{"pageNumber":"944","pageRowStart":"23575","pageSize":"25","recordCount":184904,"records":[{"id":70192306,"text":"70192306 - 2017 - Strong SH-to-Love wave scattering off the Southern California Continental Borderland","interactions":[],"lastModifiedDate":"2017-11-29T16:17:16","indexId":"70192306","displayToPublicDate":"2017-10-25T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Strong SH-to-Love wave scattering off the Southern California Continental Borderland","docAbstract":"Seismic scattering is commonly observed and results from wave propagation in heterogeneous medium. Yet, deterministic characterization of scatterers associated with lateral heterogeneities remains challenging. In this study, we analyze broadband waveforms recorded by the Southern California Seismic Network and observe strongly scattered Love waves following the arrival of teleseismic SH wave. These scattered Love waves travel approximately in the same (azimuthal) direction as the incident SH wave at a dominant period of ~10 s but at an apparent velocity of ~3.6 km/s as compared to the ~11 km/s for the SH wave. Back-projection suggests that this strong scattering is associated with pronounced bathymetric relief in the Southern California Continental Borderland, in particular the Patton Escarpment. Finite-difference simulations using a simplified 2-D bathymetric and crustal model are able to predict the arrival times and amplitudes of major scatterers. The modeling suggests a relatively low shear wave velocity in the Continental Borderland.","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2017GL075213","usgsCitation":"Yu, C., Zhan, Z., Hauksson, E., and Cochran, E.S., 2017, Strong SH-to-Love wave scattering off the Southern California Continental Borderland: Geophysical Research Letters, v. 44, no. 20, p. 10208-10215, https://doi.org/10.1002/2017GL075213.","productDescription":"8 p.","startPage":"10208","endPage":"10215","ipdsId":"IP-089078","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":469396,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2017gl075213","text":"Publisher Index Page"},{"id":347343,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, 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              -123,\n              30\n            ],\n            [\n              -113,\n              30\n            ],\n            [\n              -113,\n              38\n            ],\n            [\n              -123,\n              38\n            ],\n            [\n              -123,\n              30\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"44","issue":"20","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-21","publicationStatus":"PW","scienceBaseUri":"59f1a2a0e4b0220bbd9d9f10","contributors":{"authors":[{"text":"Yu, Chunquan","contributorId":198158,"corporation":false,"usgs":false,"family":"Yu","given":"Chunquan","email":"","affiliations":[],"preferred":false,"id":715222,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhan, Zhongwen","contributorId":195085,"corporation":false,"usgs":false,"family":"Zhan","given":"Zhongwen","email":"","affiliations":[],"preferred":false,"id":715223,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hauksson, Egill","contributorId":198159,"corporation":false,"usgs":false,"family":"Hauksson","given":"Egill","email":"","affiliations":[],"preferred":false,"id":715224,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cochran, Elizabeth S. 0000-0003-2485-4484 ecochran@usgs.gov","orcid":"https://orcid.org/0000-0003-2485-4484","contributorId":2025,"corporation":false,"usgs":true,"family":"Cochran","given":"Elizabeth","email":"ecochran@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":715221,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70192351,"text":"70192351 - 2017 - Applying citizen-science data and mark-recapture models to estimate numbers of migrant golden eagles in an important bird area in eastern North America","interactions":[],"lastModifiedDate":"2017-11-22T16:42:57","indexId":"70192351","displayToPublicDate":"2017-10-25T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3551,"text":"The Condor","active":true,"publicationSubtype":{"id":10}},"title":"Applying citizen-science data and mark-recapture models to estimate numbers of migrant golden eagles in an important bird area in eastern North America","docAbstract":"<p>Estimates of population abundance are important to wildlife management and conservation. However, it can be difficult to characterize the numbers of broadly distributed, low-density, and elusive bird species. Although Golden Eagles (Aquila chrysaetos) are rare, difficult to detect, and broadly distributed, they are concentrated during their autumn migration at monitoring sites in eastern North America. We used hawk-count data collected by citizen scientists in a virtual mark–recapture modeling analysis to estimate the numbers of Golden Eagles that migrate in autumn along Kittatinny Ridge, an Important Bird Area in Pennsylvania, USA. In order to evaluate the sensitivity of our abundance estimates to variation in eagle capture histories, we applied candidate models to 8 different sets of capture histories, constructed with or without age-class information and using known mean flight speeds 6 1, 2, 4, or 6 SE for eagles to travel between hawk-count sites. Although some abundance estimates were produced by models that poorly fitted the data (<i>ĉ</i> &gt; 3.0), 2 sets of population estimates were produced by acceptably performing models (cˆ less than or equal to 3.0). Application of these models to count data from November, 2002–2011, suggested a mean population abundance of 1,354 6 117 SE (range: 873–1,938). We found that Golden Eagles left the ridgeline at different rates and in different places along the route, and that typically ,50% of individuals were detected at the hawk-count sites. Our study demonstrates a useful technique for estimating population abundance that may be applicable to other migrant species that are repeatedly detected at multiple monitoring sites along a topographic diversion or leading line.</p>","language":"English","publisher":"BioOne","doi":"10.1650/CONDOR-16-166.1","usgsCitation":"Dennhardt, A.J., Duerr, A.E., Brandes, D., and Katzner, T., 2017, Applying citizen-science data and mark-recapture models to estimate numbers of migrant golden eagles in an important bird area in eastern North America: The Condor, v. 119, no. 4, p. 817-831, https://doi.org/10.1650/CONDOR-16-166.1.","productDescription":"15 p.","startPage":"817","endPage":"831","ipdsId":"IP-074201","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":469394,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1650/condor-16-166.1","text":"Publisher Index Page"},{"id":347305,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennslyvania","otherGeospatial":"Kittatinny Ridge","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -81.123046875,\n              39.257778150283364\n            ],\n            [\n              -73.916015625,\n              39.257778150283364\n            ],\n            [\n              -73.916015625,\n              42.78733853171998\n            ],\n            [\n              -81.123046875,\n              42.78733853171998\n            ],\n            [\n              -81.123046875,\n              39.257778150283364\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"119","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59f1a29ee4b0220bbd9d9eea","contributors":{"authors":[{"text":"Dennhardt, Andrew J.","contributorId":198247,"corporation":false,"usgs":false,"family":"Dennhardt","given":"Andrew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":715500,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duerr, Adam E.","contributorId":190590,"corporation":false,"usgs":false,"family":"Duerr","given":"Adam","email":"","middleInitial":"E.","affiliations":[{"id":16210,"text":"Division of Forestry and Natural Resources, West Virginia University","active":true,"usgs":false}],"preferred":false,"id":715501,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brandes, David","contributorId":138917,"corporation":false,"usgs":false,"family":"Brandes","given":"David","email":"","affiliations":[{"id":35653,"text":"Lafayette College, Easton, PA","active":true,"usgs":false}],"preferred":false,"id":715502,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":715499,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70192348,"text":"70192348 - 2017 - Where can wolves live and how can we live with them?","interactions":[],"lastModifiedDate":"2020-10-22T15:56:11.463004","indexId":"70192348","displayToPublicDate":"2017-10-25T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Where can wolves live and how can we live with them?","docAbstract":"<p>In the contiguous 48 United States, southern Canada, and in Europe, wolves (<i>Canis lupus</i>) have greatly increased and expanded their range during the past few decades.They are prolific, disperse long distances, readily recolonize new areas where humans allow them, and are difficult to control when populations become established.Because wolves originally lived nearly everywhere throughout North America and Eurasia, and food in the form of wild and domestic prey is abundant there, many conservation-minded people favor wolves inhabiting even more areas.On the other hand, wolves conflict in several ways with rural residents who prefer fewer wolves. This article discusses the recovery of wolves, their benefits and values, the ways in which they conflict with humans, and the potential for their expansion into new areas.It concludes that wolf conservation will best be accomplished by each responsible political entity adaptively prescribing different management strategies for different zones within its purview.Some zones for some periods can support total protection, whereas in others, wolf numbers will have to be reduced to various degrees or removed.</p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2017.04.029","usgsCitation":"Mech, L.D., 2017, Where can wolves live and how can we live with them?: Biological Conservation, v. 210, no. A, p. 310-317, https://doi.org/10.1016/j.biocon.2017.04.029.","productDescription":"8 p.","startPage":"310","endPage":"317","ipdsId":"IP-078956","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":347307,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"210","issue":"A","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59f1a29ee4b0220bbd9d9eee","contributors":{"authors":[{"text":"Mech, L. David 0000-0003-3944-7769 david_mech@usgs.gov","orcid":"https://orcid.org/0000-0003-3944-7769","contributorId":2518,"corporation":false,"usgs":true,"family":"Mech","given":"L.","email":"david_mech@usgs.gov","middleInitial":"David","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":715492,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70192399,"text":"70192399 - 2017 - Statistical design and analysis for plant cover studies with multiple sources of observation errors","interactions":[],"lastModifiedDate":"2017-12-11T13:27:34","indexId":"70192399","displayToPublicDate":"2017-10-25T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Statistical design and analysis for plant cover studies with multiple sources of observation errors","docAbstract":"<ol id=\"mee312825-list-0001\" class=\"o-list--numbered\"><li>Effective wildlife habitat management and conservation requires understanding the factors influencing distribution and abundance of plant species. Field studies, however, have documented observation errors in visually estimated plant cover including measurements which differ from the true value (measurement error) and not observing a species that is present within a plot (detection error). Unlike the rapid expansion of occupancy and N-mixture models for analysing wildlife surveys, development of statistical models accounting for observation error in plants has not progressed quickly. Our work informs development of a monitoring protocol for managed wetlands within the National Wildlife Refuge System.</li><li>Zero-augmented beta (ZAB) regression is the most suitable method for analysing areal plant cover recorded as a continuous proportion but assumes no observation errors. We present a model extension that explicitly includes the observation process thereby accounting for both measurement and detection errors. Using simulations, we compare our approach to a ZAB regression that ignores observation errors (naïve model) and an “ad hoc” approach using a composite of multiple observations per plot within the naïve model. We explore how sample size and within-season revisit design affect the ability to detect a change in mean plant cover between 2&nbsp;years using our model.</li><li>Explicitly modelling the observation process within our framework produced unbiased estimates and nominal coverage of model parameters. The naïve and “ad hoc” approaches resulted in underestimation of occurrence and overestimation of mean cover. The degree of bias was primarily driven by imperfect detection and its relationship with cover within a plot. Conversely, measurement error had minimal impacts on inferences. We found &gt;30 plots with at least three within-season revisits achieved reasonable posterior probabilities for assessing change in mean plant cover.</li><li>For rapid adoption and application, code for Bayesian estimation of our single-species ZAB with errors model is included. Practitioners utilizing our R-based simulation code can explore trade-offs among different survey efforts and parameter values, as we did, but tuned to their own investigation. Less abundant plant species of high ecological interest may warrant the additional cost of gathering multiple independent observations in order to guard against erroneous conclusions.</li></ol>","language":"English","publisher":"British Ecological Society","doi":"10.1111/2041-210X.12825","usgsCitation":"Wright, W.J., Irvine, K.M., Warren, J.M., and Barnett, J.K., 2017, Statistical design and analysis for plant cover studies with multiple sources of observation errors: Methods in Ecology and Evolution, v. 8, no. 12, p. 1832-1841, https://doi.org/10.1111/2041-210X.12825.","productDescription":"10 p.","startPage":"1832","endPage":"1841","ipdsId":"IP-084125","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":469390,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/2041-210x.12825","text":"Publisher Index Page"},{"id":347377,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"12","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-07-11","publicationStatus":"PW","scienceBaseUri":"59f1a29ae4b0220bbd9d9ed0","contributors":{"authors":[{"text":"Wright, Wilson J. 0000-0003-4276-3850 wjwright@usgs.gov","orcid":"https://orcid.org/0000-0003-4276-3850","contributorId":198317,"corporation":false,"usgs":true,"family":"Wright","given":"Wilson","email":"wjwright@usgs.gov","middleInitial":"J.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":715684,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Irvine, Kathryn M. 0000-0002-6426-940X kirvine@usgs.gov","orcid":"https://orcid.org/0000-0002-6426-940X","contributorId":2218,"corporation":false,"usgs":true,"family":"Irvine","given":"Kathryn","email":"kirvine@usgs.gov","middleInitial":"M.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":715683,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Warren, Jeffrey M .","contributorId":198318,"corporation":false,"usgs":false,"family":"Warren","given":"Jeffrey","email":"","middleInitial":"M .","affiliations":[],"preferred":false,"id":715685,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barnett, Jenny K.","contributorId":198319,"corporation":false,"usgs":false,"family":"Barnett","given":"Jenny","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":715686,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70192331,"text":"70192331 - 2017 - Partial polygon pruning of hydrographic features in automated generalization","interactions":[],"lastModifiedDate":"2017-10-25T10:06:11","indexId":"70192331","displayToPublicDate":"2017-10-25T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3618,"text":"Transactions in GIS","active":true,"publicationSubtype":{"id":10}},"title":"Partial polygon pruning of hydrographic features in automated generalization","docAbstract":"This paper demonstrates a working method to automatically detect and prune portions of waterbody polygons to support creation of a multi-scale hydrographic database. Water features are known to be sensitive to scale change; and thus multiple representations are required to maintain visual and geographic logic at smaller scales. Partial pruning of polygonal features—such as long and sinuous reservoir arms, stream channels that are too narrow at the target scale, and islands that begin to coalesce—entails concurrent management of the length and width of polygonal features as well as integrating pruned polygons with other generalized point and linear hydrographic features to maintain stream network connectivity. The implementation follows data representation standards developed by the U.S. Geological Survey (USGS) for the National Hydrography Dataset (NHD). Portions of polygonal rivers, streams, and canals are automatically characterized for width, length, and connectivity. This paper describes an algorithm for automatic detection and subsequent processing, and shows results for a sample of NHD subbasins in different landscape conditions in the United States.","language":"English","publisher":"John Wiley & Sons, Ltd.","doi":"10.1111/tgis.12270","usgsCitation":"Stum, A.K., Buttenfield, B.P., and Stanislawski, L.V., 2017, Partial polygon pruning of hydrographic features in automated generalization: Transactions in GIS, v. 21, no. 5, p. 1061-1078, https://doi.org/10.1111/tgis.12270.","productDescription":"18 p.","startPage":"1061","endPage":"1078","ipdsId":"IP-078637","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":347314,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"21","issue":"5","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-08","publicationStatus":"PW","scienceBaseUri":"59f1a29ee4b0220bbd9d9ef8","contributors":{"authors":[{"text":"Stum, Alexander K.","contributorId":198209,"corporation":false,"usgs":false,"family":"Stum","given":"Alexander","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":715372,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Buttenfield, Barbara P.","contributorId":184069,"corporation":false,"usgs":false,"family":"Buttenfield","given":"Barbara","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":715373,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stanislawski, Larry V. 0000-0002-9437-0576 lstan@usgs.gov","orcid":"https://orcid.org/0000-0002-9437-0576","contributorId":3386,"corporation":false,"usgs":true,"family":"Stanislawski","given":"Larry","email":"lstan@usgs.gov","middleInitial":"V.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true},{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"preferred":true,"id":715371,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70192372,"text":"70192372 - 2017 - Assessing models of arsenic occurrence in drinking water from bedrock aquifers in New Hampshire","interactions":[],"lastModifiedDate":"2017-10-25T09:37:46","indexId":"70192372","displayToPublicDate":"2017-10-25T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2234,"text":"Journal of Contemporary Water Research and Education","active":true,"publicationSubtype":{"id":10}},"title":"Assessing models of arsenic occurrence in drinking water from bedrock aquifers in New Hampshire","docAbstract":"Three existing multivariate logistic regression models were assessed using new data to evaluate the capacity of the models to correctly predict the probability of groundwater arsenic concentrations exceeding the threshold values of 1, 5, and 10 micrograms per liter (µg/L) in New Hampshire, USA. A recently released testing dataset includes arsenic concentrations from groundwater samples collected in 2004–2005 from a mix of 367 public-supply and private domestic wells. The use of this dataset to test three existing logistic regression models demonstrated enhanced overall predictive accuracy for the 5 and 10 μg/L models. Overall accuracies of 54.8, 76.3, and 86.4 percent were reported for the 1, 5, and 10 μg/L models, respectively. The state was divided by counties into northwest and southeast regions. Regional differences in accuracy were identified; models had an average accuracy of 83.1 percent for the counties in the northwest and 63.7 percent in the southeast. This is most likely due to high model specificity in the northwest and regional differences in arsenic occurrence. Though these models have limitations, they allow for arsenic hazard assessment across the region. The introduction of well-type (public or private), well depth, and casing length as explanatory variables may be appropriate measures to improve model performance. Our findings indicate that the original models generalize to the testing dataset, and should continue to serve as an important vehicle of preventative public health that may be applied to other groundwater contaminants in New Hampshire.","language":"English","publisher":"Wiley","doi":"10.1111/j.1936-704X.2017.03238.x","usgsCitation":"Andy, C., Fahnestock, M.F., Lombard, M.A., Hayes, L., Bryce, J., and Ayotte, J.D., 2017, Assessing models of arsenic occurrence in drinking water from bedrock aquifers in New Hampshire: Journal of Contemporary Water Research and Education, v. 160, no. 1, p. 25-41, https://doi.org/10.1111/j.1936-704X.2017.03238.x.","productDescription":"17 p.","startPage":"25","endPage":"41","ipdsId":"IP-078863","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":469389,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/j.1936-704x.2017.03238.x","text":"Publisher Index Page"},{"id":347304,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New 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mlombard@usgs.gov","orcid":"https://orcid.org/0000-0001-5924-6556","contributorId":198254,"corporation":false,"usgs":true,"family":"Lombard","given":"Melissa","email":"mlombard@usgs.gov","middleInitial":"A.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":715523,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hayes, Laura 0000-0002-4488-1343 lhayes@usgs.gov","orcid":"https://orcid.org/0000-0002-4488-1343","contributorId":2791,"corporation":false,"usgs":true,"family":"Hayes","given":"Laura","email":"lhayes@usgs.gov","affiliations":[{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":715524,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bryce, Julie","contributorId":198256,"corporation":false,"usgs":false,"family":"Bryce","given":"Julie","email":"","affiliations":[],"preferred":false,"id":715525,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ayotte, Joseph D. 0000-0002-1892-2738 jayotte@usgs.gov","orcid":"https://orcid.org/0000-0002-1892-2738","contributorId":149619,"corporation":false,"usgs":true,"family":"Ayotte","given":"Joseph","email":"jayotte@usgs.gov","middleInitial":"D.","affiliations":[{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":715526,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70192315,"text":"70192315 - 2017 - Projected warming portends seasonal shifts of stream temperatures in the Crown of the Continent Ecosystem, USA and Canada","interactions":[],"lastModifiedDate":"2017-10-26T09:31:34","indexId":"70192315","displayToPublicDate":"2017-10-25T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1252,"text":"Climatic Change","active":true,"publicationSubtype":{"id":10}},"title":"Projected warming portends seasonal shifts of stream temperatures in the Crown of the Continent Ecosystem, USA and Canada","docAbstract":"Climate warming is expected to increase stream temperatures in mountainous regions of western North America, yet the degree to which future climate change may influence seasonal patterns of stream temperature is uncertain. In this study, a spatially explicit statistical model framework was integrated with empirical stream temperature data (approximately four million bi-hourly recordings) and high-resolution climate and land surface data to estimate monthly stream temperatures and potential change under future climate scenarios in the Crown of the Continent Ecosystem, USA and Canada (72,000 km2). Moderate and extreme warming scenarios forecast increasing stream temperatures during spring, summer, and fall, with the largest increases predicted during summer (July, August, and September). Additionally, thermal regimes characteristic of current August temperatures, the warmest month of the year, may be exceeded during July and September, suggesting an earlier and extended duration of warm summer stream temperatures. Models estimate that the largest magnitude of temperature warming relative to current conditions may be observed during the shoulder months of winter (April and November). Summer stream temperature warming is likely to be most pronounced in glacial-fed streams where models predict the largest magnitude (> 50%) of change due to the loss of alpine glaciers. We provide the first broad-scale analysis of seasonal climate effects on spatiotemporal patterns of stream temperature in the Crown of the Continent Ecosystem for better understanding climate change impacts on freshwater habitats and guiding conservation and climate adaptation strategies.","language":"English","publisher":"Springer","doi":"10.1007/s10584-017-2060-7","usgsCitation":"Jones, L.A., Muhlfeld, C.C., and Marshall, L.A., 2017, Projected warming portends seasonal shifts of stream temperatures in the Crown of the Continent Ecosystem, USA and Canada: Climatic Change, v. 144, no. 4, p. 641-655, https://doi.org/10.1007/s10584-017-2060-7.","productDescription":"15 p.","startPage":"641","endPage":"655","ipdsId":"IP-081391","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":347330,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -116.27929687499999,\n              43.54854811091286\n            ],\n            [\n              -113.37890625,\n              43.13306116240612\n            ],\n            [\n              -111.884765625,\n              43.99281450048989\n            ],\n            [\n              -111.357421875,\n              45.058001435398275\n            ],\n            [\n              -110.1708984375,\n              46.437856895024204\n            ],\n            [\n              -110.302734375,\n              47.040182144806664\n            ],\n            [\n              -112.54394531249999,\n              49.66762782262194\n            ],\n            [\n              -114.08203125,\n              50.708634400828224\n            ],\n            [\n              -116.76269531249999,\n              53.35710874569601\n            ],\n            [\n              -119.267578125,\n              54.92714186454645\n            ],\n            [\n              -121.728515625,\n              55.70235509327093\n            ],\n            [\n              -122.9150390625,\n              55.25407706707272\n            ],\n            [\n              -123.04687499999999,\n              54.54657953840501\n            ],\n            [\n              -122.2119140625,\n              52.5897007687178\n            ],\n            [\n              -120.76171875,\n              50.764259357116465\n            ],\n            [\n              -119.66308593749999,\n              48.83579746243093\n            ],\n            [\n              -118.037109375,\n              47.30903424774781\n            ],\n            [\n              -117.6416015625,\n              45.706179285330855\n            ],\n            [\n              -117.24609374999999,\n              44.5278427984555\n            ],\n            [\n              -116.27929687499999,\n              43.54854811091286\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"144","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-05","publicationStatus":"PW","scienceBaseUri":"59f1a2a0e4b0220bbd9d9f0a","contributors":{"authors":[{"text":"Jones, Leslie A. 0000-0002-4953-7189 lajones@usgs.gov","orcid":"https://orcid.org/0000-0002-4953-7189","contributorId":4599,"corporation":false,"usgs":true,"family":"Jones","given":"Leslie","email":"lajones@usgs.gov","middleInitial":"A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":715260,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Muhlfeld, Clint C. 0000-0002-4599-4059 cmuhlfeld@usgs.gov","orcid":"https://orcid.org/0000-0002-4599-4059","contributorId":924,"corporation":false,"usgs":true,"family":"Muhlfeld","given":"Clint","email":"cmuhlfeld@usgs.gov","middleInitial":"C.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":715258,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Marshall, Lucy A. 0000-0003-0450-4292","orcid":"https://orcid.org/0000-0003-0450-4292","contributorId":198080,"corporation":false,"usgs":false,"family":"Marshall","given":"Lucy","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":715259,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70192325,"text":"70192325 - 2017 - Systematic observations of the slip pulse properties of large earthquake ruptures","interactions":[],"lastModifiedDate":"2017-11-10T14:04:30","indexId":"70192325","displayToPublicDate":"2017-10-25T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Systematic observations of the slip pulse properties of large earthquake ruptures","docAbstract":"<p><span>In earthquake dynamics there are two end member models of rupture: propagating cracks and self-healing pulses. These arise due to different properties of faults and have implications for seismic hazard; rupture mode controls near-field strong ground motions. Past studies favor the pulse-like mode of rupture; however, due to a variety of limitations, it has proven difficult to systematically establish their kinematic properties. Here we synthesize observations from a database of &gt;150 rupture models of earthquakes spanning&nbsp;</span><i>M</i><span>7–</span><i>M</i><span>9 processed in a uniform manner and show the magnitude scaling properties of these slip pulses indicates self-similarity. Further, we find that large and very large events are statistically distinguishable relatively early (at ~15&nbsp;s) in the rupture process. This suggests that with dense regional geophysical networks strong ground motions from a large rupture can be identified before their onset across the source region.</span></p>","language":"English","publisher":"AGU","doi":"10.1002/2017GL074916","usgsCitation":"Melgar, D., and Hayes, G.P., 2017, Systematic observations of the slip pulse properties of large earthquake ruptures: Geophysical Research Letters, v. 44, no. 19, p. 9691-9698, https://doi.org/10.1002/2017GL074916.","productDescription":"8 p.","startPage":"9691","endPage":"9698","ipdsId":"IP-090143","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":469399,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2017gl074916","text":"Publisher Index Page"},{"id":438177,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7SF2V44","text":"USGS data release","linkHelpText":"Data for Systematic Observations of the Slip-pulse Properties of Large Earthquake Ruptures"},{"id":347316,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"44","issue":"19","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-13","publicationStatus":"PW","scienceBaseUri":"59f1a29fe4b0220bbd9d9f02","contributors":{"authors":[{"text":"Melgar, Diego","contributorId":193030,"corporation":false,"usgs":false,"family":"Melgar","given":"Diego","email":"","affiliations":[],"preferred":false,"id":715354,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hayes, Gavin P. 0000-0003-3323-0112 ghayes@usgs.gov","orcid":"https://orcid.org/0000-0003-3323-0112","contributorId":147556,"corporation":false,"usgs":true,"family":"Hayes","given":"Gavin","email":"ghayes@usgs.gov","middleInitial":"P.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":715355,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70192341,"text":"70192341 - 2017 - Groundwater-level trends in the U.S. glacial aquifer system, 1964-2013","interactions":[],"lastModifiedDate":"2017-11-06T15:23:39","indexId":"70192341","displayToPublicDate":"2017-10-25T00:00:00","publicationYear":"2017","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":"Groundwater-level trends in the U.S. glacial aquifer system, 1964-2013","docAbstract":"The glacial aquifer system in the United States is a major source of water supply but previous work on historical groundwater trends across the system is lacking. Trends in annual minimum, mean, and maximum groundwater levels for 205 monitoring wells were analyzed across three regions of the system (East, Central, West Central) for four time periods: 1964-2013, 1974-2013, 1984-2013, and 1994-2013. Trends were computed separately for wells in the glacial aquifer system with low potential for human influence on groundwater levels and ones with high potential influence from activities such as groundwater pumping. Generally there were more wells with significantly increasing groundwater levels (levels closer to ground surface) than wells with significantly decreasing levels. The highest numbers of significant increases for all four time periods were with annual minimum and/or mean levels. There were many more wells with significant increases from 1964 to 2013 than from more recent periods, consistent with low precipitation in the 1960s. Overall there were low numbers of wells with significantly decreasing trends regardless of time period considered; the highest number of these were generally for annual minimum groundwater levels at wells with likely human influence. There were substantial differences in the number of wells with significant groundwater-level trends over time, depending on whether the historical time series are assumed to be independent, have short-term persistence, or have long-term persistence. Mean annual groundwater levels have significant lag-one-year autocorrelation at 26.0% of wells in the East region, 65.4% of wells in the Central region, and 100% of wells in the West Central region. Annual precipitation across the glacial aquifer system, on the other hand, has significant autocorrelation at only 5.5% of stations, about the percentage expected due to chance.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2017.07.055","usgsCitation":"Hodgkins, G.A., Dudley, R.W., Nielsen, M.G., Renard, B., and Qi, S.L., 2017, Groundwater-level trends in the U.S. glacial aquifer system, 1964-2013: Journal of Hydrology, v. 553, p. 289-303, https://doi.org/10.1016/j.jhydrol.2017.07.055.","productDescription":"15 p.","startPage":"289","endPage":"303","ipdsId":"IP-081195","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":461379,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2017.07.055","text":"Publisher Index Page"},{"id":347310,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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,{"id":70192304,"text":"70192304 - 2017 - Delayed seismicity rate changes controlled by static stress transfer","interactions":[],"lastModifiedDate":"2017-11-29T16:17:51","indexId":"70192304","displayToPublicDate":"2017-10-25T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2314,"text":"Journal of Geophysical Research B: Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Delayed seismicity rate changes controlled by static stress transfer","docAbstract":"<p><span>On 15 June 2010, a&nbsp;</span><i>M</i><sub><i>w</i></sub><span>5.7 earthquake occurred near Ocotillo, California, in the Yuha Desert. This event was the largest aftershock of the 4 April 2010<span>&nbsp;</span></span><i>M</i><sub><i>w</i></sub><span>7.2 El Mayor-Cucapah (EMC) earthquake in this region. The EMC mainshock and subsequent Ocotillo aftershock provide an opportunity to test the Coulomb failure hypothesis (CFS). We explore the spatiotemporal correlation between seismicity rate changes and regions of positive and negative CFS change imparted by the Ocotillo event. Based on simple CFS calculations we divide the Yuha Desert into three subregions, one triggering zone and two stress shadow zones. We find the nominal triggering zone displays immediate triggering, one stress shadowed region experiences immediate quiescence, and the other nominal stress shadow undergoes an immediate rate increase followed by a delayed shutdown. We quantitatively model the spatiotemporal variation of earthquake rates by combining calculations of CFS change with the rate-state earthquake rate formulation of Dieterich (1994), assuming that each subregion contains a mixture of nucleation sources that experienced a CFS change of differing signs. Our modeling reproduces the observations, including the observed delay in the stress shadow effect in the third region following the Ocotillo aftershock. The delayed shadow effect occurs because of intrinsic differences in the amplitude of the rate response to positive and negative stress changes and the time constants for return to background rates for the two populations. We find that rate-state models of time-dependent earthquake rates are in good agreement with the observed rates and thus explain the complex spatiotemporal patterns of seismicity.</span></p>","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2017JB014227","usgsCitation":"Kroll, K.A., Richards-Dinger, K.B., Dieterich, J.H., and Cochran, E.S., 2017, Delayed seismicity rate changes controlled by static stress transfer: Journal of Geophysical Research B: Solid Earth, v. 122, no. 10, p. 7951-7965, https://doi.org/10.1002/2017JB014227.","productDescription":"15 p.","startPage":"7951","endPage":"7965","ipdsId":"IP-070704","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":469402,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/2017jb014227","text":"External Repository"},{"id":347341,"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              -116.5,\n              32\n            ],\n            [\n              -115,\n              32\n            ],\n            [\n              -115,\n              33.5\n            ],\n            [\n              -116.5,\n              33.5\n            ],\n            [\n              -116.5,\n              32\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"122","issue":"10","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-14","publicationStatus":"PW","scienceBaseUri":"59f1a2a0e4b0220bbd9d9f13","contributors":{"authors":[{"text":"Kroll, Kayla A.","contributorId":146335,"corporation":false,"usgs":false,"family":"Kroll","given":"Kayla","email":"","middleInitial":"A.","affiliations":[{"id":6984,"text":"UC Riverside","active":true,"usgs":false}],"preferred":false,"id":715216,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Richards-Dinger, Keith B.","contributorId":198155,"corporation":false,"usgs":false,"family":"Richards-Dinger","given":"Keith","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":715217,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dieterich, James H.","contributorId":198156,"corporation":false,"usgs":false,"family":"Dieterich","given":"James","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":715218,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cochran, Elizabeth S. 0000-0003-2485-4484 ecochran@usgs.gov","orcid":"https://orcid.org/0000-0003-2485-4484","contributorId":2025,"corporation":false,"usgs":true,"family":"Cochran","given":"Elizabeth","email":"ecochran@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":715215,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70192302,"text":"70192302 - 2017 - Buried shallow fault slip from the South Napa earthquake revealed by near-field geodesy","interactions":[],"lastModifiedDate":"2017-10-26T09:32:58","indexId":"70192302","displayToPublicDate":"2017-10-25T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5010,"text":"Science Advances","active":true,"publicationSubtype":{"id":10}},"title":"Buried shallow fault slip from the South Napa earthquake revealed by near-field geodesy","docAbstract":"<p><span>Earthquake-related fault slip in the upper hundreds of meters of Earth’s surface has remained largely unstudied because of challenges measuring deformation in the near field of a fault rupture. We analyze centimeter-scale accuracy mobile laser scanning (MLS) data of deformed vine rows within ±300 m of the principal surface expression of the&nbsp;</span><i>M</i><span><span>&nbsp;</span>(magnitude) 6.0 2014 South Napa earthquake. Rather than assuming surface displacement equivalence to fault slip, we invert the near-field data with a model that allows for, but does not require, the fault to be buried below the surface. The inversion maps the position on a preexisting fault plane of a slip front that terminates ~3 to 25 m below the surface coseismically and within a few hours postseismically. The lack of surface-breaching fault slip is verified by two trenches. We estimate near-surface slip ranging from ~0.5 to 1.25 m. Surface displacement can underestimate fault slip by as much as 30%. This implies that similar biases could be present in short-term geologic slip rates used in seismic hazard analyses. Along strike and downdip, we find deficits in slip: The along-strike deficit is erased after ~1 month by afterslip. We find no evidence of off-fault deformation and conclude that the downdip shallow slip deficit for this event is likely an artifact. As near-field geodetic data rapidly proliferate and will become commonplace, we suggest that analyses of near-surface fault rupture should also use more sophisticated mechanical models and subsurface geomechanical tests.</span></p>","language":"English","publisher":"AAAS","doi":"10.1126/sciadv.1700525","usgsCitation":"Brooks, B.A., Minson, S.E., Glennie, C.L., Nevitt, J., Dawson, T.E., Rubin, R.S., Ericksen, T., Lockner, D.A., Hudnut, K.W., Langenheim, V., Lutz, A., Murray, J.R., Schwartz, D.P., and Zaccone, D., 2017, Buried shallow fault slip from the South Napa earthquake revealed by near-field geodesy: Science Advances, v. 3, no. 7, e1700525; 12 p., https://doi.org/10.1126/sciadv.1700525.","productDescription":"e1700525; 12 p.","ipdsId":"IP-088981","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":469391,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1126/sciadv.1700525","text":"Publisher Index Page"},{"id":347346,"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              -123,\n              37.5\n            ],\n            [\n              -122,\n              37.5\n            ],\n            [\n              -122,\n              39\n            ],\n            [\n              -123,\n              39\n            ],\n            [\n              -123,\n              37.5\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"3","issue":"7","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59f1a2a1e4b0220bbd9d9f16","contributors":{"authors":[{"text":"Brooks, Benjamin A. 0000-0001-7954-6281 bbrooks@usgs.gov","orcid":"https://orcid.org/0000-0001-7954-6281","contributorId":5237,"corporation":false,"usgs":true,"family":"Brooks","given":"Benjamin","email":"bbrooks@usgs.gov","middleInitial":"A.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":715191,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Minson, Sarah E. 0000-0001-5869-3477 sminson@usgs.gov","orcid":"https://orcid.org/0000-0001-5869-3477","contributorId":5357,"corporation":false,"usgs":true,"family":"Minson","given":"Sarah","email":"sminson@usgs.gov","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":715192,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Glennie, Craig L.","contributorId":198143,"corporation":false,"usgs":false,"family":"Glennie","given":"Craig","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":715193,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nevitt, Johanna 0000-0003-3819-1773 jnevitt@usgs.gov","orcid":"https://orcid.org/0000-0003-3819-1773","contributorId":198144,"corporation":false,"usgs":true,"family":"Nevitt","given":"Johanna","email":"jnevitt@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":715194,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dawson, Timothy E.","contributorId":24429,"corporation":false,"usgs":false,"family":"Dawson","given":"Timothy","email":"","middleInitial":"E.","affiliations":[{"id":7099,"text":"Calif. Geol. Survey","active":true,"usgs":false}],"preferred":false,"id":715195,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rubin, Ron S.","contributorId":127696,"corporation":false,"usgs":false,"family":"Rubin","given":"Ron","email":"","middleInitial":"S.","affiliations":[{"id":7099,"text":"Calif. Geol. Survey","active":true,"usgs":false}],"preferred":false,"id":715196,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ericksen, Todd 0000-0001-9340-575X tericksen@usgs.gov","orcid":"https://orcid.org/0000-0001-9340-575X","contributorId":198145,"corporation":false,"usgs":true,"family":"Ericksen","given":"Todd","email":"tericksen@usgs.gov","affiliations":[],"preferred":true,"id":715197,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lockner, David A. 0000-0001-8630-6833 dlockner@usgs.gov","orcid":"https://orcid.org/0000-0001-8630-6833","contributorId":567,"corporation":false,"usgs":true,"family":"Lockner","given":"David","email":"dlockner@usgs.gov","middleInitial":"A.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":715198,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hudnut, Kenneth W. 0000-0002-3168-4797 hudnut@usgs.gov","orcid":"https://orcid.org/0000-0002-3168-4797","contributorId":2550,"corporation":false,"usgs":true,"family":"Hudnut","given":"Kenneth","email":"hudnut@usgs.gov","middleInitial":"W.","affiliations":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":715199,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Langenheim, Victoria E. 0000-0003-2170-5213 zulanger@usgs.gov","orcid":"https://orcid.org/0000-0003-2170-5213","contributorId":151042,"corporation":false,"usgs":true,"family":"Langenheim","given":"Victoria E.","email":"zulanger@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":715200,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Lutz, Andrew","contributorId":198146,"corporation":false,"usgs":false,"family":"Lutz","given":"Andrew","email":"","affiliations":[],"preferred":false,"id":715201,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Murray, Jessica R. 0000-0002-6144-1681 jrmurray@usgs.gov","orcid":"https://orcid.org/0000-0002-6144-1681","contributorId":2759,"corporation":false,"usgs":true,"family":"Murray","given":"Jessica","email":"jrmurray@usgs.gov","middleInitial":"R.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":715203,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Schwartz, David P. 0000-0001-5193-9200 dschwartz@usgs.gov","orcid":"https://orcid.org/0000-0001-5193-9200","contributorId":1940,"corporation":false,"usgs":true,"family":"Schwartz","given":"David","email":"dschwartz@usgs.gov","middleInitial":"P.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":715204,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Zaccone, Dana","contributorId":198147,"corporation":false,"usgs":false,"family":"Zaccone","given":"Dana","email":"","affiliations":[],"preferred":false,"id":715205,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}}
,{"id":70192308,"text":"70192308 - 2017 - Shear-wave velocity model from Rayleigh wave group velocities centered on the Sacramento/San Joaquin Delta","interactions":[],"lastModifiedDate":"2017-10-25T11:34:59","indexId":"70192308","displayToPublicDate":"2017-10-25T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3208,"text":"Pure and Applied Geophysics","active":true,"publicationSubtype":{"id":10}},"title":"Shear-wave velocity model from Rayleigh wave group velocities centered on the Sacramento/San Joaquin Delta","docAbstract":"Rayleigh wave group velocities obtained from ambient noise tomography are inverted for an upper crustal model of the Central Valley, California, centered on the Sacramento/San Joaquin Delta. Two methods were tried; the first uses SURF96, a least-squares routine. It provides a good fit to the data, but convergence is dependent on the starting model. The second uses a genetic algorithm, whose starting model is random. This method was tried at several nodes in the model and compared to the output from SURF96. The genetic code is run five times and the variance of the output of all five models can be used to obtain an estimate of error. SURF96 produces a more regular solution mostly because it is typically run with a smoothing constraint. Models from the genetic code are generally consistent with the SURF96 code sometimes producing lower velocities at depth. The full model, calculated using SURF96, employed a 2-pass strategy, which used a variable damping scheme in the first pass. The resulting model shows low velocities near the surface in the Central Valley with a broad asymmetrical sedimentary basin located close to the western edge of the Central Valley near 122°W longitude. At shallow depths the Rio Vista Basin is found nestled between the Pittsburgh/Kirby Hills and Midland faults, but a significant basin also seems to exist to the west of the Kirby Hills fault. There are other possible correlations between fast and slow velocities in the Central Valley and geologic features such as the Stockton Arch, oil or gas producing regions and the fault-controlled western boundary of the Central Valley.","language":"English","publisher":"Springer","doi":"10.1007/s00024-017-1587-x","usgsCitation":"Fletcher, J.P., and Erdem, J., 2017, Shear-wave velocity model from Rayleigh wave group velocities centered on the Sacramento/San Joaquin Delta: Pure and Applied Geophysics, v. 174, no. 10, p. 3825-3839, https://doi.org/10.1007/s00024-017-1587-x.","productDescription":"15 p.","startPage":"3825","endPage":"3839","ipdsId":"IP-081360","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":461377,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00024-017-1587-x","text":"Publisher Index Page"},{"id":347340,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento/San Joaquin Delta","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -123.48358154296874,\n              37.23470197166817\n            ],\n            [\n              -121.45111083984375,\n              37.23470197166817\n            ],\n            [\n              -121.45111083984375,\n              38.57393751557591\n            ],\n            [\n              -123.48358154296874,\n              38.57393751557591\n            ],\n            [\n              -123.48358154296874,\n              37.23470197166817\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"174","issue":"10","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-21","publicationStatus":"PW","scienceBaseUri":"59f1a2a0e4b0220bbd9d9f0d","contributors":{"authors":[{"text":"Fletcher, Jon Peter B. 0000-0001-8885-6177 jfletcher@usgs.gov","orcid":"https://orcid.org/0000-0001-8885-6177","contributorId":1216,"corporation":false,"usgs":true,"family":"Fletcher","given":"Jon","email":"jfletcher@usgs.gov","middleInitial":"Peter B.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":715226,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Erdem, Jemile 0000-0003-2353-9431 jerdem@usgs.gov","orcid":"https://orcid.org/0000-0003-2353-9431","contributorId":127700,"corporation":false,"usgs":true,"family":"Erdem","given":"Jemile","email":"jerdem@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":715227,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70191853,"text":"70191853 - 2017 - Selective transport of palynomorphs in marine turbiditic deposits: An example from the Ascension-Monterey Canyon system offshore central California","interactions":[],"lastModifiedDate":"2018-04-27T16:52:31","indexId":"70191853","displayToPublicDate":"2017-10-25T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3217,"text":"Quaternary International","active":true,"publicationSubtype":{"id":10}},"title":"Selective transport of palynomorphs in marine turbiditic deposits: An example from the Ascension-Monterey Canyon system offshore central California","docAbstract":"The pollen assemblage of a deep-sea core (15G) collected at lower bathyal depths (3491 m) on a levee of Monterey Canyon off central California was investigated to gain insights into the delivery processes of terrigenous material to submarine fans and the effect this transport has on the palynological record. Thirty-two samples were obtained down the length of the core, 19 from hemipelagic and mixed mud deposits considered to be the background record, and 13 others from displaced flow deposits. The pollen record obtained from the background samples documents variations in the terrestrial flora as it adapted to changing climatic conditions over the last 19,000 cal yrs BP. A Q-mode cluster analysis defined three pollen zones: a Glacial Pollen Zone (ca. 20,000–17,000 cal yr BP), an overlying Transitional Pollen Zone (ca. 17,000–11,500 cal yr BP), and an Interglacial Pollen Zone (ca. 11,500 cal yr BP to present). Another Q-mode cluster analysis, of both the background mud and flow deposits, also defined these three pollen zones, but four of the 13 turbiditic deposits were assigned to pollen zones older than expected by their stratigraphic position. This was due to these samples containing statistically significant fewer palynomorphs than the background muds as well as being enriched (∼10–35% in some cases) in hydraulically-efficient Pinus pollen. A selective bias in the pollen assemblage, such as demonstrated here, may result in incorrect interpretations (e.g., climatic shifts or environmental perturbations) based on the floral record, indicating turbiditic deposits should be avoided in marine palynological studies. Particularly in the case of fine-grained flow deposits that may not be visually distinct, granulometry and grain size frequency distribution curves may not be enough to identify these biased deposits. Determining the relative abundance and source of displaced shallow-water benthic foraminifera entrained in these sediments serves as an excellent additional tool to do so.","language":"English","publisher":"Elsevier","doi":"10.1016/j.quaint.2016.11.003","usgsCitation":"McGann, M., 2017, Selective transport of palynomorphs in marine turbiditic deposits: An example from the Ascension-Monterey Canyon system offshore central California: Quaternary International, v. 469, no. B, p. 120-140, https://doi.org/10.1016/j.quaint.2016.11.003.","productDescription":"21 p.","startPage":"120","endPage":"140","ipdsId":"IP-074347","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":469403,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.quaint.2016.11.003","text":"Publisher Index Page"},{"id":438178,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F74F1NW7","text":"USGS data release","linkHelpText":"Grain-size data from core S3-15G, Monterey Fan, Central California"},{"id":347355,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Monterey Canyon","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -123.6676025390625,\n              36.029110596631874\n            ],\n            [\n              -121.6241455078125,\n              36.029110596631874\n            ],\n            [\n              -121.6241455078125,\n              37.483576550426996\n            ],\n            [\n              -123.6676025390625,\n              37.483576550426996\n            ],\n            [\n              -123.6676025390625,\n              36.029110596631874\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"469","issue":"B","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59f1a2a2e4b0220bbd9d9f25","contributors":{"authors":[{"text":"McGann, Mary 0000-0002-3057-2945 mmcgann@usgs.gov","orcid":"https://orcid.org/0000-0002-3057-2945","contributorId":169540,"corporation":false,"usgs":true,"family":"McGann","given":"Mary","email":"mmcgann@usgs.gov","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":713403,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70191727,"text":"70191727 - 2017 - Fractional crystallization-induced variations in sulfides from the Noril’sk-Talnakh mining district (polar Siberia, Russia)","interactions":[],"lastModifiedDate":"2019-12-21T08:38:31","indexId":"70191727","displayToPublicDate":"2017-10-25T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2954,"text":"Ore Geology Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Fractional crystallization-induced variations in sulfides from the Noril’sk-Talnakh mining district (polar Siberia, Russia)","docAbstract":"The distribution of platinum-group elements (PGE) within zoned magmatic ore bodies has been extensively studied and appears to be controlled by the partitioning behavior of the PGE during fractional crystallization of magmatic sulfide liquids. However, other chalcophile elements, especially TABS (Te, As, Bi, Sb, and Sn) have been neglected despite their critical role in forming platinum-group minerals (PGM). TABS are volatile trace elements that are considered to be mobile so investigating their primary distribution may be challenging in magmatic ore bodies that have been somewhat altered. Magmatic sulfide ore bodies from the Noril’sk-Talnakh mining district (polar Siberia, Russia) offer an exceptional opportunity to investigate the behavior of TABS during fractional crystallization of sulfide liquids and PGM formation as the primary features of the ore bodies have been relatively well preserved. In this study, new petrographic (2D and 3D) and whole-rock geochemical data from Cu-poor to Cu-rich sulfide ores of the Noril’sk-Talnakh mining district are integrated with published data to consider the role of fractional crystallization in generating mineralogical and geochemical variations across the different ore types (disseminated to massive). Despite textural variations in Cu-rich massive sulfides (lenses, veins, and breccias), these sulfides have similar chemical compositions, which suggests that Cu-rich veins and breccias formed from fractionated sulfide liquids that were injected into the surrounding rocks. Numerical modeling using the median disseminated sulfide composition as the initial sulfide liquid composition and recent DMSS/liq and DISS/liq predicts the compositional variations observed in the massive sulfides, especially in terms of Pt, Pd, and TABS. Therefore, distribution of these elements in the massive sulfides was likely controlled by their partitioning behavior during sulfide liquid fractional crystallization, prior to PGM formation. Our observations indicate that in the Cu-poor massive sulfides the PGM formed as the result of exsolution from sulfide minerals whereas in the Cu-rich massive sulfides the PGM formed by crystallization from late-stage fractionated sulfide liquids. We suggest that the significant amount of Sn-bearing PGM may be related to crustal contamination from granodiorite, whereas As, Bi, Te, and Sb were likely added to the magma along with S from sedimentary rocks. Large PGM that are scarce and randomly distributed may account for most of the whole-rock Pt budget. Based on our results, we propose a holistic genetic model for the formation of the magmatic sulfide ore bodies of the Noril’sk-Talnakh mining district.","language":"English","publisher":"Elsevier","doi":"10.1016/j.oregeorev.2017.05.016","usgsCitation":"Duran, C., Barnes, S., Plese, P., Prasek, M.K., Zientek, M.L., and Page, P., 2017, Fractional crystallization-induced variations in sulfides from the Noril’sk-Talnakh mining district (polar Siberia, Russia): Ore Geology Reviews, v. 90, p. 326-351, https://doi.org/10.1016/j.oregeorev.2017.05.016.","productDescription":"26 p.","startPage":"326","endPage":"351","ipdsId":"IP-084455","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":469395,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.oregeorev.2017.05.016","text":"Publisher Index Page"},{"id":347374,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Russia","state":"Siberia","otherGeospatial":"Noril’sk-Talnakh mining district","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              99.49218749999999,\n              60.58696734225869\n            ],\n            [\n              131.484375,\n              60.58696734225869\n            ],\n            [\n              131.484375,\n              71.96538769913127\n            ],\n            [\n              99.49218749999999,\n              71.96538769913127\n            ],\n            [\n              99.49218749999999,\n              60.58696734225869\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"90","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59f1a2a3e4b0220bbd9d9f2b","contributors":{"authors":[{"text":"Duran, C.J.","contributorId":197322,"corporation":false,"usgs":false,"family":"Duran","given":"C.J.","email":"","affiliations":[],"preferred":false,"id":713193,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barnes, S-J.","contributorId":197321,"corporation":false,"usgs":false,"family":"Barnes","given":"S-J.","affiliations":[],"preferred":false,"id":713192,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Plese, P.","contributorId":197323,"corporation":false,"usgs":false,"family":"Plese","given":"P.","email":"","affiliations":[],"preferred":false,"id":713194,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Prasek, M. Kudrna","contributorId":197324,"corporation":false,"usgs":false,"family":"Prasek","given":"M.","email":"","middleInitial":"Kudrna","affiliations":[],"preferred":false,"id":713195,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zientek, Michael L. 0000-0002-8522-9626 mzientek@usgs.gov","orcid":"https://orcid.org/0000-0002-8522-9626","contributorId":2420,"corporation":false,"usgs":true,"family":"Zientek","given":"Michael","email":"mzientek@usgs.gov","middleInitial":"L.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":713191,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Page, P.","contributorId":197325,"corporation":false,"usgs":false,"family":"Page","given":"P.","email":"","affiliations":[],"preferred":false,"id":713196,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70192415,"text":"70192415 - 2017 - Potential paths for male-mediated gene flow to and from an isolated grizzly bear population","interactions":[],"lastModifiedDate":"2017-10-25T13:52:55","indexId":"70192415","displayToPublicDate":"2017-10-25T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Potential paths for male-mediated gene flow to and from an isolated grizzly bear population","docAbstract":"<p><span>For several decades, grizzly bear populations in the Greater Yellowstone Ecosystem (GYE) and the Northern Continental Divide Ecosystem (NCDE) have increased in numbers and range extent. The GYE population remains isolated and although effective population size has increased since the early 1980s, genetic connectivity between these populations remains a long-term management goal. With only ~110&nbsp;km distance separating current estimates of occupied range for these populations, the potential for gene flow is likely greater now than it has been for many decades. We sought to delineate potential paths that would provide the opportunity for male-mediated gene flow between the two populations. We first developed step-selection functions to generate conductance layers using ecological, physical, and anthropogenic landscape features associated with non-stationary GPS locations of 124 male grizzly bears (199 bear-years). We then used a randomized shortest path (RSP) algorithm to estimate the average number of net passages for all grid cells in the study region, when moving from an origin to a destination node. Given habitat characteristics that were the basis for the conductance layer, movements follow certain grid cell sequences more than others and the resulting RSP values thus provide a measure of movement potential. Repeating this process for 100 pairs of random origin and destination nodes, we identified paths for three levels of random deviation (θ) from the least-cost path. We observed broad-scale concordance between model predictions for paths originating in the NCDE and those originating in the GYE for all three levels of movement exploration. Model predictions indicated that male grizzly bear movement between the ecosystems could involve a variety of routes, and verified observations of grizzly bears outside occupied range supported this finding. Where landscape features concentrated paths into corridors (e.g., because of anthropogenic influence), they typically followed neighboring mountain ranges, of which several could serve as pivotal stepping stones. The RSP layers provide detailed, spatially explicit information for land managers and organizations working with land owners to identify and prioritize conservation measures that maintain or enhance the integrity of potential areas conducive to male grizzly bear dispersal.</span></p>","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.1969","usgsCitation":"Peck, C.P., van Manen, F.T., Costello, C.M., Haroldson, M.A., Landenburger, L., Roberts, L.L., Bjornlie, D.D., and Mace, R.D., 2017, Potential paths for male-mediated gene flow to and from an isolated grizzly bear population: Ecosphere, v. 8, no. 10, p. 1-19, https://doi.org/10.1002/ecs2.1969.","productDescription":"e01969; 19 p.","startPage":"1","endPage":"19","ipdsId":"IP-086828","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":469392,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1969","text":"Publisher Index Page"},{"id":438176,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F72V2F2W","text":"USGS data release","linkHelpText":"Potential movement paths for male grizzly bear (Ursus arctos) dispersal between the Northern Continental Divide and Greater Yellowstone Ecosystems, 2000-2015"},{"id":347373,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Greater Yellowstone Ecosystem, Northern Conti-nental Divide Ecosystem","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -114.7412109375,\n              44.29240108529005\n            ],\n            [\n              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fvanmanen@usgs.gov","orcid":"https://orcid.org/0000-0001-5340-8489","contributorId":2267,"corporation":false,"usgs":true,"family":"van Manen","given":"Frank","email":"fvanmanen@usgs.gov","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":715749,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Costello, Cecily M.","contributorId":198346,"corporation":false,"usgs":false,"family":"Costello","given":"Cecily","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":715751,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Haroldson, Mark A. 0000-0002-7457-7676 mharoldson@usgs.gov","orcid":"https://orcid.org/0000-0002-7457-7676","contributorId":1773,"corporation":false,"usgs":true,"family":"Haroldson","given":"Mark","email":"mharoldson@usgs.gov","middleInitial":"A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":715752,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Landenburger, Lisa 0000-0002-4325-3652 lisa_landenburger@usgs.gov","orcid":"https://orcid.org/0000-0002-4325-3652","contributorId":4106,"corporation":false,"usgs":true,"family":"Landenburger","given":"Lisa","email":"lisa_landenburger@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":715756,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Roberts, Lori L.","contributorId":198347,"corporation":false,"usgs":false,"family":"Roberts","given":"Lori","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":715753,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bjornlie, Daniel D.","contributorId":198348,"corporation":false,"usgs":false,"family":"Bjornlie","given":"Daniel","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":715754,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mace, Richard D.","contributorId":150235,"corporation":false,"usgs":false,"family":"Mace","given":"Richard","email":"","middleInitial":"D.","affiliations":[{"id":5099,"text":"Montana Department of Fish, Wildlife, and Parks","active":true,"usgs":false}],"preferred":false,"id":715755,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70192058,"text":"70192058 - 2017 - Characterizing sources of uncertainty from global climate models and downscaling techniques","interactions":[],"lastModifiedDate":"2018-01-05T14:23:27","indexId":"70192058","displayToPublicDate":"2017-10-25T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5202,"text":"Journal of Applied Meteorology and Climatology","onlineIssn":"1558-8432","printIssn":"1558-8424","active":true,"publicationSubtype":{"id":10}},"title":"Characterizing sources of uncertainty from global climate models and downscaling techniques","docAbstract":"<p><span>In recent years climate model experiments have been increasingly oriented towards providing information that can support local and regional adaptation to the expected impacts of anthropogenic climate change. This shift has magnified the importance of downscaling as a means to translate coarse-scale global climate model (GCM) output to a finer scale that more closely matches the scale of interest. Applying this technique, however, introduces a new source of uncertainty into any resulting climate model ensemble. Here we present a method, based on a previously established variance decomposition method, to partition and quantify the uncertainty in climate model ensembles that is attributable to downscaling. We apply the method to the Southeast U.S. using five downscaled datasets that represent both statistical and dynamical downscaling techniques. The combined ensemble is highly fragmented, in that only a small portion of the complete set of downscaled GCMs and emission scenarios are typically available. The results indicate that the uncertainty attributable to downscaling approaches ~20% for large areas of the Southeast U.S. for precipitation and ~30% for extreme heat days (&gt; 35°C) in the Appalachian Mountains. However, attributable quantities are significantly lower for time periods when the full ensemble is considered but only a sub-sample of all models are available, suggesting that overconfidence could be a serious problem in studies that employ a single set of downscaled GCMs. We conclude with recommendations to advance the design of climate model experiments so that the uncertainty that accrues when downscaling is employed is more fully and systematically considered.</span></p>","language":"English","publisher":"American Meteorological Society","doi":"10.1175/JAMC-D-17-0087.1","usgsCitation":"Wootten, A., Terando, A., Reich, B.J., Boyles, R.P., and Semazzi, F., 2017, Characterizing sources of uncertainty from global climate models and downscaling techniques: Journal of Applied Meteorology and Climatology, v. 56, p. 3245-3262, https://doi.org/10.1175/JAMC-D-17-0087.1.","productDescription":"18 p.","startPage":"3245","endPage":"3262","ipdsId":"IP-088255","costCenters":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"links":[{"id":469388,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1175/jamc-d-17-0087.1","text":"Publisher Index Page"},{"id":347350,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"56","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59f1a2a1e4b0220bbd9d9f1c","contributors":{"authors":[{"text":"Wootten, Adrienne","contributorId":197529,"corporation":false,"usgs":false,"family":"Wootten","given":"Adrienne","affiliations":[],"preferred":false,"id":714033,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Terando, Adam 0000-0002-9280-043X aterando@usgs.gov","orcid":"https://orcid.org/0000-0002-9280-043X","contributorId":197511,"corporation":false,"usgs":true,"family":"Terando","given":"Adam","email":"aterando@usgs.gov","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":714032,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reich, Brian J.","contributorId":150871,"corporation":false,"usgs":false,"family":"Reich","given":"Brian","email":"","middleInitial":"J.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":714034,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boyles, Ryan P. 0000-0001-9272-867X rboyles@usgs.gov","orcid":"https://orcid.org/0000-0001-9272-867X","contributorId":197670,"corporation":false,"usgs":true,"family":"Boyles","given":"Ryan","email":"rboyles@usgs.gov","middleInitial":"P.","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":714035,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Semazzi, Fred","contributorId":197671,"corporation":false,"usgs":false,"family":"Semazzi","given":"Fred","affiliations":[],"preferred":false,"id":714036,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70194258,"text":"70194258 - 2017 - Quantile regression applications in ecology and the environmental sciences","interactions":[],"lastModifiedDate":"2018-02-12T15:40:53","indexId":"70194258","displayToPublicDate":"2017-10-25T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"seriesTitle":{"id":5562,"text":"Handbooks of Modern Statistical Methods","active":true,"publicationSubtype":{"id":24}},"title":"Quantile regression applications in ecology and the environmental sciences","docAbstract":"<p>No abstract available.<br></p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Handbook of Quantile Regression","largerWorkSubtype":{"id":13,"text":"Handbook"},"language":"English","publisher":"CRC Press/Taylor and Francis Group","isbn":"9781498725286","usgsCitation":"Cade, B.S., 2017, Quantile regression applications in ecology and the environmental sciences, chap. <i>of</i> Handbook of Quantile Regression: Handbooks of Modern Statistical Methods.","ipdsId":"IP-079309","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true}],"links":[{"id":349305,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":349304,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.crcpress.com/Handbook-of-Quantile-Regression/Koenker-Chernozhukov-He-Peng/p/book/9781498725286"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fb2ee4b06e28e9c22d92","contributors":{"editors":[{"text":"Koenker, Roger","contributorId":85106,"corporation":false,"usgs":false,"family":"Koenker","given":"Roger","email":"","affiliations":[],"preferred":false,"id":723387,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Chernozhukov, Victor","contributorId":46045,"corporation":false,"usgs":false,"family":"Chernozhukov","given":"Victor","email":"","affiliations":[],"preferred":false,"id":723388,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"He, Xuming","contributorId":106042,"corporation":false,"usgs":false,"family":"He","given":"Xuming","email":"","affiliations":[],"preferred":false,"id":723389,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Peng, Limin","contributorId":65328,"corporation":false,"usgs":false,"family":"Peng","given":"Limin","email":"","affiliations":[],"preferred":false,"id":723390,"contributorType":{"id":2,"text":"Editors"},"rank":4}],"authors":[{"text":"Cade, Brian S. 0000-0001-9623-9849 cadeb@usgs.gov","orcid":"https://orcid.org/0000-0001-9623-9849","contributorId":1278,"corporation":false,"usgs":true,"family":"Cade","given":"Brian","email":"cadeb@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":722903,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70191785,"text":"70191785 - 2017 - Monitoring eradication of European mouflon sheep from the Kahuku Unit of Hawai‘i Volcanoes National Park","interactions":[],"lastModifiedDate":"2018-01-04T08:31:47","indexId":"70191785","displayToPublicDate":"2017-10-25T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2990,"text":"Pacific Science","active":true,"publicationSubtype":{"id":10}},"title":"Monitoring eradication of European mouflon sheep from the Kahuku Unit of Hawai‘i Volcanoes National Park","docAbstract":"<p><span>European mouflon (</span><i>Ovis gmelini musimon</i><span>), the world's smallest wild sheep, have proliferated and degraded fragile native ecosystems in the Hawaiian Islands through browsing, bark stripping, and trampling, including native forests within Hawai‘i Volcanoes National Park (HAVO). HAVO resource managers initiated ungulate control efforts in the 469 km</span><sup>2</sup><span><span>&nbsp;</span>Kahuku Unit after it was acquired in 2003. We tracked control effort and used aerial surveys in a 64.7 km</span><sup>2</sup><span><span>&nbsp;</span>area from 2004 to 2017 and more intensive ground surveys and camera-trap monitoring to detect the last remaining animals within a 25.9 km</span><sup>2</sup><span><span>&nbsp;</span>subunit after it was enclosed by fence in 2012. Aerial shooting yielded the most removals per unit effort (3.2 animals/ hour), resulting in 261 animals. However, ground-based methods yielded 4,607 removals overall, 3,038 of which resulted from assistance of volunteers. Ground shooting with dogs, intensive aerial shooting, ground sweeps, and forward-looking infrared (FLIR)-assisted shooting were necessary to find and remove the last remaining mouflon. The Judas technique, baiting, and trapping were not successful in attracting or detecting small numbers of remaining individuals. Effort expended to remove each mouflon increased nearly 15-fold during the last 3 yr of eradication effort from 2013 to 2016. Complementary active and passive monitoring techniques allowed us to track the effectiveness of control effort and reveal locations of small groups to staff. The effort and variety of methods required to eradicate mouflon from an enclosed unit of moderate size illustrates the difficulty of scaling up to entire populations of wild ungulates from unenclosed areas.</span></p>","language":"English","publisher":"University of Hawai'i Press","doi":"10.2984/71.4.3","usgsCitation":"Judge, S., Hess, S.C., Faford, J., Pacheco, D., and Leopold, C., 2017, Monitoring eradication of European mouflon sheep from the Kahuku Unit of Hawai‘i Volcanoes National Park: Pacific Science, v. 71, no. 4, p. 425-436, https://doi.org/10.2984/71.4.3.","productDescription":"12 p.","startPage":"425","endPage":"436","ipdsId":"IP-080122","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":469393,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2984/71.4.3","text":"Publisher Index Page"},{"id":347364,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawai'i","otherGeospatial":"Hawai‘i Volcanoes National Park","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -155.7524871826172,\n              19.062117883514652\n            ],\n            [\n              -155.65635681152344,\n              19.062117883514652\n            ],\n            [\n              -155.65635681152344,\n              19.17954399635705\n            ],\n            [\n              -155.7524871826172,\n              19.17954399635705\n            ],\n            [\n              -155.7524871826172,\n              19.062117883514652\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"71","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59f1a2a3e4b0220bbd9d9f29","contributors":{"authors":[{"text":"Judge, Seth 0000-0003-3832-3246","orcid":"https://orcid.org/0000-0003-3832-3246","contributorId":189965,"corporation":false,"usgs":false,"family":"Judge","given":"Seth","email":"","affiliations":[],"preferred":false,"id":713199,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hess, Steven C. 0000-0001-6403-9922 shess@usgs.gov","orcid":"https://orcid.org/0000-0001-6403-9922","contributorId":3156,"corporation":false,"usgs":true,"family":"Hess","given":"Steven","email":"shess@usgs.gov","middleInitial":"C.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":false,"id":713198,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Faford, Jonathan K.","contributorId":177221,"corporation":false,"usgs":false,"family":"Faford","given":"Jonathan K.","affiliations":[],"preferred":false,"id":713200,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pacheco, Dexter","contributorId":156310,"corporation":false,"usgs":false,"family":"Pacheco","given":"Dexter","email":"","affiliations":[{"id":20307,"text":"US National Park Service","active":true,"usgs":false}],"preferred":false,"id":713201,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Leopold, Christina 0000-0003-0499-3196","orcid":"https://orcid.org/0000-0003-0499-3196","contributorId":178961,"corporation":false,"usgs":false,"family":"Leopold","given":"Christina","affiliations":[],"preferred":false,"id":713202,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70191687,"text":"70191687 - 2017 - The nexus of fun and nutrition: Recreational fishing is also about food","interactions":[],"lastModifiedDate":"2018-04-24T13:38:28","indexId":"70191687","displayToPublicDate":"2017-10-25T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1652,"text":"Fish and Fisheries","active":true,"publicationSubtype":{"id":10}},"title":"The nexus of fun and nutrition: Recreational fishing is also about food","docAbstract":"<p><span>Recreational fishing is a popular activity in aquatic ecosystems around the globe using a variety of gears including rod and line and to a lesser extent handlines, spears, bow and arrow, traps and nets. Similar to the propensity to engage in voluntary catch-and-release, the propensity to harvest fishes strongly varies among cultures, locations, species and fisheries. There is a misconception that because recreational fishing happens during non-work (i.e. leisure) time, the nutritional motivation is negligible; therefore, the role of recreational fishing in supporting nutrition (and thus food security) at regional, national or global scales is underappreciated. We consider the factors that influence whether fish will be harvested or released by examining the motives that underlie recreational fishing. Next, we provide an overview of the magnitude and role of recreational fishing harvest in supporting nutrition using regional case-studies. Then, we address issues such as contaminants and parasites that constrain the ability of fish harvested by recreational fishers to be consumed. Although recreational fishing is foremost a leisure activity, the harvest of fish for personal consumption by recreational fishers has contributed and will continue to contribute to human nutrition by providing an accessible, affordable and generally highly sustainable food source, notwithstanding concerns about food safety and possibly overfishing. Attempts to better quantify the role of fish harvested by recreational fishers and the relative contribution to overall food security and personal nutrition will provide resource managers and policymakers the information needed to guide management activities and policy development.</span></p>","language":"English","publisher":"Wiley","doi":"10.1111/faf.12246","usgsCitation":"Cooke, S., Twardek, W.M., Lennox, R.J., Zolderdo, A.J., Bower, S.D., Gutowsky, L.F., Danylchuk, A.J., Arlinghaus, R., and Beard, 2017, The nexus of fun and nutrition: Recreational fishing is also about food: Fish and Fisheries, v. 19, no. 2, p. 2012-224, https://doi.org/10.1111/faf.12246.","productDescription":"24 p.","startPage":"2012","endPage":"224","ipdsId":"IP-083974","costCenters":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":347323,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"19","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-18","publicationStatus":"PW","scienceBaseUri":"59f1a2a4e4b0220bbd9d9f2f","contributors":{"authors":[{"text":"Cooke, Steven J.","contributorId":56132,"corporation":false,"usgs":false,"family":"Cooke","given":"Steven J.","affiliations":[{"id":36574,"text":"Carleton University, Ottawa, Ontario","active":true,"usgs":false}],"preferred":false,"id":715563,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Twardek, William M.","contributorId":198272,"corporation":false,"usgs":false,"family":"Twardek","given":"William","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":715564,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lennox, Robert J.","contributorId":198273,"corporation":false,"usgs":false,"family":"Lennox","given":"Robert","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":715565,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zolderdo, Aaron J.","contributorId":198274,"corporation":false,"usgs":false,"family":"Zolderdo","given":"Aaron","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":715566,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bower, Shannon D.","contributorId":166921,"corporation":false,"usgs":false,"family":"Bower","given":"Shannon","email":"","middleInitial":"D.","affiliations":[{"id":17786,"text":"Carleton University","active":true,"usgs":false}],"preferred":false,"id":715567,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gutowsky, Lee F. G.","contributorId":181859,"corporation":false,"usgs":false,"family":"Gutowsky","given":"Lee","email":"","middleInitial":"F. G.","affiliations":[],"preferred":false,"id":715568,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Danylchuk, Andy J.","contributorId":138981,"corporation":false,"usgs":false,"family":"Danylchuk","given":"Andy","email":"","middleInitial":"J.","affiliations":[{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false}],"preferred":false,"id":715569,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Arlinghaus, Robert","contributorId":32425,"corporation":false,"usgs":false,"family":"Arlinghaus","given":"Robert","email":"","affiliations":[{"id":17980,"text":"Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany","active":true,"usgs":false}],"preferred":false,"id":715570,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Beard, Jr. 0000-0003-2632-2350 dbeard@usgs.gov","orcid":"https://orcid.org/0000-0003-2632-2350","contributorId":169459,"corporation":false,"usgs":true,"family":"Beard","suffix":"Jr.","email":"dbeard@usgs.gov","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":715571,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70191833,"text":"70191833 - 2017 - Implementing Nepal's national building code—A case study in patience and persistence","interactions":[],"lastModifiedDate":"2018-01-24T15:53:40","indexId":"70191833","displayToPublicDate":"2017-10-25T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1436,"text":"Earthquake Spectra","active":true,"publicationSubtype":{"id":10}},"title":"Implementing Nepal's national building code—A case study in patience and persistence","docAbstract":"<p><span>The April 2015 Gorkha Nepal earthquake revealed the relative effectiveness of the Nepal Standard, or national building code (NBC), and irregular compliance with it in different parts of Nepal. Much of the damage to more than half a million Nepal's residential structures may be attributed to the prevalence of owner-built or owner-supervised construction and the lack of owner and builder responsiveness to seismic risk and training in the appropriate means of complying with the NBC. To explain these circumstances, we review the protracted implementation of the NBC and the role played by one organization, the National Society for Earthquake Technology-Nepal (NSET), in the NBC's implementation. We also share observations on building code compliance made by individuals in Nepal participating in workshops led by the Earthquake Engineering Research Institute's 2014 class of Housner Fellows.</span></p>","language":"English","publisher":"EERI","doi":"10.1193/121716EQS242M","usgsCitation":"Arendt, L., Hortacsu, A., Jaiswal, K.S., Bevington, J., Shrestha, S., Lanning, F., Mentor-William, G., Naeem, G., and Thibert, K., 2017, Implementing Nepal's national building code—A case study in patience and persistence: Earthquake Spectra, v. 33, no. S1, p. S167-S183, https://doi.org/10.1193/121716EQS242M.","productDescription":"17 p.","startPage":"S167","endPage":"S183","ipdsId":"IP-089851","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":347336,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"33","issue":"S1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-12-01","publicationStatus":"PW","scienceBaseUri":"59f1a2a2e4b0220bbd9d9f27","contributors":{"authors":[{"text":"Arendt, Lucy","contributorId":197352,"corporation":false,"usgs":false,"family":"Arendt","given":"Lucy","email":"","affiliations":[],"preferred":false,"id":713277,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hortacsu, Ayse","contributorId":195197,"corporation":false,"usgs":false,"family":"Hortacsu","given":"Ayse","email":"","affiliations":[],"preferred":false,"id":713278,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jaiswal, Kishor S. 0000-0002-5803-8007 kjaiswal@usgs.gov","orcid":"https://orcid.org/0000-0002-5803-8007","contributorId":149796,"corporation":false,"usgs":true,"family":"Jaiswal","given":"Kishor","email":"kjaiswal@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":713279,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bevington, John","contributorId":195194,"corporation":false,"usgs":false,"family":"Bevington","given":"John","email":"","affiliations":[],"preferred":false,"id":713280,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shrestha, Surya","contributorId":150710,"corporation":false,"usgs":false,"family":"Shrestha","given":"Surya","email":"","affiliations":[{"id":18073,"text":"National Society for Earthquake Technology","active":true,"usgs":false}],"preferred":false,"id":713281,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lanning, Forrest","contributorId":197353,"corporation":false,"usgs":false,"family":"Lanning","given":"Forrest","email":"","affiliations":[],"preferred":false,"id":713282,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mentor-William, Garmalia","contributorId":197354,"corporation":false,"usgs":false,"family":"Mentor-William","given":"Garmalia","email":"","affiliations":[],"preferred":false,"id":713283,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Naeem, Ghazala","contributorId":194817,"corporation":false,"usgs":false,"family":"Naeem","given":"Ghazala","email":"","affiliations":[],"preferred":false,"id":713284,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Thibert, Kate","contributorId":197355,"corporation":false,"usgs":false,"family":"Thibert","given":"Kate","email":"","affiliations":[],"preferred":false,"id":713285,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70190582,"text":"ofr20171117 - 2017 - U.S. Geological Survey input-data forms for the assessment of the Spraberry Formation of the Midland Basin, Permian Basin Province, Texas, 2017","interactions":[],"lastModifiedDate":"2017-10-25T10:13:23","indexId":"ofr20171117","displayToPublicDate":"2017-10-24T18:15:00","publicationYear":"2017","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":"2017-1117","title":"U.S. Geological Survey input-data forms for the assessment of the Spraberry Formation of the Midland Basin, Permian Basin Province, Texas, 2017","docAbstract":"<p>In 2017, the U.S. Geological Survey (USGS) completed an updated assessment of undiscovered, technically recoverable oil and gas resources in the Spraberry Formation of the Midland Basin (Permian Basin Province) in southwestern Texas (Marra and others, 2017). The Spraberry Formation was assessed using both the standard continuous (unconventional) and conventional methodologies established by the USGS for three assessment units (AUs): (1) Lower Spraberry Continuous Oil Trend AU, (2) Middle Spraberry Continuous Oil Trend AU, and (3) Northern Spraberry Conventional Oil AU. The revised assessment resulted in total estimated mean resources of 4,245 million barrels of oil, 3,112 billion cubic feet of gas, and 311 million barrels of natural gas liquids. The purpose of this report is to provide supplemental documentation of the input parameters used in the USGS 2017 Spraberry Formation assessment.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171117","usgsCitation":"Marra, K.R., 2017, U.S. Geological Survey input-data forms for the assessment of the Spraberry Formation of the Midland Basin, Permian Basin Province, Texas, 2017: U.S. Geological Survey Open-File Report 2017–1117, 46 p., https://doi.org/10.3133/ofr20171117.","productDescription":"iii, 46 p.","numberOfPages":"54","onlineOnly":"Y","ipdsId":"IP-089773","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":347263,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/fs20173029","text":"Fact Sheet 2017–3029:","linkHelpText":"Assessment of Undiscovered Oil and Gas Resources in the Spraberry Formation of the Midland Basin, Permian Basin Province, Texas, 2017"},{"id":347257,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1117/coverthb.jpg"},{"id":347258,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1117/ofr20171117.pdf","text":"Report","size":"372 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1117"}],"country":"United States","state":"Texas","otherGeospatial":"Spraberry Formation of the Midland Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -103.75,\n              30.3333\n            ],\n            [\n              -99.25,\n              30.3333\n            ],\n            [\n              -99.25,\n              34\n            ],\n            [\n              -103.75,\n              34\n            ],\n            [\n              -103.75,\n              30.3333\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"http://energy.usgs.gov/\" data-mce-href=\"http://energy.usgs.gov/\">Central Energy Resources Science Center</a><br>U.S. Geological Survey<br>Box 25046, MS-939<br>Denver, CO 80225-0046</p>","tableOfContents":"<ul><li>Introduction</li><li>Assessment Methodology</li><li>Summary Input-Data Forms for Assessment</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"publishedDate":"2017-10-24","noUsgsAuthors":false,"publicationDate":"2017-10-24","publicationStatus":"PW","scienceBaseUri":"59f05119e4b0220bbd9a1d45","contributors":{"authors":[{"text":"Marra, Kristen R. 0000-0001-8027-5255 kmarra@usgs.gov","orcid":"https://orcid.org/0000-0001-8027-5255","contributorId":4844,"corporation":false,"usgs":true,"family":"Marra","given":"Kristen","email":"kmarra@usgs.gov","middleInitial":"R.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":709899,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70192135,"text":"sir20175091 - 2017 - Simulation of daily streamflow for 12 river basins in western Iowa using the Precipitation-Runoff Modeling System","interactions":[],"lastModifiedDate":"2017-10-24T15:14:56","indexId":"sir20175091","displayToPublicDate":"2017-10-24T14:45:00","publicationYear":"2017","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":"2017-5091","title":"Simulation of daily streamflow for 12 river basins in western Iowa using the Precipitation-Runoff Modeling System","docAbstract":"<p>The U.S. Geological Survey, in cooperation with the Iowa Department of Natural Resources, constructed Precipitation-Runoff Modeling System models to estimate daily streamflow for 12 river basins in western Iowa that drain into the Missouri River. The Precipitation-Runoff Modeling System is a deterministic, distributed-parameter, physical-process-based modeling system developed to evaluate the response of streamflow and general drainage basin hydrology to various combinations of climate and land use. Calibration periods for each basin varied depending on the period of record available for daily mean streamflow measurements at U.S. Geological Survey streamflow-gaging stations.</p><p>A geographic information system tool was used to delineate each basin and estimate initial values for model parameters based on basin physical and geographical features. A U.S. Geological Survey automatic calibration tool that uses a shuffled complex evolution algorithm was used for initial calibration, and then manual modifications were made to parameter values to complete the calibration of each basin model. The main objective of the calibration was to match daily discharge values of simulated streamflow to measured daily discharge values. The Precipitation-Runoff Modeling System model was calibrated at 42 sites located in the 12 river basins in western Iowa.</p><p>The accuracy of the simulated daily streamflow values at the 42 calibration sites varied by river and by site. The models were satisfactory at 36 of the sites based on statistical results. Unsatisfactory performance at the six other sites can be attributed to several factors: (1) low flow, no flow, and flashy flow conditions in headwater subbasins having a small drainage area; (2) poor representation of the groundwater and storage components of flow within a basin; (3) lack of accounting for basin withdrawals and water use; and (4) limited availability and accuracy of meteorological input data. The Precipitation-Runoff Modeling System models of 12 river basins in western Iowa will provide water-resource managers with a consistent and documented method for estimating streamflow at ungaged sites and aid in environmental studies, hydraulic design, water management, and water-quality projects.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175091","collaboration":"Prepared in cooperation with the Iowa Department of Natural Resources","usgsCitation":"Christiansen, D.E., Haj, A.E., and Risely, J.C., 2017, Simulation of daily streamflow for 12 river basins in western Iowa using the Precipitation-Runoff Modeling System: U.S. Geological Survey Scientific Investigations Report 2017–5091, 27 p., https://doi.org/10.3133/sir20175091. ","productDescription":"iv, 27 p.","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-080002","costCenters":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"links":[{"id":347102,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5091/sir20175091.pdf","text":"Report","size":"12.5","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5091"},{"id":347101,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5091/coverthb.jpg"}],"country":"United 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href=\"mailto:dc_ia@usgs.gov\" data-mce-href=\"mailto:dc_ia@usgs.gov\">Director</a>, <a href=\"https://ia.water.usgs.gov/\" data-mce-href=\"https://ia.water.usgs.gov/\">Iowa Water Science Center</a><br> U.S. Geological Survey<br> P.O. Box 1230<br> Iowa City, IA 52240</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Model Development</li><li>Simulation of Daily Streamflow for 12 River Basins in Western Iowa Using the Precipitation-Runoff Modeling System</li><li>Model Limitations</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"publishedDate":"2017-10-24","noUsgsAuthors":false,"publicationDate":"2017-10-24","publicationStatus":"PW","scienceBaseUri":"59f0511be4b0220bbd9a1d48","contributors":{"authors":[{"text":"Christiansen, Daniel E. 0000-0001-6108-2247 dechrist@usgs.gov","orcid":"https://orcid.org/0000-0001-6108-2247","contributorId":366,"corporation":false,"usgs":true,"family":"Christiansen","given":"Daniel","email":"dechrist@usgs.gov","middleInitial":"E.","affiliations":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"preferred":true,"id":714361,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haj, Adel E. 0000-0002-3377-7161 ahaj@usgs.gov","orcid":"https://orcid.org/0000-0002-3377-7161","contributorId":175220,"corporation":false,"usgs":true,"family":"Haj","given":"Adel E.","email":"ahaj@usgs.gov","affiliations":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"preferred":false,"id":714363,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Risley, John C. 0000-0002-8206-5443 jrisley@usgs.gov","orcid":"https://orcid.org/0000-0002-8206-5443","contributorId":2698,"corporation":false,"usgs":true,"family":"Risley","given":"John","email":"jrisley@usgs.gov","middleInitial":"C.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":714362,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70191366,"text":"ofr20171126 - 2017 - Geologic map of the Dusar area, Herat Province, Afghanistan; Modified from the 1973 original map compilations of V.I. Tarasenko and others","interactions":[],"lastModifiedDate":"2017-11-08T12:23:25","indexId":"ofr20171126","displayToPublicDate":"2017-10-24T13:45:00","publicationYear":"2017","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":"2017-1126","title":"Geologic map of the Dusar area, Herat Province, Afghanistan; Modified from the 1973 original map compilations of V.I. Tarasenko and others","docAbstract":"<p>The geologic maps and cross sections presented in this report are redrafted and modified versions of the <i>Geologic map and map of useful minerals of the Dusar area</i> (scale 1:50,000) and <i>Geologic sketch map of the Dusar and Namak-sory ore occurrences</i> (scale 1:10,000), located in the Herat Province, Afghanistan. The original maps and cross sections are contained in unpublished Soviet report no. 0290 (Tarasenko and others, 1973) prepared in cooperation with the Ministry of Mines and Industries of the Royal Government of Afghanistan, in Kabul during 1973 under contract no. 50728. The redrafted maps and cross sections (modified from Tarasenko and others, 1973) illustrate the geological structure and mineral occurrences of the Dusar copper-gold-silver-lead-zinc prospect area of western Afghanistan, located within the Dusar-Shaida copper and tin area of interest (AOI), Herat Province, Afghanistan.</p><p>Mineralization in the Dusar area is hosted within Early Jurassic to Early Cretaceous stratified volcanic and sedimentary rocks associated with numerous diabase and gabbro-diabase intrusive bodies and is generally near a major northeast-trending system of faults and quartz veins. Host rocks consist of quartz keratophyre and quartz-feldspar porphyry, with layers of schist, phyllite, and quartz-chlorite and chlorite-sericite slate; and limestone and shale, with schist and carbonate-chlorite and chlorite slate. Known mineralization includes an extensive quartz vein system, shown on the map as the “northern occurrence,” as well as the Dusar and Namak-sory gossan zones, interpreted to have formed from remnant pyrite mineralization. The veins of the northern occurrence and their altered host rocks are known to contain anomalous to economic concentrations of precious and base metals, with concentrations locally in excess of 2 parts per million gold, 100 parts per million silver, 5 percent copper, and 1 percent lead. These veins occur in swarms, and are hosted along structures that are approximately concordant with the plane of the metamorphic fabric. The veins consist mostly of quartz, with minor carbonate and sulfide minerals, and display weak alteration halos along their margins. The gossans are locally anomalous in these metals, but their size and extent makes them attractive exploration targets for potential massive sulfide mineralization.</p><p>The Dusar gossan zone is a massive, ochreous, and siliceous limonitic rock, approximately 2,200 meters long, 30 to 250 meters wide, and 2.0 to 7.2 meters thick. Drilling below the Dusar gossan intersected a siliceous, sericitic, and limonitic rock underlain by quartz keratophyre with abundant disseminated pyrite. Mineralized sections grade 0.06 weight percent copper and up to 0.05 weight percent zinc. The Namak-sory gossan zone contains a similar deposit with anomalous concentrations of copper, zinc, and gold.</p><p>The redrafted maps and cross sections reproduce the topology of rock units, contacts, and faults of the original Soviet maps and cross sections, and include minor modifications based on examination of the originals and observations made during two brief field visits by USGS staff in August, 2010, and June, 2013.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171126","collaboration":"Prepared in cooperation with the Afghan Geological Survey under the auspices of the U.S. Department of Defense","usgsCitation":"Tucker, R.D., Stettner, W.R., Masonic, L.M., and Bogdanow, A.K., comps., 2017, Geologic map of the Dusar area, Herat Province, Afghanistan; Modified from the 1973 original map compilations of V.I. Tarasenko and others: U.S. Geological Survey Open-File Report 2017–1126, 1 sheet, scales 1:50,000 and 1:10,000, https://doi.org/10.3133/ofr20171126.","productDescription":"57.56 x 39.83 inches","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-050066","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":346482,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1126/coverthb.jpg"},{"id":346483,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1126/ofr20171126.pdf","text":"Report","size":"786 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OF 2017-1126"}],"country":"Afghanistan","otherGeospatial":"Dusar Area, Herat Province","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              61.1,\n              33.5\n            ],\n            [\n              61.5,\n              33.5\n            ],\n            [\n              61.5,\n              34.020794936018724\n            ],\n            [\n              61.1,\n              34.020794936018724\n            ],\n            [\n              61.1,\n              33.5\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"https://international.usgs.gov/index.htm\" data-mce-href=\"https://international.usgs.gov/index.htm\">Office of International Programs</a><br> U.S. Geological Survey<br> 917 National Center<br> Reston, VA 20192<br></p>","tableOfContents":"<ul><li>Introduction</li><li>Description of Map Units</li><li>Intrusive Rocks</li><li>Zone of Alteration</li><li>Explanation of Map Symbols</li></ul>","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2017-10-24","noUsgsAuthors":false,"publicationDate":"2017-10-24","publicationStatus":"PW","scienceBaseUri":"59f0511be4b0220bbd9a1d4a","contributors":{"compilers":[{"text":"Tucker, Robert D. 0000-0001-8463-4358 rtucker@usgs.gov","orcid":"https://orcid.org/0000-0001-8463-4358","contributorId":2007,"corporation":false,"usgs":true,"family":"Tucker","given":"Robert","email":"rtucker@usgs.gov","middleInitial":"D.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":715350,"contributorType":{"id":3,"text":"Compilers"},"rank":1},{"text":"Stettner, Will R. wstettne@usgs.gov","contributorId":4021,"corporation":false,"usgs":true,"family":"Stettner","given":"Will","email":"wstettne@usgs.gov","middleInitial":"R.","affiliations":[{"id":6676,"text":"USGS (retired)","active":true,"usgs":false}],"preferred":true,"id":715351,"contributorType":{"id":3,"text":"Compilers"},"rank":2},{"text":"Masonic, Linda M. lmasonic@usgs.gov","contributorId":149154,"corporation":false,"usgs":true,"family":"Masonic","given":"Linda","email":"lmasonic@usgs.gov","middleInitial":"M.","affiliations":[{"id":5072,"text":"Office of Communication and Publishing","active":true,"usgs":true}],"preferred":false,"id":715352,"contributorType":{"id":3,"text":"Compilers"},"rank":3},{"text":"Bogdanow, Anya K. abogdanow@usgs.gov","contributorId":147633,"corporation":false,"usgs":true,"family":"Bogdanow","given":"Anya K.","email":"abogdanow@usgs.gov","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":715353,"contributorType":{"id":3,"text":"Compilers"},"rank":4}]}}
,{"id":70184198,"text":"70184198 - 2017 - Some results from ModEM3DMT, the freely available OSU 3D MT inversion code","interactions":[],"lastModifiedDate":"2018-10-25T08:42:51","indexId":"70184198","displayToPublicDate":"2017-10-24T11:51:08","publicationYear":"2017","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Some results from ModEM3DMT, the freely available OSU 3D MT inversion code","docAbstract":"<p>At the 3DEM-5 workshop in 2013, we presented a paper entitled \"ModEM: developing 3D EM inversion for the masses\", outlining our then recent development of a modular system for inversion of EM geophysical data, called ModEM. As promised in that presentation, we made a version of the code that is suitable for 3D modeling and inversion of magnetotelluric data freely available for academic use shortly thereafter. There are now over 250 registered users, of ModEM3DMT from around the globe. To date at least 50 publications cite use of ModEM for 3D inversion of real MT datasets to address diverse problems in applied and basic Earth Science research at a range of scales. Here we present an overview of some of these results, focusing on studies that the authors have been involved in, and are thus most familiar to us. </p>","conferenceTitle":"6th International Symposium on Three-Dimensional Electromagnetics","conferenceDate":"March 28-30, 2017","conferenceLocation":"Berkeley, CA","language":"English","publisher":"Australian Society of Exploration Geophysicists","usgsCitation":"Egbert, G.D., Meqbel, N., and Kelbert, A., 2017, Some results from ModEM3DMT, the freely available OSU 3D MT inversion code, 6th International Symposium on Three-Dimensional Electromagnetics, Berkeley, CA, March 28-30, 2017, 4 p.","productDescription":"4 p.","ipdsId":"IP-084666","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":358781,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5c10aaeee4b034bf6a7e5e41","contributors":{"authors":[{"text":"Egbert, Gary D.","contributorId":187462,"corporation":false,"usgs":false,"family":"Egbert","given":"Gary","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":680511,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Meqbel, Naser","contributorId":187463,"corporation":false,"usgs":false,"family":"Meqbel","given":"Naser","email":"","affiliations":[],"preferred":false,"id":680512,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kelbert, Anna 0000-0003-4395-398X akelbert@usgs.gov","orcid":"https://orcid.org/0000-0003-4395-398X","contributorId":184053,"corporation":false,"usgs":true,"family":"Kelbert","given":"Anna","email":"akelbert@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":680513,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70192233,"text":"70192233 - 2017 - Remote measurement of river discharge using thermal particle image velocimetry (PIV) and various sources of bathymetric information","interactions":[],"lastModifiedDate":"2017-10-24T12:21:45","indexId":"70192233","displayToPublicDate":"2017-10-24T00:00:00","publicationYear":"2017","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":"Remote measurement of river discharge using thermal particle image velocimetry (PIV) and various sources of bathymetric information","docAbstract":"<p><span>Although river discharge is a fundamental hydrologic quantity, conventional methods of streamgaging are impractical, expensive, and potentially dangerous in remote locations. This study evaluated the potential for measuring discharge via various forms of remote sensing, primarily thermal imaging of flow velocities but also spectrally-based depth retrieval from passive optical image data. We acquired thermal image time series from bridges spanning five streams in Alaska and observed strong agreement between velocities measured&nbsp;</span><i>in situ</i><span><span>&nbsp;</span>and those inferred by Particle Image Velocimetry (PIV), which quantified advection of thermal features by the flow. The resulting surface velocities were converted to depth-averaged velocities by applying site-specific, calibrated velocity indices. Field spectra from three clear-flowing streams provided strong relationships between depth and reflectance, suggesting that, under favorable conditions, spectrally-based bathymetric mapping could complement thermal PIV in a hybrid approach to remote sensing of river discharge; this strategy would not be applicable to larger, more turbid rivers, however. A more flexible and efficient alternative might involve inferring depth from thermal data based on relationships between depth and integral length scales of turbulent fluctuations in temperature, captured as variations in image brightness. We observed moderately strong correlations for a site-aggregated data set that reduced station-to-station variability but encompassed a broad range of depths. Discharges calculated using thermal PIV-derived velocities were within 15% of<span>&nbsp;</span></span><i>in situ</i><span><span>&nbsp;</span>measurements when combined with depths measured directly in the field or estimated from field spectra and within 40% when the depth information also was derived from thermal images. The results of this initial, proof-of-concept investigation suggest that remote sensing techniques could facilitate measurement of river discharge.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2017.09.004","usgsCitation":"Legleiter, C.J., Kinzel, P.J., and Nelson, J.M., 2017, Remote measurement of river discharge using thermal particle image velocimetry (PIV) and various sources of bathymetric information: Journal of Hydrology, v. 554, p. 490-506, https://doi.org/10.1016/j.jhydrol.2017.09.004.","productDescription":"17 p.","startPage":"490","endPage":"506","ipdsId":"IP-084918","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":469406,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2017.09.004","text":"Publisher Index Page"},{"id":438181,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7ST7N0J","text":"USGS data release","linkHelpText":"Thermal image time series from rivers in Alaska, September 18-20, 2016"},{"id":438180,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7J964K7","text":"USGS data release","linkHelpText":"ADCP data from rivers in Alaska, September 18-20, 2016"},{"id":438179,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7M906TJ","text":"USGS data release","linkHelpText":"Field spectra from rivers in Alaska, September 19-21, 2016"},{"id":347224,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"554","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59f0511ee4b0220bbd9a1d64","contributors":{"authors":[{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":714904,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kinzel, Paul J. 0000-0002-6076-9730 pjkinzel@usgs.gov","orcid":"https://orcid.org/0000-0002-6076-9730","contributorId":743,"corporation":false,"usgs":true,"family":"Kinzel","given":"Paul","email":"pjkinzel@usgs.gov","middleInitial":"J.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":714905,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nelson, Jonathan M. 0000-0002-7632-8526 jmn@usgs.gov","orcid":"https://orcid.org/0000-0002-7632-8526","contributorId":2812,"corporation":false,"usgs":true,"family":"Nelson","given":"Jonathan","email":"jmn@usgs.gov","middleInitial":"M.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":714906,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
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