Increasing population, agricultural development (including shifts to more water-intensive crops), and climate variability are placing increasingly larger demands on available groundwater resources in the Pajaro Valley, one of the most productive agricultural regions in the world. This study provided a refined conceptual model, geohydrologic framework, and integrated hydrologic model of the Pajaro Valley. The goal of this study was to produce a model capable of being accurate at scales relevant to water management decisions that are being considered in the revision and updates to the Basin Management Plan (BMP). The Pajaro Valley Hydrologic Model (PVHM) was designed to reproduce the most important natural and human components of the hydrologic system and related climatic factors, permitting an accurate assessment of groundwater conditions and processes that can inform the new BMP and help to improve planning for long-term sustainability of water resources. Model development included a revision of the conceptual model of the flow system, reevaluation of the previous model transformed into MODFLOW, implementation of the new geohydrologic model and conceptual model, and calibration of the transient hydrologic model.
\n\n
The PVHM model, using MODFLOW with the Farm Process (MF-FMP2), is capable of being accurate at seasonal to interannual time frames and subregional to valley-wide spatial scales for the assessment of the groundwater hydrologic budget for water years 1964–2009, as well as potential assessment of the BMP components and sustainability analysis of conjunctive use. The model provides a good representation of the regional flow system and the use and movement of water throughout the valley.
\n\n
Simulated changes in storage over time show that, prior to the 1984–92 dry period, significant withdrawals from storage occurred only during drought years. Since about 1993, growers in the Pajaro Valley have shifted to more water intensive crops, such as strawberries, bushberries, and vegetable row crops, as well as making additional rotational plantings, which have increased demand on limited groundwater resources. Simulated groundwater flow indicates that vertical hydraulic gradients between horizontal layers fluctuate and even reverse in several parts of the basin as recharge and pumpage rates change seasonally and annually. The majority of recharge predominantly enters the Alluvial aquifer system, and along with pumpage and the largest fractions of storage depletion, occurs in the inland regions. Coastal inflow as seawater intrusion replaces much of the potential storage depletion in the coastal regions. The simulated long-term imbalance between inflows and outflows indicates overdraft of the groundwater basin averaging about 12,950 acre-feet per year (acre-ft/yr) over the 46-year period of water years (1964–2009). Annual overdraft varies considerably from year to year, depending on land use, pumpage, and climate conditions. Climatically driven factors can affect inflows, outflows, and water use by as much as a factor of two between wet and dry years. Coastal inflows and outflows vary by year and by aquifer; the net coastal inflow, or seawater intrusion, ranges from about 1,000 to more than 6,000 acre-ft/yr. Maps of simulated and measured water-level elevations indicate regions with water levels below sea level in the alluvium and Aromas layers.
\n
Ongoing expansion of local hydrologic monitoring networks indicates the importance of these networks to the understanding of changes in groundwater flow, streamflow, and streamflow infiltration. In particular, the monitoring of streamflow, groundwater pumpage, and groundwater levels throughout the valley not only indicates the state of the resources, but also provides valuable information for model calibration and for model-based evaluation of management actions.
The HS-ASR was simulated for the years 2002–09, and replaced about about 1,290 acre-ft of coastal pumpage. This was combined with the simulation of additional 6,200 acre-ft of deliveries from supplemental wells, recycled water, and city connection deliveries through the CDS that also supplanted some coastal pumpage. Total simulated deliveries were 7,350 acre-ft of the 7,500 acre-ft of reported deliveries for the period 2002-09. The completed CDS should be capable of delivering about 8.8 million cubic meters (7,150 acre-ft) of water per year to coastal farms within the Pajaro Valley, if all the local supply components were fully available for this purpose. This would represent about 15 percent of the 48,300 acre-ft (59.6 million cubic meters) average agricultural pumpage for the period 2005 to 2009. Combined with the potential capture and reuse of some of the return flows and tile-drain flows, this could represent an almost 70 percent reduction of average overdraft for the entire valley and a large part of the coastal pumpage that induces seawater intrusion.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20145111","collaboration":"Prepared in cooperation with Pajaro Valley Water Management Agency","usgsCitation":"Hanson, R.T., Schmid, W., Faunt, C., Lear, J., and Lockwood, B., 2014, Integrated hydrologic model of Pajaro Valley, Santa Cruz and Monterey Counties, California: U.S. Geological Survey Scientific Investigations Report 2014-5111, x, 166 p., https://doi.org/10.3133/sir20145111.","productDescription":"x, 166 p.","numberOfPages":"180","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-003917","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":294084,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20145111.jpg"},{"id":294082,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2014/5111"},{"id":294083,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2014/5111/pdf/sir2014-5111.pdf"}],"projection":"Universal Transverse Mercator projection","country":"United States","state":"California","county":"Monterey County;Santa Cruz County","otherGeospatial":"Pajaro Valley","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -122.00,36.866667 ], [ -122.00,37.5 ], [ -121.616667,37.5 ], [ -121.616667,36.866667 ], [ -122.00,36.866667 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"541be60ce4b0e96537dda06b","contributors":{"authors":[{"text":"Hanson, Randall T. 0000-0002-9819-7141 rthanson@usgs.gov","orcid":"https://orcid.org/0000-0002-9819-7141","contributorId":801,"corporation":false,"usgs":true,"family":"Hanson","given":"Randall","email":"rthanson@usgs.gov","middleInitial":"T.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":494670,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schmid, Wolfgang","contributorId":84020,"corporation":false,"usgs":false,"family":"Schmid","given":"Wolfgang","affiliations":[{"id":13040,"text":"Department of Hydrology and Water Resources, University of Arizona","active":true,"usgs":false}],"preferred":false,"id":494674,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Faunt, Claudia C. 0000-0001-5659-7529 ccfaunt@usgs.gov","orcid":"https://orcid.org/0000-0001-5659-7529","contributorId":1491,"corporation":false,"usgs":true,"family":"Faunt","given":"Claudia C.","email":"ccfaunt@usgs.gov","affiliations":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"preferred":false,"id":494671,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lear, Jonathan","contributorId":72303,"corporation":false,"usgs":true,"family":"Lear","given":"Jonathan","email":"","affiliations":[],"preferred":false,"id":494672,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lockwood, Brian","contributorId":80202,"corporation":false,"usgs":true,"family":"Lockwood","given":"Brian","email":"","affiliations":[],"preferred":false,"id":494673,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70125410,"text":"70125410 - 2014 - Sapronosis: a distinctive type of infectious agent","interactions":[],"lastModifiedDate":"2017-06-30T13:37:58","indexId":"70125410","displayToPublicDate":"2014-09-17T14:30:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3890,"text":"Trends in Parasitology","active":true,"publicationSubtype":{"id":10}},"title":"Sapronosis: a distinctive type of infectious agent","docAbstract":"Sapronotic disease agents have evolutionary and epidemiological properties unlike other infectious organisms. Their essential saprophagic existence prevents coevolution, and no host–parasite virulence trade-off can evolve. However, the host may evolve defenses. Models of pathogens show that sapronoses, lacking a threshold of transmission, cannot regulate host populations, although they can reduce host abundance and even extirpate their hosts. Immunocompromised hosts are relatively susceptible to sapronoses. Some particularly important sapronoses, such as cholera and anthrax, can sustain an epidemic in a host population. However, these microbes ultimately persist as saprophages. One-third of human infectious disease agents are sapronotic, including nearly all fungal diseases. Recognition that an infectious disease is sapronotic illuminates a need for effective environmental control strategies.","language":"English","publisher":"Elsevier","doi":"10.1016/j.pt.2014.06.006","usgsCitation":"Kuris, A.M., Lafferty, K.D., and Sokolow, S.H., 2014, Sapronosis: a distinctive type of infectious agent: Trends in Parasitology, v. 30, no. 8, p. 386-393, https://doi.org/10.1016/j.pt.2014.06.006.","productDescription":"8 p.","startPage":"386","endPage":"393","numberOfPages":"8","ipdsId":"IP-057756","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":294066,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":293989,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.pt.2014.06.006"}],"volume":"30","issue":"8","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"541a9492e4b01571b3d4cc6f","contributors":{"authors":[{"text":"Kuris, Armand M.","contributorId":54332,"corporation":false,"usgs":true,"family":"Kuris","given":"Armand","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":501400,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lafferty, Kevin D. 0000-0001-7583-4593 klafferty@usgs.gov","orcid":"https://orcid.org/0000-0001-7583-4593","contributorId":1415,"corporation":false,"usgs":true,"family":"Lafferty","given":"Kevin","email":"klafferty@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":501398,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sokolow, Susanne H.","contributorId":52503,"corporation":false,"usgs":false,"family":"Sokolow","given":"Susanne","email":"","middleInitial":"H.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":501399,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70121118,"text":"sir20105070L - 2014 - Deposit model for heavy-mineral sands in coastal environments","interactions":[],"lastModifiedDate":"2020-07-01T19:49:29.216529","indexId":"sir20105070L","displayToPublicDate":"2014-09-17T11:33:00","publicationYear":"2014","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":"2010-5070","chapter":"L","title":"Deposit model for heavy-mineral sands in coastal environments","docAbstract":"This report provides a descriptive model of heavy-mineral sands, which are sedimentary deposits of dense minerals that accumulate with sand, silt, and clay in coastal environments, locally forming economic concentrations of the heavy minerals. This deposit type is the main source of titanium feedstock for the titanium dioxide (TiO2) pigments industry, through recovery of the minerals ilmenite (Fe2+TiO3), rutile (TiO2), and leucoxene (an alteration product of ilmenite). Heavy-mineral sands are also the principal source of zircon (ZrSiO4) and its zirconium oxide; zircon is often recovered as a coproduct. Other heavy minerals produced as coproducts from some deposits are sillimanite/kyanite, staurolite, monazite, and garnet. Monazite [(Ce,La,Nd,Th)PO4] is a source of rare earth elements as well as thorium, which is used in thorium-based nuclear power under development in India and elsewhere.
\nThe processes that form coastal deposits of heavy-mineral sands begin inland. High-grade metamorphic and igneous rocks that contain heavy minerals weather and erode, contributing detritus composed of sand, silt, clay, and heavy minerals to fluvial systems. Streams and rivers carry the detritus to the coast, where they are deposited in a variety of coastal environments, such as deltas, the beach face (foreshore), the nearshore, barrier islands or dunes, and tidal lagoons, as well as the channels and floodplains of streams and rivers in the coastal plain. The sediments are reworked by waves, tides, longshore currents, and wind, which are effective mechanisms for sorting the mineral grains on the basis of differences in their size and density. The finest-grained, most dense heavy minerals are the most effectively sorted. The result is that heavy minerals accumulate together, forming laminated or lens-shaped, heavy-mineral-rich sedimentary packages that can be several meters and even as much as tens of meters thick. Most economic deposits of heavy-mineral sands are Paleogene, Neogene, and Quaternary in age; some are modern coastal deposits.
\nSuperimposed on these basic processes of ore formation are a multitude of contributing and modifying factors, such as the following:
\nThe resulting deposits of heavy-mineral sands can be voluminous. Individual bodies of heavy mineral-rich sands are typically about 1 kilometer wide and more than 5 kilometers long. Many heavy-mineral sands districts extend for more than 10 kilometers and contain several individual deposits that are spread along an ancient or modern strandline. Reported thicknesses of economic deposits range from 3 to 45 meters. Individual ore deposits typically comprise at least 10 megatonnes of ore (the total size of the individual sand-silt body), whose overall heavy-mineral content is 2 to greater than 10 percent.
\nHeavy-mineral sands deposits are relatively easy to mine because they are weakly to poorly consolidated, and they are relatively easy to process. From a geoenvironmental standpoint, mining of heavy mineral-sands generates little or no acid or solubilized metals. However, environmental and human health concerns related to such mining include potential effects on indigenous flora and fauna, effects on local hydrology, and issues related to processing and storing thorium-bearing monazite, owing to its radioactivity.
\nRegional exploration for deposits of heavy-mineral sands can utilize the analyses of stream sediment samples for Ti, Hf, the rare earth elements, Th, and U, and geophysical surveys, particularly radiometric (gamma-ray spectrometry for K, U, and Th) and magnetic methods. Geophysical anomalies may be small, and surveys are generally more successful when conducted close to sources of interest.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Mineral deposit models for resource assessment","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20105070L","issn":"2328-0328","usgsCitation":"Van Gosen, B.S., Fey, D.L., Shah, A.K., Verplanck, P.L., and Hoefen, T.M., 2014, Deposit model for heavy-mineral sands in coastal environments: U.S. Geological Survey Scientific Investigations Report 2010-5070, viii, 51 p., https://doi.org/10.3133/sir20105070L.","productDescription":"viii, 51 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-053206","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":294045,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20105070L.jpg"},{"id":294044,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2010/5070/l/"},{"id":294046,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2010/5070/l/pdf/sir2010-5070l.pdf","text":"Report","size":"15.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"541a948be4b01571b3d4cc21","contributors":{"authors":[{"text":"Van Gosen, Bradley S. 0000-0003-4214-3811 bvangose@usgs.gov","orcid":"https://orcid.org/0000-0003-4214-3811","contributorId":1174,"corporation":false,"usgs":true,"family":"Van Gosen","given":"Bradley","email":"bvangose@usgs.gov","middleInitial":"S.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":498806,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fey, David L. dfey@usgs.gov","contributorId":713,"corporation":false,"usgs":true,"family":"Fey","given":"David","email":"dfey@usgs.gov","middleInitial":"L.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":498804,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shah, Anjana K. 0000-0002-3198-081X ashah@usgs.gov","orcid":"https://orcid.org/0000-0002-3198-081X","contributorId":2297,"corporation":false,"usgs":true,"family":"Shah","given":"Anjana","email":"ashah@usgs.gov","middleInitial":"K.","affiliations":[{"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":498807,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Verplanck, Philip L. 0000-0002-3653-6419 plv@usgs.gov","orcid":"https://orcid.org/0000-0002-3653-6419","contributorId":728,"corporation":false,"usgs":true,"family":"Verplanck","given":"Philip","email":"plv@usgs.gov","middleInitial":"L.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":498805,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hoefen, Todd M. 0000-0002-3083-5987 thoefen@usgs.gov","orcid":"https://orcid.org/0000-0002-3083-5987","contributorId":403,"corporation":false,"usgs":true,"family":"Hoefen","given":"Todd","email":"thoefen@usgs.gov","middleInitial":"M.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":498803,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70125287,"text":"70125287 - 2014 - Incubation stage and polychlorinated biphenyl (PCB) congener patterns in an altricial and precocial bird species","interactions":[],"lastModifiedDate":"2018-09-14T15:52:00","indexId":"70125287","displayToPublicDate":"2014-09-17T11:30:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1555,"text":"Environmental Pollution","active":true,"publicationSubtype":{"id":10}},"title":"Incubation stage and polychlorinated biphenyl (PCB) congener patterns in an altricial and precocial bird species","docAbstract":"The composition of polychlorinated biphenyl (PCB) congeners was compared between non-incubated and embryonated eggs of tree swallows (Tachycineta bicolor) and little terns (Sterna albifrons) to determine if measurable changes in PCB congeners occurred during the embryonic period. There was no indication of changes in PCB congener patterns over the incubation period in tree swallows in 1999 and 2000 at a site with very high PCB exposure or a site with more modest PCB exposure. Additionally, congeners known to be either quickly metabolized or conserved based on experimental studies did not generally respond as predicted. Similarly, PCB congener patterns in eggs of little terns from Bottsand, Schleswig-Holstein, Germany, did not differ between non-incubated and embryonated eggs. The results from both species suggest that the stage of incubation is not an important consideration when evaluating PCB congener patterns; comparisons and assessments can be made with eggs collected at all stages of incubation.","language":"English","publisher":"Elsevier","doi":"10.1016/j.envpol.2014.08.010","usgsCitation":"Custer, C.M., Custer, T.W., Thyen, S., and Becker, P.H., 2014, Incubation stage and polychlorinated biphenyl (PCB) congener patterns in an altricial and precocial bird species: Environmental Pollution, v. 195, p. 109-114, https://doi.org/10.1016/j.envpol.2014.08.010.","productDescription":"6 p.","startPage":"109","endPage":"114","numberOfPages":"6","ipdsId":"IP-056787","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":294039,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":293870,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.envpol.2014.08.010"}],"volume":"195","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"541a948fe4b01571b3d4cc4b","chorus":{"doi":"10.1016/j.envpol.2014.08.010","url":"http://dx.doi.org/10.1016/j.envpol.2014.08.010","publisher":"Elsevier BV","authors":"Custer Christine M., Custer Thomas W., Thyen Stefan, Becker Peter H.","journalName":"Environmental Pollution","publicationDate":"12/2014","auditedOn":"11/1/2014"},"contributors":{"authors":[{"text":"Custer, Christine M. 0000-0003-0500-1582 ccuster@usgs.gov","orcid":"https://orcid.org/0000-0003-0500-1582","contributorId":1143,"corporation":false,"usgs":true,"family":"Custer","given":"Christine","email":"ccuster@usgs.gov","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":501133,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Custer, Thomas W. 0000-0003-3170-6519 tcuster@usgs.gov","orcid":"https://orcid.org/0000-0003-3170-6519","contributorId":2835,"corporation":false,"usgs":true,"family":"Custer","given":"Thomas","email":"tcuster@usgs.gov","middleInitial":"W.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":501134,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thyen, Stefan","contributorId":50087,"corporation":false,"usgs":true,"family":"Thyen","given":"Stefan","email":"","affiliations":[],"preferred":false,"id":501135,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Becker, Peter H.","contributorId":55359,"corporation":false,"usgs":true,"family":"Becker","given":"Peter","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":501136,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70121300,"text":"ofr20141158 - 2014 - Two decision-support tools for assessing the potential effects of energy development on hydrologic resources as part of the Energy and Environment in the Rocky Mountain Area interactive energy atlas","interactions":[],"lastModifiedDate":"2018-08-10T16:13:29","indexId":"ofr20141158","displayToPublicDate":"2014-09-16T12:44:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2014-1158","title":"Two decision-support tools for assessing the potential effects of energy development on hydrologic resources as part of the Energy and Environment in the Rocky Mountain Area interactive energy atlas","docAbstract":"The U.S. Geological Survey project—Energy and Environment in the Rocky Mountain Area (EERMA)—has developed a set of virtual tools in the form of an online interactive energy atlas for Colorado and New Mexico to facilitate access to geospatial data related to energy resources, energy infrastructure, and natural resources that may be affected by energy development. The interactive energy atlas currently (2014) consists of three components: (1) a series of interactive maps; (2) downloadable geospatial datasets; and (3) decison-support tools, including two maps related to hydrologic resources discussed in this report. The hydrologic-resource maps can be used to examine the potential effects of energy development on hydrologic resources with respect to (1) groundwater vulnerability, by using the depth to water, recharge, aquifer media, soil media, topography, impact of the vadose zone, and hydraulic conductivity of the aquifer (DRASTIC) model, and (2) landscape erosion potential, by using the revised universal soil loss equation (RUSLE). The DRASTIC aquifer vulnerability index value for the two-State area ranges from 48 to 199. Higher values, indicating greater relative aquifer vulnerability, are centered in south-central Colorado, areas in southeastern New Mexico, and along riparian corridors in both States—all areas where the water table is relatively close to the land surface and the aquifer is more susceptible to surface influences. As calculated by the RUSLE model, potential mean annual erosion, as soil loss in units of tons per acre per year, ranges from 0 to 12,576 over the two-State area. The RUSLE model calculated low erosion potential over most of Colorado and New Mexico, with predictions of highest erosion potential largely confined to areas of mountains or escarpments. An example is presented of how a fully interactive RUSLE model could be further used as a decision-support tool to evaluate the potential hydrologic effects of energy development on a site-specific basis and to explore the effectiveness of various mitigation practices.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20141158","usgsCitation":"Linard, J.I., Matherne, A.M., Leib, K.J., Carr, N.B., Diffendorfer, J., Hawkins, S.J., Latysh, N., Ignizio, D., and Babel, N.C., 2014, Two decision-support tools for assessing the potential effects of energy development on hydrologic resources as part of the Energy and Environment in the Rocky Mountain Area interactive energy atlas: U.S. Geological Survey Open-File Report 2014-1158, iv, 16 p., https://doi.org/10.3133/ofr20141158.","productDescription":"iv, 16 p.","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-057229","costCenters":[{"id":191,"text":"Colorado Water Science 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this threat, scientists at the Western Ecology Division of the U.S. EPA at and the Western Fisheries Research Center of the U.S. Geological Survey, along with other partners, initiated a series of studies on the structure and vulnerability of tidal wetlands to climate change. One research thrust was to evaluate community structure of PNW marshes, experimentally assess the vulnerability of marsh plants to inundation and salinity stress (as would happen with sea level rise), and evaluate the utility of the National Wetland Inventory (NWI) classification system. Another research thrust was to develop tools that provide insights into possible impacts of climate change. This effort included enhancing the Sea Level Affecting Marshes Model (SLAMM) to predict the effects of sea level rise on submerged aquatic vegetation (Zostera marina) distributions, evaluating changes in river flow into coastal estuaries in response to precipitation changes, and synthesizing Pacific Coast estuary, watershed, and climate data in a downloadable tool. Because the research resulting from these efforts was published in multiple venues, we summarized them in this document. 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Forks and Upper Devonian to Lower Mississippian Bakken Formations comprise a major United States continuous oil resource. Current exploitation of oil is from horizontal drilling and hydraulic fracturing of the Middle Member of the Bakken and upper Three Forks, with ongoing exploration of the lower Three Forks, and the Upper, Lower, and Pronghorn Members of the Bakken Formation. In 2008, the U.S. Geological Survey (USGS) estimated a mean of 3.65 billion bbl of undiscovered, technically recoverable oil resource within the Bakken Formation. The USGS recently reassessed the Bakken Formation, which included an assessment of the underlying Three Forks Formation. The Pronghorn Member of the Bakken Formation, where present, was included as part of the Three Forks assessment due to probable fluid communication between reservoirs. For the Bakken Formation, five continuous and one conventional assessment units (AUs) were defined. These AUs are modified from the 2008 AU boundaries to incorporate expanded geologic and production information. The Three Forks Formation was defined with one continuous and one conventional AU. Within the continuous AUs, optimal regions of hydrocarbon recovery, or “sweet spots,” were delineated and estimated ultimate recoveries were calculated for each continuous AU. Resulting undiscovered, technically recoverable resource estimates were 3.65 billion bbl for the five Bakken continuous oil AUs and 3.73 billion bbl for the Three Forks Continuous Oil AU, generating a total mean resource estimate of 7.38 billion bbl. The two conventional AUs are hypothetical and represent a negligible component of the total estimated resource (8 million barrels of oil).
","language":"English","publisher":"American Association of Petroleum Geologists","publisherLocation":"Tulsa, OK","doi":"10.1306/08131414051","usgsCitation":"Gaswirth, S., and Marra, K.R., 2014, U.S. Geological Survey 2013 assessment of undiscovered resources in the Bakken and Three Forks Formations of the U.S. Williston Basin Province: AAPG Bulletin, v. 99, no. 4, p. 639-660, https://doi.org/10.1306/08131414051.","productDescription":"22 p.","startPage":"639","endPage":"660","numberOfPages":"22","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-055559","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":299445,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":299404,"type":{"id":15,"text":"Index Page"},"url":"https://dx.doi.org/10.1306/08131414051"}],"volume":"99","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5524ffb8e4b027f0aee3d495","contributors":{"authors":[{"text":"Gaswirth, Stephanie B. 0000-0001-5821-6347 sgaswirth@usgs.gov","orcid":"https://orcid.org/0000-0001-5821-6347","contributorId":140068,"corporation":false,"usgs":true,"family":"Gaswirth","given":"Stephanie B.","email":"sgaswirth@usgs.gov","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":544135,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Marra, Kristen R. 0000-0001-8027-5255 kmarra@usgs.gov","orcid":"https://orcid.org/0000-0001-8027-5255","contributorId":4844,"corporation":false,"usgs":true,"family":"Marra","given":"Kristen","email":"kmarra@usgs.gov","middleInitial":"R.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":544136,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70132473,"text":"70132473 - 2014 - Estimates of annual survival, growth, and recruitment of a white-tailed ptarmigan population in Colorado over 43 years","interactions":[],"lastModifiedDate":"2020-12-31T20:36:58.905875","indexId":"70132473","displayToPublicDate":"2014-09-13T12:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3103,"text":"Population Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Estimates of annual survival, growth, and recruitment of a white-tailed ptarmigan population in Colorado over 43 years","docAbstract":"Long-term datasets for high-elevation species are rare, and considerable uncertainty exists in understanding how high-elevation populations have responded to recent climate warming. We present estimates of demographic vital rates from a 43-year population study of white-tailed ptarmigan (Lagopus leucura), a species endemic to alpine habitats in western North America. We used capture-recapture models to estimate annual rates of apparent survival, population growth, and recruitment for breeding-age ptarmigan, and we fit winter weather covariates to models in an attempt to explain annual variation. There were no trends in survival over the study period but there was strong support for age and sex effects. The average rate of annual growth suggests a relatively stable breeding-age population (
Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, national security, recreation, and many others. For the State of Illinois, elevation data are critical for flood risk management, water supply and quality, infrastructure and construction management, agriculture and precision farming, and other business uses. Today, high-density light detection and ranging (lidar) data are the primary sources for deriving elevation models and other datasets. Federal, State, and local agencies work in partnership to (1) replace data that are older and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data.
\nThe National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 interferometric synthetic aperture radar (ifsar) data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey, the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.
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Jr. carswell@usgs.gov","contributorId":1787,"corporation":false,"usgs":true,"family":"Carswell","given":"William J.","suffix":"Jr.","email":"carswell@usgs.gov","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":false,"id":499673,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70124520,"text":"70124520 - 2014 - Bird migration and avian influenza: a comparison of hydrogen stable isotopes and satellite tracking methods","interactions":[],"lastModifiedDate":"2017-07-26T17:14:45","indexId":"70124520","displayToPublicDate":"2014-09-12T08:28:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Bird migration and avian influenza: a comparison of hydrogen stable isotopes and satellite tracking methods","docAbstract":"Satellite-based tracking of migratory waterfowl is an important tool for understanding the potential role of wild birds in the long-distance transmission of highly pathogenic avian influenza. However, employing this technique on a continental scale is prohibitively expensive. This study explores the utility of stable isotope ratios in feathers in examining both the distances traveled by migratory birds and variation in migration behavior. We compared the satellite-derived movement data of 22 ducks from 8 species captured at wintering areas in Bangladesh, Turkey, and Hong Kong with deuterium ratios (δD) in the feathers of these and other individuals captured at the same locations. We derived likely molting locations from the satellite tracking data and generated expected isotope ratios based on an interpolated map of δD in rainwater. Although δD was correlated with the distance between wintering and molting locations, surprisingly, measured δD values were not correlated with either expected values or latitudes of molting sites. However, population-level parameters derived from the satellite-tracking data, such as mean distance between wintering and molting locations and variation in migration distance, were reflected by means and variation of the stable isotope values. Our findings call into question the relevance of the rainfall isotope map for Asia for linking feather isotopes to molting locations, and underscore the need for extensive ground truthing in the form of feather-based isoscapes. Nevertheless, stable isotopes from feathers could inform disease models by characterizing the degree to which regional breeding populations interact at common wintering locations. Feather isotopes also could aid in surveying wintering locations to determine where high-resolution tracking techniques (e.g. satellite tracking) could most effectively be employed. Moreover, intrinsic markers such as stable isotopes offer the only means of inferring movement information from birds that have died as a result of infection. In the absence of feather based-isoscapes, we recommend a combination of isotope analysis and satellite-tracking as the best means of generating aggregate movement data for informing disease models.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Ecological Indicators","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2014.04.027","usgsCitation":"Bridge, E., Kelly, J., Xiao, X., Takekawa, J.Y., Hill, N., Yamage, M., Haque, E.U., Islam, M.A., Mundkur, T., Yavuz, K.E., Leader, P., Leung, C.Y., Smith, B., Spragens, K., Vandegrift, K.J., Hosseini, P.R., Saif, S., Mohsanin, S., Mikolon, A., Islam, A., George, A., Sivananinthaperumal, B., Daszak, P., and Newman, S.H., 2014, Bird migration and avian influenza: a comparison of hydrogen stable isotopes and satellite tracking methods: Ecological Indicators, v. 45, p. 266-273, https://doi.org/10.1016/j.ecolind.2014.04.027.","productDescription":"8 p.","startPage":"266","endPage":"273","numberOfPages":"8","ipdsId":"IP-035731","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":472764,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/4097340","text":"External Repository"},{"id":293794,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":293780,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.ecolind.2014.04.027"}],"country":"Bangladesh;Hong Kong;Turkey","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 30.0,10.0 ], [ 30.0,80.0 ], [ 160.0,80.0 ], [ 160.0,10.0 ], [ 30.0,10.0 ] ] ] } } ] 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Peter","contributorId":96130,"corporation":false,"usgs":true,"family":"Daszak","given":"Peter","affiliations":[],"preferred":false,"id":500866,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Newman, Scott H.","contributorId":101372,"corporation":false,"usgs":true,"family":"Newman","given":"Scott","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":500868,"contributorType":{"id":1,"text":"Authors"},"rank":24}]}} ,{"id":70124264,"text":"70124264 - 2014 - Evolutionary ecology of resprouting and seeding in fire-prone ecosystems","interactions":[],"lastModifiedDate":"2014-09-11T13:32:15","indexId":"70124264","displayToPublicDate":"2014-09-11T13:30:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2863,"text":"New Phytologist","active":true,"publicationSubtype":{"id":10}},"title":"Evolutionary ecology of resprouting and seeding in fire-prone ecosystems","docAbstract":"There are two broad mechanisms by which plant populations persist under recurrent disturbances: resprouting from surviving tissues, and seedling recruitment. Species can have one of these mechanisms or both. However, a coherent framework explaining the differential evolutionary pressures driving these regeneration mechanisms is lacking. We propose a bottom-up approach in addressing this question that considers the relative survivorship of adults and juveniles in an evolutionary context, based on two assumptions. First, resprouting and seeding can be interpreted by analogy with annual versus perennial life histories; that is, if we consider disturbance cycles to be analogous to annual cycles, then resprouting species are analogous to the perennial life history with iteroparous reproduction, and obligate seeding species that survive disturbances solely through seed banks are analogous to the annual life history with semelparous reproduction. Secondly, changes in the selective regimes differentially modify the survival rates of adults and juveniles and thus the relative costs and benefits of resprouting versus seeding. Our approach provides a framework for understanding temporal and spatial variation in resprouting and seeding under crown-fire regimes. It accounts for patterns of coexistence and environmental changes that contribute to the evolution of seeding from resprouting ancestors.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"New Phytologist","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley","doi":"10.1111/nph.12921","usgsCitation":"Pausas, J.G., and Keeley, J.E., 2014, Evolutionary ecology of resprouting and seeding in fire-prone ecosystems: New Phytologist, v. 204, no. 1, p. 55-65, https://doi.org/10.1111/nph.12921.","productDescription":"11 p.","startPage":"55","endPage":"65","numberOfPages":"11","ipdsId":"IP-056456","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":472765,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/nph.12921","text":"Publisher Index Page"},{"id":293745,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":293643,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1111/nph.12921"}],"volume":"204","issue":"1","noUsgsAuthors":false,"publicationDate":"2014-07-15","publicationStatus":"PW","scienceBaseUri":"5412ab8de4b0239f1986b9e6","contributors":{"authors":[{"text":"Pausas, Juli G.","contributorId":91347,"corporation":false,"usgs":true,"family":"Pausas","given":"Juli","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":500609,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Keeley, Jon E. 0000-0002-4564-6521 jon_keeley@usgs.gov","orcid":"https://orcid.org/0000-0002-4564-6521","contributorId":1268,"corporation":false,"usgs":true,"family":"Keeley","given":"Jon","email":"jon_keeley@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":500608,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70124259,"text":"70124259 - 2014 - Dynamics of an introduced and unexploited Lake Whitefish population in Lake Pend Oreille, Idaho","interactions":[],"lastModifiedDate":"2016-06-01T14:06:41","indexId":"70124259","displayToPublicDate":"2014-09-11T13:22:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Dynamics of an introduced and unexploited Lake Whitefish population in Lake Pend Oreille, Idaho","docAbstract":"To evaluate biological potential of a commercial fishery for an unexploited Lake Whitefish Coregonus clupeaformis population in Lake Pend Oreille, Idaho, we estimated population parameters related to production and yield. The length frequency based on trap-netting in autumn 2005 was normal with a mean of 448 mm TL, whereas the length frequency based on gillnetting in spring 2006 was bimodal with a mean of 390 mm TL. Sex composition was skewed toward females (0.66) during autumn trap-netting. Shape parameters β of weight–length models for females (β = 3.38) and males (β = 3.45) were similar to those of other unexploited populations. Instantaneous growth rates K for females (K = 0.144 per year) and males (K = 0.153 per year) were among the lowest for unexploited populations across the species’ range. Age at 50% maturity (females: 6.5 years; males: 6.0 years) and length at 50% maturity (females: 390 mm TL; males: 378 mm TL) were high for unexploited populations. The natural mortality rate M (0.149 per year, ages 11–36) was among the lowest observed for unexploited populations. Adult population density was lower than that of other populations based on total surface area (mean = 1.35 fish/ha; 95% confidence interval [CI] = 1.11–1.78 fish/ha) but was average based on lake area shallower than 70 m (4.07 fish/ha; 95% CI = 3.35–5.35 fish/ha). Population density of juveniles and adults averaged 84 fish/ha (95% CI = 52–143 fish/ha) over the entire surface area and 278 fish/ha (95% CI = 173–474 fish/ha) over depths shallower than 70 m. The difference between the low M of the unexploited population in Lake Pend Oreille (M = 0.149 per year; annual mortality rate A = 14%) and the high sustainable total mortality Z of exploited stocks in the Laurentian Great Lakes (Z = 1.204; A = 70%) suggests a large scope for sustainable fishing mortality F (1.055 per year; exploitation rate u = 61%) that is equivalent to a sustainable Lake Whitefish harvest of 55,000 individuals (50,000–60,000 individuals) and 49,000 kg (45,000–54,000 kg) from Lake Pend Oreille.
","language":"English","publisher":"American Fisheries Society","publisherLocation":"Lawrence, KS","doi":"10.1080/02755947.2014.943862","usgsCitation":"Hosack, M.A., Hansen, M.J., and Horner, N.J., 2014, Dynamics of an introduced and unexploited Lake Whitefish population in Lake Pend Oreille, Idaho: North American Journal of Fisheries Management, v. 34, no. 5, p. 1014-1027, https://doi.org/10.1080/02755947.2014.943862.","productDescription":"14 p.","startPage":"1014","endPage":"1027","numberOfPages":"14","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-057116","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":293743,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Lake Pend Oreille","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -116.579841,47.948526 ], [ -116.579841,48.323412 ], [ -116.192894,48.323412 ], [ -116.192894,47.948526 ], [ -116.579841,47.948526 ] ] ] } } ] }","volume":"34","issue":"5","noUsgsAuthors":false,"publicationDate":"2014-09-10","publicationStatus":"PW","scienceBaseUri":"5412ab8ce4b0239f1986b9dd","contributors":{"authors":[{"text":"Hosack, Michael A.","contributorId":58952,"corporation":false,"usgs":true,"family":"Hosack","given":"Michael","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":500594,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hansen, Michael J. 0000-0001-8522-3876 michaelhansen@usgs.gov","orcid":"https://orcid.org/0000-0001-8522-3876","contributorId":5006,"corporation":false,"usgs":true,"family":"Hansen","given":"Michael","email":"michaelhansen@usgs.gov","middleInitial":"J.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":500592,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Horner, Ned J.","contributorId":19488,"corporation":false,"usgs":true,"family":"Horner","given":"Ned","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":500593,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70112900,"text":"ofr20141106 - 2014 - Ecological requirements for pallid sturgeon reproduction and recruitment in the Missouri River: annual report 2011","interactions":[],"lastModifiedDate":"2014-09-11T15:06:23","indexId":"ofr20141106","displayToPublicDate":"2014-09-11T12:36:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2014-1106","title":"Ecological requirements for pallid sturgeon reproduction and recruitment in the Missouri River: annual report 2011","docAbstract":"The Comprehensive Sturgeon Research Project is a multiyear, multiagency collaborative research framework developed to provide information to support pallid sturgeon recovery and Missouri River management decisions. The project strategy integrates field and laboratory studies of sturgeon reproductive ecology, early life history, habitat requirements, and physiology. The project scope of work is developed annually with cooperating research partners and in collaboration with the U.S. Army Corps of Engineers, Missouri River Recovery—Integrated Science Program. The research consists of several interdependent and complementary tasks that engage multiple disciplines.
\nThe research tasks in the 2011 scope of work emphasized understanding of reproductive migrations and spawning of adult sturgeon, and hatch and drift of larvae. These tasks were addressed in three hydrologically and geomorphologically distinct parts of the Missouri River Basin: the Lower Missouri River downstream from Gavins Point Dam, the Upper Missouri River downstream from Fort Peck Dam and including downstream reaches of the Milk River, and the Lower Yellowstone River. The research is designed to inform management decisions related to channel re-engineering, flow modification, and pallid sturgeon population augmentation on the Missouri River, and throughout the range of the species. Research and progress made through this project are reported to the U.S. Army Corps of Engineers annually. This annual report details the research effort and progress made by the Comprehensive Sturgeon Research Project during 2011.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20141106","collaboration":"Prepared in cooperation with the Missouri River Recovery–Integrated Science Program, U.S. Army Corps of Engineers, Yankton, South Dakota","usgsCitation":"DeLonay, A.J., Jacobson, R.B., Chojnacki, K.A., Annis, M., Braaten, P., Elliott, C.M., Fuller, D.B., Haas, J.D., Haddix, T.M., Ladd, H.L., McElroy, B.J., Mestl, G.E., Papoulias, D.M., Rhoten, J.C., and Wildhaber, M.L., 2014, Ecological requirements for pallid sturgeon reproduction and recruitment in the Missouri River: annual report 2011: U.S. Geological Survey Open-File Report 2014-1106, xi, 96 p., https://doi.org/10.3133/ofr20141106.","productDescription":"xi, 96 p.","numberOfPages":"112","onlineOnly":"Y","ipdsId":"IP-043483","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":293715,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20141106.jpg"},{"id":293714,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2014/1106/pdf/ofr14-1106.pdf"},{"id":293713,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2014/1106/"}],"scale":"2000000","projection":"Albers Equal Area projection","country":"United States","otherGeospatial":"Missouri River","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -115.00,35.00 ], [ -115.00,49.00 ], [ -90.00,49.00 ], [ -90.00,35.00 ], [ -115.00,35.00 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5412ab8ce4b0239f1986b9e1","contributors":{"authors":[{"text":"DeLonay, Aaron J.","contributorId":53360,"corporation":false,"usgs":true,"family":"DeLonay","given":"Aaron","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":494891,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jacobson, Robert B. 0000-0002-8368-2064 rjacobson@usgs.gov","orcid":"https://orcid.org/0000-0002-8368-2064","contributorId":1289,"corporation":false,"usgs":true,"family":"Jacobson","given":"Robert","email":"rjacobson@usgs.gov","middleInitial":"B.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":494882,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chojnacki, Kimberly A. kchojnacki@usgs.gov","contributorId":1978,"corporation":false,"usgs":true,"family":"Chojnacki","given":"Kimberly","email":"kchojnacki@usgs.gov","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":494884,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Annis, Mandy L.","contributorId":41575,"corporation":false,"usgs":true,"family":"Annis","given":"Mandy L.","affiliations":[],"preferred":false,"id":494889,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Braaten, P. J. pbraaten@usgs.gov","contributorId":2724,"corporation":false,"usgs":true,"family":"Braaten","given":"P. J.","email":"pbraaten@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":false,"id":494886,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Elliott, Caroline M. 0000-0002-9190-7462 celliott@usgs.gov","orcid":"https://orcid.org/0000-0002-9190-7462","contributorId":2380,"corporation":false,"usgs":true,"family":"Elliott","given":"Caroline","email":"celliott@usgs.gov","middleInitial":"M.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":494885,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fuller, D. B.","contributorId":58196,"corporation":false,"usgs":true,"family":"Fuller","given":"D.","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":494892,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Haas, Justin D.","contributorId":92123,"corporation":false,"usgs":true,"family":"Haas","given":"Justin","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":494896,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Haddix, Tyler M.","contributorId":72315,"corporation":false,"usgs":true,"family":"Haddix","given":"Tyler","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":494894,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Ladd, Hallie L.A.","contributorId":81817,"corporation":false,"usgs":true,"family":"Ladd","given":"Hallie","email":"","middleInitial":"L.A.","affiliations":[],"preferred":false,"id":494895,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"McElroy, Brandon J.","contributorId":58197,"corporation":false,"usgs":true,"family":"McElroy","given":"Brandon","email":"","middleInitial":"J.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":false,"id":494893,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Mestl, Gerald E.","contributorId":49336,"corporation":false,"usgs":true,"family":"Mestl","given":"Gerald","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":494890,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Papoulias, Diana M. 0000-0002-5106-2469 dpapoulias@usgs.gov","orcid":"https://orcid.org/0000-0002-5106-2469","contributorId":2726,"corporation":false,"usgs":true,"family":"Papoulias","given":"Diana","email":"dpapoulias@usgs.gov","middleInitial":"M.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":494887,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Rhoten, Jason C.","contributorId":7633,"corporation":false,"usgs":false,"family":"Rhoten","given":"Jason","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":494888,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Wildhaber, Mark L. 0000-0002-6538-9083 mwildhaber@usgs.gov","orcid":"https://orcid.org/0000-0002-6538-9083","contributorId":1386,"corporation":false,"usgs":true,"family":"Wildhaber","given":"Mark","email":"mwildhaber@usgs.gov","middleInitial":"L.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":494883,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70119745,"text":"ofr20141170 - 2014 - Model documentation for relations between continuous real-time and discrete water-quality constituents in Indian Creek, Johnson County, Kansas, June 2004 through May 2013","interactions":[],"lastModifiedDate":"2014-09-11T12:40:38","indexId":"ofr20141170","displayToPublicDate":"2014-09-11T12:33:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2014-1170","title":"Model documentation for relations between continuous real-time and discrete water-quality constituents in Indian Creek, Johnson County, Kansas, June 2004 through May 2013","docAbstract":"Johnson County is the fastest growing county in Kansas, with a population of about 560,000 people in 2012. Urban growth and development can have substantial effects on water quality, and streams in Johnson County are affected by nonpoint-source pollutants from stormwater runoff and point-source discharges such as municipal wastewater effluent. Understanding of current (2014) water-quality conditions and the effects of urbanization is critical for the protection and remediation of aquatic resources in Johnson County, Kansas and downstream reaches located elsewhere. The Indian Creek Basin is 194 square kilometers and includes parts of Johnson County, Kansas and Jackson County, Missouri. Approximately 86 percent of the Indian Creek Basin is located in Johnson County, Kansas. The U.S. Geological Survey, in cooperation with Johnson County Wastewater, operated a series of six continuous real-time water-quality monitoring stations in the Indian Creek Basin during June 2011 through May 2013; one of these sites has been operating since February 2004. Five monitoring sites were located on Indian Creek and one site was located on Tomahawk Creek. The purpose of this report is to document regression models that establish relations between continuously measured water-quality properties and discretely collected water-quality constituents. Continuously measured water-quality properties include streamflow, specific conductance, pH, water temperature, dissolved oxygen, turbidity, and nitrate. Discrete water-quality samples were collected during June 2011 through May 2013 at five new sites and June 2004 through May 2013 at a long-term site and analyzed for sediment, nutrients, bacteria, and other water-quality constituents. Regression models were developed to establish relations between discretely sampled constituent concentrations and continuously measured physical properties to estimate concentrations of those constituents of interest that are not easily measured in real time because of limitations in sensor technology and fiscal constraints. Regression models for 28 water-quality constituents were developed and documented. The water-quality information in this report is important to Johnson County Wastewater because it allows the concentrations of many potential pollutants of interest, including nutrients and sediment, to be estimated in real time and characterized during conditions and time scales that would not be possible otherwise.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20141170","collaboration":"Prepared in cooperation with Johnson County Wastewater","usgsCitation":"Stone, M.L., and Graham, J.L., 2014, Model documentation for relations between continuous real-time and discrete water-quality constituents in Indian Creek, Johnson County, Kansas, June 2004 through May 2013: U.S. Geological Survey Open-File Report 2014-1170, Report: xcviii, 71 p.; Downloads Directory, https://doi.org/10.3133/ofr20141170.","productDescription":"Report: xcviii, 71 p.; Downloads Directory","numberOfPages":"169","onlineOnly":"Y","temporalStart":"2004-06-01","temporalEnd":"2013-05-31","ipdsId":"IP-054302","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":293709,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2014/1170/pdf/ofr2014-1170.pdf"},{"id":293710,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2014/1170/downloads/"},{"id":293711,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20141170.jpg"},{"id":293708,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2014/1170/"}],"scale":"24000","projection":"Albers Conic Equal-Area projection","country":"United States","state":"Kansas","county":"Johnson County","otherGeospatial":"Indian Creek","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -94.75,38.833333 ], [ -94.75,39.0 ], [ -94.583333,39.0 ], [ -94.583333,38.833333 ], [ -94.75,38.833333 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5412ab8de4b0239f1986b9e9","contributors":{"authors":[{"text":"Stone, Mandy L. 0000-0002-6711-1536 mstone@usgs.gov","orcid":"https://orcid.org/0000-0002-6711-1536","contributorId":4409,"corporation":false,"usgs":true,"family":"Stone","given":"Mandy","email":"mstone@usgs.gov","middleInitial":"L.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":497780,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Graham, Jennifer L. 0000-0002-6420-9335 jlgraham@usgs.gov","orcid":"https://orcid.org/0000-0002-6420-9335","contributorId":1769,"corporation":false,"usgs":true,"family":"Graham","given":"Jennifer","email":"jlgraham@usgs.gov","middleInitial":"L.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":497779,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70104683,"text":"sir20145074 - 2014 - Three-dimensional model of the hydrostratigraphy and structure of the area in and around the U.S. Army-Camp Stanley Storage Activity Area, northern Bexar County, Texas","interactions":[],"lastModifiedDate":"2014-09-11T08:44:46","indexId":"sir20145074","displayToPublicDate":"2014-09-11T08:37:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2014-5074","title":"Three-dimensional model of the hydrostratigraphy and structure of the area in and around the U.S. Army-Camp Stanley Storage Activity Area, northern Bexar County, Texas","docAbstract":"A three-dimensional model of the Camp Stanley Storage Activity area defines and illustrates the surface and subsurface hydrostratigraphic architecture of the military base and adjacent areas to the south and west using EarthVision software. The Camp Stanley model contains 11 hydrostratigraphic units in descending order: 1 model layer representing the Edwards aquifer; 1 model layer representing the upper Trinity aquifer; 6 model layers representing the informal hydrostratigraphic units that make up the upper part of the middle Trinity aquifer; and 3 model layers representing each, the Bexar, Cow Creek, and the top of the Hammett of the lower part of the middle Trinity aquifer. The Camp Stanley three-dimensional model includes 14 fault structures that generally trend northeast/southwest. The top of Hammett hydrostratigraphic unit was used to propagate and validate all fault structures and to confirm most of the drill-hole data. Differences between modeled and previously mapped surface geology reflect interpretation of fault relations at depth, fault relations to hydrostratigraphic contacts, and surface digital elevation model simplification to fit the scale of the model. In addition, changes based on recently obtained drill-hole data and field reconnaissance done during the construction of the model. The three-dimensional modeling process revealed previously undetected horst and graben structures in the northeastern and southern parts of the study area. This is atypical, as most faults in the area are en echelon that step down southeasterly to the Gulf Coast. The graben structures may increase the potential for controlling or altering local groundwater flow.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20145074","collaboration":"Prepared in cooperation with the Camp Stanley Storage Activity Environmental Management Office and the Parsons Corporation","usgsCitation":"Pantea, M.P., Blome, C.D., and Clark, A.K., 2014, Three-dimensional model of the hydrostratigraphy and structure of the area in and around the U.S. Army-Camp Stanley Storage Activity Area, northern Bexar County, Texas: U.S. Geological Survey Scientific Investigations Report 2014-5074, Report: iii, 13 p.; 3D Model; Downloads Directory, https://doi.org/10.3133/sir20145074.","productDescription":"Report: iii, 13 p.; 3D Model; Downloads Directory","numberOfPages":"21","ipdsId":"IP-050575","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":293632,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2014/5074/downloads/Video/SIR_2014_5074_ppt.wmv"},{"id":293633,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2014/5074/downloads/"},{"id":293634,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20145074.jpg"},{"id":293631,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2014/5074/downloads/Report/SIR_2014-5074.pdf"},{"id":293626,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2014/5074/"}],"scale":"100000","projection":"Universal Transverse Mercator projection","datum":"North American Datum of 1983","country":"United States","state":"Texas","county":"Bexar County","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -98.883333,29.35 ], [ -98.883333,30.125 ], [ -98.108333,30.125 ], [ -98.108333,29.35 ], [ -98.883333,29.35 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5412ab8ee4b0239f1986b9ed","contributors":{"authors":[{"text":"Pantea, Michael P. mpantea@usgs.gov","contributorId":1549,"corporation":false,"usgs":true,"family":"Pantea","given":"Michael","email":"mpantea@usgs.gov","middleInitial":"P.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":493787,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blome, Charles D. 0000-0002-3449-9378 cblome@usgs.gov","orcid":"https://orcid.org/0000-0002-3449-9378","contributorId":1246,"corporation":false,"usgs":true,"family":"Blome","given":"Charles","email":"cblome@usgs.gov","middleInitial":"D.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":493785,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clark, Allan K. 0000-0003-0099-1521 akclark@usgs.gov","orcid":"https://orcid.org/0000-0003-0099-1521","contributorId":1279,"corporation":false,"usgs":true,"family":"Clark","given":"Allan","email":"akclark@usgs.gov","middleInitial":"K.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":493786,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70124051,"text":"70124051 - 2014 - Estimating the spatial distribution of wintering little brown bat populations in the eastern United States","interactions":[],"lastModifiedDate":"2016-01-26T15:45:48","indexId":"70124051","displayToPublicDate":"2014-09-10T15:50:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Estimating the spatial distribution of wintering little brown bat populations in the eastern United States","docAbstract":"Depicting the spatial distribution of wildlife species is an important first step in developing management and conservation programs for particular species. Accurate representation of a species distribution is important for predicting the effects of climate change, land-use change, management activities, disease, and other landscape-level processes on wildlife populations. We developed models to estimate the spatial distribution of little brown bat (Myotis lucifugus) wintering populations in the United States east of the 100th meridian, based on known hibernacula locations. From this data, we developed several scenarios of wintering population counts per county that incorporated uncertainty in the spatial distribution of the hibernacula as well as uncertainty in the size of the current little brown bat population. We assessed the variability in our results resulting from effects of uncertainty. Despite considerable uncertainty in the known locations of overwintering little brown bats in the eastern United States, we believe that models accurately depicting the effects of the uncertainty are useful for making management decisions as these models are a coherent organization of the best available information.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.1215","usgsCitation":"Russell, R.E., Tinsley, K., Erickson, R.A., Thogmartin, W.E., and Szymanski, J.A., 2014, Estimating the spatial distribution of wintering little brown bat populations in the eastern United States: Ecology and Evolution, v. 4, no. 19, p. 3746-3754, https://doi.org/10.1002/ece3.1215.","productDescription":"9 p.","startPage":"3746","endPage":"3754","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-056690","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":472766,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/ece3.1215","text":"External Repository"},{"id":306624,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.986328125,\n 25.958044673317843\n ],\n [\n -100.986328125,\n 49.03786794532644\n ],\n [\n -67.060546875,\n 49.03786794532644\n ],\n [\n -67.060546875,\n 25.958044673317843\n ],\n [\n -100.986328125,\n 25.958044673317843\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"4","issue":"19","noUsgsAuthors":false,"publicationDate":"2014-09-08","publicationStatus":"PW","scienceBaseUri":"541157b3e4b0fe7e184a553b","contributors":{"authors":[{"text":"Russell, Robin E. 0000-0001-8726-7303 rerussell@usgs.gov","orcid":"https://orcid.org/0000-0001-8726-7303","contributorId":3998,"corporation":false,"usgs":true,"family":"Russell","given":"Robin","email":"rerussell@usgs.gov","middleInitial":"E.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":500581,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tinsley, Karl","contributorId":23457,"corporation":false,"usgs":false,"family":"Tinsley","given":"Karl","email":"","affiliations":[{"id":6969,"text":"U.S. Fish and Wildlife Service, Division of Endangered Species","active":true,"usgs":false}],"preferred":false,"id":500583,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":500584,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":500580,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Szymanski, Jennifer A.","contributorId":127486,"corporation":false,"usgs":false,"family":"Szymanski","given":"Jennifer","email":"","middleInitial":"A.","affiliations":[{"id":6969,"text":"U.S. Fish and Wildlife Service, Division of Endangered Species","active":true,"usgs":false}],"preferred":false,"id":500582,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70123861,"text":"70123861 - 2014 - Assessing fire effects on forest spatial structure using a fusion of Landsat and airborne LiDAR data in Yosemite National Park","interactions":[],"lastModifiedDate":"2014-09-10T11:13:48","indexId":"70123861","displayToPublicDate":"2014-09-10T11:07:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Assessing fire effects on forest spatial structure using a fusion of Landsat and airborne LiDAR data in Yosemite National Park","docAbstract":"Mosaics of tree clumps and openings are characteristic of forests dominated by frequent, low- and moderate-severity fires. When restoring these fire-suppressed forests, managers often try to reproduce these structures to increase ecosystem resilience. We examined unburned and burned forest structures for 1937 0.81 ha sample areas in Yosemite National Park, USA. We estimated severity for fires from 1984 to 2010 using the Landsat-derived Relativized differenced Normalized Burn Ratio (RdNBR) and measured openings and canopy clumps in five height strata using airborne LiDAR data. Because our study area lacked concurrent field data, we identified methods to allow structural analysis using LiDAR data alone. We found three spatial structures, canopy-gap, clump-open, and open, that differed in spatial arrangement and proportion of canopy and openings. As fire severity increased, the total area in canopy decreased while the number of clumps increased, creating a patchwork of openings and multistory tree clumps. The presence of openings > 0.3 ha, an approximate minimum gap size needed to favor shade-intolerant pine regeneration, increased rapidly with loss of canopy area. The range and variation of structures for a given fire severity were specific to each forest type. Low- to moderate-severity fires best replicated the historic clump-opening patterns that were common in forests with frequent fire regimes. Our results suggest that managers consider the following goals for their forest restoration: 1) reduce total canopy cover by breaking up large contiguous areas into variable-sized tree clumps and scattered large individual trees; 2) create a range of opening sizes and shapes, including ~ 50% of the open area in gaps > 0.3 ha; 3) create multistory clumps in addition to single story clumps; 4) retain historic densities of large trees; and 5) vary treatments to include canopy-gap, clump-open, and open mosaics across project areas to mimic the range of patterns found for each forest type in our study.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Remote Sensing of Environment","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2013.07.041","usgsCitation":"Kane, V., North, M.P., Lutz, J.A., Churchill, D.J., Roberts, S.L., Smith, D.F., McGaughey, R.J., Kane, J.T., and Brooks, M.L., 2014, Assessing fire effects on forest spatial structure using a fusion of Landsat and airborne LiDAR data in Yosemite National Park: Remote Sensing of Environment, v. 151, p. 89-101, https://doi.org/10.1016/j.rse.2013.07.041.","productDescription":"13 p.","startPage":"89","endPage":"101","ipdsId":"IP-045760","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":293600,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":293599,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.rse.2013.07.041"}],"country":"United States","state":"California","otherGeospatial":"Yosemite National Park","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -119.886496,37.494762 ], [ -119.886496,38.185228 ], [ -119.195416,38.185228 ], [ -119.195416,37.494762 ], [ -119.886496,37.494762 ] ] ] } } ] }","volume":"151","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"541157b1e4b0fe7e184a5531","contributors":{"authors":[{"text":"Kane, Van R.","contributorId":25873,"corporation":false,"usgs":true,"family":"Kane","given":"Van R.","affiliations":[],"preferred":false,"id":500417,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"North, Malcolm P.","contributorId":9975,"corporation":false,"usgs":true,"family":"North","given":"Malcolm","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":500414,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lutz, James A.","contributorId":61350,"corporation":false,"usgs":true,"family":"Lutz","given":"James","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":500419,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Churchill, Derek J.","contributorId":16763,"corporation":false,"usgs":true,"family":"Churchill","given":"Derek","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":500416,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Roberts, Susan L.","contributorId":85312,"corporation":false,"usgs":true,"family":"Roberts","given":"Susan","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":500421,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Smith, Douglas F.","contributorId":76235,"corporation":false,"usgs":true,"family":"Smith","given":"Douglas","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":500420,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McGaughey, Robert J.","contributorId":36865,"corporation":false,"usgs":true,"family":"McGaughey","given":"Robert","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":500418,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kane, Jonathan T.","contributorId":16329,"corporation":false,"usgs":true,"family":"Kane","given":"Jonathan","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":500415,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Brooks, Matthew L. 0000-0002-3518-6787 mlbrooks@usgs.gov","orcid":"https://orcid.org/0000-0002-3518-6787","contributorId":393,"corporation":false,"usgs":true,"family":"Brooks","given":"Matthew","email":"mlbrooks@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":500413,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70117082,"text":"tm11C9 - 2014 - The Pedestrian Evacuation Analyst: geographic information systems software for modeling hazard evacuation potential","interactions":[],"lastModifiedDate":"2014-09-10T10:15:53","indexId":"tm11C9","displayToPublicDate":"2014-09-10T09:16:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"11-C9","title":"The Pedestrian Evacuation Analyst: geographic information systems software for modeling hazard evacuation potential","docAbstract":"Recent disasters such as the 2011 Tohoku, Japan, earthquake and tsunami; the 2013 Colorado floods; and the 2014 Oso, Washington, mudslide have raised awareness of catastrophic, sudden-onset hazards that arrive within minutes of the events that trigger them, such as local earthquakes or landslides. Due to the limited amount of time between generation and arrival of sudden-onset hazards, evacuations are typically self-initiated, on foot, and across the landscape (Wood and Schmidtlein, 2012). Although evacuation to naturally occurring high ground may be feasible in some vulnerable communities, evacuation modeling has demonstrated that other communities may require vertical-evacuation structures within a hazard zone, such as berms or buildings, if at-risk individuals are to survive some types of sudden-onset hazards (Wood and Schmidtlein, 2013).
\nResearchers use both static least-cost-distance (LCD) and dynamic agent-based models to assess the pedestrian evacuation potential of vulnerable communities. Although both types of models help to understand the evacuation landscape, LCD models provide a more general overview that is independent of population distributions, which may be difficult to quantify given the dynamic spatial and temporal nature of populations (Wood and Schmidtlein, 2012). Recent LCD efforts related to local tsunami threats have focused on an anisotropic (directionally dependent) path distance modeling approach that incorporates travel directionality, multiple travel speed assumptions, and cost surfaces that reflect variations in slope and land cover (Wood and Schmidtlein, 2012, 2013).
\nThe Pedestrian Evacuation Analyst software implements this anisotropic path-distance approach for pedestrian evacuation from sudden-onset hazards, with a particular focus at this time on local tsunami threats. The model estimates evacuation potential based on elevation, direction of movement, land cover, and travel speed and creates a map showing travel times to safety (a time map) throughout a hazard zone. Model results provide a general, static view of the evacuation landscape at different pedestrian travel speeds and can be used to identify areas outside the reach of naturally occurring high ground. In addition, data on the size and location of different populations within the hazard zone can be integrated with travel-time maps to create tables and graphs of at-risk population counts as a function of travel time to safety. As a decision-support tool, the Pedestrian Evacuation Analyst provides the capability to evaluate the effectiveness of various vertical-evacuation structures within a study area, both through time maps of the modeled travel-time landscape with a potential structure in place and through comparisons of population counts within reach of safety.
\nThe Pedestrian Evacuation Analyst is designed for use by researchers examining the pedestrian-evacuation potential of an at-risk community. In communities where modeled evacuation times exceed the event (for example, tsunami wave) arrival time, researchers can use the software with emergency managers to assess the area and population served by potential vertical-evacuation options. By automating and managing the modeling process, the software allows researchers to concentrate efforts on providing crucial and timely information on community vulnerability to sudden-onset hazards.
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","language":"English","publisher":"American Fisheries Society","doi":"10.1080/02755947.2014.942042","usgsCitation":"Shepard, B.B., Nelson, L.M., Taper, M.L., and Zale, A.V., 2014, Factors influencing successful eradication of nonnative brook trout from four small Rocky Mountain streams using electrofishing: North American Journal of Fisheries Management, v. 34, no. 5, p. 988-997, https://doi.org/10.1080/02755947.2014.942042.","productDescription":"10 p.","startPage":"988","endPage":"997","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-054488","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":322011,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Cottonwood Creek, Craver Creek, Muskrat Creek, Spring, Staubach Creek, Whites Creek, Upper Missouri River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.48876953125,\n 45.22848059584359\n ],\n [\n -113.48876953125,\n 47.41322033016902\n ],\n [\n -104.0625,\n 47.41322033016902\n ],\n [\n -104.0625,\n 45.22848059584359\n ],\n [\n -113.48876953125,\n 45.22848059584359\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"34","issue":"5","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2014-09-10","publicationStatus":"PW","scienceBaseUri":"57500762e4b0ee97d51bb5e2","contributors":{"authors":[{"text":"Shepard, Bradley B.","contributorId":57327,"corporation":false,"usgs":true,"family":"Shepard","given":"Bradley","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":631249,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nelson, Lee M.","contributorId":169853,"corporation":false,"usgs":false,"family":"Nelson","given":"Lee","email":"","middleInitial":"M.","affiliations":[{"id":5099,"text":"Montana Department of Fish, Wildlife, and Parks","active":true,"usgs":false}],"preferred":false,"id":631250,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Taper, Mark L.","contributorId":105192,"corporation":false,"usgs":true,"family":"Taper","given":"Mark","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":631251,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zale, Alexander V. 0000-0003-1703-885X zale@usgs.gov","orcid":"https://orcid.org/0000-0003-1703-885X","contributorId":3010,"corporation":false,"usgs":true,"family":"Zale","given":"Alexander","email":"zale@usgs.gov","middleInitial":"V.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":630995,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70122729,"text":"fs20143085 - 2014 - The 3D Elevation Program: summary for Arizona","interactions":[],"lastModifiedDate":"2016-08-17T15:25:43","indexId":"fs20143085","displayToPublicDate":"2014-09-09T16:37:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2014-3085","title":"The 3D Elevation Program: summary for Arizona","docAbstract":"Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, national security, recreation, and many others. For the State of Arizona, elevation data are critical for infrastructure and construction management, natural resources conservation, flood risk management, geologic resource assessment and hazard mitigation, agriculture and precision farming, and other business uses. Today, high-density light detection and ranging (lidar) data are the primary sources for deriving elevation models and other datasets. Federal, State, Tribal, and local agencies work in partnership to (1) replace data that are older and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data.
\nThe National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 interferometric synthetic aperture radar (ifsar) data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey, the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.
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William J. Jr. carswell@usgs.gov","contributorId":1787,"corporation":false,"usgs":true,"family":"Carswell","given":"William J.","suffix":"Jr.","email":"carswell@usgs.gov","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":false,"id":499672,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70059134,"text":"70059134 - 2014 - Identifying sources of aeolian mineral dust: Present and past","interactions":[],"lastModifiedDate":"2015-11-13T14:42:44","indexId":"70059134","displayToPublicDate":"2014-09-09T15:45:00","publicationYear":"2014","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Identifying sources of aeolian mineral dust: Present and past","docAbstract":"Aeolian mineral dust is an important component of the Earth’s environmental systems, playing roles in the planetary radiation balance, as a source of fertilizer for biota in both terrestrial and marine realms and as an archive for understanding atmospheric circulation and paleoclimate in the geologic past. Crucial to understanding all of these roles of dust is the identification of dust sources. Here we review the methods used to identify dust sources active at present and in the past. Contemporary dust sources, produced by both glaciogenic and non-glaciogenic processes, can be readily identified by the use of Earth-orbiting satellites. These data show that present dust sources are concentrated in a global dust belt that encompasses large topographic basins in low-latitude arid and semiarid regions. Geomorphic studies indicate that specific point sources for dust in this zone include dry or ephemeral lakes, intermittent stream courses, dune fields, and some bedrock surfaces. Back-trajectory analyses are also used to identify dust sources, through modeling of wind fields and the movement of air parcels over periods of several days. Identification of dust sources from the past requires novel approaches that are part of the geologic toolbox of provenance studies. Identification of most dust sources of the past requires the use of physical, mineralogical, geochemical, and isotopic analyses of dust deposits. Physical properties include systematic spatial changes in dust deposit thickness and particle size away from a source. Mineralogy and geochemistry can pinpoint dust sources by clay mineral ratios and Sc-Th-La abundances, respectively. The most commonly used isotopic methods utilize isotopes of Nd, Sr, and Pb and have been applied extensively in dust archives of deep-sea cores, ice cores, and loess. All these methods have shown that dust sources have changed over time, with far more abundant dust supplies existing during glacial periods. Greater dust supplies in glacial periods are likely due to greater production of glaciogenic dust particles from expanded ice sheets and mountain glaciers, but could also include dust inputs from exposed continental and insular shelves now submerged. Future dust sources are difficult to assess, but will likely differ from those of the present because of global warming. Global warming could bring about shifts in dust sources by changes in degree or type of vegetation cover, changes in wind strength, and increases or decreases in the size of water bodies. A major uncertainty in assessing dust sources of the future is related to changes inhuman land use, which could affect land surface cover, particularly due to increased agricultural endeavors and water usage.
","largerWorkType":{"id":5,"text":"Book chapter"},"largerWorkTitle":"Mineral dust: A key player in the earth system","largerWorkSubtype":{"id":24,"text":"Book Chapter"},"language":"English","publisher":"Springer","publisherLocation":"Dordrecht","doi":"10.1007/978-94-017-8978-3","usgsCitation":"Muhs, D.R., Prospero, J., Baddock, M.C., and Gill, T., 2014, Identifying sources of aeolian mineral dust: Present and past, chap. of Mineral dust: A key player in the earth system, p. 51-74, https://doi.org/10.1007/978-94-017-8978-3.","productDescription":"14 p.","startPage":"51","endPage":"74","numberOfPages":"14","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-045657","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":311316,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Global","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"564717cbe4b0e2669b313114","contributors":{"authors":[{"text":"Muhs, Daniel R","contributorId":118290,"corporation":false,"usgs":true,"family":"Muhs","given":"Daniel","email":"","middleInitial":"R","affiliations":[],"preferred":false,"id":518428,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Prospero, Joseph M","contributorId":118520,"corporation":false,"usgs":true,"family":"Prospero","given":"Joseph M","affiliations":[],"preferred":false,"id":518429,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baddock, Matthew C","contributorId":117410,"corporation":false,"usgs":true,"family":"Baddock","given":"Matthew","email":"","middleInitial":"C","affiliations":[],"preferred":false,"id":518427,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gill, Thomas E","contributorId":119945,"corporation":false,"usgs":true,"family":"Gill","given":"Thomas E","affiliations":[],"preferred":false,"id":518430,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70116961,"text":"sir20145135 - 2014 - Pesticide trends in major rivers of the United States, 1992-2010","interactions":[],"lastModifiedDate":"2017-10-12T20:11:39","indexId":"sir20145135","displayToPublicDate":"2014-09-09T08:42:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2014-5135","title":"Pesticide trends in major rivers of the United States, 1992-2010","docAbstract":"This report is part of a series of pesticide trend assessments led by the National Water-Quality Assessment Program of the U.S. Geological Survey. This assessment focuses on major rivers of various sizes throughout the United States that have large watersheds with a range of land uses, changes in pesticide use, changes in management practices, and natural influences typical of the regions being drained.
\nTrends were assessed at 59 sites for 40 pesticides and pesticide degradates during each of three overlapping periods: 1992–2001, 1997–2006, and 2001–10. In addition to trends in concentration, trends in agricultural-use intensity (agricultural use) were also assessed at 57 of the sites for 35 parent compounds with agricultural uses during the same three periods. The SEAWAVE-Q model was used to analyze trends in concentration, and parametric survival regression for interval-censored data was used to assess trends in agricultural use. All trends are provided in downloadable electronic files.
\nA subset of 39 sites was chosen to represent non-nested, generally independent basins for a national analysis of pesticide and agricultural-use trends for the most prevalent pesticides (15 pesticides and 2 degradation products). Graphical and numerical results are presented to provide a national overview of concentration and use trends. As another perspective on understanding pesticide concentration trends in large rivers in relation to multiple tributary watersheds, this report also presents a detailed assessment of concentration and use trends for simazine, metolachlor, atrazine, deethylatrazine, and diazinon for a set of 17 nested sites in the Mississippi River Basin (including the Ohio and Missouri River Basins), for the second and third trend periods.
\nPesticides strongly dominated by agricultural use—cyanazine, metolachlor, atrazine, and alachlor—had widespread agreement between concentration trends and agricultural-use trends. Pesticides with substantial use in agricultural and urban applications—simazine, tebuthiuron, Dacthal, pendimethalin, chlorpyrifos, malathion, diazinon, fipronil, carbofuran, and carbaryl—had concentration trends that were mostly explained by a combination of agricultural-use trends and concentration trends in urban streams that were evaluated in a separate companion study. The importance of the urban stream trends for explaining concentration trends in major rivers indicates the significance of nonagricultural uses of some pesticides to concentrations in major rivers despite the much smaller area of urban land use compared to agriculture. Deethylatrazine, a degradate of atrazine, was the only pesticide compound assessed that had frequent occurrences during 1997–2006 and 2001–10 of concentration trends in the opposite direction of use trends (atrazine use). The nested analysis for the Mississippi River indicates that most trends observed in the largest rivers—multiple Mississippi River sites, the Ohio River, and the Missouri River—are consistent with streamflow contributions and concentration trends observed at tributary sites.
\nStreamflow (incorporated into the trend model and shown in the nested basin analysis), trends in agricultural use of pesticides (quantified in this report), and urban use of pesticides (represented by concentration trends in a companion study of urban streams) are all important influences on pesticide concentrations in streams and rivers. Consideration of these influences is vital to understanding trends in pesticide concentrations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20145135","collaboration":"National Water-Quality Assessment Program","usgsCitation":"Ryberg, K.R., Vecchia, A.V., Gilliom, R.J., and Martin, J.D., 2014, Pesticide trends in major rivers of the United States, 1992-2010: U.S. Geological Survey Scientific Investigations Report 2014-5135, Report: vi, 63 p.; 2 Tables, https://doi.org/10.3133/sir20145135.","productDescription":"Report: vi, 63 p.; 2 Tables","numberOfPages":"74","onlineOnly":"Y","ipdsId":"IP-052669","costCenters":[{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":293476,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20145135.jpg"},{"id":293472,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2014/5135/"},{"id":293473,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2014/5135/pdf/sir2014-5135.pdf"},{"id":293474,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2014/5135/downloads/table06.xlsx"},{"id":293475,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2014/5135/downloads/table05.xlsx"}],"projection":"Albers Equal-Area Conic projection","country":"United States","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -125.15,24.86 ], [ -125.15,48.99 ], [ -66.95,48.99 ], [ -66.95,24.86 ], [ -125.15,24.86 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"54100631e4b07ab1cd9807fd","contributors":{"authors":[{"text":"Ryberg, Karen R. 0000-0002-9834-2046 kryberg@usgs.gov","orcid":"https://orcid.org/0000-0002-9834-2046","contributorId":1172,"corporation":false,"usgs":true,"family":"Ryberg","given":"Karen","email":"kryberg@usgs.gov","middleInitial":"R.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":495906,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vecchia, Aldo V. 0000-0002-2661-4401","orcid":"https://orcid.org/0000-0002-2661-4401","contributorId":41810,"corporation":false,"usgs":true,"family":"Vecchia","given":"Aldo","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":495907,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gilliom, Robert J. rgilliom@usgs.gov","contributorId":488,"corporation":false,"usgs":true,"family":"Gilliom","given":"Robert","email":"rgilliom@usgs.gov","middleInitial":"J.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":495904,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Martin, Jeffrey D. 0000-0003-1994-5285 jdmartin@usgs.gov","orcid":"https://orcid.org/0000-0003-1994-5285","contributorId":1066,"corporation":false,"usgs":true,"family":"Martin","given":"Jeffrey","email":"jdmartin@usgs.gov","middleInitial":"D.","affiliations":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true},{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":495905,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70101015,"text":"sir20145065 - 2014 - Status and understanding of groundwater quality in the Klamath Mountains study unit, 2010: California GAMA Priority Basin Project","interactions":[],"lastModifiedDate":"2018-06-08T13:31:38","indexId":"sir20145065","displayToPublicDate":"2014-09-05T12:18:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2014-5065","title":"Status and understanding of groundwater quality in the Klamath Mountains study unit, 2010: California GAMA Priority Basin Project","docAbstract":"Groundwater quality in the Klamath Mountains (KLAM) study unit was investigated as part of the Priority Basin Project of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program. The study unit is located in Del Norte, Humboldt, Shasta, Siskiyou, Tehama, and Trinity Counties. The GAMA Priority Basin Project is being conducted by the California State Water Resources Control Board in collaboration with the U.S. Geological Survey (USGS) and the Lawrence Livermore National Laboratory.
\nThe GAMA Priority Basin Project was designed to provide a spatially unbiased, statistically robust assessment of the quality of untreated (raw) groundwater in the primary aquifer system. The assessment is based on water-quality data and explanatory factors for groundwater samples collected in 2010 by the USGS from 39 sites and on water-quality data from the California Department of Public Health (CDPH) water-quality database. The primary aquifer system was defined by the depth intervals of the wells listed in the CDPH water-quality database for the KLAM study unit. The quality of groundwater in the primary aquifer system may be different from that in the shallower or deeper water-bearing zones; shallow groundwater may be more vulnerable to surficial contamination.
\nThis study included two types of assessments: (1) a status assessment, which characterized the status of the current quality of the groundwater resource by using data from samples analyzed for volatile organic compounds, pesticides, and naturally occurring inorganic constituents, such as major ions and trace elements, and (2) an understanding assessment, which evaluated the natural and human factors potentially affecting the groundwater quality. The assessments were intended to characterize the quality of groundwater resources in the primary aquifer system of the KLAM study unit, not the quality of treated drinking water delivered to consumers by water purveyors.
\nRelative-concentrations (sample concentrations divided by the health- or aesthetic-based benchmark concentrations) were used for evaluating groundwater quality for those constituents that have Federal or California regulatory or non-regulatory benchmarks for drinking-water quality. A relative-concentration greater than (>) 1.0 indicates a concentration greater than a benchmark, and a relative-concentration less than or equal to (≤) 1.0 indicates a concentration less than or equal to a benchmark. Relative-concentrations of organic constituents were classified as “high” (relative-concentration > 1.0), “moderate” (0.1 < relative-concentration ≤ 1.0), or “low” (relative-concentration ≤ 0.1). For inorganic constituents, the boundary between low and moderate relative-concentration was set at 0.5.
\nAquifer-scale proportion was used in the status assessment as the primary metric for evaluating regional-scale groundwater quality. High aquifer-scale proportion is defined as the percentage of the area of the primary aquifer system with a relative-concentration greater than 1.0 for a particular constituent or class of constituents; percentage is based on an areal rather than a volumetric basis. Moderate and low aquifer-scale proportions were defined as the percentages of the primary aquifer system with moderate and low relative-concentrations, respectively.
\nThe KLAM study unit includes more than 8,800 square miles (mi2), but only those areas near the sampling sites, about 920 mi2, are included in the areal assessment of the study unit. Two statistical approaches—grid-based and spatially weighted—were used to evaluate aquifer-scale proportions for individual constituents and classes of constituents. To confirm this methodology, 90 percent confidence intervals were calculated for the grid-based high aquifer-scale proportions and were compared to the spatially weighted results, which were found to be within these confidence intervals in all cases. Grid-based results were selected for use in the status assessment unless, as was observed in a few cases, a grid-based result was zero and the spatially weighted result was not zero, in which case, the spatially weighted result was used.
\nThe status assessment showed that inorganic constituents with human-health benchmarks were detected at high relative-concentrations in 2.6 percent of the primary aquifer system and at moderate relative-concentrations in 10 percent of the system. The high aquifer-scale proportion for inorganic constituents mainly reflected the high aquifer-scale proportions of boron. Inorganic constituents with secondary maximum contaminant levels were detected at high relative-concentrations in 13 percent of the primary aquifer system and at moderate relative-concentrations in 10 percent of the system. The constituents present at high relative-concentrations included iron and manganese.
\nOrganic constituents with human-health benchmarks were not detected at high relative-concentrations, but were detected at moderate relative-concentrations in 1.9 percent of the primary aquifer system. The 1.9 percent reflected a spatially weighted moderate aquifer-scale proportion for the gasoline additive methyl tert-butyl ether. Of the 148 organic constituents analyzed, 14 constituents were detected. Only one organic constituent had a detection frequency of greater than 10 percent—the trihalomethane, chloroform.
\nThe second component of this study, the understanding assessment, identified the natural and human factors that may have affected the groundwater quality in the KLAM study unit by evaluating statistical correlations between water-quality constituents and potential explanatory factors. The potential explanatory factors evaluated were aquifer lithology, land use, hydrologic conditions, depth, groundwater age, and geochemical conditions. Results of the statistical evaluations were used to explain the occurrence and distribution of constituents in the KLAM study unit.
\nGroundwater age distribution (modern, mixed, or pre-modern), redox class (oxic, mixed, or anoxic), and dissolved oxygen concentration were the explanatory factors that best explained occurrence patterns of the inorganic constituents. High concentrations of boron were found to be associated with groundwater classified as mixed or pre-modern with respect to groundwater age. Boron was also negatively correlated to dissolved oxygen and positively correlated to specific conductance. Iron and manganese concentrations were strongly associated with low dissolved oxygen concentrations, anoxic and mixed redox classifications, and pre-modern groundwater. Specific conductance concentrations were found to be related to pre-modern groundwater, low dissolved oxygen concentrations, and high pH.
\nChloroform was selected for additional evaluation in the understanding assessment because it was detected in more than 10 percent of wells sampled in the KLAM study unit. Septic tank density was the only explanatory factor that was found to relate to chloroform concentrations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20145065","collaboration":"A product of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Bennett, G.L., Fram, M.S., and Belitz, K., 2014, Status and understanding of groundwater quality in the Klamath Mountains study unit, 2010: California GAMA Priority Basin Project: U.S. Geological Survey Scientific Investigations Report 2014-5065, viii, 58 p., https://doi.org/10.3133/sir20145065.","productDescription":"viii, 58 p.","numberOfPages":"70","ipdsId":"IP-043179","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":293462,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20145065.jpg"},{"id":293460,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2014/5065/"},{"id":293461,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2014/5065/pdf/sir2014-5065.pdf"}],"projection":"Albers Equal Area Conic Projection","datum":"North American Datum of 1983","country":"United States","state":"California","otherGeospatial":"Klamath Mountains","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -125.00,32.00 ], [ -125.00,42.00 ], [ -114.00,42.00 ], [ -114.00,32.00 ], [ -125.00,32.00 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"540ac032e4b023c1f29d587d","contributors":{"authors":[{"text":"Bennett, George L. V V 0000-0002-6239-1604 georbenn@usgs.gov","orcid":"https://orcid.org/0000-0002-6239-1604","contributorId":1373,"corporation":false,"usgs":true,"family":"Bennett","given":"George","suffix":"V","email":"georbenn@usgs.gov","middleInitial":"L. V","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":492543,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fram, Miranda S. 0000-0002-6337-059X mfram@usgs.gov","orcid":"https://orcid.org/0000-0002-6337-059X","contributorId":1156,"corporation":false,"usgs":true,"family":"Fram","given":"Miranda","email":"mfram@usgs.gov","middleInitial":"S.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":492542,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Belitz, Kenneth 0000-0003-4481-2345 kbelitz@usgs.gov","orcid":"https://orcid.org/0000-0003-4481-2345","contributorId":442,"corporation":false,"usgs":true,"family":"Belitz","given":"Kenneth","email":"kbelitz@usgs.gov","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":492541,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}