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 >150 rupture models of earthquakes spanning M7–M9 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 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.
","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":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":"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 M (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.
","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":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","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":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":"On 15 June 2010, a Mw5.7 earthquake occurred near Ocotillo, California, in the Yuha Desert. This event was the largest aftershock of the 4 April 2010 Mw7.2 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.
","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":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":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":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 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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":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":"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 (ĉ > 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.
","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":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":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":713403,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"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":"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 (> 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.
","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":70192391,"text":"70192391 - 2017 - Spatially explicit population estimates for black bears based on cluster sampling","interactions":[],"lastModifiedDate":"2017-10-26T09:25:19","indexId":"70192391","displayToPublicDate":"2017-10-25T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Spatially explicit population estimates for black bears based on cluster sampling","docAbstract":"We estimated abundance and density of the 5 major black bear (Ursus americanus) subpopulations (i.e., Eglin, Apalachicola, Osceola, Ocala-St. Johns, Big Cypress) in Florida, USA with spatially explicit capture-mark-recapture (SCR) by extracting DNA from hair samples collected at barbed-wire hair sampling sites. We employed a clustered sampling configuration with sampling sites arranged in 3 × 3 clusters spaced 2 km apart within each cluster and cluster centers spaced 16 km apart (center to center). We surveyed all 5 subpopulations encompassing 38,960 km2 during 2014 and 2015. Several landscape variables, most associated with forest cover, helped refine density estimates for the 5 subpopulations we sampled. Detection probabilities were affected by site-specific behavioral responses coupled with individual capture heterogeneity associated with sex. Model-averaged bear population estimates ranged from 120 (95% CI = 59–276) bears or a mean 0.025 bears/km2 (95% CI = 0.011–0.44) for the Eglin subpopulation to 1,198 bears (95% CI = 949–1,537) or 0.127 bears/km2 (95% CI = 0.101–0.163) for the Ocala-St. Johns subpopulation. The total population estimate for our 5 study areas was 3,916 bears (95% CI = 2,914–5,451). The clustered sampling method coupled with information on land cover was efficient and allowed us to estimate abundance across extensive areas that would not have been possible otherwise. Clustered sampling combined with spatially explicit capture-recapture methods has the potential to provide rigorous population estimates for a wide array of species that are extensive and heterogeneous in their distribution.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.21294","usgsCitation":"Humm, J., McCown, J.W., Scheick, B., and Clark, J.D., 2017, Spatially explicit population estimates for black bears based on cluster sampling: Journal of Wildlife Management, v. 81, no. 7, p. 1187-1201, https://doi.org/10.1002/jwmg.21294.","productDescription":"14 p.","startPage":"1187","endPage":"1201","ipdsId":"IP-085404","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":347380,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.91696166992186,\n 30.398937557618677\n ],\n [\n -86.63131713867188,\n 30.379983796443742\n ],\n [\n 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Walter","contributorId":198293,"corporation":false,"usgs":false,"family":"McCown","given":"J.","email":"","middleInitial":"Walter","affiliations":[],"preferred":false,"id":715634,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Scheick, B.K.","contributorId":25347,"corporation":false,"usgs":true,"family":"Scheick","given":"B.K.","email":"","affiliations":[],"preferred":false,"id":715635,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Clark, Joseph D. 0000-0002-8547-8112 jclark1@usgs.gov","orcid":"https://orcid.org/0000-0002-8547-8112","contributorId":2265,"corporation":false,"usgs":true,"family":"Clark","given":"Joseph","email":"jclark1@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":715632,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"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":"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 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.
","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 -109.786376953125,\n 44.29240108529005\n ],\n [\n -109.786376953125,\n 48.42920055556841\n ],\n [\n -114.7412109375,\n 48.42920055556841\n ],\n [\n -114.7412109375,\n 44.29240108529005\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","issue":"10","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-23","publicationStatus":"PW","scienceBaseUri":"59f1a297e4b0220bbd9d9eb8","contributors":{"authors":[{"text":"Peck, Christopher P.","contributorId":198345,"corporation":false,"usgs":false,"family":"Peck","given":"Christopher","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":715750,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"van Manen, Frank T. 0000-0001-5340-8489 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":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":"European mouflon (Ovis gmelini musimon), 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 km2 Kahuku Unit after it was acquired in 2003. We tracked control effort and used aerial surveys in a 64.7 km2 area from 2004 to 2017 and more intensive ground surveys and camera-trap monitoring to detect the last remaining animals within a 25.9 km2 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.
","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":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":"No abstract available.
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.
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.
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.
","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. 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Iowa Water Science Center
U.S. Geological Survey
P.O. Box 1230
Iowa City, IA 52240
The geologic maps and cross sections presented in this report are redrafted and modified versions of the Geologic map and map of useful minerals of the Dusar area (scale 1:50,000) and Geologic sketch map of the Dusar and Namak-sory ore occurrences (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.
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.
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.
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.
","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":"Office of International Programs
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917 National Center
Reston, VA 20192
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.
","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":70191811,"text":"70191811 - 2017 - Riverine discharges to Chesapeake Bay: Analysis of long-term (1927–2014) records and implications for future flows in the Chesapeake Bay basin","interactions":[],"lastModifiedDate":"2017-10-24T14:07:39","indexId":"70191811","displayToPublicDate":"2017-10-24T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Riverine discharges to Chesapeake Bay: Analysis of long-term (1927–2014) records and implications for future flows in the Chesapeake Bay basin","docAbstract":"The Chesapeake Bay (CB) basin is under a total maximum daily load (TMDL) mandate to reduce nitrogen, phosphorus, and sediment loads to the bay. Identifying shifts in the hydro-climatic regime may help explain observed trends in water quality. To identify potential shifts, hydrologic data (1927–2014) for 27 watersheds in the CB basin were analyzed to determine the relationships among long-term precipitation and stream discharge trends. The amount, frequency, and intensity of precipitation increased from 1910 to 1996 in the eastern U.S., with the observed increases greater in the northeastern U.S. than the southeastern U.S. The CB watershed spans the north-to-south gradient in precipitation increases, and hydrologic differences have been observed in watersheds north relative to watersheds south of the Pennsylvania—Maryland (PA-MD) border. Time series of monthly mean precipitation data specific to each of 27 watersheds were derived from the Precipitation-elevation Regression on Independent Slopes Model (PRISM) dataset, and monthly mean stream-discharge data were obtained from U.S. Geological Survey streamgage records. All annual precipitation trend slopes in the 18 watersheds north of the PA-MD border were greater than or equal to those of the nine south of that border. The magnitude of the trend slopes for 1927–2014 in both precipitation and discharge decreased in a north-to-south pattern. Distributions of the monthly precipitation and discharge datasets were assembled into percentiles for each year for each watershed. Multivariate correlation of precipitation and discharge within percentiles among the groups of northern and southern watersheds indicated only weak associations. Regional-scale average behaviors of trends in the distribution of precipitation and discharge annual percentiles differed between the northern and southern watersheds. In general, the linkage between precipitation and discharge was weak, with the linkage weaker in the northern watersheds compared to those in the south. On the basis of simple linear regression, 26 of the 27 watersheds are projected to have higher annual mean discharge in 2025, the target date for implementation of the TMDL for the CB basin.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2017.08.057","usgsCitation":"Rice, K.C., Moyer, D.L., and Mills, A., 2017, Riverine discharges to Chesapeake Bay: Analysis of long-term (1927–2014) records and implications for future flows in the Chesapeake Bay basin: Journal of Environmental Management, v. 204, no. 1, p. 246-254, https://doi.org/10.1016/j.jenvman.2017.08.057.","productDescription":"9 p.","startPage":"246","endPage":"254","ipdsId":"IP-078770","costCenters":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"links":[{"id":461383,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jenvman.2017.08.057","text":"Publisher Index Page"},{"id":347248,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Chesapeake Bay Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n 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0000-0001-6330-478X dlmoyer@usgs.gov","orcid":"https://orcid.org/0000-0001-6330-478X","contributorId":174389,"corporation":false,"usgs":true,"family":"Moyer","given":"Douglas","email":"dlmoyer@usgs.gov","middleInitial":"L.","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"preferred":true,"id":713215,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mills, Aaron L.","contributorId":189745,"corporation":false,"usgs":false,"family":"Mills","given":"Aaron L.","affiliations":[],"preferred":false,"id":713216,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70192253,"text":"70192253 - 2017 - A coupled metabolic-hydraulic model and calibration scheme for estimating of whole-river metabolism during dynamic flow conditions","interactions":[],"lastModifiedDate":"2017-10-26T09:38:40","indexId":"70192253","displayToPublicDate":"2017-10-24T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2622,"text":"Limnology and Oceanography: Methods","active":true,"publicationSubtype":{"id":10}},"title":"A coupled metabolic-hydraulic model and calibration scheme for estimating of whole-river metabolism during dynamic flow conditions","docAbstract":"Conventional methods for estimating whole-stream metabolic rates from measured dissolved oxygen dynamics do not account for the variation in solute transport times created by dynamic flow conditions. Changes in flow at hourly time scales are common downstream of hydroelectric dams (i.e. hydropeaking), and hydrologic limitations of conventional metabolic models have resulted in a poor understanding of the controls on biological production in these highly managed river ecosystems. To overcome these limitations, we coupled a two-station metabolic model of dissolved oxygen dynamics with a hydrologic river routing model. We designed calibration and parameter estimation tools to infer values for hydrologic and metabolic parameters based on time series of water quality data, achieving the ultimate goal of estimating whole-river gross primary production and ecosystem respiration during dynamic flow conditions. Our case study data for model design and calibration were collected in the tailwater of Glen Canyon Dam (Arizona, USA), a large hydropower facility where the mean discharge was 325 m3 s 1 and the average daily coefficient of variation of flow was 0.17 (i.e. the hydropeaking index averaged from 2006 to 2016). We demonstrate the coupled model’s conceptual consistency with conventional models during steady flow conditions, and illustrate the potential bias in metabolism estimates with conventional models during unsteady flow conditions. This effort contributes an approach to solute transport modeling and parameter estimation that allows study of whole-ecosystem metabolic regimes across a more diverse range of hydrologic conditions commonly encountered in streams and rivers.","language":"English","publisher":"Association for the Sciences of Limnology and Oceanography (ASLO)","doi":"10.1002/lom3.10204","usgsCitation":"Payn, R.A., Hall, R.O., Kennedy, T.A., Poole, G.C., and Marshall, L.A., 2017, A coupled metabolic-hydraulic model and calibration scheme for estimating of whole-river metabolism during dynamic flow conditions: Limnology and Oceanography: Methods, v. 15, no. 10, p. 847-866, https://doi.org/10.1002/lom3.10204.","productDescription":"20 p.","startPage":"847","endPage":"866","ipdsId":"IP-083968","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":469411,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/lom3.10204","text":"Publisher Index Page"},{"id":438182,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F76T0KG2","text":"USGS data release","linkHelpText":"Metabolic-hydraulic modelData"},{"id":347212,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Glen Canyon Dam","volume":"15","issue":"10","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-28","publicationStatus":"PW","scienceBaseUri":"59f0511ee4b0220bbd9a1d60","contributors":{"authors":[{"text":"Payn, Robert A.","contributorId":127363,"corporation":false,"usgs":false,"family":"Payn","given":"Robert","email":"","middleInitial":"A.","affiliations":[{"id":6765,"text":"Montana State University, Department of Land Resources and Environmental Sciences","active":true,"usgs":false}],"preferred":false,"id":715019,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hall, Robert O","contributorId":198078,"corporation":false,"usgs":false,"family":"Hall","given":"Robert","email":"","middleInitial":"O","affiliations":[],"preferred":false,"id":715020,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kennedy, Theodore A. 0000-0003-3477-3629 tkennedy@usgs.gov","orcid":"https://orcid.org/0000-0003-3477-3629","contributorId":167537,"corporation":false,"usgs":true,"family":"Kennedy","given":"Theodore","email":"tkennedy@usgs.gov","middleInitial":"A.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":715018,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Poole, Geoff C","contributorId":198079,"corporation":false,"usgs":false,"family":"Poole","given":"Geoff","email":"","middleInitial":"C","affiliations":[],"preferred":false,"id":715021,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"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":715022,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70192224,"text":"70192224 - 2017 - 3D ground‐motion simulations of Mw 7 earthquakes on the Salt Lake City segment of the Wasatch fault zone: Variability of long‐period (T≥1 s) ground motions and sensitivity to kinematic rupture parameters","interactions":[],"lastModifiedDate":"2017-10-26T09:37:24","indexId":"70192224","displayToPublicDate":"2017-10-24T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"displayTitle":"3D ground‐motion simulations of Mw 7 earthquakes on the Salt Lake City segment of the Wasatch fault zone: Variability of long‐period (T≥1 s) ground motions and sensitivity to kinematic rupture parameters","title":"3D ground‐motion simulations of Mw 7 earthquakes on the Salt Lake City segment of the Wasatch fault zone: Variability of long‐period (T≥1 s) ground motions and sensitivity to kinematic rupture parameters","docAbstract":"We examine the variability of long‐period (T≥1 s) earthquake ground motions from 3D simulations of Mw 7 earthquakes on the Salt Lake City segment of the Wasatch fault zone, Utah, from a set of 96 rupture models with varying slip distributions, rupture speeds, slip velocities, and hypocenter locations. Earthquake ruptures were prescribed on a 3D fault representation that satisfies geologic constraints and maintained distinct strands for the Warm Springs and for the East Bench and Cottonwood faults. Response spectral accelerations (SA; 1.5–10 s; 5% damping) were measured, and average distance scaling was well fit by a simple functional form that depends on the near‐source intensity level SA0(T) and a corner distance Rc:SA(R,T)=SA0(T)(1+(R/Rc))−1. Period‐dependent hanging‐wall effects manifested and increased the ground motions by factors of about 2–3, though the effects appeared partially attributable to differences in shallow site response for sites on the hanging wall and footwall of the fault. Comparisons with modern ground‐motion prediction equations (GMPEs) found that the simulated ground motions were generally consistent, except within deep sedimentary basins, where simulated ground motions were greatly underpredicted. Ground‐motion variability exhibited strong lateral variations and, at some sites, exceeded the ground‐motion variability indicated by GMPEs. The effects on the ground motions of changing the values of the five kinematic rupture parameters can largely be explained by three predominant factors: distance to high‐slip subevents, dynamic stress drop, and changes in the contributions from directivity. These results emphasize the need for further characterization of the underlying distributions and covariances of the kinematic rupture parameters used in 3D ground‐motion simulations employed in probabilistic seismic‐hazard analyses.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120160307","usgsCitation":"Moschetti, M.P., Hartzell, S.H., Ramirez-Guzman, L., Frankel, A.D., Angster, S.J., and Stephenson, W.J., 2017, 3D ground‐motion simulations of Mw 7 earthquakes on the Salt Lake City segment of the Wasatch fault zone: Variability of long‐period (T≥1 s) ground motions and sensitivity to kinematic rupture parameters: Bulletin of the Seismological Society of America, v. 107, no. 4, p. 1704-1723, https://doi.org/10.1785/0120160307.","productDescription":"20 p.","startPage":"1704","endPage":"1723","ipdsId":"IP-085767","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":347227,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Wasatch fault zone","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.25,\n 40\n ],\n [\n -111.5,\n 40\n ],\n [\n -111.5,\n 41.25\n ],\n [\n -112.25,\n 41.25\n ],\n [\n -112.25,\n 40\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"107","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-20","publicationStatus":"PW","scienceBaseUri":"59f0511fe4b0220bbd9a1d68","contributors":{"authors":[{"text":"Moschetti, Morgan P. 0000-0001-7261-0295 mmoschetti@usgs.gov","orcid":"https://orcid.org/0000-0001-7261-0295","contributorId":1662,"corporation":false,"usgs":true,"family":"Moschetti","given":"Morgan","email":"mmoschetti@usgs.gov","middleInitial":"P.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":714863,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hartzell, Stephen H. 0000-0003-0858-9043 shartzell@usgs.gov","orcid":"https://orcid.org/0000-0003-0858-9043","contributorId":2594,"corporation":false,"usgs":true,"family":"Hartzell","given":"Stephen","email":"shartzell@usgs.gov","middleInitial":"H.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":714864,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ramirez-Guzman, Leonardo","contributorId":175444,"corporation":false,"usgs":false,"family":"Ramirez-Guzman","given":"Leonardo","email":"","affiliations":[],"preferred":false,"id":714865,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Frankel, Arthur D. 0000-0001-9119-6106 afrankel@usgs.gov","orcid":"https://orcid.org/0000-0001-9119-6106","contributorId":146285,"corporation":false,"usgs":true,"family":"Frankel","given":"Arthur","email":"afrankel@usgs.gov","middleInitial":"D.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":714866,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Angster, Stephen J. 0000-0001-9250-8415 sangster@usgs.gov","orcid":"https://orcid.org/0000-0001-9250-8415","contributorId":3885,"corporation":false,"usgs":true,"family":"Angster","given":"Stephen","email":"sangster@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":true,"id":714867,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stephenson, William J. 0000-0001-8699-0786 wstephens@usgs.gov","orcid":"https://orcid.org/0000-0001-8699-0786","contributorId":695,"corporation":false,"usgs":true,"family":"Stephenson","given":"William","email":"wstephens@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":714868,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"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":"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 in situ 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 in situ 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.
","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":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":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":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},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","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":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":714906,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70192024,"text":"70192024 - 2017 - HERA: A dynamic web application for visualizing community exposure to flood hazards based on storm and sea level rise scenarios","interactions":[],"lastModifiedDate":"2017-10-24T16:24:55","indexId":"70192024","displayToPublicDate":"2017-10-24T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1315,"text":"Computers & Geosciences","printIssn":"0098-3004","active":true,"publicationSubtype":{"id":10}},"title":"HERA: A dynamic web application for visualizing community exposure to flood hazards based on storm and sea level rise scenarios","docAbstract":"The Hazard Exposure Reporting and Analytics (HERA) dynamic web application was created to provide a platform that makes research on community exposure to coastal-flooding hazards influenced by sea level rise accessible to planners, decision makers, and the public in a manner that is both easy to use and easily accessible. HERA allows users to (a) choose flood-hazard scenarios based on sea level rise and storm assumptions, (b) appreciate the modeling uncertainty behind a chosen hazard zone, (c) select one or several communities to examine exposure, (d) select the category of population or societal asset, and (e) choose how to look at results. The application is designed to highlight comparisons between (a) varying levels of sea level rise and coastal storms, (b) communities, (c) societal asset categories, and (d) spatial scales. Through a combination of spatial and graphical visualizations, HERA aims to help individuals and organizations to craft more informed mitigation and adaptation strategies for climate-driven coastal hazards. This paper summarizes the technologies used to maximize the user experience, in terms of interface design, visualization approaches, and data processing.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.cageo.2017.08.012","usgsCitation":"Jones, J.M., Henry, K., Wood, N.J., Ng, P., and Jamieson, M., 2017, HERA: A dynamic web application for visualizing community exposure to flood hazards based on storm and sea level rise scenarios: Computers & Geosciences, v. 109, p. 124-133, https://doi.org/10.1016/j.cageo.2017.08.012.","productDescription":"8 p.","startPage":"124","endPage":"133","ipdsId":"IP-085912","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":469405,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.cageo.2017.08.012","text":"Publisher Index Page"},{"id":347292,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"109","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59f0511fe4b0220bbd9a1d73","contributors":{"authors":[{"text":"Jones, Jeanne M. 0000-0001-7549-9270 jmjones@usgs.gov","orcid":"https://orcid.org/0000-0001-7549-9270","contributorId":4676,"corporation":false,"usgs":true,"family":"Jones","given":"Jeanne","email":"jmjones@usgs.gov","middleInitial":"M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":713858,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Henry, Kevin 0000-0001-9314-2531 khenry@usgs.gov","orcid":"https://orcid.org/0000-0001-9314-2531","contributorId":176934,"corporation":false,"usgs":true,"family":"Henry","given":"Kevin","email":"khenry@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":713859,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wood, Nathan J. 0000-0002-6060-9729 nwood@usgs.gov","orcid":"https://orcid.org/0000-0002-6060-9729","contributorId":3347,"corporation":false,"usgs":true,"family":"Wood","given":"Nathan","email":"nwood@usgs.gov","middleInitial":"J.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":713860,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ng, Peter 0000-0001-8509-5544 png@usgs.gov","orcid":"https://orcid.org/0000-0001-8509-5544","contributorId":3317,"corporation":false,"usgs":true,"family":"Ng","given":"Peter","email":"png@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":713861,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jamieson, Matthew 0000-0002-9371-9182","orcid":"https://orcid.org/0000-0002-9371-9182","contributorId":197590,"corporation":false,"usgs":false,"family":"Jamieson","given":"Matthew","affiliations":[],"preferred":false,"id":713862,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70192261,"text":"70192261 - 2017 - Declines revisited: Long-term recovery and spatial population dynamics oftailed frog larvae after wildfire","interactions":[],"lastModifiedDate":"2017-10-24T10:54:06","indexId":"70192261","displayToPublicDate":"2017-10-24T00:00:00","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":"Declines revisited: Long-term recovery and spatial population dynamics oftailed frog larvae after wildfire","docAbstract":"Drought has fueled an increased frequency and severity of large wildfires in many ecosystems. Despite an increase in research on wildfire effects on vertebrates, the vast majority of it has focused on short-term (< 5 years) effects and there is still little information on the time scale of population recovery for species that decline in abundance after fire. In 2003, a large wildfire in Montana (USA) burned the watersheds of four of eight streams that we sampled for larval Rocky Mountain tailed frogs (Ascaphus montanus) in 2001. Surveys during 2004–2005 revealed reduced abundance of larvae in burned streams relative to unburned streams, with greater declines associated with increased fire extent. Rocky Mountain tailed frogs have low vagility and have several unusual life-history traits that could slow population recovery, including an extended larval period (4 years), delayed sexual maturity (6–8 years), and low fecundity (< 50 eggs/year). To determine if abundance remained depressed since the 2003 wildfire, we repeated surveys during 2014–2015 and found relative abundance of larvae in burned and unburned streams had nearly converged to pre-fire conditions within two generations. The negative effects of burn extent on larval abundance weakened> 58% within 12 years after the fire. We also found moderate synchrony among populations in unburned streams and negative spatial autocorrelation among populations in burned streams. We suspect negative spatial autocorrelation among spatially-clustered burned streams reflected increased post-fire patchiness in resources and different rates of local recovery. Our results add to a growing body of work that suggests populations in intact ecosystems tend to be resilient to habitat changes caused by wildfire. Our results also provide important insights into recovery times of populations that have been negatively affected by severe wildfire.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2017.06.022","usgsCitation":"Hossack, B.R., and Honeycutt, R.K., 2017, Declines revisited: Long-term recovery and spatial population dynamics oftailed frog larvae after wildfire: Biological Conservation, v. 212, no. A, p. 274-278, https://doi.org/10.1016/j.biocon.2017.06.022.","productDescription":"5 p.","startPage":"274","endPage":"278","ipdsId":"IP-083575","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":469407,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.biocon.2017.06.022","text":"Publisher Index Page"},{"id":347204,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Flathead National Forest, Glacier National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.17953491210938,\n 48.22284281261854\n ],\n [\n -113.22235107421874,\n 48.22284281261854\n ],\n [\n -113.22235107421874,\n 48.826757381274426\n ],\n [\n -114.17953491210938,\n 48.826757381274426\n ],\n [\n -114.17953491210938,\n 48.22284281261854\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"212","issue":"A","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59f0511ee4b0220bbd9a1d5b","contributors":{"authors":[{"text":"Hossack, Blake R. 0000-0001-7456-9564 blake_hossack@usgs.gov","orcid":"https://orcid.org/0000-0001-7456-9564","contributorId":1177,"corporation":false,"usgs":true,"family":"Hossack","given":"Blake","email":"blake_hossack@usgs.gov","middleInitial":"R.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":715048,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Honeycutt, R. Ken 0000-0002-7157-7195 rhoneycutt@usgs.gov","orcid":"https://orcid.org/0000-0002-7157-7195","contributorId":156282,"corporation":false,"usgs":true,"family":"Honeycutt","given":"R.","email":"rhoneycutt@usgs.gov","middleInitial":"Ken","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":715049,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}