In river valleys, sediment moves between active river channels, near-channel deposits including bars and floodplains, and upland environments such as terraces and aeolian dunefields. Sediment availability is a prerequisite for the sustained transfer of material between these areas, and for the eco-geomorphic functioning of river networks in general. However, the difficulty of monitoring sediment availability and movement at the reach or corridor scale has hindered our ability to quantify and forecast the response of sediment transfer to hydrologic or land cover alterations. Here we leverage spatiotemporally extensive datasets quantifying sediment areal coverage along a 28 km reach of the Colorado River in Grand Canyon, southwestern USA. In concert with information on hydrologic alteration and vegetation encroachment resulting from the operation of Glen Canyon Dam (constructed in 1963) upstream of our study reach, we model the relative and combined influence of changes in (a) flow and (b) riparian vegetation extent on the areal extent of sediment available for transport in the river valley over the period from 1921 to 2016. In addition, we use projections of future streamflow and vegetation encroachment to forecast sediment availability over the 20 year period from 2016 to 2036. We find that hydrologic alteration has reduced the areal extent of bare sediment by 9% from the pre- to post-dam periods, whereas vegetation encroachment further reduced bare sediment extent by 45%. Over the next 20 years, the extent of bare sediment is forecast to be reduced by an additional 12%. Our results demonstrate the impact of river regulation, specifically the loss of annual low flows and associated vegetation encroachment, on reducing the sediment available for transfer within river valleys. This work provides an extendable framework for using high-resolution data on streamflow and land cover to assess and forecast the impact of watershed perturbation (e.g. river regulation, land cover shifts, climate change) on sediment connectivity at the corridor scale.
","language":"English","publisher":"SAGE Publishing","doi":"10.1177/0309133318795846","usgsCitation":"Kasprak, A., Sankey, J.B., Buscombe, D.D., Caster, J., East, A.E., and Grams, P.E., 2018, Quantifying and forecasting changes in the areal extent of river valley sediment in response to altered hydrology and land cover: Progress in Physical Geography: Earth and Environment, v. 42, no. 6, p. 739-764, https://doi.org/10.1177/0309133318795846.","productDescription":"26 p.","startPage":"739","endPage":"764","ipdsId":"IP-088947","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":468374,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1177/0309133318795846","text":"Publisher Index Page"},{"id":437745,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SX3MGY","text":"USGS data release","linkHelpText":"River Valley Sediment Connectivity Data, Colorado River, Grand Canyon"},{"id":357659,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon National Park, Lower Marble Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.93145751953125,\n 36.16781389727332\n ],\n [\n -111.77352905273438,\n 36.16781389727332\n ],\n [\n -111.77352905273438,\n 36.4223874864237\n ],\n [\n -111.93145751953125,\n 36.4223874864237\n ],\n [\n -111.93145751953125,\n 36.16781389727332\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"42","issue":"6","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-09-13","publicationStatus":"PW","scienceBaseUri":"5bc02f99e4b0fc368eb538d3","contributors":{"authors":[{"text":"Kasprak, Alan 0000-0001-8184-6128","orcid":"https://orcid.org/0000-0001-8184-6128","contributorId":204162,"corporation":false,"usgs":true,"family":"Kasprak","given":"Alan","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":745883,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sankey, Joel B. 0000-0003-3150-4992 jsankey@usgs.gov","orcid":"https://orcid.org/0000-0003-3150-4992","contributorId":3935,"corporation":false,"usgs":true,"family":"Sankey","given":"Joel","email":"jsankey@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":745884,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Buscombe, Daniel D. 0000-0001-6217-5584","orcid":"https://orcid.org/0000-0001-6217-5584","contributorId":198817,"corporation":false,"usgs":false,"family":"Buscombe","given":"Daniel","middleInitial":"D.","affiliations":[],"preferred":false,"id":745885,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Caster, Joshua 0000-0002-2858-1228 jcaster@usgs.gov","orcid":"https://orcid.org/0000-0002-2858-1228","contributorId":199033,"corporation":false,"usgs":true,"family":"Caster","given":"Joshua","email":"jcaster@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":745888,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"East, Amy E. 0000-0002-9567-9460 aeast@usgs.gov","orcid":"https://orcid.org/0000-0002-9567-9460","contributorId":196364,"corporation":false,"usgs":true,"family":"East","given":"Amy","email":"aeast@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":745886,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Grams, Paul E. 0000-0002-0873-0708 pgrams@usgs.gov","orcid":"https://orcid.org/0000-0002-0873-0708","contributorId":1830,"corporation":false,"usgs":true,"family":"Grams","given":"Paul","email":"pgrams@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":745887,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70198969,"text":"fs20183058 - 2018 - Water use in Washington, 2015","interactions":[],"lastModifiedDate":"2018-09-25T10:43:43","indexId":"fs20183058","displayToPublicDate":"2018-09-24T08:50:46","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-3058","title":"Water use in Washington, 2015","docAbstract":"Water use in the State of Washington has evolved during the past century from small withdrawals used for domestic and stock needs to the diverse needs of current public supply systems, domestic water users, irrigation projects, industrial plants, and aquaculture industries. Increasing demand for water makes the accountability of water use an important issue.
A few State and local agencies in Washington collect water-use information for specific categories of water use; currently, only the U.S. Geological Survey (USGS) compiles cumulative water-use information across the State for a comprehensive range of uses.
Since 1950, on a 5-year cycle, the USGS has compiled and published estimates of water withdrawal and use for specific categories aggregated at the county, State, and national level. The information is shared publicly through the USGS Water Use in the United States website (https://water.usgs.gov/watuse/) and national publications that detail water use definitions, categories, trends, and data for every state. The data are compiled individually by each state from available sources, and are augmented by estimates from national models for categories that have limited data. The USGS Washington Water Science Center is responsible for compiling their estimates and maintains the State water use webpage (https://wa.water.usgs.gov/data/wuse/) of State-level information and links to the national program.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20183058","usgsCitation":"Fasser, E.T., 2018, Water use in Washington, 2015: U.S. Geological Survey Fact Sheet 2018-3058, 4 p., https://doi.org/10.3133/fs20183058.","productDescription":"4 p.","ipdsId":"IP-098099","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":357683,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2018/3058/coverthb2.jpg"},{"id":357592,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2018/3058/fs20183058.pdf","text":"Report","size":"2.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2018-3058"}],"country":"United 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\"}}]}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
The Eagle Creek watershed, a small subbasin (125 km2) within the Maumee River Basin, Ohio, was selected as a part of the Great Lakes Restoration Initiative (GLRI) “Priority Watersheds” program to evaluate the effectiveness of agricultural Best Management Practices (BMPs) funded through GLRI at the field and watershed scales. The location and quantity of BMPs were obtained from the U.S. Department of Agriculture-Natural Resources Conservation Service National Conservation Planning (NCP) database. A Soil and Water Assessment Tool (SWAT) model was built and calibrated for this predominantly agricultural Eagle Creek watershed, incorporating NCP BMPs and monitoring data at the watershed outlet, an edge-of-field (EOF), and tile monitoring sites. Input air temperature modifications were required to induce simulated tile flow to match monitoring data. Calibration heavily incorporated tile monitoring data to correctly proportion surface and subsurface flow, but calibration statistics were unsatisfactory at the EOF and tile monitoring sites. At the watershed outlet, satisfactory to very good calibration statistics were achieved over a 2-year calibration period, and satisfactory statistics were found in the 2-year validation period. SWAT fixes parameters controlling nutrients primarily at the watershed level; a refinement of these parameters at a smaller-scale could improve field-level calibration. Field-scale modeling results indicate that filter strips (FS) are the most effective single BMPs at reducing dissolved reactive phosphorus, and FS typically decreased sediment and nutrient yields when added to any other BMP or BMP combination. Cover crops were the most effective single, in-field practice by reducing nutrient loads over winter months. Watershed-scale results indicate BMPs can reduce sediment and nutrients, but reductions due to NCP BMPs in the Eagle Creek watershed for all water-quality constituents were less than 10%. Hypothetical scenarios simulated with increased BMP acreages indicate larger investments of the appropriate BMP or BMP combination can decrease watershed level loads.
","language":"English","publisher":"MDPI","doi":"10.3390/w10101299","usgsCitation":"Merriman, K.R., Daggupati, P., Srinivasan, R., Toussant, C., Russell, A.M., and Hayhurst, B.A., 2018, Assessing the impact of site-specific BMPs using a spatially explicit, field-scale SWAT model with edge-of-field and tile hydrology and water-quality data in the Eagle Creek watershed, Ohio: Water, v. 10, no. 10, p. 1-37, https://doi.org/10.3390/w10101299.","productDescription":"Article 1299; 37 p.","startPage":"1","endPage":"37","ipdsId":"IP-092960","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":468377,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w10101299","text":"Publisher Index Page"},{"id":357665,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Ohio","otherGeospatial":"Eagle Creek Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.8333,\n 40.67\n ],\n [\n -83.5,\n 40.67\n ],\n [\n -83.5,\n 41\n ],\n [\n -83.8333,\n 41\n ],\n [\n -83.8333,\n 40.67\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"10","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2018-09-21","publicationStatus":"PW","scienceBaseUri":"5bc02f99e4b0fc368eb538d9","contributors":{"authors":[{"text":"Merriman, Katherine R. 0000-0002-1303-2410 kmerriman@usgs.gov","orcid":"https://orcid.org/0000-0002-1303-2410","contributorId":4973,"corporation":false,"usgs":true,"family":"Merriman","given":"Katherine","email":"kmerriman@usgs.gov","middleInitial":"R.","affiliations":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"preferred":false,"id":745973,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Daggupati, Prasad","contributorId":203354,"corporation":false,"usgs":false,"family":"Daggupati","given":"Prasad","affiliations":[{"id":36214,"text":"Univeristy of Guelph","active":true,"usgs":false}],"preferred":false,"id":745974,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Srinivasan, Raghavan","contributorId":203355,"corporation":false,"usgs":false,"family":"Srinivasan","given":"Raghavan","email":"","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":745975,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Toussant, Chad","contributorId":208117,"corporation":false,"usgs":true,"family":"Toussant","given":"Chad","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":745976,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Russell, Amy M. 0000-0003-0582-0094 arussell@usgs.gov","orcid":"https://orcid.org/0000-0003-0582-0094","contributorId":200011,"corporation":false,"usgs":true,"family":"Russell","given":"Amy","email":"arussell@usgs.gov","middleInitial":"M.","affiliations":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":745977,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hayhurst, Brett A. 0000-0002-1717-2015 bhayhurs@usgs.gov","orcid":"https://orcid.org/0000-0002-1717-2015","contributorId":3398,"corporation":false,"usgs":true,"family":"Hayhurst","given":"Brett","email":"bhayhurs@usgs.gov","middleInitial":"A.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":745978,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70229790,"text":"70229790 - 2018 - Dynamic occupancy modeling of temperate marine fish in area-based closures","interactions":[],"lastModifiedDate":"2022-03-17T15:46:42.731317","indexId":"70229790","displayToPublicDate":"2018-09-21T10:38:02","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Dynamic occupancy modeling of temperate marine fish in area-based closures","docAbstract":"Species distribution models (SDMs) are commonly used to model the spatial structure of species in the marine environment, however, most fail to account for detectability of the target species. This can result in underestimates of occupancy, where nondetection is conflated with absence. The site occupancy model (SOM) overcomes this failure by treating occupancy as a latent variable of the model and incorporates a detection submodel to account for variability in detection rates. These have rarely been applied in the context of marine fish and never for the multiseason dynamic occupancy model (DOM). In this study, a DOM is developed for a designated species of concern, cusk (Brosme brosme), over a four-season period. Making novel use of a high-resolution 3-dimensional hydrodynamic model, detectability of cusk is considered as a function of current speed and algae cover. Algal cover on the seabed is measured from video surveys to divide the study area into two distinct regions: those with canopy forming species of algae and those without (henceforth bottom types). Modeled estimates of the proportion of sites occupied in each season are 0.88, 0.45, 0.74, and 0.83. These are significantly greater than the proportion of occupied sites measured from underwater video observations which are 0.57, 0.28, 0.43, and 0.57. Individual fish are detected more frequently with increasing current speed in areas lacking canopy and less frequently with increasing current speed in areas with canopy. The results indicate that, where possible, SDM studies for all marine species should take account of detectability to avoid underestimating the proportion of sites occupied at a given study area. Sampling closed areas or areas of conservation often requires the use of nonphysical, low impact sampling methods like camera surveys. These methods inherently result in detection probabilities less than one, an issue compounded by time-varying features of the environment that are rarely accounted for marine studies. This work highlights the use of modeled hydrodynamics as a tool to correct some of this imbalance.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.4493","usgsCitation":"Calvert, J., McGonigle, C., Sethi, S., Harris, B., Quinn, R., and Grabowski, J., 2018, Dynamic occupancy modeling of temperate marine fish in area-based closures: Ecology and Evolution, v. 8, no. 20, p. 10192-10205, https://doi.org/10.1002/ece3.4493.","productDescription":"14 p.","startPage":"10192","endPage":"10205","ipdsId":"IP-127035","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":468378,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.4493","text":"Publisher Index Page"},{"id":397252,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Gulf of Maine","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -68.983333,\n 43\n ],\n [\n -68.916667,\n 43\n ],\n [\n -68.916667,\n 42.88\n ],\n [\n -68.983333,\n 42.88\n ],\n [\n -68.983333,\n 43\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","issue":"20","noUsgsAuthors":false,"publicationDate":"2018-09-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Calvert, Jay","contributorId":288770,"corporation":false,"usgs":false,"family":"Calvert","given":"Jay","email":"","affiliations":[{"id":61838,"text":"University of Ulster","active":true,"usgs":false}],"preferred":false,"id":838269,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McGonigle, Chris","contributorId":288771,"corporation":false,"usgs":false,"family":"McGonigle","given":"Chris","email":"","affiliations":[{"id":61838,"text":"University of Ulster","active":true,"usgs":false}],"preferred":false,"id":838270,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sethi, Suresh 0000-0002-0053-1827 ssethi@usgs.gov","orcid":"https://orcid.org/0000-0002-0053-1827","contributorId":191424,"corporation":false,"usgs":true,"family":"Sethi","given":"Suresh","email":"ssethi@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":838268,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harris, Bradley","contributorId":288772,"corporation":false,"usgs":false,"family":"Harris","given":"Bradley","affiliations":[{"id":12915,"text":"Alaska Pacific University","active":true,"usgs":false}],"preferred":false,"id":838271,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Quinn, Rory","contributorId":288773,"corporation":false,"usgs":false,"family":"Quinn","given":"Rory","email":"","affiliations":[{"id":61838,"text":"University of Ulster","active":true,"usgs":false}],"preferred":false,"id":838272,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Grabowski, Jon","contributorId":288774,"corporation":false,"usgs":false,"family":"Grabowski","given":"Jon","email":"","affiliations":[{"id":61840,"text":"Northeaster University","active":true,"usgs":false}],"preferred":false,"id":838273,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70223491,"text":"70223491 - 2018 - Responses of unimpaired flows, storage, and managed flows to scenarios of climate change in the San Francisco Bay-Delta watershed","interactions":[],"lastModifiedDate":"2021-08-30T13:08:42.994302","indexId":"70223491","displayToPublicDate":"2018-09-21T08:06:37","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Responses of unimpaired flows, storage, and managed flows to scenarios of climate change in the San Francisco Bay-Delta watershed","docAbstract":"Projections of meteorology downscaled from global climate model runs were used to drive a model of unimpaired hydrology of the Sacramento/San Joaquin watershed, which in turn drove models of operational responses and managed flows. Twenty daily climate change scenarios for water years 1980–2099 were evaluated with the goal of producing inflow boundary conditions for a watershed sediment model and for a hydrodynamical model of the San Francisco Bay-Delta estuary. The resulting time series of meteorology, snowpack, unimpaired flow, reservoir storage, and managed flow were analyzed for century-scale trends. In the Sacramento basin, which dominates Bay-Delta inflows, all 20 scenarios portrayed warming trends (with a mean of 4.1 °C) and most had precipitation increases (with a mean increase of 9%). Sacramento basin snowpack water equivalent declined sharply (by 89%), which was associated with a major shift toward earlier unimpaired runoff timing (33% more flow arriving prior to 1 April). Sacramento basin reservoirs showed large declines in end-of-September storage. Water-year averaged outflows increased for most scenarios for both unimpaired and impaired flows, and frequency of extremely high daily unimpaired and impaired flows increased (increases of 175% and 170%, respectively). Managed Delta inflows were projected to experience large increases in the wet season and declines in the dry season. Changes in management strategy and infrastructure can mitigate some of these changes, though to what degree is uncertain.
Tectonic tremor can be used to constrain seismic‐wave attenuation for use in ground‐motion prediction equations (GMPEs) in regions where moderately sized earthquakes occur infrequently. Here we quantify seismic‐wave attenuation by inverting tremor ground motion amplitudes in different frequency bands of interest, to determine frequency dependence of and spatial variations in seismic‐wave attenuation in Cascadia. Due to the density of tremor data, we are able to resolve along‐strike variations in the attenuation parameter. We find that tectonic tremor exhibits the frequency dependence expected for attenuation, as determined from GMPEs developed from moderate‐to‐large magnitude earthquakes. This implies that attenuation along these paths is independent of the source mechanism. This study demonstrates that tectonic tremor can be used to provide insight into the physical factors responsible for attenuation, and to refine estimates of attenuation for ground‐motion prediction, thus having important implications for hazard assessment and engineering seismology.
","language":"English","publisher":"AGU","doi":"10.1029/2018GL079344","usgsCitation":"Littel, G.F., Thomas, A.M., and Baltay Sundstrom, A.S., 2018, Using tectonic tremor to constrain seismic‐wave attenuation in Cascadia: Geophysical Research Letters, v. 45, no. 18, p. 9579-9587, https://doi.org/10.1029/2018GL079344.","productDescription":"9 p.","startPage":"9579","endPage":"9587","ipdsId":"IP-101242","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":468380,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2018gl079344","text":"Publisher Index Page"},{"id":357578,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -128,\n 39.5\n ],\n [\n -121,\n 39.5\n ],\n [\n -121,\n 50.5\n ],\n [\n -128,\n 50.5\n ],\n [\n -128,\n 39.5\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"45","issue":"18","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-09-27","publicationStatus":"PW","scienceBaseUri":"5bc02f99e4b0fc368eb538db","contributors":{"authors":[{"text":"Littel, Geena F.","contributorId":208081,"corporation":false,"usgs":false,"family":"Littel","given":"Geena","email":"","middleInitial":"F.","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":745834,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thomas, Amanda M.","contributorId":200641,"corporation":false,"usgs":false,"family":"Thomas","given":"Amanda","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":745835,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baltay Sundstrom, Annemarie S. 0000-0002-6514-852X abaltay@usgs.gov","orcid":"https://orcid.org/0000-0002-6514-852X","contributorId":4932,"corporation":false,"usgs":true,"family":"Baltay Sundstrom","given":"Annemarie","email":"abaltay@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":745833,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70199535,"text":"70199535 - 2018 - The ecology of movement and behaviour: a saturated tripartite network for describing animal contacts","interactions":[],"lastModifiedDate":"2018-09-21T10:58:38","indexId":"70199535","displayToPublicDate":"2018-09-20T15:35:21","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3174,"text":"Proceedings of the Royal Society B: Biological Sciences","active":true,"publicationSubtype":{"id":10}},"title":"The ecology of movement and behaviour: a saturated tripartite network for describing animal contacts","docAbstract":"Ecologists regularly use animal contact networks to describe interactions underlying pathogen transmission, gene flow, and information transfer. However, empirical descriptions of contact often overlook some features of individual movement, and decisions about what kind of network to use in a particular setting are commonly ad hoc. Here, we relate individual movement trajectories to contact networks through a tripartite network model of individual, space, and time nodes. Most networks used in animal contact studies (e.g. individual association networks, home range overlap networks, and spatial networks) are simplifications of this tripartite model. The tripartite structure can incorporate a broad suite of alternative ecological metrics like home range sizes and patch occupancy patterns into inferences about contact network metrics such as modularity and degree distribution. We demonstrate the model's utility with two simulation studies using alternative forms of ecological data to constrain the tripartite network's structure and inform expectations about the harder-to-measure metrics related to contact.
","language":"English","publisher":"The Royal Society Publishing","doi":"10.1098/rspb.2018.0670","usgsCitation":"Manlove, K.R., Aiello, C.M., Sah, P., Cummins, B., Hudson, P.J., and Cross, P.C., 2018, The ecology of movement and behaviour: a saturated tripartite network for describing animal contacts: Proceedings of the Royal Society B: Biological Sciences, v. 285, no. 1887, https://doi.org/10.1098/rspb.2018.0670.","ipdsId":"IP-092136","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":468381,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1098/rspb.2018.0670","text":"External Repository"},{"id":357568,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"285","issue":"1887","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-09-19","publicationStatus":"PW","scienceBaseUri":"5bc02f99e4b0fc368eb538df","contributors":{"authors":[{"text":"Manlove, Kezia R.","contributorId":198305,"corporation":false,"usgs":false,"family":"Manlove","given":"Kezia","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":745807,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Aiello, Christina M. 0000-0002-2399-5464 caiello@usgs.gov","orcid":"https://orcid.org/0000-0002-2399-5464","contributorId":5617,"corporation":false,"usgs":true,"family":"Aiello","given":"Christina","email":"caiello@usgs.gov","middleInitial":"M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":745808,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sah, Pratha","contributorId":127768,"corporation":false,"usgs":false,"family":"Sah","given":"Pratha","email":"","affiliations":[{"id":7145,"text":"Department of Biology, Georgetown University, Washington DC","active":true,"usgs":false}],"preferred":false,"id":745809,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cummins, Bree","contributorId":208072,"corporation":false,"usgs":false,"family":"Cummins","given":"Bree","email":"","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":745810,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hudson, Peter J.","contributorId":204377,"corporation":false,"usgs":false,"family":"Hudson","given":"Peter","email":"","middleInitial":"J.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":745811,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cross, Paul C. 0000-0001-8045-5213 pcross@usgs.gov","orcid":"https://orcid.org/0000-0001-8045-5213","contributorId":2709,"corporation":false,"usgs":true,"family":"Cross","given":"Paul","email":"pcross@usgs.gov","middleInitial":"C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":745806,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70213242,"text":"70213242 - 2018 - A method to detect discontinuities in census data","interactions":[],"lastModifiedDate":"2020-09-16T13:31:14.714975","indexId":"70213242","displayToPublicDate":"2018-09-20T13:35:09","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"A method to detect discontinuities in census data","docAbstract":"The distribution of pattern across scales has predictive power in the analysis of complex systems. Discontinuity approaches remain a fruitful avenue of research in the quest for quantitative measures of resilience because discontinuity analysis provides an objective means of identifying scales in complex systems and facilitates delineation of hierarchical patterns in processes, structure, and resources. However, current discontinuity methods have been considered too subjective, too complicated and opaque, or have become computationally obsolete; given the ubiquity of discontinuities in ecological and other complex systems, a simple and transparent method for detection is needed. In this study, we present a method to detect discontinuities in census data based on resampling of a neutral model and provide the R code used to run the analyses. This method has the potential for advancing basic and applied ecological research.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.4297","usgsCitation":"Barichievy, C., Angeler, D., Eason, T.N., Garmestani, A.S., Nash, K., Stow, C., Sundstrom, S., and Allen, C.R., 2018, A method to detect discontinuities in census data: Ecology and Evolution, v. 8, no. 19, p. 9614-9623, https://doi.org/10.1002/ece3.4297.","productDescription":"10 p.","startPage":"9614","endPage":"9623","ipdsId":"IP-098532","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true}],"links":[{"id":468382,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.4297","text":"Publisher Index Page"},{"id":378407,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"19","noUsgsAuthors":false,"publicationDate":"2018-09-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Barichievy, C. 0000-0003-4088-953X","orcid":"https://orcid.org/0000-0003-4088-953X","contributorId":240685,"corporation":false,"usgs":false,"family":"Barichievy","given":"C.","affiliations":[{"id":13431,"text":"Zoological Society of London","active":true,"usgs":false}],"preferred":false,"id":798757,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Angeler, D. G.","contributorId":240686,"corporation":false,"usgs":false,"family":"Angeler","given":"D. G.","affiliations":[{"id":12665,"text":"University of Cape Town","active":true,"usgs":false}],"preferred":false,"id":798758,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eason, T. N.","contributorId":205437,"corporation":false,"usgs":false,"family":"Eason","given":"T.","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":798759,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Garmestani, A. S.","contributorId":240687,"corporation":false,"usgs":false,"family":"Garmestani","given":"A.","email":"","middleInitial":"S.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":798760,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nash, K.L. 0000-0003-0976-3197","orcid":"https://orcid.org/0000-0003-0976-3197","contributorId":240688,"corporation":false,"usgs":false,"family":"Nash","given":"K.L.","email":"","affiliations":[{"id":48132,"text":"Centre for Marine Socioecology","active":true,"usgs":false}],"preferred":false,"id":798761,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stow, C.A.","contributorId":240689,"corporation":false,"usgs":false,"family":"Stow","given":"C.A.","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":798762,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sundstrom, S.","contributorId":240690,"corporation":false,"usgs":false,"family":"Sundstrom","given":"S.","affiliations":[{"id":36892,"text":"University of Nebraska","active":true,"usgs":false}],"preferred":false,"id":798763,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Allen, Craig R. 0000-0001-8655-8272 allencr@usgs.gov","orcid":"https://orcid.org/0000-0001-8655-8272","contributorId":1979,"corporation":false,"usgs":true,"family":"Allen","given":"Craig","email":"allencr@usgs.gov","middleInitial":"R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":798764,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70199223,"text":"ofr20181148 - 2018 - PRISM marine sites—The history of PRISM sea surface temperature estimation","interactions":[],"lastModifiedDate":"2018-09-19T16:44:54","indexId":"ofr20181148","displayToPublicDate":"2018-09-19T14:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1148","title":"PRISM marine sites—The history of PRISM sea surface temperature estimation","docAbstract":"For more than three decades, the U.S. Geological Survey (USGS) Pliocene Research, Interpretation and Synoptic Mapping (PRISM) Project has compiled paleoenvironmental data with the goal of reconstructing global conditions during the warm interval in the middle of the Piacenzian Age of the Pliocene Epoch (about 3.3 to 3.0 million years ago). Because this is the most recent interval of time in which climatic conditions were similar to those expected in the near future, a global reconstruction of conditions from this interval offers an imperfect yet useful representation of near future conditions. PRISM reconstructions have been used extensively as boundary conditions in general circulation model experiments aimed at better understanding Pliocene climate. They have also served as hindcasting targets when testing the ability of climate models to simulate real climates of the past, an exercise in estimating a model’s ability to accurately predict future climate. As data coverage has grown and model precision has improved, PRISM datasets have become important validation tools for pinpointing discrete areas of data-model disagreement and model-model disagreement. The Pliocene sea surface temperature (SST) dataset is the best developed component of the PRISM reconstructions and is the keystone of Pliocene paleoclimate research. For the first time, we compile all data related to PRISM SST estimation. This discussion chronicles the history of PRISM SST research as it evolved, responding to advances in paleochronology and paleotemperature estimation. Paleoclimatic considerations unique to each location are illustrated, as are any new developments since the initial publication of the data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181148","usgsCitation":"Robinson, M.M., Dowsett, H.J., Foley, K.M., and Riesselman, C.R., 2018, PRISM marine sites—The history of PRISM sea surface temperature estimation: U.S. Geological Survey Open-File Report 2018–1148, 49 p., https://doi.org/10.3133/ofr20181148.","productDescription":"vi, 49 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-087999","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":357306,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1148/ofr20181148.pdf","text":"Report","size":"1 MB","description":"OFR 2018-1148"},{"id":357305,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1148/coverthb3.jpg"}],"contact":"Eastern Geology and Paleoclimate Science Center
U.S. Geological Survey
12201 Sunrise Valley Drive
926A National Center
Reston, VA 20192
Species dependent upon early-successional landscapes often occupy patches at different stages of recovery after disturbance. The demographic processes that drive patch dynamics in these systems have rarely been described but are important for developing effective conservation and management plans, especially when humans have modified the timing and intensity of disturbances that drive regeneration. In riparian systems, disturbance by floods historically initiated plant regeneration, but many rivers are now regulated and stream flows disrupted by dams and reservoirs. We studied the demography and patch dynamics of an endangered, neotropical migrant bird dependent on remnant riparian patches for breeding, the southwestern willow flycatcher (Empidonax trailli extimus), over 9 yr at both a riverine and reservoir site in central Arizona. We found that at both sites, number of territories/ha within patches increased for 2–4 yr after colonization and then declined, with several patches abandoned after 6–10 yr. Age of birds increased with patch age, with younger birds in colonizing patches and older, site-faithful birds in older patches, while mean per capita reproductive success did not differ with patch age. Natal dispersal and breeding dispersal were primarily from intermediate-aged patches into either young- or other intermediate-aged patches. At both riverine and reservoir sites, both the number of patches and the number of territorial birds increased over time, with the percentage of territories shifting into younger and younger patches. The type of disturbance driving patch regeneration differed between riverine and reservoir sites (seasonal flooding vs. falling lake levels due to drought), but the demographic patterns did not, indicating that reservoirs can generate patch dynamics similar to those on rivers. Managing stream flows and reservoir levels to maintain disturbance cycles sufficient to generate riparian patches at different stages of regeneration through time would benefit succession-dependent species like the endangered flycatcher we studied, whether those disturbances arise from natural flooding events along free-flowing rivers or through changes in reservoir levels.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.2425","usgsCitation":"Theimer, T.C., Sogge, M.K., and Paxton, E.H., 2018, Patch age since disturbance drives patch dynamics for flycatchers breeding in both reservoir and riverine habitat: Ecosphere, v. 9, no. 9, e02425, 16 p., https://doi.org/10.1002/ecs2.2425.","productDescription":"e02425, 16 p.","ipdsId":"IP-099402","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":468385,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2425","text":"Publisher Index Page"},{"id":424187,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"9","noUsgsAuthors":false,"publicationDate":"2018-09-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Theimer, Tad C 0000-0002-4565-8661","orcid":"https://orcid.org/0000-0002-4565-8661","contributorId":223213,"corporation":false,"usgs":false,"family":"Theimer","given":"Tad","email":"","middleInitial":"C","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":891676,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sogge, Mark K. 0000-0002-8337-5689 mark_sogge@usgs.gov","orcid":"https://orcid.org/0000-0002-8337-5689","contributorId":3710,"corporation":false,"usgs":true,"family":"Sogge","given":"Mark","email":"mark_sogge@usgs.gov","middleInitial":"K.","affiliations":[{"id":5079,"text":"Pacific Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":891677,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Paxton, Eben H. 0000-0001-5578-7689","orcid":"https://orcid.org/0000-0001-5578-7689","contributorId":19640,"corporation":false,"usgs":true,"family":"Paxton","given":"Eben","email":"","middleInitial":"H.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":891678,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70217633,"text":"70217633 - 2018 - Submarine deposition of a subaerial landslide in Taan Fiord, Alaska","interactions":[],"lastModifiedDate":"2023-11-09T16:12:13.583505","indexId":"70217633","displayToPublicDate":"2018-09-19T07:18:03","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2312,"text":"Journal of Geophysical Research","active":true,"publicationSubtype":{"id":10}},"title":"Submarine deposition of a subaerial landslide in Taan Fiord, Alaska","docAbstract":"A large subaerial landslide entered Taan Fiord, Alaska, on 17 October 2015 producing a tsunami with runup to 193 m. We use LiDAR data to show the slide volume to be 76 + 3/−4 million cubic meters and that 51,000,000 m3 entered Taan Fiord. In 2016, we mapped the fjord with multibeam bathymetry and high‐resolution seismic data. Landslide and postlandslide deposits extend 6 km downfjord, are up to 70 ± 11 m thick, and have a total volume of ~147,000,000 m3. Seismic data image a blocky landslide unit and two units deposited immediately after the landslide. The blocky landslide unit is ~65,000,000 m3. We infer it consists dominantly of subaerially derived material and secondarily of fjord floor sediment. The overlying units are likely megaturbidites presumably deposited within minutes to days after the landslide. We infer that these deposits dominantly consist of fjord floor material mobilized and suspended as the slide entered and traveled downfjord. The lower postlandslide unit is up to 35 ± 6 m thick, and the upper unit is up to 12 ± 3 m thick. These deposits are distinctive and will leave a lasting record of the event. This subaerial‐to‐submarine landslide deposit is distinct from other submarine landslide deposits studied in Alaskan fjords because it has a much greater thickness, larger and more angular blocks, distinctive postlandslide megaturbidites, and a higher‐amplitude acoustic signature of the blocky deposit. The tight constraints on the landslide source and deposit volumes, topography, bathymetry, and tsunami runup heights and flow directions should make this a benchmark site for landslide‐tsunami models.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2018JF004608","usgsCitation":"Haeussler, P., Gulick, S.P., McCall, N., Walton, M.A., Reece, R., Larson, C., Shugar, D.H., Geertsema, M., Venditti, J.G., and Labay, K.A., 2018, Submarine deposition of a subaerial landslide in Taan Fiord, Alaska: Journal of Geophysical Research, v. 123, no. 10, p. 2443-2463, https://doi.org/10.1029/2018JF004608.","productDescription":"21 p.","startPage":"2443","endPage":"2463","ipdsId":"IP-094084","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":460847,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2018jf004608","text":"Publisher Index Page"},{"id":382581,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Taan Fiord","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -141.6,\n 60.25\n ],\n [\n -141.6,\n 59.75\n ],\n [\n -141,\n 59.75\n ],\n [\n -141,\n 60.25\n ],\n [\n -141.6,\n 60.25\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"123","issue":"10","noUsgsAuthors":false,"publicationDate":"2018-10-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Haeussler, Peter J. 0000-0002-1503-6247","orcid":"https://orcid.org/0000-0002-1503-6247","contributorId":219956,"corporation":false,"usgs":true,"family":"Haeussler","given":"Peter J.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":809031,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gulick, S. P. S 0000-0003-4740-9068","orcid":"https://orcid.org/0000-0003-4740-9068","contributorId":248396,"corporation":false,"usgs":false,"family":"Gulick","given":"S.","email":"","middleInitial":"P. S","affiliations":[{"id":49883,"text":"Institute for Geophysics and Department of Geological Sciences, University of Texas at Austin, Austin, Texas, USA","active":true,"usgs":false}],"preferred":false,"id":809032,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCall, N. 0000-0001-7133-7717","orcid":"https://orcid.org/0000-0001-7133-7717","contributorId":248397,"corporation":false,"usgs":false,"family":"McCall","given":"N.","email":"","affiliations":[{"id":49883,"text":"Institute for Geophysics and Department of Geological Sciences, University of Texas at Austin, Austin, Texas, USA","active":true,"usgs":false}],"preferred":false,"id":809033,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Walton, Maureen A. L. 0000-0001-8496-463X","orcid":"https://orcid.org/0000-0001-8496-463X","contributorId":211025,"corporation":false,"usgs":true,"family":"Walton","given":"Maureen","email":"","middleInitial":"A. L.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":809034,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reece, R. 0000-0002-0769-1698","orcid":"https://orcid.org/0000-0002-0769-1698","contributorId":248398,"corporation":false,"usgs":false,"family":"Reece","given":"R.","email":"","affiliations":[{"id":49885,"text":"Department of Geology and Geophysics, Texas A&M University, College Station, TX","active":true,"usgs":false}],"preferred":false,"id":809035,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Larson, C.","contributorId":248399,"corporation":false,"usgs":false,"family":"Larson","given":"C.","affiliations":[{"id":49886,"text":"University of Alaska, Fairbanks, Alaska, USA","active":true,"usgs":false}],"preferred":false,"id":809036,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Shugar, D. H. 0000-0002-6279-8420","orcid":"https://orcid.org/0000-0002-6279-8420","contributorId":248400,"corporation":false,"usgs":false,"family":"Shugar","given":"D.","email":"","middleInitial":"H.","affiliations":[{"id":49887,"text":"Water, Sediment, Hazards, and Earth-surface Dynamics (waterSHED) Lab, University of Washington, Tacoma, Washington, USA","active":true,"usgs":false}],"preferred":false,"id":809037,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Geertsema, M. 0000-0002-4650-8251","orcid":"https://orcid.org/0000-0002-4650-8251","contributorId":167412,"corporation":false,"usgs":false,"family":"Geertsema","given":"M.","affiliations":[],"preferred":false,"id":809038,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Venditti, J. G. 0000-0002-2876-4251","orcid":"https://orcid.org/0000-0002-2876-4251","contributorId":248401,"corporation":false,"usgs":false,"family":"Venditti","given":"J.","email":"","middleInitial":"G.","affiliations":[{"id":49888,"text":"Simon Fraser University, Burnaby, British Columbia, Canada","active":true,"usgs":false}],"preferred":false,"id":809039,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Labay, Keith A. 0000-0002-6763-3190 klabay@usgs.gov","orcid":"https://orcid.org/0000-0002-6763-3190","contributorId":217714,"corporation":false,"usgs":true,"family":"Labay","given":"Keith","email":"klabay@usgs.gov","middleInitial":"A.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":809040,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70236782,"text":"70236782 - 2018 - A digital elevation model for simulating the 1945 Makran tsunami in Karachi Harbour","interactions":[],"lastModifiedDate":"2022-09-19T11:28:27.799503","indexId":"70236782","displayToPublicDate":"2018-09-19T06:23:24","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12591,"text":"Geoscience Letters","active":true,"publicationSubtype":{"id":10}},"title":"A digital elevation model for simulating the 1945 Makran tsunami in Karachi Harbour","docAbstract":"The digital elevation model documented here provides a tool for calibrating tsunami models to effects of the 1945 Makran tsunami that were observed in Karachi Harbour. The DEM bathymetry is derived from soundings made mainly during the first 8 years post-tsunami. While deficient in its portrayal of interior tidelands and upland topography, the DEM accurately represents the setting of a tide gauge that recorded the 1945 tsunami.
High water temperatures can increase the energetic cost for salmon to migrate and spawn, which can be important for Snake River fall‐run Chinook salmon because they migrate great distances (>500 km) at a time when river temperatures (18–24°C) can be above their optimum temperatures (16.5°C). Average river temperatures and random combinations of migration and spawning dates were used to simulate fish travel times and determine the energetic consequences of different thermal experiences during migration. An energy threshold criterion (4 kJ/g) was also imposed on survival and spawning success, which was used to determine how prevailing temperatures might select against certain migration dates and thermal experiences, and in turn, explain the selection for the current spawning phenology of the population. Scenarios of tributary use for thermal refugia under increasing water temperatures (1, 2, and 3°C) were also run to determine which combinations of migration dates, travel rates, and resulting thermal experiences might be most affected by energy exhaustion. As expected, when compared to observations, the model under existing conditions and energy use could explain the onset, but not the end of the observed spawning migration. Simulations of early migrants had greater energy loss than late migrants regardless of the river temperature scenario, but higher temperatures disproportionately selected against a larger fraction of early‐migrating fish, although using cold‐water tributaries during migration provided a buffer against higher energy use at higher temperatures. The fraction of simulated fish that exceeded the threshold for migration success increased from 58% to 72% as average seasonal river temperatures over baseline temperatures increased. The model supports the conclusion that increases in average seasonal river temperatures as little as 1°C could impose greater thermal constraints on the fish, select against early migrants, and in turn, truncate the onset of the current spawning migration.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.4353","usgsCitation":"Plumb, J.M., 2018, A bioenergetics evaluation of temperature‐dependent selection for the spawning phenology by Snake River fall Chinook salmon: Ecology and Evolution, v. 62, no. 4, p. 351-354, https://doi.org/10.1002/ece3.4353.","productDescription":"4 p.","startPage":"351","endPage":"354","ipdsId":"IP-091288","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":468390,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.4353","text":"Publisher Index Page"},{"id":357442,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Columbia River, Snake River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.27783203125,\n 45.0502402697946\n ],\n [\n -116.34521484375001,\n 45.0502402697946\n ],\n [\n -116.34521484375001,\n 47.34626718205302\n ],\n [\n -122.27783203125,\n 47.34626718205302\n ],\n [\n -122.27783203125,\n 45.0502402697946\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"62","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-09-12","publicationStatus":"PW","scienceBaseUri":"5bc02f9ce4b0fc368eb538f3","contributors":{"authors":[{"text":"Plumb, John M. 0000-0003-4255-1612 jplumb@usgs.gov","orcid":"https://orcid.org/0000-0003-4255-1612","contributorId":3569,"corporation":false,"usgs":true,"family":"Plumb","given":"John","email":"jplumb@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":745343,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70199409,"text":"70199409 - 2018 - A database of natural monthly streamflow estimates from 1950 to 2015 for the conterminous United States","interactions":[],"lastModifiedDate":"2018-12-05T14:17:31","indexId":"70199409","displayToPublicDate":"2018-09-17T13:52:16","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"A database of natural monthly streamflow estimates from 1950 to 2015 for the conterminous United States","docAbstract":"Quantifying and understanding the natural streamflow regime, defined as expected streamflow that would occur in the absence of anthropogenic modification to the hydrologic system, is critically important for the development of management strategies aimed at protecting aquatic ecosystems. Water balance models have been applied frequently to estimate natural flows, but are limited in the number of predictor variables that can be included. Here, a statistical machine learning technique — random forest modeling — was applied to estimate natural flows at a monthly time‐step from 1950 to 2015 for >2.5 million stream reaches in the conterminous United States (U.S.) using 200 potential predictor variables. We describe the development and documentation of this dataset and assess model performance. Model fit statistics (mean Nash–Sutcliffe efficiency = 0.85; observed/expected ratio = 0.94) indicate good correspondence between predicted and observed flows at nearly 2,000 streamgages. As an example application of the dataset, the observed streamflow record at a site prior to and after the construction of an upstream reservoir was compared with estimated natural flows to demonstrate the magnitude of seasonal depletions in streamflow due to the reservoir. This dataset can be applied to quantify natural and anthropogenic processes contributing to streamflow depletion or augmentation, and assess associated ecological effects.
","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.12685","usgsCitation":"Miller, M.P., Carlisle, D.M., Wolock, D.M., and Wieczorek, M., 2018, A database of natural monthly streamflow estimates from 1950 to 2015 for the conterminous United States: Journal of the American Water Resources Association, v. 54, no. 6, p. 1258-1269, https://doi.org/10.1111/1752-1688.12685.","productDescription":"12 p.","startPage":"1258","endPage":"1269","ipdsId":"IP-094353","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":468395,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1752-1688.12685","text":"Publisher Index Page"},{"id":437752,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7CC0ZMG","text":"USGS data release","linkHelpText":"Natural Monthly Flow Estimates for the Conterminous United States, 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Program","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":745159,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wieczorek, Michael 0000-0003-0999-5457","orcid":"https://orcid.org/0000-0003-0999-5457","contributorId":207911,"corporation":false,"usgs":true,"family":"Wieczorek","given":"Michael","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":745160,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70199412,"text":"70199412 - 2018 - Identifying physics‐based thresholds for rainfall‐induced landsliding","interactions":[],"lastModifiedDate":"2018-10-23T16:50:13","indexId":"70199412","displayToPublicDate":"2018-09-17T13:45:04","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Identifying physics‐based thresholds for rainfall‐induced landsliding","docAbstract":"Most regional landslide warning systems utilize empirically derived rainfall thresholds that are difficult to improve without recalibration to additional landslide events. To address this limitation, we explored the use of synthetic rainfall to generate thousands of possible storm patterns and coupled them with a physics‐based hydrology and slope stability model for various antecedent soil saturation scenarios to analyze pore‐water pressure and factor of safety metrics. We used these metrics to generate two‐tiered alert thresholds that can be employed to assess shallow landslide potential for any given combination of storm and antecedent wetness. When applied to the San Francisco Bay region (California, USA), the results are consistent with events that caused widespread landsliding. Our deterministic modeling approach, which accounts for plausible ranges in soil hydraulic and mechanical properties, can inform the development of the next generation of warning systems for rainfall‐induced landsliding.
","language":"English","publisher":"AGU","doi":"10.1029/2018GL079662","usgsCitation":"Thomas, M.A., Mirus, B.B., and Collins, B.D., 2018, Identifying physics‐based thresholds for rainfall‐induced landsliding: Geophysical Research Letters, v. 45, no. 18, p. 9651-9661, https://doi.org/10.1029/2018GL079662.","productDescription":"11 p.","startPage":"9651","endPage":"9661","ipdsId":"IP-099617","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":468396,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2018gl079662","text":"Publisher Index Page"},{"id":357398,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"45","issue":"18","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-09-26","publicationStatus":"PW","scienceBaseUri":"5bc02f9de4b0fc368eb53905","contributors":{"authors":[{"text":"Thomas, Matthew A. 0000-0002-9828-5539 matthewthomas@usgs.gov","orcid":"https://orcid.org/0000-0002-9828-5539","contributorId":200616,"corporation":false,"usgs":true,"family":"Thomas","given":"Matthew","email":"matthewthomas@usgs.gov","middleInitial":"A.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":745169,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mirus, Benjamin B. 0000-0001-5550-014X bbmirus@usgs.gov","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":4064,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin","email":"bbmirus@usgs.gov","middleInitial":"B.","affiliations":[{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true},{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":745170,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Collins, Brian D. 0000-0003-4881-5359 bcollins@usgs.gov","orcid":"https://orcid.org/0000-0003-4881-5359","contributorId":149278,"corporation":false,"usgs":true,"family":"Collins","given":"Brian","email":"bcollins@usgs.gov","middleInitial":"D.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":745171,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70199421,"text":"70199421 - 2018 - Mapping the relationships between trail conditions and experiential elements of long-distance hiking","interactions":[],"lastModifiedDate":"2018-09-17T13:39:22","indexId":"70199421","displayToPublicDate":"2018-09-17T13:39:16","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2603,"text":"Landscape and Urban Planning","active":true,"publicationSubtype":{"id":10}},"title":"Mapping the relationships between trail conditions and experiential elements of long-distance hiking","docAbstract":"Trail users that experience acceptable social and ecological conditions are more likely to act as trail stewards, exhibit proper trail etiquette, and use low-impact practices. However, the relationships between specific trail conditions and experiential elements of long-distance hiking are not well understood. Therefore, the purpose of this study was to identify how trail conditions influence particular elements of the long-distance hiking experience. The researchers used a mixed-methods approach involving semi-structured interviews (n = 17), quantitative questionnaires (n = 336), ecological measurements of trail conditions (n = 21–5 km sections), and modified Recreation Suitability Mapping (RSM) techniques to quantify the relationships between five trail conditions (trail incision, muddiness, rugosity, trail width, and gradient) and four experiential elements of long-distance hiking (level of challenge, perceived impact to musculoskeletal system, valuation of tread aesthetics, and ability to maintain an ideal hiking pace). Quantified values were weighted, analyzed, and mapped using SPSS 22.0 and ArcMap 10.2.2. Significant differences exist in the scores and distributions of ecological measures across all sections, indicating that trail conditions vary significantly across sampled trail sections. Although, long-distance hikers felt all four experiential elements were important, tread aesthetics was ranked by 50.2% of sampled hikers as the most important experiential element to the overall experience. The resulting information after applying the weights suggests what particular type of experience is likely for each trail section considering the presence of trail conditions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.landurbplan.2018.06.010","usgsCitation":"Peterson, B.A., Brownlee, M.T., and Marion, J.L., 2018, Mapping the relationships between trail conditions and experiential elements of long-distance hiking: Landscape and Urban Planning, v. 180, p. 60-75, https://doi.org/10.1016/j.landurbplan.2018.06.010.","productDescription":"16 p.","startPage":"60","endPage":"75","ipdsId":"IP-098634","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":468398,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1016/j.landurbplan.2018.06.010","text":"External Repository"},{"id":357396,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Appalachian Trail","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.77294921875,\n 41.52502957323801\n ],\n [\n -68.90625,\n 41.52502957323801\n ],\n [\n -68.90625,\n 46.042735653846506\n ],\n [\n -74.77294921875,\n 46.042735653846506\n ],\n [\n -74.77294921875,\n 41.52502957323801\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"180","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5bc02f9ee4b0fc368eb53909","contributors":{"authors":[{"text":"Peterson, Brian A.","contributorId":207921,"corporation":false,"usgs":false,"family":"Peterson","given":"Brian","email":"","middleInitial":"A.","affiliations":[{"id":37666,"text":"PhD student, Clemson University","active":true,"usgs":false}],"preferred":false,"id":745212,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brownlee, Matthew T.J.","contributorId":207922,"corporation":false,"usgs":false,"family":"Brownlee","given":"Matthew","email":"","middleInitial":"T.J.","affiliations":[{"id":37667,"text":"Assistant Professor, Clemson University","active":true,"usgs":false}],"preferred":false,"id":745213,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Marion, Jeffrey L. 0000-0003-2226-689X jeff_marion@usgs.gov","orcid":"https://orcid.org/0000-0003-2226-689X","contributorId":3614,"corporation":false,"usgs":true,"family":"Marion","given":"Jeffrey","email":"jeff_marion@usgs.gov","middleInitial":"L.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":745211,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70200635,"text":"70200635 - 2018 - Inferring watershed hydraulics and cold-water habitat persistence using multi-year air and stream temperature signals","interactions":[],"lastModifiedDate":"2018-10-25T14:28:37","indexId":"70200635","displayToPublicDate":"2018-09-15T14:28:30","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Inferring watershed hydraulics and cold-water habitat persistence using multi-year air and stream temperature signals","docAbstract":"Streams strongly influenced by groundwater discharge may serve as “climate refugia” for sensitive species in regions of increasingly marginal thermal conditions. The main goal of this study is to develop paired air and stream water annual temperature signal analysis techniques to elucidate the relative groundwater contribution to stream water and the effective groundwater flowpath depth. Groundwater discharge to streams attenuates surface water temperature signals, and this attenuation can be diagnostic of groundwater gaining systems. Additionally, discharge from shallow groundwater flowpaths can theoretically transfer lagged annual temperature signals from aquifer to stream water. Here we explore this concept using multi-year temperature records from 120 stream sites located across 18 mountain watersheds of Shenandoah National Park, VA, USA and a coastal watershed in Massachusetts, USA. Both areas constitute important cold-water habitat for native brook trout (Salvelinus fontinalis). Observed annual temperature signals indicate a dominance of shallow groundwater discharge to streams in the National Park, in contrast to the coastal watershed that has strong, apparently deeper, groundwater influence. The average phase lag from air to stream signals in Shenandoah National Park is 11 d; however, extended lags of approximately 1 month were observed in a subset of streams. In contrast, the coastal stream has pronounced attenuation of annual temperature signals without notable phase lag. To better understand these observed differences in signal characteristics, analytical and numerical models are used to quantify mixing of the annual temperature signals of surface and groundwater. Simulations using a total heat budget numerical model indicate groundwater-induced annual temperature signal phase lags are likely to show greater downstream propagation than the related signal amplitude attenuation. The measurement of multi-seasonal paired air and water temperatures offers great promise toward understanding catchment processes and informing current cold-water habitat management at ecologically-relevant scales.
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C","contributorId":273181,"corporation":false,"usgs":false,"family":"Liknes","given":"Greg","email":"","middleInitial":"C","affiliations":[{"id":7134,"text":"USFS","active":true,"usgs":false}],"preferred":false,"id":832667,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Rigge, Matthew B. 0000-0003-4471-8009 mrigge@usgs.gov","orcid":"https://orcid.org/0000-0003-4471-8009","contributorId":751,"corporation":false,"usgs":true,"family":"Rigge","given":"Matthew","email":"mrigge@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":832668,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"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":832669,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70216329,"text":"70216329 - 2018 - Use of landscape simulation modeling to quantify resilience for ecological applications","interactions":[],"lastModifiedDate":"2020-11-12T13:32:54.085508","indexId":"70216329","displayToPublicDate":"2018-09-13T07:30:05","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Use of landscape simulation modeling to quantify resilience for ecological applications","docAbstract":"Goals of fostering ecological resilience are increasingly used to guide U.S. public land management in the context of anthropogenic climate change and increasing landscape disturbances. There are, however, few operational means of assessing the resilience of a landscape or ecosystem. We present a method to evaluate resilience using simulation modeling. In this method, we use historical conditions (e.g., in North America, prior to European settlement), quantified using simulation modeling, to provide a comparative reference for contemporary conditions, where substantial departures indicate loss of resilience. Contemporary ecological conditions are compared statistically to the historical time series to create a resilience index, which can be used to prioritize landscapes for treatment and inform possible treatments. However, managing for resilience based on historical conditions is tenuous in the Anthropocene, which is characterized by rapid climate change, extensive human land use, altered disturbance regimes, and exotic species introductions. To account for the future variability of ecosystems resulting from climate and disturbance regime shifts, we augment historical simulations with simulations of ecosystem dynamics under projected climate and land use changes to assess the degree of departure from benchmark historical conditions. We use a mechanistic landscape model (FireBGCv2) applied to a large landscape in western Montana, USA, to illustrate the methods presented in this paper. Spatially explicit ecosystem modeling provides the vehicle to generate the historical and future time series needed to quantify potential resilience conditions associated with past and potential future conditions. Our methods show that given selection of a useful set of metrics, managers could use simulations like ours to evaluate potential future management directions.
Large intraplate earthquakes in oceanic lithosphere are rare and usually related to regions of diffuse deformation within the oceanic plate. The 23 January 2018 MW 7.9 strike-slip Gulf of Alaska earthquake ruptured an oceanic fracture zone system offshore Kodiak Island. Bathymetric compilations show a muted topographic expression of the fracture zone due to the thick sediment that covers oceanic basement but the fracture zone system can be identified by offset N-S magnetic anomalies and E-W linear zones in the vertical gravity gradient. Back-projection from global seismic stations reveals that the initial rupture at first propagated from the epicenter to the north, likely rupturing along a weak zone parallel to the ocean crustal fabric. The rupture then changed direction to eastward directed with most energy emitted on Aka fracture zone resulting in an unusual multi-fault earthquake. Similarly, the aftershocks show complex behavior and are related to two different tectonic structures: (1) events along N-S trending oceanic fabric, which ruptured mainly strike-slip and additionally, in normal and oblique slip mechanisms and (2) strike-slip events along E-W oriented fracture zones. To explain the complex faulting behavior we adopt the classical stress and strain partitioning concept and propose a generalized model for large intra-oceanic strike-slip earthquakes of trench-oblique oriented fracture zones/ocean plate fabric near subduction zones. Taking the Kodiak asperity position of 1964 maximum afterslip and outer-rise Coulomb stress distribution into account, we propose that the unusual 2018 Gulf of Alaska moment release was stress transferred to the incoming oceanic plate from co- and post-processes of the nearby great 1964 MW 9.2 megathrust earthquake.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-018-32071-4","usgsCitation":"Krabbenhoeft, A., von Huene, R., Miller, J., Lange, D., and Vera, F., 2018, Strike-slip 23 January 2018 MW 7.9 Gulf of Alaska rare intraplate earthquake: Complex rupture of a fracture zone system: Scientific Reports, v. 8, p. 1-9, https://doi.org/10.1038/s41598-018-32071-4.","productDescription":"13706; 9 p.","startPage":"1","endPage":"9","ipdsId":"IP-096067","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":468409,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-018-32071-4","text":"Publisher Index Page"},{"id":357618,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Gulf of Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -162.68554687499997,\n 55.25407706707272\n ],\n [\n -144.3603515625,\n 52.1874047455997\n ],\n [\n -130.6494140625,\n 52.26815737376817\n ],\n [\n -130.6494140625,\n 54.6992335284814\n ],\n [\n -134.38476562499997,\n 59.108308258604964\n ],\n [\n -139.6142578125,\n 60.65164736580915\n ],\n [\n -148.1396484375,\n 61.543641475549954\n ],\n [\n -152.3583984375,\n 60.823494332539646\n ],\n [\n -162.68554687499997,\n 55.25407706707272\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-09-12","publicationStatus":"PW","scienceBaseUri":"5bc02fa1e4b0fc368eb5392f","contributors":{"authors":[{"text":"Krabbenhoeft, Anne","contributorId":208084,"corporation":false,"usgs":false,"family":"Krabbenhoeft","given":"Anne","email":"","affiliations":[{"id":37708,"text":"GEOMAR Helmholtz Center for Ocean Research Kiel, Wischhofstr. 1-3, 24148 Kiel, Germany","active":true,"usgs":false}],"preferred":false,"id":745851,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"von Huene, Roland 0000-0003-1301-3866","orcid":"https://orcid.org/0000-0003-1301-3866","contributorId":208085,"corporation":false,"usgs":false,"family":"von Huene","given":"Roland","affiliations":[{"id":37709,"text":"USGS, emeritus, 800 Blossom Hill Road, Los Gatos, CA","active":true,"usgs":false}],"preferred":false,"id":745852,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, John J. 0000-0002-9098-0967","orcid":"https://orcid.org/0000-0002-9098-0967","contributorId":208083,"corporation":false,"usgs":true,"family":"Miller","given":"John J.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":745850,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lange, Dietrich","contributorId":208086,"corporation":false,"usgs":false,"family":"Lange","given":"Dietrich","email":"","affiliations":[{"id":37708,"text":"GEOMAR Helmholtz Center for Ocean Research Kiel, Wischhofstr. 1-3, 24148 Kiel, Germany","active":true,"usgs":false}],"preferred":false,"id":745853,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vera, Felipe","contributorId":208087,"corporation":false,"usgs":false,"family":"Vera","given":"Felipe","email":"","affiliations":[{"id":37710,"text":"Helmholtz-Zentrum Potsdam, Deutsches GeoForschungsZentrum GFZ, Telegrafenberg 1, 14473, Potsdam, Germany and Freie Universität Berlin, Malteserstr. 74−100, 12249, Berlin, Germany","active":true,"usgs":false}],"preferred":false,"id":745854,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70197184,"text":"cir1440 - 2018 - Continuing progress toward a national assessment of water availability and use","interactions":[],"lastModifiedDate":"2022-04-22T16:20:30.384084","indexId":"cir1440","displayToPublicDate":"2018-09-12T09:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":307,"text":"Circular","code":"CIR","onlineIssn":"2330-5703","printIssn":"1067-084X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1440","displayTitle":"Continuing Progress Toward a National Assessment of Water Availability and Use","title":"Continuing progress toward a national assessment of water availability and use","docAbstract":"The Omnibus Public Land Management Act of 2009 (Public Law 111—11) was passed into law on March 30, 2009. Subtitle F, also known as the SECURE Water Act, calls for the establishment of a “national water availability and use assessment program” within the U.S. Geological Survey (USGS). The USGS issued the first report on the program in 2013. Program progress over the period 2013–17 is reported herein to fulfill the requirement to inform Congress on implementation of the national water availability and use assessment program, also referred to as the USGS National Water Census (the Water Census).
Much work has been accomplished during 2013–17 on producing water budgets for the nation, a goal USGS outlined in its first report on program progress to Congress. The USGS has completed three geographic focus area studies and has begun three others. Work has advanced on nationwide efforts in streamflow analysis, groundwater assessment and research, evapotranspiration studies, water use, environmental water science, and drought science. The USGS works with Federal and non-Federal agencies, universities, and other organizations to ensure that the information can be aggregated with other types of water-availability and socioeconomic information, such as data on food and energy production. The USGS has also made great strides in measures for delivering data and information on the Water Census to stakeholders and the public.
Much work remains to be accomplished for the Nation to have a comprehensive, ongoing Water Census. In this report, the USGS lays out activities to be accomplished in the next 5 years (2017–22), based upon current funding levels. These include selecting new focus area studies, conducting hydrologic modeling to complete water budgets for the conterminous United States, expanding groundwater modeling efforts, mapping a national classification system for environmental water science, and developing an inventory of interbasin water transfers. All of these steps are necessary in order for the Water Census to achieve the goals outlined by Congress in the SECURE Water Act.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1440","usgsCitation":"Evenson, E.J., Jones, S.A., Barber, N.L., Barlow, P.M., Blodgett, D.L., Bruce, B.W., Douglas-Mankin, K., Farmer, W.H., Fischer, J.M., Hughes, W.B., Kennen, J.G., Kiang, J.E., Maupin, M.A., Reeves, H.W., Senay, G.B., Stanton, J.S., Wagner, C.R., and Wilson, J.T., 2018, Continuing progress toward a national assessment of water availability and use: U.S. Geological Survey Circular 1440, 64 p., https://doi.org/10.3133/cir1440.","productDescription":"viii, 64 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-088874","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true}],"links":[{"id":354392,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1440/circ1440.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"CIRC 1440"},{"id":354391,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1440/coverthb.jpg"}],"contact":"Coordinator—Water Availability and Use Science Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
The 2014 update of the U.S. Geological Survey (USGS) National Seismic Hazard Model (NSHM) for the conterminous United States (2014 NSHM; Petersen and others, 2014, 2015) included probabilistic ground motion maps for 2 percent and 10 percent probabilities of exceedance in 50 years, derived from seismic hazard curves for peak ground acceleration (PGA) and 0.2 and 1.0 second spectral accelerations (SAs) with 5 percent damping for the National Earthquake Hazards Reduction Program (NEHRP) site class boundary B/C (time-averaged shear wave velocity in the upper 30 meters [VS30]=760 meters per second [m/s]). We now provide uniform NEHRP site class maps for 2, 5, and 10 percent probabilities of exceedance in 50 years derived from hazard curves for additional spectral periods. For the central and eastern United States (CEUS) and western United States (WUS), hazard curves and maps for PGA, 0.1, 0.2, 0.3, 0.5, 1.0, and 2.0 second SAs are now available. The WUS additionally includes hazard curves and maps for 0.75, 3.0, 4.0, and 5.0 second SAs. The use of region-specific suites of weighted ground motion models (GMMs) in the 2014 NSHM precluded the calculation of ground motions for a uniform set of periods and site classes for the conterminous United States. At the time of the development of the 2014 NSHM, there was no consensus in the CEUS on an appropriate site-amplification model to use; therefore, we calculated hazard curves and maps for NEHRP site class A, for which most stable continental GMMs were originally developed, based on simulations for hard rock site conditions (VS30=2,000 m/s). In the WUS, however, the active crustal Next Generation Attenuation Relationships for the WUS (NGA-West2 GMMs) and subduction GMMs allow amplification of ground motions based on site class (defined by VS30); so we calculated hazard curves and maps for NEHRP site classes B (VS30=1,080 m/s), C (VS30=530 m/s), D (VS30=260 m/s), and E (VS30=150 m/s) and site class boundaries A/B (VS30=1,500 m/s), B/C (VS30=760 m/s), C/D (VS30=365 m/s), and D/E (VS30=185 m/s). The 2014 NSHM introduced a set of criteria for selecting GMMs for use in the NSHMs. When calculating additional period and site class maps, we verified whether the 2014 NSHM original suites of GMMs satisfied these ground motion selection criteria at all additional periods and site classes using GMM magnitude-distance scaling relation plots. Results of our analysis show that certain GMMs give unrealistic results at longer periods, distances, and softer soils in the WUS. In these rare instances, the GMM was removed from the original suite of GMMs (for all periods and site classes) and the weights of the remaining GMMs in the suite were renormalized. Ratio maps show these updated suites of weighted GMMs result in probabilistic ground motion changes of less than 10 percent in the WUS at PGA, as well as 0.2 and 1.0 second SAs, except in the Pacific Northwest, where differences as much as 20 percent are seen. Hazard curves and uniform hazard response spectra at test sites across the conterminous United States were produced to verify that results were reasonable. The additional period and site class maps, and the hazard curves from which they were derived, are available for download from the USGS ScienceBase Catalog.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181111","usgsCitation":"Shumway, A.M., Petersen, M.D., Powers, P.M., and Rezaeian, S., 2018, Additional period and site class maps for the 2014 National Seismic Hazard Model for the conterminous United States: U.S. Geological Survey Open-File Report 2018–1111, 46 p., https://doi.org/10.3133/ofr20181111.","productDescription":"Report: v, 46 p.; Data release","onlineOnly":"Y","ipdsId":"IP-098308","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":357217,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9I6BPX5","text":"USGS data release","linkHelpText":"Data Release for Additional Period and Site Class Maps for the 2014 National Seismic Hazard Model for the Conterminous United 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\"}}]}\n\n\n","contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, MS 966
Denver, CO 80225