Newly emerging plants provide the best forage for herbivores. To exploit this fleeting resource, migrating herbivores align their movements to surf the wave of spring green-up. With new technology to track migrating animals, the Green Wave Hypothesis has steadily gained empirical support across a diversity of migratory taxa. This hypothesis assumes the green wave is controlled by variation in climate, weather, and topography, and its progression dictates the timing, pace, and extent of migrations. However, aggregate grazers that are also capable of engineering grassland ecosystems make some of the world’s most impressive migrations, and it is unclear how the green wave determines their movements. Here we show that Yellowstone’s bison (Bison bison) do not choreograph their migratory movements to the wave of spring green-up. Instead, bison modify the green wave as they migrate and graze. While most bison surfed during early spring, they eventually slowed and let the green wave pass them by. However, small-scale experiments indicated that feedback from grazing sustained forage quality. Most importantly, a 6-fold decadal shift in bison density revealed that intense grazing caused grasslands to green up faster, more intensely, and for a longer duration. Our finding broadens our understanding of the ways in which animal movements underpin the foraging benefit of migration. The widely accepted Green Wave Hypothesis needs to be revised to include large aggregate grazers that not only move to find forage, but also engineer plant phenology through grazing, thereby shaping their own migratory movements.
Constraining the magnitude of past hydrological change may improve understanding and predictions of future shifts in water availability. Here we demonstrate that water-table depth, a sensitive indicator of hydroclimate, can be quantitatively reconstructed using Kr and Xe isotopes in groundwater. We present the first-ever measurements of these dissolved noble gas isotopes in groundwater at high precision (≤0.005‰ amu−1; 1σ), which reveal depth-proportional signals set by gravitational settling in soil air at the time of recharge. Analyses of California groundwater successfully reproduce modern groundwater levels and indicate a 17.9 ± 1.3 m (±1 SE) decline in water-table depth in Southern California during the last deglaciation. This hydroclimatic transition from the wetter glacial period to more arid Holocene accompanies a surface warming of 6.2 ± 0.6 °C (±1 SE). This new hydroclimate proxy builds upon an existing paleo-temperature application of noble gases and may identify regions prone to future hydrological change.
","language":"English","publisher":"Nature","doi":"10.1038/s41467-019-13693-2","usgsCitation":"Seltzer, A.M., Ng, J., Danskin, W.R., Kulongoski, J.T., Gannon, R., Stute, M., and Severinghaus, J.P., 2019, Deglacial water-table decline in Southern California recorded by noble gas isotopes: Nature Communications, v. 10, 5739, 6 p., https://doi.org/10.1038/s41467-019-13693-2.","productDescription":"5739, 6 p.","ipdsId":"IP-108743","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":458949,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-019-13693-2","text":"Publisher Index Page"},{"id":391384,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"San Diego","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.44384765625,\n 32.56996256044998\n ],\n [\n -116.663818359375,\n 32.56996256044998\n ],\n [\n -116.663818359375,\n 32.99484290420988\n ],\n [\n -117.44384765625,\n 32.99484290420988\n ],\n [\n -117.44384765625,\n 32.56996256044998\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2019-12-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Seltzer, Alan M.","contributorId":192321,"corporation":false,"usgs":false,"family":"Seltzer","given":"Alan","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":826385,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ng, Jessica","contributorId":268304,"corporation":false,"usgs":false,"family":"Ng","given":"Jessica","email":"","affiliations":[{"id":38264,"text":"Scripps Institution of Oceanography","active":true,"usgs":false}],"preferred":false,"id":826386,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Danskin, Wesley R. 0000-0001-8672-5501 wdanskin@usgs.gov","orcid":"https://orcid.org/0000-0001-8672-5501","contributorId":1034,"corporation":false,"usgs":true,"family":"Danskin","given":"Wesley","email":"wdanskin@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":826387,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kulongoski, Justin T. 0000-0002-3498-4154 kulongos@usgs.gov","orcid":"https://orcid.org/0000-0002-3498-4154","contributorId":173457,"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":true,"id":826388,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gannon, Riley 0000-0002-1239-1083","orcid":"https://orcid.org/0000-0002-1239-1083","contributorId":205967,"corporation":false,"usgs":true,"family":"Gannon","given":"Riley","email":"","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":826389,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stute, Martin","contributorId":131127,"corporation":false,"usgs":false,"family":"Stute","given":"Martin","email":"","affiliations":[{"id":7254,"text":"Columbia University - Lamont Doherty Earth Observatory","active":true,"usgs":false}],"preferred":false,"id":826390,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Severinghaus, Jeffery P. 0000-0001-8883-3119","orcid":"https://orcid.org/0000-0001-8883-3119","contributorId":268306,"corporation":false,"usgs":false,"family":"Severinghaus","given":"Jeffery","email":"","middleInitial":"P.","affiliations":[{"id":38264,"text":"Scripps Institution of Oceanography","active":true,"usgs":false}],"preferred":false,"id":826391,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70228351,"text":"70228351 - 2019 - Evaluation of Potential Translocation Sites for an Imperiled Cyprinid, theHornyhead Chub","interactions":[],"lastModifiedDate":"2022-02-09T23:58:16.272557","indexId":"70228351","displayToPublicDate":"2019-12-15T17:52:09","publicationYear":"2019","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":"Evaluation of Potential Translocation Sites for an Imperiled Cyprinid, theHornyhead Chub","docAbstract":"Translocation of isolated species into suitable habitats may help secure vulnerable, geographically limited species. Due to the decline of Wyoming Hornyhead Chub Nocomis biguttatus, conservation actions such as translocation of populations within the plausible historical range are being considered to improve population redundancy and resiliency to disturbance events. Translocation of Wyoming Hornyhead Chub must be rigorously evaluated because a hatchery stock does not exist, so all fish used in translocations will come from the wild population. We present an approach to identify best available translocation sites prior to translocation efforts taking place. We evaluated fish community composition and habitat conditions at 54 potential translocation sites for Hornyhead Chub within 12 streams of the North Platte River Basin of Wyoming. We used two analyses to identify translocation sites most similar to currently occupied Hornyhead Chub sites on the Laramie River: hurdle models to predict hypothetical abundance of Hornyhead Chub at translocation sites and non-metric multidimensional scaling (NMDS) with fish community and habitat conditions. Presence and abundance of Hornyhead Chub was related to lack of nonnative predators and habitat features characteristic of backwater and velocity refuge habitats. We used a rank scoring system to weight the outcomes of each analysis and the highest ranking translocation sites occurred at a historical locality, the Sweetwater River. Our approach may be appropriate for other at-risk species with isolated distributions and little historical data.
","language":"English","doi":"10.1002/nafm.10261","usgsCitation":"Hickerson, B.T., and Walters, A.W., 2019, Evaluation of Potential Translocation Sites for an Imperiled Cyprinid, theHornyhead Chub: Transactions of the American Fisheries Society, v. 39, p. 205-218, https://doi.org/10.1002/nafm.10261.","productDescription":"14 p.","startPage":"205","endPage":"218","ipdsId":"IP-098524","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":395754,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Laramie River, North Platte River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.38635253906249,\n 41.970722347928096\n ],\n [\n -104.54315185546875,\n 41.970722347928096\n ],\n [\n -104.54315185546875,\n 42.20817645934742\n ],\n [\n -105.38635253906249,\n 42.20817645934742\n ],\n [\n -105.38635253906249,\n 41.970722347928096\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"39","noUsgsAuthors":false,"publicationDate":"2019-02-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Hickerson, Brian T.","contributorId":275272,"corporation":false,"usgs":false,"family":"Hickerson","given":"Brian","email":"","middleInitial":"T.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":833909,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walters, Annika W. 0000-0002-8638-6682 awalters@usgs.gov","orcid":"https://orcid.org/0000-0002-8638-6682","contributorId":4190,"corporation":false,"usgs":true,"family":"Walters","given":"Annika","email":"awalters@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":833908,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70215096,"text":"70215096 - 2019 - Time to branch out? Application of hierarchical survival models in plant phenology","interactions":[],"lastModifiedDate":"2020-10-08T11:54:37.37837","indexId":"70215096","displayToPublicDate":"2019-12-15T14:38:28","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":681,"text":"Agricultural and Forest Meteorology","active":true,"publicationSubtype":{"id":10}},"title":"Time to branch out? Application of hierarchical survival models in plant phenology","docAbstract":"The sensitivity of phenology to environmental drivers can vary across geography and species. As such, models developed to predict phenology are typically site- or taxon-specific. Generation of site- and taxon-specific models is limited by the intensive in-situ phenological monitoring effort required to generate sufficient data to parameterize each model. Where in-situ phenological observations exist, the data are often subject to analytical issues due to the limited duration of any individual monitoring program, spotty site- and species- level coverage, lack of standardized methodology, and infrequent or variable census intervals. Together, these characteristics constrain our ability to make phenological inferences outside of select sites and taxa where long-duration, intensive monitoring has occurred. In this study, we leveraged two national, standardized phenology datasets to develop a multi-species and multi-site state-space survival model of the onset of deciduous tree and shrub spring (leaf out) and fall (leaf-color) events across temperate ecoregions of the United States. We used data from two national-scale phenological databases, a 9-year, broadly distributed dataset from the USA National Phenology Network and a 4-year dataset from the National Ecological Observatory Network, to quantify regional and interspecific variation in sensitivity to environmental drivers for both spring and fall leaf phenophases. Spring leaf out was generally promoted by longer days, spring growing degree day accumulation, overwinter chilling, and was suppressed by frost events, whereas fall leaf color was promoted by shorter days and cold accumulation. The sensitivity to most environmental drivers tended to be more variable among species than among the regions as defined here (EPA ecoregions of North America, excluding desert and tropical areas). The results of this study lay the groundwork for incorporating the growing collection of phenological observations into a generalized framework for predicting the transition states for any species, in any location.","language":"English","publisher":"Elsevier","doi":"10.1016/j.agrformet.2019.107694","usgsCitation":"Elmendorf, S., Crimmins, T., Gerst, K.L., and Weltzin, J., 2019, Time to branch out? Application of hierarchical survival models in plant phenology: Agricultural and Forest Meteorology, v. 279, 107694, 8 p., https://doi.org/10.1016/j.agrformet.2019.107694.","productDescription":"107694, 8 p.","ipdsId":"IP-107695","costCenters":[{"id":433,"text":"National Phenology Network","active":true,"usgs":true}],"links":[{"id":458954,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.agrformet.2019.107694","text":"Publisher Index Page"},{"id":379194,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"279","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Elmendorf, Sarah","contributorId":147651,"corporation":false,"usgs":false,"family":"Elmendorf","given":"Sarah","affiliations":[{"id":16880,"text":"National Ecological Observatory Network (NEON), 1685 38th St., Boulder, CO 80301, USA","active":true,"usgs":false}],"preferred":false,"id":800827,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Crimmins, Theresa 0000-0001-9592-625X","orcid":"https://orcid.org/0000-0001-9592-625X","contributorId":222414,"corporation":false,"usgs":false,"family":"Crimmins","given":"Theresa","email":"","affiliations":[{"id":40537,"text":"USA National Phenology Network, National Coordinating Office; University of Arizona, School of Natural Resources and the Environment","active":true,"usgs":false}],"preferred":false,"id":800828,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gerst, Katharine L.","contributorId":175227,"corporation":false,"usgs":false,"family":"Gerst","given":"Katharine","email":"","middleInitial":"L.","affiliations":[{"id":27543,"text":"National Phenology Network, University of Arizona","active":true,"usgs":false}],"preferred":false,"id":800829,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Weltzin, Jake 0000-0001-8641-6645 jweltzin@usgs.gov","orcid":"https://orcid.org/0000-0001-8641-6645","contributorId":196323,"corporation":false,"usgs":true,"family":"Weltzin","given":"Jake","email":"jweltzin@usgs.gov","affiliations":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true},{"id":433,"text":"National Phenology Network","active":true,"usgs":true}],"preferred":true,"id":800830,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70216009,"text":"70216009 - 2019 - High rates of inflation during a noneruptive episode of seismic unrest at Semisopochnoi Volcano, Alaska in 2014–2015","interactions":[],"lastModifiedDate":"2020-11-11T14:35:23.35128","indexId":"70216009","displayToPublicDate":"2019-12-15T07:30:28","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2312,"text":"Journal of Geophysical Research","active":true,"publicationSubtype":{"id":10}},"title":"High rates of inflation during a noneruptive episode of seismic unrest at Semisopochnoi Volcano, Alaska in 2014–2015","docAbstract":"Magma intrusion rate is a key parameter in eruption triggering but is poorly quantified in existing geodetic studies. Here we examine two episodes of rapid inflation in this context. Two noneruptive microseismic swarms were recorded at Semisopochnoi Volcano, Alaska in 2014–2015. We use differential SAR techniques and TerraSAR‐X images to document surface deformation from 2011 to 2015, which comprises island‐wide radial inflation totaling ~25 cm (+/−1 cm) line of sight displacement in 2014–2015. Multiple source geometries are tested in an inversion of the deformation data, and InSAR data are best fit by a spheroid trending to the northeast and plunging to the southeast, with a major axis of ~4 km and minor axes of ~1 km, directly under the central caldera of Semisopochnoi. In 2014, a modeled influx of 0.043 km3 of magma caused line of sight displacement of ~17 cm. This magma was stored at a depth of ~8 km, until 2015 when 0.029 km3 was added. Along with the definition of inflation source parameters, the recorded seismic events are relocated using differential travel times. These relocated events outline a linear aseismic area within a larger zone of shallow (<10 km) seismicity. This aseismic region aligns with the centroid of the deformation model. Based on these geodetic and seismic models, the plumbing system at Semisopochnoi is interpreted as a spheroidal magma storage zone at a depth of ˜8 km below a linear feature of partial melt. The observed deformation and seismicity appear to result from rapid injection into this main storage region.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019GC008720","usgsCitation":"Degrandpre, K., Pesicek, J.D., Lu, Z., DeShon, H.R., and Roman, D., 2019, High rates of inflation during a noneruptive episode of seismic unrest at Semisopochnoi Volcano, Alaska in 2014–2015: Journal of Geophysical Research, v. 20, no. 12, p. 6163-6186, https://doi.org/10.1029/2019GC008720.","productDescription":"24 p.","startPage":"6163","endPage":"6186","ipdsId":"IP-098799","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":380068,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Semisopochnoi Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 179.47265625,\n 51.839171715043946\n ],\n [\n 179.77203369140625,\n 51.839171715043946\n ],\n [\n 179.77203369140625,\n 52.04742324502936\n ],\n [\n 179.47265625,\n 52.04742324502936\n ],\n [\n 179.47265625,\n 51.839171715043946\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"20","issue":"12","noUsgsAuthors":false,"publicationDate":"2019-12-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Degrandpre, Kimberly","contributorId":244311,"corporation":false,"usgs":false,"family":"Degrandpre","given":"Kimberly","email":"","affiliations":[{"id":20301,"text":"SMU","active":true,"usgs":false}],"preferred":false,"id":803746,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pesicek, Jeremy D. 0000-0001-7964-5845","orcid":"https://orcid.org/0000-0001-7964-5845","contributorId":202042,"corporation":false,"usgs":true,"family":"Pesicek","given":"Jeremy","email":"","middleInitial":"D.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":803747,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lu, Zhong","contributorId":199794,"corporation":false,"usgs":false,"family":"Lu","given":"Zhong","affiliations":[],"preferred":false,"id":803748,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DeShon, Heather R.","contributorId":244313,"corporation":false,"usgs":false,"family":"DeShon","given":"Heather","email":"","middleInitial":"R.","affiliations":[{"id":20301,"text":"SMU","active":true,"usgs":false}],"preferred":false,"id":803749,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Roman, Diana","contributorId":237832,"corporation":false,"usgs":false,"family":"Roman","given":"Diana","affiliations":[{"id":47620,"text":"Dept. of Terrestrial Magnetism, Carnegie Institution for Science, Washington DC 20015","active":true,"usgs":false}],"preferred":false,"id":803777,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70207382,"text":"70207382 - 2019 - Validating a landsat time-series of fractional component cover across western U.S. Rangelands","interactions":[],"lastModifiedDate":"2022-02-16T21:32:14.39285","indexId":"70207382","displayToPublicDate":"2019-12-13T19:22:20","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Validating a landsat time-series of fractional component cover across western U.S. Rangelands","docAbstract":"Western U.S. rangelands have been quantified as six fractional cover (0%–100%) components\nover the Landsat archive (1985–2018) at a 30 m resolution, termed the “Back-in-Time” (BIT) dataset. Robust validation through space and time is needed to quantify product accuracy. Here, we used field data collected concurrently with high-resolution satellite (HRS) images over multiple locations (n = 42) and years. Field observations were used to train regression tree models, predicting the component cover across each HRS image. Our objectives were to evaluate the spatial and temporal relationships between HRS and BIT component cover and compare spatio-temporal climate responses. First, for each HRS site-year (n = 77) we averaged both the HRS and BIT predictions within each site separately and regressed the averages to quantify the temporal accuracy. Next, we regressed individual pixel values of corresponding HRS and BIT predictions to quantify the spatio-temporal accuracy. Results showed strong temporal correlations with an average R2 of 0.63 and Root Mean Square Error (RMSE) of 5.47% as well as strong spatio-temporal correlations with an average R2 of 0.52 and RMSE of 7.89% across components. Our approach increased the validation sample size relative to direct comparison of field observations. Validation results showed robust spatio-temporal relationships between HRS and BIT data, providing increased user confidence in the data.","language":"English","publisher":"MPDI","doi":"10.3390/rs11243009","usgsCitation":"Rigge, M.B., Homer, C.G., Shi, H., and Meyer, D.K., 2019, Validating a landsat time-series of fractional component cover across western U.S. Rangelands: Remote Sensing, v. 11, no. 24, 3009, 16 p.; Data release, https://doi.org/10.3390/rs11243009.","productDescription":"3009, 16 p.; Data release","ipdsId":"IP-113763","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":458961,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11243009","text":"Publisher Index Page"},{"id":370436,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":396049,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90Q8BCP","text":"USGS data release","description":"USGS data release","linkHelpText":"Temporal and Spatio-Temporal High-Resolution Satellite Data for the Validation of a Landsat Time-Series of Fractional Component Cover Across Western United States (U.S.) Rangelands"}],"country":"Unites States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.05859375,\n 38.89103282648846\n ],\n [\n -115.31249999999999,\n 38.89103282648846\n ],\n [\n -115.31249999999999,\n 42.09822241118974\n ],\n [\n -120.05859375,\n 42.09822241118974\n ],\n [\n -120.05859375,\n 38.89103282648846\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.533203125,\n 41.04621681452063\n ],\n [\n -103.974609375,\n 41.04621681452063\n ],\n [\n -103.974609375,\n 45.213003555993964\n ],\n [\n -111.533203125,\n 45.213003555993964\n ],\n [\n -111.533203125,\n 41.04621681452063\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"24","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2019-12-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Rigge, Matthew B. 0000-0003-4471-8009 mrigge@usgs.gov","orcid":"https://orcid.org/0000-0003-4471-8009","contributorId":751,"corporation":false,"usgs":true,"family":"Rigge","given":"Matthew","email":"mrigge@usgs.gov","middleInitial":"B.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":777869,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Homer, Collin G. 0000-0003-4755-8135 homer@usgs.gov","orcid":"https://orcid.org/0000-0003-4755-8135","contributorId":2262,"corporation":false,"usgs":true,"family":"Homer","given":"Collin","email":"homer@usgs.gov","middleInitial":"G.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":777870,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shi, Hua 0000-0001-7013-1565 hshi@usgs.gov","orcid":"https://orcid.org/0000-0001-7013-1565","contributorId":646,"corporation":false,"usgs":true,"family":"Shi","given":"Hua","email":"hshi@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":777871,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Meyer, Debra K. 0000-0002-8841-697X dkmeyer@usgs.gov","orcid":"https://orcid.org/0000-0002-8841-697X","contributorId":3145,"corporation":false,"usgs":true,"family":"Meyer","given":"Debra","email":"dkmeyer@usgs.gov","middleInitial":"K.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":777872,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70207249,"text":"ofr20191128 - 2019 - Depth to bedrock based on modeling of gravity data of the eastern part of Edwards Air Force Base, California","interactions":[],"lastModifiedDate":"2019-12-14T06:09:21","indexId":"ofr20191128","displayToPublicDate":"2019-12-13T11:19:45","publicationYear":"2019","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":"2019-1128","displayTitle":"Depth to Bedrock Based on Modeling of Gravity Data of the Eastern Part of Edwards Air Force Base, California","title":"Depth to bedrock based on modeling of gravity data of the eastern part of Edwards Air Force Base, California","docAbstract":"We describe a gravity survey acquired to determine the thickness of basin-fill deposits (depth to bedrock) and to delineate geologic structures that might influence groundwater flow beneath the eastern part of Edwards Air Force Base, California. Inversion of these gravity data combined with geologic map and well information provides an estimate of the thickness of basin-fill deposits (defined here as Cenozoic sedimentary and volcanic rocks). After removing the gravitational effect of the basin-fill deposits, the inversion also results in a gravity map that reflects variations in the bedrock density. The depth to bedrock is generally less than 1 kilometer in the map area, except for localized depressions north and south of Kramer Hills, northwest-trending pockets about 4 kilometers northeast of Rogers Lake, and a large depression southwest of Rogers Lake. In the area near Leuhman Ridge, depth to bedrock is shallow. The Spring and Leuhman faults do not coincide with large variations in basin-fill thickness or with prominent gravity gradients, suggestive of minor vertical displacement and minor horizontal displacement at their southeastern mapped extents where they project across a large gravity low.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191128","collaboration":"Prepared in cooperation with the Air Force Civil Engineer Center","usgsCitation":"Langenheim, V.E., Morita, A., Christensen, A.H., Cromwell, G., and Ely, C., 2019, Depth to bedrock based on modeling of gravity data of the eastern part of Edwards Air Force Base, California: U.S. Geological Survey Open-File Report 2019–1128, 12 p., https://doi.org/10.3133/ofr20191128.\n","productDescription":"Report: iv, 12 p.; Dataset; Metadata","numberOfPages":"12","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-109233","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":370252,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1128/ofr20191128.pdf","text":"Report","size":"8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1128"},{"id":370253,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://pubs.usgs.gov/of/2019/1128/ofr20191128_basementwells.csv","text":"Basement Wells","size":"5 KB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2019-1128"},{"id":370254,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://pubs.usgs.gov/of/2019/1128/ofr20191128_basinwells.csv","text":"Basin Wells","size":"6.5 KB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2019-1128"},{"id":370251,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1128/coverthb.jpg"},{"id":370255,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://pubs.usgs.gov/of/2019/1128/ofr20191128_depthtobedrock.csv","text":"Depth to Bedrock","size":"1 MB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2019-1128"},{"id":370256,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://pubs.usgs.gov/of/2019/1128/ofr20191128_gravitydata.csv","text":"Gravity Data","size":"225 KB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2019-1128"},{"id":370257,"rank":7,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/of/2019/1128/ofr20191128_metadata.xml","size":"22 KB xml","description":"OFR 2019-1128"},{"id":370258,"rank":8,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/of/2019/1128/ofr20191128_readmedata.rtf","size":"15 KB","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2019-1128"}],"country":"United States","state":"California","otherGeospatial":"Edwards Air Force Base","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.10302734374999,\n 34.7506398050501\n ],\n [\n -117.65258789062499,\n 34.7506398050501\n ],\n [\n -117.65258789062499,\n 35.0254981588326\n ],\n [\n -118.10302734374999,\n 35.0254981588326\n ],\n [\n -118.10302734374999,\n 34.7506398050501\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Geology, Minerals, Energy, & Geophysics Science Center
Menlo Park, California
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
Worldwide, human activities have modified hydrology and nutrient loading regimes in coastal wetlands. Understanding the interplay between these drivers and subsequent response of wetland plant communities is essential to informing wetland management and restoration efforts. Recent restoration strategies in Louisiana proposes to use sediment diversions from the Mississippi River to build land in adjacent wetlands and reduce the rate of land to open water conversion. In conjunction with sediment delivery, diversions can increase nutrient loads and water levels in the receiving basins. We conducted a greenhouse mesocosm experiment in which we exposed three common tidal freshwater and brackish marsh plants (Panicum hemitomon, Sagittaria lancifolia, and Spartina patens) to two nitrate loading rates [high (35 g N m2 year−1) and low (0.25 g N m2 year−1)], and two flooding treatments (with and without diversion pulsing). Experimental units were set at two different elevations within the treatment tanks to simulate both a healthy and degraded marsh. Plant growth metrics and soil physicochemical properties were measured monthly. Final total biomass was determined at the study’s conclusion. Growth responses differed between species but were not significantly influenced by the treatments. Soil redox potential decreased significantly following the increase in flooding associated with the diversion pulse, but recovered to pre-diversion levels after a 3-month recovery period. Our study suggests short flooding pulses with a recovery period may be key for maintaining healthy marshes, however there remains a need for longer-term empirical studies to understand marsh response to pressures associated with river sediment diversions over time.
","language":"English","publisher":"Springer","doi":"10.1007/s11273-019-09699-8","usgsCitation":"McCoy, M.M., Sloey, T.M., Howard, R.J., and Hester, M.W., 2019, Response of tidal marsh vegetation to pulsed increases in flooding and nitrogen: Wetlands Ecology and Management, v. 28, p. 119-135, https://doi.org/10.1007/s11273-019-09699-8.","productDescription":"17 p.","startPage":"119","endPage":"135","ipdsId":"IP-106945","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":370302,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Jean Lafitte National Historical Park and Preserve, Lake Salvador Wildlife Management Area ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 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M","contributorId":149516,"corporation":false,"usgs":false,"family":"Sloey","given":"Taylor","email":"","middleInitial":"M","affiliations":[{"id":17763,"text":"University of Louisiana, Lafayette","active":true,"usgs":false}],"preferred":false,"id":777557,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Howard, Rebecca J. 0000-0001-7264-4364 howardr@usgs.gov","orcid":"https://orcid.org/0000-0001-7264-4364","contributorId":2429,"corporation":false,"usgs":true,"family":"Howard","given":"Rebecca","email":"howardr@usgs.gov","middleInitial":"J.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":777555,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hester, Mark W.","contributorId":195572,"corporation":false,"usgs":false,"family":"Hester","given":"Mark","email":"","middleInitial":"W.","affiliations":[{"id":34316,"text":"University of Louisiana at Lafayette, Lafayette, LA, USA","active":true,"usgs":false}],"preferred":false,"id":777558,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70207510,"text":"70207510 - 2019 - Developing and optimizing shrub parameters representing sagebrush (Artemisia spp.) ecosystems in the Northern Great Basin using the Ecosystem Demography (EDv2.2) model","interactions":[],"lastModifiedDate":"2019-12-22T14:03:15","indexId":"70207510","displayToPublicDate":"2019-12-12T14:00:55","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1818,"text":"Geoscientific Model Development","active":true,"publicationSubtype":{"id":10}},"title":"Developing and optimizing shrub parameters representing sagebrush (Artemisia spp.) ecosystems in the Northern Great Basin using the Ecosystem Demography (EDv2.2) model","docAbstract":"Ecosystem dynamic models are useful for understanding ecosystem characteristics over time and space because of their efficiency over direct field measurements and applicability to broad spatial extents. Their application, however, is challenging due to internal model uncertainties and complexities arising from distinct qualities of the ecosystems being analyzed. The sagebrush-steppe in western North America, for example, has substantial spatial and temporal heterogeneity as well as variability due to anthropogenic disturbance, invasive species, climate change, and altered fire regimes, which collectively make modelling dynamic ecosystem processes difficult. Ecosystem Demography (EDv2.2) is a robust ecosystem dynamic model, initially developed for tropical forests, that simulates energy, water, and carbon fluxes at fine scales. Although EDv2.2 has since been tested on different ecosystems via development of different Plant Function Types (PFT), it still lacks a shrub PFT. In this study, we developed and parameterized a shrub PFT representative of sagebrush (Artemisia spp.) ecosystems in order to initialize and test it within EDv2.2, and to promote future broad-scale analysis of restoration activities, climate change, and fire regimes in the sagebrush-steppe. Specifically, we parameterized the sagebrush PFT within EDv2.2 to estimate gross primary production (GPP), using data from two sagebrush study sites in the northern Great Basin. To accomplish this, we employed a three-tier approach: 1) To initially parameterize the sagebrush PFT, we fitted allometric relationships for sagebrush using field-collected data, information from existing sagebrush literature, and parameters from other land models. 2) To determine influential parameters in GPP prediction, we used a sensitivity analysis to identify the five most sensitive parameters. 3) To improve model performance and validate results, we optimized these five parameters using an exhaustive search method to estimate GPP, and compared results with observations from two Eddy Covariance (EC) sites in the study area. Our modeled results were encouraging, with reasonable fidelity to observed values, although some negative biases (i.e., seasonal underestimates of GPP) were apparent.","language":"English","publisher":"European Geosciences Union","doi":"10.5194/gmd-12-4585-2019","usgsCitation":"Pandit, K., Dasthi, H., Glenn, N., Flores, A., Maguire, K.C., Shinneman, D.J., Flerchinger, G., and Fellow, A., 2019, Developing and optimizing shrub parameters representing sagebrush (Artemisia spp.) ecosystems in the Northern Great Basin using the Ecosystem Demography (EDv2.2) model: Geoscientific Model Development, v. 12, p. 4585-4601, https://doi.org/10.5194/gmd-12-4585-2019.","productDescription":"17 p.","startPage":"4585","endPage":"4601","ipdsId":"IP-102648","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":458969,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/gmd-12-4585-2019","text":"Publisher Index Page"},{"id":370607,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Great Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.56347656249999,\n 42.032974332441405\n ],\n [\n -118.16894531249999,\n 35.35321610123823\n ],\n [\n -112.2802734375,\n 34.59704151614417\n ],\n [\n -109.248046875,\n 38.37611542403604\n ],\n [\n -110.0830078125,\n 43.13306116240612\n ],\n [\n -112.8955078125,\n 44.02442151965934\n ],\n [\n -115.6201171875,\n 43.58039085560784\n ],\n [\n -119.35546875000001,\n 44.15068115978094\n ],\n [\n -121.025390625,\n 44.08758502824516\n ],\n [\n -122.56347656249999,\n 42.032974332441405\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2019-11-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Pandit, Karun","contributorId":221464,"corporation":false,"usgs":false,"family":"Pandit","given":"Karun","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":778308,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dasthi, Hamid","contributorId":221465,"corporation":false,"usgs":false,"family":"Dasthi","given":"Hamid","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":778309,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Glenn, Nancy","contributorId":181558,"corporation":false,"usgs":false,"family":"Glenn","given":"Nancy","affiliations":[],"preferred":false,"id":778310,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Flores, Alejandro","contributorId":221466,"corporation":false,"usgs":false,"family":"Flores","given":"Alejandro","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":778311,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Maguire, Kaitlin C. 0000-0001-8193-2384","orcid":"https://orcid.org/0000-0001-8193-2384","contributorId":203419,"corporation":false,"usgs":true,"family":"Maguire","given":"Kaitlin","email":"","middleInitial":"C.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":778312,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shinneman, Douglas J. 0000-0002-4909-5181 dshinneman@usgs.gov","orcid":"https://orcid.org/0000-0002-4909-5181","contributorId":147745,"corporation":false,"usgs":true,"family":"Shinneman","given":"Douglas","email":"dshinneman@usgs.gov","middleInitial":"J.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":778307,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Flerchinger, Gerald","contributorId":221467,"corporation":false,"usgs":false,"family":"Flerchinger","given":"Gerald","affiliations":[{"id":37009,"text":"USDA Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":778313,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fellow, Aaron","contributorId":221468,"corporation":false,"usgs":false,"family":"Fellow","given":"Aaron","email":"","affiliations":[{"id":37009,"text":"USDA Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":778314,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70210612,"text":"70210612 - 2019 - Generation of lamprey monoclonal antibodies (Lampribodies) using the phage display system","interactions":[],"lastModifiedDate":"2020-06-12T17:22:56.373722","indexId":"70210612","displayToPublicDate":"2019-12-12T12:19:10","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5966,"text":"Biomolecules","active":true,"publicationSubtype":{"id":10}},"title":"Generation of lamprey monoclonal antibodies (Lampribodies) using the phage display system","docAbstract":"The variable lymphocyte receptors (VLRs) consist of leucine rich repeats (LRRs) and comprise the humoral antibodies produced by lampreys and hagfishes. The diversity of the molecules is generated by stepwise genomic rearrangements of LRR cassettes dispersed throughout the VLRB locus. Previously, target-specific monovalent VLRB antibodies were isolated from sea lamprey larvae after immunization with model antigens. Further, the cloned VLR cDNAs from activated lamprey leukocytes were transfected into human cell lines or yeast to select best binders. Here, we expand on the overall utility of the VLRB technology by introducing it into a filamentous phage display system. We first tested the efficacy of isolating phage into which known VLRB molecules were cloned after a series of dilutions. These experiments showed that targeted VLRB clones could easily be recovered even after extensive dilutions (1 to 109). We further utilized the system to isolate target-specific “lampribodies” from phage display libraries from immunized animals and observed an amplification of binders with relative high affinities by competitive binding. The lampribodies can be individually purified and ostensibly utilized for applications for which conventional monoclonal antibodies are employed.
","language":"English","publisher":"MDPI","doi":"10.3390/biom9120868","usgsCitation":"Hassan, K.M., Hansen, J.D., Herrin, B.R., and Amemiya, C.T., 2019, Generation of lamprey monoclonal antibodies (Lampribodies) using the phage display system: Biomolecules, v. 9, no. 12, 868, 18 p., https://doi.org/10.3390/biom9120868.","productDescription":"868, 18 p.","ipdsId":"IP-107248","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":458972,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/biom9120868","text":"Publisher Index Page"},{"id":375562,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"12","noUsgsAuthors":false,"publicationDate":"2019-12-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Hassan, Khan M A","contributorId":225255,"corporation":false,"usgs":false,"family":"Hassan","given":"Khan","email":"","middleInitial":"M A","affiliations":[{"id":41083,"text":"University of California-Merced, Molecular Cell Biology, Merced CA 95343","active":true,"usgs":false}],"preferred":false,"id":790844,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hansen, John D. 0000-0002-3006-2734","orcid":"https://orcid.org/0000-0002-3006-2734","contributorId":220725,"corporation":false,"usgs":true,"family":"Hansen","given":"John","middleInitial":"D.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":790845,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Herrin, Brantley R","contributorId":225256,"corporation":false,"usgs":false,"family":"Herrin","given":"Brantley","email":"","middleInitial":"R","affiliations":[{"id":41084,"text":"Emory University, Department of Pathology and Laboratory Medicine, Atlanta GA 30322 USA","active":true,"usgs":false}],"preferred":false,"id":790846,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Amemiya, Chris T","contributorId":225257,"corporation":false,"usgs":false,"family":"Amemiya","given":"Chris","email":"","middleInitial":"T","affiliations":[{"id":41083,"text":"University of California-Merced, Molecular Cell Biology, Merced CA 95343","active":true,"usgs":false}],"preferred":false,"id":790847,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70208062,"text":"70208062 - 2019 - High-resolution and accurate topography reconstruction of Mount Etna from Pleiades satellite data","interactions":[],"lastModifiedDate":"2020-01-29T16:34:42","indexId":"70208062","displayToPublicDate":"2019-12-12T07:32:03","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"High-resolution and accurate topography reconstruction of Mount Etna from Pleiades satellite data","docAbstract":"The areas characterized by dynamic and rapid morphological changes need accurate topography information with frequent updates, especially if these are populated and involve infrastructures. This is particularly true in active volcanic areas such as Mount (Mt.) Etna, located in the northeastern portion of Sicily, Italy. The Mt. Etna volcano is periodically characterized by explosive and effusive eruptions and represents a potential hazard for several thousands of local people and hundreds of tourists present on the volcano itself. In this work, a high-resolution, high vertical accuracy digital surface model (DSM) of Mt. Etna was derived from Pleiades satellite data using the National Aeronautics and Space Administration (NASA) Ames Stereo Pipeline (ASP) tool set. We believe that this is the first time that the ASP using Pleiades imagery has been applied to Mt. Etna with sub-meter vertical root mean square error (RMSE) results. The model covers an area of about 400 km2 with a spatial resolution of 2 m and centers on the summit portion of the volcano. The model was validated by using a set of reference ground control points (GCP) obtaining a vertical RMSE of 0.78 m. The described procedure provides an avenue to obtain DSMs at high spatial resolution and elevation accuracy in a relatively short amount of processing time, making the procedure itself suitable to reproduce topographies often indispensable during the emergency management case of volcanic eruptions.
","language":"English","publisher":"MDPI","doi":"10.3390/rs11242983","usgsCitation":"Palaseanu-Lovejoy, M., Bisson, M., Spinetti, C., Buongiorno, M.F., Alexandrov, O., and Cecere, T., 2019, High-resolution and accurate topography reconstruction of Mount Etna from Pleiades satellite data: Remote Sensing, v. 11, no. 24, 2983, 17 p., https://doi.org/10.3390/rs11242983.","productDescription":"2983, 17 p.","ipdsId":"IP-112349","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":458977,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11242983","text":"Publisher Index Page"},{"id":437261,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IGLDYE","text":"USGS data release","linkHelpText":"Digital Surface Model of Mt. Etna, Italy, derived from 2015 Pleiades Satellite Imagery"},{"id":371637,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Italy","otherGeospatial":"Mount Etna","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 14.84733581542969,\n 37.62238973852369\n ],\n [\n 15.128173828125,\n 37.62238973852369\n ],\n [\n 15.128173828125,\n 37.84015683604136\n ],\n [\n 14.84733581542969,\n 37.84015683604136\n ],\n [\n 14.84733581542969,\n 37.62238973852369\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"24","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-12-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Palaseanu-Lovejoy, Monica 0000-0002-3786-5118 mpal@usgs.gov","orcid":"https://orcid.org/0000-0002-3786-5118","contributorId":3639,"corporation":false,"usgs":true,"family":"Palaseanu-Lovejoy","given":"Monica","email":"mpal@usgs.gov","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true}],"preferred":true,"id":780322,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bisson, Marina 0000-0002-7104-9210","orcid":"https://orcid.org/0000-0002-7104-9210","contributorId":221724,"corporation":false,"usgs":false,"family":"Bisson","given":"Marina","email":"","affiliations":[{"id":40408,"text":"Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Pisa, via Della Faggiola, Pisa, 56126, Italy","active":true,"usgs":false}],"preferred":false,"id":780323,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Spinetti, Claudia 0000-0002-1861-5666","orcid":"https://orcid.org/0000-0002-1861-5666","contributorId":221725,"corporation":false,"usgs":false,"family":"Spinetti","given":"Claudia","email":"","affiliations":[{"id":40409,"text":"Istituto Nazionale di Geofisica e Vulcanologia, Sezione ONT, via di Vigna Murata, Roma, 00143, Italy","active":true,"usgs":false}],"preferred":false,"id":780324,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Buongiorno, Maria Fabrizia 0000-0002-6095-6974","orcid":"https://orcid.org/0000-0002-6095-6974","contributorId":221726,"corporation":false,"usgs":false,"family":"Buongiorno","given":"Maria","email":"","middleInitial":"Fabrizia","affiliations":[{"id":40409,"text":"Istituto Nazionale di Geofisica e Vulcanologia, Sezione ONT, via di Vigna Murata, Roma, 00143, Italy","active":true,"usgs":false}],"preferred":false,"id":780325,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Alexandrov, Oleg","contributorId":167662,"corporation":false,"usgs":false,"family":"Alexandrov","given":"Oleg","email":"","affiliations":[{"id":24796,"text":"NASA Ames Research Center","active":true,"usgs":false}],"preferred":false,"id":780326,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cecere, Thomas 0000-0001-5254-8404 tcecere@usgs.gov","orcid":"https://orcid.org/0000-0001-5254-8404","contributorId":221727,"corporation":false,"usgs":true,"family":"Cecere","given":"Thomas","email":"tcecere@usgs.gov","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":780327,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70208671,"text":"70208671 - 2019 - A pragmatic approach for comparing species distribution models to increasing confidence in managing piping plover habitat","interactions":[],"lastModifiedDate":"2020-02-24T19:21:44","indexId":"70208671","displayToPublicDate":"2019-12-11T19:18:17","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5803,"text":"Conservation Science and Practice","active":true,"publicationSubtype":{"id":10}},"title":"A pragmatic approach for comparing species distribution models to increasing confidence in managing piping plover habitat","docAbstract":"Conservation management often requires decision-making without perfect knowledge of the at-risk species or ecosystem. Species distribution models (SDMs) are useful but largely under-utilized due to model uncertainty. We provide a case study that utilizes an ensemble modeling approach of two independently derived SDMs to explicitly address common modeling impediments and to directly inform conservation decision-making for piping plovers in a heavily populated mid-Atlantic (USA) coastal zone. We summarized previously published Bayesian network and maximum entropy modeling approaches to highlight similarities and differences in model structure, and we compared the relative importance of predictors used. Despite marked differences in analytical approach, the relative importance of factors driving nest-site selection was consistent. Comparison of raw suitability scores revealed high dissimilarity between modeling approaches, but models demonstrated considerable agreement when comparing a binary (suitable/unsuitable) measure of suitability. Instances of model consensus (i.e., overlapping areas of predicted piping plover nesting habitat between models) provide a stronger ‘signal’ in model results, reducing uncertainty related to biases or errors associated with either model. We tested model accuracy using a common dataset of plover nests initiated within the focal areas between 2013 and 2015, and we examined congruency in model outputs. Nearly 90% of all nests occurred in areas predicted suitable by at least one model, and at least 33% of the total nests were predicted in areas suitable by both. Because models predominantly agreed on what drives piping plover nest-site selection, areas predicted suitable by a single model should not be discounted. This case study demonstrates how models can effectively inform conservation planning by explicitly identifying the management objective, presenting robust evidence to allow managers to evaluate outcomes of alternative management decisions, and clearly communicating results that address real-world conservation problems. The results presented here can greatly increase the piping plover management community’s ability to prioritize candidate sites for future protection, manage existing nesting habitat appropriately, and make a compelling case for conservation actions against competing land use objectives. ","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/csp2.150","usgsCitation":"Maslo, B., Zeigler, S., Drake, E., Pover, T., and Plant, N.G., 2019, A pragmatic approach for comparing species distribution models to increasing confidence in managing piping plover habitat: Conservation Science and Practice, v. 2, no. 2, e150, 18 p., https://doi.org/10.1111/csp2.150.","productDescription":"e150, 18 p.","ipdsId":"IP-111943","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":458978,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/csp2.150","text":"Publisher Index Page"},{"id":372594,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Jersey, New York","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.24310302734375,\n 40.697299008636755\n ],\n [\n -73.6578369140625,\n 40.64938745451835\n ],\n [\n -73.9324951171875,\n 40.66188943992171\n ],\n [\n -74.102783203125,\n 40.65355504328839\n ],\n [\n -74.24560546875,\n 40.57015381856105\n ],\n [\n -74.28131103515625,\n 40.46575594018434\n ],\n [\n -74.08355712890625,\n 40.35700974577561\n ],\n [\n -74.06158447265624,\n 40.21873275657034\n ],\n [\n -74.20440673828125,\n 39.96238554917605\n ],\n [\n -74.36920166015625,\n 39.66702799810167\n ],\n [\n -74.56146240234375,\n 39.48496522541802\n ],\n [\n -74.67681884765624,\n 39.34491849236129\n ],\n [\n -74.5751953125,\n 39.27266344858914\n ],\n [\n -74.34722900390625,\n 39.35341418045878\n ],\n [\n -73.98468017578125,\n 39.72620102617506\n ],\n [\n -73.95721435546875,\n 40.113789191575236\n ],\n [\n -73.82537841796875,\n 40.46157664398329\n ],\n [\n -73.49578857421875,\n 40.55763465737646\n ],\n [\n -73.23211669921875,\n 40.697299008636755\n ],\n [\n -73.24310302734375,\n 40.697299008636755\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"2","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-12-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Maslo, Brooke","contributorId":192146,"corporation":false,"usgs":false,"family":"Maslo","given":"Brooke","email":"","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":782951,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zeigler, Sara 0000-0002-5472-769X","orcid":"https://orcid.org/0000-0002-5472-769X","contributorId":222703,"corporation":false,"usgs":true,"family":"Zeigler","given":"Sara","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":782950,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Drake, Evan","contributorId":222704,"corporation":false,"usgs":false,"family":"Drake","given":"Evan","email":"","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":782952,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pover, Todd","contributorId":222705,"corporation":false,"usgs":false,"family":"Pover","given":"Todd","email":"","affiliations":[{"id":40592,"text":"Conserve Wildlife Foundation of New Jersey","active":true,"usgs":false}],"preferred":false,"id":782954,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Plant, Nathaniel G. 0000-0002-5703-5672 nplant@usgs.gov","orcid":"https://orcid.org/0000-0002-5703-5672","contributorId":3503,"corporation":false,"usgs":true,"family":"Plant","given":"Nathaniel","email":"nplant@usgs.gov","middleInitial":"G.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true}],"preferred":true,"id":782953,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70198586,"text":"sir20185111 - 2019 - Recent sandy deposits at five northern California coastal wetlands — Stratigraphy, diatoms, and implications for storm and tsunami hazards","interactions":[],"lastModifiedDate":"2022-04-22T21:09:08.356356","indexId":"sir20185111","displayToPublicDate":"2019-12-11T15:33:18","publicationYear":"2019","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":"2018-5111","displayTitle":"Recent Sandy Deposits at Five Northern California Coastal Wetlands — Stratigraphy, Diatoms, and Implications for Storm and Tsunami Hazards","title":"Recent sandy deposits at five northern California coastal wetlands — Stratigraphy, diatoms, and implications for storm and tsunami hazards","docAbstract":"A recent geological record of inundation by tsunamis or storm surges is evidenced by deposits found within the first few meters of the modern surface at five wetlands on the northern California coast. The study sites include three locations in the Crescent City area (Marhoffer Creek marsh, Elk Creek wetland, and Sand Mine marsh), O’rekw marsh in the lower Redwood Creek alluvial valley, and Pillar Point marsh at the northern end of Half Moon Bay.
Contact Information
Pacific Coastal & Marine Science Center
U.S. Geological Survey
Pacific Science Center
2885 Mission St.
Santa Cruz, CA 95060
The location of a point on the landscape within a stream network (hydrologic position) can be an important predictive measure in hydrology. Hydrologic position is defined here by two metrics: lateral position and distance from stream to divide, both measured horizontally. Lateral position (dimensionless) is the relative position of a point between the stream and its watershed divide. Distance from stream to divide (units of length) is an indicator of position within a watershed: generally small near a confluence and generally large in headwater areas. Watersheds and watershed divides are defined here by Thiessen polygons rather than topographic divides. Lateral position and distance from stream to divide are also defined in the context of hydrologic order. Hydrologic order “n” is defined as the network of streams, and associated divides, of order n and higher. And given that a point can have different positions in different hydrologic orders the term multiorder hydrologic position (MOHP) is used to describe the ensemble of hydrologic positions. MOHP was mapped across the conterminous United States for nine hydrologic orders at a spatial resolution of 30 m (about 8.7 billion pixels). There are 18 metrics for each pixel. Four case studies are presented that use MOHP metrics as explanatory factors in random forest machine learning models. The case studies show that lower order MOHP metrics can serve as indicators of hydrologic process while higher‐order metrics serve as indicators of location. MOHP is shown to have utility as a predictor variable across a large range of scales (50,000 to 8,000,000 km2).
Glass composition-based correlations of volcanic ash (tephra) traditionally rely on extensive manual plotting. Many previous statistical methods for testing correlations are limited by using geochemical means, masking diagnostic variability. We suggest that machine learning classifiers can expedite correlation, quickly narrowing the list of likely candidates using well-trained models. Eruptives from Alaska's Aleutian Arc-Alaska Peninsula and Wrangell volcanic field were used as a test environment for 11 supervised classification algorithms, trained on nearly 2000 electron probe microanalysis measurements of glass major oxides, representing 10 volcanic sources. Artificial neural networks and random forests were consistently among the top-performing learners (accuracy and kappa > 0.96). Their combination as an average ensemble effectively improves their performance. Using this combined model on tephras from Eklutna Lake, south-central Alaska, showed that predictions match traditional methods and can speed correlation. Although classifiers are useful tools, they should aid expert analysis, not replace it. The Eklutna Lake tephras are mostly from Redoubt Volcano. Besides tephras from known Holocene-active sources, Holocene tephra geochemically consistent with Pleistocene Emmons Lake Volcanic Center (Dawson tephra), but from a yet unknown source, is evident. These tephras are mostly anchored by a highly resolved varved chronology and represent new important regional stratigraphic markers.
Recovering species are often limited to much smaller areas than they historically occupied. Conservation planning for the recovering species is often based on this limited range, which may simply be an artifact of where the surviving population persisted. Southern sea otters (Enhydra lutris nereis) were hunted nearly to extinction but recovered from a small remnant population on a remote stretch of the California outer coast, where most of their recovery has occurred. However, studies of recently-recolonized estuaries have revealed that estuaries can provide southern sea otters with high quality habitats featuring shallow waters, high production and ample food, limited predators, and protected haul-out opportunities. Moreover, sea otters can have strong effects on estuarine ecosystems, fostering seagrass resilience through their consumption of invertebrate prey. Using a combination of literature reviews, population modeling, and prey surveys we explored the former estuarine habitats outside the current southern sea otter range to determine if these estuarine habitats can support healthy sea otter populations. We found the majority of studies and conservation efforts have focused on populations in exposed, rocky coastal habitats. Yet historical evidence indicates that sea otters were also formerly ubiquitous in estuaries. Our habitat-specific population growth model for California’s largest estuary—San Francisco Bay—determined that it alone can support about 6,600 sea otters, more than double the 2018 California population. Prey surveys in estuaries currently with (Elkhorn Slough and Morro Bay) and without (San Francisco Bay and Drakes Estero) sea otters indicated that the availability of prey, especially crabs, is sufficient to support healthy sea otter populations. Combining historical evidence with our results, we show that conservation practitioners could consider former estuarine habitats as targets for sea otter and ecosystem restoration. This study reveals the importance of understanding how recovering species interact with all the ecosystems they historically occupied, both for improved conservation of the recovering species and for successful restoration of ecosystem functions and processes.
","language":"English","publisher":"PeerJ","doi":"10.7717/peerj.8100","usgsCitation":"Hughes, B.B., Wasson, K., Tinker, M., Williams, S.L., Carswell, L., Boyer, K.E., Beck, M.W., Eby, R., Scoles, R., Staedler, M.M., Espinosa, S., Hessing-Lewis, M., Foster, E.U., Beheshti, K., Grimes, T.M., Becker, B.H., Needles, L., Tomoleoni, J.A., Rudebusch, J., Hines, E.M., and Silliman, B.R., 2019, Species recovery and recolonization of past habitats: Lessons for science and conservation from sea otters in estuaries: PeerJ, v. 7, e8100, 30 p., https://doi.org/10.7717/peerj.8100.","productDescription":"e8100, 30 p.","ipdsId":"IP-098446","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":458985,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.8100","text":"Publisher Index Page"},{"id":371544,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Elkhorn Slough, Morro Bay, San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.62390136718749,\n 37.38325280195101\n ],\n [\n -121.8878173828125,\n 37.38325280195101\n ],\n [\n -121.8878173828125,\n 38.229550455326134\n ],\n [\n -122.62390136718749,\n 38.229550455326134\n ],\n [\n -122.62390136718749,\n 37.38325280195101\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.8170928955078,\n 36.79993834872292\n ],\n [\n -121.73057556152344,\n 36.79993834872292\n ],\n [\n -121.73057556152344,\n 36.87110680999585\n ],\n [\n -121.8170928955078,\n 36.87110680999585\n ],\n [\n -121.8170928955078,\n 36.79993834872292\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.97869873046875,\n 35.25907654252574\n ],\n [\n -120.76171875,\n 35.25907654252574\n ],\n [\n -120.76171875,\n 35.458432791026304\n ],\n [\n -120.97869873046875,\n 35.458432791026304\n ],\n [\n -120.97869873046875,\n 35.25907654252574\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2019-12-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Hughes, Brent B.","contributorId":201240,"corporation":false,"usgs":false,"family":"Hughes","given":"Brent","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":780221,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wasson, Kerstin","contributorId":221786,"corporation":false,"usgs":false,"family":"Wasson","given":"Kerstin","email":"","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":780222,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tinker, M. 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2019 - Trends in streamflow and concentrations and flux of nutrients and total suspended solids in the Upper White River at Muncie, near Nora, and near Centerton, Indiana","interactions":[],"lastModifiedDate":"2022-04-25T18:47:12.543093","indexId":"sir20195119","displayToPublicDate":"2019-12-10T16:08:12","publicationYear":"2019","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":"2019-5119","displayTitle":"Trends in Streamflow and Concentrations and Flux of Nutrients and Total Suspended Solids in the Upper White River at Muncie, near Nora, and near Centerton, Indiana","title":"Trends in streamflow and concentrations and flux of nutrients and total suspended solids in the Upper White River at Muncie, near Nora, and near Centerton, Indiana","docAbstract":"The U.S. Geological Survey (USGS), in cooperation with The Nature Conservancy, completed a study to estimate and assess trends in streamflow and annual mean concentrations and flux of nutrients (nitrate plus nitrite, total Kjeldahl nitrogen, and total phosphorus) and total suspended solids at three USGS streamgages (hereafter referred to as “study gages”) on the Upper White River at Muncie (USGS station 03347000), near Nora (USGS station 03351000), and near Centerton (USGS station 03354000), Indiana. Water-quality data used in the analyses were collected by several agencies between calendar years 1991 and 2017, and streamflow (discharge) data were collected by the USGS. For most of the water-quality constituents, there were suitable data to facilitate an analysis of the 26-year period extending from calendar years 1991 to 2017 (water years 1992 to 2017); however, shorter analytical periods were necessary for total Kjeldahl nitrogen for the study gages at Muncie and near Centerton and for total suspended solids for the study gage near Centerton.
Temporal trends in streamflows at the study gages for the period extending from water years 1978 to 2017 were assessed using Exploration and Graphics for RivEr Trends (EGRET) and Mann-Kendall and Pettitt tests. With just one exception, the annual maximum and mean daily streamflows and the annual minimum 7-day mean streamflows at the study gages demonstrated upward trends (increasing streamflows) in the EGRET analyses. The exception was the annual 7-day minimum streamflow at the study gage near Nora, which indicated no trend. Mann-Kendall tests also indicated that the average trend for the annual maximum daily, annual mean daily, and annual 7-day minimum streamflow statistics between water years 1978 and 2017 was upward at each of the study gages; however, only the trends in the annual mean daily streamflows at the study gage at Muncie and the annual maximum daily streamflows at the study gages near Nora and near Centerton were statistically significant at a 0.05 probability level. The Pettitt tests indicated that a statistically significant step trend (abrupt change) in annual mean daily streamflows occurred at each of the study gages around water year 2001.
The seasonal distributions of total suspended solids, total phosphorus, nitrate plus nitrite, and total Kjeldahl nitrogen concentrations at the study gages were evaluated to identify patterns and other distinguishing characteristics by examining boxplots of concentrations as a function of month of the year. Seasonal distributions of nitrate plus nitrite concentrations and total suspended solids concentrations differed from each other but were generally similar among the three study gages for a given constituent. Median concentrations of nitrate plus nitrite were highest during the January–June months, whereas median concentrations of total suspended solids were highest during June and July. Seasonal distributions of total phosphorus concentrations were similar at the study gages near Nora and near Centerton, but the seasonal distribution was noticeably different at the study gage at Muncie, which had monthly median concentrations that were substantially lower than at the two downstream study gages (near Nora and near Centerton). The seasonal distribution of total Kjeldahl nitrogen concentrations differed in pattern among the three study gages; however, in general, some of the higher monthly median total Kjeldahl nitrogen concentrations at each study gage were associated with the late spring and summer periods.
The Weighted Regressions on Time, Discharge, and Season (WRTDS) method implemented in EGRET was used to estimate water-year annual mean daily concentrations and flux of nutrients and total suspended solids, as well as estimates of concentrations and flux that were “normalized” to remove the effect of year-to-year variation in streamflow. The approximate coefficients of determination for the WRTDS regression models ranged from a high of 0.82 for total phosphorus for the study gage near Centerton to a low of 0.19 for nitrate plus nitrite for the study gage near Nora.
Loads and yields of total suspended solids, total phosphorus, nitrate plus nitrite, and total Kjeldahl nitrogen were estimated for analytical periods consisting of the longest periods of concurrent record at the three study gages. Loads of each of the constituents increased sequentially from the most upstream study gage to the most downstream study gage; however, the same was not true for yields. The highest yields of total suspended solids, total phosphorus, and total Kjeldahl nitrogen occurred at the most upstream study gage (at Muncie); however, the highest yield of nitrate plus nitrite occurred at the most downstream study gage (near Centerton).
WRTDS bootstrap tests were used to assess the magnitude, direction, and likelihood of changes in annual flow-normalized mean daily concentrations and flux of total suspended solids, total phosphorus, nitrate plus nitrite, and total Kjeldahl nitrogen at the study gages between water years 1997 and 2017. Changes in flow-normalized concentrations and flux of the constituents between water years 1997 and 2017 were mostly downward (decreasing). The exceptions were likely to highly likely upward (increasing) changes in (1) flow-normalized annual mean daily concentration and annual flux for total suspended solids and total phosphorus at the study gage at Muncie, (2) flow-normalized annual mean daily total phosphorus concentration at the study gage near Centerton, (3) flow-normalized annual flux of total phosphorus at the study gage near Centerton, and (4) flow-normalized annual mean daily nitrate plus nitrite concentration at the study gage near Centerton. Although an upward change in flow-normalized nitrate plus nitrite concentrations was likely at the study gage near Centerton, flow-normalized annual flux of nitrate plus nitrite at that study gage was determined to have a highly likely downward change.
EGRET and Exploration and Graphics for RivEr Trends Confidence Intervals (EGRETci) analyses can be used to improve our understanding of how concentrations and flux change as functions of time and streamflow, as well as provide information on how the relations between streamflow and constituent concentrations have changed within the calendar year between any 2 years included in the analyses. Examples of those uses, illustrating changes between calendar years 1992 and 2017, were given for total suspended solids concentrations at the study gage near Nora and for nitrate plus nitrite concentrations at the study gage near Centerton.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195119","collaboration":"Prepared in cooperation with The Nature Conservancy","usgsCitation":"Koltun, G.F., 2019, Trends in streamflow and concentrations and flux of nutrients and total suspended solids in the Upper White River at Muncie, near Nora, and near Centerton, Indiana: U.S. Geological Survey Scientific Investigations Report 2019–5119, 34 p., https://doi.org/10.3133/sir20195119.","productDescription":"Report: viii, 34 p.; Data Release","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-109722","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":399602,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109513.htm"},{"id":370134,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VN5RKV","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Total suspended solids, total phosphorus, nitrate plus nitrite, and total Kjeldahl nitrogen concentration data for the White River at Muncie, near Nora, and near Centerton, Indiana, 1991–2017"},{"id":370133,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5119/sir20195119.pdf","text":"Report","size":"3.99 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5119"},{"id":370132,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5119/coverthb.jpg"}],"country":"United States","state":"Indiana","county":"Morgan County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.8311,\n 39.2633\n ],\n [\n -84.9667,\n 39.2633\n ],\n [\n -84.9667,\n 40.3608\n ],\n [\n -86.8311,\n 40.3608\n ],\n [\n -86.8311,\n 39.2633\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
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Columbus, OH 43229–1737
The largest earthquakes recorded in northern Alaska (Mw 6.4 and Mw 6.0) occurred ~6 hours apart on August 12, 2018 in the northeastern Brooks Range. The earthquakes were captured by Sentinel-1 InSAR satellites and Earthscope Transportable Array seismic data, giving insight into the little-known active tectonic processes of Arctic Alaska, obscured until recently by sparse data availability. In this study, InSAR modelling, teleseismic back projections, calibrated hypocentral relocations and regional moment tensor solutions resolve two previously unknown, SSW-dipping right-lateral fault segments. These are the first active faults identified as conjugate to the NE-trending sinistral Canning Displacement Zone directly to the west, which is therefore a more complex zone of diffuse faulting than previously thought. The northeastern Brooks Range has been characterized as an area of low to moderate seismic hazard, but these earthquakes illustrate the potential for larger, possibly destructive events in a region earmarked for rapid resource development.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019GL085651","usgsCitation":"Gaudreau, E., Nissen, E., Bergman, E.A., Benz, H.M., Tan, F., and Karasözen, E., 2019, The August 2018 Kaktovik earthquakes: Active tectonics in northeastern Alaska revealed With InSAR and seismology: Geophysical Research Letters, v. 46, no. 24, p. 14412-14420, https://doi.org/10.1029/2019GL085651.","productDescription":"9 p.","startPage":"14412","endPage":"14420","ipdsId":"IP-113641","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":370590,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -167.51953124999997,\n 64.92354174306496\n ],\n [\n -140.9765625,\n 64.92354174306496\n ],\n [\n -140.9765625,\n 71.35706654962706\n ],\n [\n -167.51953124999997,\n 71.35706654962706\n ],\n [\n -167.51953124999997,\n 64.92354174306496\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"46","issue":"24","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-12-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Gaudreau, E.","contributorId":221460,"corporation":false,"usgs":false,"family":"Gaudreau","given":"E.","email":"","affiliations":[{"id":16829,"text":"University of Victoria","active":true,"usgs":false}],"preferred":false,"id":778301,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nissen, E.K.","contributorId":221461,"corporation":false,"usgs":false,"family":"Nissen","given":"E.K.","email":"","affiliations":[{"id":16829,"text":"University of Victoria","active":true,"usgs":false}],"preferred":false,"id":778302,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bergman, Eric A. 0000-0002-7069-8286","orcid":"https://orcid.org/0000-0002-7069-8286","contributorId":84513,"corporation":false,"usgs":false,"family":"Bergman","given":"Eric","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":778303,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Benz, Harley M. 0000-0002-6860-2134 benz@usgs.gov","orcid":"https://orcid.org/0000-0002-6860-2134","contributorId":794,"corporation":false,"usgs":true,"family":"Benz","given":"Harley","email":"benz@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":778304,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tan, F.","contributorId":221462,"corporation":false,"usgs":false,"family":"Tan","given":"F.","email":"","affiliations":[{"id":16829,"text":"University of Victoria","active":true,"usgs":false}],"preferred":false,"id":778305,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Karasözen, E.","contributorId":221463,"corporation":false,"usgs":false,"family":"Karasözen","given":"E.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":778306,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70205020,"text":"sir20195093 - 2019 - Hydrogeologic framework of the Virginia Eastern Shore","interactions":[],"lastModifiedDate":"2022-04-22T21:38:34.08926","indexId":"sir20195093","displayToPublicDate":"2019-12-05T12:00:00","publicationYear":"2019","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":"2019-5093","displayTitle":"Hydrogeologic Framework of the Virginia Eastern Shore","title":"Hydrogeologic framework of the Virginia Eastern Shore","docAbstract":"The Yorktown-Eastover aquifer system of the Virginia Eastern Shore consists of upper, middle, and lower confined aquifers overlain by correspondingly named confining units and underlain by the Saint Marys confining unit. Miocene- to Pliocene-age marine-shelf sediments observed in 205 boreholes include medium- to coarse-grained sand and shells that compose the aquifers and fine-grained sand, silt, and clay that compose the confining units. The upper confining unit also includes fine-grained and organic-rich back-barrier and estuarine sediments of Pleistocene age. An overlying surficial aquifer is composed mostly of Pleistocene-age nearshore sand and gravel with smaller amounts of cobbles and boulders.
In addition, Pleistocene-age sediments that fill three buried paleochannels are for the first time explicitly delineated here as distinct hydrogeologic units. Two aquifers are composed of medium- to coarse-grained fluvial sand and gravel, and an intervening confining unit is composed of fine-grained estuarine sand, silt, clay, and organic material. Aquifer and confining-unit sediments are also mixed with reworked marine-shelf sediments eroded from the sides of the paleochannels.
Hydrogeologic units of the Yorktown-Eastover aquifer system generally dip eastward, are as much as several tens of feet thick, and have an undulating configuration possibly resulting from the underlying Chesapeake Bay impact crater. Aquifers and confining units are incised by the three paleochannels along an upward-widening and eastward-lengthening series of structural “windows.” Hydrogeologic units within mainstems and branching tributaries of the paleochannels dip southeastward parallel to slopes of the paleochannels, are as much as several tens of feet thick, and laterally abut the Yorktown-Eastover aquifer system along paleochannel sidewalls. The Yorktown-Eastover aquifer system is thereby hydraulically breached by the paleochannels to alternately create barriers to or conduits for groundwater flow.
Results of previously documented aquifer tests at 58 wells indicate that transmissivity is generally greatest in young, shallow, and coarse-grained nearshore and fluvial sediments of the surficial aquifer and paleochannels. Transmissivity progressively decreases with depth in older, deeper, and finer grained marine-shelf sediments of the Yorktown-Eastover aquifer system, probably because they have undergone compaction as a result of greater overburden pressure over longer periods of time.
Compiled chloride concentrations in samples from 330 wells generally increase downward, with most of the samples collected at altitudes above −300 feet and with most concentrations less than 250 milligrams per liter. The saltwater-transition zone has a broad trough-like shape aligned with the peninsula, being relatively shallow along the coastline and deeper along the central “spine.” Because movement of the saltwater is slow, the configuration largely reflects groundwater flow prior to widespread groundwater withdrawals. Fresh groundwater has leaked downward along deep parts of the saltwater-transition zone and leaked upward along shallower parts to discharge at the coast.
The saltwater-transition zone also exhibits an anomalous ridge across the center of the peninsula. Groundwater levels indicate that the saltwater ridge formed primarily by the Exmore paleochannel acting as a large lateral collector drain. Groundwater levels were lowered, and the position of saltwater-transition zone was elevated, by a flow conduit that intercepted groundwater that otherwise would have flowed toward and discharged along the coastline.
Nearly all freshwater on the Virginia Eastern Shore is supplied by groundwater withdrawals, which have lowered water levels, altered hydraulic gradients, and created a concern for saltwater intrusion. Previous characterizations of groundwater conditions that are relied on to manage groundwater development have been limited by a lack of hydrogeologic information, particularly data on buried paleochannels that are critical to safeguarding the groundwater supply. Using recently available expanded information, the U.S. Geological Survey undertook a study in cooperation with the Virginia Department of Environmental Quality during 2016–19 to develop an improved description of the groundwater system called a “hydrogeologic framework.”
The hydrogeologic framework can aid water-supply planning and development by providing information on broad trends in aquifer configurations, hydraulic properties, and proximity to saltwater to avoid chloride contamination. Digital models to evaluate effects of groundwater withdrawals can also be improved with expanded data and capabilities to evaluate paleochannel hydraulic connections and the potential for saltwater movement.
The hydrogeologic framework is limited by the nonuniform distribution of boreholes and the subjective delineation of aquifers and confining units, including those within paleochannels that are regarded as preliminary. The configuration of the saltwater-transition zone is also regarded as preliminary because of the nonuniform distribution of groundwater samples. Low well-sampling frequency precludes characterizing movement of the saltwater-transition zone. A monitoring strategy of sampling and possibly electromagnetic-induction well logging could be used to detect saltwater movement.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195093","collaboration":"Prepared in cooperation with the Virginia Department of Environmental Quality","usgsCitation":"McFarland, E.R., and Beach, T.A., 2019, Hydrogeologic framework of the Virginia Eastern Shore: U.S. Geological Survey Scientific Investigations Report 2019–5093, 26 p., 13 pl., https://doi.org/10.3133/sir20195093.","productDescription":"Report: viii, 26 p.; 13 Plates: 11.00 x 17.00 inches or smaller; Data Release","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-108409","costCenters":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"links":[{"id":369803,"rank":16,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093.pdf","text":"Report","size":"3.47 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5093"},{"id":369731,"rank":15,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate13.pdf","text":"Plate 13","size":"352 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Locations and Numbers of Sampled Wells and Altitude of the 250-Milligram-Per-Liter Chloride-Concentration Surface on the Virginia Eastern Shore"},{"id":369730,"rank":14,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate12.pdf","text":"Plate 12","size":"332 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Top-Surface Altitude of the Upper Confining Unit on the Virginia Eastern Shore"},{"id":369725,"rank":9,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate07.pdf","text":"Plate 7","size":"346 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Top-Surface Altitude of the Middle Confining Unit on the Virginia Eastern Shore"},{"id":369724,"rank":8,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate06.pdf","text":"Plate 6","size":"340 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Top-Surface Altitude of the Middle Aquifer on the Virginia Eastern Shore"},{"id":369723,"rank":7,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate05.pdf","text":"Plate 5","size":"332 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Top-Surface Altitude of the Lower Confining Unit on the Virginia Eastern Shore"},{"id":399542,"rank":17,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109487.htm"},{"id":369721,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate03.pdf","text":"Plate 3","size":"323 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Top-Surface Altitude of the Saint Marys Confining Unit on the Virginia Eastern Shore"},{"id":369720,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate02.pdf","text":"Plate 2","size":"336 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Hydrogeologic Section through the Virginia Eastern Shore"},{"id":369719,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate01.pdf","text":"Plate 1","size":"339 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Locations and Numbers of Boreholes on the Virginia Eastern Shore"},{"id":369714,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MPE5SD","text":"USGS data release","linkFileType":{"id":5,"text":"html"},"linkHelpText":"Borehole hydrogeologic-unit top-surface altitudes, aquifer hydraulic properties, and groundwater-sample chloride-concentration data from 1906 through 2016 for the Virginia Eastern Shore"},{"id":369728,"rank":12,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate10.pdf","text":"Plate 10","size":"319 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Top-Surface Altitude of the Paleochannel Confining Unit on the Virginia Eastern Shore"},{"id":369727,"rank":11,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate09.pdf","text":"Plate 9","size":"316 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Top-Surface Altitude of the Paleochannel Lower Aquifer on the Virginia Eastern Shore"},{"id":369726,"rank":10,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate08.pdf","text":"Plate 8","size":"350 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Top-Surface Altitude of the Upper Aquifer on the Virginia Eastern 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U.S. Geological Survey
1730 East Parham Road
Richmond, Virginia 23228