Groundwater quality in the North San Francisco Bay Shallow Aquifer study unit (NSF-SA) was investigated as part of the Priority Basin Project of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program. The study unit is in Marin, Mendocino, Napa, Solano, and Sonoma Counties and included two physiographic study areas: the Valleys and Plains area and the surrounding Highlands area. The NSF-SA focused on groundwater resources used for domestic drinking water supply, which generally correspond to shallower parts of aquifer systems than that of groundwater resources used for public drinking water supply in the same area. The assessments characterized the quality of untreated groundwater, not the quality of drinking water.
This study included three components: (1) a status assessment, which characterized the status of the quality of the groundwater resources used for domestic supply for 2012; (2) an understanding assessment, which evaluated the natural and human factors potentially affecting water quality in those resources; and (3) a comparison between the groundwater resources used for domestic supply and those used for public supply.
The status assessment was based on data collected from 71 sites sampled by the U.S. Geological Survey for the GAMA Priority Basin Project in 2012. To provide context, concentrations of constituents measured in groundwater were compared to U.S. Environmental Protection Agency (EPA) and California State Water Resources Control Board Division of Drinking Water regulatory and non-regulatory benchmarks for drinking-water quality. The status assessment used a grid-based method to estimate the proportion of the groundwater resources that has concentrations of water-quality constituents approaching or above benchmark concentrations. This method provides statistically unbiased results at the study-area scale and permits comparisons to other GAMA Priority Basin Project study areas.
In the NSF-SA study unit as a whole, inorganic constituents with human-health benchmarks were detected at high relative concentrations (RCs) in 27 percent of the shallow aquifer system, and inorganic constituents with secondary maximum contaminant levels (SMCL) were detected at high RCs in 24 percent of the system. The inorganic constituents detected at high RCs were arsenic, boron, fluoride, manganese, nitrate, iron, sulfate, and total dissolved solids (TDS). Organic constituents with human-health benchmarks were detected at high RCs in 1 percent of the shallow aquifer system. Of the 148 organic constituents analyzed, 30 constituents were detected, although only 1, chloroform, had a detection frequency greater than 10 percent.
Natural and anthropogenic factors that could affect the groundwater quality were evaluated by using results from statistical testing of associations between constituent concentrations and values of potential explanatory factors. Groundwater age class (modern, mixed, or pre-modern), redox class (oxic or anoxic), aquifer lithology class (metamorphic, sedimentary, or volcanic), and dissolved oxygen concentrations were the explanatory factors that explained distribution patterns of most of the inorganic constituents best. Groundwater classified primarily as pre-modern or mixed in age was associated with higher concentrations of arsenic and fluoride than waters classified as modern. Anoxic or mixed redox conditions were associated with higher concentrations of boron, fluoride, and manganese. Similar patterns of association with explanatory variables were seen for inorganic constituents with aesthetic-based benchmarks detected at high concentrations. Nitrate and perchlorate had higher concentrations in oxic than in the anoxic redox class and were positively correlated with urban land use.
The NSF-SA water-quality results were compared to those of the GAMA North San Francisco Bay Public-Supply Aquifer study unit (NSF-PA). The NSF-PA was sampled in 2004 and covers much of the same area as the NSF-SA, but focused on the deeper public-supply aquifer system. The comparison of the NSF-PA to the NSF-SA showed that there were more differences between the Valleys and Plains study areas of the two study units than between the Highlands study areas of the two study units. As expected from the shallower depth of wells, the NSF-SA Valleys and Plains study area had a lesser proportion of pre-modern age groundwater and greater proportion of modern age groundwater than the NSF-PA Valleys and Plains study area. In contrast, well depths and groundwater ages were not significantly different between the two Highlands study areas. Arsenic, manganese, and nitrate were present at high RCs, and perchlorate was detected in greater proportions of the NSF-SA Valleys and Plains study area than the NSF-PA Valleys and Plains study area.
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California GAMA
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
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 Priority Basin Project of the GAMA Program provides a comprehensive assessment of the State’s groundwater quality and increases public access to groundwater-quality information. The North San Francisco Bay Shallow Aquifer constitutes one of the study units being evaluated.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20183007","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Bennett, G.L., V, and Fram, M.S., 2018, Groundwater quality in the North San Francisco Bay shallow aquifer, California: U.S. Geological Survey Fact Sheet 2018–3007, 4 p., https://doi.org/10.3133/fs20183007.","productDescription":"4 p.","numberOfPages":"4","ipdsId":"IP-053825","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":351912,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2018/3007/fs20183007.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017-3007"},{"id":351911,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2018/3007/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"North San Francisco Bay Shallow Aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.29132080078125,\n 38\n ],\n [\n -122.189,\n 38\n ],\n [\n -122.189,\n 38.96154447940714\n ],\n [\n -123.29132080078125,\n 38.96154447940714\n ],\n [\n -123.29132080078125,\n 38\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"California Water Science Center
California GAMA
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With many of the world's migratory bird populations in alarming decline, broad-scale assessments of responses to migratory hazards may prove crucial to successful conservation efforts. Most birds migrate at night through increasingly light-polluted skies. Bright light sources can attract airborne migrants and lead to collisions with structures, but might also influence selection of migratory stopover habitat and thereby acquisition of food resources. We demonstrate, using multi-year weather radar measurements of nocturnal migrants across the northeastern U.S., that autumnal migrant stopover density increased at regional scales with proximity to the brightest areas, but decreased within a few kilometers of brightly-lit sources. This finding implies broad-scale attraction to artificial light while airborne, impeding selection for extensive forest habitat. Given that high-quality stopover habitat is critical to successful migration, and hindrances during migration can decrease fitness, artificial lights present a potentially heightened conservation concern for migratory bird populations.
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The Fire Island Coastal Change project objectives include understanding the morphologic evolution of the barrier island system on a variety of time scales (months to centuries) and resolving storm-related impacts, post-storm beach response, and recovery. In April 2016, scientists from the USGS St. Petersburg Coastal and Marine Science Center conducted geophysical and sediment sampling surveys on Fire Island to characterize and quantify spatial variability in the subaerial geology with the goal of subsequently integrating onshore geology with other surf zone and nearshore datasets.
This report, along with the associated USGS data release, serves as an archive of ground penetrating radar (GPR) and post-processed differential global positioning system (DGPS) data collected from beach and back-barrier environments on Fire Island, April 6–13, 2016 (USGS Field Activity Number 2016-322-FA). Data products, including unprocessed GPR trace data, processed DGPS data, elevation-corrected subsurface profile images, geographic information system files, and accompanying Federal Geographic Data Committee metadata are available for download.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1078","usgsCitation":"Forde, A.S., Bernier, J.C., and Miselis, J.L., 2018, Ground penetrating radar and differential global positioning system data collected in April 2016 from Fire Island, New York: U.S. Geological Survey Data Series 1078, https://doi.org/10.3133/ds1078.","productDescription":"Report: HTML; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-092111","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":351847,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P84B1P","text":"USGS data release","description":"USGS data release","linkHelpText":"Archive of Ground Penetrating Radar and Differential Global Positioning System Data Collected in April 2016 from Fire Island, New York\""},{"id":438004,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97YW2UL","text":"USGS data release","linkHelpText":"Ground Penetrating Radar and Global Positioning System Data Collected in 2021 From Fire Island, New York"},{"id":351845,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1078/coverthb.jpg"},{"id":351846,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1078/index.html","text":"Report HTML","description":"DS 1078"}],"country":"United States","state":"New York","otherGeospatial":"Fire Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.32962036132812,\n 40.59414233212419\n ],\n [\n -72.88742065429688,\n 40.59414233212419\n ],\n [\n -72.88742065429688,\n 40.737892702684064\n ],\n [\n -73.32962036132812,\n 40.737892702684064\n ],\n [\n -73.32962036132812,\n 40.59414233212419\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
Spatial and temporal variability in the frequency, duration, and severity of hydrological droughts across the conterminous United States (CONUS) was examined using monthly mean streamflow measured at 872 sites from 1951 through 2014. Hydrological drought is identified as starting when streamflow falls below the 20th percentile streamflow value for 3 consecutive months and ending when streamflow remains above the 20th percentile streamflow value for 3 consecutive months. Mean drought frequency for all aggregated ecoregions in CONUS is 16 droughts per 100 years. Mean drought duration is 5 months, and mean drought severity is 39 percent on a scale ranging from 0 percent to 100 percent (with 100% being the most severe). Hydrological drought frequency is highest in the Western Mountains aggregated ecoregion and lowest in the Eastern Highlands, Northeast, and Southeast Plains aggregated ecoregions. Hydrological drought frequencies of 17 or more droughts per 100 years were found for the Central Plains, Southeast Coastal Plains, Western Mountains, and Western Xeric aggregated ecoregions. Drought duration and severity indicate spatial variability among the sites, but unlike drought frequency, do not show coherent spatial patterns. A comparison of an older period (1951–82) with a recent period (1983–2014) indicates few sites have statistically significant changes in drought frequency, drought duration, or drought severity at a 95-percent confidence level.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175099","isbn":"978-1-4113-4205-7","usgsCitation":"Austin, S.H., Wolock, D.M., and Nelms, D.L., 2018, Variability of hydrological droughts in the conterminous United States, 1951 through 2014: U.S. Geological Survey Scientific Investigations Report 2017–5099, 16 p., https://doi.org/10.3133/sir20175099.","productDescription":"Report: v, 16 p.; Data release","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063758","costCenters":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"links":[{"id":351816,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F70C4T19","text":"USGS data release","description":"USGS data release","linkHelpText":"Monthly streamflows, drought indices, and supporting statistics for USGS gage stations used to identify variability of hydrological droughts in the conterminous United States, 1951 through 2014"},{"id":351810,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5099/sir20175099.pdf","text":"Report","size":"3.45 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5099"},{"id":351809,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5099/coverthb.jpg"}],"country":"United 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\"}}]}\n\n\n","contact":"Director, Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, VA 23228
Rationale
Water stable isotope ratios (δ2H and δ18O values) are widely used tracers in environmental studies; hence, accurate and precise assays are required for providing sound scientific information. We tested the analytical performance of 235 international laboratories conducting water isotope analyses using dual-inlet and continuous-flow isotope ratio mass spectrometers and laser spectrometers through a water isotope inter-comparison test.
Methods
Eight test water samples were distributed by the IAEA to international stable isotope laboratories. These consisted of a core set of five samples spanning the common δ-range of natural waters, and three optional samples (highly depleted, enriched, and saline). The fifth core sample contained unrevealed trace methanol to assess analyst vigilance to the impact of organic contamination on water isotopic measurements made by all instrument technologies.
Results
For the core and optional samples ~73 % of laboratories gave acceptable results within 0.2 ‰ and 1.5 ‰ of the reference values for δ18O and δ2H, respectively; ~27 % produced unacceptable results. Top performance for δ18O values was dominated by dual-inlet IRMS laboratories; top performance for δ2H values was led by laser spectrometer laboratories. Continuous-flow instruments yielded comparatively intermediate results. Trace methanol contamination of water resulted in extreme outlier δ-values for laser instruments, but also affected reactor-based continuous-flow IRMS systems; however, dual-inlet IRMS δ-values were unaffected.
Conclusions
Analysis of the laboratory results and their metadata suggested inaccurate or imprecise performance stemmed mainly from skill- and knowledge-based errors including: calculation mistakes, inappropriate or compromised laboratory calibration standards, poorly performing instrumentation, lack of vigilance to contamination, or inattention to unreasonable isotopic outcomes. To counteract common errors, we recommend that laboratories include 1–2 'known' control standards in all autoruns; laser laboratories should screen each autorun for spectral contamination; and all laboratories should evaluate whether derived d-excess values are realistic when both isotope ratios are measured. Combined, these data evaluation strategies should immediately inform the laboratory about fundamental mistakes or compromised samples.
","language":"English","publisher":"Wiley","doi":"10.1002/rcm.8052","usgsCitation":"Wassenaar, L.I., Terzer-Wassmuth, S., Douence, C., Araguas-Araguas, L., Aggarwal, P.K., and Coplen, T.B., 2018, Seeking excellence: An evaluation of 235 international laboratories conducting water isotope analyses by isotope-ratio and laser-absorption spectrometry: Rapid Communications in Mass Spectrometry, v. 32, no. 5, p. 393-406, https://doi.org/10.1002/rcm.8052.","productDescription":"14 p.","startPage":"393","endPage":"406","ipdsId":"IP-094049","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":488646,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rcm.8052","text":"Publisher Index Page"},{"id":351880,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"32","issue":"5","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-08","publicationStatus":"PW","scienceBaseUri":"5afee725e4b0da30c1bfc13a","contributors":{"authors":[{"text":"Wassenaar, Leonard I.","contributorId":202666,"corporation":false,"usgs":false,"family":"Wassenaar","given":"Leonard","middleInitial":"I.","affiliations":[{"id":36516,"text":"International Atomic Energy Agency, Water Resources Section, PO Box 100, Vienna. A-1400, Austria","active":true,"usgs":false}],"preferred":false,"id":729274,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Terzer-Wassmuth, S.","contributorId":202667,"corporation":false,"usgs":false,"family":"Terzer-Wassmuth","given":"S.","email":"","affiliations":[{"id":36516,"text":"International Atomic Energy Agency, Water Resources Section, PO Box 100, Vienna. A-1400, Austria","active":true,"usgs":false}],"preferred":false,"id":729275,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Douence, C.","contributorId":202668,"corporation":false,"usgs":false,"family":"Douence","given":"C.","email":"","affiliations":[{"id":36516,"text":"International Atomic Energy Agency, Water Resources Section, PO Box 100, Vienna. A-1400, Austria","active":true,"usgs":false}],"preferred":false,"id":729276,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Araguas-Araguas, L.","contributorId":202669,"corporation":false,"usgs":false,"family":"Araguas-Araguas","given":"L.","email":"","affiliations":[{"id":36516,"text":"International Atomic Energy Agency, Water Resources Section, PO Box 100, Vienna. A-1400, Austria","active":true,"usgs":false}],"preferred":false,"id":729277,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Aggarwal, P. K.","contributorId":202670,"corporation":false,"usgs":false,"family":"Aggarwal","given":"P.","email":"","middleInitial":"K.","affiliations":[{"id":36516,"text":"International Atomic Energy Agency, Water Resources Section, PO Box 100, Vienna. A-1400, Austria","active":true,"usgs":false}],"preferred":false,"id":729278,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Coplen, Tyler B. 0000-0003-4884-6008 tbcoplen@usgs.gov","orcid":"https://orcid.org/0000-0003-4884-6008","contributorId":508,"corporation":false,"usgs":true,"family":"Coplen","given":"Tyler","email":"tbcoplen@usgs.gov","middleInitial":"B.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":729273,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70195550,"text":"70195550 - 2018 - A floodplain continuum for Atlantic coast rivers of the Southeastern US: Predictable changes in floodplain biota along a river's length","interactions":[],"lastModifiedDate":"2018-02-23T10:51:59","indexId":"70195550","displayToPublicDate":"2018-02-22T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"A floodplain continuum for Atlantic coast rivers of the Southeastern US: Predictable changes in floodplain biota along a river's length","docAbstract":"Floodplains are among the world’s economically-most-valuable, environmentally-most-threatened, and yet conceptually-least-understood ecosystems. Drawing on concepts from existing riverine and wetland models, and empirical data from floodplains of Atlantic Coast rivers in the Southeastern US (and elsewhere when possible), we introduce a conceptual model to explain a continuum of longitudinal variation in floodplain ecosystem functions with a particular focus on biotic change. Our hypothesis maintains that major controls on floodplain ecology are either external (ecotonal interactions with uplands or stream/river channels) or internal (wetland-specific functions), and the relative importance of these controls changes progressively from headwater to mid-river to lower-river floodplains. Inputs of water, sediments, nutrients, flora, and fauna from uplands-to-floodplains decrease, while the impacts of wetland biogeochemistry and obligate wetland plants and animals within-floodplains increase, along the length of a river floodplain. Inputs of water, sediment, nutrients, and fauna from river/stream channels to floodplains are greatest mid-river, and lower either up- or down-stream. While the floodplain continuum we develop is regional in scope, we review how aspects may apply more broadly. Management of coupled floodplain-river ecosystems would be improved by accounting for how factors controlling the floodplain ecosystem progressively change along longitudinal riverine gradients.
","language":"English","publisher":"Springer","doi":"10.1007/s13157-017-0983-4","usgsCitation":"Batzer, D.P., Noe, G.E., Lee, L., and Galatowitsch, M., 2018, A floodplain continuum for Atlantic coast rivers of the Southeastern US: Predictable changes in floodplain biota along a river's length: Wetlands, v. 38, no. 1, p. 1-13, https://doi.org/10.1007/s13157-017-0983-4.","productDescription":"13 p.","startPage":"1","endPage":"13","ipdsId":"IP-091974","costCenters":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true}],"links":[{"id":351873,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"38","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-12-04","publicationStatus":"PW","scienceBaseUri":"5afee726e4b0da30c1bfc13c","contributors":{"authors":[{"text":"Batzer, Darold P.","contributorId":202656,"corporation":false,"usgs":false,"family":"Batzer","given":"Darold","email":"","middleInitial":"P.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":729238,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Noe, Gregory E. 0000-0002-6661-2646 gnoe@usgs.gov","orcid":"https://orcid.org/0000-0002-6661-2646","contributorId":139100,"corporation":false,"usgs":true,"family":"Noe","given":"Gregory","email":"gnoe@usgs.gov","middleInitial":"E.","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":729237,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lee, Linda","contributorId":202657,"corporation":false,"usgs":false,"family":"Lee","given":"Linda","affiliations":[{"id":36513,"text":"University of Georgia Savannah River Ecology Laboratory","active":true,"usgs":false}],"preferred":false,"id":729239,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Galatowitsch, Mark","contributorId":202658,"corporation":false,"usgs":false,"family":"Galatowitsch","given":"Mark","email":"","affiliations":[{"id":36514,"text":"Centre College","active":true,"usgs":false}],"preferred":false,"id":729240,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70194213,"text":"sir20175142 - 2018 - Groundwater conditions in Georgia, 2015–16","interactions":[],"lastModifiedDate":"2018-02-22T14:30:49","indexId":"sir20175142","displayToPublicDate":"2018-02-21T10:45:00","publicationYear":"2018","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":"2017-5142","title":"Groundwater conditions in Georgia, 2015–16","docAbstract":"The U.S. Geological Survey collects groundwater data and conducts studies to monitor hydrologic conditions, define groundwater resources, and address problems related to water supply, water use, and water quality. In Georgia, water levels were monitored continuously at 157 wells during calendar years 2015 and 2016. Because of missing data or short periods of record (less than 5 years) for several of these wells, data for 147 wells are presented in this report. These wells include 15 in the surficial aquifer system, 18 in the Brunswick aquifer system and equivalent sediments, 59 in the Upper Floridan aquifer, 13 in the Lower Floridan aquifer and underlying units, 9 in the Claiborne aquifer, 1 in the Gordon aquifer, 8 in the Clayton aquifer, 16 in the Cretaceous aquifer system, 2 in Paleozoic-rock aquifers, and 6 in crystalline-rock aquifers. Data from the well network indicate that water levels generally rose during the 10-year period from 2007 through 2016, with water levels rising in 105 wells and declining in 31 wells; insufficient data prevented determination of a 10-year trend in 11 wells. Water levels declined over the long-term period of record at 80 wells, increased at 62 wells, and remained relatively constant at 5 wells.
In addition to continuous water-level data, periodic water-level data were collected and used to construct potentiometric-surface maps for the Upper Floridan aquifer in the Brunswick–Glynn County area during October 2015 and October 2016 and in the Albany–Dougherty County area during December 2015 and November and December 2016. Periodic water-level measurements were also collected and used to construct potentiometric-surface maps for the Cretaceous aquifer system in the Augusta–Richmond County area during July 2015 and June 2016. In general, water levels in the Upper Floridan aquifer were higher during 2015 than during 2016 in the Brunswick–Glynn County and Albany–Dougherty County areas due to higher precipitation during 2015. Water levels were lower, however, during 2015 than during 2016 in the Cretaceous aquifer system in the Augusta–Richmond County area.
In the Brunswick area, maps showing the chloride concentration of water in the Upper Floridan aquifer constructed using data collected from 33 wells during October 2015 and from 30 wells during October 2016 indicate that chloride concentrations remained above the U.S. Environmental Protection Agency’s secondary drinking-water standard in an approximately 2-square-mile area. During calendar years 2015–16, chloride concentrations generally were similar to those measured during 2012–14; however, some wells did show an increase in chloride concentration, likely due to increases in pumping.
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\"}}]}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Columbia, SC 29210
The ground‐motion models provided by me to the Next Generation Attenuation‐East (NGA‐East) project in 2015 have been updated by considering three additional Fourier spectra attenuation models and by conducting a mixed‐effect analysis of the residuals between the ground‐motion intensity measures computed from the attenuation models and the data from nine relatively well‐recorded events in eastern North America. On the basis of the period trends of the bias in the residuals and the distance trends of the residuals, I recommend the ground‐motion models developed for these attenuation models, with equal weights: the BCA10D model with 1/R geometrical spreading at all distances, and two modifications of the Atkinson and Boore (2014; referred as AB14) model, with 1/R spreading within 10 km, 1/R1.3 spreading from 10 to 50 km, and 1/√R spreading beyond 50 km.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220170218","usgsCitation":"Boore, D., 2018, Ground-motion models for very-hard rock sites in eastern North America: An update: Seismological Research Letters, v. 89, no. 3, p. 1172-1184, https://doi.org/10.1785/0220170218.","productDescription":"13 p.","startPage":"1172","endPage":"1184","ipdsId":"IP-091241","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":481867,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"89","issue":"3","noUsgsAuthors":false,"publicationDate":"2018-02-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Boore, David 0000-0002-8605-9673 boore@usgs.gov","orcid":"https://orcid.org/0000-0002-8605-9673","contributorId":140502,"corporation":false,"usgs":true,"family":"Boore","given":"David","email":"boore@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":926869,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70208916,"text":"70208916 - 2018 - Examining the role of unusually warm Indo‐Pacific sea‐surface temperatures in recent African droughts","interactions":[],"lastModifiedDate":"2020-03-05T06:46:48","indexId":"70208916","displayToPublicDate":"2018-02-21T06:42:31","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5939,"text":"Journal of the Royal Meteorological Society","active":true,"publicationSubtype":{"id":10}},"title":"Examining the role of unusually warm Indo‐Pacific sea‐surface temperatures in recent African droughts","docAbstract":"Southern Africa (SA) and eastern Africa (EA) experienced a sequence of severe droughts in December–February (SA DJF) 2015–2016, October–December (EA OND) 2016 and March–April–May 2017 (EA MAM). This sequence contributed to severe food insecurity. While climate variability in these regions is very complex, the goal of this study is to analyse the role played by unusually warm Indo–Pacific SSTs, where unusual is defined as a 1‐in‐6 year event. We use observed sea‐surface temperatures (SST) and satellite–gauge rainfall observations, a 20‐member ensemble of Community Atmospheric Model version 5.1 simulations (CAM5), and a 40‐member ensemble of climate change simulations from the Community Earth Systems Model version 1 (CESM1) Large Ensemble Community Project (LENS) to explore climate conditions associated with warm events identified based on eastern and western Pacific SSTs. Our analysis suggests that strong El Niño's may be followed by warm western Pacific SST conditions, which can lead to conditions conducive to successive and potentially predictable droughts in SA DJF, EA OND and EA MAM. We show that different regions of warm SST are related to recent droughts—SA DJF: Niño 3.4; EA OND: western equatorial Pacific (WEP); and EA MAM: western North Pacific (WNP). For DJF and MAM, respectively, the CAM5 model driven with observed SST and the same model driven within a climate change experiment indicate that warmer El Niño's and WNP events produce more intense atmospheric responses, potentially associated with more severe droughts. OND climate seems to be strongly influenced by the Indian Ocean Dipole, which corresponds with some WEP events. Given global warming, we suggest that the extreme Niño 3.4 and west Pacific SST events responsible for 2015–2017 droughts are likely to reoccur, thus humanitarian agencies should prepare to predict and respond to multi‐year drought and substantial food insecurity in SA and EA.","language":"English","publisher":"Wiley","doi":"10.1002/qj.3266","usgsCitation":"Funk, C., Harrison, L., Shukla, S., Pomposi, C., Galu, G., Korecha, D., Husak, G., Magadzire, T., Davenport, F., Hillbruner, C., Eilerts, G., Zaitchik, B., and Verdin, J., 2018, Examining the role of unusually warm Indo‐Pacific sea‐surface temperatures in recent African droughts: Journal of the Royal Meteorological Society, v. 144, no. S1, p. 360-383, https://doi.org/10.1002/qj.3266.","productDescription":"24 p.","startPage":"360","endPage":"383","ipdsId":"IP-089669","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":468980,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/qj.3266","text":"Publisher Index Page"},{"id":372941,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Africa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 34.80468749999999,\n 27.371767300523047\n ],\n [\n 29.8828125,\n 34.016241889667015\n ],\n [\n 11.25,\n 41.244772343082076\n ],\n [\n -7.3828125,\n 37.996162679728116\n ],\n [\n -18.28125,\n 28.304380682962783\n ],\n [\n -22.148437499999996,\n 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ice","interactions":[],"lastModifiedDate":"2018-02-21T11:02:35","indexId":"70195536","displayToPublicDate":"2018-02-21T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3219,"text":"Quaternary Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Prospects for reconstructing paleoenvironmental conditions from organic compounds in polar snow and ice","docAbstract":"Polar ice cores provide information about past climate and environmental changes over periods ranging from a few years up to 800,000 years. The majority of chemical studies have focused on determining inorganic components, such as major ions and trace elements as well as on their isotopic fingerprint. In this paper, we review the different classes of organic compounds that might yield environmental information, discussing existing research and what is needed to improve knowledge. We also discuss the problems of sampling, analysis and interpretation of organic molecules in ice. This review highlights the great potential for organic compounds to be used as proxies for anthropogenic activities, past fire events from different types of biomass, terrestrial biogenic emissions and marine biological activity, along with the possibility of inferring past temperature fluctuations and even large-scale climate variability. In parallel, comprehensive research needs to be done to assess the atmospheric stability of these compounds, their ability to be transported long distances in the atmosphere, and their stability in the archive in order to better interpret their fluxes in ice cores. In addition, specific decontamination procedures, analytical methods with low detection limits (ng/L or lower), fast analysis time and low sample requests need to be developed in order to ensure a good time resolution in the archive.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.quascirev.2018.01.007","usgsCitation":"Giorio, C., Kehrwald, N.M., Barbante, C., Kalberer, M., King, A.C., Thomas, E.R., Wolff, E.W., and Zennaro, P., 2018, Prospects for reconstructing paleoenvironmental conditions from organic compounds in polar snow and ice: Quaternary Science Reviews, v. 183, p. 1-22, https://doi.org/10.1016/j.quascirev.2018.01.007.","productDescription":"22 p.","startPage":"1","endPage":"22","ipdsId":"IP-087010","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":468981,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.repository.cam.ac.uk/handle/1810/274040","text":"External Repository"},{"id":351851,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"183","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee727e4b0da30c1bfc142","contributors":{"authors":[{"text":"Giorio, Chiara","contributorId":202631,"corporation":false,"usgs":false,"family":"Giorio","given":"Chiara","email":"","affiliations":[{"id":36502,"text":"Department of Chemistry, University of Cambrige, Lensfield Road, Cambridge CB2 1 EW, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":729173,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kehrwald, Natalie M. 0000-0002-9160-2239 nkehrwald@usgs.gov","orcid":"https://orcid.org/0000-0002-9160-2239","contributorId":168918,"corporation":false,"usgs":true,"family":"Kehrwald","given":"Natalie","email":"nkehrwald@usgs.gov","middleInitial":"M.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":729172,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barbante, Carlo","contributorId":202632,"corporation":false,"usgs":false,"family":"Barbante","given":"Carlo","email":"","affiliations":[{"id":36503,"text":"Department of Environmental Sciences, Infomatics, and Statistics, Ca'Foscari University of Venice, Via Torino 155, 30172 Mestre (VE), Italy","active":true,"usgs":false}],"preferred":false,"id":729174,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kalberer, Markus","contributorId":202633,"corporation":false,"usgs":false,"family":"Kalberer","given":"Markus","email":"","affiliations":[{"id":36504,"text":"Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":729175,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"King, Amy C.F.","contributorId":202634,"corporation":false,"usgs":false,"family":"King","given":"Amy","email":"","middleInitial":"C.F.","affiliations":[{"id":36505,"text":"British Antarctic Survey, High Cross, Madingley Road, Cambridge, CB3 0ET, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":729176,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Thomas, Elizabeth R.","contributorId":202635,"corporation":false,"usgs":false,"family":"Thomas","given":"Elizabeth","email":"","middleInitial":"R.","affiliations":[{"id":36505,"text":"British Antarctic Survey, High Cross, Madingley Road, Cambridge, CB3 0ET, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":729178,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wolff, Eric W.","contributorId":202636,"corporation":false,"usgs":false,"family":"Wolff","given":"Eric","email":"","middleInitial":"W.","affiliations":[{"id":36506,"text":"Department of Earth Science, University of Cambridge, Downing Street, Cambridge CB2 3EQ, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":729179,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Zennaro, Piero","contributorId":202637,"corporation":false,"usgs":false,"family":"Zennaro","given":"Piero","email":"","affiliations":[{"id":36507,"text":"Department of Environmental Sciences, Informatics, and Statistics, Ca' Foscari University of Venice, Via Torino 155, 30172 Mestre (VE), Italy","active":true,"usgs":false}],"preferred":false,"id":729180,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70195533,"text":"70195533 - 2018 - The sensitivity of ecosystem service models to choices of input data and spatial resolution","interactions":[],"lastModifiedDate":"2018-02-28T10:10:34","indexId":"70195533","displayToPublicDate":"2018-02-21T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":836,"text":"Applied Geography","active":true,"publicationSubtype":{"id":10}},"title":"The sensitivity of ecosystem service models to choices of input data and spatial resolution","docAbstract":"Although ecosystem service (ES) modeling has progressed rapidly in the last 10–15 years, comparative studies on data and model selection effects have become more common only recently. Such studies have drawn mixed conclusions about whether different data and model choices yield divergent results. In this study, we compared the results of different models to address these questions at national, provincial, and subwatershed scales in Rwanda. We compared results for carbon, water, and sediment as modeled using InVEST and WaSSI using (1) land cover data at 30 and 300 m resolution and (2) three different input land cover datasets. WaSSI and simpler InVEST models (carbon storage and annual water yield) were relatively insensitive to the choice of spatial resolution, but more complex InVEST models (seasonal water yield and sediment regulation) produced large differences when applied at differing resolution. Six out of nine ES metrics (InVEST annual and seasonal water yield and WaSSI) gave similar predictions for at least two different input land cover datasets. Despite differences in mean values when using different data sources and resolution, we found significant and highly correlated results when using Spearman's rank correlation, indicating consistent spatial patterns of high and low values. Our results confirm and extend conclusions of past studies, showing that in certain cases (e.g., simpler models and national-scale analyses), results can be robust to data and modeling choices. For more complex models, those with different output metrics, and subnational to site-based analyses in heterogeneous environments, data and model choices may strongly influence study findings.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeog.2018.02.005","usgsCitation":"Bagstad, K.J., Cohen, E., Ancona, Z.H., McNulty, S., and Sun, G., 2018, The sensitivity of ecosystem service models to choices of input data and spatial resolution: Applied Geography, v. 93, p. 25-36, https://doi.org/10.1016/j.apgeog.2018.02.005.","productDescription":"12 p.","startPage":"25","endPage":"36","ipdsId":"IP-089975","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":438005,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7CR5S92","text":"USGS data release","linkHelpText":"Data Release for The sensitivity of ecosystem service models to choices of input data and spatial resolution"},{"id":351849,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Rwanda","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n 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Ge","contributorId":145893,"corporation":false,"usgs":false,"family":"Sun","given":"Ge","email":"","affiliations":[{"id":6684,"text":"USDA Forest Service, Southern Research Station, Aiken, SC","active":true,"usgs":false}],"preferred":false,"id":729148,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70195037,"text":"sir20175147 - 2018 - Temporal trends in water-quality constituent concentrations and annual loads of chemical constituents in Michigan watersheds, 1998–2013","interactions":[],"lastModifiedDate":"2018-02-22T14:33:12","indexId":"sir20175147","displayToPublicDate":"2018-02-21T00:00:00","publicationYear":"2018","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":"2017-5147","title":"Temporal trends in water-quality constituent concentrations and annual loads of chemical constituents in Michigan watersheds, 1998–2013","docAbstract":"In 1998, the Michigan Department of Environmental Quality and the U.S. Geological Survey began the Water Chemistry Monitoring Program for select streams in the State of Michigan. Objectives of this program were to provide assistance with (1) statewide water-quality assessments, (2) the National Pollutant Discharge Elimination System permitting process, and (3) water-resource management decisions. As part of this program, water-quality data collected from 1998 to 2013 were analyzed to identify potential trends for select constituents that were sampled. Sixteen water-quality constituents were analyzed at 32 stations throughout Michigan. Trend analysis on the various water-quality data was done using either the uncensored Seasonal Kendall test or through Tobit regression. In total, 79 trends were detected in the constituents analyzed for 32 river stations sampled for the study period—53 downward trends and 26 upward trends were detected. The most prevalent trend detected throughout the State was for ammonia, with 11 downward trends and 1 upward trend estimated.
In addition to trends, constituent loads were estimated for 31 stations from 2002 to 2013 for stations that were sampled 12 times per year. Loads were computed using the Autobeale load computation program, which used the Beale ratio estimator approach to estimate an annual load. Constituent loads were the largest in large watershed streams with the highest annual flows such as the Saginaw and Grand Rivers. Likewise, constituent loads were the smallest in smaller tributaries that were sampled as part of this program such as the Boardman and Thunder Bay Rivers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175147","collaboration":"Prepared in cooperation with the Michigan Department of Environmental Quality","usgsCitation":"Hoard, C.J., Fogarty, L.R., and Duris, J.W., 2018, Temporal trends in water-quality constituent concentrations and annual loads of chemical constituents in Michigan watersheds, 1998–2013: U.S. Geological Survey Scientific Investigations Report 2017–5147, 79 p., https://doi.org/10.3133/sir20175147.","productDescription":"vi, 79 p.","numberOfPages":"90","onlineOnly":"N","ipdsId":"IP-077501","costCenters":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"links":[{"id":351840,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5147/coverthb.jpg"},{"id":351841,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5147/sir20175147.pdf","text":"Report","size":"4.32 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\"}}]}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
6520 Mercantile Way
Suite 5
Lansing, MI 48911
Conserving and managing global natural capital requires an understanding of the complexity of flows of ecosystem services across geographic boundaries. Failing to understand and to incorporate these flows into national and international ecosystem assessments leads to incomplete and potentially skewed conclusions, impairing society’s ability to identify sustainable management and policy choices. In this paper, we synthesise existing knowledge and develop a conceptual framework for analysing interregional ecosystem service flows. We synthesise the types of such flows, the characteristics of sending and receiving socio-ecological systems, and the impacts of ecosystem service flows on interregional sustainability. Using four cases (trade of certified coffee, migration of northern pintails, flood protection in the Danube watershed, and information on giant pandas), we test the conceptual framework and show how an enhanced understanding of interregional telecouplings in socio-ecological systems can inform ecosystem service-based decision making and governance with respect to sustainability goals.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecoser.2018.02.003","usgsCitation":"Schroter, M., Koellner, T., Alkemade, R., Arnhold, S., Bagstad, K.J., Frank, K., Erb, K., Kastner, T., Kissinger, M., Liu, J., Lopez-Hoffman, L., Maes, J., Marques, A., Martín-López, B., Meyer, C., Schulp, C.J., Thober, J., Wolff, S., and Bonn, A., 2018, Interregional flows of ecosystem services: Concepts, typology and four cases: Ecosystem Services, v. 31, no. B, p. 231-241, https://doi.org/10.1016/j.ecoser.2018.02.003.","productDescription":"11 p.","startPage":"231","endPage":"241","ipdsId":"IP-088768","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":468982,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecoser.2018.02.003","text":"Publisher Index Page"},{"id":351848,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"B","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee727e4b0da30c1bfc144","contributors":{"authors":[{"text":"Schroter, Matthias 0000-0003-0207-7311","orcid":"https://orcid.org/0000-0003-0207-7311","contributorId":202612,"corporation":false,"usgs":false,"family":"Schroter","given":"Matthias","email":"","affiliations":[{"id":36494,"text":"UFZ – Helmholtz Centre for Environmental Research","active":true,"usgs":false}],"preferred":false,"id":729150,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Koellner, Thomas 0000-0001-5022-027X","orcid":"https://orcid.org/0000-0001-5022-027X","contributorId":202613,"corporation":false,"usgs":false,"family":"Koellner","given":"Thomas","email":"","affiliations":[{"id":36495,"text":"University of Bayeruth","active":true,"usgs":false}],"preferred":false,"id":729151,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alkemade, Rob 0000-0001-8761-1768","orcid":"https://orcid.org/0000-0001-8761-1768","contributorId":202614,"corporation":false,"usgs":false,"family":"Alkemade","given":"Rob","email":"","affiliations":[{"id":36496,"text":"PBL Netherlands Environmental Assessment Agency","active":true,"usgs":false}],"preferred":false,"id":729152,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Arnhold, Sebastian 0000-0003-4823-4570","orcid":"https://orcid.org/0000-0003-4823-4570","contributorId":202615,"corporation":false,"usgs":false,"family":"Arnhold","given":"Sebastian","email":"","affiliations":[{"id":36495,"text":"University of Bayeruth","active":true,"usgs":false}],"preferred":false,"id":729153,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bagstad, Kenneth J. 0000-0001-8857-5615 kjbagstad@usgs.gov","orcid":"https://orcid.org/0000-0001-8857-5615","contributorId":3680,"corporation":false,"usgs":true,"family":"Bagstad","given":"Kenneth","email":"kjbagstad@usgs.gov","middleInitial":"J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":729149,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Frank, Karin 0000-0002-2769-0692","orcid":"https://orcid.org/0000-0002-2769-0692","contributorId":202616,"corporation":false,"usgs":false,"family":"Frank","given":"Karin","email":"","affiliations":[{"id":36494,"text":"UFZ – Helmholtz Centre for Environmental Research","active":true,"usgs":false}],"preferred":false,"id":729154,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Erb, Karl-Heinz 0000-0002-8335-4159","orcid":"https://orcid.org/0000-0002-8335-4159","contributorId":202617,"corporation":false,"usgs":false,"family":"Erb","given":"Karl-Heinz","email":"","affiliations":[{"id":36497,"text":"Institute of Social Ecology Vienna","active":true,"usgs":false}],"preferred":false,"id":729155,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kastner, Thomas","contributorId":202618,"corporation":false,"usgs":false,"family":"Kastner","given":"Thomas","email":"","affiliations":[{"id":27439,"text":"Senckenberg Biodiversity and Climate Research 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Arizona","active":true,"usgs":false}],"preferred":false,"id":729159,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Maes, Joachim","contributorId":190801,"corporation":false,"usgs":false,"family":"Maes","given":"Joachim","email":"","affiliations":[],"preferred":false,"id":729160,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Marques, Alexandra","contributorId":202622,"corporation":false,"usgs":false,"family":"Marques","given":"Alexandra","email":"","affiliations":[{"id":36499,"text":"Leiden University","active":true,"usgs":false}],"preferred":false,"id":729161,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Martín-López, Berta","contributorId":202623,"corporation":false,"usgs":false,"family":"Martín-López","given":"Berta","affiliations":[{"id":36500,"text":"Leuphana University","active":true,"usgs":false}],"preferred":false,"id":729162,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Meyer, Carsten","contributorId":193124,"corporation":false,"usgs":false,"family":"Meyer","given":"Carsten","email":"","affiliations":[],"preferred":false,"id":729163,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Schulp, Catharina J. E.","contributorId":202624,"corporation":false,"usgs":false,"family":"Schulp","given":"Catharina","email":"","middleInitial":"J. E.","affiliations":[{"id":28162,"text":"Vrije University Amsterdam","active":true,"usgs":false}],"preferred":false,"id":729164,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Thober, Jule","contributorId":202628,"corporation":false,"usgs":false,"family":"Thober","given":"Jule","email":"","affiliations":[{"id":36501,"text":"UFZ-Helmholtz Centre for Environmental Research","active":true,"usgs":false}],"preferred":false,"id":729168,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Wolff, Sarah","contributorId":202626,"corporation":false,"usgs":false,"family":"Wolff","given":"Sarah","email":"","affiliations":[{"id":28162,"text":"Vrije University Amsterdam","active":true,"usgs":false}],"preferred":false,"id":729166,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Bonn, Aletta 0000-0002-8345-4600","orcid":"https://orcid.org/0000-0002-8345-4600","contributorId":202627,"corporation":false,"usgs":false,"family":"Bonn","given":"Aletta","email":"","affiliations":[{"id":36494,"text":"UFZ – Helmholtz Centre for Environmental Research","active":true,"usgs":false}],"preferred":false,"id":729167,"contributorType":{"id":1,"text":"Authors"},"rank":19}]}} ,{"id":70195517,"text":"70195517 - 2018 - Imperfect pathogen detection from non-invasive skin swabs biases disease inference","interactions":[],"lastModifiedDate":"2018-02-20T09:02:24","indexId":"70195517","displayToPublicDate":"2018-02-20T00:00:00","publicationYear":"2018","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":"Imperfect pathogen detection from non-invasive skin swabs biases disease inference","docAbstract":"1. Conservation managers rely on accurate estimates of disease parameters, such as pathogen prevalence and infection intensity, to assess disease status of a host population. However, these disease metrics may be biased if low-level infection intensities are missed by sampling methods or laboratory diagnostic tests. These false negatives underestimate pathogen prevalence and overestimate mean infection intensity of infected individuals. 2. Our objectives were two-fold. First, we quantified false negative error rates of Batrachochytrium dendrobatidis on non-invasive skin swabs collected from an amphibian community in El Copé, Panama. We swabbed amphibians twice in sequence, and we used a recently developed hierarchical Bayesian estimator to assess disease status of the population. Second, we developed a novel hierarchical Bayesian model to simultaneously account for imperfect pathogen detection from field sampling and laboratory diagnostic testing. We evaluated the performance of the model using simulations and varying sampling design to quantify the magnitude of bias in estimates of pathogen prevalence and infection intensity. 3. We show that Bd detection probability from skin swabs was related to host infection intensity, where Bd infections < 10 zoospores have < 95% probability of being detected. If imperfect Bd detection was not considered, then Bd prevalence was underestimated by as much as 16%. In the Bd-amphibian system, this indicates a need to correct for imperfect pathogen detection caused by skin swabs in persisting host communities with low-level infections. More generally, our results have implications for study designs in other disease systems, particularly those with similar objectives, biology, and sampling decisions. 4. Uncertainty in pathogen detection is an inherent property of most sampling protocols and diagnostic tests, where the magnitude of bias depends on the study system, type of infection, and false negative error rates. Given that it may be difficult to know this information in advance, we advocate that the most cautious approach is to assume all errors are possible and to accommodate them by adjusting sampling designs. The modeling framework presented here improves the accuracy in estimating pathogen prevalence and infection intensity.","language":"English","doi":"10.1111/2041-210X.12868","usgsCitation":"DiRenzo, G.V., Grant, E.H., Longo, A.V., Che-Castaldo, C., Zamudio, K.R., and Lips, K., 2018, Imperfect pathogen detection from non-invasive skin swabs biases disease inference: Methods in Ecology and Evolution, v. 9, no. 2, p. 380-389, https://doi.org/10.1111/2041-210X.12868.","productDescription":"10 p.","startPage":"380","endPage":"389","ipdsId":"IP-079826","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":468988,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/2041-210x.12868","text":"Publisher Index Page"},{"id":351805,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Panama","volume":"9","issue":"2","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-14","publicationStatus":"PW","scienceBaseUri":"5afee729e4b0da30c1bfc152","contributors":{"authors":[{"text":"DiRenzo, Graziella V.","contributorId":192177,"corporation":false,"usgs":false,"family":"DiRenzo","given":"Graziella","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":728984,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grant, Evan H. Campbell 0000-0003-4401-6496 ehgrant@usgs.gov","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":201360,"corporation":false,"usgs":true,"family":"Grant","given":"Evan","email":"ehgrant@usgs.gov","middleInitial":"H. Campbell","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":728983,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Longo, Ana V.","contributorId":202587,"corporation":false,"usgs":false,"family":"Longo","given":"Ana","email":"","middleInitial":"V.","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":728985,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Che-Castaldo, Christian","contributorId":202588,"corporation":false,"usgs":false,"family":"Che-Castaldo","given":"Christian","email":"","affiliations":[{"id":36488,"text":"Stony Brook University","active":true,"usgs":false}],"preferred":false,"id":728986,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zamudio, Kelly R.","contributorId":8320,"corporation":false,"usgs":true,"family":"Zamudio","given":"Kelly","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":728987,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lips, Karen","contributorId":174835,"corporation":false,"usgs":false,"family":"Lips","given":"Karen","affiliations":[],"preferred":false,"id":728988,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70195520,"text":"70195520 - 2018 - Corrugated megathrust revealed offshore from Costa Rica","interactions":[],"lastModifiedDate":"2018-03-05T15:23:45","indexId":"70195520","displayToPublicDate":"2018-02-20T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2845,"text":"Nature Geoscience","active":true,"publicationSubtype":{"id":10}},"title":"Corrugated megathrust revealed offshore from Costa Rica","docAbstract":"Exhumed faults are rough, often exhibiting topographic corrugations oriented in the direction of slip; such features are fundamental to mechanical processes that drive earthquakes and fault evolution. However, our understanding of corrugation genesis remains limited due to a lack of in situ observations at depth, especially at subducting plate boundaries. Here we present three-dimensional seismic reflection data of the Costa Rica subduction zone that image a shallow megathrust fault characterized by corrugated, and chaotic and weakly corrugated topographies. The corrugated surfaces extend from near the trench to several kilometres down-dip, exhibit high reflection amplitudes (consistent with high fluid content/pressure) and trend 11–18° oblique to subduction, suggesting 15 to 25 mm yr−1 of trench-parallel slip partitioning across the plate boundary. The corrugations form along portions of the megathrust with greater cumulative slip and may act as fluid conduits. In contrast, weakly corrugated areas occur adjacent to active plate bending faults where the megathrust has migrated up-section, forming a nascent fault surface. The variations in megathrust roughness imaged here suggest that abandonment and then reestablishment of the megathrust up-section transiently increases fault roughness. Analogous corrugations may exist along significant portions of subduction megathrusts globally.
","language":"English","publisher":"Nature","doi":"10.1038/s41561-018-0061-4","usgsCitation":"Edwards, J.H., Kluesner, J.W., Silver, E.A., Brodsky, E.E., Brothers, D.S., Bangs, N.L., Kirkpatrick, J.D., Wood, R., and Okamato, K., 2018, Corrugated megathrust revealed offshore from Costa Rica: Nature Geoscience, v. 11, p. 197-202, https://doi.org/10.1038/s41561-018-0061-4.","productDescription":"6 p.","startPage":"197","endPage":"202","ipdsId":"IP-090462","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":468985,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://escholarship.mcgill.ca/concern/articles/ht24wq37d","text":"External Repository"},{"id":351819,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Costa Rica","volume":"11","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-12","publicationStatus":"PW","scienceBaseUri":"5afee729e4b0da30c1bfc14e","contributors":{"authors":[{"text":"Edwards, Joel H.","contributorId":202599,"corporation":false,"usgs":false,"family":"Edwards","given":"Joel","email":"","middleInitial":"H.","affiliations":[{"id":27155,"text":"University of California Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":729006,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kluesner, Jared W. 0000-0003-1701-8832 jkluesner@usgs.gov","orcid":"https://orcid.org/0000-0003-1701-8832","contributorId":201261,"corporation":false,"usgs":true,"family":"Kluesner","given":"Jared","email":"jkluesner@usgs.gov","middleInitial":"W.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":729004,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Silver, Eli A.","contributorId":83505,"corporation":false,"usgs":true,"family":"Silver","given":"Eli","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":729007,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brodsky, Emily E.","contributorId":29660,"corporation":false,"usgs":true,"family":"Brodsky","given":"Emily","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":729008,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brothers, Daniel S. 0000-0001-7702-157X dbrothers@usgs.gov","orcid":"https://orcid.org/0000-0001-7702-157X","contributorId":167089,"corporation":false,"usgs":true,"family":"Brothers","given":"Daniel","email":"dbrothers@usgs.gov","middleInitial":"S.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":729009,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bangs, Nathan L.","contributorId":202602,"corporation":false,"usgs":false,"family":"Bangs","given":"Nathan","email":"","middleInitial":"L.","affiliations":[{"id":12430,"text":"University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":729010,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kirkpatrick, James D.","contributorId":202603,"corporation":false,"usgs":false,"family":"Kirkpatrick","given":"James","email":"","middleInitial":"D.","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":729011,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wood, Ruby","contributorId":202604,"corporation":false,"usgs":false,"family":"Wood","given":"Ruby","email":"","affiliations":[{"id":27155,"text":"University of California Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":729012,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Okamato, Kristina","contributorId":202605,"corporation":false,"usgs":false,"family":"Okamato","given":"Kristina","email":"","affiliations":[{"id":27155,"text":"University of California Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":729013,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70195521,"text":"70195521 - 2018 - Improving estimation of flight altitude in wildlife telemetry studies","interactions":[],"lastModifiedDate":"2018-07-03T11:37:03","indexId":"70195521","displayToPublicDate":"2018-02-20T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Improving estimation of flight altitude in wildlife telemetry studies","docAbstract":"The snow energy balance is difficult to measure during the snowmelt period, yet critical for predictions of water yield in regions characterized by snow cover. Robust simplifications of the snowmelt energy balance can aid our understanding of water resources in a changing climate. Research to date has demonstrated that the net turbulent flux (FT) between a melting snowpack and the atmosphere is negligible if the sum of atmospheric vapor pressure (ea) and temperature (Ta) equals a constant, but it is unclear how frequently this situation holds across different sites. Here, we quantified the contribution of FT to the snowpack energy balance during 59 snowmelt periods across 11 sites in the FLUXNET2015 database with a detailed analysis of snowmelt in subarctic tundra near Abisko, Sweden. At the Abisko site we investigated the frequency of occurrences during which sensible heat flux (H) and latent heat flux (λE) are of (approximately) equal but opposite sign, and if the sum of these terms, FT, is therefore negligible during the snowmelt period. H approximately equaled -λE for less than 50% of the melt period and FT was infrequently a trivial term in the snowmelt energy balance at Abisko. The reason is that the relationship between observed ea and Ta is roughly orthogonal to the “line of equality” at which H equals -λE as warmer Ta during the melt period usually resulted in greater ea. This relationship holds both within melt periods at individual sites and across different sites in the FLUXNET2015 database, where FTcomprised less than 20% of the energy available to melt snow, Qm, in 44% of the snowmelt periods studied here. FT/Qm was significantly related to the mean ea during the melt period, but not mean Ta, and FT tended to be near 0 W m−2 when ea averaged ca. 0.5 kPa. FT may become an increasingly important term in the snowmelt energy balance across many global regions as warmer temperatures are projected to cause snow to melt more slowly and earlier in the year under conditions of lower net radiation (Rn). Eddy covariance research networks such as Ameriflux must improve their ability to observe cold-season processes to enhance our understanding of water resources and surface-atmosphere exchange in a changing climate.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.agrformet.2018.01.028","usgsCitation":"Stoy, P., Peitzsch, E.H., Wood, D.J., Rottinghaus, D., Wohlfahrt, G., Goulden, M., and Ward, H., 2018, On the exchange of sensible and latent heat between the atmosphere and melting snow: Agricultural and Forest Meteorology, v. 252, p. 167-174, https://doi.org/10.1016/j.agrformet.2018.01.028.","productDescription":"8 p.","startPage":"167","endPage":"174","ipdsId":"IP-087816","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":468984,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.agrformet.2018.01.028","text":"Publisher Index Page"},{"id":351808,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"252","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee729e4b0da30c1bfc158","contributors":{"authors":[{"text":"Stoy, Paul C.","contributorId":60860,"corporation":false,"usgs":true,"family":"Stoy","given":"Paul C.","affiliations":[],"preferred":false,"id":728960,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peitzsch, Erich H. 0000-0001-7624-0455 epeitzsch@usgs.gov","orcid":"https://orcid.org/0000-0001-7624-0455","contributorId":3786,"corporation":false,"usgs":true,"family":"Peitzsch","given":"Erich","email":"epeitzsch@usgs.gov","middleInitial":"H.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":728958,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wood, David J. A. 0000-0003-4315-5160 dwood@usgs.gov","orcid":"https://orcid.org/0000-0003-4315-5160","contributorId":177588,"corporation":false,"usgs":true,"family":"Wood","given":"David","email":"dwood@usgs.gov","middleInitial":"J. A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":728959,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rottinghaus, Daniel","contributorId":202579,"corporation":false,"usgs":false,"family":"Rottinghaus","given":"Daniel","email":"","affiliations":[{"id":36485,"text":"Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, Montana, USA.","active":true,"usgs":false}],"preferred":false,"id":728961,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wohlfahrt, Georg","contributorId":202591,"corporation":false,"usgs":false,"family":"Wohlfahrt","given":"Georg","email":"","affiliations":[],"preferred":false,"id":728989,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Goulden, Michael","contributorId":192006,"corporation":false,"usgs":false,"family":"Goulden","given":"Michael","email":"","affiliations":[],"preferred":false,"id":728963,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ward, Helen","contributorId":202581,"corporation":false,"usgs":false,"family":"Ward","given":"Helen","email":"","affiliations":[{"id":36487,"text":"Department of Meteorology, University of Reading, Reading, RG6 6BB, United Kingdom and Institute of Atmospheric and Cryospheric Sciences, University of Innsbruck, 6020 Innsbruck, Austria","active":true,"usgs":false}],"preferred":false,"id":728964,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70195518,"text":"70195518 - 2018 - Delineating and identifying long-term changes in the whooping crane (Grus americana) migration corridor","interactions":[],"lastModifiedDate":"2018-02-20T12:46:47","indexId":"70195518","displayToPublicDate":"2018-02-20T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Delineating and identifying long-term changes in the whooping crane (Grus americana) migration corridor","title":"Delineating and identifying long-term changes in the whooping crane (Grus americana) migration corridor","docAbstract":"Defining and identifying changes to seasonal ranges of migratory species is required for effective conservation. Historic sightings of migrating whooping cranes (Grus americana) have served as sole source of information to define a migration corridor in the Great Plains of North America (i.e., Canadian Prairies and United States Great Plains) for this endangered species. We updated this effort using past opportunistic sightings from 1942–2016 (n = 5,055) and more recent (2010–2016) location data from 58 telemetered birds (n = 4,423) to delineate migration corridors that included 50%, 75%, and 95% core areas. All migration corridors were well defined and relatively compact, with the 95% core corridor averaging 294 km wide, although it varied approximately ±40% in width from 170 km in central Texas to 407 km at the international border of the United States and Canada. Based on historic sightings and telemetry locations, we detected easterly movements in locations over time, primarily due to locations west of the median shifting east. This shift occurred from northern Oklahoma to central Saskatchewan at an average rate of 1.2 km/year (0.3–2.8 km/year). Associated with this directional shift was a decrease in distance of locations from the median in the same region averaging -0.7 km/year (-0.3–-1.3 km/year), suggesting a modest narrowing of the migration corridor. Changes in the corridor over the past 8 decades suggest that agencies and organizations interested in recovery of this species may need to modify where conservation and recovery actions occur. Whooping cranes showed apparent plasticity in their migratory behavior, which likely has been necessary for persistence of a wetland-dependent species migrating through the drought-prone Great Plains. Behavioral flexibility will be useful for whooping cranes to continue recovery in a future of uncertain climate and land use changes throughout their annual range.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0192737","usgsCitation":"Pearse, A.T., Rabbe, M., Juliusson, L.M., Bidwell, M.T., Craig-Moore, L., Brandt, D.A., and Harrell, W.C., 2018, Delineating and identifying long-term changes in the whooping crane (Grus americana) migration corridor: PLoS ONE, v. 13, no. 2, p. 1-15, https://doi.org/10.1371/journal.pone.0192737.","productDescription":"e0192737; 15 p.","startPage":"1","endPage":"15","ipdsId":"IP-090602","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":468983,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0192737","text":"Publisher Index Page"},{"id":351820,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-15","publicationStatus":"PW","scienceBaseUri":"5afee729e4b0da30c1bfc150","contributors":{"authors":[{"text":"Pearse, Aaron T. 0000-0002-6137-1556 apearse@usgs.gov","orcid":"https://orcid.org/0000-0002-6137-1556","contributorId":1772,"corporation":false,"usgs":true,"family":"Pearse","given":"Aaron","email":"apearse@usgs.gov","middleInitial":"T.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":728990,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rabbe, Matt","contributorId":202597,"corporation":false,"usgs":false,"family":"Rabbe","given":"Matt","email":"","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":728991,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Juliusson, Lara M.","contributorId":202593,"corporation":false,"usgs":false,"family":"Juliusson","given":"Lara","email":"","middleInitial":"M.","affiliations":[{"id":36490,"text":"USFWS, Lakewood, CO","active":true,"usgs":false}],"preferred":false,"id":728992,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bidwell, Mark T.","contributorId":202007,"corporation":false,"usgs":false,"family":"Bidwell","given":"Mark","email":"","middleInitial":"T.","affiliations":[{"id":36318,"text":"CWS","active":true,"usgs":false}],"preferred":false,"id":728993,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Craig-Moore, Lea","contributorId":202595,"corporation":false,"usgs":false,"family":"Craig-Moore","given":"Lea","email":"","affiliations":[{"id":36491,"text":"Environment and Climate Change Canada, Saskatoon, SK","active":true,"usgs":false}],"preferred":false,"id":728994,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brandt, David A. 0000-0001-9786-307X dbrandt@usgs.gov","orcid":"https://orcid.org/0000-0001-9786-307X","contributorId":149929,"corporation":false,"usgs":true,"family":"Brandt","given":"David","email":"dbrandt@usgs.gov","middleInitial":"A.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":728995,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Harrell, Wade C.","contributorId":147143,"corporation":false,"usgs":false,"family":"Harrell","given":"Wade","email":"","middleInitial":"C.","affiliations":[{"id":16793,"text":"USFWS, Ecological Services, Austwell, TX","active":true,"usgs":false}],"preferred":false,"id":728996,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70195477,"text":"70195477 - 2018 - Clayey landslide initiation and acceleration strongly modulated by soil swelling","interactions":[],"lastModifiedDate":"2018-03-19T11:10:31","indexId":"70195477","displayToPublicDate":"2018-02-20T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Clayey landslide initiation and acceleration strongly modulated by soil swelling","docAbstract":"Largely unknown mechanisms restrain motion of clay-rich, slow-moving landslides that are widespread worldwide and rarely accelerate catastrophically. We studied a clayey, slow-moving landslide typical of thousands in northern California, USA, to decipher hydrologic-mechanical interactions that modulate landslide dynamics. Similar to some other studies, observed pore-water pressures correlated poorly with landslide reactivation and speed. In situ and laboratory measurements strongly suggested that variable pressure along the landslide's lateral shear boundaries resulting from seasonal soil expansion and contraction modulated its reactivation and speed. Slope-stability modeling suggested that the landslide's observed behavior could be predicted by including transient swell pressure as a resistance term, whereas modeling considering only transient hydrologic conditions predicted movement 5–6 months prior to when it was observed. All clayey soils swell to some degree; hence, our findings suggest that swell pressure likely modulates motion of many landslides and should be considered to improve forecasts of clayey landslide initiation and mobility.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2017GL076807","usgsCitation":"Schulz, W.H., Smith, J.B., Wang, G., Jiang, Y., and Roering, J., 2018, Clayey landslide initiation and acceleration strongly modulated by soil swelling: Geophysical Research Letters, v. 45, no. 4, p. 1888-1896, https://doi.org/10.1002/2017GL076807.","productDescription":"9 p.","startPage":"1888","endPage":"1896","ipdsId":"IP-093100","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":468987,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2017gl076807","text":"Publisher Index Page"},{"id":438006,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7GF0SFS","text":"USGS data release","linkHelpText":"Data from in-situ landslide monitoring, Trinity County, California"},{"id":351813,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","volume":"45","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-26","publicationStatus":"PW","scienceBaseUri":"5afee72ae4b0da30c1bfc15c","contributors":{"authors":[{"text":"Schulz, William H. 0000-0001-9980-3580 wschulz@usgs.gov","orcid":"https://orcid.org/0000-0001-9980-3580","contributorId":942,"corporation":false,"usgs":true,"family":"Schulz","given":"William","email":"wschulz@usgs.gov","middleInitial":"H.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":728780,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Joel B. 0000-0001-7219-7875 jbsmith@usgs.gov","orcid":"https://orcid.org/0000-0001-7219-7875","contributorId":4925,"corporation":false,"usgs":true,"family":"Smith","given":"Joel","email":"jbsmith@usgs.gov","middleInitial":"B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":728781,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wang, Gonghui","contributorId":202546,"corporation":false,"usgs":false,"family":"Wang","given":"Gonghui","email":"","affiliations":[{"id":36476,"text":"Disaster Prevention Research Institute, Kyoto University","active":true,"usgs":false}],"preferred":false,"id":728782,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jiang, Yao","contributorId":202547,"corporation":false,"usgs":false,"family":"Jiang","given":"Yao","email":"","affiliations":[{"id":36476,"text":"Disaster Prevention Research Institute, Kyoto University","active":true,"usgs":false}],"preferred":false,"id":728783,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Roering, Joshua J.","contributorId":194297,"corporation":false,"usgs":false,"family":"Roering","given":"Joshua J.","affiliations":[],"preferred":false,"id":728784,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70195523,"text":"70195523 - 2018 - Variability in soil-water retention properties and implications for physics-based simulation of landslide early warning criteria","interactions":[],"lastModifiedDate":"2018-07-03T11:36:21","indexId":"70195523","displayToPublicDate":"2018-02-20T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2604,"text":"Landslides","active":true,"publicationSubtype":{"id":10}},"title":"Variability in soil-water retention properties and implications for physics-based simulation of landslide early warning criteria","docAbstract":"Rainfall-induced shallow landsliding is a persistent hazard to human life and property. Despite the observed connection between infiltration through the unsaturated zone and shallow landslide initiation, there is considerable uncertainty in how estimates of unsaturated soil-water retention properties affect slope stability assessment. This source of uncertainty is critical to evaluating the utility of physics-based hydrologic modeling as a tool for landslide early warning. We employ a numerical model of variably saturated groundwater flow parameterized with an ensemble of texture-, laboratory-, and field-based estimates of soil-water retention properties for an extensively monitored landslide-prone site in the San Francisco Bay Area, CA, USA. Simulations of soil-water content, pore-water pressure, and the resultant factor of safety show considerable variability across and within these different parameter estimation techniques. In particular, we demonstrate that with the same permeability structure imposed across all simulations, the variability in soil-water retention properties strongly influences predictions of positive pore-water pressure coincident with widespread shallow landsliding. We also find that the ensemble of soil-water retention properties imposes an order-of-magnitude and nearly two-fold variability in seasonal and event-scale landslide susceptibility, respectively. Despite the reduced factor of safety uncertainty during wet conditions, parameters that control the dry end of the soil-water retention function markedly impact the ability of a hydrologic model to capture soil-water content dynamics observed in the field. These results suggest that variability in soil-water retention properties should be considered for objective physics-based simulation of landslide early warning criteria.
","language":"English","publisher":"Springer","doi":"10.1007/s10346-018-0950-z","usgsCitation":"Thomas, M.A., Mirus, B.B., Collins, B.D., Lu, N., and Godt, J.W., 2018, Variability in soil-water retention properties and implications for physics-based simulation of landslide early warning criteria: Landslides, v. 15, no. 7, p. 1265-1277, https://doi.org/10.1007/s10346-018-0950-z.","productDescription":"13 p.","startPage":"1265","endPage":"1277","ipdsId":"IP-089282","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":438007,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7M0449D","text":"USGS data release","linkHelpText":"Field data used to support hydrologic modeling for the U.S. Geological Survey's San Francisco Bay Area "BALT1" landslide monitoring site"},{"id":351832,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"7","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-12","publicationStatus":"PW","scienceBaseUri":"5afee728e4b0da30c1bfc14a","contributors":{"authors":[{"text":"Thomas, Matthew A.","contributorId":138657,"corporation":false,"usgs":false,"family":"Thomas","given":"Matthew","email":"","middleInitial":"A.","affiliations":[{"id":12482,"text":"Department of Geological and Environmental Sciences, Stanford University, 450 Serra Mall, Building 320, Stanford, California 94305-2115, USA","active":true,"usgs":false}],"preferred":false,"id":729027,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mirus, Benjamin B. 0000-0001-5550-014X bbmirus@usgs.gov","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":4064,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin","email":"bbmirus@usgs.gov","middleInitial":"B.","affiliations":[{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true}],"preferred":true,"id":729028,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Collins, Brian D. bcollins@usgs.gov","contributorId":2406,"corporation":false,"usgs":true,"family":"Collins","given":"Brian","email":"bcollins@usgs.gov","middleInitial":"D.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":false,"id":729029,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lu, Ning","contributorId":191360,"corporation":false,"usgs":false,"family":"Lu","given":"Ning","email":"","affiliations":[{"id":12620,"text":"U.S. Army Corp. of Engineers","active":true,"usgs":false}],"preferred":false,"id":729030,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Godt, Jonathan W. 0000-0002-8737-2493 jgodt@usgs.gov","orcid":"https://orcid.org/0000-0002-8737-2493","contributorId":1166,"corporation":false,"usgs":true,"family":"Godt","given":"Jonathan","email":"jgodt@usgs.gov","middleInitial":"W.","affiliations":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":729031,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}