The home‐range concept is central in animal ecology and behavior, and numerous mechanistic models have been developed to understand home range formation and maintenance. These mechanistic models usually assume a single, contiguous home range. Here we describe and implement a simple home‐range model that can accommodate multiple home‐range centers, form complex shapes, allow discontinuities in use patterns, and infer how external and internal variables affect movement and use patterns. The model assumes individuals associate with two or more home‐range centers and move among them with some estimable probability. Movement in and around home‐range centers is governed by a two‐dimensional Ornstein‐Uhlenbeck process, while transitions between centers are modeled as a stochastic state‐switching process. We augmented this base model by introducing environmental and demographic covariates that modify transition probabilities between home‐range centers and can be estimated to provide insight into the movement process. We demonstrate the model using telemetry data from sea otters (Enhydra lutris) in California. The model was fit using a Bayesian Markov Chain Monte Carlo method, which estimated transition probabilities, as well as unique Ornstein‐Uhlenbeck diffusion and centralizing tendency parameters. Estimated parameters could then be used to simulate movement and space use that was virtually indistinguishable from real data. We used Deviance Information Criterion (DIC) scores to assess model fit and determined that both wind and reproductive status were predictive of transitions between home‐range centers. Females were less likely to move between home‐range centers on windy days, less likely to move between centers when tending pups, and much more likely to move between centers just after weaning a pup. These tendencies are predicted by theoretical movement rules but were not previously known and show that our model can extract meaningful behavioral insight from complex movement data.
Earthquake ground‐motion recordings are scarce in the central and eastern United States (CEUS) for large‐magnitude events and at close distances. We use two different simulation approaches, a deterministic physics‐based method and a site‐based stochastic method, to simulate ground motions over a wide range of magnitudes. Drawing on previous results for the modeling of recordings from the 2011 Mw 5.8 Mineral, Virginia, earthquake and using the 2001 Mw 7.6 Bhuj, India, earthquake as a tectonic analog for a large magnitude CEUS event, we are able to calibrate the two simulation methods over this magnitude range. Both models show a good fit to the Mineral and Bhuj observations from 0.1 to 10 Hz. Model parameters are then adjusted to obtain simulations for Mw 6.5, 7.0, and 7.6 events in the CEUS. Our simulations are compared with the 2014 U.S. Geological Survey weighted combination of existing ground‐motion prediction equations in the CEUS. The physics‐based simulations show comparable response spectral amplitudes and a fairly similar attenuation with distance. The site‐based stochastic simulations suggest a slightly faster attenuation of the response spectral amplitudes with distance for larger magnitude events and, as a result, slightly lower amplitudes at distances greater than 200 km. Both models are plausible alternatives and, given the few available data points in the CEUS, can be used to represent the epistemic uncertainty in modeling of postulated CEUS large‐magnitude events.
Invasive species provide a unique opportunity to evaluate factors controlling biogeographic distributions; we can consider introduction success as an experiment testing suitability of environmental conditions. Predicting potential distributions of spreading species is not easy, and forecasting potential distributions with changing climate is even more difficult. Using the globally invasive coypu (Myocastor coypus [Molina, 1782]), we evaluate and compare the utility of a simplistic ecophysiological based model and a correlative model to predict current and future distribution. The ecophysiological model was based on winter temperature relationships with nutria survival. We developed correlative statistical models using the Software for Assisted Habitat Modeling and biologically relevant climate data with a global extent. We applied the ecophysiological based model to several global circulation model (GCM) predictions for mid-century. We used global coypu introduction data to evaluate these models and to explore a hypothesized physiological limitation, finding general agreement with known coypu distribution locally and globally and support for an upper thermal tolerance threshold. Global circulation model based model results showed variability in coypu predicted distribution among GCMs, but had general agreement of increasing suitable area in the USA. Our methods highlighted the dynamic nature of the edges of the coypu distribution due to climate non-equilibrium, and uncertainty associated with forecasting future distributions. Areas deemed suitable habitat, especially those on the edge of the current known range, could be used for early detection of the spread of coypu populations for management purposes. Combining approaches can be beneficial to predicting potential distributions of invasive species now and in the future and in exploring hypotheses of factors controlling distributions.
","language":"English","publisher":"Pensoft","doi":"10.3897/neobiota.32.8884","usgsCitation":"Jarnevich, C.S., Young, N.E., Sheffels, T.R., Carter, J., Systma, M.D., and Talbert, C., 2017, Evaluating simplistic methods to understand current distributions and forecast distribution changes under climate change scenarios: An example with coypu (Myocastor coypus): NeoBiota, v. 32, p. 107-125, https://doi.org/10.3897/neobiota.32.8884.","productDescription":"19 p.","startPage":"107","endPage":"125","ipdsId":"IP-065118","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":470099,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3897/neobiota.32.8884","text":"Publisher Index Page"},{"id":337613,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"32","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-04","publicationStatus":"PW","scienceBaseUri":"58ca52cce4b0849ce97c869a","contributors":{"authors":[{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 jarnevichc@usgs.gov","orcid":"https://orcid.org/0000-0002-9699-2336","contributorId":3424,"corporation":false,"usgs":true,"family":"Jarnevich","given":"Catherine","email":"jarnevichc@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":683741,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Young, Nicholas E.","contributorId":189060,"corporation":false,"usgs":false,"family":"Young","given":"Nicholas","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":683742,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sheffels, Trevor R.","contributorId":140176,"corporation":false,"usgs":false,"family":"Sheffels","given":"Trevor","email":"","middleInitial":"R.","affiliations":[{"id":6929,"text":"Portland State University","active":true,"usgs":false}],"preferred":false,"id":683743,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Carter, Jacoby 0000-0003-0110-0284 carterj@usgs.gov","orcid":"https://orcid.org/0000-0003-0110-0284","contributorId":2399,"corporation":false,"usgs":true,"family":"Carter","given":"Jacoby","email":"carterj@usgs.gov","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":683744,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Systma, Mark D.","contributorId":140177,"corporation":false,"usgs":false,"family":"Systma","given":"Mark","email":"","middleInitial":"D.","affiliations":[{"id":13401,"text":"Portland State University, Portland Oregon","active":true,"usgs":false}],"preferred":false,"id":683745,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Talbert, Colin 0000-0002-9505-1876 talbertc@usgs.gov","orcid":"https://orcid.org/0000-0002-9505-1876","contributorId":181913,"corporation":false,"usgs":true,"family":"Talbert","given":"Colin","email":"talbertc@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":683746,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70186420,"text":"70186420 - 2017 - Expanding the role of reactive transport models in critical zone processes","interactions":[],"lastModifiedDate":"2017-04-05T10:00:40","indexId":"70186420","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1431,"text":"Earth-Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Expanding the role of reactive transport models in critical zone processes","docAbstract":"Models test our understanding of processes and can reach beyond the spatial and temporal scales of measurements. Multi-component Reactive Transport Models (RTMs), initially developed more than three decades ago, have been used extensively to explore the interactions of geothermal, hydrologic, geochemical, and geobiological processes in subsurface systems. Driven by extensive data sets now available from intensive measurement efforts, there is a pressing need to couple RTMs with other community models to explore non-linear interactions among the atmosphere, hydrosphere, biosphere, and geosphere. Here we briefly review the history of RTM development, summarize the current state of RTM approaches, and identify new research directions, opportunities, and infrastructure needs to broaden the use of RTMs. In particular, we envision the expanded use of RTMs in advancing process understanding in the Critical Zone, the veneer of the Earth that extends from the top of vegetation to the bottom of groundwater. We argue that, although parsimonious models are essential at larger scales, process-based models offer tools to explore the highly nonlinear coupling that characterizes natural systems. We present seven testable hypotheses that emphasize the unique capabilities of process-based RTMs for (1) elucidating chemical weathering and its physical and biogeochemical drivers; (2) understanding the interactions among roots, micro-organisms, carbon, water, and minerals in the rhizosphere; (3) assessing the effects of heterogeneity across spatial and temporal scales; and (4) integrating the vast quantity of novel data, including “omics” data (genomics, transcriptomics, proteomics, metabolomics), elemental concentration and speciation data, and isotope data into our understanding of complex earth surface systems. With strong support from data-driven sciences, we are now in an exciting era where integration of RTM framework into other community models will facilitate process understanding across disciplines and across scales.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.earscirev.2016.09.001","usgsCitation":"Li, L., Maher, K., Navarre-Sitchler, A., Druhan, J., Meile, C., Lawrence, C., Moore, J., Perdrial, J., Sullivan, P., Thompson, A., Jin, L., Bolton, E.W., Brantley, S.L., Dietrich, W., Mayer, K.U., Steefel, C., Valocchi, A.J., Zachara, J.M., Kocar, B.D., McIntosh, J., Tutolo, B.M., Kumar, M., Sonnenthal, E., Bao, C., and Beisman, J., 2017, Expanding the role of reactive transport models in critical zone processes: Earth-Science Reviews, v. 165, p. 280-301, https://doi.org/10.1016/j.earscirev.2016.09.001.","productDescription":"22 p.","startPage":"280","endPage":"301","ipdsId":"IP-070272","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":461771,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://escholarship.org/uc/item/81f302jz","text":"Publisher Index Page"},{"id":339191,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"165","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58e60272e4b09da6799ac681","contributors":{"authors":[{"text":"Li, Li","contributorId":190439,"corporation":false,"usgs":false,"family":"Li","given":"Li","affiliations":[],"preferred":false,"id":688432,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Maher, Kate","contributorId":190440,"corporation":false,"usgs":false,"family":"Maher","given":"Kate","email":"","affiliations":[],"preferred":false,"id":688433,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Navarre-Sitchler, Alexis","contributorId":190441,"corporation":false,"usgs":false,"family":"Navarre-Sitchler","given":"Alexis","email":"","affiliations":[],"preferred":false,"id":688434,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Druhan, Jennifer","contributorId":190442,"corporation":false,"usgs":false,"family":"Druhan","given":"Jennifer","affiliations":[],"preferred":false,"id":688435,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Meile, Christof","contributorId":190443,"corporation":false,"usgs":false,"family":"Meile","given":"Christof","email":"","affiliations":[],"preferred":false,"id":688436,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lawrence, Corey 0000-0002-2179-2436 clawrence@usgs.gov","orcid":"https://orcid.org/0000-0002-2179-2436","contributorId":190438,"corporation":false,"usgs":true,"family":"Lawrence","given":"Corey","email":"clawrence@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":688431,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Moore, Joel","contributorId":190444,"corporation":false,"usgs":false,"family":"Moore","given":"Joel","email":"","affiliations":[],"preferred":false,"id":688437,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Perdrial, Julia","contributorId":190445,"corporation":false,"usgs":false,"family":"Perdrial","given":"Julia","affiliations":[],"preferred":false,"id":688438,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sullivan, Pamela","contributorId":190446,"corporation":false,"usgs":false,"family":"Sullivan","given":"Pamela","affiliations":[],"preferred":false,"id":688439,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Thompson, Aaron","contributorId":190447,"corporation":false,"usgs":false,"family":"Thompson","given":"Aaron","affiliations":[],"preferred":false,"id":688440,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Jin, Lixin","contributorId":190448,"corporation":false,"usgs":false,"family":"Jin","given":"Lixin","email":"","affiliations":[],"preferred":false,"id":688441,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Bolton, Edward W.","contributorId":190449,"corporation":false,"usgs":false,"family":"Bolton","given":"Edward","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":688442,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Brantley, Susan L. 0000-0003-4320-2342","orcid":"https://orcid.org/0000-0003-4320-2342","contributorId":184201,"corporation":false,"usgs":false,"family":"Brantley","given":"Susan","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":688443,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Dietrich, William E.","contributorId":115128,"corporation":false,"usgs":true,"family":"Dietrich","given":"William E.","affiliations":[],"preferred":false,"id":688444,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Mayer, K. Ulrich","contributorId":151069,"corporation":false,"usgs":false,"family":"Mayer","given":"K.","email":"","middleInitial":"Ulrich","affiliations":[{"id":18176,"text":"Department of Earth and Ocean Science, University of British Columbia, Vancouver, British Columbia, Canada","active":true,"usgs":false}],"preferred":false,"id":688445,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Steefel, Carl","contributorId":66932,"corporation":false,"usgs":false,"family":"Steefel","given":"Carl","email":"","affiliations":[{"id":6670,"text":"Lawrence Berkeley National Laboratory, Berkeley, CA","active":true,"usgs":false}],"preferred":false,"id":688446,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Valocchi, Albert J.","contributorId":25062,"corporation":false,"usgs":true,"family":"Valocchi","given":"Albert","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":688447,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Zachara, John M.","contributorId":7421,"corporation":false,"usgs":true,"family":"Zachara","given":"John","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":688448,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Kocar, Benjamin D.","contributorId":44460,"corporation":false,"usgs":true,"family":"Kocar","given":"Benjamin","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":688449,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"McIntosh, Jennifer","contributorId":100059,"corporation":false,"usgs":true,"family":"McIntosh","given":"Jennifer","affiliations":[],"preferred":false,"id":688450,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Tutolo, Benjamin M.","contributorId":190458,"corporation":false,"usgs":false,"family":"Tutolo","given":"Benjamin","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":688452,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Kumar, Mukesh","contributorId":190460,"corporation":false,"usgs":false,"family":"Kumar","given":"Mukesh","email":"","affiliations":[],"preferred":false,"id":688454,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Sonnenthal, Eric","contributorId":146807,"corporation":false,"usgs":false,"family":"Sonnenthal","given":"Eric","affiliations":[],"preferred":false,"id":688455,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Bao, Chen","contributorId":190457,"corporation":false,"usgs":false,"family":"Bao","given":"Chen","email":"","affiliations":[],"preferred":false,"id":688451,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Beisman, Joe","contributorId":190459,"corporation":false,"usgs":false,"family":"Beisman","given":"Joe","email":"","affiliations":[],"preferred":false,"id":688453,"contributorType":{"id":1,"text":"Authors"},"rank":25}]}} ,{"id":70182798,"text":"70182798 - 2017 - Dynamic strains for earthquake source characterization","interactions":[],"lastModifiedDate":"2017-03-01T14:26:59","indexId":"70182798","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Dynamic strains for earthquake source characterization","docAbstract":"Strainmeters measure elastodynamic deformation associated with earthquakes over a broad frequency band, with detection characteristics that complement traditional instrumentation, but they are commonly used to study slow transient deformation along active faults and at subduction zones, for example. Here, we analyze dynamic strains at Plate Boundary Observatory (PBO) borehole strainmeters (BSM) associated with 146 local and regional earthquakes from 2004–2014, with magnitudes from M 4.5 to 7.2. We find that peak values in seismic strain can be predicted from a general regression against distance and magnitude, with improvements in accuracy gained by accounting for biases associated with site–station effects and source–path effects, the latter exhibiting the strongest influence on the regression coefficients. To account for the influence of these biases in a general way, we include crustal‐type classifications from the CRUST1.0 global velocity model, which demonstrates that high‐frequency strain data from the PBO BSM network carry information on crustal structure and fault mechanics: earthquakes nucleating offshore on the Blanco fracture zone, for example, generate consistently lower dynamic strains than earthquakes around the Sierra Nevada microplate and in the Salton trough. Finally, we test our dynamic strain prediction equations on the 2011 M 9 Tohoku‐Oki earthquake, specifically continuous strain records derived from triangulation of 137 high‐rate Global Navigation Satellite System Earth Observation Network stations in Japan. Moment magnitudes inferred from these data and the strain model are in agreement when Global Positioning System subnetworks are unaffected by spatial aliasing.","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220160155","usgsCitation":"Barbour, A., and Crowell, B.W., 2017, Dynamic strains for earthquake source characterization: Seismological Research Letters, v. 88, no. 2A, p. 354-370, https://doi.org/10.1785/0220160155.","productDescription":"17 p. ","startPage":"354","endPage":"370","ipdsId":"IP-076502","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":336775,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":336351,"type":{"id":15,"text":"Index Page"},"url":"https://dx.doi.org/10.1785/0220160155"}],"volume":"88","issue":"2A","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-01","publicationStatus":"PW","scienceBaseUri":"58b7eba1e4b01ccd5500bad5","contributors":{"authors":[{"text":"Barbour, Andrew J. 0000-0002-6890-2452 abarbour@usgs.gov","orcid":"https://orcid.org/0000-0002-6890-2452","contributorId":140443,"corporation":false,"usgs":true,"family":"Barbour","given":"Andrew J.","email":"abarbour@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":false,"id":673788,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Crowell, Brendan W.","contributorId":184207,"corporation":false,"usgs":false,"family":"Crowell","given":"Brendan","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":673789,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70179238,"text":"sir20165179 - 2017 - Flood-inundation maps for the St. Joseph River at Elkhart, Indiana","interactions":[],"lastModifiedDate":"2017-02-02T10:11:06","indexId":"sir20165179","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-5179","title":"Flood-inundation maps for the St. Joseph River at Elkhart, Indiana","docAbstract":"Digital flood-inundation maps for a 6.6-mile reach of the St. Joseph River at Elkhart, Indiana, were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Office of Community and Rural Affairs. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at https://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage 04101000, St. Joseph River at Elkhart, Ind. Real-time stages at this streamgage may be obtained on the Internet from the USGS National Water Information System at https://waterdata.usgs.gov/nwis or the National Weather Service (NWS) Advanced Hydrologic Prediction Service at http:/water.weather.gov/ahps/, which also forecasts flood hydrographs at this site (NWS site EKMI3).
Flood profiles were computed for the stream reach by means of a one-dimensional, step-backwater hydraulic modeling software developed by the U.S. Army Corps of Engineers. The hydraulic model was calibrated using the current stage-discharge rating at the USGS streamgage 04101000, St. Joseph River at Elkhart, Ind., and the documented high-water marks from the flood of March 1982. The hydraulic model was then used to compute six water-surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum ranging from 23.0 ft (the NWS “action stage”) to 28.0 ft, which is the highest stage interval of the current USGS stage-discharge rating curve and 1 ft higher than the NWS “major flood stage.” The simulated water-surface profiles were then combined with a Geographic Information System digital elevation model (derived from light detection and ranging [lidar] data having a 0.49-ft root mean squared error and 4.9-ft horizontal resolution, resampled to a 10-ft grid) to delineate the area flooded at each stage.
The availability of these maps, along with Internet information regarding current stage from the USGS streamgage and forecasted high-flow stages from the NWS, will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for post-flood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165179","collaboration":"Prepared in cooperation with the Indiana Office of Community and Rural Affairs","usgsCitation":"Martin, Z.W., 2017, Flood-inundation maps for the St. Joseph River at Elkhart, Indiana: U.S. Geological Survey Scientific Investigations Report 2016–5179, 10 p., https://doi.org/10.3133/sir20165179.","productDescription":"Report: vi, 10 p.; Data Release","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-079008","costCenters":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true}],"links":[{"id":333769,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7QZ2836","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"St. Joseph River at Elkhart, Indiana, Flood-Inundation HEC-RAS Model"},{"id":333734,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5179/coverthb.jpg"},{"id":333735,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5179/sir20165179.pdf","text":"Report","size":"1.35 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5179"}],"country":"United States","state":"Indiana","otherGeospatial":"St. Joseph River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.04475021362305,\n 41.67355293097283\n ],\n [\n -86.04475021362305,\n 41.69380876113261\n ],\n [\n -85.97402572631836,\n 41.69380876113261\n ],\n [\n -85.97402572631836,\n 41.67355293097283\n ],\n [\n -86.04475021362305,\n 41.67355293097283\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Boulevard,
Indianapolis, IN 46278–1996
Previous magnetotelluric (MT) studies of the high-temperature Coso geothermal system in California identified a subvertical feature of low resistivity (2–5 Ohm m) and appreciable lateral extent (>1 km) in the producing zone of the East Flank field. However, these models could not reproduce gross 3-D effects in the recorded data. We perform 3-D full-tensor inversion and retrieve a resistivity model that out-performs previous 2-D and 3-D off-diagonal models in terms of its fit to the complete 3-D MT data set as well as the degree of modelling bias. Inclusion of secondary Zxx and Zyy data components leads to a robust east-dip (60†) to the previously identified conductive East Flank reservoir feature, which correlates strongly with recently mapped surface faults, downhole well temperatures, 3-D seismic reflection data, and local microseismicity. We perform synthetic forward modelling to test the best-fit dip of this conductor using the response at a nearby MT station. We interpret the dipping conductor as a fractured and fluidized compartment, which is structurally controlled by an unmapped blind East Flank fault zone.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gji/ggw408","usgsCitation":"Lindsey, N.J., Kaven, J., Davatzes, N.C., and Newman, G.A., 2017, Compartmentalization of the Coso East Flank geothermal field imaged by 3-D full-tensor MT inversion: Geophysical Journal International, v. 208, no. 2, p. 652-662, https://doi.org/10.1093/gji/ggw408.","productDescription":"11 p.","startPage":"652","endPage":"662","ipdsId":"IP-073610","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":470088,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/gji/ggw408","text":"Publisher Index Page"},{"id":346680,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Coso Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.8,\n 36\n ],\n [\n -117.725,\n 36\n ],\n [\n -117.725,\n 36.075\n ],\n [\n -117.8,\n 36.075\n ],\n [\n -117.8,\n 36\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"208","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-05","publicationStatus":"PW","scienceBaseUri":"59e71692e4b05fe04cd331b1","contributors":{"authors":[{"text":"Lindsey, Nathaniel J.","contributorId":197138,"corporation":false,"usgs":false,"family":"Lindsey","given":"Nathaniel","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":712679,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kaven, J. Ole 0000-0003-2625-2786 okaven@usgs.gov","orcid":"https://orcid.org/0000-0003-2625-2786","contributorId":3993,"corporation":false,"usgs":true,"family":"Kaven","given":"J. Ole","email":"okaven@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":712678,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Davatzes, Nicholas C.","contributorId":138855,"corporation":false,"usgs":false,"family":"Davatzes","given":"Nicholas","email":"","middleInitial":"C.","affiliations":[{"id":12547,"text":"Temple University","active":true,"usgs":false}],"preferred":false,"id":712680,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Newman, Gregory A.","contributorId":197140,"corporation":false,"usgs":false,"family":"Newman","given":"Gregory","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":712681,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70193659,"text":"70193659 - 2017 - Pectoral fin contact as a mechanism for social bonding among dolphins ","interactions":[],"lastModifiedDate":"2017-11-13T14:28:50","indexId":"70193659","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5550,"text":"Animal Behavior and Cognition","active":true,"publicationSubtype":{"id":10}},"title":"Pectoral fin contact as a mechanism for social bonding among dolphins ","docAbstract":"Bottlenose dolphins are large-brained social mammals residing in a fission-fusion society with relationships that are established and maintained over decades. We examined a decade-long data set of inter-individual pectoral fin contact exchanges to better understand how dolphins share information via tactile contact. Sex and age are significant factors in pectoral fin contact within non-kin dolphin dyads. Adult females shared more pectoral fin contacts with other adult females, while younger females showed no pattern of contact. Males shared more pectoral fin contacts with other males as juveniles and as adults, but showed no difference in the number of touches versus rubs as pectoral fin contacts with other males. Whether in the role of initiator as rubber or initiator as rubbee, male dolphins again preferred other males. These results support the notion that dolphins, especially male dolphins, might use pectoral fin contact as one tool in their repertoire for social bonding to establish, maintain and manage their inter-individual relationships. Additionally, it is also likely that the exchange of pectoral fin contact is developed and refined as individuals age, mature socially, and establish their place within a fission-fusion society.
","language":"English","publisher":"Animal Behavior and Cognition","doi":"10.12966/abc.03.02.2017","usgsCitation":"Dudzinski, K., and Ribic, C., 2017, Pectoral fin contact as a mechanism for social bonding among dolphins : Animal Behavior and Cognition, v. 4, no. 1, p. 30-48, https://doi.org/10.12966/abc.03.02.2017.","productDescription":"19 p.","startPage":"30","endPage":"48","ipdsId":"IP-076066","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":470093,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.12966/abc.03.02.2017","text":"Publisher Index Page"},{"id":348718,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-01","publicationStatus":"PW","scienceBaseUri":"5a60fc1be4b06e28e9c23a4b","contributors":{"authors":[{"text":"Dudzinski, Kathleen","contributorId":199697,"corporation":false,"usgs":false,"family":"Dudzinski","given":"Kathleen","affiliations":[],"preferred":false,"id":719790,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ribic, Christine 0000-0003-2583-1778 caribic@usgs.gov","orcid":"https://orcid.org/0000-0003-2583-1778","contributorId":147952,"corporation":false,"usgs":true,"family":"Ribic","given":"Christine","email":"caribic@usgs.gov","affiliations":[{"id":5068,"text":"Midwest Regional Director's Office","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":719789,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70193686,"text":"70193686 - 2017 - Generation of 3-D hydrostratigraphic zones from dense airborne electromagnetic data to assess groundwater model prediction error","interactions":[],"lastModifiedDate":"2017-11-02T16:32:12","indexId":"70193686","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Generation of 3-D hydrostratigraphic zones from dense airborne electromagnetic data to assess groundwater model prediction error","docAbstract":"We present a new methodology to combine spatially dense high-resolution airborne electromagnetic (AEM) data and sparse borehole information to construct multiple plausible geological structures using a stochastic approach. The method developed allows for quantification of the performance of groundwater models built from different geological realizations of structure. Multiple structural realizations are generated using geostatistical Monte Carlo simulations that treat sparse borehole lithological observations as hard data and dense geophysically derived structural probabilities as soft data. Each structural model is used to define 3-D hydrostratigraphical zones of a groundwater model, and the hydraulic parameter values of the zones are estimated by using nonlinear regression to fit hydrological data (hydraulic head and river discharge measurements). Use of the methodology is demonstrated for a synthetic domain having structures of categorical deposits consisting of sand, silt, or clay. It is shown that using dense AEM data with the methodology can significantly improve the estimated accuracy of the sediment distribution as compared to when borehole data are used alone. It is also shown that this use of AEM data can improve the predictive capability of a calibrated groundwater model that uses the geological structures as zones. However, such structural models will always contain errors because even with dense AEM data it is not possible to perfectly resolve the structures of a groundwater system. It is shown that when using such erroneous structures in a groundwater model, they can lead to biased parameter estimates and biased model predictions, therefore impairing the model's predictive capability.
","language":"English","publisher":"AGU","doi":"10.1002/2016WR019141","usgsCitation":"Christensen, N.K., Minsley, B.J., and Christensen, S., 2017, Generation of 3-D hydrostratigraphic zones from dense airborne electromagnetic data to assess groundwater model prediction error: Water Resources Research, v. 53, no. 2, p. 1019-1038, https://doi.org/10.1002/2016WR019141.","productDescription":"20 p.","startPage":"1019","endPage":"1038","ipdsId":"IP-081403","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":488731,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://pure.au.dk/portal/en/publications/dcdb9b5e-bf3c-4826-83aa-0fb5cd606845","text":"External Repository"},{"id":348146,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"53","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59fc2ea5e4b0531197b27f85","contributors":{"authors":[{"text":"Christensen, Nikolaj K","contributorId":199736,"corporation":false,"usgs":false,"family":"Christensen","given":"Nikolaj","email":"","middleInitial":"K","affiliations":[{"id":13419,"text":"Aarhus University, Denmark","active":true,"usgs":false}],"preferred":false,"id":719889,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Minsley, Burke J. 0000-0003-1689-1306 bminsley@usgs.gov","orcid":"https://orcid.org/0000-0003-1689-1306","contributorId":697,"corporation":false,"usgs":true,"family":"Minsley","given":"Burke","email":"bminsley@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":719888,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Christensen, Steen","contributorId":199737,"corporation":false,"usgs":false,"family":"Christensen","given":"Steen","email":"","affiliations":[{"id":13419,"text":"Aarhus University, Denmark","active":true,"usgs":false}],"preferred":false,"id":719890,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70190678,"text":"70190678 - 2017 - Integration of genetic and demographic data to assess population risk in a continuously distributed species","interactions":[],"lastModifiedDate":"2018-03-26T14:33:09","indexId":"70190678","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1324,"text":"Conservation Genetics","active":true,"publicationSubtype":{"id":10}},"title":"Integration of genetic and demographic data to assess population risk in a continuously distributed species","docAbstract":"The identification and demographic assessment of biologically meaningful populations is fundamental to species’ ecology and management. Although genetic tools are used frequently to identify populations, studies often do not incorporate demographic data to understand their respective population trends. We used genetic data to define subpopulations in a continuously distributed species. We assessed demographic independence and variation in population trends across the distribution. Additionally, we identified potential barriers to gene flow among subpopulations. We sampled greater sage-grouse (Centrocercus urophasianus) leks from across their range (≈175,000 Km2) in Wyoming and amplified DNA at 14 microsatellite loci for 1761 samples. Subsequently, we assessed population structure in unrelated individuals (n = 872) by integrating results from multiple Bayesian clustering approaches and used the boundaries to inform our assessment of long-term population trends and lek activity over the period of 1995–2013. We identified four genetic clusters of which two northern ones showed demographic independence from the others. Trends in population size for the northwest subpopulation were statistically different from the other three genetic clusters and the northeast and southwest subpopulations demonstrated a general trend of increasing proportion of inactive leks over time. Population change from 1996 to 2012 suggested population growth in the southern subpopulations and decline, or neutral, change in the northern subpopulations. We suggest that sage-grouse subpopulations in northern Wyoming are at greater risk of extirpation than the southern subpopulations due to smaller census and effective population sizes and higher variability within subpopulations. Our research is an example of incorporating genetic and demographic data and provides guidance on the identification of subpopulations of conservation concern.
","language":"English","publisher":"Springer","doi":"10.1007/s10592-016-0885-7","usgsCitation":"Fedy, B., Row, J.R., and Oyler-McCance, S.J., 2017, Integration of genetic and demographic data to assess population risk in a continuously distributed species: Conservation Genetics, v. 18, no. 1, p. 89-104, https://doi.org/10.1007/s10592-016-0885-7.","productDescription":"16 p.","startPage":"89","endPage":"104","ipdsId":"IP-060878","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":345643,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"18","issue":"1","noUsgsAuthors":false,"publicationDate":"2016-09-22","publicationStatus":"PW","scienceBaseUri":"59b8f220e4b08b1644e0aeeb","contributors":{"authors":[{"text":"Fedy, Bradley C.","contributorId":40536,"corporation":false,"usgs":true,"family":"Fedy","given":"Bradley C.","affiliations":[],"preferred":false,"id":710146,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Row, Jeffery R.","contributorId":178107,"corporation":false,"usgs":false,"family":"Row","given":"Jeffery","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":710147,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Oyler-McCance, Sara J. 0000-0003-1599-8769 sara_oyler-mccance@usgs.gov","orcid":"https://orcid.org/0000-0003-1599-8769","contributorId":1973,"corporation":false,"usgs":true,"family":"Oyler-McCance","given":"Sara","email":"sara_oyler-mccance@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":710148,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70191692,"text":"70191692 - 2017 - Relations between some horizontal‐component ground‐motion intensity measures used in practice","interactions":[],"lastModifiedDate":"2017-10-17T17:06:07","indexId":"70191692","displayToPublicDate":"2017-02-01T00:00:00","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}},"title":"Relations between some horizontal‐component ground‐motion intensity measures used in practice","docAbstract":"Various measures using the two horizontal components of recorded ground motions have been used in a number of studies that derive ground‐motion prediction equations and construct maps of shaking intensity. We update relations between a number of these measures, including those in Boore et al. (2006) and Boore (2010), using the large and carefully constructed global database of ground motions from crustal earthquakes in active tectonic regions developed as part of the Pacific Earthquake Engineering Research Center–Next Generation Attenuation‐West2 project. The ratios from the expanded datasets generally agree to within a few percent of the previously published ratios. We also provide some ratios that were not considered before, some of which will be useful in applications such as constructing ShakeMaps. Finally, we compare two important ratios with those from a large central and eastern North American database and from many records from subduction earthquakes in Japan and Taiwan. In general, the ratios from these regions are within several percent of those from crustal earthquakes in active tectonic regions.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120160250","usgsCitation":"Boore, D., and Kishida, T., 2017, Relations between some horizontal‐component ground‐motion intensity measures used in practice: Bulletin of the Seismological Society of America, v. 107, no. 1, p. 334-343, https://doi.org/10.1785/0120160250.","productDescription":"10 p.","startPage":"334","endPage":"343","ipdsId":"IP-077102","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":346772,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"107","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-12-27","publicationStatus":"PW","scienceBaseUri":"59e71692e4b05fe04cd331ae","contributors":{"authors":[{"text":"Boore, David 0000-0002-8605-9673 boore@usgs.gov","orcid":"https://orcid.org/0000-0002-8605-9673","contributorId":140502,"corporation":false,"usgs":true,"family":"Boore","given":"David","email":"boore@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":713079,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kishida, Tadahiro","contributorId":140538,"corporation":false,"usgs":false,"family":"Kishida","given":"Tadahiro","email":"","affiliations":[{"id":6643,"text":"University of California - Berkeley","active":true,"usgs":false}],"preferred":false,"id":713080,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70193924,"text":"70193924 - 2017 - Prior knowledge-based approach for associating contaminants with biological effects: A case study in the St. Croix River basin, MN, WI, USA","interactions":[],"lastModifiedDate":"2017-11-10T10:14:48","indexId":"70193924","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1555,"text":"Environmental Pollution","active":true,"publicationSubtype":{"id":10}},"title":"Prior knowledge-based approach for associating contaminants with biological effects: A case study in the St. Croix River basin, MN, WI, USA","docAbstract":"Evaluating potential adverse effects of complex chemical mixtures in the environment is challenging. One way to address that challenge is through more integrated analysis of chemical monitoring and biological effects data. In the present study, water samples from five locations near two municipal wastewater treatment plants in the St. Croix River basin, on the border of MN and WI, USA, were analyzed for 127 organic contaminants. Known chemical-gene interactions were used to develop site-specific knowledge assembly models (KAMs) and formulate hypotheses concerning possible biological effects associated with chemicals detected in water samples from each location. Additionally, hepatic gene expression data were collected for fathead minnows (Pimephales promelas) exposed in situ, for 12 d, at each location. Expression data from oligonucleotide microarrays were analyzed to identify functional annotation terms enriched among the differentially-expressed probes. The general nature of many of the terms made hypothesis formulation on the basis of the transcriptome-level response alone difficult. However, integrated analysis of the transcriptome data in the context of the site-specific KAMs allowed for evaluation of the likelihood of specific chemicals contributing to observed biological responses. Thirteen chemicals (atrazine, carbamazepine, metformin, thiabendazole, diazepam, cholesterol, p-cresol, phenytoin, omeprazole, ethyromycin, 17β-estradiol, cimetidine, and estrone), for which there was statistically significant concordance between occurrence at a site and expected biological response as represented in the KAM, were identified. While not definitive, the approach provides a line of evidence for evaluating potential cause-effect relationships between components of a complex mixture of contaminants and biological effects data, which can inform subsequent monitoring and investigation.
","language":"English","publisher":"Environmental Pollution","doi":"10.1016/j.envpol.2016.12.005","usgsCitation":"Schroeder, A.L., Martinovic-Weigelt, D., Ankley, G., Lee, K., Garcia-Reyero, N., Perkins, E.J., Schoenfuss, H.L., and Villeneuve, D.L., 2017, Prior knowledge-based approach for associating contaminants with biological effects: A case study in the St. Croix River basin, MN, WI, USA: Environmental Pollution, v. 221, p. 427-436, https://doi.org/10.1016/j.envpol.2016.12.005.","productDescription":"10 p.","startPage":"427","endPage":"436","ipdsId":"IP-065526","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"links":[{"id":470101,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6139436","text":"Publisher Index Page"},{"id":348551,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota, Wisconsin","otherGeospatial":"St. Croix River Basin","volume":"221","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a06c8d0e4b09af898c8613c","contributors":{"authors":[{"text":"Schroeder, Anthony L.","contributorId":173596,"corporation":false,"usgs":false,"family":"Schroeder","given":"Anthony","email":"","middleInitial":"L.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false},{"id":12503,"text":"University of Minnesota - Saint Paul","active":true,"usgs":false}],"preferred":false,"id":721514,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Martinovic-Weigelt, Dalma","contributorId":173655,"corporation":false,"usgs":false,"family":"Martinovic-Weigelt","given":"Dalma","affiliations":[{"id":6748,"text":"University of St. Thomas","active":true,"usgs":false}],"preferred":false,"id":721515,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ankley, Gerald T.","contributorId":177970,"corporation":false,"usgs":false,"family":"Ankley","given":"Gerald T.","affiliations":[{"id":13485,"text":"U.S. Environmental Protection Agency, Duluth, MN","active":true,"usgs":false}],"preferred":false,"id":721516,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lee, Kathy 0000-0002-7683-1367 klee@usgs.gov","orcid":"https://orcid.org/0000-0002-7683-1367","contributorId":2538,"corporation":false,"usgs":true,"family":"Lee","given":"Kathy","email":"klee@usgs.gov","affiliations":[{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":721517,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Garcia-Reyero, Natalia","contributorId":43961,"corporation":false,"usgs":false,"family":"Garcia-Reyero","given":"Natalia","affiliations":[{"id":26924,"text":"USArmy Engineer Research and Development Center, Vicksburg, MS","active":true,"usgs":false},{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":721518,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Perkins, Edward J.","contributorId":89063,"corporation":false,"usgs":false,"family":"Perkins","given":"Edward","email":"","middleInitial":"J.","affiliations":[{"id":26924,"text":"USArmy Engineer Research and Development Center, Vicksburg, MS","active":true,"usgs":false}],"preferred":false,"id":721519,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schoenfuss, Heiko L.","contributorId":76409,"corporation":false,"usgs":false,"family":"Schoenfuss","given":"Heiko","email":"","middleInitial":"L.","affiliations":[{"id":13317,"text":"Saint Cloud State University","active":true,"usgs":false}],"preferred":false,"id":721520,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Villeneuve, Daniel L.","contributorId":32091,"corporation":false,"usgs":false,"family":"Villeneuve","given":"Daniel","email":"","middleInitial":"L.","affiliations":[{"id":13485,"text":"U.S. Environmental Protection Agency, Duluth, MN","active":true,"usgs":false}],"preferred":false,"id":721521,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70194324,"text":"70194324 - 2017 - Collar temperature sensor data reveal long-term patterns in southern Beaufort Sea polar bear den distribution on pack ice and land","interactions":[],"lastModifiedDate":"2017-11-22T13:30:53","indexId":"70194324","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2663,"text":"Marine Ecology Progress Series","active":true,"publicationSubtype":{"id":10}},"title":"Collar temperature sensor data reveal long-term patterns in southern Beaufort Sea polar bear den distribution on pack ice and land","docAbstract":"In response to a changing climate, many species alter habitat use. Polar bears Ursus maritimus in the southern Beaufort Sea have increasingly used land for maternal denning. To aid in detecting denning behavior, we developed an objective method to identify polar bear denning events using temperature sensor data collected by satellite-linked transmitters deployed on adult females between 1985 and 2013. We then applied this method to determine whether southern Beaufort Sea polar bears have continued to increase land denning with recent sea-ice loss and examined whether sea-ice conditions affect the distribution of dens between pack-ice and coastal substrates. Because land use in summer and autumn has also increased, we examined potential associations between summering substrate and denning substrate. Statistical process control methods applied to temperature-sensor data identified denning events with 94.5% accuracy in comparison to direct observations (n = 73) and 95.7% accuracy relative to subjective classifications based on temperature, location, and activity sensor data (n = 116). We found an increase in land-based denning during the study period. The frequency of land denning was directly related to the distance that sea ice retreated from the coast. Among females that denned, all 14 that summered on land subsequently denned there, whereas 29% of the 69 bears summering on ice denned on land. These results suggest that denning on land may continue to increase with further loss of sea ice. While the effects that den substrate have on nutrition, energetics, and reproduction are unclear, more polar bears denning onshore will likely increase human-bear interactions.
","language":"English","publisher":"Inter-Research","doi":"10.3354/meps12000","usgsCitation":"Olson, J.W., Rode, K.D., Eggett, D.L., Smith, T.S., Wilson, R.R., Durner, G.M., Fischbach, A., Atwood, T.C., and Douglas, D., 2017, Collar temperature sensor data reveal long-term patterns in southern Beaufort Sea polar bear den distribution on pack ice and land: Marine Ecology Progress Series, v. 564, p. 211-224, https://doi.org/10.3354/meps12000.","productDescription":"14 p.","startPage":"211","endPage":"224","ipdsId":"IP-076135","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":438441,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7BP00Z5","text":"USGS data release","linkHelpText":"Denning Behavior Classifications Using Temperature Sensor Data on Collars Deployed on Polar Bears in the Southern Beaufort Sea, 1986-2013"},{"id":349283,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Beaufort Sea","volume":"564","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fc1be4b06e28e9c23a3e","contributors":{"authors":[{"text":"Olson, Jay W","contributorId":200778,"corporation":false,"usgs":false,"family":"Olson","given":"Jay","email":"","middleInitial":"W","affiliations":[],"preferred":false,"id":723307,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rode, Karyn D. 0000-0002-3328-8202 krode@usgs.gov","orcid":"https://orcid.org/0000-0002-3328-8202","contributorId":5053,"corporation":false,"usgs":true,"family":"Rode","given":"Karyn","email":"krode@usgs.gov","middleInitial":"D.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":723306,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eggett, Dennis L.","contributorId":191388,"corporation":false,"usgs":false,"family":"Eggett","given":"Dennis","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":723308,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, T. 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R.","contributorId":200779,"corporation":false,"usgs":false,"family":"Wilson","given":"R.","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":723309,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Durner, George M. 0000-0002-3370-1191 gdurner@usgs.gov","orcid":"https://orcid.org/0000-0002-3370-1191","contributorId":3576,"corporation":false,"usgs":true,"family":"Durner","given":"George","email":"gdurner@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":723310,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fischbach, Anthony S. 0000-0002-6555-865X afischbach@usgs.gov","orcid":"https://orcid.org/0000-0002-6555-865X","contributorId":200780,"corporation":false,"usgs":true,"family":"Fischbach","given":"Anthony S.","email":"afischbach@usgs.gov","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":723311,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Atwood, Todd C. 0000-0002-1971-3110 tatwood@usgs.gov","orcid":"https://orcid.org/0000-0002-1971-3110","contributorId":4368,"corporation":false,"usgs":true,"family":"Atwood","given":"Todd","email":"tatwood@usgs.gov","middleInitial":"C.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":723312,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Douglas, David C. 0000-0003-0186-1104 ddouglas@usgs.gov","orcid":"https://orcid.org/0000-0003-0186-1104","contributorId":150115,"corporation":false,"usgs":true,"family":"Douglas","given":"David C.","email":"ddouglas@usgs.gov","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":723313,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70192944,"text":"70192944 - 2017 - Temporal expansion of annual crop classification layers for the CONUS using the C5 decision tree classifier","interactions":[],"lastModifiedDate":"2017-10-30T15:00:45","indexId":"70192944","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3251,"text":"Remote Sensing Letters","active":true,"publicationSubtype":{"id":10}},"title":"Temporal expansion of annual crop classification layers for the CONUS using the C5 decision tree classifier","docAbstract":"Crop cover maps have become widely used in a range of research applications. Multiple crop cover maps have been developed to suite particular research interests. The National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) are a series of commonly used crop cover maps for the conterminous United States (CONUS) that span from 2008 to 2013. In this investigation, we sought to contribute to the availability of consistent CONUS crop cover maps by extending temporal coverage of the NASS CDL archive back eight additional years to 2000 by creating annual NASS CDL-like crop cover maps derived from a classification tree model algorithm. We used over 11 million records to train a classification tree algorithm and develop a crop classification model (CCM). The model was used to create crop cover maps for the CONUS for years 2000–2013 at 250 m spatial resolution. The CCM and the maps for years 2008–2013 were assessed for accuracy relative to resampled NASS CDLs. The CCM performed well against a withheld test data set with a model prediction accuracy of over 90%. The assessment of the crop cover maps indicated that the model performed well spatially, placing crop cover pixels within their known domains; however, the model did show a bias towards the ‘Other’ crop cover class, which caused frequent misclassifications of pixels around the periphery of large crop cover patch clusters and of pixels that form small, sparsely dispersed crop cover patches.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/2150704X.2016.1271469","usgsCitation":"Friesz, A.M., Wylie, B., and Howard, D.M., 2017, Temporal expansion of annual crop classification layers for the CONUS using the C5 decision tree classifier: Remote Sensing Letters, v. 8, no. 4, p. 389-398, https://doi.org/10.1080/2150704X.2016.1271469.","productDescription":"10 p.","startPage":"389","endPage":"398","ipdsId":"IP-075490","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":347729,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"4","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-03","publicationStatus":"PW","scienceBaseUri":"59f83a38e4b063d5d30980ef","contributors":{"authors":[{"text":"Friesz, Aaron M. 0000-0003-4096-3824 afriesz@usgs.gov","orcid":"https://orcid.org/0000-0003-4096-3824","contributorId":5943,"corporation":false,"usgs":true,"family":"Friesz","given":"Aaron","email":"afriesz@usgs.gov","middleInitial":"M.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":717392,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":197161,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce K.","email":"wylie@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":717394,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Howard, Daniel M. 0000-0002-7563-7538 danny.howard.ctr@usgs.gov","orcid":"https://orcid.org/0000-0002-7563-7538","contributorId":197063,"corporation":false,"usgs":true,"family":"Howard","given":"Daniel","email":"danny.howard.ctr@usgs.gov","middleInitial":"M.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":717393,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70192900,"text":"70192900 - 2017 - A land data assimilation system for sub-Saharan Africa food and water security applications","interactions":[],"lastModifiedDate":"2017-10-30T15:06:03","indexId":"70192900","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3907,"text":"Scientific Data","active":true,"publicationSubtype":{"id":10}},"title":"A land data assimilation system for sub-Saharan Africa food and water security applications","docAbstract":"Seasonal agricultural drought monitoring systems, which rely on satellite remote sensing and land surface models (LSMs), are important for disaster risk reduction and famine early warning. These systems require the best available weather inputs, as well as a long-term historical record to contextualize current observations. This article introduces the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), a custom instance of the NASA Land Information System (LIS) framework. The FLDAS is routinely used to produce multi-model and multi-forcing estimates of hydro-climate states and fluxes over semi-arid, food insecure regions of Africa. These modeled data and derived products, like soil moisture percentiles and water availability, were designed and are currently used to complement FEWS NET’s operational remotely sensed rainfall, evapotranspiration, and vegetation observations. The 30+ years of monthly outputs from the FLDAS simulations are publicly available from the NASA Goddard Earth Science Data and Information Services Center (GES DISC) and recommended for use in hydroclimate studies, early warning applications, and by agro-meteorological scientists in Eastern, Southern, and Western Africa.
","language":"English","publisher":"Nature","doi":"10.1038/sdata.2017.12","usgsCitation":"McNally, A., Arsenault, K., Kumar, S., Shukla, S., Peterson, P., Wang, S., Funk, C., Peters-Lidard, C., and Verdin, J., 2017, A land data assimilation system for sub-Saharan Africa food and water security applications: Scientific Data, v. 4, p. 1-19, https://doi.org/10.1038/sdata.2017.12.","productDescription":"Article number 170012; 19 p.","startPage":"1","endPage":"19","ipdsId":"IP-077287","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":470090,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/sdata.2017.12","text":"Publisher Index Page"},{"id":347730,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-14","publicationStatus":"PW","scienceBaseUri":"59f83a39e4b063d5d30980f3","contributors":{"authors":[{"text":"McNally, Amy","contributorId":145810,"corporation":false,"usgs":false,"family":"McNally","given":"Amy","email":"","affiliations":[{"id":16236,"text":"UCSB Climate Hazards Group","active":true,"usgs":false}],"preferred":false,"id":717321,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arsenault, Kristi","contributorId":198836,"corporation":false,"usgs":false,"family":"Arsenault","given":"Kristi","affiliations":[],"preferred":false,"id":717322,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kumar, Sujay","contributorId":198837,"corporation":false,"usgs":false,"family":"Kumar","given":"Sujay","email":"","affiliations":[],"preferred":false,"id":717323,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shukla, Shraddhanand","contributorId":145841,"corporation":false,"usgs":false,"family":"Shukla","given":"Shraddhanand","affiliations":[{"id":16255,"text":"Climate Hazards Group University of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":717324,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Peterson, Pete","contributorId":192379,"corporation":false,"usgs":false,"family":"Peterson","given":"Pete","affiliations":[],"preferred":false,"id":717325,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wang, Shugong","contributorId":198838,"corporation":false,"usgs":false,"family":"Wang","given":"Shugong","email":"","affiliations":[],"preferred":false,"id":717326,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Funk, Chris 0000-0002-9254-6718 cfunk@usgs.gov","orcid":"https://orcid.org/0000-0002-9254-6718","contributorId":167070,"corporation":false,"usgs":true,"family":"Funk","given":"Chris","email":"cfunk@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":717320,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Peters-Lidard, Christa","contributorId":198839,"corporation":false,"usgs":false,"family":"Peters-Lidard","given":"Christa","email":"","affiliations":[],"preferred":false,"id":717327,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Verdin, James 0000-0003-0238-9657 verdin@usgs.gov","orcid":"https://orcid.org/0000-0003-0238-9657","contributorId":145830,"corporation":false,"usgs":true,"family":"Verdin","given":"James","email":"verdin@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":717328,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70192732,"text":"70192732 - 2017 - Terrestrial ecosystem model performance in simulating productivity and its vulnerability to climate change in the northern permafrost region","interactions":[],"lastModifiedDate":"2017-11-08T13:25:23","indexId":"70192732","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2320,"text":"Journal of Geophysical Research: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Terrestrial ecosystem model performance in simulating productivity and its vulnerability to climate change in the northern permafrost region","docAbstract":"Realistic projection of future climate-carbon (C) cycle feedbacks requires better understanding and an improved representation of the C cycle in permafrost regions in the current generation of Earth system models. Here we evaluated 10 terrestrial ecosystem models for their estimates of net primary productivity (NPP) and responses to historical climate change in permafrost regions in the Northern Hemisphere. In comparison with the satellite estimate from the Moderate Resolution Imaging Spectroradiometer (MODIS; 246 ± 6 g C m−2 yr−1), most models produced higher NPP (309 ± 12 g C m−2 yr−1) over the permafrost region during 2000–2009. By comparing the simulated gross primary productivity (GPP) with a flux tower-based database, we found that although mean GPP among the models was only overestimated by 10% over 1982–2009, there was a twofold discrepancy among models (380 to 800 g C m−2 yr−1), which mainly resulted from differences in simulated maximum monthly GPP (GPPmax). Most models overestimated C use efficiency (CUE) as compared to observations at both regional and site levels. Further analysis shows that model variability of GPP and CUE are nonlinearly correlated to variability in specific leaf area and the maximum rate of carboxylation by the enzyme Rubisco at 25°C (Vcmax_25), respectively. The models also varied in their sensitivities of NPP, GPP, and CUE to historical changes in climate and atmospheric CO2 concentration. These results indicate that model predictive ability of the C cycle in permafrost regions can be improved by better representation of the processes controlling CUE and GPPmax as well as their sensitivity to climate change.
","language":"English","publisher":"AGU","doi":"10.1002/2016JG003384","usgsCitation":"Xia, J., McGuire, A.D., Lawrence, D., Burke, E.J., Chen, G., Chen, X., Delire, C., Koven, C., MacDougall, A., Peng, S., Rinke, A., Saito, K., Zhang, W., Alkama, R., Bohn, T.J., Ciais, P., Decharme, B., Gouttevin, I., Hajima, T., Hayes, D.J., Huang, K., Ji, D., Krinner, G., Lettenmaier, D.P., Miller, P.A., Moore, J., Smith, B., Sueyoshi, T., Shi, Z., Yan, L., Liang, J., Jiang, L., Zhang, Q., and Luo, Y., 2017, Terrestrial ecosystem model performance in simulating productivity and its vulnerability to climate change in the northern permafrost region: Journal of Geophysical Research: Biogeosciences, v. 122, no. 2, p. 430-446, https://doi.org/10.1002/2016JG003384.","productDescription":"17 p.","startPage":"430","endPage":"446","ipdsId":"IP-070881","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":470091,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016jg003384","text":"Publisher Index Page"},{"id":348457,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"122","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-22","publicationStatus":"PW","scienceBaseUri":"5a0425bce4b0dc0b45b453b0","contributors":{"authors":[{"text":"Xia, Jianyang","contributorId":167809,"corporation":false,"usgs":false,"family":"Xia","given":"Jianyang","email":"","affiliations":[],"preferred":false,"id":721176,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McGuire, A. 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ShakeCast is widely used by public and private emergency planners and responders, lifeline utility operators and transportation engineers to automatically receive and process ShakeMap products for situational awareness, inspection priority, or damage assessment of their own infrastructure or building portfolios. The success of ShakeCast to date and its broad, critical-user base mandates improved software usability and functionality, including improved engineering-based damage and loss functions. In order to make the software more accessible to novice users—while still utilizing advanced users’ technical and engineering background—we have developed a “ShakeCast Workbook”, a well documented, Excel spreadsheet-based user interface that allows users to input notification and inventory data and export XML files requisite for operating the ShakeCast system. Users will be able to select structure based on a minimum set of user-specified facility (building location, size, height, use, construction age, etc.). “Expert” users will be able to import user-modified structural response properties into facility inventory associated with the HAZUS Advanced Engineering Building Modules (AEBM). The goal of the ShakeCast system is to provide simplified real-time potential impact and inspection metrics (i.e., green, yellow, orange and red priority ratings) to allow users to institute customized earthquake response protocols. Previously, fragilities were approximated using individual ShakeMap intensity measures (IMs, specifically PGA and 0.3 and 1s spectral accelerations) for each facility but we are now performing capacity-spectrum damage state calculations using a more robust characterization of spectral deamnd.We are also developing methods for the direct import of ShakeMap’s multi-period spectra in lieu of the assumed three-domain design spectrum (at 0.3s for constant acceleration; 1s or 3s for constant velocity and constant displacement at very long response periods). As part of ongoing ShakeCast research and development, we will also explore the use of ShakeMap IM uncertainty estimates and evaluate the assumption of employing multiple response spectral damping values rather than the single 5%-damped value currently employed. Developing and incorporating advanced fragility assignments into the ShakeCast Workbook requires related software modifications and database improvements; these enhancements are part of an extensive rewrite of the ShakeCast application.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 16th World Conference on Earthquake Engineering","largerWorkSubtype":{"id":15,"text":"Monograph"},"conferenceTitle":"16th World Conference on Earthquake Engineering","language":"English","publisher":"16th World Conference on Earthquake Engineering","usgsCitation":"Wald, D.J., Lin, K., Kircher, C.A., Jaiswal, K.S., Luco, N., Turner, L., and Slosky, D., 2017, Development and utilization of USGS ShakeCast for rapid post-earthquake assessment of critical facilities and infrastructure, in Proceedings of the 16th World Conference on Earthquake Engineering.","ipdsId":"IP-080219","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":344164,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":344163,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://usgs.github.io/shakecast/2017_16WCEE.html"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"597312aae4b0ec1a488718d7","contributors":{"authors":[{"text":"Wald, David J. 0000-0002-1454-4514 wald@usgs.gov","orcid":"https://orcid.org/0000-0002-1454-4514","contributorId":795,"corporation":false,"usgs":true,"family":"Wald","given":"David","email":"wald@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":705900,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lin, Kuo-wan 0000-0002-7520-8151 klin@usgs.gov","orcid":"https://orcid.org/0000-0002-7520-8151","contributorId":1539,"corporation":false,"usgs":true,"family":"Lin","given":"Kuo-wan","email":"klin@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":705904,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kircher, C. A.","contributorId":194952,"corporation":false,"usgs":false,"family":"Kircher","given":"C.","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":705901,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jaiswal, Kishor S. 0000-0002-5803-8007 kjaiswal@usgs.gov","orcid":"https://orcid.org/0000-0002-5803-8007","contributorId":149796,"corporation":false,"usgs":true,"family":"Jaiswal","given":"Kishor","email":"kjaiswal@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":705905,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Luco, Nico 0000-0002-5763-9847 nluco@usgs.gov","orcid":"https://orcid.org/0000-0002-5763-9847","contributorId":145730,"corporation":false,"usgs":true,"family":"Luco","given":"Nico","email":"nluco@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":705906,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Turner, L.","contributorId":194953,"corporation":false,"usgs":false,"family":"Turner","given":"L.","email":"","affiliations":[],"preferred":false,"id":705902,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Slosky, Daniel 0000-0001-7407-3606 dslosky@usgs.gov","orcid":"https://orcid.org/0000-0001-7407-3606","contributorId":194954,"corporation":false,"usgs":true,"family":"Slosky","given":"Daniel","email":"dslosky@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":705903,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70189596,"text":"70189596 - 2017 - A discrete stage-structured model of California newt population dynamics during a period of drought","interactions":[],"lastModifiedDate":"2018-03-26T12:18:31","indexId":"70189596","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2475,"text":"Journal of Theoretical Biology","active":true,"publicationSubtype":{"id":10}},"title":"A discrete stage-structured model of California newt population dynamics during a period of drought","docAbstract":"We introduce a mathematical model for studying the population dynamics under drought of the California newt (Taricha torosa), a species of special concern in the state of California. Since 2012, California has experienced a record-setting drought, and multiple studies predict drought conditions currently underway will persist and even increase in severity. Recent declines and local extinctions of California newt populations in Santa Monica Mountain streams motivate our study of the impact of drought on newt population sizes. Although newts are terrestrial salamanders, they migrate to streams each spring to breed and lay eggs. Since egg and larval stages occur in water, a precipitation deficit due to drought conditions reduces the space for newt egg-laying and the necessary habitat for larval development. To mathematically forecast newt population dynamics, we develop a nonlinear system of discrete equations that includes demographic parameters such as survival rates for newt life stages and egg production, which depend on habitat availability and rainfall. We estimate these demographic parameters using 15 years of stream survey data collected from Cold Creek in Los Angeles County, California, and our model captures the observed decline of the parameterized Cold Creek newt population. Based upon data analysis, we predict how the number of available newt egg-laying sites varies with annual precipitation. Our model allows us to make predictions about how the length and severity of drought can affect the likelihood of persistence and the time to critical endangerment of a local newt population. We predict that sustained severe drought will critically endanger the newt population but that the newt population can rebound if a drought is sufficiently short.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jtbi.2016.11.011","usgsCitation":"Jones, M.T., Milligan, W.R., Kats, L.B., Vandergon, T.L., Honeycutt, R.L., Fisher, R.N., Davis, C.L., and Lucas, T.A., 2017, A discrete stage-structured model of California newt population dynamics during a period of drought: Journal of Theoretical Biology, v. 414, p. 245-253, https://doi.org/10.1016/j.jtbi.2016.11.011.","productDescription":"9 p.","startPage":"245","endPage":"253","ipdsId":"IP-081597","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":343986,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"414","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"596f1e25e4b0d1f9f064075b","contributors":{"authors":[{"text":"Jones, Marjorie T.","contributorId":194782,"corporation":false,"usgs":false,"family":"Jones","given":"Marjorie","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":705333,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Milligan, William R.","contributorId":194783,"corporation":false,"usgs":false,"family":"Milligan","given":"William","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":705334,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kats, Lee B.","contributorId":106034,"corporation":false,"usgs":true,"family":"Kats","given":"Lee","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":705335,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vandergon, Thomas L.","contributorId":38489,"corporation":false,"usgs":true,"family":"Vandergon","given":"Thomas","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":705336,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Honeycutt, Rodney L.","contributorId":106426,"corporation":false,"usgs":true,"family":"Honeycutt","given":"Rodney","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":705337,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fisher, Robert N. 0000-0002-2956-3240 rfisher@usgs.gov","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":1529,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert","email":"rfisher@usgs.gov","middleInitial":"N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":705338,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Davis, Courtney L.","contributorId":181922,"corporation":false,"usgs":false,"family":"Davis","given":"Courtney","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":705339,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lucas, Timothy A.","contributorId":194784,"corporation":false,"usgs":false,"family":"Lucas","given":"Timothy","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":705340,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70195760,"text":"70195760 - 2017 - Building the vegetation drought response index for Canada (VegDRI-Canada) to monitor agricultural drought: first results","interactions":[],"lastModifiedDate":"2018-02-28T14:03:02","indexId":"70195760","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1722,"text":"GIScience and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Building the vegetation drought response index for Canada (VegDRI-Canada) to monitor agricultural drought: first results","docAbstract":"Drought is a natural climatic phenomenon that occurs throughout the world and impacts many sectors of society. To help decision-makers reduce the impacts of drought, it is important to improve monitoring tools that provide relevant and timely information in support of drought mitigation decisions. Given that drought is a complex natural hazard that manifests in different forms, monitoring can be improved by integrating various types of information (e.g., remote sensing and climate) that is timely and region specific to identify where and when droughts are occurring. The Vegetation Drought Response Index for Canada (VegDRI-Canada) is a recently developed drought monitoring tool for Canada. VegDRI-Canada extends the initial VegDRI concept developed for the conterminous United States to a broader transnational coverage across North America. VegDRI-Canada models are similar to those developed for the United States, integrating satellite observations of vegetation status, climate data, and biophysical information on land use and land cover, soil characteristics, and other environmental factors. Collectively, these different types of data are integrated into the hybrid VegDRI-Canada to isolate the effects of drought on vegetation. Twenty-three weekly VegDRI-Canada models were built for the growing season (April–September) through the weekly analysis of these data using a regression tree-based data mining approach. A 15-year time series of VegDRI-Canada results (s to 2014) was produced using these models and the output was validated by randomly selecting 20% of the historical data, as well as holdout year (15% unseen data) across the growing season that the Pearson’s correlation ranged from 0.6 to 0.77. A case study was also conducted to evaluate the VegDRI-Canada results over the prairie region of Canada for two drought years and one non-drought year for three weekly periods of the growing season (i.e., early-, mid-, and late season). The comparison of the VegDRI-Canada map with the Canadian Drought Monitor (CDM), an independent drought indicator, showed that the VegDRI-Canada maps depicted key spatial drought severity patterns during the two targeted drought years consistent with the CDM. In addition, VegDRI-Canada was compared with canola yields in the Prairie Provinces at the regional scale for a period from 2000 to 2014 to evaluate the indices’ applicability for monitoring drought impacts on crop production. The result showed that VegDRI-Canada values had a relatively higher correlation (i.e., r > 0.5) with canola yield for nonirrigated croplands in the Canadian Prairies region in areas where drought is typically a limiting factor on crop growth, but showed a negative relationship in the southeastern Prairie region, where water availability is less of a limiting factor and in some cases a hindrance to crop growth when waterlogging occurs. These initial results demonstrate VegDRI-Canada’s utility for monitoring drought-related vegetation conditions, particularly in drought prone areas. In general, the results indicated that the VegDRI-Canada models showed sensitivity to known agricultural drought events in Canada over the 15-year period mainly for nonirrigated areas.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/15481603.2017.1286728","usgsCitation":"Tadesse, T., Champagne, C., Wardlow, B.D., Hadwen, T.A., Brown, J.F., Demisse, G.B., Bayissa, Y.A., and Davidson, A.M., 2017, Building the vegetation drought response index for Canada (VegDRI-Canada) to monitor agricultural drought: first results: GIScience and Remote Sensing, v. 54, no. 2, p. 230-257, https://doi.org/10.1080/15481603.2017.1286728.","productDescription":"28 p.","startPage":"230","endPage":"257","ipdsId":"IP-082660","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":499999,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/99a8bce08c6143daaa4fc548ecdb117b","text":"External Repository"},{"id":352144,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -138.779296875,\n 41.83682786072714\n ],\n [\n -51.67968749999999,\n 41.83682786072714\n ],\n [\n -51.67968749999999,\n 60\n ],\n [\n -138.779296875,\n 60\n ],\n [\n -138.779296875,\n 41.83682786072714\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"54","issue":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-08","publicationStatus":"PW","scienceBaseUri":"5afee8d3e4b0da30c1bfc4bc","contributors":{"authors":[{"text":"Tadesse, Tsegaye 0000-0002-4102-1137","orcid":"https://orcid.org/0000-0002-4102-1137","contributorId":147617,"corporation":false,"usgs":false,"family":"Tadesse","given":"Tsegaye","email":"","affiliations":[],"preferred":false,"id":729876,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Champagne, Catherine","contributorId":202836,"corporation":false,"usgs":false,"family":"Champagne","given":"Catherine","email":"","affiliations":[{"id":27920,"text":"Agriculture and Agrifood Canada","active":true,"usgs":false}],"preferred":false,"id":729877,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wardlow, Brian D. 0000-0002-4767-581X","orcid":"https://orcid.org/0000-0002-4767-581X","contributorId":191403,"corporation":false,"usgs":false,"family":"Wardlow","given":"Brian","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":729878,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hadwen, Trevor A.","contributorId":202837,"corporation":false,"usgs":false,"family":"Hadwen","given":"Trevor","email":"","middleInitial":"A.","affiliations":[{"id":27920,"text":"Agriculture and Agrifood Canada","active":true,"usgs":false}],"preferred":false,"id":729879,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brown, Jesslyn F. 0000-0002-9976-1998 jfbrown@usgs.gov","orcid":"https://orcid.org/0000-0002-9976-1998","contributorId":176609,"corporation":false,"usgs":true,"family":"Brown","given":"Jesslyn","email":"jfbrown@usgs.gov","middleInitial":"F.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":729875,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Demisse, Getachew B.","contributorId":202845,"corporation":false,"usgs":false,"family":"Demisse","given":"Getachew","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":729894,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bayissa, Yared A.","contributorId":202846,"corporation":false,"usgs":false,"family":"Bayissa","given":"Yared","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":729895,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Davidson, Andrew M.","contributorId":202847,"corporation":false,"usgs":false,"family":"Davidson","given":"Andrew","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":729896,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70188345,"text":"70188345 - 2017 - Ground motion in the presence of complex Topography II: Earthquake sources and 3D simulations","interactions":[],"lastModifiedDate":"2017-06-06T16:13:07","indexId":"70188345","displayToPublicDate":"2017-02-01T00:00:00","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}},"title":"Ground motion in the presence of complex Topography II: Earthquake sources and 3D simulations","docAbstract":"Eight seismic stations were placed in a linear array with a topographic relief of 222 m over Mission Peak in the east San Francisco Bay region for a period of one year to study topographic effects. Seventy‐two well‐recorded local earthquakes are used to calculate spectral amplitude ratios relative to a reference site. A well‐defined fundamental resonance peak is observed with individual station amplitudes following the theoretically predicted progression of larger amplitudes in the upslope direction. Favored directions of vibration are also seen that are related to the trapping of shear waves within the primary ridge dimensions. Spectral peaks above the fundamental one are also related to topographic effects but follow a more complex pattern. Theoretical predictions using a 3D velocity model and accurate topography reproduce many of the general frequency and time‐domain features of the data. Shifts in spectral frequencies and amplitude differences, however, are related to deficiencies of the model and point out the importance of contributing factors, including the shear‐wave velocity under the topographic feature, near‐surface velocity gradients, and source parameters.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120160159","usgsCitation":"Hartzell, S.H., Ramirez-Guzman, L., Meremonte, M., and Leeds, A.L., 2017, Ground motion in the presence of complex Topography II: Earthquake sources and 3D simulations: Bulletin of the Seismological Society of America, v. 107, no. 1, p. 344-358, https://doi.org/10.1785/0120160159.","productDescription":"15 p.","startPage":"344","endPage":"358","ipdsId":"IP-078909","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":342187,"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 -122.05,\n 37.85\n ],\n [\n -121.65,\n 37.85\n ],\n [\n -121.65,\n 37.3\n ],\n [\n -122.05,\n 37.3\n ],\n [\n -122.05,\n 37.85\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"107","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-12-20","publicationStatus":"PW","scienceBaseUri":"5937bf2de4b0f6c2d0d9c75e","contributors":{"authors":[{"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":697336,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ramirez-Guzman, Leonardo","contributorId":175444,"corporation":false,"usgs":false,"family":"Ramirez-Guzman","given":"Leonardo","email":"","affiliations":[],"preferred":false,"id":697337,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Meremonte, Mark","contributorId":192672,"corporation":false,"usgs":false,"family":"Meremonte","given":"Mark","email":"","affiliations":[],"preferred":false,"id":697338,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Leeds, Alena L. 0000-0002-8756-3687 aleeds@usgs.gov","orcid":"https://orcid.org/0000-0002-8756-3687","contributorId":4077,"corporation":false,"usgs":true,"family":"Leeds","given":"Alena","email":"aleeds@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":697339,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70195840,"text":"70195840 - 2017 - Using diurnal temperature signals to infer vertical groundwater-surface water exchange","interactions":[],"lastModifiedDate":"2018-03-06T11:07:46","indexId":"70195840","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Using diurnal temperature signals to infer vertical groundwater-surface water exchange","docAbstract":"Heat is a powerful tracer to quantify fluid exchange between surface water and groundwater. Temperature time series can be used to estimate pore water fluid flux, and techniques can be employed to extend these estimates to produce detailed plan-view flux maps. Key advantages of heat tracing include cost-effective sensors and ease of data collection and interpretation, without the need for expensive and time-consuming laboratory analyses or induced tracers. While the collection of temperature data in saturated sediments is relatively straightforward, several factors influence the reliability of flux estimates that are based on time series analysis (diurnal signals) of recorded temperatures. Sensor resolution and deployment are particularly important in obtaining robust flux estimates in upwelling conditions. Also, processing temperature time series data involves a sequence of complex steps, including filtering temperature signals, selection of appropriate thermal parameters, and selection of the optimal analytical solution for modeling. This review provides a synthesis of heat tracing using diurnal temperature oscillations, including details on optimal sensor selection and deployment, data processing, model parameterization, and an overview of computing tools available. Recent advances in diurnal temperature methods also provide the opportunity to determine local saturated thermal diffusivity, which can improve the accuracy of fluid flux modeling and sensor spacing, which is related to streambed scour and deposition. These parameters can also be used to determine the reliability of flux estimates from the use of heat as a tracer.
","language":"English","publisher":"Wiley","doi":"10.1111/gwat.12459","usgsCitation":"Irvine, D.J., Briggs, M.A., Lautz, L.K., Gordon, R.P., McKenzie, J.M., and Cartwright, I., 2017, Using diurnal temperature signals to infer vertical groundwater-surface water exchange: Groundwater, v. 55, no. 1, p. 10-26, https://doi.org/10.1111/gwat.12459.","productDescription":"17 p.","startPage":"10","endPage":"26","ipdsId":"IP-077274","costCenters":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"links":[{"id":470089,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gwat.12459","text":"Publisher Index Page"},{"id":352253,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"55","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-10-03","publicationStatus":"PW","scienceBaseUri":"5afee8d3e4b0da30c1bfc4b8","contributors":{"authors":[{"text":"Irvine, Dylan J.","contributorId":190404,"corporation":false,"usgs":false,"family":"Irvine","given":"Dylan","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":730252,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Briggs, Martin A. 0000-0003-3206-4132 mbriggs@usgs.gov","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":4114,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin","email":"mbriggs@usgs.gov","middleInitial":"A.","affiliations":[{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":730251,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lautz, Laura K.","contributorId":124523,"corporation":false,"usgs":false,"family":"Lautz","given":"Laura","email":"","middleInitial":"K.","affiliations":[{"id":5082,"text":"Syracuse University","active":true,"usgs":false}],"preferred":false,"id":730253,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gordon, Ryan P.","contributorId":202947,"corporation":false,"usgs":false,"family":"Gordon","given":"Ryan","email":"","middleInitial":"P.","affiliations":[{"id":7257,"text":"Maine Geological Survey","active":true,"usgs":false}],"preferred":false,"id":730254,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McKenzie, Jeffrey M.","contributorId":176299,"corporation":false,"usgs":false,"family":"McKenzie","given":"Jeffrey","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":730255,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cartwright, Ian","contributorId":190405,"corporation":false,"usgs":false,"family":"Cartwright","given":"Ian","affiliations":[],"preferred":false,"id":730256,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70188375,"text":"70188375 - 2017 - Multi-year microbial source tracking study characterizing fecal contamination in an urban watershed","interactions":[],"lastModifiedDate":"2017-06-07T14:04:58","indexId":"70188375","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3711,"text":"Water Environment Research","active":true,"publicationSubtype":{"id":10}},"title":"Multi-year microbial source tracking study characterizing fecal contamination in an urban watershed","docAbstract":"Microbiological and hydrological data were used to rank tributary stream contributions of bacteria to the Little Blue River in Independence, Missouri. Concentrations, loadings and yields of E. coli and microbial source tracking (MST) markers, were characterized during base flow and storm events in five subbasins within Independence, as well as sources entering and leaving the city through the river. The E. coli water quality threshold was exceeded in 29% of base-flow and 89% of storm-event samples. The total contribution of E. coli and MST markers from tributaries within Independence to the Little Blue River, regardless of streamflow, did not significantly increase the median concentrations leaving the city. Daily loads and yields of E. coli and MST markers were used to rank the subbasins according to their contribution of each constituent to the river. The ranking methodology used in this study may prove useful in prioritizing remediation in the different subbasins.
","language":"English","publisher":"Water Environment Federation","doi":"10.2175/106143016X14798353399412","collaboration":"City of Independence, Missouri Water Pollution Control Station","usgsCitation":"Bushon, R.N., Brady, A.M., Christensen, E.D., and Stelzer, E.A., 2017, Multi-year microbial source tracking study characterizing fecal contamination in an urban watershed: Water Environment Research, v. 89, no. 2, p. 127-143, https://doi.org/10.2175/106143016X14798353399412.","productDescription":"17 p.","startPage":"127","endPage":"143","ipdsId":"IP-069132","costCenters":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true}],"links":[{"id":342249,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","city":"Independence","otherGeospatial":"Little Blue River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": 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Center","active":true,"usgs":true}],"preferred":true,"id":697453,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stelzer, Erin A. 0000-0001-7645-7603 eastelzer@usgs.gov","orcid":"https://orcid.org/0000-0001-7645-7603","contributorId":1933,"corporation":false,"usgs":true,"family":"Stelzer","given":"Erin","email":"eastelzer@usgs.gov","middleInitial":"A.","affiliations":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":697454,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70189317,"text":"70189317 - 2017 - Human health screening and public health significance of contaminants of emerging concern detected in public water supplies","interactions":[],"lastModifiedDate":"2017-07-11T09:02:13","indexId":"70189317","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5331,"text":"Science of Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Human health screening and public health significance of contaminants of emerging concern detected in public water supplies","docAbstract":"The source water and treated drinking water from twenty five drinking water treatment plants (DWTPs) across the United States were sampled in 2010–2012. Samples were analyzed for 247 contaminants using 15 chemical and microbiological methods. Most of these contaminants are not regulated currently either in drinking water or in discharges to ambient water by the U. S. Environmental Protection Agency (USEPA) or other U.S. regulatory agencies. This analysis shows that there is little public health concern for most of the contaminants detected in treated water from the 25 DWTPs participating in this study. For vanadium, the calculated Margin of Exposure (MOE) was less than the screening MOE in two DWTPs. For silicon, the calculated MOE was less than the screening MOE in one DWTP. Additional study, for example a national survey may be needed to determine the number of people ingesting vanadium and silicon above a level of concern. In addition, the concentrations of lithium found in treated water from several DWTPs are within the range previous research has suggested to have a human health effect. Additional investigation of this issue is necessary. Finally, new toxicological data suggest that exposure to manganese at levels in public water supplies may present a public health concern which will require a robust assessment of this information.
","language":"English","publisher":"ScienceDirect","doi":"10.1016/j.scitotenv.2016.03.146","usgsCitation":"Benson, R., Conerly, O.D., Sander, W., Batt, A.L., Boone, J.S., Furlong, E.T., Glassmeyer, S., Kolpin, D.W., and Mash, H., 2017, Human health screening and public health significance of contaminants of emerging concern detected in public water supplies: Science of Total Environment, v. 579, p. 1643-1648, https://doi.org/10.1016/j.scitotenv.2016.03.146.","productDescription":"6 p.","startPage":"1643","endPage":"1648","ipdsId":"IP-061632","costCenters":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"links":[{"id":470103,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6277017","text":"External Repository"},{"id":343548,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"579","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5965b227e4b0d1f9f05b37df","contributors":{"authors":[{"text":"Benson, Robert","contributorId":194436,"corporation":false,"usgs":false,"family":"Benson","given":"Robert","email":"","affiliations":[],"preferred":false,"id":704125,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Conerly, Octavia D.","contributorId":194437,"corporation":false,"usgs":false,"family":"Conerly","given":"Octavia","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":704126,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sander, William","contributorId":194438,"corporation":false,"usgs":false,"family":"Sander","given":"William","email":"","affiliations":[],"preferred":false,"id":704127,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Batt, Angela L.","contributorId":184134,"corporation":false,"usgs":false,"family":"Batt","given":"Angela","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":704128,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Boone, J. Scott","contributorId":178697,"corporation":false,"usgs":false,"family":"Boone","given":"J.","email":"","middleInitial":"Scott","affiliations":[],"preferred":false,"id":704129,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Furlong, Edward T. 0000-0002-7305-4603 efurlong@usgs.gov","orcid":"https://orcid.org/0000-0002-7305-4603","contributorId":740,"corporation":false,"usgs":true,"family":"Furlong","given":"Edward","email":"efurlong@usgs.gov","middleInitial":"T.","affiliations":[{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":704130,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Glassmeyer, Susan T.","contributorId":72924,"corporation":false,"usgs":true,"family":"Glassmeyer","given":"Susan T.","affiliations":[],"preferred":false,"id":704131,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kolpin, Dana W. 0000-0002-3529-6505 dwkolpin@usgs.gov","orcid":"https://orcid.org/0000-0002-3529-6505","contributorId":1239,"corporation":false,"usgs":true,"family":"Kolpin","given":"Dana","email":"dwkolpin@usgs.gov","middleInitial":"W.","affiliations":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"preferred":true,"id":704119,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Mash, Heath","contributorId":184088,"corporation":false,"usgs":false,"family":"Mash","given":"Heath","affiliations":[],"preferred":false,"id":704132,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70189149,"text":"70189149 - 2017 - Paleoseismic potential of sublacustrine landslide records in a high-seismicity setting (south-central Alaska)","interactions":[],"lastModifiedDate":"2023-11-08T15:51:27.424228","indexId":"70189149","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2667,"text":"Marine Geology","active":true,"publicationSubtype":{"id":10}},"title":"Paleoseismic potential of sublacustrine landslide records in a high-seismicity setting (south-central Alaska)","docAbstract":"Sublacustrine landslide stratigraphy is considered useful for quantitative paleoseismology in low-seismicity settings. However, as the recharging of underwater slopes with sediments is one of the factors that governs the recurrence of slope failures, it is not clear if landslide deposits can provide continuous paleoseismic records in settings of frequent strong shaking. To test this, we selected three lakes in south-central Alaska that experienced a strong historical megathrust earthquake (the 1964 Mw9.2 Great Alaska Earthquake) and exhibit high sedimentation rates in their main basins (0.2 cm yr− 1–1.0 cm yr− 1). We present high-resolution reflection seismic data (3.5 kHz) and radionuclide data from sediment cores in order to investigate factors that control the establishment of a reliable landslide record. Seismic stratigraphy analysis reveals the presence of several landslide deposits in the lacustrine sedimentary infill. Most of these landslide deposits can be attributed to specific landslide events, as multiple landslide deposits sourced from different lacustrine slopes occur on a single stratigraphic horizon. We identify numerous events in the lakes: Eklutna Lake proximal basin (14 events), Eklutna Lake distal basin (8 events), Skilak Lake (7 events) and Kenai Lake (7 events). The most recent event in each basin corresponds to the historic 1964 megathrust earthquake. All events are characterized by multiple landslide deposits, which hints at a regional trigger mechanism, such as an earthquake (the synchronicity criterion). This means that the landslide record in each basin represents a record of past seismic events. Based on extrapolation of sedimentation rates derived from radionuclide dating, we roughly estimate a mean recurrence interval in the Eklutna Lake proximal basin, Eklutna Lake distal basin, Skilak Lake and Kenai Lake, at ~ 250 yrs., ~ 450 yrs., ~ 900 yrs. and ~ 450 yrs., respectively. This distinct difference in recording can be explained by variations in preconditioning factors like slope angle, slope recharging (sedimentation rate) and the sediment source area: faster slope recharging and a predominance of delta and alluvial fan failures, increase the sensitivity and lower the intensity threshold for slope instability. Also, the seismotectonic setting of the lakes has to be taken into account. This study demonstrates that sublacustrine landslides in several Alaskan lakes can be used as reliable recorders of strong earthquake shaking, when a multi-lake approach is used, and can enhance the temporal and spatial resolution of the paleoseismic record of south-central Alaska.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.margeo.2016.05.004","usgsCitation":"Praet, N., Moernaut, J., Van Daele, M., Boes, E., Haeussler, P.J., Strupler, M., Schmidt, S., Loso, M.G., and De Batist, M., 2017, Paleoseismic potential of sublacustrine landslide records in a high-seismicity setting (south-central Alaska): Marine Geology, v. 384, p. 103-119, https://doi.org/10.1016/j.margeo.2016.05.004.","productDescription":"17 p.","startPage":"103","endPage":"119","ipdsId":"IP-073802","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":489654,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GK6YZG","text":"USGS data release","linkHelpText":"Gridded Data from Multibeam Bathymetric Surveys of Eklutna, Kenai, and Skilak Lakes, Alaska"},{"id":470106,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.margeo.2016.05.004","text":"Publisher Index Page"},{"id":343261,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Kenai Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -153.5009765625,\n 59.108308258604964\n ],\n [\n -147.48046875,\n 59.108308258604964\n ],\n [\n -147.48046875,\n 61.37567331572747\n ],\n [\n -153.5009765625,\n 61.37567331572747\n ],\n [\n -153.5009765625,\n 59.108308258604964\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"384","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"595b5797e4b0d1f9f0536db1","contributors":{"authors":[{"text":"Praet, Nore","contributorId":194083,"corporation":false,"usgs":false,"family":"Praet","given":"Nore","email":"","affiliations":[],"preferred":false,"id":703165,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moernaut, Jasper","contributorId":194084,"corporation":false,"usgs":false,"family":"Moernaut","given":"Jasper","email":"","affiliations":[],"preferred":false,"id":703166,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Van Daele, Maarten 0000-0002-8530-4438","orcid":"https://orcid.org/0000-0002-8530-4438","contributorId":194085,"corporation":false,"usgs":false,"family":"Van Daele","given":"Maarten","email":"","affiliations":[{"id":27279,"text":"Department of Geology and Soil Science, Ghent University, Ghent, Belgium","active":true,"usgs":false}],"preferred":false,"id":703167,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boes, Evelien","contributorId":194086,"corporation":false,"usgs":false,"family":"Boes","given":"Evelien","email":"","affiliations":[],"preferred":false,"id":703168,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Haeussler, Peter J. 0000-0002-1503-6247 pheuslr@usgs.gov","orcid":"https://orcid.org/0000-0002-1503-6247","contributorId":503,"corporation":false,"usgs":true,"family":"Haeussler","given":"Peter","email":"pheuslr@usgs.gov","middleInitial":"J.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":703164,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Strupler, Michael","contributorId":194087,"corporation":false,"usgs":false,"family":"Strupler","given":"Michael","email":"","affiliations":[],"preferred":false,"id":703169,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schmidt, Sabine","contributorId":194088,"corporation":false,"usgs":false,"family":"Schmidt","given":"Sabine","email":"","affiliations":[],"preferred":false,"id":703170,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Loso, Michael G.","contributorId":146361,"corporation":false,"usgs":false,"family":"Loso","given":"Michael","email":"","middleInitial":"G.","affiliations":[{"id":12915,"text":"Alaska Pacific University","active":true,"usgs":false}],"preferred":false,"id":703171,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"De Batist, Marc 0000-0002-1625-2080","orcid":"https://orcid.org/0000-0002-1625-2080","contributorId":194089,"corporation":false,"usgs":false,"family":"De Batist","given":"Marc","email":"","affiliations":[],"preferred":false,"id":703172,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70188343,"text":"70188343 - 2017 - Modeling strong‐motion recordings of the 2010 Mw 8.8 Maule, Chile, earthquake with high stress‐drop subevents and background slip","interactions":[],"lastModifiedDate":"2017-06-06T16:25:26","indexId":"70188343","displayToPublicDate":"2017-02-01T00:00:00","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":"Modeling strong‐motion recordings of the 2010 Mw 8.8 Maule, Chile, earthquake with high stress‐drop subevents and background slip","title":"Modeling strong‐motion recordings of the 2010 Mw 8.8 Maule, Chile, earthquake with high stress‐drop subevents and background slip","docAbstract":"Strong‐motion recordings of the Mw 8.8 Maule earthquake were modeled using a compound rupture model consisting of (1) a background slip distribution with large correlation lengths, relatively low slip velocity, and long peak rise time of slip of about 10 s and (2) high stress‐drop subevents (asperities) on the deeper portion of the rupture with moment magnitudes 7.9–8.2, high slip velocity, and rise times of slip of about 2 s. In this model, the high‐frequency energy is not produced in the same location as the peak coseismic slip, but is generated in the deeper part of the rupture zone. Using synthetic seismograms generated for a plane‐layered velocity model, I find that the high stress‐drop subevents explain the observed Fourier spectral amplitude from about 0.1 to 1.0 Hz. Broadband synthetics (0–10 Hz) were calculated by combining deterministic synthetics derived from the background slip and asperities (≤1 Hz) with stochastic synthetics generated only at the asperities (≥1 Hz). The broadband synthetics produced response spectral accelerations with low bias compared to the data, for periods of 0.1–10 s. A subevent stress drop of 200–350 bars for the high‐frequency stochastic synthetics was found to bracket the observed spectral accelerations at frequencies greater than 1 Hz. For most of the stations, the synthetics had durations of the Arias intensity similar to the observed records.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120160127","usgsCitation":"Frankel, A.D., 2017, Modeling strong‐motion recordings of the 2010 Mw 8.8 Maule, Chile, earthquake with high stress‐drop subevents and background slip: Bulletin of the Seismological Society of America, v. 107, no. 1, p. 372-386, https://doi.org/10.1785/0120160127.","productDescription":"15 p.","startPage":"372","endPage":"386","ipdsId":"IP-074295","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":342191,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Chile","city":"Maule","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74,\n -33\n ],\n [\n -70,\n -33\n ],\n [\n -70,\n -39\n ],\n [\n -74,\n -39\n ],\n [\n -74,\n -33\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"107","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-22","publicationStatus":"PW","scienceBaseUri":"5937bf2ee4b0f6c2d0d9c760","contributors":{"authors":[{"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":697332,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ]}