{"pageNumber":"544","pageRowStart":"13575","pageSize":"25","recordCount":46677,"records":[{"id":70191613,"text":"70191613 - 2014 - Potential fitness benefits of the half-pounder life history in Klamath River steelhead","interactions":[],"lastModifiedDate":"2017-10-17T14:58:08","indexId":"70191613","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Potential fitness benefits of the half-pounder life history in Klamath River steelhead","docAbstract":"<p><span>Steelhead&nbsp;</span><i>Oncorhynchus mykiss</i><span><span>&nbsp;</span>from several of the world's rivers display the half-pounder life history, a variant characterized by an amphidromous (and, less often, anadromous) return to freshwater in the year of initial ocean entry. We evaluated factors related to expression of the half-pounder life history in wild steelhead from the lower Klamath River basin, California. We also evaluated fitness consequences of the half-pounder phenotype using a simple life history model that was parameterized with our empirical data and outputs from a regional survival equation. The incidence of the half-pounder life history differed among subbasins of origin and smolt ages. Precocious maturation occurred in approximately 8% of half-pounders and was best predicted by individual length in freshwater preceding ocean entry. Adult steelhead of the half-pounder phenotype were smaller and less fecund at age than adult steelhead of the alternative (ocean contingent) phenotype. However, our data suggest that fish of the half-pounder phenotype are more likely to spawn repeatedly than are fish of the ocean contingent phenotype. Models predicted that if lifetime survivorship were equal between phenotypes, the fitness of the half-pounder phenotype would be 17–28% lower than that of the ocean contingent phenotype. To meet the condition of equal fitness between phenotypes would require that first-year ocean survival be 21–40% higher among half-pounders in freshwater than among their cohorts at sea. We concluded that continued expression of the half-pounder phenotype is favored by precocious maturation and increased survival relative to that of the ocean contingent phenotype.</span></p>","language":"English","publisher":"Taylor & Francis","doi":"10.1080/00028487.2014.892536","usgsCitation":"Hodge, B.W., Wilzbach, P., and Duffy, W.G., 2014, Potential fitness benefits of the half-pounder life history in Klamath River steelhead: Transactions of the American Fisheries Society, v. 143, no. 4, p. 864-875, https://doi.org/10.1080/00028487.2014.892536.","productDescription":"12 p.","startPage":"864","endPage":"875","ipdsId":"IP-051296","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":346719,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -124,\n              40.5\n            ],\n            [\n              -122,\n              40.5\n            ],\n            [\n              -122,\n              42\n            ],\n            [\n              -124,\n              42\n            ],\n            [\n              -124,\n              40.5\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"143","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2014-06-16","publicationStatus":"PW","scienceBaseUri":"59e71695e4b05fe04cd331e1","contributors":{"authors":[{"text":"Hodge, Brian W.","contributorId":172966,"corporation":false,"usgs":false,"family":"Hodge","given":"Brian","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":712932,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wilzbach, Peggy 0000-0002-3559-3630 paw7002@usgs.gov","orcid":"https://orcid.org/0000-0002-3559-3630","contributorId":3908,"corporation":false,"usgs":true,"family":"Wilzbach","given":"Peggy","email":"paw7002@usgs.gov","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":712866,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Duffy, Walter G. wgd7001@usgs.gov","contributorId":2491,"corporation":false,"usgs":true,"family":"Duffy","given":"Walter","email":"wgd7001@usgs.gov","middleInitial":"G.","affiliations":[],"preferred":false,"id":712933,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70187380,"text":"70187380 - 2014 - A new map of global ecological land units — An ecophysiographic stratification approach","interactions":[],"lastModifiedDate":"2022-09-16T18:13:10.033614","indexId":"70187380","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":4,"text":"Book"},"publicationSubtype":{"id":15,"text":"Monograph"},"title":"A new map of global ecological land units — An ecophysiographic stratification approach","docAbstract":"<p>In response to the need and an intergovernmental commission for a high resolution and data-derived global ecosystem map, land surface elements of global ecological pattern were characterized in an ecophysiographic stratification of the planet. The stratification produced 3,923 terrestrial ecological land units (ELUs) at a base resolution of 250 meters. The ELUs were derived from data on land surface features in a three step approach. The first step involved acquiring or developing four global raster datalayers representing the primary components of ecosystem structure: bioclimate, landform, lithology, and land cover. These datasets generally represent the most accurate, current, globally comprehensive, and finest spatial and thematic resolution data available for each of the four inputs. The second step involved a spatial combination of the four inputs into a single, new integrated raster dataset where every cell represents a combination of values from the bioclimate, landforms, lithology, and land cover datalayers. This foundational global raster datalayer, called ecological facets (EFs), contains 47,650 unique combinations of the four inputs. The third step involved an aggregation of the EFs into the 3,923 ELUs. This subdivision of the Earth’s surface into relatively fine, ecological land areas is designed to be useful for various types of ecosystem research and management applications, including assessments of climate change impacts to ecosystems, economic and non-economic valuation of ecosystem services, and conservation planning.</p>","language":"English","publisher":"Association of American Geographers, U. S. Geological Survey, GEO BON","isbn":"978-0-89291-276-6","usgsCitation":"Sayre, R., Dangermond, J., Frye, C., Vaughan, R., Aniello, P., Breyer, S.P., Cribbs, D., Hopkins, D., Nauman, R., Derrenbacher, W., Wright, D.J., Brown, C., Convis, C., Smith, J.H., Benson, L., VanSistine, D.P., Warner, H., Cress, J.J., Danielson, J.J., Hamann, S.L., Cecere, T., Reddy, A.D., Burton, D., Grosse, A., TRUE, D., Metzger, M., Hartmann, J., Moosdorf, N., Durr, H., Paganini, M., Defourny, P., Arino, O., Maynard, S., Anderson, M., and Comer, P., 2014, A new map of global ecological land units — An ecophysiographic stratification approach, 46 p.","productDescription":"46 p.","ipdsId":"IP-060509","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"links":[{"id":340699,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":406868,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://geobon.org/documents/biodiversity-monitoring/"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59084934e4b0fc4e448ffd8a","contributors":{"authors":[{"text":"Sayre, Roger 0000-0001-6703-7105 rsayre@usgs.gov","orcid":"https://orcid.org/0000-0001-6703-7105","contributorId":191629,"corporation":false,"usgs":true,"family":"Sayre","given":"Roger","email":"rsayre@usgs.gov","affiliations":[{"id":5055,"text":"Land Change Science","active":true,"usgs":true},{"id":505,"text":"Office of the AD Climate and Land-Use Change","active":true,"usgs":true}],"preferred":true,"id":693663,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dangermond, Jack","contributorId":191630,"corporation":false,"usgs":false,"family":"Dangermond","given":"Jack","email":"","affiliations":[],"preferred":false,"id":693664,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Frye, Charlie","contributorId":191631,"corporation":false,"usgs":false,"family":"Frye","given":"Charlie","affiliations":[{"id":18946,"text":"Environmental Systems Research Institute, Inc. (ESRI), Redlands, CA","active":true,"usgs":false}],"preferred":false,"id":693665,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vaughan, Randy","contributorId":191632,"corporation":false,"usgs":false,"family":"Vaughan","given":"Randy","email":"","affiliations":[{"id":18946,"text":"Environmental Systems Research Institute, Inc. (ESRI), Redlands, CA","active":true,"usgs":false}],"preferred":false,"id":693666,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Aniello, Peter","contributorId":191633,"corporation":false,"usgs":false,"family":"Aniello","given":"Peter","affiliations":[{"id":18946,"text":"Environmental Systems Research Institute, Inc. 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,{"id":70192418,"text":"70192418 - 2014 - Major and trace element geochemistry and background concentrations for soils in Connecticut","interactions":[],"lastModifiedDate":"2021-08-02T11:37:54.198201","indexId":"70192418","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5673,"text":"Northeastern Geoscience","active":true,"publicationSubtype":{"id":10}},"title":"Major and trace element geochemistry and background concentrations for soils in Connecticut","docAbstract":"<p>Soil samples were collected throughout Connecticut (CT) to determine the relationship of soil chemistry with the underlying geology and to better understand background concentrations of major and trace elements in soils. Soil samples were collected (1) from the upper 5 cm of surficial soil at 100 sites, (2) from the A horizon at 86 of these sites, and (3) from the deeper horizon, typically the C horizon, at 79 of these sites. The &lt;2-millimeter fraction of each sample was analyzed for 44 elements by methods that yield the total or near-total elemental content. Sample sites were characterized by glacial setting, underlying bedrock geology, and soil type. These spatial data were used with element concentrations in the C-horizon to relate geologic factors to soil chemistry. </p><p>Concentrations of elements in C-horizon soils varied with grain size in surficial glacial materials and with underlying rock types, as determined using nonparametric statistical procedures. Concentrations of most elements in C-horizon soils showed a positive correlation with silt and (or) clay content and were higher in surficial materials mapped as till, thick till, and (or) fines. Element concentrations in C-horizon soils showed significant differences among the underlying geologic provinces and were highest overlying the Grenville Belt and (or) the Grenville Shelf Sequence Provinces in western CT. These rocks consist mainly of carbonates and the relatively high element concentrations in overlying soils likely result from less influence of dilution by quartz compared to other provinces. Element concentrations in C-horizon soils in CT were compared with those in samples from other New England states overlying similar lithologic bedrock types. The upper range of As concentrations in C-horizon soils overlying the New Hampshire-Maine (NH-ME) Sequence in CT was 15 mg/kg, lower than the upper range of 24 mg/kg in C-horizon soils overlying the same sequence in ME. In CT, U concentration means were significantly higher in C-horizon soils overlying Avalonian granites, and U concentrations ranged as high as 14 mg/kg, compared to those in C-horizon soil samples collected from other New England states, which ranged as high as 6.1 mg/kg in a sample in NH overlying the NH-ME Sequence. </p><p>Element concentrations in C-horizon soils in CT were compared with those in samples collected from shallower depths. Concentrations of most major elements were highest in C-horizon soil samples, including Al, Ca, Fe, K, Na, and Ti, but element concentrations showed a relatively similar pattern in A-horizon and surficial soil samples among the underlying geologic provinces. Trace element concentrations, including Ba, W, Ga, Ni, Cs, Rb, Sr, Th, Sc, and U, also were higher in C-horizon soil samples than in overlying soil samples. Concentrations of Mg, and several trace elements, including Mn, P, As, Nb, Sn, Be, Bi, Hg, Se, Sb, La, Co, Cr, Pb, V, Y, Cu, Pb, and Zn were highest in some A-horizon or surficial soils, and indicate possible contributions from anthropogenic sources. Because element concentrations in soils above the C horizon are more likely to be affected by anthropogenic factors, concentration ranges in C-horizon soils and their spatially varying geologic associations should be considered when estimating background concentrations of elements in CT soils. <br></p>","language":"English","publisher":"Northeastern Geoscience","usgsCitation":"Brown, C.J., and Thomas, M., 2014, Major and trace element geochemistry and background concentrations for soils in Connecticut: Northeastern Geoscience, v. 32, p. 1-37.","productDescription":"37 p.","startPage":"1","endPage":"37","ipdsId":"IP-054875","costCenters":[{"id":196,"text":"Connecticut Water Science Center","active":true,"usgs":true}],"links":[{"id":352951,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-71.799242,42.008065],[-71.797922,41.935395],[-71.797649,41.928556],[-71.794161,41.841101],[-71.794161,41.840141],[-71.792786,41.80867],[-71.792767,41.807001],[-71.791062,41.770273],[-71.789678,41.724734],[-71.789672,41.724569],[-71.786994,41.655992],[-71.787637,41.639917],[-71.789356,41.59691],[-71.789359,41.596852],[-71.797683,41.416709],[-71.81839,41.419599],[-71.839649,41.412119],[-71.842563,41.409855],[-71.843472,41.40583],[-71.842131,41.395359],[-71.833443,41.384524],[-71.831613,41.370899],[-71.837738,41.363529],[-71.835951,41.353935],[-71.829595,41.344544],[-71.839013,41.334042],[-71.860513,41.320248],[-71.859566,41.3224],[-71.868235,41.330941],[-71.886302,41.33641],[-71.91671,41.332217],[-71.922092,41.334518],[-71.923282,41.335113],[-71.936284,41.337959],[-71.945652,41.337799],[-71.956747,41.329871],[-71.970955,41.324526],[-71.979447,41.329987],[-71.982194,41.329861],[-71.988153,41.320577],[-72.021898,41.316838],[-72.084487,41.319634],[-72.094443,41.314164],[-72.09982,41.306998],[-72.11182,41.299098],[-72.134221,41.299398],[-72.16158,41.310262],[-72.173922,41.317597],[-72.177622,41.322497],[-72.184122,41.323997],[-72.191022,41.323197],[-72.201422,41.315697],[-72.203022,41.313197],[-72.204022,41.299097],[-72.212924,41.291365],[-72.225276,41.299047],[-72.235531,41.300413],[-72.248161,41.299488],[-72.251895,41.29862],[-72.250515,41.294386],[-72.251323,41.289997],[-72.261487,41.282926],[-72.31776,41.277782],[-72.327595,41.27846],[-72.333894,41.282916],[-72.34146,41.28011],[-72.348643,41.277446],[-72.348068,41.269698],[-72.386629,41.261798],[-72.398688,41.278172],[-72.40593,41.278398],[-72.451925,41.278885],[-72.472539,41.270103],[-72.485693,41.270881],[-72.499534,41.265866],[-72.506634,41.260099],[-72.51866,41.261253],[-72.521312,41.2656],[-72.529416,41.264421],[-72.533247,41.26269],[-72.536746,41.256207],[-72.537776,41.255646],[-72.546833,41.250718],[-72.547235,41.250499],[-72.570655,41.267744],[-72.571076,41.268054],[-72.571136,41.268098],[-72.583336,41.271698],[-72.585181,41.271321],[-72.585934,41.271168],[-72.586674,41.271017],[-72.587926,41.270761],[-72.589818,41.270375],[-72.590967,41.270141],[-72.598036,41.268698],[-72.607863,41.270387],[-72.610236,41.270795],[-72.617237,41.271998],[-72.617521,41.27194],[-72.617983,41.271845],[-72.631363,41.269092],[-72.641001,41.267108],[-72.641538,41.266998],[-72.642811,41.266884],[-72.650697,41.266178],[-72.653838,41.265897],[-72.653931,41.265931],[-72.654715,41.266219],[-72.662203,41.268964],[-72.662838,41.269197],[-72.667176,41.268192],[-72.671673,41.267151],[-72.672339,41.266997],[-72.674319,41.26552],[-72.684939,41.257597],[-72.685414,41.252607],[-72.685539,41.251297],[-72.689446,41.247629],[-72.690237,41.246887],[-72.690439,41.246697],[-72.693441,41.245493],[-72.694744,41.24497],[-72.69547,41.244948],[-72.701806,41.244752],[-72.706236,41.244615],[-72.707212,41.244585],[-72.708658,41.24454],[-72.708963,41.24453],[-72.709193,41.244523],[-72.710595,41.24448],[-72.710821,41.244812],[-72.713674,41.249007],[-72.711208,41.251018],[-72.71246,41.254167],[-72.722439,41.259138],[-72.732813,41.254727],[-72.754444,41.266913],[-72.757477,41.266913],[-72.786142,41.264796],[-72.818737,41.252244],[-72.819372,41.254061],[-72.826883,41.256755],[-72.847767,41.25669],[-72.85021,41.255544],[-72.854055,41.24774],[-72.861344,41.245297],[-72.881445,41.242597],[-72.895445,41.243697],[-72.900803,41.245864],[-72.904345,41.247297],[-72.905245,41.248297],[-72.903045,41.252797],[-72.902808,41.252894],[-72.894745,41.256197],[-72.89473,41.25626],[-72.893845,41.259897],[-72.89637,41.263949],[-72.903129,41.274794],[-72.907962,41.282549],[-72.9082,41.282932],[-72.916827,41.282033],[-72.917037,41.281905],[-72.920062,41.280056],[-72.920658,41.271574],[-72.920714,41.27078],[-72.920846,41.268897],[-72.931887,41.261139],[-72.933472,41.260024],[-72.935646,41.258497],[-72.956984,41.25292],[-72.959633,41.252228],[-72.961345,41.25178],[-72.962047,41.251597],[-72.983751,41.235364],[-72.985095,41.234358],[-72.986247,41.233497],[-72.997948,41.222697],[-73.003639,41.215287],[-73.007548,41.210197],[-73.013465,41.205479],[-73.013988,41.205062],[-73.014948,41.204297],[-73.020149,41.204097],[-73.020167,41.204237],[-73.020195,41.204446],[-73.02021,41.204568],[-73.020254,41.204906],[-73.020449,41.206397],[-73.022549,41.207197],[-73.024783,41.207435],[-73.045602,41.209658],[-73.05065,41.210197],[-73.054947,41.208468],[-73.05935,41.206697],[-73.07761,41.195176],[-73.07945,41.194015],[-73.09122,41.184153],[-73.092,41.1835],[-73.092147,41.183377],[-73.104328,41.17317],[-73.105483,41.172203],[-73.105493,41.172194],[-73.107987,41.168738],[-73.110352,41.159697],[-73.109952,41.156997],[-73.108352,41.153718],[-73.111052,41.150797],[-73.130253,41.146797],[-73.16437,41.158565],[-73.170074,41.160532],[-73.170701,41.164945],[-73.177774,41.166697],[-73.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 \"}}]}","volume":"32","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afeee10e4b0da30c1bfc753","contributors":{"authors":[{"text":"Brown, Craig J. 0000-0002-3858-3964 cjbrown@usgs.gov","orcid":"https://orcid.org/0000-0002-3858-3964","contributorId":198350,"corporation":false,"usgs":true,"family":"Brown","given":"Craig","email":"cjbrown@usgs.gov","middleInitial":"J.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":715762,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thomas, Margaret A.","contributorId":191171,"corporation":false,"usgs":false,"family":"Thomas","given":"Margaret A.","affiliations":[],"preferred":false,"id":715763,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70194120,"text":"70194120 - 2014 - Bacterial pathogen gene abundance and relation to recreational water quality at seven Great Lakes beaches","interactions":[],"lastModifiedDate":"2017-11-16T16:52:57","indexId":"70194120","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Bacterial pathogen gene abundance and relation to recreational water quality at seven Great Lakes beaches","docAbstract":"<p><span>Quantitative assessment of bacterial pathogens, their geographic variability, and distribution in various matrices at Great Lakes beaches are limited. Quantitative PCR (qPCR) was used to test for genes from&nbsp;</span><i>E. coli</i><span><span>&nbsp;</span>O157:H7 (</span><i>eae</i><sub>O157</sub><span>), shiga-toxin producing<span>&nbsp;</span></span><i>E. coli</i><span><span>&nbsp;</span>(</span><i>stx2</i><span>),<span>&nbsp;</span></span><i>Campylobacter jejuni</i><span><span>&nbsp;</span>(</span><i>mapA</i><span>),<span>&nbsp;</span></span><i>Shigella</i><span><span>&nbsp;</span>spp. (</span><i>ipaH</i><span>), and a<span>&nbsp;</span></span><i>Salmonella enterica</i><span>-specific (</span><i>SE</i><span>) DNA sequence at seven Great Lakes beaches, in algae, water, and sediment. Overall, detection frequencies were<span>&nbsp;</span></span><i>mapA</i><span>&gt;</span><i>stx2</i><span>&gt;</span><i>ipaH</i><span>&gt;</span><i>SE</i><span>&gt;</span><i>eae</i><sub><i>O157</i></sub><span>. Results were highly variable among beaches and matrices; some correlations with environmental conditions were observed for<span>&nbsp;</span></span><i>mapA</i><span>,<span>&nbsp;</span></span><i>stx2</i><span>, and<span>&nbsp;</span></span><i>ipaH</i><span><span>&nbsp;</span>detections. Beach seasonal mean<span>&nbsp;</span></span><i>mapA</i><span><span>&nbsp;</span>abundance in water was correlated with beach seasonal mean log</span><sub>10</sub><i>E. coli</i><span><span>&nbsp;</span>concentration. At one beach,<span>&nbsp;</span></span><i>stx2</i><span><span>&nbsp;</span>gene abundance was positively correlated with concurrent daily<span>&nbsp;</span></span><i>E. coli</i><span><span>&nbsp;</span>concentrations. Concentration distributions for<span>&nbsp;</span></span><i>stx2</i><span>,<span>&nbsp;</span></span><i>ipaH</i><span>, and<span>&nbsp;</span></span><i>mapA</i><span><span>&nbsp;</span>within algae, sediment, and water were statistically different (Non-Detect and Data Analysis in R). Assuming 10, 50, or 100% of gene copies represented viable and presumably infective cells, a quantitative microbial risk assessment tool developed by Michigan State University indicated a moderate probability of illness for<span>&nbsp;</span></span><i>Campylobacter jejuni</i><span><span>&nbsp;</span>at the study beaches, especially where recreational water quality criteria were exceeded. Pathogen gene quantification may be useful for beach water quality management.</span></p>","language":"English","publisher":"ACS Publications","doi":"10.1021/es5038657","usgsCitation":"Oster, R.J., Wijesinghe, R.U., Fogarty, L.R., Haack, S.K., Fogarty, L.R., Tucker, T.R., and Riley, S., 2014, Bacterial pathogen gene abundance and relation to recreational water quality at seven Great Lakes beaches: Environmental Science & Technology, v. 48, no. 24, p. 14148-14157, https://doi.org/10.1021/es5038657.","productDescription":"10 p.","startPage":"14148","endPage":"14157","ipdsId":"IP-052094","costCenters":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"links":[{"id":349032,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Great Lakes","volume":"48","issue":"24","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2014-11-25","publicationStatus":"PW","scienceBaseUri":"5a6100c8e4b06e28e9c25411","contributors":{"authors":[{"text":"Oster, Ryan J. roster@usgs.gov","contributorId":5483,"corporation":false,"usgs":true,"family":"Oster","given":"Ryan","email":"roster@usgs.gov","middleInitial":"J.","affiliations":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"preferred":true,"id":722157,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wijesinghe, Rasanthi U. rwijesinghe@usgs.gov","contributorId":5484,"corporation":false,"usgs":true,"family":"Wijesinghe","given":"Rasanthi","email":"rwijesinghe@usgs.gov","middleInitial":"U.","affiliations":[],"preferred":true,"id":722158,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fogarty, Lisa Reynolds 0000-0003-0329-3251 lrfogart@usgs.gov","orcid":"https://orcid.org/0000-0003-0329-3251","contributorId":150958,"corporation":false,"usgs":true,"family":"Fogarty","given":"Lisa","email":"lrfogart@usgs.gov","middleInitial":"Reynolds","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":722159,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Haack, Sheridan K. skhaack@usgs.gov","contributorId":1982,"corporation":false,"usgs":true,"family":"Haack","given":"Sheridan","email":"skhaack@usgs.gov","middleInitial":"K.","affiliations":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"preferred":true,"id":722160,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fogarty, Lisa R. 0000-0003-0329-3251 lrfogart@usgs.gov","orcid":"https://orcid.org/0000-0003-0329-3251","contributorId":2053,"corporation":false,"usgs":true,"family":"Fogarty","given":"Lisa","email":"lrfogart@usgs.gov","middleInitial":"R.","affiliations":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"preferred":false,"id":722571,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tucker, Taaja R. 0000-0003-1534-4677 trtucker@usgs.gov","orcid":"https://orcid.org/0000-0003-1534-4677","contributorId":5172,"corporation":false,"usgs":true,"family":"Tucker","given":"Taaja","email":"trtucker@usgs.gov","middleInitial":"R.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":722161,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Riley, Stephen 0000-0002-8968-8416 sriley@usgs.gov","orcid":"https://orcid.org/0000-0002-8968-8416","contributorId":169479,"corporation":false,"usgs":true,"family":"Riley","given":"Stephen","email":"sriley@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":722162,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70189913,"text":"70189913 - 2014 - Geophysical and hydrologic studies of lake seepage variability","interactions":[],"lastModifiedDate":"2017-08-01T08:32:18","indexId":"70189913","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Geophysical and hydrologic studies of lake seepage variability","docAbstract":"<p>Variations in lake seepage were studied along a 130 m shoreline of Mirror Lake NH. Seepage was downward from the lake to groundwater; rates measured from 28 seepage meters varied from 0 to −282 cm/d. Causes of this variation were investigated using electrical resistivity surveys and lakebed sediment characterization. Two-dimensional (2D) resistivity surveys showed a transition in lakebed sediments from outwash to till that correlated with high- and low-seepage zones, respectively. However, the 2D survey was not able to predict smaller scale variations within these facies. In the outwash, fast seepage was associated with permeability variations in a thin (2 cm) layer of sediments at the top of the lakebed. In the till, where seepage was slower than that in the outwash, a three-dimensional resistivity survey mapped a point of high seepage associated with heterogeneity (lower resistivity and likely higher permeability). Points of focused flow across the sediment–water interface are difficult to detect and can transmit a large percentage of total exchange. Using a series of electrical resistivity geophysical methods in combination with hydrologic data to locate heterogeneities that affect seepage rates can help guide seepage meter placement. Improving our understanding of the causes and types of heterogeneity in lake seepage will provide better data for lake budgets and prediction of mass transfer of solutes or contaminants between lakes and groundwater.</p>","language":"English","publisher":"Wiley","doi":"10.1111/gwat.12309","usgsCitation":"Toran, L., Nyquist, J.E., Rosenberry, D.O., Gagliano, M.P., Mitchell, N., and Mikochik, J., 2014, Geophysical and hydrologic studies of lake seepage variability: Groundwater, v. 53, no. 6, p. 841-850, https://doi.org/10.1111/gwat.12309.","productDescription":"10 p.","startPage":"841","endPage":"850","ipdsId":"IP-057392","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":344490,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"53","issue":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2014-12-31","publicationStatus":"PW","scienceBaseUri":"59819316e4b0e2f5d463b7a5","contributors":{"authors":[{"text":"Toran, Laura","contributorId":81622,"corporation":false,"usgs":false,"family":"Toran","given":"Laura","email":"","affiliations":[{"id":34225,"text":"Temple University, Philadelphia, Pa.","active":true,"usgs":false}],"preferred":false,"id":706753,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nyquist, Jonathan E.","contributorId":101801,"corporation":false,"usgs":false,"family":"Nyquist","given":"Jonathan","email":"","middleInitial":"E.","affiliations":[{"id":34225,"text":"Temple University, Philadelphia, Pa.","active":true,"usgs":false}],"preferred":false,"id":706754,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rosenberry, Donald O. 0000-0003-0681-5641 rosenber@usgs.gov","orcid":"https://orcid.org/0000-0003-0681-5641","contributorId":1312,"corporation":false,"usgs":true,"family":"Rosenberry","given":"Donald","email":"rosenber@usgs.gov","middleInitial":"O.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":706752,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gagliano, Michael P.","contributorId":176822,"corporation":false,"usgs":false,"family":"Gagliano","given":"Michael","email":"","middleInitial":"P.","affiliations":[{"id":34225,"text":"Temple University, Philadelphia, Pa.","active":true,"usgs":false}],"preferred":false,"id":706755,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mitchell, Natasha","contributorId":195321,"corporation":false,"usgs":false,"family":"Mitchell","given":"Natasha","email":"","affiliations":[{"id":34225,"text":"Temple University, Philadelphia, Pa.","active":true,"usgs":false}],"preferred":false,"id":706756,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mikochik, James","contributorId":195322,"corporation":false,"usgs":false,"family":"Mikochik","given":"James","email":"","affiliations":[{"id":34225,"text":"Temple University, Philadelphia, Pa.","active":true,"usgs":false}],"preferred":false,"id":706757,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70189202,"text":"70189202 - 2014 - Distributed Evaluation of Local Sensitivity Analysis (DELSA), with application to hydrologic models","interactions":[],"lastModifiedDate":"2017-07-05T16:57:14","indexId":"70189202","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Distributed Evaluation of Local Sensitivity Analysis (DELSA), with application to hydrologic models","docAbstract":"<p><span>This paper presents a hybrid local-global sensitivity analysis method termed the Distributed Evaluation of Local Sensitivity Analysis (DELSA), which is used here to identify important and unimportant parameters and evaluate how model parameter importance changes as parameter values change. DELSA uses derivative-based “local” methods to obtain the distribution of parameter sensitivity across the parameter space, which promotes consideration of sensitivity analysis results in the context of simulated dynamics. This work presents DELSA, discusses how it relates to existing methods, and uses two hydrologic test cases to compare its performance with the popular global, variance-based Sobol' method. The first test case is a simple nonlinear reservoir model with two parameters. The second test case involves five alternative “bucket-style” hydrologic models with up to 14 parameters applied to a medium-sized catchment (200 km</span><sup>2</sup><span>) in the Belgian Ardennes. Results show that in both examples, Sobol' and DELSA identify similar important and unimportant parameters, with DELSA enabling more detailed insight at much lower computational cost. For example, in the real-world problem the time delay in runoff is the most important parameter in all models, but DELSA shows that for about 20% of parameter sets it is not important at all and alternative mechanisms and parameters dominate. Moreover, the time delay was identified as important in regions producing poor model fits, whereas other parameters were identified as more important in regions of the parameter space producing better model fits. The ability to understand how parameter importance varies through parameter space is critical to inform decisions about, for example, additional data collection and model development. The ability to perform such analyses with modest computational requirements provides exciting opportunities to evaluate complicated models as well as many alternative models.</span></p>","language":"English","publisher":"AGU","doi":"10.1002/2013WR014063","usgsCitation":"Rakovec, O., Hill, M.C., Clark, M., Weerts, A.H., Teuling, A.J., and Uijlenhoet, R., 2014, Distributed Evaluation of Local Sensitivity Analysis (DELSA), with application to hydrologic models: Water Resources Research, v. 50, no. 1, p. 409-426, https://doi.org/10.1002/2013WR014063.","productDescription":"18 p.","startPage":"409","endPage":"426","ipdsId":"IP-053395","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":487085,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/1808/19328","text":"External Repository"},{"id":343373,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"50","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2014-01-17","publicationStatus":"PW","scienceBaseUri":"595dfab7e4b0d1f9f056a7aa","contributors":{"authors":[{"text":"Rakovec, O.","contributorId":194218,"corporation":false,"usgs":false,"family":"Rakovec","given":"O.","email":"","affiliations":[],"preferred":false,"id":703468,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hill, Mary C. mchill@usgs.gov","contributorId":974,"corporation":false,"usgs":true,"family":"Hill","given":"Mary","email":"mchill@usgs.gov","middleInitial":"C.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":703467,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clark, M.P.","contributorId":194219,"corporation":false,"usgs":false,"family":"Clark","given":"M.P.","email":"","affiliations":[],"preferred":false,"id":703469,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Weerts, A. H.","contributorId":194220,"corporation":false,"usgs":false,"family":"Weerts","given":"A.","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":703470,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Teuling, A. J.","contributorId":138517,"corporation":false,"usgs":false,"family":"Teuling","given":"A.","email":"","middleInitial":"J.","affiliations":[{"id":6920,"text":"Wageningen University, The Netherlands","active":true,"usgs":false}],"preferred":false,"id":703471,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Uijlenhoet, R.","contributorId":138518,"corporation":false,"usgs":false,"family":"Uijlenhoet","given":"R.","email":"","affiliations":[{"id":6920,"text":"Wageningen University, The Netherlands","active":true,"usgs":false}],"preferred":false,"id":703472,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70189207,"text":"70189207 - 2014 - Evaluation of statistically downscaled GCM output as input for hydrological and stream temperature simulation in the Apalachicola–Chattahoochee–Flint River Basin (1961–99)","interactions":[],"lastModifiedDate":"2017-07-05T16:20:39","indexId":"70189207","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1421,"text":"Earth Interactions","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of statistically downscaled GCM output as input for hydrological and stream temperature simulation in the Apalachicola–Chattahoochee–Flint River Basin (1961–99)","docAbstract":"<p>The accuracy of statistically downscaled general circulation model (GCM) simulations of daily surface climate for historical conditions (1961–99) and the implications when they are used to drive hydrologic and stream temperature models were assessed for the Apalachicola–Chattahoochee–Flint River basin (ACFB). The ACFB is a 50 000 km<sup>2</sup><span>&nbsp;</span>basin located in the southeastern United States. Three GCMs were statistically downscaled, using an asynchronous regional regression model (ARRM), to ⅛° grids of daily precipitation and minimum and maximum air temperature. These ARRM-based climate datasets were used as input to the Precipitation-Runoff Modeling System (PRMS), a deterministic, distributed-parameter, physical-process watershed model used to simulate and evaluate the effects of various combinations of climate and land use on watershed response. The ACFB was divided into 258 hydrologic response units (HRUs) in which the components of flow (groundwater, subsurface, and surface) are computed in response to climate, land surface, and subsurface characteristics of the basin. Daily simulations of flow components from PRMS were used with the climate to simulate in-stream water temperatures using the Stream Network Temperature (SNTemp) model, a mechanistic, one-dimensional heat transport model for branched stream networks.</p><p>The climate, hydrology, and stream temperature for historical conditions were evaluated by comparing model outputs produced from historical climate forcings developed from gridded station data (GSD) versus those produced from the three statistically downscaled GCMs using the ARRM methodology. The PRMS and SNTemp models were forced with the GSD and the outputs produced were treated as “truth.” This allowed for a spatial comparison by HRU of the GSD-based output with ARRM-based output. Distributional similarities between GSD- and ARRM-based model outputs were compared using the two-sample Kolmogorov–Smirnov (KS) test in combination with descriptive metrics such as the mean and variance and an evaluation of rare and sustained events. In general, precipitation and streamflow quantities were negatively biased in the downscaled GCM outputs, and results indicate that the downscaled GCM simulations consistently underestimate the largest precipitation events relative to the GSD. The KS test results indicate that ARRM-based air temperatures are similar to GSD at the daily time step for the majority of the ACFB, with perhaps subweekly averaging for stream temperature. Depending on GCM and spatial location, ARRM-based precipitation and streamflow requires averaging of up to 30 days to become similar to the GSD-based output.</p><p>Evaluation of the model skill for historical conditions suggests some guidelines for use of future projections; while it seems correct to place greater confidence in evaluation metrics which perform well historically, this does not necessarily mean those metrics will accurately reflect model outputs for future climatic conditions. Results from this study indicate no “best” overall model, but the breadth of analysis can be used to give the product users an indication of the applicability of the results to address their particular problem. Since results for historical conditions indicate that model outputs can have significant biases associated with them, the range in future projections examined in terms of change relative to historical conditions for each individual GCM may be more appropriate.</p>","language":"English","publisher":"American Meteorological Society","doi":"10.1175/2013EI000554.1","usgsCitation":"Hay, L.E., LaFontaine, J.H., and Markstrom, S.L., 2014, Evaluation of statistically downscaled GCM output as input for hydrological and stream temperature simulation in the Apalachicola–Chattahoochee–Flint River Basin (1961–99): Earth Interactions, v. 18, p. 1-32, https://doi.org/10.1175/2013EI000554.1.","productDescription":"32 p.","startPage":"1","endPage":"32","ipdsId":"IP-052922","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":473306,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1175/2013ei000554.1","text":"Publisher Index Page"},{"id":343366,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida, Georgia","otherGeospatial":"Apalachicola–Chattahoochee–Flint River Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -85.60546875,\n              29.6594160549124\n            ],\n            [\n              -83.7158203125,\n              29.6594160549124\n            ],\n            [\n              -83.7158203125,\n              34.470335121217474\n            ],\n            [\n              -85.60546875,\n              34.470335121217474\n            ],\n            [\n              -85.60546875,\n              29.6594160549124\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"18","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2014-04-14","publicationStatus":"PW","scienceBaseUri":"595dfab7e4b0d1f9f056a7a6","contributors":{"authors":[{"text":"Hay, Lauren E. 0000-0003-3763-4595 lhay@usgs.gov","orcid":"https://orcid.org/0000-0003-3763-4595","contributorId":1287,"corporation":false,"usgs":true,"family":"Hay","given":"Lauren","email":"lhay@usgs.gov","middleInitial":"E.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":703494,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"LaFontaine, Jacob H. 0000-0003-4923-2630 jlafonta@usgs.gov","orcid":"https://orcid.org/0000-0003-4923-2630","contributorId":2258,"corporation":false,"usgs":true,"family":"LaFontaine","given":"Jacob","email":"jlafonta@usgs.gov","middleInitial":"H.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":316,"text":"Georgia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":703495,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Markstrom, Steven L. 0000-0001-7630-9547 markstro@usgs.gov","orcid":"https://orcid.org/0000-0001-7630-9547","contributorId":146553,"corporation":false,"usgs":true,"family":"Markstrom","given":"Steven","email":"markstro@usgs.gov","middleInitial":"L.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":703496,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70193118,"text":"70193118 - 2014 - Groundwater conditions in Utah, spring of 2014","interactions":[],"lastModifiedDate":"2019-05-22T09:32:52","indexId":"70193118","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":110,"text":"Cooperative Investigations Report","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"55","title":"Groundwater conditions in Utah, spring of 2014","docAbstract":"<p>This is the fifty-first in a series of annual reports that describe groundwater conditions in Utah. Reports in this series, published cooperatively by the U.S. Geological Survey and the Utah Department of Natural Resources, Division of Water Rights, and the Utah Department of Environmental Quality, Division of Water Quality, provide data to enable interested parties to maintain awareness of changing groundwater conditions. </p><p>This report, like the others in the series, contains information on well construction, groundwater withdrawal from wells, water-level changes, precipitation, streamflow, and chemical quality of water. Information on well construction included in this report refers only to wells constructed for new appropriations of groundwater. Supplementary data are included in reports of this series only for those years or areas that are important to a discussion of changing groundwater conditions and for which applicable data are available.</p><p>This report includes individual discussions of selected significant areas of groundwater development in the State for calendar year 2013. Most of the reported data were collected by the U.S. Geological Survey in cooperation with the Utah Department of Natural Resources, Division of Water Rights, and the Utah Department of Environmental Quality, Division of Water Quality. This report is also available online at http://www.waterrights.utah.gov/techinfo/ and http://ut.water. usgs.gov/publications/GW2014.pdf. Groundwater conditions in Utah for calendar year 2012 are reported in Burden and others (2013) and are available online at http://ut.water.usgs. gov/publications/GW2013.pdf</p>","language":"English","publisher":"Utah Department of Natural Resources","usgsCitation":"Burden, C.B., 2014, Groundwater conditions in Utah, spring of 2014: Cooperative Investigations Report 55, x, 118 p.","productDescription":"x, 118 p.","numberOfPages":"132","ipdsId":"IP-056622","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":350085,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":364083,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://waterrights.utah.gov/techinfo/wwwpub/GW2014.pdf"}],"country":"United 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 \"}}]}","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a6100c9e4b06e28e9c2541d","contributors":{"authors":[{"text":"Burden, Carole B. cburden@usgs.gov","contributorId":852,"corporation":false,"usgs":true,"family":"Burden","given":"Carole","email":"cburden@usgs.gov","middleInitial":"B.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":718032,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70189782,"text":"70189782 - 2014 - CyberShake-derived ground-motion prediction models for the Los Angeles region with application to earthquake early warning","interactions":[],"lastModifiedDate":"2017-07-26T11:02:38","indexId":"70189782","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1803,"text":"Geophysical Journal International","active":true,"publicationSubtype":{"id":10}},"title":"CyberShake-derived ground-motion prediction models for the Los Angeles region with application to earthquake early warning","docAbstract":"<p><span>Real-time applications such as earthquake early warning (EEW) typically use empirical ground-motion prediction equations (GMPEs) along with event magnitude and source-to-site distances to estimate expected shaking levels. In this simplified approach, effects due to finite-fault geometry, directivity and site and basin response are often generalized, which may lead to a significant under- or overestimation of shaking from large earthquakes (</span><i>M</i><span>&nbsp;&gt;&nbsp;6.5) in some locations. For enhanced site-specific ground-motion predictions considering 3-D wave-propagation effects, we develop support vector regression (SVR) models from the SCEC CyberShake low-frequency (&lt;0.5 Hz) and broad-band (0–10 Hz) data sets. CyberShake encompasses 3-D wave-propagation simulations of&nbsp;&gt;415&nbsp;000 finite-fault rupture scenarios (6.5 ≤<span>&nbsp;</span></span><i>M</i><span><span>&nbsp;</span>≤ 8.5) for southern California defined in UCERF 2.0. We use CyberShake to demonstrate the application of synthetic waveform data to EEW as a ‘proof of concept’, being aware that these simulations are not yet fully validated and might not appropriately sample the range of rupture uncertainty. Our regression models predict the maximum and the temporal evolution of instrumental intensity (MMI) at 71 selected test sites using only the hypocentre, magnitude and rupture ratio, which characterizes uni- and bilateral rupture propagation. Our regression approach is completely data-driven (where here the CyberShake simulations are considered data) and does not enforce pre-defined functional forms or dependencies among input parameters. The models were established from a subset (∼20&nbsp;per cent) of CyberShake simulations, but can explain MMI values of all&nbsp;&gt;400 k rupture scenarios with a standard deviation of about 0.4 intensity units. We apply our models to determine threshold magnitudes (and warning times) for various active faults in southern California that earthquakes need to exceed to cause at least ‘moderate’, ‘strong’ or ‘very strong’ shaking in the Los Angeles (LA) basin. These thresholds are used to construct a simple and robust EEW algorithm: to declare a warning, the algorithm only needs to locate the earthquake and to verify that the corresponding magnitude threshold is exceeded. The models predict that a relatively moderate<span>&nbsp;</span></span><i>M</i><span>6.5–7 earthquake along the Palos Verdes, Newport-Inglewood/Rose Canyon, Elsinore or San Jacinto faults with a rupture propagating towards LA could cause ‘very strong’ to ‘severe’ shaking in the LA basin; however, warning times for these events could exceed 30 s.</span></p>","language":"English","publisher":"Oxford Academic","doi":"10.1093/gji/ggu198","usgsCitation":"Bose, M., Graves, R., Gill, D., Callaghan, S., and Maechling, P.J., 2014, CyberShake-derived ground-motion prediction models for the Los Angeles region with application to earthquake early warning: Geophysical Journal International, v. 198, no. 3, p. 1438-1457, https://doi.org/10.1093/gji/ggu198.","productDescription":"20 p.","startPage":"1438","endPage":"1457","ipdsId":"IP-054646","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":473293,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1093/gji/ggu198","text":"External Repository"},{"id":344321,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Los Angeles","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -119,\n              33\n            ],\n            [\n              -117,\n              33\n            ],\n            [\n              -117,\n              35\n            ],\n            [\n              -119,\n              35\n            ],\n            [\n              -119,\n              33\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"198","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2014-07-03","publicationStatus":"PW","scienceBaseUri":"5979aa58e4b0ec1a488b8c3f","contributors":{"authors":[{"text":"Bose, Maren","contributorId":195135,"corporation":false,"usgs":false,"family":"Bose","given":"Maren","affiliations":[],"preferred":false,"id":706331,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Graves, Robert 0000-0001-9758-453X rwgraves@usgs.gov","orcid":"https://orcid.org/0000-0001-9758-453X","contributorId":140738,"corporation":false,"usgs":true,"family":"Graves","given":"Robert","email":"rwgraves@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":706330,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gill, David","contributorId":195159,"corporation":false,"usgs":false,"family":"Gill","given":"David","email":"","affiliations":[],"preferred":false,"id":706332,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Callaghan, Scott","contributorId":195136,"corporation":false,"usgs":false,"family":"Callaghan","given":"Scott","email":"","affiliations":[],"preferred":false,"id":706333,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Maechling, Phillip J.","contributorId":117072,"corporation":false,"usgs":false,"family":"Maechling","given":"Phillip","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":706334,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70189669,"text":"70189669 - 2014 - Transcriptomic effects-based monitoring for endocrine active chemicals: Assessing relative contribution of treated wastewater to downstream pollution","interactions":[],"lastModifiedDate":"2018-09-14T16:02:33","indexId":"70189669","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Transcriptomic effects-based monitoring for endocrine active chemicals: Assessing relative contribution of treated wastewater to downstream pollution","docAbstract":"<p><span>The present study investigated whether a combination of targeted analytical chemistry information with unsupervised, data-rich biological methodology (i.e., transcriptomics) could be utilized to evaluate relative contributions of wastewater treatment plant (WWTP) effluents to biological effects. The effects of WWTP effluents on fish exposed to ambient, receiving waters were studied at three locations with distinct WWTP and watershed characteristics. At each location, 4 d exposures of male fathead minnows to the WWTP effluent and upstream and downstream ambient waters were conducted. Transcriptomic analyses were performed on livers using 15 000 feature microarrays, followed by a canonical pathway and gene set enrichment analyses. Enrichment of gene sets indicative of teleost brain–pituitary–gonadal–hepatic (BPGH) axis function indicated that WWTPs serve as an important source of endocrine active chemicals (EACs) that affect the BPGH axis (e.g., cholesterol and steroid metabolism were altered). The results indicated that transcriptomics may even pinpoint pertinent adverse outcomes (i.e., liver vacuolization) and groups of chemicals that preselected chemical analytes may miss. Transcriptomic Effects-Based monitoring was capable of distinguishing sites, and it reflected chemical pollution gradients, thus holding promise for assessment of relative contributions of point sources to pollution and the efficacy of pollution remediation.</span></p>","language":"English","publisher":"ACS","doi":"10.1021/es404027n","usgsCitation":"Martinovic-Weigelt, D., Mehinto, A.C., Ankley, G., Denslow, N., Barber, L.B., Lee, K., King, R.J., Schoenfuss, H.L., Schroeder, A.L., and Villeneuve, D.L., 2014, Transcriptomic effects-based monitoring for endocrine active chemicals: Assessing relative contribution of treated wastewater to downstream pollution: Environmental Science & Technology, v. 48, no. 4, p. 2385-2394, https://doi.org/10.1021/es404027n.","productDescription":"10 p.","startPage":"2385","endPage":"2394","ipdsId":"IP-053126","costCenters":[{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":344075,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"48","issue":"4","noUsgsAuthors":false,"publicationDate":"2014-01-10","publicationStatus":"PW","scienceBaseUri":"59706fbce4b0d1f9f065a911","contributors":{"authors":[{"text":"Martinovic-Weigelt, Dalma","contributorId":173655,"corporation":false,"usgs":false,"family":"Martinovic-Weigelt","given":"Dalma","affiliations":[{"id":6748,"text":"University of St. Thomas","active":true,"usgs":false}],"preferred":false,"id":705708,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mehinto, Alvine C.","contributorId":104387,"corporation":false,"usgs":true,"family":"Mehinto","given":"Alvine","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":705709,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ankley, Gerald T.","contributorId":177970,"corporation":false,"usgs":false,"family":"Ankley","given":"Gerald T.","affiliations":[{"id":13485,"text":"U.S. Environmental Protection Agency, Duluth, MN","active":true,"usgs":false}],"preferred":false,"id":705710,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Denslow, Nancy D.","contributorId":72831,"corporation":false,"usgs":true,"family":"Denslow","given":"Nancy D.","affiliations":[],"preferred":false,"id":705711,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barber, Larry B. 0000-0002-0561-0831 lbbarber@usgs.gov","orcid":"https://orcid.org/0000-0002-0561-0831","contributorId":921,"corporation":false,"usgs":true,"family":"Barber","given":"Larry","email":"lbbarber@usgs.gov","middleInitial":"B.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":705712,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lee, Kathy 0000-0002-7683-1367 klee@usgs.gov","orcid":"https://orcid.org/0000-0002-7683-1367","contributorId":2538,"corporation":false,"usgs":true,"family":"Lee","given":"Kathy","email":"klee@usgs.gov","affiliations":[{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":705713,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"King, Ryan J.","contributorId":194914,"corporation":false,"usgs":false,"family":"King","given":"Ryan","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":705714,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Schoenfuss, Heiko L.","contributorId":76409,"corporation":false,"usgs":false,"family":"Schoenfuss","given":"Heiko","email":"","middleInitial":"L.","affiliations":[{"id":13317,"text":"Saint Cloud State University","active":true,"usgs":false}],"preferred":false,"id":705715,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Schroeder, Anthony L.","contributorId":173596,"corporation":false,"usgs":false,"family":"Schroeder","given":"Anthony","email":"","middleInitial":"L.","affiliations":[{"id":12503,"text":"University of Minnesota - Saint Paul","active":true,"usgs":false},{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":705716,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Villeneuve, Daniel L.","contributorId":32091,"corporation":false,"usgs":false,"family":"Villeneuve","given":"Daniel","email":"","middleInitial":"L.","affiliations":[{"id":13485,"text":"U.S. Environmental Protection Agency, Duluth, MN","active":true,"usgs":false}],"preferred":false,"id":705717,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70187708,"text":"70187708 - 2014 - Monitoring conterminous United States (CONUS) land cover change with Web-Enabled Landsat Data (WELD)","interactions":[],"lastModifiedDate":"2017-05-31T16:12:20","indexId":"70187708","displayToPublicDate":"2014-01-01T00:00:00","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":"Monitoring conterminous United States (CONUS) land cover change with Web-Enabled Landsat Data (WELD)","docAbstract":"<p>Forest cover loss and bare ground gain from 2006 to 2010 for the conterminous United States (CONUS) were quantified at a 30 m spatial resolution using Web-Enabled Landsat Data available from the USGS Center for Earth Resources Observation and Science (EROS) (http://landsat.usgs.gov/WELD.php). The approach related multi-temporal WELD metrics and expert-derived training data for forest cover loss and bare ground gain through a decision tree classification algorithm. Forest cover loss was reported at state and ecoregional scales, and the identification of core forests' absent of change was made and verified using LiDAR data from the GLAS (Geoscience Laser Altimetry System) instrument. Bare ground gain correlated with population change for large metropolitan statistical areas (MSAs) outside of desert or semi-desert environments. GoogleEarth™ time-series images were used to validate the products. Mapped forest cover loss totaled 53,084 km2 and was found to be depicted conservatively, with a user's accuracy of 78% and a producer's accuracy of 68%. Excluding errors of adjacency, user's and producer's accuracies rose to 93% and 89%, respectively. Mapped bare ground gain equaled 5974 km2 and nearly matched the estimated area from the reference (GoogleEarth™) classification; however, user's (42%) and producer's (49%) accuracies were much less than those of the forest cover loss product. Excluding errors of adjacency, user's and producer's accuracies rose to 62% and 75%, respectively. Compared to recent 2001–2006 USGS National Land Cover Database validation data for forest loss (82% and 30% for respective user's and producer's accuracies) and urban gain (72% and 18% for respective user's and producer's accuracies), results using a single CONUS-scale model with WELD data are promising and point to the potential for national-scale operational mapping of key land cover transitions. However, validation results highlighted limitations, some of which can be addressed by improving training data, creating a more robust image feature space, adding contemporaneous Landsat 5 data to the inputs, and modifying definition sets to account for differences in temporal and spatial observational scales. The presented land cover extent and change data are available via the official WELD website (ftp://weldftp.cr.usgs.gov/CONUS_5Y_LandCover/ftp://weldftp.cr.usgs.gov/CONUS_5Y_LandCover/).</p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2013.08.014","usgsCitation":"Hansen, M., Egorov, A., Potapov, P., Stehman, S., Tyukavina, A., Turubanova, S., Roy, D.P., Goetz, S., Loveland, T., Ju, J., Kommareddy, A., Kovalskyy, V., Forsyth, C., and Bents, T., 2014, Monitoring conterminous United States (CONUS) land cover change with Web-Enabled Landsat Data (WELD): Remote Sensing of Environment, v. 140, p. 466-484, https://doi.org/10.1016/j.rse.2013.08.014.","productDescription":"19 p.","startPage":"466","endPage":"484","ipdsId":"IP-046262","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":341346,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"140","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"591c0fcce4b0a7fdb43ddf00","contributors":{"authors":[{"text":"Hansen, M.C.","contributorId":69690,"corporation":false,"usgs":false,"family":"Hansen","given":"M.C.","email":"","affiliations":[{"id":33433,"text":"University of Maryland, College Park","active":true,"usgs":false}],"preferred":false,"id":695302,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Egorov, Alexey","contributorId":81719,"corporation":false,"usgs":false,"family":"Egorov","given":"Alexey","email":"","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":695303,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Potapov, P.V.","contributorId":19677,"corporation":false,"usgs":false,"family":"Potapov","given":"P.V.","email":"","affiliations":[{"id":33433,"text":"University of Maryland, College Park","active":true,"usgs":false}],"preferred":false,"id":695304,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stehman, S.V.","contributorId":91974,"corporation":false,"usgs":false,"family":"Stehman","given":"S.V.","email":"","affiliations":[{"id":27852,"text":"State University of New York, Syracuse","active":true,"usgs":false}],"preferred":false,"id":695306,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tyukavina, A.","contributorId":19872,"corporation":false,"usgs":false,"family":"Tyukavina","given":"A.","email":"","affiliations":[{"id":33433,"text":"University of Maryland, College Park","active":true,"usgs":false}],"preferred":false,"id":695307,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Turubanova, S.A.","contributorId":108388,"corporation":false,"usgs":false,"family":"Turubanova","given":"S.A.","email":"","affiliations":[{"id":33433,"text":"University of Maryland, College Park","active":true,"usgs":false}],"preferred":false,"id":695308,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Roy, David P.","contributorId":54761,"corporation":false,"usgs":false,"family":"Roy","given":"David","email":"","middleInitial":"P.","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false},{"id":33433,"text":"University of Maryland, College Park","active":true,"usgs":false},{"id":26958,"text":"South Dakota State University, Brookings, SD","active":true,"usgs":false}],"preferred":false,"id":695309,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Goetz, S.J.","contributorId":55186,"corporation":false,"usgs":false,"family":"Goetz","given":"S.J.","email":"","affiliations":[{"id":25456,"text":"Woods Hole Research Center, Falmouth, MA, United States","active":true,"usgs":false}],"preferred":false,"id":695310,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Loveland, Thomas R. 0000-0003-3114-6646","orcid":"https://orcid.org/0000-0003-3114-6646","contributorId":106125,"corporation":false,"usgs":true,"family":"Loveland","given":"Thomas R.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":false,"id":695311,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Ju, J.","contributorId":85801,"corporation":false,"usgs":false,"family":"Ju","given":"J.","email":"","affiliations":[{"id":12721,"text":"NASA GSFC SSAI","active":true,"usgs":false}],"preferred":false,"id":695312,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Kommareddy, A.","contributorId":105638,"corporation":false,"usgs":false,"family":"Kommareddy","given":"A.","email":"","affiliations":[{"id":26958,"text":"South Dakota State University, Brookings, SD","active":true,"usgs":false}],"preferred":false,"id":695317,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Kovalskyy, Valeriy","contributorId":192062,"corporation":false,"usgs":false,"family":"Kovalskyy","given":"Valeriy","email":"","affiliations":[{"id":26958,"text":"South Dakota State University, Brookings, SD","active":true,"usgs":false}],"preferred":false,"id":695318,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Forsyth, C.","contributorId":192034,"corporation":false,"usgs":false,"family":"Forsyth","given":"C.","email":"","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":695319,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Bents, T.","contributorId":139577,"corporation":false,"usgs":false,"family":"Bents","given":"T.","email":"","affiliations":[{"id":33302,"text":"University of Kansas, Lawrence","active":true,"usgs":false}],"preferred":false,"id":695320,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}}
,{"id":70193117,"text":"70193117 - 2014 - Advantages of active love wave techniques in geophysical characterizations of seismographic station - Case studies in California and the central and eastern United States","interactions":[],"lastModifiedDate":"2018-02-02T14:51:15","indexId":"70193117","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Advantages of active love wave techniques in geophysical characterizations of seismographic station - Case studies in California and the central and eastern United States","docAbstract":"<p>Active-source Love waves, recorded by the multi-channel analysis of surface wave (MASLW) technique, were recently analyzed in two site characterization projects. Between 2010 and 2012, the 2009 American Recovery and Reinvestment Act (ARRA) funded GEOVision to conduct geophysical investigations at 191 seismographic stations in California and the Central Eastern U.S. (CEUS). The original project plan was to utilize active and passive Rayleigh wave-based techniques to obtain shear-wave velocity (VS) profiles to a minimum depth of 30 m and the time-averaged VS of the upper 30 meters (VS30). Early in this investigation it became clear that Rayleigh wave techniques, such as multi-channel analysis of surface waves (MASRW), were not suited for characterizing all sites. Shear-wave seismic refraction and MASLW techniques were therefore applied. In 2012, the Electric Power Research Institute funded characterization of 33 CEUS station sites. Based on experience from the ARRA investigation, both MASRW and MASLW data were acquired by GEOVision at 24 CEUS sites. At shallow rock sites, sites with steep velocity gradients, and, sites with a thin, low velocity, surficial soil layer overlying stiffer sediments, Love wave techniques generally were found to be easier to interpret, i.e., Love wave data typically yielded unambiguous fundamental mode dispersion curves and thus, reduce uncertainty in the resultant VS model. These types of velocity structure often excite dominant higher modes in Rayleigh wave data, but not in the Love wave data. It is possible to model Rayleigh wave data using multi- or effective-mode techniques; however, extraction of Rayleigh wave dispersion data was found to be difficult in many cases. These results imply that field procedures should include careful scrutiny of Rayleigh wave-based dispersion data in order to also collect Love wave data when warranted.</p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Tenth U.S. National Conference on Earthquake Engineering: Frontiers of Earthquake Engineering ","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Tenth U.S. National Conference on Earthquake Engineering","conferenceDate":"July 21-25, 2014","conferenceLocation":"Anchorage, AK","language":"English","publisher":"10NCEE","usgsCitation":"Martin, A., Yong, A.K., and Salomone, L.A., 2014, Advantages of active love wave techniques in geophysical characterizations of seismographic station - Case studies in California and the central and eastern United States, <i>in</i> Tenth U.S. National Conference on Earthquake Engineering: Frontiers of Earthquake Engineering , Anchorage, AK, July 21-25, 2014, 11 p.","productDescription":"11 p.","ipdsId":"IP-056051","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":350984,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a7586dde4b00f54eb1d8210","contributors":{"authors":[{"text":"Martin, Antony","contributorId":199052,"corporation":false,"usgs":false,"family":"Martin","given":"Antony","affiliations":[],"preferred":false,"id":718030,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yong, Alan K. 0000-0003-1807-5847 yong@usgs.gov","orcid":"https://orcid.org/0000-0003-1807-5847","contributorId":1554,"corporation":false,"usgs":true,"family":"Yong","given":"Alan","email":"yong@usgs.gov","middleInitial":"K.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":718029,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Salomone, Larry A.","contributorId":199053,"corporation":false,"usgs":false,"family":"Salomone","given":"Larry","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":718031,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70187261,"text":"70187261 - 2014 - Predicting impacts of future human population growth and development on occupancy rates of forest-dependent birds","interactions":[],"lastModifiedDate":"2017-04-27T11:17:37","indexId":"70187261","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Predicting impacts of future human population growth and development on occupancy rates of forest-dependent birds","docAbstract":"<p><span>Forest loss and fragmentation are among the largest threats to forest-dwelling wildlife species today, and projected increases in human population growth are expected to increase these threats in the next century. We combined spatially-explicit growth models with wildlife distribution models to predict the effects of human development on 5 forest-dependent bird species in Vermont, New Hampshire, and Massachusetts, USA. We used single-species occupancy models to derive the probability of occupancy for each species across the study area in the years 2000 and 2050. Over half a million new housing units were predicted to be added to the landscape. The maximum change in housing density was nearly 30 houses per hectare; however, 30% of the towns in the study area were projected to add less than 1 housing unit per hectare. In the face of predicted human growth, the overall occupancy of each species decreased by as much as 38% (ranging from 19% to 38% declines in the worst-case scenario) in the year 2050. These declines were greater outside of protected areas than within protected lands. Ninety-seven percent of towns experienced some decline in species occupancy within their borders, highlighting the value of spatially-explicit models. The mean decrease in occupancy probability within towns ranged from 3% for hairy woodpecker to 8% for ovenbird and hermit thrush. Reductions in occupancy probability occurred on the perimeters of cities and towns where exurban development is predicted to increase in the study area. This spatial approach to wildlife planning provides data to evaluate trade-offs between development scenarios and forest-dependent wildlife species.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2013.07.039","usgsCitation":"Brown, M.L., Donovan, T., Schwenk, W.S., and Theobald, D.M., 2014, Predicting impacts of future human population growth and development on occupancy rates of forest-dependent birds: Biological Conservation, v. 170, p. 311-320, https://doi.org/10.1016/j.biocon.2013.07.039.","productDescription":"10 p.","startPage":"311","endPage":"320","ipdsId":"IP-039574","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":340499,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"170","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59030328e4b0e862d230f751","contributors":{"authors":[{"text":"Brown, Michelle L.","contributorId":168990,"corporation":false,"usgs":false,"family":"Brown","given":"Michelle","email":"","middleInitial":"L.","affiliations":[{"id":7147,"text":"Wayne State University","active":true,"usgs":false}],"preferred":false,"id":693186,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Donovan, Therese tdonovan@usgs.gov","contributorId":171599,"corporation":false,"usgs":true,"family":"Donovan","given":"Therese","email":"tdonovan@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":693119,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schwenk, W. Scott","contributorId":172274,"corporation":false,"usgs":false,"family":"Schwenk","given":"W.","email":"","middleInitial":"Scott","affiliations":[],"preferred":false,"id":693187,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Theobald, David M. 0000-0002-1271-9368","orcid":"https://orcid.org/0000-0002-1271-9368","contributorId":10271,"corporation":false,"usgs":false,"family":"Theobald","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":13470,"text":"Conservation Science Partners","active":true,"usgs":false}],"preferred":true,"id":693188,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70188054,"text":"70188054 - 2014 - Earth observation based assessment of the water production and water consumption of Nile Basin agro-ecosystems","interactions":[],"lastModifiedDate":"2017-05-31T16:11:56","indexId":"70188054","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Earth observation based assessment of the water production and water consumption of Nile Basin agro-ecosystems","docAbstract":"<p><span>The increasing competition for water resources requires a better understanding of flows, fluxes, stocks, and the services and benefits related to water consumption. This paper explains how public domain Earth Observation data based on Moderate Resolution Imaging Spectroradiometer (MODIS), Second Generation Meteosat (MSG), Tropical Rainfall Measurement Mission (TRMM) and various altimeter measurements can be used to estimate net water production (rainfall (P) &gt; evapotranspiration (ET)) and net water consumption (ET &gt; P) of Nile Basin agro-ecosystems. Rainfall data from TRMM and the Famine Early Warning System Network (FEWS-NET) RainFall Estimates (RFE) products were used in conjunction with actual evapotranspiration from the Operational Simplified Surface Energy Balance (SSEBop) and ETLook models. Water flows laterally between net water production and net water consumption areas as a result of runoff and withdrawals. This lateral flow between the 15 sub-basins of the Nile was estimated, and partitioned into stream flow and non-stream flow using the discharge data. A series of essential water metrics necessary for successful integrated water management are explained and computed. Net water withdrawal estimates (natural and humanly instigated) were assumed to be the difference between net rainfall (P</span><sub>net</sub><span>) and actual evapotranspiration (ET) and some first estimates of withdrawals—without flow meters—are provided. Groundwater-dependent ecosystems withdraw large volumes of groundwater, which exceed water withdrawals for the irrigation sector. There is a strong need for the development of more open-access Earth Observation databases, especially for information related to actual ET. The fluxes, flows and storage changes presented form the basis for a global framework to describe monthly and annual water accounts in ungauged river basins. </span></p>","language":"English","publisher":"MDPI","doi":"10.3390/rs61110306","usgsCitation":"Bastiaanssen, W., Karimi, P., Rebelo, L., Duan, Z., Senay, G., Muthuwatte, L., and Smakhtin, V., 2014, Earth observation based assessment of the water production and water consumption of Nile Basin agro-ecosystems: Remote Sensing, v. 6, no. 11, p. 10306-10334, https://doi.org/10.3390/rs61110306.","productDescription":"29 p.","startPage":"10306","endPage":"10334","ipdsId":"IP-057431","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":473300,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs61110306","text":"Publisher Index Page"},{"id":341872,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Nile Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              23.818359375,\n              -3.688855143147035\n            ],\n            [\n              37.6171875,\n              -3.688855143147035\n            ],\n            [\n              37.6171875,\n              31.57853542647338\n            ],\n            [\n              23.818359375,\n              31.57853542647338\n            ],\n            [\n              23.818359375,\n              -3.688855143147035\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"6","issue":"11","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2014-10-24","publicationStatus":"PW","scienceBaseUri":"592e84c6e4b092b266f10da3","contributors":{"authors":[{"text":"Bastiaanssen, Wim","contributorId":192421,"corporation":false,"usgs":false,"family":"Bastiaanssen","given":"Wim","email":"","affiliations":[],"preferred":false,"id":696478,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Karimi, Poolad","contributorId":192422,"corporation":false,"usgs":false,"family":"Karimi","given":"Poolad","email":"","affiliations":[],"preferred":false,"id":696479,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rebelo, Lisa-Maria","contributorId":192423,"corporation":false,"usgs":false,"family":"Rebelo","given":"Lisa-Maria","email":"","affiliations":[],"preferred":false,"id":696480,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Duan, Zheng","contributorId":192424,"corporation":false,"usgs":false,"family":"Duan","given":"Zheng","email":"","affiliations":[],"preferred":false,"id":696481,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":166812,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696333,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Muthuwatte, Lal","contributorId":192425,"corporation":false,"usgs":false,"family":"Muthuwatte","given":"Lal","email":"","affiliations":[],"preferred":false,"id":696482,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Smakhtin, Vladimir","contributorId":192426,"corporation":false,"usgs":false,"family":"Smakhtin","given":"Vladimir","email":"","affiliations":[],"preferred":false,"id":696483,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70059572,"text":"70059572 - 2014 - Spatially explicit modeling of 1992-2100 land cover and forest stand age for the conterminous United States","interactions":[],"lastModifiedDate":"2022-03-31T19:37:52.175526","indexId":"70059572","displayToPublicDate":"2013-12-23T09:21:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Spatially explicit modeling of 1992-2100 land cover and forest stand age for the conterminous United States","docAbstract":"Information on future land-use and land-cover (LULC) change is needed to analyze the impact of LULC change on ecological processes. The U.S. Geological Survey has produced spatially explicit, thematically detailed LULC projections for the conterminous United States. Four qualitative and quantitative scenarios of LULC change were developed, with characteristics consistent with the Intergovernmental Panel on Climate Change (IPCC) Special Report on 5 Emission Scenarios (SRES). The four quantified scenarios (A1B, A2, B1, and B2) served as input to the Forecasting Scenarios of Land-use Change (FORE-SCE) model. Four spatially explicit datasets consistent with scenario storylines were produced for the conterminous United States, with annual LULC maps from 1992 through 2100. The future projections are characterized by a loss of natural land covers in most scenarios, with corresponding expansion of 10 anthropogenic land uses. Along with the loss of natural land covers, remaining natural land covers experience increased fragmentation under most scenarios, with only the B2 scenario remaining relatively stable in both proportion of remaining natural land covers and basic fragmentation measures. Forest stand age was also modeled. By 2100, scenarios and ecoregions with heavy forest cutting have relatively lower mean stand ages compared to those with less 15 forest cutting. Stand ages differ substantially between unprotected and protected forest lands, as well as between different forest classes. The modeled data were compared to the National Land Cover Database (NLCD) and other data sources to assess model characteristics. The consistent, spatially explicit, and thematically detailed LULC projections and the associated forest stand age data layers have been used to analyze LULC impacts on carbon and greenhouse gas fluxes, 20 biodiversity, climate and weather variability, hydrologic change, and other ecological processes.","language":"English","publisher":"Ecological Society of America","doi":"10.1890/13-1245.1","usgsCitation":"Sohl, T.L., Sayler, K., Bouchard, M., Reker, R.R., Friesz, A.M., Bennett, S.L., Sleeter, B.M., Sleeter, R., Wilson, T., Soulard, C.E., Knuppe, M., and Van Hofwegen, T., 2014, Spatially explicit modeling of 1992-2100 land cover and forest stand age for the conterminous United States: Ecological Applications, v. 24, no. 5, p. 1015-1036, https://doi.org/10.1890/13-1245.1.","productDescription":"22 p. ; Data release","startPage":"1015","endPage":"1036","numberOfPages":"22","ipdsId":"IP-042928","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) 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 \"}}]}","volume":"24","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"52b95be3e4b0a747b3e7e7b1","contributors":{"authors":[{"text":"Sohl, Terry L. 0000-0002-9771-4231 sohl@usgs.gov","orcid":"https://orcid.org/0000-0002-9771-4231","contributorId":648,"corporation":false,"usgs":true,"family":"Sohl","given":"Terry","email":"sohl@usgs.gov","middleInitial":"L.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":487685,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sayler, Kristi L. 0000-0003-2514-242X sayler@usgs.gov","orcid":"https://orcid.org/0000-0003-2514-242X","contributorId":2988,"corporation":false,"usgs":true,"family":"Sayler","given":"Kristi","email":"sayler@usgs.gov","middleInitial":"L.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":487687,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bouchard, Michelle 0000-0002-6353-3491 mbouchard@usgs.gov","orcid":"https://orcid.org/0000-0002-6353-3491","contributorId":3765,"corporation":false,"usgs":true,"family":"Bouchard","given":"Michelle","email":"mbouchard@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":487689,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reker, Ryan R. 0000-0001-7524-0082 rreker@usgs.gov","orcid":"https://orcid.org/0000-0001-7524-0082","contributorId":174136,"corporation":false,"usgs":true,"family":"Reker","given":"Ryan","email":"rreker@usgs.gov","middleInitial":"R.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":487693,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Friesz, Aaron M. 0000-0003-4096-3824 afriesz@usgs.gov","orcid":"https://orcid.org/0000-0003-4096-3824","contributorId":5943,"corporation":false,"usgs":true,"family":"Friesz","given":"Aaron","email":"afriesz@usgs.gov","middleInitial":"M.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":487691,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bennett, Stacie L.","contributorId":42820,"corporation":false,"usgs":true,"family":"Bennett","given":"Stacie","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":487695,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sleeter, Benjamin M. 0000-0003-2371-9571 bsleeter@usgs.gov","orcid":"https://orcid.org/0000-0003-2371-9571","contributorId":3479,"corporation":false,"usgs":true,"family":"Sleeter","given":"Benjamin","email":"bsleeter@usgs.gov","middleInitial":"M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":487688,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sleeter, Rachel R.","contributorId":7946,"corporation":false,"usgs":true,"family":"Sleeter","given":"Rachel R.","affiliations":[],"preferred":false,"id":487692,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Wilson, Tamara 0000-0001-7399-7532 tswilson@usgs.gov","orcid":"https://orcid.org/0000-0001-7399-7532","contributorId":2975,"corporation":false,"usgs":true,"family":"Wilson","given":"Tamara","email":"tswilson@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":487686,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Soulard, Christopher E. 0000-0002-5777-9516 csoulard@usgs.gov","orcid":"https://orcid.org/0000-0002-5777-9516","contributorId":2642,"corporation":false,"usgs":true,"family":"Soulard","given":"Christopher","email":"csoulard@usgs.gov","middleInitial":"E.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":725409,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Knuppe, Michelle 0000-0002-0374-9477","orcid":"https://orcid.org/0000-0002-0374-9477","contributorId":42125,"corporation":false,"usgs":true,"family":"Knuppe","given":"Michelle","email":"","affiliations":[],"preferred":false,"id":487694,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Van Hofwegen, Travis tvanhofwegen@usgs.gov","contributorId":5529,"corporation":false,"usgs":true,"family":"Van Hofwegen","given":"Travis","email":"tvanhofwegen@usgs.gov","affiliations":[],"preferred":true,"id":487690,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}}
,{"id":70073924,"text":"70073924 - 2014 - Contaminants from Cretaceous black shale: II. Effect of geology, weathering, climate, and land use on salinity and selenium cycling, Mancos Shale landscapes, southwestern United States","interactions":[],"lastModifiedDate":"2020-12-30T16:47:04.54835","indexId":"70073924","displayToPublicDate":"2013-12-20T11:31:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":835,"text":"Applied Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Contaminants from Cretaceous black shale: II. Effect of geology, weathering, climate, and land use on salinity and selenium cycling, Mancos Shale landscapes, southwestern United States","docAbstract":"<p><span>The Cretaceous Mancos Shale (MS) is a known nonpoint source for a significant portion of the salinity and selenium (Se) loads in the Colorado River in the southwestern United States and northwestern corner of Mexico. These two contaminants pose a serious threat to rivers in these arid regions where water supplies are especially critical. Tuttle et al. (companion paper) investigates the cycling of contaminants in a Colorado River tributary watershed (Uncompahgre River, southwestern Colorado) where the MS weathers under natural conditions. This paper builds on those results and uses regional soil data in the same watershed to investigate the impact of MS geology, weathering intensity, land use, and climate on salt and Se storage in and flux from soils on the natural landscape, irrigated agriculture fields, areas undergoing urban development, and wetlands. The size of salinity and Se reservoirs in the MS soils is quantified. Flux calculations show that during modern weathering, natural landscapes cycle salt and Se; however, little of it is released for transport to the Uncompahgre River (10% of the annual salinity and 6% of the annual Se river loads). When irrigated, salinity and Se loads from the MS soil increase (26% and 57% of the river load, respectively), causing the river to be out of compliance with Federal and State Se standards. During 100</span><span>&nbsp;</span><span>years of irrigation, seven times more Se has been removed from agricultural soil than what was lost from natural landscapes during the entire period of pedogenesis. Under more arid conditions, even less salt and Se are expected to be transported from the natural landscape. However, if wetter climates prevail, transport could increase dramatically due to storage of soluble phases in the non-irrigated soil. These results are critical input for water-resource and land-use managers who must decide whether or not the salinity and Se in a watershed can be managed, what sustainable mitigation strategies are possible, and what landscapes should be targeted. The broader implications include providing a reliable approach for quantifying nonpoint-source contamination from MS and other rock units elsewhere that weather under similar conditions and, together with results from our companion paper, address the complex interplay of geology, weathering, climate, and land use on contaminant cycling in the arid Southwest.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2013.12.011","usgsCitation":"Tuttle, M., Fahy, J.W., Elliott, J.G., Grauch, R.I., and Stillings, L., 2014, Contaminants from Cretaceous black shale: II. Effect of geology, weathering, climate, and land use on salinity and selenium cycling, Mancos Shale landscapes, southwestern United States: Applied Geochemistry, v. 46, p. 72-84, https://doi.org/10.1016/j.apgeochem.2013.12.011.","productDescription":"13 p.","startPage":"72","endPage":"84","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":281483,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Colorado River Basin, Mancos Shale","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -107.993508,38.537542 ], [ -107.993508,38.818839 ], [ -107.749497,38.818839 ], [ -107.749497,38.537542 ], [ -107.993508,38.537542 ] ] ] } } ] }","volume":"46","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd52abe4b0b290850f4aa1","contributors":{"authors":[{"text":"Tuttle, Michele L. mtuttle@usgs.gov","contributorId":1028,"corporation":false,"usgs":true,"family":"Tuttle","given":"Michele L.","email":"mtuttle@usgs.gov","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":false,"id":489231,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fahy, Juli W. jfahy@usgs.gov","contributorId":57362,"corporation":false,"usgs":true,"family":"Fahy","given":"Juli","email":"jfahy@usgs.gov","middleInitial":"W.","affiliations":[],"preferred":false,"id":489234,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Elliott, John G. jelliott@usgs.gov","contributorId":832,"corporation":false,"usgs":true,"family":"Elliott","given":"John","email":"jelliott@usgs.gov","middleInitial":"G.","affiliations":[],"preferred":true,"id":489230,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Grauch, Richard I. 0000-0002-1763-0813 rgrauch@usgs.gov","orcid":"https://orcid.org/0000-0002-1763-0813","contributorId":1193,"corporation":false,"usgs":true,"family":"Grauch","given":"Richard","email":"rgrauch@usgs.gov","middleInitial":"I.","affiliations":[],"preferred":true,"id":489232,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stillings, Lisa L. 0000-0002-9011-8891 stilling@usgs.gov","orcid":"https://orcid.org/0000-0002-9011-8891","contributorId":3143,"corporation":false,"usgs":true,"family":"Stillings","given":"Lisa L.","email":"stilling@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":false,"id":489233,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70059128,"text":"70059128 - 2014 - Impact of climate variability on runoff in the north-central United States","interactions":[],"lastModifiedDate":"2017-10-12T20:15:37","indexId":"70059128","displayToPublicDate":"2013-12-17T12:03:53","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2341,"text":"Journal of Hydrologic Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Impact of climate variability on runoff in the north-central United States","docAbstract":"Large changes in runoff in the north-central United States have occurred during the past century, with larger floods and increases in runoff tending to occur from the 1970s to the present. The attribution of these changes is a subject of much interest. Long-term precipitation, temperature, and streamflow records were used to compare changes in precipitation and potential evapotranspiration (PET) to changes in runoff within 25 stream basins. The basins studied were organized into four groups, each one representing basins similar in topography, climate, and historic patterns of runoff. Precipitation, PET, and runoff data were adjusted for near-decadal scale variability to examine longer-term changes. A nonlinear water-balance analysis shows that changes in precipitation and PET explain the majority of multidecadal spatial/temporal variability of runoff and flood magnitudes, with precipitation being the dominant driver. Historical changes in climate and runoff in the region appear to be more consistent with complex transient shifts in seasonal climatic conditions than with gradual climate change. A portion of the unexplained variability likely stems from land-use change.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Hydrologic Engineering","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"American Society of Civil Engineers","doi":"10.1061/(ASCE)HE.1943-5584.0000775","usgsCitation":"Ryberg, K.R., Lin, W., and Vecchia, A.V., 2014, Impact of climate variability on runoff in the north-central United States: Journal of Hydrologic Engineering, v. 19, no. 1, p. 148-158, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000775.","productDescription":"11 p.","startPage":"148","endPage":"158","ipdsId":"IP-036799","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":280403,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, South Dakota","volume":"19","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd620be4b0b290850fdec0","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":487475,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lin, Wei","contributorId":93805,"corporation":false,"usgs":true,"family":"Lin","given":"Wei","email":"","affiliations":[],"preferred":false,"id":487477,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":487476,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70058881,"text":"70058881 - 2014 - The relation between invertebrate drift and two primary controls, discharge and benthic densities, in a large regulated river","interactions":[],"lastModifiedDate":"2014-01-24T09:32:06","indexId":"70058881","displayToPublicDate":"2013-12-17T10:19:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1696,"text":"Freshwater Biology","active":true,"publicationSubtype":{"id":10}},"title":"The relation between invertebrate drift and two primary controls, discharge and benthic densities, in a large regulated river","docAbstract":"1. Invertebrate drift is a fundamental process in streams and rivers. Studies from laboratory experiments and small streams have identified numerous extrinsic (e.g. discharge, light intensity, water quality) and intrinsic factors (invertebrate life stage, benthic density, behaviour) that govern invertebrate drift concentrations (# m−3), but the factors that govern invertebrate drift in larger rivers remain poorly understood. For example, while large increases or decreases in discharge can lead to large increases in invertebrate drift, the role of smaller, incremental changes in discharge is poorly described. In addition, while we might expect invertebrate drift concentrations to be proportional to benthic densities (# m−2), the benthic–drift relation has not been rigorously evaluated.\n<br>\n2. Here, we develop a framework for modelling invertebrate drift that is derived from sediment transport studies. We use this framework to guide the analysis of high-resolution data sets of benthic density and drift concentration for four important invertebrate taxa from the Colorado River downstream of Glen Canyon Dam (mean daily discharge 325 m3 s−1) that were collected over 18 months and include multiple observations within days. Ramping of regulated flows on this river segment provides an experimental treatment that is repeated daily and allowed us to describe the functional relations between invertebrate drift and two primary controls, discharge and benthic densities.\n<br>\n3. Twofold daily variation in discharge resulted in a >10-fold increase in drift concentrations of benthic invertebrates associated with pools and detritus (i.e. Gammarus lacustris and Potamopyrgus antipodarum). In contrast, drift concentrations of sessile blackfly larvae (Simuliium arcticum), which are associated with high-velocity cobble microhabitats, decreased by over 80% as discharge doubled. Drift concentrations of Chironomidae increased proportional to discharge.\n<br>\n4. Drift of all four taxa was positively related to benthic density. Drift concentrations of Gammarus, Potamopyrgus and Chironomidae were proportional to benthic density. Drift concentrations of Simulium were positively related to benthic density, but the benthic–drift relation was less than proportional (i.e. a doubling of benthic density only led to a 40% increase in drift concentrations).\n<br>\n5. Our study demonstrates that invertebrate drift concentrations in the Colorado River are jointly controlled by discharge and benthic densities, but these controls operate at different timescales. Twofold daily variation in discharge associated with hydropeaking was the primary control on within-day variation in invertebrate drift concentrations. In contrast, benthic density, which varied 10- to 1000-fold among sampling dates, depending on the taxa, was the primary control on invertebrate drift concentrations over longer timescales (weeks to months).","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Freshwater Biology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley","doi":"10.1111/fwb.12285","usgsCitation":"Kennedy, T., Yackulic, C.B., Cross, W.F., Grams, P.E., Yard, M., and Copp, A.J., 2014, The relation between invertebrate drift and two primary controls, discharge and benthic densities, in a large regulated river: Freshwater Biology, v. 59, no. 3, p. 557-572, https://doi.org/10.1111/fwb.12285.","productDescription":"16 p.","startPage":"557","endPage":"572","numberOfPages":"16","ipdsId":"IP-045496","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":280362,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":280361,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1111/fwb.12285"}],"country":"United States","state":"Arizona","otherGeospatial":"Glen Canyon Dam","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -111.666667,36.833333 ], [ -111.666667,36.966667 ], [ -111.416667,36.966667 ], [ -111.416667,36.833333 ], [ -111.666667,36.833333 ] ] ] } } ] }","volume":"59","issue":"3","noUsgsAuthors":false,"publicationDate":"2013-12-11","publicationStatus":"PW","scienceBaseUri":"52b172c1e4b0d9b325224604","contributors":{"authors":[{"text":"Kennedy, Theodore A. 0000-0003-3477-3629","orcid":"https://orcid.org/0000-0003-3477-3629","contributorId":50227,"corporation":false,"usgs":true,"family":"Kennedy","given":"Theodore A.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":487413,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yackulic, Charles B. 0000-0001-9661-0724 cyackulic@usgs.gov","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":4662,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","email":"cyackulic@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":487410,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cross, Wyatt F.","contributorId":70881,"corporation":false,"usgs":true,"family":"Cross","given":"Wyatt","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":487414,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Grams, Paul E. 0000-0002-0873-0708 pgrams@usgs.gov","orcid":"https://orcid.org/0000-0002-0873-0708","contributorId":1830,"corporation":false,"usgs":true,"family":"Grams","given":"Paul","email":"pgrams@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":487409,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Yard, Michael D. 0000-0002-6580-6027","orcid":"https://orcid.org/0000-0002-6580-6027","contributorId":8577,"corporation":false,"usgs":true,"family":"Yard","given":"Michael D.","affiliations":[],"preferred":false,"id":487412,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Copp, Adam J. 0000-0001-7385-0055 acopp@usgs.gov","orcid":"https://orcid.org/0000-0001-7385-0055","contributorId":5194,"corporation":false,"usgs":true,"family":"Copp","given":"Adam","email":"acopp@usgs.gov","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":487411,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70058744,"text":"70058744 - 2014 - Climatic variation and tortoise survival: has a desert species met its match?","interactions":[],"lastModifiedDate":"2013-12-13T12:06:21","indexId":"70058744","displayToPublicDate":"2013-12-13T11:57:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Climatic variation and tortoise survival: has a desert species met its match?","docAbstract":"While demographic changes in short-lived species may be observed relatively quickly in response to climate changes, measuring population responses of long-lived species requires long-term studies that are not always available. We analyzed data from a population of threatened Agassiz’s desert tortoises (Gopherus agassizii) at a 2.59 km<sup>2</sup> study plot in the Sonoran Desert ecosystem of Joshua Tree National Park, California, USA from 1978 to 2012 to examine variation in apparent survival and demography in this long-lived species. Transect-based, mark-recapture surveys were conducted in 10 of those years to locate living and dead tortoises. Previous modeling suggested that this area would become unsuitable as tortoise habitat under a warming and drying climate scenario. Estimated adult population size declined greatly from 1996 to 2012. The population appeared to have high apparent survival from 1978 to 1996 but apparent survival decreased from 1997 to 2002, concurrent with persistent drought. The best model relating apparent survivorship of tortoises &ge;18 cm over time was based on a three year moving average of estimated winter precipitation. The postures and positions of a majority of dead tortoises found in 2012 were consistent with death by dehydration and starvation. Some live and many dead tortoises found in 2012 showed signs of predation or scavenging by mammalian carnivores. Coyote (Canis latrans) scats and other evidence from the site confirmed their role as tortoise predators and scavengers. Predation rates may be exacerbated by drought if carnivores switch from preferred mammalian prey to tortoises during dry years. Climate modeling suggests that the region will be subjected to even longer duration droughts in the future and that the plot may become unsuitable for continued tortoise survival. Our results showing wide fluctuations in apparent survival and decreasing tortoise density over time may be early signals of that possible outcome.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Biological Conservation","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2013.09.027","usgsCitation":"Lovich, J.E., Yackulic, C.B., Freilich, J., Agha, M., Austin, M., Meyer, K.P., Arundel, T., Hansen, J., Vamstad, M., and Root, S., 2014, Climatic variation and tortoise survival: has a desert species met its match?: Biological Conservation, v. 169, p. 214-224, https://doi.org/10.1016/j.biocon.2013.09.027.","productDescription":"11 p.","startPage":"214","endPage":"224","numberOfPages":"11","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":473324,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.biocon.2013.09.027","text":"Publisher Index Page"},{"id":280295,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":280294,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.biocon.2013.09.027"}],"country":"United States","state":"California","otherGeospatial":"Joshua Tree National Park","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -116.458031,33.670186 ], [ -116.458031,34.129343 ], [ -115.262191,34.129343 ], [ -115.262191,33.670186 ], [ -116.458031,33.670186 ] ] ] } } ] }","volume":"169","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"52ac2c62e4b004a77d23c4c9","contributors":{"authors":[{"text":"Lovich, Jeffrey E. 0000-0002-7789-2831 jeffrey_lovich@usgs.gov","orcid":"https://orcid.org/0000-0002-7789-2831","contributorId":458,"corporation":false,"usgs":true,"family":"Lovich","given":"Jeffrey","email":"jeffrey_lovich@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":487334,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yackulic, Charles B. 0000-0001-9661-0724 cyackulic@usgs.gov","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":4662,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","email":"cyackulic@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":487335,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Freilich, Jerry","contributorId":42518,"corporation":false,"usgs":true,"family":"Freilich","given":"Jerry","email":"","affiliations":[],"preferred":false,"id":487340,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Agha, Mickey","contributorId":22235,"corporation":false,"usgs":false,"family":"Agha","given":"Mickey","email":"","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false},{"id":12425,"text":"University of Kentucky","active":true,"usgs":false}],"preferred":false,"id":487337,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Austin, Meaghan","contributorId":37244,"corporation":false,"usgs":true,"family":"Austin","given":"Meaghan","affiliations":[],"preferred":false,"id":487339,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Meyer, Katherine P.","contributorId":95789,"corporation":false,"usgs":true,"family":"Meyer","given":"Katherine","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":487343,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Arundel, Terence R.","contributorId":11080,"corporation":false,"usgs":true,"family":"Arundel","given":"Terence R.","affiliations":[],"preferred":false,"id":487336,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hansen, Jered","contributorId":34034,"corporation":false,"usgs":true,"family":"Hansen","given":"Jered","affiliations":[],"preferred":false,"id":487338,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Vamstad, Michael S.","contributorId":66590,"corporation":false,"usgs":true,"family":"Vamstad","given":"Michael S.","affiliations":[],"preferred":false,"id":487341,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Root, Stephanie A.","contributorId":87449,"corporation":false,"usgs":true,"family":"Root","given":"Stephanie A.","affiliations":[],"preferred":false,"id":487342,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70058014,"text":"70058014 - 2014 - Integration of stable carbon isotope, microbial community, dissolved hydrogen gas, and <sup>2</sup>H<sub>H<sub>2</sub>O</sub> tracer data to assess bioaugmentation for chlorinated ethene degradation in fractured rocks","interactions":[],"lastModifiedDate":"2018-09-18T16:15:49","indexId":"70058014","displayToPublicDate":"2013-12-05T09:56:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2233,"text":"Journal of Contaminant Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Integration of stable carbon isotope, microbial community, dissolved hydrogen gas, and <sup>2</sup>H<sub>H<sub>2</sub>O</sub> tracer data to assess bioaugmentation for chlorinated ethene degradation in fractured rocks","docAbstract":"An in situ bioaugmentation (BA) experiment was conducted to understand processes controlling microbial dechlorination of trichloroethene (TCE) in groundwater at the Naval Air Warfare Center (NAWC), West Trenton, NJ. In the BA experiment, an electron donor (emulsified vegetable oil and sodium lactate) and a chloro-respiring microbial consortium were injected into a well in fractured mudstone of Triassic age. Water enriched in <sup>2</sup>H was also injected as a tracer of the BA solution, to monitor advective transport processes. The changes in concentration and the δ<sup>13</sup>C of TCE, cis-dichloroethene (cis-DCE), and vinyl chloride (VC); the δ<sup>2</sup>H of water; changes in the abundance of the microbial communities; and the concentration of dissolved H2 gas compared to pre- test conditions, provided multiple lines of evidence that enhanced biodegradation occurred in the injection well and in two downgradient wells. For those wells where the biodegradation was stimulated intensively, the sum of the molar chlorinated ethene (CE) concentrations in post-BA water was higher than that of the sum of the pre-BA background molar CE concentrations. The concentration ratios of TCE/(cis-DCE + VC) indicated that the increase in molar CE concentration may result from additional TCE mobilized from the rock matrix in response to the oil injection or due to desorption/diffusion. The stable carbon isotope mass-balance calculations show that the weighted average <sup>13</sup>C isotope of the CEs was enriched for around a year compared to the background value in a two year monitoring period, an effective indication that dechlorination of VC was occurring. Insights gained from this study can be applied to efforts to use BA in other fractured rock systems. The study demonstrates that a BA approach can substantially enhance in situ bioremediation not only in fractures connected to the injection well, but also in the rock matrix around the well due to processes such as diffusion and desorption. Because the effect of the BA was intensive only in wells where an amendment was distributed during injection, it is necessary to adequately distribute the amendments throughout the fractured rock to achieve substantial bioremediation. The slowdown in BA effect after a year is due to some extend to the decrease abundant of appropriate microbes, but more likely the decreased concentration of electron donor.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jconhyd.2013.10.004","usgsCitation":"Revesz, K.M., Lollar, B.S., Kirshtein, J.D., Tiedeman, C.R., Imbrigiotta, T., Goode, D., Shapiro, A.M., Voytek, M.A., Lancombe, P.J., and Busenberg, E., 2014, Integration of stable carbon isotope, microbial community, dissolved hydrogen gas, and <sup>2</sup>H<sub>H<sub>2</sub>O</sub> tracer data to assess bioaugmentation for chlorinated ethene degradation in fractured rocks: Journal of Contaminant Hydrology, v. 156, p. 62-77, https://doi.org/10.1016/j.jconhyd.2013.10.004.","productDescription":"16 p.","startPage":"62","endPage":"77","numberOfPages":"16","ipdsId":"IP-044573","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":280190,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":280189,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.jconhyd.2013.10.004"}],"country":"United States","state":"New Jersey","city":"Ewing Township","otherGeospatial":"Naval Air Warfare Center, West Trenton","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -74.838496,40.209396 ], [ -74.838496,40.283997 ], [ -74.725712,40.283997 ], [ -74.725712,40.209396 ], [ -74.838496,40.209396 ] ] ] } } ] }","volume":"156","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"52a1a089e4b02938ec05883c","contributors":{"authors":[{"text":"Revesz, Kinga M.","contributorId":18258,"corporation":false,"usgs":true,"family":"Revesz","given":"Kinga","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":486998,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lollar, Barbara Sherwood","contributorId":18668,"corporation":false,"usgs":false,"family":"Lollar","given":"Barbara","email":"","middleInitial":"Sherwood","affiliations":[{"id":7044,"text":"University of Toronto","active":true,"usgs":false}],"preferred":false,"id":486999,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kirshtein, Julie D.","contributorId":26033,"corporation":false,"usgs":true,"family":"Kirshtein","given":"Julie","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":487000,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tiedeman, Claire R. 0000-0002-0128-3685 tiedeman@usgs.gov","orcid":"https://orcid.org/0000-0002-0128-3685","contributorId":196777,"corporation":false,"usgs":true,"family":"Tiedeman","given":"Claire","email":"tiedeman@usgs.gov","middleInitial":"R.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":487002,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Imbrigiotta, Thomas E. 0000-0003-1716-4768 timbrig@usgs.gov","orcid":"https://orcid.org/0000-0003-1716-4768","contributorId":2466,"corporation":false,"usgs":true,"family":"Imbrigiotta","given":"Thomas E.","email":"timbrig@usgs.gov","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":false,"id":486997,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Goode, Daniel J. 0000-0002-8527-2456 djgoode@usgs.gov","orcid":"https://orcid.org/0000-0002-8527-2456","contributorId":2433,"corporation":false,"usgs":true,"family":"Goode","given":"Daniel J.","email":"djgoode@usgs.gov","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":false,"id":486996,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Shapiro, Allen M. 0000-0002-6425-9607 ashapiro@usgs.gov","orcid":"https://orcid.org/0000-0002-6425-9607","contributorId":2164,"corporation":false,"usgs":true,"family":"Shapiro","given":"Allen","email":"ashapiro@usgs.gov","middleInitial":"M.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":486994,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Voytek, Mary A.","contributorId":91943,"corporation":false,"usgs":true,"family":"Voytek","given":"Mary","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":487003,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lancombe, Pierre J.","contributorId":33614,"corporation":false,"usgs":true,"family":"Lancombe","given":"Pierre","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":487001,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Busenberg, Eurybiades ebusenbe@usgs.gov","contributorId":2271,"corporation":false,"usgs":true,"family":"Busenberg","given":"Eurybiades","email":"ebusenbe@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":486995,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70058428,"text":"70058428 - 2014 - Fitting statistical distributions to sea duck count data: implications for survey design and abundance estimation","interactions":[],"lastModifiedDate":"2013-12-05T09:39:16","indexId":"70058428","displayToPublicDate":"2013-12-05T09:34:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3475,"text":"Statistical Methodology","active":true,"publicationSubtype":{"id":10}},"title":"Fitting statistical distributions to sea duck count data: implications for survey design and abundance estimation","docAbstract":"Determining appropriate statistical distributions for modeling animal count data is important for accurate estimation of abundance, distribution, and trends. In the case of sea ducks along the U.S. Atlantic coast, managers want to estimate local and regional abundance to detect and track population declines, to define areas of high and low use, and to predict the impact of future habitat change on populations. In this paper, we used a modified marked point process to model survey data that recorded flock sizes of Common eiders, Long-tailed ducks, and Black, Surf, and White-winged scoters. The data come from an experimental aerial survey, conducted by the United States Fish & Wildlife Service (USFWS) Division of Migratory Bird Management, during which east-west transects were flown along the Atlantic Coast from Maine to Florida during the winters of 2009–2011. To model the number of flocks per transect (the points), we compared the fit of four statistical distributions (zero-inflated Poisson, zero-inflated geometric, zero-inflated negative binomial and negative binomial) to data on the number of species-specific sea duck flocks that were recorded for each transect flown. To model the flock sizes (the marks), we compared the fit of flock size data for each species to seven statistical distributions: positive Poisson, positive negative binomial, positive geometric, logarithmic, discretized lognormal, zeta and Yule–Simon. Akaike’s Information Criterion and Vuong’s closeness tests indicated that the negative binomial and discretized lognormal were the best distributions for all species for the points and marks, respectively. These findings have important implications for estimating sea duck abundances as the discretized lognormal is a more skewed distribution than the Poisson and negative binomial, which are frequently used to model avian counts; the lognormal is also less heavy-tailed than the power law distributions (e.g., zeta and Yule–Simon), which are becoming increasingly popular for group size modeling. Choosing appropriate statistical distributions for modeling flock size data is fundamental to accurately estimating population summaries, determining required survey effort, and assessing and propagating uncertainty through decision-making processes.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Statistical Methodology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.stamet.2012.10.002","usgsCitation":"Zipkin, E., Leirness, J.B., Kinlan, B.P., O’Connell, A.F., and Silverman, E.D., 2014, Fitting statistical distributions to sea duck count data: implications for survey design and abundance estimation: Statistical Methodology, v. 17, p. 67-81, https://doi.org/10.1016/j.stamet.2012.10.002.","productDescription":"15 p.","startPage":"67","endPage":"81","numberOfPages":"15","ipdsId":"IP-041848","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":280187,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":280186,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.stamet.2012.10.002"}],"volume":"17","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"52a1a089e4b02938ec058835","contributors":{"authors":[{"text":"Zipkin, Elise F.","contributorId":70528,"corporation":false,"usgs":true,"family":"Zipkin","given":"Elise F.","affiliations":[],"preferred":false,"id":487035,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Leirness, Jeffery B.","contributorId":24678,"corporation":false,"usgs":true,"family":"Leirness","given":"Jeffery","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":487033,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kinlan, Brian P.","contributorId":24679,"corporation":false,"usgs":true,"family":"Kinlan","given":"Brian","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":487034,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"O’Connell, Allan F. 0000-0001-7032-7023 aoconnell@usgs.gov","orcid":"https://orcid.org/0000-0001-7032-7023","contributorId":471,"corporation":false,"usgs":true,"family":"O’Connell","given":"Allan","email":"aoconnell@usgs.gov","middleInitial":"F.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":487032,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Silverman, Emily D.","contributorId":79220,"corporation":false,"usgs":true,"family":"Silverman","given":"Emily","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":487036,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70156236,"text":"70156236 - 2014 - Band reporting probablilities of mallards, American black ducks, and wood ducks in eastern North America","interactions":[],"lastModifiedDate":"2022-11-10T17:17:14.126777","indexId":"70156236","displayToPublicDate":"2013-12-05T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Band reporting probablilities of mallards, American black ducks, and wood ducks in eastern North America","docAbstract":"<p><span>Estimates of band reporting probabilities are used for managing North American waterfowl to convert band recovery probabilities into harvest probabilities, which are used to set harvest regulations. Band reporting probability is the probability that someone who has shot and retrieved a banded bird will report the band. This probability can vary relative to a number of factors, particularly the inscription on the band and the ease with which it can be reported. Other factors, such as geographic reporting region, and species and sex of the bird may also play a role. We tested whether reporting probabilities of wood ducks (</span><i>Aix sponsa</i><span>) and American black ducks (black ducks; </span><i>Anas rubripes</i><span>) differed from those of mallards (</span><i>Anas platyrhynchos</i><span>) and whether band reporting varied geographically or by the sex of the banded bird. In the analysis of spatially comparable wood duck and mallard data, a band reporting probability of 0.73 (95% CI = 0.67–0.78) was appropriate for use across species, sex, and reporting region within the United States. In the black duck–mallard comparison, the band reporting probability of black ducks in Eastern Canada (0.50, 95% CI = 0.44–0.57) was lower than in the Eastern United States (0.73, 95% CI = 0.62–0.83). These estimates reflected an increase in overall band reporting probability following the addition of a toll-free telephone number to band inscriptions. Lower reporting in Eastern Canada may be because of cultural, linguistic, or logistical barriers.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/jwmg.647","usgsCitation":"Garrettson, P., Raftovich, R.V., Hines, J.E., and Zimmerman, G.S., 2014, Band reporting probablilities of mallards, American black ducks, and wood ducks in eastern North America: Journal of Wildlife Management, v. 78, no. 1, p. 50-57, https://doi.org/10.1002/jwmg.647.","productDescription":"7 p.","startPage":"50","endPage":"57","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-052406","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":306826,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": 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System","active":true,"usgs":false}],"preferred":false,"id":568122,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hines, James E. 0000-0001-5478-7230 jhines@usgs.gov","orcid":"https://orcid.org/0000-0001-5478-7230","contributorId":146530,"corporation":false,"usgs":true,"family":"Hines","given":"James","email":"jhines@usgs.gov","middleInitial":"E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":568120,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zimmerman, Guthrie S.","contributorId":42473,"corporation":false,"usgs":false,"family":"Zimmerman","given":"Guthrie","email":"","middleInitial":"S.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":568123,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70058431,"text":"70058431 - 2014 - Precise determination of δ<sup>88</sup>Sr in rocks, minerals, and waters by double-spike TIMS: A powerful tool in the study of chemical, geologic, hydrologic and biologic processes","interactions":[],"lastModifiedDate":"2013-12-05T10:25:37","indexId":"70058431","displayToPublicDate":"2013-12-04T10:21:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2155,"text":"Journal of Analytical Atomic Spectrometry","active":true,"publicationSubtype":{"id":10}},"title":"Precise determination of δ<sup>88</sup>Sr in rocks, minerals, and waters by double-spike TIMS: A powerful tool in the study of chemical, geologic, hydrologic and biologic processes","docAbstract":"We present strontium isotopic (<sup>88</sup>Sr/<sup>86</sup>Sr and <sup>87</sup>Sr/<sup>86</sup>Sr) results obtained by <sup>87</sup>Sr–<sup>84</sup>Sr double spike thermal ionization mass-spectrometry (DS-TIMS) for several standards as well as natural water samples and mineral samples of abiogenic and biogenic origin. The detailed data reduction algorithm and a user-friendly Sr-specific stand-alone computer program used for the spike calibration and the data reduction are also presented. Accuracy and precision of our δ<sup>88</sup>Sr measurements, calculated as permil (‰) deviations from the NIST SRM-987 standard, were evaluated by analyzing the NASS-6 seawater standard, which yielded δ<sup>88</sup>Sr = 0.378 ± 0.009‰. The first DS-TIMS data for the NIST SRM-607 potassium feldspar standard and for several US Geological Survey carbonate, phosphate, and silicate standards (EN-1, MAPS-4, MAPS-5, G-3, BCR-2, and BHVO-2) are also reported. Data obtained during this work for Sr-bearing solids and natural waters show a range of δ<sup>88</sup>Sr values of about 2.4‰, the widest observed so far in terrestrial materials. This range is easily resolvable analytically because the demonstrated external error (±SD, standard deviation) for measured δ<sup>88</sup>Sr values is typically ≤0.02‰. It is shown that the “true” <sup>87</sup>Sr/<sup>86</sup>Sr value obtained by the DS-TIMS or any other external normalization method combines radiogenic and mass-dependent mass-fractionation effects, which cannot be separated. Therefore, the “true” <sup>87</sup>Sr/<sup>86</sup>Sr and the δ<sup>87</sup>Sr parameter derived from it are not useful isotope tracers. Data presented in this paper for a wide range of naturally occurring sample types demonstrate the potential of the δ<sup>88</sup>Sr isotope tracer in combination with the traditional radiogenic <sup>87</sup>Sr/<sup>86</sup>Sr tracer for studying a variety of biological, hydrological, and geological processes.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Analytical Atomic Spectrometry","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Royal Society of Chemistry","doi":"10.1039/C3JA50310K","usgsCitation":"Neymark, L.A., Premo, W.R., Mel’nikov, N.N., and Emsbo, P., 2014, Precise determination of δ<sup>88</sup>Sr in rocks, minerals, and waters by double-spike TIMS: A powerful tool in the study of chemical, geologic, hydrologic and biologic processes: Journal of Analytical Atomic Spectrometry, v. 29, p. 65-75, https://doi.org/10.1039/C3JA50310K.","productDescription":"11 p.","startPage":"65","endPage":"75","numberOfPages":"11","ipdsId":"IP-050748","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":280192,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":280191,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1039/C3JA50310K"}],"volume":"29","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"52a1aea5e4b02938ec05c900","contributors":{"authors":[{"text":"Neymark, Leonid A. lneymark@usgs.gov","contributorId":532,"corporation":false,"usgs":true,"family":"Neymark","given":"Leonid","email":"lneymark@usgs.gov","middleInitial":"A.","affiliations":[{"id":218,"text":"Denver Federal Center","active":false,"usgs":true}],"preferred":false,"id":487037,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Premo, Wayne R. 0000-0001-9904-4801 wpremo@usgs.gov","orcid":"https://orcid.org/0000-0001-9904-4801","contributorId":1697,"corporation":false,"usgs":true,"family":"Premo","given":"Wayne","email":"wpremo@usgs.gov","middleInitial":"R.","affiliations":[],"preferred":true,"id":487039,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mel’nikov, Nikolay N.","contributorId":37246,"corporation":false,"usgs":true,"family":"Mel’nikov","given":"Nikolay","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":487040,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Emsbo, Poul 0000-0001-9421-201X pemsbo@usgs.gov","orcid":"https://orcid.org/0000-0001-9421-201X","contributorId":997,"corporation":false,"usgs":true,"family":"Emsbo","given":"Poul","email":"pemsbo@usgs.gov","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":487038,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70048865,"text":"70048865 - 2014 - Hysteresis and uncertainty in soil water-retention curve parameters","interactions":[],"lastModifiedDate":"2018-03-08T15:54:54","indexId":"70048865","displayToPublicDate":"2013-12-01T14:48:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2327,"text":"Journal of Geotechnical and Geoenvironmental Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Hysteresis and uncertainty in soil water-retention curve parameters","docAbstract":"Accurate estimates of soil hydraulic parameters representing wetting and drying paths are required for predicting hydraulic and mechanical responses in a large number of applications. A comprehensive suite of laboratory experiments was conducted to measure hysteretic soil-water characteristic curves (SWCCs) representing a wide range of soil types. Results were used to quantitatively assess differences and uncertainty in three simplifications frequently adopted to estimate wetting-path SWCC parameters from more easily measured drying curves. They are the following: (1) α<sup>w</sup>=2α<sup>d</sup>, (2) n<sup>w</sup>=n<sup>d</sup>, and (3) θ<sup>w</sup><sub>s</sub>=θ<sup>d</sup><sub>s</sub>, where α, n, and θ<sub>s</sub> are fitting parameters entering van Genuchten’s commonly adopted SWCC model, and the superscripts w and d indicate wetting and drying paths, respectively. The average ratio αw/αd for the data set was 2.24±1.25. Nominally cohesive soils had a lower α<sup>w</sup>/α<sup>d</sup> ratio (1.73±0.94) than nominally cohesionless soils (3.14±1.27). The average n<sup>w</sup>/n<sup>d</sup> ratio was 1.01±0.11 with no significant dependency on soil type, thus confirming the n<sup>w</sup>=n<sup>d</sup> simplification for a wider range of soil types than previously available. Water content at zero suction during wetting (θ<sup>w</sup><sub>s</sub>) was consistently less than during drying (θ<sup>d</sup><sub>s</sub>) owing to air entrapment. The θ<sup>w</sup><sub>s</sub>/θ<sup>d</sup>s</sup> ratio averaged 0.85±0.10 and was comparable for nominally cohesive (0.87±0.11) and cohesionless (0.81±0.08) soils. Regression statistics are provided to quantitatively account for uncertainty in estimating hysteretic retention curves. Practical consequences are demonstrated for two case studies.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Geotechnical and Geoenvironmental Engineering","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"American Society of Civil Engineers","doi":"10.1061/(ASCE)GT.1943-5606.0001071","usgsCitation":"Likos, W.J., Lu, N., and Godt, J.W., 2014, Hysteresis and uncertainty in soil water-retention curve parameters: Journal of Geotechnical and Geoenvironmental Engineering, v. 140, no. 4, 11 p., https://doi.org/10.1061/(ASCE)GT.1943-5606.0001071.","productDescription":"11 p.","numberOfPages":"11","ipdsId":"IP-052321","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":280761,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":280760,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1061/(ASCE)GT.1943-5606.0001071"}],"volume":"140","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd61dee4b0b290850fdcdf","contributors":{"authors":[{"text":"Likos, William J.","contributorId":14725,"corporation":false,"usgs":true,"family":"Likos","given":"William","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":485767,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lu, Ning","contributorId":191360,"corporation":false,"usgs":false,"family":"Lu","given":"Ning","email":"","affiliations":[{"id":12620,"text":"U.S. Army Corp. of Engineers","active":true,"usgs":false}],"preferred":false,"id":485768,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Godt, Jonathan W. 0000-0002-8737-2493 jgodt@usgs.gov","orcid":"https://orcid.org/0000-0002-8737-2493","contributorId":1166,"corporation":false,"usgs":true,"family":"Godt","given":"Jonathan","email":"jgodt@usgs.gov","middleInitial":"W.","affiliations":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":485766,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70098946,"text":"70098946 - 2014 - Seasonal cultivated and fallow cropland mapping using MODIS-based automated cropland classification algorithm","interactions":[],"lastModifiedDate":"2017-04-06T16:13:58","indexId":"70098946","displayToPublicDate":"2013-12-01T14:31:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2172,"text":"Journal of Applied Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Seasonal cultivated and fallow cropland mapping using MODIS-based automated cropland classification algorithm","docAbstract":"Increasing drought occurrences and growing populations demand accurate, routine, and consistent cultivated and fallow cropland products to enable water and food security analysis. The overarching goal of this research was to develop and test automated cropland classification algorithm (ACCA) that provide accurate, consistent, and repeatable information on seasonal cultivated as well as seasonal fallow cropland extents and areas based on the Moderate Resolution Imaging Spectroradiometer remote sensing data. Seasonal ACCA development process involves writing series of iterative decision tree codes to separate cultivated and fallow croplands from noncroplands, aiming to accurately mirror reliable reference data sources. A pixel-by-pixel accuracy assessment when compared with the U.S. Department of Agriculture (USDA) cropland data showed, on average, a producer’s accuracy of 93% and a user’s accuracy of 85% across all months. Further, ACCA-derived cropland maps agreed well with the USDA Farm Service Agency crop acreage-reported data for both cultivated and fallow croplands with R-square values over 0.7 and field surveys with an accuracy of ≥95% for cultivated croplands and ≥76% for fallow croplands. Our results demonstrated the ability of ACCA to generate cropland products, such as cultivated and fallow cropland extents and areas, accurately, automatically, and repeatedly throughout the growing season.","language":"English","publisher":"SPIE","doi":"10.1117/1.JRS.8.083685","usgsCitation":"Wu, Z., Thenkabail, P.S., Mueller, R., Zakzeski, A., Melton, F., Johnson, L., Rosevelt, C., Dwyer, J., Jones, J., and Verdin, J.P., 2014, Seasonal cultivated and fallow cropland mapping using MODIS-based automated cropland classification algorithm: Journal of Applied Remote Sensing, v. 8, no. 1, Article 083685; 17 p., https://doi.org/10.1117/1.JRS.8.083685.","productDescription":"Article 083685; 17 p.","numberOfPages":"17","ipdsId":"IP-044862","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":473328,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1117/1.jrs.8.083685","text":"Publisher Index Page"},{"id":284264,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":284265,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1117/1.JRS.8.083685"}],"country":"United States","state":"California","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -125.0,33.0 ], [ -125.0,42.0 ], [ -115.0,42.0 ], [ -115.0,33.0 ], [ -125.0,33.0 ] ] ] } } ] }","volume":"8","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd71abe4b0b29085107d2e","contributors":{"authors":[{"text":"Wu, Zhuoting 0000-0001-7393-1832 zwu@usgs.gov","orcid":"https://orcid.org/0000-0001-7393-1832","contributorId":4953,"corporation":false,"usgs":true,"family":"Wu","given":"Zhuoting","email":"zwu@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":498,"text":"Office of Land Remote Sensing (Geography)","active":true,"usgs":true}],"preferred":true,"id":491771,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thenkabail, Prasad S. 0000-0002-2182-8822 pthenkabail@usgs.gov","orcid":"https://orcid.org/0000-0002-2182-8822","contributorId":570,"corporation":false,"usgs":true,"family":"Thenkabail","given":"Prasad","email":"pthenkabail@usgs.gov","middleInitial":"S.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":491769,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mueller, Rick","contributorId":101182,"corporation":false,"usgs":false,"family":"Mueller","given":"Rick","email":"","affiliations":[{"id":6622,"text":"US Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":491778,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zakzeski, Audra","contributorId":79796,"corporation":false,"usgs":true,"family":"Zakzeski","given":"Audra","email":"","affiliations":[],"preferred":false,"id":491777,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Melton, F.","contributorId":34039,"corporation":false,"usgs":true,"family":"Melton","given":"F.","affiliations":[{"id":24796,"text":"NASA Ames Research Center","active":true,"usgs":false}],"preferred":false,"id":491774,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Johnson, Lee","contributorId":60122,"corporation":false,"usgs":true,"family":"Johnson","given":"Lee","email":"","affiliations":[],"preferred":false,"id":491776,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rosevelt, Carolyn","contributorId":25455,"corporation":false,"usgs":true,"family":"Rosevelt","given":"Carolyn","email":"","affiliations":[],"preferred":false,"id":491773,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dwyer, John","contributorId":45042,"corporation":false,"usgs":true,"family":"Dwyer","given":"John","affiliations":[],"preferred":false,"id":491775,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Jones, Jeanine","contributorId":6758,"corporation":false,"usgs":true,"family":"Jones","given":"Jeanine","email":"","affiliations":[],"preferred":false,"id":491772,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Verdin, James P. 0000-0003-0238-9657 verdin@usgs.gov","orcid":"https://orcid.org/0000-0003-0238-9657","contributorId":720,"corporation":false,"usgs":true,"family":"Verdin","given":"James","email":"verdin@usgs.gov","middleInitial":"P.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":491770,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
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