Social science offers rich descriptions of relationships between wildland–urban interface residents and wildfire, but syntheses across different contexts might gloss over important differences. We investigate the potential extent of such differences using data collected consistently in sixty-eight Colorado communities and hierarchical modeling. We find substantial variation across responses for all considered measures, much of which occurs at the community-level. Our results show that many aspects of relationships with wildfire meaningfully differ both within and across communities. Our analysis suggests that some wildfire social science results will be relatively consistent across communities, whereas others will not, and this study contributes evidence to broader efforts for understanding which is which. As such, it provides important guidance for transferring the lessons of wildfire social science studies across contexts, and for practitioners who seek to understand the breadth of viewpoints within the communities with which they work.
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PSC"},"noUsgsAuthors":false,"publicationDate":"2018-06-04","publicationStatus":"PW","scienceBaseUri":"5befe5bde4b045bfcadf7f42","contributors":{"authors":[{"text":"Meldrum, James R. 0000-0001-5250-3759 jmeldrum@usgs.gov","orcid":"https://orcid.org/0000-0001-5250-3759","contributorId":195484,"corporation":false,"usgs":true,"family":"Meldrum","given":"James","email":"jmeldrum@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":751384,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brenkert-Smith, Hannah 0000-0001-6117-8863","orcid":"https://orcid.org/0000-0001-6117-8863","contributorId":195485,"corporation":false,"usgs":false,"family":"Brenkert-Smith","given":"Hannah","email":"","affiliations":[],"preferred":false,"id":751385,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Champ, Patricia A.","contributorId":195486,"corporation":false,"usgs":false,"family":"Champ","given":"Patricia","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":751386,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Falk, Lilia C.","contributorId":210655,"corporation":false,"usgs":false,"family":"Falk","given":"Lilia","email":"","middleInitial":"C.","affiliations":[{"id":38125,"text":"West Region Wildfire Council","active":true,"usgs":false}],"preferred":false,"id":751387,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wilson, Pamela","contributorId":210656,"corporation":false,"usgs":false,"family":"Wilson","given":"Pamela","email":"","affiliations":[{"id":38126,"text":"FireWise of Southwest Colorado","active":true,"usgs":false}],"preferred":false,"id":751389,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Barth, Christopher M.","contributorId":195487,"corporation":false,"usgs":false,"family":"Barth","given":"Christopher","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":751388,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70255616,"text":"70255616 - 2018 - Prediction uncertainty and data worth assessment for groundwater transport times in an agricultural catchment","interactions":[],"lastModifiedDate":"2024-06-26T13:22:26.75759","indexId":"70255616","displayToPublicDate":"2018-06-01T08:11:56","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Prediction uncertainty and data worth assessment for groundwater transport times in an agricultural catchment","docAbstract":"Uncertainties about the age of base-flow discharge can have serious implications for the management of degraded environmental systems where subsurface pathways, and the ongoing release of pollutants that accumulated in the subsurface during past decades, dominate the water quality signal. Numerical groundwater models may be used to estimate groundwater return times and base-flow ages and thus predict the time required for stakeholders to see the results of improved agricultural management practices. However, the uncertainty inherent in the relationship between (i) the observations of atmospherically-derived tracers that are required to calibrate such models and (ii) the predictions of system age that the observations inform have not been investigated. For example, few if any studies have assessed the uncertainty of numerically-simulated system ages or evaluated the uncertainty reductions that may result from the expense of collecting additional subsurface tracer data. In this study we combine numerical flow and transport modeling of atmospherically-derived tracers with prediction uncertainty methods to accomplish four objectives. First, we show the relative importance of head, discharge, and tracer information for characterizing response times in a uniquely data rich catchment that includes 266 age-tracer measurements (SF6, CFCs, and 3H) in addition to long term monitoring of water levels and stream discharge. Second, we calculate uncertainty intervals for model-simulated base-flow ages using both linear and non-linear methods, and find that the prediction sensitivity vector used by linear first-order second-moment methods results in much larger uncertainties than non-linear Monte Carlo methods operating on the same parameter uncertainty. Third, by combining prediction uncertainty analysis with multiple models of the system, we show that data-worth calculations and monitoring network design are sensitive to variations in the amount of water leaving the system via stream discharge and irrigation withdrawals. Finally, we demonstrate a novel model-averaged computation of potential data worth that can account for these uncertainties in model structure.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2018.02.006","usgsCitation":"Zell, W.O., Culver, T.B., and Sanford, W.E., 2018, Prediction uncertainty and data worth assessment for groundwater transport times in an agricultural catchment: Journal of Hydrology, v. 561, p. 1019-1036, https://doi.org/10.1016/j.jhydrol.2018.02.006.","productDescription":"18 p.","startPage":"1019","endPage":"1036","ipdsId":"IP-088953","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":430519,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Morgan Creek, Upper Chester watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76,\n 39.333\n ],\n [\n -76,\n 39.25\n ],\n [\n -75.916667,\n 39.25\n ],\n [\n -75.916667,\n 39.333\n ],\n [\n -76,\n 39.333\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"561","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Zell, Wesley O. 0000-0002-8782-6627","orcid":"https://orcid.org/0000-0002-8782-6627","contributorId":339721,"corporation":false,"usgs":true,"family":"Zell","given":"Wesley","email":"","middleInitial":"O.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":904939,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Culver, Teresa B.","contributorId":339727,"corporation":false,"usgs":false,"family":"Culver","given":"Teresa","email":"","middleInitial":"B.","affiliations":[{"id":25492,"text":"University of Virginia","active":true,"usgs":false}],"preferred":false,"id":904940,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sanford, Ward E. 0000-0002-6624-0280 wsanford@usgs.gov","orcid":"https://orcid.org/0000-0002-6624-0280","contributorId":2268,"corporation":false,"usgs":true,"family":"Sanford","given":"Ward","email":"wsanford@usgs.gov","middleInitial":"E.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":904941,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70197436,"text":"70197436 - 2018 - The utility of point count surveys to predict wildlife interactions with wind energy facilities: An example focused on golden eagles","interactions":[],"lastModifiedDate":"2018-06-05T09:53:39","indexId":"70197436","displayToPublicDate":"2018-06-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"The utility of point count surveys to predict wildlife interactions with wind energy facilities: An example focused on golden eagles","docAbstract":"Wind energy development is rapidly expanding in North America, often accompanied by requirements to survey potential facility locations for existing wildlife. Within the USA, golden eagles (Aquila chrysaetos) are among the most high-profile species of birds that are at risk from wind turbines. To minimize golden eagle fatalities in areas proposed for wind development, modified point count surveys are usually conducted to estimate use by these birds. However, it is not always clear what drives variation in the relationship between on-site point count data and actual use by eagles of a wind energy project footprint. We used existing GPS-GSM telemetry data, collected at 15 min intervals from 13 golden eagles in 2012 and 2013, to explore the relationship between point count data and eagle use of an entire project footprint. To do this, we overlaid the telemetry data on hypothetical project footprints and simulated a variety of point count sampling strategies for those footprints. We compared the time an eagle was found in the sample plots with the time it was found in the project footprint using a metric we called “error due to sampling”. Error due to sampling for individual eagles appeared to be influenced by interactions between the size of the project footprint (20, 40, 90 or 180 km2) and the sampling type (random, systematic or stratified) and was greatest on 90 km2 plots. However, use of random sampling resulted in lowest error due to sampling within intermediate sized plots. In addition sampling intensity and sampling frequency both influenced the effectiveness of point count sampling. Although our work focuses on individual eagles (not the eagle populations typically surveyed in the field), our analysis shows both the utility of simulations to identify specific influences on error and also potential improvements to sampling that consider the context-specific manner that point counts are laid out on the landscape.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2018.01.024","usgsCitation":"Sur, M., Belthoff, J.R., Bjerre, E.R., Millsap, B.A., and Katzner, T., 2018, The utility of point count surveys to predict wildlife interactions with wind energy facilities: An example focused on golden eagles: Ecological Indicators, v. 88, p. 126-133, https://doi.org/10.1016/j.ecolind.2018.01.024.","productDescription":"8 p.","startPage":"126","endPage":"133","ipdsId":"IP-081663","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":468711,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2018.01.024","text":"Publisher Index Page"},{"id":354710,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.5667,\n 33.4333\n ],\n [\n -115.3833,\n 33.4333\n ],\n [\n -115.3833,\n 36.1333\n ],\n [\n -118.5667,\n 36.1333\n ],\n [\n -118.5667,\n 33.4333\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"88","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b46e577e4b060350a15d1ad","contributors":{"authors":[{"text":"Sur, Maitreyi","contributorId":191354,"corporation":false,"usgs":false,"family":"Sur","given":"Maitreyi","email":"","affiliations":[],"preferred":false,"id":737147,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Belthoff, James R. 0000-0002-6051-2353","orcid":"https://orcid.org/0000-0002-6051-2353","contributorId":190592,"corporation":false,"usgs":false,"family":"Belthoff","given":"James","email":"","middleInitial":"R.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":737148,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bjerre, Emily R.","contributorId":205390,"corporation":false,"usgs":false,"family":"Bjerre","given":"Emily","email":"","middleInitial":"R.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":737149,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Millsap, Brian A.","contributorId":205391,"corporation":false,"usgs":false,"family":"Millsap","given":"Brian","email":"","middleInitial":"A.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":737150,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":737146,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70197465,"text":"70197465 - 2018 - Remote sensing analysis of vegetation at the San Carlos Apache Reservation, Arizona and surrounding area","interactions":[],"lastModifiedDate":"2018-06-06T11:01:01","indexId":"70197465","displayToPublicDate":"2018-06-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2172,"text":"Journal of Applied Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Remote sensing analysis of vegetation at the San Carlos Apache Reservation, Arizona and surrounding area","docAbstract":"Mapping of vegetation types is of great importance to the San Carlos Apache Tribe and their management of forestry and fire fuels. Various remote sensing techniques were applied to classify multitemporal Landsat 8 satellite data, vegetation index, and digital elevation model data. A multitiered unsupervised classification generated over 900 classes that were then recoded to one of the 16 generalized vegetation/land cover classes using the Southwest Regional Gap Analysis Project (SWReGAP) map as a guide. A supervised classification was also run using field data collected in the SWReGAP project and our field campaign. Field data were gathered and accuracy assessments were generated to compare outputs. Our hypothesis was that a resulting map would update and potentially improve upon the vegetation/land cover class distributions of the older SWReGAP map over the 24,000 km2 study area. The estimated overall accuracies ranged between 43% and 75%, depending on which method and field dataset were used. The findings demonstrate the complexity of vegetation mapping, the importance of recent, high-quality-field data, and the potential for misleading results when insufficient field data are collected.
","language":"English","publisher":"SPIE","doi":"10.1117/1.JRS.12.026017","usgsCitation":"Norman, L.M., Middleton, B.R., and Wilson, N.R., 2018, Remote sensing analysis of vegetation at the San Carlos Apache Reservation, Arizona and surrounding area: Journal of Applied Remote Sensing, v. 12, no. 2, p. 1-19, https://doi.org/10.1117/1.JRS.12.026017.","productDescription":"Article 026017; 19 p.","startPage":"1","endPage":"19","ipdsId":"IP-093007","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":468713,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1117/1.jrs.12.026017","text":"Publisher Index Page"},{"id":437886,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OCZ17X","text":"USGS data release","linkHelpText":"Vegetation Survey of the San Carlos Apache Reservation, Arizona and Surrounding Area (September to November 2017)."},{"id":354725,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111,\n 32.5\n ],\n [\n -109,\n 32.5\n ],\n [\n -109,\n 34\n ],\n [\n -111,\n 34\n ],\n [\n -111,\n 32.5\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b46e577e4b060350a15d1a5","contributors":{"authors":[{"text":"Norman, Laura M. 0000-0002-3696-8406 lnorman@usgs.gov","orcid":"https://orcid.org/0000-0002-3696-8406","contributorId":967,"corporation":false,"usgs":true,"family":"Norman","given":"Laura","email":"lnorman@usgs.gov","middleInitial":"M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":737279,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Middleton, Barry R. 0000-0001-8924-4121 bmiddleton@usgs.gov","orcid":"https://orcid.org/0000-0001-8924-4121","contributorId":3947,"corporation":false,"usgs":true,"family":"Middleton","given":"Barry","email":"bmiddleton@usgs.gov","middleInitial":"R.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":737281,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wilson, Natalie R. 0000-0001-5145-1221 nrwilson@usgs.gov","orcid":"https://orcid.org/0000-0001-5145-1221","contributorId":5770,"corporation":false,"usgs":true,"family":"Wilson","given":"Natalie","email":"nrwilson@usgs.gov","middleInitial":"R.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":737280,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70197445,"text":"70197445 - 2018 - Estimating freshwater productivity, overwinter survival, and migration patterns of Klamath River Coho Salmon","interactions":[],"lastModifiedDate":"2018-06-12T11:03:14","indexId":"70197445","displayToPublicDate":"2018-06-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5708,"text":"Arcata Fisheries Technical Report","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"TR 2018-33","title":"Estimating freshwater productivity, overwinter survival, and migration patterns of Klamath River Coho Salmon","docAbstract":"An area of great importance to resource management and conservation biology in the Klamath Basin is balancing water usage against the life history requirements of threatened Coho Salmon. One tool for addressing this topic is a freshwater dynamics model to forecast Coho Salmon productivity based on environmental inputs. Constructing such a forecasting tool requires local data to quantify the unique life history processes of Coho Salmon inhabiting this region. Here, we describe analytical methods for estimating a series of sub-models, each capturing a different life history process, which will eventually be synchronized as part of a freshwater dynamics model for Klamath River Coho Salmon. Specifically, we draw upon extensive population monitoring data collected in the basin to estimate models of freshwater productivity, overwinter survival, and migration patterns. Our models of freshwater productivity indicated that high summer temperatures and high winter flows can both adversely affect smolt production and that such relationships are more likely in tributaries with naturally regulated flows due to substantial intraannual environmental variation. Our models of overwinter survival demonstrated extensive variability in survival among years, but not among rearing locations, and demonstrated that a substantial proportion (~ 20%) of age-0+ fish emigrate from some rearing sites in the winter. Our models of migration patterns indicated that many age-0+ fish redistribute in the basin during the summer and winter. Further, we observed that these redistributions can entail long migrations in the mainstem where environmental stressors likely play a role in cueing refuge entry. Finally, our models of migration patterns indicated that changes in discharge are important in cueing the seaward migration of smolts, but that the nature of this behavioral response can differ dramatically between tributaries with naturally and artificially regulated flows. Collectively, these analyses demonstrate that environmental variation interacts with most phases of the freshwater life history of Klamath River Coho Salmon and that anthropogenic environmental variation can have a particularly large bearing on productivity.
","language":"English","publisher":"U.S. Fish and Wildlife Service, Arcata Fish and Wildlife Office","usgsCitation":"Manhard, C.V., Som, N.A., Perry, R.W., Faukner, J., and Soto, T., 2018, Estimating freshwater productivity, overwinter survival, and migration patterns of Klamath River Coho Salmon: Arcata Fisheries Technical Report TR 2018-33, x, 74 p.","productDescription":"x, 74 p.","numberOfPages":"84","ipdsId":"IP-088669","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":354933,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":354702,"type":{"id":15,"text":"Index Page"},"url":"https://www.fws.gov/arcata/fisheries/reports/technical/2018/EstimatingFreshwaterProductivityOverwinterSurvivalandMigrationPatternsofKlamathRiverCohoSalmon.pdf"}],"publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b46e577e4b060350a15d1ab","contributors":{"authors":[{"text":"Manhard, Christopher V.","contributorId":203911,"corporation":false,"usgs":false,"family":"Manhard","given":"Christopher","email":"","middleInitial":"V.","affiliations":[{"id":36754,"text":"U.S. Fish and Wildlife Service, California Cooperative Fish and Wildlife Research Unit, Humboldt State University, 1 Harpst Street, Arcata, CA 95521, USA","active":true,"usgs":false}],"preferred":false,"id":737180,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Som, Nicholas A.","contributorId":203773,"corporation":false,"usgs":false,"family":"Som","given":"Nicholas","email":"","middleInitial":"A.","affiliations":[{"id":36713,"text":"Statistician, USFWS - Arcata Fisheries Program, Humboldt State University","active":true,"usgs":false}],"preferred":false,"id":737181,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Perry, Russell W. 0000-0003-4110-8619 rperry@usgs.gov","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":2820,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","email":"rperry@usgs.gov","middleInitial":"W.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":737179,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Faukner, Jimmy","contributorId":205405,"corporation":false,"usgs":false,"family":"Faukner","given":"Jimmy","email":"","affiliations":[{"id":37098,"text":"Yurok Tribal Fisheries Program","active":true,"usgs":false}],"preferred":false,"id":737182,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Soto, Toz","contributorId":205406,"corporation":false,"usgs":false,"family":"Soto","given":"Toz","email":"","affiliations":[{"id":37099,"text":"Karuk Tribe Fisheries Program","active":true,"usgs":false}],"preferred":false,"id":737183,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70197403,"text":"70197403 - 2018 - Evaluating indices of lipid and protein content in lesser snow and Ross's geese during spring migration","interactions":[],"lastModifiedDate":"2018-07-03T11:10:56","indexId":"70197403","displayToPublicDate":"2018-06-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating indices of lipid and protein content in lesser snow and Ross's geese during spring migration","docAbstract":"Assessing nutrient stores in avian species is important for understanding the extent to which body condition influences success or failure in life‐history events. We evaluated predictive models using morphometric characteristics to estimate total body lipids (TBL) and total body protein (TBP), based on traditional proximate analyses, in spring migrating lesser snow geese (Anser caerulescens caerulescens) and Ross's geese (A. rossii). We also compared performance of our lipid model with a previously derived predictive equation for TBL developed for nesting lesser snow geese. We used external and internal measurements on 612 lesser snow and 125 Ross's geese collected during spring migration in 2015 and 2016 within the Central and Mississippi flyways to derive and evaluate predictive models. Using a validation data set, our best performing lipid model for snow geese better predicted TBL (root mean square error [RMSE] of 23.56) compared with a model derived from nesting individuals (RMSE = 48.60), suggesting the importance of season‐specific models for accurate lipid estimation. Models that included body mass and abdominal fat deposit best predicted TBL determined by proximate analysis in both species (lesser snow goose, R2 = 0.87, RMSE = 23.56: Ross's geese, R2 = 0.89, RMSE = 13.75). Models incorporating a combination of external structural measurements in addition to internal muscle and body mass best predicted protein values (R2 = 0.85, RMSE = 19.39 and R2 = 0.85, RMSE = 7.65, lesser snow and Ross's geese, respectively), but protein models including only body mass and body size were also competitive and provided extended utility to our equations for field applications. Therefore, our models indicated the importance of specimen dissection and measurement of the abdominal fat pad to provide the most accurate lipid estimates and provide alternative dissection‐free methods for estimating protein.
","language":"English","publisher":"Wiley","doi":"10.1002/wsb.867","usgsCitation":"Webb, E.B., Fowler, D.N., Woodall, B.A., and Vrtiska, M.P., 2018, Evaluating indices of lipid and protein content in lesser snow and Ross's geese during spring migration: Wildlife Society Bulletin, v. 42, no. 2, p. 295-303, https://doi.org/10.1002/wsb.867.","productDescription":"9 p.","startPage":"295","endPage":"303","ipdsId":"IP-086754","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":499991,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/b506db9d3c3242bc8767f8d690d7f8c1","text":"External Repository"},{"id":354662,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Missouri, Nebraska, South Dakota","volume":"42","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b155d6fe4b092d9651e1ae0","contributors":{"authors":[{"text":"Webb, Elisabeth B. 0000-0003-3851-6056 ewebb@usgs.gov","orcid":"https://orcid.org/0000-0003-3851-6056","contributorId":3981,"corporation":false,"usgs":true,"family":"Webb","given":"Elisabeth","email":"ewebb@usgs.gov","middleInitial":"B.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":737022,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fowler, Drew N.","contributorId":205356,"corporation":false,"usgs":false,"family":"Fowler","given":"Drew","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":737053,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Woodall, Brendan A.","contributorId":205358,"corporation":false,"usgs":false,"family":"Woodall","given":"Brendan","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":737054,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vrtiska, Mark P.","contributorId":54008,"corporation":false,"usgs":true,"family":"Vrtiska","given":"Mark","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":737055,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70197968,"text":"70197968 - 2018 - Critically assessing the utility of portable lead analyzers for wildlife conservation","interactions":[],"lastModifiedDate":"2018-07-02T11:12:16","indexId":"70197968","displayToPublicDate":"2018-06-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"Critically assessing the utility of portable lead analyzers for wildlife conservation","docAbstract":"Lead (Pb) exposure in wildlife is a widespread management and conservation concern. Quantitative determination of Pb concentrations in wildlife tissues is the foundation for estimating exposure and risk. Development of low‐cost, portable instruments has improved access and cost‐effectiveness of determining Pb concentrations in blood samples, while also facilitating the ability for wildlife researchers to conduct near real‐time Pb testing. However, these instruments, which use anodic stripping voltammetry (ASV) methodology, may produce an analytical bias in wildlife‐blood Pb concentrations. Additionally, their simplicity invites use without appropriate quality‐assurance–quality‐control measures. Together, these factors can reduce data quality and hamper the ability to evaluate it, raising concerns about use of these instruments to inform important conservation issues. We document the extent to which this bias is addressed in the wildlife toxicology literature, develop quantitative approaches for correcting the bias, and provide recommendations to ensure robust data quality when using these instruments. Of the 25 studies we reviewed that referenced ASV use for determining Pb exposure in wildlife, only 32% acknowledged the existence of bias from the instrument. Importantly, another 20% of the studies actually reported ASV and spectroscopic‐based results together without acknowledging their lack of equivalence. Using a multispecies data set of avian blood Pb concentrations, we found that ASV‐based estimates of paired blood Pb concentrations were 30–38% lower than those from standard spectrometric‐based methods. We provide regression equations based on this analysis of 453 blood samples to allow users of ASV instruments to adjust Pb concentrations to spectrometric‐equivalent values, and propose a series of guidelines to follow when using these instruments to improve data validity.
","language":"English","publisher":"Wiley","doi":"10.1002/wsb.892","usgsCitation":"Herring, G., Eagles-Smith, C.A., Bedrosian, B., Craighead, D., Domenech, R., Langner, H.W., Parish, C.N., Shreading, A., Welch, A., and Wolstenholme, R., 2018, Critically assessing the utility of portable lead analyzers for wildlife conservation: Wildlife Society Bulletin, v. 42, no. 2, p. 284-294, https://doi.org/10.1002/wsb.892.","productDescription":"11 p.","startPage":"284","endPage":"294","ipdsId":"IP-092490","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":488774,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wsb.892","text":"Publisher Index Page"},{"id":355445,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"42","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-06-15","publicationStatus":"PW","scienceBaseUri":"5b46e576e4b060350a15d197","contributors":{"authors":[{"text":"Herring, Garth 0000-0003-1106-4731 gherring@usgs.gov","orcid":"https://orcid.org/0000-0003-1106-4731","contributorId":4403,"corporation":false,"usgs":true,"family":"Herring","given":"Garth","email":"gherring@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":739365,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eagles-Smith, Collin A. 0000-0003-1329-5285 ceagles-smith@usgs.gov","orcid":"https://orcid.org/0000-0003-1329-5285","contributorId":505,"corporation":false,"usgs":true,"family":"Eagles-Smith","given":"Collin","email":"ceagles-smith@usgs.gov","middleInitial":"A.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":739364,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bedrosian, Bryan","contributorId":199738,"corporation":false,"usgs":false,"family":"Bedrosian","given":"Bryan","affiliations":[{"id":35591,"text":"Teton Raptor Center","active":true,"usgs":false}],"preferred":false,"id":739366,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Craighead, Derek","contributorId":206080,"corporation":false,"usgs":false,"family":"Craighead","given":"Derek","email":"","affiliations":[{"id":6657,"text":"Craighead Beringia South","active":true,"usgs":false}],"preferred":false,"id":739367,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Domenech, Robert","contributorId":199743,"corporation":false,"usgs":false,"family":"Domenech","given":"Robert","email":"","affiliations":[{"id":35594,"text":"Raptor View Research Institute","active":true,"usgs":false}],"preferred":false,"id":739423,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Langner, Heiko W.","contributorId":206081,"corporation":false,"usgs":false,"family":"Langner","given":"Heiko","email":"","middleInitial":"W.","affiliations":[{"id":37234,"text":"King Adbdullah University of Science and Technology","active":true,"usgs":false}],"preferred":false,"id":739368,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Parish, Chris N.","contributorId":206082,"corporation":false,"usgs":false,"family":"Parish","given":"Chris","email":"","middleInitial":"N.","affiliations":[{"id":37235,"text":"The Peregrin Fund","active":true,"usgs":false}],"preferred":false,"id":739369,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Shreading, Adam","contributorId":199745,"corporation":false,"usgs":false,"family":"Shreading","given":"Adam","email":"","affiliations":[{"id":35594,"text":"Raptor View Research Institute","active":true,"usgs":false}],"preferred":false,"id":739370,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Welch, Alacia","contributorId":206083,"corporation":false,"usgs":false,"family":"Welch","given":"Alacia","email":"","affiliations":[{"id":37236,"text":"Pinnacles National Park","active":true,"usgs":false}],"preferred":false,"id":739371,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Wolstenholme, Rachel","contributorId":206084,"corporation":false,"usgs":false,"family":"Wolstenholme","given":"Rachel","email":"","affiliations":[{"id":37236,"text":"Pinnacles National Park","active":true,"usgs":false}],"preferred":false,"id":739372,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70197446,"text":"70197446 - 2018 - Estimation of stream conditions in tributaries of the Klamath River, northern California","interactions":[],"lastModifiedDate":"2018-06-12T11:11:00","indexId":"70197446","displayToPublicDate":"2018-06-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5708,"text":"Arcata Fisheries Technical Report","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"TR 2018-32","title":"Estimation of stream conditions in tributaries of the Klamath River, northern California","docAbstract":"Because of their critical ecological role, stream temperature and discharge are requisite inputs for models of salmonid population dynamics. Coho Salmon inhabiting the Klamath Basin spend much of their freshwater life cycle inhabiting tributaries, but environmental data are often absent or only seasonally available at these locations. To address this information gap, we constructed daily averaged water temperature models that used simulated meteorological data to estimate daily tributary temperatures, and we used flow differentials recorded on the mainstem Klamath River to estimate daily tributary discharge.\n\nObserved temperature data were available for fourteen of the major salmon bearing tributaries, which enabled estimation of tributary-specific model parameters at those locations. Water temperature data from six mid-Klamath Basin tributaries were used to estimate a global set of parameters for predicting water temperatures in the remaining tributaries. The resulting parameter sets were used to simulate water temperatures for each of 75 tributaries from 1980-2015. Goodness-of-fit statistics computed from a cross-validation analysis demonstrated a high precision of the tributary-specific models in predicting temperature in unobserved years and of the global model in predicting temperatures in unobserved streams.\n\nKlamath River discharge has been monitored by four gages that broadly intersperse the 292 kilometers from the Iron Gate Dam to the Klamath River mouth. These gages defined the upstream and downstream margins of three reaches. Daily discharge of tributaries within a reach was estimated from 1980-2015 based on drainage-area proportionate allocations of the discharge differential between the upstream and downstream margin. Comparisons with measured discharge on Indian Creek, a moderate-sized tributary with naturally regulated flows, revealed that the estimates effectively approximated both the variability and magnitude of discharge.","language":"English","publisher":"U.S. Fish and Wildlife Service. Arcata Fish and Wildlife Office","usgsCitation":"Manhard, C.V., Som, N.A., Jones, E.C., and Perry, R.W., 2018, Estimation of stream conditions in tributaries of the Klamath River, northern California: Arcata Fisheries Technical Report TR 2018-32, vi, 28 p.","productDescription":"vi, 28 p.","numberOfPages":"34","ipdsId":"IP-088667","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":354934,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":354703,"type":{"id":15,"text":"Index Page"},"url":"https://www.fws.gov/arcata/fisheries/reports/technical/2018/EstimationofStreamConditionsinTributariesoftheKlamathRiverNorthernCalifornia.pdf"}],"country":"United States","state":"California","otherGeospatial":"Klamath River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.667,\n 41\n ],\n [\n -122.3333,\n 41\n ],\n [\n -122.3333,\n 42\n ],\n [\n -123.667,\n 42\n ],\n [\n -123.667,\n 41\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b46e577e4b060350a15d1a9","contributors":{"authors":[{"text":"Manhard, Christopher V.","contributorId":203911,"corporation":false,"usgs":false,"family":"Manhard","given":"Christopher","email":"","middleInitial":"V.","affiliations":[{"id":36754,"text":"U.S. Fish and Wildlife Service, California Cooperative Fish and Wildlife Research Unit, Humboldt State University, 1 Harpst Street, Arcata, CA 95521, USA","active":true,"usgs":false}],"preferred":false,"id":737185,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Som, Nicholas A.","contributorId":203773,"corporation":false,"usgs":false,"family":"Som","given":"Nicholas","email":"","middleInitial":"A.","affiliations":[{"id":36713,"text":"Statistician, USFWS - Arcata Fisheries Program, Humboldt State University","active":true,"usgs":false}],"preferred":false,"id":737186,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jones, Edward C. 0000-0001-7255-1475 ejones@usgs.gov","orcid":"https://orcid.org/0000-0001-7255-1475","contributorId":203917,"corporation":false,"usgs":true,"family":"Jones","given":"Edward","email":"ejones@usgs.gov","middleInitial":"C.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":737187,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Perry, Russell W. 0000-0003-4110-8619 rperry@usgs.gov","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":2820,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","email":"rperry@usgs.gov","middleInitial":"W.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":737184,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70197125,"text":"70197125 - 2018 - World distribution of uranium deposits","interactions":[],"lastModifiedDate":"2018-06-12T11:33:29","indexId":"70197125","displayToPublicDate":"2018-06-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"title":"World distribution of uranium deposits","docAbstract":"Deposit data derived from IAEA UDEPO (http://infcis.iaea.org/UDEPO/About.cshtml) database with assistance from P. Bruneton (France) and M. Mihalasky (U.S.A.). The map is an updated companion to \"World Distribution of Uranium Deposits (UDEPO) with Uranium Deposit Classification, IAEA Tech-Doc-1629\".\n\nGeology was derived from L.B. Chorlton, Generalized Geology of the World, Geological Survey of Canada, Open File 5529 , 2007.\n\nMap production by M.C. Fairclough (IAEA), J.A. Irvine (Austrailia), L.F. Katona (Australia) and W.L. Slimmon (Canada). World Distribution of Uranium Deposits, International Atomic Energy Agency, Vienna, Austria. Cartographic Assistance was supplied by the Geological Survey of South Australia, the Saskatchewan Geological Survey and United States Geological Survey to the IAEA.\n\nCoastlines, drainage, and country boundaries were obtained from ArcMap, 1:25 000 000 scale, and are copyrighted data containing the intellectual property of Environmental Systems Research Institute (ESRI). The use of particular designations of countries or territories does not imply any judgment by the publisher, the IAEA, as to the legal status of such countries or territories, of their authorities and institutions or of the delimitation of their boundaries.\n\nAny revisions or additional geological information known to the user would be welcomed by the International Atomic Energy Agency and the Geological Survey of Canada.","language":"English","publisher":"International Atomic Energy Agency","publisherLocation":"Vienna, Austria","usgsCitation":"Fairclough, M.C., Irvine, J.A., Katona, L.F., Simmon, W.L., Bruneton, P., Mihalasky, M.J., Cuney, M., Aranha, M., Pylypenko, O., and Poliakovska, K., 2018, World distribution of uranium deposits (Second Edition), 46.81 x 33.11 inches.","productDescription":"46.81 x 33.11 inches","ipdsId":"IP-089497","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":354941,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":354940,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www-pub.iaea.org/books/IAEABooks/12314/World-Distribution-of-Uranium-Deposits-Second-Edition"}],"edition":"Second Edition","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b46e578e4b060350a15d1af","contributors":{"authors":[{"text":"Fairclough, M. C.","contributorId":205544,"corporation":false,"usgs":false,"family":"Fairclough","given":"M.","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":737713,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Irvine, J. A.","contributorId":205545,"corporation":false,"usgs":false,"family":"Irvine","given":"J.","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":737714,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Katona, L. F.","contributorId":205546,"corporation":false,"usgs":false,"family":"Katona","given":"L.","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":737715,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Simmon, W. L.","contributorId":205547,"corporation":false,"usgs":false,"family":"Simmon","given":"W.","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":737716,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bruneton, P.","contributorId":205548,"corporation":false,"usgs":false,"family":"Bruneton","given":"P.","email":"","affiliations":[],"preferred":false,"id":737717,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mihalasky, Mark J. 0000-0002-0082-3029 mjm@usgs.gov","orcid":"https://orcid.org/0000-0002-0082-3029","contributorId":3692,"corporation":false,"usgs":true,"family":"Mihalasky","given":"Mark","email":"mjm@usgs.gov","middleInitial":"J.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true},{"id":662,"text":"Western Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":737718,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cuney, M.","contributorId":205549,"corporation":false,"usgs":false,"family":"Cuney","given":"M.","email":"","affiliations":[],"preferred":false,"id":737719,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Aranha, M.","contributorId":205550,"corporation":false,"usgs":false,"family":"Aranha","given":"M.","affiliations":[],"preferred":false,"id":737720,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Pylypenko, O.","contributorId":205551,"corporation":false,"usgs":false,"family":"Pylypenko","given":"O.","email":"","affiliations":[],"preferred":false,"id":737721,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Poliakovska, K.","contributorId":205552,"corporation":false,"usgs":false,"family":"Poliakovska","given":"K.","email":"","affiliations":[],"preferred":false,"id":737722,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70195691,"text":"ofr20181031 - 2018 - Assessment of capacity-building activities for forest measurement, reporting, and verification, 2011–15 ","interactions":[],"lastModifiedDate":"2018-05-31T09:44:13","indexId":"ofr20181031","displayToPublicDate":"2018-05-31T09:15:00","publicationYear":"2018","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":"2018-1031","title":"Assessment of capacity-building activities for forest measurement, reporting, and verification, 2011–15 ","docAbstract":"This report was written as a collaborative effort between the U.S. Geological Survey, SilvaCarbon, and Wageningen University with funding provided by the U.S. Agency for International Development and the European Space Agency, respectively, to address a pressing need for enhanced result-based monitoring and evaluation of delivered capacity-building activities. For this report, the capacity-building activities delivered by capacity-building providers (referred to as “providers” hereafter) during 2011–15 (the study period) to support countries in building measurement, reporting, and verification (MRV) systems for reducing emissions from deforestation and forest degradation (REDD+) were assessed and evaluated.
Summarizing capacity-building activities and outcomes across multiple providers was challenging. Many of the providers did not have information readily available, which precluded them from participating in this study despite the usefulness of their information. This issue led to a key proposed future action: Capacity-building providers could establish a central repository within the Global Forestry Observation Initiative (GFOI; http://www.gfoi.org/) where data from past, current, and future activities of all capacity-building providers could be stored. The repository could be maintained in a manner to continually learn from previous lessons.
Although various providers monitored and evaluated the success of their capacity-building activities, such evaluations only assessed the success of immediate outcomes and not the overarching outcomes and impacts of activities implemented by multiple providers. Good monitoring and evaluation should continuously monitor and periodically evaluate all factors affecting the outcomes of a provided capacity-building activity.
The absence of a methodology to produce quantitative evidence of a causal link between multiple capacity-building activities delivered and successful outcomes left only a plausible association. A previous publication argued that plausible association, although not a precise measurement of cause and effect, was a realistic tool. Our review of the available literature on this subject did not find another similar assessment to assess capacity-building activities for supporting the countries in building MRV system for REDD+.
Four countries from the main forested regions of Africa, the Americas, and Asia were chosen as subjects for this report based on the length of time SilvaCarbon and other providers have provided capacity-building activities toward MRV system for REDD+: Colombia (the Americas), the Democratic Republic of the Congo (DRC; Africa), Peru (the Americas), and the Republic of the Philippines (referred to as “the Philippines” hereafter; Asia).
Several providers were contacted for information to include in this report, but, because of various constraints, only SilvaCarbon, the Food and Agriculture Organization of the United Nations (FAO), and the World Wildlife Fund (WWF) participated in this study. These three providers supported various targeted capacity-building activities through-out Africa, the Americas, and Asia, including the following: technical workshops at national and regional levels (referred to as “workshops” hereafter), hands on training, study tours, technical details by experts, technical consultation between providers and recipients, sponsorship for travel, organizing network meetings, developing sampling protocols, assessing deforestation and degradation drivers, estimating carbon stock and flow, designing monitoring systems for multiple uses, promoting public-private partnerships to scale up investments on MRV system for REDD+, and assisting with the design of national forest monitoring systems.
Their activities were planned in coordination with key partners in each country and region and with the support and assistance of other providers. Note that several other organizations and institutions assisted the providers to deliver capacity-building activities, including Boston University, Conservation International, Stanford University, University of Maryland, and Wageningen University & Research.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181031","collaboration":"Prepared in cooperation with Wageningen University, the U.S. Agency for International Development, the U.S. Department of State, and the European Space Agency ","usgsCitation":"Peneva-Reed, E.I., and Romijn, J.E, 2018, Assessment of capacity-building activities for forest measurement, reporting, and verification, 2011–15: U.S. Geological Survey Open-File Report 2018–1031, 35 p., https://doi.org/10.3133/ofr20181031. ","productDescription":"v, 35 p.","numberOfPages":"46","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-088895","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"links":[{"id":354567,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1031/ofr20181031.pdf","text":"Report","size":"1.07 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1031"},{"id":354566,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1031/coverthb.jpg"}],"contact":"Director, U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Under-ice discharge is estimated using open-water reference hydrographs; however, the ratings for ice-affected sites are generally qualified as poor. The U.S. Geological Survey (USGS), in collaboration with the Colorado Water Conservation Board, conducted a proof-of-concept to develop an alternative method for computing under-ice discharge using hydroacoustics and the Probability Concept.
The study site was located south of Minturn, Colorado (CO), USA, and was selected because of (1) its proximity to the existing USGS streamgage 09064600 Eagle River near Minturn, CO, and (2) its ease-of-access to verify discharge using a variety of conventional methods. From late September 2014 to early March 2015, hydraulic conditions varied from open water to under ice. These temporal changes led to variations in water depth and velocity. Hydroacoustics (tethered and uplooking acoustic Doppler current profilers and acoustic Doppler velocimeters) were deployed to measure the vertical-velocity profile at a singularly important vertical of the channel-cross section. Because the velocity profile was non-standard and cannot be characterized using a Power Law or Log Law, velocity data were analyzed using the Probability Concept, which is a probabilistic formulation of the velocity distribution. The Probability Concept-derived discharge was compared to conventional methods including stage-discharge and index-velocity ratings and concurrent field measurements; each is complicated by the dynamics of ice formation, pressure influences on stage measurements, and variations in cross-sectional area due to ice formation.
No particular discharge method was assigned as truth. Rather one statistical metric (Kolmogorov-Smirnov; KS), agreement plots, and concurrent measurements provided a measure of comparability between various methods. Regardless of the method employed, comparisons between each method revealed encouraging results depending on the flow conditions and the absence or presence of ice cover.
For example, during lower discharges dominated by under-ice and transition (intermittent open-water and under-ice) conditions, the KS metric suggests there is not sufficient information to reject the null hypothesis and implies that the Probability Concept and index-velocity rating represent similar distributions. During high-flow, open-water conditions, the comparisons are less definitive; therefore, it is important that the appropriate analytical method and instrumentation be selected. Six conventional discharge measurements were collected concurrently with Probability Concept-derived discharges with percent differences (%) of −9.0%, −21%, −8.6%, 17.8%, 3.6%, and −2.3%.
This proof-of-concept demonstrates that riverine discharges can be computed using the Probability Concept for a range of hydraulic extremes (variations in discharge, open-water and under-ice conditions) immediately after the siting phase is complete, which typically requires one day. Computing real-time discharges is particularly important at sites, where (1) new streamgages are planned, (2) river hydraulics are complex, and (3) shifts in the stage-discharge rating are needed to correct the streamflow record. Use of the Probability Concept does not preclude the need to maintain a stage-area relation. Both the Probability Concept and index-velocity rating offer water-resource managers and decision makers alternatives for computing real-time discharge for open-water and under-ice conditions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2018.04.073","usgsCitation":"Fulton, J.W., Henneberg, M.F., Mills, T.J., Kohn, M.S., Epstein, B., Hittle, E.A., Damschen, W., Laveau, C., Lambrecht, J.M., and Farmer, W.H., 2018, Computing under-ice discharge: A proof-of-concept using hydroacoustics and the Probability Concept: Journal of Hydrology, v. 562, p. 733-748, https://doi.org/10.1016/j.jhydrol.2018.04.073.","productDescription":"16 p.","startPage":"733","endPage":"748","ipdsId":"IP-072689","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":468717,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2018.04.073","text":"Publisher Index Page"},{"id":354617,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Eagle River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.40344033567112,\n 39.55549288908489\n ],\n [\n -106.40344033567112,\n 39.552795008656176\n ],\n [\n -106.40000726492562,\n 39.552795008656176\n ],\n [\n -106.40000726492562,\n 39.55549288908489\n ],\n [\n -106.40344033567112,\n 39.55549288908489\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"562","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b155d72e4b092d9651e1af8","contributors":{"authors":[{"text":"Fulton, John W. 0000-0002-5335-0720 jwfulton@usgs.gov","orcid":"https://orcid.org/0000-0002-5335-0720","contributorId":2298,"corporation":false,"usgs":true,"family":"Fulton","given":"John","email":"jwfulton@usgs.gov","middleInitial":"W.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":736892,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Henneberg, Mark F. 0000-0002-6991-1211 mfhenneb@usgs.gov","orcid":"https://orcid.org/0000-0002-6991-1211","contributorId":173569,"corporation":false,"usgs":true,"family":"Henneberg","given":"Mark","email":"mfhenneb@usgs.gov","middleInitial":"F.","affiliations":[],"preferred":false,"id":736893,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mills, Taylor J. 0000-0001-7252-0521 tmills@usgs.gov","orcid":"https://orcid.org/0000-0001-7252-0521","contributorId":4658,"corporation":false,"usgs":true,"family":"Mills","given":"Taylor","email":"tmills@usgs.gov","middleInitial":"J.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":736894,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kohn, Michael S. 0000-0002-5989-7700 mkohn@usgs.gov","orcid":"https://orcid.org/0000-0002-5989-7700","contributorId":4549,"corporation":false,"usgs":true,"family":"Kohn","given":"Michael","email":"mkohn@usgs.gov","middleInitial":"S.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":736895,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Epstein, Brian","contributorId":205319,"corporation":false,"usgs":false,"family":"Epstein","given":"Brian","email":"","affiliations":[],"preferred":false,"id":736896,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hittle, Elizabeth A. 0000-0002-1771-7724 ehittle@usgs.gov","orcid":"https://orcid.org/0000-0002-1771-7724","contributorId":2038,"corporation":false,"usgs":true,"family":"Hittle","given":"Elizabeth","email":"ehittle@usgs.gov","middleInitial":"A.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":736897,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Damschen, William C. wcdamsch@usgs.gov","contributorId":1610,"corporation":false,"usgs":true,"family":"Damschen","given":"William C.","email":"wcdamsch@usgs.gov","affiliations":[{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":736898,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Laveau, Christopher D. 0000-0002-4009-1889","orcid":"https://orcid.org/0000-0002-4009-1889","contributorId":205320,"corporation":false,"usgs":true,"family":"Laveau","given":"Christopher D.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":false,"id":736899,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lambrecht, Jason M. jmlambre@usgs.gov","contributorId":4019,"corporation":false,"usgs":true,"family":"Lambrecht","given":"Jason","email":"jmlambre@usgs.gov","middleInitial":"M.","affiliations":[],"preferred":true,"id":736900,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Farmer, William H. 0000-0002-2865-2196 wfarmer@usgs.gov","orcid":"https://orcid.org/0000-0002-2865-2196","contributorId":4374,"corporation":false,"usgs":true,"family":"Farmer","given":"William","email":"wfarmer@usgs.gov","middleInitial":"H.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":736901,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70197385,"text":"70197385 - 2018 - Habitat selection, movement patterns, and hazards encountered by northern leopard frogs (Lithobates pipiens) in an agricultural landscape","interactions":[],"lastModifiedDate":"2018-05-31T14:57:37","indexId":"70197385","displayToPublicDate":"2018-05-31T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1894,"text":"Herpetological Conservation and Biology","onlineIssn":"2151-0733","printIssn":"1931-7603","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Habitat selection, movement patterns, and hazards encountered by northern leopard frogs (Lithobates pipiens) in an agricultural landscape","title":"Habitat selection, movement patterns, and hazards encountered by northern leopard frogs (Lithobates pipiens) in an agricultural landscape","docAbstract":"Telemetry data for 59 Northern Leopard Frogs (Lithobates pipiens) breeding in ponds in Houston and Winona Counties, MN; 2001-2002. Agricultural intensification is causing declines in many wildlife species, including Northern Leopard Frogs (Lithobates pipiens). Specific information about frog movements, habitat selection, and sources of mortality can be used to inform conservation-focused land management and acquisition. We studied Northern Leopard Frogs in southeastern Minnesota, part of the Driftless Area ecoregion, characterized by hills and valleys and a mix of agriculture, forests, small towns and farmsteads. In this area, small farm ponds, originally built to control soil erosion are used by the species for breeding and wintering in addition to riparian wetlands. But, this agricultural landscape may be hazardous for frogs moving between breeding, feeding, and wintering habitats. We surgically implanted transmitters into the peritoneal cavity of 59 Northern Leopard Frogs and tracked them from May to October 2001-2002. The total distance traveled by radio-tagged frogs ranged from 12 to 3316 m, the 95% home range averaged 5.3 ± 1.2 (SE) ha, and the 50% core area averaged 1.05 ± 0.3 (SE) ha. As expected, Northern Leopard Frogs selected wetlands over all other land cover classes and row crops were generally avoided at all levels of selection. Only a few tracked frogs were successful at dispersing (n = 6). Most frogs attempting to disperse (n =31) ended up missing (n = 14), died due to mowing (n = 8), or were recorded as transmitter failure (n = 2) or unknown mortalities (n = 1). For the conservation of Northern Leopard Frogs in this agricultural setting, we must consider both the aquatic and the terrestrial needs of this species. Conservation agencies that restore, manage, and acquire wetlands should consider the hazards posed by land uses adjacent to frog breeding and wintering sites and plan for movement corridors between these locations. For example, grasslands that are mowed or hayed between April and October in the north central U.S. and are adjacent to wetlands, pose a direct threat to frogs because these cultivated grasslands are primary locations for summer occupancy. When conservation land managers are selecting sites for acquisition or restoration they should avoid investments that will situate the wetland adjacent to heavily travelled roads and agricultural lands likely to be mowed or hayed. Increasing habitat amount and quality at amphibian breeding, feeding and wintering sites should reduce the energy required and hazards associated with moving long distances. Large, diverse wetlands probably provide all of the requirements needed by Northern Leopard Frogs for survival including food, shelter, breeding and overwintering areas.","language":"English","publisher":"Herpetological Conservation and Biology","usgsCitation":"Knutson, M.G., Herner-Thogmartin, J., Thogmartin, W.E., Kapfer, J.M., and Nelson, J.C., 2018, Habitat selection, movement patterns, and hazards encountered by northern leopard frogs (Lithobates pipiens) in an agricultural landscape: Herpetological Conservation and Biology, v. 13, no. 1, p. 113-130.","productDescription":"18 p.","startPage":"113","endPage":"130","ipdsId":"IP-085717","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":354645,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":354643,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org10.5066/F7930S4R"},{"id":354629,"type":{"id":15,"text":"Index Page"},"url":"https://www.herpconbio.org/contents_vol13_issue1.html"}],"country":"United States","state":"Minnesota","county":"Houston County, Winona County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.0379638671875,\n 43.375108633273086\n ],\n [\n -91.08489990234375,\n 43.375108633273086\n ],\n [\n -91.08489990234375,\n 44.319918120477425\n ],\n [\n -92.0379638671875,\n 44.319918120477425\n ],\n [\n -92.0379638671875,\n 43.375108633273086\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"1","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b155d71e4b092d9651e1af0","contributors":{"authors":[{"text":"Knutson, Melinda G.","contributorId":205325,"corporation":false,"usgs":false,"family":"Knutson","given":"Melinda","email":"","middleInitial":"G.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":736941,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Herner-Thogmartin, Jennifer H.","contributorId":205326,"corporation":false,"usgs":false,"family":"Herner-Thogmartin","given":"Jennifer H.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":736942,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":736940,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kapfer, Joshua M.","contributorId":176248,"corporation":false,"usgs":false,"family":"Kapfer","given":"Joshua","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":736943,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nelson, John C. 0000-0002-7105-0107 jcnelson@usgs.gov","orcid":"https://orcid.org/0000-0002-7105-0107","contributorId":149361,"corporation":false,"usgs":true,"family":"Nelson","given":"John","email":"jcnelson@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":736944,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70197365,"text":"sir20185064 - 2018 - Conceptual framework and trend analysis of water-level responses to hydrologic stresses, Pahute Mesa–Oasis Valley groundwater basin, Nevada, 1966-2016","interactions":[],"lastModifiedDate":"2018-06-06T14:16:17","indexId":"sir20185064","displayToPublicDate":"2018-05-31T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5064","title":"Conceptual framework and trend analysis of water-level responses to hydrologic stresses, Pahute Mesa–Oasis Valley groundwater basin, Nevada, 1966-2016","docAbstract":"This report identifies water-level trends in wells and provides a conceptual framework that explains the hydrologic stresses and factors causing the trends in the Pahute Mesa–Oasis Valley (PMOV) groundwater basin, southern Nevada. Water levels in 79 wells were analyzed for trends between 1966 and 2016. The magnitude and duration of water-level responses to hydrologic stresses were analyzed graphically, statistically, and with water-level models.
The conceptual framework consists of multiple stress-specific conceptual models to explain water-level responses to the following hydrologic stresses: recharge, evapotranspiration, pumping, nuclear testing, and wellbore equilibration. Dominant hydrologic stresses affecting water-level trends in each well were used to categorize trends as nonstatic, transient, or steady state.
The conceptual framework of water-level responses to hydrologic stresses and trend analyses provide a comprehensive understanding of the PMOV basin and vicinity. The trend analysis links water-level fluctuations in wells to hydrologic stresses and potential factors causing the trends. Transient and steady-state trend categorizations can be used to determine the appropriate water-level data for groundwater studies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185064","collaboration":"Prepared in cooperation with the Department of Energy, National Nuclear Security Administration Nevada Site Office, Office of Environmental Management under Interagency Agreement, DE-NA0001654","usgsCitation":"Jackson, T.R., and Fenelon, J.M., 2018, Conceptual framework and trend analysis of water-level responses to hydrologic stresses, Pahute Mesa–Oasis Valley groundwater basin, Nevada, 1966-2016: U.S. Geological Survey Scientific Investigations Report 2018-5064, 89 p., https://doi.org/10.3133/sir20185064.","productDescription":"ix, 89 p.","numberOfPages":"104","onlineOnly":"Y","ipdsId":"IP-086316","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":354612,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F77942XB","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Supplemental data for conceptual framework and trend analysis of water-level responses to hydrologic stresses, Pahute Mesa–Oasis Valley Groundwater Basin, Nevada, 1966-2016"},{"id":354610,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5064/coverthb.jpg"},{"id":354611,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5064/sir20185064.pdf","text":"Report","size":"4.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5064"}],"country":"United States","state":"Nevada","otherGeospatial":"Pahute Mesa–Oasis Valley Groundwater Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117,\n 36\n ],\n [\n -115,\n 36\n ],\n [\n -115,\n 38\n ],\n [\n -117,\n 38\n ],\n [\n -117,\n 36\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Rd.
Carson City, Nevada 89701
The U.S. Geological Survey developed a groundwater-flow model for the uppermost principal aquifer systems in the Williston Basin in parts of Montana, North Dakota, and South Dakota in the United States and parts of Manitoba and Saskatchewan in Canada as part of a detailed assessment of the groundwater availability in the area. The assessment was done because of the potential for increased demands and stresses on groundwater associated with large-scale energy development in the area. As part of this assessment, a three-dimensional groundwater-flow model was developed as a tool that can be used to simulate how the groundwater-flow system responds to changes in hydrologic stresses at a regional scale.
The three-dimensional groundwater-flow model was developed using the U.S. Geological Survey’s numerical finite-difference groundwater model with the Newton-Rhapson solver, MODFLOW–NWT, to represent the glacial, lower Tertiary, and Upper Cretaceous aquifer systems for steady-state (mean) hydrological conditions for 1981‒2005 and for transient (temporally varying) conditions using a combination of a steady-state period for pre-1960 and transient periods for 1961‒2005. The numerical model framework was constructed based on existing and interpreted hydrogeologic and geospatial data and consisted of eight layers. Two layers were used to represent the glacial aquifer system in the model; layer 1 represented the upper one-half and layer 2 represented the lower one-half of the glacial aquifer system. Three layers were used to represent the lower Tertiary aquifer system in the model; layer 3 represented the upper Fort Union aquifer, layer 4 represented the middle Fort Union hydrogeologic unit, and layer 5 represented the lower Fort Union aquifer. Three layers were used to represent the Upper Cretaceous aquifer system in the model; layer 6 represented the upper Hell Creek hydrogeologic unit, layer 7 represented the lower Hell Creek aquifer, and layer 8 represented the Fox Hills aquifer. The numerical model was constructed using a uniform grid with square cells that are about 1 mile (1,600 meters) on each side with a total of about 657,000 active cells.
Model calibration was completed by linking Parameter ESTimation (PEST) software with MODFLOW–NWT. The PEST software uses statistical parameter estimation techniques to identify an optimum set of input parameters by adjusting individual model input parameters and assessing the differences, or residuals, between observed (measured or estimated) data and simulated values. Steady-state model calibration consisted of attempting to match mean simulated values to measured or estimated values of (1) hydraulic head, (2) hydraulic head differences between model layers, (3) stream infiltration, and (4) discharge to streams. Calibration of the transient model consisted of attempting to match simulated and measured temporally distributed values of hydraulic head changes, stream base flow, and groundwater discharge to artesian flowing wells. Hydraulic properties estimated through model calibration included hydraulic conductivity, vertical hydraulic conductivity, aquifer storage, and riverbed hydraulic conductivity in addition to groundwater recharge and well skin.
The ability of the numerical model to accurately simulate groundwater flow in the Williston Basin was assessed primarily by its ability to match calibration targets for hydraulic head, stream base flow, and flowing well discharge. The steady-state model also was used to assess the simulated potentiometric surfaces in the upper Fort Union aquifer, the lower Fort Union aquifer, and the Fox Hills aquifer. Additionally, a previously estimated regional groundwater-flow budget was compared with the simulated steady-state groundwater-flow budget for the Williston Basin. The simulated potentiometric surfaces typically compared well with the estimated potentiometric surfaces based on measured hydraulic head data and indicated localized groundwater-flow gradients that were topographically controlled in outcrop areas and more generalized regional gradients where the aquifers were confined. The differences between the measured and simulated (residuals) hydraulic head values for 11,109 wells were assessed, which indicated that the steady-state model generally underestimated hydraulic head in the model area. This underestimation is indicated by a positive mean residual of 11.2 feet for all model layers. Layer 7, which represents the lower Hell Creek aquifer, is the only layer for which the steady-state model overestimated hydraulic head. Simulated groundwater-level changes for the transient model matched within plus or minus 2.5 feet of the measured values for more than 60 percent of all measurements and to within plus or minus 17.5 feet for 95 percent of all measurements; however, the transient model underestimated groundwater-level changes for all model layers. A comparison between simulated and estimated base flows for the steady-state and transient models indicated that both models overestimated base flow in streams and underestimated annual fluctuations in base flow.
The estimated and simulated groundwater budgets indicate the model area received a substantial amount of recharge from precipitation and stream infiltration. The steady-state model indicated that reservoir seepage was a larger component of recharge in the Williston Basin than was previously estimated. Irrigation recharge and groundwater inflow from outside the Williston Basin accounted for a relatively small part of total groundwater recharge when compared with recharge from precipitation, stream infiltration, and reservoir seepage. Most of the estimated and simulated groundwater discharge in the Williston Basin was to streams and reservoirs. Simulated groundwater withdrawal, discharge to reservoirs, and groundwater outflow in the Williston Basin accounted for a smaller part of total groundwater discharge.
The transient model was used to simulate discharge to 571 flowing artesian wells within the model area. Of the 571 established flowing artesian wells simulated by the model, 271 wells did not flow at any time during the simulation because hydraulic head was always below the land-surface altitude. As hydraulic head declined throughout the simulation, 68 of these wells responded by ceasing to flow by the end of 2005. Total mean simulated discharge for the 571 flowing artesian wells was 55.1 cubic feet per second (ft3/s), and the mean simulated flowing well discharge for individual wells was 0.118 ft3/s. Simulated discharge to individual flowing artesian wells increased from 0.039 to 0.177 ft3/s between 1961 and 1975 and decreased to 0.102 ft3/s by 2005. The mean residual for 34 flowing wells with measured discharge was 0.014 ft3/s, which indicates the transient model overestimated discharge to flowing artesian wells in the model area.
Model limitations arise from aspects of the conceptual model and from simplifications inherent in the construction and calibration of a regional-scale numerical groundwater-flow model. Simplifying assumptions in defining hydraulic parameters in space and hydrologic stresses and time-varying observational data in time can limit the capabilities of this tool to simulate how the groundwater-flow system responds to changes in hydrologic stresses, particularly at the local scale; nevertheless, the steady-state model adequately simulated flow in the uppermost principal aquifer systems in the Williston Basin based on the comparison between the simulated and estimated groundwater-flow budget, the comparison between simulated and estimated potentiometric surfaces, and the results of the calibration process.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175158","collaboration":"Water Availability and Use Science Program","usgsCitation":"Davis, K.W., and Long, A.J., 2018, Construction and calibration of a groundwater-flow model to assess groundwater availability in the uppermost principal aquifer systems of the Williston Basin, United States and Canada: U.S. Geological Survey Scientific Investigations Report 2017–5158, 70 p., https://doi.org/10.3133/sir20175158.","productDescription":"Report: ix, 70; Appendixes 1-2; Data Release","numberOfPages":"84","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-080007","costCenters":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":354478,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F75B01CZ","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW-NWT model used to assess groundwater availability in the uppermost principal aquifer systems of the Williston structural basin, United States and Canada"},{"id":354477,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5158/sir20175158.pdf","text":"Report","size":"97.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5158"},{"id":354510,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5158/sir20175158_appendix_1.xlsx","text":"Appendix Table 1","size":"1.77 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017–5158 Appendix 1"},{"id":354511,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5158/sir20175158_appendix_2.xlsx","text":"Appendix Table 2","size":"25.1 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017–5158 Appendix 2"},{"id":354476,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5158/coverthb2.jpg"}],"country":"United States","state":"Montana, North Dakota, South Dakota, Wyoming","otherGeospatial":"Williston Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.3359375,\n 42.35854391749705\n ],\n [\n -97.734375,\n 42.35854391749705\n ],\n [\n -97.734375,\n 49.89463439573421\n ],\n [\n -109.3359375,\n 49.89463439573421\n ],\n [\n -109.3359375,\n 42.35854391749705\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
South Dakota Office
U.S. Geological Survey
1608 Mountain View Rd.
Rapid City, SD 57702
**September 28, 2018: The purpose of a USGS Open-file report (OFR) is dissemination of information that must be released immediately to fill a public need or information that is not sufficiently refined to warrant publication in one of the other USGS series. As part of that refinement process, an error was discovered in one of the input data sets of the Rio Grande Transboundary Integrated Hydrologic Model (RGTIHM) that this OFR was based upon. The error involved the assignment of storage properties to “phantom cells.”
Phantom cells are required for most variants of MODFLOW that use a structured finite-difference grid when individual stratigraphic layers are represented as separate layers. Using phantom cells is a common practice that allows separate model layers to be maintained without having to combine stratigraphic layers into equivalent model layers or to use an unstructured grid. Typically, phantom cell horizontal hydraulic conductivities and storage properties are set to a small number and vertical hydraulic conductivities are set to a number large enough to allow vertical flow between the vertically adjacent layers.
In the RGTIHM, the specific storage properties of the phantom cells for the upper (RGTIHM layers 3 and 4), middle (RGTIHM layers 5 and 6), and lower (RGTIHM layers 7 and 8) members of the Santa Fe Group were inadvertently assigned a value of 1 feet-1. The revision of these specific storage values to a small number (1.0 x 10-09 feet-1) required additional trial-and-error model calibration and a new sensitivity analysis. After calibration, the overall model fit remained similar to the fit described in the OFR, but the fit for many individual features such as project water available for diversions at the American Canal and Acequia Madre improved due to the reduction in flow coming from lower layers. Overall, there is still an average net depletion of groundwater flow, and the conclusions of the report are not changed. The revised average annual groundwater flow depletion simulated for the period 1953-2014 is -1,480 acre-feet/year for the entire model region, and -3,660 acre-feet/year for the portion of the model in the United States. The final version of the model will be the basis of the USGS Scientific Investigations Report that will supersede this OFR. An updated Model Archive of RGTIHM is available upon request to the USGS California Water Science Center.
The corrected version of the model WAS the basis for the USGS Scientific Investigations Report that SUPERSEDED this Open-File Report.**
Changes in population, agricultural development and practices (including shifts to more water-intensive crops), and climate variability are increasing demands on available water resources, particularly groundwater, in one of the most productive agricultural regions in the Southwest—the Rincon and Mesilla Valley parts of Rio Grande Valley, Doña Ana and Sierra Counties, New Mexico, and El Paso County, Texas. The goal of this study was to produce an integrated hydrological simulation model to help evaluate water-management strategies, including conjunctive use of surface water and groundwater for historical conditions, and to support long-term planning for the Rio Grande Project. This report describes model construction and applications by the U.S. Geological Survey, working in cooperation and collaboration with the Bureau of Reclamation.
This model, the Rio Grande Transboundary Integrated Hydrologic Model, simulates the most important natural and human components of the hydrologic system, including selected components related to variations in climate, thereby providing a reliable assessment of surface-water and groundwater conditions and processes that can inform water users and help improve planning for future conditions and sustained operations of the Rio Grande Project (RGP) by the Bureau of Reclamation. Model development included a revision of the conceptual model of the flow system, construction of a Transboundary Rio Grande Watershed Model (TRGWM) water-balance model using the Basin Characterization Model (BCM), and construction of an integrated hydrologic flow model with MODFLOW-One-Water Hydrologic Flow Model (referred to as One Water). The hydrologic models were developed for and calibrated to historical conditions of water and land use, and parameters were adjusted so that simulated values closely matched available measurements (calibration). The calibrated model was then used to assess the use and movement of water in the Rincon Valley, Mesilla Basin, and northern part of the Conejos-Médanos Basin, with the entire region referred to as the “Transboundary Rio Grande” or TRG. These tools provide a means to understand hydrologic system response to the evolution of water use in the region, its availability, and potential operational constraints of the RGP.
The conceptual model identified surface-water and groundwater inflows and outflows that included the movement and use of water both in natural and in anthropogenic systems. The groundwater-flow system is characterized by a layered geologic sedimentary sequence combined with the effects of groundwater pumping, operation of the RGP, natural runoff and recharge, and the application of irrigation water at the land surface that is captured and reused in an extensive network of canals and drains as part of the conjunctive use of water in the region.
Historical groundwater-level fluctuations followed a cyclic pattern that were aligned with climate cycles, which collectively resulted in alternating periods of wet or dry years. Periods of drought that persisted for one or more years are associated with low surface-water availability that resulted in higher rates of groundwater-level decline. Rates of groundwater-level decline also increased during periods of agricultural intensification, which necessitated increasing use of groundwater as a source of irrigation water. Agriculture in the area was initially dominated by alfalfa and cotton, but since 1970 more water-intensive pecan orchards and vegetable production have become more common. Groundwater levels substantially declined in subregions where drier climate combined with increased demand, resulting in periods of reduced streamflows.
Most of the groundwater was recharged in the Rio Grande Valley floor, and most of the pumpage and aquifer storage depletion was in Mesilla Basin agricultural subregions. A cyclic imbalance between inflows and outflows resulted in the modeled cyclic depletion (groundwater withdrawals in excess of natural recharge) of the groundwater basin during the 75-year simulation period of 1940–2014. Changes in groundwater storage can vary considerably from year to year, depending on land use, pumpage, and climate conditions. Climatic drivers of wet and dry years can greatly affect all inflows, outflows, and water use. Although streamflow and, to a minor extent, precipitation during inter-decadal wet-year periods replenished the groundwater historically, contemporary water use and storage depletion could have reduced the effects of these major recharge events. The average net groundwater flow-rate deficit for 1953–2014 was estimated to be about 8,990 acre-feet per year.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181091","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Hanson, R.T., Ritchie, A.B., Boyce, S.E., Galanter, A.E., Ferguson, I.A., Flint, L.E., and Henson, W.R., 2018, Rio Grande transboundary integrated hydrologic model and water-availability analysis, New Mexico and Texas, United States, and Northern Chihuahua, Mexico: U.S Geological Survey Open-File Report 2018–1091, 185 p., https://doi.org/10.3133/ofr20181091.","productDescription":"Report: x, 185 p.; Dataset; Data release; Errata","numberOfPages":"200","onlineOnly":"Y","ipdsId":"IP-071162","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":354790,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1091/ofr20181091.pdf","text":"Report","size":"25 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":354791,"rank":2,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.3133/ofr20181091","linkHelpText":"- This Open-File report (OFR) was superseded by USGS Scientific Investigations report (SIR) SIR 2019-5120. The final model archive will be available on the national USGS archive site."},{"id":357946,"rank":4,"type":{"id":12,"text":"Errata"},"url":"https://pubs.usgs.gov/of/2018/1091/erratum.txt","size":"3 KB","linkFileType":{"id":2,"text":"txt"}},{"id":363155,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9J9NYND","linkHelpText":"Digital hydrologic and geospatial data for the Rio Grande transboundary integrated hydrologic model and water-availability analysis, New Mexico and Texas, United States, and Northern Chihuahua, Mexico"},{"id":354795,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1091/coverthb_.jpg"}],"country":"Mexico, United States","state":"New Mexico, Northern Chihuahua, Texas","otherGeospatial":"Rio Grande","publicComments":"This Open-File report (OFR) will be superseded by a USGS Scientific Investigations report (SIR) once the USGS Techniques and Methods report (T&M) documenting the numerical code is published. Once the SIR is released, the final model archive will be available on the national USGS archive site. For the interim archive for this model, please contact CaWSC for directions on downloading 916-278-3026.","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Forster’s terns (Sterna forsteri), historically one of the most numerous colonial-breeding waterbirds in South San Francisco Bay, California, have had recent decreases in the number of nesting colonies and overall breeding population size. The South Bay Salt Pond (SBSP) Restoration Project aims to restore 50–90 percent of former salt evaporation ponds to tidal marsh habitat in South San Francisco Bay. This restoration will remove much of the historical island nesting habitat used by Forster’s terns, American avocets (Recurvirostra americana), and other waterbirds. To address this issue, the SBSP Restoration Project organized the construction of new nesting islands in managed ponds that will not be restored to tidal marsh, thereby providing enduring island nesting habitat for waterbirds. In 2012, 16 new islands were constructed in Pond A16 in the Alviso complex of the Don Edwards San Francisco Bay National Wildlife Refuge, increasing the number of islands in this pond from 4 to 20. However, despite a history of nesting on the four historical islands in Pond A16 before 2012, no Forster’s terns have nested in Pond A16 since the new islands were constructed.
In 2017, we used social attraction measures (decoys and electronic call systems) to attract Forster’s terns to islands within Pond A16 to re-establish nesting colonies. We maintained these systems from March through August 2017. To evaluate the effect of these social attraction measures, we also completed waterbird surveys between April and August, where we recorded the number and location of all Forster’s terns and other waterbirds using Pond A16, and monitored waterbird nests. We compared bird survey and nest monitoring data collected in 2017 to data collected in 2015 and 2016, prior to the implementation of social attraction measures, allowing for direct evaluation of social attraction efforts on Forster’s terns.
To increase the visibility and stakeholder involvement of this project, we engaged in multiple outreach activities, including the development of a project web site (https://apps.usgs.gov/shorebirds/) and educational video (https://www.youtube.com/watch?v=-IaZD0YlAvM&feature=youtu.be); publication of a popular article (http://www.sfestuary.org/estuary-news-caspian-push-and-pull/); and public presentations to relay findings to managers, stakeholders, and the general public.
The relative number of Forster’s terns using Pond A16, after adjusting for the overall South San Francisco Bay breeding population each year, was higher during the nesting period in 2017 (after social attraction was used) than in 2015 and 2016 (before social attraction was used). Furthermore, in 2017, more Forster’s terns were observed in the areas of Pond A16 where decoys and call systems were deployed during the pre-nesting and nesting periods. Although no Forster’s tern nests were recorded in Pond A16 before (2015, 2016) or after (2017) implementation of social attraction measures, bird survey results indicate that Forster’s terns were attracted to areas within Pond A16 where decoys and call systems were deployed, suggesting that terns may have been prospecting for future breeding sites. As social attraction efforts often benefit from multiple years of decoy and call system deployment, these first-year results suggest that continued implementation of social attraction measures could help to re-establish Forster’s tern breeding colonies in Pond A16 and other areas of South San Francisco Bay.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181090","collaboration":"Prepared in cooperation with the San Francisco Bay Bird Observatory","usgsCitation":"Hartman, C.A., Ackerman, J.T., Herzog, M.P., Wang, Y., and Strong, C., 2018, Evaluation of social attraction measures to establish Forster’s tern (Sterna forsteri) nesting colonies for the South Bay Salt Pond Restoration Project, San Francisco Bay, California—2017 annual report: U.S. Geological Survey Open-File Report 2018–1090, 25 p., https://doi.org/10.3133/ofr20181090.","productDescription":"iv, 25 p.","numberOfPages":"33","onlineOnly":"Y","ipdsId":"IP-096847","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":354652,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1090/coverthb2.jpg"},{"id":354653,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1090/ofr20181090.pdf","text":"Report","size":"12.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1090"}],"country":"United States","state":"California","otherGeospatial":"Don Edwards San Francisco Bay National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.15492248535156,\n 37.38379840307495\n ],\n [\n -121.89674377441405,\n 37.38379840307495\n ],\n [\n -121.89674377441405,\n 37.555465068186955\n ],\n [\n -122.15492248535156,\n 37.555465068186955\n ],\n [\n -122.15492248535156,\n 37.38379840307495\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
The right‐lateral San Gregorio‐Hosgri fault system (SGHF) extends mainly offshore for about 400 km along the central California coast and is a major structure in the distributed transform margin of western North America. We mapped a poorly known 64‐km‐long section of the SGHF offshore Big Sur between Piedras Blancas and Point Sur using high‐resolution bathymetry, seismic reflection, and marine magnetic data. In this region, the SGHF is characterized by multiple strands, step overs, scarps and lineaments, shutter ridges, deflected drainages, and other geomorphic features consistent with strike‐slip faulting. Analysis of offset shelfbreak gullies suggest a lateral slip rate of about 3.35 mm/year. Vertical slip rates range as high as 0.8 to 1.2 mm/year. Lateral slip combined with high vertical slip rates result in a northwest decrease in shelf width, a northward increase in shelf and upper slope gradient, and progressive incision of submarine canyon heads. The SGHF between Point Sur and Piedras Blancas trends ~321° and forms a 105‐km‐long transpressive bend (the Big Sur Bend) between more north trending transtensional fault sections to the south (120 km long, ~336° trend) and north (180 km long, ~337° trend). This transpressional bend and SGHF splay faults have had a significant role in shaping the modern geomorphology of the central California coast, controlling or influencing the locations of mountainous uplifts, large coastal headlands, embayments, and rivers.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2017TC004724","usgsCitation":"Johnson, S.Y., Watt, J., Hartwell, S., and Kluesner, J.W., 2018, Neotectonics of the Big Sur Bend, San Gregorio‐Hosgri fault system, central California: Tectonics, v. 37, no. 7, p. 1930-1954, https://doi.org/10.1029/2017TC004724.","productDescription":"25 p.","startPage":"1930","endPage":"1954","ipdsId":"IP-088681","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":468715,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2017tc004724","text":"Publisher Index Page"},{"id":355620,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Gregorio‐Hosgri fault","volume":"37","issue":"7","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-07-10","publicationStatus":"PW","scienceBaseUri":"5b46e578e4b060350a15d1b1","contributors":{"authors":[{"text":"Johnson, Samuel Y. 0000-0001-7972-9977 sjohnson@usgs.gov","orcid":"https://orcid.org/0000-0001-7972-9977","contributorId":2607,"corporation":false,"usgs":true,"family":"Johnson","given":"Samuel","email":"sjohnson@usgs.gov","middleInitial":"Y.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":739770,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Watt, Janet 0000-0002-4759-3814 jwatt@usgs.gov","orcid":"https://orcid.org/0000-0002-4759-3814","contributorId":146222,"corporation":false,"usgs":true,"family":"Watt","given":"Janet","email":"jwatt@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":739771,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hartwell, Stephen 0000-0002-3522-7526 shartwell@usgs.gov","orcid":"https://orcid.org/0000-0002-3522-7526","contributorId":146221,"corporation":false,"usgs":true,"family":"Hartwell","given":"Stephen","email":"shartwell@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":739772,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kluesner, Jared W. 0000-0003-1701-8832 jkluesner@usgs.gov","orcid":"https://orcid.org/0000-0003-1701-8832","contributorId":201261,"corporation":false,"usgs":true,"family":"Kluesner","given":"Jared","email":"jkluesner@usgs.gov","middleInitial":"W.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":739773,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70198335,"text":"70198335 - 2018 - Remotely sensing the morphometrics and dynamics of a cold region dune field using historical aerial photography and airborne LiDAR data","interactions":[],"lastModifiedDate":"2018-07-30T16:11:03","indexId":"70198335","displayToPublicDate":"2018-05-30T15:39:29","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Remotely sensing the morphometrics and dynamics of a cold region dune field using historical aerial photography and airborne LiDAR data","docAbstract":"This study uses an airborne Light Detection and Ranging (LiDAR) survey, historical aerial photography and historical climate data to describe the character and dynamics of the Nogahabara Sand Dunes, a sub-Arctic dune field in interior Alaska’s discontinuous permafrost zone. The Nogahabara Sand Dunes consist of a 43-km2 area of active transverse and barchanoid dunes within a 3200-km2 area of vegetated dune and sand sheet deposits. The average dune height in the active portion of the dune field is 5.8 m, with a maximum dune height of 28 m. Dune spacing is variable with average crest-to-crest distances for select transects ranging from 66–132 m. Between 1952 and 2015, dunes migrated at an average rate of 0.52 m a−1. Dune movement was greatest between 1952 and 1978 (0.68 m a−1) and least between 1978 and 2015 (0.43 m a−1). Dunes migrated predominantly to the southeast; however, along the dune field margin, net migration was towards the edge of the dune field regardless of heading. Better constraining the processes controlling dune field dynamics at the Nogahabara dunes would provide information that can be used to model possible reactivation of more northerly dune fields and sand sheets in response to climate change, shifting fire regimes and permafrost thaw.
","language":"English","publisher":"Multidisciplinary Digital Publishing Institute","doi":"10.3390/rs10050792","usgsCitation":"Baughman, C., Jones, B.M., Bodony, K.L., Mann, D.H., Larsen, C.F., Himmelstoss, E., and Smith, J., 2018, Remotely sensing the morphometrics and dynamics of a cold region dune field using historical aerial photography and airborne LiDAR data: Remote Sensing, v. 10, no. 5, p. 1-19, https://doi.org/10.3390/rs10050792.","productDescription":"Article 792; 19 p.","startPage":"1","endPage":"19","ipdsId":"IP-082492","costCenters":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":468719,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs10050792","text":"Publisher Index Page"},{"id":356010,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"5","noUsgsAuthors":false,"publicationDate":"2018-05-19","publicationStatus":"PW","scienceBaseUri":"5b6fc444e4b0f5d57878ea3b","contributors":{"authors":[{"text":"Baughman, Carson 0000-0002-9423-9324 cbaughman@usgs.gov","orcid":"https://orcid.org/0000-0002-9423-9324","contributorId":169657,"corporation":false,"usgs":true,"family":"Baughman","given":"Carson","email":"cbaughman@usgs.gov","affiliations":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"preferred":true,"id":741101,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, Benjamin M. 0000-0002-1517-4711 bjones@usgs.gov","orcid":"https://orcid.org/0000-0002-1517-4711","contributorId":2286,"corporation":false,"usgs":true,"family":"Jones","given":"Benjamin","email":"bjones@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"preferred":true,"id":741124,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bodony, Karin L.","contributorId":206563,"corporation":false,"usgs":false,"family":"Bodony","given":"Karin","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":741125,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mann, Daniel H.","contributorId":67010,"corporation":false,"usgs":true,"family":"Mann","given":"Daniel","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":741126,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Larsen, Christopher F.","contributorId":147408,"corporation":false,"usgs":false,"family":"Larsen","given":"Christopher","email":"","middleInitial":"F.","affiliations":[{"id":6695,"text":"UAF","active":true,"usgs":false}],"preferred":false,"id":741127,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Himmelstoss, Emily A. ehimmelstoss@usgs.gov","contributorId":2508,"corporation":false,"usgs":true,"family":"Himmelstoss","given":"Emily A.","email":"ehimmelstoss@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":741128,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Smith, Jeremy","contributorId":62919,"corporation":false,"usgs":true,"family":"Smith","given":"Jeremy","affiliations":[],"preferred":false,"id":741129,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70200590,"text":"70200590 - 2018 - The Mystic subterrane (partly) demystified: New data from the Farewell terrane and adjacent rocks, interior Alaska","interactions":[],"lastModifiedDate":"2018-10-25T11:50:24","indexId":"70200590","displayToPublicDate":"2018-05-30T11:50:16","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"The Mystic subterrane (partly) demystified: New data from the Farewell terrane and adjacent rocks, interior Alaska","docAbstract":"The youngest part of the Farewell terrane in interior Alaska (USA) is the enigmatic Devonian–Cretaceous Mystic subterrane. New U-Pb detrital zircon, fossil, geochemical, neodymium isotopic, and petrographic data illuminate the origin of the rocks of this subterrane. The Devonian–Permian Sheep Creek Formation yielded youngest detrital zircons of Devonian age, major detrital zircon age probability peaks between ca. 460 and 405 Ma, and overall age spectra like those from the underlying Dillinger subterrane. Samples are sandstones rich in sedimentary lithic clasts, and differ from approximately coeval strata to the east that have abundant volcanic lithic clasts and late Paleozoic detrital zircons. The Permian Mount Dall conglomerate has mainly carbonate and chert clasts and yielded youngest detrital zircons of latest Pennsylvanian age. Permian quartz-carbonate sandstone in the northern Farewell terrane yielded abundant middle to late Permian detrital zircons.
Late Triassic–Early Jurassic mafic igneous rocks occur in the central and eastern Mystic subterrane. New whole-rock geochemical and isotopic data indicate that magmas were rift related and derived from subcontinental mantle. Triassic and Jurassic strata have detrital zircon age spectra much like those of the Sheep Creek Formation, with major age populations between ca. 430 and 410 Ma. These rocks include conglomerate with clasts of carbonate ± chert and youngest detrital zircons of Late Triassic age and quartz-carbonate sandstone with youngest detrital zircons of Early Jurassic age. Lithofacies indicating highly productive oceanographic conditions (upwelling?) bracket the main part of the Mystic succession: Upper Devonian bedded barite and phosphatic Upper Devonian and Lower Jurassic rocks.
The youngest part of the Mystic subterrane consists of Lower Cretaceous (Valanginian–Aptian) limestone, calcareous sandstone, and related strata. These rocks are partly coeval with the oldest parts of the Kahiltna assemblage, an overlap succession exposed along the southern margin of the Farewell terrane.
Our findings support previous models suggesting that the Farewell terrane was proximal to the Alexander-Wrangellia-Peninsular composite terrane during the late Paleozoic, and further suggest that such proximity continued into (or recurred during) the Late Triassic–Early Jurassic. But middle to late Permian detrital zircons in northern Farewell require another source; the Yukon-Tanana terrane is one possibility.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES01588.1","usgsCitation":"Dumoulin, J.A., Jones, J.V., Box, S.E., Bradley, D., Ayuso, R.A., and O’Sullivan, P.B., 2018, The Mystic subterrane (partly) demystified: New data from the Farewell terrane and adjacent rocks, interior Alaska: Geosphere, v. 14, no. 4, p. 1501-1543, https://doi.org/10.1130/GES01588.1.","productDescription":"43 p.","startPage":"1501","endPage":"1543","ipdsId":"IP-095640","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":468720,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges01588.1","text":"Publisher Index Page"},{"id":437889,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7765DN7","text":"USGS data release","linkHelpText":"U-Pb Isotopic Data and Ages of Detrital Zircon Grains, Whole Rock Major and Trace-element Geochemistry, and Whole Rock Isotopic Data from Selected Rocks from the Western Alaska Range, Medfra area, and Livengood area, Alaska"},{"id":358807,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","volume":"14","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-30","publicationStatus":"PW","scienceBaseUri":"5c10a9abe4b034bf6a7e53b3","contributors":{"authors":[{"text":"Dumoulin, Julie A. 0000-0003-1754-1287 dumoulin@usgs.gov","orcid":"https://orcid.org/0000-0003-1754-1287","contributorId":203209,"corporation":false,"usgs":true,"family":"Dumoulin","given":"Julie","email":"dumoulin@usgs.gov","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":749660,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, James V. III 0000-0002-6602-5935 jvjones@usgs.gov","orcid":"https://orcid.org/0000-0002-6602-5935","contributorId":201245,"corporation":false,"usgs":true,"family":"Jones","given":"James","suffix":"III","email":"jvjones@usgs.gov","middleInitial":"V.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":749661,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Box, Stephen E. 0000-0002-5268-8375 sbox@usgs.gov","orcid":"https://orcid.org/0000-0002-5268-8375","contributorId":1843,"corporation":false,"usgs":true,"family":"Box","given":"Stephen","email":"sbox@usgs.gov","middleInitial":"E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":749662,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bradley, Dwight 0000-0001-9116-5289 bradleyorchard2@gmail.com","orcid":"https://orcid.org/0000-0001-9116-5289","contributorId":2358,"corporation":false,"usgs":true,"family":"Bradley","given":"Dwight","email":"bradleyorchard2@gmail.com","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":749663,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ayuso, Robert A. 0000-0002-8496-9534 rayuso@usgs.gov","orcid":"https://orcid.org/0000-0002-8496-9534","contributorId":2654,"corporation":false,"usgs":true,"family":"Ayuso","given":"Robert","email":"rayuso@usgs.gov","middleInitial":"A.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":749664,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"O’Sullivan, Paul B.","contributorId":193544,"corporation":false,"usgs":false,"family":"O’Sullivan","given":"Paul","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":749665,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70203040,"text":"70203040 - 2018 - Gas hydrate quantification using full-waveform inversion of sparse ocean-bottom seismic data: A case study from Green Canyon Block 955, Gulf of Mexico","interactions":[],"lastModifiedDate":"2019-04-15T10:46:45","indexId":"70203040","displayToPublicDate":"2018-05-30T09:29:48","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1808,"text":"Geophysics","active":true,"publicationSubtype":{"id":10}},"title":"Gas hydrate quantification using full-waveform inversion of sparse ocean-bottom seismic data: A case study from Green Canyon Block 955, Gulf of Mexico","docAbstract":"We present a case study of gas hydrate quantification using dense short-offset multichannel seismic (MCS) and sparse long-offset ocean-bottom-seismometer (OBS) data in lease block Green Canyon 955 (GC955), Gulf of Mexico (GOM), where the presence of gas hydrate was interpreted using logging while drilling (LWD) data acquired by the GOM Gas Hydrate Joint Industry Project Leg II expedition. We use frequency-domain full-waveform inversion (FWI) of seven OBS gathers to invert for a P-wave velocity model of an approximately 7 km long MCS profile connecting two LWD sites, GC955-H and GC955-Q. We build the starting model for FWI using traveltime inversion (TI) of the MCS and OBS data. In addition, we use the TI model for depth migrating the MCS stack. At the LWD sites, we constrain the hydrate saturation (Sgh) using sonic and resistivity logs. Unfortunately, as is typical of seismic quantification problems, the FWI model resolution is not sufficient to extrapolate the LWD-based Sgh. Therefore, we apply Backus averaging to the sonic log, at 60 m wavelength, bringing it within approximately 8% of the FWI model and make the assumption that averaging the sonic log is same as redistributing the gas hydrate within the Backus wavelength. In this manner, instead of Sgh, the FWI model is able to estimate the total gas hydrate volume. In the end, we use the FWI model and the migrated stack to constrain the locations and bulk volumes of free gas and gas hydrate. Our results demonstrate that with careful processing, reasonable estimates on locations and bulk volumes of submarine gas hydrate accumulations can be achieved even with sparse seismic data that are not adequate for amplitude-based assessments.","language":"English","publisher":"SEG","doi":"10.1190/geo2017-0414.1","usgsCitation":"Wang, J., Jaiswal, P., Haines, S.S., Hart, P.E., and Wu, S., 2018, Gas hydrate quantification using full-waveform inversion of sparse ocean-bottom seismic data: A case study from Green Canyon Block 955, Gulf of Mexico: Geophysics, v. 83, no. 4, p. B167-B181, https://doi.org/10.1190/geo2017-0414.1.","productDescription":"15 p.","startPage":"B167","endPage":"B181","ipdsId":"IP-065223","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":362942,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Gulf of Mexico","volume":"83","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wang, Jiliang","contributorId":214827,"corporation":false,"usgs":false,"family":"Wang","given":"Jiliang","email":"","affiliations":[{"id":32415,"text":"Chinese Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":760908,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jaiswal, Priyank","contributorId":214828,"corporation":false,"usgs":false,"family":"Jaiswal","given":"Priyank","email":"","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":760909,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haines, Seth S. 0000-0003-2611-8165 shaines@usgs.gov","orcid":"https://orcid.org/0000-0003-2611-8165","contributorId":1344,"corporation":false,"usgs":true,"family":"Haines","given":"Seth","email":"shaines@usgs.gov","middleInitial":"S.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":760907,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hart, Patrick E. 0000-0002-5080-1426 hart@usgs.gov","orcid":"https://orcid.org/0000-0002-5080-1426","contributorId":2879,"corporation":false,"usgs":true,"family":"Hart","given":"Patrick","email":"hart@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":760910,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wu, Shiguo","contributorId":214829,"corporation":false,"usgs":false,"family":"Wu","given":"Shiguo","email":"","affiliations":[{"id":32415,"text":"Chinese Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":760911,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70197340,"text":"70197340 - 2018 - Green‐wave surfing increases fat gain in a migratory ungulate","interactions":[],"lastModifiedDate":"2018-07-03T11:15:21","indexId":"70197340","displayToPublicDate":"2018-05-30T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2939,"text":"Oikos","active":true,"publicationSubtype":{"id":10}},"title":"Green‐wave surfing increases fat gain in a migratory ungulate","docAbstract":"Each spring, migratory herbivores around the world track or ‘surf’ green waves of newly emergent vegetation to distant summer or wet‐season ranges. This foraging tactic may help explain the great abundance of migratory herbivores on many seasonal landscapes. However, the underlying fitness benefits of this life‐history strategy remain poorly understood. A fundamental prediction of the green‐wave hypothesis is that migratory herbivores obtain fitness benefits from surfing waves of newly emergent vegetation more closely than their resident counterparts. Here we evaluate whether this behavior increases body‐fat levels – a critically important correlate of reproduction and survival for most ungulates – in elk Cervus elaphus of the Greater Yellowstone Ecosystem. Using satellite imagery and GPS tracking data, we found evidence that migrants (n = 23) indeed surfed the green wave, occupying sites 12.7 days closer to peak green‐up than residents (n = 16). Importantly, individual variation in surfing may help account for up to 6 kg of variation in autumn body‐fat levels. Our findings point to a pathway for anthropogenic changes to the green wave (e.g. climate change) or migrants’ ability to surf it (e.g. development) to impact migratory populations. To explore this possibility, we evaluated potential population‐level consequences of constrained surfing with a heuristic model. If green‐wave surfing deteriorates by 5–15 days from observed, our model predicts up to a 20% decrease in pregnancy rates, a 2.5% decrease in population growth, and a 30% decrease in abundance over 50 years. By linking green‐wave surfing to fitness and illustrating potential effects on population growth, our study provides new insights into the evolution of migratory behavior and the prospects for the persistence of migratory ungulate populations in a changing world.
","language":"English","publisher":"Wiley","doi":"10.1111/oik.05227","usgsCitation":"Middleton, A., Merkle, J., McWhirter, D.E., Cook, J.G., Cook, R.C., White, P., and Kauffman, M., 2018, Green‐wave surfing increases fat gain in a migratory ungulate: Oikos, v. 127, no. 7, p. 1060-1068, https://doi.org/10.1111/oik.05227.","productDescription":"9 p.","startPage":"1060","endPage":"1068","ipdsId":"IP-084519","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":460913,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/oik.05227","text":"Publisher Index Page"},{"id":354565,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.09374999999999,\n 42.50450285299051\n ],\n [\n -107.6220703125,\n 42.50450285299051\n ],\n [\n -107.6220703125,\n 44.99588261816546\n ],\n [\n -111.09374999999999,\n 44.99588261816546\n ],\n [\n -111.09374999999999,\n 42.50450285299051\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"127","issue":"7","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-22","publicationStatus":"PW","scienceBaseUri":"5b155d75e4b092d9651e1b10","contributors":{"authors":[{"text":"Middleton, Arthur D.","contributorId":99440,"corporation":false,"usgs":true,"family":"Middleton","given":"Arthur D.","affiliations":[],"preferred":false,"id":736764,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Merkle, Jerod","contributorId":172972,"corporation":false,"usgs":false,"family":"Merkle","given":"Jerod","affiliations":[{"id":35288,"text":"Wyoming Cooperative Fish and Wildlife Research Unit, University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":736765,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McWhirter, Douglas E.","contributorId":90623,"corporation":false,"usgs":true,"family":"McWhirter","given":"Douglas","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":736766,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cook, John G.","contributorId":12903,"corporation":false,"usgs":true,"family":"Cook","given":"John","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":736767,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cook, Rachel C.","contributorId":19064,"corporation":false,"usgs":true,"family":"Cook","given":"Rachel","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":736768,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"White, P.J.","contributorId":194049,"corporation":false,"usgs":false,"family":"White","given":"P.J.","email":"","affiliations":[],"preferred":false,"id":736769,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kauffman, Matthew J. 0000-0003-0127-3900 mkauffman@usgs.gov","orcid":"https://orcid.org/0000-0003-0127-3900","contributorId":189179,"corporation":false,"usgs":true,"family":"Kauffman","given":"Matthew J.","email":"mkauffman@usgs.gov","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"preferred":false,"id":736745,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70197339,"text":"70197339 - 2018 - Combining genetic, isotopic, and field data to better describe the influence of dams and diversions on Burbot Movement in the Wind River Drainage, Wyoming","interactions":[],"lastModifiedDate":"2018-05-30T11:14:07","indexId":"70197339","displayToPublicDate":"2018-05-30T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Combining genetic, isotopic, and field data to better describe the influence of dams and diversions on Burbot Movement in the Wind River Drainage, Wyoming","docAbstract":"Dams and water diversions fragment habitat, entrain fish, and alter fish movement. Many Burbot Lota lota populations are declining, with dams and water diversions thought to be a major threat. We used multiple methods to identify Burbot movement patterns and assess entrainment into an irrigation system in the Wind River, Wyoming. We assessed seasonal movement of Burbot with a mark–recapture (PIT tagging) study, natal origins of entrained fish with otolith microchemistry, and historic movement with genotyping by sequencing. We found limited evidence of entrainment in irrigation waters across all approaches. The mark–recapture study indicated that out‐migration from potential source populations could be influenced by flow regime but was generally low. Otolith and genomic results suggested the presence of a self‐sustaining population within the irrigation network. We conclude that emigration from natural tributary populations is not the current source of the majority of Burbot found in irrigation waters. Instead, reservoir and irrigation canal construction has created novel habitat in which Burbot have established a population. Using a multi‐scale approach increased our inferential abilities and mechanistic understanding of movement patterns between natural and managed systems.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/tafs.10062","usgsCitation":"Hooley-Underwood, Z., Mandeville, E.G., Gerrity, P.C., Deromedi, J.W., Johnson, K., and Walters, A.W., 2018, Combining genetic, isotopic, and field data to better describe the influence of dams and diversions on Burbot Movement in the Wind River Drainage, Wyoming: Transactions of the American Fisheries Society, v. 147, no. 3, p. 606-620, https://doi.org/10.1002/tafs.10062.","productDescription":"15 p.","startPage":"606","endPage":"620","ipdsId":"IP-084464","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":354580,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Wind River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.632568359375,\n 42.19596877629178\n ],\n [\n -108.21533203125,\n 42.19596877629178\n ],\n [\n -108.21533203125,\n 43.89789239125797\n ],\n [\n -109.632568359375,\n 43.89789239125797\n ],\n [\n -109.632568359375,\n 42.19596877629178\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"147","issue":"3","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-22","publicationStatus":"PW","scienceBaseUri":"5b155d75e4b092d9651e1b12","contributors":{"authors":[{"text":"Hooley-Underwood, Zachary","contributorId":205292,"corporation":false,"usgs":false,"family":"Hooley-Underwood","given":"Zachary","affiliations":[],"preferred":false,"id":736787,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mandeville, Elizabeth G.","contributorId":166947,"corporation":false,"usgs":false,"family":"Mandeville","given":"Elizabeth","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":736788,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gerrity, Paul C.","contributorId":104198,"corporation":false,"usgs":true,"family":"Gerrity","given":"Paul","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":736789,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Deromedi, J. W.","contributorId":200247,"corporation":false,"usgs":false,"family":"Deromedi","given":"J.","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":736790,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnson, Kevin","contributorId":181825,"corporation":false,"usgs":false,"family":"Johnson","given":"Kevin","email":"","affiliations":[],"preferred":false,"id":736791,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Walters, Annika W. 0000-0002-8638-6682 awalters@usgs.gov","orcid":"https://orcid.org/0000-0002-8638-6682","contributorId":4190,"corporation":false,"usgs":true,"family":"Walters","given":"Annika","email":"awalters@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":736744,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70197337,"text":"70197337 - 2018 - Synthesizing models useful for ecohydrology and ecohydraulic approaches: An emphasis on integrating models to address complex research questions","interactions":[],"lastModifiedDate":"2018-10-12T16:08:22","indexId":"70197337","displayToPublicDate":"2018-05-30T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1447,"text":"Ecohydrology","active":true,"publicationSubtype":{"id":10}},"title":"Synthesizing models useful for ecohydrology and ecohydraulic approaches: An emphasis on integrating models to address complex research questions","docAbstract":"Ecohydrology combines empiricism, data analytics, and the integration of models to characterize linkages between ecological and hydrological processes. A challenge for practitioners is determining which models best generalizes heterogeneity in hydrological behaviour, including water fluxes across spatial and temporal scales, integrating environmental and socio‐economic activities to determine best watershed management practices and data requirements. We conducted a literature review and synthesis of hydrologic, hydraulic, water quality, and ecological models designed for solving interdisciplinary questions. We reviewed 1,275 papers and identified 178 models that have the capacity to answer an array of research questions about ecohydrology or ecohydraulics. Of these models, 43 were commonly applied due to their versatility, accessibility, user‐friendliness, and excellent user‐support. Forty‐one of 43 reviewed models were linked to at least 1 other model especially: Water Quality Analysis Simulation Program (linked to 21 other models), Soil and Water Assessment Tool (19), and Hydrologic Engineering Center's River Analysis System (15). However, model integration was still relatively infrequent. There was substantial variation in model applications, possibly an artefact of the regional focus of research questions, simplicity of use, quality of user‐support efforts, or a limited understanding of model applicability. Simply increasing the interoperability of model platforms, transformation of models to user‐friendly forms, increasing user‐support, defining the reliability and risk associated with model results, and increasing awareness of model applicability may promote increased use of models across subdisciplines. Nonetheless, the current availability of models allows an array of interdisciplinary questions to be addressed, and model choice relates to several factors including research objective, model complexity, ability to link to other models, and interface choice.
","language":"English","publisher":"Wiley","doi":"10.1002/eco.1966","usgsCitation":"Brewer, S.K., Worthington, T., Mollenhauer, R., Stewart, D., McManamay, R., Guertault, L., and Moore, D., 2018, Synthesizing models useful for ecohydrology and ecohydraulic approaches: An emphasis on integrating models to address complex research questions: Ecohydrology, v. 11, no. 7, p. 1-26, https://doi.org/10.1002/eco.1966.","productDescription":"e1966; 26 p.","startPage":"1","endPage":"26","ipdsId":"IP-083229","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":468724,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/1435332","text":"External Repository"},{"id":354585,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"7","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-06","publicationStatus":"PW","scienceBaseUri":"5b155d75e4b092d9651e1b16","contributors":{"authors":[{"text":"Brewer, Shannon K. 0000-0002-1537-3921 skbrewer@usgs.gov","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":2252,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon","email":"skbrewer@usgs.gov","middleInitial":"K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":736736,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Worthington, Thomas","contributorId":205274,"corporation":false,"usgs":false,"family":"Worthington","given":"Thomas","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":736737,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mollenhauer, Robert","contributorId":205275,"corporation":false,"usgs":false,"family":"Mollenhauer","given":"Robert","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":736738,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stewart, David","contributorId":205276,"corporation":false,"usgs":false,"family":"Stewart","given":"David","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":736739,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McManamay, Ryan","contributorId":205277,"corporation":false,"usgs":false,"family":"McManamay","given":"Ryan","affiliations":[{"id":37070,"text":"Oak Ridge National Laboratory","active":true,"usgs":false}],"preferred":false,"id":736740,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Guertault, Lucie","contributorId":205278,"corporation":false,"usgs":false,"family":"Guertault","given":"Lucie","email":"","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":736741,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Moore, Desiree","contributorId":205279,"corporation":false,"usgs":false,"family":"Moore","given":"Desiree","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":736742,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70196624,"text":"sir20185057 - 2018 - Status and understanding of groundwater quality in the Monterey-Salinas Shallow Aquifer Study Unit, 2012–13: California GAMA Priority Basin Project","interactions":[],"lastModifiedDate":"2018-09-21T15:03:20","indexId":"sir20185057","displayToPublicDate":"2018-05-30T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5057","title":"Status and understanding of groundwater quality in the Monterey-Salinas Shallow Aquifer Study Unit, 2012–13: California GAMA Priority Basin Project","docAbstract":"Groundwater quality in the approximately 7,820-square-kilometer (km2) Monterey-Salinas Shallow Aquifer (MS-SA) study unit was investigated from October 2012 to May 2013 as part of the second phase of the Priority Basin Project of the Groundwater Ambient Monitoring and Assessment (GAMA) Program. The study unit is in the central coast region of California in the counties of Santa Cruz, Monterey, and San Luis Obispo. The GAMA Priority Basin Project is being conducted by the California State Water Resources Control Board in cooperation with the U.S. Geological Survey and the Lawrence Livermore National Laboratory.
The MS-SA study was designed to provide a statistically robust assessment of untreated-groundwater quality in the shallow aquifer systems. The assessment was based on water-quality samples collected by the U.S. Geological Survey from 100 groundwater sites and 70 household tap sites, along with ancillary data such as land use and well-construction information. The shallow aquifer systems were defined by the depth interval of wells associated with domestic supply. The MS-SA study unit consisted of four study areas—Santa Cruz (210 km2), Pajaro Valley (360 km2), Salinas Valley (2,000 km2), and Highlands (5,250 km2).
This study had two primary components: the status assessment and the understanding assessment. The first primary component of this study—the status assessment—assessed the quality of the groundwater resource indicated by data from samples analyzed for volatile organic compounds (VOCs), pesticides, and naturally present inorganic constituents, such as major ions and trace elements. The status assessment is intended to characterize the quality of groundwater resources in the shallow aquifer system of the MS-SA study unit, not the treated drinking water delivered to consumers by water purveyors. As opposed to the public wells, however, water from private wells, which often tap the shallow aquifer, is usually consumed without any treatment. The second component of this study—the understanding assessment—identified the natural and human factors that potentially affect groundwater quality by evaluating land-use characteristics, measures of location, geologic factors, groundwater age, and geochemical conditions of the shallow aquifer. An additional component of this study was a comparison of MS-SA water-quality results to those of the GAMA Monterey Bay and Salinas Valley Groundwater Basins study unit. This study unit covered much of the same areal extent as the MS-SA, but assessed the deeper, public drinking-water aquifer system.
Relative concentrations (sample concentration divided by the benchmark concentration) were used to evaluate concentrations of constituents in groundwater samples relative to water-quality benchmarks for those constituents that have Federal or California benchmarks, such as maximum contaminant levels. For organic and special-interest constituents, relative concentrations were classified as high, greater than 1.0; moderate, greater than 0.1 and less than or equal to 1.0; or low, less than or equal to 0.1. For inorganic constituents, relative concentrations were classified as high, greater than 1.0; moderate, greater than 0.5 and less than or equal to 1.0; or low, less than or equal to 0.5. A relative concentration greater than 1.0 indicates that the concentration was greater than a benchmark. Aquifer-scale proportions were used to quantify regional-scale groundwater quality. The aquifer-scale proportions are the areal percentages of the shallow aquifer system where relative concentrations for a given constituent or class of constituents were high, moderate, or low.
Inorganic constituents were measured at high and moderate relative concentrations more frequently than organic constituents. In the MS-SA study unit, inorganic constituents with benchmarks were detected at high relative concentrations in 51 percent of the study unit. The greatest proportions of high relative concentrations of trace elements and radioactive constituents were in the Highlands and Santa Cruz study areas, whereas high relative concentrations of nutrients were most often detected in the Salinas Valley and Pajaro Valley study areas and salinity indicators were most often detected in the Highlands and Salinas Valley study areas. The trace elements detected at high relative concentrations were arsenic, boron, iron, manganese, molybdenum, selenium, and strontium. The radioactive constituents detected at high relative concentrations were adjusted gross alpha radioactivity and uranium. The nutrient detected at high relative concentrations was nitrate plus nitrite. The salinity indicators detected at high relative concentrations were chloride, sulfate, and total dissolved solids.
Organic constituents (VOCs and pesticides) were not detected at high relative concentrations in any of the study areas. The fumigant 1,2-dichloropropane was detected at moderate relative concentrations. The VOC chloroform and the pesticide simazine were the only organic constituents detected in more than 10 percent of samples. The constituents of special interest NDMA (N-nitrosodimethylamine) and perchlorate were detected at high relative concentrations in the MS-SA study unit.
Selected constituents were evaluated with explanatory factors to identify potential sources or processes that could explain their presence and distribution. Trace elements and radioactive constituents came from natural sources and were not elevated by anthropogenic sources or processes, except for selenium and the radioactive constituent uranium. Arsenic, manganese, iron, selenium, and uranium concentrations were all influenced by oxidation-reduction conditions.
Unlike other trace elements and radioactive constituents, uranium and selenium can be affected by agricultural practices. Uranium and selenium can be released from aquifer sediments as a result of irrigation recharge water interacting with bicarbonate systems.
Nitrate can be strongly affected by anthropogenic sources. Nitrate concentrations were significantly higher in modern groundwater, indicating recent inputs of nitrate to the shallow aquifer system. Nitrate was positively correlated with agricultural land use, indicating that irrigation-return water could be leaching nitrogen fertilizer and naturally present nitrate to elevate nitrate concentrations in shallow groundwater.
The salinity indicators total dissolved solids, chloride, and sulfate all had natural sources in the MS-SA study unit, primarily marine sediments. Concentrations of the constituents were elevated as a result of evaporative concentration of irrigation water or precipitation. Sulfate concentrations were significantly correlated to agricultural land use, indicating that agricultural land-use practices are a contributing source of sulfate to groundwater.
The samples with most of the detections of VOCs were from sites in the Pajaro Valley and northern part of the Salinas Valley. Most of the samples with pesticide detections were from sites in the Salinas Valley study area. The herbicide simazine was positively correlated to the percentage of agricultural land use, and its concentrations were higher in modern groundwater than in pre-modern groundwater.
Perchlorate, similar to nitrate, has natural and anthropogenic sources. Correlations of perchlorate to dissolved oxygen, nitrate, and percentage of agricultural land use indicated that the irrigation-return water could be leaching naturally present perchlorate, as well as perchlorate from historical applications of Chilean nitrate fertilizer, to increase perchlorate concentrations in groundwater.
The quality of the water in the shallow aquifer system from this study was compared with the quality of water in the public drinking-water aquifer in a previous GAMA (MS-PA) study in the same area. The shallow system was more oxic and had more sites with modern groundwater than the public drinking-water aquifer, which was more anoxic and had sites with more pre-modern groundwater. Arsenic and selenium were found at high relative concentrations in a greater proportion of the shallow system. Manganese and iron were found at high relative concentrations in a greater proportion of the public drinking-water aquifer. Uranium was found at higher relative concentrations in a greater proportion of the shallow system. Concentrations of arsenic, iron, manganese, and molybdenum are not likely to change much as groundwater percolates from the shallow system to the public drinking-water aquifer because there are no anthropogenic sources affecting these constituents. Uranium and selenium concentrations in the public drinking-water aquifer could be affected by the higher concentrations in the shallow system because of irrigation-return water, however.
Nitrate and salinity indicators had concentrations that were much higher in the shallow system than the deeper public drinking-water aquifer. High concentrations of these constituents in the shallow system could lead to increased concentrations in the public drinking-water aquifer in parts of the study units because of land-use practices, such as irrigated agriculture.
Organic constituents were detected more frequently in the public drinking-water aquifer than in the shallow system, possibly because more of the sites sampled in the public drinking-water aquifer were in urban areas compared to the sites sampled for the shallow system or because sources of contamination have decreased as a result of changes in use at the land surface.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185057","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Burton, C.A., and Wright, M.T., 2018, Status and understanding of groundwater quality in the Monterey-Salinas Shallow Aquifer study unit, 2012–13: California GAMA Priority Basin Project (ver. 1.1, September 2018): U.S. Geological Survey Scientific Investigations Report 2018–5057, 116 p., https://doi.org/10.3133/sir20185057.","productDescription":"Report: x, 116 p.","numberOfPages":"132","onlineOnly":"Y","ipdsId":"IP-056428","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":357554,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2018/5057/sir20185057_versionhist.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2018-5057"},{"id":354600,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5057/coverthb.jpg"},{"id":354601,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5057/sir20185057_v1.1.pdf","text":"Report","size":"38.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5057"}],"country":"United States","state":"California","otherGeospatial":"Monterey-Salinas Shallow Aquifer study unit","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.904296875,\n 36.53170884914869\n ],\n [\n -121.761474609375,\n 36.527294814546245\n ],\n [\n -121.58569335937501,\n 36.49638952000399\n ],\n [\n -121.5142822265625,\n 36.40359962073253\n ],\n [\n -121.4813232421875,\n 36.363798554158635\n ],\n [\n -121.33300781249999,\n 36.33282808737917\n ],\n [\n -121.234130859375,\n 36.27085020723902\n ],\n [\n -121.17370605468749,\n 36.19995805932895\n ],\n [\n -121.08581542968751,\n 36.10237644873644\n ],\n [\n -121.01989746093749,\n 36.02688935430189\n ],\n [\n -120.94299316406249,\n 35.96911507577482\n ],\n [\n -120.88256835937499,\n 35.89795019335754\n ],\n [\n -120.80566406250001,\n 35.79999392988527\n ],\n [\n -120.89080810546875,\n 35.66622234103479\n ],\n [\n -120.8551025390625,\n 35.61488368245436\n ],\n [\n -120.6683349609375,\n 35.46961797120201\n ],\n [\n -120.56945800781249,\n 35.37113502280101\n ],\n [\n -120.465087890625,\n 35.37113502280101\n ],\n [\n -120.2838134765625,\n 35.37113502280101\n ],\n [\n -120.12451171875,\n 35.37561413174875\n ],\n [\n -120.0146484375,\n 35.44277092585766\n ],\n [\n -119.94323730468749,\n 35.46514408578589\n ],\n [\n -119.99267578124999,\n 35.53222622770337\n ],\n [\n -120.10253906249999,\n 35.59925232772949\n ],\n [\n -120.2838134765625,\n 35.755428369259626\n ],\n [\n -120.421142578125,\n 35.88905007936091\n ],\n [\n -120.50903320312501,\n 35.97356075349624\n ],\n [\n -120.673828125,\n 36.09349937380574\n ],\n [\n -120.80566406250001,\n 36.22211876039103\n ],\n [\n -120.94299316406249,\n 36.359374956015856\n ],\n [\n -121.06933593749999,\n 36.46988944681576\n ],\n [\n -121.55273437499999,\n 36.83127162140714\n ],\n [\n -121.61315917968749,\n 36.88840804313823\n ],\n [\n -121.72302246093749,\n 36.9806150652861\n ],\n [\n -121.84936523437499,\n 37.020098201368114\n ],\n [\n -121.9976806640625,\n 37.03325468997236\n ],\n [\n -122.06909179687501,\n 37.01132594307015\n ],\n [\n -122.1075439453125,\n 36.96306042436515\n ],\n [\n -122.0855712890625,\n 36.92793899776678\n ],\n [\n -121.9976806640625,\n 36.94989178681327\n ],\n [\n -121.92626953124999,\n 36.96306042436515\n ],\n [\n -121.86584472656251,\n 36.94989178681327\n ],\n [\n -121.8438720703125,\n 36.88401445049676\n ],\n [\n -121.8109130859375,\n 36.80928470205937\n ],\n [\n -121.8109130859375,\n 36.760891249565624\n ],\n [\n -121.83288574218749,\n 36.69044623523481\n ],\n [\n -121.8438720703125,\n 36.641977814705946\n ],\n [\n -121.8878173828125,\n 36.602299135790446\n ],\n [\n -121.9317626953125,\n 36.602299135790446\n ],\n [\n -121.915283203125,\n 36.56260003738545\n ],\n [\n -121.904296875,\n 36.53170884914869\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.1: September 2018; Version 1.0: May 2018","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
We compare the thermophysical properties and particle sizes derived from the Mars Science Laboratory rover's Ground Temperature Sensor of the Bagnold dunes, specifically Namib dune, to those derived orbitally from Thermal Emission Imaging System, ultimately linking these measurements to ground truth particle sizes determined from Mars Hand Lens Imager images. In general, we find that all three datasets report consistent particle sizes for the Bagnold dunes (~110–350 μm and are within measurement and model uncertainties), indicating that particle sizes of homogeneous materials inferred from temperature measurements and thermophysical models are reliable. Furthermore, we examine the effects of two physical characteristics that could influence the modeled thermal inertia and particle sizes, including (1) fine‐scale (centimeter to meter scale) ripples and (2) thin layering of indurated/armored materials. To first order, we find that small‐scale ripples and thin (approximately centimeter scale) layers do not significantly affect the determination of bulk thermal inertia from orbital thermal data using a single nighttime temperature. Modeling of a layer of coarse or indurated material reveals that a thin layer (< ~5 mm; similar to what was observed by the Curiosity rover) would not significantly change the observed thermal properties of the surface and would be dominated by the properties of the underlying material. Thermal inertia and particle sizes of relatively homogeneous materials derived from nighttime orbital data should be considered as reliable, as long as there are no significant subpixel anisothermality effects (e.g., lateral mixing of multiple thermophysically distinct materials).
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2017JE005501","usgsCitation":"Edwards, C., Piqueux, S., Hamilton, V.E., Fergason, R.L., Herkenhoff, K., Vasavada, A.R., Bennett, K.A., Sacks, L., Lewis, K., and Smith, M.D., 2018, The thermophysical properties of the Bagnold Dunes, Mars: Ground truthing orbital data: Journal of Geophysical Research: Planets, v. 123, no. 5, p. 1307-1326, https://doi.org/10.1029/2017JE005501.","productDescription":"15 p.","startPage":"1307","endPage":"1326","ipdsId":"IP-085162","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":468722,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2017je005501","text":"Publisher Index Page"},{"id":355551,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Bagnold Dunes, Mars","volume":"123","issue":"5","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-31","publicationStatus":"PW","scienceBaseUri":"5b46e584e4b060350a15d1c0","contributors":{"authors":[{"text":"Edwards, Christopher S.","contributorId":206168,"corporation":false,"usgs":false,"family":"Edwards","given":"Christopher S.","affiliations":[{"id":7202,"text":"NAU","active":true,"usgs":false}],"preferred":false,"id":739692,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Piqueux, Sylvain","contributorId":56986,"corporation":false,"usgs":false,"family":"Piqueux","given":"Sylvain","email":"","affiliations":[{"id":7023,"text":"Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":739693,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hamilton, Victoria E.","contributorId":206169,"corporation":false,"usgs":false,"family":"Hamilton","given":"Victoria","email":"","middleInitial":"E.","affiliations":[{"id":37270,"text":"Southwest Research Institute, Boulder, Colo.","active":true,"usgs":false}],"preferred":false,"id":739694,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fergason, Robin L. 0000-0002-2044-1714","orcid":"https://orcid.org/0000-0002-2044-1714","contributorId":206167,"corporation":false,"usgs":true,"family":"Fergason","given":"Robin","email":"","middleInitial":"L.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":739691,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Herkenhoff, Kenneth E. 0000-0002-3153-6663","orcid":"https://orcid.org/0000-0002-3153-6663","contributorId":206170,"corporation":false,"usgs":true,"family":"Herkenhoff","given":"Kenneth E.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":739695,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vasavada, Ashwin R.","contributorId":200409,"corporation":false,"usgs":false,"family":"Vasavada","given":"Ashwin","email":"","middleInitial":"R.","affiliations":[],"preferred":true,"id":739696,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bennett, Kristen A.","contributorId":206171,"corporation":false,"usgs":false,"family":"Bennett","given":"Kristen","email":"","middleInitial":"A.","affiliations":[{"id":7202,"text":"NAU","active":true,"usgs":false}],"preferred":false,"id":739697,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sacks, Leah","contributorId":206172,"corporation":false,"usgs":false,"family":"Sacks","given":"Leah","email":"","affiliations":[{"id":37271,"text":"Carelton College, Northfield, Minn.","active":true,"usgs":false}],"preferred":false,"id":739698,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lewis, Kevin","contributorId":195296,"corporation":false,"usgs":false,"family":"Lewis","given":"Kevin","affiliations":[],"preferred":false,"id":739699,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Smith, Michael D.","contributorId":206173,"corporation":false,"usgs":false,"family":"Smith","given":"Michael","email":"","middleInitial":"D.","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":739700,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ]}