Large rivers carry terrestrial sediment, contaminants, and other materials to the coastal zone where they can affect marine biogeochemical cycles and ecosystems. This U.S. Geological Survey study combined river and marine sediment geochemistry and organic contaminant analyses to identify riverborne sediment and associated contaminants at shoreline sites in Commencement Bay, Puget Sound, Washington, that could be used by adult forage fish and other marine organisms. Geochemical signatures distinguished the fine fraction (<0.063 millimeter, mm) of Puyallup River sediment—which originates from Mount Rainier, a Cascade volcano—from glacial fine sediment in lowland bluffs that supply sediment to beaches. In combination with activities of beryllium-7 (7Be), a short-lived radionuclide, geochemical signatures showed that winter 2013–14 sediment runoff from the Puyallup River was transported to and deposited along the north shore of Commencement Bay, then mixed downward into the sediment column. The three Commencement Bay sites at which organic contaminants were measured in surface sediment did not have measurable 7Be activities in that layer, so their contaminant assemblages were attributed to sources from previous years. Concentrations of organic contaminants (the most common of which were polycyclic aromatic hydrocarbons, polychlorinated biphenyls, and fecal sterols) were higher in the <0.063-mm fraction compared to the <2-mm fraction, in winter compared to summer, in river suspended sediment compared to river bar and bank sediment, and in marine sediment compared to river sediment. The geochemical property barium/aluminum (Ba/Al) showed that the median percentage of Puyallup River derived fine surface sediment along the shoreline of Commencement Bay was 77 percent. This finding, in combination with higher concentrations of organic contaminants in marine rather than river sediment, indicates that riverborne sediment-bound contaminants are retained in shallow marine habitats of Commencement Bay. The retention of earlier inputs complicates efforts to identify recent inputs and sources. Understanding modern sources and fates of riverborne sediment and contaminants and their potential ecological impacts will therefore require a suite of targeted geochemical studies in such marine depositional environments.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171124","usgsCitation":"Takesue, R.K., Conn, K.E., and Dinicola, R.S., 2017, Tracking riverborne sediment and contaminants in Commencement Bay, Washington, using geochemical signatures: U.S. Geological Survey Open-File Report 2017–1124, 31 p., https://doi.org/10.3133/ofr20171124.","productDescription":"vii, 31 p.","numberOfPages":"41","onlineOnly":"Y","ipdsId":"IP-086001","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":346256,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1124/coverthb.jpg"},{"id":346257,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1124/ofr20171124.pdf","text":"Report","size":"5.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1124"}],"country":"United States","state":"Washington","otherGeospatial":"Puyallup River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.46185302734375,\n 46.86582490125156\n ],\n [\n -121.60491943359375,\n 46.86582490125156\n ],\n [\n -121.60491943359375,\n 47.23262467463881\n ],\n [\n -122.46185302734375,\n 47.23262467463881\n ],\n [\n -122.46185302734375,\n 46.86582490125156\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Pacific Coastal and Marine Science Center
U.S. Geological Survey
Pacific Science Center
2885 Mission St.
Santa Cruz, CA 95060
Climate change and habitat loss are projected to be the two greatest drivers of biodiversity loss over the coming century. While public lands have the potential to increase regional resilience of bird populations to these threats, long-term data are necessary to document species responses to changes in climate and habitat to better understand population vulnerabilities. We used generalized linear mixed models to determine the importance of stand-level characteristics, multi-scale land cover, and annual weather factors to the abundance of 61 bird species over a 20-year time frame in Chippewa National Forest, Minnesota, USA. Of the 61 species modeled, we were able to build final models with R-squared values that ranged from 26% to 69% for 37 species; the remaining 24 species models had issues with convergence or low explanatory power (R-squared < 20%). Models for the 37 species show that stand-level characteristics, land cover factors, and annual weather effects on species abundance were species-specific and varied within guilds. Forty-one percent of the final species models included stand-level characteristics, 92% included land cover variables at the 200 m scale, 51% included land cover variables at the 500 m scale, 46% included land cover variables at the 1000 m scale, and 38% included weather variables in best models. Three species models (8%) included significant weather and land cover interaction terms. Overall, models indicated that aboveground tree biomass and land cover variables drove changes in the majority of species. Of those species models including weather variables, more included annual variation in precipitation or drought than temperature. Annual weather variability was significantly more likely to impact abundance of species associated with deciduous forests and bird species that are considered climate sensitive. The long-term data and models we developed are particularly suited to informing science-based adaptive forest management plans that incorporate climate sensitivity, aim to conserve large areas of forest habitat, and maintain an historical mosaic of cover types for conserving a diverse and abundant avian assemblage.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2017.09.057","usgsCitation":"Grinde, A.R., Hiemi, G.J., Sturtevant, B.R., Panci, H., Thogmartin, W.E., and Wolter, P., 2017, Importance of scale, land cover, and weather on the abundance of bird species in a managed forest: Forest Ecology and Management, v. 405, p. 295-308, https://doi.org/10.1016/j.foreco.2017.09.057.","productDescription":"14 p.","startPage":"295","endPage":"308","ipdsId":"IP-083833","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":469494,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://lib.dr.iastate.edu/nrem_pubs/239","text":"Publisher Index Page"},{"id":352524,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","otherGeospatial":"Chippewa National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.71588134765625,\n 46.80193957664001\n ],\n [\n -93.22998046875,\n 46.80193957664001\n ],\n [\n -93.22998046875,\n 47.8666165573186\n ],\n [\n -94.71588134765625,\n 47.8666165573186\n ],\n [\n -94.71588134765625,\n 46.80193957664001\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"405","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee7ebe4b0da30c1bfc3b3","contributors":{"authors":[{"text":"Grinde, Alexis R.","contributorId":196778,"corporation":false,"usgs":false,"family":"Grinde","given":"Alexis","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":711557,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hiemi, Gerald J.","contributorId":196780,"corporation":false,"usgs":false,"family":"Hiemi","given":"Gerald","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":711560,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sturtevant, Brian R.","contributorId":190143,"corporation":false,"usgs":false,"family":"Sturtevant","given":"Brian","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":711559,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Panci, Hannah","contributorId":196779,"corporation":false,"usgs":false,"family":"Panci","given":"Hannah","email":"","affiliations":[],"preferred":false,"id":711558,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":711556,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wolter, Peter","contributorId":196781,"corporation":false,"usgs":false,"family":"Wolter","given":"Peter","affiliations":[],"preferred":false,"id":711561,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70191195,"text":"ofr20171125 - 2017 - Skagit River coho salmon life history model—Users’ guide","interactions":[],"lastModifiedDate":"2017-11-22T12:08:31","indexId":"ofr20171125","displayToPublicDate":"2017-09-29T00:00:00","publicationYear":"2017","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":"2017-1125","title":"Skagit River coho salmon life history model—Users’ guide","docAbstract":"Natural resource management is conducted in the context of multiple anthropogenic stressors and is further challenged owing to changing climate. Experiments to determine the effects of climate change on complex ecological systems are nearly impossible. However, using a simulation model to synthesize current understanding of key ecological processes through the life cycle of a fish population can provide a platform for exploring potential effects of and management responses to changing conditions. Potential climate-change scenarios can be imposed, responses can be observed, and the effectiveness of potential actions can be evaluated. This approach is limited owing to future conditions likely deviating in range and timing from conditions used to create the model so that the model is expected to become obsolete. In the meantime, however, the modeling process explicitly states assumptions, clarifies information gaps, and provides a means to better understand which relationships are robust and which are vulnerable to changing climate by observing whether and why model output diverges from actual observations through time. The purpose of the model described herein is to provide such a decision-support tool regarding coho (Oncorhynchus kisutch) salmon for the Sauk-Suiattle Indian Tribe of Washington State.
The Skagit coho salmon model is implemented in a system dynamics format and has three primary stocks—(1) predicted smolts, (2) realized smolts, and (3) escapement. “Predicted smolts” are the number of smolts expected based on the number of spawners in any year and the Ricker production curve. Pink salmon (Oncorhynchus gorbuscha) return to the Skagit River in odd years, and when they overlap with juvenile rearing coho salmon, coho smolt production is substantially higher than in non-pink years. Therefore, the model uses alternative Ricker equations to predict smolts depending on whether their juvenile year was a pink or non-pink year. The stock “realized smolts” is calculated based on the expected effect of streamflow conditions to alter the productivity predicted by the Ricker curve. Adverse conditions include scouring flow events that occur when redds are present; high-flow events during winter on juveniles, which can cause fish displacement and adverse water turbidity; and extremely low flows in summer. The stock “escapement” represents the fish remaining after accounting for ocean mortality and harvest. Ocean mortality has been linked with indices of ocean conditions, which are related to ocean biological productivity. Ocean survival also may have a density-dependent component such that lower survival is associated with higher numbers of smolts. The model allows the user to change certain model parameters and inputs, and choose among alternative predictors for certain modeled relations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171125","collaboration":"Prepared in cooperation with Sauk-Suiattle Indian Tribe","usgsCitation":"Woodward, Andrea, Kirby, Grant, and Morris, Scott, 2017, Skagit River coho salmon life history model—Users’ guide: U.S. Geological Survey Open-File Report 2017–1125, 57 p., https://doi.org/10.3133/ofr20171125.","productDescription":"vi, 57 p.","numberOfPages":"68","onlineOnly":"Y","ipdsId":"IP-081907","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":346300,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1125/ofr20171125.pdf","text":"Report","size":"3.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1125"},{"id":346299,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1125/coverthb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Skagit River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.5,\n 47.95314495015594\n ],\n [\n -120.75347900390624,\n 47.95314495015594\n ],\n [\n -120.75347900390624,\n 49\n ],\n [\n -122.5,\n 49\n ],\n [\n -122.5,\n 47.95314495015594\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Forest and Rangeland Ecosystem Science Center
U.S. Geological Survey
777 NW 9th St., Suite 400
Corvallis, Oregon 97330
The 2017 census of southern sea otters (Enhydra lutris nereis) was conducted between late April and early July along the mainland coast of central California and in April at San Nicolas Island in southern California. The 3-year average of combined counts from the mainland range and San Nicolas Island was 3,186, down by 86 sea otters from the previous year. This is the second year that the official index has exceeded 3,090, the Endangered Species Act delisting threshold identified in the U.S. Fish and Wildlife Service’s Southern Sea Otter Recovery Plan (the threshold would need to be exceeded for 3 consecutive years before delisting consideration). The 5-year average trend in abundance, including both the mainland range and San Nicolas Island populations, remains positive at 2.3 percent per year. Continuing lack of growth in the range peripheries likely explains the cessation of range expansion.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1067","usgsCitation":"Tinker, M.T., and Hatfield, B.B., 2017, California sea otter (Enhydra lutris nereis) census results, spring 2017: U.S. Geological Survey Data Series 1067, 9 p., https://doi.org/10.3133/ds1067.","productDescription":"Report: iv, 9 p.; Data Release","numberOfPages":"18","onlineOnly":"Y","ipdsId":"IP-090685","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":346058,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7R2109J","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Annual California sea otter census—2017 spring census summary"},{"id":346008,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1067/coverthb.jpg"},{"id":346009,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1067/ds1067.pdf","text":"Report","size":"1.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1067"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.37670898437499,\n 33.15594830078649\n ],\n [\n -119.35546875000001,\n 33.15594830078649\n ],\n [\n -119.35546875000001,\n 37.18657859524883\n ],\n [\n -122.37670898437499,\n 37.18657859524883\n ],\n [\n -122.37670898437499,\n 33.15594830078649\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive
Modoc Hall, Room 4004
Sacramento, California 95819
Two sets of digital flood-inundation map libraries that spanned a combined 16.7-mile reach of the Meramec River that extends upstream from Valley Park, Missouri, to downstream from Fenton, Mo., were created by the U.S. Geological Survey (USGS) in cooperation with the U.S. Army Corps of Engineers, St. Louis Metropolitan Sewer District, Missouri Department of Transportation, Missouri American Water, and Federal Emergency Management Agency Region 7. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science website at https://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the cooperative USGS streamgages on the Meramec River at Valley Park, Mo., (USGS station number 07019130) and the Meramec River at Fenton, Mo. (USGS station number 07019210). Near-real-time stage data at these streamgages may be obtained from the USGS National Water Information System at https://waterdata.usgs.gov/nwis or the National Weather Service (NWS) Advanced Hydrologic Prediction Service at http:/water.weather.gov/ahps/, which also forecasts flood hydrographs at these sites (listed as NWS sites vllm7 and fnnm7, respectively).
Flood profiles were computed for the stream reaches by means of a calibrated one-dimensional step-backwater hydraulic model. The model was calibrated using a stage-discharge relation at the Meramec River near Eureka streamgage (USGS station number 07019000) and documented high-water marks from the flood of December 2015 through January 2016.
The calibrated hydraulic model was used to compute two sets of water-surface profiles: one set for the streamgage at Valley Park, Mo. (USGS station number 07019130), and one set for the USGS streamgage on the Meramec River at Fenton, Mo. (USGS station number 07019210). The water-surface profiles were produced for stages at 1-foot (ft) intervals referenced to the datum from each streamgage and ranging from the NWS action stage, or near bankfull discharge, to the stage corresponding to the estimated 0.2-percent annual exceedance probability (500-year recurrence interval) flood, as determined at the Eureka streamgage (USGS station number 07019000). The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from light detection and ranging data having a 0.28-ft vertical accuracy and 3.28-ft horizontal resolution) to delineate the area flooded at each flood stage (water level).
The availability of these maps, along with internet information regarding current stage from the USGS streamgages and forecasted high-flow stages from the NWS, will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures and for postflood recovery efforts.
Director, Missouri Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
We surveyed for Least Bell’s Vireos (LBVI) (Vireo bellii pusillus) and Southwestern Willow Flycatchers (SWFL) (Empidonax traillii extimus) along the San Luis Rey River, between College Boulevard in Oceanside and Interstate 15 in Fallbrook, California (middle San Luis Rey River), in 2016. Surveys were done from March 30 to July 11 (LBVI) and from May 18 to July 30 (SWFL). We found 142 LBVI territories, at least 106 of which were occupied by pairs. Six additional transient LBVIs were detected. Of 20 banded LBVIs detected in the survey area, 9 had been given full color-band combinations prior to 2016, although we were unable to determine the exact color combination of 1 female LBVI. Seven other LBVIs with single (natal) federal bands were recaptured and banded in 2016. Four vireos with single dark blue federal bands indicating that they were banded as nestlings on the lower San Luis Rey River could not be recaptured for identification.
Three SFWL territories were observed in the survey area in 2016. Two territories were occupied by pairs and one by a male of unknown breeding status. Both pairs attempted to nest at least once, and both pairs were successful, fledging three young each. Nesting began in early June and continued into July. Brown-Headed Cowbird (Molothrus ater) eggs were not observed in either nest. An additional 12 transient Willow Flycatchers of unknown subspecies were detected in 2016.
Two of the five resident SWFLs were originally banded as nestlings on Marine Corps Base Camp Pendleton. One male and one female were banded as nestlings on Camp Pendleton in 2009 and 2011, respectively. One natal male of unknown breeding status, originally banded as a nestling on the middle San Luis Rey River in 2015, was recaptured and given a unique color combination in 2016. This male was later detected on Marine Corps Base Camp Pendleton.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1065","usgsCitation":"Allen, L.D., Howell, S.L., and Kus, B.E., 2017, Distribution and abundance of Least Bell’s Vireos (Vireo bellii pusillus) and Southwestern Willow Flycatchers (Empidonax traillii extimus) on the Middle San Luis Rey River, San Diego, southern California—2016 data summary: U.S. Geological Survey Data Series 1065, 11 p., https://doi.org/10.3133/ds1065.","productDescription":"iv, 11 p.","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-087469","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":346297,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1065/coverthb.jpg"},{"id":346298,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1065/ds1065.pdf","text":"Report","size":"2.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1065"}],"country":"United States","state":"California","county":"San Diego County","otherGeospatial":"Middle San Luis Rey River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.3020553588867,\n 33.244430403478276\n ],\n [\n -117.158203125,\n 33.244430403478276\n ],\n [\n -117.158203125,\n 33.324504165325195\n ],\n [\n -117.3020553588867,\n 33.324504165325195\n ],\n [\n -117.3020553588867,\n 33.244430403478276\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Evaluating the conditions where a species can persist is an important question in ecology both to understand tolerances of organisms and to predict distributions across landscapes. Presence data combined with background or pseudo-absence locations are commonly used with species distribution modeling to develop these relationships. However, there is not a standard method to generate background or pseudo-absence locations, and method choice affects model outcomes. We evaluated combinations of both model algorithms (simple and complex generalized linear models, multivariate adaptive regression splines, Maxent, boosted regression trees, and random forest) and background methods (random, minimum convex polygon, and continuous and binary kernel density estimator (KDE)) to assess the sensitivity of model outcomes to choices made. We evaluated six questions related to model results, including five beyond the common comparison of model accuracy assessment metrics (biological interpretability of response curves, cross-validation robustness, independent data accuracy and robustness, and prediction consistency). For our case study with cheatgrass in the western US, random forest was least sensitive to background choice and the binary KDE method was least sensitive to model algorithm choice. While this outcome may not hold for other locations or species, the methods we used can be implemented to help determine appropriate methodologies for particular research questions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2017.08.017","usgsCitation":"Jarnevich, C.S., Talbert, M., Morisette, J.T., Aldridge, C.L., Brown, C., Kumar, S., Manier, D.J., Talbert, C., and Holcombe, T.R., 2017, Minimizing effects of methodological decisions on interpretation and prediction in species distribution studies: An example with background selection: Ecological Modelling, v. 363, p. 48-56, https://doi.org/10.1016/j.ecolmodel.2017.08.017.","productDescription":"9 p.","startPage":"48","endPage":"56","ipdsId":"IP-073503","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":346154,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.8486328125,\n 31.615965936476076\n ],\n [\n -101.90917968749999,\n 31.615965936476076\n ],\n [\n -101.90917968749999,\n 49.06666839558117\n ],\n [\n -124.8486328125,\n 49.06666839558117\n ],\n [\n -124.8486328125,\n 31.615965936476076\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"363","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59ce0a26e4b05fe04cc020f6","contributors":{"authors":[{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 jarnevichc@usgs.gov","orcid":"https://orcid.org/0000-0002-9699-2336","contributorId":3424,"corporation":false,"usgs":true,"family":"Jarnevich","given":"Catherine","email":"jarnevichc@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":711394,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Talbert, Marian 0000-0003-0588-0265 mtalbert@usgs.gov","orcid":"https://orcid.org/0000-0003-0588-0265","contributorId":196740,"corporation":false,"usgs":true,"family":"Talbert","given":"Marian","email":"mtalbert@usgs.gov","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":477,"text":"North Central Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":711395,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Morisette, Jeffrey T. 0000-0002-0483-0082 morisettej@usgs.gov","orcid":"https://orcid.org/0000-0002-0483-0082","contributorId":307,"corporation":false,"usgs":true,"family":"Morisette","given":"Jeffrey","email":"morisettej@usgs.gov","middleInitial":"T.","affiliations":[{"id":477,"text":"North Central Climate Science Center","active":true,"usgs":true},{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":711396,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":711397,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brown, Cynthia 0000-0001-8486-7119","orcid":"https://orcid.org/0000-0001-8486-7119","contributorId":196741,"corporation":false,"usgs":false,"family":"Brown","given":"Cynthia","affiliations":[],"preferred":false,"id":711398,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kumar, Sunil","contributorId":195493,"corporation":false,"usgs":false,"family":"Kumar","given":"Sunil","affiliations":[],"preferred":false,"id":711399,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Manier, Daniel J. 0000-0002-1105-1327 manierd@usgs.gov","orcid":"https://orcid.org/0000-0002-1105-1327","contributorId":127553,"corporation":false,"usgs":true,"family":"Manier","given":"Daniel","email":"manierd@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":711400,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Talbert, Colin 0000-0002-9505-1876 talbertc@usgs.gov","orcid":"https://orcid.org/0000-0002-9505-1876","contributorId":181913,"corporation":false,"usgs":true,"family":"Talbert","given":"Colin","email":"talbertc@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":711401,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Holcombe, Tracy R. holcombet@usgs.gov","contributorId":3694,"corporation":false,"usgs":true,"family":"Holcombe","given":"Tracy","email":"holcombet@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":711402,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70191171,"text":"70191171 - 2017 - Riparian plant composition along hydrologic gradients in a dryland river basin and implications for a warming climate","interactions":[],"lastModifiedDate":"2017-09-28T11:50:32","indexId":"70191171","displayToPublicDate":"2017-09-28T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1447,"text":"Ecohydrology","active":true,"publicationSubtype":{"id":10}},"title":"Riparian plant composition along hydrologic gradients in a dryland river basin and implications for a warming climate","docAbstract":"Droughts in dryland regions on all continents are expected to increase in severity and duration under future climate projections. In dryland regions, it is likely that minimum streamflow will decrease with some perennial streams shifting to intermittent flow under climate-driven changes in precipitation and runoff and increases in temperature. Decreasing base flow and shifting flow regimes from perennial to intermittent could have significant implications for stream-dependent biota, including riparian vegetation. In this study, we asked, how do riparian plant communities vary along wet-to-dry hydrologic gradients on small (first–third order) streams? We collected data on geomorphic, hydrologic, and plant community characteristics on 54 stream sites ranging in hydrology from intermittent to perennial flow across the Upper Colorado River Basin (284,898 km2). We found that plant communities varied along hydrologic gradients from high to low elevation between streams, and perennial to intermittent flow. We identified indicator species associated with different hydrologic conditions and suggest how plant communities may shift under warmer, drier conditions. Our results indicate that species richness and cover of total, perennial, wetland, and native plant groups will decrease while annual plants will increase under drying conditions. Understanding how plant communities respond to regional drivers such as hydroclimate requires broad-scale approaches such as sampling across whole river basins. With increasingly arid conditions in many regions of the globe, understanding plant community shifts is key to understanding the future of riparian ecosystems.
","language":"English","publisher":"Wiley","doi":"10.1002/eco.1864","usgsCitation":"Reynolds, L., and Shafroth, P.B., 2017, Riparian plant composition along hydrologic gradients in a dryland river basin and implications for a warming climate: Ecohydrology, v. 10, no. 6, p. 1-13, https://doi.org/10.1002/eco.1864.","productDescription":"e1864; 13 p.","startPage":"1","endPage":"13","ipdsId":"IP-073730","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":346153,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Upper Colorado River Basin","volume":"10","issue":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-05-31","publicationStatus":"PW","scienceBaseUri":"59ce0a25e4b05fe04cc020f3","contributors":{"authors":[{"text":"Reynolds, Lindsay 0000-0001-9973-9312 reynoldsl@usgs.gov","orcid":"https://orcid.org/0000-0001-9973-9312","contributorId":150076,"corporation":false,"usgs":true,"family":"Reynolds","given":"Lindsay","email":"reynoldsl@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":711408,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shafroth, Patrick B. 0000-0002-6064-871X shafrothp@usgs.gov","orcid":"https://orcid.org/0000-0002-6064-871X","contributorId":2000,"corporation":false,"usgs":true,"family":"Shafroth","given":"Patrick","email":"shafrothp@usgs.gov","middleInitial":"B.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":711407,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70190752,"text":"sir20175104 - 2017 - 2015 Volcanic activity in Alaska—Summary of events and response of the Alaska Volcano Observatory","interactions":[],"lastModifiedDate":"2017-10-02T10:36:25","indexId":"sir20175104","displayToPublicDate":"2017-09-28T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5104","title":"2015 Volcanic activity in Alaska—Summary of events and response of the Alaska Volcano Observatory","docAbstract":"The Alaska Volcano Observatory (AVO) responded to eruptions, volcanic unrest or suspected unrest, and seismic events at 14 volcanic centers in Alaska during 2015. The most notable volcanic activity consisted of continuing intermittent ash eruptions from Cleveland and Shishaldin volcanoes in the Aleutian Islands. Two eruptive episodes, at Veniaminof and Pavlof, on the Alaska Peninsula ended in 2015. During 2015, AVO re-established the seismograph network at Aniakchak, installed six new broadband seismometers throughout the Aleutian Islands, and added a Multiple component Gas Analyzer System (MultiGAS) station on Augustine.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175104","collaboration":"Prepared in collaboration with University of Alaska Fairbanks Geophysical Institute and the Alaska Division of Geological and Geophysical Surveys","usgsCitation":"Dixon, J.P., Cameron, C.E., Iezzi, A.M., and Wallace, Kristi, 2017, 2015 Volcanic activity in Alaska—Summary of events and response of the Alaska Volcano Observatory: U.S. Geological Survey Scientific Investigations Report 2017-5104, 61 p., https://doi.org/10.3133/sir20175104.","productDescription":"vi, 61 p.","numberOfPages":"72","onlineOnly":"Y","ipdsId":"IP-082299","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":346181,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5104/coverthb.jpg"},{"id":346182,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5104/sir20175104.pdf","text":"Report","size":"2.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5104"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -182.724609375,\n 50.98609893339354\n ],\n [\n -148.798828125,\n 50.98609893339354\n ],\n [\n -148.798828125,\n 61.66902436927201\n ],\n [\n -182.724609375,\n 61.66902436927201\n ],\n [\n -182.724609375,\n 50.98609893339354\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Volcano Science Center
U.S. Geological Survey
4230 University Drive
Anchorage, Alaska 99508
Detecting populations of rare or cryptic species is essential for their conservation. For species like giant gartersnakes (Thamnophis gigas), conventional survey methods can be expensive and inefficient. These sampling difficulties might be overcome by modern techniques that detect deoxyribonucleic acid (DNA) shed by organisms into the environment (eDNA). We evaluated the efficacy of detecting giant gartersnake eDNA in water samples from the laboratory and at locations with known giant gartersnake populations in the Sacramento Valley of California, and failed to detect giant gartersnake DNA in most laboratory and all field samples. Aspects of giant gartersnake biology—such as highly keratinized skin and spending extensive time in the terrestrial environment, as well as hot, sunny, and turbid conditions in wetlands and canals of the Sacramento Valley—likely contributed to low detection probabilities. Although detection of eDNA shows promise under many conditions, further development is needed before sampling for eDNA is a viable option for detecting giant gartersnake populations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171123","collaboration":"Prepared in cooperation with the Central Valley Project—Bureau of Reclamation and U.S. Fish and Wildlife Service","usgsCitation":"Halstead, B.J., Wood, D.A, Bowen, Lizabeth, Waters, Shannon, Vandergast, A.G., Ersan, J.S.M., Skalos, S.M., and Casazza, M.L., 2017, An evaluation of the efficacy of using environmental DNA (eDNA) to detect giant gartersnakes (Thamnophis gigas): U.S. Geological Survey Open-File Report 2017-1123, 41 p., https://doi.org/10.3133/ofr20171123.","productDescription":"vi, 41 p.","numberOfPages":"52","onlineOnly":"Y","ipdsId":"IP-086792","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":346165,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1123/ofr20171123.pdf","text":"Report","size":"4.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1123"},{"id":346180,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1123/coverthb2.jpg"}],"country":"United States","state":"California","otherGeospatial":"Colusa National Wildlife Refuge, Natomas Basin","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Plague impacts prairie dogs (Cynomys spp.), the endangered black-footed ferret (Mustela nigripes) and other sensitive wildlife species. We compared efficacy of prophylactic treatments (burrow dusting with deltamethrin or oral vaccination with recombinant “sylvatic plague vaccine” [RCN-F1/V307]) to placebo treatment in black-tailed prairie dog (C. ludovicianus) colonies. Between 2013 and 2015, we measured prairie dog apparent survival, burrow activity and flea abundance on triplicate plots (“blocks”) receiving dust, vaccine or placebo treatment. Epizootic plague affected all three blocks but emerged asynchronously. Dust plots had fewer fleas per burrow (P < 0.0001), and prairie dogs captured on dust plots had fewer fleas (P < 0.0001) than those on vaccine or placebo plots. Burrow activity and prairie dog density declined sharply in placebo plots when epizootic plague emerged. Patterns in corresponding dust and vaccine plots were less consistent and appeared strongly influenced by timing of treatment applications relative to plague emergence. Deltamethrin or oral vaccination enhanced apparent survival within two blocks. Applying insecticide or vaccine prior to epizootic emergence blunted effects of plague on prairie dog survival and abundance, thereby preventing colony collapse. Successful plague mitigation will likely entail strategic combined uses of burrow dusting and oral vaccination within large colonies or colony complexes.
","language":"English","publisher":"Springer","doi":"10.1007/s10393-017-1236-y","usgsCitation":"Tripp, D.W., Rocke, T.E., Runge, J.P., Abbott, R.C., and Miller, M.W., 2017, Burrow dusting or oral vaccination prevents plague-associated prairie dog colony collapse: EcoHealth, v. 14, no. 3, p. 451-462, https://doi.org/10.1007/s10393-017-1236-y.","productDescription":"12 p.","startPage":"451","endPage":"462","ipdsId":"IP-085018","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":461395,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10393-017-1236-y","text":"Publisher Index Page"},{"id":346158,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","county":"Larimer County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.10517120361328,\n 40.868872344643655\n ],\n [\n -104.93385314941406,\n 40.868872344643655\n ],\n [\n -104.93385314941406,\n 40.97186275286114\n ],\n [\n -105.10517120361328,\n 40.97186275286114\n ],\n [\n -105.10517120361328,\n 40.868872344643655\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","issue":"3","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-22","publicationStatus":"PW","scienceBaseUri":"59ce0a24e4b05fe04cc020eb","contributors":{"authors":[{"text":"Tripp, Daniel W.","contributorId":17910,"corporation":false,"usgs":false,"family":"Tripp","given":"Daniel","email":"","middleInitial":"W.","affiliations":[{"id":13449,"text":"Colorado Division of Parks and Wildlife","active":true,"usgs":false}],"preferred":false,"id":711430,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rocke, Tonie E. 0000-0003-3933-1563 trocke@usgs.gov","orcid":"https://orcid.org/0000-0003-3933-1563","contributorId":2665,"corporation":false,"usgs":true,"family":"Rocke","given":"Tonie","email":"trocke@usgs.gov","middleInitial":"E.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":711429,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Runge, Jonathan P.","contributorId":196756,"corporation":false,"usgs":false,"family":"Runge","given":"Jonathan","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":711431,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Abbott, Rachel C. 0000-0003-4820-9295 rabbott@usgs.gov","orcid":"https://orcid.org/0000-0003-4820-9295","contributorId":1183,"corporation":false,"usgs":true,"family":"Abbott","given":"Rachel","email":"rabbott@usgs.gov","middleInitial":"C.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":711432,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Miller, Michael W.","contributorId":140308,"corporation":false,"usgs":false,"family":"Miller","given":"Michael","email":"","middleInitial":"W.","affiliations":[{"id":13449,"text":"Colorado Division of Parks and Wildlife","active":true,"usgs":false}],"preferred":false,"id":711433,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70190590,"text":"sir20175098 - 2017 - Hydrogeology and simulated groundwater flow and availability in the North Fork Red River aquifer, southwest Oklahoma, 1980–2013","interactions":[],"lastModifiedDate":"2017-09-28T14:29:06","indexId":"sir20175098","displayToPublicDate":"2017-09-28T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5098","displayTitle":"Hydrogeology and simulated groundwater flow and availability in the North Fork Red River aquifer, southwest Oklahoma,On September 8, 1981, the Oklahoma Water Resources Board established regulatory limits on the maximum annual yield of groundwater (343,042 acre-feet per year) and equal-proportionate-share (EPS) pumping rate (1.0 acre-foot per acre per year) for the North Fork Red River aquifer. The maximum annual yield and EPS were based on a hydrologic investigation that used a numerical groundwater-flow model to evaluate the effects of potential groundwater withdrawals on groundwater availability in the North Fork Red River aquifer. The Oklahoma Water Resources Board is statutorily required (every 20 years) to update the hydrologic investigation on which the maximum annual yield and EPS were based. Because 20 years have elapsed since the final order was issued, the U.S. Geological Survey, in cooperation with the Oklahoma Water Resources Board, conducted an updated hydrologic investigation and evaluated the effects of potential groundwater withdrawals on groundwater flow and availability in the North Fork Red River aquifer in Oklahoma. This report describes a hydrologic investigation of the North Fork Red River aquifer that includes an updated summary of the aquifer hydrogeology. As part of this investigation, groundwater flow and availability were simulated by using a numerical groundwater-flow model.
The North Fork Red River aquifer in Beckham, Greer, Jackson, Kiowa, and Roger Mills Counties in Oklahoma is composed of about 777 square miles (497,582 acres) of alluvium and terrace deposits along the North Fork Red River and tributaries, including Sweetwater Creek, Elk Creek, Otter Creek, and Elm Fork Red River. The North Fork Red River is the primary source of surface-water inflow to Lake Altus, which overlies the North Fork Red River aquifer. Lake Altus is a U.S. Bureau of Reclamation reservoir with the primary purpose of supplying irrigation water to the Lugert-Altus Irrigation District.
A hydrogeologic framework was developed for the North Fork Red River aquifer and included a definition of the aquifer extent and potentiometric surface, as well as a description of the textural and hydraulic properties of aquifer materials. The hydrogeologic framework was used in the construction of a numerical groundwater-flow model of the North Fork Red River aquifer described in this report. A conceptual model of aquifer inflows and outflows was developed for the North Fork Red River aquifer to constrain the construction and calibration of a numerical groundwater-flow model that reasonably represented the groundwater-flow system. The conceptual-model water budget estimated mean annual inflows to and outflows from the North Fork Red River aquifer for the period 1980–2013 and included a sub-accounting of mean annual inflows and outflows for the portions of the aquifer that were upgradient and downgradient from Lake Altus. The numerical groundwater-flow model simulated the period 1980–2013 and was calibrated to water-table-altitude observations at selected wells, monthly base flow at selected streamgages, net streambed seepage as estimated for the conceptual model, and Lake Altus stage.
Groundwater-availability scenarios were performed by using the calibrated numerical groundwater-flow model to (1) estimate the EPS pumping rate that guarantees a minimum 20-, 40-, and 50-year life of the aquifer, (2) quantify the potential effects of projected well withdrawals on groundwater storage over a 50-year period, and (3) simulate the potential effects of a hypothetical (10-year) drought on base flow and groundwater storage. The results of the groundwater-availability scenarios could be used by the Oklahoma Water Resources Board to reevaluate the maximum annual yield of groundwater from the North Fork Red River aquifer.
EPS scenarios for the North Fork Red River aquifer were run for periods of 20, 40, and 50 years. The 20-, 40-, and 50-year EPS pumping rates under normal recharge conditions were 0.59, 0.52, and 0.52 acre-foot per acre per year, respectively. Given the 497,582-acre aquifer area, these rates correspond to annual yields of about 294,000, 259,000, and 259,000 acre-feet per year, respectively. Groundwater storage at the end of the 20-year EPS scenario was about 951,000 acre-feet, or about 1,317,000 acre-feet (58 percent) less than the starting EPS scenario storage. This decrease in storage was equivalent to a mean water-level decline of about 22 feet. Most areas of the active alluvium near the North Fork Red River, Elk Creek, and Elm Fork Red River remained partially saturated through the end of the EPS scenario because of streambed seepage. Lake Altus storage was reduced to zero after 6–7 years of EPS pumping in each scenario.
Projected 50-year pumping scenarios were used to simulate the effects of selected well withdrawal rates on groundwater storage of the North Fork Red River aquifer and base flows in the North Fork Red River upstream from Lake Altus. The effects of well withdrawals were evaluated by comparing changes in groundwater storage and base flow between four 50-year scenarios using (1) no groundwater pumping, (2) mean pumping rates for the study period (1980–2013), (3) 2013 pumping rates, and (4) increasing demand pumping rates. The increasing demand pumping rates assumed a 20.4-percent increase in pumping over 50 years based on 2010–60 demand projections for southwest Oklahoma.
Groundwater storage after 50 years with no pumping was about 2,606,000 acre-feet, or 137,000 acre-feet (5.5 percent) greater than the initial groundwater storage; this groundwater storage increase is equivalent to a mean water-level increase of 2.3 feet. Groundwater storage after 50 years with the mean pumping rate for the study period (1980–2013) was about 2,476,000 acre-feet, or about 7,000 acre-feet (0.3 percent) greater than the initial groundwater storage; this groundwater storage increase is equivalent to a mean water-level increase of 0.1 foot. Groundwater storage at the end of the 50-year period with 2013 pumping rates was about 2,398,000 acre-feet, or about 70,000 acre-feet (2.8 percent) less than the initial storage; this groundwater storage decrease is equivalent to a mean water-level decline of 1.2 feet. Groundwater storage at the end of the 50-year period with increasing demand pumping rates was about 2,361,000 acre-feet, or about 107,000 acre-feet (4.3 percent) less than the initial storage; this groundwater storage decrease is equivalent to a mean water-level decline of 1.8 feet. Mean annual base flow simulated at the Carter streamgage (07301500) on North Fork Red River increased by about 4,000 acre-feet (10 percent) after 50 years with no pumping and decreased by about 5,400 acre-feet (13 percent) after 50 years with increasing demand pumping rates. Mean annual base flow simulated at the North Fork Red River inflow to Lake Altus increased by about 7,400 acre-feet (15 percent) after 50 years with no pumping and decreased by about 5,800 acre-feet (12 percent) after 50 years with increasing demand pumping rates.
A hypothetical 10-year drought scenario was used to simulate the effects of a prolonged period of reduced recharge on groundwater storage and Lake Altus stage and storage. Drought effects were quantified by comparing the results of the drought scenario to those of the calibrated numerical model (no drought). To simulate the hypothetical drought, recharge in the calibrated numerical model was reduced by 50 percent during the simulated drought period (1984–1993). Groundwater storage at the end of the drought period was about 2,271,000 acre-feet, or about 426,000 acre-feet (15.8 percent) less than the groundwater storage of the calibrated numerical model. This decrease in groundwater storage is equivalent to a mean water-table-altitude decline of 7.1 feet. At the end of the 10-year hypothetical drought period, base flows at the Sweetwater (07301420), Carter (07301500), Headrick (07305000), and Snyder (07307010) streamgages had decreased by about 37, 61, 44, and 45 percent, respectively. The minimum Lake Altus storage simulated during the drought period was 403 acre-feet, which was a decline of 92 percent from the nondrought storage. Reduced base flows in the North Fork Red River were the primary cause of Lake Altus storage declines.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175098","collaboration":"Prepared in cooperation with the Oklahoma Water Resources Board","usgsCitation":"Smith, S.J., Ellis, J.H., Wagner, D.L., and Peterson, S.M., 2017, Hydrogeology and simulated groundwater flow and availability in the North Fork Red River aquifer, southwest Oklahoma, 1980–2013: U.S. Geological Survey Scientific Investigations Report 2017–5098, 107 p., https://doi.org/10.3133/sir20175098.","productDescription":"Report: xii, 107 p.; Data Release","numberOfPages":"124","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-071702","costCenters":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"links":[{"id":346139,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5098/sir20175098.pdf","text":"Report","size":"29.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5098"},{"id":346138,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5098/coverthb.jpg"},{"id":346140,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7JQ0ZXH","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"MODFLOW-NWT model used in simulation of groundwater flow and availability in the North Fork Red River aquifer, southwest Oklahoma, 1980–2013"}],"country":"United States","state":"Oklahoma","otherGeospatial":"North Fork Red River Aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100,\n 34.5\n ],\n [\n -98.8,\n 34.5\n ],\n [\n -98.8,\n 35.45\n ],\n [\n -100,\n 35.45\n ],\n [\n -100,\n 34.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oklahoma Water Science Center
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Oklahoma City, OK 73116
The water we drink, air we breathe, and soil we come into contact with have the potential to adversely affect our health because of contaminants in the environment. Environmental samples can characterize the extent of potential contamination, but traditional methods for collecting water, air, and soil samples below the ground (for example, well drilling or direct-push soil sampling) are expensive and time consuming. Trees are closely connected to the subsurface and sampling tree trunks can indicate subsurface pollutants, a process called phytoforensics. Scientists at the Missouri Water Science Center were among the first to use phytoforensics to screen sites for contamination before using traditional sampling methods, to guide additional sampling, and to show the large cost savings associated with tree sampling compared to traditional methods.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173076","usgsCitation":"Wilson, J.L., 2017, Phytoforensics—Using trees to find contamination: U.S. Geological Survey Fact Sheet 2017–3076, 2 p., https://doi.org/10.3133/fs20173076.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-087356","costCenters":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"links":[{"id":346125,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3076/fs20173076.pdf","text":"Report","size":"6.99 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017–3076"},{"id":346124,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3076/coverthb.jpg"}],"contact":"Director, Missouri Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
No abstract available.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/jme/tjx096","usgsCitation":"Hahn, M., Jarnevich, C.S., Monaghan, A.J., and Eisen, R.J., 2017, Response: The geographic distribution of Ixodes scapularis (Acari: Ixodidae) revisited: The importance of assumptions about error balance: Journal of Medical Entomology, v. 54, no. 5, p. 1104-1106, https://doi.org/10.1093/jme/tjx096.","productDescription":"3 p.","startPage":"1104","endPage":"1106","ipdsId":"IP-084078","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":469495,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/jme/tjx096","text":"Publisher Index Page"},{"id":346155,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"54","issue":"5","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-14","publicationStatus":"PW","scienceBaseUri":"59ce0a26e4b05fe04cc020f8","contributors":{"authors":[{"text":"Hahn, Micah","contributorId":179215,"corporation":false,"usgs":false,"family":"Hahn","given":"Micah","email":"","affiliations":[],"preferred":false,"id":711391,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 jarnevichc@usgs.gov","orcid":"https://orcid.org/0000-0002-9699-2336","contributorId":3424,"corporation":false,"usgs":true,"family":"Jarnevich","given":"Catherine","email":"jarnevichc@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":711390,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Monaghan, Andrew J.","contributorId":179216,"corporation":false,"usgs":false,"family":"Monaghan","given":"Andrew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":711392,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Eisen, Rebecca J.","contributorId":179217,"corporation":false,"usgs":false,"family":"Eisen","given":"Rebecca","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":711393,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70191179,"text":"70191179 - 2017 - Contaminants of emerging concern in tributaries to the Laurentian Great Lakes: I. Patterns of occurrence","interactions":[],"lastModifiedDate":"2017-09-28T16:22:38","indexId":"70191179","displayToPublicDate":"2017-09-28T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Contaminants of emerging concern in tributaries to the Laurentian Great Lakes: I. Patterns of occurrence","docAbstract":"Human activities introduce a variety of chemicals to the Laurentian Great Lakes including pesticides, pharmaceuticals, flame retardants, plasticizers, and solvents (collectively referred to as contaminants of emerging concern or CECs) potentially threatening the vitality of these valuable ecosystems. We conducted a basin-wide study to identify the presence of CECs and other chemicals of interest in 12 U.S. tributaries to the Laurentian Great Lakes during 2013 and 2014. A total of 292 surface-water and 80 sediment samples were collected and analyzed for approximately 200 chemicals. A total of 32 and 28 chemicals were detected in at least 30% of water and sediment samples, respectively. Concentrations ranged from 0.0284 (indole) to 72.2 (cholesterol) μg/L in water and 1.75 (diphenhydramine) to 20,800 μg/kg (fluoranthene) in sediment. Cluster analyses revealed chemicals that frequently co-occurred such as pharmaceuticals and flame retardants at sites receiving similar inputs such as wastewater treatment plant effluent. Comparison of environmental concentrations to water and sediment-quality benchmarks revealed that polycyclic aromatic hydrocarbon concentrations often exceeded benchmarks in both water and sediment. Additionally, bis(2-ethylhexyl) phthalate and dichlorvos concentrations exceeded water-quality benchmarks in several rivers. Results from this study can be used to understand organism exposure, prioritize river basins for future management efforts, and guide detailed assessments of factors influencing transport and fate of CECs in the Great Lakes Basin.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0182868","usgsCitation":"Elliott, S.M., Brigham, M.E., Lee, K., Banda, J.A., Choy, S.J., Gefell, D.J., Minarik, T.A., Moore, J.N., and Jorgenson, Z.G., 2017, Contaminants of emerging concern in tributaries to the Laurentian Great Lakes: I. Patterns of occurrence: PLoS ONE, v. 12, no. 9, p. 1-21, https://doi.org/10.1371/journal.pone.0182868.","productDescription":"e0182868; 21 p.","startPage":"1","endPage":"21","ipdsId":"IP-086440","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"links":[{"id":469497,"rank":4,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0182868","text":"Publisher Index Page"},{"id":438206,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7TH8JS6","text":"USGS data release","linkHelpText":"Chemicals of Emerging Concern in Water and Bottom Sediment in Great Lakes Tributaries, 2014 - Collection Methods, Analytical Methods, Quality Assurance Analyses, and Data"},{"id":438205,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7DF6P9D","text":"USGS data release","linkHelpText":"Chemicals of Emerging Concern in Water and Bottom Sediment in Great Lakes Areas of Concern, 2013 - Analytical Methods, Collection Methods, Environmental Data, and Quality Assurance"},{"id":346175,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":346174,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7DF6P9D","text":"Chemicals of Emerging Concern in Water and Bottom Sediment in Great Lakes Areas of Concern, 2013—Analytical Methods, Collection Methods, Environmental Data, and Quality Assurance"},{"id":346173,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7TH8JS6","text":"Chemicals of Emerging Concern in Water and Bottom Sediment in the Great Lakes Basin, 2014 - Analytical Methods, Collection Methods,Quality-Assurance Analyses, and Data"}],"country":"United States","otherGeospatial":"Great Lakes","volume":"12","issue":"9","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-27","publicationStatus":"PW","scienceBaseUri":"59ce0a22e4b05fe04cc020e3","contributors":{"authors":[{"text":"Elliott, Sarah M. 0000-0002-1414-3024 selliott@usgs.gov","orcid":"https://orcid.org/0000-0002-1414-3024","contributorId":1472,"corporation":false,"usgs":true,"family":"Elliott","given":"Sarah","email":"selliott@usgs.gov","middleInitial":"M.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":711446,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brigham, Mark E. 0000-0001-7412-6800 mbrigham@usgs.gov","orcid":"https://orcid.org/0000-0001-7412-6800","contributorId":1840,"corporation":false,"usgs":true,"family":"Brigham","given":"Mark","email":"mbrigham@usgs.gov","middleInitial":"E.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":711447,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lee, Kathy 0000-0002-7683-1367 klee@usgs.gov","orcid":"https://orcid.org/0000-0002-7683-1367","contributorId":2538,"corporation":false,"usgs":true,"family":"Lee","given":"Kathy","email":"klee@usgs.gov","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"preferred":true,"id":711448,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Banda, Jo A.","contributorId":196761,"corporation":false,"usgs":false,"family":"Banda","given":"Jo","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":711449,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Choy, Steven J.","contributorId":138668,"corporation":false,"usgs":false,"family":"Choy","given":"Steven","email":"","middleInitial":"J.","affiliations":[{"id":6678,"text":"U.S. Fish and Wildlife Service, Alaska Maritime National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":711450,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gefell, Daniel J.","contributorId":138671,"corporation":false,"usgs":false,"family":"Gefell","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":6678,"text":"U.S. Fish and Wildlife Service, Alaska Maritime National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":711451,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Minarik, Thomas A.","contributorId":139816,"corporation":false,"usgs":false,"family":"Minarik","given":"Thomas","email":"","middleInitial":"A.","affiliations":[{"id":13283,"text":"Metropolitan Water Reclamation District of Greater Chicago","active":true,"usgs":false}],"preferred":false,"id":711454,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Moore, Jeremy N.","contributorId":138669,"corporation":false,"usgs":false,"family":"Moore","given":"Jeremy","email":"","middleInitial":"N.","affiliations":[{"id":6678,"text":"U.S. Fish and Wildlife Service, Alaska Maritime National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":711452,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Jorgenson, Zachary G.","contributorId":69476,"corporation":false,"usgs":false,"family":"Jorgenson","given":"Zachary","email":"","middleInitial":"G.","affiliations":[{"id":13317,"text":"Saint Cloud State University","active":true,"usgs":false}],"preferred":false,"id":711453,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70191174,"text":"70191174 - 2017 - Long-term trends of surface-water mercury and methylmercury concentrations downstream of historic mining within the Carson River watershed","interactions":[],"lastModifiedDate":"2017-09-28T13:23:41","indexId":"70191174","displayToPublicDate":"2017-09-28T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1555,"text":"Environmental Pollution","active":true,"publicationSubtype":{"id":10}},"title":"Long-term trends of surface-water mercury and methylmercury concentrations downstream of historic mining within the Carson River watershed","docAbstract":"The Carson River is a vital water resource for local municipalities and migratory birds travelling the Pacific Flyway. Historic mining practices that used mercury (Hg) to extract gold from Comstock Lode ore has left much of the river system heavily contaminated with Hg, a practice that continues in many parts of the world today. Between 1998 and 2013, the United States Geological Survey (USGS) collected and analyzed Carson River water for Hg and methylmercury (MeHg) concentrations resulting in a sixteen year record of unfiltered total mercury (uf.THg), filtered (dissolved) Hg (f.THg), total methylmercury (uf.MeHg), filtered MeHg (f.MeHg), and particulate-bound THg (p.THg) and MeHg (p.MeHg) concentrations. This represents one of the longest continuous records of Hg speciation data for any riverine system, thereby providing a unique opportunity to evaluate long-term trends in concentrations and annual loads. During the period of analysis, uf.THg concentration and load trended downward at rates of −0.85% and −1.8% per year, respectively. Conversely, the f.THg concentration increased at a rate of 1.7% per year between 1998 and 2005, and 4.9% per year between 2005 and 2013. Trends in flow-normalized partition coefficients for both Hg and MeHg suggest a statistically significant shift from the particulate to the filtered phase. The upwardly accelerating f.THg concentration and observed shift from the solid phase to the aqueous phase among the pools of Hg and MeHg within the river water column signals an increased risk of deteriorating ecological conditions in the lower basin with respect to Hg contamination. More broadly, the 16-year trend analysis, completed 140 years after the commencement of major Hg releases to the Carson River, provides a poignant example of the ongoing legacy left behind by gold and silver mining techniques that relied on Hg amalgamation, and a cautionary tale for regions still pursuing the practice in other countries.
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\"}}]}\n","contact":"Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
The U.S. Geological Survey (USGS) recently conducted an assessment of the undiscovered, technically recoverable oil and gas potential of Tertiary strata underlying the onshore areas and State waters of the northern Gulf of Mexico coastal region. The assessment was based on a number of geologic elements including an evaluation of hydrocarbon source rocks, suitable reservoir rocks, and hydrocarbon traps in an Upper Jurassic-Cretaceous-Tertiary Composite Total Petroleum System defined for the region by the USGS. Five conventional assessment units (AUs) were defined for the Midway (Paleocene) and Wilcox (Paleocene-Eocene) Groups, and the Carrizo Sand of the Claiborne Group (Eocene) interval including: (1) the Wilcox Stable Shelf Oil and Gas AU; (2) the Wilcox Expanded Fault Zone Gas and Oil AU; (3) the Wilcox-Lobo Slide Block Gas AU; (4) the Wilcox Slope and Basin Floor Gas AU; and (5) the Wilcox Mississippi Embayment AU (not quantitatively assessed).
The USGS assessment of undiscovered oil and gas resources for the Midway-Wilcox-Carrizo interval resulted in estimated mean values of 110 million barrels of oil (MMBO), 36.9 trillion cubic feet of gas (TCFG), and 639 million barrels of natural gas liquids (MMBNGL) in the four assessed units. The undiscovered oil resources are almost evenly divided between fluvial-deltaic sandstone reservoirs within the Wilcox Stable Shelf (54 MMBO) AU and deltaic sandstone reservoirs of the Wilcox Expanded Fault Zone (52 MMBO) AU. Greater than 70 percent of the undiscovered gas and 66 percent of the natural gas liquids (NGL) are estimated to be in deep (13,000 to 30,000 feet), untested distal deltaic and slope sandstone reservoirs within the Wilcox Slope and Basin Floor Gas AU.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171111","usgsCitation":"Warwick, P.D., 2017, Geologic assessment of undiscovered conventional oil and gas resources in the lower Paleogene Midway and Wilcox Groups, and the Carrizo Sand of the Claiborne Group, of the northern Gulf Coast region: U.S. Geological Survey Open-File Report 2017–1111, 67 p., https://doi.org/10.3133/ofr20171111.","productDescription":"Report: vi, 60 p.; Appendixes 1-4","numberOfPages":"78","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-063993","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":410834,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/ofr20171167","text":"Open-File Report 2017–1167","linkHelpText":"- Geologic Assessment of Undiscovered Gas Resources in Cretaceous–Tertiary Coal Beds of the U.S. Gulf of Mexico Coastal Plain"},{"id":346065,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2017/1111/ofr20171111_appendix2.pdf","text":"Appendix 2","size":"490 KB","linkHelpText":"- Input Data Form for the Wilcox Expanded Fault Zone Gas and Oil Assessment Unit (50470117)"},{"id":346063,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1111/ofr20171111.pdf","text":"Report","size":"14.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1111"},{"id":346062,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1111/coverthb.jpg"},{"id":346066,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2017/1111/ofr20171111_appendix3.pdf","text":"Appendix 3","size":"510 KB","linkHelpText":"- Input Data Form for the Wilcox-Lobo Slide Block Gas Assessment Unit (50470119)"},{"id":346067,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2017/1111/ofr20171111_appendix4.pdf","text":"Appendix 4","size":"396 KB","linkHelpText":"- Input Data Form for the Wilcox Slope and Basin Floor Gas Assessment Unit (50470118)"},{"id":346064,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2017/1111/ofr20171111_appendix1.pdf","text":"Appendix 1","size":"389 KB","linkHelpText":"- Input Data Form for the Wilcox Stable Shelf Oil and Gas Assessment Unit (50470116)"}],"country":"United States","otherGeospatial":"Gulf of Mexico","contact":"Director, Eastern Energy Resources Science Center
U.S. Geological Survey
Mail Stop 956
12201 Sunrise Valley Drive
Reston, VA 20192
http://energy.usgs.gov/
The Laurentian Great Lakes contain one fifth of the world’s surface freshwater and have been impacted by human activity since the Industrial Revolution. In addition to legacy contaminants, nitrification and invasive species, this aquatic ecosystem is also the recipient of Contaminants of Emerging Concern (CECs) with poorly understood biological consequences. In the current study, we documented the presence, concentrations, and biological effects of CECs across 27 field sites in six Great Lakes tributaries by examining over 2250 resident and caged sunfish (Lepomis ssp.) for a variety of morphological and physiological endpoints and related these results to CEC occurrence. CEC were ubiquitous across studies sites and their presence and concentrations in water and sediment were highest in effluent dominated rivers and downstream of municipal wastewater treatment plant discharges. However, even putative upstream reference sites were not free of CEC presence and fish at these sites exhibited biological effects consistent with CEC exposure. Only the Fox River exhibited consistent adverse biological effects, including increased relative liver size, greater prominence of hepatocyte vacuoles and increased plasma glucose concentrations. Canonical Redundancy Analysis revealed consistent patterns of biological consequences of CEC exposure across all six tributaries. Increasing plasma glucose concentrations, likely as a result of pollutant-induced metabolic stress, were associated with increased relative liver size and greater prominence of hepatocyte vacuoles. These indicators of pollutant exposure were inversely correlated with indicators of reproductive potential including smaller gonad size and less mature gametes. The current study highlights the need for greater integration of chemical and biological studies and suggests that CECs in the Laurentian Great Lakes Basin may adversely affect the reproductive potential of exposed fish populations.
","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pone.0184725","usgsCitation":"Thomas, L., Jorgenson, Z.G., Brigham, M.E., Choy, S.J., Moore, J.N., Banda, J.A., Gefell, D., Minarik, T.A., and Schoenfuss, H.L., 2017, Contaminants of emerging concern in tributaries to the Laurentian Great Lakes: II. Biological consequences of exposure: PLoS ONE, v. 12, no. 9, Article e0184725; 36 p., https://doi.org/10.1371/journal.pone.0184725.","productDescription":"Article e0184725; 36 p.","ipdsId":"IP-087970","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"links":[{"id":469499,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0184725","text":"Publisher Index Page"},{"id":348862,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Great Lakes","volume":"12","issue":"9","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-27","publicationStatus":"PW","scienceBaseUri":"5a60fb44e4b06e28e9c22ea3","contributors":{"authors":[{"text":"Thomas, Linnea M.","contributorId":146311,"corporation":false,"usgs":false,"family":"Thomas","given":"Linnea M.","affiliations":[],"preferred":false,"id":721922,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jorgenson, Zachary G.","contributorId":69476,"corporation":false,"usgs":false,"family":"Jorgenson","given":"Zachary","email":"","middleInitial":"G.","affiliations":[{"id":13317,"text":"Saint Cloud State University","active":true,"usgs":false}],"preferred":false,"id":721923,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brigham, Mark E. 0000-0001-7412-6800 mbrigham@usgs.gov","orcid":"https://orcid.org/0000-0001-7412-6800","contributorId":1840,"corporation":false,"usgs":true,"family":"Brigham","given":"Mark","email":"mbrigham@usgs.gov","middleInitial":"E.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":721921,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Choy, Steven J.","contributorId":138668,"corporation":false,"usgs":false,"family":"Choy","given":"Steven","email":"","middleInitial":"J.","affiliations":[{"id":6678,"text":"U.S. Fish and Wildlife Service, Alaska Maritime National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":721924,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moore, Jeremy N.","contributorId":138669,"corporation":false,"usgs":false,"family":"Moore","given":"Jeremy","email":"","middleInitial":"N.","affiliations":[{"id":6678,"text":"U.S. Fish and Wildlife Service, Alaska Maritime National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":721925,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Banda, Jo A.","contributorId":196761,"corporation":false,"usgs":false,"family":"Banda","given":"Jo","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":721926,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gefell, D.J.","contributorId":200326,"corporation":false,"usgs":false,"family":"Gefell","given":"D.J.","affiliations":[],"preferred":false,"id":721927,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Minarik, Thomas A.","contributorId":139816,"corporation":false,"usgs":false,"family":"Minarik","given":"Thomas","email":"","middleInitial":"A.","affiliations":[{"id":13283,"text":"Metropolitan Water Reclamation District of Greater Chicago","active":true,"usgs":false}],"preferred":false,"id":721928,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Schoenfuss, Heiko L.","contributorId":76409,"corporation":false,"usgs":false,"family":"Schoenfuss","given":"Heiko","email":"","middleInitial":"L.","affiliations":[{"id":13317,"text":"Saint Cloud State University","active":true,"usgs":false}],"preferred":false,"id":721929,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70190107,"text":"sir20175087 - 2017 - A review of surface energy balance models for estimating actual evapotranspiration with remote sensing at high spatiotemporal resolution over large extents","interactions":[],"lastModifiedDate":"2017-09-27T16:05:02","indexId":"sir20175087","displayToPublicDate":"2017-09-27T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5087","title":"A review of surface energy balance models for estimating actual evapotranspiration with remote sensing at high spatiotemporal resolution over large extents","docAbstract":"Many approaches have been developed for measuring or estimating actual evapotranspiration (ETa), and research over many years has led to the development of remote sensing methods that are reliably reproducible and effective in estimating ETa. Several remote sensing methods can be used to estimate ETa at the high spatial resolution of agricultural fields and the large extent of river basins. More complex remote sensing methods apply an analytical approach to ETa estimation using physically based models of varied complexity that require a combination of ground-based and remote sensing data, and are grounded in the theory behind the surface energy balance model. This report, funded through cooperation with the International Joint Commission, provides an overview of selected remote sensing methods used for estimating water consumed through ETa and focuses on Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) and Operational Simplified Surface Energy Balance (SSEBop), two energy balance models for estimating ETa that are currently applied successfully in the United States. The METRIC model can produce maps of ETa at high spatial resolution (30 meters using Landsat data) for specific areas smaller than several hundred square kilometers in extent, an improvement in practice over methods used more generally at larger scales. Many studies validating METRIC estimates of ETa against measurements from lysimeters have shown model accuracies on daily to seasonal time scales ranging from 85 to 95 percent. The METRIC model is accurate, but the greater complexity of METRIC results in greater data requirements, and the internalized calibration of METRIC leads to greater skill required for implementation. In contrast, SSEBop is a simpler model, having reduced data requirements and greater ease of implementation without a substantial loss of accuracy in estimating ETa. The SSEBop model has been used to produce maps of ETa over very large extents (the conterminous United States) using lower spatial resolution (1 kilometer) Moderate Resolution Imaging Spectroradiometer (MODIS) data. Model accuracies ranging from 80 to 95 percent on daily to annual time scales have been shown in numerous studies that validated ETa estimates from SSEBop against eddy covariance measurements. The METRIC and SSEBop models can incorporate low and high spatial resolution data from MODIS and Landsat, but the high spatiotemporal resolution of ETa estimates using Landsat data over large extents takes immense computing power. Cloud computing is providing an opportunity for processing an increasing amount of geospatial “big data” in a decreasing period of time. For example, Google Earth EngineTM has been used to implement METRIC with automated calibration for regional-scale estimates of ETa using Landsat data. The U.S. Geological Survey also is using Google Earth EngineTM to implement SSEBop for estimating ETa in the United States at a continental scale using Landsat data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175087","collaboration":"Prepared in cooperation with the International Joint Commission","usgsCitation":"McShane, R.R., Driscoll, K.P., and Sando, Roy, 2017, A review of surface energy balance models for estimating actual evapotranspiration with remote sensing at high spatiotemporal resolution over large extents: U.S. Geological Survey Scientific Investigations Report 2017–5087, 19 p., https://doi.org/10.3133/sir20175087.","productDescription":"vi, 19 p.","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-083112","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":346073,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5087/coverthb.jpg"},{"id":346074,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5087/sir20175087.pdf","text":"Report","size":"678 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5087"}],"contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
Groundwater provides more than 40 percent of California’s drinking water. To protect this vital resource, the State of California created the Groundwater Ambient Monitoring and Assessment (GAMA) Program. The GAMA Program’s Priority Basin Project assesses the quality of groundwater resources used for drinking water supply and increases public access to groundwater-quality information. In the Yuba River and Bear River Watersheds of the Sierra Nevada, many rural households rely on private wells for their drinking water supplies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171115","collaboration":"Prepared in collaboration with California State Water Resources Control Board","usgsCitation":"Fram, M.S., Jasper, Monica, and Taylor, K.A., 2017, Groundwater Quality in the Yuba River and Bear River watersheds, Sierra Nevada, California: U.S. Geological Survey Open-File Report 2017–1115, 4 p., https://doi.org/10.3133/ofr20171115.","productDescription":"4 p.","numberOfPages":"4","ipdsId":"IP-087403","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":438208,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F73F4MS9","text":"USGS data release","linkHelpText":"Groundwater-Quality Data in the Yuba and Bear Watersheds Shallow Aquifer Study Unit, 2015-2016: Results from the California GAMA Priority Basin Project"},{"id":346137,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1115/ofr20171115.pdf","text":"Report","size":"3.75 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1115"},{"id":346136,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1115/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Bear River Watershed, Yuba River Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.5,\n 39\n ],\n [\n -120.4167,\n 39\n ],\n [\n -120.4167,\n 39.75\n ],\n [\n -121.5,\n 39.75\n ],\n [\n -121.5,\n 39\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"California Water Science Center
California GAMA
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Microbiomes are the communities of microorganisms (for example, bacteria, viruses, and fungi) that live on, in, and around people, plants, animals, soil, water, and the atmosphere. Microbiomes are active in the functioning of diverse ecosystems, for instance, by influencing water quality, nutrient acquisition and stress tolerance in plants, and stability of soil and aquatic environments. Microbiome research conducted by the U.S. Geological Survey spans many of our mission areas. Key research areas include water quality, understanding climate effects on soil and permafrost, ecosystem and wildlife health, invasive species, contaminated environments to improve bioremediation, and enhancing energy production. Microbiome research will fundamentally strengthen the ability to address the global challenges of maintaining clean water, ensuring adequate food supply, meeting energy needs, and preserving human and ecosystem health.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/fs20173074","usgsCitation":"Kellogg, C.A., and Hopkins, M.C., 2017, USGS microbiome research: U.S. Geological Survey Fact Sheet 2017–3074, 4 p., https://doi.org/10.3133/fs20173074.","productDescription":"4 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-087448","costCenters":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"links":[{"id":346030,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3074/coverthb.jpg"},{"id":346031,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3074/fs20173074l.pdf","text":"Report","size":"2.61 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017-3074","linkHelpText":"- Low resolution"},{"id":346032,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3074/fs20173074h.pdf","text":"Report","size":"18.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017-3074","linkHelpText":"- High resolution"}],"contact":"Ecosystems Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
https://answers.usgs.gov/