","productDescription":"v, 28 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-067697","costCenters":[{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true}],"links":[{"id":327911,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5027/coverthb.jpg"},{"id":327912,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5027/sir20165027.pdf","text":"Report","size":"1.21 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5027"}],"country":"United States","state":"Massachusetts","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.61688232421875,\n 42.731378652044185\n ],\n [\n -72.60452270507812,\n 42.718263627309774\n ],\n [\n -72.58804321289061,\n 42.693539313660004\n ],\n [\n -72.55989074707031,\n 42.653656930109726\n ],\n [\n -72.55302429199219,\n 42.64153569439977\n ],\n [\n -72.5592041015625,\n 42.628906895633456\n ],\n [\n -72.55851745605469,\n 42.61122227199062\n ],\n [\n -72.56813049316406,\n 42.603641609996586\n ],\n [\n -72.57637023925781,\n 42.59606002548659\n ],\n [\n -72.58049011230469,\n 42.59454359788448\n ],\n [\n -72.57980346679686,\n 42.58493869951935\n ],\n [\n -72.57568359375,\n 42.578871685346364\n ],\n [\n -72.57431030273438,\n 42.560161341916064\n ],\n [\n -72.58323669433594,\n 42.54296310520116\n ],\n [\n -72.59078979492188,\n 42.52677220056902\n ],\n [\n -72.60246276855469,\n 42.50804623455747\n ],\n [\n -72.62718200683594,\n 42.50500906265383\n ],\n [\n -72.652587890625,\n 42.50956476517422\n ],\n [\n -72.6800537109375,\n 42.51766297209017\n ],\n [\n -72.69515991210938,\n 42.52879629320373\n ],\n [\n -72.70683288574219,\n 42.51766297209017\n ],\n [\n -72.71781921386719,\n 42.50754004948742\n ],\n [\n -72.74116516113281,\n 42.49387150323448\n ],\n [\n -72.78923034667969,\n 42.47817430242153\n ],\n [\n -72.80845642089844,\n 42.46753847696729\n ],\n [\n -72.82699584960938,\n 42.50500906265383\n ],\n [\n -72.84347534179688,\n 42.52069952914966\n ],\n [\n -72.85308837890624,\n 42.53638605645048\n ],\n [\n -72.89840698242188,\n 42.55055114674488\n ],\n [\n -72.90252685546875,\n 42.54448078729858\n ],\n [\n -72.90390014648438,\n 42.53739795519656\n ],\n [\n -72.94166564941406,\n 42.53638605645048\n ],\n [\n -72.94647216796874,\n 42.525760129656845\n ],\n [\n -72.96363830566406,\n 42.51968735985934\n ],\n [\n -72.97531127929688,\n 42.53588010092859\n ],\n [\n -72.9876708984375,\n 42.54852775918642\n ],\n [\n -73.00277709960938,\n 42.55560932868032\n ],\n [\n -73.01239013671875,\n 42.562184352321886\n ],\n [\n -73.02200317382812,\n 42.57027573801005\n ],\n [\n -73.02818298339844,\n 42.58645536081647\n ],\n [\n -73.04054260253906,\n 42.601619944327965\n ],\n [\n -73.04672241210938,\n 42.605157816197334\n ],\n [\n -73.06938171386719,\n 42.58696090638245\n ],\n [\n -73.06800842285156,\n 42.5798828953732\n ],\n [\n -73.06869506835938,\n 42.560161341916064\n ],\n [\n -73.05770874023438,\n 42.54144538620976\n ],\n [\n -73.05770874023438,\n 42.53486817758702\n ],\n [\n -73.07212829589844,\n 42.52677220056902\n ],\n [\n -73.10508728027344,\n 42.51158941527828\n ],\n [\n -73.11126708984375,\n 42.49690921618136\n ],\n [\n -73.14628601074219,\n 42.50500906265383\n ],\n [\n -73.15864562988281,\n 42.49944053092116\n ],\n [\n -73.16963195800781,\n 42.489820989777066\n ],\n [\n -73.18954467773438,\n 42.48830197960227\n ],\n [\n -73.21563720703125,\n 42.49032731830467\n ],\n [\n -73.22181701660156,\n 42.50905859240087\n ],\n [\n -73.22181701660156,\n 42.52829027619378\n ],\n [\n -73.21769714355469,\n 42.54448078729858\n ],\n [\n -73.21151733398436,\n 42.553586105099654\n ],\n [\n -73.20533752441406,\n 42.56370156708076\n ],\n [\n -73.19366455078125,\n 42.587971985214814\n ],\n [\n -73.201904296875,\n 42.58594981115061\n ],\n [\n -73.23211669921875,\n 42.58493869951935\n ],\n [\n -73.24928283691406,\n 42.5995982130586\n ],\n [\n -73.25889587402344,\n 42.5995982130586\n ],\n [\n -73.26301574707031,\n 42.58746644784856\n ],\n [\n -73.32069396972655,\n 42.59252163701558\n ],\n [\n -73.26507568359375,\n 42.74600367786244\n ],\n [\n -72.61688232421875,\n 42.731378652044185\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Or visit our Web site at:
http://newengland.water.usgs.gov
The vertical avoidance behavior of the tadpole madtom (Noturus gyrinus) exposed to environmentally relevant concentrations of the granular formulation of the lampricide Bayluscide® was evaluated. The lampricide formulation (3.2 percent active ingredient coated on a sand granule) is used to control larval sea lamprey populations in the Great Lakes. The tadpole madtom was chosen as a surrogate to the federally endangered northern madtom (Noturus stigmosus) based on similar life history characteristics and habitat requirements. Vertical avoidance of tadpole madtoms in response to the granular formulation was documented in clear Plexiglas columns (107 centimeters in height, 30.5 centimeters in diameter) for 1 hour after chemical application. Each avoidance trial produced data consisting of the number of tadpole madtoms avoiding the chemical at a given time. Based on the overall data, tadpole madtoms in treated columns were 11.7 times more likely to display avoidance compared to those in untreated controls. Results indicate that it is likely that northern madtoms will be able to detect and avoid Bayluscide® from granular applications if their response is similar to that of the tadpole madtom.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161130","usgsCitation":"Boogaard, M.A., Erickson, R.A., and Hubert, T.D, 2016, Evaluation of avoidance behavior of tadpole madtoms (Noturus gyrinus) as a surrogate for the endangered northern madtom (Noturus stigmosus) in response to granular Bayluscide: U.S. Geological Survey Open-File Report 2016‒1130, 6 p., https://dx.doi.org/10.3133/ofr20161130. ","productDescription":"Report: iv, 6 p. ","startPage":"1","endPage":"6","numberOfPages":"12","onlineOnly":"Y","ipdsId":"IP-075622","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":438554,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7K35RS7","text":"USGS data release","linkHelpText":"Evaluation of avoidance behavior of tadpole madtoms (Noturus gyrinus) as a surrogate to the endangered northern madtom (Noturus stigmosus) in response to granular Bayluscide&amp;amp;amp;reg;"},{"id":326363,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1130/coverthb.jpg"},{"id":326364,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1130/ofr20161130.pdf","text":"Report","size":"509 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1130"}],"country":"United States","otherGeospatial":"Great Lakes ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.935546875,\n 46.49839225859763\n ],\n [\n -82.880859375,\n 46.49839225859763\n ],\n [\n -81.82617187499999,\n 46.31658418182218\n ],\n [\n -80.2001953125,\n 46.07323062540838\n ],\n [\n -79.8046875,\n 45.55252525134013\n ],\n [\n -79.453125,\n 45.089035564831036\n ],\n [\n -79.8046875,\n 44.62175409623324\n ],\n [\n -80.5078125,\n 44.402391829093915\n ],\n [\n -81.03515625,\n 44.59046718130883\n ],\n [\n -81.4306640625,\n 44.18220395771566\n ],\n [\n -81.650390625,\n 43.73935207915473\n ],\n [\n -81.6943359375,\n 43.13306116240612\n ],\n [\n -82.08984375,\n 42.87596410238254\n ],\n [\n -82.2216796875,\n 42.68243539838623\n ],\n [\n -81.2548828125,\n 42.87596410238254\n ],\n [\n -80.5517578125,\n 42.84375132629021\n ],\n [\n -80.068359375,\n 43.197167282501276\n ],\n [\n -79.716796875,\n 43.67581809328344\n ],\n [\n -79.1455078125,\n 43.866218006556394\n ],\n [\n -77.87109375,\n 44.08758502824518\n ],\n [\n -77.2998046875,\n 44.05601169578525\n ],\n [\n -76.9482421875,\n 44.308126684886126\n ],\n [\n -75.9814453125,\n 44.308126684886126\n ],\n [\n -75.7177734375,\n 43.929549935614595\n ],\n [\n -76.5087890625,\n 43.32517767999296\n ],\n [\n -77.51953125,\n 43.068887774169625\n ],\n [\n -78.92578124999999,\n 43.13306116240612\n ],\n [\n -78.75,\n 42.65012181368025\n ],\n [\n -79.40917968749999,\n 42.09822241118974\n ],\n [\n -81.03515625,\n 41.672911819602085\n ],\n [\n -81.8701171875,\n 41.3108238809182\n ],\n [\n -82.8369140625,\n 41.37680856570233\n ],\n [\n -83.4521484375,\n 41.57436130598913\n ],\n [\n -83.4521484375,\n 42.22851735620852\n ],\n [\n -82.6611328125,\n 42.90816007196054\n ],\n [\n -82.7490234375,\n 43.67581809328344\n ],\n [\n -83.0126953125,\n 43.8028187190472\n ],\n [\n -83.671875,\n 43.58039085560786\n ],\n [\n -84.111328125,\n 43.83452678223684\n ],\n [\n -83.75976562499999,\n 44.33956524809713\n ],\n [\n -83.5400390625,\n 44.84029065139799\n ],\n [\n -83.84765625,\n 45.24395342262324\n ],\n [\n -84.462890625,\n 45.521743896993634\n ],\n [\n -84.9462890625,\n 45.36758436884978\n ],\n [\n -85.95703125,\n 44.5278427984555\n ],\n [\n -86.17675781249999,\n 44.02442151965934\n ],\n [\n -86.17675781249999,\n 43.51668853502909\n ],\n [\n -85.9130859375,\n 42.84375132629021\n ],\n [\n -85.9130859375,\n 41.96765920367816\n ],\n [\n -86.4404296875,\n 41.64007838467894\n ],\n [\n -87.3193359375,\n 41.37680856570233\n ],\n [\n -88.06640625,\n 41.64007838467894\n ],\n [\n -88.06640625,\n 42.45588764197166\n ],\n [\n -88.11035156249999,\n 43.42100882994726\n ],\n [\n -88.11035156249999,\n 43.89789239125797\n ],\n [\n -88.41796875,\n 43.67581809328344\n ],\n [\n -88.76953125,\n 44.24519901522129\n ],\n [\n -88.2861328125,\n 45.089035564831036\n ],\n [\n -87.802734375,\n 45.55252525134013\n ],\n [\n -87.275390625,\n 46.042735653846506\n ],\n [\n -86.572265625,\n 46.31658418182218\n ],\n [\n -86.17675781249999,\n 46.31658418182218\n ],\n [\n -85.4296875,\n 46.34692761055676\n ],\n [\n -85.078125,\n 46.46813299215554\n ],\n [\n -83.935546875,\n 46.49839225859763\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Environmental Sciences Center
U.S. Geological Survey
2630 Fanta Reed Road
La Crosse, WI 54603
http://www.umesc.usgs.gov/
The combined geothermal discharge from over 10,000 features in Yellowstone National Park (YNP) can be can be estimated from the Cl flux in the Madison, Yellowstone, Falls, and Snake Rivers. Over the last 30 years, the Cl flux in YNP Rivers has been calculated using discharge measurements and Cl concentrations determined in discrete water samples and it has been determined that approximately 12% of the Cl flux exiting YNP is from the Snake River. The relationship between electrical conductivity and concentrations of Cl and other geothermal solutes was quantified at a monitoring site located downstream from the thermal inputs in the Snake River. Beginning in 2012, continuous (15 min) electrical conductivity measurements have been made at the monitoring site. Combining continuous electrical conductivity and discharge data, the Cl and other geothermal solute fluxes were determined. The 2013–2015 Cl fluxes (5.3–5.8 kt/yr) determined using electrical conductivity are comparable to historical data. In addition, synoptic water samples and discharge data were obtained from sites along the Snake River under low-flow conditions of September 2014. The synoptic water study extended 17 km upstream from the monitoring site. Surface inflows were sampled to identify sources and to quantify solute loading. The Lewis River was the primary source of Cl, Na, K, Cl, SiO2, Rb, and As loads (50–80%) in the Snake River. The largest source of SO4 was from the upper Snake River (50%). Most of the Ca and Mg (50–55%) originate from the Snake Hot Springs. Chloride, Ca, Mg, Na, K, SiO2, F, HCO3, SO4, B, Li, Rb, and As behave conservatively in the Snake River, and therefore correlate well with conductivity (R2 ≥ 0.97).
","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2016.08.006","usgsCitation":"McCleskey, R.B., Lowenstern, J.B., Schaper, J., Nordstrom, D.K., Heasler, H.P., and Mahony, D., 2016, Geothermal solute flux monitoring and the source and fate of solutes in the Snake River, Yellowstone National Park, WY: Applied Geochemistry, v. 73, p. 142-156, https://doi.org/10.1016/j.apgeochem.2016.08.006.","productDescription":"15 p.","startPage":"142","endPage":"156","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-077823","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":470591,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.apgeochem.2016.08.006","text":"Publisher Index Page"},{"id":328226,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.52005767822266,\n 44.14218189856008\n ],\n [\n -110.52297592163086,\n 44.14698597918344\n ],\n [\n -110.52726745605469,\n 44.15215916724574\n ],\n [\n -110.53396224975586,\n 44.15388346250658\n ],\n [\n -110.54271697998047,\n 44.15634665405655\n ],\n [\n -110.54769515991211,\n 44.16275047090301\n ],\n [\n -110.55387496948242,\n 44.168784200722875\n ],\n [\n -110.56537628173828,\n 44.17691028989048\n ],\n [\n -110.58425903320312,\n 44.175925369020135\n ],\n [\n -110.64760208129883,\n 44.15573086580812\n ],\n [\n -110.66442489624023,\n 44.14489194082416\n ],\n [\n -110.66116333007812,\n 44.13996449871928\n ],\n [\n -110.65326690673828,\n 44.14168915023695\n ],\n [\n -110.63764572143553,\n 44.14957262989197\n ],\n [\n -110.6022834777832,\n 44.16250418310723\n ],\n [\n -110.5854606628418,\n 44.16582898159794\n ],\n [\n -110.57413101196289,\n 44.17026175476358\n ],\n [\n -110.56554794311523,\n 44.168291674483854\n ],\n [\n -110.55644989013672,\n 44.16275047090301\n ],\n [\n -110.55112838745116,\n 44.15696243587891\n ],\n [\n -110.54546356201172,\n 44.1510506651172\n ],\n [\n -110.52194595336914,\n 44.140334071142775\n ],\n [\n -110.52005767822266,\n 44.14218189856008\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"73","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57ca94aae4b0f2f0cec194e6","chorus":{"doi":"10.1016/j.apgeochem.2016.08.006","url":"http://dx.doi.org/10.1016/j.apgeochem.2016.08.006","publisher":"Elsevier BV","authors":"McCleskey R. Blaine, Lowenstern Jacob B., Schaper Jonas, Nordstrom D. Kirk, Heasler Henry P., Mahony Dan","journalName":"Applied Geochemistry","publicationDate":"10/2016"},"contributors":{"authors":[{"text":"McCleskey, R. 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The NAAMP is a longterm monitoring program in which observers collect anuran calling observation data at fixed locations along random roadside routes. We assessed occupancy trends for 14 species. We found weak evidence for a general regional pattern of decline in calling anurans within breeding habitats along roads in the southeastern USA over the last 13 y. Two species had positive regional trends with 95% posterior intervals that did not include zero (Hyla cinerea and Pseudacris crucifer). Five other species also showed an increasing trend, while eight species showed a declining trend, although 95% posterior intervals included zero. We also assessed state level trends for 107 species/state combinations. Of these, 14 showed a significant decline and 12 showed a significant increase in occupancy (i.e., credible intervals did not include zero for these 26 trends).
","language":"English","publisher":"Partners in Amphibian and Reptile Conservation","publisherLocation":"Texarkana, TX","usgsCitation":"Villena Carpio, O., Royle, J., Weir, L., Foreman, T.M., Gazenski, K.D., and Campbell Grant, E., 2016, Southeast regional and state trends in anuran occupancy from calling survey data (2001-2013) from the North American Amphibian Monitoring Program: Herpetological Conservation and Biology, v. 11, no. 2, p. 373-385.","startPage":"373","endPage":"385","numberOfPages":"13","ipdsId":"IP-073199","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":328215,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":328214,"type":{"id":15,"text":"Index Page"},"url":"https://www.herpconbio.org/contents_vol11_issue2.html"}],"country":"United States","state":"Florida, Georgia, Louisiana, Mississippi, North Carolina, South Carolina, Tennessee, 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This study employed Maxent modeling to investigate the geographic distribution of goitered gazelle, Gazella subgutturosa (Güldenstädt, 1780), in central Iran using uncorrelated variables at a spatial resolution of 250 m. We used spatial downscaling to downscale WorldClim data to 250-m resolution. We evaluated the sensitivity of the model to different grain and extent sizes from 250 m to 3 km. We compared the performance of the model at different scales using suitability indexes (AUC) and predicted habitat areas. Two models performed with AUC values higher than random (AUCun = 0.957, AUCpu = 0.953). The distribution of potential habitats at 250- m grid size was strongly influenced by bioclimatic data, vegetation type and density, and elevation. There were few spatial divergences between uncorrelated and pruned models. The mean AUC across eight different spatial scales ranged from 0.936 to 0.959. There was a significant negative correlation between grain size and AUC (R2 = 0.57). An increase in grain size increased the predicted habitat area. The extent size and AUC showed a positive correlation (R2 = 0.18). Predicted suitability habitat also decreased as extent size increased (R2 = 0.49). Spatial congruence AUC fluctuated within a small range and the maximum difference occurred between models of 1 × 1 and 2.5 × 2.5 km. These results showed that an increase in extent size is more accurate than an increase in grain size, and the maximum accuracy for predicting distribution of goitered gazelle in Iran was obtained if the grain size and extent size were 750 m.
","language":"English","publisher":"Tubitak","doi":"10.3906/zoo-1505-38","usgsCitation":"Khosravi, R., Hemami, M., Malekian, M., Flint, A.L., and Flint, L.E., 2016, Maxent modeling for predicting potential distribution of goitered gazelle in central Iran: the effect of extent and grain size on performance of the model: Turkish Journal of Zoology, v. 40, p. 574-585, https://doi.org/10.3906/zoo-1505-38.","productDescription":"12 p.","startPage":"574","endPage":"585","ipdsId":"IP-070743","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":470594,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.3906/zoo-1505-38","text":"External Repository"},{"id":357988,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Iran","volume":"40","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5bc032a7e4b0fc368eb53a6b","contributors":{"authors":[{"text":"Khosravi, Rasoul","contributorId":208380,"corporation":false,"usgs":false,"family":"Khosravi","given":"Rasoul","email":"","affiliations":[{"id":37792,"text":"Isfahan University of Technology, Iran","active":true,"usgs":false}],"preferred":false,"id":746911,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hemami, Mahmoud-Reza","contributorId":208381,"corporation":false,"usgs":false,"family":"Hemami","given":"Mahmoud-Reza","email":"","affiliations":[{"id":37792,"text":"Isfahan University of Technology, Iran","active":true,"usgs":false}],"preferred":false,"id":746912,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Malekian, Mansoureh","contributorId":208382,"corporation":false,"usgs":false,"family":"Malekian","given":"Mansoureh","email":"","affiliations":[{"id":37792,"text":"Isfahan University of Technology, Iran","active":true,"usgs":false}],"preferred":false,"id":746913,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Flint, Alan L. 0000-0002-5118-751X aflint@usgs.gov","orcid":"https://orcid.org/0000-0002-5118-751X","contributorId":1492,"corporation":false,"usgs":true,"family":"Flint","given":"Alan","email":"aflint@usgs.gov","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":746914,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Flint, Lorraine E. 0000-0002-7868-441X lflint@usgs.gov","orcid":"https://orcid.org/0000-0002-7868-441X","contributorId":1184,"corporation":false,"usgs":true,"family":"Flint","given":"Lorraine","email":"lflint@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":746910,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70199656,"text":"70199656 - 2016 - Shale-gas assessment: Comparison of gas-in-place versus performance-based approaches","interactions":[],"lastModifiedDate":"2018-09-24T11:51:06","indexId":"70199656","displayToPublicDate":"2016-09-01T11:51:01","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2832,"text":"Natural Resources Research","onlineIssn":"1573-8981","printIssn":"1520-7439","active":true,"publicationSubtype":{"id":10}},"title":"Shale-gas assessment: Comparison of gas-in-place versus performance-based approaches","docAbstract":"The recent interest in exploration for shale gas increases the demand for a reliable, compatible resource assessment. Many different assessment methods are used, commonly depending on types and quantity of data available, which may lead to significantly divergent results for the same shale-gas play. This study compares results obtained using performance-based and gas-in-place methodologies to assess a well-developed and active shale-gas play (Woodford Shale, Arkoma Basin, USA) and two untested, hypothetical shale-gas plays (Shublik and Brookian, Alaska North Slope, USA). Results show that the two assessment methods produce comparable results when assessment units are identically defined and similar geological constraints are used as input parameters. Inherent uncertainties are associated with both assessment methods, and these are related to aspects of shale-gas production that are not well understood. The performance-based method relies on decline trend analysis to generate distributions of estimated ultimate recovery (EUR), and uncertainty increases in cases of short production history. The gas-in-place method requires the application of a recovery factor to estimate technically recoverable resources, and both absolute values of recovery factors and their spatial variability are poorly documented, and therefore a source of uncertainty.
","language":"English","publisher":"Springer","doi":"10.1007/s11053-015-9283-y","usgsCitation":"Stueck, H., Houseknecht, D.W., Franke, D., Gautier, D., Bahr, A., and Ladage, S., 2016, Shale-gas assessment: Comparison of gas-in-place versus performance-based approaches: Natural Resources Research, v. 25, no. 3, p. 315-329, https://doi.org/10.1007/s11053-015-9283-y.","productDescription":"15 p.","startPage":"315","endPage":"329","ipdsId":"IP-068231","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":357673,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"25","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-04","publicationStatus":"PW","scienceBaseUri":"5bc032a7e4b0fc368eb53a6d","contributors":{"authors":[{"text":"Stueck, H.","contributorId":208137,"corporation":false,"usgs":false,"family":"Stueck","given":"H.","email":"","affiliations":[{"id":37755,"text":"Federal Institute for Geosciences and Natural Resources, 30655 Hanover, Germany","active":true,"usgs":false}],"preferred":false,"id":746086,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Houseknecht, David W. 0000-0002-9633-6910 dhouse@usgs.gov","orcid":"https://orcid.org/0000-0002-9633-6910","contributorId":645,"corporation":false,"usgs":true,"family":"Houseknecht","given":"David","email":"dhouse@usgs.gov","middleInitial":"W.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":746085,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Franke, D.","contributorId":208138,"corporation":false,"usgs":false,"family":"Franke","given":"D.","email":"","affiliations":[{"id":37755,"text":"Federal Institute for Geosciences and Natural Resources, 30655 Hanover, Germany","active":true,"usgs":false}],"preferred":false,"id":746087,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gautier, Donald L.","contributorId":208139,"corporation":false,"usgs":false,"family":"Gautier","given":"Donald L.","affiliations":[{"id":27856,"text":"USGS-retired","active":true,"usgs":false}],"preferred":false,"id":746088,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bahr, A.","contributorId":208140,"corporation":false,"usgs":false,"family":"Bahr","given":"A.","email":"","affiliations":[{"id":37755,"text":"Federal Institute for Geosciences and Natural Resources, 30655 Hanover, Germany","active":true,"usgs":false}],"preferred":false,"id":746089,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ladage, S.","contributorId":208141,"corporation":false,"usgs":false,"family":"Ladage","given":"S.","email":"","affiliations":[{"id":37755,"text":"Federal Institute for Geosciences and Natural Resources, 30655 Hanover, Germany","active":true,"usgs":false}],"preferred":false,"id":746090,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70209673,"text":"70209673 - 2016 - Highly conductive horizons in the Mesoproterozoic Belt-Purcell Basin: Sulfidic early basin strata as key markers of Cordilleran shortening and Eocene extension","interactions":[],"lastModifiedDate":"2020-04-21T14:45:32.418007","indexId":"70209673","displayToPublicDate":"2016-09-01T09:45:09","publicationYear":"2016","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Highly conductive horizons in the Mesoproterozoic Belt-Purcell Basin: Sulfidic early basin strata as key markers of Cordilleran shortening and Eocene extension","docAbstract":"We investigated the crustal structure of the central Mesoproterozoic Belt Basin in northwestern Montana and northern Idaho using a crustal resistivity section derived from a transect of new short- and long-period magnetotelluric (MT) stations. Two- and three-dimensional resistivity models were generated from these data in combination with data collected previously along three parallel short-period MT profiles and from EarthScope MT stations. The models were interpreted together with coincident deep seismic-reflection data collected during the Consortium for Continental Reflection Profiling (COCORP) program. The upper-crustal portion of the resistivity model correlates well with the mapped surface geology and reveals a three-layer resistivity stratigraphy, best expressed beneath the axis of the Libby syncline. Prominent features in the resistivity models are thick conductive horizons that serve as markers in reconstructing the disrupted basin stratigraphy. The uppermost unit (up to 5 km thick), consisting of all of the Belt Supergroup above the Prichard Formation, is highly resistive (1000–10,000 Ω·m) and has relatively low seismic layer velocities. The intermediate unit (up to 7 km thick) consists of the exposed Prichard Formation and 3+ km of stratigraphy below the deepest stratigraphic exposures of the unit. The intermediate unit has low to moderate resistivity (30–200 Ω·m), relatively high seismic velocities, and high seismic reflectivity, with the latter two characteristics resulting from an abundance of thick syndepositional mafic sills. The lowest unit (5–10 km thick) is nowhere exposed but underlies the intermediate unit and has very high conductivity (4–8 Ω·m) and intermediate seismic velocities. This 17–22-km-thick three-layer stratigraphy is repeated below the Libby syncline, with a base at ~37 km depth. Seismic layer velocities indicate high mantle-like velocities below 37 km beneath the Libby syncline. The continuous high-conductivity layer in the lower repeated section is apparently displaced ~26 km to the east above a low-angle normal fault inferred to be the downdip continuation of the Eocene, east-dipping Purcell Trench detachment fault. Reversal of that and other Eocene displacements reveals a >50-km-thick crustal section at late Paleocene time. Further reversal of apparent thrust displacements of the three-layer stratigraphy along the Lewis, Pinkham, Libby, and Moyie thrusts allows construction of a restored cross section prior to the onset of Cordilleran thrusting in the Jurassic. A total of ~220 km of Jurassic–Paleocene shortening along these faults is indicated. The enhanced conductivity within the lowest (unexposed) Belt stratigraphic unit is primarily attributed to one or more horizons of laminated metallic sulfides; graphite, though not described within the Belt Supergroup, may also contribute to the enhanced conductivity of the lowest stratigraphic unit. A narrow conductive horizon observed within the Prichard Formation in the eastern part of the transect correlates with the stratigraphic position of the world-class Sullivan sedimentary exhalative massive sulfide deposit in southern British Columbia, and it may represent a distal sulfide blanket deposit broadly dispersed across the Belt Basin. By analogy, the thick conductive sub–Prichard Formation unit may represent repeated sulfide depositional events within the early rift history of the basin, potentially driven by hydrothermal fluids released during basaltic underplating of attenuated continental crust.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Belt basin: Window to Mesoproterozoic Earth","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Geological Society of America","doi":"10.1130/2016.2522(12)","collaboration":"","usgsCitation":"Bedrosian, P.A., and Box, S.E., 2016, Highly conductive horizons in the Mesoproterozoic Belt-Purcell Basin: Sulfidic early basin strata as key markers of Cordilleran shortening and Eocene extension, chap. of Belt basin: Window to Mesoproterozoic Earth, v. 522, p. 305-339, https://doi.org/10.1130/2016.2522(12).","productDescription":"36 p.","startPage":"305","endPage":"339","ipdsId":"IP-058401","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":470598,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/2016.2522(12)","text":"Publisher Index Page"},{"id":374153,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alberta, British Columbia, Idaho, Montana, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.14672851562499,\n 45.034714778688624\n ],\n [\n -110.775146484375,\n 45.034714778688624\n ],\n [\n -110.775146484375,\n 50.15578588538455\n ],\n [\n -119.14672851562499,\n 50.15578588538455\n ],\n [\n -119.14672851562499,\n 45.034714778688624\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"522","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"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":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":787470,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Box, Stephen E. 0000-0002-5268-8375 sbox@usgs.gov","orcid":"https://orcid.org/0000-0002-5268-8375","contributorId":1843,"corporation":false,"usgs":true,"family":"Box","given":"Stephen","email":"sbox@usgs.gov","middleInitial":"E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":787471,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70192912,"text":"70192912 - 2016 - Landsat-7 ETM+ radiometric calibration status","interactions":[],"lastModifiedDate":"2017-12-20T10:56:58","indexId":"70192912","displayToPublicDate":"2016-09-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Landsat-7 ETM+ radiometric calibration status","docAbstract":"Now in its 17th year of operation, the Enhanced Thematic Mapper + (ETM+), on board the Landsat-7 satellite, continues to systematically acquire imagery of the Earth to add to the 40+ year archive of Landsat data. Characterization of the ETM+ on-orbit radiometric performance has been on-going since its launch in 1999. The radiometric calibration of the reflective bands is still monitored using on-board calibration devices, though the Pseudo-Invariant Calibration Sites (PICS) method has proven to be an effective tool as well. The calibration gains were updated in April 2013 based primarily on PICS results, which corrected for a change of as much as -0.2%/year degradation in the worst case bands. A new comparison with the SADE database of PICS results indicates no additional degradation in the updated calibration. PICS data are still being tracked though the recent trends are not well understood. The thermal band calibration was updated last in October 2013 based on a continued calibration effort by NASA/Jet Propulsion Lab and Rochester Institute of Technology. The update accounted for a 0.036 W/m2 sr μm or 0.26K at 300K bias error. The updated lifetime trend is now stable to within +/- 0.4K.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings Volume 9972, Earth Observing Systems XXI","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"SPIE","doi":"10.1117/12.2238625","usgsCitation":"Barsi, J.A., Markham, B.L., Czapla-Myers, J.S., Helder, D.L., Hook, S., Schott, J.R., and Haque, O., 2016, Landsat-7 ETM+ radiometric calibration status, in Proceedings Volume 9972, Earth Observing Systems XXI, v. 9972, https://doi.org/10.1117/12.2238625.","ipdsId":"IP-079294","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":470629,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://doi.org/10.1117/12.2238625","text":"External Repository"},{"id":350125,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9972","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fcd4e4b06e28e9c24390","contributors":{"authors":[{"text":"Barsi, Julia A.","contributorId":71822,"corporation":false,"usgs":false,"family":"Barsi","given":"Julia","email":"","middleInitial":"A.","affiliations":[{"id":12721,"text":"NASA GSFC SSAI","active":true,"usgs":false}],"preferred":false,"id":725247,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Markham, Brian L.","contributorId":90482,"corporation":false,"usgs":false,"family":"Markham","given":"Brian","email":"","middleInitial":"L.","affiliations":[{"id":12721,"text":"NASA GSFC SSAI","active":true,"usgs":false}],"preferred":false,"id":725248,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Czapla-Myers, J. S.","contributorId":101968,"corporation":false,"usgs":true,"family":"Czapla-Myers","given":"J.","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":725249,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Helder, Dennis L.","contributorId":105613,"corporation":false,"usgs":true,"family":"Helder","given":"Dennis","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":725250,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hook, Simon","contributorId":150339,"corporation":false,"usgs":false,"family":"Hook","given":"Simon","affiliations":[{"id":7218,"text":"California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":725251,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schott, John R.","contributorId":199175,"corporation":false,"usgs":false,"family":"Schott","given":"John","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":725252,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Haque, Obaidul 0000-0002-0914-1446 ohaque@usgs.gov","orcid":"https://orcid.org/0000-0002-0914-1446","contributorId":4691,"corporation":false,"usgs":true,"family":"Haque","given":"Obaidul","email":"ohaque@usgs.gov","affiliations":[{"id":40546,"text":"KBR, Contractor to the USGS Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":true,"id":717349,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70192911,"text":"70192911 - 2016 - Radiometric calibration updates to the Landsat collection","interactions":[],"lastModifiedDate":"2018-04-23T09:09:51","indexId":"70192911","displayToPublicDate":"2016-09-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Radiometric calibration updates to the Landsat collection","docAbstract":"The Landsat Project is planning to implement a new collection management strategy for Landsat products generated at the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center. The goal of the initiative is to identify a collection of consistently geolocated and radiometrically calibrated images across the entire Landsat archive that is readily suitable for time-series analyses. In order to perform an accurate land change analysis, the data from all Landsat sensors must be on the same radiometric scale. Landsat 7 Enhanced Thematic Mapper Plus (ETM+) is calibrated to a radiance standard and all previous sensors are cross-calibrated to its radiometric scale. Landsat 8 Operational Land Imager (OLI) is calibrated to both radiance and reflectance standards independently. The Landsat 8 OLI reflectance calibration is considered to be most accurate. To improve radiometric calibration accuracy of historical data, Landsat 1-7 sensors also need to be cross-calibrated to the OLI reflectance scale. Results of that effort, as well as other calibration updates including the absolute and relative radiometric calibration and saturated pixel replacement for Landsat 8 OLI and absolute calibration for Landsat 4 and 5 Thematic Mappers (TM), will be implemented into Landsat products during the archive reprocessing campaign planned within the new collection management strategy. This paper reports on the planned radiometric calibration updates to the solar reflective bands of the new Landsat collection.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings Volume 9972, Earth Observing Systems XXI","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Society of Photo-Optical Instrumentation Engineers","doi":"10.1117/12.2239426","usgsCitation":"Micijevic, E., Haque, O., and Mishra, N., 2016, Radiometric calibration updates to the Landsat collection, in Proceedings Volume 9972, Earth Observing Systems XXI, v. 9972, 12 p., https://doi.org/10.1117/12.2239426.","productDescription":"12 p.","ipdsId":"IP-079592","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":350127,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9972","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fcd5e4b06e28e9c24393","contributors":{"authors":[{"text":"Micijevic, Esad 0000-0002-3828-9239 emicijevic@usgs.gov","orcid":"https://orcid.org/0000-0002-3828-9239","contributorId":3075,"corporation":false,"usgs":true,"family":"Micijevic","given":"Esad","email":"emicijevic@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":717346,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haque, Obaidul 0000-0002-0914-1446 ohaque@usgs.gov","orcid":"https://orcid.org/0000-0002-0914-1446","contributorId":4691,"corporation":false,"usgs":true,"family":"Haque","given":"Obaidul","email":"ohaque@usgs.gov","affiliations":[{"id":40546,"text":"KBR, Contractor to the USGS Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":true,"id":717347,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mishra, Nischal nischal.mishra.ctr@usgs.gov","contributorId":198842,"corporation":false,"usgs":true,"family":"Mishra","given":"Nischal","email":"nischal.mishra.ctr@usgs.gov","affiliations":[],"preferred":false,"id":717348,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70191926,"text":"70191926 - 2016 - The effects of anthropogenic land cover change on pollen-vegetation relationships in the American Midwest","interactions":[],"lastModifiedDate":"2017-10-19T12:38:18","indexId":"70191926","displayToPublicDate":"2016-09-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":815,"text":"Anthropocene","active":true,"publicationSubtype":{"id":10}},"title":"The effects of anthropogenic land cover change on pollen-vegetation relationships in the American Midwest","docAbstract":"Fossil pollen assemblages provide information about vegetation dynamics at time scales ranging from centuries to millennia. Pollen-vegetation models and process-based models of dispersal typically assume stable relationships between source vegetation and corresponding pollen in surface sediments, as well as stable parameterizations of dispersal and productivity. These assumptions, however, are largely unevaluated. This paper reports a test of the stability of pollen-vegetation relationships using vegetation and pollen data from the Midwestern region of the United States, during a period of large changes in land use and vegetation driven by Euro-American settlement. We compared a dataset of pollen records for the early settlement-era with three other datasets of pollen and forest composition for two time periods: before Euro-American settlement, and the late 20th century. Results from generalized linear models for thirteen genera indicate that pollen-vegetation relationships significantly differ (p < 0.05) between pre-settlement and the modern era for several genera: Fagus, Betula, Tsuga, Quercus, Pinus, and Picea. The estimated pollen source radius for the 8 km gridded vegetation data and associated pollen data is 25–85 km, consistent with prior studies using similar methods and spatial resolutions.
Hence, the rapid changes in land cover associated with the Anthropocene affect the accuracy of ecological predictions for both the future and the past. In the Anthropocene, paleoecology should move beyond the assumption that pollen-vegetation relationships are stable over time. Multi-temporal calibration datasets are increasingly possible and enable paleoecologists to better understand the complex processes governing pollen-vegetation relationships through space and time.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ancene.2016.09.005","usgsCitation":"Kujawa, E.R., Goring, S., Dawson, A., Calcote, R., Grimm, E., Hotchkiss, S.C., Jackson, S.T., Lynch, E.A., McLachlan, J.S., St-Jacques, J., Umbanhowar, C., and Williams, J.W., 2016, The effects of anthropogenic land cover change on pollen-vegetation relationships in the American Midwest: Anthropocene, v. 15, p. 60-71, https://doi.org/10.1016/j.ancene.2016.09.005.","productDescription":"12 p.","startPage":"60","endPage":"71","ipdsId":"IP-076183","costCenters":[{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true}],"links":[{"id":470625,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1101/037051","text":"External Repository"},{"id":346964,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan, Minnesota, 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Water-resource managers need tools to quickly predict when and where toxin-producing cyanoHABs will occur. This could be done by using site-specific models that estimate the potential for elevated toxin concentrations that cause public health concerns. With this study, samples were collected at three Ohio lakes to identify environmental and water-quality factors to develop linear-regression models to estimate microcystin levels. Measures of the algal community (phycocyanin, cyanobacterial biovolume, and cyanobacterial gene concentrations) and pH were most strongly correlated with microcystin concentrations. Cyanobacterial genes were quantified for general cyanobacteria, general Microcystis and Dolichospermum, and for microcystin synthetase (mcyE) for Microcystis, Dolichospermum, and Planktothrix. For phycocyanin, the relations were different between sites and were different between hand-held measurements on-site and nearby continuous monitor measurements for the same site. Continuous measurements of parameters such as phycocyanin, pH, and temperature over multiple days showed the highest correlations to microcystin concentrations. The development of models with high R2values (0.81–0.90), sensitivities (92%), and specificities (100%) for estimating microcystin concentrations above or below the Ohio Recreational Public Health Advisory level of 6 μg L−1 was demonstrated for one site; these statistics may change as more data are collected in subsequent years. This study showed that models could be developed for estimates of exceeding a microcystin threshold concentration at a recreational freshwater lake site, with potential to expand their use to provide relevant public health information to water resource managers and the public for both recreational and drinking waters.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.hal.2016.07.003","usgsCitation":"Francy, D.S., Brady, A.M., Ecker, C.D., Graham, J.L., Stelzer, E.A., Struffolino, P., and Loftin, K.A., 2016, Estimating microcystin levels at recreational sites in western Lake Erie and Ohio: Harmful Algae, v. 58, p. 23-34, https://doi.org/10.1016/j.hal.2016.07.003.","productDescription":"12 p.","startPage":"23","endPage":"34","ipdsId":"IP-068433","costCenters":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true}],"links":[{"id":352264,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Ohio","otherGeospatial":"Lake Erie","volume":"58","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee98be4b0da30c1bfc568","contributors":{"authors":[{"text":"Francy, Donna S. 0000-0001-9229-3557 dsfrancy@usgs.gov","orcid":"https://orcid.org/0000-0001-9229-3557","contributorId":1853,"corporation":false,"usgs":true,"family":"Francy","given":"Donna","email":"dsfrancy@usgs.gov","middleInitial":"S.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true},{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true}],"preferred":true,"id":730225,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brady, Amie M.G. 0000-0002-7414-0992 amgbrady@usgs.gov","orcid":"https://orcid.org/0000-0002-7414-0992","contributorId":2544,"corporation":false,"usgs":true,"family":"Brady","given":"Amie","email":"amgbrady@usgs.gov","middleInitial":"M.G.","affiliations":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":730222,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ecker, Christopher D. 0000-0003-0353-5855 cdecker@usgs.gov","orcid":"https://orcid.org/0000-0003-0353-5855","contributorId":149530,"corporation":false,"usgs":true,"family":"Ecker","given":"Christopher","email":"cdecker@usgs.gov","middleInitial":"D.","affiliations":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":false,"id":730221,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Graham, Jennifer L. 0000-0002-6420-9335 jlgraham@usgs.gov","orcid":"https://orcid.org/0000-0002-6420-9335","contributorId":1769,"corporation":false,"usgs":true,"family":"Graham","given":"Jennifer","email":"jlgraham@usgs.gov","middleInitial":"L.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":730220,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stelzer, Erin A. 0000-0001-7645-7603 eastelzer@usgs.gov","orcid":"https://orcid.org/0000-0001-7645-7603","contributorId":1933,"corporation":false,"usgs":true,"family":"Stelzer","given":"Erin","email":"eastelzer@usgs.gov","middleInitial":"A.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true},{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true}],"preferred":true,"id":730224,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Struffolino, Pamela","contributorId":202922,"corporation":false,"usgs":false,"family":"Struffolino","given":"Pamela","affiliations":[{"id":12455,"text":"University of Toledo","active":true,"usgs":false}],"preferred":false,"id":730219,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Loftin, Keith A. 0000-0001-5291-876X kloftin@usgs.gov","orcid":"https://orcid.org/0000-0001-5291-876X","contributorId":868,"corporation":false,"usgs":true,"family":"Loftin","given":"Keith","email":"kloftin@usgs.gov","middleInitial":"A.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":730223,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70188438,"text":"70188438 - 2016 - Holocene climate changes in eastern Beringia (NW North America) – A systematic review of multi-proxy evidence","interactions":[],"lastModifiedDate":"2017-06-09T14:10:37","indexId":"70188438","displayToPublicDate":"2016-09-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3219,"text":"Quaternary Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Holocene climate changes in eastern Beringia (NW North America) – A systematic review of multi-proxy evidence","docAbstract":"Reconstructing climates of the past relies on a variety of evidence from a large number of sites to capture the varied features of climate and the spatial heterogeneity of climate change. This review summarizes available information from diverse Holocene paleoenvironmental records across eastern Beringia (Alaska, westernmost Canada and adjacent seas), and it quantifies the primary trends of temperature- and moisture-sensitive records based in part on midges, pollen, and biogeochemical indicators (compiled in the recently published Arctic Holocene database, and updated here to v2.1). The composite time series from these proxy records are compared with new summaries of mountain-glacier and lake-level fluctuations, terrestrial water-isotope records, sea-ice and sea-surface-temperature analyses, and peatland and thaw-lake initiation frequencies to clarify multi-centennial- to millennial-scale trends in Holocene climate change. To focus the synthesis, the paleo data are used to frame specific questions that can be addressed with simulations by Earth system models to investigate the causes and dynamics of past and future climate change. This systematic review shows that, during the early Holocene (11.7–8.2 ka; 1 ka = 1000 cal yr BP), rather than a prominent thermal maximum as suggested previously, temperatures were highly variable, at times both higher and lower than present (approximate mid-20th-century average), with no clear spatial pattern. Composited pollen, midge and other proxy records average out the variability and show the overall lowest summer and mean-annual temperatures across the study region during the earliest Holocene, followed by warming over the early Holocene. The sparse data available on early Holocene glaciation show that glaciers in southern Alaska were as extensive then as they were during the late Holocene. Early Holocene lake levels were low in interior Alaska, but moisture indicators show pronounced differences across the region. The highest frequency of both peatland and thaw-lake initiation ages also occurred during the early Holocene. During the middle Holocene (8.2–4.2 ka), glaciers retreated as the regional average temperature increased to a maximum between 7 and 5 ka, as reflected in most proxy types. Following the middle Holocene thermal maximum, temperatures decreased starting between 4 and 3 ka, signaling the onset of Neoglacial cooling. Glaciers in the Brooks and Alaska Ranges advanced to their maximum Holocene extent as lakes generally rose to modern levels. Temperature differences for averaged 500-year time steps typically ranged by 1–2 °C for individual records in the Arctic Holocene database, with a transition to a cooler late Holocene that was neither abrupt nor spatially coherent. The longest and highest-resolution terrestrial water isotope records previously interpreted to represent changes in the Aleutian low-pressure system around this time are here shown to be largely contradictory. Furthermore, there are too few records with sufficient resolution to identify sub-centennial-scale climate anomalies, such as the 8.2 ka event. The review concludes by suggesting some priorities for future paleoclimate research in the region.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.quascirev.2015.10.021","usgsCitation":"Kaufman, D.S., Axford, Y.L., Henderson, A.C., McKay, N.P., Oswald, W., Saenger, C., Anderson, R., Bailey, H.L., Clegg, B., Gajewski, K., Hu, F.S., Jones, M.C., Massa, C., Routson, C.C., Werner, A., Wooller, M.J., and Yu, Z., 2016, Holocene climate changes in eastern Beringia (NW North America) – A systematic review of multi-proxy evidence: Quaternary Science Reviews, v. 147, p. 312-339, https://doi.org/10.1016/j.quascirev.2015.10.021.","productDescription":"28 p.","startPage":"312","endPage":"339","ipdsId":"IP-068458","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":470606,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.quascirev.2015.10.021","text":"Publisher Index Page"},{"id":342340,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"147","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"593bb3a0e4b0764e6c60e7b4","contributors":{"authors":[{"text":"Kaufman, Darrell S.","contributorId":192787,"corporation":false,"usgs":false,"family":"Kaufman","given":"Darrell","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":697736,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Axford, Yarrow L.","contributorId":192788,"corporation":false,"usgs":false,"family":"Axford","given":"Yarrow","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":697737,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Henderson, Andrew C.G.","contributorId":192789,"corporation":false,"usgs":false,"family":"Henderson","given":"Andrew","email":"","middleInitial":"C.G.","affiliations":[],"preferred":false,"id":697738,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McKay, Nicolas P.","contributorId":192790,"corporation":false,"usgs":false,"family":"McKay","given":"Nicolas","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":697739,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Oswald, W. 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Scott","affiliations":[{"id":7034,"text":"School of Earth Sciences and Environmental Sustainability at Northern Arizona University, in Flagstaff","active":true,"usgs":false}],"preferred":false,"id":697742,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bailey, Hannah L.","contributorId":192793,"corporation":false,"usgs":false,"family":"Bailey","given":"Hannah","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":697743,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Clegg, Benjamin","contributorId":192794,"corporation":false,"usgs":false,"family":"Clegg","given":"Benjamin","email":"","affiliations":[],"preferred":false,"id":697744,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Gajewski, Konrad","contributorId":192795,"corporation":false,"usgs":false,"family":"Gajewski","given":"Konrad","email":"","affiliations":[],"preferred":false,"id":697745,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Hu, Feng Sheng","contributorId":192796,"corporation":false,"usgs":false,"family":"Hu","given":"Feng","email":"","middleInitial":"Sheng","affiliations":[],"preferred":false,"id":697746,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Jones, Miriam C. 0000-0002-6650-7619 miriamjones@usgs.gov","orcid":"https://orcid.org/0000-0002-6650-7619","contributorId":4056,"corporation":false,"usgs":true,"family":"Jones","given":"Miriam","email":"miriamjones@usgs.gov","middleInitial":"C.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":697735,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Massa, Charly","contributorId":192797,"corporation":false,"usgs":false,"family":"Massa","given":"Charly","email":"","affiliations":[],"preferred":false,"id":697747,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Routson, Cody C. 0000-0001-8694-7809","orcid":"https://orcid.org/0000-0001-8694-7809","contributorId":187600,"corporation":false,"usgs":false,"family":"Routson","given":"Cody","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":697748,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Werner, Al","contributorId":192798,"corporation":false,"usgs":false,"family":"Werner","given":"Al","email":"","affiliations":[],"preferred":false,"id":697749,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Wooller, Matthew J.","contributorId":192799,"corporation":false,"usgs":false,"family":"Wooller","given":"Matthew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":697750,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Yu, Zicheng 0000-0003-2358-2712","orcid":"https://orcid.org/0000-0003-2358-2712","contributorId":147521,"corporation":false,"usgs":false,"family":"Yu","given":"Zicheng","email":"","affiliations":[{"id":16857,"text":"Lehigh Univ.","active":true,"usgs":false}],"preferred":false,"id":697751,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70187173,"text":"70187173 - 2016 - Environmental covariates associated with Cambarus veteranus (Decapoda: Cambaridae), an imperiled Appalachian crayfish endemic to West Virginia, USA","interactions":[],"lastModifiedDate":"2018-03-16T15:31:45","indexId":"70187173","displayToPublicDate":"2016-09-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2235,"text":"Journal of Crustacean Biology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Environmental covariates associated with Cambarus veteranus (Decapoda: Cambaridae), an imperiled Appalachian crayfish endemic to West Virginia, USA","title":"Environmental covariates associated with Cambarus veteranus (Decapoda: Cambaridae), an imperiled Appalachian crayfish endemic to West Virginia, USA","docAbstract":"Cambarus veteranus Faxon, 1914, a narrow endemic crayfish native to the Upper Guyandotte River Basin (UGB) in West Virginia, USA, was petitioned in 2014 by the United States Fish and Wildlife Service to be listed as endangered, but a status survey was recommended to determine if listing was warranted. During May and June 2015, surveys were undertaken across the UGB to determine the current distribution of the species. A total of 71 sites were sampled, including all streams where the species was previously recorded, as well as semi-randomly selected streams, with 1-9 125 m long sites sampled per wadeable stream. Physiochemical and physical habitat data (based on the Qualitative Habitat Evaluation Index, QHEI) were obtained at each site to determine abiotic factors that were associated with the presence of C. veteranus. Site detection or non-detection of C. veteranus and associated site covariates were modeled using logistic regression to determine covariates associated with the presence of the species. Cambarus veteranus was present in both the Pinnacle Creek and Clear Fork/Laurel Fork watersheds at 10 sites, but it was not observed in the remaining 61 sites. An additive effects model with conductivity and QHEI was selected as the best approximating model. Cambarusveteranus was associated with lower than average UGB conductivity (379 µS) and high (>80) QHEI score. All sites where C. veteranus was not detected had higher conductivity and/or lower QHEI scores.
","language":"English","publisher":"Oxford Academic","doi":"10.1163/1937240x-00002456","usgsCitation":"Loughman, Z.J., Welsh, S., Sadecky, N., Dillard, Z.W., and Scott, R.K., 2016, Environmental covariates associated with Cambarus veteranus (Decapoda: Cambaridae), an imperiled Appalachian crayfish endemic to West Virginia, USA: Journal of Crustacean Biology, v. 36, no. 5, p. 642-648, https://doi.org/10.1163/1937240x-00002456.","productDescription":"7 p.","startPage":"642","endPage":"648","ipdsId":"IP-078754","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":470619,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1163/1937240x-00002456","text":"Publisher Index Page"},{"id":340355,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"West Virginia","otherGeospatial":"Upper Guyandotte River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.14501953125,\n 38.37611542403604\n ],\n [\n -80.83740234375,\n 38.315801006824984\n ],\n [\n -80.5517578125,\n 38.22091976683121\n ],\n [\n -80.26611328125,\n 38.08268954483802\n ],\n [\n -80.22216796875,\n 37.93553306183642\n ],\n [\n -80.343017578125,\n 37.75334401310656\n ],\n [\n -80.66162109375,\n 37.61423141542417\n ],\n [\n -81.01318359375,\n 37.501010429493284\n ],\n [\n -81.76025390625,\n 37.50972584293751\n ],\n [\n -81.968994140625,\n 37.58811876638322\n ],\n [\n -82.276611328125,\n 37.735969208590504\n ],\n [\n -82.37548828125,\n 37.95286091815649\n ],\n [\n -82.496337890625,\n 38.14319750166766\n ],\n [\n -82.4853515625,\n 38.28993659801203\n ],\n [\n -82.30957031249999,\n 38.41055825094609\n ],\n [\n -82.0458984375,\n 38.57393751557591\n ],\n [\n -81.82617187499999,\n 38.57393751557591\n ],\n [\n -81.507568359375,\n 38.53957267203905\n ],\n [\n -81.287841796875,\n 38.46219172306828\n ],\n [\n -81.14501953125,\n 38.37611542403604\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"36","issue":"5","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59006063e4b0e85db3a5ddd7","contributors":{"authors":[{"text":"Loughman, Zachary J.","contributorId":76157,"corporation":false,"usgs":false,"family":"Loughman","given":"Zachary","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":692929,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Welsh, Stuart A. 0000-0003-0362-054X swelsh@usgs.gov","orcid":"https://orcid.org/0000-0003-0362-054X","contributorId":152088,"corporation":false,"usgs":true,"family":"Welsh","given":"Stuart A.","email":"swelsh@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":692923,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sadecky, Nicole M.","contributorId":179375,"corporation":false,"usgs":false,"family":"Sadecky","given":"Nicole M.","affiliations":[],"preferred":false,"id":692930,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dillard, Zachary W.","contributorId":179376,"corporation":false,"usgs":false,"family":"Dillard","given":"Zachary","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":692931,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Scott, R. Katie","contributorId":179377,"corporation":false,"usgs":false,"family":"Scott","given":"R.","email":"","middleInitial":"Katie","affiliations":[],"preferred":false,"id":692932,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70192761,"text":"70192761 - 2016 - Toxicity of potassium chloride to veliger and byssal stage dreissenid mussels related to water quality","interactions":[],"lastModifiedDate":"2017-11-07T14:58:56","indexId":"70192761","displayToPublicDate":"2016-09-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2655,"text":"Management of Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Toxicity of potassium chloride to veliger and byssal stage dreissenid mussels related to water quality","docAbstract":"Natural resource managers are seeking appropriate chemical eradication and control protocols for infestations of zebra mussels, Dreissena polymorpha (Pallas, 1769), and quagga mussels. D. rostiformis bugensis (Andrusov, 1897) that have limited effect on non-target species. Applications of low concentrations of potassium salt (as potash) have shown promise for use where the infestation and treatment can be contained or isolated. To further our understanding of such applications and obtain data that could support a pesticide registration, we conducted studies of the acute and chronic toxicity of potassium chloride to dreissenid mussels in four different water sources from infested and non-infested locations (ground water from northern Idaho, surface water from the Snake River, Idaho, USA, surface water from Lake Ontario, Ontario, Canada, and surface water from the Colorado River, Arizona, USA). We found short term exposure of veligers (< 24 h) to concentrations of 960 mg/L KCl produced rapid mortality in water from three locations, but veligers tested in Colorado River water were resistant. We used probit models to compare the mortality responses, predicted median lethal times and 95% confidence intervals. In separate experiments, we explored the sensitivity of byssal stage mussels in chronic exposures (>29 d) at concentrations of 100 and 200 mg/L KCl. Rapid mortality occurred within 10 d of exposure to concentrations of 200 mg/L KCl, regardless of water source. Kaplan-Meier estimates of mean survival of byssal mussels in 100 mg/L KCl prepared in surface water from Idaho and Lake Ontario were 4.9 or 6.9 d, respectively; however, mean survival of mussels tested in the Colorado River water was > 23 d. The sodium content of the Colorado River water was nearly three times that measured in waters from the other locations, and we hypothesized sodium concentrations may affect mussel survival. To test our hypothesis, we supplemented Snake River and Lake Ontario water with NaCl to equivalent conductivity as the Colorado River, and found mussel survival increased to levels observed in tests of veliger and byssal mussels in Colorado River water. We recommend KCl disinfection and eradication protocols must be developed to carefully consider the water quality characteristics of treatment locations.
","language":"English","publisher":"REABIC","doi":"10.3391/mbi.2016.7.3.05","usgsCitation":"Moffitt, C.M., Stockton-Fiti, K.A., and Claudi, R., 2016, Toxicity of potassium chloride to veliger and byssal stage dreissenid mussels related to water quality: Management of Biological Invasions, v. 7, no. 3, p. 257-268, https://doi.org/10.3391/mbi.2016.7.3.05.","productDescription":"12 p.","startPage":"257","endPage":"268","ipdsId":"IP-073121","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":470626,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3391/mbi.2016.7.3.05","text":"Publisher Index Page"},{"id":348406,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"3","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a07e9dbe4b09af898c8cc5c","contributors":{"authors":[{"text":"Moffitt, Christine M. 0000-0001-6020-9728 cmoffitt@usgs.gov","orcid":"https://orcid.org/0000-0001-6020-9728","contributorId":2583,"corporation":false,"usgs":true,"family":"Moffitt","given":"Christine","email":"cmoffitt@usgs.gov","middleInitial":"M.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":716850,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stockton-Fiti, Kelly A.","contributorId":200103,"corporation":false,"usgs":false,"family":"Stockton-Fiti","given":"Kelly","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":721003,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Claudi, Renata","contributorId":171420,"corporation":false,"usgs":false,"family":"Claudi","given":"Renata","email":"","affiliations":[{"id":26908,"text":"RNT Consulting Inc., Canada","active":true,"usgs":false}],"preferred":false,"id":721004,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70171462,"text":"70171462 - 2016 - Migratory routes and at-sea threats to Pink-footed Shearwaters","interactions":[],"lastModifiedDate":"2016-09-08T11:56:42","indexId":"70171462","displayToPublicDate":"2016-09-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Migratory routes and at-sea threats to Pink-footed Shearwaters","docAbstract":"The Pink-footed Shearwater (Ardenna creatopus) is a seabird with a breeding range restricted to three islands in Chile and an estimated world population of approximately 56,000 breeding individuals (Muñoz 2011, Oikonos unpublished data). Due to multiple threats on breeding colonies and at-sea, Pink-footed Shearwaters are listed as Endangered by the government of Chile (Reglamento de Clasificación de Especies, 2011), Threatened by the government of Canada (Environment Canada 2008), and are listed under Appendix 1 of the Agreement on the Conservation of Albatrosses and Petrels (ACAP 2013).\r\nA principal conservation concern for the species is mortality from fisheries bycatch during the breeding and non-breeding seasons; thus, identification of areas of overlap between at-sea use by Pink-footed Shearwaters and fisheries is a high priority conservation objective (Hinojosa Sáez and Hodum 1997, Mangel et al. 2013, ACAP 2013). During the non-breeding period, Pink-footed Shearwaters range as far north as Canada, although little was known until recently about migration routes and important wintering areas where fisheries bycatch could be a risk. Additionally, Pink-footed Shearwaters face at-sea threats during the non-breeding season off the west coast of North America. Recently, areas used by wintering Pink-footed Shearwaters have been identified as areas of interest for developing alternative energy offshore in North America (e.g., floating wind generators; Trident Winds 2016). The goal of our study was to track Pink-footed Shearwater post-breeding movements with satellite tags to identify timing and routes of migration, locate important non-breeding foraging habitats, and determine population distribution among different wintering regions.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Seventh Meeting of the Seabird Bycatch Working Group","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Seventh Meeting of the Seabird Bycatch Working Group","conferenceDate":"May 2-4, 2016","conferenceLocation":"La Serena, Chile","language":"English","publisher":"Agreement on the Conservation of Albatrosses and Petrels","usgsCitation":"Adams, J., Felis, J.J., Hodum, P., Colodro, V., Carle, R., and López, V., 2016, Migratory routes and at-sea threats to Pink-footed Shearwaters, in Seventh Meeting of the Seabird Bycatch Working Group, La Serena, Chile, May 2-4, 2016.","ipdsId":"IP-075471","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":328369,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":321936,"type":{"id":15,"text":"Index Page"},"url":"https://www.acap.aq/en/search14?q=Migratory+routes+and+at-sea+threats+to+Pink-footed+Shearwaters"}],"publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57d28baee4b0571647d0f93a","contributors":{"authors":[{"text":"Adams, Josh 0000-0003-3056-925X josh_adams@usgs.gov","orcid":"https://orcid.org/0000-0003-3056-925X","contributorId":2422,"corporation":false,"usgs":true,"family":"Adams","given":"Josh","email":"josh_adams@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":631080,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Felis, Jonathan J. 0000-0002-0608-8950 jfelis@usgs.gov","orcid":"https://orcid.org/0000-0002-0608-8950","contributorId":4825,"corporation":false,"usgs":true,"family":"Felis","given":"Jonathan","email":"jfelis@usgs.gov","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":631081,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hodum, Peter 0000-0003-2160-5132","orcid":"https://orcid.org/0000-0003-2160-5132","contributorId":169797,"corporation":false,"usgs":false,"family":"Hodum","given":"Peter","email":"","affiliations":[{"id":25597,"text":"Oikonos Ecosystem Knowledge","active":true,"usgs":false}],"preferred":false,"id":631082,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Colodro, Valentina 0000-0001-9285-3171","orcid":"https://orcid.org/0000-0001-9285-3171","contributorId":169798,"corporation":false,"usgs":false,"family":"Colodro","given":"Valentina","email":"","affiliations":[{"id":25597,"text":"Oikonos Ecosystem Knowledge","active":true,"usgs":false}],"preferred":false,"id":631083,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Carle, Ryan 0000-0002-8213-4306","orcid":"https://orcid.org/0000-0002-8213-4306","contributorId":169799,"corporation":false,"usgs":false,"family":"Carle","given":"Ryan","email":"","affiliations":[{"id":25597,"text":"Oikonos Ecosystem Knowledge","active":true,"usgs":false}],"preferred":false,"id":631084,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"López, Verónica","contributorId":169800,"corporation":false,"usgs":false,"family":"López","given":"Verónica","affiliations":[{"id":25597,"text":"Oikonos Ecosystem Knowledge","active":true,"usgs":false}],"preferred":false,"id":631085,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70175152,"text":"ofr20161126 - 2016 - Evaluating integration of inland bathymetry in the U.S. Geological Survey 3D Elevation Program, 2014","interactions":[],"lastModifiedDate":"2016-09-01T15:31:00","indexId":"ofr20161126","displayToPublicDate":"2016-09-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-1126","title":"Evaluating integration of inland bathymetry in the U.S. Geological Survey 3D Elevation Program, 2014","docAbstract":"Inland bathymetry survey collections, survey data types, features, sources, availability, and the effort required to integrate inland bathymetric data into the U.S. Geological Survey 3D Elevation Program are assessed to help determine the feasibility of integrating three-dimensional water feature elevation data into The National Map. Available data from wading, acoustic, light detection and ranging, and combined technique surveys are provided by the U.S. Geological Survey, National Oceanic and Atmospheric Administration, U.S. Army Corps of Engineers, and other sources. Inland bathymetric data accessed through Web-hosted resources or contacts provide useful baseline parameters for evaluating survey types and techniques used for collection and processing, and serve as a basis for comparing survey methods and the quality of results. Historically, boat-mounted acoustic surveys have provided most inland bathymetry data. Light detection and ranging techniques that are beneficial in areas hard to reach by boat, that can collect dense data in shallow water to provide comprehensive coverage, and that can be cost effective for surveying large areas with good water clarity are becoming more common; however, optimal conditions and techniques for collecting and processing light detection and ranging inland bathymetry surveys are not yet well defined.
Assessment of site condition parameters important for understanding inland bathymetry survey issues and results, and an evaluation of existing inland bathymetry survey coverage are proposed as steps to develop criteria for implementing a useful and successful inland bathymetry survey plan in the 3D Elevation Program. These survey parameters would also serve as input for an inland bathymetry survey data baseline. Integration and interpolation techniques are important factors to consider in developing a robust plan; however, available survey data are usually in a triangulated irregular network format or other format compatible with the 3D Elevation Program so that data can be integrated with a minimal level of effort. Geomorphic site conditions are known to affect the success and accuracy of light detection and ranging and other bathymetric surveys, and a baseline that includes geomorphic data is recommended to help in evaluation of limitations imposed by geomorphology for surveys completed in the variable physiographic provinces across the United States. The geographic distribution for existing surveys identifies regions where inland bathymetry data have been collected and, conversely, where little or no survey data seem to be available to provide hydrologic and hydraulic information. This distribution, in conjunction with local to regional data needs to characterize and monitor river and lake resources, provides another important set of criteria to propose and guide acquisition of new bathymetry data for the 3D Elevation Program. An initial evaluation of needs can be based on the importance of water resources that provide primary water supplies for communities, agriculture, energy, and ecological systems; the importance of flood plain analyses; and projected population growth across the United States.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161126","usgsCitation":"Miller-Corbett, Cynthia, 2016, Evaluating integration of inland bathymetry in the U.S. Geological Survey 3D Elevation Program, 2014: U.S. Geological Survey Open-File Report 2016–1126, 44 p., https://dx.doi.org/10.3133/ofr20161126.\n","productDescription":"vi, 44 p.","numberOfPages":"54","onlineOnly":"Y","ipdsId":"IP-065698","costCenters":[{"id":5047,"text":"NGTOC Denver","active":true,"usgs":true}],"links":[{"id":328148,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1126/coverthb.jpg"},{"id":328149,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1126/ofr20161126.pdf","text":"Report","size":"10.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016–1126"}],"contact":"Director, National Geospatial Technical Operations Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Owing to human activity, global nitrogen (N) cycles have been altered. In the past 100 years, global N deposition has increased. Currently, the monitoring and estimating of N deposition and the evaluation of its effects on global carbon budgets are the focus of many researchers. NO2 columns retrieved by space-borne sensors provide us with a new way of exploring global N cycles and these have the ability to estimate N deposition. However, the time range limitation of NO2 columns makes the estimation of long timescale N deposition difficult. In this study we used ground-based NOx emission data to expand the density of NO2columns, and 40 years of N deposition (1970–2009) was inverted using the multivariate linear model with expanded NO2 columns. The dynamic of N deposition was examined in both global and biome scales. The results show that the average N deposition was 0.34 g N m–2 year–1 in the 2000s, which was an increase of 38.4% compared with the 1970s’. The total N deposition in different biomes is unbalanced. N deposition is only 38.0% of the global total in forest biomes; this is made up of 25.9%, 11.3, and 0.7% in tropical, temperate, and boreal forests, respectively. As N-limited biomes, there was little increase of N deposition in boreal forests. However, N deposition has increased by a total of 59.6% in tropical forests and croplands, which are N-rich biomes. Such characteristics may influence the effects on global carbon budgets.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/01431161.2016.1225178","usgsCitation":"Lu, X., Zhang, X., Liu, J., and Jin, J., 2016, Estimating 40 years of nitrogen deposition in global biomes using the SCIAMACHY NO2 column: International Journal of Remote Sensing, v. 37, no. 20, p. 4964-4978, https://doi.org/10.1080/01431161.2016.1225178.","productDescription":"15 p.","startPage":"4964","endPage":"4978","ipdsId":"IP-076950","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":331434,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"37","issue":"20","noUsgsAuthors":false,"publicationDate":"2016-09-21","publicationStatus":"PW","scienceBaseUri":"584144e0e4b04fc80e5073ac","contributors":{"authors":[{"text":"Lu, Xuehe","contributorId":73517,"corporation":false,"usgs":true,"family":"Lu","given":"Xuehe","affiliations":[],"preferred":false,"id":654763,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhang, Xiuying","contributorId":175218,"corporation":false,"usgs":false,"family":"Zhang","given":"Xiuying","email":"","affiliations":[{"id":27538,"text":"International Institute for Earth System Science, Nanjing University, Xianlin Avenue 163, Nanjing 210093","active":true,"usgs":false}],"preferred":false,"id":654764,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Liu, Jinxun 0000-0003-0561-8988 jxliu@usgs.gov","orcid":"https://orcid.org/0000-0003-0561-8988","contributorId":3414,"corporation":false,"usgs":true,"family":"Liu","given":"Jinxun","email":"jxliu@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":654765,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jin, Jiaxin","contributorId":13561,"corporation":false,"usgs":true,"family":"Jin","given":"Jiaxin","affiliations":[],"preferred":false,"id":654766,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}