Avian droppings (combination of fecal matter and urates) provide a non-lethal and non-invasive matrix for measuring pesticide exposures. In the field, droppings may be collected days or weeks after excretion and the persistence of pesticide residues in weathered droppings is not known. Thus, we studied the effects of weathering on pesticide residues in droppings. Domestic chicken (Gallus gallus domesticus) hens were used as a representative species for Order Galliformes. We collected droppings from hens before they were exposed to the pesticides (reference or pre-dose droppings ). Thereafter, the hens were orally administered encapsulated wheat seeds coated with Raxil® PRO Shield (containing the active ingredients imidacloprid, prothioconazole, metalaxyl, and tebuconazole) for consecutive 7 days. During this time, their droppings were collected on days 3, 5, and 8 from the start of the exposure period (post-dose droppings ). The pre-dose and post-dose droppings were weathered for up to 30 days in autumn and spring in shrubsteppe habitat. Droppings were analyzed using HPLC coupled to triple quad LC/MS for parent compound and metabolite residues. No pesticide or its metabolite residues were detected in the weathered reference droppings. No parent pesticide compounds were detected in weathered post-dose droppings but imidacloprid metabolites, imidacloprid-5-hydroxy and imidacloprid-olefin, and the prothioconazole metabolite, desthio-prothioconazole, were detected in all post-dose weathered samples from both seasons. The active ingredients metalaxyl and tebuconazole and their metabolites were not detected in any of the samples. Our results suggest that, depending on the pesticide, its concentration, and the environmental conditions, residues of some pesticides can be detected in droppings weathered for at least 30 days. Knowledge of pesticide persistence in weathered droppings can help refine the quality and quantity of fecal samples that are collected for monitoring pesticide exposures to birds.
The Coastal Science Navigator is an online gateway to a wide variety of U.S. Geological Survey (USGS) coastal change hazards-related information, data, and tools relevant to stakeholders’ scientific and decision-making needs. The products within the Coastal Science Navigator provide data related to past, present, and future threats to our coastlines. The filter search allows users to see all available products and identify relevant options by time scale, geographic scope, coastal hazard theme, and other filters. The guided search suggests products based on users’ answers to a short series of questions. A comprehensive summary is available for each product.
The idea for the Coastal Science Navigator arose in 2020 in response to stakeholder feedback identifying the need for a central source for USGS coastal science information. It was published in July 2023 and initially included 55 products. Regular updates are planned to integrate other existing and new products.
This guide introduces some of the many coastal change hazards-related products available through the USGS. In it, we showcase the products included in the Coastal Science Navigator’s initial publication in July 2023. While it is not representative of all the information, tools, and data available, we hope it serves as a compelling snapshot of what the USGS has to offer and encourages you to explore the Coastal Science Navigator to discover more of the products you need.
To navigate this guide, the products have been organized by the time scale they are best suited for—past, present, or future—although many products cover multiple time scales. An additional section features software, one of the many product types available as filters within the Coastal Science Navigator. Other products include downloadable data, websites, and geonarratives (web pages that combine text, images, and interactive maps into narratives you can scroll through). Featured geographic scopes are also highlighted within this guide, detailing some of the many regions in which the USGS conducts research and illustrating another way to filter products within the Coastal Science Navigator.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1523","usgsCitation":"Anderberg, M., and Ernst, S., 2024, Coastal Science Navigator companion guide—Discover the U.S. Geological Survey coastal science products you need: U.S. Geological Survey Circular 1523, 31 p., https://doi.org/10.3133/cir1523.","productDescription":"iv, 31 p.","numberOfPages":"31","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-159018","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":499076,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117116.htm","linkFileType":{"id":5,"text":"html"}},{"id":430649,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1523/coverthb.jpg"},{"id":430650,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1523/cir1523.pdf","text":"Report","size":"15.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Circular 1523"},{"id":430680,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://www.usgs.gov/apps/coastalsciencenavigator/index.html","text":"Coastal Science Navigator"}],"contact":"Director, Woods Hole Coastal and Marine Science Center
U.S. Geological Survey
384 Woods Hole Road
Quissett Campus
Woods Hole, MA 02543–1598
Estimates of nitrogen loading from nonpoint sources on Long Island, New York, at or just below the land surface, are essential for assessing the current and future effects of nitrogen on the island’s drinking water and fresh and marine surface receiving waters. Annual estimates of nitrogen loading for the 120 years from 1900 to 2019 for major nonpoint nitrogen sources—septic systems, residential fertilizer, agricultural fertilizer, livestock waste, pet waste, and atmospheric deposition—were made by using a geographic information system to analyze, visualize, and process data sources, and format output data. This analysis provided spatial and temporal estimates of nitrogen loading derived from each nonpoint source at a 500- by 500-foot gridded resolution and represents the total mass of nitrogen applied on, or just below, the land surface annually from 1900 to 2019. These mass estimates are considered unattenuated as they do not reflect the various mechanisms of nitrogen loss, such as plant uptake, overland runoff, and chemical transformations in the soil and unsaturated zones that likely reduce the amount of nitrogen that reaches the water table.
Island-wide and countywide summaries of the estimated nitrogen loading were analyzed to describe the long-term average nitrogen totals and the contributions from the six nonpoint sources. The island-wide average annual nitrogen load from 1900 to 2019 was 14.92 million kilograms (Mkg) nitrogen, which represents the aggregate of individual contributions from septic systems (4.15 Mkg), residential fertilizer (3.28 Mkg), agricultural fertilizer (3.19 Mkg), livestock waste (1.22 Mkg), pet waste (0.98 Mkg), and atmospheric deposition (2.10 Mkg). The island-wide average annual nitrogen load, normalized by area, was 4,100 kilograms nitrogen per square kilometer (kg N/km2), which represents the aggregate of individual contributions from septic systems (1,100 kg N/km2), residential fertilizer (910 kg N/km2), agricultural fertilizer (880 kg N/km2), livestock waste (340 kg N/km2), pet waste (270 kg N/km2), and atmospheric deposition (580 kg N/km2).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245047","programNote":"National Water Quality Program","usgsCitation":"Monti, J., Jr., Walter, D.A., and Jahn, K.L., 2024, Nitrogen load estimates from six nonpoint sources on Long Island, New York, from 1900 to 2019: U.S. Geological Survey Scientific Investigations Report 2024–5047, 40 p., https://doi.org/10.3133/sir20245047.","productDescription":"Report: vi, 40 p.; Data Release","numberOfPages":"40","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-113797","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":430660,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5047/coverthb.jpg"},{"id":430665,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MC2LAJ","text":"USGS data release","linkHelpText":"Annual nitrogen load estimates from six nonpoint sources on Long Island, New York, from 1900 to 2019"},{"id":499468,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117117.htm","linkFileType":{"id":5,"text":"html"}},{"id":430664,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5047/images/"},{"id":430663,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5047/sir20245047.XML"},{"id":430662,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245047/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5047 HTML"},{"id":430661,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5047/sir20245047.pdf","text":"Report","size":"4.25 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5047 PDF"}],"country":"United States","state":"New York","otherGeospatial":"Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.02511928852329,\n 40.68218751210432\n ],\n [\n -74.04241711735317,\n 40.61984146636081\n ],\n [\n -73.96024588897663,\n 40.51470305180803\n ],\n [\n -73.57529563875842,\n 40.61328064665312\n ],\n [\n -73.00872461020937,\n 40.711699206964624\n ],\n [\n -72.82706852535102,\n 40.72809246976527\n ],\n [\n -72.51134253267655,\n 40.803437312254374\n ],\n [\n -71.82364810847187,\n 41.03873449309313\n ],\n [\n -71.89284965601844,\n 41.113723998451945\n ],\n [\n -72.21290681342221,\n 41.217914881287896\n ],\n [\n -72.6627165098169,\n 41.01915680925052\n ],\n [\n -73.13847751185946,\n 41.002839709762156\n ],\n [\n -73.18604492083178,\n 40.95059491778545\n ],\n [\n -73.31145759896312,\n 40.94732919735483\n ],\n [\n -73.41095044220684,\n 40.97019137918906\n ],\n [\n -73.65314825931426,\n 40.94406140051893\n ],\n [\n -73.78288230142525,\n 40.84925526234707\n ],\n [\n -73.79584578341924,\n 40.79361766159383\n ],\n [\n -73.87367687293711,\n 40.79362055072983\n ],\n [\n -73.90903980024608,\n 40.79462167829007\n ],\n [\n -73.92389759495337,\n 40.78249751209276\n ],\n [\n -73.93657311063014,\n 40.77857350644817\n ],\n [\n -73.93928570688715,\n 40.77163712685007\n ],\n [\n -73.94979738187155,\n 40.758534591172804\n ],\n [\n -73.9630315224905,\n 40.743113924810274\n ],\n [\n -73.96778515613994,\n 40.72948789268287\n ],\n [\n -73.97083994930827,\n 40.71380180805102\n ],\n [\n -73.97999494183452,\n 40.70814705453665\n ],\n [\n -74.00883757884912,\n 40.7017148459239\n ],\n [\n -74.01188750615336,\n 40.68756571016257\n ],\n [\n -74.02511928852329,\n 40.68218751210432\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
The U.S. Geological Survey, in cooperation with the Ohio Department of Natural Resources, performed a two-part study to (1) assess water use and temporal trends and changes in streamflow, and to (2) characterize 2021–23 baseline water quality, as they relate to oil and gas extraction activities in selected eastern Ohio watersheds. Between calendar years 2010 and 2019, hydraulic fracturing water withdrawals totaling about 27,168 million gallons were reported at 643 locations in Ohio. In 2021, wells developed with hydraulic fracturing were the source of most of the oil and gas produced in Ohio.
Daily streamflow time-series data from seven study gages and two reference gages were used to assess temporal trends and changes in streamflow. The study gages were in basins with reported water withdrawals for hydraulic fracturing. The reference gages, which have long periods of record and were subject to minimal streamflow regulation, were in nearby basins with no hydraulic fracturing water withdrawals.
Trend slopes for the period of record annual minimum and median daily streamflows and for annual daily streamflow nonexceedance probabilities less than 0.9 were all uniformly positive at the study and reference gages. This trend indicates a consistently increasing pattern over the periods of record, except for high flows. In addition, analyses of annual streamflow statistics showed no general indication that low flows or extreme low flows at the reference or study gages have lowered, become more frequent, or lengthened in duration since 2010, when records for hydraulic fracturing water withdrawals began in Ohio. In fact, in almost all cases, the opposite was indicated.
Nonexceedance percentiles of daily streamflows were compared between the full and pre-2012 periods of record for reference and study gages. The streamflows associated with nonexceedance percentiles in the lower quartile of daily streamflows determined for the full period of record were larger than or equal to those determined for the pre-2012 period of record for all study and reference gages. This indicates that low flows did not decrease during the post-2011 period of record when water was withdrawn for hydraulic fracturing.
Water-quality data were collected eight times at each of eight sampling sites (six of which were colocated with the study gages). Sampling was done during a variety of flow conditions to assess baseline water quality. In 2021, the 8 sampling sites had drainage basins that were wholly or partially within 7 of the 10 most active counties in Ohio for oil and gas development. As part of the record of baseline conditions, water-quality data were used to assess (1) water types based on major-ion chemistry; (2) sources of salinity to streams; (3) exceedances of aquatic life use criteria; and (4) the correlations between water chemistry and drainage-basin characteristics, such as density of oil and gas wells, density of wastewater treatment plants, or the percentage of different types of land cover (agriculture, developed, forest).
Seven of the water-quality sampling sites were designated as coal-mine impacted based on criteria developed for assessing mine-drainage impacts in Ohio. Mine drainage from historical coal mining in the region likely affected the quality of these streams and complicated the use of some constituents typically used as indicators of oil and gas influence. Based on major-ion chemistry, three main types of water were in the study area―sulfate (three sites), calcium-bicarbonate (one site), and mixed bicarbonate-chloride (four sites) type waters. One site had samples with a higher proportion of sodium and chloride ions than other stream samples, indicating potential contamination with oil-field brine or road salt. Binary mixing curves revealed that 11 samples from 4 of the sampling sites likely contained a component of brine. The results of the baseline assessment of surface-water quality in the study area showed no exceedances of Ohio Environmental Protection Agency aquatic life use criteria. Spearman’s rank correlation coefficients indicated no significant positive correlations with the density of vertical or horizontal oil and gas wells.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245045","collaboration":"Prepared in cooperation with the Ohio Department of Natural Resources","usgsCitation":"Covert, S.A., and Koltun, G.F., 2024, Analysis of water use associated with hydraulic fracturing and determination of baseline water quality in watersheds within the shale play of eastern Ohio, 2021–23: U.S. Geological Survey Scientific Investigations Report 2024–5045, 61 p., https://doi.org/10.3133/sir20245045.","productDescription":"Report: viii, 61 p.; 2 Data Releases","numberOfPages":"61","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-159681","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":430672,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1G2W3JQ","text":"USGS data release","linkHelpText":"Annual streamflow statistics for selected streamgages in and near the shale play area of eastern Ohio (through water year 2021)"},{"id":499467,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117118.htm","linkFileType":{"id":5,"text":"html"}},{"id":430670,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5045/images/"},{"id":430668,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245045/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5045 HTML"},{"id":430667,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5045/sir20245045.pdf","text":"Report","size":"28.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5045 PDF"},{"id":430669,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5045/sir20245045.XML","description":"SIR 2024-5045 XML"},{"id":430666,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5045/coverthb.jpg"},{"id":430671,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1EDHXB9","text":"USGS data release","linkHelpText":"Data from quality-control equipment blanks, field blanks, and field replicates for baseline water quality in watersheds within the shale play of eastern Ohio, 2021–23"}],"country":"United States","state":"Ohio","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.333,\n 41\n ],\n [\n -82.333,\n 39.125\n ],\n [\n -80.666,\n 39.125\n ],\n [\n -80.666,\n 41\n ],\n [\n -82.333,\n 41\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Blvd.
Indianapolis, IN 46278-1996
Managers invest substantial resources to promote recovery of declining anadromous fish stocks. Recovery strategies are manifold and often include management actions intended to stimulate somatic growth, increase in-river survival, and motivate juvenile outmigration during favorable environmental conditions. Evaluating the efficacy of these management actions is difficult, however, because monitoring data that explicitly track individuals from egg deposition to juvenile outmigration are typically lacking. We developed an integrated population model that links two different and often collected types of anadromous fish monitoring data: spawning ground surveys and rotary screw trap juvenile catch data. The integrated model accounts for incomplete detection and uses the two sources of data to estimate juvenile demographic parameters in a multistate framework. We evaluated the model's performance using simulated data under a range of conditions typically encountered in similar surveys. Simulation results indicated that the model estimated juvenile survival, growth, and movement with no-to-minimal bias (i.e., ≥ 50 % of simulations ± 0–0.05). As an example case study, we fit the model to empirical fall-run Chinook Salmon (Oncorhynchus tshawytscha) monitoring data collected in California's Central Valley, U.S.A. In doing so, we evaluated the influence of environmental conditions (e.g., discharge, water temperature) and habitat availability on juvenile demographic rates. We demonstrated that through our integrated approach we could estimate state transition probabilities that are typically inestimable for naturally produced, unmarked juvenile fish when using traditional statistical approaches to analyze these types of monitoring data. Furthermore, the structure of our model can serve as a useful foundation for decision-support models within adaptive management programs by directly linking management actions, decision-support-model predictions, and monitoring.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2024.110780","usgsCitation":"Wohner, P.J., Duarte, A., and Peterson, J., 2024, An integrated analysis for estimation of survival, growth, and movement of unmarked juvenile anadromous fish: Ecological Modelling, v. 495, 110780, 9 p., https://doi.org/10.1016/j.ecolmodel.2024.110780.","productDescription":"110780, 9 p.","ipdsId":"IP-165440","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":488126,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolmodel.2024.110780","text":"Publisher Index Page"},{"id":485452,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Clear Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.5906056592707,\n 40.63419131375724\n ],\n [\n -122.5906056592707,\n 40.47941759385591\n ],\n [\n -122.33253791183354,\n 40.47941759385591\n ],\n [\n -122.33253791183354,\n 40.63419131375724\n ],\n [\n -122.5906056592707,\n 40.63419131375724\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"495","noUsgsAuthors":false,"publicationDate":"2024-07-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Wohner, Patti J.","contributorId":338233,"corporation":false,"usgs":false,"family":"Wohner","given":"Patti","email":"","middleInitial":"J.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":935785,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duarte, Adam","contributorId":339254,"corporation":false,"usgs":false,"family":"Duarte","given":"Adam","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":935786,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peterson, James T. 0000-0002-7709-8590 james_peterson@usgs.gov","orcid":"https://orcid.org/0000-0002-7709-8590","contributorId":2111,"corporation":false,"usgs":true,"family":"Peterson","given":"James","email":"james_peterson@usgs.gov","middleInitial":"T.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":935787,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70269686,"text":"70269686 - 2024 - A comparative analysis of OpenET for evaluating evapotranspiration in California almond orchards","interactions":[],"lastModifiedDate":"2025-07-30T14:53:21.858355","indexId":"70269686","displayToPublicDate":"2024-07-03T09:49:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":681,"text":"Agricultural and Forest Meteorology","active":true,"publicationSubtype":{"id":10}},"title":"A comparative analysis of OpenET for evaluating evapotranspiration in California almond orchards","docAbstract":"The almond industry in California faces water management challenges that are being exacerbated by droughts, climate change, and groundwater sustainability legislation. The Tree-crop Remote sensing of Evapotranspiration eXperiment (T-REX) aims to explore opportunities to improve precision irrigation management for woody perennial cropping systems. Almond orchards in the California Central Valley were equipped with eddy covariance flux measurements to evaluate satellite remote sensing-based evapotranspiration (RSET) models. OpenET provides high-resolution (30-m spatial and daily temporal) RSET data, synthesizing decades of research for practical water management. This study provides an evaluation of OpenET performance at six almond sites covering a large range in soils, age, and variety. It also compares OpenET ensemble evapotranspiration (ET) data with applied irrigation and precipitation records over an additional 148 almond orchards located in the Central Valley of California. Results show OpenET models, including the ensemble ET value, produced reasonable and actionable ET values, with overall coefficient of determination (R2) and mean absolute error values of 0.73- and 0.95-mm d−1 at the daily time step, respectively. However, given the temporal sampling of Landsat (8-day revisit) and the interpolation methods used, the assessed ET models had difficulty in capturing short-term variability in almond ET; for example, the rapid decline in measured ET observed as a response to lack of irrigation preceding and during almond harvest. The study also drew attention to the spatial complexity in scenarios where irrigated orchards are surrounded by hot/dry areas, causing discrepancies between measured and modeled ET values. In comparison with irrigation records, OpenET ensemble ET was capable of quantifying water input (applied irrigation + precipitation) in almond orchards to within 13 % when evaluating monthly data. Initial results presented here reinforce the idea that RSET models, such as in OpenET, are powerful tools, yet their application requires nuanced understanding and careful consideration of local conditions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.agrformet.2024.110146","usgsCitation":"Knipper, K., Anderson, M., Bambach, N., Melton, F., Ellis, Z., Yang, Y., Volk, J.M., McElrone, A., Kustas, W.P., Roby, M., Carrara, W., Castro, S., Kilic, A., Fisher, J.B., Ruhoff, A., Senay, G.B., Morton, C., Saa, S., and Allen, R., 2024, A comparative analysis of OpenET for evaluating evapotranspiration in California almond orchards: Agricultural and Forest Meteorology, v. 355, 110146, 18 p., https://doi.org/10.1016/j.agrformet.2024.110146.","productDescription":"110146, 18 p.","ipdsId":"IP-167142","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":493302,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.agrformet.2024.110146","text":"Publisher Index Page"},{"id":493185,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.24346411280976,\n 35.36581404018972\n ],\n [\n -119.04389955946334,\n 35.98229970980546\n ],\n [\n -119.09665048217278,\n 36.489548538637436\n ],\n [\n -120.48964025685427,\n 37.8956809895726\n ],\n [\n -121.30837256179902,\n 39.0131951853744\n ],\n [\n -121.62152001465728,\n 39.13926422978071\n ],\n [\n -122.21612821856195,\n 38.932254764781106\n ],\n [\n -121.71286410491697,\n 37.92123720196997\n ],\n [\n -120.92135482117862,\n 37.048426115873994\n ],\n [\n -120.01788903644467,\n 36.065623999918515\n ],\n [\n -119.49460792137992,\n 35.23527037396478\n ],\n [\n -119.24346411280976,\n 35.36581404018972\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"355","noUsgsAuthors":false,"publicationDate":"2024-07-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Knipper, Kyle","contributorId":333373,"corporation":false,"usgs":false,"family":"Knipper","given":"Kyle","email":"","affiliations":[{"id":79855,"text":"USDA Agriculture Research Service","active":true,"usgs":false}],"preferred":false,"id":944430,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Martha","contributorId":269899,"corporation":false,"usgs":false,"family":"Anderson","given":"Martha","affiliations":[{"id":37009,"text":"USDA Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":944431,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bambach, Nicolas","contributorId":358904,"corporation":false,"usgs":false,"family":"Bambach","given":"Nicolas","affiliations":[{"id":12711,"text":"UC Davis","active":true,"usgs":false}],"preferred":false,"id":944432,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Melton, Forrest","contributorId":223919,"corporation":false,"usgs":false,"family":"Melton","given":"Forrest","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":944433,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ellis, Zac","contributorId":358905,"corporation":false,"usgs":false,"family":"Ellis","given":"Zac","affiliations":[{"id":85705,"text":"Olan Food Ingredients","active":true,"usgs":false}],"preferred":false,"id":944434,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yang, Yun","contributorId":333379,"corporation":false,"usgs":false,"family":"Yang","given":"Yun","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":944435,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Volk, J. 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Presently, measures of the in-situ distribution of fault damage remain limited and along-strike studies are rare. This study focuses on an offshore section Palos Verdes Fault damage zone that spans 28 km, near Los Angeles, California. To investigate the previously unresolved shallow (∼400 m below the seafloor) fault damage zone we use densely spaced (∼500 m line separation) newly collected sparker multichannel seismic lines and sub-bottom profiles. The combination of high-resolution acquisition methods and specialized seismic processing workflows provide improved imaging of shallow faulting. We apply a multi-trace similarity technique to identify discontinuities in the seismic data that may be attributed to faults and fractures. This fault detection approach reveals diverse fault damage patterns on adjacent seismic profiles. However, a discernible damage zone pattern emerges by stacking multiple damage detection profiles along strike. We find that peak damage identified in this way corresponds to the active main fault strand, confirmed in this study, and thus the technique may be useful for identifying active fault strands elsewhere. Additionally, we observe that the variable width of the damage zone along strike is controlled by fault obliquity. Furthermore, our observations reveal a correlation between fault damage and seafloor fluid seeps visible in the water column, suggesting that damage plays a role in controlling fluid flow around the fault.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023AV001155","usgsCitation":"Alongi, T., Brodsky, E., Kluesner, J., and Brothers, D., 2024, Characteristics of the fault damage zone From high-resolution seismic imaging along the Palos Verdes Fault, California: AGU Advances, v. 5, no. 4, e2023AV001155, 20 p., https://doi.org/10.1029/2023AV001155.","productDescription":"e2023AV001155, 20 p.","ipdsId":"IP-160708","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":488271,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023av001155","text":"Publisher Index Page"},{"id":484677,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Palos Verdes Fault","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.4,\n 33.75\n ],\n [\n -118.4,\n 33.375\n ],\n [\n -118,\n 33.375\n ],\n [\n -118,\n 33.75\n ],\n [\n -118.4,\n 33.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"5","issue":"4","noUsgsAuthors":false,"publicationDate":"2024-07-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Alongi, Travis Vincent 0000-0002-0865-8064","orcid":"https://orcid.org/0000-0002-0865-8064","contributorId":335029,"corporation":false,"usgs":true,"family":"Alongi","given":"Travis Vincent","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":933744,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brodsky, Emily","contributorId":299735,"corporation":false,"usgs":false,"family":"Brodsky","given":"Emily","affiliations":[{"id":27155,"text":"University of California Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":933745,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kluesner, Jared W. 0000-0003-1701-8832","orcid":"https://orcid.org/0000-0003-1701-8832","contributorId":206367,"corporation":false,"usgs":true,"family":"Kluesner","given":"Jared W.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":933746,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brothers, Daniel S. 0000-0001-7702-157X","orcid":"https://orcid.org/0000-0001-7702-157X","contributorId":210199,"corporation":false,"usgs":true,"family":"Brothers","given":"Daniel S.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":933747,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70255996,"text":"70255996 - 2024 - Invertebrate trophic structure on marine ferromanganese and phosphorite hardgrounds","interactions":[],"lastModifiedDate":"2024-07-30T14:55:12.863126","indexId":"70255996","displayToPublicDate":"2024-07-03T06:58:49","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2620,"text":"Limnology and Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Invertebrate trophic structure on marine ferromanganese and phosphorite hardgrounds","docAbstract":"The Southern California Borderland hosts a variety of geologic and oceanographic features that allow for diverse habitats to occur in a restricted region with a strong oxygen minimum zone (OMZ) and hard substrates. These include ferromanganese (FeMn) crusts and phosphorites targeted for deep-seabed mining in other regions. Baseline studies regarding hardground macro- (> 0.3 mm) and megafaunal (> 2 cm) invertebrates are lacking, although they contribute to understanding nutrient cycling and resilience of deep-sea communities to ocean deoxygenation, fishing, or mineral extraction. With the goal of understanding how substrate type, depth, and dissolved oxygen concentration influence invertebrate trophic structure, we surveyed δ13C and δ15N values of invertebrates on hard substrates on the Southern California Borderland margin along a depth gradient (120–2400 m) through the OMZ at inshore (< 100 km from shore) and offshore (100–250 km from shore) sites, using generalized additive models and community-level metrics. Macrofaunal isotopic values correlate with substrate type, exhibiting higher trophic diversity on FeMn crusts and specialized communities on phosphorites. Megafaunal isotopic values correlate with proximity to shore; animals offshore seem to depend more on phytoplanktonic production than animals inshore. In general, δ15N increased with decreasing dissolved oxygen and increasing depth, possibly due to remineralization processes within the OMZ and with depth. We discuss how feeding modes and community composition might influence the observed patterns. This study elucidates the importance of the environmental context in shaping invertebrate trophic structure on continental margins and provides baseline knowledge that may be useful in regions where these minerals are targeted for extraction.
Introduction: Habitat loss and degradation pose significant threats to global fish and wildlife populations, prompting substantial investments in habitat creation and restoration efforts. Not all habitats provide equal benefits, leading to challenges in prioritizing restoration actions. For example, juvenile anadromous salmonids require high quality rearing aquatic habitats to achieve the physiological requirements needed to successfully migrate to the ocean. However, there are profound disagreements among anadromous salmon restoration managers whether it is best to focus efforts on restoring in-channel habitats that are available for the entire rearing period or floodplain habitats that, while facilitating greater growth and survival than in-channel habitats, are only available for a few weeks at a time and are typically only activated every two-to-three years.
Methods: We used an existing fall-run Chinook salmon decision-support model to evaluate under what conditions floodplain restoration would provide greater benefits than in-channel habitat restoration. The simulations included a wide range of floodplain inundation frequencies and durations and floodplain benefits in the form of increased survival and growth relative to in-channel habitats.
Results: The simulations results indicated that in-channel habitat restoration was always the best habitat restoration action when there was no existing in-channel habitat despite simulating a wide range of flood frequency, duration, and growth and survival benefits. Floodplain restoration was generally best when there was sufficient in-channel habitat available to successfully rear most of the juveniles produced by the returning adult salmon.
Discussion: We hypothesize that in-channel and floodplain habitats have different roles in salmon population maintenance with in-channel habitats regulating the overall population size and floodplains acting as recurrent resource pulses. Our study provides a quantitative framework to evaluate the benefit of these two habitat types and provides generalizable rulesets that can be used by managers when implementing habitat restoration strategies for species that inhabit both in-channel and floodplain habitats.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fcosc.2024.1428697","usgsCitation":"Peterson, J., and Duarte, A., 2024, An evaluation of tradeoffs in restoring ephemeral vs. perennial habitats to conserve animal populations: Frontiers in Conservation Science, v. 5, 1428697, 12 p., https://doi.org/10.3389/fcosc.2024.1428697.","productDescription":"1428697, 12 p.","ipdsId":"IP-165963","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":488153,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fcosc.2024.1428697","text":"Publisher Index Page"},{"id":485520,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.15324571467127,\n 38.645272222200845\n ],\n [\n -122.15324571467127,\n 35.22532646207205\n ],\n [\n -118.48275085421352,\n 35.22532646207205\n ],\n [\n -118.48275085421352,\n 38.645272222200845\n ],\n [\n -122.15324571467127,\n 38.645272222200845\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"5","noUsgsAuthors":false,"publicationDate":"2024-07-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Peterson, James T. 0000-0002-7709-8590 james_peterson@usgs.gov","orcid":"https://orcid.org/0000-0002-7709-8590","contributorId":2111,"corporation":false,"usgs":true,"family":"Peterson","given":"James","email":"james_peterson@usgs.gov","middleInitial":"T.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":935994,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duarte, Adam","contributorId":337608,"corporation":false,"usgs":false,"family":"Duarte","given":"Adam","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":935995,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70256509,"text":"70256509 - 2024 - Spawning run estimates and phenology for an extremely small population of Atlantic Sturgeon in the Marshyhope Creek–Nanticoke River system, Chesapeake Bay","interactions":[],"lastModifiedDate":"2024-08-12T16:08:21.662133","indexId":"70256509","displayToPublicDate":"2024-07-02T10:55:49","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2680,"text":"Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science","active":true,"publicationSubtype":{"id":10}},"title":"Spawning run estimates and phenology for an extremely small population of Atlantic Sturgeon in the Marshyhope Creek–Nanticoke River system, Chesapeake Bay","docAbstract":"Once thought to be extirpated from the Chesapeake Bay, fall spawning runs of Atlantic Sturgeon Acipenser oxyrinchus have been rediscovered in the Marshyhope Creek (MC)–Nanticoke River (NR) system of Maryland, United States. High recapture rates in past telemetry surveys suggested a small population in the two connected tributaries. This study aims to generate estimates of abundance and understand within system connectivity for spawning runs in 2020 and 2021.
Data from mobile side-scan sonar surveys and detections of acoustically tagged adults on stationary telemetry receivers were analyzed in an integrated model to estimate spawning season abundance and examine run timing and system connectivity for this population. An array of acoustic receivers was deployed throughout the MC–NR system to monitor the movement of tagged fish during the spawning run period from mid-August to late October. Side-scan sonar surveys were conducted weekly in September in an area of high spawner aggregation to generate count data on spawning run abundance.
In 2020 and 2021, 32 (95% credible interval [CRI] = 23–47) and 70 (95% CRI = 49–105) Atlantic Sturgeon, respectively, used the MC–NR system. The lower estimate for 2020 coincided with an earlier end to the spawning run related to cooler September temperatures in that year.
In both years, high spawning run connectivity between MC and the upper NR was observed. Overall, run estimates supported previous hypotheses that the MC–NR system supports a very small population and that both MC and the upper NR serve as important areas for spawning activity.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/mcf2.10292","usgsCitation":"Coleman, N., Fox, D., Horne, A., Hostetter, N.J., Madsen, J., O’Brien, M., Park, I., Stence, C., and Secor, D., 2024, Spawning run estimates and phenology for an extremely small population of Atlantic Sturgeon in the Marshyhope Creek–Nanticoke River system, Chesapeake Bay: Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science, v. 16, no. 3, e10292, 16 p., https://doi.org/10.1002/mcf2.10292.","productDescription":"e10292, 16 p.","ipdsId":"IP-152527","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":439304,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/mcf2.10292","text":"Publisher Index Page"},{"id":432489,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Chesapeake Bay, Marshyhope Creek–Nanticoke River system","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.89424670173172,\n 38.22898121612636\n ],\n [\n -75.80356837182107,\n 38.3806027695843\n ],\n [\n -75.65092235600899,\n 38.5420516415999\n ],\n [\n -75.51423724108903,\n 38.538485023124736\n ],\n [\n -75.51310206970021,\n 38.701340540846786\n ],\n [\n -75.85480590117533,\n 38.71288976149938\n ],\n [\n -75.86506053032485,\n 38.57767393758033\n ],\n [\n -75.88558745668757,\n 38.40381670546191\n ],\n [\n -75.94442936459689,\n 38.33482586063296\n ],\n [\n -75.96657879053116,\n 38.253499920059284\n ],\n [\n -75.89424670173172,\n 38.22898121612636\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-07-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Coleman, Nicholas","contributorId":340953,"corporation":false,"usgs":false,"family":"Coleman","given":"Nicholas","email":"","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":907728,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fox, Dewayne","contributorId":340954,"corporation":false,"usgs":false,"family":"Fox","given":"Dewayne","affiliations":[{"id":37219,"text":"Delaware State University","active":true,"usgs":false}],"preferred":false,"id":907729,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Horne, Ashlee","contributorId":340955,"corporation":false,"usgs":false,"family":"Horne","given":"Ashlee","email":"","affiliations":[{"id":33964,"text":"Maryland Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":907730,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hostetter, Nathan J. 0000-0001-6075-2157 nhostetter@usgs.gov","orcid":"https://orcid.org/0000-0001-6075-2157","contributorId":198843,"corporation":false,"usgs":true,"family":"Hostetter","given":"Nathan","email":"nhostetter@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":true,"id":907731,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Madsen, John","contributorId":340956,"corporation":false,"usgs":false,"family":"Madsen","given":"John","affiliations":[{"id":36379,"text":"Delaware Division of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":907732,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"O’Brien, Michael","contributorId":340957,"corporation":false,"usgs":false,"family":"O’Brien","given":"Michael","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":907733,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Park, Ian","contributorId":340958,"corporation":false,"usgs":false,"family":"Park","given":"Ian","affiliations":[{"id":36379,"text":"Delaware Division of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":907734,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Stence, Chuck","contributorId":340959,"corporation":false,"usgs":false,"family":"Stence","given":"Chuck","email":"","affiliations":[{"id":33964,"text":"Maryland Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":907735,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Secor, David","contributorId":340960,"corporation":false,"usgs":false,"family":"Secor","given":"David","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":907736,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70255701,"text":"ofr20241041 - 2024 - Geospatial PDF map of the compilation of GIS data for the mineral industries and related infrastructure of Africa","interactions":[],"lastModifiedDate":"2024-07-17T15:49:48.797057","indexId":"ofr20241041","displayToPublicDate":"2024-07-02T10:45:00","publicationYear":"2024","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":"2024-1041","displayTitle":"Geospatial PDF Map of the Compilation of GIS Data for the Mineral Industries and Related Infrastructure of Africa","title":"Geospatial PDF map of the compilation of GIS data for the mineral industries and related infrastructure of Africa","docAbstract":"In 2021, the U.S. Geological Survey's (USGS) National Minerals Information Center (NMIC) completed the project titled \"Compilation of geospatial data for the mineral industries and related infrastructure of Africa.\" This project aimed to leverage the expertise and capabilities of the NMIC to collect, synthesize, and interpret geospatial data to inform on the extractive resources of the African region and expand the NMIC's understanding on the impact of mineral industry of African nations in the global economy. The African region, which comprises the independent nations that make up the African continent and its associated islands and dependencies, consists of a total of 58 mineral producing countries. The primary objective of this effort was to create a fully attributed Geographic Information System (GIS) portraying existing mining infrastructure, resources, and development capacity across Africa along with the related infrastructure capable of supporting current (for the reference year 2018) and future extractive industry operations in the region. The compiled GIS geodatabase with supporting documentation including comprehensive metadata was published as a USGS data release titled \"Compilation of Geospatial Data (GIS) for the Mineral Industries and Related Infrastructure of Africa.\"
This georeferenced portable document format (GeoPDF) map sheet presents a new geographic information product containing a partial representation of the GIS data. This GeoPDF map provides a visual comparison of the distribution of mineral industry and related infrastructure GIS data, which contributes to a deeper understanding of the intersections and complexities of the extractive industries within Africa.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241041","usgsCitation":"Neustaedter, E.R., Kemna, R.F., Padilla, A.J., and Otarod, D., 2024, Geospatial PDF map of the compilation of GIS data for the mineral industries and related infrastructure of Africa: U.S. Geological Survey Open-File Report 2024–1041, 1 geospatial map, scale 1:38,504,000, https://doi.org/10.3133/ofr20241041.","productDescription":"1 Map: 18.00 x 12.00 inches; Data Release","numberOfPages":"1","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-130230","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":431024,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241041/full"},{"id":431025,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"http://pubs.usgs.gov/of/2024/1041/images/"},{"id":430978,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1041/ofr20241041.XML","description":"OFR 2024-1041 XML"},{"id":430674,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"http://pubs.usgs.gov/of/2024/1041/coverthb.jpg"},{"id":430676,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97EQWXP","text":"USGS data release","linkHelpText":"Compilation of geospatial data (GIS) for the mineral industries and related infrastructure of Africa"},{"id":430675,"rank":2,"type":{"id":11,"text":"Document"},"url":"http://pubs.usgs.gov/of/2024/1041/ofr20241041.pdf","text":"Report","size":"12.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024-1041 PDF"}],"otherGeospatial":"Africa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -22.324219500055847,\n 41.39808853602034\n ],\n [\n -22.324219500055847,\n -37.83510956130807\n ],\n [\n 53.9648429999433,\n -37.83510956130807\n ],\n [\n 53.9648429999433,\n 41.39808853602034\n ],\n [\n -22.324219500055847,\n 41.39808853602034\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, National Minerals Information Center
U.S. Geological Survey
12201 Sunrise Valley Drive
988 National Center
Reston, VA 20192
Anadromous river herring populations, collectively alewife (Alosa pseudoharengus) and blueback herring (Alosa aestivalis), have experienced a multi-century decline in abundance and distribution. These declines have been attributed in part to anthropogenic threats in freshwater ecosystems (e.g., habitat fragmentation, overharvest, water pollution, watershed development). An understanding of variability in juvenile productivity and growth is critical to developing restoration approaches. We characterized variability in juvenile river herring growth among 11 freshwater lakes in the northeastern USA. We used age estimates from otoliths and length measurements to calculate growth rates of juvenile river herring (n = 1452). We tested the effects of juvenile river herring densities, zooplankton (biomass and size), habitat area (based on thermocline depth), and water quality (temperature, nutrients, chlorophyll a) on juvenile growth. Mean monthly growth rates ranged from 0.56 to 1.41 mm/d and typically increased throughout the summer. Increased juvenile growth was best predicted by lower juvenile density (β = − 0.104, P < 0.001) and higher zooplankton biomass (β = 0.032, P < 0.05). Combined with information about juvenile densities and mortality, these results broaden the understanding of anadromous juvenile river herring productivity, provide information that can contribute to refining stock assessment and life cycle models, and help to better understand the potential impacts of habitat conservation and restoration decisions.
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Fisheries","active":true,"usgs":false}],"preferred":false,"id":910803,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jordaan, Adrian","contributorId":343340,"corporation":false,"usgs":false,"family":"Jordaan","given":"Adrian","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":910804,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70257461,"text":"70257461 - 2024 - Accounting for missing ticks: Use (or lack thereof) of hierarchical models in tick ecology studies","interactions":[],"lastModifiedDate":"2024-09-06T16:40:27.763678","indexId":"70257461","displayToPublicDate":"2024-07-02T09:29:54","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5082,"text":"Ticks and Tick-borne Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Accounting for missing ticks: Use (or lack thereof) of hierarchical models in tick ecology studies","docAbstract":"Ixodid (hard) ticks play important ecosystem roles and have significant impacts on animal and human health via tick-borne diseases and physiological stress from parasitism. Tick occurrence, abundance, activity, and key life-history traits are highly influenced by host availability, weather, microclimate, and landscape features. As such, changes in the environment can have profound impacts on ticks, their hosts, and the spread of diseases. Researchers recognize that spatial and temporal factors influence activity and abundance and attempt to account for both by conducting replicate sampling bouts spread over the tick questing period. However, common field methods notoriously underestimate abundance, and it is unclear how (or if) tick studies model the confounding effects of factors influencing activity and abundance. This step is critical as unaccounted variance in detection can lead to biased estimates of occurrence and abundance. We performed a descriptive review to evaluate the extent to which studies account for the detection process while modeling tick data. We also categorized the types of analyses that are commonly used to model tick data. We used hierarchical models (HMs) that account for imperfect detection to analyze simulated and empirical tick data, demonstrating that inference is muddled when detection probability is not accounted for in the modeling process. Our review indicates that only 5 of 412 (1 %) papers explicitly accounted for imperfect detection while modeling ticks. By comparing HMs with the most common approaches used for modeling tick data (e.g., ANOVA), we show that population estimates are biased low for simulated and empirical data when using non-HMs, and that confounding occurs due to not explicitly modeling factors that influenced both detection and abundance. Our review and analysis of simulated and empirical data shows that it is important to account for our ability to detect ticks using field methods with imperfect detection. Not doing so leads to biased estimates of occurrence and abundance which could complicate our understanding of parasite-host relationships and the spread of tick-borne diseases. We highlight the resources available for learning HM approaches and applying them to analyzing tick data.
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consumers and growth across climatic conditions in the northern Gulf of Alaska","docAbstract":"Macroalgae and phytoplankton support the base of highly productive nearshore ecosystems in cold-temperate regions. To better understand their relative importance to nearshore food webs, this study considered four regions in the northern Gulf of Alaska where three indicator consumers were collected, filter-feeding mussels (Mytilus trossulus), pelagic-feeding Black Rockfish (Sebastes melanops), and benthic-feeding Kelp Greenling (Hexagrammos decagrammus). The study objectives were to (1) estimate the proportional contributions of macroalgal and phytoplankton organic matter using carbon and nitrogen stable isotopes, (2) determine if macroalgal use affected consumer growth using annual growth rings in shells or otoliths, and (3) describe changes in organic matter use and growth during the Pacific Marine Heatwave (PMH; 2014–2016) in one consumer, mussels. Macroalgae were the major organic matter source (> 60%) to the diet for all three consumers. The relationships between macroalgal contribution and growth were neutral for both fish species and significantly positive for mussels. During the PMH, mussels had a drop (> 10%) in macroalgal contributions and grew 45% less than in other time periods. Simultaneously, the relationship between macroalgal contribution and mussel growth was strongest during the PMH, explaining 48% variation compared to 3–12% before or after the PMH. Collectively, the results suggest that macroalgae is likely more important to cold-temperate nearshore food webs than phytoplankton. Management actions aimed at conserving and expanding macroalgae are likely to benefit nearshore food webs under all climate scenarios and especially during marine heatwaves.
","language":"English","publisher":"Springer","doi":"10.1007/s12237-024-01371-6","usgsCitation":"Corliss, K., von Biela, V.R., Coletti, H., Bodkin, J., Esler, D., and Iken, K., 2024, Relative importance of macroalgae and phytoplankton to nearshore consumers and growth across climatic conditions in the northern Gulf of Alaska: Estuaries and Coasts, v. 47, p. 1579-1597, https://doi.org/10.1007/s12237-024-01371-6.","productDescription":"19 p.","startPage":"1579","endPage":"1597","ipdsId":"IP-159405","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":439306,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s12237-024-01371-6","text":"Publisher Index Page"},{"id":433605,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Northern Gulf of Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -147.28389374481324,\n 60.97022879148497\n ],\n [\n -154.1275136007386,\n 60.97022879148497\n ],\n [\n -154.1275136007386,\n 58.24586357566474\n ],\n [\n -147.28389374481324,\n 58.24586357566474\n ],\n [\n -147.28389374481324,\n 60.97022879148497\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"47","noUsgsAuthors":false,"publicationDate":"2024-07-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Corliss, Katherine","contributorId":343993,"corporation":false,"usgs":false,"family":"Corliss","given":"Katherine","email":"","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":912578,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"von Biela, Vanessa R. 0000-0002-7139-5981 vvonbiela@usgs.gov","orcid":"https://orcid.org/0000-0002-7139-5981","contributorId":3104,"corporation":false,"usgs":true,"family":"von Biela","given":"Vanessa","email":"vvonbiela@usgs.gov","middleInitial":"R.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":912579,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coletti, Heather","contributorId":258849,"corporation":false,"usgs":false,"family":"Coletti","given":"Heather","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":912580,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bodkin, James L. 0000-0003-1641-4438","orcid":"https://orcid.org/0000-0003-1641-4438","contributorId":264733,"corporation":false,"usgs":false,"family":"Bodkin","given":"James L.","affiliations":[{"id":40616,"text":"former USGS PI","active":true,"usgs":false}],"preferred":false,"id":912581,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Esler, Daniel 0000-0001-5501-4555 desler@usgs.gov","orcid":"https://orcid.org/0000-0001-5501-4555","contributorId":5465,"corporation":false,"usgs":true,"family":"Esler","given":"Daniel","email":"desler@usgs.gov","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":12437,"text":"Simon Fraser University, Centre for Wildlife Ecology","active":true,"usgs":false},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":912582,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Iken, Katrin","contributorId":199008,"corporation":false,"usgs":false,"family":"Iken","given":"Katrin","email":"","affiliations":[],"preferred":false,"id":912583,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70255685,"text":"sir20245050 - 2024 - Water-quality trends in the Kansas River, Kansas, since enactment of the Clean Water Act, 1972–2020","interactions":[],"lastModifiedDate":"2026-02-03T19:34:03.41537","indexId":"sir20245050","displayToPublicDate":"2024-07-02T07:51:23","publicationYear":"2024","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":"2024-5050","displayTitle":"Water-Quality Trends in the Kansas River, Kansas, since Enactment of the Clean Water Act, 1972–2020","title":"Water-quality trends in the Kansas River, Kansas, since enactment of the Clean Water Act, 1972–2020","docAbstract":"The Clean Water Act was passed by Congress in 1972 to regulate pollution within the waters of the United States. The U.S. Geological Survey (USGS), in cooperation with the Kansas Department of Health and Environment (KDHE), the Kansas Water Office, the Nature Conservancy, the City of Lawrence, the City of Manhattan, the City of Olathe, the City of Topeka, WaterOne, and Evergy, compiled and analyzed historical streamflow and water-quality data collected by USGS and KDHE to characterize trends in water-quality constituents of interest because of their relation to water supply, drinking-water treatment, and sediment and nutrient transport, among others (total dissolved solids, chloride, ammonia, dissolved inorganic nitrogen [ammonia and nitrate plus nitrite], total nitrogen, orthophosphate, total phosphorus, total suspended solids, and fecal coliform bacteria) during mean- and low-flow conditions in the Kansas River since the passage of the Clean Water Act in 1972 through 2020. Trends in water-quality concentrations, or densities, and loads were analyzed using the Exploration and Graphics for RivER Trends R package and Weighted Regressions on Time, Discharge, and Season (WRTDS) model at upstream (Kansas River at Wamego, Kansas; USGS station 06887500) and downstream (Kansas River at De Soto, Kansas; USGS station 06892350) locations along the Kansas River using streamflow and water-quality data collected by the USGS and KDHE during 1972 through 2020. The Exploration and Graphics for RivER Trends Confidence Intervals R package and WRTDS bootstrap test estimated direction, uncertainty, and likelihood of trends in concentration and loads for each water-quality constituent of interest.
Downward trends in concentration and load were observed for 5 of the 9 water-quality constituents at both sites during mean-flow conditions during the study period. During low-flow conditions, 7 of the 9 constituents exhibited downward trends, possibly reflecting reductions in point-source contributions to the Kansas River. Downward trends in ammonia, dissolved inorganic nitrogen, and total nitrogen during mean- and low-flow conditions were observed at both Kansas River sites, which were similar to patterns observed nationally. Upward trends were generally observed for orthophosphate and total phosphorus, which were similar to patterns observed at sites in the Mississippi River Basin. Downward trends, or no trend, were observed for chloride. Upward and downward trends were observed for total dissolved solids. Downward trends in total suspended solids and fecal coliform bacteria were observed at both sites, which were also similar to patterns observed nationally. The long-term trend analyses in this report are an essential step to understanding how water-quality conditions have changed in the Kansas River since the passage of the Clean Water Act.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245050","collaboration":"Prepared in cooperation with the Kansas Water Office, the Kansas Department of Health and Environment, The Nature Conservancy, the City of Lawrence, the City of Manhattan, the City of Olathe, the City of Topeka, WaterOne, and Evergy","usgsCitation":"Williams, T.J., Klager, B.J., and Stiles, T.C., 2024, Water-quality trends in the Kansas River, Kansas, since enactment of the Clean Water Act, 1972–2020: U.S. Geological Survey Scientific Investigations Report 2024–5050, 29 p., https://doi.org/10.3133/sir20245050.","productDescription":"Report: viii, 29 p.; Data Release; Dataset","numberOfPages":"40","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-158483","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":430605,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WVZ8X1","text":"USGS data release","linkHelpText":"Water-quality data and computed flow-normalized and low-flow concentrations and loads in the Kansas River, Kansas, 1972–2020"},{"id":430602,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5050/images/"},{"id":430601,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5050/sir20245050.XML"},{"id":430599,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5050/coverthb.jpg"},{"id":430600,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5050/sir20245050.pdf","text":"Report","size":"3.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024–5050"},{"id":430731,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245050/full"},{"id":499470,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117100.htm","linkFileType":{"id":5,"text":"html"}},{"id":430604,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"}],"country":"United States","state":"Kansas","otherGeospatial":"Kansas River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -97.5,\n 40\n ],\n [\n -97.5,\n 38.75\n ],\n [\n -94.5,\n 38.75\n ],\n [\n -94.5,\n 40\n ],\n [\n -97.5,\n 40\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
Globally, glaciers and icefields contribute significantly to sea level rise. Here we show that ice loss from Juneau Icefield, a plateau icefield in Alaska, accelerated after 2005 AD. Rates of area shrinkage were 5 times faster from 2015–2019 than from 1979–1990. Glacier volume loss remained fairly consistent (0.65–1.01 km3 a−1) from 1770–1979 AD, rising to 3.08–3.72 km3 a−1 from 1979–2010, and then doubling after 2010 AD, reaching 5.91 ± 0.80 km3 a−1 (2010–2020). Thinning has become pervasive across the icefield plateau since 2005, accompanied by glacier recession and fragmentation. Rising equilibrium line altitudes and increasing ablation across the plateau has driven a series of hypsometrically controlled melt-accelerating feedbacks and resulted in the observed acceleration in mass loss. As glacier thinning on the plateau continues, a mass balance-elevation feedback is likely to inhibit future glacier regrowth, potentially pushing glaciers beyond a dynamic tipping point.
The first multidisciplinary characterisation of Bowditch Seamount in the Sargasso Sea was conducted to provide new baseline knowledge of the biodiversity, geomorphology, oceanography and glacial history of this seamount. A dropframe camera transect 1483–1562 m deep on the seamount documented 77 megafaunal taxa including Vulnerable Marine Ecosystem indicator taxa such as sponges, cold-water corals, and stalked crinoids. Seabed terrain analysis of multibeam echosounder data showed species varied significantly along this transect in response to local geomorphological variability (R2adj = 31%, p < 0.0001), with changes in seafloor relief and substrata driving species composition over the seamount. 14C-calibrated and 230Th-ages of fossil corals (Desmophyllum dianthus) collected by Van Veen grabs 1517 m deep showed corals thrived on the seamount ∼24 ka BP and ∼17 ka BP. Abrupt excursions between higher and lower radiogenic εNd-composition values of the skeletons suggested that D. dianthus persisted on the seamount over times of southern source water input and detrital sediments from the melting Laurentide Ice Sheet, respectively. In agreement with other studies from the western North Atlantic, living D. dianthus were absent in the contemporary setting at these depths, and suggest a significant re-organisation of the seamount community since the deglacial when ice-rafted debris of carbonates likely resulted in a lower aragonite compensation depth allowing D. dianthus to proliferate at deeper depths. New conductivity-depth-temperature profiling revealed the seamount at these depths is now bathed by highly oxygenated Labrador Sea Water (LSW) formed at high latitudes. Co-analysis of a newly constructed 70-year long time series of temperature and salinity for the Labrador Sea and Bermuda regions revealed a 10-year transit time from high latitudes to Bowditch Seamount. This multidisciplinary approach shows how geomorphology drives local biodiversity patterns, but also how upstream climatic forcing in subpolar regions may influence Bermuda's subtropical seamount ecosystem.
Dust transported from rangelands of the Southwestern United States (US) to mountain snowpack in the Upper Colorado River Basin during spring (March-May) forces earlier and faster snowmelt, which creates problems for water resources and agriculture. To better understand the drivers of dust events, we investigated large-scale meteorology responsible for organizing two Southwest US dust events from two different dominant geographic locations: (a) the Colorado Plateau and (b) the northern Chihuahuan Desert. High-resolution Weather Research and Forecasting coupled with Chemistry model (WRF-Chem) simulations with the Air Force Weather Agency dust emission scheme incorporating a MODIS albedo-based drag-partition was used to explore land surface-atmosphere interactions driving two dust events. We identified commonalities in their meteorological setups. The meteorological analyses revealed that Polar and Sub-tropical jet stream interaction was a common upper-level meteorological feature before each of the two dust events. When the two jet streams merged, a strong northeast-directed pressure gradient upstream and over the source areas resulted in strong near-surface winds, which lifted available dust into the atmosphere. Concurrently, a strong mid-tropospheric flow developed over the dust source areas, which transported dust to the San Juan Mountains and southern Colorado snowpack. The WRF-Chem simulations reproduced both dust events, indicating that the simulations represented the dust sources that contributed to dust-on-snow events reasonably well. The representativeness of the simulated dust emission and transport in different geographic and meteorological conditions with our use of albedo-based drag partition provides a basis for additional dust-on-snow simulations to assess the hydrologic impact in the Southwest US.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023JD040650","usgsCitation":"Dhital, S., Webb, N.P., Chappell, A., Kaplan, M.L., Nauman, T.W., Tyree, G.L., Duniway, M.C., Edwards, B.L., LeGrand, S.L., Letcher, T.W., Skiles, S.M., Naple, P., Chaney, N.W., and Cai, J., 2024, Synoptic analysis and WRF-Chem model simulation of dust events in the southwestern United States: JGR Atmospheres, v. 129, no. 13, e2023JD040650, 22 p., https://doi.org/10.1029/2023JD040650.","productDescription":"e2023JD040650, 22 p.","ipdsId":"IP-161089","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":495722,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023jd040650","text":"Publisher Index Page"},{"id":495406,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"southwestern United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.01835497016609,\n 41.90825861082291\n ],\n [\n -121.01835497016609,\n 31.275127575649392\n ],\n [\n -102.51551924830923,\n 31.275127575649392\n ],\n [\n -102.51551924830923,\n 41.90825861082291\n ],\n [\n -121.01835497016609,\n 41.90825861082291\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"129","issue":"13","noUsgsAuthors":false,"publicationDate":"2024-07-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Dhital, Saroj","contributorId":310520,"corporation":false,"usgs":false,"family":"Dhital","given":"Saroj","email":"","affiliations":[{"id":67202,"text":"USDA-ARS-Jornada Experimental Range. P.O. Box 30003, MSC 3JER, NMSU, Las Cruces, NM 88003","active":true,"usgs":false}],"preferred":false,"id":948654,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Webb, Nicholas P.","contributorId":361353,"corporation":false,"usgs":false,"family":"Webb","given":"Nicholas","middleInitial":"P.","affiliations":[{"id":80080,"text":"USDA-ARS Jornada Experimental Range, Las Cruces, NM, USA","active":true,"usgs":false}],"preferred":false,"id":948655,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chappell, Adrian","contributorId":167797,"corporation":false,"usgs":false,"family":"Chappell","given":"Adrian","email":"","affiliations":[],"preferred":false,"id":948656,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kaplan, Michael L.","contributorId":361354,"corporation":false,"usgs":false,"family":"Kaplan","given":"Michael","middleInitial":"L.","affiliations":[{"id":86241,"text":"Division of Atmospheric Sciences, Desert Research Institute, Reno, NV, USA","active":true,"usgs":false}],"preferred":false,"id":948657,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nauman, Travis W.","contributorId":361355,"corporation":false,"usgs":false,"family":"Nauman","given":"Travis","middleInitial":"W.","affiliations":[{"id":82620,"text":"USDA-NRCS National Soil Survey Center, Lincoln, NE, USA","active":true,"usgs":false}],"preferred":false,"id":948658,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tyree, Gayle Loren 0000-0002-9949-6426","orcid":"https://orcid.org/0000-0002-9949-6426","contributorId":257744,"corporation":false,"usgs":true,"family":"Tyree","given":"Gayle","email":"","middleInitial":"Loren","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":948659,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":219284,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":948660,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Edwards, Brandon L.","contributorId":215510,"corporation":false,"usgs":false,"family":"Edwards","given":"Brandon","email":"","middleInitial":"L.","affiliations":[{"id":39270,"text":"USDA-ARS Jornada Experimental Range, Las Cruces, NM 88003, USA","active":true,"usgs":false}],"preferred":false,"id":948661,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"LeGrand, Sandra L.","contributorId":361357,"corporation":false,"usgs":false,"family":"LeGrand","given":"Sandra","middleInitial":"L.","affiliations":[{"id":86243,"text":"U.S. Army Engineer Research and Development Center, Geospatial Research Laboratory, Alexandria, Virginia, USA","active":true,"usgs":false}],"preferred":false,"id":948662,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Letcher, Theodore W.","contributorId":361358,"corporation":false,"usgs":false,"family":"Letcher","given":"Theodore","middleInitial":"W.","affiliations":[{"id":86244,"text":"U.S. Army Engineer Research and Development Center, Cold Regions Research and Engineering Laboratory, Hanover, New Hampshire, USA","active":true,"usgs":false}],"preferred":false,"id":948663,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Skiles, S. McKenzie","contributorId":361359,"corporation":false,"usgs":false,"family":"Skiles","given":"S.","middleInitial":"McKenzie","affiliations":[{"id":86245,"text":"Department of Geography, University of Utah, Salt Lake City, UT, USA","active":true,"usgs":false}],"preferred":false,"id":948664,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Naple, Patrick","contributorId":361360,"corporation":false,"usgs":false,"family":"Naple","given":"Patrick","affiliations":[{"id":86245,"text":"Department of Geography, University of Utah, Salt Lake City, UT, USA","active":true,"usgs":false}],"preferred":false,"id":948665,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Chaney, Nathaniel W.","contributorId":361361,"corporation":false,"usgs":false,"family":"Chaney","given":"Nathaniel","middleInitial":"W.","affiliations":[{"id":86246,"text":"Department of Civil and Environmental Engineering, Duke University, Durham, NC, USA","active":true,"usgs":false}],"preferred":false,"id":948666,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Cai, Jiaxuan","contributorId":361362,"corporation":false,"usgs":false,"family":"Cai","given":"Jiaxuan","affiliations":[{"id":86246,"text":"Department of Civil and Environmental Engineering, Duke University, Durham, NC, USA","active":true,"usgs":false}],"preferred":false,"id":948667,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70263614,"text":"70263614 - 2024 - The 17 January 1994 Northridge, California, earthquake: A retrospective analysis","interactions":[],"lastModifiedDate":"2025-02-19T16:27:36.326526","indexId":"70263614","displayToPublicDate":"2024-07-01T13:22:58","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10542,"text":"The Seismic Record","active":true,"publicationSubtype":{"id":10}},"title":"The 17 January 1994 Northridge, California, earthquake: A retrospective analysis","docAbstract":"The 17 January 1994 Northridge, California, earthquake was a watershed event, with far-reaching societal and scientific impacts. The earthquake, which occurred in the early days of both broadband seismic networks and the Internet, spurred advances in seismic monitoring, real-time systems, and development of data products. Motivated by the 30th anniversary of the earthquake, we present a brief retrospective of the earthquake and its impact, and reconsider both ground motions and the aftershock distribution using modern tools and best-available data. With improvements in instrumentation and analysis methodology, recent earthquakes continue to reveal increasing complexity of ground motions, fault systems, and earthquake ruptures. Even in the absence of data from state-of-the art instrumentation, a retrospective consideration of ground motion data from the Northridge earthquake reveals complexities beyond what could be characterized (and modeled) thirty years ago. Aftershock relocations for both the 1971 Sylmar and 1994 Northridge earthquakes also reveal an updated view of fault complexity. Our study does provide a cautionary tale regarding legacy data sets and research results that are not easily accessible, which can result in discrepancies between catalog data and products from best-available science. We also briefly describe outreach products produced as part of the anniversary commemoration.","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0320240012","usgsCitation":"Hough, S.E., Graves, R., Cochran, E.S., Yoon, C., Blair, J.L., Haefner, S., Wald, D.J., and Quitoriano, V., 2024, The 17 January 1994 Northridge, California, earthquake: A retrospective analysis: The Seismic Record, v. 4, no. 3, p. 151-160, https://doi.org/10.1785/0320240012.","productDescription":"10 p.","startPage":"151","endPage":"160","ipdsId":"IP-164904","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":489848,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1785/0320240012","text":"Publisher Index Page"},{"id":482225,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Northridge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.60523974052802,\n 34.27362638562232\n ],\n [\n -118.60523974052802,\n 34.17466422238154\n ],\n [\n -118.47245060179975,\n 34.17466422238154\n ],\n [\n -118.47245060179975,\n 34.27362638562232\n ],\n [\n -118.60523974052802,\n 34.27362638562232\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"4","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Hough, Susan E. 0000-0002-5980-2986","orcid":"https://orcid.org/0000-0002-5980-2986","contributorId":263442,"corporation":false,"usgs":true,"family":"Hough","given":"Susan","email":"","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927573,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Graves, Robert 0000-0001-9758-453X rwgraves@usgs.gov","orcid":"https://orcid.org/0000-0001-9758-453X","contributorId":140738,"corporation":false,"usgs":true,"family":"Graves","given":"Robert","email":"rwgraves@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927574,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cochran, Elizabeth S. 0000-0003-2485-4484 ecochran@usgs.gov","orcid":"https://orcid.org/0000-0003-2485-4484","contributorId":2025,"corporation":false,"usgs":true,"family":"Cochran","given":"Elizabeth","email":"ecochran@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927575,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yoon, Clara 0000-0003-4521-3889","orcid":"https://orcid.org/0000-0003-4521-3889","contributorId":222019,"corporation":false,"usgs":true,"family":"Yoon","given":"Clara","email":"","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927576,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Blair, James Luke 0000-0002-6980-6446","orcid":"https://orcid.org/0000-0002-6980-6446","contributorId":213724,"corporation":false,"usgs":true,"family":"Blair","given":"James","email":"","middleInitial":"Luke","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927577,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Haefner, Scott","contributorId":350679,"corporation":false,"usgs":true,"family":"Haefner","given":"Scott","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927578,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"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":927579,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Quitoriano, Vince 0000-0003-4157-1101 vinceq@usgs.gov","orcid":"https://orcid.org/0000-0003-4157-1101","contributorId":2582,"corporation":false,"usgs":true,"family":"Quitoriano","given":"Vince","email":"vinceq@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":927580,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70273741,"text":"70273741 - 2024 - Adaptive fine-tuning for transferring a U-net hydrography extraction model using K-means","interactions":[],"lastModifiedDate":"2026-01-27T16:31:23.793842","indexId":"70273741","displayToPublicDate":"2024-07-01T10:29:44","publicationYear":"2024","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Adaptive fine-tuning for transferring a U-net hydrography extraction model using K-means","docAbstract":"The United States Geological Survey (USGS) coordinates the collection of hydrographic features derived from remotely sensed interferometric synthetic aperture radar (IfSAR) elevation and intensity data in Alaska. Hydrographic features are cartographic representations of surface water features such as stream, rivers, lakes, ponds, canals, etc. Collection and validation procedures involve complex automated and manual techniques that furnish snapshots of hydrographic vector data that exist during the IfSAR surveys. The dynamic nature of fluvial conditions warrants monitoring and updating hydrographic data, but extraction procedures for updates can be cost prohibitive. This paper overviews progress on automated workflows to extract hydrography from IfSAR data using deep learning methods trained and tested with USGS collected hydrography data. This research tests transfer learning methods on a well-performing U-net model trained on a 4600-square kilometer (sq km) base model area in northcentral Alaska. The base model is transferred and fine-tuned to regions in the target domain covering roughly 127,000 sq km. The target domain is subdivided into areas with similar hydrogeomorphic conditions using principal components and k-means clustering, and the base model is adaptively fine-tuned to each hydrogeomorphic class by selecting training watersheds from each cluster within the target domain. Results are compared with transfer learning that is fine-tuned with a random sample of watersheds in the target domain.","conferenceTitle":"Cartography and Geographic Information Society (CaGIS) and the University Consortium for Geographic Information Science (UCGIS) 2024 Symposium","conferenceDate":"June 3-6, 2024","conferenceLocation":"Columbus, OH","language":"English","publisher":"The Cartography and Geographic Information Society (CaGIS) and the University Consortium for Geographic Information Science (UCGIS)","usgsCitation":"Stanislawski, L., Shavers, E.J., Pastick, N.J., Thiem, P.T., Wang, S., Jaroenchai, N., Jiang, Z., Kronenfeld, B.J., Buttenfield, B.P., and Camerer, A., 2024, Adaptive fine-tuning for transferring a U-net hydrography extraction model using K-means, Cartography and Geographic Information Society (CaGIS) and the University Consortium for Geographic Information Science (UCGIS) 2024 Symposium, Columbus, OH, June 3-6, 2024, 45, 6 p.","productDescription":"45, 6 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electrofishing","interactions":[],"lastModifiedDate":"2024-07-10T15:16:13.598471","indexId":"70255905","displayToPublicDate":"2024-07-01T10:12:18","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"title":"Pacific Lamprey responses to stressors: Dewatering and electrofishing","docAbstract":"The Pacific Lamprey Conservation Initiative (PLCI) is a collaboration of Tribes, Federal, and State agencies working together to protect and restore Pacific Lamprey (Entosphenus tridentatus) and other native lampreys (i.e., Lampetra spp.) in Alaska, Washington, Oregon, California, and Idaho. The U.S. Fish and Wildlife Service hosts and facilitates the PLCI, and the Columbia River Tribes play a large role in setting conservation goals and defining research needs. The PLCI annually solicits proposals for research and restoration activities, which are reviewed and ranked in collaboration with the Bonneville Power Administration (BPA), who annually provides funding to support PLCI priority proposals. This report summarizes two research projects selected through PLCI, and funded under one BPA contract, in support of Pacific Lamprey conservation. The two projects were not topically related apart from a common theme of potential stressors to lampreys and are being reported together because they were combined under one BPA agreement for contracting.
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D.","contributorId":343469,"corporation":false,"usgs":false,"family":"Hamlington","given":"Benjamin","email":"","middleInitial":"D.","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":911200,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hill, Kristina","contributorId":343472,"corporation":false,"usgs":false,"family":"Hill","given":"Kristina","email":"","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":911204,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Landerer, Felix","contributorId":343532,"corporation":false,"usgs":false,"family":"Landerer","given":"Felix","email":"","affiliations":[],"preferred":false,"id":911205,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Thompson, Phillip","contributorId":343473,"corporation":false,"usgs":false,"family":"Thompson","given":"Phillip","affiliations":[{"id":36402,"text":"University of Hawaii","active":true,"usgs":false}],"preferred":false,"id":911206,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70267248,"text":"70267248 - 2024 - Framework for implementing damping scaling factors in U.S. Geological Survey National Seismic Hazard Models","interactions":[],"lastModifiedDate":"2025-05-20T14:56:34.636132","indexId":"70267248","displayToPublicDate":"2024-07-01T09:56:08","publicationYear":"2024","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Framework for implementing damping scaling factors in U.S. Geological Survey National Seismic Hazard Models","docAbstract":"Traditionally, probabilistic seismic hazard analysis (PSHA) has focused on calculating ground motion hazard curves for elastic, 5%-damped pseudo spectral accelerations, Sa(T,5%), which are used as the basis for engineering design parameters and targets for ground motion selection and modification. However, structures and geotechnical systems can exhibit a wide range of damping ratios both above and below the 5% level, depending on the construction material, structural system, nonstructural elements, or subsurface soil properties. When spectral parameters at such damping levels are required for certain applications, 5%-damped accelerations have traditionally been extracted from PSHA-based hazard curves and adjusted outside of the hazard integral using damping scaling factors (DSF) such as those from Newmark & Hall (1982). Recent advances in the development of more rigorous and comprehensive damping scaling models (e.g., Rezaeian et al., 2014; Rezaeian et al., 2021) have allowed for the modeling of means and standard deviations of DSFs as functions of earthquake source and path properties for crustal, intraslab, and subduction interface tectonic environments. These DSF models can be applied to ground motion model (GMM) estimates of Sa(T,5%) for a given earthquake rupture scenario to produce a corresponding mean and standard deviation Sa at a specified damping ratio β, Sa(T,β). In this study, the DSF models of Rezaeian et al. (2014) and Rezaeian et al. (2021) are implemented within the U.S. Geological Survey National Seismic Hazard Model (NSHM) PSHA framework to calculate probabilistic hazard curves for spectral accelerations at damping ratios from 0.5% to 30%. The DSF models are applied directly to the mean and standard deviation of Sa(T,5%) predictions from each GMM in the NSHM logic tree. Resulting hazard curves and uniform hazard and risk spectra for Sa(T,β) are presented for several geographic locations and compared with corresponding spectra estimated using current design practices by applying the same DSFs outside of the PSHA calculation. Key differences between the two methods for estimating Sa(T,β) are discussed, and potential strategies are presented for the implementation and usage of the hazard-consistent Sa(T,β) in building codes. Comparing the results to those from DSFs used in current design practices that are mainly based on Newmark & Hall (1982) is not explored in this study.
","conferenceTitle":"18th World Conference on Earthquake Engineering","conferenceDate":"June 30- July 5, 2024","conferenceLocation":"Milan, Italy","language":"English","publisher":"International Association of Earthquake Engineering","usgsCitation":"Makdisi, A.J., Smith, D., Rezaeian, S., Powers, P.M., and Withers, K., 2024, Framework for implementing damping scaling factors in U.S. Geological Survey National Seismic Hazard Models, 18th World Conference on Earthquake Engineering, Milan, Italy, June 30- July 5, 2024, 11 p.","productDescription":"11 p.","ipdsId":"IP-159606","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":486139,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://proceedings-wcee.org/view.html?id=23633&conference=18WCEE"},{"id":486214,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Makdisi, Andrew James 0000-0002-8239-0692","orcid":"https://orcid.org/0000-0002-8239-0692","contributorId":267917,"corporation":false,"usgs":true,"family":"Makdisi","given":"Andrew","email":"","middleInitial":"James","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":937507,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Dallin","contributorId":355505,"corporation":false,"usgs":false,"family":"Smith","given":"Dallin","affiliations":[{"id":6681,"text":"Brigham Young University","active":true,"usgs":false}],"preferred":false,"id":937508,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rezaeian, Sanaz 0000-0001-7589-7893","orcid":"https://orcid.org/0000-0001-7589-7893","contributorId":238513,"corporation":false,"usgs":true,"family":"Rezaeian","given":"Sanaz","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":937509,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Powers, Peter M. 0000-0003-2124-6184 pmpowers@usgs.gov","orcid":"https://orcid.org/0000-0003-2124-6184","contributorId":176814,"corporation":false,"usgs":true,"family":"Powers","given":"Peter","email":"pmpowers@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":937510,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Withers, Kyle 0000-0001-7863-3930","orcid":"https://orcid.org/0000-0001-7863-3930","contributorId":203492,"corporation":false,"usgs":true,"family":"Withers","given":"Kyle","email":"","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":937511,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70267251,"text":"70267251 - 2024 - Effects of stochastically-simulated near-fault ground motions on soil liquefaction","interactions":[],"lastModifiedDate":"2025-05-20T14:51:11.280957","indexId":"70267251","displayToPublicDate":"2024-07-01T09:50:05","publicationYear":"2024","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Effects of stochastically-simulated near-fault ground motions on soil liquefaction","docAbstract":"The scarcity of historically recorded near-fault ground motions poses a challenge to systematically understanding the influence of near-fault effects on various types of seismic demands for engineering purposes. In particular, the current state of knowledge of the influence of ground-shaking intensity on soil liquefaction and its consequences does not specifically account for the effects of near-fault ground motion characteristics. In this study, the influence of near-fault ground motions on liquefaction triggering and lateral spreading are investigated using non-linear modeling of a hypothetical liquefiable soil column in the finite-element computational platform OpenSees subjected to simulated ground motion time series that represent strong earthquake shaking in the near field. The simulated ground motion time series and resulting datasets are based on a parametric stochastic model and are developed for a range of source and path parameters to represent a realistic variability of ground motion characteristics. Dependencies between ground motion intensity measures (IMs) and liquefaction demand parameters are investigated for near-fault pulse and nonpulse-like ground motion sets. Evolutionary IMs, such as cumulative absolute velocity (CAV) and the time-varying magnitude-adjusted peak ground acceleration (PGAM), are considered in developing liquefaction triggering probability density functions. Post-liquefaction triggering responses such as lateral spreading displacements are examined in relation to PGAM and CAV. The ground motion simulations are validated by comparing their liquefaction-capacity PGAM fragilities and post-triggering CAV vulnerability relationships to historical records from the 1994 Northridge earthquake in California, USA. Finally, a path forward for future studies that includes finding systematic differences in the IM-liquefaction demand relationships between near-fault and far-field stochastic ground motion sets is outlined.
","conferenceTitle":"18th World Conference on Earthquake Engineering","conferenceDate":"June 30-July 5., 2024","conferenceLocation":"Milan, Italy","language":"English","publisher":"International Association of Earthquake Engineering","usgsCitation":"Makdisi, A.J., Dabaghi, M., Brito Silveira, L., Rezaeian, S., Haynie, K.L., and Mason, H., 2024, Effects of stochastically-simulated near-fault ground motions on soil liquefaction, 18th World Conference on Earthquake Engineering, Milan, Italy, June 30-July 5., 2024, 12 p.","productDescription":"12 p.","ipdsId":"IP-159292","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":486140,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://proceedings-wcee.org/view.html?id=25504&conference=18WCEE","linkFileType":{"id":5,"text":"html"}},{"id":486213,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2024-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Makdisi, Andrew James 0000-0002-8239-0692","orcid":"https://orcid.org/0000-0002-8239-0692","contributorId":267917,"corporation":false,"usgs":true,"family":"Makdisi","given":"Andrew","email":"","middleInitial":"James","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":937512,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dabaghi, Mayssa","contributorId":221888,"corporation":false,"usgs":false,"family":"Dabaghi","given":"Mayssa","email":"","affiliations":[{"id":40455,"text":"American University of Beirut","active":true,"usgs":false}],"preferred":false,"id":937513,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brito Silveira, Lianne 0000-0002-8331-7104","orcid":"https://orcid.org/0000-0002-8331-7104","contributorId":355508,"corporation":false,"usgs":true,"family":"Brito Silveira","given":"Lianne","affiliations":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"preferred":true,"id":937514,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rezaeian, Sanaz 0000-0001-7589-7893","orcid":"https://orcid.org/0000-0001-7589-7893","contributorId":238513,"corporation":false,"usgs":true,"family":"Rezaeian","given":"Sanaz","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":937515,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Haynie, Kirstie Lafon 0000-0001-9930-6736","orcid":"https://orcid.org/0000-0001-9930-6736","contributorId":289894,"corporation":false,"usgs":true,"family":"Haynie","given":"Kirstie","email":"","middleInitial":"Lafon","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":937516,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mason, Henry 0000-0003-4279-2854","orcid":"https://orcid.org/0000-0003-4279-2854","contributorId":293188,"corporation":false,"usgs":true,"family":"Mason","given":"Henry","email":"","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":937517,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70257647,"text":"70257647 - 2024 - Lake Ontario April prey fish survey results and Alewife assessment, 2024","interactions":[],"lastModifiedDate":"2024-08-21T14:56:36.633289","indexId":"70257647","displayToPublicDate":"2024-07-01T09:46:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"title":"Lake Ontario April prey fish survey results and Alewife assessment, 2024","docAbstract":"The Lake Ontario April bottom trawl survey assesses pelagic prey fish populations, in particular Alewife Alosa pseudoharengus, which are the primary prey supporting the lake’s sport fish populations. The 2024 survey included 234 trawls in the main lake and embayments and sampled depths from 3.9 to 245 m (13 – 809 ft). The survey captured 441,942 fish from 28 species with a total weight of 10,519 kg (23,142 lbs.). Alewife were 89% of the total catch by number while Deepwater Sculpin Myoxocephalus thompsonii, Round Goby Neogobius melanostomus, and Rainbow Smelt Osmerus mordax, comprised 4%, 3%, and 2% of the catch respectively.
The estimated Alewife biomass increases slightly from 2023 to 2024 (83.9 to 84.2 kg·ha-1) and was the largest biomass value since whole lake sampling began in 2016. Adult Alewife abundance increased in 2024 as predicted in 2023, and most of the total Alewife biomass was comprised of adult fish (97%), predominantly from the 2020 and 2022 year classes. In contrast, Age-1 Alewife biomass (2.2 kg·ha-1) was the lowest estimated since whole lake sampling began in 2016 (previous range: 2.7 – 26.7 kg·ha-1), indicating reproductive success was poor in 2023. Adult Alewife biomass is predicted to remain relatively high but decline slightly in 2025 and 2026, due to the smaller year classes produced in 2021 and 2023. Alewife condition as measured by the weight of a standard length fish (165 mm; ~6.5 inches), was 32.8 g, which was within of the range of previously observed values (28.0 – 35.9 g, 1997 – 2023). Acoustic-based prey fish densities, in the water above the bottom trawl, were similar to observations from 2021 – 2023 and were orders of magnitude lower than bottom trawl densities. These acoustic results support the seasonal timing of the April survey, when the majority of Alewife and other pelagic prey fishes are near the lake bottom and susceptible to capture with bottom trawls.
The trawl survey also provides information on the status of other pelagic prey fishes and native fish restorations. In 2024, biomass indices for Rainbow Smelt, Emerald Shiner Notropis atherinoides, and Threespine Stickleback Gasterosteus aculeatus, were similar to 2023 values while the index for Cisco Coregonus artedi declined. The density index for naturally reproduced, juvenile Lake Trout Salvelinus namaycush declined relative to 2023. Density estimates of Lake Whitefish Coregonus clupeaformis continue to be orders of magnitude lower in U.S. waters relative to Canadian waters. A single purported Bloater Coregonus hoyi (total length = 148 mm, sampling depth = 105 m) was captured near Rochester, NY during the 2024 survey. This is the eighth Bloater recaptured during this survey since restoration stocking began in 2012.
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