Sex determination of fish species is difficult to assess when sexual dimorphism and gametes are not apparent. For threatened and endangered fish species, noninvasive techniques are needed when determining sex to minimize stress and the potential for mortality. We evaluated the use of a portable ultrasound unit to determine sex of Lake Sturgeon Acipenser fulvescens in the field. Ultrasound images were collected from 9 yellow-egg (F2, F3), 32 black-egg (F4, F5), and 107 fully developed male (M2) Lake Sturgeon. Two readers accurately assigned sex to 88–96% of fish, but accuracy varied in relation to maturity stage. Black-egg females and fully developed males were correctly identified for 89–100% of the fish sampled, while these two readers identified yellow-egg females only 33% and 67% of the time. Time spent collecting images ranged between 2 and 3 min once the user was comfortable with operating procedures. Discriminant analysis revealed the total length : girth ratio was a strong predictor of sex and maturity, correctly classifying 81% of black-egg females and 97% of the fully developed males. However, yellow-egg females were incorrectly classified on all occasions. This study shows the utility of using ultrasonography and a total length : girth ratio for sex determination of Lake Sturgeon in later reproductive stages around the spawning season.
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2016 - Age, growth, and size of Lake Superior Pygmy Whitefish (Prosopium coulterii)","interactions":[],"lastModifiedDate":"2016-01-13T13:36:38","indexId":"70162102","displayToPublicDate":"2016-01-13T14:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":737,"text":"American Midland Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"Age, growth, and size of Lake Superior Pygmy Whitefish (Prosopium coulterii)","docAbstract":"Pygmy Whitefish (Prosopium coulterii) are a small, glacial relict species with a disjunct distribution in North America and Siberia. In 2013 we collected Pygmy Whitefish at 28 stations from throughout Lake Superior. Total length was recorded for all fish and weight and sex were recorded and scales and otoliths were collected from a subsample. We compared the precision of estimated ages between readers and between scales and otoliths, estimated von Bertalanffy growth parameters for male and female Pygmy Whitefish, and reported the first weight-length relationship for Pygmy Whitefish. Age estimates between scales and otoliths differed significantly with otolith ages significantly greater for most ages after age-3. Maximum otolith age was nine for females and seven for males, which is older than previously reported for Pygmy Whitefish from Lake Superior. Growth was initially fast but slowed considerably after age-3 for males and age-4 for females, falling to 3–4 mm per year at maximum estimated ages. Females were longer than males after age-3. Our results suggest the size, age, and growth of Pygmy Whitefish in Lake Superior have not changed appreciably since 1953.
","language":"English","publisher":"University of Notre Dame","doi":"10.1674/amid-175-01-24-36.1","usgsCitation":"Stewart, T., Derek Ogle, Gorman, O.T., and Vinson, M., 2016, Age, growth, and size of Lake Superior Pygmy Whitefish (Prosopium coulterii): American Midland Naturalist, v. 175, no. 1, p. 24-36, https://doi.org/10.1674/amid-175-01-24-36.1.","productDescription":"13 p.","startPage":"24","endPage":"36","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-062004","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":314281,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lake Superior","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.2412109375,\n 46.46813299215554\n ],\n [\n -92.2412109375,\n 49.023461463214126\n ],\n [\n -84.3310546875,\n 49.023461463214126\n ],\n [\n -84.3310546875,\n 46.46813299215554\n ],\n [\n -92.2412109375,\n 46.46813299215554\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"175","issue":"1","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5697752be4b039675d00a6b6","contributors":{"authors":[{"text":"Stewart, Taylor trstewart@usgs.gov","contributorId":145494,"corporation":false,"usgs":true,"family":"Stewart","given":"Taylor","email":"trstewart@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":588520,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Derek Ogle","contributorId":152217,"corporation":false,"usgs":false,"family":"Derek Ogle","affiliations":[{"id":18886,"text":"Northland College","active":true,"usgs":false}],"preferred":false,"id":588522,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gorman, Owen T. 0000-0003-0451-110X otgorman@usgs.gov","orcid":"https://orcid.org/0000-0003-0451-110X","contributorId":2888,"corporation":false,"usgs":true,"family":"Gorman","given":"Owen","email":"otgorman@usgs.gov","middleInitial":"T.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":588521,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vinson, Mark R. 0000-0001-5256-9539 mvinson@usgs.gov","orcid":"https://orcid.org/0000-0001-5256-9539","contributorId":3800,"corporation":false,"usgs":true,"family":"Vinson","given":"Mark","email":"mvinson@usgs.gov","middleInitial":"R.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":588519,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70162101,"text":"70162101 - 2016 - Diel feeding ecology of Slimy Sculpin in a tributary to Skaneateles Lake, New York","interactions":[],"lastModifiedDate":"2016-01-13T13:20:02","indexId":"70162101","displayToPublicDate":"2016-01-13T14:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":737,"text":"American Midland Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"Diel feeding ecology of Slimy Sculpin in a tributary to Skaneateles Lake, New York","docAbstract":"Interactions among the benthic community are typically overlooked but play an important role in fish community dynamics. We examined the diel feeding ecology of Slimy Sculpin (Cottus cognatus) from Grout Brook, a tributary to Skaneateles Lake. Of the six time periods examined, Slimy Sculpin consumed the least during the nighttime (2400 h and 0400 h). Chironomids were the major prey consumed during all time periods except for 2400 h when ephemeropterans were the major prey consumed. There was a moderate preference by Slimy Sculpin for food from the benthos (0.59 ± 0.06) with Diptera (Chironomids), Ephemeroptera (Baetidae), and Trichoptera (Brachycentridae) representing the major taxa. Slimy Sculpin appear to be opportunistic feeders selecting what is most available in the brook. Index of fullness was variable and averaged 1.15% across the diel cycle. Daily ration was measured as a function of fish dry body weight and ranged from 0.12 to 0.22. Estimates of daily consumption ranged from 0.007% to 4.0% of body weight, which corresponds to reports for other species. These findings have application in gauging the relative importance of Slimy Sculpin in streams where highly valued salmonid species also occur.
","language":"English","publisher":"University of Notre Dame","doi":"10.1674/amid-175-01-37-46.1","usgsCitation":"Chalupnicki, M.A., and Johnson, J.H., 2016, Diel feeding ecology of Slimy Sculpin in a tributary to Skaneateles Lake, New York: American Midland Naturalist, v. 175, no. 1, p. 37-46, https://doi.org/10.1674/amid-175-01-37-46.1.","productDescription":"10 p.","startPage":"37","endPage":"46","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-055360","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":314280,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Grout Brook, Skaneateles Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.44905090332031,\n 42.76314586689494\n ],\n [\n -76.44905090332031,\n 42.94838139765314\n ],\n [\n -76.26091003417969,\n 42.94838139765314\n ],\n [\n -76.26091003417969,\n 42.76314586689494\n ],\n [\n -76.44905090332031,\n 42.76314586689494\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"175","issue":"1","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5697752ee4b039675d00a6bc","contributors":{"authors":[{"text":"Chalupnicki, Marc A. mchalupnicki@usgs.gov","contributorId":3236,"corporation":false,"usgs":true,"family":"Chalupnicki","given":"Marc","email":"mchalupnicki@usgs.gov","middleInitial":"A.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":588517,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, James H. 0000-0002-5619-3871 jhjohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-5619-3871","contributorId":389,"corporation":false,"usgs":true,"family":"Johnson","given":"James","email":"jhjohnson@usgs.gov","middleInitial":"H.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":588518,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70204449,"text":"70204449 - 2016 - Integrative modelling reveals mechanisms linking productivity and plant species richness","interactions":[],"lastModifiedDate":"2019-07-24T13:43:37","indexId":"70204449","displayToPublicDate":"2016-01-13T13:03:52","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2840,"text":"Nature","active":true,"publicationSubtype":{"id":10}},"title":"Integrative modelling reveals mechanisms linking productivity and plant species richness","docAbstract":"How ecosystem productivity and species richness are interrelated is one of the most debated subjects in the history of ecology. Decades of intensive study have yet to discern the actual mechanisms behind observed global patterns. Here, by integrating the predictions from multiple theories into a single model and using data from 1,126 grassland plots spanning five continents, we detect the clear signals of numerous underlying mechanisms linking productivity and richness. We find that an integrative model has substantially higher explanatory power than traditional bivariate analyses. In addition, the specific results unveil several surprising findings that conflict with classical models. These include the isolation of a strong and consistent enhancement of productivity by richness, an effect in striking contrast with superficial data patterns. Also revealed is a consistent importance of competition across the full range of productivity values, in direct conflict with some (but not all) proposed models. The promotion of local richness by macroecological gradients in climatic favourability, generally seen as a competing hypothesis, is also found to be important in our analysis. The results demonstrate that an integrative modelling approach leads to a major advance in our ability to discern the underlying processes operating in ecological systems.
","language":"English","publisher":"Nature Publishing Group","doi":"10.1038/nature16524","usgsCitation":"Grace, J.B., Anderson, T.M., Seabloom, E.W., Borer, E.T., Adler, P.B., Harpole, W., Hautier, Y., Hillebrand, H., Lind, E.M., Partel, M., Bakker, J.D., Buckley, Y.M., Crawley, M.J., Damschen, E.I., Davies, K.F., Fay, P.A., Firn, J., Gruner, D.S., Hector, A., Knops, J.M., MacDougall, A.S., Melbourne, B.A., Morgan, J.W., Orrock, J., Prober, S.M., and Smith, M., 2016, Integrative modelling reveals mechanisms linking productivity and plant species richness: Nature, v. 529, p. 390-393, https://doi.org/10.1038/nature16524.","productDescription":"4 p.","startPage":"390","endPage":"393","ipdsId":"IP-051258","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":471330,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://dspace.library.uu.nl/handle/1874/344413","text":"External Repository"},{"id":365909,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"529","noUsgsAuthors":false,"publicationDate":"2016-01-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Grace, James B. 0000-0001-6374-4726 gracej@usgs.gov","orcid":"https://orcid.org/0000-0001-6374-4726","contributorId":884,"corporation":false,"usgs":true,"family":"Grace","given":"James","email":"gracej@usgs.gov","middleInitial":"B.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":766959,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, T. 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Regression Model for Reporting Nutrient and Sediment Concentrations, Fluxes, and Trends in Concentration and Flux for the Chesapeake Bay Nontidal Water-Quality Monitoring Network, Results Through Water Year 2012","interactions":[],"lastModifiedDate":"2021-07-02T13:50:02.84497","indexId":"sir20155133","displayToPublicDate":"2016-01-13T11:15:00","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":"2015-5133","title":"Application of a Weighted Regression Model for Reporting Nutrient and Sediment Concentrations, Fluxes, and Trends in Concentration and Flux for the Chesapeake Bay Nontidal Water-Quality Monitoring Network, Results Through Water Year 2012","docAbstract":"In the Chesapeake Bay watershed, estimated fluxes of nutrients and sediment from the bay’s nontidal tributaries into the estuary are the foundation of decision making to meet reductions prescribed by the Chesapeake Bay Total Maximum Daily Load (TMDL) and are often the basis for refining scientific understanding of the watershed-scale processes that influence the delivery of these constituents to the bay. Two regression-based flux and trend estimation models, ESTIMATOR and Weighted Regressions on Time, Discharge, and Season (WRTDS), were compared using data from 80 watersheds in the Chesapeake Bay Nontidal Water-Quality Monitoring Network (CBNTN). The watersheds range in size from 62 to 70,189 square kilometers and record lengths range from 6 to 28 years. ESTIMATOR is a constant-parameter model that estimates trends only in concentration; WRTDS uses variable parameters estimated with weighted regression, and estimates trends in both concentration and flux. WRTDS had greater explanatory power than ESTIMATOR, with the greatest degree of improvement evident for records longer than 25 years (30 stations; improvement in median model R2= 0.06 for total nitrogen, 0.08 for total phosphorus, and 0.05 for sediment) and the least degree of improvement for records of less than 10 years, for which the two models performed nearly equally. Flux bias statistics were comparable or lower (more favorable) for WRTDS for any record length; for 30 stations with records longer than 25 years, the greatest degree of improvement was evident for sediment (decrease of 0.17 in median statistic) and total phosphorus (decrease of 0.05). The overall between-station pattern in concentration trend direction and magnitude for all constituents was roughly similar for both models. A detailed case study revealed that trends in concentration estimated by WRTDS can operationally be viewed as a less-constrained equivalent to trends in concentration estimated by ESTIMATOR. Estimates of annual mean flow-adjusted (ESTIMATOR) and flow-normalized (WRTDS) concentration for years initially constituting the end of a water-quality record showed a similar degree of variability as data for additional years were incrementally added and the initial estimates “aged.” On the basis of the results of this broad comparison of the two models, the U.S. Geological Survey is adopting WRTDS as the primary model for estimating constituent fluxes and trends throughout the CBNTN. Nutrient and sediment flux and trend estimates, based on WRTDS, are summarized narratively and tabulated in appendixes for all stations for which fluxes or trends were reported through water year 2012.
\nWRTDS also was used to explore the sensitivity of flux and trend estimates to three data-quality issues common in many large-scale monitoring networks and evident in some of the CBNTN records. The potential effects of inconsistency in annual sampling effort and inconsistency in storm sampling effort were explored by way of a subsampling experiment using eight of the most densely sampled long-term (1985–2012) stations in the CBNTN as baseline datasets. From each dataset, a set of 10 “design guideline” subsamples was selected, consisting of 12 monthly samples and 8 targeted storm samples per year. The selection was conducted in a manner that preserved the overall intensity of storm sampling in the baseline data. These 10 subsamples were further manipulated to create “heterogeneous” subsamples by removing storm samples prior to 2003. The maximum relative difference between flow-normalized flux estimated in a single year from any of the 10 design guideline subsamples and values estimated in the corresponding year from baseline data was smallest for dissolved inorganic nitrogen (median of 8 stations = 6 percent of baseline estimate), but more appreciable for total phosphorus and sediment (medians of 22 and 32 percent, respectively). The maximum relative difference between flow-normalized flux estimated from from the 10 heterogeneous subsamples and values estimated in the corresponding year from baseline data was more pronounced, with medians for 8 stations of 15, 30, and 53 percent of the corresponding baseline estimates for dissolved inorganic nitrogen, total phosphorus, and sediment, respectively. The worst-case maximum relative differences between flow-normalize flux estimated in a single year from the 10 heterogeneous subsamples and values estimated in the corresponding year from baseline data were 25 percent for dissolved inorganic nitrogen, 37 percent for total phosphorus, and 250 percent for sediment. The results for the heterogeneous subsamples indicate that changes in storm sampling frequency can result in appreciable distortion of estimated trends in flow-normalized flux, especially for total phosphorus and sediment. Trend lines estimated from heterogeneous subsamples tended to converge with the trend lines estimated from baseline data after 2003. In contrast, 2003–12 trends based on subsamples truncated by discarding all data prior to the induced heterogeneity in 2003 showed appreciable biases and differences in slope, relative to the corresponding 2003–12 segment of the trend computed from the design guideline subsamples. Overall, the results indicate that for particulate constituents, load and trend estimates computed using long-term records recently converted to CBNTN design guideline sampling protocols will be most reliable if the trend is computed using the entire record, but reported only for the period that design guideline sampling protocols were followed.
\nInconsistencies related to changing laboratory methods were also examined via two manipulative experiments. In the first experiment, increasing and decreasing “stair-step” patterns of changes in censoring level, overall representing a factor-of-five change in the laboratory reporting limit, were artificially imposed on a 27-year record with no censoring and a period-of-record concentration trend of –68.4 percent. Trends estimated on the basis of the manipulated records were broadly similar to the original trend (–63.6 percent for decreasing censoring levels and –70.3 percent for increasing censoring levels), lending a degree of confidence that the survival regression routines upon which WRTDS is based are generally robust to data censoring. The second experiment considered an abrupt disappearance of low-concentration observations of total phosphorus, associated with a laboratory method change and not reflected through censoring, near the middle of a 28-year record. By process of elimination, an upward shift in the estimated flow-normalize concentration trend line around the same time was identified as a likely artifact resulting from the laboratory method change, although a contemporaneous change in watershed processes cannot be ruled out. Decisions as to how to treat records with potential sampling protocol or laboratory methods-related artifacts should be made on a case-by-case basis, and trend results should be appropriately qualified.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155133","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency Chesapeake Bay Program","usgsCitation":"Chanat, J.G., Moyer, D.L., Blomquist, J.D., Hyer, K.E., and Langland, M.J., 2016, Application of a weighted regression model for reporting nutrient and sediment concentrations, fluxes, and trends in concentration and flux for the Chesapeake Bay Nontidal Water-Quality Monitoring Network, results through water year 2012: U.S. Geological Survey Scientific Investigations Report 2015–5133, 76 p., https://dx.doi.org/10.3133/sir20155133.","productDescription":"Report: viii, 74 p.; 5 Appendixes","numberOfPages":"88","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-063310","costCenters":[{"id":614,"text":"Virginia Water Science 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3","size":"452 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2015-5133","linkHelpText":"Table 1 - Annual Results"},{"id":314015,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5133/pdf/sir20155133_app3_intro.pdf","text":"Appendix 3","size":"421 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5133","linkHelpText":"Introduction (Table 1 and 2)"},{"id":314014,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5133/pdf/sir20155133_appendix2.pdf","text":"Appendix 2","size":"211 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5133"},{"id":314013,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5133/pdf/sir20155133_appendix1.pdf","text":"Appendix 1","size":"523 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 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http://va.water.usgs.gov/
Bioenergetics modeling was used to determine individual and population consumption by Bloater Coregonus hoyi in Lake Michigan during three time periods with variable Bloater density: 1993–1996 (high), 1998–2002 (intermediate), and 2009–2011 (low). Despite declines in Bloater abundance between 1993 and 2011, our results did not show any density-dependent compensatory response in annual individual consumption, specific consumption, or proportion of maximum consumption consumed. Diporeia spp. accounted for a steadily decreasing fraction of annual consumption, and Bloater were apparently unable to eat enough Mysis diluviana or other prey to account for the loss of Diporeia in the environment. The fraction of production of both Diporeia and Mysis that was consumed by the Bloater population decreased over time so that the consumption-to-production ratio for Diporeia + Mysis was 0.74, 0.26, and 0.14 in 1993–1996, 1998–2002, and 2009–2011, respectively. Although high Bloater numbers in the 1980s to 1990s may have had an influence on populations of Diporeia, Bloater were not the main factor driving Diporeia to a nearly complete disappearance because Diporeia continued to decline when Bloater predation demands were lessening. Thus, there appears to be a decoupling in the inverse relationship between predator and prey abundance in Lake Michigan. Compared with Alewife Alosa pseudoharengus, the other dominant planktivore in the lake, Bloater have a lower specific consumption and higher gross conversion efficiency (GCE), indicating that the lake can support a higher biomass of Bloater than Alewife. However, declines in Bloater GCE since the 1970s and the absence of positive responses in consumption variables following declines in abundance suggest that productivity in Lake Michigan might not be able to support the same biomass of Bloater as in the past.
","language":"English","publisher":"Taylor & Franics","doi":"10.1080/00028487.2015.1094130","usgsCitation":"Pothoven, S.A., and Bunnell, D., 2016, A shift in bloater consumption in Lake Michigan between 1993 and 2011 and its effects on Diporeia and Mysis prey: Transactions of the American Fisheries Society, v. 145, no. 1, p. 59-68, https://doi.org/10.1080/00028487.2015.1094130.","productDescription":"10 p.","startPage":"59","endPage":"68","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065492","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":314263,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lake Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.08862304687499,\n 41.590796851056005\n ],\n [\n -86.63818359375,\n 41.812267143599804\n ],\n [\n -86.36352539062499,\n 42.13082130188811\n ],\n [\n -86.143798828125,\n 42.60970621339408\n ],\n [\n -86.12182617187499,\n 43.13306116240612\n ],\n [\n -86.396484375,\n 43.56447158721811\n ],\n [\n -86.36352539062499,\n 43.810747313446996\n ],\n [\n -86.37451171875,\n 44.07969327425713\n ],\n [\n -86.099853515625,\n 44.512176171071054\n ],\n [\n -85.902099609375,\n 44.793530904744074\n ],\n [\n -85.6494140625,\n 44.66865287227321\n ],\n [\n -85.308837890625,\n 44.85586880735725\n ],\n [\n -85.25390625,\n 45.158800738352134\n ],\n [\n -85.25390625,\n 45.251688256117646\n ],\n [\n -84.847412109375,\n 45.37530235052552\n ],\n [\n -84.8583984375,\n 45.4986468234261\n ],\n [\n -84.979248046875,\n 45.506346901083425\n ],\n [\n -85.05615234375,\n 45.583289756006316\n ],\n [\n -84.92431640625,\n 45.72152152227954\n ],\n [\n -84.66064453125,\n 45.79050946752472\n ],\n [\n -84.79248046875,\n 45.98169518512228\n ],\n [\n -85.198974609375,\n 46.11894150610708\n ],\n [\n -85.528564453125,\n 46.17222297845542\n ],\n [\n -85.6494140625,\n 46.057985244793024\n ],\n [\n -86.165771484375,\n 46.01985337287634\n ],\n [\n -86.385498046875,\n 45.98169518512228\n ],\n [\n -86.978759765625,\n 45.94351068030587\n ],\n [\n -87.3193359375,\n 45.73685954736049\n ],\n [\n -87.4951171875,\n 45.35214524585177\n ],\n [\n -87.791748046875,\n 45.042478050891546\n ],\n [\n -88.033447265625,\n 44.83249999349062\n ],\n [\n -88.099365234375,\n 44.629573191951046\n ],\n [\n -88.13232421875,\n 44.44162421758805\n ],\n [\n -87.879638671875,\n 44.49650533109348\n ],\n [\n -87.5830078125,\n 44.78573392716592\n ],\n [\n -87.440185546875,\n 44.809121700077355\n ],\n [\n -87.440185546875,\n 44.66865287227321\n ],\n [\n -87.64892578125,\n 44.29240108529005\n ],\n [\n -87.637939453125,\n 44.20583500104184\n ],\n [\n -87.703857421875,\n 44.04811573082351\n ],\n [\n -87.857666015625,\n 43.82660134505384\n ],\n [\n -87.802734375,\n 43.731414013769\n ],\n [\n -87.890625,\n 43.50075243569041\n ],\n [\n -87.978515625,\n 43.15710884095329\n ],\n [\n -87.945556640625,\n 42.90816007196054\n ],\n [\n -87.91259765625,\n 42.72280375732727\n ],\n [\n -87.91259765625,\n 42.382894009614056\n ],\n [\n -87.890625,\n 42.187829010590825\n ],\n [\n -87.7587890625,\n 41.934976500546604\n ],\n [\n -87.681884765625,\n 41.73033005046653\n ],\n [\n -87.4951171875,\n 41.582579601430346\n ],\n [\n -87.08862304687499,\n 41.590796851056005\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"145","issue":"1","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2016-01-08","publicationStatus":"PW","scienceBaseUri":"56977528e4b039675d00a6b4","contributors":{"authors":[{"text":"Pothoven, Steven A.","contributorId":92998,"corporation":false,"usgs":false,"family":"Pothoven","given":"Steven","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":588535,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bunnell, David B. 0000-0003-3521-7747 dbunnell@usgs.gov","orcid":"https://orcid.org/0000-0003-3521-7747","contributorId":3139,"corporation":false,"usgs":true,"family":"Bunnell","given":"David B.","email":"dbunnell@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":588534,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70162100,"text":"70162100 - 2016 - Use of terrestrial field studies in the derivation of bioaccumulation potential of chemicals","interactions":[],"lastModifiedDate":"2018-08-10T09:54:48","indexId":"70162100","displayToPublicDate":"2016-01-13T10:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2006,"text":"Integrated Environmental Assessment and Management","active":true,"publicationSubtype":{"id":10}},"title":"Use of terrestrial field studies in the derivation of bioaccumulation potential of chemicals","docAbstract":"Field-based studies are an essential component of research addressing the behavior of organic chemicals, and a unique line of evidence that can be used to assess bioaccumulation potential in chemical registration programs and aid in development of associated laboratory and modeling efforts. To aid scientific and regulatory discourse on the application of terrestrial field data in this manner, this article provides practical recommendations regarding the generation and interpretation of terrestrial field data. Currently, biota-to-soil-accumulation factors (BSAFs), biomagnification factors (BMFs), and bioaccumulation factors (BAFs) are the most suitable bioaccumulation metrics that are applicable to bioaccumulation assessment evaluations and able to be generated from terrestrial field studies with relatively low uncertainty. Biomagnification factors calculated from field-collected samples of terrestrial carnivores and their prey appear to be particularly robust indicators of bioaccumulation potential. The use of stable isotope ratios for quantification of trophic relationships in terrestrial ecosystems needs to be further developed to resolve uncertainties associated with the calculation of terrestrial trophic magnification factors (TMFs). Sampling efforts for terrestrial field studies should strive for efficiency, and advice on optimization of study sample sizes, practical considerations for obtaining samples, selection of tissues for analysis, and data interpretation is provided. Although there is still much to be learned regarding terrestrial bioaccumulation, these recommendations provide some initial guidance to the present application of terrestrial field data as a line of evidence in the assessment of chemical bioaccumulation potential and a resource to inform laboratory and modeling efforts.
","language":"English","publisher":"Wiley","doi":"10.1002/ieam.1717","usgsCitation":"van den Brink, N.W., Arblaster, J.A., Bowman, S.R., Conder, J.M., Elliott, J., Johnson, M.S., Muir, D.C., Natal-da-Luz, T., Rattner, B.A., Sample, B.E., and Shore, R.F., 2016, Use of terrestrial field studies in the derivation of bioaccumulation potential of chemicals: Integrated Environmental Assessment and Management, v. 12, no. 1, p. 135-145, https://doi.org/10.1002/ieam.1717.","productDescription":"11 p.","startPage":"135","endPage":"145","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068305","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":471331,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ieam.1717","text":"Publisher Index Page"},{"id":314256,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"1","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2015-10-01","publicationStatus":"PW","scienceBaseUri":"56977530e4b039675d00a6c2","contributors":{"authors":[{"text":"van den Brink, Nico W.","contributorId":39229,"corporation":false,"usgs":true,"family":"van den Brink","given":"Nico","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":588524,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arblaster, Jennifer A.","contributorId":152218,"corporation":false,"usgs":false,"family":"Arblaster","given":"Jennifer","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":588525,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bowman, Sarah R.","contributorId":152219,"corporation":false,"usgs":false,"family":"Bowman","given":"Sarah","email":"","middleInitial":"R.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":588526,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Conder, Jason M.","contributorId":81294,"corporation":false,"usgs":true,"family":"Conder","given":"Jason","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":588527,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Elliott, John E.","contributorId":127368,"corporation":false,"usgs":false,"family":"Elliott","given":"John E.","affiliations":[{"id":6779,"text":"Environment Canada, Burlington, Ontario, Canada","active":true,"usgs":false}],"preferred":false,"id":588528,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Johnson, Mark S.","contributorId":86058,"corporation":false,"usgs":true,"family":"Johnson","given":"Mark","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":588529,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Muir, Derek C.G.","contributorId":68679,"corporation":false,"usgs":true,"family":"Muir","given":"Derek","email":"","middleInitial":"C.G.","affiliations":[],"preferred":false,"id":588530,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Natal-da-Luz, Tiago","contributorId":152220,"corporation":false,"usgs":false,"family":"Natal-da-Luz","given":"Tiago","email":"","affiliations":[],"preferred":false,"id":588531,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rattner, Barnett A. 0000-0003-3676-2843 brattner@usgs.gov","orcid":"https://orcid.org/0000-0003-3676-2843","contributorId":4142,"corporation":false,"usgs":true,"family":"Rattner","given":"Barnett","email":"brattner@usgs.gov","middleInitial":"A.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":588516,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Sample, Bradley E.","contributorId":61135,"corporation":false,"usgs":true,"family":"Sample","given":"Bradley","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":588532,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Shore, Richard F.","contributorId":127369,"corporation":false,"usgs":false,"family":"Shore","given":"Richard","email":"","middleInitial":"F.","affiliations":[{"id":6919,"text":"Natural Environment Research Council, UK","active":true,"usgs":false}],"preferred":false,"id":588533,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70173941,"text":"70173941 - 2016 - Combined effects of projected sea level rise, storm surge, and peak river flows on water levels in the Skagit Floodplain","interactions":[],"lastModifiedDate":"2016-06-21T09:16:38","indexId":"70173941","displayToPublicDate":"2016-01-13T09:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2900,"text":"Northwest Science","onlineIssn":"2161-9859","printIssn":"0029-344X","active":true,"publicationSubtype":{"id":10}},"title":"Combined effects of projected sea level rise, storm surge, and peak river flows on water levels in the Skagit Floodplain","docAbstract":"Current understanding of the combined effects of sea level rise (SLR), storm surge, and changes in river flooding on near-coastal environments is very limited. This project uses a suite of numerical models to examine the combined effects of projected future climate change on flooding in the Skagit floodplain and estuary. Statistically and dynamically downscaled global climate model scenarios from the ECHAM-5 GCM were used as the climate forcings. Unregulated daily river flows were simulated using the VIC hydrology model, and regulated river flows were simulated using the SkagitSim reservoir operations model. Daily tidal anomalies (TA) were calculated using a regression approach based on ENSO and atmospheric pressure forcing simulated by the WRF regional climate model. A 2-D hydrodynamic model was used to estimate water surface elevations in the Skagit floodplain using resampled hourly hydrographs keyed to regulated daily flood flows produced by the reservoir simulation model, and tide predictions adjusted for SLR and TA. Combining peak annual TA with projected sea level rise, the historical (1970–1999) 100-yr peak high water level is exceeded essentially every year by the 2050s. The combination of projected sea level rise and larger floods by the 2080s yields both increased flood inundation area (+ 74%), and increased average water depth (+ 25 cm) in the Skagit floodplain during a 100-year flood. Adding sea level rise to the historical FEMA 100-year flood resulted in a 35% increase in inundation area by the 2040's, compared to a 57% increase when both SLR and projected changes in river flow were combined.
","language":"English","publisher":"BioOne","doi":"10.3955/046.090.0106","usgsCitation":"Hamman, J.J., Hamlet, A.F., Fuller, R., and Grossman, E., 2016, Combined effects of projected sea level rise, storm surge, and peak river flows on water levels in the Skagit Floodplain: Northwest Science, v. 90, no. 1, p. 57-78, https://doi.org/10.3955/046.090.0106.","productDescription":"21 p.","startPage":"57","endPage":"78","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063851","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":471332,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3955/046.090.0106","text":"Publisher Index Page"},{"id":323967,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":323930,"type":{"id":15,"text":"Index Page"},"url":"https://www.bioone.org/doi/abs/10.3955/046.090.0106"}],"country":"United States","state":"Washington","county":"Skagit","otherGeospatial":"Skagit Floodplain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.11279296875001,\n 47.56540738772849\n ],\n [\n -123.11279296875001,\n 48.680080770292875\n ],\n [\n -119.4049072265625,\n 48.680080770292875\n ],\n [\n -119.4049072265625,\n 47.56540738772849\n ],\n [\n -123.11279296875001,\n 47.56540738772849\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"90","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"576913b4e4b07657d19fefe2","contributors":{"authors":[{"text":"Hamman, Josheph J","contributorId":172118,"corporation":false,"usgs":false,"family":"Hamman","given":"Josheph","email":"","middleInitial":"J","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":639641,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hamlet, Alan F.","contributorId":15529,"corporation":false,"usgs":true,"family":"Hamlet","given":"Alan","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":639642,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fuller, Roger","contributorId":172119,"corporation":false,"usgs":false,"family":"Fuller","given":"Roger","email":"","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":639643,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Grossman, Eric E. 0000-0003-0269-6307 egrossman@usgs.gov","orcid":"https://orcid.org/0000-0003-0269-6307","contributorId":140908,"corporation":false,"usgs":true,"family":"Grossman","given":"Eric E.","email":"egrossman@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":false,"id":639640,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70173995,"text":"70173995 - 2016 - Forcing and variability of nonstationary rip currents","interactions":[],"lastModifiedDate":"2018-03-26T13:51:49","indexId":"70173995","displayToPublicDate":"2016-01-13T04:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2315,"text":"Journal of Geophysical Research C: Oceans","active":true,"publicationSubtype":{"id":10}},"title":"Forcing and variability of nonstationary rip currents","docAbstract":"Surface wave transformation and the resulting nearshore circulation along a section of coast with strong alongshore bathymetric gradients outside the surf zone are modeled for a consecutive 4 week time period. The modeled hydrodynamics are compared to in situ measurements of waves and currents collected during the Nearshore Canyon Experiment and indicate that for the entire range of observed conditions, the model performance is similar to other studies along this stretch of coast. Strong alongshore wave height gradients generate rip currents that are observed by remote sensing data and predicted qualitatively well by the numerical model. Previous studies at this site have used idealized scenarios to link the rip current locations to undulations in the offshore bathymetry but do not explain the dichotomy between permanent offshore bathymetric features and intermittent rip current development. Model results from the month‐long simulation are used to track the formation and location of rip currents using hourly statistics, and results show that the direction of the incoming wave energy strongly controls whether rip currents form. In particular, most of the offshore wave spectra were bimodal and we find that the ratio of energy contained in each mode dictates rip current development, and the alongshore rip current position is controlled by the incident wave period. Additionally, model simulations performed with and without updating the nearshore morphology yield no significant change in the accuracy of the predicted surf zone hydrodyanmics indicating that the large‐scale offshore features (e.g., submarine canyon) predominately control the nearshore wave‐circulation system.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2015JC010990","usgsCitation":"Long, J.W., and Ozkan-Haller, H., 2016, Forcing and variability of nonstationary rip currents: Journal of Geophysical Research C: Oceans, v. 121, no. 1, p. 520-539, https://doi.org/10.1002/2015JC010990.","productDescription":"20 p.","startPage":"520","endPage":"539","onlineOnly":"N","additionalOnlineFiles":"N","temporalStart":"2003-10-01","ipdsId":"IP-065750","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":471333,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015jc010990","text":"Publisher Index Page"},{"id":324145,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"121","issue":"1","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2016-01-14","publicationStatus":"PW","scienceBaseUri":"576a653ae4b07657d1a11da3","contributors":{"authors":[{"text":"Long, Joseph W. 0000-0003-2912-1992 jwlong@usgs.gov","orcid":"https://orcid.org/0000-0003-2912-1992","contributorId":3303,"corporation":false,"usgs":true,"family":"Long","given":"Joseph","email":"jwlong@usgs.gov","middleInitial":"W.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":640099,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ozkan-Haller, H.T.","contributorId":172266,"corporation":false,"usgs":false,"family":"Ozkan-Haller","given":"H.T.","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":640100,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70173528,"text":"70173528 - 2016 - Global perspectives on the urban stream syndrome","interactions":[],"lastModifiedDate":"2016-06-21T15:11:11","indexId":"70173528","displayToPublicDate":"2016-01-13T02:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1699,"text":"Freshwater Science","active":true,"publicationSubtype":{"id":10}},"title":"Global perspectives on the urban stream syndrome","docAbstract":"Urban streams commonly express degraded physical, chemical, and biological conditions that have been collectively termed the “urban stream syndrome”. The description of the syndrome highlights the broad similarities among these streams relative to their less-impaired counterparts. Awareness of these commonalities has fostered rapid improvements in the management of urban stormwater for the protection of downstream watercourses, but the focus on the similarities among urban streams has obscured meaningful differences among them. Key drivers of stream responses to urbanization can vary greatly among climatological and physiographic regions of the globe, and the differences can be manifested in individual stream channels even through the homogenizing veneer of urban development. We provide examples of differences in natural hydrologic and geologic settings (within similar regions) that can result in different mechanisms of stream ecosystem response to urbanization and, as such, should lead to different management approaches. The idea that all urban streams can be cured using the same treatment is simplistic, but overemphasizing the tremendous differences among natural (or human-altered) systems also can paralyze management. Thoughtful integration of work that recognizes the commonalities of the urban stream syndrome across the globe has benefitted urban stream management. Now we call for a more nuanced understanding of the regional, subregional, and local attributes of any given urban stream and its watershed to advance the physical, chemical, and ecological recovery of these systems.
","language":"English","publisher":"University of Chicago Press","publisherLocation":"Chicago, IL","doi":"10.1086/684940","usgsCitation":"Roy, A.H., Booth, D.B., Capps, K.A., and Smith, B., 2016, Global perspectives on the urban stream syndrome: Freshwater Science, v. 35, no. 1, p. 412-420, https://doi.org/10.1086/684940.","productDescription":"9 p.","startPage":"412","endPage":"420","numberOfPages":"9","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063957","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":324151,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"35","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"576a653be4b07657d1a11dac","contributors":{"authors":[{"text":"Roy, Allison H. 0000-0002-8080-2729 aroy@usgs.gov","orcid":"https://orcid.org/0000-0002-8080-2729","contributorId":4240,"corporation":false,"usgs":true,"family":"Roy","given":"Allison","email":"aroy@usgs.gov","middleInitial":"H.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":637264,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Booth, Derek B.","contributorId":100873,"corporation":false,"usgs":false,"family":"Booth","given":"Derek","email":"","middleInitial":"B.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":640110,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Capps, Krista A.","contributorId":35456,"corporation":false,"usgs":true,"family":"Capps","given":"Krista","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":640111,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Benjamin","contributorId":171838,"corporation":false,"usgs":false,"family":"Smith","given":"Benjamin","affiliations":[],"preferred":false,"id":640112,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70157983,"text":"ofr20151190 - 2016 - Reconnaissance sediment budget for selected watersheds of West Maui, Hawai‘i","interactions":[],"lastModifiedDate":"2016-01-13T08:49:08","indexId":"ofr20151190","displayToPublicDate":"2016-01-12T18:00:00","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":"2015-1190","title":"Reconnaissance sediment budget for selected watersheds of West Maui, Hawai‘i","docAbstract":"Episodic runoff brings suspended sediment to the nearshore waters of West Maui, Hawaiʻi. Even small rainfalls create visible plumes over a few hours. We used mapping, field experiments, and analysis of recent (July 19–20, 2014) and historic rainfall to estimate sources of land-based pollution for two watersheds in West Maui: Honolua, and Honokōwai. Former agricultural fields and some unimproved roads are plausible sources for polluted runoff, but have saturated hydraulic conductivities greater than the 10–15 millimeters per hour (mm/hr) rainfalls of July 2014. These fields and roads showed minor evidence for storm runoff, and could not have contributed substantially to July 2014 plume generation. Since 1978, rain at intensities capable of causing runoff from former agricultural fields sustained for 1–2 hours is also rare; such intensities have 2–5 year recurrence rates in the north, and greater than 25 year recurrence rates to the south near Lahaina. Streambanks now eroding into historic terraces of sands, silts, and clays are a more plausible source. Although past large storms contributed to sediment loading, annual plume generation is now caused by smaller rainfalls eroding these near-stream legacy deposits. Treatments of former agricultural fields, roads, and reserve forests are consequently not likely to measurably affect sediment pollution from smaller, more frequent storms. Increased runoff from the development of West Maui has the potential to exacerbate sediment plumes from such storms unless there is an effective strategy to reduce bank erosion. Uncertainties in the extent and erosion rate of historic terraces, however, limit our ability to plan mitigation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151190","usgsCitation":"Stock, J.D., Falinksi, K.A., Callender, T., 2015, Reconnaissance sediment budget for selected watersheds of West Maui, Hawai‘i: U.S. Geological Survey Open-File Report 2015–1190, 42 p., https://www.dx.doi.org/10.3133/ofr20151190.","productDescription":"v, 42 p.","numberOfPages":"52","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-066158","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":314194,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1190/coverthb.jpg"},{"id":314195,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1190/ofr20151190.pdf","text":"Report","size":"22.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1190"}],"country":"United States","state":"Hawaii","otherGeospatial":"West Maui","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.70074462890625,\n 20.785646688202153\n ],\n [\n -156.70074462890625,\n 21.03804387657284\n ],\n [\n -156.55620574951172,\n 21.03804387657284\n ],\n [\n -156.55620574951172,\n 20.785646688202153\n ],\n [\n -156.70074462890625,\n 20.785646688202153\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"GMEG staff, Geology, Minerals, Energy, & Geophysics Science Center—Flagstaff
U.S. Geological Survey
2255 N. Gemini Drive
Flagstaff, AZ 86001-1600
http://geomaps.wr.usgs.gov/gmeg/
The biological and hydraulic performance of a new portable floating fish collector (PFFC) located in a cul-de-sac within the forebay of Cougar Dam, Oregon, was evaluated during 2014. The purpose of the PFFC was to explore surface collection as a means to capture juvenile salmonids at one or more sites using a small, cost-effective, pilot-scale device. The PFFC used internal pumps to draw attraction flow over an inclined plane about 3 meters (m) deep, through a flume at a design velocity of as much as 6 feet per second (ft/s), and to empty a small amount of water and any entrained fish into a collection box. Performance of the PFFC was evaluated at 64 cubic feet per second (ft3/s) (Low) and 109 ft3/s (High) inflow rates alternated using a randomized-block schedule from May 27 to December 16, 2014. The evaluation of the biological performance was based on trap catch; behaviors, locations, and collection of juvenile Chinook salmon (Oncorhynchus tshawytscha) tagged with acoustic transmitters plus passive integrated transponder (PIT) tags; collection of juvenile Chinook salmon implanted with only PIT tags; and untagged fish monitored near and within the PFFC using acoustic cameras. The evaluation of hydraulic performance was based on measurements of water velocity and direction of flow in the PFFC.
\nThe PFFC collected 156 juvenile Chinook salmon and 280 individuals of other species, primarily dace (Cyprinidae) and largemouth bass (Micropterus salmoides). The collection included one of the 212 acoustic+PIT-tagged fish detected near the PFFC and two of the 1,505 PIT-tagged fish released near the head of the reservoir. No juvenile salmonids were collected between early July and early September when water temperatures near the water surface were greater than about 16 degrees Celsius (°C). Depths of acoustic+PIT-tagged fish indicated a preferential selection of water temperature of 13–15 °C, which was often deeper than the entrance to the PFFC, and those fish rarely were at depths with water temperatures greater than 16 °C. Dam passage of acoustic+PIT-tagged fish was similar to previous years, but much of the passage occurred prior to the date the PFFC began operation. Discovery Efficiency, the proportion of acoustic+PIT-tagged fish detected in the cul-de-sac that were within 10 m of the PFFC entrance and 0–6 m deep (the Discovery Zone), was 0.736 during the Low treatment and 0.639 during the High treatment. Entrance Efficiency, the proportion of fish in the Discovery Zone that were collected by the PFFC, was 0.007 during the Low treatment and 0.000 during the High treatment. Fish Collection Efficiency, the proportion of acoustic+PIT-tagged fish collected of those detected in the cul-de-sac, was 0.005 and 0.000 during the Low and High treatments, respectively. The areas of highest use by acoustic+PIT-tagged fish were between the stern of the PFFC and the outlet of the reservoir (a water temperature control tower), with the greatest use being near the tower.
\nResults from untagged fish detected with acoustic cameras indicated that most fish near and within the PFFC were in the 90–250-millimeter length bin and few were less than 60 millimeters long; most fish were present during crepuscular periods; trajectories of fish outside the PFFC were rarely directed toward the entrance; and many fish entering the PFFC swam back out before they could be collected.
\nThe hydraulic performance of the PFFC did not achieve the design goals of smooth acceleration of inflow culminating in a peak water velocity of 6 ft/s and, as a result, the hydraulic performance likely contributed to the low biological performance. The greatest water velocity measured in the PFFC (1.87 ft/s) was lower than designed due at least in part to the PFFC being lower in the water column than expected. Additionally, difficulties during anchor deployment prevented placement of the PFFC as near to the reservoir outlet as planned, resulting in a PFFC position outside the prevailing flow field and known areas of high fish densities. Overall, the results indicate that location, hydraulic conditions, water temperature, and shallow depth of the entrance were among the factors contributing to the low biological performance of the PFFC in 2014.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161003","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Beeman, J.W., Evans, S.D., Haner, P.V., Hansel, H.C., Hansen, A.C., Hansen, G.S., Hatton, T.W., Sprando, J.M., Smith, C.D., and Adams, N.S., 2016, Evaluation of the biological and hydraulic performance of the portable floating fish collector at Cougar Reservoir and Dam, Oregon, 2014: U.S. Geological Survey Open-File Report 2016-1003, 127 p., https://dx.doi.org/ 10.3133/ofr20161003.","productDescription":"xii, 127 p.","numberOfPages":"143","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-066415","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":314044,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1003/coverthb.jpg"},{"id":314045,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1003/ofr20161003.pdf","text":"Report","size":"11 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1003 PDF"}],"country":"United States","state":"Oregon","otherGeospatial":"Cougar Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.25173950195311,\n 44.06464670206631\n ],\n [\n -122.25173950195311,\n 44.132449357705454\n ],\n [\n -122.2071075439453,\n 44.132449357705454\n ],\n [\n -122.2071075439453,\n 44.06464670206631\n ],\n [\n -122.25173950195311,\n 44.06464670206631\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115
http://wfrc.usgs.gov/
Evapotranspiration (ET) mapping at the Landsat spatial resolution (100 m) is essential to fully understand water use and water availability at the field scale. Water use estimates in the Colorado River Basin (CRB), which has diverse ecosystems and complex hydro-climatic regions, will be helpful to water planners and managers. Availability of Landsat 8 images, starting in 2013, provides the opportunity to map ET in the CRB to assess spatial distribution and patterns of water use. The Operational Simplified Surface Energy Balance (SSEBop) model was used with 528 Landsat 8 images to create seamless monthly and annual ET estimates at the inherent 100 m thermal band resolution. Annual ET values were summarized by land use/land cover classes. Croplands were the largest consumer of “blue” water while shrublands consumed the most “green” water. Validation using eddy covariance (EC) flux towers and water balance approaches showed good accuracy levels with R2 ranging from 0.74 to 0.95 and the Nash–Sutcliffe model efficiency coefficient ranging from 0.66 to 0.91. The root mean square error (and percent bias) ranged from 0.48 mm (13%) to 0.60 mm (22%) for daily (days of satellite overpass) ET and from 7.75 mm (2%) to 13.04 mm (35%) for monthly ET. The spatial and temporal distribution of ET indicates the utility of Landsat 8 for providing important information about ET dynamics across the landscape. Annual crop water use was estimated for five selected irrigation districts in the Lower CRB where annual ET per district ranged between 681 mm to 772 mm. Annual ET by crop type over the Maricopa Stanfield irrigation district ranged from a low of 384 mm for durum wheat to a high of 990 mm for alfalfa fields. A rainfall analysis over the five districts suggested that, on average, 69% of the annual ET was met by irrigation. Although the enhanced cloud-masking capability of Landsat 8 based on the cirrus band and utilization of the Fmask algorithm improved the removal of contaminated pixels, the ability to reliably estimate ET over clouded areas remains an important challenge. Overall, the performance of Landsat 8 based ET compared to available EC datasets and water balance estimates for a complex basin such as the CRB demonstrates the potential of using Landsat 8 for annual water use estimation at a national scale. Future efforts will focus on (a) use of consistent methodology across years, (b) integration of multiple sensors to maximize images used, and (c) employing cloud-computing platforms for large scale processing capabilities.
","language":"English","publisher":"American Elsevier Pub. Co.","publisherLocation":"New York, NY","doi":"10.1016/j.rse.2015.12.043","usgsCitation":"Senay, G., Friedrichs, M., Singh, R.K., and Velpuri, N.M., 2016, Evaluating Landsat 8 evapotranspiration for water use mapping in the Colorado River Basin: Remote Sensing of Environment, v. 185, p. 171-185, https://doi.org/10.1016/j.rse.2015.12.043.","productDescription":"15 p.","startPage":"171","endPage":"185","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069332","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":471334,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2015.12.043","text":"Publisher Index Page"},{"id":318088,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":335400,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7DF6PDR","text":"Satellite-based water use dynamics using historical Landsat data (1984-2014) in the southwestern United States"}],"country":"United States","state":"Arizona, California, Colorado, Nevada, New Mexico, Utah, Wyoming","otherGeospatial":"Colorado River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.06152343749999,\n 43.54854811091286\n ],\n [\n -106.69921875,\n 42.65012181368025\n ],\n [\n -106.12792968749999,\n 41.44272637767212\n ],\n [\n -105.908203125,\n 40.613952441166596\n ],\n [\n -106.3037109375,\n 38.8225909761771\n ],\n [\n -107.490234375,\n 34.488447837809304\n ],\n [\n -108.19335937499999,\n 32.06395559466043\n ],\n [\n -109.599609375,\n 31.240985378021307\n ],\n [\n -111.22558593749999,\n 31.353636941500987\n ],\n [\n -114.82910156249999,\n 32.54681317351517\n ],\n [\n -115.00488281250001,\n 32.84267363195431\n ],\n [\n -116.76269531249999,\n 35.639441068973916\n ],\n [\n -116.01562499999999,\n 37.125286284966776\n ],\n [\n -114.3017578125,\n 37.96152331396616\n ],\n [\n -113.02734374999999,\n 38.376115424036016\n ],\n [\n -111.09374999999999,\n 41.918628865183045\n ],\n [\n -110.89599609375,\n 42.32606244456202\n ],\n [\n -110.4345703125,\n 42.956422511073335\n ],\n [\n -109.70947265625,\n 43.389081939117496\n ],\n [\n -109.0283203125,\n 43.56447158721811\n ],\n [\n -108.06152343749999,\n 43.54854811091286\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"185","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56c45640e4b0946c65218507","contributors":{"authors":[{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":166812,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":620124,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Friedrichs, MacKenzie 0000-0002-9602-321X mfriedrichs@usgs.gov","orcid":"https://orcid.org/0000-0002-9602-321X","contributorId":5847,"corporation":false,"usgs":true,"family":"Friedrichs","given":"MacKenzie","email":"mfriedrichs@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":620125,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Singh, Ramesh K. 0000-0002-8164-3483 rsingh@usgs.gov","orcid":"https://orcid.org/0000-0002-8164-3483","contributorId":3895,"corporation":false,"usgs":true,"family":"Singh","given":"Ramesh","email":"rsingh@usgs.gov","middleInitial":"K.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":620126,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Velpuri, Naga Manohar 0000-0002-6370-1926 nvelpuri@usgs.gov","orcid":"https://orcid.org/0000-0002-6370-1926","contributorId":166813,"corporation":false,"usgs":true,"family":"Velpuri","given":"Naga","email":"nvelpuri@usgs.gov","middleInitial":"Manohar","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":620127,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70168549,"text":"70168549 - 2016 - A semi-structured MODFLOW-USG model to evaluate local water sources to wells for decision support","interactions":[],"lastModifiedDate":"2019-12-12T12:50:22","indexId":"70168549","displayToPublicDate":"2016-01-12T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1861,"text":"Ground Water","active":true,"publicationSubtype":{"id":10}},"title":"A semi-structured MODFLOW-USG model to evaluate local water sources to wells for decision support","docAbstract":"In order to better represent the configuration of the stream network and simulate local groundwater-surface water interactions, a version of MODFLOW with refined spacing in the topmost layer was applied to a Lake Michigan Basin (LMB) regional groundwater-flow model developed by the U.S. Geological. Regional MODFLOW models commonly use coarse grids over large areas; this coarse spacing precludes model application to local management issues (e.g., surface-water depletion by wells) without recourse to labor-intensive inset models. Implementation of an unstructured formulation within the MODFLOW framework (MODFLOW-USG) allows application of regional models to address local problems. A “semi-structured” approach (uniform lateral spacing within layers, different lateral spacing among layers) was tested using the LMB regional model. The parent 20-layer model with uniform 5000-foot (1524-m) lateral spacing was converted to 4 layers with 500-foot (152-m) spacing in the top glacial (Quaternary) layer, where surface water features are located, overlying coarser resolution layers representing deeper deposits. This semi-structured version of the LMB model reproduces regional flow conditions, whereas the finer resolution in the top layer improves the accuracy of the simulated response of surface water to shallow wells. One application of the semi-structured LMB model is to provide statistical measures of the correlation between modeled inputs and the simulated amount of water that wells derive from local surface water. The relations identified in this paper serve as the basis for metamodels to predict (with uncertainty) surface-water depletion in response to shallow pumping within and potentially beyond the modeled area, see Fienen et al. (2015a).
","language":"English","publisher":"Wiley","doi":"10.1111/gwat.12389","usgsCitation":"Feinstein, D.T., Fienen, M., Reeves, H.W., and Langevin, C.D., 2016, A semi-structured MODFLOW-USG model to evaluate local water sources to wells for decision support: Ground Water, v. 54, no. 4, p. 532-544, https://doi.org/10.1111/gwat.12389.","productDescription":"13 p.","startPage":"532","endPage":"544","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066913","costCenters":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":318155,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Indiana, Michigan, Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.97802734375,\n 41.261291493919884\n ],\n [\n -83.84765625,\n 41.261291493919884\n ],\n [\n -83.84765625,\n 46.800059446787316\n ],\n [\n -89.97802734375,\n 46.800059446787316\n ],\n [\n -89.97802734375,\n 41.261291493919884\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"54","issue":"4","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2016-01-12","publicationStatus":"PW","scienceBaseUri":"56c6f93be4b0946c65240718","contributors":{"authors":[{"text":"Feinstein, Daniel T. 0000-0003-1151-2530 dtfeinst@usgs.gov","orcid":"https://orcid.org/0000-0003-1151-2530","contributorId":1907,"corporation":false,"usgs":true,"family":"Feinstein","given":"Daniel","email":"dtfeinst@usgs.gov","middleInitial":"T.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":620879,"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":893,"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":620880,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reeves, Howard W. 0000-0001-8057-2081 hwreeves@usgs.gov","orcid":"https://orcid.org/0000-0001-8057-2081","contributorId":2307,"corporation":false,"usgs":true,"family":"Reeves","given":"Howard","email":"hwreeves@usgs.gov","middleInitial":"W.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":620881,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Langevin, Christian D. 0000-0001-5610-9759 langevin@usgs.gov","orcid":"https://orcid.org/0000-0001-5610-9759","contributorId":1030,"corporation":false,"usgs":true,"family":"Langevin","given":"Christian","email":"langevin@usgs.gov","middleInitial":"D.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":620882,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70159498,"text":"ofr20151209 - 2016 - USGS lidar science strategy—Mapping the technology to the science","interactions":[],"lastModifiedDate":"2017-05-16T16:07:30","indexId":"ofr20151209","displayToPublicDate":"2016-01-11T17:00:00","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":"2015-1209","title":"USGS lidar science strategy—Mapping the technology to the science","docAbstract":"The U.S. Geological Survey (USGS) utilizes light detection and ranging (lidar) and enabling technologies to support many science research activities. Lidar-derived metrics and products have become a fundamental input to complex hydrologic and hydraulic models, flood inundation models, fault detection and geologic mapping, topographic and land-surface mapping, landslide and volcano hazards mapping and monitoring, forest canopy and habitat characterization, coastal and fluvial erosion mapping, and a host of other research and operational activities. This report documents the types of lidar being used by the USGS, discusses how lidar technology facilitates the achievement of individual mission area goals within the USGS, and offers recommendations and suggested changes in direction in terms of how a mission area could direct work using lidar as it relates to the mission area goals that have already been established.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151209","usgsCitation":"Stoker, J.M., Brock, J.C., Soulard, C.E., Ries, K.G., Sugarbaker, L.J., Newton, W.E., Haggerty, P.K., Lee, K.E., and Young, J.A., 2016, USGS lidar science strategy—Mapping the technology to the science: U.S. Geological Survey Open-File Report 2015–1209, 33 p., https://dx.doi.org/10.3133/ofr20151209.","productDescription":"v, 33 p.","numberOfPages":"39","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-065301","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":313846,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1209/coverthb.jpg"},{"id":313847,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1209/ofr20151209.pdf","text":"Report","size":"4.19 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1209"}],"contact":"Director, National Geospatial Program
U.S. Geological Survey
12201 Sunrise Valley Drive
511 National Center
Reston, VA 20192
Email:3dep@usgs.gov
http://www.usgs.gov/ngpo/
http://nationalmap.gov/3DEP/
A liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was developed for determination of 229 pesticides compounds (113 pesticides and 116 pesticide degradates) in filtered water samples from stream and groundwater sites. The pesticides represent a broad range of chemical classes and were selected based on criteria such as current-use intensity, probability of occurrence in streams and groundwater, and toxicity to humans or aquatic organisms. More than half of the analytes are pesticide degradates. The method involves direct injection of a 100-microliter (μL) sample onto the LC-MS/MS without any sample preparation other than filtration. Samples are analyzed with two injections, one in electrospray ionization (ESI) positive mode and one in ESI negative mode, using dynamic multiple reaction monitoring (MRM) conditions, with two MRM transitions for each analyte. The LC-MS/MS instrument parameters were optimized for highest sensitivity for the most analytes. This report describes the analytical method and presents characteristics of the method validation including bias and variability, detection levels, and holding-time studies.
\nMean recoveries of most analytes (223 of 229) were within data-quality objectives of 100±30 percent at spike concentrations above method detection levels (MDLs) in all four matrices. The calculated MDLs ranged from 1 to 103 nanograms per liter (ng/L) for 182 analytes analyzed in the ESI positive mode, and from 2 to 106 ng/L for 42 analytes analyzed in the ESI negative mode. Five analytes had MDLs between 100 and 250 ng/L. The stability studies in reagent water demonstrated that the largest number of the pesticide compounds (227 of 229) were stable after 14 days of storage at 4 degrees Celsius, so these were selected as the practical holding time and storage temperature for routine sample processing. The use of antimicrobial reagent citric acid to adjust the sample pH to about 4 also resulted in lower recoveries of some analytes, so it should not be used as a routine sample preservative.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section B: Methods of the National Water Quality Laboratory in Book 5 Laboratory Analysis","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm5B11","usgsCitation":"Sandstrom, M.W., Kanagy, L.K., Anderson, C.A., and Kanagy, C.J., 2015, Determination of pesticides and pesticide\ndegradates in filtered water by direct aqueous-injection liquid chromatography-tandem mass spectrometry: U.S.\nGeological Survey Techniques and Methods, book 5, chap. B11, 54 p., https://dx.doi.org/10.3133/tm5B11.","productDescription":"Report: xv, 54 p.; Tables 1-62; 1 Figure; Appendix","numberOfPages":"73","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-054757","costCenters":[{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true},{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true}],"links":[{"id":323562,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/tm/05/b11/tables/t15_t20_mdl_study.xlsx","text":"Tables 15-20"},{"id":323561,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/tm/05/b11/tables/t1_t14_method_description.xlsx","text":"Tables 1-14"},{"id":313244,"rank":5,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/tm/05/b11/figure3.pdf","text":"Figure 3 - High-resolution"},{"id":399813,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/05/b11/appendix/pdf/","text":"Supporting Figures S1 - S14"},{"id":313230,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/05/b11/appendix/tm_supporting_tables.xlsx","text":"Supporting Tables S1 - S12"},{"id":313167,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/05/b11/tm5b11.pdf","text":"Report","size":"7.44 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Techniques and Methods 5–B11"},{"id":323567,"rank":12,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/tm/05/b11/tables/t62_s2437_tm_summary.xlsx","text":"Table 62"},{"id":323566,"rank":11,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/tm/05/b11/tables/t43_t61_stability_studies.xlsx","text":"Tables 43-61"},{"id":323565,"rank":10,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/tm/05/b11/tables/t28_t42_field_study.xlsx","text":"Tables 28-42"},{"id":323564,"rank":9,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/tm/05/b11/tables/t24_t27_lab_qc.xlsx","text":"Tables 24-27"},{"id":323563,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/tm/05/b11/tables/t21_t23_matrix_effects.xlsx","text":"Tables 21-23"},{"id":313166,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/05/b11/coverthb.jpg"}],"publicComments":"This report is Chapter 11 of Section B: Methods of the National Water Quality Laboratory in Book 5 Laboratory Analysis","contact":"Chief, National Water Quality Laboratory
U.S. Geological Survey
Box 25585, Mail Stop 407
Denver, CO 80225-0585
http://nwql.usgs.gov/
\n
\n
\n
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","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"publishedDate":"2016-01-11","noUsgsAuthors":false,"publicationDate":"2016-01-11","publicationStatus":"PW","scienceBaseUri":"5694d22ce4b039675d005dbc","contributors":{"authors":[{"text":"Sandstrom, Mark W. 0000-0003-0006-5675 sandstro@usgs.gov","orcid":"https://orcid.org/0000-0003-0006-5675","contributorId":706,"corporation":false,"usgs":true,"family":"Sandstrom","given":"Mark","email":"sandstro@usgs.gov","middleInitial":"W.","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true}],"preferred":true,"id":564420,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kanagy, Leslie K. 0000-0001-5073-8538 lkkanagy@usgs.gov","orcid":"https://orcid.org/0000-0001-5073-8538","contributorId":4543,"corporation":false,"usgs":true,"family":"Kanagy","given":"Leslie","email":"lkkanagy@usgs.gov","middleInitial":"K.","affiliations":[{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true}],"preferred":true,"id":564421,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, Cyrissa A. cadamson@usgs.gov","contributorId":4379,"corporation":false,"usgs":true,"family":"Anderson","given":"Cyrissa","email":"cadamson@usgs.gov","middleInitial":"A.","affiliations":[{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true}],"preferred":true,"id":564422,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kanagy, Christopher J. ckanagy@usgs.gov","contributorId":1201,"corporation":false,"usgs":true,"family":"Kanagy","given":"Christopher","email":"ckanagy@usgs.gov","middleInitial":"J.","affiliations":[{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true}],"preferred":true,"id":564423,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70160018,"text":"fs20153087 - 2016 - Assessment of undiscovered continuous gas resources of the Ordos Basin Province, China, 2015","interactions":[],"lastModifiedDate":"2019-11-11T12:22:05","indexId":"fs20153087","displayToPublicDate":"2016-01-11T13:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-3087","title":"Assessment of undiscovered continuous gas resources of the Ordos Basin Province, China, 2015","docAbstract":"
Using a geology-based assessment methodology, the U.S. Geological Survey estimated mean resources of 28 trillion cubic feet of tight gas and 5.6 trillion cubic feet of coalbed gas in upper Paleozoic rocks in the Ordos Basin Province, China.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20153087","usgsCitation":"Charpentier, R.R., Klett, T.R., Schenk, C.J., Brownfield, M.E., Gaswirth, S.B., Le, P.A., Leathers-Miller, H.M., Marra, K.R., and Mercier, T.J., 2016, Assessment of undiscovered continuous gas resources of the Ordos Basin Province, China, 2015: U.S. Geological Survey Fact Sheet 2015–3087, 2 p., https://dx.doi.org/10.3133/fs20153087.","productDescription":"2 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069150","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":313993,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2015/3087/coverthb.jpg"},{"id":313994,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2015/3087/fs20153087.pdf","text":"Report","size":"11.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2015-3087"}],"country":"China","otherGeospatial":"Ordos Basin Province","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 107.09472656249999,\n 34.65128519895413\n ],\n [\n 111.55517578125,\n 34.65128519895413\n ],\n [\n 111.55517578125,\n 41.918628865183045\n ],\n [\n 107.09472656249999,\n 41.918628865183045\n ],\n [\n 107.09472656249999,\n 34.65128519895413\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS–939
Denver Federal Center
Denver, CO 80225–0046
http://energy.usgs.gov/
Surface tension plays an important role in the nucleation of H2O gas bubbles in magmatic melts and in the time-dependent rheology of bubble-bearing magmas. Despite several experimental studies, a physics based model of the surface tension of magmatic melts in contact with H2O is lacking. This paper employs gradient theory to develop a thermodynamical model of equilibrium surface tension of silicate melts in contact with H2O gas at low to moderate pressures. In the last decades, this approach has been successfully applied in studies of industrial mixtures but never to magmatic systems. We calibrate and verify the model against literature experimental data, obtained by the pendant drop method, and by inverting bubble nucleation experiments using the Classical Nucleation Theory (CNT). Our model reproduces the systematic decrease in surface tension with increased H2O pressure observed in the experiments. On the other hand, the effect of temperature is confirmed by the experiments only at high pressure. At atmospheric pressure, the model shows a decrease of surface tension with temperature. This is in contrast with a number of experimental observations and could be related to microstructural effects that cannot be reproduced by our model. Finally, our analysis indicates that the surface tension measured inverting the CNT may be lower than the value measured by the pendant drop method, most likely because of changes in surface tension controlled by the supersaturation.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gca.2015.10.037","usgsCitation":"Colucci, S., Battaglia, M., and Trigila, R., 2016, A thermodynamical model for the surface tension of silicate melts in contact with H2O gas: Geochimica et Cosmochimica Acta, v. 175, p. 113-127, https://doi.org/10.1016/j.gca.2015.10.037.","productDescription":"15 p.","startPage":"113","endPage":"127","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065296","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":314086,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"175","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5694d22ae4b039675d005db6","contributors":{"authors":[{"text":"Colucci, Simone","contributorId":152109,"corporation":false,"usgs":false,"family":"Colucci","given":"Simone","affiliations":[{"id":18867,"text":"INGV-sezione di Pisa,Italy","active":true,"usgs":false}],"preferred":false,"id":587970,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Battaglia, Maurizio mbattaglia@usgs.gov","contributorId":139631,"corporation":false,"usgs":true,"family":"Battaglia","given":"Maurizio","email":"mbattaglia@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":587969,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Trigila, Raffaello","contributorId":152110,"corporation":false,"usgs":false,"family":"Trigila","given":"Raffaello","email":"","affiliations":[{"id":18868,"text":"Sapienza - University of Rome","active":true,"usgs":false}],"preferred":false,"id":587971,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70173780,"text":"70173780 - 2016 - Using standardized fishery data to inform rehabilitation efforts","interactions":[],"lastModifiedDate":"2016-06-09T12:09:37","indexId":"70173780","displayToPublicDate":"2016-01-11T05:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2592,"text":"Lake and Reservoir Management","active":true,"publicationSubtype":{"id":10}},"title":"Using standardized fishery data to inform rehabilitation efforts","docAbstract":"Lakes and reservoirs progress through an aging process often accelerated by human activities, resulting in degradation or loss of ecosystem services. Resource managers thus attempt to slow or reverse the negative effects of aging using a myriad of rehabilitation strategies. Sustained monitoring programs to assess the efficacy of rehabilitation strategies are often limited; however, long-term standardized fishery surveys may be a valuable data source from which to begin evaluation. We present 3 case studies using standardized fishery survey data to assess rehabilitation efforts stemming from the Nebraska Aquatic Habitat Plan, a large-scale program with the mission to rehabilitate waterbodies within the state. The case studies highlight that biotic responses to rehabilitation efforts can be assessed, to an extent, using standardized fishery data; however, there were specific areas where minor increases in effort would clarify the effectiveness of rehabilitation techniques. Management of lakes and reservoirs can be streamlined by maximizing the utility of such datasets to work smarter, not harder. To facilitate such efforts, we stress collecting both biotic (e.g., fish lengths and weight) and abiotic (e.g., dissolved oxygen, pH, and turbidity) data during standardized fishery surveys and designing rehabilitation actions with an appropriate experimental design.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/10402381.2015.1118418","usgsCitation":"Spurgeon, J., Stewart, N.T., Pegg, M.A., Pope, K.L., and Porath, M.T., 2016, Using standardized fishery data to inform rehabilitation efforts: Lake and Reservoir Management, v. 32, no. 1, p. 41-50, https://doi.org/10.1080/10402381.2015.1118418.","productDescription":"10 p.","startPage":"41","endPage":"50","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066248","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":471335,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/10402381.2015.1118418","text":"Publisher Index Page"},{"id":323373,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nebraska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.1064453125,\n 39.8928799002948\n ],\n [\n -104.1064453125,\n 43.02071359427862\n ],\n [\n -95.33935546875,\n 43.02071359427862\n ],\n [\n -95.33935546875,\n 39.8928799002948\n ],\n [\n -104.1064453125,\n 39.8928799002948\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"32","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-01-11","publicationStatus":"PW","scienceBaseUri":"575a9337e4b04f417c275190","contributors":{"authors":[{"text":"Spurgeon, Jonathan J.","contributorId":146395,"corporation":false,"usgs":false,"family":"Spurgeon","given":"Jonathan J.","affiliations":[],"preferred":false,"id":638167,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stewart, Nathaniel T.","contributorId":171639,"corporation":false,"usgs":false,"family":"Stewart","given":"Nathaniel","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":638168,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pegg, Mark A.","contributorId":45212,"corporation":false,"usgs":true,"family":"Pegg","given":"Mark","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":638169,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pope, Kevin L. 0000-0003-1876-1687 kpope@usgs.gov","orcid":"https://orcid.org/0000-0003-1876-1687","contributorId":1574,"corporation":false,"usgs":true,"family":"Pope","given":"Kevin","email":"kpope@usgs.gov","middleInitial":"L.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":638165,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Porath, Mark T.","contributorId":28846,"corporation":false,"usgs":true,"family":"Porath","given":"Mark","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":638170,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70185996,"text":"70185996 - 2016 - Iterative ecological forecasting: Needs, opportunities, and challenges","interactions":[],"lastModifiedDate":"2017-04-10T10:43:46","indexId":"70185996","displayToPublicDate":"2016-01-09T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Iterative ecological forecasting: Needs, opportunities, and challenges","docAbstract":"A fundamental environmental challenge facing humanity in the 21st century and beyond is predicting the impacts of global environmental change. This challenge is complicated by the fact that we live on a non-stationary, unreplicated planet that is rapidly moving outside the envelope of natural variability into an historical non-analog world. In other words, while the past helps inform us about how the world has worked, it may no longer be the relevant frame of reference for management, conservation, and sustainability. In this future world the two questions at the foundation of sustainability are “How are ecosystems and the services they provide going to change in the future?” and “How do human decisions affect this trajectory?” These are, at their heart, questions about ecological forecasting.
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The samples were tested in a triaxial loading machine at a confining pressure of 50 MPa, pore pressure of 20 MPa, and temperature of 260°C, simulating a depth of 2 km under hydrostatic conditions. A pore pressure difference of up to 2 MPa was imposed across the ends of the sample. Fracture permeability decreased by 1–2 orders of magnitude during the 200–330 h experiments. Electron microprobe and SEM data indicated the formation of needle-shaped crystals of serpentine composition along the walls of the fracture, and chemical analyses of sampled pore fluids were consistent with dissolution of ferro-magnesian minerals. By comparing the difference between fracture permeability and matrix permeability measured on intact samples of the same rock types, we concluded that the contribution of the low matrix permeability to flow is negligible and essentially all of the flow is focused in the tensile fracture. The experimental results suggest that the fracture network in long-lived hydrothermal circulation systems can be sealed rapidly as a result of mineral precipitation, and generation of new permeability resulting from a combination of tectonic and crystallization-induced stresses is required to maintain fluid circulation.
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junema@usgs.gov","orcid":"https://orcid.org/0000-0002-7428-219X","contributorId":156307,"corporation":false,"usgs":true,"family":"Unema","given":"Joel","email":"junema@usgs.gov","middleInitial":"A.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":597391,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ort, Michael H.","contributorId":156308,"corporation":false,"usgs":false,"family":"Ort","given":"Michael","email":"","middleInitial":"H.","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":true,"id":597392,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Larsen, Jessica D","contributorId":156309,"corporation":false,"usgs":false,"family":"Larsen","given":"Jessica","email":"","middleInitial":"D","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":597393,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Neal, Christina A. 0000-0002-7697-7825 tneal@usgs.gov","orcid":"https://orcid.org/0000-0002-7697-7825","contributorId":131135,"corporation":false,"usgs":true,"family":"Neal","given":"Christina","email":"tneal@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":597390,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schaefer, Janet R.","contributorId":82224,"corporation":false,"usgs":true,"family":"Schaefer","given":"Janet","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":597394,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70227155,"text":"70227155 - 2016 - Geostatistical analysis of tritium, groundwater age and other noble gas derived parameters in California","interactions":[],"lastModifiedDate":"2022-01-03T16:47:07.686751","indexId":"70227155","displayToPublicDate":"2016-01-08T10:39:59","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3716,"text":"Water Research","onlineIssn":"1879-2448","printIssn":"0043-1354","active":true,"publicationSubtype":{"id":10}},"title":"Geostatistical analysis of tritium, groundwater age and other noble gas derived parameters in California","docAbstract":"Key characteristics of California groundwater systems related to aquifer vulnerability, sustainability, recharge locations and mechanisms, and anthropogenic impact on recharge are revealed in a spatial geostatistical analysis of a unique data set of tritium, noble gases and other isotopic analyses unprecedented in size at nearly 4000 samples.
The correlation length of key groundwater residence time parameters varies between tens of kilometers (3H; age) to the order of a hundred kilometers (4Heter; 14C; 3Hetrit). The correlation length of parameters related to climate, topography and atmospheric processes is on the order of several hundred kilometers (recharge temperature; δ18O). Young groundwater ages that highlight regional recharge areas are located in the eastern San Joaquin Valley, in the southern Santa Clara Valley Basin, in the upper LA basin and along unlined canals carrying Colorado River water, showing that much of the recent recharge in central and southern California is dominated by river recharge and managed aquifer recharge. Modern groundwater is found in wells with the top open intervals below 60 m depth in the southeastern San Joaquin Valley, Santa Clara Valley and Los Angeles basin, as the result of intensive pumping and/or managed aquifer recharge operations.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.watres.2016.01.004","usgsCitation":"Visser, A., Moran, J.E., Hillegonds, D., Singleton, M., Kulongoski, J.T., Belitz, K., and Esser, B., 2016, Geostatistical analysis of tritium, groundwater age and other noble gas derived parameters in California: Water Research, v. 91, p. 314-330, https://doi.org/10.1016/j.watres.2016.01.004.","productDescription":"17 p.","startPage":"314","endPage":"330","ipdsId":"IP-116710","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":471337,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1359965","text":"Publisher Index Page"},{"id":393750,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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While climate change is predicted to increase the frequency and magnitude of extreme precipitation events, the resulting impact on groundwater recharge remains poorly understood. We quantify empirical relations between precipitation characteristics and episodic groundwater recharge for a wide variety of geographic and land use types across North Carolina. We extract storm duration, magnitude, average rate, and hourly weighted intensity from long-term precipitation records over periods of 12–35 years at 10 locations. Using time series of water table fluctuations from nearby monitoring wells, we estimate relative recharge to precipitation ratios (RPR) to identify statistical trends. Increased RPR correlates with increased storm duration, whereas RPR decreases with increasing magnitude, average rate, and intensity of precipitation. Agricultural and urban areas exhibit the greatest decrease in RPR due to increasing storm magnitude, average rate, and intensity, while naturally vegetated areas exhibit a larger increase in RPR with increased storm duration. Though RPR is generally higher during the winter than the summer, this seasonal effect is magnified in the Appalachian and Piedmont regions. These statistical trends provide valuable insights into the likely consequences of climate and land use change for water resources in subtropical climates. If, as predicted, growing seasons lengthen and the intensity of storms increases with a warming climate, decreased recharge in Appalachia, the Piedmont, and rapidly growing urban areas of the American Southeast could further limit groundwater availability.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2015WR017876","usgsCitation":"Tashie, A., Mirus, B.B., and Pavelsky, T., 2016, Identifying long term empirical relationships between storm characteristics and episodic groundwater recharge: Water Resources Research, v. 52, no. 1, p. 21-35, https://doi.org/10.1002/2015WR017876.","productDescription":"15 p.","startPage":"21","endPage":"35","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-070354","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":471338,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.17615/5qy1-7985","text":"Publisher Index Page"},{"id":314716,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North 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Hazards","active":true,"usgs":true},{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":578999,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pavelsky, Tamlin","contributorId":149629,"corporation":false,"usgs":false,"family":"Pavelsky","given":"Tamlin","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":579000,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70177887,"text":"70177887 - 2016 - Long-term changes in sediment and nutrient delivery from Conowingo Dam to Chesapeake Bay: Effects of reservoir sedimentation","interactions":[],"lastModifiedDate":"2017-07-19T15:46:21","indexId":"70177887","displayToPublicDate":"2016-01-08T06:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Long-term changes in sediment and nutrient delivery from Conowingo Dam to Chesapeake Bay: Effects of reservoir sedimentation","docAbstract":"Reduction of suspended sediment (SS), total phosphorus (TP), and total nitrogen is an important focus for Chesapeake Bay watershed management. The Susquehanna River, the bay’s largest tributary, has drawn attention because SS loads from behind Conowingo Dam (near the river’s mouth) have been rising dramatically. To better understand these changes, we evaluated histories of concentration and loading (1986–2013) using data from sites above and below Conowingo Reservoir. First, observed concentration-discharge relationships show that SS and TP concentrations at the reservoir inlet have declined under most discharges in recent decades, but without corresponding declines at the outlet, implying recently diminished reservoir trapping. Second, best estimates of mass balance suggest decreasing net deposition of SS and TP in recent decades over a wide range of discharges, with cumulative mass generally dominated by the 75∼99.5th percentile of daily Conowingo discharges. Finally, stationary models that better accommodate effects of riverflow variability also support the conclusion of diminished trapping of SS and TP under a range of discharges that includes those well below the literature-reported scour threshold. Overall, these findings suggest that decreased net deposition of SS and TP has occurred at subscour levels of discharge, which has significant implications for the Chesapeake Bay ecosystem.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.5b04073","usgsCitation":"Zhang, Q., Hirsch, R.M., and Ball, W.P., 2016, Long-term changes in sediment and nutrient delivery from Conowingo Dam to Chesapeake Bay: Effects of reservoir sedimentation: Environmental Science & Technology, v. 50, no. 4, p. 1877-1886, https://doi.org/10.1021/acs.est.5b04073.","productDescription":"10 p.","startPage":"1877","endPage":"1886","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-072134","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":330431,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Chesapeake Bay, Conowingo Dam, Susquehanna River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.7,\n 38.136716904135376\n ],\n [\n -76.7,\n 39.89\n ],\n [\n -75.87158203125,\n 39.89\n ],\n [\n -75.87158203125,\n 38.136716904135376\n ],\n [\n -76.7,\n 38.136716904135376\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"50","issue":"4","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-01","publicationStatus":"PW","scienceBaseUri":"5811c0f4e4b0f497e79a5a89","contributors":{"authors":[{"text":"Zhang, Qian 0000-0003-0500-5655","orcid":"https://orcid.org/0000-0003-0500-5655","contributorId":174393,"corporation":false,"usgs":false,"family":"Zhang","given":"Qian","email":"","affiliations":[{"id":38802,"text":"University of Maryland Center for Environmental Studies","active":true,"usgs":false}],"preferred":false,"id":652028,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hirsch, Robert M. 0000-0002-4534-075X rhirsch@usgs.gov","orcid":"https://orcid.org/0000-0002-4534-075X","contributorId":2005,"corporation":false,"usgs":true,"family":"Hirsch","given":"Robert","email":"rhirsch@usgs.gov","middleInitial":"M.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":652027,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ball, William P.","contributorId":174394,"corporation":false,"usgs":false,"family":"Ball","given":"William","email":"","middleInitial":"P.","affiliations":[{"id":27446,"text":"Johns Hopkins University, Department of Geography and Environmental Engineering","active":true,"usgs":false}],"preferred":false,"id":652029,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}