Watersheds recently burned by wildfires can have an increased susceptibility to debris flow, although little is known about how long this susceptibility persists, and how it changes over time. We here use a compilation of 75 debris-flow response and fire-ignition dates, vegetation and bedrock class, rainfall regime, and initiation process from throughout the western U.S. to address these issues. The great majority (85 percent) of debris flows occurred within the first 12 months following wildfire, with 71 percent within the first six months. Seven percent of the debris flows occurred between 1 and 1.5 years after a fire, or during the second rainy season to impact an area. Within the first 1.5 years following fires, all but one of the debris flows initiated through runoff-dominated processes, and debris flows occurred in similar proportions in forested and non-forested landscapes. Geologic materials affected how long debris-flow activity persisted, and the timing of debris flows varied within different rainfall regimes. A second, later period of increased debris flow susceptibility between 2.2 and 10 years after fires is indicated by the remaining 8 percent of events, which occurred primarily in forested terrains and initiated largely through landslide processes. The short time period between fire and debris-flow response within the first 1.5 years after ignition, and the longer-term response between 2.2 and 10 years after fire, demonstrate the necessity of both rapid and long-term reactions by land managers and emergency-response agencies to mitigate hazards from debris flows from recently burned areas in the western U.S.
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Information is communicated to people from volcano observatories and emergency management agencies and from informal sources and social and environmental cues. Any individual or agency can be both a message sender and a recipient and multiple messages received from multiple sources is the norm in a volcanic crisis. Significant challenges to developing effective warning systems for volcanic hazards stem from the great diversity in unrest, eruption, and post-eruption processes and the rapidly advancing digital technologies that people use to seek real-time risk information. Challenges also involve the need to invest resources before unrest to help people develop shared mental models of important risk factors. Two populations of people are the target of volcano notifications–ground- and aviation-based populations, and volcano warning systems must address both distinctly different populations.
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\nThese historical datasets represent some of the longest term datasets on nesting ducks in North America, if not the world. They are extremely valuable for ongoing waterfowl management and habitat conservation efforts in California, as well as throughout the world. However, without organization and electronic access, these data are an untapped resource and are not being used to the full extent possible. Prior to this project, these datasets were scattered among various agencies and organizations, and original paper nest cards were being stored in cardboard boxes in attics and storage containers that were not suitable for long-term archival storage. In addition, most of these data had not been entered into a computerized database and thus were at high risk for permanent data loss.
\nTo protect this irreplaceable dataset, we submitted a series of proposals to obtain funds to complete this data archival project over the past 5 years. The Central Valley Joint Venture, USGS Data Rescue Program, and USGS Ecosystems Mission Area funded this data archival project. In addition, we leveraged other USGS projects on nesting shorebirds, songbirds, and seabirds to use further resources to more fully develop the nest database structure for use on nesting waterfowl. Specifically, this large dataset on ducks was archived by USGS, but the dataset is owned and managed by a consortium of organizations. Therefore, any access and use of this data must occur through the principal investigators, who contributed data and resources to this archival project, as detailed in section, “Data Availability.”
\nWith the conclusion of this project, most duck nest data have been entered, but all nest-captured hen data and other breeding waterfowl data that were outside the scope of this project have still not been entered and electronically archived. Maintaining an up-to-date archive will require additional resources to archive and enter the new duck nest data each year in an iterative process. Further, data proofing should be conducted whenever possible, and also should be considered an iterative process as there was sometimes missing data that could not be filled in without more direct knowledge of specific projects. Despite these disclaimers, this duck data archive represents a massive and useful dataset to inform future research and management questions.
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\"}}]}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
http://werc.usgs.gov/
As part of the U.S. Geological Survey’s Groundwater Resources Program study of the Appalachian Plateaus aquifers, annual and average estimates of water-budget components based on hydrograph separation and precipitation data from parameter-elevation regressions on independent slopes model (PRISM) were determined at 849 continuous-record streamflow-gaging stations from Mississippi to New York and covered the period of 1900 to 2011. Only complete calendar years (January to December) of streamflow record at each gage were used to determine estimates of base flow, which is that part of streamflow attributed to groundwater discharge; such estimates can serve as a proxy for annual recharge. For each year, estimates of annual base flow, runoff, and base-flow index were determined using computer programs—PART, HYSEP, and BFI—that have automated the separation procedures. These streamflow-hydrograph analysis methods are provided with version 1.0 of the U.S. Geological Survey Groundwater Toolbox, which is a new program that provides graphing, mapping, and analysis capabilities in a Windows environment. Annual values of precipitation were estimated by calculating the average of cell values intercepted by basin boundaries where previously defined in the GAGES–II dataset. Estimates of annual evapotranspiration were then calculated from the difference between precipitation and streamflow.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds944","collaboration":"Groundwater Resources Program","usgsCitation":"Nelms, D.L., Messinger, Terence, and McCoy, K.J., 2015, Annual and average estimates of water-budget components based on hydrograph separation and PRISM precipitation for gaged basins in the Appalachian Plateaus Region, 1900–2011: U.S. Geological Survey Data Series 944, 10 p., https://dx.doi.org/10.3133/ds944.","productDescription":"Report: iv, 10 p.; 3 Appendices; Database; Metadata","numberOfPages":"18","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-060622","costCenters":[{"id":614,"text":"Virginia Water Science 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KB","linkFileType":{"id":3,"text":"xlsx"},"description":"List of streamflow-gaging stations in the Appalachian Plateaus region used to estimate annual water-budget components based on hydrograph separation and PRISM precipitation, 1900-2011"},{"id":305626,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/0944/ds944_appendix2.xlsx","text":"Appendix 2","size":"6.32 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"Annual estimates of water-budget components based on hydrograph separation and PRISM precipitation for gaged basins in the Appalachian Plateaus region, 1900-2011"},{"id":305627,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/0944/ds944_appendix3.xlsx","text":"Appendix 3","size":"193 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Average estimates for the period of analysis of water-budget compo-nents based on hydrograph separation and PRISM precipitation for gaged basins in the Appalachian Plateaus region, 1900–2011"},{"id":305628,"rank":8,"type":{"id":9,"text":"Database"},"url":"https://water.usgs.gov/GIS/dsdl/HydrographSeparation_PMAS_DS944_mdb.zip","text":"Geodatabase","linkFileType":{"id":6,"text":"zip"},"description":"HydrographSeparation_PMAS_DS944"},{"id":305623,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/0944/coverthb.jpg"}],"country":"United States","otherGeospatial":"Appalachian Plateaus Region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.41796875,\n 32.175612478499325\n ],\n [\n -86.923828125,\n 31.690781806136822\n ],\n [\n -85.62744140625,\n 31.952162238024975\n ],\n [\n -85.2978515625,\n 33.50475906922606\n ],\n [\n -85.01220703125,\n 34.161818161230386\n ],\n [\n -84.83642578125,\n 34.74161249883172\n ],\n [\n -84.638671875,\n 35.0120020431607\n ],\n [\n -84.70458984375,\n 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U.S. Geological Survey
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Richmond, VA 23228
http://va.water.usgs.gov
In cooperation with the Bureau of Land Management, groundwater levels in wells located in the northern Green River Basin in Wyoming, an area of ongoing energy development, were measured by the U.S. Geological Survey from 2010 to 2014. The wells were completed in the uppermost aquifers of the Green River Basin lower Tertiary aquifer system, which is a complex regional aquifer system that provides water to most wells in the area. Except for near perennial streams, groundwater-level altitudes in most aquifers generally decreased with increasing depth, indicating a general downward potential for groundwater movement in the study area. Drilled depth of the wells was observed as a useful indicator of depth to groundwater such that deeper wells typically had a greater depth to groundwater. Comparison of a subset of wells included in this study that had historical groundwater levels that were measured during the 1960s and 1970s and again between 2012 and 2014 indicated that, overall, most of the wells showed a net decline in groundwater levels.
\nThe groundwater-level measurements were used to construct a generalized potentiometric-surface map of the Green River Basin lower Tertiary aquifer system. Groundwater-level altitudes measured in nonflowing and flowing wells used to construct the potentiometric-surface map ranged from 6,451 to 7,307 feet (excluding four unmeasured flowing wells used for contour construction purposes). The potentiometric-surface map indicates that groundwater in the study area generally moves from north to south, but this pattern of flow is altered locally by groundwater divides, groundwater discharge to the Green River, and possibly to a tributary river (Big Sandy River) and two reservoirs (Fontenelle and Big Sandy Reservoirs).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155090","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Bartos, T.T., Hallberg, L.L., and Eddy-Miller, C.A., 2015, Hydrogeology, groundwater levels, and generalized potentiometric-surface map of the Green River Basin lower Tertiary aquifer system, 2010–14, in the northern\nGreen River structural basin, Wyoming: U.S. Geological Survey Scientific Investigations Report 2015–5090, 33 p., https://dx.doi.org/10.3133/sir20155090.","productDescription":"Report: v, 33p.; 1 Plate: 11.0 x 17.0 inches","numberOfPages":"44","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-062549","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":305662,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5090/coverthb.jpg"},{"id":305663,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5090/sir20155090.pdf","text":"Report","size":"3.81 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5090"},{"id":305664,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2015/5090/downloads/sir20155090_figure10.pdf","text":"Figure 10","size":"349 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Generalized potentiometric surface of the Green River Basin lower Tertiary aquifer system, 2010-14, northern Green River structural basin, Wyoming"}],"country":"United States","state":"Wyoming","otherGeospatial":"Green River structural basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.335693359375,\n 41.6770148220322\n ],\n [\n -110.335693359375,\n 42.35448465106744\n ],\n [\n -109.2645263671875,\n 42.35448465106744\n ],\n [\n -109.2645263671875,\n 41.6770148220322\n ],\n [\n -110.335693359375,\n 41.6770148220322\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Ave.
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http://wy-mt.water.usgs.gov/
The primary goal of this research project was to evaluate the response of Moapa dace (Moapa coriacea) to the potential effects of changes in the amount of available habitat due to human influences such as ground water pumping, barriers to movement, and extirpation of Moapa dace from the mainstem Muddy River. To understand how these factors affect Moapa dace populations and to provide a tool to guide recovery actions, we developed a stochastic model to simulate Moapa dace population dynamics. Specifically, we developed an individual based model (IBM) to incorporate the critical components that drive Moapa dace population dynamics. Our model is composed of several interlinked submodels that describe changes in Moapa dace habitat as translated into carrying capacity, the influence of carrying capacity on demographic rates of dace, and the consequent effect on equilibrium population sizes. The model is spatially explicit and represents the stream network as eight discrete stream segments. The model operates at a monthly time step to incorporate seasonally varying reproduction. Growth rates of individuals vary among stream segments, with growth rates increasing along a headwater to mainstem gradient. Movement and survival of individuals are driven by density-dependent relationships that are influenced by the carrying capacity of each stream segment.
\nFirst, we calibrated the model to a historical time series of Moapa dace abundance estimates. The goal of the calibration was to estimate unknown parameters such as larval survival, carrying capacity of the tributary streams harboring the population of Moapa dace upstream of the gabion barrier, and carrying capacity of the mainstem Muddy River and tributaries. Based on historical abundance estimates, we found that the carrying capacity of the mainstem Muddy River was nearly twice the capacity of the tributary streams where Moapa dace have resided for the past 20 years.
\nGiven the calibrated model, we then conducted simulations to assess (1) the effect of altering migration barriers that restrict upstream and downstream movement of dace, and (2) the effect of changes in carrying capacity on equilibrium population sizes. We found that barriers to upstream movement led to extinction of subpopulations upstream of the barriers when initial population sizes were small. The probability of one or more subpopulations going extinct over a 50-year time horizon was >0.80 at initial population sizes of 10 non-larval and 70 larval dace, and was >0.40 at initial population sizes of 50 non-larval and 350 larval dace. The probability of a subpopulation going extinct decreased to zero when the initial population size exceeded 100 non-larval dace. Removal of upstream migration barriers eliminated extinctions of subpopulations, even at low initial population sizes. Compensatory mechanisms such as density-dependent survival and movement acted to buffer against local extinctions because stream segments could be quickly repopulated by dispersal when fish could access all stream segments.
\nProviding access to the mainstem Muddy River through removal of a gabion barrier that restricted upstream and downstream movement increased total population size from about 875 to 3,000 individuals. Additionally, because of higher growth rates of individuals in the mainstem Muddy River, the size structure of the population shifted towards larger individuals with higher fecundity, thereby increasing reproductive capacity of the population.
\nIncreasing or decreasing the total carrying capacity of all stream segments resulted in changes in equilibrium population size that were directly proportional to the change in capacity. However, changes in carrying capacity to some stream segments but not others could result in disproportionate changes in equilibrium population sizes by altering density-dependent movement and survival in the stream network. These simulations show how our IBM can provide a useful management tool for understanding the effect of restoration actions or reintroductions on carrying capacity, and, in turn, how these changes affect Moapa dace abundance. Such tools are critical for devising management strategies to achieve recovery goals.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151126","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Perry, R.W., Jones, E.C., and Scoppettone, G.G., 2015, A stochastic population model to evaluate Moapa dace (Moapa coriacea) population growth under alternative management scenarios: U.S. Geological Survey Open-File Report 2015-1126, 46 p., https://dx.doi.org/10.3133/ofr20151126.","productDescription":"iv, 46 p.","numberOfPages":"50","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-062968","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":305694,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1126/coverthb.jpg"},{"id":305695,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1126/ofr20151126.pdf","text":"Report","size":"2.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1126 Report"}],"country":"United States","state":"Nevada","otherGeospatial":"Muddy River System","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.52423095703124,\n 36.44448503928196\n ],\n [\n -114.52423095703124,\n 36.65850456897558\n ],\n [\n -114.31686401367188,\n 36.65850456897558\n ],\n [\n -114.31686401367188,\n 36.44448503928196\n ],\n [\n -114.52423095703124,\n 36.44448503928196\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
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http://wfrc.usgs.gov/
In the Mojave Desert of the southwestern United States, adult Agassiz’s desert tortoises Gopherus agassizii typically experience high survival, but population declines associated with anthropogenic impacts led to their listing as a threatened Species under the US Endangered Species Act in 1990. Predation of adult tortoises is not often considered a significant threat as they are adapted to deter most predation attempts. Despite these adaptations, some populations have experienced elevated mortality attributed to predators, suggesting that predation pressure may occasionally increase. During the tortoise activity seasons of 2012 and 2013, we observed unsustainably high mortality in 1 of 4 populations of adult desert tortoises (22 and 84%, respectively) in the western Mojave Desert in the vicinity of Barstow, CA. Photographic evidence from trail cameras and examination of carcass condition suggest that American badgers Taxidea taxus— a sometimes cited but unconfirmed predator of adult tortoises — may have been responsible for some of the mortality observed. We discuss the American badger as a plausible predator of a local tortoise population, but recommend further investigation into these events and the impacts such mortality can have on tortoise persistence.
","language":"English","publisher":"Inter-Research","publisherLocation":"Oldendorf, Germany","doi":"10.3354/esr00680","usgsCitation":"Emblidge, P.G., Nussear, K.E., Esque, T., Aiello, C.M., and Walde, A.D., 2015, Severe mortality of a population of threatened Agassiz’s desert tortoises: the American badger as a potential predator: Endangered Species Research, v. 28, p. 109-116, https://doi.org/10.3354/esr00680.","productDescription":"8 p.","startPage":"109","endPage":"116","numberOfPages":"8","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-059277","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":471942,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/esr00680","text":"Publisher Index Page"},{"id":306285,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"28","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55bc9c2ee4b033ef52100f38","contributors":{"authors":[{"text":"Emblidge, Patrick G. pemblidge@usgs.gov","contributorId":127767,"corporation":false,"usgs":false,"family":"Emblidge","given":"Patrick","email":"pemblidge@usgs.gov","middleInitial":"G.","affiliations":[{"id":7144,"text":"Center for Infectious Disease Dynamics, Penn State U, University Park, PA","active":true,"usgs":false}],"preferred":false,"id":564958,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nussear, Ken E.","contributorId":103596,"corporation":false,"usgs":true,"family":"Nussear","given":"Ken","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":564954,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Esque, Todd C. tesque@usgs.gov","contributorId":145679,"corporation":false,"usgs":true,"family":"Esque","given":"Todd C.","email":"tesque@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":564955,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Aiello, Christina M. 0000-0002-2399-5464 caiello@usgs.gov","orcid":"https://orcid.org/0000-0002-2399-5464","contributorId":5617,"corporation":false,"usgs":true,"family":"Aiello","given":"Christina","email":"caiello@usgs.gov","middleInitial":"M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":564956,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walde, Andrew D.","contributorId":127765,"corporation":false,"usgs":false,"family":"Walde","given":"Andrew","email":"","middleInitial":"D.","affiliations":[{"id":7143,"text":"Walde Resoarch & Environmental Consulting, Atascadero, CA","active":true,"usgs":false}],"preferred":false,"id":564957,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70157419,"text":"70157419 - 2015 - Regional variability in dust-on-snow processes and impacts in the Upper Colorado River Basin","interactions":[],"lastModifiedDate":"2015-12-21T13:28:52","indexId":"70157419","displayToPublicDate":"2015-07-14T11:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Regional variability in dust-on-snow processes and impacts in the Upper Colorado River Basin","docAbstract":"Dust deposition onto mountain snow cover in the Upper Colorado River Basin frequently occurs in the spring when wind speeds and dust emission peaks on the nearby Colorado Plateau. Dust loading has increased since the intensive settlement in the western USA in the mid 1880s. The effects of dust-on-snow have been well studied at Senator Beck Basin Study Area (SBBSA) in the San Juan Mountains, CO, the first high-altitude area of contact for predominantly southwesterly winds transporting dust from the southern Colorado Plateau. To capture variability in dust transport from the broader Colorado Plateau and dust deposition across a larger area of the Colorado River water sources, an additional study plot was established in 2009 on Grand Mesa, 150 km to the north of SBBSA in west central, CO. Here, we compare the 4-year (2010–2013) dust source, deposition, and radiative forcing records at Grand Mesa Study Plot (GMSP) and Swamp Angel Study Plot (SASP), SBBSA's subalpine study plot. The study plots have similar site elevations/environments and differ mainly in the amount of dust deposited and ensuing impacts. At SASP, end of year dust concentrations ranged from 0.83 mg g−1 to 4.80 mg g−1, and daily mean spring dust radiative forcing ranged from 50–65 W m−2, advancing melt by 24–49 days. At GMSP, which received 1.0 mg g−1 less dust per season on average, spring radiative forcings of 32–50 W m−2 advanced melt by 15–30 days. Remote sensing imagery showed that observed dust events were frequently associated with dust emission from the southern Colorado Plateau. Dust from these sources generally passed south of GMSP, and back trajectory footprints modelled for observed dust events were commonly more westerly and northerly for GMSP relative to SASP. These factors suggest that although the southern Colorado Plateau contains important dust sources, dust contributions from other dust sources contribute to dust loading in this region, and likely account for the majority of dust loading at GMSP.
","language":"English","publisher":"John Wiley & Sons","publisherLocation":"Chichester, Sussex, England","doi":"10.1002/hyp.10569","usgsCitation":"Skiles, S.M., Painter, T.H., Belnap, J., Holland, L., Reynolds, R.L., Goldstein, H.L., and Lin, J., 2015, Regional variability in dust-on-snow processes and impacts in the Upper Colorado River Basin: Hydrological Processes, v. 29, no. 26, p. 5397-5413, https://doi.org/10.1002/hyp.10569.","productDescription":"27 p.","startPage":"5397","endPage":"5413","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066323","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":308422,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"29","issue":"26","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-14","publicationStatus":"PW","scienceBaseUri":"5603cd58e4b03bc34f544b37","contributors":{"authors":[{"text":"Skiles, S. McKenzie","contributorId":147878,"corporation":false,"usgs":false,"family":"Skiles","given":"S.","email":"","middleInitial":"McKenzie","affiliations":[{"id":16952,"text":"University of California- Los Angeles, Joint Intitute for Regional Earth System Science and Engineering","active":true,"usgs":false}],"preferred":false,"id":573098,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Painter, Thomas H.","contributorId":12378,"corporation":false,"usgs":true,"family":"Painter","given":"Thomas","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":573099,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Belnap, Jayne 0000-0001-7471-2279 jayne_belnap@usgs.gov","orcid":"https://orcid.org/0000-0001-7471-2279","contributorId":1332,"corporation":false,"usgs":true,"family":"Belnap","given":"Jayne","email":"jayne_belnap@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":573097,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Holland, Lacey","contributorId":147879,"corporation":false,"usgs":false,"family":"Holland","given":"Lacey","email":"","affiliations":[{"id":16953,"text":"University of Utah, Atmospheric Sciences","active":true,"usgs":false}],"preferred":false,"id":573100,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reynolds, Richard L. 0000-0002-4572-2942 rreynolds@usgs.gov","orcid":"https://orcid.org/0000-0002-4572-2942","contributorId":147880,"corporation":false,"usgs":true,"family":"Reynolds","given":"Richard","email":"rreynolds@usgs.gov","middleInitial":"L.","affiliations":[{"id":271,"text":"Federal Center","active":false,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":573101,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Goldstein, Harland L. 0000-0002-6092-8818 hgoldstein@usgs.gov","orcid":"https://orcid.org/0000-0002-6092-8818","contributorId":147881,"corporation":false,"usgs":true,"family":"Goldstein","given":"Harland","email":"hgoldstein@usgs.gov","middleInitial":"L.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":false,"id":573102,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lin, J.","contributorId":33065,"corporation":false,"usgs":true,"family":"Lin","given":"J.","email":"","affiliations":[],"preferred":false,"id":573103,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70154740,"text":"ofr20151123 - 2015 - User’s guide to the North Pacific Pelagic Seabird Database 2.0","interactions":[],"lastModifiedDate":"2016-08-22T15:19:27","indexId":"ofr20151123","displayToPublicDate":"2015-07-13T16:45:00","publicationYear":"2015","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":"2015-1123","title":"User’s guide to the North Pacific Pelagic Seabird Database 2.0","docAbstract":"The North Pacific Pelagic Seabird Database (NPPSD) was created in 2005 to consolidate data on the oceanic distribution of marine bird species in the North Pacific. Most of these data were collected on surveys by counting species within defined areas and at known locations (that is, on strip transects). The NPPSD also contains observations of other bird species and marine mammals. The original NPPSD combined data from 465 surveys conducted between 1973 and 2002, primarily in waters adjacent to Alaska. These surveys included 61,195 sample transects with location, environment, and metadata information, and the data were organized in a flat-file format. In developing NPPSD 2.0, our goals were to add new datasets, to make significant improvements to database functionality and to provide the database online. NPPSD 2.0 includes data from a broader geographic range within the North Pacific, including new observations made offshore of the Russian Federation, Japan, Korea, British Columbia (Canada), Oregon, and California. These data were imported into a relational database, proofed, and structured in a common format. NPPSD 2.0 contains 351,674 samples (transects) collected between 1973 and 2012, representing a total sampled area of 270,259 square kilometers, and extends the time series of samples in some areas—notably the Bering Sea—to four decades. It contains observations of 16,988,138 birds and 235,545 marine mammals and is available on the NPPSD Web site. Supplementary materials include an updated set of standardized taxonomic codes, reference maps that show the spatial and temporal distribution of the survey efforts and a downloadable query tool.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151123","usgsCitation":"Drew, G.S., Piatt, J.F., and Renner, M., 2015, User’s guide to the North Pacific Pelagic Seabird Database 2.0:\nU.S. Geological Survey Open-File Report 2015-1123, 52 p., https://dx.doi.org/10.3133/ofr20151123.","productDescription":"iv, 52 p.","numberOfPages":"60","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-057923","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":438690,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7WQ01T3","text":"USGS data release","linkHelpText":"North Pacific Pelagic Seabird Database (NPPSD)"},{"id":305444,"rank":2,"type":{"id":9,"text":"Database"},"url":"https://dx.doi.org/10.5066/F7WQ01T3","text":"North Pacific Pelagic Seabird Database (NPPSD)","size":"67.4 MB","linkFileType":{"id":6,"text":"zip"},"description":"Database"},{"id":305552,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1123/cover.jpg"},{"id":305443,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1123/ofr20151123.pdf","text":"Report","size":"16.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1123"}],"contact":"Director, Alaska Science Center
U.S. Geological Survey
4210 University Dr
Anchorage, Alaska 99508-4560
http://alaska.usgs.gov
LANDFIRE, also known as the Landscape Fire and Resource Management Planning Tools Program, is a vegetation, fire, and fuel characteristic mapping program managed by the U.S. Department of Agriculture Forest Service and the U.S. Department of the Interior. LANDFIRE represents the first, and only, complete, nationally consistent collection of spatial resource datasets with an ecological foundation that can be used across multiple disciplines.
\nLANDFIRE data products are primarily designed and developed to be used at the landscape level to facilitate national and regional strategic planning and reporting of wild land fire and other natural resource management activities. However, LANDFIRE’s spatially comprehensive dataset can also be adapted to support a variety of local management applications that need current and comprehensive geospatial data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20153047","usgsCitation":"Zahn, S.G., 2015, LANDFIRE: U.S. Geological Survey Fact Sheet 2015-3047, 2 p., https://dx.doi.org/10.3133/fs20153047.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064616","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":305605,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2015/3047/coverthb.jpg"},{"id":305606,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2015/3047/fs20153047.pdf","text":"Report","size":"3.28","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2015-3047"}],"contact":"Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, South Dakota 57198
http://eros.usgs.gov/
The quantities of water and hydraulic fracturing proppant required for producing petroleum (oil, gas, and natural gas liquids) from continuous accumulations, and the quantities of water extracted during petroleum production, can be quantitatively assessed using a probabilistic approach. The water and proppant assessment methodology builds on the U.S. Geological Survey methodology for quantitative assessment of undiscovered technically recoverable petroleum resources in continuous accumulations. The U.S. Geological Survey assessment methodology for continuous petroleum accumulations includes fundamental concepts such as geologically defined assessment units, and probabilistic input values including well-drainage area, sweet- and non-sweet-spot areas, and success ratio within the untested area of each assessment unit. In addition to petroleum-related information, required inputs for the water and proppant assessment methodology include probabilistic estimates of per-well water usage for drilling, cementing, and hydraulic-fracture stimulation; the ratio of proppant to water for hydraulic fracturing; the percentage of hydraulic fracturing water that returns to the surface as flowback; and the ratio of produced water to petroleum over the productive life of each well. Water and proppant assessments combine information from recent or current petroleum assessments with water- and proppant-related input values for the assessment unit being studied, using Monte Carlo simulation, to yield probabilistic estimates of the volume of water for drilling, cementing, and hydraulic fracture stimulation; the quantity of proppant for hydraulic fracture stimulation; and the volumes of water produced as flowback shortly after well completion, and produced over the life of the well.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151117","usgsCitation":"Haines, S.S., 2015, Methodology for assessing quantities of water and proppant injection, and water production associated with development of continuous petroleum accumulations: U.S. Geological Survey Open-File Report 2015–1117, 18 p., https://dx.doi.org/10.3133/ofr20151117.","productDescription":"Report: iii, 18 p.; 3 Appendices","numberOfPages":"21","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-063289","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":305632,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1117/ofr20151117.pdf","text":"Report","size":"1.25","linkFileType":{"id":1,"text":"pdf"},"description":"OF 2015-1117"},{"id":305633,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2015/1117/App_1_Water_Proppant_Assmt_Input_Form_6.8.15.pdf","text":"Appendix 1","size":"77.4 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OF 2015-1117 Appendix 1"},{"id":305634,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2015/1117/App_2_Continuous_water_proppant_6.8.2015.xlsm","text":"Appendix 2","size":"55.7 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"OF 2015-1117 Appendix 2"},{"id":305631,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1117/coverthb.jpg"},{"id":305635,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2015/1117/App_4_CORE cover letter.pdf","text":"Appendix 4","size":"119 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OF 2015-1117 Appendix 4"}],"contact":"Director, Central Energy Science Center
U.S. Geological Survey
P.O. Box 25046
Denver, CO 80225
http://energy.usgs.gov/
The Ozark aquifer is the largest aquifer, both in area of outcrop and thickness, and the most important source of freshwater in the Ozark Plateaus physiographic province, supplying water to northern Arkansas, southeastern Kansas, southern Missouri, and northeastern Oklahoma. The study area includes 16 Arkansas counties lying completely or partially within the Ozark Plateaus of the Interior Highlands major physiographic division. The U.S. Geological Survey, in cooperation with the Arkansas Natural Resources Commission and the Arkansas Geological Survey, conducted a study of water levels in the Ozark aquifer within Arkansas. This report presents a potentiometric-surface map of the Ozark aquifer within the Ozark Plateaus of northern Arkansas, representing water-level conditions for the early spring of 2013 and selected water-level hydrographs.
\nThe Ozark aquifer in Arkansas is composed of dolomites, limestones, sandstones, and shales of Late Cambrian to Middle Devonian age and ranges in thickness from approximately 1,100 feet (ft) in northwestern Arkansas to more than 4,000 ft in the west-central part of Arkansas. Most wells completed in the aquifer yield between 50 and 100 gallons per minute (gal/min), although some wells may yield as much as 600 gal/min.
\nWater-level measurements were made in wells completed in the Ozark aquifer from February to May 2013. Hydrographs were constructed for nine wells that have water-level measurements with a minimum 20-year period of record.
\nWater-level altitudes in wells used to construct the potentiometric-surface map range from about 1,159 ft to 313 ft above National Geodetic Vertical Datum of 1929 (NGVD 29). The highest water-level altitudes occur in Carroll and Washington Counties while water-level altitudes of less than 400 ft above NGVD 29 are mapped along the eastern and southeastern part of the study area in Independence, Lawrence, Randolph, and Sharp Counties. The lowest water level of 313 ft above NGVD 29 was measured in southwestern Randolph County.
\nThe direction of groundwater flow generally is affected by local topography, flowing from high altitudes toward stream valleys. In southern Baxter, eastern Fulton, Independence, eastern Izard, Lawrence, Randolph, and Sharp Counties, the groundwater flow is generally to the south and southeast. In western Fulton and Izard Counties, the groundwater flow is generally to the southwest. In Boone, Marion, Newton, Searcy, and Stone Counties, the groundwater flow is generally to the east and northeast. In eastern Benton, Carroll, Madison, and eastern Washington Counties, the groundwater flow is generally to the north and northeast. In western Benton and western Washington Counties, the groundwater flow is generally to the west and northwest.
\nThe general level and shape of the potentiometric surface has changed little since predevelopment. A comparison of the predevelopment potentiometric surface and the 2007, 2010, and 2013 potentiometric surfaces indicate general agreement between the mapped surfaces with the exception of parts of Benton, Boone, Marion, and Washington Counties. In Boone and northern Marion Counties in 2013, water levels have declined when compared to the predevelopment potentiometric surface, although the direction of flow is still to the northeast and north. In southern Marion County, water levels have declined when compared to the predevelopment, 2007, and 2010 potentiometric surfaces, although the direction of flow is towards and along the stream valleys. In western Benton and northwestern Washington Counties, water levels are similar when compared to the predevelopment potentiometric surface, and the direction of flow is to the west and northwest, similar to the predevelopment direction of flow. The mapped 2007 and 2010 potentiometric surfaces are very different from the mapped 2013 potentiometric surface in western Benton and northwestern Washington Counties. The mapped 2013 potentiometric surface in western Benton and northwestern Washington Counties follows the contours of the top of the formation, similar to the predevelopment potentiometric surface. Since 1975, water use in the Ozark aquifer has declined 45 percent, while water levels in Benton, Boone, Marion, and Washington Counties continue to decline.
\nNine hydrographs were selected as representative of the water-level conditions in their respective counties. Wells in Fulton, Izard, and Newton Counties (station names 20N08W27ABD1, 18N09W15BCB1, and 16N21W34ABC1, respectively) have water levels that are within the usual range of values for their respective counties. Wells in Boone, Marion, and Washington Counties (station names 18N19W19BCC1, 19N15W20ACC1, and 16N32W09ABD1, respectively) have water levels that have recently declined or are declining for the period of record. Wells in Benton, Carroll, and Sharp Counties (station names 19N29W07DAA1, 21N26W17BCC1, and 15N05W06DDD1, respectively) have water levels that have been rising recently.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155088","collaboration":"Prepared in cooperation with the Arkansas Natural Resources Commission and the Arkansas Geological Survey","usgsCitation":"Schrader, T.P., 2015, Water levels of the Ozark aquifer in northern Arkansas, 2013: U.S. Geological Survey Scientific Investigations Report 2015–5088, 17 p., 1 pl., https://dx.doi.org/10.3133/sir20155088.","productDescription":"Report: iv, 17 p.; 1 Plate: 17.0 x 11.0 inches","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-059919","costCenters":[{"id":129,"text":"Arkansas Water Science Center","active":true,"usgs":true}],"links":[{"id":305590,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2015/5088/pdf/sir20155088-pl1.pdf","text":"Plate","size":"628 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5088 Plate"},{"id":305588,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5088/coverthb.jpg"},{"id":305589,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5088/pdf/sir20155088.pdf","text":"Report","size":"971 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5088"}],"country":"United States","state":"Arkansas","otherGeospatial":"Ozark Aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.625244140625,\n 36.500805317604794\n ],\n [\n -90.72509765625,\n 36.474306755095206\n ],\n [\n -90.670166015625,\n 36.19995805932895\n ],\n [\n -90.7470703125,\n 36.06686213257888\n ],\n [\n -91.131591796875,\n 35.93354064249312\n ],\n [\n -91.1865234375,\n 35.755428369259626\n ],\n [\n -91.49414062499999,\n 35.639441068973916\n ],\n [\n -91.73583984374999,\n 35.7286770448517\n ],\n [\n -91.8017578125,\n 35.89795019335754\n ],\n [\n -94.537353515625,\n 35.79999392988527\n ],\n [\n -94.625244140625,\n 36.500805317604794\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
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401 Hardin Road
Little Rock, Arkansas 72211-3528
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Methylmercury is the environmental form of neurotoxic mercury that is biomagnified in the food chain. Methylation rates are reduced when the metal is sequestered in crystalline mercury sulfides or bound to thiol groups in macromolecular natural organic matter. Mercury sulfide minerals are known to nucleate in anoxic zones, by reaction of the thiol-bound mercury with biogenic sulfide, but not in oxic environments. We present experimental evidence that mercury sulfide forms from thiol-bound mercury alone in aqueous dark systems in contact with air. The maximum amount of nanoparticulate mercury sulfide relative to thiol-bound mercury obtained by reacting dissolved mercury and soil organic matter matches that detected in the organic horizon of a contaminated soil situated downstream from Oak Ridge, TN, in the United States. The nearly identical ratios of the two forms of mercury in field and experimental systems suggest a common reaction mechanism for nucleating the mineral. We identified a chemical reaction mechanism that is thermodynamically favorable in which thiol-bound mercury polymerizes to mercury–sulfur clusters. The clusters form by elimination of sulfur from the thiol complexes via breaking of mercury–sulfur bonds as in an alkylation reaction. Addition of sulfide is not required. This nucleation mechanism provides one explanation for how mercury may be immobilized, and eventually sequestered, in oxygenated surface environments.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.5b02522","usgsCitation":"Alain Manceau, Lemouchi, C., Enescu, M., Gaillot, A., Lanson, M., Magnin, V., Pieter Glatzel, Poulin, B., Ryan, J.N., Aiken, G.R., Gautier-Lunea, I., and Kathryn L. Nagy, 2015, Formation of mercury sulfide from Hg(II)−thiolate complexes in natural organic matter: Environmental Science & Technology, v. 49, no. 16, p. 9787-9796, https://doi.org/10.1021/acs.est.5b02522.","productDescription":"10 p.","startPage":"9787","endPage":"9796","ipdsId":"IP-064729","costCenters":[{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":344453,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.4024658203125,\n 35.87792352995116\n ],\n [\n -84.08660888671875,\n 35.87792352995116\n ],\n [\n -84.08660888671875,\n 36.053540128339755\n ],\n [\n -84.4024658203125,\n 36.053540128339755\n ],\n [\n -84.4024658203125,\n 35.87792352995116\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"49","issue":"16","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-28","publicationStatus":"PW","scienceBaseUri":"5980419ae4b0a38ca2789343","contributors":{"authors":[{"text":"Alain Manceau","contributorId":195252,"corporation":false,"usgs":false,"family":"Alain Manceau","affiliations":[],"preferred":false,"id":706622,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lemouchi, Cyprien","contributorId":195253,"corporation":false,"usgs":false,"family":"Lemouchi","given":"Cyprien","email":"","affiliations":[],"preferred":false,"id":706623,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Enescu, Mironel","contributorId":195254,"corporation":false,"usgs":false,"family":"Enescu","given":"Mironel","email":"","affiliations":[],"preferred":false,"id":706624,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gaillot, Anne-Claire","contributorId":195256,"corporation":false,"usgs":false,"family":"Gaillot","given":"Anne-Claire","email":"","affiliations":[],"preferred":false,"id":706626,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lanson, Martine","contributorId":195257,"corporation":false,"usgs":false,"family":"Lanson","given":"Martine","email":"","affiliations":[],"preferred":false,"id":706627,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Magnin, Valerie","contributorId":195258,"corporation":false,"usgs":false,"family":"Magnin","given":"Valerie","email":"","affiliations":[],"preferred":false,"id":706628,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pieter Glatzel","contributorId":195259,"corporation":false,"usgs":false,"family":"Pieter Glatzel","affiliations":[],"preferred":false,"id":706629,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Poulin, Brett 0000-0002-5555-7733 bpoulin@usgs.gov","orcid":"https://orcid.org/0000-0002-5555-7733","contributorId":194253,"corporation":false,"usgs":true,"family":"Poulin","given":"Brett","email":"bpoulin@usgs.gov","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":706621,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ryan, Joseph N.","contributorId":54290,"corporation":false,"usgs":false,"family":"Ryan","given":"Joseph","email":"","middleInitial":"N.","affiliations":[{"id":604,"text":"University of Colorado- Boulder","active":false,"usgs":true}],"preferred":false,"id":706630,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Aiken, George R. 0000-0001-8454-0984 graiken@usgs.gov","orcid":"https://orcid.org/0000-0001-8454-0984","contributorId":1322,"corporation":false,"usgs":true,"family":"Aiken","given":"George","email":"graiken@usgs.gov","middleInitial":"R.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":706631,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Gautier-Lunea, Isabelle","contributorId":195260,"corporation":false,"usgs":false,"family":"Gautier-Lunea","given":"Isabelle","email":"","affiliations":[],"preferred":false,"id":706632,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Kathryn L. Nagy","contributorId":195261,"corporation":false,"usgs":false,"family":"Kathryn L. Nagy","affiliations":[],"preferred":false,"id":706633,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70161924,"text":"70161924 - 2015 - The effects of numerical-model complexity and observation type on estimated porosity values","interactions":[],"lastModifiedDate":"2016-01-11T12:54:35","indexId":"70161924","displayToPublicDate":"2015-07-12T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1923,"text":"Hydrogeology Journal","active":true,"publicationSubtype":{"id":10}},"title":"The effects of numerical-model complexity and observation type on estimated porosity values","docAbstract":"The relative merits of model complexity and types of observations employed in model calibration are compared. An existing groundwater flow model coupled with an advective transport simulation of the Salt Lake Valley, Utah (USA), is adapted for advective transport, and effective porosity is adjusted until simulated tritium concentrations match concentrations in samples from wells. Two calibration approaches are used: a “complex” highly parameterized porosity field and a “simple” parsimonious model of porosity distribution. The use of an atmospheric tracer (tritium in this case) and apparent ages (from tritium/helium) in model calibration also are discussed. Of the models tested, the complex model (with tritium concentrations and tritium/helium apparent ages) performs best. Although tritium breakthrough curves simulated by complex and simple models are very generally similar, and there is value in the simple model, the complex model is supported by a more realistic porosity distribution and a greater number of estimable parameters. Culling the best quality data did not lead to better calibration, possibly because of processes and aquifer characteristics that are not simulated. Despite many factors that contribute to shortcomings of both the models and the data, useful information is obtained from all the models evaluated. Although any particular prediction of tritium breakthrough may have large errors, overall, the models mimic observed trends.
","language":"English","publisher":"Springer","publisherLocation":"Berlin","doi":"10.1007/s10040-015-1289-3","usgsCitation":"Starn, J., Bagtzoglou, A., and Green, C.T., 2015, The effects of numerical-model complexity and observation type on estimated porosity values: Hydrogeology Journal, v. 23, no. 6, p. 1121-1128, https://doi.org/10.1007/s10040-015-1289-3.","productDescription":"8 p.","startPage":"1121","endPage":"1128","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-059357","costCenters":[{"id":196,"text":"Connecticut Water Science Center","active":true,"usgs":true}],"links":[{"id":471943,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10040-015-1289-3","text":"Publisher Index Page"},{"id":314146,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Salt Lake Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.5,\n 40\n ],\n [\n -112.5,\n 41\n ],\n [\n -112,\n 41\n ],\n [\n -112,\n 40\n ],\n [\n -112.5,\n 40\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"23","issue":"6","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-12","publicationStatus":"PW","scienceBaseUri":"5694e066e4b039675d005e9f","contributors":{"authors":[{"text":"Starn, Jeffrey jjstarn@usgs.gov","contributorId":149231,"corporation":false,"usgs":true,"family":"Starn","given":"Jeffrey","email":"jjstarn@usgs.gov","affiliations":[{"id":196,"text":"Connecticut Water Science Center","active":true,"usgs":true}],"preferred":true,"id":588090,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bagtzoglou, Amvrossios C.","contributorId":30146,"corporation":false,"usgs":true,"family":"Bagtzoglou","given":"Amvrossios C.","affiliations":[],"preferred":false,"id":588092,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Green, Christopher T. 0000-0002-6480-8194 ctgreen@usgs.gov","orcid":"https://orcid.org/0000-0002-6480-8194","contributorId":1343,"corporation":false,"usgs":true,"family":"Green","given":"Christopher","email":"ctgreen@usgs.gov","middleInitial":"T.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":588091,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70154864,"text":"70154864 - 2015 - Coastal vertebrate exposure to predicted habitat changes due to sea level rise","interactions":[],"lastModifiedDate":"2015-10-23T15:04:58","indexId":"70154864","displayToPublicDate":"2015-07-11T16:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1547,"text":"Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Coastal vertebrate exposure to predicted habitat changes due to sea level rise","docAbstract":"Sea level rise (SLR) may degrade habitat for coastal vertebrates in the Southeastern United States, but it is unclear which groups or species will be most exposed to habitat changes. We assessed 28 coastal Georgia vertebrate species for their exposure to potential habitat changes due to SLR using output from the Sea Level Affecting Marshes Model and information on the species’ fundamental niches. We assessed forecasted habitat change up to the year 2100 using three structural habitat metrics: total area, patch size, and habitat permanence. Almost all of the species (n = 24) experienced negative habitat changes due to SLR as measured by at least one of the metrics. Salt marsh and ocean beach habitats experienced the most change (out of 16 categorical land cover types) across the three metrics and species that used salt marsh extensively (rails and marsh sparrows) were ranked highest for exposure to habitat changes. Species that nested on ocean beaches (Diamondback Terrapins, shorebirds, and terns) were also ranked highly, but their use of other foraging habitats reduced their overall exposure. Future studies on potential effects of SLR on vertebrates in southeastern coastal ecosystems should focus on the relative importance of different habitat types to these species’ foraging and nesting requirements. Our straightforward prioritization approach is applicable to other coastal systems and can provide insight to managers on which species to focus resources, what components of their habitats need to be protected, and which locations in the study area will provide habitat refuges in the face of SLR.
","language":"English","publisher":"Springer","publisherLocation":"New York","doi":"10.1007/s00267-015-0580-3","usgsCitation":"Hunter, E., Nibbelink, N.P., Alexander, C.R., Barrett, K., Mengak, L.F., Guy, R., Moore, C.T., and Cooper, R.J., 2015, Coastal vertebrate exposure to predicted habitat changes due to sea level rise: Environmental Management, p. 1-10, https://doi.org/10.1007/s00267-015-0580-3.","productDescription":"10 p.","startPage":"1","endPage":"10","numberOfPages":"10","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-055464","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":310611,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia","otherGeospatial":"Altamaha Estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.4306640625,\n 30.704058230919504\n ],\n [\n -81.97448730468749,\n 30.831497881307943\n ],\n [\n -81.89208984375,\n 31.28793989264176\n ],\n [\n -81.507568359375,\n 32.040676557717454\n ],\n [\n -81.1285400390625,\n 32.310348764525806\n ],\n [\n -80.82092285156249,\n 31.994100723260804\n ],\n [\n -81.14501953125,\n 31.70947636001935\n ],\n [\n -81.10107421874999,\n 31.59725256170666\n ],\n [\n -81.2933349609375,\n 31.367708915120826\n ],\n [\n -81.2548828125,\n 31.236288641793006\n ],\n [\n -81.39770507812499,\n 31.1140915948987\n ],\n [\n -81.4306640625,\n 30.704058230919504\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-11","publicationStatus":"PW","scienceBaseUri":"562b5a29e4b00162522207c0","contributors":{"authors":[{"text":"Hunter, Elizabeth A.","contributorId":149399,"corporation":false,"usgs":false,"family":"Hunter","given":"Elizabeth A.","affiliations":[],"preferred":false,"id":578297,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nibbelink, Nathan P.","contributorId":141326,"corporation":false,"usgs":false,"family":"Nibbelink","given":"Nathan","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":578298,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alexander, Clark R.","contributorId":149400,"corporation":false,"usgs":false,"family":"Alexander","given":"Clark","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":578299,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barrett, Kyle","contributorId":149401,"corporation":false,"usgs":false,"family":"Barrett","given":"Kyle","email":"","affiliations":[],"preferred":false,"id":578300,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mengak, Lara F.","contributorId":149402,"corporation":false,"usgs":false,"family":"Mengak","given":"Lara","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":578301,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Guy, Rachel","contributorId":35681,"corporation":false,"usgs":true,"family":"Guy","given":"Rachel","email":"","affiliations":[],"preferred":false,"id":578302,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Moore, Clinton T. 0000-0002-6053-2880 cmoore@usgs.gov","orcid":"https://orcid.org/0000-0002-6053-2880","contributorId":3643,"corporation":false,"usgs":true,"family":"Moore","given":"Clinton","email":"cmoore@usgs.gov","middleInitial":"T.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":564291,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Cooper, Robert J.","contributorId":99245,"corporation":false,"usgs":false,"family":"Cooper","given":"Robert","email":"","middleInitial":"J.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":578303,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70148091,"text":"tm4C4 - 2015 - Design, analysis, and interpretation of field quality-control data for water-sampling projects","interactions":[],"lastModifiedDate":"2021-05-27T13:58:28.962369","indexId":"tm4C4","displayToPublicDate":"2015-07-10T16:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"4-C4","title":"Design, analysis, and interpretation of field quality-control data for water-sampling projects","docAbstract":"The process of obtaining and analyzing water samples from the environment includes a number of steps that can affect the reported result. The equipment used to collect and filter samples, the bottles used for specific subsamples, any added preservatives, sample storage in the field, and shipment to the laboratory have the potential to affect how accurately samples represent the environment from which they were collected. During the early 1990s, the U.S. Geological Survey implemented policies to include the routine collection of quality-control samples in order to evaluate these effects and to ensure that water-quality data were adequately representing environmental conditions. Since that time, the U.S. Geological Survey Office of Water Quality has provided training in how to design effective field quality-control sampling programs and how to evaluate the resultant quality-control data. This report documents that training material and provides a reference for methods used to analyze quality-control data.
\nQuality-control data are those generated from the collection and analysis of quality-control samples, and are used to estimate the magnitude of errors in the process of obtaining environmental data. “Bias” and “variability” are the terms used in this report for the two types of errors in environmental data that are quantified by the data from quality-control samples. Bias is the systematic error inherent in a method or measurement system. Variability is the random error that occurs in independent measurements. The types of field quality-control samples discussed in this report include blanks, spikes, and replicates. Blanks are samples prepared with water that is intended to be free of measurable constituents that will be analyzed by the laboratory; blanks are used to estimate bias caused by contamination. Spiked samples are modified by addition of specific analytes; spikes are used to determine the performance of analytical methods and to estimate the potential bias due to matrix interference or analyte degradation. Replicate samples are two or more samples that are considered to be essentially identical in composition. Replicates are used to evaluate variability in analytical results. Various sub-types of these quality-control samples are defined and discussed in this report, and guidance is provided for incorporating the proper samples into the design for a project. The concept of inference space is introduced to help determine where and when quality-control samples should be collected as well as which environmental samples are related to a set of quality-control samples. The recommended basic quality-control design incorporates project-specific considerations, such as the objectives and scale of the study, and hydrologic and chemical conditions within the study area.
\nThe report provides extensive information about statistical methods used to analyze quality-control data in order to estimate potential bias and variability in environmental data. These methods include construction of confidence intervals on various statistical measures, such as the mean, percentiles and percentages, and standard deviation. The methods are used to compare quality-control results with the larger set of environmental data in order to determine whether the effects of bias and variability might interfere with interpretation of these data. Examples from published reports are presented to illustrate how the methods are applied, how bias and variability are reported, and how the interpretation of environmental data can be qualified based on the quality-control analysis.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section C in Book 4 Hydrologic analysis and interpretation","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/tm4C4","usgsCitation":"Mueller, D.K., Schertz, T.L., Martin, J.D., and Sandstrom, M.W., 2015, Design, analysis, and interpretation of field quality-control data for water-sampling projects: U.S. Geological Survey Techniques and Methods 4-C4, viii, 54 p., https://doi.org/10.3133/tm4C4.","productDescription":"viii, 54 p.","numberOfPages":"65","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-056948","costCenters":[{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"links":[{"id":305661,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/tm4C4.jpg"},{"id":305660,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/04/c04/pdf/tm4c4.pdf","text":"Report","size":"1.72 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"},{"id":305622,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/tm/04/c04/"}],"publicComments":"This report is Chapter 4 of Section C in Book 4 Hydrologic analysis and interpretation","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57f7eee2e4b0bc0bec09edae","contributors":{"authors":[{"text":"Mueller, David K. mueller@usgs.gov","contributorId":1585,"corporation":false,"usgs":true,"family":"Mueller","given":"David","email":"mueller@usgs.gov","middleInitial":"K.","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":564508,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schertz, Terry L. tschertz@usgs.gov","contributorId":188,"corporation":false,"usgs":true,"family":"Schertz","given":"Terry","email":"tschertz@usgs.gov","middleInitial":"L.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":564509,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martin, Jeffrey D. 0000-0003-1994-5285 jdmartin@usgs.gov","orcid":"https://orcid.org/0000-0003-1994-5285","contributorId":1066,"corporation":false,"usgs":true,"family":"Martin","given":"Jeffrey","email":"jdmartin@usgs.gov","middleInitial":"D.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true},{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true}],"preferred":true,"id":564510,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":452,"text":"National Water Quality Laboratory","active":true,"usgs":true},{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":564511,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70159195,"text":"70159195 - 2015 - When do we need more data? A primer on calculating the value of information for applied ecologists","interactions":[],"lastModifiedDate":"2015-10-19T09:10:47","indexId":"70159195","displayToPublicDate":"2015-07-10T15:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"When do we need more data? A primer on calculating the value of information for applied ecologists","docAbstract":"When emergencies occur, first responders and disaster response teams often need rapid access to aerial photography and satellite imagery that is acquired before and after the event. The U.S. Geological Survey (USGS) Hazards Data Distribution System (HDDS) provides quick and easy access to pre- and post-event imagery and geospatial datasets that support emergency response and recovery operations. The HDDS provides a single, consolidated point-of-entry and distribution system for USGS-hosted remotely sensed imagery and other geospatial datasets related to an event response. The data delivery services are provided through an interactive map-based interface that allows emergency response personnel to rapidly select and download pre-event (\"baseline\") and post-event emergency response imagery.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20153048","usgsCitation":"Jones, B.K., and Lamb, R.M., 2015, Hazards data distribution system (HDDS): U.S. Geological Survey Fact Sheet 2015–3048, 2 p., https://dx.doi.org/10.3133/fs20153048.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-065098","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":305592,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2015/3048/coverthb.jpg"},{"id":305593,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2015/3048/fs20153048.pdf","text":"Report","size":"4.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2015-3048"}],"contact":"Earth Resources Observation
and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, South Dakota 57198
http://eros.usgs.gov/
Coal is a chemically complex commodity that often contains most of the natural elements in the periodic table. Coal constituents are conventionally grouped into four components (proximate analysis): fixed carbon, ash, inherent moisture, and volatile matter. These four parts, customarily measured as weight losses and expressed as percentages, share all properties and statistical challenges of compositional data. Consequently, adequate modeling should be done in terms of a logratio transformation, a requirement that is commonly overlooked by modelers. The transformation of choice is the isometric logratio transformation because of its geometrical and statistical advantages. The modeling is done through a series of realizations prepared by applying sequential simulation for the purpose of displaying the parts in maps incorporating uncertainty. The approach makes realistic assumptions and the results honor the data and basic considerations, such as percentages between 0 and 100, all four parts adding to 100% at any location in the study area, and a style of spatial fluctuation in the realizations equal to that of the data. The realizations are used to prepare different results, including probability distributions across a deposit, E-type maps displaying average properties, and probability maps summarizing joint fluctuations of several parts. Application of these maps to a lignite bed clearly delineates the deposit boundary, reveals a channel cutting across, and shows that the most favorable coal quality is to the north and deteriorates toward the southeast.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coal.2015.10.003","usgsCitation":"Olea, R.A., and Luppens, J.A., 2015, Mapping of coal quality using stochastic simulation and isometric logratio transformation with an application to a Texas lignite: International Journal of Coal Geology, v. 152, no. Part B, p. 80-93, https://doi.org/10.1016/j.coal.2015.10.003.","productDescription":"14 p.","startPage":"80","endPage":"93","ipdsId":"IP-069055","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":342888,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"152","issue":"Part B","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59521d22e4b062508e3c3691","contributors":{"authors":[{"text":"Olea, Ricardo A. 0000-0003-4308-0808 rolea@usgs.gov","orcid":"https://orcid.org/0000-0003-4308-0808","contributorId":139555,"corporation":false,"usgs":true,"family":"Olea","given":"Ricardo","email":"rolea@usgs.gov","middleInitial":"A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":700520,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Luppens, James A. 0000-0001-7607-8750 jluppens@usgs.gov","orcid":"https://orcid.org/0000-0001-7607-8750","contributorId":550,"corporation":false,"usgs":true,"family":"Luppens","given":"James","email":"jluppens@usgs.gov","middleInitial":"A.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":700521,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70173550,"text":"70173550 - 2015 - Influence of sectioning location on age estimates from common carp dorsal spines","interactions":[],"lastModifiedDate":"2016-06-09T16:10:50","indexId":"70173550","displayToPublicDate":"2015-07-09T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Influence of sectioning location on age estimates from common carp dorsal spines","docAbstract":"Dorsal spines have been shown to provide precise age estimates for Common CarpCyprinus carpio and are commonly used by management agencies to gain information on Common Carp populations. However, no previous studies have evaluated variation in the precision of age estimates obtained from different sectioning locations along Common Carp dorsal spines. We evaluated the precision, relative readability, and distribution of age estimates obtained from various sectioning locations along Common Carp dorsal spines. Dorsal spines from 192 Common Carp were sectioned at the base (section 1), immediately distal to the basal section (section 2), and at 25% (section 3), 50% (section 4), and 75% (section 5) of the total length of the dorsal spine. The exact agreement and within-1-year agreement among readers was highest and the coefficient of variation lowest for section 2. In general, age estimates derived from sections 2 and 3 had similar age distributions and displayed the highest concordance in age estimates with section 1. Our results indicate that sections taken at ≤ 25% of the total length of the dorsal spine can be easily interpreted and provide precise estimates of Common Carp age. The greater consistency in age estimates obtained from section 2 indicates that by using a standard sectioning location, fisheries scientists can expect age-based estimates of population metrics to be more comparable and thus more useful for understanding Common Carp population dynamics.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/02755947.2015.1042561","usgsCitation":"Watkins, C.J., Klein, Z.B., Terrazas, M.M., and Quist, M.C., 2015, Influence of sectioning location on age estimates from common carp dorsal spines: North American Journal of Fisheries Management, v. 35, no. 4, p. 690-697, https://doi.org/10.1080/02755947.2015.1042561.","productDescription":"8 p.","startPage":"690","endPage":"697","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-055728","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":323441,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"35","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-09","publicationStatus":"PW","scienceBaseUri":"575a9333e4b04f417c275159","contributors":{"authors":[{"text":"Watkins, Carson J.","contributorId":171708,"corporation":false,"usgs":false,"family":"Watkins","given":"Carson","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":638352,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Klein, Zachary B.","contributorId":171709,"corporation":false,"usgs":false,"family":"Klein","given":"Zachary","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":638353,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Terrazas, Marc M.","contributorId":171710,"corporation":false,"usgs":false,"family":"Terrazas","given":"Marc","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":638354,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Quist, Michael C. 0000-0001-8268-1839 mquist@usgs.gov","orcid":"https://orcid.org/0000-0001-8268-1839","contributorId":171392,"corporation":false,"usgs":true,"family":"Quist","given":"Michael","email":"mquist@usgs.gov","middleInitial":"C.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":637289,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70154802,"text":"70154802 - 2015 - Truth, models, model sets, AIC, and multimodel inference: a Bayesian perspective","interactions":[],"lastModifiedDate":"2016-04-13T12:35:45","indexId":"70154802","displayToPublicDate":"2015-07-08T14:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Truth, models, model sets, AIC, and multimodel inference: a Bayesian perspective","docAbstract":"Statistical inference begins with viewing data as realizations of stochastic processes. Mathematical models provide partial descriptions of these processes; inference is the process of using the data to obtain a more complete description of the stochastic processes. Wildlife and ecological scientists have become increasingly concerned with the conditional nature of model-based inference: what if the model is wrong? Over the last 2 decades, Akaike's Information Criterion (AIC) has been widely and increasingly used in wildlife statistics for 2 related purposes, first for model choice and second to quantify model uncertainty. We argue that for the second of these purposes, the Bayesian paradigm provides the natural framework for describing uncertainty associated with model choice and provides the most easily communicated basis for model weighting. Moreover, Bayesian arguments provide the sole justification for interpreting model weights (including AIC weights) as coherent (mathematically self consistent) model probabilities. This interpretation requires treating the model as an exact description of the data-generating mechanism. We discuss the implications of this assumption, and conclude that more emphasis is needed on model checking to provide confidence in the quality of inference.
","language":"English","publisher":"Wildlife Society","doi":"10.1002/jwmg.890","usgsCitation":"Barker, R., and Link, W., 2015, Truth, models, model sets, AIC, and multimodel inference: a Bayesian perspective: Journal of Wildlife Management, v. 79, no. 5, p. 730-738, https://doi.org/10.1002/jwmg.890.","productDescription":"9 p.","startPage":"730","endPage":"738","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063229","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":471945,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.890","text":"Publisher Index Page"},{"id":305619,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"79","issue":"5","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2015-05-07","publicationStatus":"PW","scienceBaseUri":"559e3ba6e4b0b94a64018f54","contributors":{"authors":[{"text":"Barker, Richard J.","contributorId":6987,"corporation":false,"usgs":true,"family":"Barker","given":"Richard J.","affiliations":[],"preferred":false,"id":564201,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Link, William A. wlink@usgs.gov","contributorId":145491,"corporation":false,"usgs":true,"family":"Link","given":"William A.","email":"wlink@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":564200,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70154797,"text":"70154797 - 2015 - Rapid water quality change in the Elwha River estuary complex during dam removal","interactions":[],"lastModifiedDate":"2015-09-17T13:42:56","indexId":"70154797","displayToPublicDate":"2015-07-08T14:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2620,"text":"Limnology and Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Rapid water quality change in the Elwha River estuary complex during dam removal","docAbstract":"Dam removal in the United States is increasing as a result of structural concerns, sedimentation of reservoirs, and declining riverine ecosystem conditions. The removal of the 32 m Elwha and 64 m Glines Canyon dams from the Elwha River in Washington, U.S.A., was the largest dam removal project in North American history. During the 3 yr of dam removal—from September 2011 to August 2014—more than ten million cubic meters of sediment was eroded from the former reservoirs, transported downstream, and deposited throughout the lower river, river delta, and nearshore waters of the Strait of Juan de Fuca. Water quality data collected in the estuary complex at the mouth of the Elwha River document how conditions in the estuary changed as a result of sediment deposition over the 3 yr the dams were removed. Rapid and large-scale changes in estuary conditions—including salinity, depth, and turbidity—occurred 1 yr into the dam removal process. Tidal propagation into the estuary ceased following a large sediment deposition event that began in October 2013, resulting in decreased salinity, and increased depth and turbidity in the estuary complex. These changes have persisted in the system through dam removal, significantly altering the structure and functioning of the Elwha River estuary ecosystem.
","language":"English","publisher":"Wiley","doi":"10.1002/lno.10129","usgsCitation":"Foley, M.M., Duda, J.J., Beirne, M., Paradis, R., Ritchie, A., and Warrick, J., 2015, Rapid water quality change in the Elwha River estuary complex during dam removal: Limnology and Oceanography, v. 60, no. 5, p. 1719-1732, https://doi.org/10.1002/lno.10129.","productDescription":"14 p.","startPage":"1719","endPage":"1732","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065467","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":471946,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/lno.10129","text":"Publisher Index Page"},{"id":305620,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Elwha River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.61679077148438,\n 47.96050238891509\n ],\n [\n -123.61679077148438,\n 48.15600899174947\n ],\n [\n -123.475341796875,\n 48.15600899174947\n ],\n [\n -123.475341796875,\n 47.96050238891509\n ],\n [\n -123.61679077148438,\n 47.96050238891509\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"60","issue":"5","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-06","publicationStatus":"PW","scienceBaseUri":"559e3ba5e4b0b94a64018f51","contributors":{"authors":[{"text":"Foley, Melissa M. 0000-0002-5832-6404 mfoley@usgs.gov","orcid":"https://orcid.org/0000-0002-5832-6404","contributorId":4861,"corporation":false,"usgs":true,"family":"Foley","given":"Melissa","email":"mfoley@usgs.gov","middleInitial":"M.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":564188,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duda, Jeffrey J. 0000-0001-7431-8634 jduda@usgs.gov","orcid":"https://orcid.org/0000-0001-7431-8634","contributorId":145486,"corporation":false,"usgs":true,"family":"Duda","given":"Jeffrey","email":"jduda@usgs.gov","middleInitial":"J.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":false,"id":564189,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beirne, Matthew M.","contributorId":66984,"corporation":false,"usgs":true,"family":"Beirne","given":"Matthew M.","affiliations":[],"preferred":false,"id":564190,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Paradis, Rebecca","contributorId":145488,"corporation":false,"usgs":false,"family":"Paradis","given":"Rebecca","affiliations":[{"id":13135,"text":"Lower Elwha Klallam Tribe, Port Angeles, WA","active":true,"usgs":false}],"preferred":false,"id":564191,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ritchie, Andrew","contributorId":35443,"corporation":false,"usgs":true,"family":"Ritchie","given":"Andrew","affiliations":[],"preferred":false,"id":564192,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Warrick, Jonathan A. 0000-0002-0205-3814 jwarrick@usgs.gov","orcid":"https://orcid.org/0000-0002-0205-3814","contributorId":139314,"corporation":false,"usgs":true,"family":"Warrick","given":"Jonathan A.","email":"jwarrick@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":564193,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70154817,"text":"70154817 - 2015 - Comparing spatial capture–recapture modeling and nest count methods to estimate orangutan densities in the Wehea Forest, East Kalimantan, Indonesia","interactions":[],"lastModifiedDate":"2015-07-08T13:35:48","indexId":"70154817","displayToPublicDate":"2015-07-08T14:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Comparing spatial capture–recapture modeling and nest count methods to estimate orangutan densities in the Wehea Forest, East Kalimantan, Indonesia","docAbstract":"Accurate information on the density and abundance of animal populations is essential for understanding species' ecology and for conservation planning, but is difficult to obtain. The endangered orangutan (Pongo spp.) is an example; due to its elusive behavior and low densities, researchers have relied on methods that convert nest counts to orangutan densities and require substantial effort for reliable results. Camera trapping and spatial capture–recapture (SCR) models could provide an alternative but have not been used for primates. We compared density estimates calculated using the two methods for orangutans in the Wehea Forest, East Kalimantan, Indonesia. Camera trapping/SCR modeling produced a density estimate of 0.16 ± 0.09–0.29 indiv/km2, and nest counts produced a density estimate of 1.05 ± 0.18–6.01 indiv/km2. The large confidence interval of the nest count estimate is probably due to high variance in nest encounter rates, indicating the need for larger sample size and the substantial effort required to produce reliable results using this method. The SCR estimate produced a very low density estimate and had a narrower but still fairly wide confidence interval. This was likely due to unmodeled heterogeneity and small sample size, specifically a low number of individual captures and recaptures. We propose methodological fixes that could address these issues and improve precision. A comparison of the overall costs and benefits of the two methods suggests that camera trapping/SCR modeling can potentially be a useful tool for assessing the densities of orangutans and other elusive primates, and warrant further investigation to determine broad applicability and methodological adjustments needed.
\n\n
Reducing turbulence and associated air entrainment is generally considered advantageous in the engineering design of fish passage facilities. The well-known energy dissipation factor, or EDF, correlates with observations of the phenomena. However, inconsistencies in EDF forms exist and the bases for volumetric energy dissipation rate criteria are often misunderstood. A comprehensive survey of EDF criteria is presented. Clarity in the application of the EDF and resolutions to these inconsistencies are provided through formal derivations; it is demonstrated that kinetic energy represents only 1/3 of the total energy input for the special case of a broad-crested weir. Specific errors in published design manuals are identified and resolved. New, fundamentally sound, design equations for culvert outlet pools and standard Denil Fishway resting pools are developed. The findings underscore the utility of EDF equations, demonstrate the transferability of volumetric energy dissipation rates, and provide a foundation for future refinement of component-, species-, and life-stage-specific EDF criteria.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecoleng.2015.06.014","usgsCitation":"Towler, B., Mulligan, K., and Haro, A.J., 2015, Derivation and application of the energy dissipation factor in the design of fishways: Ecological Engineering, v. 83, p. 208-217, https://doi.org/10.1016/j.ecoleng.2015.06.014.","productDescription":"10 p.","startPage":"208","endPage":"217","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-059508","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":305614,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"83","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"559e3ba3e4b0b94a64018f4d","contributors":{"authors":[{"text":"Towler, Brett","contributorId":141164,"corporation":false,"usgs":false,"family":"Towler","given":"Brett","email":"","affiliations":[{"id":6927,"text":"USFWS, National Wildlife Refuge System","active":true,"usgs":false}],"preferred":false,"id":548626,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mulligan, Kevin 0000-0002-3534-4239","orcid":"https://orcid.org/0000-0002-3534-4239","contributorId":141165,"corporation":false,"usgs":false,"family":"Mulligan","given":"Kevin","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":548627,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haro, Alexander J. 0000-0002-7188-9172 aharo@usgs.gov","orcid":"https://orcid.org/0000-0002-7188-9172","contributorId":2917,"corporation":false,"usgs":true,"family":"Haro","given":"Alexander","email":"aharo@usgs.gov","middleInitial":"J.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":548625,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}