Hennig Brandt's discovery of phosphorus (P) occurred during the early European colonization of the Chesapeake Bay region. Today, P, an essential nutrient on land and water alike, is one of the principal threats to the health of the bay. Despite widespread implementation of best management practices across the Chesapeake Bay watershed following the implementation in 2010 of a total maximum daily load (TMDL) to improve the health of the bay, P load reductions across the bay's 166,000‐km2 watershed have been uneven, and dissolved P loads have increased in a number of the bay's tributaries. As the midpoint of the 15‐yr TMDL process has now passed, some of the more stubborn sources of P must now be tackled. For nonpoint agricultural sources, strategies that not only address particulate P but also mitigate dissolved P losses are essential. Lingering concerns include legacy P stored in soils and reservoir sediments, mitigation of P in artificial drainage and stormwater from hotspots and converted farmland, manure management and animal heavy use areas, and critical source areas of P in agricultural landscapes. While opportunities exist to curtail transport of all forms of P, greater attention is required toward adapting P management to new hydrologic regimes and transport pathways imposed by climate change.
","language":"English","publisher":"Wiley","doi":"10.2134/jeq2019.03.0112","usgsCitation":"Kleinman, P., Fanelli, R., Hirsch, R.M., Buda, A.R., Easton, Z.M., Wainger, L.A., Brosch, C., Lowenfish, M., Collick, A.S., Shirmohammadi, A., Boomer, K., Hubbart, J.A., Bryant, R.B., and Shenk, G., 2019, Phosphorus and the Chesapeake Bay: Lingering issues and emerging concerns for agriculture: Journal of Environmental Quality, v. 48, no. 5, p. 1191-1203, https://doi.org/10.2134/jeq2019.03.0112.","productDescription":"13 p.","startPage":"1191","endPage":"1203","ipdsId":"IP-106511","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":467365,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2134/jeq2019.03.0112","text":"Publisher Index Page"},{"id":379941,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, Maryland, Virginia","otherGeospatial":"Chesapeake Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.11328125,\n 36.92793899776678\n ],\n [\n -75.948486328125,\n 37.23470197166817\n ],\n [\n -75.673828125,\n 37.896530447543\n ],\n [\n -75.816650390625,\n 38.28993659801203\n ],\n [\n -75.8221435546875,\n 38.436379603\n ],\n [\n -76.0858154296875,\n 38.44498466889473\n ],\n [\n -76.0308837890625,\n 38.71980474264237\n ],\n [\n -75.7781982421875,\n 39.614152077002664\n ],\n [\n -76.1956787109375,\n 39.592990390285024\n ],\n [\n -76.7230224609375,\n 39.21948715423953\n ],\n [\n -76.629638671875,\n 38.565347844885466\n ],\n [\n -76.629638671875,\n 38.40194908237822\n ],\n [\n -77.0635986328125,\n 38.487994609214795\n ],\n [\n -77.05810546875,\n 38.21660403859855\n ],\n [\n -76.4373779296875,\n 37.92686760148135\n ],\n [\n -77.04711914062499,\n 38.190704293996504\n ],\n [\n -77.156982421875,\n 38.043765107439675\n ],\n [\n -76.497802734375,\n 37.501010429493284\n ],\n [\n -76.4813232421875,\n 37.322120359451766\n ],\n [\n -76.4813232421875,\n 37.14718209972376\n ],\n [\n -76.234130859375,\n 36.85764758564407\n ],\n [\n -76.11328125,\n 36.92793899776678\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"48","issue":"5","noUsgsAuthors":false,"publicationDate":"2019-08-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Kleinman, Peter","contributorId":244141,"corporation":false,"usgs":false,"family":"Kleinman","given":"Peter","email":"","affiliations":[{"id":48855,"text":"USDA-ARS, Pasture Syst. and Watershed Mgmt. 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B.","contributorId":191824,"corporation":false,"usgs":false,"family":"Bryant","given":"R.","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":803488,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Shenk, Gary","contributorId":244194,"corporation":false,"usgs":false,"family":"Shenk","given":"Gary","affiliations":[],"preferred":false,"id":803489,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70203767,"text":"sir20195057 - 2019 - Paleoliquefaction field reconnaissance in eastern North Carolina—Is there evidence for large magnitude earthquakes between the central Virginia seismic zone and Charleston seismic zone?","interactions":[],"lastModifiedDate":"2019-08-15T09:08:37","indexId":"sir20195057","displayToPublicDate":"2019-08-14T10:45:00","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":"2019-5057","displayTitle":"Paleoliquefaction Field Reconnaissance in Eastern North Carolina—Is There Evidence for Large Magnitude Earthquakes Between the Central Virginia Seismic Zone and Charleston Seismic Zone?","title":"Paleoliquefaction field reconnaissance in eastern North Carolina—Is there evidence for large magnitude earthquakes between the central Virginia seismic zone and Charleston seismic zone?","docAbstract":"In June 2016, approximately 64 kilometers (km) of riverbank were examined along the Tar and Neuse Rivers near Tarboro and Kinston, North Carolina, for evidence of liquefaction-forming earthquakes. The study area is in the vicinity of the Grainger’s fault zone in eastern North Carolina. The Grainger’s fault zone is a fault zone in the inner Coastal Plain Province that has well-documented Paleogene and younger deformation of Cretaceous to Eocene strata. Low-magnitude earthquakes near the fault zone (for example, magnitude [M] 2.1 in 2013, 13 km south-southwest of Kinston) suggest larger earthquakes may have struck this region in the past. The study area is about equidistant from newly documented Holocene paleoliquefaction sites in the Central Virginia Seismic Zone (CVSZ) and liquefaction sites formed during the 1886 M7.1 Charleston, South Carolina earthquake. The northernmost Holocene paleoliquefaction features associated with the Charleston Seismic Zone (CSZ) are in Southport, North Carolina.
Conditions suitable for liquefaction were identified at 38 sites on both rivers, but only one site was classified as highly susceptible. Stratigraphy consists of Paleozoic gneiss; Cretaceous sandstone/shale; Paleocene mudstone/claystone to Eocene fossiliferous limestone; Quaternary unconsolidated, crossbedded sand and gravel; and Holocene alluvium. Three sets of stratigraphic conditions suitable for liquefaction—unconsolidated source sand beneath capping strata—were identified in detailed examinations at 105 sites: (1) Holocene alluvial sand beneath alluvial silt and clay beds; (2) Quaternary terrace sand beneath beds of silt and clay; and (3) Holocene alluvial sand or Quaternary terrace sand capped by clay-rich Bt soil horizons. Weathered and unconsolidated Cretaceous sand capped by a Bt soil horizon was identified at one site, but the weathered sand is likely too compacted to liquefy readily. One outcrop containing three small sand dikes, and four outcrops of soft-sediment deformation features—mostly load casts—were observed, but none of these features could be conclusively established as seismogenic. A few examples of pseudo-sand-dikes were also identified: sand-filled cypress root casts and pedogenic weathering fronts created the appearance of sand dikes and sills.
A comparable survey in 2015 of 119 km of riverbank exposures in the CVSZ yielded 19 paleoliquefaction sites of probable earthquake origin; these features formed from at least one M~6 earthquake in the past 6,000 years (6 ka). This survey in eastern North Carolina revealed no definitive paleoliquefaction features; earthquakes of sufficient magnitude to produce liquefaction likely have not affected this region during the Holocene.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195057","usgsCitation":"Carter, M.W., and McLaurin, B.T., 2019, Paleoliquefaction field reconnaissance in eastern North Carolina—Is there evidence for large magnitude earthquakes between the Central Virginia Seismic Zone and Charleston Seismic Zone?: U.S. Geological Survey Scientific Investigations Report 2019–5057, 54 p., https://doi.org/10.3133/sir20195057. ","productDescription":"vi, 54 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-092950","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":366514,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5057/coverthb.jpg"},{"id":366515,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5057/sir20195057.pdf","text":"Report","size":"18.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5057"}],"country":"United States","state":"North Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.7607421875,\n 32.37996146435729\n ],\n [\n -74.50927734375,\n 32.37996146435729\n ],\n [\n -74.50927734375,\n 37.61423141542417\n ],\n [\n -79.7607421875,\n 37.61423141542417\n ],\n [\n -79.7607421875,\n 32.37996146435729\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Florence Bascom Geoscience Center
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12201 Sunrise Valley Drive
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The Big Chino subbasin is located in central-northwest Arizona in the transition zone between the Colorado Plateau and the Basin and Range Province. The controlled source audio-frequency magnetotelluric (CSAMT) geophysical method, a low-impact, non-intrusive, electrical resistance sounding technique, was used to evaluate the subsurface hydrogeology of the southern third of the Big Chino subbasin. The Big Chino subbasin is a northwest-trending, late Tertiary graben bordered by the Big Chino Fault along its northeast flank where there is as much as 1,100 meters of displacement. The main water-bearing stratigraphic unit of the basin is Tertiary alluvial-fill sediment. The Devonian Martin Formation provides water to wells near Drake and the Mississippian Redwall Limestone provides water to wells east of the basin and in the Paulden area.
The purpose of the CSAMT surveys was to improve the conceptual model of the aquifer by constraining the basin geometry and identifying stratigraphic units and their subsurface extents. CSAMT methods were used to map the subsurface along 100 kilometers (62 miles) of survey lines across the southern third of the subbasin. Of 21 survey lines, 14 were west of the town of Paulden and another 7 were east of Paulden. Data were cleaned and prepared for entry into Zonge SCS2D software and then inverted to provide a two-dimensional resistivity profile for each survey line. Final inversion models representing the best fit to measured data were compared to driller’s logs or borehole data where present.
Data from the CSAMT lines west and north of Paulden are consistent with thicker alluvial basin deposits that range from 100 meters thick to a few hundred meters thick. Data from the CSAMT lines east of Paulden are consistent with thinner alluvial and basalt deposits overlying Paleozoic Martin Formation and Redwall Limestone, Tapeats Sandstone, and Precambrian granite and schist.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195082","collaboration":"Prepared in cooperation with the City of Prescott, the Town of Prescott Valley, and Salt River Project","usgsCitation":"Macy, J.P., Gungle, B., and Mason, J.P., 2019, Characterization of Big Chino subbasin hydrogeology near Paulden, Arizona, using controlled source audio-frequency magnetotellursurveys: U.S. Geological Survey Scientific Investigations Report 2019–5082, 39 p., https://doi.org/10.3133/sir20195082.\nic ","productDescription":"vii, 39 p.","numberOfPages":"39","onlineOnly":"Y","ipdsId":"IP-098264","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":366547,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5082/sir20195082.pdf","text":"Report","size":"50 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 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Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
Soils collected along a predominately north-south transect in Alaska were used to evaluate regional differences in the soil mineralogy and geochemistry in the context of a geotectonic framework for Alaska. The approximately 1,395-kilometer-long transect followed the Dalton, Elliott, and Richardson Highways from near Prudhoe Bay to Valdez. Sites were selected with a site spacing of approximately 10 road-kilometers; soil was sampled by soil horizon at 175 sites. Terrane boundaries were estimated from digitized versions of the lithotectonic terrane map of Alaska (Silberling and others, 1994). Terrane assignments for each site were based on the site’s distance along the transect. We also present data for 15 minerals or mineral groups and 58 elements, as well as total, inorganic, and organic carbon. Quantitative mineralogy of the mineral-soil horizons was characterized by X-ray diffraction. Elemental contents were determined by a combination of inductively coupled plasma-atomic emission spectrometry (ICP-AES) and inductively coupled plasma-mass spectrometry (ICP-MS) analysis following a multi-acid or sodium-sinter decomposition of the samples. Total carbon and carbonate carbon contents were determined using an automated carbon analyzer and coulometric titration, respectively; organic carbon content was obtained by calculating the difference between total and carbonate carbon. Mercury and selenium were analyzed using cold-vapor atomic absorption (CV-AA), and hydride-generation atomic absorption spectrometry (HG-AAS), respectively. The mineralogical and geochemical patterns from these soils are used to assess the relation between soil characteristics and the geology of surrounding terranes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1814E","usgsCitation":"Wang, B., Hults, C., Eberl, D., Woodruff, L., Cannon, W., and Gough, L., 2019, Soil mineralogy and geochemistry along a north-south transect in Alaska and the relation to source-rock terrane in Dumoulin, J.A., ed., Studies by the U.S. Geological Survey in Alaska, vol. 15: U.S. Geological Survey Professional Paper 1814–E, 27 p., https://doi.org/10.3133/pp1814E.","productDescription":"Report: v, 27 p.; 4 Appendixes","numberOfPages":"27","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-092422","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":366450,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1814/e/pp1814e_appendix1.pdf","text":"Appendix 1","size":"964 KB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1814 Chapter E Appendix 1","linkHelpText":" — Summary Statisitics for Chemical Analyses of Soil Samples from the North-South Transect of Alaska"},{"id":366449,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1814/e/pp1814e.pdf","text":"Report","size":"7.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1814 Chapter E"},{"id":366448,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1814/e/coverthb.jpg"},{"id":366451,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1814/e/pp1814e_appendix_2.pdf","text":"Appendix 2","size":"777 KB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1814 Chapter E Appendix 2","linkHelpText":" — Plots of mineral contents in soil samples from the upper and lower mineral soil horizons at sites along the north-south transect of Alaska"},{"id":366452,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1814/e/pp1814e_appendix_3.pdf","text":"Appendix 3","size":"4.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1814 Chapter E Appendix 3","linkHelpText":" — Box plots of elemental contents in soil samples at sites along the north-south transect of Alaska"},{"id":366453,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1814/e/pp1814e_appendix_4.xlsx","text":"Appendix 4","size":"531 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"PP 1814 Chapter E Appendix 4","linkHelpText":" — Mineralogical and chemical data for all transect soil samples, standard reference materials, and laboratory splits"}],"country":"United 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Science Center staff
U.S. Geological Survey
4210 University Dr.
Anchorage, AK 99508
Alaska Mineral Resources
Alaska Science Center
The Upper Devonian Ignacio Formation (as stratigraphically revised) comprises a transgressive, tide-dominated estuarine depositional system in the San Juan Mountains (Colorado, USA). The unit backfills at least three bedrock paleovalleys (10–30 km wide and ≥42 m deep) with a consistent stratigraphy of tidally influenced fluvial, bayhead-delta, central estuarine-basin, mixed tidal-flat, and estuarine-mouth tidal sandbar deposits. Paleovalleys were oriented northwest while longshore transport was to the north. The deposits represent Upper Devonian lowstand and transgressive systems tracts. The overlying Upper Devonian Elbert Formation (upper member) consists of geographically extensive tidal-flat deposits and is interpreted as mixed siliciclastic-carbonate bay-fill facies that represents an early highstand systems tract. Stratigraphic revision of the Ignacio Formation includes reassigning the basal conglomerate to the East Lime Creek Conglomerate, recognizing an unconformity separating these two units, and incorporating strata previously mapped as the McCracken Sandstone Member (Elbert Formation) into the Ignacio Formation. The Ignacio Formation was previously interpreted as Cambrian, but evidence that it is Devonian includes reexamined fossil data and detrital zircon U-Pb geochronology. The Ignacio Formation has a stratigraphic trend of detrital zircon ages shifting from a single ca. 1.7 Ga age peak to bimodal ca. 1.4 Ga and ca. 1.7 Ga age peaks, which represents local source-area unroofing history. Specifically, the upper plate of a Proterozoic thrust system (ca. 1.7 Ga Twilight Gneiss) was eroded prior to exposure of the lower plate (ca. 1.4 Ga Uncompahgre Formation). These results are a significant alternative interpretation of the geologic history of the southern Rocky Mountains.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02085.1","usgsCitation":"Evans, J.E., Maurer, J.T., and Holm-Denoma, C.S., 2019, Recognition and significance of Late Devonian fluvial, estuarine, and mixed siliciclastic-carbonate nearshore marine environments in the San Juan Mountains (southwestern Colorado, U.S.A.): Multiple incised valleys backfilled by lowstand and transgressive system tracts: Geosphere, v. 15, no. 5, p. 1497-1507, https://doi.org/10.1130/GES02085.1.","productDescription":"11 p.","startPage":"1497","endPage":"1507","ipdsId":"IP-103463","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":467377,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02085.1","text":"Publisher Index Page"},{"id":437368,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SYHGUV","text":"USGS data release","linkHelpText":"U-Pb detrital zircon data for: lower Paleozoic sedimentary rocks near Silverton, CO USA"},{"id":367539,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"San Juan Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.04092407226562,\n 37.137329767248794\n ],\n [\n -107.63168334960936,\n 37.137329767248794\n ],\n [\n -107.63168334960936,\n 37.847748103485365\n ],\n [\n -108.04092407226562,\n 37.847748103485365\n ],\n [\n -108.04092407226562,\n 37.137329767248794\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"15","issue":"5","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-08-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Evans, James E.","contributorId":194435,"corporation":false,"usgs":false,"family":"Evans","given":"James","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":771316,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Maurer, Joshua T","contributorId":219120,"corporation":false,"usgs":false,"family":"Maurer","given":"Joshua","email":"","middleInitial":"T","affiliations":[{"id":13587,"text":"Bowling Green State University","active":true,"usgs":false}],"preferred":false,"id":771317,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holm-Denoma, Christopher S. 0000-0003-3229-5440 cholm-denoma@usgs.gov","orcid":"https://orcid.org/0000-0003-3229-5440","contributorId":2442,"corporation":false,"usgs":true,"family":"Holm-Denoma","given":"Christopher","email":"cholm-denoma@usgs.gov","middleInitial":"S.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":771315,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70204702,"text":"70204702 - 2019 - Mapping crop residue by combining Landsat and WorldView-3 satellite imagery","interactions":[],"lastModifiedDate":"2019-08-09T12:33:40","indexId":"70204702","displayToPublicDate":"2019-08-09T12:27:48","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":"Mapping crop residue by combining Landsat and WorldView-3 satellite imagery","docAbstract":"A unique, multi-tiered approach was applied to map crop-residue cover on the Eastern Shore of the Chesapeake Bay, USA. Field measurements of crop-residue cover were used to calibrate residue mapping using shortwave infrared (SWIR) indices derived from WorldView-3 imagery for an 8-km x 8-km footprint. The resulting map was then used to calibrate and subsequently classify residue mapping of Landsat imagery at a larger spatial resolution and extent. This manuscript describes how the method was applied and presents results in the form of crop-residue cover maps, validation statistics, and quantification of conservation tillage implementation in the agricultural landscape. Overall accuracy for maps derived from Landsat 7 (ETM+) and Landsat 8 (OLI) were comparable at roughly 92% (+/- 10%). Tillage class specific accuracy was also strong and ranged from 75% to 99%. The approach, which employed a 12-band image stack of six tillage spectral indices and six individual Landsat bands, was shown to be adaptable to variable soil-moisture conditions: under dry conditions (Landsat 7, May 14, 2015) the majority of predictive power was attributed to SWIR indices, and under wet conditions (Landsat 8, May 22, 2015) single band reflectance values were more effective at explaining variability in residue cover. Summary statistics of resulting tillage class occurrence matched closely with conservation tillage implementation totals reported by Maryland and Delaware to the Chesapeake Bay Program. This hybrid method combining WorldView-3 and Landsat imagery sources shows promise for monitoring progress in the adoption of conservation tillage practices and for describing crop-residue outcomes associated with a variety of agricultural management practices.","language":"English","publisher":"MDPI","doi":"10.3390/rs11161857","usgsCitation":"Hively, W.D., Shermeyer, J., Lamb, B.T., Daughtry, C.S., Quemada, M., and Keppler, J., 2019, Mapping crop residue by combining Landsat and WorldView-3 satellite imagery: Remote Sensing, v. 11, no. 16, 1857, 21 p., https://doi.org/10.3390/rs11161857.","productDescription":"1857, 21 p.","ipdsId":"IP-090242","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":467379,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11161857","text":"Publisher Index Page"},{"id":366446,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","county":"Talbot County","otherGeospatial":"Choptank River Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.19842529296875,\n 38.565347844885466\n ],\n [\n -75.728759765625,\n 38.565347844885466\n ],\n [\n -75.728759765625,\n 39.02345139405935\n ],\n [\n -76.19842529296875,\n 39.02345139405935\n ],\n [\n -76.19842529296875,\n 38.565347844885466\n ]\n ]\n ]\n }\n }\n ]\n}\n","volume":"11","issue":"16","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2019-08-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Hively, W. Dean 0000-0002-5383-8064","orcid":"https://orcid.org/0000-0002-5383-8064","contributorId":201565,"corporation":false,"usgs":true,"family":"Hively","given":"W.","email":"","middleInitial":"Dean","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":768123,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shermeyer, Jacob 0000-0002-8143-2790","orcid":"https://orcid.org/0000-0002-8143-2790","contributorId":218038,"corporation":false,"usgs":true,"family":"Shermeyer","given":"Jacob","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":768124,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lamb, Brian T.","contributorId":211092,"corporation":false,"usgs":false,"family":"Lamb","given":"Brian","email":"","middleInitial":"T.","affiliations":[{"id":38178,"text":"City College of New York","active":true,"usgs":false}],"preferred":false,"id":768125,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Daughtry, Craig S.T.","contributorId":214079,"corporation":false,"usgs":false,"family":"Daughtry","given":"Craig","email":"","middleInitial":"S.T.","affiliations":[{"id":38179,"text":"USDA Agricultural Research Service, Hydrology and Remote Sensing Laboratory","active":true,"usgs":false}],"preferred":false,"id":768126,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Quemada, Miguel","contributorId":211094,"corporation":false,"usgs":false,"family":"Quemada","given":"Miguel","email":"","affiliations":[{"id":38180,"text":"School of Agricultural Engineering and CEIGRAM, Technical University of Madrid","active":true,"usgs":false}],"preferred":false,"id":768127,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Keppler, Jason","contributorId":218039,"corporation":false,"usgs":false,"family":"Keppler","given":"Jason","email":"","affiliations":[{"id":39731,"text":"Maryland Department of Agriculture, Office of Resource Conservation","active":true,"usgs":false}],"preferred":false,"id":768128,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70228058,"text":"70228058 - 2019 - Soil chemistry, and not short-term (1–2 year) deer exclusion, explains understory plant occupancy in forests affected by acid deposition","interactions":[],"lastModifiedDate":"2022-02-03T15:35:42.120801","indexId":"70228058","displayToPublicDate":"2019-08-09T09:25:58","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5538,"text":"AoB PLANTS","active":true,"publicationSubtype":{"id":10}},"title":"Soil chemistry, and not short-term (1–2 year) deer exclusion, explains understory plant occupancy in forests affected by acid deposition","docAbstract":"The loss of species diversity and plant community structure throughout the temperate deciduous forests of North America have often been attributed to overbrowsing by white-tailed deer (Odocoileus virginanus). Slow species recovery following removal from browsing, or reduction in deer density, has been termed a legacy effect of past deer herbivory. However, vegetation legacy effects have also coincided with changes to soil chemistry throughout the north-eastern USA. In this paper, we assess the viability of soil chemistry (i.e. pH, extractable nutrients and extractable metals) and other factors (topography, light, overstory basal area and location) as alternative explanations for a lack of vegetation recovery. We compared the relative effects of soil chemistry, site conditions and short-term (1–2 year) deer exclusion on single-species occupancy probabilities of 10 plant taxa common to oak-hickory forests in central Pennsylvania. We found detection for all modelled species was constant and high (
The eastern North American white oaks, a complex of approximately 16 potentially interbreeding species, have become a classic model for studying the genetic nature of species in a syngameon. Genetic work over the past two decades has demonstrated the reality of oak species, but gene flow between sympatric oaks raises the question of whether there are conserved regions of the genome that define oak species. Does gene flow homogenize the entire genome? Do the regions of the genome that distinguish a species in one part of its range differ from the regions that distinguish it in other parts of its range, where it grows in sympatry with
different species? Or are there regions of the genome that are relatively conserved across species ranges? In this study, we revisit seven species of the eastern North American white oak syngameon using a set of 80 single-nucleotide polymorphisms (SNPs) selected in a previous study because they show differences among, and consistency within, the species. We test the hypothesis that there exist segments of the genome that do not become homogenized by repeated introgression, but retain distinct alleles characteristic of each species. We undertake a range-wide sampling to investigate whether SNPs that appeared to be fixed based on a relatively small sample in our previous work are fixed or nearly fixed across the range of the species. Each of the seven species remains genetically distinct across its range, given our diagnostic set of markers, with relatively few individuals exhibiting admixture of multiple species. SNPs map back to all 12 Quercus linkage groups (chromosomes) and are separated from each other by an average of 7.47 million bp (± 8.74 million bp, SD), but are significantly clustered relative to a random null distribution, suggesting that our SNP toolkit reflects genome-wide patterns of divergence while potentially being concentrated in regions of the genome that reflect a higher-than-average history of among-species divergence. This application of a DNA toolkit designed for the simple problem of identifying species in the field has two important implications. First, the eastern North American white oak syngameon is composed of entities that most taxonomists would consider “good species.” Second, and more fundamentally, species in the syngameon are genetically coherent because characteristic portions of the genome remain divergent despite a history of introgression. Understanding the conditions under which some loci diverge while others introgress is key to understanding the origins and maintenance of global tree diversity.
American woodcock (Scolopax minor; woodcock) migratory connectivity (i.e., association between breeding and wintering areas) is largely unknown, even though current woodcock management is predicated on such associations. Woodcock are currently managed in the Eastern and Central management regions in the United States with the boundary between management regions analogous to the boundary between the Atlantic and Mississippi flyways, based largely on analysis of band returns from hunters. Factors during migration influence survival and fitness, and existing data derived from banding and very high frequency telemetry provide only coarse-scale information to assess factors influencing woodcock migratory movement patterns and behavior. To assess whether current management-region boundaries correspond with woodcock migratory connectivity in the Central Management Region and to describe migration patterns with higher resolution than has been previously possible, we deployed satellite transmitters on 73 woodcock (25 adult and 28 juvenile females, and 8 adult and 12 juvenile males) and recorded 87 autumn or spring migration paths from 2014 to 2016. Marked woodcock used 2 primary migrations routes: a Western Route and a Central Route. The Western Route ran north-south, connecting the breeding and wintering grounds within the Central Management Region. The hourglass-shaped Central Route connected an area on the wintering grounds reaching from Texas to Florida, to sites throughout northeastern North America in both the Eastern Management Region and Central Management Region and woodcock following this route migrated through the area between the Appalachian Mountains and the Mississippi Alluvial Valley in western Tennessee during autumn and spring. Two of 17 woodcock captured associated with breeding areas in Michigan, Wisconsin, or Minnesota migrated to wintering sites in the Eastern Management Region and 12 marked woodcock captured on wintering areas in Texas and Louisiana migrated to breeding sites in the Eastern Management Region. Woodcock that used the Western Route exhibited high concentrations of stopovers during spring in the Arkansas Ozark Mountains and northern Missouri, and along the Mississippi River on the border between Wisconsin and Minnesota, and autumn concentrations of stopovers in southwestern Iowa, central Missouri, the Arkansas portion of the Ozark Mountains, and around the junction of Texas, Louisiana, Oklahoma, and Arkansas. Woodcock that used the Central Route exhibited high concentrations of stopovers during spring in northern Mississippi through western Tennessee, western Kentucky, and the Missouri Bootheel, and autumn concentrations of stopovers in northern Illinois, southwestern Ohio, and the portions of Kentucky and Tennessee west of the Appalachian Mountains. We suggest that current management of woodcock based on 2 management regions may not be consistent with the apparent lack of strong migratory connectivity we observed. Our results also suggest where management of migration habitat might be most beneficial to woodcock.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.21741","usgsCitation":"Moore, J.D., Andersen, D.E., Cooper, T.R., Duguay, J.P., Oldenburger, S., Stewart, C.A., and Krementz, D.G., 2019, Migratory connectivity of American woodcock derived using satellite telemetry: Journal of Wildlife Management, v. 83, no. 7, p. 1617-1627, https://doi.org/10.1002/jwmg.21741.","productDescription":"11 p.","startPage":"1617","endPage":"1627","ipdsId":"IP-098884","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":395699,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.85546875,\n 25.958044673317843\n ],\n [\n -94.74609375,\n 29.075375179558346\n ],\n [\n -90.17578124999999,\n 28.998531814051795\n ],\n [\n -84.638671875,\n 30.44867367928756\n ],\n [\n -85.69335937499999,\n 35.24561909420681\n ],\n [\n -83.75976562499999,\n 34.95799531086792\n ],\n [\n -81.474609375,\n 37.020098201368114\n ],\n [\n -83.3203125,\n 36.80928470205937\n ],\n [\n -79.62890625,\n 37.78808138412046\n ],\n [\n -74.1796875,\n 40.713955826286046\n ],\n [\n -69.08203125,\n 40.84706035607122\n ],\n [\n -63.6328125,\n 46.800059446787316\n ],\n [\n -63.6328125,\n 49.03786794532644\n ],\n [\n -68.64257812499999,\n 49.781264058178344\n ],\n [\n -83.232421875,\n 50.45750402042058\n ],\n [\n -99.49218749999999,\n 52.53627304145948\n ],\n [\n -97.55859375,\n 44.02442151965934\n ],\n [\n -98.0859375,\n 36.73888412439431\n ],\n [\n -98.701171875,\n 30.751277776257812\n ],\n [\n -97.998046875,\n 26.27371402440643\n ],\n [\n -96.85546875,\n 25.958044673317843\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"83","issue":"7","noUsgsAuthors":false,"publicationDate":"2019-08-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Moore, J. D.","contributorId":275291,"corporation":false,"usgs":false,"family":"Moore","given":"J.","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":833938,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Andersen, David E. 0000-0001-9535-3404 dea@usgs.gov","orcid":"https://orcid.org/0000-0001-9535-3404","contributorId":199408,"corporation":false,"usgs":true,"family":"Andersen","given":"David","email":"dea@usgs.gov","middleInitial":"E.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":833939,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cooper, Thomas R.","contributorId":191468,"corporation":false,"usgs":false,"family":"Cooper","given":"Thomas","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":834075,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Duguay, J. P.","contributorId":275292,"corporation":false,"usgs":false,"family":"Duguay","given":"J.","email":"","middleInitial":"P.","affiliations":[{"id":12717,"text":"Louisiana Department of Wildlife and Fisheries","active":true,"usgs":false}],"preferred":false,"id":833940,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Oldenburger, Shaun L.","contributorId":275294,"corporation":false,"usgs":false,"family":"Oldenburger","given":"Shaun L.","affiliations":[{"id":56759,"text":"Texas Parks & Wildlife","active":true,"usgs":false}],"preferred":false,"id":833942,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stewart, C. A.","contributorId":275295,"corporation":false,"usgs":false,"family":"Stewart","given":"C.","email":"","middleInitial":"A.","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":833943,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Krementz, David G. 0000-0002-5661-4541 dkrementz@usgs.gov","orcid":"https://orcid.org/0000-0002-5661-4541","contributorId":2827,"corporation":false,"usgs":true,"family":"Krementz","given":"David","email":"dkrementz@usgs.gov","middleInitial":"G.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":833944,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70206160,"text":"70206160 - 2019 - Quantifying trends and uncertainty in prehistoric forest composition","interactions":[],"lastModifiedDate":"2019-12-04T06:27:40","indexId":"70206160","displayToPublicDate":"2019-08-05T06:57:15","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying trends and uncertainty in prehistoric forest composition","docAbstract":"Forest ecosystems in eastern North America were in flux over the last\nseveral thousand years, well before Euro-American land clearance and the\n20th-century onset of anthropogenic climate change. However, the\nmagnitude and uncertainty of prehistoric vegetation change have been\ndifficult to quantify because of the multiple ecological, dispersal, and\nsedimentary processes that govern the relationship between forest\ncomposition and fossil pollen assemblages. Here we extend STEPPS, a\nBayesian hierarchical spatio-temporal pollen-vegetation model, to estimate\nchanges in forest composition in the upper Midwestern United States from\nabout 2000 to 200 years ago. Using this approach, we identify areas of\nstatistically and ecologically significant change. Between 2000 and 200\nyears ago, forest composition significantly changed across broad regions of\nnorth-central Wisconsin and Minnesota. Rates of compositional change\nvaried spatially, and can be linked to previously reported events. The single\nlargest change is the infilling of Tsuga canadensis in northern Wisconsin\nover the past 2000 years. Despite this range in-filling, the range limit of T.\ncanadensis was largely stable, with modest expansion westward. The\nregional ecotone between temperate hardwood forests and northern mixed\nhardwood/conifer forests shifted southwestward by 15-20 km in Minnesota\nand Northwestern Wisconsin. Fraxinus, Ulmus, and other mesic hardwoods\nexpanded in the Big Woods region of southern Minnesota. However, some\nareas showed no significant change, suggesting high complexity in the\nspatiotemporal patterns of past forest dynamics. The increasing density of\npaleoecological data networks and advances in statistical modeling\napproaches now enables the confident detection of subtle but significant\nchanges in forest composition over the last 2000 years.","language":"English","publisher":"Wiley","doi":"10.1002/ecy.2856","usgsCitation":"Andria Dawson, Christopher J. Paciorek, Goring, S., Jackson, S., Jason S. McLachlan, and John W. Williams, 2019, Quantifying trends and uncertainty in prehistoric forest composition: Ecology, v. 100, no. 12, e02856, https://doi.org/10.1002/ecy.2856.","productDescription":"e02856","ipdsId":"IP-096645","costCenters":[{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true}],"links":[{"id":467395,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecy.2856","text":"Publisher Index Page"},{"id":368548,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota, Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.328125,\n 42.85985981506279\n ],\n [\n -87.56103515625,\n 42.85985981506279\n ],\n [\n -87.56103515625,\n 44.653024159812\n ],\n [\n -96.328125,\n 44.653024159812\n ],\n [\n -96.328125,\n 42.85985981506279\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"100","issue":"12","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-09-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Andria Dawson","contributorId":219996,"corporation":false,"usgs":false,"family":"Andria Dawson","affiliations":[{"id":40107,"text":"Mount Royal University","active":true,"usgs":false}],"preferred":false,"id":773745,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Christopher J. Paciorek","contributorId":219997,"corporation":false,"usgs":false,"family":"Christopher J. Paciorek","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":773746,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Goring, Simon","contributorId":219998,"corporation":false,"usgs":false,"family":"Goring","given":"Simon","email":"","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":773747,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jackson, Stephen 0000-0002-1487-4652","orcid":"https://orcid.org/0000-0002-1487-4652","contributorId":219995,"corporation":false,"usgs":true,"family":"Jackson","given":"Stephen","affiliations":[{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":773744,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jason S. McLachlan","contributorId":219999,"corporation":false,"usgs":false,"family":"Jason S. McLachlan","affiliations":[{"id":39516,"text":"University of Notre Dame","active":true,"usgs":false}],"preferred":false,"id":773748,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"John W. Williams","contributorId":197556,"corporation":false,"usgs":false,"family":"John W. Williams","affiliations":[],"preferred":false,"id":773749,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70206467,"text":"70206467 - 2019 - Artificial intelligence and avian influenza: Using machine learning to enhance active surveillance for avian influenza viruses","interactions":[],"lastModifiedDate":"2023-06-21T15:28:30.150596","indexId":"70206467","displayToPublicDate":"2019-08-03T10:38:24","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3849,"text":"Transboundary and Emerging Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Artificial intelligence and avian influenza: Using machine learning to enhance active surveillance for avian influenza viruses","docAbstract":"Influenza A viruses are one of the most significant viral groups globally with substantial impacts on human, domestic animal and wildlife health. Wild birds are the natural reservoirs for these viruses, and active surveillance within wild bird populations provides critical information about viral evolution forming the basis of risk assessments and countermeasure development. Unfortunately, active surveillance programs are often resource‐intensive, and thus, enhancing programs for increased efficiency is paramount. Machine learning, a branch of artificial intelligence applications, provides statistical learning procedures that can be used to gain novel insights into disease surveillance systems. We use a form of machine learning, gradient boosted trees, to estimate the probability of isolating avian influenza viruses (AIV) from wild bird samples collected during surveillance for AIVs from 2006 to 2011 in the United States. We examined several predictive features including age, sex, bird type, geographic location and matrix gene rRT‐PCR results. Our final model had high predictive power and only included geographic location and rRT‐PCR results as important predictors. The highest predicted viral isolation probability was for samples collected from the north‐central states and the south‐eastern region of Alaska. Lower rRT‐PCR Ct‐values are associated with increased likelihood of AIV isolation, and the model estimated 16% probability of isolating AIV from samples declared negative (i.e., ≥35 Ct‐value) using the rRT‐PCR screening test and standard protocols. Our model can be used to prioritize previously collected samples for isolation and rapidly evaluate AIV surveillance designs to maximize the probability of viral isolation given limited resources and laboratory capacity.
","language":"English","publisher":"Wiley","doi":"10.1111/tbed.13318","usgsCitation":"Walsh, D.P., Ma, T.F., Ip, S., and Zhu, J., 2019, Artificial intelligence and avian influenza: Using machine learning to enhance active surveillance for avian influenza viruses: Transboundary and Emerging Diseases, v. 66, no. 6, p. 2537-2545, https://doi.org/10.1111/tbed.13318.","productDescription":"9 p.; Data Release","startPage":"2537","endPage":"2545","ipdsId":"IP-109212","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":467396,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/tbed.13318","text":"Publisher Index Page"},{"id":368966,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":418298,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96YJRWR"}],"volume":"66","issue":"6","noUsgsAuthors":false,"publicationDate":"2019-08-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Walsh, Daniel P. 0000-0002-7772-2445 dwalsh@usgs.gov","orcid":"https://orcid.org/0000-0002-7772-2445","contributorId":4758,"corporation":false,"usgs":true,"family":"Walsh","given":"Daniel","email":"dwalsh@usgs.gov","middleInitial":"P.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":774746,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ma, Ting Fung","contributorId":220321,"corporation":false,"usgs":false,"family":"Ma","given":"Ting","email":"","middleInitial":"Fung","affiliations":[{"id":18002,"text":"University of Wisconsin - Madison","active":true,"usgs":false}],"preferred":false,"id":774747,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ip, S. 0000-0003-4844-7533 hip@usgs.gov","orcid":"https://orcid.org/0000-0003-4844-7533","contributorId":727,"corporation":false,"usgs":true,"family":"Ip","given":"S.","email":"hip@usgs.gov","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":774748,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zhu, Jun","contributorId":73485,"corporation":false,"usgs":true,"family":"Zhu","given":"Jun","email":"","affiliations":[],"preferred":false,"id":774749,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70223689,"text":"70223689 - 2019 - Magmatic-hydrothermal gold mineralization at the Lone Tree Mine, Battle Mountain district, Nevada","interactions":[],"lastModifiedDate":"2021-09-01T14:45:20.201078","indexId":"70223689","displayToPublicDate":"2019-08-01T09:40:47","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1472,"text":"Economic Geology","active":true,"publicationSubtype":{"id":10}},"title":"Magmatic-hydrothermal gold mineralization at the Lone Tree Mine, Battle Mountain district, Nevada","docAbstract":"The Lone Tree deposit is located in the northern Battle Mountain mining district, Nevada. Prior to mine closure in 2006, Santa Fe Pacific Gold and Newmont produced 4.2 Moz of gold at an average grade of 2.06 g/t at Lone Tree, primarily from the N-S– to NNW-SSE–striking Wayne zone. The ore is located between the Roberts Mountain and Golconda thrusts in siliciclastic rocks of the Ordovician Valmy Formation and in the Pennsylvanian-Permian Battle Mountain and Edna Mountain Formations, and above the Golconda thrust in siliciclastic and carbonate rocks of the Mississippian to Permian Havallah sequence. Ore is also hosted by rhyolitic dikes that were emplaced at 40.95 ± 0.06 Ma based on zircon U-Pb chemical abrasion-thermal ionization mass spectrometry.
The gold is associated with sericitic and argillic alteration of the siliciclastic rocks and dikes and with decarbonatization and Fe carbonate alteration of the carbonate-bearing units, as well as in Fe-As sulfide and finegrained quartz alteration of all rock types. Oxidation affects 30 to 45% of the deposit, penetrating into the stratigraphy along numerous steeply dipping north-south, east-west, and north-northeast–south-southwest structures. Gold is positively correlated with Ag, As, Hg, and Sb. The highest Au grades occur in quartz-sulfide ore hosted in siliciclastic and carbonate sedimentary rocks and rhyolitic intrusions. In this ore style, fine-grained quartz and sericite are intergrown with disseminated sulfide minerals (quartz-sericite-pyrite alteration), constituting cores of weakly mineralized pyrite or marcasite, which are surrounded by fuzzy arsenopyrite rims that contain up to ~2,000 ppm Au. Low gold grades occur in late-stage banded pyrite breccias consisting of a finely zoned Au-poor pyrite matrix surrounding jigsaw-fit clasts of quartz-, illite-, barite-, and adularia-altered siliciclastic rock. The timing of main-stage mineralization is bracketed between the emplacement of the dikes and an adularia 40Ar/39Ar age of 40.14 ± 0.74 Ma.
Sericite intergrown with arsenopyrite-rimmed pyrite in phenocrysts of the rhyolite dikes gave δ18O values of 1.6 to 9.5‰ and δD values of –105 to –145‰. For temperatures of 300 ± 100°C, the calculated fluid isotopic compositions are consistent with felsic magmatic water and minor modifications by mixing with meteoric water and exchange with wall rocks. In the silica-sulfide ore, in situ isotopic laser ablation-multicollector-inductively coupled plasma-mass spectrometry (LA-MC-ICP-MS) analyses of pyrite cores yielded δ34S values ranging from 3.4 to 7.7‰, with average values of 5.6‰ in the felsic dikes, 4.5‰ in the siliciclastic rocks, and 5.3‰ in the carbonate rocks. These values match conventional pyrite δ34S data reported for Eocene porphyry systems elsewhere in the district. Nanoscale secondary ion mass spectrometry analyses show that gold and associated trace elements occur in submicron-scale zones within arsenopyrite rims on pyrite. The average δ34S values of the arsenopyrite rims are 5.3 to 6.5‰ heavier than the pyrite cores, indicating cooling and an increasing H2S/SO2 ratio. The highest grades resulted from episodic pulses of a gold-rich fluid that was partly derived from, or exchanged with, the sedimentary host rocks. In situ LA-MC-ICP-MS δ34S values for the late-stage banded pyrite breccia become progressively lighter from veinlet margin to center, reaching a low of –32‰. These veinlets indicate a shift from main-stage quartz-sericite-pyrite and intermediate argillic alteration to more neutral pH and oxidizing conditions during late-stage mineralization, indicating either increasing interaction between the fluid and sedimentary sulfur sources in the host-rock package or bacterial sulfate reduction and supergene sulfide precipitation.
","language":"English","publisher":"Society of Economic Geologists","doi":"10.5382/econgeo.4665","usgsCitation":"Holley, E.A., Lowe, J., Johnson, C.A., and Pribil, M., 2019, Magmatic-hydrothermal gold mineralization at the Lone Tree Mine, Battle Mountain district, Nevada: Economic Geology, v. 114, no. 5, p. 811-856, https://doi.org/10.5382/econgeo.4665.","productDescription":"46 p.","startPage":"811","endPage":"856","ipdsId":"IP-104133","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":388732,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","otherGeospatial":"Battle Mountain district, Lone Tree Mine","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.003662109375,\n 38.272688535980976\n ],\n [\n -114.202880859375,\n 38.272688535980976\n ],\n [\n -114.202880859375,\n 41.99624282178583\n ],\n [\n -120.003662109375,\n 41.99624282178583\n ],\n [\n -120.003662109375,\n 38.272688535980976\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"114","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Holley, Elizabeth A. 0000-0003-2504-4555","orcid":"https://orcid.org/0000-0003-2504-4555","contributorId":265154,"corporation":false,"usgs":false,"family":"Holley","given":"Elizabeth","email":"","middleInitial":"A.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":822332,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lowe, Justin","contributorId":265155,"corporation":false,"usgs":false,"family":"Lowe","given":"Justin","email":"","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":822333,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Craig A. 0000-0002-1334-2996 cjohnso@usgs.gov","orcid":"https://orcid.org/0000-0002-1334-2996","contributorId":909,"corporation":false,"usgs":true,"family":"Johnson","given":"Craig","email":"cjohnso@usgs.gov","middleInitial":"A.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":822334,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pribil, Michael J. 0000-0003-4859-8673 mpribil@usgs.gov","orcid":"https://orcid.org/0000-0003-4859-8673","contributorId":141158,"corporation":false,"usgs":true,"family":"Pribil","given":"Michael","email":"mpribil@usgs.gov","middleInitial":"J.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":822335,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70236154,"text":"70236154 - 2019 - Hydroclimatology of the Mississippi River Basin","interactions":[],"lastModifiedDate":"2022-08-30T14:18:05.915762","indexId":"70236154","displayToPublicDate":"2019-08-01T09:11:00","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Hydroclimatology of the Mississippi River Basin","docAbstract":"Model estimated monthly water balance (WB) components (i.e., potential evapotranspiration, actual evapotranspiration, and runoff [R]) for 848 United States (U.S.) Geological Survey 8-digit hydrologic units located in the Mississippi River Basin (MRB) are used to examine the temporal and spatial variability of the MRB WB for water years 1901 through 2014. Results indicate the MRB can be divided into nine subregions with similar temporal variability in R. The WB analyses indicated ~79% of total water-year MRB runoff is generated by four of the nine subregions and most of the R in the basin is derived from surplus (S) water during the months of December through May. Furthermore, the analyses showed temporal variability in S is largely controlled by the occurrence of negative atmospheric pressure anomalies over the western U.S. and positive atmospheric pressure anomalies over the eastern U.S. coast. This combination of atmospheric pressure anomalies results in an anomalous flow of moist air from the Gulf of Mexico into the MRB. In the context of paleo-climate reconstructions of the Palmer Drought Severity Index, since about 1900 the MRB has experienced wetter conditions than were experienced during the previous 500 years.
","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.12749","usgsCitation":"McCabe, G.J., and Wolock, D.M., 2019, Hydroclimatology of the Mississippi River Basin: Journal of the American Water Resources Association, v. 55, no. 4, p. 1053-1064, https://doi.org/10.1111/1752-1688.12749.","productDescription":"12 p.","startPage":"1053","endPage":"1064","ipdsId":"IP-101403","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":467399,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/1752-1688.12749","text":"External Repository"},{"id":405907,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Mississippi River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.5986328125,\n 48.8936153614802\n ],\n [\n -112.939453125,\n 47.96050238891509\n ],\n [\n -112.67578124999999,\n 47.30903424774781\n ],\n [\n -111.357421875,\n 46.195042108660154\n ],\n [\n -112.3681640625,\n 45.61403741135093\n ],\n [\n -111.357421875,\n 44.715513732021336\n ],\n [\n -109.5556640625,\n 44.809121700077355\n ],\n [\n -109.423828125,\n 44.02442151965934\n ],\n [\n -109.0283203125,\n 43.61221676817573\n ],\n [\n -107.7978515625,\n 42.779275360241904\n ],\n [\n -106.2158203125,\n 41.57436130598913\n ],\n [\n -105.4248046875,\n 40.3130432088809\n ],\n [\n -105.29296874999999,\n 38.89103282648846\n ],\n [\n -105.5126953125,\n 37.82280243352756\n ],\n [\n -105.29296874999999,\n 36.06686213257888\n ],\n [\n -104.80957031249999,\n 34.84987503195418\n ],\n [\n -102.7001953125,\n 34.34343606848294\n ],\n [\n -99.404296875,\n 34.08906131584994\n ],\n [\n -97.1630859375,\n 33.43144133557529\n ],\n [\n -94.7021484375,\n 32.21280106801518\n ],\n [\n -94.04296874999999,\n 29.49698759653577\n ],\n [\n -89.82421875,\n 28.76765910569123\n ],\n [\n -89.6484375,\n 31.015278981711266\n ],\n [\n -90.615234375,\n 32.62087018318113\n ],\n [\n -90.263671875,\n 34.379712580462204\n ],\n [\n -88.06640625,\n 34.92197103616377\n ],\n [\n -84.7705078125,\n 34.994003757575776\n ],\n [\n -82.44140625,\n 36.24427318493909\n ],\n [\n -80.9912109375,\n 37.37015718405753\n ],\n [\n -79.0576171875,\n 38.993572058209466\n ],\n [\n -78.79394531249999,\n 42.84375132629021\n ],\n [\n -82.0458984375,\n 41.57436130598913\n ],\n [\n -84.90234375,\n 40.78054143186033\n ],\n [\n -87.099609375,\n 41.86956082699455\n ],\n [\n -87.890625,\n 42.779275360241904\n ],\n [\n -89.8681640625,\n 43.866218006556394\n ],\n [\n -90.087890625,\n 45.089035564831036\n ],\n [\n -89.82421875,\n 46.34692761055676\n ],\n [\n -90.791015625,\n 47.100044694025215\n ],\n [\n -94.6142578125,\n 47.69497434186282\n ],\n [\n -96.767578125,\n 46.255846818480315\n ],\n [\n -99.6240234375,\n 47.517200697839414\n ],\n [\n -100.37109375,\n 49.009050809382046\n ],\n [\n -113.5986328125,\n 48.8936153614802\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"55","issue":"4","noUsgsAuthors":false,"publicationDate":"2019-04-22","publicationStatus":"PW","contributors":{"authors":[{"text":"McCabe, Gregory J. 0000-0002-9258-2997 gmccabe@usgs.gov","orcid":"https://orcid.org/0000-0002-9258-2997","contributorId":200854,"corporation":false,"usgs":true,"family":"McCabe","given":"Gregory","email":"gmccabe@usgs.gov","middleInitial":"J.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":850265,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wolock, David M. 0000-0002-6209-938X","orcid":"https://orcid.org/0000-0002-6209-938X","contributorId":219213,"corporation":false,"usgs":true,"family":"Wolock","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":850266,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70204532,"text":"70204532 - 2019 - (U-Th)/He zircon dating of Chesapeake Bay distal impact ejecta from ODP site 1073","interactions":[],"lastModifiedDate":"2019-08-05T09:34:18","indexId":"70204532","displayToPublicDate":"2019-08-01T07:54:58","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2715,"text":"Meteoritics and Planetary Science","active":true,"publicationSubtype":{"id":10}},"title":"(U-Th)/He zircon dating of Chesapeake Bay distal impact ejecta from ODP site 1073","docAbstract":"Single crystal (U‐Th)/He dating has been undertaken on 21 detrital zircon grains extracted from a core sample from Ocean Drilling Project (ODP) site 1073, which is located ~390 km northeast of the center of the Chesapeake Bay impact structure. Optical and electron imaging in combination with energy dispersive X‐ray microanalysis (EDS) of zircon grains from this late Eocene sediment shows clear evidence of shock metamorphism in some zircon grains, which suggests that these shocked zircon crystals are distal ejecta from the formation of the ~40 km diameter Chesapeake Bay impact structure. (U‐Th/He) dates for zircon crystals from this sediment range from 33.49 ± 0.94 to 305.1 ± 8.6 Ma (2σ), implying crystal‐to‐crystal variability in the degree of impact‐related resetting of (U‐Th)/He systematics and a range of different possible sources. The two youngest zircon grains yield an inverse‐variance weighted mean (U‐Th)/He age of 33.99 ± 0.71 Ma (2σ uncertainties n = 2; mean square weighted deviation = 2.6; probability [P] = 11%), which is interpreted to be the (U‐Th)/He age of formation of the Chesapeake Bay impact structure. This age is in agreement with K/Ar, 40Ar/39Ar, and fission track dates for tektites from the North American strewn field, which have been interpreted as associated with the Chesapeake Bay impact event.
","language":"English","publisher":"Wiley","doi":"10.1111/maps.13316","usgsCitation":"Biren, M., Wartho, J., Soest, V., Hodges, K., Cathey, H., Glass, B., Koeberl, C., Horton, J.W., and Hale, W., 2019, (U-Th)/He zircon dating of Chesapeake Bay distal impact ejecta from ODP site 1073: Meteoritics and Planetary Science, v. 54, no. 8, p. 1840-1852, https://doi.org/10.1111/maps.13316.","productDescription":"13 p.","startPage":"1840","endPage":"1852","ipdsId":"IP-101948","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":467400,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/maps.13316","text":"External Repository"},{"id":366099,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Chesapeake Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.508544921875,\n 36.78289206199065\n ],\n [\n -74.827880859375,\n 36.78289206199065\n ],\n [\n -74.827880859375,\n 39.70718665682654\n ],\n [\n -77.508544921875,\n 39.70718665682654\n ],\n [\n -77.508544921875,\n 36.78289206199065\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"54","issue":"8","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-06-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Biren, M.B.","contributorId":217742,"corporation":false,"usgs":false,"family":"Biren","given":"M.B.","email":"","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":767421,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wartho, J.-A.","contributorId":217743,"corporation":false,"usgs":false,"family":"Wartho","given":"J.-A.","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":767422,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Soest, van","contributorId":217744,"corporation":false,"usgs":false,"family":"Soest","given":"van","email":"","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":767423,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hodges, K.V.","contributorId":217745,"corporation":false,"usgs":false,"family":"Hodges","given":"K.V.","email":"","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":767424,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cathey, H.","contributorId":217746,"corporation":false,"usgs":false,"family":"Cathey","given":"H.","email":"","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":767425,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Glass, B.P.","contributorId":217747,"corporation":false,"usgs":false,"family":"Glass","given":"B.P.","email":"","affiliations":[{"id":13359,"text":"University of Delaware","active":true,"usgs":false}],"preferred":false,"id":767426,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Koeberl, C.","contributorId":217748,"corporation":false,"usgs":false,"family":"Koeberl","given":"C.","affiliations":[{"id":39691,"text":"University of Vienna, Austria","active":true,"usgs":false}],"preferred":false,"id":767427,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Horton, J. Wright Jr. 0000-0001-6756-6365 whorton@usgs.gov","orcid":"https://orcid.org/0000-0001-6756-6365","contributorId":173694,"corporation":false,"usgs":true,"family":"Horton","given":"J.","suffix":"Jr.","email":"whorton@usgs.gov","middleInitial":"Wright","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":false,"id":767420,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hale, W.","contributorId":217749,"corporation":false,"usgs":false,"family":"Hale","given":"W.","email":"","affiliations":[{"id":39692,"text":"IODP Core Repository, Bremen, Germany","active":true,"usgs":false}],"preferred":false,"id":767428,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70204412,"text":"sir20195042 - 2019 - Lithostratigraphic, geophysical, and hydrogeologic observations from a boring drilled to bedrock in glacial sediments near Nantucket Sound in East Falmouth, Massachusetts","interactions":[],"lastModifiedDate":"2019-08-01T07:11:08","indexId":"sir20195042","displayToPublicDate":"2019-07-31T14:15:00","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":"2019-5042","displayTitle":"Lithostratigraphic, Geophysical, and Hydrogeologic Observations From a Boring Drilled to Bedrock in Glacial Sediments Near Nantucket Sound in East Falmouth, Massachusetts","title":"Lithostratigraphic, geophysical, and hydrogeologic observations from a boring drilled to bedrock in glacial sediments near Nantucket Sound in East Falmouth, Massachusetts","docAbstract":"In spring 2016, a 310-foot-deep boring (named MA–FSW 750) was drilled by the U.S. Geological Survey near Nantucket Sound in East Falmouth, Massachusetts, to investigate the hydrogeology of the southern coast of western Cape Cod. Few borings that are drilled to bedrock exist in the area, and the study area was selected to fill a gap between comprehensive geologic datasets inland to the north and marine geophysical data from beneath Nantucket Sound to the south. A permanent monitoring well (MA–FSW 750–0100) was installed in the boring upon the completion of the drilling and core collection. Observations from sediment cores and surface and borehole geophysical measurements were used to delineate three zones relevant to understanding groundwater flow at the study location. Shallow sands and gravels (0–107 feet [ft] below land surface [bls]) underlain by silt-rich fine and very fine sand (107–175 ft bls) form a zone of high permeability underlain by a zone of relatively lower permeability, referred to as the “shallow high-permeability” and “low-permeability” zones, respectively. A sharp lithological contact separating the shallow high-permeability and low-permeability zones may affect vertical flow of groundwater. Fine to coarse sand with intervals of clay and silt from 175 to 300 ft bls represent a deep zone of relatively high permeability, referred to as the “deep high-permeability” zone. A compacted, nonsorted unit (identified as basal till) and the bedrock surface were encountered at 300 and 305 ft bls, respectively. Hydraulic conductivity estimates from nuclear magnetic resonance logs and sediment grain-size distribution analyses indicated that the shallow high-permeability zone contributes substantially to the capacity of the aquifer to transmit groundwater at the study location. Results from geophysical surveys indicate a gradual transition from fresh to saline groundwater in the interval from 105 to 160 ft bls. Freshwater at the study site is present in the saturated unconsolidated sediments only in the 75 ft between 30 ft (the water table) and 105 ft bls in the shallow high-permeability zone. Sediments shallower than 175 ft bls closely resemble the downward fining post-Wisconsinan age deltaic and lacustrine deposits present in many parts of western Cape Cod; sediments deeper than 175 ft appear to be the product of earlier depositional processes more local to the southern coast of western Cape Cod. This study highlights how high-resolution observations of cored material coupled with a multitool geophysical approach can characterize a single boring to help better understand regional glacial history and hydrogeology.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195042","collaboration":"Prepared in cooperation with the Cape Cod Commission","usgsCitation":"Hull, R.B, Johnson, C.D., Stone, B.D., LeBlanc, D.R., McCobb, T.D., Phillips, S.N., Pappas, K.L., and Lane, J.W., 2019, Lithostratigraphic, geophysical, and hydrogeologic observations from a boring drilled to bedrock in glacial sediments near Nantucket sound in East Falmouth, Massachusetts: U.S. Geological Survey Scientific Investigations Report 2019–5042, 27 p., https://doi.org/10.3133/sir20195042.","productDescription":"Report: 27 p.; Data Releases","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-088627","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":365906,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P26X0Z ","text":"USGS data release","description":"USGS data release","linkHelpText":"Geophysical data"},{"id":365905,"rank":3,"type":{"id":30,"text":"Data Release"},"url":" https://doi.org/10.5066/F7W66JPM","text":"USGS data release","description":"USGS data release","linkHelpText":"Lithostratigraphic and hydraulic data"},{"id":437379,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7W66JPM","text":"USGS data release","linkHelpText":"Lithostratigrapic, Geophysical, and Hydrogeologic Observations from a Deep Boring in Glacial Sediments on Davis Neck near Nantucket Sound, East Falmouth, Western Cape Cod, Massachusetts"},{"id":365822,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5042/coverthb.jpg"},{"id":365823,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5042/sir20195042.pdf","text":"Report","size":"3.40 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5042"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Nantucket Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.69290161132811,\n 41.22824901518529\n ],\n [\n -69.90875244140625,\n 41.22824901518529\n ],\n [\n -69.90875244140625,\n 41.60312076451184\n ],\n [\n -70.69290161132811,\n 41.60312076451184\n ],\n [\n -70.69290161132811,\n 41.22824901518529\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
331 Commerce Road, Suite 2
Pembroke, NH 03275
The Santa Fe Group aquifer is an important source of water to communities within the Middle Rio Grande Basin, including the Albuquerque-Rio Rancho metropolitan area and Kirtland Air Force Base, New Mexico. In November 1999, Kirtland Air Force Base personnel observed fuel-stained soils at the Bulk Fuels Facility on the base. Subsequent pressure tests identified pipeline leaks. Fuels stored at the Bulk Fuels Facility have included aviation gasoline, jet propellant 4, and jet propellant 8. The fuels migrated about 480 feet down to the water table. Ethylene dibromide, the constituent making up the most extensive part of the plume and a component of leaded aviation gasoline, has formed a plume that, in December 2016, was 400 to 1,300 feet wide, extended about 5,800 feet northeast from the Bulk Fuels Facility, and was about 3,700 feet from the nearest downgradient water-supply well.
Prior to widespread development of groundwater resources in southeastern Albuquerque, groundwater near the present-day location of the Bulk Fuels Facility flowed to the southwest. Groundwater began flowing northeast in about 1980 towards a large area of lowered water levels caused by groundwater pumping.
In 2013 and 2014 the Albuquerque Bernalillo County Water Utility Authority, the U.S. Air Force, and the U.S. Geological Survey began a cooperative study to characterize the geology and hydrology of the Santa Fe Group aquifer in the vicinity of the ethylene dibromide plume and to develop a local-scale groundwater flow model to delineate areas contributing recharge and zones of contribution to selected water-supply wells.
For this study, a previously developed Middle Rio Grande Basin regional groundwater-flow model was updated, and a smaller local-scale model was developed. Advective groundwater-flow paths were delineated and visualized with the MODPATH particle-tracking program.
Of 11 wells included in the historical pumping analysis of areas contributing recharge, only wells K-3, K-7, and RC-4 derived a portion of their water from simulated recharge sources within the local-scale model. None of the areas contributing recharge overlap the Bulk Fuels Facility area or the ethylene dibromide plume footprint as delineated using December 2016 ethylene dibromide data.
For the historical pumping analysis of zones of contribution, particles for the 11 selected wells generally moved southwest from the north and east boundaries of the local-scale model, moved past their target well, but reversed direction and moved back towards their target well after 1980 when groundwater flow changed to the northeast. Of the 11 wells, only BR-5, RC-5, and VH-2 had 1980–2013 particle pathlines that overlap the December 2016 ethylene dibromide plume footprint, and wells BR-5 and VH-2 have 1980–2013 particle pathlines that overlap the Bulk Fuels Facility area. Particles that were north of the Bulk Fuels Facility when groundwater flow reversed direction would not have the opportunity to interact with the ethylene dibromide plume. Wells BR-5, K-15, and VH-2 did have particles southwest of the Bulk Fuels Facility in 1980. Particles traveling to BR-5 and K-15 passed under or very near the Bulk Fuels Facility area in the 1980–2013 period, but none of the pathlines were shallow enough to interact with ethylene dibromide at the Bulk Fuels Facility. A few particles traveling to VH-2 passed through the Bulk Fuels Facility area at shallow enough depths to interact with ethylene dibromide at the Bulk Fuels Facility in the 1980–2013 period. Ethylene dibromide has not been detected in water samples collected in 2012 through 2015 from the VH-2 well.
Of 10 water-supply wells near the ethylene dibromide plume included in the future pumping analysis of areas contributing recharge, only wells K-3, RC-3, and RC-4 had areas contributing recharge within the local-scale model. The areas contributing recharge for wells RC-3 and RC-4 do not overlap the Bulk Fuels Facility area or the December 2016 ethylene dibromide plume footprint, but K-3 derives part of its recharge prior to 1980 and during 1980–2015 from within the area of the December 2016 plume footprint.
The analysis of the future pumping scenarios indicated that wells BR-5, K-3, K-16, RC-5, and VH-2 have pathlines for 1980–2015 and wells K-16 and VH-2 have pathlines for 2015–50 that when projected in plan view pass through the December 2016 plume footprint. Of these five wells, only K-3 and RC-5 have pathlines for 1980–2015 that are above an elevation of 4,800 feet and could interact with the ethylene dibromide plume if ethylene dibromide was present when the particles were present.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195052","collaboration":"Prepared in cooperation with the Albuquerque Bernalillo County Water Utility Authority and the U.S. Air Force","usgsCitation":"Myers, N.C., and Friesz, P.J., 2019, Hydrogeologic framework and delineation of transient areas contributing recharge and zones of contribution to selected wells in the upper Santa Fe Group aquifer, southeastern Albuquerque, New Mexico, 1900–2050: U.S. Geological Survey Scientific Investigations Report 2019–5052, 73 p., https://doi.org/10.3133/sir20195052.","productDescription":"Report: viii, 73 p.; Data Release","numberOfPages":"86","onlineOnly":"Y","ipdsId":"IP-080008","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":365539,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5052/sir20195052.pdf","text":"Report","size":"38.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5052"},{"id":365538,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5052/coverthb.jpg"},{"id":365540,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F79P303S","text":"USGS data release ","description":"USGS Data Release","linkHelpText":"MODFLOW–LGR2 groundwater-flow model used to delineate transient areas contributing recharge and zones of contribution to selected wells in the upper Santa Fe Group aquifer, southeastern Albuquerque, New Mexico"}],"country":"United States","state":"New Mexico","county":"Bernalillo County","city":"Albuquerque","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-106.242,35.2147],[-106.2387,35.0549],[-106.2386,35.0408],[-106.2373,34.9568],[-106.1453,34.9547],[-106.1446,34.872],[-106.3328,34.8712],[-106.3569,34.8702],[-106.409,34.8687],[-106.4097,34.8914],[-106.417,34.8945],[-106.4221,34.9013],[-106.6755,34.9065],[-106.6838,34.9006],[-106.6917,34.901],[-106.6922,34.896],[-106.7139,34.8772],[-106.7127,34.8713],[-107.0181,34.8727],[-107.0227,34.8817],[-107.0641,34.9618],[-107.104,35.0395],[-107.1068,35.0454],[-107.1769,35.1809],[-107.1972,35.2197],[-107.1628,35.2192],[-107.1623,35.2192],[-107.1578,35.2192],[-107.1262,35.2186],[-107.1105,35.2188],[-107.0936,35.2189],[-107.0801,35.2186],[-107.0761,35.2186],[-107.0345,35.2185],[-106.9416,35.217],[-106.9337,35.2171],[-106.8808,35.2171],[-106.8622,35.2172],[-106.5955,35.2184],[-106.5645,35.2186],[-106.4964,35.2184],[-106.479,35.2176],[-106.4531,35.2172],[-106.3822,35.2175],[-106.3765,35.2175],[-106.242,35.2147]]]},\"properties\":{\"name\":\"Bernalillo\",\"state\":\"NM\"}}]}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE, Suite B
Albuquerque, NM 87113
Improved understanding of the origin of produced volatiles from conventional reservoirs and unconventional source rocks is critical for petroleum exploration and production. A series of hydrous heating experiments using two immature Type II siliciclastic source rocks, Pennsylvanian Turner Mine shale (TMS) and Devonian New Albany Shale (NAS), at 130 °C over one to two years were conducted to assess gas generation at low temperature. Elemental sulfur (ES) was added to the NAS samples to evaluate the role of sulfur on thermochemical sulfate reduction (TSR). The produced volatile composition was investigated in situ using Raman spectroscopy at the end of the heating experiments. Results show that the two source rocks yield different types and concentrations of volatiles. Only CH4 and CO2 were detected following hydrous heating of the TMS source rock in contrast to CH4, C2H6, C3H8, and CO2 which were observed in experiments using NAS. Variations in the produced volatiles are likely the result of compositional differences within the respective source rock organic matter. Experiments involving ES show strong H2S signals that are likely due to the formation of H2S from the reaction of ES with water at 130 °C. H2S signals correlate with a greater relative concentration of CH4 and CO2 compared to experiments where ES was not added, on a time-normalized basis. The correlation between the presence of H2S and an increase in CH4 and CO2 concentration could indicate the occurrence of TSR. Here we propose that H2S in siliciclastic shale can be generated in the presence of ES at low temperatures via both disproportionation of ES into H2S and SO42–, and TSR. Our findings from this study provide experimental evidence that may aid efforts to interpret the origin of H2S in low-temperature sedimentary basins.
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\"}}]}","volume":"137","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Alrowaie, M.A.","contributorId":217903,"corporation":false,"usgs":false,"family":"Alrowaie","given":"M.A.","email":"","affiliations":[{"id":37145,"text":"Indiana University","active":true,"usgs":false}],"preferred":false,"id":767776,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jubb, Aaron M. 0000-0001-6875-1079","orcid":"https://orcid.org/0000-0001-6875-1079","contributorId":201978,"corporation":false,"usgs":true,"family":"Jubb","given":"Aaron M.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":767775,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schimmelmann, A.","contributorId":217904,"corporation":false,"usgs":false,"family":"Schimmelmann","given":"A.","affiliations":[{"id":37145,"text":"Indiana University","active":true,"usgs":false}],"preferred":false,"id":767777,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mastalerz, M.","contributorId":217905,"corporation":false,"usgs":false,"family":"Mastalerz","given":"M.","affiliations":[{"id":33640,"text":"Indiana Geological Survey","active":true,"usgs":false}],"preferred":false,"id":767778,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pratt, L.M.","contributorId":217906,"corporation":false,"usgs":false,"family":"Pratt","given":"L.M.","email":"","affiliations":[{"id":37145,"text":"Indiana University","active":true,"usgs":false}],"preferred":false,"id":767779,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70200916,"text":"70200916 - 2019 - Right-lateral fault motion along the slope-basin transition, Gulf of Santa Catalina, southern California","interactions":[],"lastModifiedDate":"2019-12-05T09:44:43","indexId":"70200916","displayToPublicDate":"2019-07-31T09:43:45","publicationYear":"2019","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Right-lateral fault motion along the slope-basin transition, Gulf of Santa Catalina, southern California","docAbstract":"An active fault system carrying a significant component of right-lateral strike-slip motion extends for ~60 km along the slope–basin transition, ~10 to 20 km offshore of the southern California coast from La Jolla to Dana Point. From south to north, this fault system includes the Carlsbad, San Onofre, and San Mateo fault zones. High-resolution single channel minisparker and chirp seismic reflection data gathered from 2006 to 2011 reveal complex and variable fault zones that are generally characterized by nearly vertical to steeply east-dipping faults with a reverse slip component. The Carlsbad fault zone shows evidence of reverse motion followed by normal separation and probably also includes a component of strike-slip offset. The San Onofre fault zone shows clear evidence of right-lateral slip, offsetting submarine gullies near the base of the slope by approximately 60 m. North of these offset gullies, the base of the slope bends about 30° to the west, following the trend of the San Mateo fault zone, but strands of the San Onofre fault zone trend obliquely up slope, appearing to merge with the Newport–Inglewood fault zone at the shelf edge. These San Onofre fault strands consist of several en echelon left-stepping segments separated by “pop-up” structures, which imply a significant component of right-lateral offset that may serve to transfer right-lateral slip from faults along the base of the slope to the Newport–Inglewood fault zone. Using approximate base Quaternary and base Holocene reflections, segments of the Carlsbad and San Onofre fault zones appear to have experienced right-lateral motion in the Holocene, whereas deformation along the San Mateo fault zone appears to represent a period of mostly pre-Quaternary transpression.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"From the Mountains to the Abyss: The California Borderland as an Archive of Southern California Geologic Evolution","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Society for Sedimentary Geology","usgsCitation":"Conrad, J., Brothers, D., Coble, K., Holly F. Ryan, Dartnell, P., and Sliter, R., 2019, Right-lateral fault motion along the slope-basin transition, Gulf of Santa Catalina, southern California, chap. of From the Mountains to the Abyss: The California Borderland as an Archive of Southern California Geologic Evolution, v. 110, 17 p.","productDescription":"17 p.","ipdsId":"IP-093176","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":369969,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":359434,"type":{"id":15,"text":"Index Page"},"url":"https://sedimentary-geology-store.com/catalog/book/mountains-abyss-california-borderland-archive-southern-california-geologic-evolution"}],"country":"United States","state":"California","otherGeospatial":"Gulf of Santa Catalina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.79791259765625,\n 32.55144352864431\n ],\n [\n -117.04010009765625,\n 32.55144352864431\n ],\n [\n -117.04010009765625,\n 33.46810795527896\n ],\n [\n -118.79791259765625,\n 33.46810795527896\n ],\n [\n -118.79791259765625,\n 32.55144352864431\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"110","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Cochran, Susan 0000-0002-2442-8787 scochran@usgs.gov","orcid":"https://orcid.org/0000-0002-2442-8787","contributorId":210619,"corporation":false,"usgs":true,"family":"Cochran","given":"Susan","email":"scochran@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":751276,"contributorType":{"id":2,"text":"Editors"},"rank":7}],"authors":[{"text":"Conrad, James 0000-0001-6655-694X jconrad@usgs.gov","orcid":"https://orcid.org/0000-0001-6655-694X","contributorId":210620,"corporation":false,"usgs":true,"family":"Conrad","given":"James","email":"jconrad@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":751270,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brothers, Daniel","contributorId":210621,"corporation":false,"usgs":true,"family":"Brothers","given":"Daniel","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":751271,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coble, Katherine","contributorId":210622,"corporation":false,"usgs":true,"family":"Coble","given":"Katherine","email":"","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":751272,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Holly F. Ryan","contributorId":210623,"corporation":false,"usgs":false,"family":"Holly F. Ryan","affiliations":[{"id":7065,"text":"USGS emeritus","active":true,"usgs":false}],"preferred":false,"id":751273,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dartnell, Peter 0000-0002-9554-729X pdartnell@usgs.gov","orcid":"https://orcid.org/0000-0002-9554-729X","contributorId":210624,"corporation":false,"usgs":true,"family":"Dartnell","given":"Peter","email":"pdartnell@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":751274,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sliter, Ray 0000-0003-0337-3454 rsliter@usgs.gov","orcid":"https://orcid.org/0000-0003-0337-3454","contributorId":210625,"corporation":false,"usgs":true,"family":"Sliter","given":"Ray","email":"rsliter@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":751275,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70205899,"text":"70205899 - 2019 - Reduced soil macropores and forest cover reduce warm-season baseflow below ecological thresholds in the upper Delaware River Basin","interactions":[],"lastModifiedDate":"2019-10-09T12:58:42","indexId":"70205899","displayToPublicDate":"2019-07-30T12:53:41","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Reduced soil macropores and forest cover reduce warm-season baseflow below ecological thresholds in the upper Delaware River Basin","docAbstract":"We examined the impacts of changes in land cover and soil conditions on the flow regime of the upper Delaware River Basin using the Water Availability Tool for Environmental Resources (WATER). We simulated flows for two periods, circa 1600 and 1940, at three sites using the same temperature and precipitation conditions: the East Branch (EB), West Branch (WB), and mainstem Delaware River at Callicoon, NY. The 1600 period represented pristine forest and soils. The 1940 period included reduced forest cover, increased agriculture, and degraded soils with reduced soil macropore fractions. A model-sensitivity test examined the impact of soil macropore and land cover change separately. We assessed changes in flow regimes between the 1600 and 1940 periods using a variety of flow statistics, including established ecological limits of hydrologic alteration (ELOHA) thresholds. Reduced forest soil macropore fraction significantly reduced summer and fall base flows. The 1940 period had significantly lower Q50 flows (50% exceedance) than the 1600 period, as well as summer and fall Q90 and Q75-90 flows below the ELOHA thresholds. The 1- to 7-day minimum flows were also lower for the 1940 period, by 17% on the mainstem. 1940 flows were 6% more likely than the 1600 period to fall below the low-flow threshold for federally endangered dwarf wedgemussel (Alasmidonta heterodon) habitat. In contrast, the 1940 period had higher flows than the 1600 period from late fall to early winter.","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.12777","usgsCitation":"Endreny, T.A., Kwon, P.Y., Williamson, T.N., and Evans, R., 2019, Reduced soil macropores and forest cover reduce warm-season baseflow below ecological thresholds in the upper Delaware River Basin: Journal of the American Water Resources Association, v. 55, no. 5, p. 1268-1287, https://doi.org/10.1111/1752-1688.12777.","productDescription":"20 p.","startPage":"1268","endPage":"1287","ipdsId":"IP-091449","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":368171,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York, Pennsylvania","otherGeospatial":"Upper Delaware River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.5966796875,\n 40.9964840143779\n ],\n [\n -74.3389892578125,\n 40.9964840143779\n ],\n [\n -74.3389892578125,\n 42.85583308674893\n ],\n [\n -76.5966796875,\n 42.85583308674893\n ],\n [\n -76.5966796875,\n 40.9964840143779\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"55","issue":"5","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2019-07-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Endreny, Theodore A.","contributorId":195489,"corporation":false,"usgs":false,"family":"Endreny","given":"Theodore","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":772809,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kwon, Peter Yong Seuk","contributorId":219658,"corporation":false,"usgs":false,"family":"Kwon","given":"Peter","email":"","middleInitial":"Yong Seuk","affiliations":[{"id":34139,"text":"Anchor QEA","active":true,"usgs":false}],"preferred":false,"id":772810,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Williamson, Tanja N. 0000-0002-7639-8495 tnwillia@usgs.gov","orcid":"https://orcid.org/0000-0002-7639-8495","contributorId":198329,"corporation":false,"usgs":true,"family":"Williamson","given":"Tanja","email":"tnwillia@usgs.gov","middleInitial":"N.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":772808,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Evans, Richard","contributorId":216306,"corporation":false,"usgs":false,"family":"Evans","given":"Richard","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":772811,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70207040,"text":"70207040 - 2019 - Growth and mortality of invasive Flathead Catfish in the tidal James River, Virginia","interactions":[],"lastModifiedDate":"2020-01-08T14:14:03","indexId":"70207040","displayToPublicDate":"2019-07-26T15:10:44","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Growth and mortality of invasive Flathead Catfish in the tidal James River, Virginia","docAbstract":"Invasive species are a major threat to biodiversity of native fishes in North America. In Atlantic coastal rivers of the United States, large catfishes introduced from the Gulf of Mexico drainages have become established and contributed to native species declines. Flathead Catfish Pylodictis olivaris were introduced to the Chesapeake Bay drainage in the 1960s and 1970s in the James and Potomac river systems in the eastern United States. Diet studies have found James River Flathead Catfish function as apex predators and are known to consume at-risk Alosa spp. To limit further range expansion and impacts to native species, resource management agencies need information on population characteristics to support population assessments and management plan development. Thus, we examined temporal trends in growth rates and estimated total instantaneous mortality for tidal James River Flathead Catfish collected by Virginia Department of Game and Inland Fisheries from 1997 to 2015. Parameters of the von Bertalanffy growth model with length-at-age observations pooled across sampling years were estimated as L∞ = 1,059 mm, k = 0.231/y, and t0 = 0.55 y. Flathead Catfish growth differed among sampling years, especially for the years 2007 and 2014, which had the largest sample sizes. However, there were no obvious temporal trends in growth trajectories. James River Flathead Catfish tend to grow much faster than most populations used in development of the relative growth index, but the species is known to grow faster in its nonnative range. Consequently, scientists and managers should use caution when applying growth indices if native and nonnative populations are not expressly considered in development of the index. We estimated total instantaneous mortality as Z = 0.50 and mean natural mortality from six estimators as M = 0.30. A lack of older individuals in the population means that mortality rates may be overestimated as a result of gear selectivity or ongoing maturation of the population. These data provide information to support future work examining the species in the James River and development of population models to evaluate management strategies and management plans.
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/052019-JFWM-033","usgsCitation":"Hilling, C., Bunch, A.J., Emmel, J.A., Schmitt, J., and Orth, D.J., 2019, Growth and mortality of invasive Flathead Catfish in the tidal James River, Virginia: Journal of Fish and Wildlife Management, v. 10, no. 2, p. 641-652, https://doi.org/10.3996/052019-JFWM-033.","productDescription":"12 p.","startPage":"641","endPage":"652","ipdsId":"IP-109933","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":460321,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/052019-jfwm-033","text":"Publisher Index Page"},{"id":369916,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia ","otherGeospatial":"James River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.4208984375,\n 37.06394430056685\n ],\n [\n -76.387939453125,\n 37.28279464911045\n ],\n [\n -77.398681640625,\n 37.61423141542417\n ],\n [\n -79.89257812499999,\n 38.09998264736481\n ],\n [\n -80.716552734375,\n 37.98750437106374\n ],\n [\n -78.760986328125,\n 37.640334898059486\n ],\n [\n -77.53051757812499,\n 37.49229399862877\n ],\n [\n -76.31103515625,\n 36.82687474287728\n ],\n [\n -76.4208984375,\n 37.06394430056685\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"2","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2019-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Hilling, Corbin D.","contributorId":221021,"corporation":false,"usgs":false,"family":"Hilling","given":"Corbin D.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":776611,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bunch, Aaron J.","contributorId":221022,"corporation":false,"usgs":false,"family":"Bunch","given":"Aaron","email":"","middleInitial":"J.","affiliations":[{"id":35592,"text":"Virginia Department of Game and Inland Fisheries","active":true,"usgs":false}],"preferred":false,"id":776612,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Emmel, Jason A.","contributorId":221023,"corporation":false,"usgs":false,"family":"Emmel","given":"Jason","email":"","middleInitial":"A.","affiliations":[{"id":40312,"text":"Solitude Lake Management","active":true,"usgs":false}],"preferred":false,"id":776613,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schmitt, Joseph 0000-0002-8354-4067","orcid":"https://orcid.org/0000-0002-8354-4067","contributorId":221020,"corporation":false,"usgs":true,"family":"Schmitt","given":"Joseph","email":"","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":776610,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Orth, Donald J.","contributorId":221024,"corporation":false,"usgs":false,"family":"Orth","given":"Donald","email":"","middleInitial":"J.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":776614,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70203598,"text":"pp1842A - 2019 - The effects of management practices on grassland birds — An introduction to North American grasslands and the practices used to manage grasslands and grassland birds","interactions":[{"subject":{"id":70203598,"text":"pp1842A - 2019 - The effects of management practices on grassland birds — An introduction to North American grasslands and the practices used to manage grasslands and grassland birds","indexId":"pp1842A","publicationYear":"2019","noYear":false,"chapter":"A","displayTitle":"The Effects of Management Practices on Grassland Birds—An Introduction to North American Grasslands and the Practices Used to Manage Grasslands and Grassland Birds","title":"The effects of management practices on grassland birds — An introduction to North American grasslands and the practices used to manage grasslands and grassland birds"},"predicate":"IS_PART_OF","object":{"id":70203022,"text":"pp1842 - 2019 - The effects of management practices on grassland birds","indexId":"pp1842","publicationYear":"2019","noYear":false,"title":"The effects of management practices on grassland birds"},"id":1}],"isPartOf":{"id":70203022,"text":"pp1842 - 2019 - The effects of management practices on grassland birds","indexId":"pp1842","publicationYear":"2019","noYear":false,"title":"The effects of management practices on grassland birds"},"lastModifiedDate":"2023-12-20T21:00:48.691686","indexId":"pp1842A","displayToPublicDate":"2019-07-26T15:03:05","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1842","chapter":"A","displayTitle":"The Effects of Management Practices on Grassland Birds—An Introduction to North American Grasslands and the Practices Used to Manage Grasslands and Grassland Birds","title":"The effects of management practices on grassland birds — An introduction to North American grasslands and the practices used to manage grasslands and grassland birds","docAbstract":"The Great Plains of North America is defined as the land mass that encompasses the entire central portion of the North American continent that, at the time of European settlement, was an unbroken expanse of primarily herbaceous vegetation. The Great Plains extend from central Saskatchewan and Alberta to central Mexico and from Indiana to the Rocky Mountains. The expanses of herbaceous vegetation are often referred to as native prairie or native grasslands. Native grasslands share the characteristics of a general uniformity in vegetation structure, dominance by grasses and forbs, a near absence of trees and shrubs, annual precipitation ranging from 25 to 100 centimeters, extreme intra-annual fluctuations in temperature and precipitation, and a flat to rolling topography over which fires can spread. To the west of the Great Plains lie the sagebrush communities of the Great Basin, which extend from British Columbia and Saskatchewan to northern Arizona and New Mexico and from the eastern slopes of the Sierra Nevada and Cascade mountain ranges to western South Dakota. Sagebrush communities share similar characteristics to native grasslands, but their location east of the Rocky Mountains creates a more moderating influence from prevailing westerly winds that affect timing of peak precipitation and growth form of dominant vegetation. Native grasslands and sagebrush communities harbor a diverse array of grassland, wetland, and woodland plant and animal communities that are uniquely adapted to the natural forces of the Great Plains and Great Basin, namely the interactive forces of climate, fire, and grazing. The arrival of European settlers to North America brought profound change to native grassland and sagebrush communities, including the establishment of permanent towns and cities, the proliferation of cropland-based agricultural systems, and the suppression of wildfires. The near extirpation of bison by the 1860s paved the way for dramatic changes in the dominant grazers and a shift in the disturbance patterns that historically influenced vegetation structure. The greatest threat to native grasslands and sagebrush communities in modern times is their loss due to conversion to rowcrop agriculture and to urbanization. Concomitant with habitat loss is a precipitous decline in populations of bird species that evolved with, and are uniquely adapted to, the native grassland and sagebrush habitats. Avian population trends are linked strongly to agricultural land use. Besides outright loss of suitable breeding habitat, agricultural practices affect birds through factors such as pesticide exposure, habitat fragmentation, shifts in predator community composition, and occurrence of brood parasites. Bird populations face other stressors, such as loss of habitat to and behavioral avoidance of urbanized areas, roads, and infrastructure associated with energy production.
Despite the many anthropogenic changes to North American grassland and sagebrush communities, some bird species are adaptable and opportunistic in their habitat selection and now utilize one or more human-created habitats. Human-created habitats include pastures, hayfields, agricultural terraces, crop buffer strips, field borders, grassed waterways, fencerows, road rights-of-way, airports, reclaimed coal mines, and planted wildlife cover. Fields of seeded grasslands enrolled in Federal long-term set-aside programs, such as the Conservation Reserve Program in the United States and the Permanent Cover Program in Canada, provide important nesting habitat for grassland bird species. The array of habitats used by birds makes habitat and avian management a complex undertaking, and the scale (for example, local, regional, international) at which management actions can be implemented are such that a universal approach to managing grasslands for the conservation of the entire suite of bird species does not exist. Experienced land managers recognize that it is impossible to manage for all bird species simultaneously, and thus, prioritization is necessary towards those habitats or bird species that the manager or management agency ranks highest for a specific region or management unit. The primary tools available for management are burning, grazing, mowing, herbicide application, and idling, but before choosing a particular practice, a manager will want to consider issues of seasonality, intensity, and frequency.
Despite the thousands of studies that are cited in this compendium, much remains unknown about the effects of management practices on bird species. The series of species accounts in this compendium review the current state of knowledge regarding management of grassland and sagebrush bird species and summarize information on the effects of management practices on individual species. The accounts do not give definitive statements on the effects of management practices for any particular species, primarily because there are very few replicated studies in which identical management practices have been applied in the same geographical area with consistent results, which are elements necessary to provide concrete recommendations for the management of a particular species in a particular area. Documentation of the effects of management treatments on individual species through statistically sound methods that incorporate multiple years and locations will further scientists’ and land managers’ knowledge far more than 1–2-year studies that are limited in scope as well as time, but studies of that scope and breadth are rare.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"The effects of management practices on grassland birds (Professional Paper 1842)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1842A","usgsCitation":"Shaffer, J.A., and DeLong, J.P., 2019, The effects of management practices on grassland birds—An introduction to North American grasslands and the practices used to manage grasslands and grassland birds (ver. 1.1, March 2022), chap. A of Johnson, D.H., Igl, L.D., Shaffer, J.A., and DeLong, J.P., eds., The effects of management practices on grassland birds: U.S. Geological Survey Professional Paper 1842, 63 p., https://doi.org/10.3133/pp1842A.","productDescription":"v, 63 p.","numberOfPages":"74","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-097670","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":397809,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/pp/1842/a/versionhist.txt","size":"1 kB","linkFileType":{"id":2,"text":"txt"}},{"id":365495,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1842/a/pp1842a.pdf","text":"Report","size":"8.74 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1842 Chapter A"},{"id":365494,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1842/a/coverthb2.jpg"}],"edition":"Version 1.0: July 26, 2019; Version 1.1: March 31, 2022","contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
This scientific investigations report describes an effort by the U.S. Geological Survey (USGS) that used research, monitoring data, and modeling to develop a methodology to assess both the current and future population-level consequences of wind energy development on species of birds and bats that are present in the United States during any part of their life cycle. The methodology is currently applicable to birds and bats, focuses primarily on the effects of collisions with turbines, and can be applied to any species that breeds in, migrates through, or otherwise uses any part of the United States. The methodology assesses species at the national and regional scales and identifies those species potentially in need of more detailed study, as well as those species that are likely at low risk from wind energy development. This approach is fundamentally different from existing methods focusing on impacts at individual facilities.
This report supersedes USGS Scientific Investigations Report 2015–5066 by the same authors, which described a preliminary version of the methodology. Following reviews of the preliminary methodology by a panel of external experts, public comments, and additional internal review, the methodology was revised and finalized.
The three components of the refined methodology described in this new report rely on publicly available fatality information, population estimates, species range maps, turbine location data, biological characteristics of species, and population models. First, three metrics are combined to determine direct and indirect relative effects from wind energy facilities to generate a list of species scores. Second, a generic population model estimates the expected change in population trend caused by the additive mortality from collisions with wind turbines. Third, the methodology combines an estimate of observed fatalities and an estimate of potential biological removal to assess the possibility of a decrease in population size. The latter two components are quantitative. In a test case, the methodology was used to analyze data for six bird species and three bat species.
Components of the methodology are based on simplifying assumptions and require information that, for many species, may be sparse or unreliable or may require further study. These assumptions should be carefully considered when using outputs from the methodology. Increases in the quality of data for fatalities from collisions with wind turbines, species distributions, abundance, and demography will likely improve results for uses of the methodology.
The methodology’s design identifies and prioritizes a subset of the bird and bat species that may experience population-level impacts from collisions with wind turbines, both currently and from future wind energy development in the United States. Results of an assessment using this methodology could focus future research to improve our understanding of those impacts and to guide avoidance and minimization strategies. In addition, this methodology can be used to identify species for more intensive demographic modeling or to highlight those species that may not require any additional research because effects of wind energy development on their populations are projected to be small. The effects of wind energy facilities on nine unidentified species used in the test case described in this report have not been assessed. Their data were simply used to show the application of the methodology to real-world data and the types of outputs it would produce.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185157","usgsCitation":"Diffendorfer, J.E., Beston, J.A., Merrill, M.D., Stanton, J.C., Corum, M.D., Loss, S.R., Thogmartin, W.E., Johnson, D.H., Erickson, R.A., and Heist, K.W., 2019, A methodology to assess the national and regional impacts of U.S. wind energy development on birds and bats: U.S. Geological Survey Scientific Investigations Report 2018–5157, 45 p., https://doi.org/10.3133/sir20185157. [Supersedes USGS Scientific Investigations Report 2015–5066.]","productDescription":"ix, 45 p.","onlineOnly":"Y","ipdsId":"IP-079749","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":365690,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5157/coverthb.jpg"},{"id":365691,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5157/sir20185157.pdf","text":"Report","size":"1.94 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5157"}],"publicComments":"Scientific Investigations Report 2018-5157 supersedes Scientific Investigations Report 2015-5066.","contact":"Director, Eastern Energy Resources Science Center
U.S. Geological Survey
Mail Stop 956
12201 Sunrise Valley Drive
Reston, VA 20192
Energy Resources Program
Wind Energy
The U.S. Geological Survey in cooperation with the Colorado Water Conservation Board and the Upper Big Sandy Groundwater Management District carried out a study in 2016 to evaluate potential groundwater storage changes within the Upper Big Sandy Designated Groundwater Basin (UBSDGB) alluvial aquifer, including groundwater flow between the UBSDGB alluvial aquifer and the Denver Basin bedrock aquifers. The UBSDGB alluvial aquifer is located along the ephemeral Big Sandy Creek on the east-central edge of the Denver Basin aquifer system and covers an area of about 66,560 acres within the UBSDGB. The UBSDGB alluvial aquifer consists of unconsolidated Quaternary sand and gravel deposits that contain an unconfined (water table) groundwater system. The western three-fourths of the UBSDGB alluvial aquifer overlies the Tertiary and Cretaceous bedrock formations that compose the Denver Basin aquifer system. The updated water budget for the UBSDGB alluvial aquifer, including annual change in groundwater storage in 2016, was determined by combining water-budget information from an existing Denver Basin model for about three-fourths of the study area with best estimates for the major water-budget components for the area outside the Denver Basin aquifer system. The western part of the UBSDGB was included in the Denver Basin model (modeled area), whereas the eastern part of the UBSDGB was not included in the Denver Basin model (unmodeled area). The water-budget components were first estimated for the modeled area using outputs from the Denver Basin model, which uses the modular finite-difference groundwater flow computer model MODFLOW-2000 with 1-mile grid cells. For this study, the Denver Basin model was updated with additional data from 2004 through 2016 to generate current (2016) estimates of water consumption in the UBSDGB alluvial aquifer. A basin-specific water budget for the UBSDGB alluvial aquifer from the Denver Basin model was computed using a modeling tool called ZONEBUDGET. The modeled area groundwater budget, along with previous studies, was used to estimate a groundwater budget for the unmodeled area, and results for the modeled and unmodeled areas were combined for an overall water-budget estimate for the entire UBSDGB alluvial aquifer.
The net groundwater flow into the basin from adjacent alluvial aquifers was positive with flow entering the UBSDGB alluvial aquifer. Combining the total inflow from adjacent alluvial and the total outflow to adjacent alluvial aquifers resulted in a net flow from adjacent alluvial aquifers to UBSDGB alluvial aquifer of 5,125 acre-feet (ac-ft) in 2016. The net flow between the underlying bedrock aquifers and the UBSDGB alluvial aquifer was positive with flow entering the UBSDGB alluvial aquifer from the bedrock aquifers. The net flow from the bedrock aquifers to the UBSDGB alluvial aquifer was 347 ac-ft in 2016. Net recharge (precipitation and irrigation return flows minus evaporation) into the UBSDGB alluvial aquifer was negative with groundwater being removed from the UBSDGB alluvial aquifer over the total area of the basin. Combining the total inflow from recharge to the UBSDGB alluvial aquifer of 11,153 ac-ft in 2016 and the total evapo-transpiration of −11,656 ac-ft from the UBSDGB alluvial aquifer in 2016 resulted in a net recharge from UBSDGB alluvial aquifer of −503 ac-ft in 2016. Combining the modeled and unmodeled well pumping resulted in a total well pumping volume of −3,735 ac-ft in 2016 from the UBSDGB alluvial aquifer. The net groundwater flow to the stream network in the basin was negative with flow discharging from the UBSDGB alluvial aquifer into streams. Combining the total inflow from streams and the total outflow to streams for the UBSDGB alluvial aquifer resulted in −1,032 ac-ft in 2016 that was lost to the stream network in the UBSDGB. The net groundwater flow out of the UBSDGB was negative with flow leaving the UBSDGB alluvial aquifer. Combining the total area inflow to the basin from upgradient areas and the total area outflow from the basin for the UBSDGB alluvial aquifer resulted in a net flow out of the basin of −2,300 ac-ft. In the annual groundwater budget for 2016, groundwater storage in the UBSDGB alluvial aquifer system was removed because annual groundwater outflows from storage exceeded groundwater inflows to storage; in other words, water was removed from storage to balance the annual water budget. Combining the net flow from storage for the modeled area of 73 ac-ft and the inflow from storage for the unmodeled area of 2,025 ac-ft resulted in a net positive flow from storage of the UBSDGB alluvial aquifer of 2,098 ac-ft.
Increased pumping since 1958 in the Denver and upper Arapahoe aquifers, not necessarily in the UBSDGB, has caused a change in flow from bedrock units, which were minor or non-contributors of inflow to the UBSDGB alluvial aquifer, to receiving outflow from the UBSDGB alluvial aquifer. Since 2000, aquifer storage has been an inflow component of the water budget, which means that outflow from the modeled area exceeded inflow for the UBSDGB alluvial aquifer. Increased recharge from wetter than average years could replenish the UBSDGB alluvial aquifer. From 2003 through 2016, 13 of the 25 observation wells completed in the UBSDGB alluvial aquifer had a decline in the groundwater-level elevation with an average decline of −2.21 feet, and 12 of the 25 observation wells had an increase in the groundwater-level elevation with an average increase of 1.54 feet. In general, wells at the eastern and western edges of the UBSDGB showed an increase in groundwater-level elevation that appears related to areas of groundwater discharge from the lower Dawson and Laramie-Fox Hills bedrock aquifers to the UBSDGB alluvial aquifer. The remaining wells exhibited water-level declines. Future work could include the development of a basin-specific model to serve as a basin management tool for modeling changes in groundwater levels and storage under various future groundwater recharge and withdrawal scenarios.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20195049","collaboration":"Prepared in cooperation with the Colorado Water Conservation Board and the Upper Big Sandy Groundwater Management District","usgsCitation":"Kohn, M.S., Oden, J.H., and Arnold, L.R., 2019, Water-budget analysis of the Upper Big Sandy Designated Ground-water Basin alluvial aquifer, Elbert, El Paso, and Lincoln Counties, Colorado, 2016: U.S. Geological Survey Scientific Investigations Report 2019-5049, 25 p., https://dx.doi.org/10.3133/sir20195049.","productDescription":"Report: vi, 25 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-091541","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":365584,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5049/sir20195049.pdf","text":"Report","size":"7.12 M","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5049"},{"id":365581,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5049/coverthb.jpg"},{"id":365748,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DEOYGZ","text":"USGS data release","linkHelpText":"MODFLOW2000 model and ZONEBUDGET computer program used to simulate the Upper Big Sandy Designated Groundwater Basin alluvial aquifer, Elbert, El Paso, and Lincoln Counties, Colorado, 2016"}],"country":"United States","state":"Colorado","county":"Elbert County, El Paso County, Lincoln County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-104.054,38.523],[-104.1629,38.5215],[-104.2759,38.5204],[-104.2794,38.5205],[-104.2836,38.5201],[-104.3759,38.52],[-104.4971,38.5192],[-104.6071,38.5187],[-104.7171,38.5186],[-104.736,38.5183],[-104.8295,38.5183],[-104.943,38.5175],[-104.9432,38.5479],[-104.943,38.5624],[-104.9429,38.6041],[-104.9427,38.6186],[-104.9429,38.6467],[-104.9429,38.6503],[-104.9427,38.6621],[-104.9427,38.6648],[-104.9428,38.6938],[-104.9399,38.6938],[-104.9386,38.7808],[-104.939,38.7949],[-105.0671,38.7946],[-105.0674,38.8666],[-105.0502,38.8665],[-105.0296,38.8668],[-105.026,39.0413],[-105.032,39.1311],[-104.9371,39.1312],[-104.9175,39.131],[-104.8303,39.1311],[-104.6642,39.1308],[-104.6638,39.2165],[-104.664,39.3026],[-104.663,39.3892],[-104.6626,39.4762],[-104.6627,39.5665],[-104.6054,39.5663],[-104.5374,39.5655],[-104.4927,39.5636],[-104.4891,39.5636],[-104.4742,39.5629],[-104.3841,39.5627],[-104.3763,39.5631],[-104.2695,39.5639],[-104.2647,39.5638],[-104.1602,39.5646],[-104.1543,39.565],[-104.0468,39.5652],[-104.0427,39.5651],[-103.9305,39.5646],[-103.9293,39.5646],[-103.8189,39.5646],[-103.8129,39.5649],[-103.7126,39.5649],[-103.7066,39.5648],[-103.6004,39.5646],[-103.595,39.5645],[-103.4882,39.5647],[-103.4804,39.5645],[-103.3748,39.5651],[-103.3658,39.5654],[-103.2631,39.5659],[-103.253,39.5657],[-103.1533,39.5657],[-103.1539,39.475],[-103.1537,39.3879],[-103.1542,39.3009],[-103.154,39.2147],[-103.1527,39.1258],[-103.161,39.1255],[-103.1615,39.0376],[-103.1626,38.9492],[-103.163,38.863],[-103.1634,38.7765],[-103.1638,38.6912],[-103.1709,38.6909],[-103.1705,38.6837],[-103.1731,38.6796],[-103.1716,38.6111],[-103.1714,38.5236],[-103.2809,38.5224],[-103.3897,38.5239],[-103.5086,38.5236],[-103.5089,38.5159],[-103.6118,38.5171],[-103.6116,38.5225],[-103.7228,38.5223],[-103.8328,38.523],[-103.9411,38.523],[-104.054,38.523]]]},\"properties\":{\"name\":\"Elbert\",\"state\":\"CO\"}}]}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225
Sediment cores from Florida Bay, Everglades National Park were examined to determine ecosystem response to relative sea-level rise (RSLR) over the Holocene. High-resolution multiproxy analysis from four sites show freshwater wetlands transitioned to mangrove environments 4–3.6 ka, followed by estuarine environments 3.4–2.8 ka, during a period of enhanced climate variability. We calculate a RSLR rate of 0.67 ± 0.1 mm yr−1 between ~4.2–2.8 ka, 4–6 times lower than current rates. Despite low RSLR rates, the rapid mangrove to estuarine transgression was facilitated by a period of prolonged droughts and frequent storms. These findings suggest that with higher and accelerating RSLR today, enhanced climate variability could further hasten the loss of mangrove-lined coastlines, compounded by the reductions in natural flow to the coast caused by water management. Climate variability is nonlinear, and when superimposed on increases in RSLR, can complicate estimated trajectories of coastal inundation for resource management and urban planning.
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