Site characterization is a critical component of seismic hazards studies, especially in the development and use of ground motion models (GMMs). One such parameter, kappa (Κ0), represents local site attenuation and effectively describes regional variations in ground motion [1]. However, estimates of Κ0 are limited. We estimate the site parameter Κ0 for 296 broadband and accelerometer stations in the San Francisco Bay area using 225 local events with M>3.5. We first invert for kappa on the high-frequency slope of every record at a station, which represents full attenuation effects [2]. We then perform weighted regression of kappa versus distance to obtain expected kappa at the site (Κ0). We find that Κ0 varies extensively in the San Francisco Bay area, with values ranging from 0.004-0.113 s, thus representing the heterogeneous geologic conditions in the region. Κ0 estimates from this study will be useful to consider in future seismic hazard analyses and ground motion studies in the Bay Area.
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The products of aggregation vary from simple ash clusters to large, complexly layered accretionary lapilli. Here we detail the micro-stratigraphy of a single population of accretionary lapilli that grew during the ∼431 CE Tierra Blanca Joven eruption from Ilopango Caldera, El Salvador. The accretionary lapilli were sampled 10 km from the caldera source within a sequence of ash-rich pyroclastic density current deposits and intercalated fall material, known as unit D, which is traceable >40 km from Ilopango. Scanning electron microscopy and image analysis reveal common facies that form distinct layers within the accretionary lapilli. Each facies is distinguished by quantitative and qualitative variations in particle size distribution, porosity, and particle fabric. We infer that these textures resulted from aggregation conditions that differed in terms of liquid water availability, particle concentration and grain size distributions. In our proposed model, a characteristic sequence of facies accreted from core to rim in the accretionary lapilli during passage through ash clouds generated by vent-derived plumes and pyroclastic density currents. The accretionary lapilli are mostly composed of smaller aggregates (ash clusters, ash pellets) and grew predominantly by accretion of already-formed aggregates, rather than by grain-by-grain accretion of individual particles. This finding is consistent with observations of rapid aggregate growth in volcanic plumes, suggesting a common evolutionary pathway for accretionary lapilli formation across diverse eruptions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2022.107670","usgsCitation":"Hoult, H., Brown, R., Van Eaton, A.R., Hernandez, W., Dobson, K., and Woodward, B., 2022, Growth of complex volcanic ash aggregates in the Tierra Blanca Joven eruption of Ilopango Caldera, El Salvador: Journal of Volcanology and Geothermal Research, v. 431, 107670, 14 p., https://doi.org/10.1016/j.jvolgeores.2022.107670.","productDescription":"107670, 14 p.","ipdsId":"IP-130466","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467162,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dro.dur.ac.uk/37183/","text":"Publisher Index Page"},{"id":466640,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"El Salvador","otherGeospatial":"Ilopango caldera","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.5,\n 13.9\n ],\n [\n -89.5,\n 13.5\n ],\n [\n -88.9,\n 13.5\n ],\n [\n -88.9,\n 13.9\n ],\n [\n -89.5,\n 13.9\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"431","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hoult, Henry","contributorId":349109,"corporation":false,"usgs":false,"family":"Hoult","given":"Henry","affiliations":[{"id":68342,"text":"Durham University, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":924017,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brown, Richard J.","contributorId":191216,"corporation":false,"usgs":false,"family":"Brown","given":"Richard J.","affiliations":[],"preferred":false,"id":924018,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Van Eaton, Alexa R. 0000-0001-6646-4594 avaneaton@usgs.gov","orcid":"https://orcid.org/0000-0001-6646-4594","contributorId":184079,"corporation":false,"usgs":true,"family":"Van Eaton","given":"Alexa","email":"avaneaton@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":924019,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hernandez, Walter","contributorId":218214,"corporation":false,"usgs":false,"family":"Hernandez","given":"Walter","email":"","affiliations":[{"id":39782,"text":"Ministerio de Medio Ambiente y Recursos Naturales, San Salvador, El Salvador","active":true,"usgs":false}],"preferred":false,"id":924020,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dobson, Katherine J","contributorId":349112,"corporation":false,"usgs":false,"family":"Dobson","given":"Katherine J","affiliations":[{"id":68342,"text":"Durham University, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":924021,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Woodward, Bryan","contributorId":349113,"corporation":false,"usgs":false,"family":"Woodward","given":"Bryan","affiliations":[{"id":68342,"text":"Durham University, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":924022,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70236917,"text":"70236917 - 2022 - Earthquake early warning: Toward modeling optimal protective actions","interactions":[],"lastModifiedDate":"2023-01-13T18:00:20.141967","indexId":"70236917","displayToPublicDate":"2022-09-20T10:16:43","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Earthquake early warning: Toward modeling optimal protective actions","docAbstract":"Over the past few years early earthquake warning systems have been incorporated into earthquake preparation efforts in many locations around the globe. These systems provide an excellent opportunity for advanced warning of ground shaking and other hazards associated with earthquakes. This study aims to optimize this advanced warning for individuals inside a building when the alert is received. A conceptual framework is presented for modeling optimal protective actions during earthquakes when an earthquake early warning system is available to the individual. Various factors impacting the ability of an individual to take protective action are considered for the modeling process.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings from the 12th national conference on earthquake engineering","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"12th National Conference on Earthquake Engineering","conferenceDate":"Jun 27 - Jul 1, 2022","conferenceLocation":"Salt Lake City, UT","language":"English","publisher":"Earthquake Engineering Research Institute","usgsCitation":"Wood, M., Zhang, X., Zhao, X., McBride, S., Luco, N., Baldwin, D., and Covas, T., 2022, Earthquake early warning: Toward modeling optimal protective actions, in Proceedings from the 12th national conference on earthquake engineering, Salt Lake City, UT, Jun 27 - Jul 1, 2022, 6 p.","productDescription":"6 p.","ipdsId":"IP-134821","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":411873,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":411872,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://12ncee.org/program/proceedings","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wood, M.","contributorId":300884,"corporation":false,"usgs":false,"family":"Wood","given":"M.","email":"","affiliations":[],"preferred":false,"id":861618,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhang, X.","contributorId":30193,"corporation":false,"usgs":true,"family":"Zhang","given":"X.","email":"","affiliations":[],"preferred":false,"id":861619,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhao, X.","contributorId":68486,"corporation":false,"usgs":true,"family":"Zhao","given":"X.","email":"","affiliations":[],"preferred":false,"id":861620,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McBride, Sara K. 0000-0002-8062-6542","orcid":"https://orcid.org/0000-0002-8062-6542","contributorId":206933,"corporation":false,"usgs":true,"family":"McBride","given":"Sara K.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":852700,"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":852701,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Baldwin, D.","contributorId":300876,"corporation":false,"usgs":false,"family":"Baldwin","given":"D.","email":"","affiliations":[],"preferred":false,"id":861621,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Covas, T.","contributorId":300885,"corporation":false,"usgs":false,"family":"Covas","given":"T.","email":"","affiliations":[],"preferred":false,"id":861622,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70248894,"text":"70248894 - 2022 - Integrated strategies for enhanced rapid earthquake shaking, ground failure, and impact estimation employing remotely sensed and ground truth constraints","interactions":[],"lastModifiedDate":"2023-09-25T14:45:00.964132","indexId":"70248894","displayToPublicDate":"2022-09-20T09:42:14","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Integrated strategies for enhanced rapid earthquake shaking, ground failure, and impact estimation employing remotely sensed and ground truth constraints","docAbstract":"Estimating earthquake impacts using physical or empirical models is challenging because the three components of loss estimation-shaking, exposure, and vulnerabilities-entail inherent uncertainties. Loss modeling in near-real-time adds additional uncertainties, yet expectations for actionable information with a reasonable level of confidence in the results are real. The modeling approaches described herein augment inherently uncertain prior hazard and loss models with an integrated strategy for updating these priors with ground-truth observations, thereby greatly reducing their uncertainties. Two strategies are employed. Early reports of casualties are used in a Bayesian updating fashion to constrain the possible range of fatalities and to lower the prior models' uncertainties. Additionally, remotely sensed satellite radar data, in the form of a Damage Proxy Map (or DPM), are used in a Bayesian causal graph framework combined with machine learning to optimize the mapping among the physical processes that cause shaking-based building damage, landslides, and liquefaction to prior expectation models. The casual graph framework also affords the potential for removing anthropogenetic noise contained in the imagery. Ultimately, our two-fold model updating strategy will accommodate key ground-truth observations such as fatality reports, locations of building damage, and ground failure reports to converge on actual losses more rapidly.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings from the 12th national conference on earthquake engineering","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"12th National Conference on Earthquake Engineering","conferenceDate":"June 27 - July 1, 2022","conferenceLocation":"Salt Lake City, UT","language":"English","usgsCitation":"Wald, D.J., Xu, S., Noh, H., Dimasaka, J., Jaiswal, K.S., Allstadt, K.E., and Engler, D.T., 2022, Integrated strategies for enhanced rapid earthquake shaking, ground failure, and impact estimation employing remotely sensed and ground truth constraints, in Proceedings from the 12th national conference on earthquake engineering, Salt Lake City, UT, June 27 - July 1, 2022, 5 p.","productDescription":"5 p.","ipdsId":"IP-134859","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":421130,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":421115,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://eeri.org/what-we-offer/digital-library/?lid=13294","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","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":884119,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Xu, Susu","contributorId":300127,"corporation":false,"usgs":false,"family":"Xu","given":"Susu","email":"","affiliations":[{"id":65025,"text":"Stony Brook University, NY, USA","active":true,"usgs":false}],"preferred":false,"id":884120,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Noh, H.","contributorId":330155,"corporation":false,"usgs":false,"family":"Noh","given":"H.","email":"","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":884121,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dimasaka, J.","contributorId":330154,"corporation":false,"usgs":false,"family":"Dimasaka","given":"J.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":884122,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"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":884123,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Allstadt, Kate E. 0000-0003-4977-5248","orcid":"https://orcid.org/0000-0003-4977-5248","contributorId":138704,"corporation":false,"usgs":true,"family":"Allstadt","given":"Kate","email":"","middleInitial":"E.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":884124,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Engler, Davis T. 0000-0002-7133-3545","orcid":"https://orcid.org/0000-0002-7133-3545","contributorId":265962,"corporation":false,"usgs":true,"family":"Engler","given":"Davis","email":"","middleInitial":"T.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":884125,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70237183,"text":"70237183 - 2022 - Potential effects of environmental conditions on prairie dog flea development and implications for sylvatic plague epizootics","interactions":[],"lastModifiedDate":"2022-10-17T16:39:15.527088","indexId":"70237183","displayToPublicDate":"2022-09-20T08:31:54","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1443,"text":"EcoHealth","active":true,"publicationSubtype":{"id":10}},"title":"Potential effects of environmental conditions on prairie dog flea development and implications for sylvatic plague epizootics","docAbstract":"Fleas are common ectoparasites of vertebrates worldwide and vectors of many pathogens causing disease, such as sylvatic plague in prairie dog colonies. Development of fleas is regulated by environmental conditions, especially temperature and relative humidity. Development rates are typically slower at low temperatures and faster at high temperatures, which are bounded by lower and upper thresholds where development is reduced. Prairie dogs and their associated fleas (mostly Oropsylla spp) live in burrows that moderate outside environmental conditions, remaining cooler in summer and warmer in winter. We found burrow microclimates were characterized by stable daily temperatures and high relative humidity, with temperatures increasing from spring through summer. We previously showed temperature increases corresponded with increasing off-host flea abundance. To evaluate how changes in temperature could affect future prairie dog flea development and abundance, we used development rates of O. montana (a species related to prairie dog fleas), determined how prairie dog burrow microclimates are affected by ambient weather, and combined these results to develop a predictive model. Our model predicts burrow temperatures and flea development rates will increase during the twenty-first century, potentially leading to higher flea abundance and an increased probability of plague epizootics if Y. pestis is present.
","language":"English","publisher":"Springer","doi":"10.1007/s10393-022-01615-6","usgsCitation":"Samuel, M., Poje, J.E., Rocke, T.E., and Metzger, M.E., 2022, Potential effects of environmental conditions on prairie dog flea development and implications for sylvatic plague epizootics: EcoHealth, v. 19, p. 365-377, https://doi.org/10.1007/s10393-022-01615-6.","productDescription":"13 p.","startPage":"365","endPage":"377","ipdsId":"IP-136023","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":435688,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93TCY21","text":"USGS data release","linkHelpText":"Temperatures of black-tailed prairie dog burrows through the U.S. Great Plains"},{"id":407857,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","state":"Colorado, Montana, New Mexico, North Dakota, South Dakota","otherGeospatial":"Buffalo Gap National Grassland, Bureau of Land Management land surrounding Roswell, Charles M. Russell National Wildlife Refuge, Lower Brule Sioux Reservation, Pueblo Chemical Depot, Theodore Roosevelt National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.841796875,\n 47.42437092240519\n ],\n [\n -106.09771728515625,\n 47.42437092240519\n ],\n [\n -106.09771728515625,\n 48.050545996347665\n ],\n [\n -107.841796875,\n 48.050545996347665\n ],\n [\n -107.841796875,\n 47.42437092240519\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.62579345703125,\n 46.84704298339386\n ],\n [\n -103.271484375,\n 46.84704298339386\n ],\n [\n -103.271484375,\n 47.0439255239614\n ],\n [\n -103.62579345703125,\n 47.0439255239614\n ],\n [\n -103.62579345703125,\n 46.84704298339386\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.48640441894531,\n 47.53806480221706\n ],\n [\n -103.23921203613281,\n 47.53806480221706\n ],\n [\n -103.23921203613281,\n 47.63717164223885\n ],\n [\n -103.48640441894531,\n 47.63717164223885\n ],\n [\n -103.48640441894531,\n 47.53806480221706\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.2440185546875,\n 43.014689161895184\n ],\n [\n -101.62078857421874,\n 43.014689161895184\n ],\n [\n -101.62078857421874,\n 44.01652134387754\n ],\n [\n -103.2440185546875,\n 44.01652134387754\n ],\n [\n -103.2440185546875,\n 43.014689161895184\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.118408203125,\n 43.99281450048989\n ],\n [\n -99.525146484375,\n 43.99281450048989\n ],\n [\n -99.525146484375,\n 44.31795304574349\n ],\n [\n -100.118408203125,\n 44.31795304574349\n ],\n [\n -100.118408203125,\n 43.99281450048989\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.41818237304688,\n 38.22307753495298\n ],\n [\n -104.2547607421875,\n 38.22307753495298\n ],\n [\n -104.2547607421875,\n 38.38795699631396\n ],\n [\n -104.41818237304688,\n 38.38795699631396\n ],\n [\n -104.41818237304688,\n 38.22307753495298\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.9139404296875,\n 32.974107959689235\n ],\n [\n -104.1119384765625,\n 32.974107959689235\n ],\n [\n -104.1119384765625,\n 33.696922692957685\n ],\n [\n -104.9139404296875,\n 33.696922692957685\n ],\n [\n -104.9139404296875,\n 32.974107959689235\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"19","noUsgsAuthors":false,"publicationDate":"2022-09-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Samuel, Michael D.","contributorId":206351,"corporation":false,"usgs":false,"family":"Samuel","given":"Michael D.","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":853582,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Poje, Julia E.","contributorId":206595,"corporation":false,"usgs":false,"family":"Poje","given":"Julia","email":"","middleInitial":"E.","affiliations":[{"id":37348,"text":"Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin – Madison, Madison, WI, 53705","active":true,"usgs":false}],"preferred":false,"id":853583,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rocke, Tonie E. 0000-0003-3933-1563 trocke@usgs.gov","orcid":"https://orcid.org/0000-0003-3933-1563","contributorId":2665,"corporation":false,"usgs":true,"family":"Rocke","given":"Tonie","email":"trocke@usgs.gov","middleInitial":"E.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":853584,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Metzger, Marco E.","contributorId":297166,"corporation":false,"usgs":false,"family":"Metzger","given":"Marco","email":"","middleInitial":"E.","affiliations":[{"id":64309,"text":"Vector-Borne Disease Section, California Department of Public Health, Ontario, California, 91764","active":true,"usgs":false}],"preferred":false,"id":853585,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70236827,"text":"sir20225087 - 2022 - Sixty years of channel adjustments to dams in the two segments of the Missouri National Recreational River, South Dakota and Nebraska","interactions":[],"lastModifiedDate":"2022-09-27T12:22:18.320502","indexId":"sir20225087","displayToPublicDate":"2022-09-20T06:44:21","publicationYear":"2022","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":"2022-5087","displayTitle":"Sixty Years of Channel Adjustments to Dams in the Two Segments of the Missouri National Recreational River, South Dakota and Nebraska","title":"Sixty years of channel adjustments to dams in the two segments of the Missouri National Recreational River, South Dakota and Nebraska","docAbstract":"The Missouri National Recreational River (MNRR) consists of two Missouri River segments managed by the National Park Service on the border of South Dakota and Nebraska. Both river segments are unchannelized and maintain much of their pre-dam channel form, but upstream dams have caused reductions in peak flow magnitudes and sediment supply. The 39-mile segment is located between Fort Randall and Gavins Point Dams, transitioning from a riverine process domain to a distributary delta process domain in the headwaters of Lewis and Clark Lake. The 59-mile segment, an entirely riverine process domain, is downstream from Gavins Point Dam, the most downstream main channel dam on the Missouri River, and upstream from a highly altered navigation channel extending more than 1,000 kilometers downstream to St. Louis, Missouri. The National Park Service seeks to preserve the outstandingly remarkable natural, cultural, and recreational values of the MNRR. There is a particular need to understand bank-erosion processes to guide management decisions related to bank-erosion controls.
Changes in channel shape, as measured in topographic cross sections surveyed every 5–10 years since the mid-20th century, document bed incision (bed-elevation lowering) in riverine process domains, a mix of aggradation and incision in the delta, and aggradation in Lewis and Clark Lake. Channel incision is greatest in the 59-mile segment, where mean thalweg (deepest point in a cross section) incision is 3.5 meters, and net incision in the thalweg greater than 5 meters was observed at a cross section 93 kilometers downstream from Gavins Point Dam. Analysis of topographic cross sections also indicates that rates of bed-elevation change since 1960 were lowest in the 39-mile river segment and in Lewis and Clark Lake. Rates of bed-elevation change were higher in the delta and 59-mile segments but lower in cross sections near Gavins Point Dam where the channel is confined by bank revetment on both banks and the bed has coarsened substantially since completion of the dam. Several large floods in recent decades, including a post-dam record flood event in 2011, scoured the bed and deposited large high-elevation sandbars in both river segments, especially in the 59-mile segment. Analysis of topographic cross-sections indicates the 2011 flood event caused substantial erosion and deposition, low magnitude net incision in the river segments and delta, and considerable sediment aggradation in the lake. Surveys taken after the 2011 flood in the 59-mile segment indicate a trend of sediment rearrangement and channel recovery following large floods, with the highest parts of the bed, sandbars, eroding and lowering while sediment was deposited on the deepest parts of the channel, which increased in elevation.
Inundation modeling results indicate that the narrower valley in the 39-mile segment results in a higher percentage of the flood plain being inundated by flooding relative to the 59-mile segment, which has a much wider valley. Likewise, bed incision in the 59-mile segment has increased channel capacity and resulted in a modern channel corridor inset into a higher flood-plain surface. The inset flood plain was inundated by the 2011 flood, but the pre-dam flood plain is rarely inundated. Analysis of channel boundaries over time indicates that pre-dam channel-migration rates were as much as five times larger than modern channel-migration rates in the 59-mile segment. Bank erosion in the 59-mile segment has primarily been into post-1894 channel deposits; bank-erosion rates are comparably very low in the 39-mile segment. Analysis of channel-migration zones indicates that most erosion is isolated to local hot spots and is used to establish predictions for 10 and 20 years into the future based on past movement rates in both MNRR segments. Long-term bed-elevation and planform trends indicate that rates of adjustment in the 59-mile segment are slowing and may be approaching a new equilibrium, but recent large floods and spatial variability contribute to considerable uncertainty. Additional monitoring of channel morphology would be needed to confirm trends observed in this analysis.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225087","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Elliott, C.M., and Jacobson, R.B., 2022, Sixty years of channel adjustments to dams in the two segments of the Missouri National Recreational River, South Dakota and Nebraska: U.S. Geological Survey Scientific Investigations Report 2022–5087, 75 p., https://doi.org/10.3133/sir20225087.","productDescription":"Report: ix, 75 p.; Data Release","numberOfPages":"90","onlineOnly":"Y","ipdsId":"IP-128033","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":407044,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225087/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":406985,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RZPNJR","text":"USGS data release","linkHelpText":"Channel geometry, banklines and floodplain inundation over a range of discharges in two segments of the Missouri National Recreational River, South Dakota and Nebraska, 1955–2018"},{"id":406984,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5087/images"},{"id":406983,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5087/sir20225087.XML"},{"id":406982,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5087/sir20225087.pdf","text":"Report","size":"27.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5087"},{"id":406981,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5087/coverthb.jpg"}],"country":"United States","state":"Nebraska, South Dakota","otherGeospatial":"Missouri National Recreational River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.61328125,\n 42.49640294093705\n ],\n [\n -96.5313720703125,\n 42.49640294093705\n ],\n [\n -96.5313720703125,\n 43.1450861841603\n ],\n [\n -98.61328125,\n 43.1450861841603\n ],\n [\n -98.61328125,\n 42.49640294093705\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Columbia Environmental Research Center
U.S. Geological Survey
4200 New Haven Road
Columbia, MO 65201
Rockland habitat in South Florida, USA, is a threatened ecosystem that has been lost, fragmented, or degraded because of urbanization or other anthropogenic disturbance. Furthermore, low-lying islands and coastal areas are experiencing sea level rise (SLR) and an increased frequency and intensity of tidal flooding, putting rockland habitats there at increasing risk of ecological change. We evaluated changes in the extent of rockland habitat under various scenarios of future SLR, tidal flooding, and human development for two endemic state-listed threatened species of snakes, the Rim Rock Crowned Snake (Tantilla oolitica) and the Key Ring-necked Snake (Diadophis punctatus acricus). Both snakes are restricted to South Florida. We used recent and historical species' records to determine each species' habitat range. We then estimated the extent of future habitat loss due to SLR and continued human development, as well as degradation of the remaining habitat. We also asked whether the future potential drivers of habitat loss and degradation differ between the two species and across their habitat ranges. We predicted that saltwater intrusion could negatively affect rocklands by 2050, resulting in degradation of 80% of the existing habitat because of an anticipated 42 cm of SLR. Moreover, our model suggests short-term stochastic events such as storm surge and high tides may increasingly saturate the root zone of rockland vegetation before complete inundation. Under the extreme scenario, we predict most of the rockland habitat used by these two species of snakes may be inundated by 2080. Under the extreme SLR scenario, current rocklands are likely to convert to more halophytic habitat (mangrove or salt marsh wetland) within 50–60 years. Under the low scenario, 31% of rockland habitat may be lost due to human development by 2030. Therefore, mitigation actions may help to conserve specialist species within rockland habitat threatened by human activities and climate change.
","language":"English","publisher":"Wiley","doi":"10.1111/csp2.12802","usgsCitation":"Subedi, S.C., Walls, S., Barichivich, W., Boyles, R., Ross, M.S., Hogan, J.A., and Tupy, J.A., 2022, Future changes in habitat availability for two specialist snake species in the imperiled rocklands of South Florida, U.S.A.: Conservation Science and Practice, v. 4, no. 10, e12802, 12 p.; Data Release, https://doi.org/10.1111/csp2.12802.","productDescription":"e12802, 12 p.; Data Release","ipdsId":"IP-137625","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":446400,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/csp2.12802","text":"Publisher Index Page"},{"id":406959,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":414897,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TRHCLU"}],"country":"United States","state":"Florida","county":"Miami-Dade County, Monroe County","otherGeospatial":"Florida Keys","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.80895642460405,\n 24.453130854268252\n ],\n [\n -81.0587041726492,\n 24.65004795330806\n ],\n [\n -80.4614884497,\n 24.968530156464837\n ],\n [\n -80.12555950968002,\n 25.52220481330157\n ],\n [\n -80.21887446639069,\n 25.505361805372416\n ],\n [\n -80.45776075557028,\n 25.296312583579905\n ],\n [\n -80.69664704475028,\n 24.975295417537964\n ],\n [\n -81.30506056250486,\n 24.741608543790022\n ],\n [\n -81.58873803090547,\n 24.77889253696236\n ],\n [\n -81.857485106233,\n 24.60254250373599\n ],\n [\n -81.80895642460405,\n 24.453130854268252\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"4","issue":"10","noUsgsAuthors":false,"publicationDate":"2022-08-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Subedi, Suresh C. 0000-0001-8689-0689","orcid":"https://orcid.org/0000-0001-8689-0689","contributorId":217984,"corporation":false,"usgs":false,"family":"Subedi","given":"Suresh","email":"","middleInitial":"C.","affiliations":[{"id":7017,"text":"Florida International University","active":true,"usgs":false}],"preferred":false,"id":852138,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walls, Susan C. 0000-0001-7391-9155","orcid":"https://orcid.org/0000-0001-7391-9155","contributorId":3055,"corporation":false,"usgs":true,"family":"Walls","given":"Susan C.","affiliations":[],"preferred":true,"id":852139,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barichivich, William 0000-0003-1103-6861","orcid":"https://orcid.org/0000-0003-1103-6861","contributorId":215988,"corporation":false,"usgs":true,"family":"Barichivich","given":"William","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":852140,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boyles, Ryan 0000-0001-9272-867X","orcid":"https://orcid.org/0000-0001-9272-867X","contributorId":221983,"corporation":false,"usgs":true,"family":"Boyles","given":"Ryan","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":852141,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ross, Michael S.","contributorId":202431,"corporation":false,"usgs":false,"family":"Ross","given":"Michael","email":"","middleInitial":"S.","affiliations":[{"id":36434,"text":"Florida International University, Miami, FL","active":true,"usgs":false}],"preferred":false,"id":852142,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hogan, J. Aaron","contributorId":266106,"corporation":false,"usgs":false,"family":"Hogan","given":"J.","email":"","middleInitial":"Aaron","affiliations":[{"id":54906,"text":"Department of Biological Sciences, Florida International University, Miami, FL 33199, USA","active":true,"usgs":false}],"preferred":false,"id":852143,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Tupy, John A.","contributorId":296678,"corporation":false,"usgs":false,"family":"Tupy","given":"John","email":"","middleInitial":"A.","affiliations":[{"id":64127,"text":"U.S. Fish and Wildlife Service Mississippi Ecological Services Office","active":true,"usgs":false}],"preferred":false,"id":852144,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70236764,"text":"70236764 - 2022 - Comparative susceptibilities of selected California Chinook salmon and steelhead populations to isolates of L Genogroup Infectious Hematopoietic Necrosis Virus (IHNV)","interactions":[],"lastModifiedDate":"2022-09-19T13:15:31.160078","indexId":"70236764","displayToPublicDate":"2022-09-19T08:09:56","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5762,"text":"Animals","active":true,"publicationSubtype":{"id":10}},"title":"Comparative susceptibilities of selected California Chinook salmon and steelhead populations to isolates of L Genogroup Infectious Hematopoietic Necrosis Virus (IHNV)","docAbstract":"Salmonid species demonstrate varied susceptibility to the viral pathogen infectious hematopoietic necrosis virus (IHNV). In California conservation hatcheries, juvenile Chinook salmon (Oncorhynchus tshawytscha) have experienced disease outbreaks due to L genogroup IHNV since the 1940s, while indigenous steelhead (anadromous O. mykiss) appear relatively resistant. To characterize factors contributing to the losses of California salmonid fish due to IHNV, three populations of Chinook salmon and two populations of steelhead native to California watersheds were compared in controlled waterborne challenges with California L genogroup IHNV isolates at viral doses of 104–106 pfu mL−1. Chinook salmon fry were moderately to highly susceptible (CPM = 47–87%) when exposed to subgroup LI and LII IHNV. Susceptibility to mortality decreased with increasing age and also with a higher temperature. Mortality for steelhead fry exposed to two IHNV isolates was low (CPM = 1.3–33%). There was little intraspecies variation in susceptibility among populations of Chinook salmon and no differences in virulence between viruses strains. Viral persistence was demonstrated by the isolation of low levels of infectious IHNV from the skin of two juvenile Chinook salmon at 215 d post exposure. The persistence of the virus among Chinook salmon used for stocking into Lake Oroville may be an explanation for the severe epidemics of IHN at the Feather River hatchery in 1998–2002.
","language":"English","publisher":"MDPI","doi":"10.3390/ani12131733","usgsCitation":"Bendorf, C.M., Yun, S.C., Kurath, G., and Hedrick, R.P., 2022, Comparative susceptibilities of selected California Chinook salmon and steelhead populations to isolates of L Genogroup Infectious Hematopoietic Necrosis Virus (IHNV): Animals, v. 12, no. 13, 1733, 16 p., https://doi.org/10.3390/ani12131733.","productDescription":"1733, 16 p.","ipdsId":"IP-141052","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":446405,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/ani12131733","text":"Publisher Index Page"},{"id":406949,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"12","issue":"13","noUsgsAuthors":false,"publicationDate":"2022-07-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Bendorf, Christin M.","contributorId":296669,"corporation":false,"usgs":false,"family":"Bendorf","given":"Christin","email":"","middleInitial":"M.","affiliations":[{"id":64125,"text":"Department of Medicine and Epidemiology, School of Veterinary Medicine, University of California, Davis, California 95616 USA","active":true,"usgs":false}],"preferred":false,"id":852118,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yun, Susan C.","contributorId":296670,"corporation":false,"usgs":false,"family":"Yun","given":"Susan","email":"","middleInitial":"C.","affiliations":[{"id":64125,"text":"Department of Medicine and Epidemiology, School of Veterinary Medicine, University of California, Davis, California 95616 USA","active":true,"usgs":false}],"preferred":false,"id":852119,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kurath, Gael 0000-0003-3294-560X","orcid":"https://orcid.org/0000-0003-3294-560X","contributorId":220175,"corporation":false,"usgs":true,"family":"Kurath","given":"Gael","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":852120,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hedrick, Ronald P.","contributorId":120917,"corporation":false,"usgs":false,"family":"Hedrick","given":"Ronald","email":"","middleInitial":"P.","affiliations":[{"id":6643,"text":"University of California - Berkeley","active":true,"usgs":false}],"preferred":false,"id":852121,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70247986,"text":"70247986 - 2022 - Western U.S. deformation models for the 2023 update to the U.S. National Seismic Hazard Model","interactions":[],"lastModifiedDate":"2023-08-31T13:26:05.795028","indexId":"70247986","displayToPublicDate":"2022-09-19T06:58:03","publicationYear":"2022","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":"Western U.S. deformation models for the 2023 update to the U.S. National Seismic Hazard Model","docAbstract":"This report describes geodetic and geologic information used to constrain deformation models of the 2023 update to the National Seismic Hazard Model (NSHM), a set of deformation models to interpret these data, and their implications for earthquake rates in the western United States. Recent updates provide a much larger data set of Global Positioning System crustal velocities than used in the 2014 NSHM, as well as hundreds of new faults considered as active sources for the 2023 NSHM. These data are interpreted by four geodetic models of deformation that estimate fault slip rates and their uncertainties together with off‐fault moment release rates. Key innovations in the 2023 NSHM relative to past practice include (1) the addition of two new (in addition to two existing) deformation models, (2) the revision and expansion of the geologic slip rate database, (3) accounting for fault creep through development of a creep‐rate model that is employed by the four deformation models, and (4) accounting for time‐dependent earthquake‐cycle effects through development of viscoelastic models of the earthquake cycle along the San Andreas fault and the Cascadia subduction zone. The effort includes development of a geologic deformation model that complements the four geodetic models. The current deformation models provide a new assessment of outstanding discrepancies between geologic and geodetic slip rates, at the same time highlighting the need for both geologic and geodetic slip rates to robustly inform the earthquake rate model.
Global Positioning System (GPS) velocity solutions of the western United States (WUS) are compiled from several sources of field networks and data processing centers for the 2023 U.S. Geological Survey National Seismic Hazard Model (NSHM). These solutions include both survey and continuous‐mode GPS velocity measurements. I follow the data processing procedure of Parsons et al. (2013) for the Uniform California Earthquake Rupture Forecast, version 3 and McCaffrey, Bird, et al. (2013) and Zeng and Shen (2013) for their WUS deformation models in support of the 2014 NSHM update. All GPS velocity vectors are first rotated to a common North American reference frame. I edit the velocities to remove outliers and data with significant influence from volcanism. The solutions are then combined into a final GPS velocity field consisting of 4979 horizontal velocity vectors. I compute strain rates based on these GPS velocities using the method of Shen et al. (2015). These strain rates correlate closely with seismicity rates in the WUS. The results are used for WUS geodetic and geologic deformation modeling in support of the 2023 NSHM update.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220220180","usgsCitation":"Zeng, Y., 2022, GPS velocity field of the Western United States for the 2023 National Seismic Hazard Model update: Seismological Research Letters, v. 93, no. 6, p. 3121-3134, https://doi.org/10.1785/0220220180.","productDescription":"14 p.","startPage":"3121","endPage":"3134","ipdsId":"IP-142151","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":407046,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -125.94726562499999,\n 28.459033019728043\n ],\n [\n -103.0078125,\n 28.459033019728043\n ],\n [\n -103.0078125,\n 49.61070993807422\n ],\n [\n -125.94726562499999,\n 49.61070993807422\n ],\n [\n -125.94726562499999,\n 28.459033019728043\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"93","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-09-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Zeng, Yuehua 0000-0003-1161-1264 zeng@usgs.gov","orcid":"https://orcid.org/0000-0003-1161-1264","contributorId":145693,"corporation":false,"usgs":true,"family":"Zeng","given":"Yuehua","email":"zeng@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":852329,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70242153,"text":"70242153 - 2022 - Western U.S. geologic deformation model for use in the U.S. National Seismic Hazard Model 2023","interactions":[],"lastModifiedDate":"2023-04-10T11:52:49.463107","indexId":"70242153","displayToPublicDate":"2022-09-19T06:48:31","publicationYear":"2022","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":"Western U.S. geologic deformation model for use in the U.S. National Seismic Hazard Model 2023","docAbstract":"Fault geometry and slip rates are key input data for geologic deformation models, which are a fundamental component of probabilistic seismic hazard analyses (PSHAs). However, geologic sources for PSHA have traditionally been limited to faults with field‐based slip rate constraints, which results in underrepresentation of known, but partially characterized, active faults. Here, we evaluate fault geometries and geologic fault slip rates for the western United States to construct a new geologic deformation model for the U.S. National Seismic Hazard Model 2023 update (NSHM23). In previous NSHM iterations, only faults with published geologic slip rates were included. In the NSHM23 fault sections database compilation, this inclusion criterion was expanded to include faults without known slip rates. In this updated geologic deformation model, preferred slip rates and associated uncertainty distributions are incorporated for faults with slip rates derived from field studies. For faults without site‐specific slip rates, we evaluate a suite of uncertainty distributions derived from broad slip rate categories in the U.S. Geological Survey Quaternary Fault and Fold Database. Preferred slip rate distributions are selected via comparison with geodetic strain rates in tectonic subregions. The resultant moment of the geologic deformation model is generally in deficit compared with the geodetic moment within each region. Primary advances in the NSHM23 geologic deformation model include the following: (1) slip rates are presented as preferred values with uncertainties rather than single values; (2) the representation of the western U.S. active fault network is more complete; and (3) the geologic deformation model leverages geodetic information to assess regional constraints on geologic fault slip rates.
Mason Spur is a deeply eroded Middle Miocene to Pleistocene (c. 13 to 0.37 Ma) volcanic complex in southern Victoria Land, within the West Antarctic Rift System (WARS). The oldest rocks include a large volume of trachyte ignimbrites that provided abundant volcanic detritus recovered in McMurdo Sound drill cores. The ignimbrites together with early-formed intrusions were strongly deformed during a substantial caldera collapse at c. 13 Ma. Intense erosion modified the volcanic landscape, creating a paleo-relief of several hundred metres. Deep ravines were cut and filled by deposits of multiple lahars probably linked to gravitational collapses of trachyte dome(s). Small-volume trachytic magmas were also erupted, forming lavas and at least one tuff cone. The youngest trachytic activity comprises a lava dome and related block-and-ash-flow deposits, erupted at 6 Ma. Basanite erupted throughout the history of the complex and eruptions younger than 12 Ma are almost exclusively basanite, forming scoria cones, water-cooled lavas, and tuff cones. Three peripheral outcrops are composed of basanitic ‘a‘ā lava-fed deltas, probably erupted from vents on neighbouring volcanoes at Mount Discovery and Mount Morning. Abundant ignimbrite deposits at Mason Spur differentiate this volcanic complex from others in the WARS. Eruptions were triggered by rift extension initially, yielding the voluminous trachytes sourced from a magma chamber on the margin of the WARS. Later mafic eruptions were associated with deep crustal faults related to residual intraplate deformation. These results add important details to the eruptive history of the intracontinental WARS.
This report summarizes data-collection activities associated with the U.S. Geological Survey Humboldt Bay Water-Quality and Salt Marsh Monitoring Project. This work was undertaken to gain a comprehensive understanding of water-quality conditions, salt marsh accretion processes, marsh-edge erosion, and soil-carbon storage in Humboldt Bay, California. Multiparameter sondes recorded water temperature, specific conductance, and turbidity at a 15-minute timestep at two U.S. Geological Survey water-quality stations: (1) Mad River Slough near Arcata, California (U.S. Geological Survey station 405219124085601) and (2) Hookton Slough near Loleta, California (U.S. Geological Survey station 404038124131801). At each station, discrete water samples were collected to develop surrogate regression models that were used to compute a continuous time series of suspended-sediment concentration from continuously measured turbidity. Data loggers recorded water depth at a 6-minute timestep in the primary tidal channels (Mad River Slough and Hookton Slough) in two adjacent marshes (Mad River marsh and Hookton marsh). The marsh monitoring network included five study marshes. Three marshes (Mad River, Manila, and Jacoby) are in the northern embayment of Humboldt Bay and two marshes (White and Hookton) are in the southern embayment. Surface deposition and elevation change were measured using deep rod surface elevation tables and feldspar marker horizons. Sediment characteristics and soil-carbon storage were measured using a total of 10 shallow cores, distributed across 5 study marshes, collected using an Eijkelkamp peat sampler. Rates of marsh edge erosion (2010–19) were quantified in four marshes (Mad River, Manila, Jacoby, and White) by estimating changes in the areal extent of the vegetated marsh plain using repeat aerial imagery and light detection and ranging (LiDAR)-derived elevation data. During the monitoring period (2016–19), the mean suspended-sediment concentration computed for Hookton Slough (50±20 milligrams per liter [mg/L]) was higher than Mad River Slough (18±7 mg/L). Uncertainty in mean suspended-sediment concentration values is reported using a 90-percent confidence interval. Across the five study marshes, elevation change (+1.8±0.6 millimeters per year [mm/yr]) and surface deposition (+2.5±0.5 mm/yr) were lower than published values of local sea-level rise (4.9±0.8 mm/yr), and mean carbon density was 0.029±0.005 grams of carbon per cubic centimeter. From 2010 to 2019, marsh edge erosion and soil carbon loss were greatest in low-elevation marshes with the marsh edge characterized by a gentle transition from mudflat to vegetated marsh (herein, ramped edge morphology) and larger wind-wave exposure. Jacoby Creek marsh experienced the greatest edge erosion. In total, marsh edge erosion was responsible for 62.3 metric tons of estuarine soil carbon storage loss across four study marshes. Salt marshes are an important component of coastal carbon, which is frequently referred to as “blue carbon.” The monitoring data presented in this report provide fundamental information needed to manage blue carbon stocks, assess marsh vulnerability, inform sea-level rise adaptation planning, and build coastal resiliency to climate change.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221076","collaboration":"Prepared in cooperation with the California State Coastal Conservancy, California Department of Fish and Wildlife, and U.S. Fish and Wildlife Service—Humboldt Bay National Wildlife Refuge","usgsCitation":"Curtis, J.A., Thorne, K.M., Freeman, C.M., Buffington, K.J., and Drexler, J.Z., 2022, A summary of water-quality and salt marsh monitoring, Humboldt Bay, California: U.S. Geological Survey Open-File Report 2022–1076, 30 p., https://doi.org/10.3133/ofr20221076.","productDescription":"Report: viii, 30 p.; 3 Data Releases","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-133425","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":501826,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113519.htm","linkFileType":{"id":5,"text":"html"}},{"id":406874,"rank":8,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221076/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":406865,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TVX0Z8","text":"Model archive summary for a suspended-sediment concentration surrogate regression model for station 405219124085601; Mad River Slough near Arcata, CA","description":"Curtis, J.A., 2021b, Model archive summary for a suspended-sediment concentration surrogate regression model for station 405219124085601; Mad River Slough near Arcata, CA: U.S. Geological Survey data release, https://doi.org/10.5066/P9TVX0Z8."},{"id":406863,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1076/images"},{"id":406864,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RJTAIL","text":"Model archive summary for a suspended-sediment concentration surrogate regression model for station 404038124131801; Hookton Slough near Loleta, CA","description":"Curtis, J.A., 2021a, Model archive summary for a suspended-sediment concentration surrogate regression model for station 404038124131801; Hookton Slough near Loleta, CA: U.S. Geological Survey data release, https://doi.org/10.5066/P9RJTAIL."},{"id":406866,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QLAL7B","text":"Salt marsh monitoring during water years 2013 to 2019, Humboldt Bay, CA—Water levels, surface deposition, elevation change, and soil carbon storage","description":"Curtis, J.A., Thorne, K.M., Freeman, C.M., Buffington, K.J., and Drexler, J.Z., 2022, Salt marsh monitoring during water years 2013 to 2019, Humboldt Bay, CA—Water levels, surface deposition, elevation change, and soil carbon storage: U.S. Geological Survey data release, https://doi.org/10.5066/P9QLAL7B."},{"id":406860,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1076/covrthb.jpg"},{"id":406861,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1076/ofr20221076.pdf","text":"Report","size":"12 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":406862,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1076/ofr20221076.xml"}],"country":"United States","state":"California","otherGeospatial":"Humboldt Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.35699462890624,\n 40.55972134684838\n ],\n [\n -124.0191650390625,\n 40.55972134684838\n ],\n [\n -124.0191650390625,\n 40.97678774053031\n ],\n [\n -124.35699462890624,\n 40.97678774053031\n ],\n [\n -124.35699462890624,\n 40.55972134684838\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Preliminary models for gate operations at the new outlet control structure for Reelfoot Lake were developed by the U.S. Geological Survey, using calibrated ratings of the lift gates, to support the U.S. Fish and Wildlife Service in managing lake level. In 2018, the old structure at the outlet of Reelfoot Lake was buried and lake level control was transferred to a new structure. The transition from lake-level management of the old control structure to the new control structure was documented using historical lake level and discharge measurements and records of stop-log management from March 7, 2013, to August 12, 2018. Discharge into Running Reelfoot Bayou was determined using a standard stage-discharge rating curve. Discharge measured using an acoustic Doppler current profiler was used to calibrate gate-discharge equations for free and submerged orifice flow at the new structure.
Two lake operation models, one for the summer season and another for the winter season, are provided for the new structure based on data from this period. The summer operation model is based on operation of the gates once the lake level exceeds an elevation of 282.7 feet (ft) above the North American Vertical Datum of 1988 (NAVD 88). Free flow begins when lake level reaches 282.3 ft above NAVD 88 and becomes transitional once the lake level exceeds 282.8 ft above NAVD 88. Submerged flow begins once the lake level reaches 283 ft above NAVD 88 and the tail-water depth is above critical flow depth. The winter operation model is based on operation of the gates once the lake level exceeds 283.2 ft above NAVD 88. Submerged flow begins when the lake rises to an elevation of 283.5 ft above NAVD 88 and the tail-water depth is above critical flow depth.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20221073","collaboration":"Prepared in cooperation with the Tennessee Wildlife Resources Agency","usgsCitation":"Heal, E.N., Diehl, T.H., and Garrett, J.W., 2022, Preliminary models relating lake level gate operation and discharge at Reelfoot Lake in Tennessee and Kentucky: U.S. Geological Survey Open-File Report 2022–1073, 27 p., https://doi.org/10.3133/ofr20221073.","productDescription":"Report: vii, 27 p.; Data Release; Database","onlineOnly":"Y","ipdsId":"IP-103756","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":406684,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GY1UF4","text":"USGS data release","linkHelpText":"Preliminary model data for lake level gate operation and discharge at Reelfoot Lake—Tennessee and Kentucky"},{"id":501823,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113520.htm","linkFileType":{"id":5,"text":"html"}},{"id":406683,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1073/ofr20221073.pdf","text":"Report","size":"2.57 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1073"},{"id":406682,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1073/coverthb.jpg"},{"id":406685,"rank":4,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS water data for the Nation—","linkHelpText":"U.S. Geological Survey National Water Information System database"},{"id":406844,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1073/images"},{"id":406845,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1073/ofr20221073.xml"},{"id":409059,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20221073/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1073"}],"country":"United States","state":"Kentucky, Tennessee","otherGeospatial":"Reelfoot Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.46441650390625,\n 36.30350540784278\n ],\n [\n -89.25155639648438,\n 36.30350540784278\n ],\n [\n -89.25155639648438,\n 36.52453591500483\n ],\n [\n -89.46441650390625,\n 36.52453591500483\n ],\n [\n -89.46441650390625,\n 36.30350540784278\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Sandbars are an important resource in the Colorado River corridor in Marble and Grand Canyons, Arizona, downstream from Glen Canyon Dam. Sandbars provide aquatic and riparian habitat and are used as campsites by river runners and hikers. The study area is the Colorado River between Glen Canyon Dam and Diamond Creek, which is about 388 kilometers (241 miles) downstream from the dam. Closure of Glen Canyon Dam in 1963 and subsequent flow regulation reduced the sediment supply, limited the magnitude and frequency of floods, and increased the magnitude of baseflows. The result has been widespread erosion of sandbars and expansion of native and non-native vegetation on previously bare sand deposits in this debris-fan dominated canyon river. This study reports on the on-going long-term measurement program of Northern Arizona University, initiated in 1990 with the Bureau of Reclamation, and now also with the U.S. Geological Survey’s Grand Canyon Monitoring and Research Center. We report on all sandbar measurements made between 1990 and 2020 to demonstrate the multi-decadal response of the sandbar monitoring sites resulting from flow regulation by Glen Canyon Dam. Because only one study site is located in Glen Canyon, the 25 kilometer (15.5 miles) reach just below Glen Canyon Dam, analyses of sandbar response are only made for the next two canyon segments in the down-river direction, Marble Canyon (388 kilometers [99 miles]) and Grand Canyon (265 kilometers [165 miles]), respectively, where the majority of study sites are located.
We show that a majority of monitoring sites increased in volume during a period of frequent controlled floods intended to rebuild sandbars. In the period from 2004 to 2020, which included seven controlled floods, a median discharge of 350 cubic meters per second (m3/s), and greater than average tributary sand inputs in more than half of the years, net deposition occurred at 86 percent of long-term monitoring sites. This period was preceded by a period of net erosion (1990–2003) when there was one controlled flood greater than the nominal powerplant capacity of 940 m3/s. During this period the median discharge from Glen Canyon Dam was 376 m3/s and greater than average sand inputs occurred in only 36 percent of those years. At the end of the monitoring period in 2020, 61 percent of the study sites measured since 1990 underwent a net increase in sand volume. For the entire 31-year period, these trends were statistically significant for all six sandbar types studied, indicating that increased frequency of controlled flooding maintained sandbar volume at the majority of sites monitored. These floods, also referred to as high-flow experiments (HFEs), are part of a decision-making protocol approved in 2012 for coordinating dam releases timed to occur following large sand inputs to the Colorado River by a major tributary.
These findings are based on digital elevation models (DEMs) derived from approximately (~)1,800 repeat surveys of sandbar and channel bed topography made annually, or more frequently, at the 45 long-term monitoring sites, of which 31 have been monitored since 1990 and 14 were added between 1990 and 2008. This large collection of monitoring sites comprises just 7 to 9 percent of all sandbars in Marble and Grand Canyons, respectively. Nevertheless, when compared with measurements of a larger sample, these sites provide consistent characterization of average sandbar response, despite the local variability in channel and debris fan geometry. We use sand volume and normalized sand volume for tracking geomorphic changes of sandbars, because these metrics are sensitive to both changes in sandbar area and sandbar elevation. Based on checkpoint comparisons and repeat measurements, DEM elevation uncertainty was determined to be ±0.05 meter (m) and this uncertainty was used in a spatially uniform estimate of volume uncertainty. We find that the magnitudes of the topographic changes were substantially greater than the measurement uncertainty.
Sandbars of similar type throughout both Marble and Grand Canyons have responded similarly during the period of the HFE protocol, despite variations in sand supply and longitudinal extent of those inputs. It should be noted that tributary-supplied sand to Glen Canyon is negligible, much of the riverbed is now armored with cobbles, and the channel bed degradation is irreversible in the current flow and sediment supply regime. Because all these HFEs have been conducted during periods of sediment enrichment, other factors such as vegetation and geomorphic setting are likely the primary causes of variation among the monitoring sites. A larger percentage of the sandbar population, predominantly located in narrow reaches where stage changes are greater, is composed of sandbar types that remain dynamic and consistently aggrade during HFEs. In contrast, wide reaches of the river corridor where stage change is not as great are characterized by sandbars that have been stabilized by vegetation and progressive aggradation during floods. In the former case, a majority of sandbars are likely to remain dynamic, requiring continued use of HFEs to achieve desired management goals. In the latter case, HFEs can do no better than replace the sediment eroded during normal dam operation between high-flow events, as they become less effective because of a diminishing amount of accommodation space available for deposition. Long-term sandbar trajectory and the continued effectiveness of HFEs are related to the differential vegetation establishment at each bar type. Future sandbar monitoring may need to consider the effects of riparian vegetation removal.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1873","collaboration":"Prepared in cooperation with Northern Arizona University","usgsCitation":"Hazel, J.E., Jr., Kaplinski, M.A., Hamill, D., Buscombe, D., Mueller, E.R., Ross, R.P., Kohl, K., and Grams, P.E., 2022, Multi-decadal sandbar response to flow management downstream from a large dam—The Glen Canyon Dam on the Colorado River in Marble and Grand Canyons, Arizona: U.S. Geological Survey Professional Paper 1873, 104 p., https://doi.org/10.3133/pp1873.","productDescription":"Report: ix, 104 p.; Data Release","numberOfPages":"104","onlineOnly":"Y","ipdsId":"IP-123041","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":501887,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113518.htm","linkFileType":{"id":5,"text":"html"}},{"id":406788,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93F8JJK","text":"Long-term sandbar monitoring data along the Colorado River in Marble and Grand Canyons, Arizona","description":"Grams, P.E., Hazel, J.E., Jr., Kaplinski, M.A., Ross, R.P., Hamill, D., Hensleigh, J., and Gushue, T., 2020, Long-term sandbar monitoring data along the Colorado River in Marble and Grand Canyons, Arizona: U.S. Geological Survey data release, https://doi.org/10.5066/P93F8JJK."},{"id":406787,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1873/pp1873.pdf","text":"Report","size":"30 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":406786,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1873/covrthb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Glen Canyon Dam, Marble Canyon, Grand Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.06005859375,\n 35.35321610123823\n ],\n [\n -111.42333984375,\n 35.35321610123823\n ],\n [\n -111.42333984375,\n 36.79169061907076\n ],\n [\n -114.06005859375,\n 36.79169061907076\n ],\n [\n -114.06005859375,\n 35.35321610123823\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Dissolved atmogenic gasses in groundwater provide significant information about recharge conditions, flowpath, and age. Free phase gas in aquifers is largely ignored in these analyses and there is a lack of quantitative analysis for gas flux mechanisms. Many related fields encountering multiphase flow acknowledge that the presence of bubbles allows for the rapid exsolution of dissolved gasses and volatile compounds through diffusive and polar forces. By measuring the mass flow of the exsolved gas at a spring, coupled with compositional analysis in the free and dissolved phases, we show that not incorporating the effects of the free gas phase of bubbling springs introduces error in the estimation of total gas quantities, particularly light noble gasses. This can significantly affect the corresponding estimation of noble gas temperature (NGT) and apparent age. We examine the transport of free and dissolved gas from the recharge zone, using water level variation data, to the discharge location where the gases are measured. This technique of using the free gas phase for assessing aquifer dynamics will improve groundwater conceptual models, particularly in karstic aquifers where rapid fluctuations in the water table facilitate the development of excess air, generating multiphase spring discharge.
Remote sensing geodetic observations (Interferometric Synthetic Aperture Radar [InSAR] and optical correlation [“pixel tracking”]) serve an increasingly diverse and important role in earthquake monitoring and response. This study introduces the Geodetic Centroid (gCent) catalog—an earthquake catalog derived solely from space‐based geodetic observations—and analysis of 74 earthquakes (MW4.3–7.4) imaged from 1 August 2019 to 01 February 2022. For gCent, we use InSAR and optical correlation observations derived from the Sentinel‐1 satellites and various publicly available optical satellites to systematically image all global earthquakes MW 5.5 or larger and shallower than 25 km, MW 7.0 or larger at any depth, and other high‐impact earthquakes or seismic events of special interest. We invert surface displacements from successfully imaged earthquakes for the location, orientation, and dimensions of a single slipping fault patch that describes the centroid characteristics of the earthquake. These centroid models, in turn, are compiled into a catalog and used in U.S. Geological Survey/Advanced National Seismic System (ANSS) operational earthquake response products such as ShakeMaps and finite‐fault models. We provide a comparison of the gCent catalog to the ANSS Comprehensive Catalog and Global Centroid Moment Tensor (Global CMT) catalog to compare reported locations, depths, and magnitudes. We find that global earthquake catalogs not only generally provide reasonably comparable locations (within 10 km on average), but also they systematically overestimate depth that may have implications for earthquake shaking predictions based solely on earthquake origin information. Geodetic magnitudes are comparable to seismically inferred magnitudes, indicating that gCent models are unlikely to be systematically biased by the presence of postseismic deformation. We additionally highlight limitations of the gCent catalog induced by both the limitations of remote sensing imaging of earthquakes and our imposition of a simplified earthquake source description that does not include spatially distributed slip.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120220072","usgsCitation":"Barnhart, W.D., and Shea, H.N., 2022, The Geodetic Centroid (gCent) Catalog: Global earthquake monitoring with satellite imaging geodesy: Bulletin of the Seismological Society of America, v. 112, no. 6, p. 2646-2957, https://doi.org/10.1785/0120220072.","productDescription":"12 p.","startPage":"2646","endPage":"2957","ipdsId":"IP-139951","costCenters":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"links":[{"id":502008,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"112","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-09-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Barnhart, William D. 0000-0003-0498-1697 wbarnhart@usgs.gov","orcid":"https://orcid.org/0000-0003-0498-1697","contributorId":294678,"corporation":false,"usgs":true,"family":"Barnhart","given":"William","email":"wbarnhart@usgs.gov","middleInitial":"D.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":958504,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shea, Hannah N.","contributorId":215980,"corporation":false,"usgs":false,"family":"Shea","given":"Hannah","email":"","middleInitial":"N.","affiliations":[{"id":6768,"text":"University of Iowa","active":true,"usgs":false}],"preferred":false,"id":958610,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70236701,"text":"70236701 - 2022 - In-reservoir physical processes modulate aqueous and biological methylmercury export from a seasonally anoxic reservoir","interactions":[],"lastModifiedDate":"2022-10-17T16:12:28.886629","indexId":"70236701","displayToPublicDate":"2022-09-15T09:08:34","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5925,"text":"Environmental Science and Technology","active":true,"publicationSubtype":{"id":10}},"title":"In-reservoir physical processes modulate aqueous and biological methylmercury export from a seasonally anoxic reservoir","docAbstract":"Anoxic conditions within reservoirs related to thermal stratification and oxygen depletion lead to methylmercury (MeHg) production, a key process governing the uptake of mercury in aquatic food webs. Once formed within a reservoir, the timing and magnitude of the biological uptake of MeHg and the relative importance of MeHg export in water versus biological compartments remain poorly understood. We examined the relations between the reservoir stratification state, anoxia, and the concentrations and export loads of MeHg in aqueous and biological compartments at the outflow locations of two reservoirs of the Hells Canyon Complex (Snake River, Idaho-Oregon). Results show that (1) MeHg concentrations in filter-passing water, zooplankton, suspended particles, and detritus increased in response to reservoir destratification; (2) zooplankton MeHg strongly correlated with MeHg in filter-passing water during destratification; (3) reservoir anoxia appeared to be a key control on MeHg export; and (4) biological MeHg, primarily in zooplankton, accounted for only 5% of total MeHg export from the reservoirs (the remainder being aqueous compartments). These results improve our understanding of the role of biological incorporation of MeHg and the subsequent downstream release from seasonally stratified reservoirs and demonstrate that in-reservoir physical processes strongly influence MeHg incorporation at the base of the aquatic food web.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.2c03958","usgsCitation":"Baldwin, A.K., Eagles-Smith, C., Willacker, J., Poulin, B., Krabbenhoft, D.P., Naymik, J., Tate, M., Bates, D., Gastelecutto, N., Hoovestol, C., Larsen, C.F., Yoder, A.M., Chandler, J.A., and Myers, R., 2022, In-reservoir physical processes modulate aqueous and biological methylmercury export from a seasonally anoxic reservoir: Environmental Science and Technology, v. 56, no. 19, p. 13751-13760, https://doi.org/10.1021/acs.est.2c03958.","productDescription":"10 p.","startPage":"13751","endPage":"13760","ipdsId":"IP-135446","costCenters":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":446424,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.2c03958","text":"Publisher Index Page"},{"id":435690,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96E3DYN","text":"USGS data release","linkHelpText":"Biomass and methylmercury concentrations in biweekly biological samples from Brownlee and Oxbow Reservoir outflows, Snake River Hells Canyon Complex (Idaho-Oregon), 2018-2019"},{"id":406837,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Oregon","otherGeospatial":"Brownlee Reservoir, Hells Canyon Reservoir, Oxbow Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.3065185546875,\n 44.337600831495635\n ],\n [\n -117.12799072265625,\n 44.351350365612326\n ],\n [\n -116.400146484375,\n 45.596743928454124\n ],\n [\n -116.630859375,\n 45.625563438215984\n ],\n [\n -117.28179931640626,\n 44.69599298172069\n ],\n [\n -117.3065185546875,\n 44.337600831495635\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"56","issue":"19","noUsgsAuthors":false,"publicationDate":"2022-09-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Baldwin, Austin K. 0000-0002-6027-3823 akbaldwi@usgs.gov","orcid":"https://orcid.org/0000-0002-6027-3823","contributorId":4515,"corporation":false,"usgs":true,"family":"Baldwin","given":"Austin","email":"akbaldwi@usgs.gov","middleInitial":"K.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":851934,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eagles-Smith, Collin A. 0000-0003-1329-5285","orcid":"https://orcid.org/0000-0003-1329-5285","contributorId":221745,"corporation":false,"usgs":true,"family":"Eagles-Smith","given":"Collin A.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":851935,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Willacker, James 0000-0002-6286-5224","orcid":"https://orcid.org/0000-0002-6286-5224","contributorId":207883,"corporation":false,"usgs":true,"family":"Willacker","given":"James","email":"","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":851936,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Poulin, Brett 0000-0002-5555-7733","orcid":"https://orcid.org/0000-0002-5555-7733","contributorId":260893,"corporation":false,"usgs":false,"family":"Poulin","given":"Brett","affiliations":[{"id":52706,"text":"Department of Environmental Toxicology, University of California Davis, Davis, CA 95616, USA","active":true,"usgs":false}],"preferred":false,"id":851937,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Krabbenhoft, David P. 0000-0003-1964-5020 dpkrabbe@usgs.gov","orcid":"https://orcid.org/0000-0003-1964-5020","contributorId":1658,"corporation":false,"usgs":true,"family":"Krabbenhoft","given":"David","email":"dpkrabbe@usgs.gov","middleInitial":"P.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":851938,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Naymik, Jesse","contributorId":229386,"corporation":false,"usgs":false,"family":"Naymik","given":"Jesse","affiliations":[{"id":41632,"text":"Idaho Power Company","active":true,"usgs":false}],"preferred":false,"id":851939,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Tate, Michael T. 0000-0003-1525-1219 mttate@usgs.gov","orcid":"https://orcid.org/0000-0003-1525-1219","contributorId":3144,"corporation":false,"usgs":true,"family":"Tate","given":"Michael T.","email":"mttate@usgs.gov","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":851940,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bates, Dain","contributorId":296596,"corporation":false,"usgs":false,"family":"Bates","given":"Dain","email":"","affiliations":[{"id":41632,"text":"Idaho Power Company","active":true,"usgs":false}],"preferred":false,"id":851941,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gastelecutto, Nick","contributorId":296597,"corporation":false,"usgs":false,"family":"Gastelecutto","given":"Nick","email":"","affiliations":[{"id":41632,"text":"Idaho Power Company","active":true,"usgs":false}],"preferred":false,"id":851942,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hoovestol, Charles","contributorId":229387,"corporation":false,"usgs":false,"family":"Hoovestol","given":"Charles","email":"","affiliations":[{"id":41632,"text":"Idaho Power Company","active":true,"usgs":false}],"preferred":false,"id":851943,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Larsen, Christopher F.","contributorId":147408,"corporation":false,"usgs":false,"family":"Larsen","given":"Christopher","email":"","middleInitial":"F.","affiliations":[{"id":6695,"text":"UAF","active":true,"usgs":false}],"preferred":false,"id":851944,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Yoder, Alysa Muir 0000-0002-3683-6729","orcid":"https://orcid.org/0000-0002-3683-6729","contributorId":296598,"corporation":false,"usgs":true,"family":"Yoder","given":"Alysa","email":"","middleInitial":"Muir","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":851945,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Chandler, James A.","contributorId":210045,"corporation":false,"usgs":false,"family":"Chandler","given":"James","email":"","middleInitial":"A.","affiliations":[{"id":38056,"text":"Idaho Power Company 1221 West Idaho Street, Boise, ID 83702","active":true,"usgs":false}],"preferred":true,"id":851947,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Myers, Ralph","contributorId":172701,"corporation":false,"usgs":false,"family":"Myers","given":"Ralph","email":"","affiliations":[{"id":12541,"text":"Idaho Power Company, P.O. Box 70, Boise ID 83707","active":true,"usgs":false}],"preferred":false,"id":851946,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70237003,"text":"70237003 - 2022 - Quantifying flow and nonflow management impacts on an endangered fish by integrating data, research, and expert opinion","interactions":[],"lastModifiedDate":"2022-09-27T15:28:07.8792","indexId":"70237003","displayToPublicDate":"2022-09-14T10:24:31","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying flow and nonflow management impacts on an endangered fish by integrating data, research, and expert opinion","docAbstract":"Managers charged with recovering endangered species in regulated river segments often have limited flexibility to alter flow regimes and want estimates of the expected population benefits associated with both flow and nonflow management actions. Disentangling impacts on different life stages from concurrently applied actions is essential for determining the effectiveness of each action, but difficult without models that integrate multiple information sources. Here, we develop and fit an integrated population model for endangered Rio Grande Silvery Minnow (Hybognathus amarus) in the Middle Rio Grande, New Mexico. We integrate catch per unit effort monitoring data collected during 2002–2018 with population estimates, data collected during rescue of minnow from drying pools, habitat availability estimates, laboratory results, releases of hatchery reared minnow, and expert opinion. We use expert elicitation to develop a larval carrying capacity index as an informed proxy for the complex interactions among flow, habitat, and life history in this species. We evaluate the model using out-of-sample forecasts of 2019 and 2020, develop an algorithm to identify supplemental water releases that maximize benefits to the minnow, and quantify the effectiveness of various actions. Experts generally agreed on the duration and timing of flow requirements and disagreed regarding the importance of different magnitudes. The integrated model with the larval carrying capacity index outperformed two alternative models in forecasting catch in 2019 and 2020. The model estimates that minnow abundance varied by more than three orders of magnitude between 2002 and 2018 and that in a few years recruitment was limited by spawner abundance. Evaluation of the expected benefits of flow and nonflow management actions to fall population abundance across different years suggests that efficient addition of water to the base hydrograph is the most effective action in most, but not all years. Many actions are effective only under certain hydrologic and population conditions and the effectiveness of different actions varies in different sections of the study area. Widespread water extraction and river regulation combined with periodic drought and ongoing climate change may necessitate creative management of federally listed fish species in arid systems informed by thorough analyses of management effectiveness.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4240","usgsCitation":"Yackulic, C., Archdeacon, T.P., Valdez, R.A., Hobbs, M., Porter, M., Lusk, J., Tanner, A.M., Gonzales, E., Lee, D.Y., and Haggerty, G.M., 2022, Quantifying flow and nonflow management impacts on an endangered fish by integrating data, research, and expert opinion: Ecosphere, v. 13, no. 9, e4240, 22 p., https://doi.org/10.1002/ecs2.4240.","productDescription":"e4240, 22 p.","ipdsId":"IP-138221","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":446432,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4240","text":"Publisher Index Page"},{"id":407407,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"9","noUsgsAuthors":false,"publicationDate":"2022-09-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Yackulic, Charles B. 0000-0001-9661-0724","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":218825,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":853029,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Archdeacon, Thomas P","contributorId":296980,"corporation":false,"usgs":false,"family":"Archdeacon","given":"Thomas","email":"","middleInitial":"P","affiliations":[{"id":64264,"text":"U.S. Fish & Wildlife Service, New Mexico Fish & Wildlife Conservation Office, Albuquerque, NM, USA","active":true,"usgs":false}],"preferred":false,"id":853030,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Valdez, Richard A.","contributorId":204243,"corporation":false,"usgs":false,"family":"Valdez","given":"Richard","email":"","middleInitial":"A.","affiliations":[{"id":34515,"text":"SWCA Environmental Consultants","active":true,"usgs":false}],"preferred":false,"id":853031,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hobbs, Monika","contributorId":296981,"corporation":false,"usgs":false,"family":"Hobbs","given":"Monika","email":"","affiliations":[{"id":64265,"text":"Albuquerque Bernalillo County Water Utility Authority, Albuquerque, NM, USA","active":true,"usgs":false}],"preferred":false,"id":853032,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Porter, Michael D.","contributorId":139912,"corporation":false,"usgs":false,"family":"Porter","given":"Michael D.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":853033,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lusk, Joel","contributorId":296982,"corporation":false,"usgs":false,"family":"Lusk","given":"Joel","email":"","affiliations":[{"id":64266,"text":"US Bureau of Reclamation, Environment and Lands Division, Albuquerque, NM","active":true,"usgs":false}],"preferred":false,"id":853034,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Tanner, Ashley M.","contributorId":264589,"corporation":false,"usgs":false,"family":"Tanner","given":"Ashley","email":"","middleInitial":"M.","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":853035,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gonzales, Eric J","contributorId":296983,"corporation":false,"usgs":false,"family":"Gonzales","given":"Eric J","affiliations":[{"id":64267,"text":"U.S. Bureau of Reclamation, Albuquerque Area Office, Environment & Lands Division, Albuquerque, NM, USA","active":true,"usgs":false}],"preferred":false,"id":853036,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lee, Debbie Y","contributorId":296984,"corporation":false,"usgs":false,"family":"Lee","given":"Debbie","email":"","middleInitial":"Y","affiliations":[{"id":64268,"text":"Western EcoSystems Technology, Inc., Albuquerque, NM, USA","active":true,"usgs":false}],"preferred":false,"id":853037,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Haggerty, Grace M","contributorId":296985,"corporation":false,"usgs":false,"family":"Haggerty","given":"Grace","email":"","middleInitial":"M","affiliations":[{"id":64269,"text":"New Mexico Interstate Stream Commission, Albuquerque, NM, USA","active":true,"usgs":false}],"preferred":false,"id":853038,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70236690,"text":"70236690 - 2022 - Development of the LCMAP annual land cover product across Hawai'i","interactions":[],"lastModifiedDate":"2023-11-08T16:45:41.692299","indexId":"70236690","displayToPublicDate":"2022-09-14T09:22:33","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2027,"text":"International Journal of Applied Earth Observation and Geoinformation","active":true,"publicationSubtype":{"id":10}},"title":"Development of the LCMAP annual land cover product across Hawai'i","docAbstract":"Following the completion of land cover and change (LCC) products for the conterminous United States (CONUS), the U.S. Geological Survey's (USGS’s) Land Change Monitoring, Assessment, and Projection initiative has broadened the capability of characterizing continuous historical land change across the full Landsat records for Hawaiʻi at 30-meter resolution. One of the challenges of implementing the LCMAP framework to process annual land cover maps in Hawaiʻi is to collect sufficient high-quality training data. Although multiple datasets depicting land cover information are available in Hawaiʻi, they covered limited time frames and were produced from various remote sensing sources with different, classification categories, spatial resolution, and mapping accuracies. No solo product is suitable to provide LCMAP training data labels on its own. In this paper, we focused on enhancing the LCMAP training datasets to generate land cover products from 2000 to 2019 in Hawaiʻi. A total of 200 independent reference data plots were generated and manually interpreted for validating the mapping results produced by the training datasets. The results revealed that using the appropriate filter of multiple products as training data pools improved the classification model performance. The effect of training datasets (e.g., spatial coverage, quality) on accuracies for different land cover types were summarized. The LCMAP land surface change products for Hawaiʻi are available at https://doi.org/10.5066/P91E8M23.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jag.2022.103015","usgsCitation":"Li, C., Xian, G.Z., Wellington, D., Smith, K., Horton, J., and Zhou, Q., 2022, Development of the LCMAP annual land cover product across Hawai'i: International Journal of Applied Earth Observation and Geoinformation, v. 113, 103015, 17 p., https://doi.org/10.1016/j.jag.2022.103015.","productDescription":"103015, 17 p.","ipdsId":"IP-144117","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":37273,"text":"Advanced Research Computing (ARC)","active":true,"usgs":true}],"links":[{"id":446437,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jag.2022.103015","text":"Publisher Index Page"},{"id":406839,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"113","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Li, Congcong 0000-0002-4311-4169","orcid":"https://orcid.org/0000-0002-4311-4169","contributorId":270142,"corporation":false,"usgs":false,"family":"Li","given":"Congcong","email":"","affiliations":[{"id":52693,"text":"ASRC Federal","active":true,"usgs":false}],"preferred":false,"id":851901,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Xian, George Z. 0000-0001-5674-2204","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":238919,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":851902,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wellington, Danika F. 0000-0002-2130-0075","orcid":"https://orcid.org/0000-0002-2130-0075","contributorId":237074,"corporation":false,"usgs":false,"family":"Wellington","given":"Danika F.","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":851903,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Kelcy 0000-0001-6811-1485","orcid":"https://orcid.org/0000-0001-6811-1485","contributorId":272037,"corporation":false,"usgs":false,"family":"Smith","given":"Kelcy","affiliations":[{"id":56338,"text":"KBR, Inc., Contractor under USGS","active":true,"usgs":false}],"preferred":false,"id":851904,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Horton, Josephine 0000-0001-8436-4095","orcid":"https://orcid.org/0000-0001-8436-4095","contributorId":191430,"corporation":false,"usgs":false,"family":"Horton","given":"Josephine","affiliations":[],"preferred":false,"id":851905,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zhou, Qiang 0000-0002-1282-8177","orcid":"https://orcid.org/0000-0002-1282-8177","contributorId":265886,"corporation":false,"usgs":false,"family":"Zhou","given":"Qiang","affiliations":[{"id":54817,"text":"AFDS, contractor to U.S. Geological Survey","active":true,"usgs":false}],"preferred":false,"id":851906,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70247988,"text":"70247988 - 2022 - Viscoelastic fault-based model of crustal deformation for the 2023 update to the U.S. National Seismic Hazard Model","interactions":[],"lastModifiedDate":"2023-08-30T12:05:47.311699","indexId":"70247988","displayToPublicDate":"2022-09-14T07:03:55","publicationYear":"2022","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":"Viscoelastic fault-based model of crustal deformation for the 2023 update to the U.S. National Seismic Hazard Model","docAbstract":"The 2023 update to the National Seismic Hazard (NSHM) model is informed by several deformation models that furnish geodetically estimated fault slip rates. Here I describe a fault‐based model that permits estimation of long‐term slip rates on discrete faults and the distribution of off‐fault moment release. It is based on quantification of the earthquake cycle on a viscoelastic model of the seismogenic upper crust and ductile lower crust and mantle. I apply it to a large dataset of horizontal and vertical Global Positioning System (GPS) interseismic velocities in the western United States, resulting in long‐term slip rates on more than 1000 active faults defined for the NSHM. A reasonable fit to the GPS dataset is achieved with a set of slip rates designed to lie strictly within a priori geologic slip rate bounds. Time‐dependent effects implemented via a “ghost transient” have a profound effect on slip rate estimation and tend to raise calculated slip rates along the northern and southern San Andreas fault by up to several mm/yr.
Two slow-moving developments are threatening reservoir aquatic habitats globally: aging and climate change. These events are projected to transform reservoir aquatic habitats in various and often unpredictable ways. Aging affects in-lake habitats directly, whereas climate change affects both in-lake and off-lake conditions. Climate change is expected to accelerate and, in some instances, possibly decelerate aging. Aging can be indexed as functional age, an index that signals the position of a reservoir along its lifespan relying on in-lake descriptors of aquatic habitat. Using existing habitat datasets and climate projections, we developed semi-quantitative predictions about the effect of climate change on reservoir functional age in the USA. Driven by increased warming, functional age was predicted to increase latitudinally from south to north with no obvious longitudinal gradient. Functional age also changed with precipitation, increasing latitudinally from south to north and longitudinally in the east and west but decreasing in the central USA. Our projections are tentative because of the uncertain nature of reservoir aging and climate change sciences, as well as the inexactness of available data and models. We review general strategies suitable for systematically dealing with the unpredictable and constantly changing conditions expected to occur this century as reservoirs certainly continue to get older, within the scope of uncertain climate change projections.
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Dakota"},"lastModifiedDate":"2026-04-01T15:53:37.245749","indexId":"sir20175070D","displayToPublicDate":"2022-09-13T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5070","chapter":"D","displayTitle":"Potential Effects of Energy Development on Environmental Resources of the Williston Basin in Montana, North Dakota, and South Dakota—Species of Conservation Concern","title":"Potential effects of energy development on environmental resources of the Williston Basin in Montana, North Dakota, and South Dakota—Species of conservation concern","docAbstract":"The ecosystems of the Williston Basin provide direct and indirect benefits to society. These benefits include carbon sequestration, flood control, nutrient rich soils for agricultural productivity, and habitat for wildlife. This chapter’s main focus is on the effects of energy development on species that occupy the ecosystems in the Williston Basin. We compiled a list of documented species of conservation concern that are of most interest to Federal regulators and resource managers. Species of concern were either listed as endangered or threatened under the Endangered Species Act or listed by States as species of concern in Natural Heritage Program checklists or State Wildlife Action Plans. All told, we determined that 357 species of concern likely occupy the Williston Basin. These species represented seven different taxonomic groups: plants (native and nonnative), terrestrial invertebrates, birds, mammals, reptiles and amphibians, and fish and mussels.
We reviewed the existing scientific information pertaining to potential effects of energy development on these taxonomic groups. Currently, little is known about the abundance and distribution of many of these species. But some information exists that may be useful in predicting the potential effects of energy development on certain taxonomic groups. Most of this information has been developed through scientific research focused on effects to mammal and bird populations. Effects to other taxonomic groups seems to be understudied. In general, it seems that disturbances and modifications associated with development have the potential to negatively affect a wide range of species; however, many studies produce uncertain results because they are not designed to compare populations before and after energy development takes place. Most of these studies also do not monitor resources over multiple years and thus cannot detect population trends. Likewise, there are few examples of landscape-scale assessments of the cumulative effects of energy development that could be used for species or habitat management purposes. We suggest that more research needs to be completed to measure potential effects to a broad range of species in multiple taxonomic groups. This may require also developing some understanding about the basic ecology of many of the species covered in this report. In concert with this more basic research, we also suggest that more comprehensive assessments of potential negative cumulative effects across the Williston Basin should be developed in an effort to guide more strategic management of biological resources.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Potential effects of energy development on environmental resources of the Williston Basin in Montana, North Dakota, and South Dakota (Scientific Investigations Report 2017–5070)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175070D","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Post van der Burg, M., Symstad, A.J., Igl, L.D., Mushet, D.M., Larson, D.L., Sargeant, G.A., Harper, D.D., Farag, A.M., Tangen, B.A., and Anteau, M.J., 2022, Potential effects of energy development on environmental resources of the Williston Basin in Montana, North Dakota, and South Dakota—Species of conservation concern: U.S. Geological Survey Scientific Investigations Report 2017–5070–D, 41 p., https://doi.org/10.3133/sir20175070D.","productDescription":"Report: vii, 41 p.; 5 Tables","numberOfPages":"54","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-077345","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":501946,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_106212.htm","linkFileType":{"id":5,"text":"html"}},{"id":346079,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5070/d/coverthb2.jpg"},{"id":346080,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5070/d/sir20175070d.pdf","text":"Report","size":"5.50 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5070–D"},{"id":346081,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5070/d/sir20175070d_tableD1-1.csv","text":"Table D1–1","size":"41.4 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2017–5070–D Table D1–1"},{"id":346082,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5070/d/sir20175070d_tableD1-2.csv","text":"Table D1–2","size":"9.41 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2017–5070–D Table D1–2"},{"id":346083,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5070/d/sir20175070d_tableD1-3.csv","text":"Table D1–3","size":"11.1 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2017–5070–D Table D1–3"},{"id":346084,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5070/d/sir20175070d_tableD1-4.csv","text":"Table D1–4","size":"7.77 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2017–5070–D Table D1–4"},{"id":346085,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5070/d/sir20175070d_tableD1-5.csv","text":"Table D1–5","size":"5.79 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2017–5070–D Table D1–5"}],"country":"United States","state":"Montana, North Dakota, South Dakota","otherGeospatial":"Bakken Formation, Williston Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.5,\n 45.120052841530544\n ],\n [\n -96.94335937499999,\n 45.120052841530544\n ],\n [\n -96.94335937499999,\n 49.009050809382046\n ],\n [\n -108.5,\n 49.009050809382046\n ],\n [\n -108.5,\n 45.120052841530544\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.850830078125,\n 47.24194882163242\n ],\n [\n -108.544921875,\n 47.24194882163242\n ],\n [\n -108.544921875,\n 48.98742700601184\n ],\n [\n -115.850830078125,\n 48.98742700601184\n ],\n [\n -115.850830078125,\n 47.24194882163242\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
Around the world, wetland vulnerability to sea-level rise (SLR) depends on different factors including tidal regimes, topography, creeks and estuary geometry, sediment availability, vegetation type, etc. The Plum Island estuary (PIE) is a mesotidal wetland system on the east coast of the United States. This research applied a newly updated Hydro-MEM (integrated hydrodynamic-marsh) model to assess the impacts of intermediate-low (50 cm), intermediate (1 m), and intermediate-high (1.5 m) SLR on marsh evolution by the year 2100. Model advancements include capturing vegetation change, inorganic and below and aboveground organic matter portion of marsh platform accretion, and mudflat creation. Although the results indicate a low vulnerability marsh at the PIE, the vegetation changes from high to low marsh under all SLR scenarios (2%–22%), with the higher bounds belonging to higher rise scenarios. Lower SLR produces more productive marsh (13% gain in high productivity regions), whereas the highest SLR scenario causes increased tidal inundation, which leads to loss in productivity (12% change from high to low productivity regions), generation of mudflats (17% of the domain land), and marsh migration to higher lands. Sensitive nonlinear tidal flow changes, which may be increased or decreased with SLR as a result of mudflat creation, marsh migration, and bottom friction change, emphasize the importance of integrated modeling approaches that include dynamic marsh feedbacks in hydrodynamic modeling and varying hydrodynamic effects on the marsh system.
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