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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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":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","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":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":70168436,"text":"70168436 - 2016 - Evaluating Landsat 8 evapotranspiration for water use mapping in the Colorado River Basin","interactions":[],"lastModifiedDate":"2017-02-14T15:48:22","indexId":"70168436","displayToPublicDate":"2016-01-12T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating Landsat 8 evapotranspiration for water use mapping in the Colorado River Basin","docAbstract":"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":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":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":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":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":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
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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":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true},{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","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":70161861,"text":"70161861 - 2016 - A thermodynamical model for the surface tension of silicate melts in contact with H2O gas","interactions":[],"lastModifiedDate":"2016-01-11T09:18:39","indexId":"70161861","displayToPublicDate":"2016-01-11T10:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1759,"text":"Geochimica et Cosmochimica Acta","active":true,"publicationSubtype":{"id":10}},"title":"A thermodynamical model for the surface tension of silicate melts in contact with H2O gas","docAbstract":"
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":70164447,"text":"70164447 - 2016 - Water-magma interaction and plume processes in the 2008 Okmok eruption, Alaska","interactions":[],"lastModifiedDate":"2016-12-16T10:48:48","indexId":"70164447","displayToPublicDate":"2016-01-08T11:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1786,"text":"Geological Society of America Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"Water-magma interaction and plume processes in the 2008 Okmok eruption, Alaska","docAbstract":"Eruptions of similar explosivity can have divergent effects on the surroundings due to differences in the behavior of the tephra in the eruption column and atmosphere. Okmok volcano, located on Umnak Island in the eastern Aleutian Islands, erupted explosively between 12 July and 19 August 2008. The basaltic andesitic eruption ejected ∼0.24 km3dense rock equivalent (DRE) of tephra, primarily directed to the northeast of the vent area. The first 4 h of the eruption produced dominantly coarse-grained tephra, but the following 5 wk of the eruption deposited almost exclusively ash, much of it very fine and deposited as ash pellets and ashy rain and mist. Meteorological storms combined with abundant plume water to efficiently scrub ash from the eruption column, with a rapid decrease in deposit thickness with distance from the vent. Grain-size analysis shows that the modes (although not their relative proportions) are very constant throughout the deposit, implying that the fragmentation mechanisms did not vary much. Grain-shape features consistent with molten fuel-coolant interaction are common. Surface and groundwater drainage into the vents provided the water for phreatomagmatic fragmentation. The available water (water that could reach the vent area during the eruption) was ∼2.8 × 1010 kg, and the erupted magma totaled ∼7 × 1011 kg, which yield an overall water:magma mass ratio of ∼0.04, but much of the water was not interactive. Although magma flux dropped from 1 × 107 kg/s during the initial 4 h to 1.8 × 105 kg/s for the remainder of the eruption, most of the erupted material was ejected during the lower-mass-flux period due to its much greater length, and this tephra was dominantly deposited within 10 km downwind of the vent. This highlights the importance of ash scrubbing in the evaluation of hazards from explosive eruptions.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/B31360.1","usgsCitation":"Unema, J.A., Ort, M.H., Larsen, J.D., Neal, C.A., and Schaefer, J.R., 2016, Water-magma interaction and plume processes in the 2008 Okmok eruption, Alaska: Geological Society of America Bulletin, v. 128, no. 5-6, p. 792-806, https://doi.org/10.1130/B31360.1.","productDescription":"15 p.","startPage":"792","endPage":"806","numberOfPages":"15","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063687","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":316594,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Umnak Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 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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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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":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction 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}]}} ,{"id":70161860,"text":"70161860 - 2016 - Post-disaster supply chain interdependent critical infrastructure system restoration: A review of data necessary and available for modeling","interactions":[],"lastModifiedDate":"2019-06-04T08:44:09","indexId":"70161860","displayToPublicDate":"2016-01-07T11:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1368,"text":"Data Science Journal","active":true,"publicationSubtype":{"id":10}},"title":"Post-disaster supply chain interdependent critical infrastructure system restoration: A review of data necessary and available for modeling","docAbstract":"The majority of restoration strategies in the wake of large-scale disasters have focused on short-term emergency response solutions. Few consider medium- to long-term restoration strategies to reconnect urban areas to national supply chain interdependent critical infrastructure systems (SCICI). These SCICI promote the effective flow of goods, services, and information vital to the economic vitality of an urban environment. To re-establish the connectivity that has been broken during a disaster between the different SCICI, relationships between these systems must be identified, formulated, and added to a common framework to form a system-level restoration plan. To accomplish this goal, a considerable collection of SCICI data is necessary. The aim of this paper is to review what data are required for model construction, the accessibility of these data, and their integration with each other. While a review of publicly available data reveals a dearth of real-time data to assist modeling long-term recovery following an extreme event, a significant amount of static data does exist and these data can be used to model the complex interdependencies needed. For the sake of illustration, a particular SCICI (transportation) is used to highlight the challenges of determining the interdependencies and creating models capable of describing the complexity of an urban environment with the data publicly available. Integration of such data as is derived from public domain sources is readily achieved in a geospatial environment, after all geospatial infrastructure data are the most abundant data source and while significant quantities of data can be acquired through public sources, a significant effort is still required to gather, develop, and integrate these data from multiple sources to build a complete model. Therefore, while continued availability of high quality, public information is essential for modeling efforts in academic as well as government communities, a more streamlined approach to a real-time acquisition and integration of these data is essential.
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The best‐fit demographic model for the three taxa is concordant with the *BEAST species tree, and the ∂a∂i analysis does not indicate gene flow among any of the three lineages during their divergence. These analyses suggest that divergence among the lineages occurred in the absence of gene flow and in this scenario the genetic signature of ecological isolation (parapatric model) cannot be differentiated from geographic isolation (allopatric model).
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Marc","contributorId":167533,"corporation":false,"usgs":false,"family":"Tollis","given":"Marc","email":"","affiliations":[],"preferred":false,"id":622601,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hsieh, PingHsun","contributorId":167534,"corporation":false,"usgs":false,"family":"Hsieh","given":"PingHsun","email":"","affiliations":[],"preferred":false,"id":622602,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gutenkunst, Ryan N.","contributorId":167535,"corporation":false,"usgs":false,"family":"Gutenkunst","given":"Ryan","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":622603,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Liu, Zhen","contributorId":57750,"corporation":false,"usgs":true,"family":"Liu","given":"Zhen","email":"","affiliations":[],"preferred":false,"id":622604,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kusumi, Kenro","contributorId":167536,"corporation":false,"usgs":false,"family":"Kusumi","given":"Kenro","email":"","affiliations":[],"preferred":false,"id":622605,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Culver, Melanie 0000-0001-5380-3059 mculver@usgs.gov","orcid":"https://orcid.org/0000-0001-5380-3059","contributorId":4327,"corporation":false,"usgs":true,"family":"Culver","given":"Melanie","email":"mculver@usgs.gov","affiliations":[{"id":12625,"text":"School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, 85721, USA","active":true,"usgs":false},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":127,"text":"Arizona Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true}],"preferred":false,"id":622559,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Murphy, Robert W.","contributorId":147498,"corporation":false,"usgs":false,"family":"Murphy","given":"Robert","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":622606,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70161857,"text":"70161857 - 2016 - The effect of particle size distribution on the design of urban stormwater control measures","interactions":[],"lastModifiedDate":"2016-01-07T09:20:13","indexId":"70161857","displayToPublicDate":"2016-01-07T10:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"The effect of particle size distribution on the design of urban stormwater control measures","docAbstract":"An urban pollutant loading model was used to demonstrate how incorrect assumptions on the particle size distribution (PSD) in urban runoff can alter the design characteristics of stormwater control measures (SCMs) used to remove solids in stormwater. Field-measured PSD, although highly variable, is generally coarser than the widely-accepted PSD characterized by the Nationwide Urban Runoff Program (NURP). PSDs can be predicted based on environmental surrogate data. There were no appreciable differences in predicted PSD when grouped by season. Model simulations of a wet detention pond and catch basin showed a much smaller surface area is needed to achieve the same level of solids removal using the median value of field-measured PSD as compared to NURP PSD. Therefore, SCMs that used the NURP PSD in the design process could be unnecessarily oversized. The median of measured PSDs, although more site-specific than NURP PSDs, could still misrepresent the efficiency of an SCM because it may not adequately capture the variability of individual runoff events. Future pollutant loading models may account for this variability through regression with environmental surrogates, but until then, without proper site characterization, the adoption of a single PSD to represent all runoff conditions may result in SCMs that are under- or over-sized, rendering them ineffective or unnecessarily costly.
","language":"English","publisher":"MDPI","doi":"10.3390/w8010017","collaboration":"Wisconsin Department of Natural Resources","usgsCitation":"Selbig, W.R., Fienen, M., Horwatich, J.A., and Bannerman, R.T., 2016, The effect of particle size distribution on the design of urban stormwater control measures: Water, v. 8, no. 1, w8010017: 17 p., https://doi.org/10.3390/w8010017.","productDescription":"w8010017: 17 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069813","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":471342,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w8010017","text":"Publisher Index Page"},{"id":314000,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"1","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2016-01-06","publicationStatus":"PW","scienceBaseUri":"568f8c3ce4b0e7a44bc5ec97","contributors":{"authors":[{"text":"Selbig, William R. 0000-0003-1403-8280 wrselbig@usgs.gov","orcid":"https://orcid.org/0000-0003-1403-8280","contributorId":877,"corporation":false,"usgs":true,"family":"Selbig","given":"William","email":"wrselbig@usgs.gov","middleInitial":"R.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":587952,"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":587953,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Horwatich, Judy A. 0000-0003-0582-0836 jahorwat@usgs.gov","orcid":"https://orcid.org/0000-0003-0582-0836","contributorId":1388,"corporation":false,"usgs":true,"family":"Horwatich","given":"Judy","email":"jahorwat@usgs.gov","middleInitial":"A.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":587954,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bannerman, Roger T. 0000-0001-9221-2905 rbannerman@usgs.gov","orcid":"https://orcid.org/0000-0001-9221-2905","contributorId":5560,"corporation":false,"usgs":true,"family":"Bannerman","given":"Roger","email":"rbannerman@usgs.gov","middleInitial":"T.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":587955,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70161810,"text":"70161810 - 2016 - Ca, Sr and Ba stable isotopes reveal the fate of soil nutrients along a tropical climosequence","interactions":[],"lastModifiedDate":"2016-01-11T10:42:47","indexId":"70161810","displayToPublicDate":"2016-01-07T10:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1213,"text":"Chemical Geology","active":true,"publicationSubtype":{"id":10}},"title":"Ca, Sr and Ba stable isotopes reveal the fate of soil nutrients along a tropical climosequence","docAbstract":"Nutrient biolifting is an important pedogenic process in which plant roots obtain inorganic nutrients such as phosphorus (P) and calcium (Ca) from minerals at depth and concentrate those nutrients at the surface. Here we use soil chemistry and stable isotopes of the alkaline earth elements Ca, strontium (Sr) and barium (Ba) to test the hypothesis that biolifting of P has been an important pedogenic process across a soil climosequence developed on volcanic deposits at Kohala Mountain, Hawaii. The geochemical linkage between these elements is revealed as generally positive site-specific relationships in soil mass gains and losses, particularly for P, Ba and Ca, using the ratio of immobile elements titanium and niobium (Ti/Nb) to link individual soil samples to a restricted compositional range of the chemically and isotopically diverse volcanic parent materials. At sites where P is enriched in surface soils relative to abundances in deeper soils, the isotope compositions of exchangeable Ca, Sr and Ba in the shallowest soil horizons (< 10 cm depth) are lighter than those of the volcanic parent materials and trend toward those of plants growing on fresh volcanic deposits. In contrast the isotope composition of exchangeable Ba in deeper soil horizons (> 10 cm depth) at those sites is consistently heavier than the volcanic parent materials. The isotope compositions of exchangeable Ca and Sr trend toward heavier compositions with depth more gradually, reflecting increasing leakiness from these soils in the order Ba < Sr < Ca and downward transfer of light biocycled Ca and Sr to deeper exchange sites. Given the long-term stability of ecosystem properties at the sites where P is enriched in surface soils, a simple box model demonstrates that persistence of isotopically light exchangeable Ca, Sr and Ba in the shallowest soil horizons requires that the uptake flux to plants from those near-surface layers is less than the recycling flux returned to the surface as litterfall. This observation implicates an uptake flux from an additional source which we attribute to biolifting. We view the heavy exchangeable Ba relative to soil parent values in deeper soils at sites where P is enriched in surface soils, and indeed at all but the wettest site across the climosequence, to represent the complement of an isotopically light Ba fraction removed from these soils by plant roots consistent with the biolifting hypothesis. We further suggest that decreasing heaviness of depth-integrated exchangeable Ba in deeper soils with increasing median annual precipitation across the climosequence reflects greater reliance on shallow nutrient sources as site water balance increases. While the Ca, Sr and Ba isotopes considered together were useful in confirming an important role for nutrient biolifting across the climosequence, the Ba isotopes provided the most robust tracer of biolifting and have the greatest potential to find application as an isotopic proxy for P dynamics in soils.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.chemgeo.2015.12.008","usgsCitation":"Bullen, T.D., and Chadwick, O.A., 2016, Ca, Sr and Ba stable isotopes reveal the fate of soil nutrients along a tropical climosequence: Chemical Geology, v. 422, p. 25-45, https://doi.org/10.1016/j.chemgeo.2015.12.008.","productDescription":"21 p.","startPage":"25","endPage":"45","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-071551","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":471343,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.chemgeo.2015.12.008","text":"Publisher Index Page"},{"id":313999,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kohala Mountain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.71197509765625,\n 20.029483785566846\n ],\n [\n -155.71197509765625,\n 20.114615840542655\n ],\n [\n -155.5938720703125,\n 20.114615840542655\n ],\n [\n -155.5938720703125,\n 20.029483785566846\n ],\n [\n -155.71197509765625,\n 20.029483785566846\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"422","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"568f8c30e4b0e7a44bc5ec80","chorus":{"doi":"10.1016/j.chemgeo.2015.12.008","url":"http://dx.doi.org/10.1016/j.chemgeo.2015.12.008","publisher":"Elsevier BV","authors":"Bullen Thomas, Chadwick Oliver","journalName":"Chemical Geology","publicationDate":"3/2016"},"contributors":{"authors":[{"text":"Bullen, Thomas D. 0000-0003-2281-1691 tdbullen@usgs.gov","orcid":"https://orcid.org/0000-0003-2281-1691","contributorId":1969,"corporation":false,"usgs":true,"family":"Bullen","given":"Thomas","email":"tdbullen@usgs.gov","middleInitial":"D.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":587844,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chadwick, Oliver A.","contributorId":88244,"corporation":false,"usgs":false,"family":"Chadwick","given":"Oliver","email":"","middleInitial":"A.","affiliations":[{"id":6710,"text":"University of California, Santa Barbara, CA","active":true,"usgs":false}],"preferred":false,"id":587845,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70162616,"text":"70162616 - 2016 - The impacts of human recreation on brown bears (Ursus arctos): A review and new management tool","interactions":[],"lastModifiedDate":"2018-05-21T09:56:40","indexId":"70162616","displayToPublicDate":"2016-01-06T14:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"displayTitle":"The impacts of human recreation on brown bears (Ursus arctos): A review and new management tool","title":"The impacts of human recreation on brown bears (Ursus arctos): A review and new management tool","docAbstract":"Increased popularity of recreational activities in natural areas has led to the need to better understand their impacts on wildlife. The majority of research conducted to date has focused on behavioral effects from individual recreations, thus there is a limited understanding of the potential for population-level or cumulative effects. Brown bears (Ursus arctos) are the focus of a growing wildlife viewing industry and are found in habitats frequented by recreationists. Managers face difficult decisions in balancing recreational opportunities with habitat protection for wildlife. Here, we integrate results from empirical studies with expert knowledge to better understand the potential population-level effects of recreational activities on brown bears. We conducted a literature review and Delphi survey of brown bear experts to better understand the frequencies and types of recreations occurring in bear habitats and their potential effects, and to identify management solutions and research needs. We then developed a Bayesian network model that allows managers to estimate the potential effects of recreational management decisions in bear habitats. A higher proportion of individual brown bears in coastal habitats were exposed to recreation, including photography and bear-viewing than bears in interior habitats where camping and hiking were more common. Our results suggest that the primary mechanism by which recreation may impact brown bears is through temporal and spatial displacement with associated increases in energetic costs and declines in nutritional intake. Killings in defense of life and property were found to be minimally associated with recreation in Alaska, but are important considerations in population management. Regulating recreation to occur predictably in space and time and limiting recreation in habitats with concentrated food resources reduces impacts on food intake and may thereby, reduce impacts on reproduction and survival. Our results suggest that decisions managers make about regulating recreational activities in time and space have important consequences for bear populations. The Bayesian network model developed here provides a new tool for managers to balance demands of multiple recreational activities while supporting healthy bear populations.
","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pone.0141983","usgsCitation":"Fortin-noreus, J., Rode, K.D., Hilderbrand, G., Wilder, J., Farley, S., Jorgensen, C., and Marcot, B.G., 2016, The impacts of human recreation on brown bears (Ursus arctos): A review and new management tool: PLoS ONE, v. 11, no. 1, p. 1-26, https://doi.org/10.1371/journal.pone.0141983.","productDescription":"e0141983; 26 p.","startPage":"1","endPage":"26","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064745","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":471344,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0141983","text":"Publisher Index Page"},{"id":438645,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F71V5C1F","text":"USGS data release","linkHelpText":"Recreation Survey Results in Brown Bear Habitats, 2013"},{"id":314925,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-01-05","publicationStatus":"PW","scienceBaseUri":"56a9f84fe4b012c193aa3eea","contributors":{"authors":[{"text":"Fortin-noreus, Jennifer jfortin-noreus@usgs.gov","contributorId":152608,"corporation":false,"usgs":true,"family":"Fortin-noreus","given":"Jennifer","email":"jfortin-noreus@usgs.gov","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":589907,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rode, Karyn D. 0000-0002-3328-8202 krode@usgs.gov","orcid":"https://orcid.org/0000-0002-3328-8202","contributorId":5053,"corporation":false,"usgs":true,"family":"Rode","given":"Karyn","email":"krode@usgs.gov","middleInitial":"D.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":589906,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hilderbrand, Grant V. 0000-0002-0051-8315 ghilderbrand@usgs.gov","orcid":"https://orcid.org/0000-0002-0051-8315","contributorId":199764,"corporation":false,"usgs":true,"family":"Hilderbrand","given":"Grant V.","email":"ghilderbrand@usgs.gov","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":false,"id":589908,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wilder, James","contributorId":152610,"corporation":false,"usgs":false,"family":"Wilder","given":"James","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":589909,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Farley, Sean","contributorId":83415,"corporation":false,"usgs":true,"family":"Farley","given":"Sean","affiliations":[],"preferred":false,"id":589910,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jorgensen, Carole","contributorId":152611,"corporation":false,"usgs":false,"family":"Jorgensen","given":"Carole","email":"","affiliations":[{"id":18943,"text":"Chugach National Forest","active":true,"usgs":false}],"preferred":false,"id":589911,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Marcot, Bruce G.","contributorId":152612,"corporation":false,"usgs":false,"family":"Marcot","given":"Bruce","email":"","middleInitial":"G.","affiliations":[{"id":18944,"text":"Pacific Northwest Research Station, USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":589912,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70160900,"text":"70160900 - 2016 - The scaling of geographic ranges: implications for species distribution models","interactions":[],"lastModifiedDate":"2016-07-15T14:45:48","indexId":"70160900","displayToPublicDate":"2016-01-05T11:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"The scaling of geographic ranges: implications for species distribution models","docAbstract":"The geographic ranges of many species are responding to ongoing environmental change. Processes operating at different levels of biological organization, with corresponding spatial extents and grains and temporal rates, interact with the evolving configuration of environmental conditions to determine range dynamics.
\nTo synthesize understanding of scales and scaling, including relevant biological levels of organization, focusing on the processes that mediate species-environment relationships and the models used to make inferences about species distributions.
\nWe review concepts related to the scaling of geographic ranges and implications for the most commonly used analytic methods, using simple simulations to illustrate important issues.
\nMany processes lead to species distributions being dependent on environmental conditions within sites and within a neighborhood. Studies with large extents and fine grains can cut across several levels of biological organization (individual, within-population, and metapopulation processes) complicating interpretation. Many geographic ranges are not in dynamic equilibrium, but common models used for inference assume equilibrium. Interspecific interactions shape species distributions at multiple scales, and arguments for ignoring species interactions also assume equilibrium.
\nThere is a need for timely science to inform policy and management decisions; however, we must also strive to provide predictions that best reflect our understanding of ecological systems. Species distributions evolve through time and reflect responses to environmental conditions that are mediated through individual and population processes. Species distribution models that reflect this understanding, and explicitly model dynamics, are likely to give more accurate predictions.
\nCritical water-resources issues ranging from flood response to water scarcity make access to integrated water information, services, tools, and models essential. Since 1995 when the first water data web pages went online, the U.S. Geological Survey has been at the forefront of water data distribution and integration. Today, real-time and historical streamflow observations are available via web pages and a variety of web service interfaces. The Survey has built partnerships with Federal and State agencies to integrate hydrologic data providing continuous observations of surface and groundwater, temporally discrete water quality data, groundwater well logs, aquatic biology data, water availability and use information, and tools to help characterize the landscape for modeling. In this paper, we summarize the status and design patterns implemented for selected data systems. We describe how these systems contribute to a U.S. Federal Open Water Data Initiative and present some gaps and lessons learned that apply to global hydroinformatics data infrastructure.
","language":"English","publisher":"IWA Publishing","doi":"10.2166/hydro.2015.067","usgsCitation":"Blodgett, D.L., Lucido, J., and Kreft, J., 2016, Progress on water data integration and distribution: a summary of select U.S. Geological Survey data systems: Journal of Hydroinformatics, v. 18, no. 2, p. 226-237, https://doi.org/10.2166/hydro.2015.067.","productDescription":"12 p.","startPage":"226","endPage":"237","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064680","costCenters":[],"links":[{"id":471345,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2166/hydro.2015.067","text":"Publisher Index Page"},{"id":313323,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"18","issue":"2","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-31","publicationStatus":"PW","scienceBaseUri":"568ce931e4b0e7a44bc0f111","contributors":{"authors":[{"text":"Blodgett, David L. 0000-0001-9489-1710 dblodgett@usgs.gov","orcid":"https://orcid.org/0000-0001-9489-1710","contributorId":3868,"corporation":false,"usgs":true,"family":"Blodgett","given":"David","email":"dblodgett@usgs.gov","middleInitial":"L.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5054,"text":"Office of Water Information","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":584260,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lucido, Jessica M. jlucido@usgs.gov","contributorId":4695,"corporation":false,"usgs":true,"family":"Lucido","given":"Jessica M.","email":"jlucido@usgs.gov","affiliations":[{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true}],"preferred":true,"id":584261,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kreft, James M. jkreft@usgs.gov","contributorId":250,"corporation":false,"usgs":true,"family":"Kreft","given":"James M.","email":"jkreft@usgs.gov","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":false,"id":584262,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70173968,"text":"70173968 - 2016 - Beat-the-wave evacuation mapping for tsunami hazards in Seaside, Oregon, USA","interactions":[],"lastModifiedDate":"2016-06-21T07:51:33","indexId":"70173968","displayToPublicDate":"2016-01-05T10:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2822,"text":"Natural Hazards","active":true,"publicationSubtype":{"id":10}},"title":"Beat-the-wave evacuation mapping for tsunami hazards in Seaside, Oregon, USA","docAbstract":"Previous pedestrian evacuation modeling for tsunamis has not considered variable wave arrival times or critical junctures (e.g., bridges), nor does it effectively communicate multiple evacuee travel speeds. We summarize an approach that identifies evacuation corridors, recognizes variable wave arrival times, and produces a map of minimum pedestrian travel speeds to reach safety, termed a “beat-the-wave” (BTW) evacuation analysis. We demonstrate the improved approach by evaluating difficulty of pedestrian evacuation of Seaside, Oregon, for a local tsunami generated by a Cascadia subduction zone earthquake. We establish evacuation paths by calculating the least cost distance (LCD) to safety for every grid cell in a tsunami-hazard zone using geospatial, anisotropic path distance algorithms. Minimum BTW speed to safety on LCD paths is calculated for every grid cell by dividing surface distance from that cell to safety by the tsunami arrival time at safety. We evaluated three scenarios of evacuation difficulty: (1) all bridges are intact with a 5-minute evacuation delay from the start of earthquake, (2) only retrofitted bridges are considered intact with a 5-minute delay, and (3) only retrofitted bridges are considered intact with a 10-minute delay. BTW maps also take into account critical evacuation points along complex shorelines (e.g., peninsulas, bridges over shore-parallel estuaries) where evacuees could be caught by tsunami waves. The BTW map is able to communicate multiple pedestrian travel speeds, which are typically visualized by multiple maps with current LCD-based mapping practices. Results demonstrate that evacuation of Seaside is problematic seaward of the shore-parallel waterways for those with any limitations on mobility. Tsunami vertical-evacuation refuges or additional pedestrian bridges may be effective ways of reducing loss of life seaward of these waterways.
","language":"English","publisher":"Springer","doi":"10.1007/s11069-015-2011-4","collaboration":"State of Oregon Department of Geology and Mineral Industries","usgsCitation":"Priest, G., Stimely, L., Wood, N.J., Madin, I., and Watzig, R., 2016, Beat-the-wave evacuation mapping for tsunami hazards in Seaside, Oregon, USA: Natural Hazards, v. 80, no. 2, p. 1031-1056, https://doi.org/10.1007/s11069-015-2011-4.","productDescription":"25 p.","startPage":"1031","endPage":"1056","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065533","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":324028,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"id\":\"40\",\"properties\":{\"name\":\"Oregon\",\"nation\":\"USA 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Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-10-19","publicationStatus":"PW","scienceBaseUri":"576913b1e4b07657d19fefa5","contributors":{"authors":[{"text":"Priest, George R.","contributorId":50950,"corporation":false,"usgs":true,"family":"Priest","given":"George R.","affiliations":[],"preferred":false,"id":639869,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stimely, Laura","contributorId":71092,"corporation":false,"usgs":true,"family":"Stimely","given":"Laura","email":"","affiliations":[],"preferred":false,"id":639872,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wood, Nathan J. 0000-0002-6060-9729 nwood@usgs.gov","orcid":"https://orcid.org/0000-0002-6060-9729","contributorId":3347,"corporation":false,"usgs":true,"family":"Wood","given":"Nathan","email":"nwood@usgs.gov","middleInitial":"J.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":639868,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Madin, Ian","contributorId":83558,"corporation":false,"usgs":true,"family":"Madin","given":"Ian","affiliations":[],"preferred":false,"id":639870,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Watzig, Rudie","contributorId":172194,"corporation":false,"usgs":false,"family":"Watzig","given":"Rudie","email":"","affiliations":[{"id":26998,"text":"State of Oregon Department of Geology and Mineral Industries","active":true,"usgs":false}],"preferred":false,"id":639871,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70227260,"text":"70227260 - 2016 - Northern long-eared bat day-roosting and prescribed fire in the Central Appalachians, USA","interactions":[],"lastModifiedDate":"2022-01-05T13:03:27.471584","indexId":"70227260","displayToPublicDate":"2016-01-05T07:00:12","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1636,"text":"Fire Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Northern long-eared bat day-roosting and prescribed fire in the Central Appalachians, USA","docAbstract":"The northern long-eared bat (Myotis septentrionalis Trovessart) is a cavity-roosting species that forages in cluttered upland and riparian forests throughout the oak-dominated Appalachian and Central Hardwoods regions. Common prior to white-nose syndrome, the population of this bat species has declined to functional extirpation in some regions in the Northeast and Mid-Atlantic, including portions of the central Appalachians. Our long-term research in the central Appalachians has shown that maternity colonies of this species form non-random assorting networks in patches of suitable trees that result from long- and short-term forest disturbance processes, and that roost loss can occur with these disturbances. Following two consecutive prescribed burns on the Fernow Experimental Forest in the central Appalachians, West Virginia, USA, in 2007 to 2008, post-fire counts of suitable black locust (Robinia pseudoacacia L.; the most selected species for roosting) slightly decreased by 2012. Conversely, post-fire numbers of suitable maple (Acer spp. L.), primarily red maple (Acer rubrum L.), increased by a factor of three, thereby ameliorating black locust reduction. Maternity colony network metrics such as roost degree (use) and network density for two networks in the burned compartment were similar to the single network observed in unburned forest. However, roost clustering and degree of roost centralization was greater for the networks in the burned forest area. Accordingly, the short-term effects of prescribed fire are slightly or moderately positive in impact to day-roost habitat for the northern long-eared bat in the central Appalachians from a social dynamic perspective. Listing of northern long-eared bats as federally threatened will bring increased scrutiny of immediate fire impacts from direct take as well as indirect impacts from long-term changes to roosting and foraging habitat in stands being returned to historic fire-return conditions. Unfortunately, definitive impacts will remain speculative owing to the species’ current rarity and the paucity of forest stand data that considers tree condition or that adequately tracks snags spatially and temporally.
Epidemiological studies indicate that fecal indicator bacteria (FIB) in beach water are associated with illnesses among people having contact with the water. In order to mitigate public health impacts, many beaches are posted with an advisory when the concentration of FIB exceeds a beach action value. The most commonly used method of measuring FIB concentration takes 18–24 h before returning a result. In order to avoid the 24 h lag, it has become common to ”nowcast” the FIB concentration using statistical regressions on environmental surrogate variables. Most commonly, nowcast models are estimated using ordinary least squares regression, but other regression methods from the statistical and machine learning literature are sometimes used. This study compares 14 regression methods across 7 Wisconsin beaches to identify which consistently produces the most accurate predictions. A random forest model is identified as the most accurate, followed by multiple regression fit using the adaptive LASSO.
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