{"pageNumber":"636","pageRowStart":"15875","pageSize":"25","recordCount":40807,"records":[{"id":70150419,"text":"70150419 - 2013 - Links between riparian landcover, instream environment and fish assemblages in headwater streams of south-eastern Brazil","interactions":[],"lastModifiedDate":"2015-06-24T14:03:36","indexId":"70150419","displayToPublicDate":"2013-10-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1471,"text":"Ecology of Freshwater Fish","active":true,"publicationSubtype":{"id":10}},"title":"Links between riparian landcover, instream environment and fish assemblages in headwater streams of south-eastern Brazil","docAbstract":"

We hypothesised and tested a hierarchical organisation model where riparian landcover would influence bank composition and light availability, which in turn would influence instream environments and control fish assemblages. The study was conducted during the dry season in 11 headwater tributaries of the Sorocaba River in the upper Paraná River Basin, south-eastern Brazil. We focused on seven environmental factors each represented by one or multiple environmental variables and seven fish functional traits each represented by two or more classes. Multivariate direct gradient analyses suggested that riparian zone landcover can be considered a higher level causal factor in a network of relations that control instream characteristics and fish assemblages. Our results provide a framework for a hierarchical conceptual model that identifies singular and collective influences of variables from different scales on each other and ultimately on different aspects related to stream fish functional composition. This conceptual model is focused on the relationships between riparian landcover and instream variables as causal factors on the organisation of stream fish assemblages. Our results can also be viewed as a model for headwater stream management in that landcover can be manipulated to influence factors such as bank composition, substrates and water quality, whereas fish assemblage composition can be used as indicators to monitor the success of such efforts.

","language":"English","publisher":"Wiley","doi":"10.1111/eff.12065","usgsCitation":"Cruz, B.B., Miranda, L.E., and Cetra, M., 2013, Links between riparian landcover, instream environment and fish assemblages in headwater streams of south-eastern Brazil: Ecology of Freshwater Fish, v. 22, no. 4, p. 607-616, https://doi.org/10.1111/eff.12065.","productDescription":"10 p.","startPage":"607","endPage":"616","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-040676","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":302301,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Brazil","otherGeospatial":"Sorocaba River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -47.669677734375,\n -23.61432859499169\n ],\n [\n -47.669677734375,\n -23.35486416841885\n ],\n [\n -47.23297119140625,\n -23.35486416841885\n ],\n [\n -47.23297119140625,\n -23.61432859499169\n ],\n [\n -47.669677734375,\n -23.61432859499169\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"22","issue":"4","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2013-05-06","publicationStatus":"PW","scienceBaseUri":"558bd4bbe4b0b6d21dd65310","contributors":{"authors":[{"text":"Cruz, Bruna B.","contributorId":97129,"corporation":false,"usgs":true,"family":"Cruz","given":"Bruna","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":556826,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miranda, Leandro E. 0000-0002-2138-7924 smiranda@usgs.gov","orcid":"https://orcid.org/0000-0002-2138-7924","contributorId":531,"corporation":false,"usgs":true,"family":"Miranda","given":"Leandro","email":"smiranda@usgs.gov","middleInitial":"E.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":556823,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cetra, Mauricio","contributorId":143697,"corporation":false,"usgs":false,"family":"Cetra","given":"Mauricio","email":"","affiliations":[],"preferred":false,"id":556827,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70048495,"text":"ofr20131189 - 2013 - Center of Excellence for Geospatial Information Science research plan 2013-18","interactions":[],"lastModifiedDate":"2013-09-30T16:07:39","indexId":"ofr20131189","displayToPublicDate":"2013-09-30T15:59:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-1189","title":"Center of Excellence for Geospatial Information Science research plan 2013-18","docAbstract":"The U.S. Geological Survey Center of Excellence for Geospatial Information Science (CEGIS) was created in 2006 and since that time has provided research primarily in support of The National Map. The presentations and publications of the CEGIS researchers document the research accomplishments that include advances in electronic topographic map design, generalization, data integration, map projections, sea level rise modeling, geospatial semantics, ontology, user-centered design, volunteer geographic information, and parallel and grid computing for geospatial data from The National Map. A research plan spanning 2013–18 has been developed extending the accomplishments of the CEGIS researchers and documenting new research areas that are anticipated to support The National Map of the future. In addition to extending the 2006–12 research areas, the CEGIS research plan for 2013–18 includes new research areas in data models, geospatial semantics, high-performance computing, volunteered geographic information, crowdsourcing, social media, data integration, and multiscale representations to support the Three-Dimensional Elevation Program (3DEP) and The National Map of the future of the U.S. Geological Survey.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131189","usgsCitation":"Usery, E.L., 2013, Center of Excellence for Geospatial Information Science research plan 2013-18: U.S. Geological Survey Open-File Report 2013-1189, v, 50 p., https://doi.org/10.3133/ofr20131189.","productDescription":"v, 50 p.","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":161,"text":"Center of Excellence for Geospatial Information Science (CEGIS)","active":false,"usgs":true}],"links":[{"id":278234,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131189.gif"},{"id":278232,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1189/"},{"id":278233,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1189/pdf/of2013-1189.pdf"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"524a8f67e4b017cb43afb104","contributors":{"authors":[{"text":"Usery, E. Lynn 0000-0002-2766-2173 usery@usgs.gov","orcid":"https://orcid.org/0000-0002-2766-2173","contributorId":231,"corporation":false,"usgs":true,"family":"Usery","given":"E.","email":"usery@usgs.gov","middleInitial":"Lynn","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":484836,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70175230,"text":"70175230 - 2013 - A comparison of adaptive sampling designs and binary spatial models: A simulation study using a census of Bromus inermis","interactions":[],"lastModifiedDate":"2016-08-03T12:33:13","indexId":"70175230","displayToPublicDate":"2013-09-30T13:30:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1577,"text":"Environmetrics","active":true,"publicationSubtype":{"id":10}},"title":"A comparison of adaptive sampling designs and binary spatial models: A simulation study using a census of Bromus inermis","docAbstract":"

Commonly in environmental and ecological studies, species distribution data are recorded as presence or absence throughout a spatial domain of interest. Field based studies typically collect observations by sampling a subset of the spatial domain. We consider the effects of six different adaptive and two non-adaptive sampling designs and choice of three binary models on both predictions to unsampled locations and parameter estimation of the regression coefficients (species–environment relationships). Our simulation study is unique compared to others to date in that we virtually sample a true known spatial distribution of a nonindigenous plant species, Bromus inermis. The census of B. inermis provides a good example of a species distribution that is both sparsely (1.9 % prevalence) and patchily distributed. We find that modeling the spatial correlation using a random effect with an intrinsic Gaussian conditionally autoregressive prior distribution was equivalent or superior to Bayesian autologistic regression in terms of predicting to un-sampled areas when strip adaptive cluster sampling was used to survey B. inermis. However, inferences about the relationships between B. inermis presence and environmental predictors differed between the two spatial binary models. The strip adaptive cluster designs we investigate provided a significant advantage in terms of Markov chain Monte Carlo chain convergence when trying to model a sparsely distributed species across a large area. In general, there was little difference in the choice of neighborhood, although the adaptive king was preferred when transects were randomly placed throughout the spatial domain.

","language":"English","publisher":"International Environmetrics Society","publisherLocation":"London","doi":"10.1002/env.2223","usgsCitation":"Irvine, K.M., Thornton, J., Backus, V.M., Hohmann, M.G., Lehnhoff, E.A., Maxwell, B., Michels, K., and Rew, L., 2013, A comparison of adaptive sampling designs and binary spatial models: A simulation study using a census of Bromus inermis: Environmetrics, v. 24, no. 6, p. 407-417, https://doi.org/10.1002/env.2223.","startPage":"407","endPage":"417","numberOfPages":"11","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-037370","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":326035,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"24","issue":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2013-08-13","publicationStatus":"PW","scienceBaseUri":"57a315b9e4b006cb45558a1e","contributors":{"authors":[{"text":"Irvine, Kathryn M. 0000-0002-6426-940X kirvine@usgs.gov","orcid":"https://orcid.org/0000-0002-6426-940X","contributorId":2218,"corporation":false,"usgs":true,"family":"Irvine","given":"Kathryn","email":"kirvine@usgs.gov","middleInitial":"M.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":644436,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thornton, Jamie","contributorId":173411,"corporation":false,"usgs":false,"family":"Thornton","given":"Jamie","email":"","affiliations":[],"preferred":false,"id":644557,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Backus, Vickie M.","contributorId":173380,"corporation":false,"usgs":false,"family":"Backus","given":"Vickie","email":"","middleInitial":"M.","affiliations":[{"id":27218,"text":"Montana State University, Earth Sciences","active":true,"usgs":false}],"preferred":false,"id":644441,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hohmann, Matthew G.","contributorId":173379,"corporation":false,"usgs":false,"family":"Hohmann","given":"Matthew","email":"","middleInitial":"G.","affiliations":[{"id":26926,"text":"Us Army Engineer Research and Development Center, Vicksburg, MS","active":true,"usgs":false}],"preferred":false,"id":644440,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lehnhoff, Erik A.","contributorId":173377,"corporation":false,"usgs":false,"family":"Lehnhoff","given":"Erik","email":"","middleInitial":"A.","affiliations":[{"id":6765,"text":"Montana State University, Department of Land Resources and Environmental Sciences","active":true,"usgs":false}],"preferred":false,"id":644438,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Maxwell, Bruce D.","contributorId":173376,"corporation":false,"usgs":false,"family":"Maxwell","given":"Bruce D.","affiliations":[{"id":6765,"text":"Montana State University, Department of Land Resources and Environmental Sciences","active":true,"usgs":false}],"preferred":false,"id":644437,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Michels, Kurt","contributorId":173378,"corporation":false,"usgs":false,"family":"Michels","given":"Kurt","email":"","affiliations":[{"id":27217,"text":"University of Arizona, Department of Mathematical Sciences","active":true,"usgs":false}],"preferred":false,"id":644558,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rew, Lisa","contributorId":167882,"corporation":false,"usgs":false,"family":"Rew","given":"Lisa","email":"","affiliations":[{"id":5120,"text":"Montana State University, Department of Mathematical Sciences, Bozeman, MT 59717","active":true,"usgs":false}],"preferred":false,"id":644559,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70048483,"text":"70048483 - 2013 - Validating predictions from climate envelope models","interactions":[],"lastModifiedDate":"2013-10-30T11:06:49","indexId":"70048483","displayToPublicDate":"2013-09-30T10:05:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Validating predictions from climate envelope models","docAbstract":"Climate envelope models are a potentially important conservation tool, but their ability to accurately forecast species’ distributional shifts using independent survey data has not been fully evaluated. We created climate envelope models for 12 species of North American breeding birds previously shown to have experienced poleward range shifts. For each species, we evaluated three different approaches to climate envelope modeling that differed in the way they treated climate-induced range expansion and contraction, using random forests and maximum entropy modeling algorithms. All models were calibrated using occurrence data from 1967–1971 (t1) and evaluated using occurrence data from 1998–2002 (t2). Model sensitivity (the ability to correctly classify species presences) was greater using the maximum entropy algorithm than the random forest algorithm. Although sensitivity did not differ significantly among approaches, for many species, sensitivity was maximized using a hybrid approach that assumed range expansion, but not contraction, in t2. Species for which the hybrid approach resulted in the greatest improvement in sensitivity have been reported from more land cover types than species for which there was little difference in sensitivity between hybrid and dynamic approaches, suggesting that habitat generalists may be buffered somewhat against climate-induced range contractions. Specificity (the ability to correctly classify species absences) was maximized using the random forest algorithm and was lowest using the hybrid approach. Overall, our results suggest cautious optimism for the use of climate envelope models to forecast range shifts, but also underscore the importance of considering non-climate drivers of species range limits. The use of alternative climate envelope models that make different assumptions about range expansion and contraction is a new and potentially useful way to help inform our understanding of climate change effects on species.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"PLoS ONE","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"PLOS One","doi":"10.1371/journal.pone.0063600","usgsCitation":"Watling, J., Bucklin, D., Speroterra, C., Brandt, L., Cabal, C., Romañach, S., and Mazzotti, F., 2013, Validating predictions from climate envelope models: PLoS ONE, v. 8, no. 5, 12 p., https://doi.org/10.1371/journal.pone.0063600.","productDescription":"12 p.","ipdsId":"IP-036644","costCenters":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"links":[{"id":473520,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0063600","text":"Publisher Index Page"},{"id":278219,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":278218,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1371/journal.pone.0063600"}],"volume":"8","issue":"5","noUsgsAuthors":false,"publicationDate":"2013-05-23","publicationStatus":"PW","scienceBaseUri":"524a8f6ae4b017cb43afb10d","contributors":{"authors":[{"text":"Watling, J.","contributorId":13125,"corporation":false,"usgs":true,"family":"Watling","given":"J.","email":"","affiliations":[],"preferred":false,"id":484807,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bucklin, D.","contributorId":107179,"corporation":false,"usgs":true,"family":"Bucklin","given":"D.","affiliations":[],"preferred":false,"id":484811,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Speroterra, C.","contributorId":75842,"corporation":false,"usgs":true,"family":"Speroterra","given":"C.","affiliations":[],"preferred":false,"id":484810,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brandt, L.","contributorId":24548,"corporation":false,"usgs":true,"family":"Brandt","given":"L.","email":"","affiliations":[],"preferred":false,"id":484808,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cabal, C.","contributorId":52479,"corporation":false,"usgs":true,"family":"Cabal","given":"C.","email":"","affiliations":[],"preferred":false,"id":484809,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Romañach, Stephanie S. 0000-0003-0271-7825 sromanach@usgs.gov","orcid":"https://orcid.org/0000-0003-0271-7825","contributorId":2331,"corporation":false,"usgs":true,"family":"Romañach","given":"Stephanie S.","email":"sromanach@usgs.gov","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":false,"id":484805,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mazzotti, Frank J.","contributorId":12358,"corporation":false,"usgs":false,"family":"Mazzotti","given":"Frank J.","affiliations":[{"id":12604,"text":"Department of Wildlife Ecology and Conservation, Fort Lauderdale Research and Education Center, 3205 College Avenue, University of Florida, Davie, FL 33314, USA","active":true,"usgs":false}],"preferred":false,"id":484806,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70048481,"text":"70048481 - 2013 - A comparative assessment of tools for ecosystem services quantification and valuation","interactions":[],"lastModifiedDate":"2013-10-30T11:07:29","indexId":"70048481","displayToPublicDate":"2013-09-30T09:37:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1477,"text":"Ecosystem Services","active":true,"publicationSubtype":{"id":10}},"title":"A comparative assessment of tools for ecosystem services quantification and valuation","docAbstract":"To enter widespread use, ecosystem service assessments need to be quantifiable, replicable, credible, flexible, and affordable. With recent growth in the field of ecosystem services, a variety of decision-support tools has emerged to support more systematic ecosystem services assessment. Despite the growing complexity of the tool landscape, thorough reviews of tools for identifying, assessing, modeling and in some cases monetarily valuing ecosystem services have generally been lacking. In this study, we describe 17 ecosystem services tools and rate their performance against eight evaluative criteria that gauge their readiness for widespread application in public- and private-sector decision making. We describe each of the tools′ intended uses, services modeled, analytical approaches, data requirements, and outputs, as well time requirements to run seven tools in a first comparative concurrent application of multiple tools to a common location – the San Pedro River watershed in southeast Arizona, USA, and northern Sonora, Mexico. Based on this work, we offer conclusions about these tools′ current ‘readiness’ for widespread application within both public- and private-sector decision making processes. Finally, we describe potential pathways forward to reduce the resource requirements for running ecosystem services models, which are essential to facilitate their more widespread use in environmental decision making.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Ecosystem Services","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.ecoser.2013.07.004","usgsCitation":"Bagstad, K.J., Semmens, D., Waage, S., and Winthrop, R., 2013, A comparative assessment of tools for ecosystem services quantification and valuation: Ecosystem Services, v. 5, p. 27-39, https://doi.org/10.1016/j.ecoser.2013.07.004.","productDescription":"13 p.","startPage":"27","endPage":"39","numberOfPages":"13","ipdsId":"IP-036066","costCenters":[{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true}],"links":[{"id":278217,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":278216,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.ecoser.2013.07.004"}],"volume":"5","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"524a8f52e4b017cb43afb0fe","contributors":{"authors":[{"text":"Bagstad, Kenneth J. 0000-0001-8857-5615 kjbagstad@usgs.gov","orcid":"https://orcid.org/0000-0001-8857-5615","contributorId":3680,"corporation":false,"usgs":true,"family":"Bagstad","given":"Kenneth","email":"kjbagstad@usgs.gov","middleInitial":"J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":484798,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Semmens, Darius J. 0000-0001-7924-6529","orcid":"https://orcid.org/0000-0001-7924-6529","contributorId":64201,"corporation":false,"usgs":true,"family":"Semmens","given":"Darius J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":484799,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Waage, Sissel","contributorId":81786,"corporation":false,"usgs":true,"family":"Waage","given":"Sissel","email":"","affiliations":[],"preferred":false,"id":484801,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Winthrop, Robert","contributorId":76216,"corporation":false,"usgs":true,"family":"Winthrop","given":"Robert","email":"","affiliations":[],"preferred":false,"id":484800,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70048473,"text":"70048473 - 2013 - Changes in the structure and function of northern Alaskan ecosystems when considering variable leaf-out times across groupings of species in a dynamic vegetation model","interactions":[],"lastModifiedDate":"2014-02-03T10:52:02","indexId":"70048473","displayToPublicDate":"2013-09-30T09:25:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Changes in the structure and function of northern Alaskan ecosystems when considering variable leaf-out times across groupings of species in a dynamic vegetation model","docAbstract":"The phenology of arctic ecosystems is driven primarily by abiotic forces, with temperature acting as the main determinant of growing season onset and leaf budburst in the spring. However, while the plant species in arctic ecosystems require differing amounts of accumulated heat for leaf-out, dynamic vegetation models simulated over regional to global scales typically assume some average leaf-out for all of the species within an ecosystem. Here, we make use of air temperature records and observations of spring leaf phenology collected across dominant groupings of species (dwarf birch shrubs, willow shrubs, other deciduous shrubs, grasses, sedges, and forbs) in arctic and boreal ecosystems in Alaska. We then parameterize a dynamic vegetation model based on these data for four types of tundra ecosystems (heath tundra, shrub tundra, wet sedge tundra, and tussock tundra), as well as ecotonal boreal white spruce forest, and perform model simulations for the years 1970 -2100. Over the course of the model simulations, we found changes in ecosystem composition under this new phenology algorithm compared to simulations with the previous phenology algorithm. These changes were the result of the differential timing of leaf-out, as well as the ability for the groupings of species to compete for nitrogen and light availability. Regionally, there were differences in the trends of the carbon pools and fluxes between the new phenology algorithm and the previous phenology algorithm, although these differences depended on the future climate scenario. These findings indicate the importance of leaf phenology data collection by species and across the various ecosystem types within the highly heterogeneous Arctic landscape, and that dynamic vegetation models should consider variation in leaf-out by groupings of species within these ecosystems to make more accurate projections of future plant distributions and carbon cycling in Arctic regions.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Global Change Biology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.1111/gcb.12392","usgsCitation":"Euskirchen, E., Carman, T., and McGuire, A.D., 2013, Changes in the structure and function of northern Alaskan ecosystems when considering variable leaf-out times across groupings of species in a dynamic vegetation model: Global Change Biology, v. 20, no. 3, p. 963-978, https://doi.org/10.1111/gcb.12392.","productDescription":"16 p.","startPage":"963","endPage":"978","numberOfPages":"16","ipdsId":"IP-049058","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":473521,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gcb.12392","text":"Publisher Index Page"},{"id":278215,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":278214,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1111/gcb.12392"}],"volume":"20","issue":"3","noUsgsAuthors":false,"publicationDate":"2014-01-23","publicationStatus":"PW","scienceBaseUri":"524a8f68e4b017cb43afb107","contributors":{"authors":[{"text":"Euskirchen, E.S.","contributorId":44737,"corporation":false,"usgs":true,"family":"Euskirchen","given":"E.S.","affiliations":[],"preferred":false,"id":484765,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carman, T.B.","contributorId":33210,"corporation":false,"usgs":true,"family":"Carman","given":"T.B.","email":"","affiliations":[],"preferred":false,"id":484764,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McGuire, Anthony D. 0000-0003-4646-0750 ffadm@usgs.gov","orcid":"https://orcid.org/0000-0003-4646-0750","contributorId":2493,"corporation":false,"usgs":true,"family":"McGuire","given":"Anthony","email":"ffadm@usgs.gov","middleInitial":"D.","affiliations":[],"preferred":false,"id":484763,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70048440,"text":"ofr20131222 - 2013 - Design tradeoffs for trend assessment in aquatic biological monitoring programs","interactions":[],"lastModifiedDate":"2013-09-26T12:57:25","indexId":"ofr20131222","displayToPublicDate":"2013-09-26T11:50:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-1222","title":"Design tradeoffs for trend assessment in aquatic biological monitoring programs","docAbstract":"Assessments of long-term (multiyear) temporal trends in biological monitoring programs are generally undertaken without an adequate understanding of the temporal variability of biological communities. When the sources and levels of variability are unknown, managers cannot make informed choices in sampling design to achieve monitoring goals in a cost-effective manner. We evaluated different trend sampling designs by estimating components of both short- and long-term variability in biological indicators of water quality in streams. Invertebrate samples were collected from 32 sites—9 urban, 6 agricultural, and 17 relatively undisturbed (reference) streams—distributed throughout the United States. Between 5 and 12 yearly samples were collected at each site during the period 1993–2008, plus 2 samples within a 10-week index period during either 2007 or 2008. These data allowed calculation of four sources of variance for invertebrate indicators: among sites, among years within sites, interaction among sites and years (site-specific annual variation), and among samples collected within an index period at a site (residual). When estimates of these variance components are known, changes to sampling design can be made to improve trend detection. Design modifications that result in the ability to detect the smallest trend with the fewest samples are, from most to least effective: (1) increasing the number of years in the sampling period (duration of the monitoring program), (2) decreasing the interval between samples, and (3) increasing the number of repeat-visit samples per year (within an index period). This order of improvement in trend detection, which achieves the greatest gain for the fewest samples, is the same whether trends are assessed at an individual site or an average trend of multiple sites. In multiple-site surveys, increasing the number of sites has an effect similar to that of decreasing the sampling interval; the benefit of adding sites is greater when a new set of different sites is selected for each sampling effort than when the same sites are sampled each time. Understanding variance components of the ecological attributes of interest can lead to more cost-effective monitoring designs to detect trends.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131222","collaboration":"National Water-Quality Assessment Program; Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Gurtz, M.E., Van Sickle, J., Carlisle, D.M., and Paulsen, S., 2013, Design tradeoffs for trend assessment in aquatic biological monitoring programs: U.S. Geological Survey Open-File Report 2013-1222, v, 17 p., https://doi.org/10.3133/ofr20131222.","productDescription":"v, 17 p.","numberOfPages":"27","onlineOnly":"Y","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":278142,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131222.gif"},{"id":278140,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1222/"},{"id":278141,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1222/pdf/ofr2013-1222.pdf"}],"country":"United States","state":"Alabama;Arizona;Arkansas;California;Colorado;Georgia;Idaho;Indiana;Massachusetts;Michigan;New Jersey;North Carolina;Pennsylvania;Ohio;Oregon;Texas;Utah;Virginia;Washington;Wyoming","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -124.8,24.5 ], [ -124.8,49.383333 ], [ -66.95,49.383333 ], [ -66.95,24.5 ], [ -124.8,24.5 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"52454a24e4b0b3d37307e14d","contributors":{"authors":[{"text":"Gurtz, Martin E. megurtz@usgs.gov","contributorId":2987,"corporation":false,"usgs":true,"family":"Gurtz","given":"Martin","email":"megurtz@usgs.gov","middleInitial":"E.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":false,"id":484655,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Van Sickle, John","contributorId":72698,"corporation":false,"usgs":true,"family":"Van Sickle","given":"John","email":"","affiliations":[],"preferred":false,"id":484657,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carlisle, Daren M. 0000-0002-7367-348X dcarlisle@usgs.gov","orcid":"https://orcid.org/0000-0002-7367-348X","contributorId":513,"corporation":false,"usgs":true,"family":"Carlisle","given":"Daren","email":"dcarlisle@usgs.gov","middleInitial":"M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":484654,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Paulsen, Steven G.","contributorId":23837,"corporation":false,"usgs":true,"family":"Paulsen","given":"Steven G.","affiliations":[],"preferred":false,"id":484656,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70048439,"text":"sir20135053 - 2013 - Status and understanding of groundwater quality in the South Coast Range-Coastal study unit, 2008: California GAMA Priority Basin Project","interactions":[],"lastModifiedDate":"2013-10-30T11:15:23","indexId":"sir20135053","displayToPublicDate":"2013-09-26T11:43:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-5053","title":"Status and understanding of groundwater quality in the South Coast Range-Coastal study unit, 2008: California GAMA Priority Basin Project","docAbstract":"

Groundwater quality in the South Coast Range–Coastal (SCRC) study unit was investigated from May through November 2008 as part of the Priority Basin Project of the Groundwater Ambient Monitoring and Assessment (GAMA) Program. The study unit is located in the Southern Coast Range hydrologic province and includes parts of Santa Barbara and San Luis Obispo Counties. The GAMA Priority Basin Project is conducted by the U.S. Geological Survey (USGS) in collaboration with the California State Water Resources Control Board and the Lawrence Livermore National Laboratory.

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The GAMA Priority Basin Project was designed to provide a statistically unbiased, spatially distributed assessment of untreated groundwater quality within the primary aquifer system. The primary aquifer system is defined as that part of the aquifer corresponding to the perforation interval of wells listed in the California Department of Public Health (CDPH) database for the SCRC study unit.

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The assessments for the SCRC study unit were based on water-quality and ancillary data collected in 2008 by the USGS from 55 wells on a spatially distributed grid, and water-quality data from the CDPH database. Two types of assessments were made: (1) status, assessment of the current quality of the groundwater resource, and (2) understanding, identification of the natural and human factors affecting groundwater quality. Water-quality and ancillary data were collected from an additional 15 wells for the understanding assessment. The assessments characterize untreated groundwater quality, not the quality of treated drinking water delivered to consumers by water purveyors.

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The first component of this study, the status assessment of groundwater quality, used data from samples analyzed for anthropogenic constituents such as volatile organic compounds (VOCs) and pesticides, as well as naturally occurring inorganic constituents such as major ions and trace elements. Although the status assessment applies to untreated groundwater, Federal and California regulatory and non-regulatory water-quality benchmarks that apply to drinking water are used to provide context for the results. Relative-concentrations (sample concentration divided by benchmark concentration) were used for evaluating groundwater. A relative-concentration greater than (>) 1.0 indicates a concentration greater than the benchmark and is classified as high. Inorganic constituents are classified as moderate if relative-concentrations are >0.5 and less than or equal to (≤) 1.0, or low if relative-concentrations are ≤0.5. For organic constituents, the boundary between moderate and low relative-concentrations was set at 0.1.

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Aquifer-scale proportion was used in the status assessment as the primary metric for evaluating regional-scale groundwater quality. High aquifer-scale proportion is defined as the areal percentage of the primary aquifer system with a high relative-concentration for a particular constituent or class of constituents. Moderate and low aquifer-scale proportions were defined as the areal percentage of the primary aquifer system with moderate and low relative-concentrations, respectively. Two statistical approaches—grid-based and spatially weighted—were used to evaluate aquifer-scale proportions for individual constituents and classes of constituents. Grid-based and spatially weighted estimates were comparable for the study (within 90 percent confidence intervals).

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For inorganic constituents with human-health benchmarks, relative-concentrations were high for at least one constituent for 33 percent of the primary aquifer system in the SCRC study unit. Arsenic, molybdenum, and nitrate were the primary inorganic constituents with human-health benchmarks that were detected at high relative-concentrations. Inorganic constituents with aesthetic benchmarks, referred to as secondary maximum contaminant levels (SMCLs), had high relative-concentrations for 35 percent of the primary aquifer system. Iron, manganese, total dissolved solids (TDS), and sulfate were the inorganic constituents with SMCLs detected at high relative-concentrations.

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In contrast to inorganic constituents, organic constituents with human-health benchmarks were not detected at high relative-concentrations in the primary aquifer system in the SCRC study unit. Of the 205 organic constituents analyzed, 21 were detected—13 with human-health benchmarks. Perchloroethene (PCE) was the only VOC detected at moderate relative-concentrations. PCE, dichlorodifluoromethane (CFC-12), and chloroform were detected in more than 10 percent of the primary aquifer system. Of the two special-interest constituents, one was detected; perchlorate, which has a human-health benchmark, was detected at moderate relative-concentrations in 29 percent of the primary aquifer system and had a detection frequency of 60 percent in the SCRC study unit.

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The second component of this study, the understanding assessment, identified the natural and human factors that may have affected groundwater quality in the SCRC study unit by evaluating statistical correlations between water-quality constituents and potential explanatory factors. The potential explanatory factors evaluated were land use, septic tank density, well depth and depth to top-of-perforations, groundwater age, density and distance to the nearest formerly leaking underground fuel tank (LUFT), pH, and dissolved oxygen (DO) concentration. Results of the statistical evaluations were used to explain the occurrence and distribution of constituents in the study unit.

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DO was the primary explanatory factor influencing the concentrations of many inorganic constituents. Arsenic, iron, and manganese concentrations increased as DO concentrations decreased, consistent with patterns expected as a result of reductive dissolution of iron and (or) manganese oxides in aquifer sediments. Molybdenum concentrations increased in anoxic conditions and in oxic conditions with high pH, reflecting two mechanisms for the mobilization of molybdenum—reductive dissolution and pH-dependent desorption under oxic conditions from aquifer sediments. Nitrate concentrations decreased as DO concentrations decreased which would be consistent with degradation of nitrate under anoxic conditions (denitrification). It also is possible that nitrate concentrations decreased in relation to increasing depth and groundwater age and not as a result of denitrification.

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Groundwater age was another explanatory factor frequently correlated to several inorganic constituents. Iron and manganese concentrations were higher in pre-modern (water recharged before 1952) or mixed-age groundwater. This correlation is one indication that iron and manganese are from natural sources. Nitrate, TDS, and sulfate concentrations were higher in modern groundwater (water recharged since 1952) and may indicate that human activities increase concentrations of nitrate, TDS, and sulfate.

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Land use was a third explanatory factor frequently correlated with inorganic constituents. Nitrate, TDS, and sulfate concentrations were higher in agricultural land-use areas than in natural land-use areas, indicating that increased concentrations may be a result of agricultural practices.

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Organic constituents usually were detected at low relative-concentrations; therefore, statistical analyses of relations to explanatory factors usually were done for classes of constituents (for example, pesticides or solvents) as well as for selected constituents. The number of VOCs detected in a well was not correlated to any of the explanatory factors evaluated. The number of pesticide and solvent detections and PCE and CFC-12 concentrations were higher in modern groundwater than in pre-modern groundwater. PCE and CFC-12 also were positively correlated to the density of LUFTs. PCE was negatively correlated to natural land use. Chloroform concentrations were positively correlated to the density of septic systems.

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Perchlorate concentrations were greater in agricultural areas than in urban or natural areas. Correlation of perchlorate with DO may indicate that perchlorate biodegradation under anoxic conditions may occur. Anthropogenic sources have contributed perchlorate to groundwater in the SCRC study unit, although low levels of perchlorate may occur naturally.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135053","collaboration":"A product of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program, Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Burton, C., Land, M., and Belitz, K., 2013, Status and understanding of groundwater quality in the South Coast Range-Coastal study unit, 2008: California GAMA Priority Basin Project: U.S. Geological Survey Scientific Investigations Report 2013-5053, ix, 86 p., https://doi.org/10.3133/sir20135053.","productDescription":"ix, 86 p.","numberOfPages":"100","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":278137,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135053.jpg"},{"id":278135,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5053/"},{"id":278136,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5053/pdf/sir2013-5053.pdf"}],"projection":"Albers Equal Area Conic Projection","country":"United States","state":"California","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -0.01611111111111111,8.333333333333334E-4 ], [ -0.01611111111111111,0.0011111111111111111 ], [ -0.01638888888888889,0.0011111111111111111 ], [ -0.01638888888888889,8.333333333333334E-4 ], [ -0.01611111111111111,8.333333333333334E-4 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"52454a27e4b0b3d37307e15f","contributors":{"authors":[{"text":"Burton, Carmen A. 0000-0002-6381-8833","orcid":"https://orcid.org/0000-0002-6381-8833","contributorId":41793,"corporation":false,"usgs":true,"family":"Burton","given":"Carmen A.","affiliations":[],"preferred":false,"id":484653,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Land, Michael 0000-0001-5141-0307 mtland@usgs.gov","orcid":"https://orcid.org/0000-0001-5141-0307","contributorId":1479,"corporation":false,"usgs":true,"family":"Land","given":"Michael","email":"mtland@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":484652,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Belitz, Kenneth 0000-0003-4481-2345 kbelitz@usgs.gov","orcid":"https://orcid.org/0000-0003-4481-2345","contributorId":442,"corporation":false,"usgs":true,"family":"Belitz","given":"Kenneth","email":"kbelitz@usgs.gov","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":484651,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70048408,"text":"sir20135102 - 2013 - Simulating stream transport of nutrients in the eastern United States, 2002, using a spatially-referenced regression model and 1:100,000-scale hydrography","interactions":[],"lastModifiedDate":"2013-09-25T13:04:05","indexId":"sir20135102","displayToPublicDate":"2013-09-25T12:38:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-5102","title":"Simulating stream transport of nutrients in the eastern United States, 2002, using a spatially-referenced regression model and 1:100,000-scale hydrography","docAbstract":"Existing Spatially Referenced Regression on Watershed attributes (SPARROW) nutrient models for the northeastern and southeastern regions of the United States were recalibrated to achieve a hydrographically consistent model with which to assess nutrient sources and stream transport and investigate specific management questions about the effects of wetlands and atmospheric deposition on nutrient transport. Recalibrated nitrogen models for the northeast and southeast were sufficiently similar to be merged into a single nitrogen model for the eastern United States. The atmospheric deposition source in the nitrogen model has been improved to account for individual components of atmospheric input, derived from emissions from agricultural manure, agricultural livestock, vehicles, power plants, other industry, and background sources. This accounting makes it possible to simulate the effects of altering an individual component of atmospheric deposition, such as nitrate emissions from vehicles or power plants. Regional differences in transport of phosphorus through wetlands and reservoirs were investigated and resulted in two distinct phosphorus models for the northeast and southeast. The recalibrated nitrogen and phosphorus models account explicitly for the influence of wetlands on regional-scale land-phase and aqueous-phase transport of nutrients and therefore allow comparison of the water-quality functions of different wetland systems over large spatial scales. Seven wetland systems were associated with enhanced transport of either nitrogen or phosphorus in streams, probably because of the export of dissolved organic nitrogen and bank erosion. Six wetland systems were associated with mitigating the delivery of either nitrogen or phosphorus to streams, probably because of sedimentation, phosphate sorption, and ground water infiltration.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135102","collaboration":"National Water-Quality Assessment Program","usgsCitation":"Hoos, A.B., Moore, R.B., Garcia, A., Noe, G., Terziotti, S., Johnston, C.M., and Dennis, R.L., 2013, Simulating stream transport of nutrients in the eastern United States, 2002, using a spatially-referenced regression model and 1:100,000-scale hydrography: U.S. Geological Survey Scientific Investigations Report 2013-5102, vii, 33 p., https://doi.org/10.3133/sir20135102.","productDescription":"vii, 33 p.","numberOfPages":"46","onlineOnly":"Y","temporalStart":"2002-01-01","temporalEnd":"2002-12-31","costCenters":[{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true}],"links":[{"id":278096,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135102.gif"},{"id":278095,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5102/pdf/sir2013-5102.pdf"},{"id":278094,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5102/"}],"projection":"Albers Equal-Area Conic Projection","datum":"North American Datum of 1983","country":"United States","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -91.11,24.35 ], [ -91.11,47.47 ], [ -64.51,47.47 ], [ -64.51,24.35 ], [ -91.11,24.35 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5243f812e4b05b217bad9ffd","contributors":{"authors":[{"text":"Hoos, Anne B. abhoos@usgs.gov","contributorId":2236,"corporation":false,"usgs":true,"family":"Hoos","given":"Anne","email":"abhoos@usgs.gov","middleInitial":"B.","affiliations":[],"preferred":true,"id":484549,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moore, Richard B. rmoore@usgs.gov","contributorId":1464,"corporation":false,"usgs":true,"family":"Moore","given":"Richard","email":"rmoore@usgs.gov","middleInitial":"B.","affiliations":[{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":484547,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Garcia, Ana Maria 0000-0002-5388-1281","orcid":"https://orcid.org/0000-0002-5388-1281","contributorId":44634,"corporation":false,"usgs":true,"family":"Garcia","given":"Ana Maria","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":484551,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Noe, Gregory B.","contributorId":77805,"corporation":false,"usgs":true,"family":"Noe","given":"Gregory B.","affiliations":[],"preferred":false,"id":484552,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Terziotti, Silvia E.","contributorId":90204,"corporation":false,"usgs":true,"family":"Terziotti","given":"Silvia E.","affiliations":[],"preferred":false,"id":484553,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Johnston, Craig M. cmjohnst@usgs.gov","contributorId":1814,"corporation":false,"usgs":true,"family":"Johnston","given":"Craig","email":"cmjohnst@usgs.gov","middleInitial":"M.","affiliations":[{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":484548,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dennis, Robin L.","contributorId":42849,"corporation":false,"usgs":true,"family":"Dennis","given":"Robin","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":484550,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70048395,"text":"70048395 - 2013 - Reevaluation of a walleye (Sander vitreus) bioenergetics model","interactions":[],"lastModifiedDate":"2013-09-25T12:00:49","indexId":"70048395","displayToPublicDate":"2013-09-25T11:55:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1651,"text":"Fish Physiology and Biochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Reevaluation of a walleye (Sander vitreus) bioenergetics model","docAbstract":"Walleye (Sander vitreus) is an important sport fish throughout much of North America, and walleye populations support valuable commercial fisheries in certain lakes as well. Using a corrected algorithm for balancing the energy budget, we reevaluated the performance of the Wisconsin bioenergetics model for walleye in the laboratory. Walleyes were fed rainbow smelt (Osmerus mordax) in four laboratory tanks each day during a 126-day experiment. Feeding rates ranged from 1.4 to 1.7 % of walleye body weight per day. Based on a statistical comparison of bioenergetics model predictions of monthly consumption with observed monthly consumption, we concluded that the bioenergetics model estimated food consumption by walleye without any significant bias. Similarly, based on a statistical comparison of bioenergetics model predictions of weight at the end of the monthly test period with observed weight, we concluded that the bioenergetics model predicted walleye growth without any detectable bias. In addition, the bioenergetics model predictions of cumulative consumption over the 126-day experiment differed fromobserved cumulative consumption by less than 10 %. Although additional laboratory and field testing will be needed to fully evaluate model performance, based on our laboratory results, the Wisconsin bioenergetics model for walleye appears to be providing unbiased predictions of food consumption.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Fish Physiology and Biochemistry","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Springer","doi":"10.1007/s10695-012-9737-7","usgsCitation":"Madenjian, C.P., and Wang, C., 2013, Reevaluation of a walleye (Sander vitreus) bioenergetics model: Fish Physiology and Biochemistry, v. 39, no. 4, p. 749-754, https://doi.org/10.1007/s10695-012-9737-7.","productDescription":"6 p.","startPage":"749","endPage":"754","numberOfPages":"6","ipdsId":"IP-041962","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":278091,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":278088,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1007/s10695-012-9737-7"}],"volume":"39","issue":"4","noUsgsAuthors":false,"publicationDate":"2012-11-04","publicationStatus":"PW","scienceBaseUri":"5243f812e4b05b217bad9ff9","contributors":{"authors":[{"text":"Madenjian, Charles P. 0000-0002-0326-164X cmadenjian@usgs.gov","orcid":"https://orcid.org/0000-0002-0326-164X","contributorId":2200,"corporation":false,"usgs":true,"family":"Madenjian","given":"Charles","email":"cmadenjian@usgs.gov","middleInitial":"P.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":484518,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wang, Chunfang","contributorId":40884,"corporation":false,"usgs":true,"family":"Wang","given":"Chunfang","email":"","affiliations":[],"preferred":false,"id":484519,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70048405,"text":"sir20135161 - 2013 - Enhancements to the Mississippi Embayment Regional Aquifer Study (MERAS) groundwater-flow model and simulations of sustainable water-level scenarios","interactions":[],"lastModifiedDate":"2019-06-20T13:10:14","indexId":"sir20135161","displayToPublicDate":"2013-09-25T11:48:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-5161","title":"Enhancements to the Mississippi Embayment Regional Aquifer Study (MERAS) groundwater-flow model and simulations of sustainable water-level scenarios","docAbstract":"

Arkansas continues to be one of the largest users of groundwater in the Nation. As such, long-term planning and management are essential to ensure continued availability of groundwater and surface water for years to come. The Mississippi Embayment Regional Aquifer Study (MERAS) model was developed previously as a tool to evaluate groundwater availability within the Mississippi embayment, which encompasses much of eastern Arkansas where the majority of groundwater is used. The Arkansas Water Plan is being updated for the first time since 1990 and serves as the State’s primary, comprehensive water-resources planning and guidance document. The MERAS model was selected as the best available tool for evaluation of specific water-use pumping scenarios that are currently being considered by the State of Arkansas. The model, developed as part of the U.S. Geological Survey Groundwater Resources Program’s assessment of the Nation’s groundwater availability, is proving to be invaluable to the State as it works toward development of a sustained yield pumping strategy. One aspect of this investigation was to evaluate multiple methods to improve the match of observed to simulated groundwater levels within the Mississippi River Valley alluvial and middle Claiborne (Sparta) aquifers in the MERAS model. Five primary methods were evaluated: (1) explicit simulation of evapotranspiration (ET), (2) upgrade of the Multi-Node Well (MNW2) Package, (3) geometry improvement within the Streamflow Routing (SFR) Package, (4) parameter estimation of select aquifer properties with pilot points, and (5) modification of water-use estimates. For the planning purposes of the Arkansas Water Plan, three scenarios were developed to evaluate potential future conditions: (1) simulation of previously optimized pumping values within the Mississippi River Valley alluvial and the middle Claiborne aquifers, (2) simulated prolonged effects of pumping at average recent (2000–5) rates, and (3) simulation of drawdown constraints on most pumping wells.

\n
\n

The explicit simulation of ET indicated little, if any, improvement of model fit at the expense of much longer simulation time and was not included in further simulations. Numerous attempts to fully utilize the MNW2 Package were unsuccessful in achieving model stability, though modifications made to the water-use dataset remained intact. Final improvements in the residual statistics may be attributed to a single method, or a cumulative effect of all other methods (geometry improvement with the SFR Package, parameter estimation with pilot points, and modification of water-use estimates) attempted. The root mean squared error (RMSE) for all observations in the model is 22.65 feet (ft) over a range in observed hydraulic head of 741.66 ft. The RMSE for water-level observations in the Mississippi River Valley alluvial aquifer is 14.14 ft (an improvement of almost 3 ft) over a range in observed hydraulic head of 297.25 ft. The RMSE for the Sparta aquifer is 32.02 ft (an improvement of approximately 3 ft) over a range in observed hydraulic head of 634.94 ft.

\n
\n

Three scenarios were developed to utilize a steady-state version of the MERAS model. Scenario 1 was developed to use pumping values resulting from the optimization of baseline rates (typically 1997 pumping rates) from previous optimization modeling of the alluvial aquifer and the Sparta aquifer. Scenario 2 was developed to evaluate the prolonged effects of pumping from the alluvial aquifer at recent pumping rates. Scenario 3A was designed to evaluate withdrawal limits from the alluvial aquifer by utilizing drawdown constraints equal to an altitude of approximately 50 percent of the predevelopment saturated thickness of the alluvial aquifer or 30 ft above the bottom of the alluvial aquifer, whichever was greater. The results of scenario 1 indicate large water-level declines throughout the area of the alluvial aquifer, regardless of the substitution of the optimized pumping values from earlier model simulations. The results of scenario 2 also indicate large areas of water-level decline, as compared to half of the saturated thickness, throughout the alluvial aquifer. The results of scenario 3A reveal some effects from the inclusion of multiple aquifers in a single simulation. The initial configuration of scenario 3A resulted in water levels well below the defined drawdown constraint, and some areas of depleted aquifer (water levels that are near or below the bottom of the aquifer) in east-central Arkansas. A fourth simulation (scenario 3B) was configured to apply the same drawdown constraints from the alluvial aquifer wells to the Sparta aquifer wells in the depleted area. These drawdown constraints reduce leakage from the alluvial aquifer to the underlying Sparta aquifer. This configuration did not produce depleted areas within the alluvial aquifer. Scenarios 3A and 3B indicate that even when pumping is limited in the alluvial aquifer, water levels in the alluvial aquifer may continue to decline in some areas because of pumping in the underlying Sparta aquifer.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135161","collaboration":"Prepared in cooperation with the Arkansas Natural Resources Commission","usgsCitation":"Clark, B.R., Westerman, D.A., and Fugitt, D.T., 2013, Enhancements to the Mississippi Embayment Regional Aquifer Study (MERAS) groundwater-flow model and simulations of sustainable water-level scenarios: U.S. Geological Survey Scientific Investigations Report 2013-5161, iv, 29 p., https://doi.org/10.3133/sir20135161.","productDescription":"iv, 29 p.","numberOfPages":"36","onlineOnly":"Y","costCenters":[{"id":129,"text":"Arkansas Water Science Center","active":true,"usgs":true}],"links":[{"id":278090,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135161.gif"},{"id":278148,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5161/pdf/sir2013-5161.pdf"},{"id":278089,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5161/"}],"projection":"Albers Equal-Area Conic projection","country":"United States","state":"Arkansas","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -94.054,30.4913 ], [ -94.054,38.5052 ], [ -86.5118,38.5052 ], [ -86.5118,30.4913 ], [ -94.054,30.4913 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5243f810e4b05b217bad9fed","contributors":{"authors":[{"text":"Clark, Brian R. 0000-0001-6611-3807 brclark@usgs.gov","orcid":"https://orcid.org/0000-0001-6611-3807","contributorId":1502,"corporation":false,"usgs":true,"family":"Clark","given":"Brian","email":"brclark@usgs.gov","middleInitial":"R.","affiliations":[{"id":38131,"text":"WMA - Office of Planning and Programming","active":true,"usgs":true}],"preferred":true,"id":484539,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Westerman, Drew A. 0000-0002-8522-776X dawester@usgs.gov","orcid":"https://orcid.org/0000-0002-8522-776X","contributorId":4526,"corporation":false,"usgs":true,"family":"Westerman","given":"Drew","email":"dawester@usgs.gov","middleInitial":"A.","affiliations":[{"id":129,"text":"Arkansas Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":484540,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fugitt, D. Todd","contributorId":7835,"corporation":false,"usgs":true,"family":"Fugitt","given":"D.","email":"","middleInitial":"Todd","affiliations":[],"preferred":false,"id":484541,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70048404,"text":"tm6A47 - 2013 - Use of multi-node wells in the Groundwater-Management Process of MODFLOW-2005 (GWM-2005)","interactions":[],"lastModifiedDate":"2013-09-25T10:07:43","indexId":"tm6A47","displayToPublicDate":"2013-09-25T10:04:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"6-A47","title":"Use of multi-node wells in the Groundwater-Management Process of MODFLOW-2005 (GWM-2005)","docAbstract":"Many groundwater wells are open to multiple aquifers or to multiple intervals within a single aquifer. These types of wells can be represented in numerical simulations of groundwater flow by use of the Multi-Node Well (MNW) Packages developed for the U.S. Geological Survey’s MODFLOW model. However, previous versions of the Groundwater-Management (GWM) Process for MODFLOW did not allow the use of multi-node wells in groundwater-management formulations. This report describes modifications to the MODFLOW–2005 version of the GWM Process (GWM–2005) to provide for such use with the MNW2 Package. Multi-node wells can be incorporated into a management formulation as flow-rate decision variables for which optimal withdrawal or injection rates will be determined as part of the GWM–2005 solution process. In addition, the heads within multi-node wells can be used as head-type state variables, and, in that capacity, be included in the objective function or constraint set of a management formulation. Simple head bounds also can be defined to constrain water levels at multi-node wells. The report provides instructions for including multi-node wells in the GWM–2005 data-input files and a sample problem that demonstrates use of multi-node wells in a typical groundwater-management problem.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm6A47","collaboration":"Groundwater Resources Program","usgsCitation":"Ahlfeld, D.P., and Barlow, P.M., 2013, Use of multi-node wells in the Groundwater-Management Process of MODFLOW-2005 (GWM-2005): U.S. Geological Survey Techniques and Methods 6-A47, vi, 26 p., https://doi.org/10.3133/tm6A47.","productDescription":"vi, 26 p.","numberOfPages":"36","onlineOnly":"Y","costCenters":[{"id":494,"text":"Office of Groundwater","active":false,"usgs":true}],"links":[{"id":278080,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/tm6a47.gif"},{"id":278078,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/tm/06/a47/"},{"id":278079,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/06/a47/pdf/tm6-a47.pdf"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5243f813e4b05b217bada001","contributors":{"authors":[{"text":"Ahlfeld, David P.","contributorId":49464,"corporation":false,"usgs":true,"family":"Ahlfeld","given":"David","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":484538,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barlow, Paul M. 0000-0003-4247-6456 pbarlow@usgs.gov","orcid":"https://orcid.org/0000-0003-4247-6456","contributorId":1200,"corporation":false,"usgs":true,"family":"Barlow","given":"Paul","email":"pbarlow@usgs.gov","middleInitial":"M.","affiliations":[{"id":493,"text":"Office of Ground Water","active":true,"usgs":true}],"preferred":true,"id":484537,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70048390,"text":"70048390 - 2013 - Plant responses, climate pivot points, and trade-offs in water-limited ecosystems","interactions":[],"lastModifiedDate":"2013-09-24T15:22:01","indexId":"70048390","displayToPublicDate":"2013-09-24T15:14:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Plant responses, climate pivot points, and trade-offs in water-limited ecosystems","docAbstract":"Plant species in dryland ecosystems are limited by water availability and may be vulnerable to increases in aridity. Methods are needed to monitor and assess the rate of change in plant abundance and composition in relation to climate, understand the potential for degradation in dryland ecosystems, and forecast future changes in plant species assemblages. I employ nearly a century of vegetation monitoring data from three North American deserts to demonstrate an approach to determine plant species responses to climate and critical points over a range of climatic conditions at which plant species shift from increases to decreases in abundance (climate pivot points). I assess these metrics from a site to regional scale and highlight how these indicators of plant performance can be modified by the physical and biotic environment. For example, shrubs were more responsive to drought and high temperatures on shallow soils with limited capacity to store water and fine-textured soils with slow percolation rates, whereas perennial grasses were more responsive to precipitation in sparse shrublands than in relatively dense grasslands and shrublands, where competition for water is likely more intense. The responses and associated climate pivot points of plant species aligned with their lifespan and structural characteristics, and the relationship between responses and climate pivot points provides evidence of the trade-off between the capacity of a plant species to increase in abundance when water is available and its drought resistance.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Ecosphere","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Ecological Society of America","doi":"10.1890/ES13-00132.1","usgsCitation":"Munson, S.M., 2013, Plant responses, climate pivot points, and trade-offs in water-limited ecosystems: Ecosphere, v. 4, no. 9, 15 p., https://doi.org/10.1890/ES13-00132.1.","productDescription":"15 p.","numberOfPages":"15","ipdsId":"IP-042024","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":473524,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1890/es13-00132.1","text":"Publisher Index Page"},{"id":278051,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":278044,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1890/ES13-00132.1"}],"country":"United States","state":"Arizona;New Mexico;Texas;Utah","otherGeospatial":"Chihuahuan Desert;Colorado Plateau;Sonoran Desert","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -111.48,28.97 ], [ -111.48,38.86 ], [ -102.84,38.86 ], [ -102.84,28.97 ], [ -111.48,28.97 ] ] ] } } ] }","volume":"4","issue":"9","noUsgsAuthors":false,"publicationDate":"2013-09-23","publicationStatus":"PW","scienceBaseUri":"5242a696e4b096ee624641d0","contributors":{"authors":[{"text":"Munson, Seth M. 0000-0002-2736-6374 smunson@usgs.gov","orcid":"https://orcid.org/0000-0002-2736-6374","contributorId":1334,"corporation":false,"usgs":true,"family":"Munson","given":"Seth","email":"smunson@usgs.gov","middleInitial":"M.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":484514,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70048387,"text":"70048387 - 2013 - A hybrid double-observer sightability model for aerial surveys","interactions":[],"lastModifiedDate":"2013-10-30T10:31:15","indexId":"70048387","displayToPublicDate":"2013-09-24T14:46:00","publicationYear":"2013","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":"A hybrid double-observer sightability model for aerial surveys","docAbstract":"Raw counts from aerial surveys make no correction for undetected animals and provide no estimate of precision with which to judge the utility of the counts. Sightability modeling and double-observer (DO) modeling are 2 commonly used approaches to account for detection bias and to estimate precision in aerial surveys. We developed a hybrid DO sightability model (model MH) that uses the strength of each approach to overcome the weakness in the other, for aerial surveys of elk (Cervus elaphus). The hybrid approach uses detection patterns of 2 independent observer pairs in a helicopter and telemetry-based detections of collared elk groups. Candidate MH models reflected hypotheses about effects of recorded covariates and unmodeled heterogeneity on the separate front-seat observer pair and back-seat observer pair detection probabilities. Group size and concealing vegetation cover strongly influenced detection probabilities. The pilot's previous experience participating in aerial surveys influenced detection by the front pair of observers if the elk group was on the pilot's side of the helicopter flight path. In 9 surveys in Mount Rainier National Park, the raw number of elk counted was approximately 80–93% of the abundance estimated by model MH. Uncorrected ratios of bulls per 100 cows generally were low compared to estimates adjusted for detection bias, but ratios of calves per 100 cows were comparable whether based on raw survey counts or adjusted estimates. The hybrid method was an improvement over commonly used alternatives, with improved precision compared to sightability modeling and reduced bias compared to DO modeling.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Wildlife Management","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley","doi":"10.1002/jwmg.612","usgsCitation":"Griffin, P., Lubow, B., Jenkins, K.J., Vales, D.J., Moeller, B.J., Reid, M., Happe, P.J., Mccorquodale, S.M., Tirhi, M.J., Schaberi, J.P., and Beirne, K., 2013, A hybrid double-observer sightability model for aerial surveys: Journal of Wildlife Management, v. 77, no. 8, p. 1532-1544, https://doi.org/10.1002/jwmg.612.","productDescription":"13 p.","startPage":"1532","endPage":"1544","numberOfPages":"13","onlineOnly":"Y","ipdsId":"IP-045719","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":278043,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":278038,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1002/jwmg.612"}],"country":"United States","state":"Washington","otherGeospatial":"Mount Rainier National Park","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -122.0,46.5 ], [ -122.0,47.0 ], [ -121.25,47.0 ], [ -121.25,46.5 ], [ -122.0,46.5 ] ] ] } } ] }","volume":"77","issue":"8","noUsgsAuthors":false,"publicationDate":"2013-09-19","publicationStatus":"PW","scienceBaseUri":"5242a655e4b096ee624641b0","contributors":{"authors":[{"text":"Griffin, Paul C.","contributorId":7802,"corporation":false,"usgs":true,"family":"Griffin","given":"Paul C.","affiliations":[],"preferred":false,"id":484501,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lubow, Bruce C.","contributorId":59520,"corporation":false,"usgs":true,"family":"Lubow","given":"Bruce C.","affiliations":[],"preferred":false,"id":484506,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jenkins, Kurt J. 0000-0003-1415-6607 kurt_jenkins@usgs.gov","orcid":"https://orcid.org/0000-0003-1415-6607","contributorId":3415,"corporation":false,"usgs":true,"family":"Jenkins","given":"Kurt","email":"kurt_jenkins@usgs.gov","middleInitial":"J.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":484500,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vales, David J.","contributorId":74662,"corporation":false,"usgs":true,"family":"Vales","given":"David","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":484508,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moeller, Barbara J.","contributorId":87446,"corporation":false,"usgs":true,"family":"Moeller","given":"Barbara","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":484510,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Reid, Mason","contributorId":51639,"corporation":false,"usgs":true,"family":"Reid","given":"Mason","affiliations":[],"preferred":false,"id":484504,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Happe, Patricia J.","contributorId":50983,"corporation":false,"usgs":false,"family":"Happe","given":"Patricia","email":"","middleInitial":"J.","affiliations":[{"id":16133,"text":"National Park Service, Olympic National Park","active":true,"usgs":false}],"preferred":false,"id":484503,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mccorquodale, Scott M.","contributorId":62921,"corporation":false,"usgs":true,"family":"Mccorquodale","given":"Scott","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":484507,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Tirhi, Michelle J.","contributorId":36839,"corporation":false,"usgs":true,"family":"Tirhi","given":"Michelle","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":484502,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Schaberi, Jim P.","contributorId":76218,"corporation":false,"usgs":true,"family":"Schaberi","given":"Jim","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":484509,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Beirne, Katherine","contributorId":58754,"corporation":false,"usgs":true,"family":"Beirne","given":"Katherine","affiliations":[],"preferred":false,"id":484505,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70154866,"text":"70154866 - 2013 - Evaluating changes to reservoir rule curves using historical water-level data","interactions":[],"lastModifiedDate":"2015-07-10T11:41:13","indexId":"70154866","displayToPublicDate":"2013-09-24T12:45:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3876,"text":"International Journal of River Basin Management","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating changes to reservoir rule curves using historical water-level data","docAbstract":"

Flood control reservoirs are typically managed through rule curves (i.e. target water levels) which control the storage and release timing of flood waters. Changes to rule curves are often contemplated and requested by various user groups and management agencies with no information available about the actual flood risk of such requests. Methods of estimating flood risk in reservoirs are not easily available to those unfamiliar with hydrological models that track water movement through a river basin. We developed a quantile regression model that uses readily available daily water-level data to estimate risk of spilling. Our model provided a relatively simple process for estimating the maximum applicable water level under a specific flood risk for any day of the year. This water level represents an upper-limit umbrella under which water levels can be operated in a variety of ways. Our model allows the visualization of water-level management under a user-specified flood risk and provides a framework for incorporating the effect of a changing environment on water-level management in reservoirs, but is not designed to replace existing hydrological models. The model can improve communication and collaboration among agencies responsible for managing natural resources dependent on reservoir water levels.

","language":"English","publisher":"International Association of Hydraulic Engineering and Research","publisherLocation":"Madrid, Spain","doi":"10.1080/15715124.2013.823979","usgsCitation":"Mower, E., and Miranda, L.E., 2013, Evaluating changes to reservoir rule curves using historical water-level data: International Journal of River Basin Management, v. 11, no. 3, p. 323-328, https://doi.org/10.1080/15715124.2013.823979.","productDescription":"6 p.","startPage":"323","endPage":"328","numberOfPages":"6","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-048954","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":305655,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"3","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55a0ecb1e4b0183d66e43039","contributors":{"authors":[{"text":"Mower, Ethan","contributorId":143702,"corporation":false,"usgs":false,"family":"Mower","given":"Ethan","email":"","affiliations":[],"preferred":false,"id":564617,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miranda, Leandro E. 0000-0002-2138-7924 smiranda@usgs.gov","orcid":"https://orcid.org/0000-0002-2138-7924","contributorId":531,"corporation":false,"usgs":true,"family":"Miranda","given":"Leandro","email":"smiranda@usgs.gov","middleInitial":"E.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":564293,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70048362,"text":"sir20135075 - 2013 - Ranking contributing areas of salt and selenium in the Lower Gunnison River Basin, Colorado, using multiple linear regression models","interactions":[],"lastModifiedDate":"2013-09-23T16:01:07","indexId":"sir20135075","displayToPublicDate":"2013-09-23T15:42:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-5075","title":"Ranking contributing areas of salt and selenium in the Lower Gunnison River Basin, Colorado, using multiple linear regression models","docAbstract":"Mitigating the effects of salt and selenium on water quality in the Grand Valley and lower Gunnison River Basin in western Colorado is a major concern for land managers. Previous modeling indicated means to improve the models by including more detailed geospatial data and a more rigorous method for developing the models. After evaluating all possible combinations of geospatial variables, four multiple linear regression models resulted that could estimate irrigation-season salt yield, nonirrigation-season salt yield, irrigation-season selenium yield, and nonirrigation-season selenium yield. The adjusted r-squared and the residual standard error (in units of log-transformed yield) of the models were, respectively, 0.87 and 2.03 for the irrigation-season salt model, 0.90 and 1.25 for the nonirrigation-season salt model, 0.85 and 2.94 for the irrigation-season selenium model, and 0.93 and 1.75 for the nonirrigation-season selenium model. The four models were used to estimate yields and loads from contributing areas corresponding to 12-digit hydrologic unit codes in the lower Gunnison River Basin study area. Each of the 175 contributing areas was ranked according to its estimated mean seasonal yield of salt and selenium.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135075","collaboration":"Prepared in cooperation with the Bureau of Reclamation and the Colorado River Water Conservation District","usgsCitation":"Linard, J.I., 2013, Ranking contributing areas of salt and selenium in the Lower Gunnison River Basin, Colorado, using multiple linear regression models: U.S. Geological Survey Scientific Investigations Report 2013-5075, v, 45 p., https://doi.org/10.3133/sir20135075.","productDescription":"v, 45 p.","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":278018,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135075.gif"},{"id":278016,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5075/pdf/SIR13-5075.pdf"},{"id":278017,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5075/"}],"country":"United States","state":"Colorado","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -109.0009,37.762 ], [ -109.0009,39.5273 ], [ -107.037,39.5273 ], [ -107.037,37.762 ], [ -109.0009,37.762 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"524154fae4b0ec672f073ab7","contributors":{"authors":[{"text":"Linard, Joshua I. jilinard@usgs.gov","contributorId":1465,"corporation":false,"usgs":true,"family":"Linard","given":"Joshua","email":"jilinard@usgs.gov","middleInitial":"I.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":484420,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70047159,"text":"70047159 - 2013 - Updating the planetary time scale: focus on Mars","interactions":[],"lastModifiedDate":"2013-10-30T11:22:11","indexId":"70047159","displayToPublicDate":"2013-09-23T13:48:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1238,"text":"Ciencias Da Terra","active":true,"publicationSubtype":{"id":10}},"title":"Updating the planetary time scale: focus on Mars","docAbstract":"Formal stratigraphic systems have been developed for the surface materials of the Moon, Mars, Mercury, and the Galilean satellite Ganymede. These systems are based on geologic mapping, which establishes relative ages of surfaces delineated by superposition, morphology, impact crater densities, and other relations and features. Referent units selected from the mapping determine time-stratigraphic bases and/or representative materials characteristic of events and periods for definition of chronologic units. Absolute ages of these units in some cases can be estimated using crater size-frequency data. For the Moon, the chronologic units and cratering record are calibrated by radiometric ages measured from samples collected from the lunar surface. Model ages for other cratered planetary surfaces are constructed primarily by estimating cratering rates relative to that of the Moon. Other cratered bodies with estimated surface ages include Venus and the Galilean satellites of Jupiter. New global geologic mapping and crater dating studies of Mars are resulting in more accurate and detailed reconstructions of its geologic history.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Ciencias Da Terra","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Department of Earth Sciences Lisbon University","usgsCitation":"Tanaka, K.L., and Quantin-Nataf, C., 2013, Updating the planetary time scale: focus on Mars: Ciencias Da Terra.","ipdsId":"IP-044682","costCenters":[],"links":[{"id":278011,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":278012,"type":{"id":11,"text":"Document"},"url":"https://www.cienciasdaterra.com/index.php/vol/article/view/278"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"524154fce4b0ec672f073ac7","contributors":{"authors":[{"text":"Tanaka, Kenneth L. ktanaka@usgs.gov","contributorId":610,"corporation":false,"usgs":true,"family":"Tanaka","given":"Kenneth","email":"ktanaka@usgs.gov","middleInitial":"L.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":481187,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Quantin-Nataf, Cathy","contributorId":26615,"corporation":false,"usgs":true,"family":"Quantin-Nataf","given":"Cathy","email":"","affiliations":[],"preferred":false,"id":481188,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70048358,"text":"70048358 - 2013 - SSR_pipeline: a bioinformatic infrastructure for identifying microsatellites from paired-end Illumina high-throughput DNA sequencing data","interactions":[],"lastModifiedDate":"2013-10-23T14:54:22","indexId":"70048358","displayToPublicDate":"2013-09-23T12:51:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2333,"text":"Journal of Heredity","active":true,"publicationSubtype":{"id":10}},"title":"SSR_pipeline: a bioinformatic infrastructure for identifying microsatellites from paired-end Illumina high-throughput DNA sequencing data","docAbstract":"SSR_pipeline is a flexible set of programs designed to efficiently identify simple sequence repeats (e.g., microsatellites) from paired-end high-throughput Illumina DNA sequencing data. The program suite contains 3 analysis modules along with a fourth control module that can automate analyses of large volumes of data. The modules are used to 1) identify the subset of paired-end sequences that pass Illumina quality standards, 2) align paired-end reads into a single composite DNA sequence, and 3) identify sequences that possess microsatellites (both simple and compound) conforming to user-specified parameters. The microsatellite search algorithm is extremely efficient, and we have used it to identify repeats with motifs from 2 to 25bp in length. Each of the 3 analysis modules can also be used independently to provide greater flexibility or to work with FASTQ or FASTA files generated from other sequencing platforms (Roche 454, Ion Torrent, etc.). We demonstrate use of the program with data from the brine fly Ephydra packardi (Diptera: Ephydridae) and provide empirical timing benchmarks to illustrate program performance on a common desktop computer environment. We further show that the Illumina platform is capable of identifying large numbers of microsatellites, even when using unenriched sample libraries and a very small percentage of the sequencing capacity from a single DNA sequencing run. All modules from SSR_pipeline are implemented in the Python programming language and can therefore be used from nearly any computer operating system (Linux, Macintosh, and Windows).","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Heredity","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Oxford University Press","doi":"10.1093/jhered/est056","usgsCitation":"Miller, M.P., Knaus, B.J., Mullins, T., and Haig, S.M., 2013, SSR_pipeline: a bioinformatic infrastructure for identifying microsatellites from paired-end Illumina high-throughput DNA sequencing data: Journal of Heredity, v. 104, no. 6, p. 881-885, https://doi.org/10.1093/jhered/est056.","productDescription":"5 p.","startPage":"881","endPage":"885","numberOfPages":"5","ipdsId":"IP-046152","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":473525,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/jhered/est056","text":"Publisher Index Page"},{"id":278009,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":278006,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1093/jhered/est056"},{"id":278007,"type":{"id":15,"text":"Index Page"},"url":"https://jhered.oxfordjournals.org/cgi/content/full/est056?"}],"volume":"104","issue":"6","noUsgsAuthors":false,"publicationDate":"2013-09-19","publicationStatus":"PW","scienceBaseUri":"524154fce4b0ec672f073ac3","contributors":{"authors":[{"text":"Miller, Mark P. 0000-0003-1045-1772 mpmiller@usgs.gov","orcid":"https://orcid.org/0000-0003-1045-1772","contributorId":1967,"corporation":false,"usgs":true,"family":"Miller","given":"Mark","email":"mpmiller@usgs.gov","middleInitial":"P.","affiliations":[{"id":38131,"text":"WMA - Office of Planning and Programming","active":true,"usgs":true}],"preferred":true,"id":484413,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Knaus, Brian J.","contributorId":107167,"corporation":false,"usgs":true,"family":"Knaus","given":"Brian","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":484415,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mullins, Thomas D.","contributorId":12819,"corporation":false,"usgs":true,"family":"Mullins","given":"Thomas D.","affiliations":[],"preferred":false,"id":484414,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Haig, Susan M. 0000-0002-6616-7589 susan_haig@usgs.gov","orcid":"https://orcid.org/0000-0002-6616-7589","contributorId":719,"corporation":false,"usgs":true,"family":"Haig","given":"Susan","email":"susan_haig@usgs.gov","middleInitial":"M.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":484412,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70048355,"text":"70048355 - 2013 - Baseline monitoring of the western Arctic Ocean estimates 20% of the Canadian Basin surface waters are undersaturated with respect to aragonite","interactions":[],"lastModifiedDate":"2016-09-22T12:36:32","indexId":"70048355","displayToPublicDate":"2013-09-23T11:31:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Baseline monitoring of the western Arctic Ocean estimates 20% of the Canadian Basin surface waters are undersaturated with respect to aragonite","docAbstract":"Marine surface waters are being acidified due to uptake of anthropogenic carbon dioxide, resulting in surface ocean areas of undersaturation with respect to carbonate minerals, including aragonite. In the Arctic Ocean, acidification is expected to occur at an accelerated rate with respect to the global oceans, but a paucity of baseline data has limited our understanding of the extent of Arctic undersaturation and of regional variations in rates and causes. The lack of data has also hindered refinement of models aimed at projecting future trends of ocean acidification. Here, based on more than 34,000 data records collected in 2010 and 2011, we establish a baseline of inorganic carbon data (pH, total alkalinity, dissolved inorganic carbon, partial pressure of carbon dioxide, and aragonite saturation index) for the western Arctic Ocean. This data set documents aragonite undersaturation in ~20% of the surface waters of the combined Canada and Makarov basins, an area characterized by recent acceleration of sea ice loss. Conservative tracer studies using stable oxygen isotopic data from 307 sites show that while the entire surface of this area receives abundant freshwater from meteoric sources, freshwater from sea ice melt is most closely linked to the areas of carbonate mineral undersaturation. These data link the Arctic Ocean’s largest area of aragonite undersaturation to sea ice melt and atmospheric CO2 absorption in areas of low buffering capacity. Some relatively supersaturated areas can be linked to localized biological activity. Collectively, these observations can be used to project trends of ocean acidification in higher latitude marine surface waters where inorganic carbon chemistry is largely influenced by sea ice meltwater.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"PLoS ONE","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"PLOS ONE","doi":"10.1371/journal.pone.0073796","usgsCitation":"Robbins, L.L., Wynn, J.G., Lisle, J.T., Yates, K.K., Knorr, P.O., Byrne, R., Liu, X., Patsavas, M.C., Azetsu-Scott, K., and Takahashi, T., 2013, Baseline monitoring of the western Arctic Ocean estimates 20% of the Canadian Basin surface waters are undersaturated with respect to aragonite: PLoS ONE, v. 8, no. 9, 15 p., https://doi.org/10.1371/journal.pone.0073796.","productDescription":"15 p.","numberOfPages":"15","ipdsId":"IP-036765","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":473528,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0073796","text":"Publisher Index Page"},{"id":278003,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":277996,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1371/journal.pone.0073796"}],"geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -166.9,66.5 ], [ -166.9,77.3 ], [ -105.2,77.3 ], [ -105.2,66.5 ], [ -166.9,66.5 ] ] ] } } ] }","volume":"8","issue":"9","noUsgsAuthors":false,"publicationDate":"2013-09-11","publicationStatus":"PW","scienceBaseUri":"524154f9e4b0ec672f073aaf","contributors":{"authors":[{"text":"Robbins, Lisa L. 0000-0003-3681-1094 lrobbins@usgs.gov","orcid":"https://orcid.org/0000-0003-3681-1094","contributorId":422,"corporation":false,"usgs":true,"family":"Robbins","given":"Lisa","email":"lrobbins@usgs.gov","middleInitial":"L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":484396,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wynn, Jonathan G.","contributorId":92960,"corporation":false,"usgs":true,"family":"Wynn","given":"Jonathan","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":484403,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lisle, John T. 0000-0002-5447-2092 jlisle@usgs.gov","orcid":"https://orcid.org/0000-0002-5447-2092","contributorId":2944,"corporation":false,"usgs":true,"family":"Lisle","given":"John","email":"jlisle@usgs.gov","middleInitial":"T.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":484397,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yates, Kimberly K. 0000-0001-8764-0358 kyates@usgs.gov","orcid":"https://orcid.org/0000-0001-8764-0358","contributorId":420,"corporation":false,"usgs":true,"family":"Yates","given":"Kimberly","email":"kyates@usgs.gov","middleInitial":"K.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":484395,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Knorr, Paul O. pknorr@usgs.gov","contributorId":3691,"corporation":false,"usgs":true,"family":"Knorr","given":"Paul","email":"pknorr@usgs.gov","middleInitial":"O.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":484398,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Byrne, Robert H.","contributorId":83260,"corporation":false,"usgs":true,"family":"Byrne","given":"Robert H.","affiliations":[],"preferred":false,"id":484401,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Liu, Xuewu","contributorId":87676,"corporation":false,"usgs":true,"family":"Liu","given":"Xuewu","email":"","affiliations":[],"preferred":false,"id":484402,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Patsavas, Mark C.","contributorId":99881,"corporation":false,"usgs":true,"family":"Patsavas","given":"Mark","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":484404,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Azetsu-Scott, Kumiko","contributorId":78636,"corporation":false,"usgs":true,"family":"Azetsu-Scott","given":"Kumiko","email":"","affiliations":[],"preferred":false,"id":484400,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Takahashi, Taro","contributorId":55319,"corporation":false,"usgs":true,"family":"Takahashi","given":"Taro","email":"","affiliations":[],"preferred":false,"id":484399,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70048351,"text":"70048351 - 2013 - Regional signatures of plant response to drought and elevated temperature across a desert ecosystem","interactions":[],"lastModifiedDate":"2013-10-30T11:33:16","indexId":"70048351","displayToPublicDate":"2013-09-23T10:56:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Regional signatures of plant response to drought and elevated temperature across a desert ecosystem","docAbstract":"The performance of many desert plant species in North America may decline with the warmer and drier conditions predicted by climate change models, thereby accelerating land degradation and reducing ecosystem productivity. We paired repeat measurements of plant canopy cover with climate at multiple sites across the Chihuahuan Desert over the last century to determine which plant species and functional types may be the most sensitive to climate change. We found that the dominant perennial grass, Bouteloua eriopoda, and species richness had nonlinear responses to summer precipitation, decreasing more in dry summers than increasing with wet summers. Dominant shrub species responded differently to the seasonality of precipitation and drought, but winter precipitation best explained changes in the cover of woody vegetation in upland grasslands and may contribute to woody-plant encroachment that is widespread throughout the southwestern United States and northern Mexico. Temperature explained additional variability of changes in cover of dominant and subdominant plant species. Using a novel empirically based approach we identified ‘‘climate pivot points’’ that were indicative of shifts from increasing to decreasing plant cover over a range of climatic conditions. Reductions in cover of annual and several perennial plant species, in addition to declines in species richness below the long-term summer precipitation mean across plant communities, indicate a decrease in the productivity for all but the most drought-tolerant perennial grasses and shrubs in the Chihuahuan Desert. Overall, our regional synthesis of long-term data provides a robust foundation for forecasting future shifts in the composition and structure of plant assemblages in the largest North American warm desert.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Ecology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Ecological Society of America","doi":"10.1890/12-1586.1","usgsCitation":"Munson, S.M., Muldavin, E.H., Belnap, J., Peters, D.P., Anderson, J.P., Reiser, M.H., Gallo, K., Melgoza-Castillo, A., Herrick, J.E., and Christiansen, T.A., 2013, Regional signatures of plant response to drought and elevated temperature across a desert ecosystem: Ecology, v. 94, no. 9, p. 2030-2041, https://doi.org/10.1890/12-1586.1.","productDescription":"12 p.","startPage":"2030","endPage":"2041","numberOfPages":"12","ipdsId":"IP-040978","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":277998,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":277991,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1890/12-1586.1"}],"volume":"94","issue":"9","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"524154fbe4b0ec672f073abb","contributors":{"authors":[{"text":"Munson, Seth M. 0000-0002-2736-6374 smunson@usgs.gov","orcid":"https://orcid.org/0000-0002-2736-6374","contributorId":1334,"corporation":false,"usgs":true,"family":"Munson","given":"Seth","email":"smunson@usgs.gov","middleInitial":"M.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":484375,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Muldavin, Esteban H.","contributorId":88260,"corporation":false,"usgs":true,"family":"Muldavin","given":"Esteban","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":484383,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Belnap, Jayne 0000-0001-7471-2279 jayne_belnap@usgs.gov","orcid":"https://orcid.org/0000-0001-7471-2279","contributorId":1332,"corporation":false,"usgs":true,"family":"Belnap","given":"Jayne","email":"jayne_belnap@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":484374,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Peters, Debra P.C.","contributorId":81007,"corporation":false,"usgs":true,"family":"Peters","given":"Debra","email":"","middleInitial":"P.C.","affiliations":[],"preferred":false,"id":484381,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Anderson, John P.","contributorId":23060,"corporation":false,"usgs":true,"family":"Anderson","given":"John","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":484376,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Reiser, M. Hildegard","contributorId":38465,"corporation":false,"usgs":true,"family":"Reiser","given":"M.","email":"","middleInitial":"Hildegard","affiliations":[],"preferred":false,"id":484378,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gallo, Kirsten","contributorId":82414,"corporation":false,"usgs":true,"family":"Gallo","given":"Kirsten","email":"","affiliations":[],"preferred":false,"id":484382,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Melgoza-Castillo, Alicia","contributorId":76639,"corporation":false,"usgs":true,"family":"Melgoza-Castillo","given":"Alicia","email":"","affiliations":[],"preferred":false,"id":484380,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Herrick, Jeffrey E.","contributorId":26054,"corporation":false,"usgs":false,"family":"Herrick","given":"Jeffrey","email":"","middleInitial":"E.","affiliations":[{"id":12627,"text":"USDA-ARS Jornada Experimental Range, New Mexico State University, Las Cruces, NM 88003-8003, USA","active":true,"usgs":false}],"preferred":false,"id":484377,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Christiansen, Tim A.","contributorId":64550,"corporation":false,"usgs":true,"family":"Christiansen","given":"Tim","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":484379,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70048325,"text":"70048325 - 2013 - A scenario and forecast model for Gulf of Mexico hypoxic area and volume","interactions":[],"lastModifiedDate":"2013-10-30T11:34:09","indexId":"70048325","displayToPublicDate":"2013-09-23T09:22:00","publicationYear":"2013","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":"A scenario and forecast model for Gulf of Mexico hypoxic area and volume","docAbstract":"For almost three decades, the relative size of the hypoxic region on the Louisiana-Texas continental shelf has drawn scientific and policy attention. During that time, both simple and complex models have been used to explore hypoxia dynamics and to provide management guidance relating the size of the hypoxic zone to key drivers. Throughout much of that development, analyses had to accommodate an apparent change in hypoxic sensitivity to loads and often cull observations due to anomalous meteorological conditions. Here, we describe an adaptation of our earlier, simple biophysical model, calibrated to revised hypoxic area estimates and new hypoxic volume estimates through Bayesian estimation. This application eliminates the need to cull observations and provides revised hypoxic extent estimates with uncertainties, corresponding to different nutrient loading reduction scenarios. We compare guidance from this model application, suggesting an approximately 62% nutrient loading reduction is required to reduce Gulf hypoxia to the Action Plan goal of 5,000 km2, to that of previous applications. In addition, we describe for the first time, the corresponding response of hypoxic volume. We also analyze model results to test for increasing system sensitivity to hypoxia formation, but find no strong evidence of such change.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Environmental Science and Technology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"ACS Publications","doi":"10.1021/es4025035","usgsCitation":"Scavia, D., Evans, M.A., and Obenour, D.R., 2013, A scenario and forecast model for Gulf of Mexico hypoxic area and volume: Environmental Science & Technology, v. 47, no. 18, 6 p., https://doi.org/10.1021/es4025035.","productDescription":"6 p.","numberOfPages":"6","ipdsId":"IP-048828","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":277995,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":277970,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1021/es4025035"}],"volume":"47","issue":"18","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"524154f9e4b0ec672f073aab","contributors":{"authors":[{"text":"Scavia, Donald","contributorId":19068,"corporation":false,"usgs":true,"family":"Scavia","given":"Donald","affiliations":[],"preferred":false,"id":484320,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Evans, Mary Anne 0000-0002-1627-7210 maevans@usgs.gov","orcid":"https://orcid.org/0000-0002-1627-7210","contributorId":4883,"corporation":false,"usgs":true,"family":"Evans","given":"Mary","email":"maevans@usgs.gov","middleInitial":"Anne","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":484319,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Obenour, Daniel R.","contributorId":66588,"corporation":false,"usgs":true,"family":"Obenour","given":"Daniel","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":484321,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70048352,"text":"70048352 - 2013 - Characterizing regional soil mineral composition using spectroscopyand geostatistics","interactions":[],"lastModifiedDate":"2013-09-23T09:12:42","indexId":"70048352","displayToPublicDate":"2013-09-23T09:07:00","publicationYear":"2013","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":"Characterizing regional soil mineral composition using spectroscopyand geostatistics","docAbstract":"This work aims at improving the mapping of major mineral variability at regional scale using scale-dependent spatial variability observed in remote sensing data. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data and statistical methods were combined with laboratory-based mineral characterization of field samples to create maps of the distributions of clay, mica and carbonate minerals and their abundances. The Material Identification and Characterization Algorithm (MICA) was used to identify the spectrally-dominant minerals in field samples; these results were combined with ASTER data using multinomial logistic regression to map mineral distributions. X-ray diffraction (XRD)was used to quantify mineral composition in field samples. XRD results were combined with ASTER data using multiple linear regression to map mineral abundances. We testedwhether smoothing of the ASTER data to match the scale of variability of the target sample would improve model correlations. Smoothing was donewith Fixed Rank Kriging (FRK) to represent the mediumand long-range spatial variability in the ASTER data. Stronger correlations resulted using the smoothed data compared to results obtained with the original data. Highest model accuracies came from using both medium and long-range scaled ASTER data as input to the statistical models. High correlation coefficients were obtained for the abundances of calcite and mica (R2 = 0.71 and 0.70, respectively). Moderately-high correlation coefficients were found for smectite and kaolinite (R2 = 0.57 and 0.45, respectively). Maps of mineral distributions, obtained by relating ASTER data to MICA analysis of field samples, were found to characterize major soil mineral variability (overall accuracies for mica, smectite and kaolinite were 76%, 89% and 86% respectively). The results of this study suggest that the distributions of minerals and their abundances derived using FRK-smoothed ASTER data more closely match the spatial variability of soil and environmental properties at regional scale.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Remote Sensing of Environment","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2013.08.018","usgsCitation":"Mulder, V., de Bruin, S., Weyermann, J., Kokaly, R., and Schaepman, M., 2013, Characterizing regional soil mineral composition using spectroscopyand geostatistics: Remote Sensing of Environment, v. 139, no. December 2013, p. 415-429, https://doi.org/10.1016/j.rse.2013.08.018.","productDescription":"15 p.","startPage":"415","endPage":"429","numberOfPages":"15","ipdsId":"IP-049662","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":488159,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://zenodo.org/record/3422237","text":"External Repository"},{"id":277994,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":277992,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.rse.2013.08.018"}],"volume":"139","issue":"December 2013","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"524154fae4b0ec672f073ab3","contributors":{"authors":[{"text":"Mulder, V.L.","contributorId":12764,"corporation":false,"usgs":true,"family":"Mulder","given":"V.L.","email":"","affiliations":[],"preferred":false,"id":484385,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"de Bruin, S.","contributorId":49693,"corporation":false,"usgs":true,"family":"de Bruin","given":"S.","affiliations":[],"preferred":false,"id":484386,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Weyermann, J.","contributorId":9564,"corporation":false,"usgs":true,"family":"Weyermann","given":"J.","email":"","affiliations":[],"preferred":false,"id":484384,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kokaly, Raymond F. 0000-0003-0276-7101","orcid":"https://orcid.org/0000-0003-0276-7101","contributorId":81442,"corporation":false,"usgs":true,"family":"Kokaly","given":"Raymond F.","affiliations":[],"preferred":false,"id":484388,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schaepman, M.E.","contributorId":66466,"corporation":false,"usgs":true,"family":"Schaepman","given":"M.E.","email":"","affiliations":[],"preferred":false,"id":484387,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70048334,"text":"sir20105070H - 2013 - Nickel-cobalt laterites: a deposit model","interactions":[],"lastModifiedDate":"2022-12-13T17:11:43.972738","indexId":"sir20105070H","displayToPublicDate":"2013-09-20T13:48:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2010-5070","chapter":"H","title":"Nickel-cobalt laterites: a deposit model","docAbstract":"

Nickel-cobalt (Ni-Co) laterite deposits are supergene enrichments of Ni±Co that form from intense chemical and mechanical weathering of ultramafic parent rocks. These regolith deposits typically form within 26 degrees of the equator, although there are a few exceptions. They form in active continental margins and stable cratonic settings. It takes as little as one million years for a laterite profile to develop. Three subtypes of Ni-Co laterite deposits are classified according to the dominant Ni-bearing mineralogy, which include hydrous magnesium (Mg)-silicate, smectite, and oxide. These minerals form in weathering horizons that begin with the unweathered protolith at the base, saprolite next, a smectite transition zone only in profiles where drainage is very poor, followed by limonite, and then capped with ferricrete at the top. The saprolite contains Ni-rich hydrous Mg-silicates, the Ni-rich clays occur in the transition horizon, and Ni-rich goethite occurs in the limonite. Although these subtypes of deposits are the more widely used terms for classification of Ni-Co laterite deposits, most deposits have economic concentrations of Ni in more than one horizon. Because of their complex mineralogy and heterogeneous concentrations, mining of these metallurgically complex deposits can be challenging. Deposits range in size from 2.5 to about 400 million tonnes, with Ni and Co grades of 0.66–2.4 percent (median 1.3) and 0.01–0.15 percent (median 0.08), respectively. Modern techniques of ore delineation and mineralogical identification are being developed to aid in streamlining the Ni-Co laterite mining process, and low-temperature and low-pressure ore processing techniques are being tested that will treat the entire weathered profile. There is evidence that the production of Ni and Co from laterites is more energy intensive than that of sulfide ores, reflecting the environmental impact of producing a Ni-Co laterite deposit. Tailings may include high levels of magnesium, sulfate, and manganese and have the potential to be physically unstable.

","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Mineral deposit models for resource assessment (Scientific Investigations Report 2010-5070)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20105070H","usgsCitation":"Marsh, E.E., Anderson, E.D., and Gray, F., 2013, Nickel-cobalt laterites: a deposit model: U.S. Geological Survey Scientific Investigations Report 2010-5070, vii, 38 p., https://doi.org/10.3133/sir20105070H.","productDescription":"vii, 38 p.","numberOfPages":"49","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"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}],"links":[{"id":277977,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20105070H.png"},{"id":277975,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2010/5070/h/","linkFileType":{"id":5,"text":"html"}},{"id":277976,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2010/5070/h/pdf/SIR10-5070-H.pdf","text":"Report","size":"6.19 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"523d6bade4b097188d6c7696","contributors":{"authors":[{"text":"Marsh, Erin E. 0000-0001-5245-9532 emarsh@usgs.gov","orcid":"https://orcid.org/0000-0001-5245-9532","contributorId":1250,"corporation":false,"usgs":true,"family":"Marsh","given":"Erin","email":"emarsh@usgs.gov","middleInitial":"E.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":484346,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Eric D. 0000-0002-0138-6166 ericanderson@usgs.gov","orcid":"https://orcid.org/0000-0002-0138-6166","contributorId":1733,"corporation":false,"usgs":true,"family":"Anderson","given":"Eric","email":"ericanderson@usgs.gov","middleInitial":"D.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":484347,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gray, Floyd 0000-0002-0223-8966 fgray@usgs.gov","orcid":"https://orcid.org/0000-0002-0223-8966","contributorId":603,"corporation":false,"usgs":true,"family":"Gray","given":"Floyd","email":"fgray@usgs.gov","affiliations":[{"id":662,"text":"Western Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":484345,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70048310,"text":"sir20135101 - 2013 - Geohydrology, geochemistry, and groundwater simulation (1992-2011) and analysis of potential water-supply management options, 2010-60, of the Langford Basin, California","interactions":[],"lastModifiedDate":"2013-10-30T11:35:55","indexId":"sir20135101","displayToPublicDate":"2013-09-20T08:42:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-5101","title":"Geohydrology, geochemistry, and groundwater simulation (1992-2011) and analysis of potential water-supply management options, 2010-60, of the Langford Basin, California","docAbstract":"Groundwater withdrawals began in 1992 from the Langford Basin within the Fort Irwin National Training Center (NTC), California. From April 1992 to December 2010, approximately 12,300 acre-feet of water (averaging about 650 acre-feet per year) has been withdrawn from the basin and transported to the adjacent Irwin Basin. Since withdrawals began, water levels in the basin have declined by as much as 40 feet, and the quality of the groundwater withdrawn from the basin has deteriorated. The U.S. Geological Survey collected geohydrologic data from Langford Basin during 1992–2011 to determine the quantity and quality of groundwater available in the basin. Geophysical surveys, including gravity, seismic refraction, and time-domain electromagnetic induction surveys, were conducted to determine the depth and shape of the basin, to delineate depths to the Quaternary-Tertiary interface, and to map the depth to the water table and changes in water quality. Data were collected from existing wells and test holes, as well as 11 monitor wells that were installed at 5 sites as part of this study. Water-quality samples collected from wells in the basin were used to determine the groundwater chemistry within the basin and to delineate potential sources of poor-quality groundwater. Analysis of stable isotopes of oxygen and hydrogen in groundwater indicates that present-day precipitation is not a major source of recharge to the basin. Tritium and carbon-14 data indicate that most of the basin was recharged prior to 1952, and the groundwater in the basin has an apparent age of 12,500 to 30,000 years. Recharge to the basin, estimated to be less than 50 acre-feet per year, has not been sufficient to replenish the water that is being withdrawn from the basin. A numerical groundwater-flow model was developed for the Langford Basin to better understand the aquifer system used by the Fort Irwin NTC as part of its water supply, and to provide a tool to help manage groundwater resources at the NTC. Measured groundwater-level declines since the initiation of withdrawals (1992–2011) were used to calibrate the groundwater-flow model. The simulated recharge was about 46 acre-feet per year, including approximately 6 acre-feet per year of natural recharge derived from precipitation runoff and as much as 40 acre-feet per year of underflow from the Irwin Basin. Between April 1992 and December 2010, an average of about 650 acre-feet per year of water was withdrawn from the Langford Basin. Groundwater withdrawals in excess of natural recharge resulted in a net loss of 11,670 acre-feet of groundwater storage within the basin for the simulation period. The Fort Irwin NTC is considering various groundwater-management options to address the limited water resources in the Langford Basin. The calibrated Langford Basin groundwater-flow model was used to evaluate the hydrologic effects of four groundwater-withdrawal scenarios being considered by the Fort Irwin NTC over the next 50 years (January 2011 through December 2060). Continuation of the 2010 withdrawal rate in the three existing production wells will result in 70 feet of additional drawdown in the central part of the basin. Redistributing the 2010 withdrawal rate equally to the three existing wells and two proposed new wells in the northern and southern parts of the basin would result in about 10 feet less drawdown in the central part of the basin but about 100 feet of additional drawdown in the new well in the northern part of the basin and about 50 feet of additional drawdown in the new well in the southern part of the basin. Reducing the withdrawals from the three existing production wells in the central part of the basin from about 45,000 acre-feet to about 32,720 acre-feet would result in about 40 feet of additional drawdown in the central basin near the pumping wells, about 25 feet less than if withdrawals were not reduced. The combination of reducing and redistributing the cumulative withdrawals to the three existing and two proposed new wells results in about 40 feet of additional drawdown in the central and southern parts of the basin and about 70 feet in the northern part of the basin. These results show that reducing and redistributing the groundwater withdrawals would maintain the upper aquifer at greater than 50 percent of its predevelopment saturated thickness throughout the groundwater basin. The scenarios simulated for this study demonstrate how the calibrated model can be utilized to evaluate the hydrologic effects of different water-management strategies.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135101","usgsCitation":"Voronin, L.M., Densmore, J., Martin, P., Brush, C.F., Carlson, C.S., and Miller, D., 2013, Geohydrology, geochemistry, and groundwater simulation (1992-2011) and analysis of potential water-supply management options, 2010-60, of the Langford Basin, California: U.S. Geological Survey Scientific Investigations Report 2013-5101, x, 86 p., https://doi.org/10.3133/sir20135101.","productDescription":"x, 86 p.","numberOfPages":"100","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":277948,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135101.jpg"},{"id":277946,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5101/"},{"id":277947,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5101/pdf/sir2013-5101.pdf"}],"country":"United States","state":"California","otherGeospatial":"Fort Irwin National Training Center","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -11.118611111111111,34.5 ], [ -11.118611111111111,8.333333333333334E-4 ], [ -0.01638888888888889,8.333333333333334E-4 ], [ -0.01638888888888889,34.5 ], [ -11.118611111111111,34.5 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"523d6b91e4b097188d6c7692","contributors":{"authors":[{"text":"Voronin, Lois M. 0000-0002-1064-1675 lvoronin@usgs.gov","orcid":"https://orcid.org/0000-0002-1064-1675","contributorId":1475,"corporation":false,"usgs":true,"family":"Voronin","given":"Lois","email":"lvoronin@usgs.gov","middleInitial":"M.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":484292,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Densmore, Jill N. 0000-0002-5345-6613","orcid":"https://orcid.org/0000-0002-5345-6613","contributorId":89179,"corporation":false,"usgs":true,"family":"Densmore","given":"Jill N.","affiliations":[],"preferred":false,"id":484295,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martin, Peter pmmartin@usgs.gov","contributorId":799,"corporation":false,"usgs":true,"family":"Martin","given":"Peter","email":"pmmartin@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":484291,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brush, Charles F.","contributorId":93140,"corporation":false,"usgs":true,"family":"Brush","given":"Charles","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":484296,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Carlson, Carl S. 0000-0001-7142-3519 cscarlso@usgs.gov","orcid":"https://orcid.org/0000-0001-7142-3519","contributorId":1694,"corporation":false,"usgs":true,"family":"Carlson","given":"Carl","email":"cscarlso@usgs.gov","middleInitial":"S.","affiliations":[{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":484293,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Miller, David M. 0000-0003-3711-0441 dmiller@usgs.gov","orcid":"https://orcid.org/0000-0003-3711-0441","contributorId":1707,"corporation":false,"usgs":true,"family":"Miller","given":"David M.","email":"dmiller@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":false,"id":484294,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70048296,"text":"sir20135136 - 2013 - The distribution and modeling of nitrate transport in the Carson Valley alluvial aquifer, Douglas County, Nevada","interactions":[],"lastModifiedDate":"2013-09-27T08:55:23","indexId":"sir20135136","displayToPublicDate":"2013-09-19T14:52:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-5136","title":"The distribution and modeling of nitrate transport in the Carson Valley alluvial aquifer, Douglas County, Nevada","docAbstract":"Residents of Carson Valley in Douglas County, Nevada, rely on groundwater from an alluvial aquifer for domestic use and agricultural irrigation. Since the 1970s, there has been a rapid increase in population in several parts of the valley that rely on domestic wells for drinking water and septic systems for treatment of household waste. As a result, the density of septic systems in the developed areas is greater than one septic system per 3 acres, and the majority of the domestic wells are shallow (screened within 250 feet of the land surface).

\nThe distribution of nitrate as nitrogen (referred to herein as nitrate-N) concentrations in groundwater was determined by collecting more than 200 samples from 8 land-use categories: single family residential, multifamily residential, rural (including land use for agriculture), vacant land, commercial, industrial, utilities, and unclassified. Nitrate-N concentrations ranged from below detection (less than 0.05 milligrams per liter) to 18 milligrams per liter. The results of nitrate-N concentrations that were sampled from three wells equalled or exceeded the maximum contaminant level of 10 milligrams per liter set by the U.S. Environmental Protection Agency. Nitrate-N concentrations in sampled wells showed a positive correlation between elevated nitrate-N concentrations and the percentage of single-family land use and septic-system density. Wells sampled in other land-use categories did not have any correlation to nitrate-N concentrations. In areas with greater than 50-percent single-family land use, nitrate-N concentrations were two times greater than in areas with less than 50 percent single-family land use. Nitrate-N concentrations in groundwater near septic systems that had been used more than 20 years were more than two times greater than in areas where septic systems had been used less than 20 years. Lower nitrate-N concentrations in the areas where septic systems were less than 20 years old probably result from temporary storage of nitrogen leaching from septic systems into the unsaturated zone.

In areas where septic systems are abundant, nitrate-N concentrations were predicted to 2059 by using numerical models within the Ruhenstroth and Johnson Lane subdivisions in the Carson Valley. Model results indicated that nitrate-N concentrations will continue to increase and could exceed the maximum contaminant level over extended areas inside and outside the subdivisions. Two modeling scenarios were used to simulate future transport as a result of removal of septic systems (source of nitrate-N contamination) and the termination of domestic pumping of groundwater. The models showed the largest decrease in nitrate-N concentrations when septic systems were removed and wells continued to pump. Nitrate-N concentrations probably will continue to increase in areas that are dependent on septic systems for waste disposal either under current land-use conditions in the valley or with continued growth and change in land use in the valley.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135136","collaboration":"Prepared in cooperation with Douglas County and the Carson Water Subconservancy District","usgsCitation":"Naranjo, R.C., Welborn, T.L., and Rosen, M.R., 2013, The distribution and modeling of nitrate transport in the Carson Valley alluvial aquifer, Douglas County, Nevada: U.S. Geological Survey Scientific Investigations Report 2013-5136, vii, 51 p., https://doi.org/10.3133/sir20135136.","productDescription":"vii, 51 p.","numberOfPages":"58","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":277941,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135136.jpg"},{"id":277939,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5136/pdf/sir2013-5136.pdf"},{"id":277940,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5136/"}],"country":"United States","state":"Nevada","county":"Douglas County","otherGeospatial":"Carson Valley","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -119.6783,38.4682 ], [ -119.6783,39.1034 ], [ -119.1676,39.1034 ], [ -119.1676,38.4682 ], [ -119.6783,38.4682 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"523c0efee4b024b60d40726e","contributors":{"authors":[{"text":"Naranjo, Ramon C. 0000-0003-4469-6831 rnaranjo@usgs.gov","orcid":"https://orcid.org/0000-0003-4469-6831","contributorId":3391,"corporation":false,"usgs":true,"family":"Naranjo","given":"Ramon","email":"rnaranjo@usgs.gov","middleInitial":"C.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":484262,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Welborn, Toby L. 0000-0003-4839-2405 tlwelbor@usgs.gov","orcid":"https://orcid.org/0000-0003-4839-2405","contributorId":2295,"corporation":false,"usgs":true,"family":"Welborn","given":"Toby","email":"tlwelbor@usgs.gov","middleInitial":"L.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":484261,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rosen, Michael R. 0000-0003-3991-0522 mrosen@usgs.gov","orcid":"https://orcid.org/0000-0003-3991-0522","contributorId":495,"corporation":false,"usgs":true,"family":"Rosen","given":"Michael","email":"mrosen@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":484260,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}