{"pageNumber":"406","pageRowStart":"10125","pageSize":"25","recordCount":46619,"records":[{"id":70176878,"text":"70176878 - 2016 - Mercury and methylmercury in aquatic sediment across western North America","interactions":[],"lastModifiedDate":"2018-08-07T12:23:42","indexId":"70176878","displayToPublicDate":"2016-10-11T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Mercury and methylmercury in aquatic sediment across western North America","docAbstract":"

Large-scale assessments are valuable in identifying primary factors controlling total mercury (THg) and monomethyl mercury (MeHg) concentrations, and distribution in aquatic ecosystems. Bed sediment THg and MeHg concentrations were compiled for > 16,000 samples collected from aquatic habitats throughout the West between 1965 and 2013. The influence of aquatic feature type (canals, estuaries, lakes, and streams), and environmental setting (agriculture, forest, open-water, range, wetland, and urban) on THg and MeHg concentrations was examined. THg concentrations were highest in lake (29.3 ± 6.5 μg kg− 1) and canal (28.6 ± 6.9 μg kg− 1) sites, and lowest in stream (20.7 ± 4.6 μg kg− 1) and estuarine (23.6 ± 5.6 μg kg− 1) sites, which was partially a result of differences in grain size related to hydrologic gradients. By environmental setting, open-water (36.8 ± 2.2 μg kg− 1) and forested (32.0 ± 2.7 μg kg− 1) sites generally had the highest THg concentrations, followed by wetland sites (28.9 ± 1.7 μg kg− 1), rangeland (25.5 ± 1.5 μg kg− 1), agriculture (23.4 ± 2.0 μg kg− 1), and urban (22.7 ± 2.1 μg kg− 1) sites. MeHg concentrations also were highest in lakes (0.55 ± 0.05 μg kg− 1) and canals (0.54 ± 0.11 μg kg− 1), but, in contrast to THg, MeHg concentrations were lowest in open-water sites (0.22 ± 0.03 μg kg− 1). The median percent MeHg (relative to THg) for the western region was 0.7%, indicating an overall low methylation efficiency; however, a significant subset of data (n > 100) had percentages that represent elevated methylation efficiency (> 6%). MeHg concentrations were weakly correlated with THg (r2 = 0.25) across western North America. Overall, these results highlight the large spatial variability in sediment THg and MeHg concentrations throughout western North America and underscore the important roles that landscape and land-use characteristics have on the MeHg cycle.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2016.03.044","usgsCitation":"Fleck, J., Marvin-DiPasquale, M.C., Eagles-Smith, C.A., Ackerman, J., Lutz, M.A., Tate, M., Alpers, C.N., Hall, B.D., Krabbenhoft, D.P., and Eckley, C.S., 2016, Mercury and methylmercury in aquatic sediment across western North America: Science of the Total Environment, v. 568, p. 727-738, https://doi.org/10.1016/j.scitotenv.2016.03.044.","productDescription":"12 p.","startPage":"727","endPage":"738","ipdsId":"IP-070290","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true}],"links":[{"id":470509,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2016.03.044","text":"Publisher Index Page"},{"id":329462,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"568","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57fe679ae4b0824b2d1436f5","chorus":{"doi":"10.1016/j.scitotenv.2016.03.044","url":"http://dx.doi.org/10.1016/j.scitotenv.2016.03.044","publisher":"Elsevier BV","authors":"Fleck Jacob A., Marvin-DiPasquale Mark, Eagles-Smith Collin A., Ackerman Joshua T., Lutz Michelle A., Tate Michael, Alpers Charles N., Hall Britt D., Krabbenhoft David P., Eckley Chris S.","journalName":"Science of The Total Environment","publicationDate":"10/2016"},"contributors":{"authors":[{"text":"Fleck, Jacob 0000-0002-3217-3972 jafleck@usgs.gov","orcid":"https://orcid.org/0000-0002-3217-3972","contributorId":168694,"corporation":false,"usgs":true,"family":"Fleck","given":"Jacob","email":"jafleck@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":650582,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Marvin-DiPasquale, Mark C. 0000-0002-8186-9167 mmarvin@usgs.gov","orcid":"https://orcid.org/0000-0002-8186-9167","contributorId":1485,"corporation":false,"usgs":true,"family":"Marvin-DiPasquale","given":"Mark","email":"mmarvin@usgs.gov","middleInitial":"C.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":650583,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eagles-Smith, Collin A. 0000-0003-1329-5285 ceagles-smith@usgs.gov","orcid":"https://orcid.org/0000-0003-1329-5285","contributorId":505,"corporation":false,"usgs":true,"family":"Eagles-Smith","given":"Collin","email":"ceagles-smith@usgs.gov","middleInitial":"A.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"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":650584,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ackerman, Joshua T. 0000-0002-3074-8322 jackerman@usgs.gov","orcid":"https://orcid.org/0000-0002-3074-8322","contributorId":147078,"corporation":false,"usgs":true,"family":"Ackerman","given":"Joshua T.","email":"jackerman@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":650585,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lutz, Michelle A. malutz@usgs.gov","contributorId":167259,"corporation":false,"usgs":true,"family":"Lutz","given":"Michelle","email":"malutz@usgs.gov","middleInitial":"A.","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":650586,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":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":650587,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Alpers, Charles N. 0000-0001-6945-7365 cnalpers@usgs.gov","orcid":"https://orcid.org/0000-0001-6945-7365","contributorId":411,"corporation":false,"usgs":true,"family":"Alpers","given":"Charles","email":"cnalpers@usgs.gov","middleInitial":"N.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":650588,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hall, Britt D.","contributorId":27161,"corporation":false,"usgs":true,"family":"Hall","given":"Britt","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":650589,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Krabbenhoft, David P. 0000-0003-1964-5020 dpkrabbe@usgs.gov","orcid":"https://orcid.org/0000-0003-1964-5020","contributorId":1658,"corporation":false,"usgs":true,"family":"Krabbenhoft","given":"David","email":"dpkrabbe@usgs.gov","middleInitial":"P.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":650590,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Eckley, Chris S.","contributorId":167256,"corporation":false,"usgs":false,"family":"Eckley","given":"Chris","email":"","middleInitial":"S.","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":650591,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70176849,"text":"70176849 - 2016 - High resolution mapping of development in the wildland-urban interface using object based image extraction","interactions":[],"lastModifiedDate":"2016-10-11T10:58:51","indexId":"70176849","displayToPublicDate":"2016-10-11T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5211,"text":"Heliyon","active":true,"publicationSubtype":{"id":10}},"title":"High resolution mapping of development in the wildland-urban interface using object based image extraction","docAbstract":"

The wildland-urban interface (WUI), the area where human development encroaches on undeveloped land, is expanding throughout the western United States resulting in increased wildfire risk to homes and communities. Although census based mapping efforts have provided insights into the pattern of development and expansion of the WUI at regional and national scales, these approaches do not provide sufficient detail for fine-scale fire and emergency management planning, which requires maps of individual building locations. Although fine-scale maps of the WUI have been developed, they are often limited in their spatial extent, have unknown accuracies and biases, and are costly to update over time. In this paper we assess a semi-automated Object Based Image Analysis (OBIA) approach that utilizes 4-band multispectral National Aerial Image Program (NAIP) imagery for the detection of individual buildings within the WUI. We evaluate this approach by comparing the accuracy and overall quality of extracted buildings to a building footprint control dataset. In addition, we assessed the effects of buffer distance, topographic conditions, and building characteristics on the accuracy and quality of building extraction. The overall accuracy and quality of our approach was positively related to buffer distance, with accuracies ranging from 50 to 95% for buffer distances from 0 to 100 m. Our results also indicate that building detection was sensitive to building size, with smaller outbuildings (footprints less than 75 m2) having detection rates below 80% and larger residential buildings having detection rates above 90%. These findings demonstrate that this approach can successfully identify buildings in the WUI in diverse landscapes while achieving high accuracies at buffer distances appropriate for most fire management applications while overcoming cost and time constraints associated with traditional approaches. This study is unique in that it evaluates the ability of an OBIA approach to extract highly detailed data on building locations in a WUI setting.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.heliyon.2016.e00174","usgsCitation":"Caggiano, M.D., Tinkham, W.T., Hoffman, C., Cheng, A.S., and Hawbaker, T., 2016, High resolution mapping of development in the wildland-urban interface using object based image extraction: Heliyon, v. 2, no. 10, Article e00174; 19 p., https://doi.org/10.1016/j.heliyon.2016.e00174.","productDescription":"Article e00174; 19 p.","ipdsId":"IP-075187","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":470513,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.heliyon.2016.e00174","text":"Publisher Index Page"},{"id":329419,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107,\n 39\n ],\n [\n -107,\n 41\n ],\n [\n -104,\n 41\n ],\n [\n -104,\n 39\n ],\n [\n -107,\n 39\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"2","issue":"10","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57fe679be4b0824b2d1436fb","contributors":{"authors":[{"text":"Caggiano, Michael D.","contributorId":175232,"corporation":false,"usgs":false,"family":"Caggiano","given":"Michael","email":"","middleInitial":"D.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":650507,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tinkham, Wade T.","contributorId":175233,"corporation":false,"usgs":false,"family":"Tinkham","given":"Wade","email":"","middleInitial":"T.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":650508,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hoffman, Chad ","contributorId":175234,"corporation":false,"usgs":false,"family":"Hoffman","given":"Chad ","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":650509,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cheng, Antony S.","contributorId":175235,"corporation":false,"usgs":false,"family":"Cheng","given":"Antony","email":"","middleInitial":"S.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":650510,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hawbaker, Todd 0000-0003-0930-9154 tjhawbaker@usgs.gov","orcid":"https://orcid.org/0000-0003-0930-9154","contributorId":568,"corporation":false,"usgs":true,"family":"Hawbaker","given":"Todd","email":"tjhawbaker@usgs.gov","affiliations":[{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":650506,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70176859,"text":"70176859 - 2016 - Large-scale changes in bloater growth and condition in Lake Huron","interactions":[],"lastModifiedDate":"2016-10-11T15:14:08","indexId":"70176859","displayToPublicDate":"2016-10-11T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Large-scale changes in bloater growth and condition in Lake Huron","docAbstract":"

Native Bloaters Coregonus hoyi have exhibited multiple strong year-classes since 2005 and now are the most abundant benthopelagic offshore prey fish in Lake Huron, following the crash of nonnative AlewivesAlosa pseudoharengus and substantial declines in nonnative Rainbow Smelt Osmerus mordax. Despite recent recoveries in Bloater abundance, marketable-size (>229 mm) Bloaters remain scarce. We used annual survey data to assess temporal and spatial dynamics of Bloater body condition and lengths at age in the main basin of Lake Huron from 1973 to 2014. Basinwide lengths at age were modeled by cohort for the 1973–2003 year-classes using a von Bertalanffy growth model with time-varying Brody growth coefficient (k) and asymptotic length (\"\") parameters. Median Bloater weights at selected lengths were estimated to assess changes in condition by modeling weight–length relations with an allometric growth model that allowed growth parameters to vary spatially and temporally. Estimated Bloater lengths at age declined 14–24% among ages 4–8 for all year-classes between 1973 and 2004. Estimates of \"\" declined from a peak of 394 mm (1973 year-class) to a minimum of 238 mm (1998 year-class). Observed mean lengths at age in 2014 were at all-time lows, suggesting that year-classes comprising the current Bloater population would have to follow growth trajectories unlike those characterizing the 1973–2003 year-classes to attain marketable size. Furthermore, estimated weights of 250-mm Bloaters (i.e., a large, commercially valuable size-class) declined 17% among all regions from 1976 to 2007. Decreases in body condition of large Bloaters are associated with lower lipid content and may be linked to marked declines in abundance of the amphipodsDiporeia spp. in Lake Huron. We hypothesize that since at least 1976, large Bloaters have become more negatively buoyant and may have incurred an increasingly greater metabolic cost performing diel vertical migrations to prey upon the opossum shrimp Mysis diluviana and zooplankton.

","language":"English","publisher":"Taylor & Francis","doi":"10.1080/00028487.2016.1214176","usgsCitation":"Prichard, C.G., Roseman, E., Keeler, K.M., O’Brien, T.P., and Riley, S.C., 2016, Large-scale changes in bloater growth and condition in Lake Huron: Transactions of the American Fisheries Society, v. 145, no. 6, p. 1241-1251, https://doi.org/10.1080/00028487.2016.1214176.","productDescription":"11 p.","startPage":"1241","endPage":"1251","ipdsId":"IP-074730","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":329461,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lake Huron","volume":"145","issue":"6","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2016-10-07","publicationStatus":"PW","scienceBaseUri":"57fe679ae4b0824b2d1436f7","contributors":{"authors":[{"text":"Prichard, Carson G. cprichard@usgs.gov","contributorId":168429,"corporation":false,"usgs":true,"family":"Prichard","given":"Carson","email":"cprichard@usgs.gov","middleInitial":"G.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":650542,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roseman, Edward F. eroseman@usgs.gov","contributorId":534,"corporation":false,"usgs":true,"family":"Roseman","given":"Edward F.","email":"eroseman@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":650541,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Keeler, Kevin M. 0000-0002-8118-0060 kkeeler@usgs.gov","orcid":"https://orcid.org/0000-0002-8118-0060","contributorId":4377,"corporation":false,"usgs":true,"family":"Keeler","given":"Kevin","email":"kkeeler@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":650543,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"O’Brien, Timothy P. 0000-0003-4502-5204 tiobrien@usgs.gov","orcid":"https://orcid.org/0000-0003-4502-5204","contributorId":2662,"corporation":false,"usgs":true,"family":"O’Brien","given":"Timothy","email":"tiobrien@usgs.gov","middleInitial":"P.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":650544,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Riley, Stephen C. 0000-0002-8968-8416 sriley@usgs.gov","orcid":"https://orcid.org/0000-0002-8968-8416","contributorId":2661,"corporation":false,"usgs":true,"family":"Riley","given":"Stephen","email":"sriley@usgs.gov","middleInitial":"C.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":650545,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70184237,"text":"70184237 - 2016 - Evaluating land cover influences on model uncertainties—A case study of cropland carbon dynamics in the Mid-Continent Intensive Campaign region","interactions":[],"lastModifiedDate":"2017-05-09T12:44:23","indexId":"70184237","displayToPublicDate":"2016-10-10T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating land cover influences on model uncertainties—A case study of cropland carbon dynamics in the Mid-Continent Intensive Campaign region","docAbstract":"

Quantifying spatial and temporal patterns of carbon sources and sinks and their uncertainties across agriculture-dominated areas remains challenging for understanding regional carbon cycles. Characteristics of local land cover inputs could impact the regional carbon estimates but the effect has not been fully evaluated in the past. Within the North American Carbon Program Mid-Continent Intensive (MCI) Campaign, three models were developed to estimate carbon fluxes on croplands: an inventory-based model, the Environmental Policy Integrated Climate (EPIC) model, and the General Ensemble biogeochemical Modeling System (GEMS) model. They all provided estimates of three major carbon fluxes on cropland: net primary production (NPP), net ecosystem production (NEP), and soil organic carbon (SOC) change. Using data mining and spatial statistics, we studied the spatial distribution of the carbon fluxes uncertainties and the relationships between the uncertainties and the land cover characteristics. Results indicated that uncertainties for all three carbon fluxes were not randomly distributed, but instead formed multiple clusters within the MCI region. We investigated the impacts of three land cover characteristics on the fluxes uncertainties: cropland percentage, cropland richness and cropland diversity. The results indicated that cropland percentage significantly influenced the uncertainties of NPP and NEP, but not on the uncertainties of SOC change. Greater uncertainties of NPP and NEP were found in counties with small cropland percentage than the counties with large cropland percentage. Cropland species richness and diversity also showed negative correlations with the model uncertainties. Our study demonstrated that the land cover characteristics contributed to the uncertainties of regional carbon fluxes estimates. The approaches we used in this study can be applied to other ecosystem models to identify the areas with high uncertainties and where models can be improved to reduce overall uncertainties for regional carbon flux estimates.

","language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam","doi":"10.1016/j.ecolmodel.2016.07.002","usgsCitation":"Li, Z., Liu, S., Zhang, X., West, T.O., Ogle, S.M., and Zhou, N., 2016, Evaluating land cover influences on model uncertainties—A case study of cropland carbon dynamics in the Mid-Continent Intensive Campaign region: Ecological Modelling, v. 337, p. 176-187, https://doi.org/10.1016/j.ecolmodel.2016.07.002.","productDescription":"12 p.","startPage":"176","endPage":"187","ipdsId":"IP-076132","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":470514,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1398090","text":"Publisher Index Page"},{"id":336836,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Mid-Continent Intensive Campaign 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Texas","interactions":[],"lastModifiedDate":"2017-05-04T10:45:47","indexId":"sim3365","displayToPublicDate":"2016-10-07T13:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3365","title":"Water-level altitudes 2016 and water-level changes in the Chicot, Evangeline, and Jasper aquifers and compaction 1973–2015 in the Chicot and Evangeline aquifers, Houston-Galveston region, Texas","docAbstract":"

Most of the land-surface subsidence in the Houston-Galveston region, Texas, has occurred as a direct result of groundwater withdrawals for municipal supply, commercial and industrial use, and irrigation that depressured and dewatered the Chicot and Evangeline aquifers, thereby causing compaction of the aquifer sediments, mostly in the fine-grained silt and clay layers. This report, prepared by the U.S. Geological Survey in cooperation with the Harris-Galveston Subsidence District, City of Houston, Fort Bend Subsidence District, Lone Star Groundwater Conservation District, and Brazoria County Groundwater Conservation District, is one in an annual series of reports depicting water-level altitudes and water-level changes in the Chicot, Evangeline, and Jasper aquifers and measured cumulative compaction of subsurface sediments in the Chicot and Evangeline aquifers in the Houston-Galveston region. The report contains regional-scale maps depicting approximate 2016 water-level altitudes (represented by measurements made during December 2015–March 2016) for the Chicot, Evangeline, and Jasper aquifers; maps depicting 1-year (2015–16) water-level changes for each aquifer; maps depicting approximate contoured 5-year (2011–16) water-level changes for each aquifer; maps depicting approximate contoured long-term (1990–2016 and 1977–2016) water-level changes for the Chicot and Evangeline aquifers; a map depicting approximate contoured long-term (2000–16) water-level changes for the Jasper aquifer; a map depicting locations of borehole-extensometer sites; and graphs depicting measured long-term cumulative compaction of subsurface sediments at the extensometers during 1973–2015. Tables listing the water-level data used to construct each water-level map for each aquifer and the measured long-term cumulative compaction data for each extensometer site are included. Graphs depicting water-level measurement data also are included; these graphs can be used to approximate changes in effective stress caused by changes in groundwater withdrawal from the Chicot and Evangeline aquifers.

In 2016, water-level-altitude contours for the Chicot aquifer ranged from 200 feet (ft) below the vertical datum (North American Vertical Datum of 1988; hereinafter, datum) in a localized area in northwestern Harris County to 200 ft above datum in west-central Montgomery County. Water-level changes during 2015–16 in the Chicot aquifer ranged from a 39-ft decline to a 26-ft rise. Contoured 5-year and long-term changes in water-level altitudes of the Chicot aquifer ranged from a 30-ft decline to a 20-ft rise (2011–16), from a 140-ft decline to a 160-ft rise (1990–2016), and from a 120-ft decline to a 200-ft rise (1977–2016). In 2016, water-level-altitude contours for the Evangeline aquifer ranged from 250 ft below datum in three separate areas in south-central Montgomery County and extending into north-central Harris County, in west-central Harris County, and in southwestern Harris County to 200 ft above datum in southeastern Grimes and northwestern Montgomery Counties. Water-level changes during 2015–16 in the Evangeline aquifer ranged from a 65-ft decline to a 61-ft rise. Contoured 5-year and long-term changes in water-level altitudes of the Evangeline aquifer ranged from a 60-ft decline to a 40-ft rise (2011–16), from a 160-ft decline to a 160-ft rise (1990–2016), and from a 320-ft decline to a 240-ft rise (1977–2016). In 2016, water-level-altitude contours for the Jasper aquifer ranged from 200 ft below datum in south-central Montgomery County extending into north-central Harris County to 250 ft above datum in northwestern Montgomery County and extending into eastern Grimes County and southwestern Walker County. Water-level changes during 2015–16 in the Jasper aquifer ranged from a 38-ft decline to a 51-ft rise. Contoured 5-year and long-term changes in water-level altitudes of the Jasper aquifer ranged from a 60-ft decline to a 40-ft rise (2011–16) and from a 220-ft decline to a 20-ft decline (2000–16).

Compaction of subsurface sediments (mostly in the fine-grained silt and clay layers) in the Chicot and Evangeline aquifers was recorded continuously by using 13 extensometers at 11 sites that were either activated or installed between 1973 and 1980. During the period of record beginning in 1973 (or later depending on activation or installation date) and ending in December 2015, measured cumulative compaction at the 13 extensometers ranged from 0.095 ft at the Texas City-Moses Lake extensometer to 3.666 ft at the Addicks extensometer. From January through December 2015, the Northeast, Southwest, Addicks, Johnson Space Center, and Clear Lake (deep) extensometers recorded net decreases in land-surface elevation, but the Lake Houston, East End, Texas City-Moses Lake, Baytown C–1 (shallow), Baytown C–2 (deep), Seabrook, Clear Lake (shallow), and Pasadena extensometers recorded net increases in land-surface elevation. For the 11 extensometer sites during the selected years 1988, 1998, 2008, 2012, and 2015, the smallest effective stress (20.12 pounds per square inch [psi]) was estimated at the Texas City-Moses Lake extensometer site and was produced by a measured water level of 46.42 ft below land-surface datum (blsd) in January 2008. The corresponding net compaction during 2007 at this site was 0.001 ft. The largest effective stress (174.86 psi) was estimated at the Addicks extensometer site and was produced by a measured water level of 403.38 ft blsd in January 1998. The corresponding net compaction at the Addicks site was 0.067 ft in 1997.

The 2011 drought caused water-level declines in the aquifers that were documented by the water-level-measurement data collected in January 2012. During the 2011 drought, the 13 extensometers recorded varying amounts of compaction that ranged from a net compaction value of 0.002 ft recorded by the Texas City-Moses Lake extensometer to a net compaction value of 0.192 ft recorded by the Pasadena extensometer. Water-level data for 1988, 1998, 2008, 2012, and 2015 and the corresponding net compaction values recorded by the extensometers for 1987, 1997, 2007, 2011, and 2014 were used to illustrate the cause and effect relations between changes in water level caused by groundwater withdrawals and resulting changes in effective stress. Changes in effective stress are related to changes in land-surface elevations caused by compaction of the fine-grained sediments composing the Chicot and Evangeline aquifers.

The rate of compaction varies from site to site because of differences in rates of groundwater withdrawal in the areas adjacent to each extensometer site; differences among sites in the ratios of sand, silt, and clay and their corresponding compressibilities; and previously established preconsolidation heads. It is not appropriate, therefore, to extrapolate or infer a rate of compaction for an adjacent area on the basis of the rate of compaction recorded by proximal extensometers.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3365","collaboration":"Prepared in cooperation with the Harris-Galveston Subsidence District, City of Houston, Fort Bend Subsidence District, Lone Star Groundwater Conservation District, and Brazoria County Groundwater Conservation District","usgsCitation":"Kasmarek, M.C., Ramage, J.K., and Johnson, M.R., 2016, Water-level altitudes 2016 and water-level changes in the Chicot, Evangeline, and Jasper aquifers and compaction 1973–2015 in the Chicot and Evangeline aquifers, Houston-Galveston region, Texas: U.S. Geological Survey Scientific Investigations Map 3365, pamphlet, 16 sheets, scale 1:100,000, https://dx.doi.org/10.3133/sim3365.","productDescription":"Report: ix, 39 p.; 16 Sheets: 21.99 x 22.00 inches or smaller; Tables 1-5; Appendix 1; Datasets; Read Me","numberOfPages":"53","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-072338","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":329386,"rank":7,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3365/ReadMe_2.txt","size":"2.46 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3365"},{"id":329382,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3365/sheets","text":"Sheets 1-16","description":"SIM 3365"},{"id":329381,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sim/3365/tables","text":"Tables 1-5","linkFileType":{"id":3,"text":"xlsx"},"description":"SIM 3365"},{"id":329375,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3365/coverthb.jpg"},{"id":329385,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://pubs.usgs.gov/sim/3365/datasets","text":"Datasets (Revised May 2017)","description":"SIM 3365"},{"id":329377,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sim/3365/appendix1","text":"Appendix 1","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3365"},{"id":329376,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3365/sim3365_pamphlet.pdf","text":"Report ","size":"5.32 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3365"}],"country":"United States","state":"Texas","city":"Galveston, Houston","otherGeospatial":"Chicot Aquifer, Evangeline Aquifer, Jasper Aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.3505859375,\n 29.554345125748267\n ],\n [\n -94.52636718749999,\n 30.031055426540206\n ],\n [\n -94.7021484375,\n 30.29701788337205\n ],\n [\n -94.976806640625,\n 30.675715404167743\n ],\n [\n -95.07568359375,\n 30.829139422013956\n ],\n [\n -95.25970458984374,\n 30.954057859276126\n ],\n [\n -95.614013671875,\n 30.95876857077987\n ],\n [\n -96.064453125,\n 30.798474179567823\n ],\n [\n -96.2841796875,\n 30.64027517241868\n ],\n [\n -96.3446044921875,\n 30.462879341709886\n ],\n [\n -96.2237548828125,\n 30.073847754270204\n ],\n [\n -96.03149414062499,\n 29.410890376109\n ],\n [\n -95.82275390625,\n 29.080175989623203\n ],\n [\n -95.6304931640625,\n 28.9072060763367\n ],\n [\n -95.3558349609375,\n 28.8831596093235\n ],\n [\n -94.7515869140625,\n 29.291189838184863\n ],\n [\n -94.3505859375,\n 29.554345125748267\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
http://tx.usgs.gov/

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State and local water-resource managers need modeling tools to help them manage and protect water-supply resources for both human consumption and ecological needs. The U.S. Geological Survey, in cooperation with the Massachusetts Department of Environmental Protection, has developed a decision-support tool to estimate the effects of reservoirs on natural streamflow. The Massachusetts Reservoir Simulation Tool is a model that simulates the daily water balance of a reservoir. The reservoir simulation tool provides estimates of daily outflows from reservoirs and compares the frequency, duration, and magnitude of the volume of outflows from reservoirs with estimates of the unaltered streamflow that would occur if no dam were present. This tool will help environmental managers understand the complex interactions and tradeoffs between water withdrawals, reservoir operational practices, and reservoir outflows needed for aquatic habitats.

A sensitivity analysis of the daily water balance equation was performed to identify physical and operational features of reservoirs that could have the greatest effect on reservoir outflows. For the purpose of this report, uncontrolled releases of water (spills or spillage) over the reservoir spillway were considered to be a proxy for reservoir outflows directly below the dam. The ratio of average withdrawals to the average inflows had the largest effect on spillage patterns, with the highest withdrawals leading to the lowest spillage. The size of the surface area relative to the drainage area of the reservoir also had an effect on spillage; reservoirs with large surface areas have high evaporation rates during the summer, which can contribute to frequent and long periods without spillage, even in the absence of water withdrawals. Other reservoir characteristics, such as variability of inflows, groundwater interactions, and seasonal demand patterns, had low to moderate effects on the frequency, duration, and magnitude of spillage. The reservoir simulation tool was used to simulate 35 single- and multiple-reservoir systems in Massachusetts over a 44-year period (water years 1961 to 2004) under two water-use scenarios. The no-pumping scenario assumes no water withdrawal pumping, and the pumping scenario incorporates average annual pumping rates from 2000 to 2004. By comparing the results of the two scenarios, the total streamflow alteration can be parsed into the portion of streamflow alteration caused by the presence of a reservoir and the additional streamflow alteration caused by the level of water use of the system.

For each reservoir system, the following metrics were computed to characterize the frequency, duration, and magnitude of reservoir outflow volumes compared with unaltered streamflow conditions: (1) the median number of days per year in which the reservoir did not spill, (2) the median duration of the longest consecutive period of no-spill days per year, and (3) the lowest annual flow duration exceedance probability at which the outflows are significantly different from estimated unaltered streamflow at the 95-percent confidence level. Most reservoirs in the study do not spill during the summer months even under no-pumping conditions. The median number of days during which there was no spillage was less than 365 for all reservoirs in the study, indicating that, even under reported pumping conditions, the reservoirs refill to full volume and spill at least once during nondrought years, typically in the spring.

Thirteen multiple-reservoir systems consisting of two or three hydrologically connected reservoirs were included in the study. Because operating rules used to manage multiple-reservoir systems are not available, these systems were simulated under two pumping scenarios, one in which water transfers between reservoirs are minimal and one in which reservoirs continually transferred water to intermediate or terminal reservoirs. These two scenarios provided upper and lower estimates of spillage under average pumping conditions from 2000 to 2004.

For sites with insufficient data to simulate daily water balances, a proxy method to estimate the three spillage metrics was developed. A series of 4,000 Monte Carlo simulations of the reservoir water balance were run. In each simulation, streamflow, physical reservoir characteristics, and daily climate inputs were randomly varied. Tobit regression equations that quantify the relation between streamflow alteration and physical and operational characteristics of reservoirs were developed from the results of the Monte Carlo simulations and can be used to estimate each of the three spillage metrics using only the withdrawal ratio and the ratio of the surface area to the drainage area, which are available statewide for all reservoirs.

A graphical user-interface for the Massachusetts Reservoir Simulation Tool was developed in a Microsoft Access environment. The simulation tool contains information for 70 reservoirs in Massachusetts and allows for simulation of additional scenarios than the ones considered in this report, including controlled releases, dam seepage and leakage, demand management plans, and alternative water withdrawal and transfer rules.

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\"}}]}","contact":"

Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532

Or visit our Web site at:
http://newengland.water.usgs.gov/

","tableOfContents":"","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2016-10-06","noUsgsAuthors":false,"publicationDate":"2016-10-06","publicationStatus":"PW","scienceBaseUri":"57f7c089e4b0bc0bec09c7cf","contributors":{"authors":[{"text":"Levin, Sara B. 0000-0002-2448-3129 slevin@usgs.gov","orcid":"https://orcid.org/0000-0002-2448-3129","contributorId":1870,"corporation":false,"usgs":true,"family":"Levin","given":"Sara","email":"slevin@usgs.gov","middleInitial":"B.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":646705,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70176429,"text":"ofr20161157 - 2016 - Bathymetric survey and estimation of storage capacity of lower Sixmile Creek reservoir, Ithaca, New York","interactions":[],"lastModifiedDate":"2016-10-05T16:54:35","indexId":"ofr20161157","displayToPublicDate":"2016-10-05T15:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-1157","title":"Bathymetric survey and estimation of storage capacity of lower Sixmile Creek reservoir, Ithaca, New York","docAbstract":"

During 2015, the U.S. Geological Survey, in cooperation with the City of Ithaca, New York, and the New York State Department of State, conducted a bathymetric survey of the lower Sixmile Creek reservoir in Tompkins County, New York. A former water-supply reservoir for the City of Ithaca, the reservoir is no longer a functional component of Ithaca’s water-supply system, having been replaced by a larger reservoir less than a mile upstream in 1911. Excessive sedimentation has substantially reduced the reservoir’s water-storage capacity and made the discharge gate at the base of the 30-foot dam, which creates the reservoir, inoperable. U.S. Geological Survey personnel collected bathymetric data by using an acoustic Doppler current profiler. Across more than half of the approximately 14-acre reservoir, depths were manually measured because of interference from aquatic vegetation with the acoustic Doppler current profiler. City of Ithaca personnel created a bottom-elevation surface from these depth data. A second surface was created from depths that were manually measured by City of Ithaca personnel during 1938. Surface areas and storage capacities were computed at 1-foot increments of elevation for both bathymetric surveys. The results indicate that the current storage capacity of the reservoir at its normal water-surface elevation is about 84 acre-feet and that sediment accumulated between 1938 and 2015 has decreased the reservoir’s capacity by about 68 acre-feet. This sediment load is attributed to annual inputs from the watershed above the reservoir, as well as from an episodic landslide that filled a large part of the reservoir along its northern edge in 1949.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161157","collaboration":"Prepared in cooperation with the City of Ithaca, New York, and the New York State Department of State","usgsCitation":"Wernly, J.F., Zajd, H.J., Jr., and Coon, W.F., 2016, Bathymetric survey and estimation of storage capacity of lower Sixmile Creek reservoir, Ithaca, New York: U.S. Geological Survey Open-File Report 2016–1157, 13 p., https://dx.doi.org/10.3133/ofr20161157.","productDescription":"Report: vii, 13 p.; Data Release","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-074656","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":438539,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7G15Z0S","text":"USGS data release","linkHelpText":"Geospatial data set of bathymetric survey of lower Sixmile Creek Reservoir, Ithaca, New York, 2015"},{"id":329057,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1157/coverthb.jpg"},{"id":329058,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1157/ofr20161157.pdf","text":"Report","size":"7.89 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1157"},{"id":329059,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7G15Z0S","text":"USGS data release","description":"USGS data release","linkHelpText":"Geospatial Dataset of Bathymetric Survey of Lower Sixmile Creek Reservoir, Ithaca, New York, 2015"}],"country":"United States","state":"New York","city":"Ithaca","otherGeospatial":"Lower Sixmile Creek Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.47645235061646,\n 42.42269621215634\n ],\n [\n -76.47645235061646,\n 42.42519885057981\n ],\n [\n -76.47053003311157,\n 42.42519885057981\n ],\n [\n -76.47053003311157,\n 42.42269621215634\n ],\n [\n -76.47645235061646,\n 42.42269621215634\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, New York Water Science Center
U.S. Geological Survey
30 Brown Road
Ithaca, NY 14850

Information requests:
(518) 285-5602
or visit our Web site at:
http://ny.water.usgs.gov

","tableOfContents":"","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2016-10-05","noUsgsAuthors":false,"publicationDate":"2016-10-05","publicationStatus":"PW","scienceBaseUri":"57f7c639e4b0bc0bec09c80a","contributors":{"authors":[{"text":"Wernly, John F. jwernly@usgs.gov","contributorId":174610,"corporation":false,"usgs":true,"family":"Wernly","given":"John F.","email":"jwernly@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":false,"id":648730,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zajd hzajd@usgs.gov","contributorId":1085,"corporation":false,"usgs":true,"family":"Zajd","email":"hzajd@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":false,"id":648731,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coon, William F. 0000-0002-7007-7797 wcoon@usgs.gov","orcid":"https://orcid.org/0000-0002-7007-7797","contributorId":1765,"corporation":false,"usgs":true,"family":"Coon","given":"William","email":"wcoon@usgs.gov","middleInitial":"F.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":648732,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70176712,"text":"sir20165138 - 2016 - Delineation of areas contributing groundwater to selected receiving surface water bodies for long-term average hydrologic conditions from 1968 to 1983 for Long Island, New York","interactions":[],"lastModifiedDate":"2016-10-05T16:38:00","indexId":"sir20165138","displayToPublicDate":"2016-10-05T13:15:00","publicationYear":"2016","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":"2016-5138","title":"Delineation of areas contributing groundwater to selected receiving surface water bodies for long-term average hydrologic conditions from 1968 to 1983 for Long Island, New York","docAbstract":"

To assist resource managers and planners in developing informed strategies to address nitrogen loading to coastal water bodies of Long Island, New York, the U.S. Geological Survey and the New York State Department of Environmental Conservation initiated a program to delineate a comprehensive dataset of groundwater recharge areas (or areas contributing groundwater), travel times, and outflows to streams and saline embayments on Long Island. A four-layer regional three-dimensional finite-difference groundwater-flow model of hydrologic conditions from 1968 to 1983 was used to provide delineations of 48 groundwater watersheds on Long Island. Sixteen particle starting points were evenly spaced within each of the 4,000- by 4,000-foot model cells that receive water-table recharge and tracked using forward particle-tracking analysis modeling software to outflow zones. For each particle, simulated travel times were grouped by age as follows: less than or equal to 10 years, greater than 10 years and less than or equal to 100 years, greater than 100 years and less than or equal to 1,000 years, and greater than 1,000 years; and simulated ending zones were grouped into 48 receiving water bodies, based on the New York State Department of Environmental Conservation Waterbody Inventory/Priority Waterbodies List. Areal delineation of travel time zones and groundwater contributing areas were generated and a table was prepared presenting the sum of groundwater outflow for each area.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165138","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Misut, P.E., and Monti, Jack, Jr., 2016, Delineation of areas contributing groundwater to selected receiving surface water bodies for long-term average hydrologic conditions from 1968 to 1983 for Long Island, New York:U.S. Geological Survey Scientific Investigations Report 2016–5138, 22 p., https://dx.doi.org/10.3133/sir20165138.","productDescription":"Report: iv, 22 p.; Figures: 1-5; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[],"links":[{"id":329253,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5138/coverthb.jpg"},{"id":329257,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7TB151D ","text":"USGS data release","description":"USGS data release ","linkHelpText":"MODFLOW-2005 and MODPATH6 models "},{"id":329254,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5138/sir20165138.pdf","text":"Report","size":"5.21 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5138"},{"id":329255,"rank":3,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2016/5138/sir20165138_figs1-5.zip","text":"Figures 1-5 ","size":"10.2 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2016-5138","linkHelpText":"- Large-format versions of figures in report"}],"country":"United States","state":"New York","otherGeospatial":"Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.25,\n 40.5\n ],\n [\n -73.25,\n 40.9\n ],\n [\n -74.25,\n 40.9\n ],\n [\n -74.25,\n 40.5\n ],\n [\n -73.25,\n 40.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180
http://ny.water.usgs.gov

","tableOfContents":"","publishedDate":"2016-10-05","noUsgsAuthors":false,"publicationDate":"2016-10-05","publicationStatus":"PW","scienceBaseUri":"584e41fae4b0260a373816ec","contributors":{"authors":[{"text":"Misut, Paul E. 0000-0002-6502-5255 pemisut@usgs.gov","orcid":"https://orcid.org/0000-0002-6502-5255","contributorId":1073,"corporation":false,"usgs":true,"family":"Misut","given":"Paul","email":"pemisut@usgs.gov","middleInitial":"E.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":650106,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Monti,, Jack Jr. jmonti@usgs.gov","contributorId":145900,"corporation":false,"usgs":true,"family":"Monti,","given":"Jack","suffix":"Jr.","email":"jmonti@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":false,"id":650107,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70176539,"text":"fs20163074 - 2016 - USGS mineral-resource assessment of Sagebrush Focal Areas in the western United States","interactions":[],"lastModifiedDate":"2016-10-04T14:19:28","indexId":"fs20163074","displayToPublicDate":"2016-10-04T13:50:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-3074","title":"USGS mineral-resource assessment of Sagebrush Focal Areas in the western United States","docAbstract":"

U.S. Geological Survey (USGS) scientists have completed an assessment of the mineral-resource potential of nearly 10 million acres of Federal and adjacent lands in Idaho, Montana, Nevada, Oregon, Utah, and Wyoming. The assessment of these lands, identified as Sagebrush Focal Areas, was done at the request of the Bureau of Land Management. The assessment results will be used in the decision-making process that the Department of the Interior is pursuing toward the protection of large areas of contiguous sagebrush habitat for the greater sage-grouse (Centrocercus urophasianus) in the Western United States. The detailed results of this ambitious study are published in the five volumes of USGS Scientific Investigations Report 2016–5089 and seven accompanying data releases.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163074","usgsCitation":"Frank, D.G., Frost, T.P., Day, W.C., and the USGS SaMiRA team, 2016, U.S. Geological Survey mineral-resource assessment of Sagebrush Focal Areas in the Western United States: U.S. Geological Survey Fact Sheet 2016–3074, 4 p., https://dx.doi.org/10.3133/fs20163074.","productDescription":"4 p.","onlineOnly":"Y","ipdsId":"IP-078129","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":329275,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/sir20165089","text":"Scientific Investigations Report 2016-5089","linkHelpText":"Mineral resources of the Sagebrush Focal Areas of Idaho, Montana, Nevada, Oregon, Utah, and 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\"}}]}","contact":"

Contact Information, Mineral Resources Program
U.S. Geological Survey
12201 Sunrise Valley Drive
913 National Center
Reston, VA 20192
http://minerals.usgs.gov/

","tableOfContents":"","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2016-10-04","noUsgsAuthors":false,"publicationDate":"2016-10-04","publicationStatus":"PW","scienceBaseUri":"57f7c639e4b0bc0bec09c80e","contributors":{"authors":[{"text":"Frank, David G. dfrank@usgs.gov","contributorId":3274,"corporation":false,"usgs":true,"family":"Frank","given":"David","email":"dfrank@usgs.gov","middleInitial":"G.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":649152,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Frost, Thomas P. 0000-0001-8348-8432 tfrost@usgs.gov","orcid":"https://orcid.org/0000-0001-8348-8432","contributorId":203,"corporation":false,"usgs":true,"family":"Frost","given":"Thomas","email":"tfrost@usgs.gov","middleInitial":"P.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":649153,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Day, Warren C. 0000-0002-9278-2120 wday@usgs.gov","orcid":"https://orcid.org/0000-0002-9278-2120","contributorId":1308,"corporation":false,"usgs":true,"family":"Day","given":"Warren","email":"wday@usgs.gov","middleInitial":"C.","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":649154,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"the USGS SaMiRA team","contributorId":174960,"corporation":true,"usgs":false,"organization":"the USGS SaMiRA team","id":649800,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70176465,"text":"ds1020 - 2016 - Characterization of fractures and flow zones in a contaminated crystalline-rock aquifer in the Tylerville section of Haddam, Connecticut","interactions":[],"lastModifiedDate":"2016-10-04T10:56:37","indexId":"ds1020","displayToPublicDate":"2016-10-04T07:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1020","title":"Characterization of fractures and flow zones in a contaminated crystalline-rock aquifer in the Tylerville section of Haddam, Connecticut","docAbstract":"

The U.S. Geological Survey, in cooperation with the Connecticut Department of Energy and Environmental Protection, investigated the characteristics of the bedrock aquifer in the Tylerville section of Haddam, Connecticut, from June to August 2014. As part of this investigation, geophysical logs were collected from six water-supply wells and were analyzed to (1) identify well construction, (2) determine the rock type and orientation of the foliation and layering of the rock, (3) characterize the depth and orientation of fractures, (4) evaluate fluid properties of the water in the well, and (5) determine the relative transmissivity and head of discrete fractures or fracture zones. The logs included the following: caliper, electromagnetic induction, gamma, acoustic and (or) optical televiewer, heat-pulse flowmeter under ambient and pumped conditions, hydraulic head data, fluid electrical conductivity and temperature under postpumping conditions, and borehole-radar reflection collected in single-hole mode. In a seventh borehole, a former water-supply well, only caliper, fluid electrical conductivty, and temperature logs were collected, because of a constriction in the borehole.

This report includes a description of the methods used to collect and process the borehole geophysical data, the description of the data collected in each of the wells, and a comparison of the results collected in all of the wells. The data are presented in plots of the borehole geophysical logs, tables, and figures. Collectively these data provide valuable characterizations that can be used to improve or inform site conceptual models of groundwater flow in the study area.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1020","collaboration":"Prepared in cooperation with the Connecticut Department of Energy and Environmental Protection","usgsCitation":"Johnson, C.D., Kiel, K.F., Joesten, P.K., and Pappas, K.L., 2016, Characterization of fractures and flow zones in a contaminated crystalline-rock aquifer in the Tylerville section of Haddam, Connecticut: U.S. Geological Survey Data Series 1020, 40 p., https://dx.doi.org/10.3133/ds1020.","productDescription":"Report: viii, 40 p.; Appendixes: 1-7","numberOfPages":"52","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-067211","costCenters":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"links":[{"id":329127,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/1020/appendix/ds1020_appendix2.zip","text":"Appendix 2","size":"7.27 MB","linkFileType":{"id":6,"text":"zip"},"description":"DS 1020","linkHelpText":"- Borehole-Geophysical Logs From Borehole 76–BR in the Tylerville Study Area, Haddam, Connecticut, 2014"},{"id":329131,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/1020/appendix/ds1020_appendix6.zip","text":"Appendix 6","size":"19.9 MB","linkFileType":{"id":6,"text":"zip"},"description":"DS 1020","linkHelpText":"- Borehole-Geophysical Logs From Borehole 130–LMR in the Tylerville Study Area, Haddam, Connecticut, 2014"},{"id":329132,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/1020/appendix/ds1020_appendix7.zip","text":"Appendix 7","size":"658 KB","linkFileType":{"id":6,"text":"zip"},"description":"DS 1020","linkHelpText":"- Borehole-Geophysical Logs From Borehole 95–BR in the Tylerville Study Area, Haddam, Connecticut, 2014"},{"id":329133,"rank":10,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/1020/appendix/ds1020_appendixes_1-7.zip","text":"Appendixes 1–7","size":"76.9 MB","linkFileType":{"id":6,"text":"zip"},"description":"DS 1020","linkHelpText":"- Borehole-Geophysical Logs From Seven Boreholes in the Tylerville Study Area, Haddam, Connecticut, 2014"},{"id":329128,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/1020/appendix/ds1020_appendix3.zip","text":"Appendix 3","size":"5.84 MB","linkFileType":{"id":6,"text":"zip"},"description":"DS 1020","linkHelpText":"- Borehole-Geophysical Logs From Borehole 85–BR in the Tylerville Study Area, Haddam, Connecticut, 2014"},{"id":329130,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/1020/appendix/ds1020_appendix5.zip","text":"Appendix 5","size":"18 MB","linkFileType":{"id":6,"text":"zip"},"description":"DS 1020","linkHelpText":"- Borehole-Geophysical Logs From Borehole 77–LMR in the Tylerville Study Area, Haddam, Connecticut, 2014"},{"id":329129,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/1020/appendix/ds1020_appendix4.zip","text":"Appendix 4","size":"10.7 MB","linkFileType":{"id":6,"text":"zip"},"description":"DS 1020","linkHelpText":"- Borehole-Geophysical Logs From Borehole 79/81–BR in the Tylerville Study Area, Haddam, Connecticut, 2014"},{"id":329124,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1020/coverthb.jpg"},{"id":329125,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1020/ds1020.pdf","text":"Report","size":"8.19 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1020"},{"id":329126,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/1020/appendix/ds1020_appendix1.zip","text":"Appendix 1","size":"14.4 MB","linkFileType":{"id":6,"text":"zip"},"description":"DS 1020","linkHelpText":"- Borehole-Geophysical Logs From Borehole 1640–SR in the Tylerville Study Area, Haddam, Connecticut, 2014"}],"country":"United States","state":"Connecticut","county":"Middlesex County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.48126983642578,\n 41.43944494429659\n ],\n [\n -72.48126983642578,\n 41.45745861169602\n ],\n [\n -72.46135711669922,\n 41.45745861169602\n ],\n [\n -72.46135711669922,\n 41.43944494429659\n ],\n [\n -72.48126983642578,\n 41.43944494429659\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Chief, Branch of Geophysics
Office of Groundwater
U.S. Geological Survey
11 Sherman Place, Unit 5015
Storrs, CT 06269
http://water.usgs.gov/ogw/bgas

","tableOfContents":"","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2016-10-04","noUsgsAuthors":false,"publicationDate":"2016-10-04","publicationStatus":"PW","scienceBaseUri":"57f7c639e4b0bc0bec09c818","contributors":{"authors":[{"text":"Johnson, Carole D. 0000-0001-6941-1578 cjohnson@usgs.gov","orcid":"https://orcid.org/0000-0001-6941-1578","contributorId":1891,"corporation":false,"usgs":true,"family":"Johnson","given":"Carole","email":"cjohnson@usgs.gov","middleInitial":"D.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":649902,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kiel, Kristal F.","contributorId":174636,"corporation":false,"usgs":false,"family":"Kiel","given":"Kristal","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":649903,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Joesten, Peter K. pjoesten@usgs.gov","contributorId":1929,"corporation":false,"usgs":true,"family":"Joesten","given":"Peter","email":"pjoesten@usgs.gov","middleInitial":"K.","affiliations":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":649904,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pappas, Katherine L.","contributorId":175026,"corporation":false,"usgs":true,"family":"Pappas","given":"Katherine L.","affiliations":[],"preferred":false,"id":649908,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70176702,"text":"70176702 - 2016 - Economic value of angling on the Colorado River at Lees Ferry: Using secondary data to estimate the influence of seasonality","interactions":[],"lastModifiedDate":"2016-10-04T12:10:11","indexId":"70176702","displayToPublicDate":"2016-10-04T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Economic value of angling on the Colorado River at Lees Ferry: Using secondary data to estimate the influence of seasonality","docAbstract":"

Glen Canyon Dam (GCD) on the Colorado River in northern Arizona provides water storage, flood control, and power system benefits to approximately 40 million people who rely on water and energy resources in the Colorado River basin. Downstream resources (e.g., angling, whitewater floating) in Glen Canyon National Recreation Area (GCNRA) and Grand Canyon National Park are impacted by the operation of GCD. The GCD Adaptive Management Program was established in 1997 to monitor and research the effects of dam operations on the downstream environment. We utilized secondary survey data and an individual observation travel cost model to estimate the net economic benefit of angling in GCNRA for each season and each type of angler. As expected, the demand for angling decreased with increasing travel cost; the annual value of angling at Lees Ferry totaled US$2.7 million at 2014 visitation levels. Demand for angling was also affected by season, with per-trip values of $210 in the summer, $237 in the spring, $261 in the fall, and $399 in the winter. This information provides insight into the ways in which anglers are potentially impacted by seasonal GCD operations and adaptive management experiments aimed at improving downstream resource conditions.

","language":"English","publisher":"Taylor & Francis","doi":"10.1080/02755947.2016.1204388","usgsCitation":"Bair, L.S., Rogowski, D.L., and Neher, C., 2016, Economic value of angling on the Colorado River at Lees Ferry: Using secondary data to estimate the influence of seasonality: North American Journal of Fisheries Management, v. 36, no. 6, p. 1229-1239, https://doi.org/10.1080/02755947.2016.1204388.","productDescription":"11 p.","startPage":"1229","endPage":"1239","ipdsId":"IP-066706","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":329258,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Colorado River, Lees Ferry","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.63894653320311,\n 36.824676208856175\n ],\n [\n -111.63894653320311,\n 36.943855400282494\n ],\n [\n -111.47758483886719,\n 36.943855400282494\n ],\n [\n -111.47758483886719,\n 36.824676208856175\n ],\n [\n -111.63894653320311,\n 36.824676208856175\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"36","issue":"6","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-30","publicationStatus":"PW","scienceBaseUri":"57f7c63ae4b0bc0bec09c81c","contributors":{"authors":[{"text":"Bair, Lucas S. 0000-0002-9911-3624 lbair@usgs.gov","orcid":"https://orcid.org/0000-0002-9911-3624","contributorId":5270,"corporation":false,"usgs":true,"family":"Bair","given":"Lucas","email":"lbair@usgs.gov","middleInitial":"S.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":649934,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rogowski, David L.","contributorId":175084,"corporation":false,"usgs":false,"family":"Rogowski","given":"David","email":"","middleInitial":"L.","affiliations":[{"id":27527,"text":"AZ Game and FIsh Department","active":true,"usgs":false}],"preferred":false,"id":649935,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Neher, Christopher","contributorId":175085,"corporation":false,"usgs":false,"family":"Neher","given":"Christopher","email":"","affiliations":[{"id":27528,"text":"Uni. of Montana, Dept. of Mathematical Sciences","active":true,"usgs":false}],"preferred":false,"id":649936,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70176690,"text":"fs20163070 - 2016 - The Land Processes Distributed Active Archive Center (LP DAAC)","interactions":[],"lastModifiedDate":"2017-01-17T19:06:46","indexId":"fs20163070","displayToPublicDate":"2016-10-03T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-3070","title":"The Land Processes Distributed Active Archive Center (LP DAAC)","docAbstract":"

The Land Processes Distributed Active Archive Center (LP DAAC) operates as a partnership with the U.S. Geological Survey and is 1 of 12 DAACs within the National Aeronautics and Space Administration (NASA) Earth Observing System Data and Information System (EOSDIS). The LP DAAC ingests, archives, processes, and distributes NASA Earth science remote sensing data. These data are provided to the public at no charge. Data distributed by the LP DAAC provide information about Earth’s surface from daily to yearly intervals and at 15 to 5,600 meter spatial resolution. Data provided by the LP DAAC can be used to study changes in agriculture, vegetation, ecosystems, elevation, and much more. The LP DAAC provides several ways to access, process, and interact with these data. In addition, the LP DAAC is actively archiving new datasets to provide users with a variety of data to study the Earth.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163070","collaboration":"Prepared in cooperation with the National Aeronautics and Space Administration","usgsCitation":"Golon, D.K., 2016, The Land Processes Distributed Active Archive Center (LP DAAC): U.S. Geological Survey Fact Sheet 2016–3070, 2 p., https://dx.doi.org/10.3133/fs20163070.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":329073,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3070/fs20163070.pdf","text":"Fact Sheet","size":"11.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016–3070"},{"id":329072,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3070/coverthb.jpg"}],"contact":"

Director, Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198

http://eros.usgs.gov

https://lpdaac.usgs.gov/

","tableOfContents":"


","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2016-10-03","noUsgsAuthors":false,"publicationDate":"2016-10-03","publicationStatus":"PW","scienceBaseUri":"57f7c63ae4b0bc0bec09c828","contributors":{"authors":[{"text":"Golon, Danielle K. 0000-0001-5179-2093 dgolon@usgs.gov","orcid":"https://orcid.org/0000-0001-5179-2093","contributorId":168397,"corporation":false,"usgs":true,"family":"Golon","given":"Danielle","email":"dgolon@usgs.gov","middleInitial":"K.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":649847,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70175322,"text":"sir20165112 - 2016 - Status of groundwater quality in the Santa Barbara Study Unit, 2011: California GAMA Priority Basin Project","interactions":[],"lastModifiedDate":"2016-10-03T16:15:22","indexId":"sir20165112","displayToPublicDate":"2016-10-03T00:00:00","publicationYear":"2016","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":"2016-5112","title":"Status of groundwater quality in the Santa Barbara Study Unit, 2011: California GAMA Priority Basin Project","docAbstract":"

Groundwater quality in the 48-square-mile Santa Barbara study unit was investigated in 2011 as part of the California State Water Resources Control Board’s Groundwater Ambient Monitoring and Assessment (GAMA) Program Priority Basin Project. The study unit is mostly in Santa Barbara County and is in the Transverse and Selected Peninsular Ranges hydrogeologic province. The GAMA Priority Basin Project is carried out by the U.S. Geological Survey in collaboration with the California State Water Resources Control Board and Lawrence Livermore National Laboratory.

The GAMA Priority Basin Project was designed to provide a statistically unbiased, spatially distributed assessment of the quality of untreated groundwater in the primary aquifer system of California. The primary aquifer system is defined as that part of the aquifer corresponding to the perforation interval of wells listed in the California Department of Public Health database for the Santa Barbara study unit. This status assessment is intended to characterize the quality of groundwater resources in the primary aquifer system of the Santa Barbara study unit, not the treated drinking water delivered to consumers by water purveyors.

The status assessment for the Santa Barbara study unit was based on water-quality and ancillary data collected in 2011 by the U.S. Geological Survey from 23 sites and on water-quality data from the California Department of Public Health database for January 24, 2008–January 23, 2011. The data used for the assessment included volatile organic compounds; pesticides; pharmaceutical compounds; two constituents of special interest, perchlorate and N-nitrosodimethylamine (NDMA); and naturally present inorganic constituents, such as major ions and trace elements. Relative-concentrations (sample concentration divided by the health- or aesthetic-based benchmark concentration) were used to evaluate groundwater quality for those constituents that have federal or California regulatory and non-regulatory benchmarks for drinking-water quality. For inorganic, organic, and special-interest constituents, a relative-concentration greater than 1.0 indicates a concentration greater than the benchmark and is classified as high. Inorganic constituents are classified as moderate if relative-concentrations are greater than 0.5 and less than or equal to 1.0 and are classified as low if relative-concentrations are less than or equal to 0.5. For organic and special-interest constituents, the boundary between moderate and low relative-concentrations was set at 0.1.

Aquifer-scale proportion was used as the primary metric for evaluating regional-scale groundwater quality. High aquifer-scale proportion is defined as the areal percentage of the primary aquifer system with a relative-concentration greater than 1.0 for a particular constituent or class of constituents. Moderate and low aquifer-scale proportions were defined as the areal percentage of the primary aquifer system that had moderate and low relative-concentrations, respectively. Two statistical approaches—grid based and spatially weighted—were used to calculate aquifer-scale proportions for individual constituents and constituent classes. Grid-based and spatially weighted estimates were comparable in this the study (within 90-percent confidence intervals). Grid-based results were selected for use in the status assessment unless, as was observed in a few cases, a grid-based result was zero and the spatially weighted result was not zero, in which case, the spatially weighted result was used.

Inorganic constituents that have human-health benchmarks were present at high relative-concentrations in 5.3 percent of the primary aquifer system and at moderate concentrations in 32 percent. High aquifer-scale proportions of inorganic constituents primarily were a result of high aquifer-scale proportions of boron (5.3 percent) and fluoride (5.3 percent). Inorganic constituents that have aesthetic-based benchmarks, referred to as secondary maximum contaminant levels, were present at high relative-concentrations in 58 percent of the primary aquifer system and at moderate concentrations in 37 percent. Iron, manganese, sulfate, and total dissolved solids were the inorganic constituents with secondary maximum contaminant levels present at high relative-concentrations.

In contrast, organic and special-interest constituents that have health-based benchmarks were not detected at high relative-concentrations in the primary aquifer system. Of the 218 organic constituents analyzed, 10 were detected—9 that had human-health benchmarks. Organic constituents were present at moderate relative-concentrations in 11 percent of the primary aquifer system. The moderate aquifer-scale proportions were a result of moderate relative-concentrations of the volatile organic compounds methyl tert-butyl ether (MTBE, 11 percent) and 1,2-dichloroethane (5.6 percent). The volatile organic compounds 1,1,1-trichloroethane, 1,1-dichloroethane, bromodichloromethane, chloroform, MTBE, and perchloroethene (PCE); the pesticide simazine; and the special-interest constituent perchlorate were detected at more than 10 percent of the sites in the Santa Barbara study unit. Perchlorate was present at moderate relative-concentrations in 50 percent of the primary aquifer system. Pharmaceutical compounds and NDMA were not detected in the Santa Barbara study unit.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165112","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Davis, T.A., and Kulongoski, J.T., 2016, Status of groundwater quality in the Santa Barbara Study Unit, 2011: California GAMA Priority Basin Project: U.S. Geological Survey Scientific Investigations Report 2016–5112, 70 p., https://dx.doi.org/10.3133/sir20165112.","productDescription":"viii, 70 p.","numberOfPages":"82","ipdsId":"IP-077335","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":329221,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5112/sir20165112.pdf","text":"Report","size":"14.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5112"},{"id":329220,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5112/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Santa Barbara Study Unit","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.92813110351561,\n 34.37461214493789\n ],\n [\n -119.92813110351561,\n 34.47203335543746\n ],\n [\n -119.43237304687499,\n 34.47203335543746\n ],\n [\n -119.43237304687499,\n 34.37461214493789\n ],\n [\n -119.92813110351561,\n 34.37461214493789\n ]\n ]\n ]\n }\n }\n ]\n}","publicComments":"A product of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program","contact":"

Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
http://ca.water.usgs.gov

","tableOfContents":"","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2016-10-03","noUsgsAuthors":false,"publicationDate":"2016-10-03","publicationStatus":"PW","scienceBaseUri":"57f7c63ae4b0bc0bec09c82e","contributors":{"authors":[{"text":"Davis, Tracy A. 0000-0003-0253-6661 tadavis@usgs.gov","orcid":"https://orcid.org/0000-0003-0253-6661","contributorId":2715,"corporation":false,"usgs":true,"family":"Davis","given":"Tracy","email":"tadavis@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":644759,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kulongoski, Justin T. 0000-0002-3498-4154 kulongos@usgs.gov","orcid":"https://orcid.org/0000-0002-3498-4154","contributorId":156272,"corporation":false,"usgs":true,"family":"Kulongoski","given":"Justin","email":"kulongos@usgs.gov","middleInitial":"T.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":644760,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70176468,"text":"sir20165131 - 2016 - Paleomagnetic correlation of basalt flows in selected coreholes near the Advanced Test Reactor Complex, the Idaho Nuclear Technology and Engineering Center, and along the southern boundary, Idaho National Laboratory, Idaho","interactions":[],"lastModifiedDate":"2016-10-04T10:41:58","indexId":"sir20165131","displayToPublicDate":"2016-10-03T00:00:00","publicationYear":"2016","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":"2016-5131","title":"Paleomagnetic correlation of basalt flows in selected coreholes near the Advanced Test Reactor Complex, the Idaho Nuclear Technology and Engineering Center, and along the southern boundary, Idaho National Laboratory, Idaho","docAbstract":"

The U.S. Geological Survey, in cooperation with the U.S. Department of Energy, used paleomagnetic data from 18 coreholes to construct three cross sections of subsurface basalt flows in the southern part of the Idaho National Laboratory (INL). These cross sections, containing descriptions of the subsurface horizontal and vertical distribution of basalt flows and sediment layers, will be used in geological studies, and to construct numerical models of groundwater flow and contaminant transport.

Subsurface cross sections were used to correlate surface vents to their subsurface flows intersected by coreholes, to correlate subsurface flows between coreholes, and to identify possible subsurface vent locations of subsurface flows. Correlations were identified by average paleomagnetic inclinations of flows, and depth from land surface in coreholes, normalized to the North American Datum of 1927. Paleomagnetic data were combined, in some cases, with other data, such as radiometric ages of flows. Possible vent locations of buried basalt flows were identified by determining the location of the maximum thickness of flows penetrated by more than one corehole.

Flows from the surface volcanic vents Quaking Aspen Butte, Vent 5206, Mid Butte, Lavatoo Butte, Crater Butte, Pond Butte, Vent 5350, Vent 5252, Tin Cup Butte, Vent 4959, Vent 5119, and AEC Butte are found in coreholes, and were correlated to the surface vents by matching their paleomagnetic inclinations, and in some cases, their stratigraphic positions.

Some subsurface basalt flows that do not correlate to surface vents, do correlate over several coreholes, and may correlate to buried vents. Subsurface flows which correlate across several coreholes, but not to a surface vent include the D3 flow, the Big Lost flow, the CFA buried vent flow, the Early, Middle, and Late Basal Brunhes flows, the South Late Matuyama flow, the Matuyama flow, and the Jaramillo flow. The location of vents buried in the subsurface by younger basalt flows can be inferred if their flows are penetrated by several coreholes, by tracing the flows in the subsurface, and determining where the greatest thickness occurs.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165131","collaboration":"DOE/ID-22240
Prepared in cooperation with the U.S. Department of Energy","usgsCitation":"Hodges, M.K.V., and Champion, D.E., 2016, Paleomagnetic correlation of basalt flows in selected coreholes near the Advanced Test Reactor Complex, the Idaho Nuclear Technology and Engineering Center, and along the southern boundary, Idaho National Laboratory, Idaho: U.S. Geological Survey Scientific Investigations Report 2016-5131\n(DOE/ID-22240), 65 p., 1 pl., https://dx.doi.org/10.3133/sir20165131.","productDescription":"Report: v, 65 p.; Plate: 34.00 x 40.00 inches","numberOfPages":"76","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-065469","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":329237,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5131/sir20165131.pdf","text":"Report","size":"965 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5131"},{"id":329238,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2016/5131/sir20165131_plate01.pdf","text":"Plate 1","size":"543 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5131 Plate 1","linkHelpText":"Map and subsurface stratigraphic cross sections interpreted from paleomagnetic inclination data from coreholes in the southern part of the Idaho National Laboratory, Idaho."},{"id":329236,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5131/coverthb.jpg"}],"country":"United States","state":"Idaho","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.45581054687499,\n 43.22519255488632\n ],\n [\n -113.45581054687499,\n 44.11914151643737\n ],\n [\n -112.3516845703125,\n 44.11914151643737\n ],\n [\n -112.3516845703125,\n 43.22519255488632\n ],\n [\n -113.45581054687499,\n 43.22519255488632\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702
http://id.water.usgs.gov

","tableOfContents":"","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"publishedDate":"2016-10-03","noUsgsAuthors":false,"publicationDate":"2016-10-03","publicationStatus":"PW","scienceBaseUri":"57f7c63ae4b0bc0bec09c82c","contributors":{"authors":[{"text":"Hodges, Mary K.V.","contributorId":66848,"corporation":false,"usgs":true,"family":"Hodges","given":"Mary K.V.","affiliations":[],"preferred":false,"id":648850,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Champion, Duane E. 0000-0001-7854-9034 dchamp@usgs.gov","orcid":"https://orcid.org/0000-0001-7854-9034","contributorId":2912,"corporation":false,"usgs":true,"family":"Champion","given":"Duane","email":"dchamp@usgs.gov","middleInitial":"E.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":648851,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70160116,"text":"fs20153086 - 2016 - Enhanced canopy fuel mapping by integrating lidar data","interactions":[],"lastModifiedDate":"2017-01-17T19:08:52","indexId":"fs20153086","displayToPublicDate":"2016-10-03T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-3086","title":"Enhanced canopy fuel mapping by integrating lidar data","docAbstract":"

Background

The Wildfire Sciences Team at the U.S. Geological Survey’s Earth Resources Observation and Science Center produces vegetation type, vegetation structure, and fuel products for the United States, primarily through the Landscape Fire and Resource Management Planning Tools (LANDFIRE) program. LANDFIRE products are used across disciplines for a variety of applications. The LANDFIRE data retain their currency and relevancy through periodic updating or remapping. These updating and remapping efforts provide opportunities to improve the LANDFIRE product suite by incorporating data from other sources. Light detection and ranging (lidar) is uniquely suitable for gathering information on vegetation structure and spatial arrangement because it can collect data in three dimensions. The Wildfire Sciences Team has several completed and ongoing studies focused on integrating lidar into vegetation and fuels mapping.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20153086","usgsCitation":"Peterson, B.E., and Nelson, K.J., 2016, Enhanced canopy fuel mapping by integrating lidar data: U.S. Geological Survey Fact Sheet 2015–3086, 2 p., https://dx.doi.org/10.3133/fs20153086.","productDescription":"2 p.","onlineOnly":"N","ipdsId":"IP-057944","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":328868,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2015/3086/coverthb.jpg"},{"id":328869,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2015/3086/fs20153086.pdf","text":"Fact Sheet","size":"1.05 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Fact Sheet 2015–3086"}],"contact":"

Fire Science Team Lead
Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47194 252nd Street
Sioux Falls, SD 57198

 http://eros.usgs.gov

","tableOfContents":"


","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2016-10-03","noUsgsAuthors":false,"publicationDate":"2016-10-03","publicationStatus":"PW","scienceBaseUri":"57f7c63ae4b0bc0bec09c832","contributors":{"authors":[{"text":"Peterson, Birgit E. 0000-0002-4356-1540 bpeterson@usgs.gov","orcid":"https://orcid.org/0000-0002-4356-1540","contributorId":3599,"corporation":false,"usgs":true,"family":"Peterson","given":"Birgit","email":"bpeterson@usgs.gov","middleInitial":"E.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":581951,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nelson, Kurtis J. 0000-0003-4911-4511","orcid":"https://orcid.org/0000-0003-4911-4511","contributorId":105629,"corporation":false,"usgs":true,"family":"Nelson","given":"Kurtis J.","affiliations":[],"preferred":false,"id":581952,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70178579,"text":"70178579 - 2016 - Remote estimation of surface pCO2 on the West Florida Shelf","interactions":[],"lastModifiedDate":"2018-08-07T14:13:27","indexId":"70178579","displayToPublicDate":"2016-10-01T14:13:20","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1333,"text":"Continental Shelf Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Remote estimation of surface pCO2 on the West Florida Shelf","title":"Remote estimation of surface pCO2 on the West Florida Shelf","docAbstract":"

Surface pCO2 data from the West Florida Shelf (WFS) have been collected during 25 cruise surveys between 2003 and 2012. The data were scaled up using remote sensing measurements of surface water properties in order to provide a more nearly synoptic map of pCO2 spatial distributions and describe their temporal variations. This investigation involved extensive tests of various model forms through parsimony and Principal Component Analysis, which led to the development of a multi-variable empirical surface pCO2 model based on concurrent MODIS (Moderate Resolution Imaging Spectroradiometer) estimates of surface chlorophyll a concentrations (CHL, mg m−3), diffuse light attenuation at 490 nm (Kd_Lee, m−1), and sea surface temperature (SST, °C). Validation using an independent dataset showed a pCO2 Root Mean Square Error (RMSE) of <12 µatm and a 0.88 coefficient of determination (R2) for measured and model-predicted pCO2 ranging from 300 to 550 µatm. The model was more sensitive to SST than to CHL and Kd_Lee, with a 1 °C change in SST leading to a ~16 µatm change in the predicted pCO2. Application of the model to the entire WFS MODIS time series between 2002 and 2014 showed clear seasonality, with maxima (~450 µatm) in summer and minima (~350 µatm) in winter. The seasonality was positively correlated to SST (high in summer and low in winter) and negatively correlated to CHL and Kd_Lee (high in winter and low in summer). Inter-annual variations of pCO2 were consistent with inter-annual variations of SST, CHL, and Kd_Lee. These results suggest that surface water pCO2 of the WFS can be estimated, with known uncertainties, from remote sensing. However, while the general approach of empirical regression may work for waters from other areas of the Gulf of Mexico, model coefficients need to be empirically determined in a similar fashion.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.csr.2016.09.004","usgsCitation":"Chen, S., Hu, C., Byrne, R., Robbins, L.L., and Yang, B., 2016, Remote estimation of surface pCO2 on the West Florida Shelf: Continental Shelf Research, v. 128, p. 10-25, https://doi.org/10.1016/j.csr.2016.09.004.","productDescription":"16 p.","startPage":"10","endPage":"25","ipdsId":"IP-071209","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":462067,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.csr.2016.09.004","text":"Publisher Index Page"},{"id":356293,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"West Florida Shelf","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85,\n 24\n ],\n [\n -80,\n 24\n ],\n [\n -80,\n 30\n ],\n [\n -85,\n 30\n ],\n [\n -85,\n 24\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"128","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b6fc864e4b0f5d57878ec28","contributors":{"authors":[{"text":"Chen, Shuangling","contributorId":177054,"corporation":false,"usgs":false,"family":"Chen","given":"Shuangling","email":"","affiliations":[],"preferred":false,"id":654429,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hu, Chuanmin","contributorId":177055,"corporation":false,"usgs":false,"family":"Hu","given":"Chuanmin","email":"","affiliations":[],"preferred":false,"id":654430,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Byrne, Robert H.","contributorId":83260,"corporation":false,"usgs":true,"family":"Byrne","given":"Robert H.","affiliations":[],"preferred":false,"id":654431,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Robbins, Lisa L. 0000-0003-3681-1094 lrobbins@usgs.gov","orcid":"https://orcid.org/0000-0003-3681-1094","contributorId":422,"corporation":false,"usgs":true,"family":"Robbins","given":"Lisa","email":"lrobbins@usgs.gov","middleInitial":"L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":654428,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Yang, Bo","contributorId":149369,"corporation":false,"usgs":false,"family":"Yang","given":"Bo","email":"","affiliations":[{"id":13653,"text":"University South Florida","active":true,"usgs":false}],"preferred":false,"id":741896,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70178623,"text":"70178623 - 2016 - First direct evidence of long-distance seasonal movements and hibernation in a migratory bat","interactions":[],"lastModifiedDate":"2017-04-27T10:16:44","indexId":"70178623","displayToPublicDate":"2016-10-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"First direct evidence of long-distance seasonal movements and hibernation in a migratory bat","docAbstract":"

Understanding of migration in small bats has been constrained by limitations of techniques that were labor-intensive, provided coarse levels of resolution, or were limited to population-level inferences. Knowledge of movements and behaviors of individual bats have been unknowable because of limitations in size of tracking devices and methods to attach them for long periods. We used sutures to attach miniature global positioning system (GPS) tags and data loggers that recorded light levels, activity, and temperature to male hoary bats (Lasiurus cinereus). Results from recovered GPS tags illustrated profound differences among movement patterns by individuals, including one that completed a >1000 km round-trip journey during October 2014. Data loggers allowed us to record sub-hourly patterns of activity and torpor use, in one case over a period of 224 days that spanned an entire winter. In this latter bat, we documented 5 torpor bouts that lasted ≥16 days and a flightless period that lasted 40 nights. These first uses of miniature tags on small bats allowed us to discover that male hoary bats can make multi-directional movements during the migratory season and sometimes hibernate for an entire winter.

","language":"English","publisher":"Nature","doi":"10.1038/srep34585","usgsCitation":"Weller, T.J., Castle, K.T., Liechti, F., Hein, C.D., Schirmacher, M.R., and Cryan, P.M., 2016, First direct evidence of long-distance seasonal movements and hibernation in a migratory bat: Scientific Reports, v. 6, Article number 34585; 7 p., https://doi.org/10.1038/srep34585.","productDescription":"Article number 34585; 7 p.","ipdsId":"IP-077969","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":470528,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/srep34585","text":"Publisher Index Page"},{"id":331386,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","noUsgsAuthors":false,"publicationDate":"2016-10-04","publicationStatus":"PW","scienceBaseUri":"584144dfe4b04fc80e5073a5","contributors":{"authors":[{"text":"Weller, Theodore J.","contributorId":105961,"corporation":false,"usgs":false,"family":"Weller","given":"Theodore","email":"","middleInitial":"J.","affiliations":[{"id":13261,"text":"USDA Forest Service, Pacific Southwest Research Station, Davis, California","active":true,"usgs":false}],"preferred":false,"id":654600,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Castle, Kevin T.","contributorId":90616,"corporation":false,"usgs":true,"family":"Castle","given":"Kevin","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":654601,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Liechti, Felix","contributorId":177094,"corporation":false,"usgs":false,"family":"Liechti","given":"Felix","email":"","affiliations":[],"preferred":false,"id":654602,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hein, Cris D.","contributorId":73910,"corporation":false,"usgs":false,"family":"Hein","given":"Cris","email":"","middleInitial":"D.","affiliations":[{"id":12591,"text":"Bat Conservation International","active":true,"usgs":false}],"preferred":false,"id":654603,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schirmacher, Michael R.","contributorId":76635,"corporation":false,"usgs":false,"family":"Schirmacher","given":"Michael","email":"","middleInitial":"R.","affiliations":[{"id":12591,"text":"Bat Conservation International","active":true,"usgs":false}],"preferred":false,"id":654604,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cryan, Paul M. 0000-0002-2915-8894 cryanp@usgs.gov","orcid":"https://orcid.org/0000-0002-2915-8894","contributorId":2356,"corporation":false,"usgs":true,"family":"Cryan","given":"Paul","email":"cryanp@usgs.gov","middleInitial":"M.","affiliations":[{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":654605,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70184238,"text":"70184238 - 2016 - Potential interactions among disease, pesticides, water quality and adjacent land cover in amphibian habitats in the United States","interactions":[],"lastModifiedDate":"2018-08-09T12:24:22","indexId":"70184238","displayToPublicDate":"2016-10-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Potential interactions among disease, pesticides, water quality and adjacent land cover in amphibian habitats in the United States","docAbstract":"

To investigate interactions among disease, pesticides, water quality, and adjacent land cover, we collected samples of water, sediment, and frog tissue from 21 sites in 7 States in the United States (US) representing a variety of amphibian habitats. All samples were analyzed for > 90 pesticides and pesticide degradates, and water and frogs were screened for the amphibian chytrid fungus Batrachochytrium dendrobatidis (Bd) using molecular methods. Pesticides and pesticide degradates were detected frequently in frog breeding habitats (water and sediment) as well as in frog tissue. Fungicides occurred more frequently in water, sediment, and tissue than was expected based upon their limited use relative to herbicides or insecticides. Pesticide occurrence in water or sediment was not a strong predictor of occurrence in tissue, but pesticide concentrations in tissue were correlated positively to agricultural and urban land, and negatively to forested land in 2-km buffers around the sites. Bd was detected in water at 45% of sites, and on 34% of swabbed frogs. Bd detections in water were not associated with differences in land use around sites, but sites with detections had colder water. Frogs that tested positive for Bd were associated with sites that had higher total fungicide concentrations in water and sediment, but lower insecticide concentrations in sediments relative to frogs that were Bd negative. Bd concentrations on frog swabs were positively correlated to dissolved organic carbon, and total nitrogen and phosphorus, and negatively correlated to pH and water temperature.

Data were collected from a range of locations and amphibian habitats and represent some of the first field-collected information aimed at understanding the interactions between pesticides, land use, and amphibian disease. These interactions are of particular interest to conservation efforts as many amphibians live in altered habitats and may depend on wetlands embedded in these landscapes to survive.

","language":"English","publisher":"Elsevier","publisherLocation":"New York, NY","doi":"10.1016/j.scitotenv.2016.05.062","usgsCitation":"Battaglin, W.A., Smalling, K., Anderson, C.W., Calhoun, D.L., Chestnut, T.E., and Muths, E.L., 2016, Potential interactions among disease, pesticides, water quality and adjacent land cover in amphibian habitats in the United States: Science of the Total Environment, v. 566-567, p. 320-332, https://doi.org/10.1016/j.scitotenv.2016.05.062.","productDescription":"13 p.","startPage":"320","endPage":"332","ipdsId":"IP-073673","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":336833,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Colorado, Georgia, Idaho, 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Evaluation of the progress of management programs for invasive species is crucial for demonstrating impacts to stakeholders and strategic planning of resource allocation. Estimates of abundance before and after management activities can serve as a useful metric of population management programs. However, many methods of estimating population size are too labor intensive and costly to implement, posing restrictive levels of burden on operational programs. Removal models are a reliable method for estimating abundance before and after management using data from the removal activities exclusively, thus requiring no work in addition to management. We developed a Bayesian hierarchical model to estimate abundance from removal data accounting for varying levels of effort, and used simulations to assess the conditions under which reliable population estimates are obtained. We applied this model to estimate site-specific abundance of an invasive species, feral swine (Sus scrofa), using removal data from aerial gunning in 59 site/time-frame combinations (480–19,600 acres) throughout Oklahoma and Texas, USA. Simulations showed that abundance estimates were generally accurate when effective removal rates (removal rate accounting for total effort) were above 0.40. However, when abundances were small (<50) the effective removal rate needed to accurately estimates abundances was considerably higher (0.70). Based on our post-validation method, 78% of our site/time frame estimates were accurate. To use this modeling framework it is important to have multiple removals (more than three) within a time frame during which demographic changes are minimized (i.e., a closed population; ≤3 months for feral swine). Our results show that the probability of accurately estimating abundance from this model improves with increased sampling effort (8+ flight hours across the 3-month window is best) and increased removal rate. Based on the inverse relationship between inaccurate abundances and inaccurate removal rates, we suggest auxiliary information that could be collected and included in the model as covariates (e.g., habitat effects, differences between pilots) to improve accuracy of removal rates and hence abundance estimates.

","language":"English","publisher":"Wiley","doi":"10.1002/eap.1383","usgsCitation":"Davis, A.J., Hooten, M., Miller, R.S., Farnsworth, M.L., Lewis, J., Moxcey, M., and Pepin, K., 2016, Inferring invasive species abundance using removal data from management actions: Ecological Applications, v. 26, no. 7, p. 2339-2346, https://doi.org/10.1002/eap.1383.","productDescription":"8 p.","startPage":"2339","endPage":"2346","ipdsId":"IP-067270","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":336748,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"26","issue":"7","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-19","publicationStatus":"PW","scienceBaseUri":"58b7eba6e4b01ccd5500bb01","contributors":{"authors":[{"text":"Davis, Amy J.","contributorId":149854,"corporation":false,"usgs":false,"family":"Davis","given":"Amy","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":680416,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false}],"preferred":true,"id":673716,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, Ryan S.","contributorId":49005,"corporation":false,"usgs":false,"family":"Miller","given":"Ryan","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":680417,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Farnsworth, Matthew L.","contributorId":56473,"corporation":false,"usgs":false,"family":"Farnsworth","given":"Matthew","email":"","middleInitial":"L.","affiliations":[{"id":12434,"text":"USDA, Wildlife Services, National Wildlife Research Center","active":true,"usgs":false}],"preferred":false,"id":680418,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lewis, Jesse S.","contributorId":147540,"corporation":false,"usgs":false,"family":"Lewis","given":"Jesse S.","affiliations":[{"id":12556,"text":"Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":680419,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Moxcey, Michael","contributorId":187442,"corporation":false,"usgs":false,"family":"Moxcey","given":"Michael","email":"","affiliations":[],"preferred":false,"id":680420,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pepin, Kim M. 0000-0002-9931-8312","orcid":"https://orcid.org/0000-0002-9931-8312","contributorId":187441,"corporation":false,"usgs":false,"family":"Pepin","given":"Kim M.","affiliations":[],"preferred":false,"id":680421,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70176821,"text":"70176821 - 2016 - Scaling relation between earthquake magnitude and the departure time from P wave similar growth","interactions":[],"lastModifiedDate":"2021-08-24T15:45:38.136043","indexId":"70176821","displayToPublicDate":"2016-10-01T00:00:00","publicationYear":"2016","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}},"displayTitle":"Scaling relation between earthquake magnitude and the departure time from P wave similar growth","title":"Scaling relation between earthquake magnitude and the departure time from P wave similar growth","docAbstract":"

We introduce a new scaling relation between earthquake magnitude (M) and a characteristic of initial P wave displacement. By examining Japanese K-NET data averaged in bins partitioned by Mw and hypocentral distance, we demonstrate that the P wave displacement briefly displays similar growth at the onset of rupture and that the departure time (Tdp), which is defined as the time of departure from similarity of the absolute displacement after applying a band-pass filter, correlates with the final M in a range of 4.5 ≤ Mw ≤ 7. The scaling relation between Mw and Tdp implies that useful information on the final M can be derived while the event is still in progress because Tdp occurs before the completion of rupture. We conclude that the scaling relation is important not only for earthquake early warning but also for the source physics of earthquakes.

","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2016GL070069","usgsCitation":"Noda, S., and Ellsworth, W.L., 2016, Scaling relation between earthquake magnitude and the departure time from P wave similar growth: Geophysical Research Letters, v. 43, no. 17, p. 9053-9060, https://doi.org/10.1002/2016GL070069.","productDescription":"8 p.","startPage":"9053","endPage":"9060","ipdsId":"IP-076652","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":470524,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016gl070069","text":"Publisher Index Page"},{"id":329428,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"43","issue":"17","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-15","publicationStatus":"PW","scienceBaseUri":"57fe679ee4b0824b2d14370d","contributors":{"authors":[{"text":"Noda, Shunta snoda@usgs.gov","contributorId":173999,"corporation":false,"usgs":true,"family":"Noda","given":"Shunta","email":"snoda@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":650458,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ellsworth, William L. ellsworth@usgs.gov","contributorId":787,"corporation":false,"usgs":true,"family":"Ellsworth","given":"William","email":"ellsworth@usgs.gov","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":650459,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70176822,"text":"70176822 - 2016 - Latest Pleistocene and Holocene glacial events in the Colonia valley, Northern Patagonia Icefield, southern Chile","interactions":[],"lastModifiedDate":"2016-10-11T11:54:47","indexId":"70176822","displayToPublicDate":"2016-10-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2437,"text":"Journal of Quaternary Science","active":true,"publicationSubtype":{"id":10}},"title":"Latest Pleistocene and Holocene glacial events in the Colonia valley, Northern Patagonia Icefield, southern Chile","docAbstract":"

The Northern Patagonia Icefield (NPI) is the primary glaciated terrain worldwide at its latitude (46.5–47.5°S), and constraining its glacial history provides unique information for reconstructing Southern Hemisphere paleoclimate. The Colonia Glacier is the largest outlet glacier draining the eastern NPI. Ages were determined using dendrochronology, lichenometry, radiocarbon, cosmogenic 10Be and optically stimulated luminescence. Dated moraines in the Colonia valley defined advances at 13.2 ± 0.95, 11.0 ± 0.47 and 4.96 ± 0.21 ka, with the last being the first constraint on the onset of Neoglaciation for the eastern NPI from a directly dated landform. Dating in the tributary Cachet valley, which contains an ice-dammed lake during periods of Colonia Glacier expansion, defined an advance at ca. 2.95 ± 0.21 ka, periods of advancement at 810 ± 49 cal a BP and 245 ± 13 cal a BP, and retreat during the intervening periods. Recent Colonia Glacier thinning, which began in the late 1800s, opened a lower-elevation outlet channel for Lago Cachet Dos in ca. 1960. Our data provide the most comprehensive set of Latest Pleistocene and Holocene ages for a single NPI outlet glacier and expand previously developed NPI glacial chronologies.

","language":"English","publisher":"Wiley","doi":"10.1002/jqs.2847","usgsCitation":"Nimick, D.A., Mcgrath, D., Mahan, S.A., Friesen, B.A., and Leidich, J., 2016, Latest Pleistocene and Holocene glacial events in the Colonia valley, Northern Patagonia Icefield, southern Chile: Journal of Quaternary Science, v. 31, no. 6, p. 551-564, https://doi.org/10.1002/jqs.2847.","productDescription":"14 p.","startPage":"551","endPage":"564","ipdsId":"IP-061075","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":470529,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/jqs.2847","text":"External Repository"},{"id":329423,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Chile","otherGeospatial":"Colonia valley, Northern Patagonia Icefield","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.0863037109375,\n -47.65428791076271\n ],\n [\n -74.0863037109375,\n -46.38862233816169\n ],\n [\n -72.66632080078125,\n -46.38862233816169\n ],\n [\n -72.66632080078125,\n -47.65428791076271\n ],\n [\n -74.0863037109375,\n -47.65428791076271\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"31","issue":"6","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2016-07-29","publicationStatus":"PW","scienceBaseUri":"57fe679de4b0824b2d14370b","contributors":{"authors":[{"text":"Nimick, David A. dnimick@usgs.gov","contributorId":421,"corporation":false,"usgs":true,"family":"Nimick","given":"David","email":"dnimick@usgs.gov","middleInitial":"A.","affiliations":[{"id":573,"text":"Special Applications Science Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":650460,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mcgrath, Daniel 0000-0002-9462-6842 dmcgrath@usgs.gov","orcid":"https://orcid.org/0000-0002-9462-6842","contributorId":145635,"corporation":false,"usgs":true,"family":"Mcgrath","given":"Daniel","email":"dmcgrath@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":650461,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mahan, Shannon A. 0000-0001-5214-7774 smahan@usgs.gov","orcid":"https://orcid.org/0000-0001-5214-7774","contributorId":147159,"corporation":false,"usgs":true,"family":"Mahan","given":"Shannon","email":"smahan@usgs.gov","middleInitial":"A.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":650462,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Friesen, Beverly A. bafriesen@usgs.gov","contributorId":3216,"corporation":false,"usgs":true,"family":"Friesen","given":"Beverly","email":"bafriesen@usgs.gov","middleInitial":"A.","affiliations":[{"id":573,"text":"Special Applications Science Center","active":true,"usgs":true}],"preferred":true,"id":650463,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Leidich, Jonathan","contributorId":139703,"corporation":false,"usgs":false,"family":"Leidich","given":"Jonathan","email":"","affiliations":[{"id":12885,"text":"Patagonia Adventure Expeditions","active":true,"usgs":false}],"preferred":false,"id":650464,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70179646,"text":"70179646 - 2016 - DOM composition and transformation in boreal forest soils: The effects of temperature and organic-horizon decomposition state","interactions":[],"lastModifiedDate":"2017-01-10T11:13:30","indexId":"70179646","displayToPublicDate":"2016-10-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2320,"text":"Journal of Geophysical Research: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"DOM composition and transformation in boreal forest soils: The effects of temperature and organic-horizon decomposition state","docAbstract":"

The boreal region stores large amounts of organic carbon (C) in organic-soil horizons, which are vulnerable to destabilization via warming and disturbance. Decomposition of soil organic matter (SOM) contributes to the production and turnover of dissolved organic matter (DOM). While temperature is a primary control on rates of SOM and DOM cycling, little is known about temperature effects on DOM composition in soil leachate. Here we conducted a 30 day incubation to examine the effects of temperature (20 versus 5°C) and SOM decomposition state (moss versus fibric versus amorphous horizons) on DOM composition in organic soils of interior Alaska. We characterized DOM using bulk dissolved organic C (DOC) concentration, chemical fractionation, optical properties, and ultrahigh-resolution mass spectrometry. We observed an increase in DOC concentration and DOM aromaticity in the 20°C treatment compared to the 5°C treatment. Leachate from fibric horizons had higher DOC concentration than shallow moss or deep amorphous horizons. We also observed chemical shifts in DOM leachate over time, including increases in hydrophobic organic acids, polyphenols, and condensed aromatics and decreases in low-molecular weight hydrophilic compounds and aliphatics. We compared ultrahigh-resolution mass spectrometry and optical data and observed strong correlations between polyphenols, condensed aromatics, SUVA254, and humic-like fluorescence intensities. These findings suggest that biolabile DOM was preferentially mineralized, and the magnitude of this transformation was determined by kinetics (i.e., temperature) and substrate quality (i.e., soil horizon). With future warming, our findings indicate that organic soils may release higher concentrations of aromatic DOM to aquatic ecosystems.

","language":"English","publisher":"AGU Publications","doi":"10.1002/2016JG003431","usgsCitation":"O’Donnell, J.A., Aiken, G.R., Butler, K.D., Guillemette, F., Podgorski, D.C., and Spencer, R., 2016, DOM composition and transformation in boreal forest soils: The effects of temperature and organic-horizon decomposition state: Journal of Geophysical Research: Biogeosciences, v. 121, no. 10, p. 2727-2744, https://doi.org/10.1002/2016JG003431.","productDescription":"18 p.","startPage":"2727","endPage":"2744","ipdsId":"IP-077855","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":470533,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016jg003431","text":"Publisher Index Page"},{"id":333013,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"121","issue":"10","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-10-29","publicationStatus":"PW","scienceBaseUri":"58760116e4b04eac8e0746e1","contributors":{"authors":[{"text":"O’Donnell, Jonathan A.","contributorId":178151,"corporation":false,"usgs":false,"family":"O’Donnell","given":"Jonathan","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":658042,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Aiken, George R. 0000-0001-8454-0984 graiken@usgs.gov","orcid":"https://orcid.org/0000-0001-8454-0984","contributorId":1322,"corporation":false,"usgs":true,"family":"Aiken","given":"George","email":"graiken@usgs.gov","middleInitial":"R.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":658041,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Butler, Kenna D. kebutler@usgs.gov","contributorId":3283,"corporation":false,"usgs":true,"family":"Butler","given":"Kenna","email":"kebutler@usgs.gov","middleInitial":"D.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":false,"id":658043,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Guillemette, Francois","contributorId":178152,"corporation":false,"usgs":false,"family":"Guillemette","given":"Francois","affiliations":[],"preferred":false,"id":658044,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Podgorski, David C.","contributorId":178153,"corporation":false,"usgs":false,"family":"Podgorski","given":"David","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":658045,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Spencer, Robert G. M.","contributorId":139731,"corporation":false,"usgs":false,"family":"Spencer","given":"Robert G. M.","affiliations":[{"id":12894,"text":"Department of Land, Air, and Water Resources, University of California, One Shields Avenue, Davis, CA, 95616, USA","active":true,"usgs":false}],"preferred":false,"id":658046,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70182773,"text":"70182773 - 2016 - The timing of compositionally-zoned magma reservoirs and mafic 'priming' weeks before the 1912 Novarupta-Katmai rhyolite eruption","interactions":[],"lastModifiedDate":"2017-03-01T14:43:11","indexId":"70182773","displayToPublicDate":"2016-10-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1427,"text":"Earth and Planetary Science Letters","active":true,"publicationSubtype":{"id":10}},"title":"The timing of compositionally-zoned magma reservoirs and mafic 'priming' weeks before the 1912 Novarupta-Katmai rhyolite eruption","docAbstract":"The June 6, 1912 eruption of more than 13 km3 of dense rock equivalent (DRE) magma at Novarupta vent, Alaska was the largest of the 20th century. It ejected >7 km3 of rhyolite, ~1.3 km3 of andesite and ~4.6 km3 of dacite. Early ideas about the origin of pyroclastic flows and magmatic differentiation (e.g., compositional zonation of reservoirs) were shaped by this eruption. Despite being well studied, the timing of events that led to the chemically and mineralogically zoned magma reservoir remain poorly known. Here we provide new insights using the textures and chemical compositions of plagioclase and orthopyroxene crystals and by reevaluating previous U-Th isotope data. Compositional zoning of the magma reservoir likely developed a few thousand years before the eruption by several additions of mafic magma below an extant silicic reservoir. Melt compositions calculated from Sr contents in plagioclase fill the compositional gap between 68 and 76% SiO2 in whole pumice clasts, consistent with uninterrupted crystal growth from a continuum of liquids. Thus, our findings support a general model in which large volumes of crystal-poor rhyolite are related to intermediate magmas through gradual separation of melt from crystal-rich mush. The rhyolite is incubated by, but not mixed with, episodic recharge pulses of mafic magma that interact thermochemically with the mush and intermediate magmas. Hot, Mg-, Ca-, and Al-rich mafic magma intruded into, and mixed with, deeper parts of the reservoir (andesite and dacite) multiple times. Modeling the relaxation of the Fe-Mg concentrations in orthopyroxene and Mg in plagioclase rims indicates that the final recharge event occurred just weeks prior to the eruption. Rapid addition of mass, volatiles, and heat from the recharge magma, perhaps aided by partial melting of cumulate mush below the andesite and dacite, pressurized the reservoir and likely propelled a ~10 km lateral dike that allowed the overlying rhyolite to reach the surface.","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2016.07.015","usgsCitation":"Singer, B.S., Costa, F., Herrin, J.S., Hildreth, W., and Fierstein, J., 2016, The timing of compositionally-zoned magma reservoirs and mafic 'priming' weeks before the 1912 Novarupta-Katmai rhyolite eruption: Earth and Planetary Science Letters, v. 451, p. 125-137, https://doi.org/10.1016/j.epsl.2016.07.015.","productDescription":"13 p. ","startPage":"125","endPage":"137","ipdsId":"IP-078234","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":470525,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.epsl.2016.07.015","text":"Publisher Index Page"},{"id":336778,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"451","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58b7eba6e4b01ccd5500bb03","contributors":{"authors":[{"text":"Singer, Brad S.","contributorId":184168,"corporation":false,"usgs":false,"family":"Singer","given":"Brad","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":673703,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Costa, Fidel","contributorId":184169,"corporation":false,"usgs":false,"family":"Costa","given":"Fidel","email":"","affiliations":[],"preferred":false,"id":673704,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Herrin, Jason S.","contributorId":184170,"corporation":false,"usgs":false,"family":"Herrin","given":"Jason","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":673705,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hildreth, Wes 0000-0002-7925-4251 hildreth@usgs.gov","orcid":"https://orcid.org/0000-0002-7925-4251","contributorId":2221,"corporation":false,"usgs":true,"family":"Hildreth","given":"Wes","email":"hildreth@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":680460,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fierstein, Judith 0000-0001-8024-1426 jfierstn@usgs.gov","orcid":"https://orcid.org/0000-0001-8024-1426","contributorId":147000,"corporation":false,"usgs":true,"family":"Fierstein","given":"Judith","email":"jfierstn@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":673707,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70185034,"text":"70185034 - 2016 - Controls on selenium distribution and mobilization in an irrigated shallow groundwater system underlain by Mancos Shale, Uncompahgre River Basin, Colorado, USA","interactions":[],"lastModifiedDate":"2017-03-15T11:16:36","indexId":"70185034","displayToPublicDate":"2016-10-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Controls on selenium distribution and mobilization in an irrigated shallow groundwater system underlain by Mancos Shale, Uncompahgre River Basin, Colorado, USA","docAbstract":"

Elevated selenium (Se) concentrations in surface water and groundwater have become a concern in areas of the Western United States due to the deleterious effects of Se on aquatic ecosystems. Elevated Se concentrations are most prevalent in irrigated alluvial valleys underlain by Se-bearing marine shales where Se can be leached from geologic materials into the shallow groundwater and surface water systems. This study presents groundwater chemistry and solid-phase geochemical data from the Uncompahgre River Basin in Western Colorado, an irrigated alluvial landscape underlain by Se-rich Cretaceous marine shale. We analyzed Se species, major and trace elements, and stable nitrogen and oxygen isotopes of nitrate in groundwater and aquifer sediments to examine processes governing selenium release and transport in the shallow groundwater system. Groundwater Se concentrations ranged from below detection limit (< 0.5 μg L− 1) to 4070 μg L− 1, and primarily are controlled by high groundwater nitrate concentrations that maintain oxidizing conditions in the aquifer despite low dissolved oxygen concentrations. High nitrate concentrations in non-irrigated soils and nitrate isotopes indicate nitrate is largely derived from natural sources in the Mancos Shale and alluvial material. Thus, in contrast to areas that receive substantial NO3 inputs through inorganic fertilizer application, Se mitigation efforts that involve limiting NO3 application might have little impact on groundwater Se concentrations in the study area. Soluble salts are the primary source of Se to the groundwater system in the study area at-present, but they constitute a small percentage of the total Se content of core material. Sequential extraction results indicate insoluble Se is likely composed of reduced Se in recalcitrant organic matter or discrete selenide phases. Oxidation of reduced Se species that constitute the majority of the Se pool in the study area could be a potential source of Se in the future as soluble salts are progressively depleted.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2016.06.063","usgsCitation":"Mills, T.J., Mast, M.A., Thomas, J.C., and Keith, G.L., 2016, Controls on selenium distribution and mobilization in an irrigated shallow groundwater system underlain by Mancos Shale, Uncompahgre River Basin, Colorado, USA: Science of the Total Environment, v. 566-567, p. 1621-1631, https://doi.org/10.1016/j.scitotenv.2016.06.063.","productDescription":"11 p.","startPage":"1621","endPage":"1631","ipdsId":"IP-072320","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":337598,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Uncompahgre River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.17550659179688,\n 38.41378642476067\n ],\n [\n -107.78961181640625,\n 38.41378642476067\n ],\n [\n -107.78961181640625,\n 38.79476766282312\n ],\n [\n -108.17550659179688,\n 38.79476766282312\n ],\n [\n -108.17550659179688,\n 38.41378642476067\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"566-567","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58ca52cee4b0849ce97c86aa","contributors":{"authors":[{"text":"Mills, Taylor J. 0000-0001-7252-0521 tmills@usgs.gov","orcid":"https://orcid.org/0000-0001-7252-0521","contributorId":4658,"corporation":false,"usgs":true,"family":"Mills","given":"Taylor","email":"tmills@usgs.gov","middleInitial":"J.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":684023,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mast, M. Alisa 0000-0001-6253-8162 mamast@usgs.gov","orcid":"https://orcid.org/0000-0001-6253-8162","contributorId":827,"corporation":false,"usgs":true,"family":"Mast","given":"M.","email":"mamast@usgs.gov","middleInitial":"Alisa","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":684024,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thomas, Judith C. 0000-0001-7883-1419 juthomas@usgs.gov","orcid":"https://orcid.org/0000-0001-7883-1419","contributorId":1468,"corporation":false,"usgs":true,"family":"Thomas","given":"Judith","email":"juthomas@usgs.gov","middleInitial":"C.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":684025,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Keith, Gabrielle L. gkeith@usgs.gov","contributorId":5247,"corporation":false,"usgs":true,"family":"Keith","given":"Gabrielle","email":"gkeith@usgs.gov","middleInitial":"L.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":684026,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70181012,"text":"70181012 - 2016 - Use of mineral/solution equilibrium calculations to assess the potential for carnotite precipitation from groundwater in the Texas Panhandle, USA","interactions":[],"lastModifiedDate":"2018-08-06T13:08:08","indexId":"70181012","displayToPublicDate":"2016-10-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":835,"text":"Applied Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Use of mineral/solution equilibrium calculations to assess the potential for carnotite precipitation from groundwater in the Texas Panhandle, USA","docAbstract":"

This study investigated the potential for the uranium mineral carnotite (K2(UO2)2(VO4)2·3H2O) to precipitate from evaporating groundwater in the Texas Panhandle region of the United States. The evolution of groundwater chemistry during evaporation was modeled with the USGS geochemical code PHREEQC using water-quality data from 100 groundwater wells downloaded from the USGS National Water Information System (NWIS) database. While most modeled groundwater compositions precipitated calcite upon evaporation, not all groundwater became saturated with respect to carnotite with the system open to CO2. Thus, the formation of calcite is not a necessary condition for carnotite to form. Rather, the determining factor in achieving carnotite saturation was the evolution of groundwater chemistry during evaporation following calcite precipitation. Modeling in this study showed that if the initial major-ion groundwater composition was dominated by calcium-magnesium-sulfate (>70 precent Ca + Mg and >50 percent SO4 + Cl) or calcium-magnesium-bicarbonate (>70 percent Ca + Mg and <70 percent HCO3 + CO3) and following the precipitation of calcite, the concentration of calcium was greater than the carbonate alkalinity (2mCa+2 > mHCO3− + 2mCO3−2) carnotite saturation was achieved. If, however, the initial major-ion groundwater composition is sodium-bicarbonate (varying amounts of Na, 40–100 percent Na), calcium-sodium-sulfate, or calcium-magnesium-bicarbonate composition (>70 percent HCO3 + CO3) and following the precipitation of calcite, the concentration of calcium was less than the carbonate alkalinity (2mCa+2 < mHCO3- + 2mCO3−2) carnotite saturation was not achieved. In systems open to CO2, carnotite saturation occurred in most samples in evaporation amounts ranging from 95 percent to 99 percent with the partial pressure of CO2 ranging from 10−3.5 to 10−2.5 atm. Carnotite saturation occurred in a few samples in evaporation amounts ranging from 98 percent to 99 percent with the partial pressure of CO2 equal to 10−2.0 atm. Carnotite saturation did not occur in any groundwater with the system closed to CO2.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2016.08.004","usgsCitation":"Ranalli, A.J., and Yager, D.B., 2016, Use of mineral/solution equilibrium calculations to assess the potential for carnotite precipitation from groundwater in the Texas Panhandle, USA: Applied Geochemistry, v. 73, p. 118-131, https://doi.org/10.1016/j.apgeochem.2016.08.004.","productDescription":"14 p.","startPage":"118","endPage":"131","ipdsId":"IP-069663","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":335173,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.07373046875,\n 33.925129700072\n ],\n [\n -103.07373046875,\n 36.50963615733049\n ],\n [\n -99.97558593749999,\n 36.50963615733049\n ],\n [\n -99.97558593749999,\n 33.925129700072\n ],\n [\n -103.07373046875,\n 33.925129700072\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"73","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"589fff23e4b099f50d3e0450","contributors":{"authors":[{"text":"Ranalli, Anthony J. tranalli@usgs.gov","contributorId":1195,"corporation":false,"usgs":true,"family":"Ranalli","given":"Anthony","email":"tranalli@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":true,"id":663275,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yager, Douglas B. 0000-0001-5074-4022 dyager@usgs.gov","orcid":"https://orcid.org/0000-0001-5074-4022","contributorId":798,"corporation":false,"usgs":true,"family":"Yager","given":"Douglas","email":"dyager@usgs.gov","middleInitial":"B.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":663274,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}