Livestock grazing is an important land use in the western USA and can have positive or negative effects on amphibians. Columbia Spotted Frog (Rana luteiventris) often use ponds that provide water for cattle. We conducted a long-term manipulative study on US Forest Service land in northeastern Oregon to determine the effects of full and partial exclosures that limited cattle access to ponds used by frogs. We found weak evidence of a short-term increase in abundance that did not differ between full and partial exclosures and that diminished with continuing exclusion of cattle. The benefit of exclosures was small relative to the overall decline in breeding numbers that we documented. This suggests that some protection can provide a short-term boost to populations.
","language":"English","publisher":"Springer","doi":"10.1007/s11273-018-9596-9","usgsCitation":"Adams, M.J., Pearl, C., Chambert, T., McCreary, B., Galvan, S., and Rowe, J., 2018, Effect of cattle exclosures on Columbia Spotted Frog abundance: Wetlands Ecology and Management, v. 26, no. 4, p. 627-634, https://doi.org/10.1007/s11273-018-9596-9.","productDescription":"8 p.","startPage":"627","endPage":"634","ipdsId":"IP-088665","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":352015,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"26","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-26","publicationStatus":"PW","scienceBaseUri":"5afee716e4b0da30c1bfc10c","contributors":{"authors":[{"text":"Adams, M. J. 0000-0001-8844-042X mjadams@usgs.gov","orcid":"https://orcid.org/0000-0001-8844-042X","contributorId":3133,"corporation":false,"usgs":false,"family":"Adams","given":"M.","email":"mjadams@usgs.gov","middleInitial":"J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":729574,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pearl, Christopher 0000-0003-2943-7321 christopher_pearl@usgs.gov","orcid":"https://orcid.org/0000-0003-2943-7321","contributorId":172669,"corporation":false,"usgs":true,"family":"Pearl","given":"Christopher","email":"christopher_pearl@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":729575,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chambert, Thierry 0000-0002-9450-9080 tchambert@usgs.gov","orcid":"https://orcid.org/0000-0002-9450-9080","contributorId":191979,"corporation":false,"usgs":false,"family":"Chambert","given":"Thierry","email":"tchambert@usgs.gov","affiliations":[],"preferred":false,"id":729576,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCreary, Brome 0000-0002-0313-7796 brome_mccreary@usgs.gov","orcid":"https://orcid.org/0000-0002-0313-7796","contributorId":3130,"corporation":false,"usgs":true,"family":"McCreary","given":"Brome","email":"brome_mccreary@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":729577,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Galvan, Stephanie 0000-0002-9864-3674 stephanie_galvan@usgs.gov","orcid":"https://orcid.org/0000-0002-9864-3674","contributorId":3135,"corporation":false,"usgs":true,"family":"Galvan","given":"Stephanie","email":"stephanie_galvan@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":729578,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rowe, Jennifer 0000-0002-5253-2223 jrowe@usgs.gov","orcid":"https://orcid.org/0000-0002-5253-2223","contributorId":172670,"corporation":false,"usgs":true,"family":"Rowe","given":"Jennifer","email":"jrowe@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":729579,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70122872,"text":"sir20145168 - 2018 - Spatially distributed groundwater recharge estimated using a water-budget model for the Island of Maui, Hawai`i, 1978–2007","interactions":[],"lastModifiedDate":"2019-10-04T07:23:44","indexId":"sir20145168","displayToPublicDate":"2018-02-26T00:00:00","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":"2014-5168","title":"Spatially distributed groundwater recharge estimated using a water-budget model for the Island of Maui, Hawai`i, 1978–2007","docAbstract":"Demand for freshwater on the Island of Maui is expected to grow. To evaluate the availability of fresh groundwater, estimates of groundwater recharge are needed. A water-budget model with a daily computation interval was developed and used to estimate the spatial distribution of recharge on Maui for average climate conditions (1978–2007 rainfall and 2010 land cover) and for drought conditions (1998–2002 rainfall and 2010 land cover). For average climate conditions, mean annual recharge for Maui is about 1,309 million gallons per day, or about 44 percent of precipitation (rainfall and fog interception). Recharge for average climate conditions is about 39 percent of total water inflow consisting of precipitation, irrigation, septic leachate, and seepage from reservoirs and cesspools. Most recharge occurs on the wet, windward slopes of Haleakalā and on the wet, uplands of West Maui Mountain. Dry, coastal areas generally have low recharge. In the dry isthmus, however, irrigated fields have greater recharge than nearby unirrigated areas. For drought conditions, mean annual recharge for Maui is about 1,010 million gallons per day, which is 23 percent less than recharge for average climate conditions. For individual aquifer-system areas used for groundwater management, recharge for drought conditions is about 8 to 51 percent less than recharge for average climate conditions. The spatial distribution of rainfall is the primary factor determining spatially distributed recharge estimates for most areas on Maui. In wet areas, recharge estimates are also sensitive to water-budget parameters that are related to runoff, fog interception, and forest-canopy evaporation. In dry areas, recharge estimates are most sensitive to irrigated crop areas and parameters related to evapotranspiration.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20145168","collaboration":"Prepared in cooperation with the County of Maui Department of Water Supply and the State of Hawai‘i Commission on Water Resource Management","usgsCitation":"Johnson, A.G., Engott, J.A., Bassiouni, Maoya, and Rotzoll, Kolja, 2018, Spatially distributed groundwater recharge estimated using a water-budget model for the Island of Maui, Hawai`i, 1978–2007 (ver. 2.0, February 2018): U.S. Geological Survey Scientific Investigations Report 2014–5168, 53 p., https://doi.org/10.3133/sir20145168.","productDescription":"Report: v, 53 p.; Data Release","numberOfPages":"64","ipdsId":"IP-036379","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":351916,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2014/5168/pdf/sir20145168.pdf","text":"Report","size":"9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2014-5168"},{"id":351915,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2014/5168/images/coverthb.jpg"},{"id":351918,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://www.sciencebase.gov/catalog/item/get/5a0f91bee4b09af898d09be6","linkHelpText":"Mean annual water-budget components for the Island of Maui, Hawaii, for average climate conditions, 1978-2007 rainfall and 2010 land cover (version 2.0)"},{"id":351917,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2014/5168/sir20145168_versionhist.txt","size":"2 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2014-5168"}],"country":"United States","state":"Hawai'i","otherGeospatial":"Maui","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.73507690429688,\n 20.56208154725951\n ],\n [\n -155.972900390625,\n 20.56208154725951\n ],\n [\n -155.972900390625,\n 21.042209507614245\n ],\n [\n -156.73507690429688,\n 21.042209507614245\n ],\n [\n -156.73507690429688,\n 20.56208154725951\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: Originally posted December 14, 2014; Version 2.0: February 26, 2018","contact":"Director,
Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
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
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
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.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175051","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Bennett, G.L., V, 2018, Status and understanding of groundwater quality in the North San Francisco Bay Shallow Aquifer study unit, 2012; California GAMA Priority Basin Project (ver. 1.1, February 2018): U.S. Geological Survey Scientific Investigations Report 2017–5051, 74 p., https://doi.org/10.3133/sir20175051.","productDescription":"Report: x, 74 p.","numberOfPages":"74","onlineOnly":"Y","ipdsId":"IP-053824","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":344100,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5051/sir20175051_v1.1.pdf","text":"Report","size":"11.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5051 Version 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2018","contact":"California Water Science Center
California GAMA
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
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.
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\"}}]}\n\n\n","contact":"Director, Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, VA 23228
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":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","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":70195559,"text":"70195559 - 2018 - Seeking excellence: An evaluation of 235 international laboratories conducting water isotope analyses by isotope-ratio and laser-absorption spectrometry","interactions":[],"lastModifiedDate":"2018-02-22T14:05:58","indexId":"70195559","displayToPublicDate":"2018-02-22T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3233,"text":"Rapid Communications in Mass Spectrometry","active":true,"publicationSubtype":{"id":10}},"title":"Seeking excellence: An evaluation of 235 international laboratories conducting water isotope analyses by isotope-ratio and laser-absorption spectrometry","docAbstract":"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":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","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":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.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175142","usgsCitation":"Gordon, D.W., and Painter, J.A., 2018, Groundwater conditions in Georgia, 2015–16: U.S. Geological Survey Scientific Investigations Report 2017–5142, 59 p., https://doi.org/10.3133/sir20175142.","productDescription":"iv, 59 p.","numberOfPages":"67","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-088486","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":351599,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5142/coverthb.jpg"},{"id":351600,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5142/sir20175142.pdf","text":"Report","size":"8.09 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5142"}],"country":"United 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\"}}]}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Columbia, SC 29210
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
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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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 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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, 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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":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":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","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":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true}],"preferred":true,"id":729031,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70195515,"text":"70195515 - 2018 - Contaminants of emerging concern presence and adverse effects in fish: A case study in the Laurentian Great Lakes","interactions":[],"lastModifiedDate":"2018-03-26T11:55:57","indexId":"70195515","displayToPublicDate":"2018-02-20T00:00:00","publicationYear":"2018","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":"Contaminants of emerging concern presence and adverse effects in fish: A case study in the Laurentian Great Lakes","docAbstract":"The Laurentian Great Lakes are a valuable natural resource that is affected by contaminants of emerging concern (CECs), including sex steroid hormones, personal care products, pharmaceuticals, industrial chemicals, and new generation pesticides. However, little is known about the fate and biological effects of CECs in tributaries to the Great Lakes. In the current study, 16 sites on three rivers in the Great Lakes basin (Fox, Cuyahoga, and Raquette Rivers) were assessed for CEC presence using polar organic chemical integrative samplers (POCIS) and grab water samplers. Biological activity was assessed through a combination of in vitro bioassays (focused on estrogenic activity) and in vivo assays with larval fathead minnows. In addition, resident sunfish, largemouth bass, and white suckers were assessed for changes in\nbiological endpoints associated with CEC exposure. CECs were present in all water samples and POCIS extracts. A total of 111 and 97 chemicals were detected in at least one water sample and POCIS extract, respectively. Known estrogenic chemicals were detected in water samples at all 16 sites and in POCIS extracts at 13 sites. Most sites elicited estrogenic activity in bioassays. Ranking sites and rivers based on water chemistry, POCIS chemistry, or total in vitro estrogenicity produced comparable patterns with the Cuyahoga River ranking as most and the Raquette River as least affected by CECs. Changes in biological responses grouped according to physiological processes, and differed between species but not sex. The Fox and Cuyahoga Rivers often had significantly different patterns in biological response Our study supports the need for multiple lines of evidence and provides a framework to assess CEC presence and\neffects in fish in the Laurentian Great Lakes basin.","language":"English","publisher":"ScienceDirect","doi":"10.1016/j.envpol.2018.01.070","usgsCitation":"Jorgenson, Z.G., Thomas, L., Elliott, S.M., Cavallin, J.E., Randolph, E.C., Choy, S.J., Alvarez, D., Banda, J.A., Gefell, D.J., Lee, K., Furlong, E.T., and Schoenfuss, H.L., 2018, Contaminants of emerging concern presence and adverse effects in fish: A case study in the Laurentian Great Lakes: Environmental Pollution, v. 236, p. 718-733, https://doi.org/10.1016/j.envpol.2018.01.070.","productDescription":"16 p.","startPage":"718","endPage":"733","ipdsId":"IP-090883","costCenters":[{"id":392,"text":"Minnesota Water Science 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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":728966,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thomas, Linnea M.","contributorId":146311,"corporation":false,"usgs":false,"family":"Thomas","given":"Linnea M.","affiliations":[],"preferred":false,"id":728967,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":728965,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cavallin, Jenna E.","contributorId":146304,"corporation":false,"usgs":false,"family":"Cavallin","given":"Jenna","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":728969,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Randolph, Eric C.","contributorId":202582,"corporation":false,"usgs":false,"family":"Randolph","given":"Eric","email":"","middleInitial":"C.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":728970,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":728971,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Alvarez, David 0000-0002-6918-2709 dalvarez@usgs.gov","orcid":"https://orcid.org/0000-0002-6918-2709","contributorId":150499,"corporation":false,"usgs":true,"family":"Alvarez","given":"David","email":"dalvarez@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":728972,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Banda, Jo A.","contributorId":196761,"corporation":false,"usgs":false,"family":"Banda","given":"Jo","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":728973,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"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":728974,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"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":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":728975,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Furlong, Edward T. 0000-0002-7305-4603 efurlong@usgs.gov","orcid":"https://orcid.org/0000-0002-7305-4603","contributorId":740,"corporation":false,"usgs":true,"family":"Furlong","given":"Edward","email":"efurlong@usgs.gov","middleInitial":"T.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":728976,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"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":728968,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70195514,"text":"70195514 - 2018 - On the exchange of sensible and latent heat between the atmosphere and melting snow","interactions":[],"lastModifiedDate":"2018-02-20T10:13:24","indexId":"70195514","displayToPublicDate":"2018-02-20T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":681,"text":"Agricultural and Forest Meteorology","active":true,"publicationSubtype":{"id":10}},"title":"On the exchange of sensible and latent heat between the atmosphere and melting snow","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":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":70191345,"text":"tm6A58 - 2018 - Volume-weighted particle-tracking method for solute-transport modeling; Implementation in MODFLOW–GWT","interactions":[],"lastModifiedDate":"2019-08-21T11:39:08","indexId":"tm6A58","displayToPublicDate":"2018-02-16T13:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"6-A58","title":"Volume-weighted particle-tracking method for solute-transport modeling; Implementation in MODFLOW–GWT","docAbstract":"In the traditional method of characteristics for groundwater solute-transport models, advective transport is represented by moving particles that track concentration. This approach can lead to global mass-balance problems because in models of aquifers having complex boundary conditions and heterogeneous properties, particles can originate in cells having different pore volumes and (or) be introduced (or removed) at cells representing fluid sources (or sinks) of varying strengths. Use of volume-weighted particles means that each particle tracks solute mass. In source or sink cells, the changes in particle weights will match the volume of water added or removed through external fluxes. This enables the new method to conserve mass in source or sink cells as well as globally. This approach also leads to potential efficiencies by allowing the number of particles per cell to vary spatially—using more particles where concentration gradients are high and fewer where gradients are low. The approach also eliminates the need for the model user to have to distinguish between “weak” and “strong” fluid source (or sink) cells. The new model determines whether solute mass added by fluid sources in a cell should be represented by (1) new particles having weights representing appropriate fractions of the volume of water added by the source, or (2) distributing the solute mass added over all particles already in the source cell. The first option is more appropriate for the condition of a strong source; the latter option is more appropriate for a weak source. At sinks, decisions whether or not to remove a particle are replaced by a reduction in particle weight in proportion to the volume of water removed. A number of test cases demonstrate that the new method works well and conserves mass. The method is incorporated into a new version of the U.S. Geological Survey’s MODFLOW–GWT solute-transport model.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section A: Groundwater in Book 6: Modeling techniques","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm6A58","usgsCitation":"Winston, R.B., Konikow, L.F., and Hornberger, G.Z., 2018, Volume-weighted particle-tracking method for solute-transport modeling; Implementation in MODFLOW–GWT: U.S. Geological Survey Techniques and Methods, book 6, chap. A58, 44 p., https://doi.org/10.3133/tm6A58.","productDescription":"v, 44 p.","numberOfPages":"54","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-082949","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":351613,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/06/a58/coverthb.jpg"},{"id":351614,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/06/a58/tm6a58.pdf","text":"Report","size":"2.91 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 6-A58"}],"publicComments":"This report is Chapter 58 of Section A: Groundwater in Book 6 Modeling techniques.","contact":"Director, Integrated Modeling and Prediction Division
U.S. Geological Survey
MS 415 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
Human exposure to volatile organic compounds (VOCs) via vapor intrusion (VI) is an emerging public health concern with notable detrimental impacts on public health. Phytoforensics, plant sampling to semi-quantitatively delineate subsurface contamination, provides a potential non-invasive screening approach to detect VI potential, and plant sampling is effective and also time- and cost-efficient. Existing VI assessment methods are time- and resource-intensive, invasive, and require access into residential and commercial buildings to drill holes through basement slabs to install sampling ports or require substantial equipment to install groundwater or soil vapor sampling outside the home. Tree-core samples collected in 2 days at the PCE Southeast Contamination Site in York, Nebraska were analyzed for tetrachloroethene (PCE) and results demonstrated positive correlations with groundwater, soil, soil-gas, sub-slab, and indoor-air samples collected over a 2-year period. Because tree-core samples were not collocated with other samples, interpolated surfaces of PCE concentrations were estimated so that comparisons could be made between pairs of data. Results indicate moderate to high correlation with average indoor-air and sub-slab PCE concentrations over long periods of time (months to years) to an interpolated tree-core PCE concentration surface, with Spearman’s correlation coefficients (ρ) ranging from 0.31 to 0.53 that are comparable to the pairwise correlation between sub-slab and indoor-air PCE concentrations (ρ = 0.55, n = 89). Strong correlations between soil-gas, sub-slab, and indoor-air PCE concentrations and an interpolated tree-core PCE concentration surface indicate that trees are valid indicators of potential VI and human exposure to subsurface environment pollutants. The rapid and non-invasive nature of tree sampling are notable advantages: even with less than 60 trees in the vicinity of the source area, roughly 12 hours of tree-core sampling with minimal equipment at the PCE Southeast Contamination Site was sufficient to delineate vapor intrusion potential in the study area and offered comparable delineation to traditional sub-slab sampling performed at 140 properties over a period of approximately 2 years.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0193247","usgsCitation":"Wilson, J.L., Samaranayake, V., Limmer, M.A., and Burken, J.G., 2018, Phytoforensics: Trees as bioindicators of potential indoor exposure via vapor intrusion: PLoS ONE, v. 13, no. 2, p. 1-17, https://doi.org/10.1371/journal.pone.0193247.","productDescription":"e0193247; 17 p.","startPage":"1","endPage":"17","ipdsId":"IP-085495","costCenters":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"links":[{"id":468991,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0193247","text":"Publisher Index Page"},{"id":438010,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7CF9P06","text":"USGS data release","linkHelpText":"Concentrations of tetrachloroethylene in tree-core, groundwater, soil, soil-gas, indoor-air, and sub-slab samples from the Tetrachloroethene Southeast Contamination Site in York, Nebraska, 2014-2016."},{"id":351746,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nebraska","city":"York","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.595,\n 40.87\n ],\n [\n -97.575,\n 40.87\n ],\n [\n -97.575,\n 40.8639\n ],\n [\n -97.595,\n 40.8639\n ],\n [\n -97.595,\n 40.87\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-16","publicationStatus":"PW","scienceBaseUri":"5afee72be4b0da30c1bfc168","contributors":{"authors":[{"text":"Wilson, Jordan L. 0000-0003-0490-9062 jlwilson@usgs.gov","orcid":"https://orcid.org/0000-0003-0490-9062","contributorId":5416,"corporation":false,"usgs":true,"family":"Wilson","given":"Jordan","email":"jlwilson@usgs.gov","middleInitial":"L.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":728825,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Samaranayake, V.A. 0000-0002-1892-8363","orcid":"https://orcid.org/0000-0002-1892-8363","contributorId":201176,"corporation":false,"usgs":false,"family":"Samaranayake","given":"V.A.","email":"","affiliations":[],"preferred":false,"id":728826,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Limmer, Matthew A.","contributorId":200927,"corporation":false,"usgs":false,"family":"Limmer","given":"Matthew","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":728827,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Burken, Joel G.","contributorId":21218,"corporation":false,"usgs":true,"family":"Burken","given":"Joel","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":728828,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70195463,"text":"70195463 - 2018 - Analysis of vegetation recovery surrounding a restored wetland using the normalized difference infrared index (NDII) and normalized difference vegetation index (NDVI)","interactions":[],"lastModifiedDate":"2018-02-16T10:33:21","indexId":"70195463","displayToPublicDate":"2018-02-16T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2068,"text":"International Journal of Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Analysis of vegetation recovery surrounding a restored wetland using the normalized difference infrared index (NDII) and normalized difference vegetation index (NDVI)","docAbstract":"Watershed restoration efforts seek to rejuvenate vegetation, biological diversity, and land productivity at Cienega San Bernardino, an important wetland in southeastern Arizona and northern Sonora, Mexico. Rock detention and earthen berm structures were built on the Cienega San Bernardino over the course of four decades, beginning in 1984 and continuing to the present. Previous research findings show that restoration supports and even increases vegetation health despite ongoing drought conditions in this arid watershed. However, the extent of restoration impacts is still unknown despite qualitative observations of improvement in surrounding vegetation amount and vigor. We analyzed spatial and temporal trends in vegetation greenness and soil moisture by applying the normalized difference vegetation index (NDVI) and normalized difference infrared index (NDII) to one dry summer season Landsat path/row from 1984 to 2016. The study area was divided into zones and spectral data for each zone was analyzed and compared with precipitation record using statistical measures including linear regression, Mann– Kendall test, and linear correlation. NDVI and NDII performed differently due to the presence of continued grazing and the effects of grazing on canopy cover; NDVI was better able to track changes in vegetation in areas without grazing while NDII was better at tracking changes in areas with continued grazing. Restoration impacts display higher greenness and vegetation water content levels, greater increases in greenness and water content through time, and a decoupling of vegetation greenness and water content from spring precipitation when compared to control sites in nearby tributary and upland areas. Our results confirm the potential of erosion control structures to affect areas up to 5 km downstream of restoration sites over time and to affect 1 km upstream of the sites.","language":"English","publisher":"Taylor & Francis","doi":"10.1080/01431161.2018.1437297","usgsCitation":"Wilson, N., and Norman, L., 2018, Analysis of vegetation recovery surrounding a restored wetland using the normalized difference infrared index (NDII) and normalized difference vegetation index (NDVI): International Journal of Remote Sensing, v. 39, no. 10, p. 3243-3274, https://doi.org/10.1080/01431161.2018.1437297.","productDescription":"30 p.","startPage":"3243","endPage":"3274","ipdsId":"IP-087663","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":468994,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/01431161.2018.1437297","text":"Publisher Index Page"},{"id":438011,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F798867T","text":"USGS data release","linkHelpText":"Data Release for Analysis of Vegetation Recovery Surrounding a Restored Wetland using the Normalized Difference Infrared Index (NDII) and Normalized Difference Vegetation Index (NDVI)"},{"id":351692,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","otherGeospatial":"Cuenca Los Ojos, San Bernardino National Wildlife Refuge, San Bernadino Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.51995849609375,\n 31.049404461655996\n ],\n [\n -108.93630981445312,\n 31.049404461655996\n ],\n [\n -108.93630981445312,\n 31.468496379205966\n ],\n [\n -109.51995849609375,\n 31.468496379205966\n ],\n [\n -109.51995849609375,\n 31.049404461655996\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"39","issue":"10","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-12","publicationStatus":"PW","scienceBaseUri":"5afee72be4b0da30c1bfc16e","contributors":{"authors":[{"text":"Wilson, Natalie R. 0000-0001-5145-1221","orcid":"https://orcid.org/0000-0001-5145-1221","contributorId":202534,"corporation":false,"usgs":true,"family":"Wilson","given":"Natalie R.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":728707,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Norman, Laura","contributorId":202535,"corporation":false,"usgs":true,"family":"Norman","given":"Laura","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":728708,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70195402,"text":"ofr20171166 - 2018 - Landsat classification of surface-water presence during multiple years to assess response of playa wetlands to climatic variability across the Great Plains Landscape Conservation Cooperative region","interactions":[],"lastModifiedDate":"2022-04-22T16:28:36.224437","indexId":"ofr20171166","displayToPublicDate":"2018-02-15T16:30:00","publicationYear":"2018","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":"2017-1166","title":"Landsat classification of surface-water presence during multiple years to assess response of playa wetlands to climatic variability across the Great Plains Landscape Conservation Cooperative region","docAbstract":"To improve understanding of the distribution of ecologically important, ephemeral wetland habitats across the Great Plains, the occurrence and distribution of surface water in playa wetland complexes were documented for four different years across the Great Plains Landscape Conservation Cooperative (GPLCC) region. This information is important because it informs land and wildlife managers about the timing and location of habitat availability. Data with an accurate timestamp that indicate the presence of water, the percent of the area inundated with water, and the spatial distribution of playa wetlands with water are needed for a host of resource inventory, monitoring, and research applications. For example, the distribution of inundated wetlands forms the spatial pattern of available habitat for resident shorebirds and water birds, stop-over habitats for migratory birds, connectivity and clustering of wetland habitats, and surface waters that recharge the Ogallala aquifer; there is considerable variability in the distribution of playa wetlands holding water through time. Documentation of these spatially and temporally intricate processes, here, provides data required to assess connections between inundation and multiple environmental drivers, such as climate, land use, soil, and topography. Climate drivers are understood to interact with land cover, land use and soil attributes in determining the amount of water that flows overland into playa wetlands. Results indicated significant spatial variability represented by differences in the percent of playas inundated among States within the GPLCC. Further, analysis-of-variance comparison of differences in inundation between years showed significant differences in all cases. Although some connections with seasonal moisture patterns may be observed, the complex spatial-temporal gradients of precipitation, temperature, soils, and land use need to be combined as covariates in multivariate models to effectively account for these patterns. We demonstrate the feasibility of using classification of Landsat satellite imagery to describe playa-wetland inundation across years and seasons. Evaluating classifications representing only 4 years of imagery, we found significant year-to-year and state-to-state differences in inundation rates.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171166","collaboration":"Prepared in cooperation with the Great Plains Landscape Conservation Cooperative, U.S. Fish and Wildlife Service, Albuquerque, New Mexico","usgsCitation":"Manier, D.J., and Rover, J.R., 2018, Landsat classification of surface-water presence during multiple years to assess response of playa wetlands to climatic variability across the Great Plains Landscape Conservation Cooperative region: U.S. Geological Survey Open-File Report 2017–1166, 20 p., https://doi.org/10.3133/ofr20171166.","productDescription":"iv, 20 p.","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-073569","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":438012,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7MW2GCN","text":"USGS data release","linkHelpText":"Landsat classification of surface water for multiple seasons to monitor inundation of playa wetlands"},{"id":351569,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1166/ofr20171166.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1166"},{"id":351568,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1166/coverthb.jpg"}],"country":"United States","otherGeospatial":"Great Plains Landscape Conservation Cooperative","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106,\n 30\n ],\n [\n -96,\n 30\n ],\n [\n -96,\n 44\n ],\n [\n -106,\n 44\n ],\n [\n -106,\n 30\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Building C
Fort Collins, CO 80526-8118
Identifying the sources of methylmercury (MeHg) and tracing the transformations of mercury (Hg) in the aquatic food web are important components of effective strategies for managing current and legacy Hg sources. In our previous work, we measured stable isotopes of Hg (δ202Hg, Δ199Hg, and Δ200Hg) in the Laurentian Great Lakes and estimated source contributions of Hg to bottom sediment. Here, we identify isotopically distinct Hg signatures for Great Lakes trout (Salvelinus namaycush) and walleye (Sander vitreus), driven by both food-web and water-quality characteristics. Fish contain high values for odd-isotope mass independent fractionation (MIF) with averages ranging from 2.50 (western Lake Erie) to 6.18‰ (Lake Superior) in Δ199Hg. The large range in odd-MIF reflects variability in the depth of the euphotic zone, where Hg is most likely incorporated into the food web. Even-isotope MIF (Δ200Hg), a potential tracer for Hg from precipitation, appears both disconnected from lake sedimentary sources and comparable in fish among the five lakes. We suggest that similar to the open ocean, water-column methylation also occurs in the Great Lakes, possibly transforming recently deposited atmospheric Hg deposition. We conclude that the degree of photochemical processing of Hg is controlled by phytoplankton uptake rather than by dissolved organic carbon quantity among lakes.
","language":"English","publisher":"ACS","doi":"10.1021/acs.est.7b06120","usgsCitation":"Lepak, R., Janssen, S., Yin, R., Ogorek, J.M., Krabbenhoft, D., DeWild, J.F., Tate, M., Holsen, T.M., and Hurley, J., 2018, Factors affecting mercury stable isotopic distribution in piscivorous fish of the Laurentian Great Lakes: Environmental Science & Technology, v. 52, no. 5, p. 2768-2776, https://doi.org/10.1021/acs.est.7b06120.","productDescription":"9 p.","startPage":"2768","endPage":"2776","ipdsId":"IP-094499","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":359865,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"52","issue":"5","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-15","publicationStatus":"PW","scienceBaseUri":"5c064ee3e4b0815414cecb12","contributors":{"editors":[{"text":"Janssen, Sarah E. 0000-0003-4432-3154","orcid":"https://orcid.org/0000-0003-4432-3154","contributorId":210991,"corporation":false,"usgs":true,"family":"Janssen","given":"Sarah E.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":752911,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Yin, Runsheng","contributorId":150057,"corporation":false,"usgs":false,"family":"Yin","given":"Runsheng","email":"","affiliations":[{"id":17896,"text":"State Key Laboratory of Ore Deposit Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang, China","active":true,"usgs":false}],"preferred":false,"id":752910,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Ogorek, Jacob M. 0000-0002-6327-0740 jmogorek@usgs.gov","orcid":"https://orcid.org/0000-0002-6327-0740","contributorId":4960,"corporation":false,"usgs":true,"family":"Ogorek","given":"Jacob","email":"jmogorek@usgs.gov","middleInitial":"M.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":752912,"contributorType":{"id":2,"text":"Editors"},"rank":5},{"text":"DeWild, John F. 0000-0003-4097-2798 jfdewild@usgs.gov","orcid":"https://orcid.org/0000-0003-4097-2798","contributorId":2525,"corporation":false,"usgs":true,"family":"DeWild","given":"John","email":"jfdewild@usgs.gov","middleInitial":"F.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science 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E.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":752954,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yin, Runsheng","contributorId":150057,"corporation":false,"usgs":false,"family":"Yin","given":"Runsheng","email":"","affiliations":[{"id":17896,"text":"State Key Laboratory of Ore Deposit Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang, China","active":true,"usgs":false}],"preferred":false,"id":752953,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ogorek, Jacob M. 0000-0002-6327-0740 jmogorek@usgs.gov","orcid":"https://orcid.org/0000-0002-6327-0740","contributorId":4960,"corporation":false,"usgs":true,"family":"Ogorek","given":"Jacob","email":"jmogorek@usgs.gov","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":752948,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Krabbenhoft, David P. 0000-0003-1964-5020 dpkrabbe@usgs.gov","orcid":"https://orcid.org/0000-0003-1964-5020","contributorId":118001,"corporation":false,"usgs":true,"family":"Krabbenhoft","given":"David P.","email":"dpkrabbe@usgs.gov","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":false,"id":752947,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"DeWild, John F. 0000-0003-4097-2798 jfdewild@usgs.gov","orcid":"https://orcid.org/0000-0003-4097-2798","contributorId":2525,"corporation":false,"usgs":true,"family":"DeWild","given":"John","email":"jfdewild@usgs.gov","middleInitial":"F.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":752949,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Tate, Michael T. 0000-0003-1525-1219 mttate@usgs.gov","orcid":"https://orcid.org/0000-0003-1525-1219","contributorId":3144,"corporation":false,"usgs":true,"family":"Tate","given":"Michael T.","email":"mttate@usgs.gov","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":752950,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Holsen, Thomas M.","contributorId":150058,"corporation":false,"usgs":false,"family":"Holsen","given":"Thomas","email":"","middleInitial":"M.","affiliations":[{"id":17897,"text":"Department of Civil and Environmental Engineering, Clarkson University, Potsdam, New York","active":true,"usgs":false}],"preferred":false,"id":752951,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hurley, James P.","contributorId":147931,"corporation":false,"usgs":false,"family":"Hurley","given":"James P.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":752952,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70195431,"text":"70195431 - 2018 - Consortial brown tide − picocyanobacteria blooms in Guantánamo Bay, Cuba","interactions":[],"lastModifiedDate":"2018-02-15T10:12:49","indexId":"70195431","displayToPublicDate":"2018-02-15T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1878,"text":"Harmful Algae","active":true,"publicationSubtype":{"id":10}},"title":"Consortial brown tide − picocyanobacteria blooms in Guantánamo Bay, Cuba","docAbstract":"A brown tide bloom of Aureoumbra lagunensis developed in Guantánamo Bay, Cuba during a period of drought in 2013 that followed heavy winds and rainfall from Hurricane Sandy in late October 2012. Based on satellite images and water turbidity measurements, the bloom appeared to initiate in January 2013. The causative species (A. lagunensis) was confirmed by microscopic observation, and pigment and genetic analyses of bloom samples collected on May 28 of that year. During that time, A. lagunensis reached concentrations of 900,000 cells ml−1 (28 ppm by biovolume) in the middle portion of the Bay. Samples could not be collected from the northern (Cuban) half of the Bay because of political considerations. Subsequent sampling of the southern half of the Bay in November 2013, April 2014, and October 2014 showed persistent lower concentrations of A. lagunensis, with dominance shifting to the cyanobacterium Synechococcus (up to 33 ppm in April), an algal group that comprised a minor bloom component on May 28. Thus, unlike the brown tide bloom in Laguna Madre, which lasted 8 years, the bloom in Guantánamo Bay was short-lived, much like recent blooms in the Indian River, Florida. Although hypersaline conditions have been linked to brown tide development in the lagoons of Texas and Florida, observed euhaline conditions in Guantánamo Bay (salinity 35–36) indicate that strong hypersalinity is not a requirement for A. lagunensis bloom formation. Microzooplankton biomass dominated by ciliates was high during the observed peak of the brown tide, and ciliate abundance was high compared to other systems not impacted by brown tide. Preferential grazing by zooplankton on non-brown tide species, as shown in A. lagunensis blooms in Texas and Florida, may have been a factor in the development of the Cuban brown tide bloom. However, subsequent selection of microzooplankton capable of utilizing A. lagunensis as a primary food source may have contributed to the short-lived duration of the brown tide bloom in Guantánamo Bay.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.hal.2018.01.003","usgsCitation":"Hall, N.S., Litaker, R.W., Kenworthy, W.J., Vandersea, M.W., Sunda, W.G., Reid, J.P., Slone, D., and Butler, S.M., 2018, Consortial brown tide − picocyanobacteria blooms in Guantánamo Bay, Cuba: Harmful Algae, v. 73, p. 30-43, https://doi.org/10.1016/j.hal.2018.01.003.","productDescription":"14 p.","startPage":"30","endPage":"43","ipdsId":"IP-086188","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":468996,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.hal.2018.01.003","text":"Publisher Index Page"},{"id":351644,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Cuba","otherGeospatial":"Guantánamo Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.234375,\n 19.883620357758062\n ],\n [\n -75.0918960571289,\n 19.883620357758062\n ],\n [\n -75.0918960571289,\n 19.973348786110602\n ],\n [\n -75.234375,\n 19.973348786110602\n ],\n [\n -75.234375,\n 19.883620357758062\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"73","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee72ee4b0da30c1bfc186","contributors":{"authors":[{"text":"Hall, Nathan S","contributorId":202494,"corporation":false,"usgs":false,"family":"Hall","given":"Nathan","email":"","middleInitial":"S","affiliations":[{"id":36459,"text":"University of North Carolina at Chapel Hill, Institute of Marine Sciences","active":true,"usgs":false}],"preferred":false,"id":728570,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Litaker, R. 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The Prairie Pothole Region is characterized by millions of small water bodies (or surface-water depressions) that provide numerous ecosystem services and are considered an important contribution to the hydrologic cycle. The Upper Pipestem PRMS model was extracted from the U.S. Geological Survey's (USGS) National Hydrologic Model (NHM), developed to support consistent hydrologic modelling across the conterminous United States. The Geospatial Fabric database, created for the USGS NHM, contains hydrologic model parameter values derived from datasets that characterize the physical features of the entire conterminous United States for 109,951 hydrologic response units. Each hydrologic response unit in the Geospatial Fabric was parameterized using aggregated surface-water depression area derived from the National Hydrography Dataset Plus, an integrated suite of application-ready geospatial datasets. This paper presents a calibration strategy for the Upper Pipestem PRMS model that uses normalized lake elevation measurements to calibrate the parameters influencing simulated fractional surface-water depression storage. Results indicate that inclusion of measurements that give an indication of the change in surface-water depression storage in the calibration procedure resulted in accurate changes in surface-water depression storage in the water balance. Regionalized parameterization of the USGS NHM will require a proxy for change in surface-storage to accurately parameterize surface-water depression storage within the USGS NHM.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.11416","usgsCitation":"Hay, L.E., Norton, P.A., Viger, R.J., Markstrom, S.L., Regan, R.S., and Vanderhoof, M.K., 2018, Modelling surface-water depression storage in a Prairie Pothole Region: Hydrological Processes, v. 32, no. 4, p. 462-479, https://doi.org/10.1002/hyp.11416.","productDescription":"18 p.","startPage":"462","endPage":"479","ipdsId":"IP-080013","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":352175,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota","otherGeospatial":"Upper Pipestem Creek basin","volume":"32","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-08","publicationStatus":"PW","scienceBaseUri":"5afee72de4b0da30c1bfc182","contributors":{"authors":[{"text":"Hay, Lauren E. 0000-0003-3763-4595 lhay@usgs.gov","orcid":"https://orcid.org/0000-0003-3763-4595","contributorId":1287,"corporation":false,"usgs":true,"family":"Hay","given":"Lauren","email":"lhay@usgs.gov","middleInitial":"E.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":729974,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Norton, Parker A. 0000-0002-4638-2601 pnorton@usgs.gov","orcid":"https://orcid.org/0000-0002-4638-2601","contributorId":2257,"corporation":false,"usgs":true,"family":"Norton","given":"Parker","email":"pnorton@usgs.gov","middleInitial":"A.","affiliations":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":729978,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Viger, Roland J. 0000-0003-2520-714X rviger@usgs.gov","orcid":"https://orcid.org/0000-0003-2520-714X","contributorId":168799,"corporation":false,"usgs":true,"family":"Viger","given":"Roland","email":"rviger@usgs.gov","middleInitial":"J.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":729976,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Markstrom, Steven L. 0000-0001-7630-9547 markstro@usgs.gov","orcid":"https://orcid.org/0000-0001-7630-9547","contributorId":146553,"corporation":false,"usgs":true,"family":"Markstrom","given":"Steven","email":"markstro@usgs.gov","middleInitial":"L.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":729977,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Regan, R. Steve 0000-0003-4803-8596 rsregan@usgs.gov","orcid":"https://orcid.org/0000-0003-4803-8596","contributorId":196973,"corporation":false,"usgs":true,"family":"Regan","given":"R.","email":"rsregan@usgs.gov","middleInitial":"Steve","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":729975,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vanderhoof, Melanie K. 0000-0002-0101-5533 mvanderhoof@usgs.gov","orcid":"https://orcid.org/0000-0002-0101-5533","contributorId":168395,"corporation":false,"usgs":true,"family":"Vanderhoof","given":"Melanie","email":"mvanderhoof@usgs.gov","middleInitial":"K.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":729979,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70191170,"text":"sir20175108 - 2018 - Stream-channel and watershed delineations and basin-characteristic measurements using lidar elevation data for small drainage basins within the Des Moines Lobe landform region in Iowa","interactions":[],"lastModifiedDate":"2018-02-14T15:01:18","indexId":"sir20175108","displayToPublicDate":"2018-02-14T13:00:00","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":"2017-5108","title":"Stream-channel and watershed delineations and basin-characteristic measurements using lidar elevation data for small drainage basins within the Des Moines Lobe landform region in Iowa","docAbstract":"Basin-characteristic measurements related to stream length, stream slope, stream density, and stream order have been identified as significant variables for estimation of flood, flow-duration, and low-flow discharges in Iowa. The placement of channel initiation points, however, has always been a matter of individual interpretation, leading to differences in stream definitions between analysts.
This study investigated five different methods to define stream initiation using 3-meter light detection and ranging (lidar) digital elevation models (DEMs) data for 17 streamgages with drainage areas less than 50 square miles within the Des Moines Lobe landform region in north-central Iowa. Each DEM was hydrologically enforced and the five stream initiation methods were used to define channel initiation points and the downstream flow paths. The five different methods to define stream initiation were tested side-by-side for three watershed delineations: (1) the total drainage-area delineation, (2) an effective drainage-area delineation of basins based on a 2-percent annual exceedance probability (AEP) 12-hour rainfall, and (3) an effective drainage-area delineation based on a 20-percent AEP 12-hour rainfall.
Generalized least squares regression analysis was used to develop a set of equations for sites in the Des Moines Lobe landform region for estimating discharges for ungaged stream sites with 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent AEPs. A total of 17 streamgages were included in the development of the regression equations. In addition, geographic information system software was used to measure 58 selected basin-characteristics for each streamgage.
Results of the regression analyses of the 15 lidar datasets indicate that the datasets that produce regional regression equations (RREs) with the best overall predictive accuracy are the National Hydrographic Dataset, Iowa Department of Natural Resources, and profile curvature of 0.5 stream initiation methods combined with the 20-percent AEP 12-hour rainfall watershed delineation method. These RREs have a mean average standard error of prediction (SEP) for 4-, 2-, and 1-percent AEP discharges of 53.9 percent and a mean SEP for all eight AEPs of 55.5 percent. Compared to the RREs developed in this study using the basin characteristics from the U.S. Geological Survey StreamStats application, the lidar basin characteristics provide better overall predictive accuracy.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175108","collaboration":"Prepared in cooperation with the Iowa Department of Transportation and the Iowa Highway Research Board (Project TR–692) ","usgsCitation":"Eash, D.A., Barnes, K.K., O’Shea, P.S., and Gelder, B.K., 2018, Stream-channel and watershed delineations and basin-characteristic measurements using lidar elevation data for small drainage basins within the Des Moines Lobe landform region in Iowa: U.S. Geological Survey Scientific Investigations Report 2017–5108, 23 p., https://doi.org/10.3133/sir20175108. ","productDescription":"vi, 23 p.","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-081688","costCenters":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"links":[{"id":351551,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5108/coverthb.jpg"},{"id":351552,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5108/sir20175108.pdf","text":"Report","size":"1.48 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5108"}],"country":"United States","state":"Iowa","otherGeospatial":"Des Moines Lobe landform region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96,\n 41.5\n ],\n [\n -93,\n 41.5\n ],\n [\n -93,\n 43.50075243569041\n ],\n [\n -96,\n 43.50075243569041\n ],\n [\n -96,\n 41.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
400 S. Clinton St., Rm 269
Iowa City, IA 52240