There is a paucity of data evaluating water use by raptors. Although raptors are believed to satisfy their water requirements through metabolic processes, they are known to experience reduced reproductive success during periods of drought, and there is evidence of water being important for site occupancy in arid landscapes. Several raptor species have a seasonal or year-round presence in west Texas, a drought-prone, semi-arid region of the Southern Great Plains. We examined species-specific timing of free water use by common raptors in this region, and examined environmental conditions associated with water use. We collected 4549 camera trap-days of data across 4 yr at ten human-made water sources placed for cattle. We recorded 14 species of raptors among the 1177 detections of raptors visiting water sources; of these, 1084 raptors (92.1%) perched at tanks, and 93 (7.1%) flew by tanks. Of the raptors that perched at tanks, 63.5% drank and 20.8% both bathed and drank. Barn Owls (Tyto alba; 35.6%), Swainson's Hawks (Buteo swainsoni; 32.0%), and Northern Harriers (Circus hudsonius; 21.0%) were the predominate species detected. Visits by Northern Harriers and Swainson's Hawks increased with increasing temperature and decreasing precipitation. Visits by Barn Owls increased with increasing drought severity. Further, detections per 100 trap-days increased substantively across our 4-yr study period during which the region experienced one of the worst droughts on record. Although our data do not demonstrate these raptors require free water, they do reveal an increasing use of free water in relation to hotter and drier conditions. How this influences survival and reproduction remains unknown, but may become a pressing question because current climate models predict the study area will experience increases in heat and decreases in precipitation.
","language":"English","publisher":"BioOne","doi":"10.3356/JRR-21-70","usgsCitation":"Boal, C.W., Bibles, B.D., and Gicklhorn, T., 2023, Patterns of water use by raptors in the southern Great Plains: Journal of Raptor Research, v. 57, no. 3, p. 444-455, https://doi.org/10.3356/JRR-21-70.","productDescription":"12 p.","startPage":"444","endPage":"455","ipdsId":"IP-134224","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":432384,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"57","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Boal, Clint W. 0000-0001-6008-8911 cboal@usgs.gov","orcid":"https://orcid.org/0000-0001-6008-8911","contributorId":1909,"corporation":false,"usgs":true,"family":"Boal","given":"Clint","email":"cboal@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908580,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bibles, Brent D.","contributorId":341539,"corporation":false,"usgs":false,"family":"Bibles","given":"Brent","email":"","middleInitial":"D.","affiliations":[{"id":81739,"text":"Unity College","active":true,"usgs":false}],"preferred":false,"id":908581,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gicklhorn, Trevor S.","contributorId":341540,"corporation":false,"usgs":false,"family":"Gicklhorn","given":"Trevor S.","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":908582,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70243941,"text":"sir20235050 - 2023 - Bathymetric and velocimetric surveys at highway bridges crossing the Missouri and Mississippi Rivers near St. Louis, Missouri, August 3–10, 2020","interactions":[],"lastModifiedDate":"2026-03-09T16:14:58.563672","indexId":"sir20235050","displayToPublicDate":"2023-05-25T14:04:57","publicationYear":"2023","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":"2023-5050","displayTitle":"Bathymetric and Velocimetric Surveys at Highway Bridges Crossing the Missouri and Mississippi Rivers near St. Louis, Missouri, August 3–10, 2020","title":"Bathymetric and velocimetric surveys at highway bridges crossing the Missouri and Mississippi Rivers near St. Louis, Missouri, August 3–10, 2020","docAbstract":"Bathymetric and velocimetric data were collected by the U.S. Geological Survey, in cooperation with the Missouri Department of Transportation, near 15 bridges at 10 highway crossings of the Missouri and Mississippi Rivers near Washington, Louisiana, and St. Louis, Missouri, on August 3–10, 2020. A multibeam echosounder mapping system was used to obtain channel-bed elevations for river reaches about 1,640 to 1,970 feet longitudinally and generally extending laterally across the active channel from bank to bank during moderate flood-flow conditions. These surveys provided channel geometry and hydraulic conditions at the time of the surveys and provided characteristics of scour holes that may be useful in developing predictive guidelines or equations for computing potential scour depth. These data also may be useful to the Missouri Department of Transportation as a low to moderate flood-flow assessment of the bridges for stability and integrity issues with respect to bridge scour during floods.
Bathymetric data were collected around every in-channel pier. Scour holes were present at most piers for which bathymetry could be obtained, except those on banks or surrounded by riprap. All the bridge sites in this study were previously surveyed and documented in previous studies, including the two new bridge structures at Louisiana and Washington (structures A8141 and A8504, sites 22 and 32, respectively). Comparisons between bathymetric surfaces from the previous surveys and those of the current (2020) study do not indicate any consistent correlation in channel-bed elevations with streamflow conditions. The comparisons of the 2020 surveys to two previous surveys at the new bridge structure A8141 at Washington (site 22) resulted in net erosion of the channel bed in both comparisons, despite the 2020 streamflow being less than either previous survey. Alternatively, there was a net gain of sediment at new bridge structure A8504 at Louisiana (site 32) between 2014 and 2020, which was the most substantial increase in the surveys detailed in this report; substantially less flow in 2020 than in 2014 or changes to the channel and spur dikes near the bridge may have contributed to the observed sediment gain.
Pier size, nose shape, and skew to approach flow had a substantial effect on the size of the scour hole observed at a given pier. Larger and deeper scour holes were present at piers with wide or blunt noses caused by exposed footings, seal courses, or caissons. When a pier was skewed to primary approach flow, the scour hole was generally deeper and larger than at a similar pier without skew; however, the shape of the scour hole near skewed piers in this study generally was longer and deeper on the leeward side, contrary to the general shape of scour holes for skewed piers. However, this phenomenon has been observed historically at these sites, and likely is exacerbated by debris rafts or other turbulence-inducing features near the atypical scour holes. A substantial scour hole was observed near pier 11 of structure A6500 (site 33), which was deeper than in the 2016 survey. The scour holes observed at pier 17 of structure L0561 (site 25) and piers 3 and 4 of structure A1500 (site 34) also were slightly deeper and wider in 2020 than in 2016. At new bridge structures A8141 at Washington (site 22) and A8504 at Louisiana (site 32), the smaller cross-sectional area and configuration of the piers of the new bridges resulted in substantially less scour than with the wider old piers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235050","collaboration":"Prepared in cooperation with Missouri Department of Transportation","usgsCitation":"Huizinga, R.J., 2023, Bathymetric and velocimetric surveys at highway bridges crossing the Missouri and Mississippi Rivers near St. Louis, Missouri, August 3–10, 2020 (ver. 1.1, June 2023): U.S. Geological Survey Scientific Investigations Report 2023–5050, 129 p., https://doi.org/10.3133/sir20235050.","productDescription":"Report: xii, 129 p.; 4 Data Releases","numberOfPages":"146","onlineOnly":"Y","ipdsId":"IP-137672","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":500928,"rank":11,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114743.htm","linkFileType":{"id":5,"text":"html"}},{"id":417650,"rank":9,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2023/5050/versionHist.txt","text":"Version History","size":"1.19 kB","linkFileType":{"id":2,"text":"txt"}},{"id":417532,"rank":8,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5050/images"},{"id":417434,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9F04JC5","text":"USGS data release","linkHelpText":"Bathymetry and velocity data from surveys at highway bridges crossing the Missouri and Mississippi Rivers near St. Louis, Missouri, August 3–10, 2020"},{"id":417432,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WDI9YF","text":"USGS data release","linkHelpText":"Bathymetry and velocity data from surveys at highway bridges crossing the Missouri and Mississippi Rivers on the periphery of Missouri, December 2008 through August 2018"},{"id":417431,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94M4US7","text":"USGS data release","linkHelpText":"Bathymetry and velocity data from surveys at highway bridges crossing the Missouri River between Kansas City and St. Louis, Missouri, January 2010 through May 2017"},{"id":417429,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F71C1VCC","text":"USGS data release","linkHelpText":"Bathymetry and velocity data from surveys at highway bridges crossing the Missouri and Mississippi Rivers near St. Louis, Missouri, October 2008 through May 2016"},{"id":417428,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5050/sir20235050.XML","linkFileType":{"id":8,"text":"xml"}},{"id":417427,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5050/sir20235050.pdf","text":"Report","size":"26.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5050"},{"id":417426,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5050/coverthb2.jpg"},{"id":417651,"rank":10,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235050/full"}],"country":"United States","state":"Illinois, Missouri","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.7316541640954,\n 39.115075665885314\n ],\n [\n -90.7316541640954,\n 38.326846254515004\n ],\n [\n -90.02883149031791,\n 38.326846254515004\n ],\n [\n -90.02883149031791,\n 39.115075665885314\n ],\n [\n -90.7316541640954,\n 39.115075665885314\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: May 25, 2023; Version 1.1: June 1, 2023","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Escalated wildfire activity within the western U.S. has widespread societal impacts and long-term consequences for the imperiled sagebrush (Artemisia spp.) biome. Shifts from historical fire regimes and the interplay between frequent disturbance and invasive annual grasses may initiate permanent state transitions as wildfire frequency outpaces sagebrush communities’ innate capacity to recover. Therefore, wildfire management is at the core of conservation plans for sagebrush ecosystems, especially critical habitat for species of conservation concern such as the greater sage-grouse (Centrocercus urophasianus; hereafter sage-grouse). Fuel breaks help facilitate wildfire suppression by modifying behavior through fuels modification and allowing safe access points for containment by firefighters. The Bureau of Land Management has proposed to roughly double the existing fuel break network in the western U.S., centered on the Great Basin. To our knowledge, no broad-scale examination of fuel break effectiveness or the environmental conditions under which fuel breaks are expected to be most effective has been conducted. We performed a retrospective assessment of probability of fuel break contributing to wildfire containment on recorded wildfire and fuel break interactions from 1985 to 2018 within the western U.S. We characterized environmental, fuels, and weather conditions within 500 m of wildfire contact, and within 5 km of the approaching wildfire. We used a binomial mixed model within a Bayesian framework to identify relationships between these variables and fuel break success. Fuel breaks were least successful in areas classified as having low resilience to disturbance and low resistance to invasion, in areas composed of primarily woody fuels, and when operating in high temperature and low precipitation conditions. Fuel breaks were most effective in areas where fine fuels dominated and in areas that were readily accessible. Maintenance history and fuel break type also contributed to the probability of containment. Overall results indicate a complex and sometimes paradoxical relationship between landscape characteristics that promote wildfire spread and those that impact fuel break effectiveness. Finally, we developed predictive maps of fuel break effectiveness by fuel break type to further elucidate these complex relationships and to inform urgently needed fuel break placement and maintenance priorities across the sagebrush biome.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2023.117903","usgsCitation":"Weise, C.L., Brussee, B.E., Crist, M.R., Shinneman, D.J., Coates, P.S., Aldridge, C.L., Heinrichs, J.A., and Ricca, M.A., 2023, A retrospective assessment of fuel break effectiveness for containing rangeland wildfires in the sagebrush biome: Journal of Environmental Management, v. 341, 117903, 15 p., https://doi.org/10.1016/j.jenvman.2023.117903.","productDescription":"117903, 15 p.","ipdsId":"IP-145845","costCenters":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":435315,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OPI1N3","text":"USGS data release","linkHelpText":"Predictive Maps of Fuel Break Effectiveness by Treatment Type and Underlying Resilience to Disturbance and Resistance to Invasion Across the Western U.S."},{"id":417439,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.72004583519214,\n 49.050273759992564\n ],\n [\n -121.72004583519214,\n 36.57497861377678\n ],\n [\n -103.96634293196246,\n 36.57497861377678\n ],\n [\n -103.96634293196246,\n 49.050273759992564\n ],\n [\n -121.72004583519214,\n 49.050273759992564\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"341","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Weise, Cali L.","contributorId":305785,"corporation":false,"usgs":false,"family":"Weise","given":"Cali","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":873833,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brussee, Brianne E. 0000-0002-2452-7101 bbrussee@usgs.gov","orcid":"https://orcid.org/0000-0002-2452-7101","contributorId":4249,"corporation":false,"usgs":true,"family":"Brussee","given":"Brianne","email":"bbrussee@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":873834,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research 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Center","active":false,"usgs":true}],"preferred":true,"id":873835,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":873836,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Heinrichs, Julie A. 0000-0001-7733-5034 jheinrichs@usgs.gov","orcid":"https://orcid.org/0000-0001-7733-5034","contributorId":193742,"corporation":false,"usgs":true,"family":"Heinrichs","given":"Julie","email":"jheinrichs@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":873837,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ricca, Mark A. 0000-0003-1576-513X mark_ricca@usgs.gov","orcid":"https://orcid.org/0000-0003-1576-513X","contributorId":139103,"corporation":false,"usgs":true,"family":"Ricca","given":"Mark","email":"mark_ricca@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":873747,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70243920,"text":"70243920 - 2023 - Elevated road segment (ERS) passage design may provide enhanced connectivity for amphibians, reptiles, and small mammals","interactions":[],"lastModifiedDate":"2023-05-25T14:37:47.454826","indexId":"70243920","displayToPublicDate":"2023-05-25T09:06:20","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3910,"text":"Frontiers in Ecology and Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"title":"Elevated road segment (ERS) passage design may provide enhanced connectivity for amphibians, reptiles, and small mammals","docAbstract":"Introduction: Designs for safe and effective road crossing structures for small animals are typically under-road microtunnels and culverts which have varying levels of effectiveness reported in the scientific literature. Many species, particularly migratory amphibians, may have limited ability to find and use passages if they are too far apart, resulting in substantial barrier effects.
Methods: We designed a novel open elevated passage (elevated road segment: ERS), similar to a low terrestrial bridge, that could theoretically be built to any length based upon species needs and movement characteristics. A 30 m length prototype ERS was installed along a forest road with a history of amphibian road mortality in Sierra National Forest, Fresno County, CA, USA. From 2018 to 2021, we monitored small animal activity under the ERS in relation to surrounding roadside and forest habitats using active infrared cameras.
Results: We documented a total of 8,815 unique use events, using species specific independence criteria, across 22 species of amphibians (3), reptiles (4), and small mammals (15). Poisson regression modeling of taxonomic group activity under the ERS, roadside and forest, showed that amphibian activity was highest in the forest habitat, no differences were observed for reptiles, and small mammal activity was highest under the ERS. However, mean activity estimates under the ERS were equal to or greater than the open roadside habitat for all 22 species, suggesting that adding cover objects, such as downed logs and vegetation may further enhance passage use.
Discussion: Overall, results showed that the design of the ERS crossing has potential to provide high connectivity for a wide range of amphibian, reptile, and small mammal species while reducing road mortality. ERS systems can also be used in areas with challenging terrain and other hydrological and environmental constraints. Incorporating current road ecology science, we provide supplemental ERS concept designs for secondary roads, primary roads and highways to help increase the options available for road mitigation planning for small animals.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2023.1145322","usgsCitation":"Brehme, C.S., Barnes, S., Ewing, B., Gould, P.R., Vaughan, C., Hobbs, M., Tornaci, C., Holm, S., Sheldon, H., Fiutak, J., and Fisher, R., 2023, Elevated road segment (ERS) passage design may provide enhanced connectivity for amphibians, reptiles, and small mammals: Frontiers in Ecology and Evolution, v. 11, 1145322, 16 p., https://doi.org/10.3389/fevo.2023.1145322.","productDescription":"1145322, 16 p.","ipdsId":"IP-152345","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":443347,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2023.1145322","text":"Publisher Index Page"},{"id":417436,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Fresno 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Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":873739,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70243695,"text":"sir20235005 - 2023 - Moderate flood level scenarios—Synthetic storm-driven flood-inundation maps for coastal communities in 10 New Jersey counties","interactions":[],"lastModifiedDate":"2026-03-02T17:59:11.412021","indexId":"sir20235005","displayToPublicDate":"2023-05-25T09:05:00","publicationYear":"2023","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":"2023-5005","displayTitle":"Moderate Flood Level Scenarios: Synthetic Storm-Driven Flood-Inundation Maps for Coastal Communities in 10 New Jersey Counties","title":"Moderate flood level scenarios—Synthetic storm-driven flood-inundation maps for coastal communities in 10 New Jersey counties","docAbstract":"The U.S. Geological Survey (USGS), in cooperation with the New Jersey Department of Environmental Protection (NJDEP) and the New Jersey Office of Emergency Management (NJOEM), created digital flood-inundation maps for approximately 1,430 square miles of the New Jersey coast and tidewaters through 10 coastal counties stretching from Cumberland County through Bergen County, New Jersey. The maps depict extent and depth estimates of coastal flooding corresponding to selected tidal elevations recorded by 25 real-time USGS tide gages located within the study area. The flood-inundation maps can be accessed through the USGS Interagency Flood Risk Management (InFRM) Flood Decision Support Toolbox (FDST).
Previously published modeled data were utilized from the coupled ADvanced CIRCulation Model (ADCIRC) and Simulating Waves Nearshore (SWAN) model. Simulated tropical storm events were selected based on parameters including landfall location or closest approach location, maximum wind speed, central pressure, and radii of winds. Two storm events were selected per tide gage providing two “scenarios” and accompanying inundation-map libraries for each gage. Flood-inundation maps reflect between 9 to 30 stages (elevations) at each tide gage that correspond to areal extents and depths for ADCIRC-SWAN storm time steps extracted from modeled hydrographs at the gage locations. Water-surface elevations from ADCIRC-SWAN node points extending through each tide gage station extent were used to interpolate a water surface. Combining these surfaces with a geographic information system (GIS) topobathymetric digital elevation model (TBDEM) delineated the area flooded by coastal waters at each tide gage elevation.
The availability of these maps to visualize potential inundation for selected water levels along with real-time water level data available online from USGS tide gages, coastal impact statements, and forecasted tide elevations from the National Weather Service (NWS) will provide emergency management personnel and residents with a link between numeric and text warning information and images of estimated inundation extents in their community. User selected display of inundation allows early response activities to NWS forecasted water level elevations or mitigation planning by selecting targeted water levels and planning critical pre-flood activities such as building elevations, early traffic pattern changes because of neighborhood building inundation levels, improved understanding about when major road access is affected, as well as for post-flood recovery efforts.
A subsequent analysis of several community metrics including total structures, structure density, percent of buildings inundated, and roads and bridges affected by flooding was used to evaluate moderate flooding impacts among the mapped station extents. Initial comparisons are presented to show the variability of these characteristics within each mapped station extent then extended to evaluate impacts from moderate flooding on these same areas. The analysis used simulated inundation layers at the moderate flood stage to investigate the magnitude of inundation on building structures and major roads among the mapped station extents. Experimental equations were developed to begin testing if a mathematical equation could help identify communities that were disproportionately impacted at moderate flood stage. The community analysis of impacts to moderate flooding based on these inundation scenario maps should provide community leaders and local and state planning officials with tools to better visualize and understand how flooding begins to disrupt and damage building structures and major roads as a surrogate for direct increased risk to human life and property.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235005","collaboration":"Prepared in cooperation with New Jersey Department of Environmental Protection","usgsCitation":"Suro, T.P., Niemoczynski, M.J., Boetsma, A., and Niemoczynski, L.M., 2023, Moderate flood level scenarios—Synthetic storm-driven flood-inundation maps for coastal communities in 10 New Jersey counties: U.S. Geological Survey Scientific Investigations Report 2023–5005, 49 p., https://doi.org/10.3133/sir20235005.","productDescription":"Report: viii, 49 p.; Data Release","numberOfPages":"49","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-136043","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":417140,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5005/images/"},{"id":500685,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114740.htm","linkFileType":{"id":5,"text":"html"}},{"id":417141,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RVF9P8","text":"USGS data release","linkHelpText":"Synthetic storm-driven flood-inundation grids for coastal communities in 10 New Jersey counties"},{"id":417139,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5005/sir20235005.XML"},{"id":417138,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235005/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5005"},{"id":417137,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5005/sir20235005.pdf","text":"Report","size":"21.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5005"},{"id":417136,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5005/coverthb.jpg"}],"country":"United States","state":"New Jersey","county":"Atlantic County, Bergen County, Cape May County, Cumberland County, Essex County, Middlesex County, Monmouth County, Ocean County, Salem County, Union County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.1277080228522,\n 40.638935692142724\n ],\n [\n -74.41159623268268,\n 40.50386433220055\n ],\n [\n -74.13706697482502,\n 40.31620957676867\n ],\n [\n -74.07155431101805,\n 40.27813984647429\n ],\n [\n -74.2088189399465,\n 39.95129023758125\n ],\n [\n -74.58317622656678,\n 39.43518753081048\n ],\n [\n -74.78283386864535,\n 39.20591467723193\n ],\n [\n -75.42236225342704,\n 39.6781159291034\n ],\n [\n -75.58146443695806,\n 39.64689563243181\n ],\n [\n -75.51907142380868,\n 39.57479497276381\n ],\n [\n -75.55650723169862,\n 39.449642684385594\n ],\n [\n -75.2258242620061,\n 39.23250052123328\n ],\n [\n -75.13535439293969,\n 39.15996996348281\n ],\n [\n -75.03864522255803,\n 39.19382687463093\n ],\n [\n -74.92009849757379,\n 39.14303539458916\n ],\n [\n -75.00120941466811,\n 38.92494240360199\n ],\n [\n -74.90138059362884,\n 38.89338447021902\n ],\n [\n -74.52078321341743,\n 39.249413543217884\n ],\n [\n -74.25873255818952,\n 39.44482463320827\n ],\n [\n -74.05907491611174,\n 39.72851891657447\n ],\n [\n -73.93740900673615,\n 40.3827789234833\n ],\n [\n -73.99044306791338,\n 40.49911897812299\n ],\n [\n -74.17762210736144,\n 40.48488407551238\n ],\n [\n -74.0372378277754,\n 40.579726378448925\n ],\n [\n -74.06219503303558,\n 40.65787019904499\n ],\n [\n -74.1277080228522,\n 40.638935692142724\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville, NJ 08648
The tire wear transformation product 6PPD-quinone (6PPDQ) has been implicated as the causative factor for broad scale mortality events for coho salmon in the Pacific Northwest. Highly variable sensitivity to 6PPDQ in closely related salmonids complicates efforts to evaluate the broader toxicological impacts to aquatic ecosystems. Our goals were to (1) validate the large range of in vivo species sensitivities reported for coho, Chinook, and sockeye salmon and (2) develop an in vitro platform for assessing 6PPDQ toxicity. In vivo studies confirmed the acute sensitivity of juvenile coho (12 h LC50 = 80.4 ng/L) and demonstrated that sockeye salmon were not vulnerable to mortality. Chinook salmon were sensitive to 6PPDQ mortality at initial concentrations >25 μg/L, ∼10-fold greater than reported environmental measurements. In vitro, the coho salmon cell line CSE-119 was acutely sensitive to 6PPDQ (metabolic EC50 = 7.9 μg/L, cytotoxicity EC50 = 6.1 μg/L). Analogous Chinook (CHSE-214) and sockeye salmon (SSE-5) cell lines were nonresponsive in both assays, and rainbow trout RTG-2 cells began showing metabolic effects at 68 μg/L (EC5). Recreation of species-specific 6PPDQ sensitivity in vitro implicates conserved modes of action in CSE-119 that could be utilized for mechanistic studies of 6PPDQ toxicity and screening of other PPD transformation products.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.estlett.3c00196","usgsCitation":"Greer, J.B., Dalsky, E.M., Lane, R.F., and Hansen, J.D., 2023, Establishing an in vitro model to assess the toxicity of 6PPD-quinone and other tire wear transformation products: Environmental Science and Technology Letters, v. 10, no. 6, p. 533-537, https://doi.org/10.1021/acs.estlett.3c00196.","productDescription":"5 p.","startPage":"533","endPage":"537","ipdsId":"IP-151440","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":443356,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.estlett.3c00196","text":"Publisher Index Page"},{"id":435317,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98HOI3I","text":"USGS data release","linkHelpText":"Assessing cell line models for species differences in 6PPD-quinone sensitivity"},{"id":417424,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"6","noUsgsAuthors":false,"publicationDate":"2023-05-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Greer, Justin Blaine 0000-0001-6660-9976","orcid":"https://orcid.org/0000-0001-6660-9976","contributorId":265183,"corporation":false,"usgs":true,"family":"Greer","given":"Justin","email":"","middleInitial":"Blaine","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":873696,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dalsky, Ellie Maureen 0000-0001-8299-7198","orcid":"https://orcid.org/0000-0001-8299-7198","contributorId":265182,"corporation":false,"usgs":true,"family":"Dalsky","given":"Ellie","email":"","middleInitial":"Maureen","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":873697,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lane, Rachael F. 0000-0001-9202-0612","orcid":"https://orcid.org/0000-0001-9202-0612","contributorId":222471,"corporation":false,"usgs":true,"family":"Lane","given":"Rachael","email":"","middleInitial":"F.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":873698,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hansen, John D. 0000-0002-3006-2734","orcid":"https://orcid.org/0000-0002-3006-2734","contributorId":220725,"corporation":false,"usgs":true,"family":"Hansen","given":"John","middleInitial":"D.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":873699,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70243938,"text":"70243938 - 2023 - Application of habitat association models across regions: Useful explanatory power retained in wetland bird case study","interactions":[],"lastModifiedDate":"2023-05-25T12:05:21.55694","indexId":"70243938","displayToPublicDate":"2023-05-25T06:46:14","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Application of habitat association models across regions: Useful explanatory power retained in wetland bird case study","docAbstract":"Species often exhibit regionally specific habitat associations, so habitat association models developed in one region might not be accurate or even appropriate for other regions. Three programs to survey wetland-breeding birds covering (respectively) Great Lakes coastal wetlands, inland Great Lakes wetlands, and the Prairie Pothole Region offer an opportunity to test whether regionally specific models of habitat use by wetland-obligate breeding birds are transferrable across regions. We first developed independent, regional population density models for four species of wetland-obligate birds: Pied-billed Grebe (Podilymbus podiceps), Virginia Rail (Rallus limicola), Sora (Porzana carolina), and American Bittern (Botaurus lentiginosus). We then used adjusted pseudo-R2 values to compare the amount of variation explained by each model when applied to data collected in each of the three regions. Although certain habitat characteristics, such as emergent vegetation and wetland area, were consistently important across regions, models for each species differed by region—both in variables selected for inclusion and often in the directionality of relationships for common variables—indicating that habitat associations for these species are regionally specific. When we applied a model developed in one region to data collected in another region, we found that explanatory power was reduced in most (71%) models. Therefore, we suggest that ecological analyses should emphasize regionally specific habitat association models whenever possible. Nonetheless, models created from inland Great Lakes wetland data had higher median explanatory power when applied to other regions, and the amount of explanatory power lost by other transferred models was relatively small. Thus, while regionally specific habitat association models are preferable, in the absence of reliable regional data, habitat association models developed in one region may be applied to another region, but the results need to be cautiously interpreted. Additionally, we found that median explanatory power was higher when local-scale habitat characteristics were included in the models, indicating that regionally specific models should ideally be based on a combination of local- and landscape-scale habitat characteristics. Conservation practitioners can leverage such regionally specific models and associated monitoring data to help prioritize areas for management activities that contribute to regional conservation efforts.
","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.4499","usgsCitation":"Elliott, L.H., Bracey, A.M., Niemi, G.J., Johnson, D., Gehring, T.M., Gnass Giese, E.E., Fiorino, G.E., Howe, R.W., Lawrence, G.A., Norment, C.J., Tozer, D.C., and Igl, L., 2023, Application of habitat association models across regions: Useful explanatory power retained in wetland bird case study: Ecosphere, v. 14, no. 5, e4499, 19 p., https://doi.org/10.1002/ecs2.4499.","productDescription":"e4499, 19 p.","ipdsId":"IP-109710","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":443363,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4499","text":"Publisher Index Page"},{"id":417421,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Illinois, Indiana, Michigan, Minnesota, New York, North Dakota, 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Bay","active":true,"usgs":false}],"preferred":false,"id":873809,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fiorino, Giuseppe E. 0000-0002-1569-0767","orcid":"https://orcid.org/0000-0002-1569-0767","contributorId":305775,"corporation":false,"usgs":false,"family":"Fiorino","given":"Giuseppe","email":"","middleInitial":"E.","affiliations":[{"id":36681,"text":"Environment and Climate Change Canada","active":true,"usgs":false}],"preferred":false,"id":873810,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Howe, Robert W.","contributorId":197823,"corporation":false,"usgs":false,"family":"Howe","given":"Robert","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":873811,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lawrence, Gregory A. 0000-0001-8854-8123","orcid":"https://orcid.org/0000-0001-8854-8123","contributorId":305777,"corporation":false,"usgs":false,"family":"Lawrence","given":"Gregory","middleInitial":"A.","affiliations":[{"id":36972,"text":"University of British Columbia","active":true,"usgs":false}],"preferred":false,"id":873812,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Norment, Christopher J. 0000-0002-5080-0271","orcid":"https://orcid.org/0000-0002-5080-0271","contributorId":305778,"corporation":false,"usgs":false,"family":"Norment","given":"Christopher","email":"","middleInitial":"J.","affiliations":[{"id":66292,"text":"State University of New York-Brockport","active":true,"usgs":false}],"preferred":false,"id":873813,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Tozer, Douglas C. 0000-0001-9516-876X","orcid":"https://orcid.org/0000-0001-9516-876X","contributorId":305779,"corporation":false,"usgs":false,"family":"Tozer","given":"Douglas","email":"","middleInitial":"C.","affiliations":[{"id":66293,"text":"Long Point Waterfowl and Wetlands Research Program, Birds Canada","active":true,"usgs":false}],"preferred":false,"id":873814,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Igl, Lawrence 0000-0003-0530-7266","orcid":"https://orcid.org/0000-0003-0530-7266","contributorId":221711,"corporation":false,"usgs":true,"family":"Igl","given":"Lawrence","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":873815,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70244170,"text":"70244170 - 2023 - Introduction to the digitization of seismic data: A user’s guide","interactions":[],"lastModifiedDate":"2023-06-28T15:26:29.061119","indexId":"70244170","displayToPublicDate":"2023-05-25T06:46:08","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Introduction to the digitization of seismic data: A user’s guide","docAbstract":"Modern seismic data are collected, distributed, and analyzed using digital formats, and this has become a standard for the field. Although most modern seismometers still make use of analog electronic circuits, their data are converted from an analog voltage output to time‐tagged counts by way of digitization. Although much of the digitization process is not complicated to conceptualize, there is a fair bit of jargon in digitizer specifications, and a few pitfalls that can arise in the processes of recording and analyzing ground‐motion data. In this article, we review some of the fundamental physical properties of data acquisition systems and the basic steps in digitizing data from an analog instrument (specifically a seismometer). We then briefly discuss the digitization process and some of the key properties needed to make these data useful for seismological applications. Finally, we discuss some of the filtering processes that naturally arise from digitization and how it can affect the processing workflow. The end goal is to provide a user guide that will enable seismologists to have a working knowledge of the digitization process. We focus on aspects central to seismological applications and have tried to avoid getting bogged down in signal processing formalism.
We evaluated wildlife population health from the perspective of statistical means vs. variances. We outlined the choices necessary to provide the framework for our study. These consisted of spatial and temporal boundaries (e.g., choice of sentinel species, populations, time frame), measurement techniques (molecular to population level), and appropriate statistical analyses. We chose to assess the health of 19 sea otter populations, located in the north Pacific from the Aleutian Islands, AK, to Santa Barbara, CA, and varying in population growth rates and length of occupancy. Our focal metric was gene expression (i.e., mRNA transcripts) data that we had previously generated across sea otter populations as a measure of population health. We used statistical methods with different approaches (i.e., means vs. variances) and examined the subsequent interpretive outcomes and how these influence our assessment of “health.” Interpretations based on analyses using variances versus means overlapped to some degree. In general, sea otter populations with low variation in gene expression were limited by food resources and at or near carrying capacity. In populations where the variation in gene expression was moderate or high, four out of five populations were increasing in abundance, or had been recently increasing. Where we had additional information on sources of stressors at the level of the population, we were able to draw inferences from those stressors to specific gene expression results. For example, gene expression patterns of sea otters from Western Prince William Sound were consistent with long term exposure to petroleum hydrocarbons, whereas in Kachemak Bay, patterns were consistent with exposure to algal toxins. Ultimately, determination of population or ecosystem health will be most informative when multiple metrics are examined across disciplines in the context of specific scenarios and goals.
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Keith 0000-0002-3108-808X keith_miles@usgs.gov","orcid":"https://orcid.org/0000-0002-3108-808X","contributorId":196,"corporation":false,"usgs":true,"family":"Miles","given":"A.","email":"keith_miles@usgs.gov","middleInitial":"Keith","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":873756,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70243887,"text":"70243887 - 2023 - Recognizing political influences in participatory social-ecological systems modeling","interactions":[],"lastModifiedDate":"2023-05-24T19:19:55.098091","indexId":"70243887","displayToPublicDate":"2023-05-24T14:05:52","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":14448,"text":"Socio-Environmental Systems Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Recognizing political influences in participatory social-ecological systems modeling","docAbstract":"Stakeholder participation in social-ecological systems (SES) modeling is increasingly considered a desirable way to elicit diverse sources of knowledge about SES behavior and to promote inclusive decision-making in SES. Understanding how participatory modeling processes function in the context of long-term adaptive management of SES may allow for better design of participatory processes to achieve the intended outcomes of inclusionary knowledge, representativeness, and social learning, while avoiding unintended outcomes. Long-term adaptive management contexts often include political influences -- attempts to shift or preserve power structures and authority, and efforts to represent the political and economic interests of stakeholders -- in the computer models that are used to shape policy making and implementation. In this research, we examine a period that included a major transition in the watershed model used for management of the Chesapeake Bay in the United States. The Chesapeake Bay watershed model has been in development since the 1980s, and is considered by many to be an exemplary case of participatory modeling. We use documentary analysis and interviews with participants involved in the model application and development transition to reveal a variety of ways in which participatory modeling may be subject to different kinds of political influences, some of which resulted in unintended outcomes, including: perceptions of difficulty updating the model in substantive ways, “gaming” of the model/participatory process by stakeholders, and increasing resistance against considering uncertainty in the system not captured by the model. This research suggests unintended or negative outcomes may be associated with both participatory decision-making and stakeholder learning even though they are so often touted as the benefits of participatory modeling. We end with a hypothesis that further development of a theory of computer model governance to bridge model impact and broader theories of environmental governance at the science-policy interface may result in improved SES modeling outcomes.
","language":"English","publisher":"International Environmental Modelling and Software Society","doi":"10.18174/sesmo.18509","usgsCitation":"Lim, T.C., Glynn, P.D., Shenk, G.W., Bitterman, P., Guillaume, J., Little, J., and Webster, D.G., 2023, Recognizing political influences in participatory social-ecological systems modeling: Socio-Environmental Systems Modelling, v. 5, 18509, 25 p., https://doi.org/10.18174/sesmo.18509.","productDescription":"18509, 25 p.","ipdsId":"IP-147282","costCenters":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true},{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"links":[{"id":443374,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.18174/sesmo.18509","text":"Publisher Index Page"},{"id":417408,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, Virginia","otherGeospatial":"Chesapeake Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.51635702888042,\n 37.912980532966955\n ],\n [\n -75.71280241735982,\n 38.58150889183236\n ],\n [\n -75.95290230901038,\n 38.64120407293515\n ],\n [\n -75.77828418591723,\n 39.269223836039686\n ],\n [\n -75.64732059359784,\n 39.57273156992801\n ],\n [\n -75.86559324746412,\n 39.723989493914246\n ],\n [\n -76.17117496287733,\n 39.757557416910515\n ],\n [\n -76.520411209063,\n 39.438002411462605\n ],\n [\n -76.65137480138303,\n 39.18468171617553\n ],\n [\n -76.79325202639652,\n 38.717881959277776\n ],\n [\n -77.08792010911627,\n 38.74342299584799\n ],\n [\n -77.39350182452881,\n 38.59003985313274\n ],\n [\n -77.38258819183537,\n 37.12528463444586\n ],\n [\n -76.11660679941075,\n 36.74144568362692\n ],\n [\n -75.51635702888042,\n 37.912980532966955\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"5","noUsgsAuthors":false,"publicationDate":"2023-05-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Lim, Theodore C. 0000-0002-7896-4964","orcid":"https://orcid.org/0000-0002-7896-4964","contributorId":305702,"corporation":false,"usgs":true,"family":"Lim","given":"Theodore","email":"","middleInitial":"C.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":873621,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Glynn, Pierre D. 0000-0001-8804-7003","orcid":"https://orcid.org/0000-0001-8804-7003","contributorId":305703,"corporation":false,"usgs":false,"family":"Glynn","given":"Pierre","email":"","middleInitial":"D.","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":873622,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shenk, Gary W. 0000-0001-6451-2513","orcid":"https://orcid.org/0000-0001-6451-2513","contributorId":225440,"corporation":false,"usgs":true,"family":"Shenk","given":"Gary","email":"","middleInitial":"W.","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873623,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bitterman, Patrick 0000-0002-2715-606X","orcid":"https://orcid.org/0000-0002-2715-606X","contributorId":305704,"corporation":false,"usgs":false,"family":"Bitterman","given":"Patrick","email":"","affiliations":[{"id":36892,"text":"University of Nebraska","active":true,"usgs":false}],"preferred":false,"id":873624,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Guillaume, Joseph H. A.","contributorId":248835,"corporation":false,"usgs":false,"family":"Guillaume","given":"Joseph H. A.","affiliations":[{"id":50037,"text":"Water and Development Research Group, Aalto University, Finland","active":true,"usgs":false}],"preferred":false,"id":873625,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Little, John","contributorId":305705,"corporation":false,"usgs":false,"family":"Little","given":"John","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":873626,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Webster, D. G.","contributorId":305706,"corporation":false,"usgs":false,"family":"Webster","given":"D.","email":"","middleInitial":"G.","affiliations":[{"id":36404,"text":"Dartmouth University","active":true,"usgs":false}],"preferred":false,"id":873627,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70243873,"text":"70243873 - 2023 - Sensitivity of the Penman-Monteith reference evapotranspiration equation to meteorological variables for Puerto Rico","interactions":[],"lastModifiedDate":"2023-05-24T19:04:03.81162","indexId":"70243873","displayToPublicDate":"2023-05-24T13:55:34","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10778,"text":"Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Sensitivity of the Penman-Monteith reference evapotranspiration equation to meteorological variables for Puerto Rico","docAbstract":"Spatiotemporal variations in reference evapotranspiration (ETo) are sensitive to the meteorological data used in its estimation. The sensitivity of the ASCE standardized ETo equation to meteorological variables from GOES-PRWEB dataset was evaluated for the island of Puerto Rico. Island wide, ETo is most sensitive to daily mean relative humidity (RHmean), followed by solar radiation, daily maximum (Tmax) and minimum (Tmin) air temperatures, and wind speed with average absolute relative sensitivity coefficients (SCs) of 0.98, 0.57, 0.50, 0.27, and 0.12, respectively. The derived SCs guided the prioritization of bias correction of meteorological data for ETo estimation from two downscaled climate models (CNRM and CESM). The SCs were applied to evaluate how meteorological variables contribute to model errors and projected future changes in ETo from 1985–2005 to 2040–2060 at irrigated farms in the south. Both models project a 5.6% average increase in annual ETo due to projected increases in Tmax and Tmin and a decrease in RHmean. Despite ETo being most sensitive to relative changes in RHmean, the contributions from RHmean, Tmax, and Tmin to future changes in ETo are similar. CESM projects increases in ETo in March, November, and December, increasing the potential for crop water stress. Study limitations are discussed.
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Rico","active":true,"usgs":false}],"preferred":false,"id":873577,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70243904,"text":"70243904 - 2023 - Assessing individual movement, habitat use, and behavior of non-breeding marine birds in relation to prey availability in the US Atlantic","interactions":[],"lastModifiedDate":"2023-05-24T18:53:49.243988","indexId":"70243904","displayToPublicDate":"2023-05-24T13:24:43","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2663,"text":"Marine Ecology Progress Series","active":true,"publicationSubtype":{"id":10}},"title":"Assessing individual movement, habitat use, and behavior of non-breeding marine birds in relation to prey availability in the US Atlantic","docAbstract":"Resource availability is a key factor driving marine bird movements and distributions, but direct information on prey availability is difficult to obtain at relevant scales. We present novel methods for describing multi-scale trophic associations, combining movement analyses of marine birds with estimates of forage fish surface aggregations from digital aerial survey data and species occupancy from bottom trawl survey data. We analyzed satellite telemetry data from northern gannets Morus bassanus, red-throated loons Gavia stellata, and long-tailed ducks Clangula hyemalis in the US Atlantic during the non-breeding period. Using discrete-time hidden Markov models to distinguish area-restricted (i.e. putative foraging) from transit movements, we examined how environmental factors influence movement, and how forage fish species distributions and surface aggregations influence habitat use by gannets and loons that have greater dietary reliance. Our results suggest that chlorophyll a concentration significantly affected movement behavior across species, highlighting the importance of higher-productivity areas around estuaries during colder months when regional productivity is low. Though variable across species and seasons, spatial cross-correlation analysis revealed that herring species (Family Clupeidae), including Atlantic menhaden Brevoortia tyrannus, may be important resources; it also showed positive spatial correlations with forage fish aggregations. This suggests that prey patch dynamics and factors driving aggregation formation may be as important as species composition. However, spatial patterns were generally low (<0.3), suggesting a mismatch in spatiotemporal resolution, exemplifying the challenges in quantifying trophic relationships in marine systems. Disentangling predator-prey relationships is critical to understanding the mechanisms driving marine bird behavior in rapidly changing marine systems.
","language":"English","publisher":"Inter-Research","doi":"10.3354/meps14316","usgsCitation":"Gulka, J., Berlin, A., Friedland, K., Gilbert, A., Goetsch, C., Montevecchi, W., Perry, M., Stenhouse, I., Williams, K.A., and Adams, E.A., 2023, Assessing individual movement, habitat use, and behavior of non-breeding marine birds in relation to prey availability in the US Atlantic: Marine Ecology Progress Series, v. 711, p. 77-99, https://doi.org/10.3354/meps14316.","productDescription":"23 p.","startPage":"77","endPage":"99","ipdsId":"IP-150930","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":443380,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/meps14316","text":"Publisher Index Page"},{"id":417406,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Atlantic Ocean","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.25987342428122,\n 25.49083684996951\n ],\n [\n -60.361707727220605,\n 25.49083684996951\n ],\n [\n -60.361707727220605,\n 45.94936874492856\n ],\n [\n -81.25987342428122,\n 45.94936874492856\n ],\n [\n -81.25987342428122,\n 25.49083684996951\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"711","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gulka, Julia","contributorId":303827,"corporation":false,"usgs":false,"family":"Gulka","given":"Julia","email":"","affiliations":[],"preferred":false,"id":873676,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Berlin, Alicia 0000-0002-5275-3077","orcid":"https://orcid.org/0000-0002-5275-3077","contributorId":216023,"corporation":false,"usgs":true,"family":"Berlin","given":"Alicia","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":873677,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Friedland, Kevin","contributorId":292483,"corporation":false,"usgs":false,"family":"Friedland","given":"Kevin","affiliations":[],"preferred":false,"id":873678,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gilbert, Andrew","contributorId":194560,"corporation":false,"usgs":false,"family":"Gilbert","given":"Andrew","affiliations":[],"preferred":false,"id":873679,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Goetsch, Chandra","contributorId":214868,"corporation":false,"usgs":false,"family":"Goetsch","given":"Chandra","email":"","affiliations":[],"preferred":false,"id":873680,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Montevecchi, William","contributorId":171895,"corporation":false,"usgs":false,"family":"Montevecchi","given":"William","affiliations":[{"id":26965,"text":"Memorial University of Newfoundland","active":true,"usgs":false}],"preferred":false,"id":873681,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Perry, Matthew","contributorId":194761,"corporation":false,"usgs":false,"family":"Perry","given":"Matthew","affiliations":[],"preferred":false,"id":873682,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Stenhouse, Iain","contributorId":194567,"corporation":false,"usgs":false,"family":"Stenhouse","given":"Iain","affiliations":[],"preferred":false,"id":873683,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Williams, Kate A.","contributorId":219079,"corporation":false,"usgs":false,"family":"Williams","given":"Kate","email":"","middleInitial":"A.","affiliations":[{"id":37436,"text":"Biodiversity Research Institute","active":true,"usgs":false}],"preferred":false,"id":873684,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Adams, Evan A.","contributorId":204599,"corporation":false,"usgs":false,"family":"Adams","given":"Evan","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":873685,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70243860,"text":"ofr20231044 - 2023 - ECCOE Landsat quarterly Calibration and Validation report—Quarter 4, 2022","interactions":[],"lastModifiedDate":"2023-05-25T13:29:39.490622","indexId":"ofr20231044","displayToPublicDate":"2023-05-24T11:48:52","publicationYear":"2023","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":"2023-1044","displayTitle":"ECCOE Landsat Quarterly Calibration and Validation Report—Quarter 4, 2022","title":"ECCOE Landsat quarterly Calibration and Validation report—Quarter 4, 2022","docAbstract":"The U.S. Geological Survey Earth Resources Observation and Science Calibration and Validation (Cal/Val) Center of Excellence (ECCOE) focuses on improving the accuracy, precision, calibration, and product quality of remote-sensing data, leveraging years of multiscale optical system geometric and radiometric calibration and characterization experience. The ECCOE Landsat Cal/Val Team continually monitors the geometric and radiometric performance of active Landsat missions and makes calibration adjustments, as needed, to maintain data quality at the highest level.
This report provides observed geometric and radiometric analysis results for Landsats 7–8 for quarter 4 (October–December) of 2022. All data used to compile the Cal/Val analysis results presented in this report are freely available from the U.S. Geological Survey EarthExplorer website: https://earthexplorer.usgs.gov.
One specific activity that the ECCOE Landsat Cal/Val Team closely monitored was the lowering of the Landsat 7 orbit. On April 6, 2022, the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensor was placed into standby mode, and a series of spacecraft burns was completed through the month of April to lower the satellite’s orbit by 8 kilometers. Imaging resumed at a lower orbit of 697 kilometers on May 5, 2022, extending the science mission. Additional information about the Landsat 7 orbit lowering is here: https://www.usgs.gov/centers/eros/news/landsat-7-lowered-standard-landsat-orbit#:~:text=The%20satellite's%20primary%20science%20mission%20has%20ended&text=On%20April%206%2C%202022%2C%20the,satellite's%20orbit%20by%208%20kilometers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231044","usgsCitation":"Haque, M.O., Rengarajan, R., Lubke, M., Hasan, M.N., Shrestha, A., Tuli, F.T.Z., Shaw, J.L., Denevan, A., Franks, S., Micijevic, E., Choate, M.J., Anderson, C., Thome, K., Kaita, E., Barsi, J., Levy, R., and Miller, J., 2023, ECCOE Landsat quarterly Calibration and Validation report—Quarter 4, 2022: U.S. Geological Survey Open-File Report 2023–1044, 39 p., https://doi.org/10.3133/ofr20231044.","productDescription":"Report: vii, 39 p.; Dataset","numberOfPages":"52","onlineOnly":"Y","ipdsId":"IP-149560","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":417400,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231044/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":417365,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov","text":"USGS database","linkHelpText":"—EarthExplorer"},{"id":417362,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1044/ofr20231044.pdf","text":"Report","size":"4.01 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023–1044"},{"id":417361,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1044/coverthb.jpg"},{"id":417363,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1044/ofr20231044.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":417364,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1044/images"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
The northern spotted owl (Strix occidentalis caurina; hereinafter NSO) was listed as “threatened” under the Endangered Species Act in 1990 and population declines have continued since that listing. Given the species’ protected status, any proposed activities on Federal lands that might impact NSO require consultation with U.S. Fish and Wildlife Service and part of that consultation often includes surveys to determine presence and occupancy status of the species in the proposed activity area. The objective of this report is to present study-area specific estimates of the probability of detection for NSO pairs from twelve 2-week seasonal survey periods using data from a recent range-wide meta-analysis. These estimates were a by-product of pair occupancy modeling but might provide insight into potential changes in the effect of the invasive barred owl on NSO detection rates. We used two-species multi-season occupancy models to estimate the probability of detection for NSOs on each of 11 study areas for each 2-week survey period and relative to the range-wide effect of barred owl presence or absence. Detection probabilities within the season generally increased from the earliest surveys in March through mid-season, decreasing again in the late season on five study areas. For three other study areas, detection rates were highest during the earliest survey periods in late March or early April. Estimates of cumulative seasonal detection of NSO (across a maximum of six within-season surveys) were less than 0.90 when barred owls (BO) were present on all but one study area, regardless of when surveys were conducted within a season. However, despite low detection rates, the probability that a territory was occupied when an NSO pair was not detected over six within-season surveys was also very low. When BO are not present on a territory, a six-survey protocol had a high probability of detecting an NSO pair at least once during the season on all study areas, except for the very lowest per-survey estimates. Conducting most surveys earlier in the season, when the probability of detecting pairs is highest (through May on most areas) could improve seasonal detection rates. However, alternative methods of population monitoring—such as the use of passive acoustic recorders—may be needed to continue monitoring NSO for research and management.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231012","collaboration":"Prepared in cooperation with Oregon State University","usgsCitation":"Dugger, K.M., Franklin, A.B., Lesmeister, D.B., Davis, R.J., Wiens, J.D., White, G.C., Nichols, J.D., Hines, J.E., Yackulic, C.B., Schwarz, C.J., Ackers, S.H., Andrews, L.S., Bailey, L.L., Bown, R., Burgher, J., Burnham, K.P., Carlson, P.C., Chestnut, T., Conner, M.M., Dilione, K.E., Forsman, E.D., Gremel, S.A., Hamm, K.A., Herter, D.R., Higley, J.M., Horn, R.B., Jenkins, J.M., Kendall, W.L., Lamphear, D.W., McCafferty, C., McDonald, T.L., Reid, J.A., Rockweit, J.T., Simon, D.C., Sovern, S.G., Swingle, J.K., and Wise, H., 2023, Estimating northern spotted owl (Strix occidentalis caurina) pair detection probabilities based on call-back surveys associated with long-term mark-recapture studies, 1993–2018: U.S. Geological Survey Open-File Report 2023–1012, 25 p., https://doi.org/10.3133/ofr20231012.","productDescription":"vii, 25 p.","onlineOnly":"Y","ipdsId":"IP-133003","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":417167,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231012/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2023-1012"},{"id":417166,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1012/ofr20231012.pdf","text":"Report","size":"6.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1012"},{"id":417169,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1012/ofr20231012.XML"},{"id":417168,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1012/images"},{"id":417165,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1012/coverthb.jpg"}],"country":"United States","state":"California, Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.82419135781177,\n 37.90972622681076\n ],\n [\n -122.3277779102568,\n 38.08861577320431\n ],\n [\n -120.925210233316,\n 42.44504507790097\n ],\n [\n -120.0290356856878,\n 48.995370896990295\n ],\n [\n -123.35793848445479,\n 48.94274756685837\n ],\n [\n -123.44660126094462,\n 48.247927989659445\n ],\n [\n -125.06690973675748,\n 48.590141468479914\n ],\n [\n -124.10299464056828,\n 46.115944811333236\n ],\n [\n -124.32184346408252,\n 43.795550298890845\n ],\n [\n -124.71979791193871,\n 42.67568668540227\n ],\n [\n -124.10920825253021,\n 41.43162232110191\n ],\n [\n -124.53261803772267,\n 40.21134172936314\n ],\n [\n -123.80095675617886,\n 38.77121416707598\n ],\n [\n -122.82419135781177,\n 37.90972622681076\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Forest and Rangeland Ecosystem Science Center
777 NW 9th Street
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Surveys are important tools in business resilience and recovery research because of their ability to capture disaggregated economic information; however, they can be difficult and costly due to business operational dynamics and the larger challenges of disaster research. The COVID-19 pandemic serves as a recent example where demand for business data was high across both research and practice. Yet, the methods and modes for collecting data were limited due to safety, health, and ethical concerns. This research seeks to address the lack of tailored guidance for conducting business resilience and recovery surveys by collecting and synthesizing instruments and best practices from previous survey efforts. These previous surveys were undertaken by a diverse group of organizations with varied research questions, objectives, and hazard events of interest. This paper discusses six broad lessons: clearly define purpose, objectives, and concepts; recognize that response rates will be low, consider disaster dynamics in the research design, address bias that can be exacerbated by disasters, take care to acknowledge the unique ethical considerations of disaster resilience surveys in the business and economic context, and verify and validate data at all stages of the survey process. These lessons, in addition to the published instruments themselves, support researchers or practitioners who wish to conduct their own business resilience and recovery surveys in the future.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ijdrr.2023.103743","usgsCitation":"Watson, M., Brown, C., Handmer, J., Kroll, C., Wein, A., Helgeson, J., Rose, A., Dormady, N., and Kim, J., 2023, Methods and lessons for business resilience and recovery surveys: International Journal of Disaster Risk Reduction, v. 93, 103743, 14 p., https://doi.org/10.1016/j.ijdrr.2023.103743.","productDescription":"103743, 14 p.","ipdsId":"IP-143482","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":443384,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ijdrr.2023.103743","text":"Publisher Index Page"},{"id":417437,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"93","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Watson, Maria","contributorId":305734,"corporation":false,"usgs":false,"family":"Watson","given":"Maria","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":873757,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brown, Charlotte","contributorId":305735,"corporation":false,"usgs":false,"family":"Brown","given":"Charlotte","email":"","affiliations":[{"id":66278,"text":"Resilient Organisations Ltd","active":true,"usgs":false}],"preferred":false,"id":873758,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Handmer, John","contributorId":305736,"corporation":false,"usgs":false,"family":"Handmer","given":"John","email":"","affiliations":[{"id":66279,"text":"International Institute for Applied Systems Analysis (IIASA)","active":true,"usgs":false}],"preferred":false,"id":873759,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kroll, Cynthia","contributorId":305737,"corporation":false,"usgs":false,"family":"Kroll","given":"Cynthia","affiliations":[{"id":66280,"text":"Cynthia Kroll Consulting","active":true,"usgs":false}],"preferred":false,"id":873760,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wein, Anne 0000-0002-5516-3697 awein@usgs.gov","orcid":"https://orcid.org/0000-0002-5516-3697","contributorId":589,"corporation":false,"usgs":true,"family":"Wein","given":"Anne","email":"awein@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":873761,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Helgeson, Jennifer","contributorId":305738,"corporation":false,"usgs":false,"family":"Helgeson","given":"Jennifer","affiliations":[{"id":66281,"text":"National Institute of Standards","active":true,"usgs":false}],"preferred":false,"id":873762,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rose, Adam","contributorId":305740,"corporation":false,"usgs":false,"family":"Rose","given":"Adam","affiliations":[{"id":13249,"text":"University of Southern California","active":true,"usgs":false}],"preferred":false,"id":873763,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dormady, Noah","contributorId":305741,"corporation":false,"usgs":false,"family":"Dormady","given":"Noah","email":"","affiliations":[{"id":49186,"text":"University of Ohio","active":true,"usgs":false}],"preferred":false,"id":873764,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kim, Juri","contributorId":305742,"corporation":false,"usgs":false,"family":"Kim","given":"Juri","email":"","affiliations":[{"id":36972,"text":"University of British Columbia","active":true,"usgs":false}],"preferred":false,"id":873765,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70243829,"text":"sir20235011 - 2023 - The Science Application for Risk Reduction (SAFRR) Scenario Retrospective 2006–21","interactions":[],"lastModifiedDate":"2026-03-02T19:03:04.474237","indexId":"sir20235011","displayToPublicDate":"2023-05-23T11:25:00","publicationYear":"2023","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":"2023-5011","displayTitle":"The Science Application for Risk Reduction (SAFRR) Scenario Retrospective 2006–21","title":"The Science Application for Risk Reduction (SAFRR) Scenario Retrospective 2006–21","docAbstract":"The U.S. Geological Survey Science Application for Risk Reduction (SAFRR) Project has created four major hazard scenarios—ShakeOut, ARkStorm, Tsunami Scenario, and HayWired—with multidisciplinary teams of scientists, academics, and practitioners. By presenting a clear and highly detailed narrative of potential damage from earthquakes, tsunamis, and winter storms, the scenarios are intended to foster science-based preparedness strategies and disaster risk reduction innovations.
This evaluation explores the presence of these scenarios in cultures of preparedness and their role in disaster risk reduction, and reports barriers and enablers to creating and using these scenarios. To do this, the evaluation team developed a mixed-methods study that includes background research for each scenario, qualitative interviews, data collection of media and academic engagement, and examples of SAFRR scenario use in hazard planning. The data collection led to the development of a hazard scenario evaluation tool that combines theories from multiple disciplines to create a best practice set of categories that aid in scenario use and efficacy. The evaluation tool categories—actionable, longitudinal, educational, relevant, and thorough—are organized as a series of checklists that are used to determine how the scenario planners prioritized different aspects of the scenarios to achieve their goals. The tool could also be used to aid scenario planning for other regional disaster risk reduction scenarios of a similar scope.
Findings from this evaluation include detailed narratives of scenario use over time, demonstrating that the scenarios have continued to be useful in hazard planning and preparedness across the globe. Examples of use include using the scenarios to advocate for resilient building and development policy, to promote hazard response exercises, and as source data for the development of new hazard models and science. The scenarios themselves are innovative, both in the hazard science created for scenario development and in their branding and public engagement as U.S. Geological Survey products. This SAFFR retrospective is a descriptive evaluation and does not formally address the effects of the scenarios. Nevertheless, this report does include evidence of scenario affects as discovered through qualitative interviews and research, which is presented to explore how the SAFRR scenarios have been received by cultures of preparedness.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20235011","usgsCitation":"Smithhisler, N., and Burkardt, N., 2023, The Science Application for Risk Reduction (SAFRR) Scenario Retrospective 2006–21: U.S. Geological Survey Scientific Investigations Report 2023–5011, 116 p., https://doi.org/10.3133/sir20235011.","productDescription":"Report: viii, 116 p.; Program Agenda","onlineOnly":"Y","ipdsId":"IP-129947","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":417356,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5011/sir20235011.xml"},{"id":417355,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5011/images"},{"id":417314,"rank":3,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sir/2023/5011/agenda.pdf","text":"ARkSTORM Summit Program","size":"3.10 MB","linkFileType":{"id":1,"text":"pdf"},"description":"ARkSTORM Summit Program"},{"id":417313,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5011/sir20235011.pdf","text":"Report","size":"15.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5011"},{"id":417312,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5011/coverthb.jpg"},{"id":500692,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114738.htm","linkFileType":{"id":5,"text":"html"}}],"contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526-8118
Individual variation in life-history traits can have important implications for the ability of populations to respond to environmental variability and change. In migratory animals, flexibility in the timing of life-history events, such as juvenile emigration from natal areas, can influence the effects of population density and environmental conditions on habitat use and population dynamics. We evaluated the functional relationships between population density and environmental covariates and the abundance of juveniles expressing different life-history pathways in a migratory fish, Chinook salmon (Oncorhynchus tshawytscha), in the Wenatchee River basin in Washington State, USA. We found that the abundance of younger emigrants from natal streams was best described by an accelerating or near-linear function of spawners, whereas the abundance of older emigrants was best described by a decelerating function of spawners. This supports the hypothesis that emigration timing varies in response to density in natal areas, with younger-emigrating life-history pathways comprising a larger proportion of emigrants when densities of conspecifics are high. We also observed positive relationships between winter stream discharge and abundance of younger emigrants, supporting the hypothesis that habitat conditions can also influence the prevalence of different life-history pathways. Our results suggest that early emigration, and a resultant increase in the use of downstream rearing habitats, may increase at higher population densities and with greater winter precipitation. Winter precipitation is projected to increase in this system due to climate warming. Characterizing relationships between life-history prevalence and environmental conditions may improve our understanding of species habitat requirements and is a first step in understanding the dynamics of species with diverse life-history strategies. As environmental conditions change—due to climate change, management, or other factors—resultant life-history changes are likely to have important demographic implications that will be challenging to predict when life-history diversity is not accounted for in population models.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.10087","usgsCitation":"Sorel, M.H., Murdoch, A.R., Zabel, R.W., Kamphaus, C.M., Buhle, E.R., Scheuerell, M.D., and Converse, S.J., 2023, Effects of population density and environmental conditions on life-history prevalence in a migratory fish: Ecology and Evolution, v. 13, no. 5, e10087, 13 p., https://doi.org/10.1002/ece3.10087.","productDescription":"e10087, 13 p.","ipdsId":"IP-141269","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":443411,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.10087","text":"Publisher Index Page"},{"id":430137,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Wenatchee River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.2,\n 48.2\n ],\n [\n -121.2,\n 47.4\n ],\n [\n -120.2,\n 47.4\n ],\n [\n -120.2,\n 48.2\n ],\n [\n -121.2,\n 48.2\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"13","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-05-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Sorel, Mark H.","contributorId":171739,"corporation":false,"usgs":false,"family":"Sorel","given":"Mark","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":903776,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Murdoch, Andrew R.","contributorId":339213,"corporation":false,"usgs":false,"family":"Murdoch","given":"Andrew","email":"","middleInitial":"R.","affiliations":[{"id":12438,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":903777,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zabel, Richard W.","contributorId":272049,"corporation":false,"usgs":false,"family":"Zabel","given":"Richard","email":"","middleInitial":"W.","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":903778,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kamphaus, Cory M.","contributorId":339215,"corporation":false,"usgs":false,"family":"Kamphaus","given":"Cory","email":"","middleInitial":"M.","affiliations":[{"id":39287,"text":"Yakama Nation Fisheries","active":true,"usgs":false}],"preferred":false,"id":903779,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Buhle, Eric R.","contributorId":339062,"corporation":false,"usgs":false,"family":"Buhle","given":"Eric","email":"","middleInitial":"R.","affiliations":[{"id":81244,"text":"Biomark Applied Biological Services","active":true,"usgs":false}],"preferred":false,"id":903780,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Scheuerell, Mark David 0000-0002-8284-1254","orcid":"https://orcid.org/0000-0002-8284-1254","contributorId":288621,"corporation":false,"usgs":true,"family":"Scheuerell","given":"Mark","email":"","middleInitial":"David","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903781,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":903782,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70243980,"text":"70243980 - 2023 - Influence of increased freshwater inflow on nitrogen and phosphorus budgets in a dynamic subtropical estuary, Barataria Basin, Louisiana","interactions":[],"lastModifiedDate":"2023-05-30T14:19:45.884972","indexId":"70243980","displayToPublicDate":"2023-05-23T08:48:22","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Influence of increased freshwater inflow on nitrogen and phosphorus budgets in a dynamic subtropical estuary, Barataria Basin, Louisiana","docAbstract":"Coastal Louisiana is currently experiencing high rates of wetland loss and large-scale ecosystem restoration is being implemented. One of the largest and most novel restoration projects is a controlled sediment diversion, proposed to rebuild and sustain wetlands by diverting sediment- and nutrient-rich water from the Mississippi River. However, the impact of this proposed sediment diversion on the nutrient budget of the receiving basin is largely unknown. A water quality model was developed to investigate the impact of the planned Mid-Barataria Sediment Diversion on the nutrient budget of the Barataria Basin (herein referred to as ‘the Basin’). The model results indicate that the planned diversion will increase TN and TP pools by about 38% and 17%, respectively, even with TN and TP loadings that increase by >300%. Water quality model results suggest that the increase of nutrients in the basin will be mitigated by increased advection transport (i.e., decreased residence time from ~170 days to ~40 days, leading to greater flushing) and increased removal via assimilation, denitrification, and settling within the Basin. Advection transport resulted in higher TN removal in the Basin than other processes, such as uptake or denitrification. Approximately 25% of the additional TN loading and 30% of the additional TP loading were processed within the Basin through the assimilation of phytoplankton and wetland vegetation, denitrification, and burial in the sediment/soils. These nutrient budgets help to better understand how the planned large-scale sediment diversion project may change the future ecological conditions within the estuaries of coastal Louisiana and near-shore northern Gulf of Mexico.
","language":"English","publisher":"MDPI","doi":"10.3390/w15111974","usgsCitation":"Jung, H., Nuttle, W.K., Baustian, M.M., and Carruthers, T., 2023, Influence of increased freshwater inflow on nitrogen and phosphorus budgets in a dynamic subtropical estuary, Barataria Basin, Louisiana: Water, v. 15, no. 11, 1974, 26 p., https://doi.org/10.3390/w15111974.","productDescription":"1974, 26 p.","ipdsId":"IP-147358","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":443414,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w15111974","text":"Publisher Index Page"},{"id":417528,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Barataria Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.95930658584572,\n 28.576529058181706\n ],\n [\n -89.35817902592156,\n 29.01036697967102\n ],\n [\n -89.24031087691701,\n 29.18888336467873\n ],\n [\n -89.67249408993412,\n 29.421859677181203\n ],\n [\n -90.17539819235454,\n 29.835100651604293\n ],\n [\n -90.3954187371632,\n 29.739625500016345\n ],\n [\n -90.2186165136559,\n 29.182023077646647\n ],\n [\n -89.95930658584572,\n 28.576529058181706\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"11","noUsgsAuthors":false,"publicationDate":"2023-05-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Jung, Hoonshin","contributorId":305843,"corporation":false,"usgs":false,"family":"Jung","given":"Hoonshin","email":"","affiliations":[{"id":13499,"text":"The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":873997,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nuttle, William K.","contributorId":189603,"corporation":false,"usgs":false,"family":"Nuttle","given":"William","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":873998,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baustian, Melissa Millman 0000-0003-2467-2533","orcid":"https://orcid.org/0000-0003-2467-2533","contributorId":304015,"corporation":false,"usgs":true,"family":"Baustian","given":"Melissa","email":"","middleInitial":"Millman","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":873999,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Carruthers, Tim J. B.","contributorId":140566,"corporation":false,"usgs":false,"family":"Carruthers","given":"Tim J. B.","affiliations":[],"preferred":false,"id":874000,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70243839,"text":"70243839 - 2023 - Spatiotemporal patterns and environmental drivers of eastern redcedar (Juniperus virginiana) abundance along the Missouri River, USA","interactions":[],"lastModifiedDate":"2023-06-09T15:26:11.659239","indexId":"70243839","displayToPublicDate":"2023-05-23T08:14:25","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Spatiotemporal patterns and environmental drivers of eastern redcedar (Juniperus virginiana) abundance along the Missouri River, USA","title":"Spatiotemporal patterns and environmental drivers of eastern redcedar (Juniperus virginiana) abundance along the Missouri River, USA","docAbstract":"Context: Changes in disturbance regimes, including reductions in flooding and geomorphic dynamism from dam construction and flow regulation, have facilitated invasion by eastern redcedar (Juniperus virginiana L.), an upland tree species, in the understory of floodplain forests along the Missouri National Recreational River (MNRR).
Objectives: Our aim was to determine the spatiotemporal patterns and environmental drivers of redcedar invasion along the MNRR.
Methods: We used the Normalized Difference Vegetation Index (NDVI) calculated from winter Landsat imagery to construct a time series of maps showing spatial changes in redcedar abundance and distribution from 1982 to 2017 in both riparian and upland habitats along the MNRR. We determined how environmental factors (e.g., soil drainage ability, flood recurrence interval, 1980s LULC, lateral distance to the river, and channel incision) have influenced current (2017) redcedar occurrence and abundance in riparian habitats using random forest models (RFM).
Results: Time-series maps indicated that detectable redcedar cover occurred over less than 5% of the study area before 1985, increased steadily from 1985 to 2000, and more than tripled from 2000 to 2010. After 2010, redcedar abundance continued to increase in upland areas but declined following the 2011 Missouri River flood in the floodplain. RFMs indicated that river incision, distance to the river, soil drainage, 1984 LULC, and flood recurrence interval were important features influencing redcedar occurrence and abundance in the floodplain.
Conclusion: Unless preventive measures are implemented, lack of floods and ongoing flow regulation will enable the continued spread of redcedar along the MNRR and other regulated rivers in the eastern Great Plains.
The Muddy River provides habitat for several wildlife and endemic aquatic species protected under the Endangered Species Act. Near Moapa, Nevada, in the Bureau of Land Management’s Muddy River Floodplain Restoration Project Area, a previously constructed levee on the east side of the river alters the natural hydrology and decreases connectivity between the river and its floodplain. The Bureau of Land Management is interested in restoring the project area to a more natural state and proposed removing the existing levee (at the time of this study in 2019) on the east bank of the river and replacing it with a new levee farther away from the river. The 50-, 20-, 10-, 4-, 2-, and 1-percent annual exceedance probability flood streamflows were estimated based on a flood-frequency analysis of a streamgage in the study area. River cross-sections were surveyed and combined with a digital elevation model of the floodplains to create a coupled one- and two-dimensional hydraulic model of the study area. The estimated flood streamflows were used as inputs in the hydraulic model to simulate how flood-inundation extents would change with the proposed restoration. Simulated inundation extents expand with increasing flood magnitudes, with nearly the entire valley inundated by the 2-percent flood streamflow. Within the project area, inundation extents with restoration increased on the east floodplain and decreased on the west floodplain for the 20-, 10-, 4-, 2-, and 1-percent flood streamflows. Outside the Muddy River Floodplain Restoration Project Area, inundation extents decreased with restoration east of the project area for the 20- and 10-percent flood streamflows, but changes in extent for larger streamflows were minor because most of the streamflow leaves the main river channel upstream of the restoration area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235033","usgsCitation":"Morris, C.M., and Childres, H.K., 2023, Flood-inundation maps for the Muddy River, near Moapa, Nevada: U.S. Geological Survey Scientific Investigations Report 2023–5033, 22 p., https://doi.org/10.3133/sir20235033.","productDescription":"Report: viii, 22 p.; Data Release","numberOfPages":"22","onlineOnly":"Y","ipdsId":"IP-104618","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":416999,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5033/sir20235033.pdf","text":"Report","size":"23 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":417002,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5033/images"},{"id":416997,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K68IWI","text":"Geospatial data, flood-frequency analysis, and surface-water model archive for flood-inundation maps of the Muddy River, near Moapa, Nevada","description":"Morris, C.M., 2023, Geospatial data, flood-frequency analysis, and surface-water model archive for flood-inundation maps of the Muddy River, near Moapa, Nevada: U.S. Geological Survey data release, at https://doi.org/10.5066/P9K68IWI."},{"id":417000,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5033/covrthb.jpg"},{"id":417001,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5033/sir20235033.xml"},{"id":417003,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20235033/full"},{"id":500898,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114737.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Nevada","city":"Moapa","otherGeospatial":"Muddy River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.716667,\n 36.723611\n ],\n [\n -114.716667,\n 36.675\n ],\n [\n -114.675,\n 36.675\n ],\n [\n -114.675,\n 36.723611\n ],\n [\n -114.716667,\n 36.723611\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road
Carson City, Nevada 89701
In contrast to many other arid region rivers, streamflow in the South Platte River is heavily augmented by trans-basin water imports and irrigation return flows. Hydrological changes began in the 1880s, resulting in channel narrowing and the development of a continuous Populus-Salix forest by the mid-twentieth century. We assessed the composition, structure and regeneration status of the riparian forest and identified environmental variables affecting annual Populus deltoides tree growth. We sampled forest structure at four sites in 2015, and conducted dendroecological analysis at seven additional sites in 2019. The riparian forest was dominated by P. deltoides, which occurred at all sites, comprising 79% of total tree basal area and 62% of total tree density. Age structure data indicated ongoing though episodic recruitment of P. deltoides, at least over the past ~130 years. We tested 14 linear mixed effects models to describe the effect of climate and streamflow on individual tree growth (modeled as the log of BAI, n = 237 trees). The most parsimonious model selected with AICc explained 28.6% of BAI variability, and included hydrology and climate factors during the growing season (i.e., June–August streamflow, June–July PDSI), some aspects of off-season (i.e., previous November and March) streamflow, along with tree age and study site effects. The riparian forest developed in response to, and has been maintained by, current climate conditions and water management regimes. It may be negatively affected by future climate change and increased urban water demand in the basin.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.4151","usgsCitation":"Christensen, C., Katz, G.L., Friedman, J.M., Redmond, M.D., and Norton, A.S., 2023, No evidence for cottonwood forest decline along a flow-augmented western U.S. river: River Research and Applications, v. 39, no. 8, p. 1602-1615, https://doi.org/10.1002/rra.4151.","productDescription":"14 p.","startPage":"1602","endPage":"1615","ipdsId":"IP-147312","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":443426,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rra.4151","text":"Publisher Index Page"},{"id":417328,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"South Platte River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n 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Despite the importance of early warning systems for these hazards, existing real-time meteorological and oceanographic forecast systems at regional and national scales, until now, have lacked estimates of runup necessary to predict wave-driven overwash and erosion. To address this need, we present an approach that includes wave runup in an operational, national-scale modeling system. Using this system, we quantify the contribution of waves to potential dune erosion events along 4,700 km of U.S. Atlantic and Gulf of Mexico sandy coastlines for a one-year period. Dune erosion events were predicted to occur at over 80% of coastal locations, where waves dominated shoreline total water levels, representing 73% of the signal. This shows that models that neglect the wave component underestimate the hazard. This new, national-scale operational modeling system provides communities with timely, local-scale (0.5 km resolution) coastal hazard warnings for all wave conditions, allowing for rapid decision-making related to safety and emergency management. 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California","interactions":[],"lastModifiedDate":"2023-05-25T15:59:15.380077","indexId":"70243807","displayToPublicDate":"2023-05-22T08:29:31","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Comparison of nonergodic ground-motion components from CyberShake and NGA-West2 datasets in California","docAbstract":"In this study, we compare the Southern California Earthquake Center CyberShake platform against the Next Generation Attenuation‐West2 empirical datasets. Because the CyberShake and empirical datasets cover very different magnitude ranges and site conditions, we develop ground‐motion models (GMMs) for CyberShake datasets to compare trends with empirical GMMs and decompose the residuals for further analysis. We apply mixed effects regression to four CyberShake datasets in southern, central, and northern California at 2, 3, 5, and 10 s periods, and compare the results with the empirical datasets using the same approach. CyberShake captures the total variability of ground motions in the empirical datasets but tends to predict larger median ground motions relative to the empirical GMMs. We then calculate and compare the repeatable source‐specific location, site, and path effects between CyberShake and empirical datasets. We find that the correlations of site effects between the CyberShake and empirical datasets are generally satisfactory, but the variability of site effects is slightly smaller for CyberShake datasets. There is no apparent correlation of source‐specific location effects between the CyberShake and empirical datasets. Comparison of path effects shows a wide range of correlation coefficients. Finally, we investigate the source of observed differences between the CyberShake and empirical datasets. We attribute the larger median ground‐motion levels in CyberShake to a combination of the homogeneous slip patterns of the earthquake ruptures, the low resolution of near‐surface materials in the velocity models, and strong reflections at high‐contrast boundaries in the velocity models. These factors also impact the correlations of site and path effects between the CyberShake and empirical datasets. Moreover, the leakage from location effects into site and path terms further weakens the correlations. In summary, we find that CyberShake could be improved, but it is still very useful to supplement empirical datasets for ground‐motion studies, especially to inform their nonergodic components.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120220049","usgsCitation":"Meng, X., Goulet, C., Milner, K.R., Graves, R., and Callaghan, S., 2023, Comparison of nonergodic ground-motion components from CyberShake and NGA-West2 datasets in California: Bulletin of the Seismological Society of America, v. 113, no. 3, p. 1152-1175, https://doi.org/10.1785/0120220049.","productDescription":"24 p.","startPage":"1152","endPage":"1175","ipdsId":"IP-140662","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":417292,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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US","interactions":[],"lastModifiedDate":"2023-05-22T13:08:24.434575","indexId":"70243816","displayToPublicDate":"2023-05-22T07:57:11","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1551,"text":"Environmental Modelling and Software","active":true,"publicationSubtype":{"id":10}},"title":"Generating a reference flow network with improved connectivity to support durable data integration and reproducibility in the coterminous US","docAbstract":"This report presents a reference flow network for the conterminous United States that is built from the best available information from the U.S. Geological Survey, the National Oceanic and Atmospheric Administration National Weather Service, and the U.S. Environmental Protection Agency. The work is intended to support durable data integration and reproducibility. Originating from the National Hydrography Dataset Plus (NHDPlus) V2.1, the reference flow network incorporates network connectivity enhancements from federal agency efforts. After incorporating these network improvements, many original NHDPlus attributes were regenerated to enable network navigation and related operations. After introducing the motivation and background for this work, this report describes the attribute generation workflow and data quality checks that were performed in preparation of the dataset. The reference flow network follows the NHDPlus data model and is described using terms defined in the Mainstem and Drainage Basin logical model and WaterML2 Part3: Surface Hydrology Features conceptual model.
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