The U.S. Army Corps of Engineers (USACE), Southwestern Division, Little Rock District Civil Works program has a mission to maintain cohesion between physical and naturally developed environments. The USACE authorized the development of an off-channel harbor (hereinafter referred to as the “proposed slack water harbor”) along the McClellan-Kerr Arkansas River Navigation System at river mile 202.6, and an initial evaluation of shoreline stability and adjacent land near the proposed harbor was considered essential in establishing a baseline for potential effects and future monitoring. In October 2021, the U.S. Geological Survey, in cooperation with the USACE, completed high-resolution bathymetric (underwater elevation) and topographic surveys of the Arkansas River and a quarry at the location of the proposed slack water harbor near Dardanelle, Arkansas, using a combination of multibeam sound navigation and ranging (sonar) and high-resolution, low-altitude aerial light detection and ranging (lidar) data to provide data and analysis needed for as-built information and future monitoring of river shoreline and floodplain management and maintenance.
Bathymetric data were collected using a high-resolution multibeam mapping system, which consists of a multibeam echosounder and an inertial navigation system mounted on a marine survey vessel. Data were collected as the vessel traversed the river and quarry along overlapping survey lines distributed throughout the areas.
Topographic data were collected as a lidar point cloud using an unmanned aircraft system (UAS) with a YellowScan Vx20–100 lidar payload, which consists of the lidar scanner and an inertial navigation system. The lidar point cloud data were collected as the UAS followed two sets of parallel transect lines, oriented perpendicular to each other (nominally north to south and east to west) on separate flights. The bathymetric and UAS topographic datasets were combined with topographic data extracted from publicly available aerial lidar data collected in 2014 to create a multisource point cloud classified as “ground” (code 2) according to the American Society for Photogrammetry and Remote Sensing standard lidar point classes in the proposed harbor area and surroundings, from which topographic contours were derived.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3494","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Southwestern Division, Little Rock District","usgsCitation":"Huizinga, R.J., Richards, J.M., and Rivers, B.C., 2022, Use of high-resolution topobathymetry to assess shoreline topography and potential future development of a slack water harbor near Dardanelle, Arkansas, October 2021: U.S. Geological Survey Scientific Investigations Map 3494, 1 sheet, https://doi.org/10.3133/sim3494.","productDescription":"Sheet: 36.00 x 39.50 inches; Data Release","numberOfPages":"1","onlineOnly":"Y","ipdsId":"IP-137686","costCenters":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":408700,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3494/sim3494.pdf","text":"Report","size":"2.76 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3494"},{"id":408699,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3494/coverthb.jpg"},{"id":408701,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sim/3494/sim3494.XML"},{"id":408702,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sim/3494/images"},{"id":408722,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sim3494/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":408703,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KW1D2D","text":"USGS data release","linkHelpText":"Use of high-resolution topobathymetry to assess shoreline topography and future development of a slack water harbor near Dardanelle, Arkansas, October 2021"},{"id":501937,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113784.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Arkansas","county":"Dardanelle","otherGeospatial":"Slack Water Harbor","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92.1753984002736,\n 34.7148432917307\n ],\n [\n -92.1753984002736,\n 34.70185497290542\n ],\n [\n -92.15152711351902,\n 34.70185497290542\n ],\n [\n -92.15152711351902,\n 34.7148432917307\n ],\n [\n -92.1753984002736,\n 34.7148432917307\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
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In this study we illustrate the value of social data compiled at the community scale to guide a local wildfire mitigation and education effort. The four contiguous fire-prone study communities in North Central Washington, US, fall within the same jurisdictional fire service boundary and within one US census block group. Across the four communities, similar attitudes toward wildfire were observed. However, significant differences were found on the measures critical to tailoring wildfire preparation and mitigation programs to the local context such as risk mitigation behaviors, reported barriers to mitigation, and communication preferences across the four communities.
Groundwater-stream connectivity across mountain watersheds is critical for supporting streamflow during dry times and keeping streams cool during warm times, yet U.S. Geological Survey (USGS) stream measurements are often sparse in headwaters. Starting in 2019, the USGS Next Generation Water Observing System Program developed a multiscale methods and technology testbed approach to monitoring groundwater discharge to streams in the Neversink Reservoir watershed in the Catskill Mountains of New York. Groundwater discharge dynamics are complex across space and time because of geographic variability, topography, and preferential groundwater flow patterns, and the monitoring of discharge processes necessitates an innovative approach that includes emerging water tracing methods and enhanced local geologic mapping. This fact sheet describes the multiscale monitoring approach applied in the Neversink Reservoir watershed and specifically how the varied data types are complimentary in understanding groundwater-stream connectivity, with elements transferable to other mountain watersheds.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223077","usgsCitation":"Briggs, M.A., Gazoorian, C.L., Doctor, D.H., and Burns, D.A., 2022, A multiscale approach for monitoring groundwater discharge to headwater streams by the U.S. Geological Survey Next Generation Water Observing System Program—An example from the Neversink Reservoir watershed, New York: U.S. Geological Survey Fact Sheet 2022–3077, 6 p., https://doi.org/10.3133/fs20223077.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-140810","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":501511,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113787.htm","linkFileType":{"id":5,"text":"html"}},{"id":435646,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9R3TYOZ","text":"USGS data release","linkHelpText":"Stream Temperature, Dissolved Radon, and Stable Water Isotope Data Collected along Headwater Streams in the Upper Neversink River Watershed, NY, USA (ver. 2.0, April 2023)"},{"id":408663,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3077/images/"},{"id":408662,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3077/fs20223077.XML"},{"id":408661,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223077/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2022-3077"},{"id":408660,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3077/fs20223077.pdf","text":"Report","size":"10.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022-3077"},{"id":408659,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3077/coverthb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Neversink Reservoir Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.20847061475345,\n 42.188348269216135\n ],\n [\n -74.59712682347332,\n 42.248700527889696\n ],\n [\n -75.01085762630467,\n 41.923046154032335\n ],\n [\n -74.86354438590236,\n 41.71281459504877\n ],\n [\n -74.42317182682854,\n 41.88105505768368\n ],\n [\n -74.19906764196153,\n 42.012767732647546\n ],\n [\n -74.20847061475345,\n 42.188348269216135\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Program Manager, Next Generation Water Observing System
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U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
The coastal waters of Cuba are home to a small, endangered population of West Indian manatee, which would benefit from a comprehensive characterization of the population’s genetic variation. We conducted the first genetic assessment of Cuban manatees to determine the extent of the population's genetic structure and characterize the neutral genetic diversity among regions within the archipelago. We genotyped 49 manatees at 18 microsatellite loci, a subset of 27 samples on 1703 single nucleotide polymorphisms (SNPs), and sequenced 59 manatees at the mitochondrial control region. The Cuba manatee population had low nuclear (microsatellites HE = 0.44, and SNP HE = 0.29) and mitochondrial genetic diversity (h = 0.068 and π = 0.00025), and displayed moderate departures from random mating (microsatellite FIS = 0.12, SNP FIS = 0.10). Our results suggest that the western portion of the archipelago undergoes periodic exchange of alleles based on the evidence of shared ancestry and low but significant differentiation. The southeast Guantanamo Bay region and the western portion of the archipelago were more differentiated than southwest and northwest manatees. The genetic distinctiveness observed in the southeast supports its recognition as a demographically independent unit for natural resource management regardless of whether it is due to historical isolation or isolation by distance. Estimates of the regional effective population sizes, with the microsatellite and SNP datasets, were small (all Ne < 60). Subsequent analyses using additional samples could better examine how the observed structure is masking simple isolation by distance patterns or whether ecological or biogeographic forces shape genetic patterns.
Seasonal snow melt dominates the hydrologic budget across a large portion of the globe. Snow accumulation and melt vary over a broad range of spatial scales, preventing accurate extrapolation of sparse in situ observations to watershed scales. The lidar onboard the Ice, Cloud, and land Elevation, Satellite (ICESat-2) was designed for precise mapping of ice sheets and sea ice, and here we assess the feasibility of snow depth-mapping using ICESat-2 data in more complex and rugged mountain landscapes. We explore the utility of ATL08 Land and Vegetation Height and ATL06 Land Ice Height differencing from reference elevation datasets in two end member study sites. We analyze ∼3 years of data for Reynolds Creek Experimental Watershed in Idaho's Owyhee Mountains and Wolverine Glacier in southcentral Alaska's Kenai Mountains. Our analysis reveals decimeter-scale uncertainties in derived snow depth and glacier mass balance at the watershed scale. Both accuracy and precision decrease as slope increases: the magnitudes of the median and median of the absolute deviation of elevation errors (MAD) vary from ∼0.2 m for slopes <5° to >1 m for slopes >20°. For glacierized regions, failure to account for intra- and inter-annual evolution of glacier surface elevations can strongly bias ATL06 elevations, resulting in under-estimation of the mass balance gradient with elevation. Based on these results, we conclude that ATL08 and ATL06 observations are best suited for characterization of watershed-scale snow depth and mass balance gradients over relatively shallow slopes with thick snowpacks. In these regions, ICESat-2 elevation residual-derived snow depth and mass balance transects can provide valuable watershed scale constraints on terrain parameter- and model-derived estimates of snow accumulation and melt.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2022.113307","usgsCitation":"Enderlin, E., Elkin, C., Gendreau, M., Marshall, H., O'Neel, S., McNeil, C., Florentine, C., and Sass, L., 2022, Uncertainty of ICESat-2 ATL06- and ATL08-derived snow depths for glacierized and vegetated mountain regions: Remote Sensing of Environment, v. 283, 113307, 17 p., https://doi.org/10.1016/j.rse.2022.113307.","productDescription":"113307, 17 p.","ipdsId":"IP-141547","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":446058,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2022.113307","text":"Publisher Index Page"},{"id":486323,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F76Q1WHK","text":"USGS data release","linkHelpText":"Point Raw Glaciological Data: Ablation Stake, Snow Pit, and Probed Snow Depth Data on USGS Benchmark Glaciers"},{"id":408693,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska, Idaho","otherGeospatial":"Reynolds Creek Experimental Watershed, Wolverine Glacier","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.9169563546656,\n 43.36296558422342\n ],\n [\n -116.9169563546656,\n 43.1201924666604\n ],\n [\n -116.63496346150279,\n 43.1201924666604\n ],\n [\n -116.63496346150279,\n 43.36296558422342\n ],\n [\n -116.9169563546656,\n 43.36296558422342\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -148.84207609134586,\n 60.42699332401864\n ],\n [\n -148.8772978314237,\n 60.46763185035496\n ],\n [\n -148.92384084509808,\n 60.44126913255184\n ],\n [\n -148.95214402908923,\n 60.43009729404224\n ],\n [\n -148.9219539661653,\n 60.37666770702921\n ],\n [\n -148.9112616522131,\n 60.37542411458642\n ],\n [\n -148.84207609134586,\n 60.42699332401864\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"283","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Enderlin, Ellyn","contributorId":187445,"corporation":false,"usgs":false,"family":"Enderlin","given":"Ellyn","email":"","affiliations":[],"preferred":false,"id":855759,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Elkin, Colten","contributorId":298508,"corporation":false,"usgs":false,"family":"Elkin","given":"Colten","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":855760,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gendreau, Madeline","contributorId":298509,"corporation":false,"usgs":false,"family":"Gendreau","given":"Madeline","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":855761,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Marshall, H. P.","contributorId":298510,"corporation":false,"usgs":false,"family":"Marshall","given":"H. P.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":855762,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"O'Neel, Shad 0000-0002-9185-0144","orcid":"https://orcid.org/0000-0002-9185-0144","contributorId":289666,"corporation":false,"usgs":false,"family":"O'Neel","given":"Shad","affiliations":[{"id":62222,"text":"Cold Regions Research Laboratory","active":true,"usgs":false}],"preferred":false,"id":855763,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McNeil, Christopher J. 0000-0003-4170-0428 cmcneil@usgs.gov","orcid":"https://orcid.org/0000-0003-4170-0428","contributorId":5803,"corporation":false,"usgs":true,"family":"McNeil","given":"Christopher J.","email":"cmcneil@usgs.gov","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":855764,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Florentine, Caitlyn 0000-0002-7028-0963","orcid":"https://orcid.org/0000-0002-7028-0963","contributorId":205964,"corporation":false,"usgs":true,"family":"Florentine","given":"Caitlyn","email":"","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":855766,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sass, Louis C. 0000-0003-4677-029X lsass@usgs.gov","orcid":"https://orcid.org/0000-0003-4677-029X","contributorId":3555,"corporation":false,"usgs":true,"family":"Sass","given":"Louis C.","email":"lsass@usgs.gov","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":855765,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70259706,"text":"70259706 - 2022 - Exploring declustering methodology for addressing geothermal exploration bias","interactions":[],"lastModifiedDate":"2024-10-21T12:29:48.729606","indexId":"70259706","displayToPublicDate":"2022-10-21T07:28:20","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1827,"text":"Geothermal Resources Council Transactions","active":true,"publicationSubtype":{"id":10}},"title":"Exploring declustering methodology for addressing geothermal exploration bias","docAbstract":"Geothermal resources assessments use data that are unevenly distributed in space, with more data collected in areas with known thermal features. To meet the assumptions for geostatistical modeling (e.g., variography and kriging) such as having a random sample representative of the population, declustering may be needed to correct for spatial sample bias. Several declustering methods exist and to understand how best to use these methods, we apply these to real data and samples of that data. The work described herein summarizes the application of cell-based declustering to shallow temperature data (~20 cm) collected in a survey across a thermal feature in the Lower Geyser Basin, Yellowstone National Park, Wyoming. The sample dataset is a regular grid (3-m spacing) of temperatures across a 72-m square area, providing a shallow, subsurface temperature dataset collected with minimal spatial bias (a few grid locations near a hot spring could not be sampled). To test the influence of sample clustering on geothermal estimates, this dense dataset is sub-sampled irregularly to evaluate bias on temperature estimation. Three sampling strategies were tested: a simple random sample, a stratified random sample, and a stratified biased random sample. The naive mean (before declustering) values for each dataset were compared to the post-declustering mean to evaluate the effectiveness of declustering on correcting the mean for spatial bias. For the limited number of sample datasets evaluated, we found that although cell-based declustering did partially correct the mean, some bias remained (i.e., the estimate was improved, but not fully corrected). It is possible that the procedure documented herein (applied here to only a few random samples) could be applied to many random samples, so that robust conclusions might be drawn (e.g., Is there always some remaining bias in declustered estimates? Does it depend on the number of sample points?). In particular, bias could be evaluated for persistency, and uncertainty could be evaluated.","language":"English","publisher":"Geothermal Rising","usgsCitation":"Lindsey, C.R., Price, A.N., and Burns, E.R., 2022, Exploring declustering methodology for addressing geothermal exploration bias: Geothermal Resources Council Transactions, v. 46, p. 1109-1119.","productDescription":"11 p.","startPage":"1109","endPage":"1119","ipdsId":"IP-141054","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":463063,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.geothermal-library.org/index.php?mode=pubs&action=view&record=1034661"},{"id":463064,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"46","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lindsey, Cary Ruth","contributorId":345373,"corporation":false,"usgs":true,"family":"Lindsey","given":"Cary","email":"","middleInitial":"Ruth","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":916395,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Price, Adam N. 0000-0002-7211-4758","orcid":"https://orcid.org/0000-0002-7211-4758","contributorId":295971,"corporation":false,"usgs":false,"family":"Price","given":"Adam","email":"","middleInitial":"N.","affiliations":[{"id":27155,"text":"University of California Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":916396,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Burns, Erick R. 0000-0002-1747-0506 eburns@usgs.gov","orcid":"https://orcid.org/0000-0002-1747-0506","contributorId":192154,"corporation":false,"usgs":true,"family":"Burns","given":"Erick","email":"eburns@usgs.gov","middleInitial":"R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":916397,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70241136,"text":"70241136 - 2022 - Climate disequilibrium dominates uncertainty in long-term projections of primary productivity","interactions":[],"lastModifiedDate":"2023-03-13T12:07:56.782111","indexId":"70241136","displayToPublicDate":"2022-10-21T07:05:50","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1466,"text":"Ecology Letters","active":true,"publicationSubtype":{"id":10}},"title":"Climate disequilibrium dominates uncertainty in long-term projections of primary productivity","docAbstract":"Rapid climate change may exceed ecosystems' capacities to respond through processes including phenotypic plasticity, compositional turnover and evolutionary adaption. However, consequences of the resulting climate disequilibria for ecosystem functioning are rarely considered in projections of climate change impacts. Combining statistical models fit to historical climate data and remotely-sensed estimates of herbaceous net primary productivity with an ensemble of climate models, we demonstrate that assumptions concerning the magnitude of climate disequilibrium are a dominant source of uncertainty: models assuming maximum disequilibrium project widespread decreases in productivity in the western US by 2100, while models assuming minimal disequilibrium project productivity increases. Uncertainty related to climate disequilibrium is larger than uncertainties from variation among climate models or emissions pathways. A better understanding of processes that regulate climate disequilibria is essential for improving long-term projections of ecological responses and informing management to maintain ecosystem functioning at historical baselines.
Here we describe the methods and results for biological characterization of the benthos on a previously unexplored area of central California, USA seafloor. We conducted 40 remotely operated vehicle dives from 371 to 1173 m water depth. Seafloor habitats and megafauna (fish and invertebrates) were documented from 46.8 km of seafloor video footage. Our expanded development and analysis of biotopes from quantitative data allowed us to describe detailed biological communities, along with the physical characteristics and habitat associations within the study area. This method provides a framework for potential monitoring, detection of future environmental change (natural and anthropogenic) and comprehensive comparison to other deep water regions. From 185 h of observational video at 25 sites, nearly 120,000 annotations of organisms, habitat characters, biological detritus and anthropogenic debris were recorded and analyzed. We identified a total of 228 taxa, with 173 of them present on linear quantitative transects. Species richness for transects ranged from 0.04 to 0.28 m-2 (8–55 taxa), with densities ranging from 0.07 to 5.20 ind. m−2. Both were highest on hard substrate with greatest surface area. Densities decreased with depth. Within soft substratum zones was a large field of pockmarks, which are seafloor depressions averaging 175 m in diameter and 5 m in depth. Pockmarks have sometimes been associated with seafloor gas seepage, but here we found no biological evidence of chemosynthetic organisms. No significant differences were found in either density nor species richness at pockmark sites vs. non-pockmark sites. Mud draped greenish-black coarse sand occurred only in low oxygen areas, while hummocky, rugose mud supported somewhat different species than flat mud plains. Seventy percent of the transects occurred inside the oxygen minimum zone. We conclude that high rugosity, slope, and the presence of hard substratum were better predictors of species richness and density than oxygen concentration in this specific study. Abundant biological detritus, in the form of dead and dying pelagic pyrosomes and salps, created a large, presumably ephemeral flux of carbon to the seafloor during the study period.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.dsr.2022.103872","usgsCitation":"Kuhnz, L.A., Gilbane, L., Cochrane, G.R., and Paull, C.K., 2022, Multi-factor biotopes as a method for detailed site characterization in diverse benthic megafaunal communities and habitats in deep-water off Morro Bay, California: Deep Sea Research Part I: Oceanographic Research Papers, v. 190, 103872, 19 p., https://doi.org/10.1016/j.dsr.2022.103872.","productDescription":"103872, 19 p.","ipdsId":"IP-137867","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":446078,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.dsr.2022.103872","text":"Publisher Index Page"},{"id":408743,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Morro Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.66975787964981,\n 35.804510935101575\n ],\n [\n -121.66975787964981,\n 34.864126610922014\n ],\n [\n -120.43438488666736,\n 34.864126610922014\n ],\n [\n -120.43438488666736,\n 35.804510935101575\n ],\n [\n -121.66975787964981,\n 35.804510935101575\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"190","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kuhnz, Linda A. 0000-0002-8359-3803","orcid":"https://orcid.org/0000-0002-8359-3803","contributorId":289638,"corporation":false,"usgs":false,"family":"Kuhnz","given":"Linda","email":"","middleInitial":"A.","affiliations":[{"id":13620,"text":"Monterey Bay Aquarium Research Institute, Moss Landing, California","active":true,"usgs":false}],"preferred":true,"id":855821,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gilbane, Lisa 0000-0001-9170-5388","orcid":"https://orcid.org/0000-0001-9170-5388","contributorId":289639,"corporation":false,"usgs":false,"family":"Gilbane","given":"Lisa","email":"","affiliations":[{"id":20318,"text":"Bureau of Ocean Energy Management","active":true,"usgs":false}],"preferred":true,"id":855822,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cochrane, Guy R. 0000-0002-8094-4583 gcochrane@usgs.gov","orcid":"https://orcid.org/0000-0002-8094-4583","contributorId":2870,"corporation":false,"usgs":true,"family":"Cochrane","given":"Guy","email":"gcochrane@usgs.gov","middleInitial":"R.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":855823,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Paull, Charles K. 0000-0001-5940-3443","orcid":"https://orcid.org/0000-0001-5940-3443","contributorId":55825,"corporation":false,"usgs":false,"family":"Paull","given":"Charles","email":"","middleInitial":"K.","affiliations":[{"id":7043,"text":"University of North Carolina","active":true,"usgs":false}],"preferred":true,"id":855824,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70237677,"text":"ofr20221092 - 2022 - ECCOE Landsat Quarterly Calibration and Validation report—Quarter 2, 2022","interactions":[],"lastModifiedDate":"2022-10-20T10:57:08.281875","indexId":"ofr20221092","displayToPublicDate":"2022-10-19T14:35:42","publicationYear":"2022","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":"2022-1092","displayTitle":"ECCOE Landsat Quarterly Calibration and Validation Report—Quarter 2, 2022","title":"ECCOE Landsat Quarterly Calibration and Validation report—Quarter 2, 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 2 (April–June), 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 throughout the month of April to lower the satellite’s orbit by 8 kilometers. Imaging resumed at the lower orbit of 697 kilometers on May 5, 2022, extending the science mission to allow for essential data to be acquired during the 2022 Northern Hemisphere fire and growing season. 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.
Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Water, bed sediment, and invertebrate tissue were sampled in streams from Butte to near Missoula, Montana, as part of a monitoring program in the Clark Fork Basin. The sampling program was completed by the U.S. Geological Survey, in cooperation with the U.S. Environmental Protection Agency, to characterize aquatic resources in the Clark Fork Basin and monitor trace elements associated with historical mining and smelting activities. Sampling sites were on the Clark Fork River and a subset of its tributaries. Water samples were collected periodically at 22 sites from October 2019 through September 2020. Bed-sediment and tissue samples were collected once at 12 sites during July 2020.
Water-quality data included concentrations of major ions, dissolved organic carbon, nitrogen (nitrate plus nitrite), trace elements, and suspended sediment. Daily values of turbidity were determined at four sites. Bed-sediment data included trace-element concentrations in the fine-grained (less than 0.063 millimeter) fraction. Biological data included trace-element concentrations in whole-body tissue of selected aquatic benthic invertebrates. Statistical summaries of water-quality, bed-sediment, and invertebrate tissue trace-element data for sites in the Clark Fork Basin were provided for the period of record: March 1985–September 2020.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221090","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Clark, G.D., Hornberger, M.I., Hepler, E.J., and Heinert, T.L., 2022, Water-quality, bed-sediment, and invertebrate tissue trace-element concentrations for tributaries in the Clark Fork Basin, Montana, October 2019–September 2020: U.S. Geological Survey Open-File Report 2022–1090, 17 p., https://doi.org/10.3133/ofr20221090.","productDescription":"Report: vii, 17 p.; Data Release; Dataset","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-138065","costCenters":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"links":[{"id":501834,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113781.htm","linkFileType":{"id":5,"text":"html"}},{"id":408546,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221090/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":408387,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":408386,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93BP9P8","text":"USGS data release","linkHelpText":"Results of water-quality, bed-sediment, and invertebrate tissue trace-element concentrations for tributaries in the Clark Fork Basin, Montana, October 2019– September 2020"},{"id":408385,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1090/images"},{"id":408384,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1090/ofr20221090.XML"},{"id":408383,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1090/ofr20221090.pdf","text":"Report","size":"0.97 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022–1090"},{"id":408381,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1090/coverthb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Clark Fork Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -115.67714251796224,\n 47.58172589143089\n ],\n [\n -115.67714251796224,\n 45.00795879114483\n ],\n [\n -111.56647259158387,\n 45.00795879114483\n ],\n [\n -111.56647259158387,\n 47.58172589143089\n ],\n [\n -115.67714251796224,\n 47.58172589143089\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
Quantifying the marine radiocarbon reservoir effect, offsets (ΔR), and ΔR variability over time is critical to improving dating estimates of marine samples while also providing a proxy of water mass dynamics. In the northeastern Pacific, where no high-resolution time series of ΔR has yet been established, we sampled radiocarbon (14C) from exactly dated growth increments in a multicentennial chronology of the long-lived bivalve, Pacific geoduck (Paneopea generosa) at the Tree Nob site, coastal British Columbia, Canada. Samples were taken at approximately decadal time intervals from 1725 CE to 1920 CE and indicate average ΔR values of 256 ± 22 years (1σ) consistent with existing discrete estimates. Temporal variability in ΔR is small relative to analogous Atlantic records except for an unusually old-water event, 1802–1812. The correlation between ΔR and sea surface temperature (SST) reconstructed from geoduck increment width is weakly significant (r2 = .29, p = .03), indicating warm water is generally old, when the 1802–1812 interval is excluded. This interval contains the oldest (–2.1σ) anomaly, and that is coincident with the coldest (–2.7σ) anomalies of the temperature reconstruction. An additional 32 14C values spanning 1952–1980 were detrended using a northeastern Pacific bomb pulse curve. Significant positive correlations were identified between the detrended 14C data and annual El Niño Southern Oscillation (ENSO) and summer SST such that cooler conditions are associated with older water. Thus, 14C is generally relatively stable with weak, potentially inconsistent associations to climate variables, but capable of infrequent excursions as illustrated by the unusually cold, old-water 1802–1812 interval.
Hydrologic and geophysical components interact to produce streambed scour. This study investigates methods for improving the utility of estimates of hydrologic flow in streams and rivers used when evaluating potential pier scour over the design-life of highway bridges in Virginia. Recent studies of streambed composition identify potential bridge design cost savings when attributes of cohesive soil and weathered rock unique to certain streambeds are considered within the bridge planning design. To achieve potential cost savings, however, attributes and effects of scour forces caused by water movement across the streambed surface must be accurately described and estimated.
This study explores the potential for improving estimates of the hydrologic component, namely hydrologic flow, afforded by empirically based deterministic, probabilistic, and statistical modeling of flows using streamgage data from 10 selected sites in Virginia. Methods are described and tools are provided that may assist with estimating hydrological components of flow duration and potential cumulative stream power for bridge designs in specific settings, and calculation of comprehensive projections of anticipated individual bridge pier scour rates. Examples of hydrologic properties needed to determine the rates of streambed scour are described for sites spanning a range of basin sizes and locations in Virginia. Deterministic, probabilistic, and statistical modeling methods are demonstrated for estimating hydrological components of streambed scour over a bridge design lifespan. Eight tools provide examples of streamflow analysis using daily and instantaneous streamflow data collected at 10 study sites in Virginia. Tool 1 provides a generalized system dynamics model of streamflow and sediment motion that may be used to estimate hydrologic flow over time. Tool 2 illustrates at-a-station hydraulic geometry using methods pioneered by Leopold and others. Tool 3 provides a system dynamics model developed to test the use of Monte-Carlo sampling of instantaneous streamflow measurements to augment and increase precision of site-specific period-of-record daily-flow values useful for driving stream-power and streambed scour estimates. Tool 4 integrates deterministic modeling, maximum likelihood logistic regression, and Monte-Carlo sampling to identify probable hydrologic flows. Tool 5 provides instantaneous flow hydrologic envelope profiles, using measured instantaneous flow data integrated with measured daily-flow value data. Tool 6 provides precise estimates of hydrologic flow over entire data time-series suitable for driving scour simulation models. Tool 7 provides a threshold of flow and probability of time-under-load interactive calculator that allows selection of a desired bridge design lifespan, ranging from 1 to 250 years, and identification of a flow interval of interest. Tool 8 provides a flow-random sampling interactive tool, developed to facilitate easy access to large datasets of randomly sampled flow data measurements from unique locations for purposes of computing and testing future models of bridge pier scour.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225059","collaboration":"Prepared in cooperation with the Virginia Department of Transportation","usgsCitation":"Austin, S.H., 2022, Virginia Bridge Scour Pilot Study—Hydrological Tools: U.S. Geological Survey Scientific Investigations Report 2022–5059, 46 p., https://doi.org/10.3133/sir20225059.","productDescription":"Report: vii, 46 p.; Data Release; Dataset","numberOfPages":"46","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-137495","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":408486,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P957ABZN","text":"USGS data release","linkHelpText":"Virginia bridge scour pilot study streamflow data"},{"id":408487,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the nation"},{"id":408485,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5059/sir20225059.XML"},{"id":408484,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5059/images/"},{"id":408483,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225059/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5059"},{"id":408482,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5059/sir20225059.pdf","text":"Report","size":"9.59 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5059"},{"id":408481,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5059/coverthb.jpg"}],"country":"United States","state":"Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.28857421875,\n 39.554883059924016\n ],\n [\n -80.39794921875,\n 38.18638677411551\n ],\n [\n -80.4638671875,\n 37.52715361723378\n ],\n [\n -77.49755859375,\n 37.59682400108367\n ],\n [\n -77.32177734375,\n 39.53793974517628\n ],\n [\n -78.28857421875,\n 39.554883059924016\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, Virginia 23228
Planning stormwater projects requires estimates of current and future extreme precipitation depths for events with specified return periods and durations. In this study, precipitation data from four downscaled climate datasets are used to determine changes in precipitation depth-duration-frequency curves from the period 1966–2005 to the period 2050–89 primarily on the basis of Representative Concentration Pathways 4.5 and 8.5 emission scenarios from the Coupled Model Intercomparison Project Phase 5. The four downscaled climate datasets are (1) the Coordinated Regional Downscaling Experiment (CORDEX) dataset, (2) the Localized Constructed Analogs (LOCA) dataset, (3) the Multivariate Adaptive Constructed Analogs (MACA) dataset, and (4) the Jupiter Intelligence Weather Research and Forecasting Model (JupiterWRF) dataset. Change factors—multiplicative changes in expected extreme precipitation magnitude from current to future period—were computed for grid cells from the downscaled climate datasets containing National Oceanic and Atmospheric Administration Atlas 14 stations in central and south Florida. Change factors for specific durations and return periods may be used to scale the National Oceanic and Atmospheric Administration Atlas 14 historical depth-duration-frequency values to the period 2050–89 on the basis of changes in extreme precipitation derived from downscaled climate datasets. Model culling was implemented to select downscaled climate models that best captured observed historical patterns of precipitation extremes in central and south Florida.
Overall, a large variation in change factors across downscaled climate datasets was found, with change factors generally greater than one and increasing with return period. In general, median change factors were higher for the south-central Florida climate region (1.05–1.55 depending on downscaled climate dataset, duration, and return period) than for the south Florida climate region (1–1.4 depending on downscaled climate dataset, duration, and return period) when considering best performing models for both areas, indicating a projected overall increase in future extreme precipitation events.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225093","collaboration":"Prepared in cooperation with the South Florida Water Management District","usgsCitation":"Irizarry-Ortiz, M.M., Stamm, J.F., Maran, C., and Obeysekera, J., 2022, Development of projected depth-duration frequency curves (2050–89) for south Florida: U.S. Geological Survey Scientific Investigations Report 2022–5093, 114 p., https://doi.org/10.3133/sir20225093.","productDescription":"Report: xii, 114 p.; 1 Table; Data Release","numberOfPages":"130","onlineOnly":"Y","ipdsId":"IP-134493","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":408474,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P935WRTG","text":"USGS data release","linkHelpText":"Change factors to derive projected future precipitation depth-duration-frequency (DDF) curves at 174 National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in central and south Florida"},{"id":435653,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Q3LEIL","text":"USGS data release","linkHelpText":"Change factors to derive projected future precipitation depth-duration-frequency (DDF) curves at 242 National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in Florida (ver 2.0, May 2024)"},{"id":408853,"rank":8,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225093/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":408472,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5093/sir20225093_table1.1.xlsx","text":"Table 1.1","size":"50.0 kB","linkFileType":{"id":3,"text":"xlsx"}},{"id":408471,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5093/images"},{"id":408470,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5093/sir20225093.XML"},{"id":408469,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5093/sir20225093.pdf","text":"Report","size":"23.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5093"},{"id":408468,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5093/coverthb.jpg"},{"id":408473,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5093/sir20225093_table1.1.csv","text":"Table 1.1","size":"18.6 kB","linkFileType":{"id":7,"text":"csv"}}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.111572265625,\n 24.327076540018634\n ],\n [\n -79.43115234375,\n 24.327076540018634\n ],\n [\n -79.43115234375,\n 28.98892237190413\n ],\n [\n -83.111572265625,\n 28.98892237190413\n ],\n [\n -83.111572265625,\n 24.327076540018634\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
The horizontal‐to‐vertical spectral ratios from microtremor (mHVSR) data obtained at 196 seismic stations in California are used to evaluate three alternative microtremor‐based proxies for site amplification for use in ground‐motion models (GMMs): the site fundamental period (
The Navajo (N) aquifer is an extensive aquifer and the primary source of groundwater in the 5,400-square-mile Black Mesa area in northeastern Arizona. Water availability is an important issue in the Black Mesa area because of the arid climate, past industrial water use, and continued water requirements for municipal use by a growing population. Precipitation in the area typically ranges from less than 6 to more than 16 inches per year depending on location.
The U.S. Geological Survey water-monitoring program in the Black Mesa area began in 1971 and provides information about the long-term effects of groundwater withdrawals from the N aquifer for industrial and municipal uses. This report presents results of data collected as part of the monitoring program in the Black Mesa area from calendar year 2019, and additionally uses streamflow statistics from November and December 2018. The monitoring program includes measurements of (1) groundwater withdrawals (pumping), (2) groundwater levels, (3) spring discharge, (4) surface-water discharge, and (5) groundwater chemistry.
In calendar year 2019, total groundwater withdrawals were estimated to be 3,070 acre-feet (acre-ft), industrial withdrawals were 670 acre-ft, and municipal withdrawals were estimated to be 2,400 acre-ft. Total withdrawals during 2019 were about 58 percent less than total withdrawals in 2005 because of Peabody Western Coal Company’s discontinued use of water to transport coal in a coal slurry pipeline after 2005 and cessation of mining operations in 2019.
Water levels measured in 2019 from wells completed in the unconfined areas of the N aquifer within the Black Mesa area showed a decline in 10 of 16 wells when compared with water levels from the prestress period (prior to 1965). The changes in water levels across all 16 wells ranged from +8.2 feet (ft) to −40.0 ft, and the median change was −1.7 ft. Water levels also showed decline in 16 of 18 wells measured in the confined area of the aquifer when compared to the prestress period. The median change for the confined area of the aquifer was −38.8 ft, with changes across all 18 wells ranging from +12.9 ft to −185.0 ft.
Spring flow was measured at four springs in 2019. Flow fluctuated during the period of record for Burro Spring and Pasture Canyon Spring, but a decreasing trend was statistically significant (p<0.05) at Moenkopi School Spring and Unnamed Spring near Dennehotso. Discharge at Burro Spring has remained relatively constant since it was first measured in the 1980s and discharge at Pasture Canyon Spring has fluctuated for the period of record.
Continuous records of surface-water discharge in the Black Mesa area were collected from streamflow-gaging stations at the following sites: Moenkopi Wash at Moenkopi 09401260 (1976 to 2019), Dinnebito Wash near Sand Springs 09401110 (1993 to 2019), Polacca Wash near Second Mesa 09400568 (1994 to 2019), and Pasture Canyon Springs 09401265 (2004 to 2019). Median winter flows (November through February) of each winter were used as an estimate of the amount of groundwater discharge at the above-named sites. For the period of record, the median winter flows have generally remained constant at Polacca Wash and Pasture Canyon Springs, whereas a decreasing trend was indicated at Moenkopi Wash and Dinnebito Wash.
In 2019, water samples collected from four springs and three wells in the Black Mesa area were analyzed for selected chemical constituents. Results from the four springs were compared with previous analyses from the same springs. Concentrations of dissolved solids, chloride, and sulfate increased at Moenkopi School Spring during the more than 30 years of record at that site. Concentrations of dissolved solids, chloride, and sulfate at Pasture Canyon Spring have not varied significantly (p>0.05) since the early 1980s, and there is no increasing or decreasing trend in those data. Concentrations of dissolved solids, chloride, and sulfate at Unnamed Spring near Dennehotso have varied for the period of record, but there is no statistical trend in the data. Concentrations of dissolved solids and chloride at Burro Spring have varied for the period of record, but there is no statistical trend in the data; however, concentrations of sulfate from Burro Spring now show a trend towards lower concentrations. No statistical trend tests were performed for the three wells sampled in 2019 since less historical water-quality data were available for comparison.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221086","collaboration":"Prepared in cooperation with the Navajo Nation and Peabody Western Coal Company","usgsCitation":"Mason, J.P., 2022, Groundwater, surface-water, and water-chemistry data, Black Mesa area, northeastern Arizona—2018–2019: U.S. Geological Survey Open-File Report 2022–1086, 47 p., https://doi.org/10.3133/ofr20221086.","productDescription":"vii, 47 p.","numberOfPages":"47","onlineOnly":"Y","ipdsId":"IP-119897","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":501833,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113767.htm","linkFileType":{"id":5,"text":"html"}},{"id":408436,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20211124","text":"Open-File Report 2021-1124","linkHelpText":"- Groundwater, Surface-Water, and Water-Chemistry Data, Black Mesa Area, Northeastern Arizona—2016–2018"},{"id":408427,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1086/covrthb.jpg"},{"id":408428,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1086/ofr20221086.pdf","text":"Report","size":"11 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2022-1086"}],"country":"United States","state":"Arizona","otherGeospatial":"Black Mesa area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.06054687499999,\n 34.94899072578227\n ],\n [\n -109.390869140625,\n 34.94899072578227\n ],\n [\n -109.390869140625,\n 36.96744946416934\n ],\n [\n -112.06054687499999,\n 36.96744946416934\n ],\n [\n -112.06054687499999,\n 34.94899072578227\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
Earthquake scientists have traditionally relied on relatively small data sets recorded on small numbers of instruments. With advances in both instrumentation and computational resources, the big-data era, including an established norm of open data-sharing, allows seismologists to explore important issues using data volumes that would have been unimaginable in earlier decades. Alongside with these developments, the community has moved towards routine production of interpreted data products such as seismic moment tensor catalogs that have provided an additional boon to earthquake science. As these products have become increasingly familiar and useful, it is important to bear in mind that they are not data, but rather interpreted data products. As such, they differ from data in ways that can be important, but not always appreciated. Important - and sometimes surprising - issues can arise if methodology is not fully described, data from multiple sources are included, or data products are not versioned (time-stamped). The line between data and data products is sometimes blurred, leading to an underappreciation of issues that affect data products. This note illustrates examples from two widely used data products: moment tensor catalogs and Did You Feel It? (DYFI) macroseismic intensity values. These examples show that increasing a data product’s documentation, independence, and stability can make it even more useful. To ensure the reproducibility of studies using data products, time-stamped products should be preserved, for example as electronic supplements to published papers, or, ideally, a more permanent repository.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/feart.2022.988098","usgsCitation":"Rosler, B., Stein, S., and Hough, S.E., 2022, On the documentation, independence, and stability of widely used seismological data products: Frontiers in Earth Science, v. 10, 988098, 10 p., https://doi.org/10.3389/feart.2022.988098.","productDescription":"988098, 10 p.","ipdsId":"IP-141028","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":487651,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2022.988098","text":"Publisher Index Page"},{"id":482161,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","noUsgsAuthors":false,"publicationDate":"2022-10-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Rosler, Boris","contributorId":350977,"corporation":false,"usgs":false,"family":"Rosler","given":"Boris","affiliations":[],"preferred":false,"id":927623,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stein, Seth","contributorId":263457,"corporation":false,"usgs":false,"family":"Stein","given":"Seth","affiliations":[{"id":25254,"text":"Northwestern University","active":true,"usgs":false}],"preferred":false,"id":927624,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hough, Susan E. 0000-0002-5980-2986","orcid":"https://orcid.org/0000-0002-5980-2986","contributorId":263442,"corporation":false,"usgs":true,"family":"Hough","given":"Susan","email":"","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927625,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70237596,"text":"sir20225010 - 2022 - Sources and characteristics of dissolved organic carbon in the McKenzie River, Oregon, related to the formation of disinfection by-products in treated drinking water","interactions":[],"lastModifiedDate":"2026-04-08T17:23:29.228484","indexId":"sir20225010","displayToPublicDate":"2022-10-14T12:12:02","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5010","displayTitle":"Sources and Characteristics of Dissolved Organic Carbon in the McKenzie River, Oregon, Related to the Formation of Disinfection By-Products in Treated Drinking Water","title":"Sources and characteristics of dissolved organic carbon in the McKenzie River, Oregon, related to the formation of disinfection by-products in treated drinking water","docAbstract":"This study characterized the concentration and quality of dissolved organic carbon (DOC) in the McKenzie River, a relatively undeveloped watershed in western Oregon, and its link to forming disinfection by-products (DBPs) in treated drinking water. The study aimed to identify the primary source(s) of DOC in source water for the Eugene Water & Electric Board’s (EWEB) conventional treatment plant on the McKenzie River near river mile 11, upstream of Hayden Bridge. The two classes of regulated compounds examined—trihalomethanes (THMs) and haloacetic acids (HAAs)—form when organic carbon in raw source water reacts with chlorine and (or) bromine during water treatment.
The objectives of the study were to:
Eleven storms were sampled synoptically in upstream-to-downstream fashion to provide a “snapshot” of water quality conditions at four sites on the McKenzie River from Frissell Bridge (6 miles downstream from Trail Bridge Reservoir) to the EWEB water treatment plant at Hayden Bridge and nine contributing tributaries. Storms included late summer and early autumn “first flush” events and late autumn, winter, and spring storms spanning a range in streamflows from 3,000 to 26,000 cubic feet per second as measured in the main stem McKenzie River at the EWEB water intake.
Water samples were analyzed for DOC concentrations and optical properties (fluorescence and ultraviolet absorbance [UVA]) across a range of wavelengths to characterize the quantity and quality of dissolved organic matter (DOM) in the McKenzie River at the drinking water intake and upstream locations. Paired sets of source and finished water samples were collected at the EWEB treatment plant to identify DOC quality parameters in raw source water that might predict DBP concentrations in finished drinking water.
DOC concentrations were relatively low in the McKenzie River (0.4–3 milligrams per liter [mg/L]; average 1.5 mg/L) but much higher in the tributaries. The highest DOC concentrations occurred during “first flush” storms in October 2012 and September 2013; the highest value (16 mg/L) was measured at the 52nd Street stormwater outfall. The average DOC concentration in the lower basin-tributaries was 3.8 mg/L; three middle basin tributaries—Quartz, Gate, and Haagen Creeks, which drain private forestland with less coniferous forest compared with other higher elevation tributaries— had slightly lower average DOC concentrations (2.8 mg/L). These middle-basin watersheds may be important sources of DOC and DBP precursors to the McKenzie River, even more so than the lower basin tributaries, depending on their flows (and loads). This is particularly true after the September 2020 Holiday Farm fire, which burned much of this area.
DOC concentrations increased 68 percent in the McKenzie River between the uppermost reference site at Frissell Bridge and Vida; this includes drainage from Quartz Creek, Blue River Lake and Cougar Reservoir, which all contributed DOC to the main stem. In contrast, the lowermost tributaries draining most of the agricultural and urban land did not have a large effect on DOC in the McKenzie River despite their higher DOC concentrations because of their presumed relatively low streamflows and, consequently, DOC loads. Apart from the continuous flow monitors in the McKenzie River and some tributaries (Blue River and South Fork McKenzie River, and streamflow at Hayden Bridge and Vida, Camp Creek and some other locations), streamflow was not assessed during sample collection for this study. This lack of streamflow data precludes a detailed analysis of loads, which is discussed in the future studies section.
All DBP concentrations in finished drinking water were less than EPA maximum contaminant levels (MCLs) of 0.080 mg/L for the four trihalomethanes (THM4) and 0.060 mg/L for five haloacetic acids (HAA5). During the 11 storm sampling events the maximum summed concentrations were about 0.040 mg/L for both THM4 and HAA5. Compliance monitoring samples, collected separately by EWEB, yielded some higher concentrations—0.046 mg/L THM4 and 0.047 HAA5—during the December 2012 storm. The corresponding benchmark quotient (BQ) values, which indicate how close a measured DBP concentration is to the MCL, were 0.58 and 0.78, respectively, for THM4 and HAA5. Compared with a similar 2007–08 McKenzie River study that did not target storm events, concentrations of THM4 and HAA5 in finished water were 68 percent and 33 percent higher, respectively, during the current study.
Due to the high dilution rates in the McKenzie River main stem, many of the individual fluorescence excitation-emission measurements were low (<0.1 Raman units) and approached analytical detection limits. Parallel factor analysis (PARAFAC) resulted in a five-component model (C1–C5) that represents five unique organic fluorophores. Components C1, C2, and C3 represent DOM associated with soil-derived, humic-like, more degraded organic matter. In contrast, components C4 and C5 represent “fresher” DOM, derived from terrestrial and aquatic plants, including algae and cyanobacteria that are common in the McKenzie River and its tributaries and reservoirs. The fluorescence data and PARAFAC modeling suggest that most of the DOC in the McKenzie River originated from terrestrial sources (primarily components C1 and C2). The largest increases in DOC in the main stem occurred in the reach upstream of Vida, from inflows by Quartz Creek, Blue River, South Fork McKenzie River, and other tributaries.
Concentrations of DBPs in EWEB’s finished drinking water were positively correlated with DOC concentrations in raw source water (THM4, p<0.05; HAA5, p<0.01) for paired samples collected 12−24 hours apart. DOC concentrations were significantly positively correlated (p<0.001) with laboratory-based fluorescent dissolved organic matter (fDOM) measurements, suggesting fDOM as a useful parameter for monitoring and predicting DOC concentration in surface water and DBP concentrations in finished water.
Of all the PARAFAC components in surface water, C5 had the highest correlations with DBPs in finished water (rho = 0.77–0.84, p<0.01), followed by components C1 and C2 (rho = 0.75 and 0.71, respectively, p<0.01). This C5 carbon is associated with recently produced DOM, possibly from decomposed terrestrial and aquatic vegetation. Model loadings of these three components were considerably higher in the sampled tributaries relative to the main stem McKenzie River, with most of the observed increases in the main stem apparent at Vida. This points to Quartz Creek or other tributaries in the reach between Frissell Bridge and the sampling site near Vida (South Fork McKenzie and Blue Rivers) as potentially key contributors of DOM source material that leads to the production of DBPs in treated drinking water. A limited load analysis showed that the reservoirs contributed 8–37 percent of the instantaneous DOC loads observed at Vida at the time of sampling, which suggests other sources such as Quartz Creek and other streams in the reach between Frissell Bridge and Vida are more important.
Random forest analyses identified PARAFAC components C1 and C5 and fluorescence peaks A, C, M, T and N as the best predictors for HAA5 concentrations in finished drinking water, explaining 62.5 percent of the variation. The best predictors for THM4 were C1, C4 + C5, and peaks T, A, and N, which explained 33 percent of the variation.
Several land cover and vegetation classes were correlated with DOC concentration and other optical measurements. The percentage of evergreen forest in each of the subwatersheds sampled was negatively correlated (p<0.001) with DOC concentration and many optical indicators of DOM quantity: UVA254, fDOM, and all of the fluorescence peaks. In contrast, mixed (deciduous) forest was positively correlated (p<0.001) with DOC, fDOM, UVA254, and several fluorescence peaks, demonstrating the importance of deciduous leaf fall in generating DOC and DBP precursors.
The high level of human activities in the middle and lower portion of the basin—including timber harvesting and road construction on private forestland, agricultural, rural, industrial, and urban development—have resulted in the greatest loss in native coniferous and mixed deciduous forests in the basin. DOC loading from these tributaries and reservoir releases, which contain DOC from terrestrial and aquatic productivity, both enrich the McKenzie River. Concentrations of DOC increased an average of 71 percent (range 30–120 percent) in the McKenzie River between Frissell Bridge, the upstream reference site, and Vida. PARAFAC components C1, C2, and C5—which were correlated with DBPs in finished water—increased, on average, 109–136 percent (range 20–250 percent) in this same Frissell-to-Vida reach. These increases occur from input of tributaries in the middle basin such as Quartz Creek and others, as noted above.
Future monitoring, field, and lab studies can improve our understanding of seasonal and spatial sources of organic carbon contributing DBP precursors to the McKenzie River and allow detection of long-term trends resulting from the recent Holiday Farm Fire, which burned 173,393 acres of forestland, including riparian areas along the main stem, and numerous structures, homes, and outbuildings in September 2020. Future studies could examine DOC fluxes and flushing of carbon from the watershed, investigate the role of precipitation amount and intensity in mobilizing carbon and sediment, and evaluate impacts to aquatic communities and human health as part of a post-fire assessment. Other areas ripe for study include evaluating the impacts of potential temperature increases on carbon sequestration and decomposition in the burned and unburned forests and identifying practices that foster sequestration of carbon in forest soils.
The use of fluorescence sensors such as fDOM to monitor the concentration and composition of raw water supplies may be improved for detection of specific DBP precursors, to provide continuous and real-time information to treatment plant operators. Future studies that monitor DOM amount and quality, and DBP Formation Potential (FP), particularly during storm events, paired with streamflow measurements, as suggested above, could help identify areas that contribute high DOC loads and thus help managers identify the key areas to focus restoration activities. Other studies could examine treatment options for currently regulated DBPs and potentially unregulated compounds, including advanced biological treatments for their removal.
This study was a collaboration between the U.S. Geological Survey (USGS) and EWEB in Eugene, Oregon, with additional funding provided from USGS Cooperative Matching Funds Program.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225010","collaboration":"Prepared in Cooperation with Eugene Water & Electric Board","usgsCitation":"Carpenter, K.D., Kraus, T.E., Hansen, A.M., Downing, B.D., Goldman, J.H., Haynes, J., Donahue, D., and Morgenstern, K., 2022, Sources and characteristics of dissolved organic carbon in the McKenzie River, Oregon, related to the formation of disinfection by-products in treated drinking water: U.S. Geological Survey Scientific Investigations Report 2022–5010, 50 p., https://doi.org/10.3133/sir20225010.","productDescription":"Report: viii, 50 p.; Table","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-117763","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":408395,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QPSIG3","text":"USGS data release","description":"USGS data release.","linkHelpText":"Absorbance and fluorescence measurements and concentrations of disinfection by-products in source water and finished water in the McKenzie River Basin, Oregon: 2012-2014"},{"id":408366,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5010/sir20225010_table1.1.xlsx","text":"Table 1.1","size":"37 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2022-5010 table 1.1"},{"id":408301,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5010/images"},{"id":408299,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5010/coverthb2.jpg"},{"id":408302,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5010/sir20225010.XML"},{"id":408300,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5010/sir20225010.pdf","text":"Report","size":"4.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5010"},{"id":502297,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113766.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Oregon","otherGeospatial":"McKenzie River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.125,\n 43.8\n ],\n [\n -121.875,\n 43.8\n ],\n [\n -121.875,\n 44.3\n ],\n [\n -123.125,\n 44.3\n ],\n [\n -123.125,\n 43.8\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
Fragile geologic features (FGFs), which are extant on the landscape but vulnerable to earthquake ground shaking, may provide geological constraints on the intensity of prior shaking. These empirical constraints are particularly important in regions such as the Pacific Northwest that have not experienced a megathrust earthquake in written history. Here, we describe our field survey of FGFs in southern Oregon. We documented 58 features with fragile geometric characteristics, as determined from field measurements of size and strength, historical photographs, and light detection and ranging point clouds. Among the surveyed FGFs, sea stacks have particular advantages for use as ground‐motion constraints: (1) they are frequently tall and thin; (2) they are widely distributed parallel to the coast, proximal to the trench and the likely megathrust rupture surface; and (3) they are formed by sea cliff retreat, meaning that their ages may be coarsely estimated as a function of distance from the coast. About 40% of the surveyed sea stacks appear to have survived multiple Cascadia megathrust earthquakes. Using a quasi‐static analysis, we estimate the minimum horizontal ground accelerations that could fracture the rock pillars. We provide context for the quasi‐static results by comparing them with predictions from kinematic simulations and ground‐motion prediction equations. Among the sea stacks old enough to have survived multiple megathrust earthquakes (n = 16), eight yield breaking accelerations lower than the predictions, although they generally overlap within uncertainty. FGFs with the lowest breaking accelerations are distributed uniformly over 130 km of coastline. Results for inland features, such as speleothems, are in close agreement with the predictions. We conclude that FGFs show promise for investigating both past earthquake shaking and its spatial variability along the coasts of Oregon and Washington, where sea stacks are often prevalent. Future work can refine our understanding of FGF age and evolution.
The late Pleistocene was a climatically dynamic period, with abrupt shifts between cool-wet and warm-dry conditions. Increased effective precipitation supported large pluvial lakes and long-lived spring ecosystems in valleys and basins throughout the western and southwestern U.S., but the source and seasonality of the increased precipitation are debated. Increases in the proportions of C4/(C4+ C3) grasses in the diets of large grazers have been ascribed both to increases in summer precipitation and lower atmospheric CO2 levels. Here we present stable carbon and oxygen isotope data from tooth enamel of late Pleistocene herbivores recovered from paleowetland deposits at Tule Spring Fossil Beds National Monument in the Las Vegas Valley of southern Nevada, as well as modern herbivores from the surrounding area. We use these data to investigate whether winter or summer precipitation was responsible for driving the relatively wet hydroclimate conditions that prevailed in the region during the late Pleistocene. We also evaluate whether late Pleistocene grass C4/(C4+ C3) was higher than today, and potential drivers of any changes.
Tooth enamel δ18O values for Pleistocene Equus, Bison, and Mammuthus are generally low (average 22.0 ± 0.7‰, 2 s.e., VSMOW) compared to modern equids (27.8 ± 1.5‰), and imply lower water δ18O values (−16.1 ± 0.8‰) than modern precipitation (−10.5‰) or in waters present in active springs and wells in the Las Vegas Valley (−12.9‰), an area dominated by winter precipitation. In contrast, tooth enamel of Camelops (a browser) generally yielded higher δ18O values (23.9 ± 1.1‰), possibly suggesting drought tolerance. Mean δ13C values for the Pleistocene grazers (−6.6 ± 0.7‰, 2 s.e., VPDB) are considerably higher than for modern equids (−9.6 ± 0.4‰) and indicate more consumption of C4 grass (17 ± 5%) than today (4 ± 4%). However, calculated C4 grass consumption in the late Pleistocene is strikingly lower than the proportion of C4 grass taxa currently present in the valley (55–60%). δ13C values in Camelops tooth enamel (−7.7 ± 1.0‰) are interpreted as reflecting moderate consumption (14 ± 8%) of Atriplex (saltbush), a C4 shrub that flourishes in regions with hot, dry summers.
Lower water δ18O values, lower abundance of C4 grasses, and the inferred presence of Atriplex are all consistent with general circulation models for the late Pleistocene that show enhanced delivery of winter precipitation, sourced from the north Pacific, into the interior western U.S. but do not support alternative models that infer enhanced delivery of summer precipitation, sourced from the tropics. In addition, we hypothesize that dietary competition among the diverse and abundant Pleistocene fauna may have driven the grazers analyzed here to feed preferentially on C4 grasses. Dietary partitioning, especially when combined with decreased pCO2 levels during the late Pleistocene, can explain the relatively high δ13C values observed in late Pleistocene grazers in the Las Vegas Valley and elsewhere in the southwestern U.S. without requiring additional summer precipitation. Pleistocene hydroclimate parameters derived from dietary and floral records may need to be reevaluated in the context of the potential effects of dietary preferences and lower pCO2 levels on the stability of C3 vs. C4 plants.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.quascirev.2022.107784","usgsCitation":"Kohn, M.J., Springer, K.B., Pigati, J.S., Reynard, L., Drewicz, A.E., Crevier, J., and Scott, E., 2022, Seasonality of precipitation in the southwestern United States during the late Pleistocene inferred from stable isotopes in herbivore tooth enamel: Quaternary Science Reviews, v. 296, 107784, 21 p., https://doi.org/10.1016/j.quascirev.2022.107784.","productDescription":"107784, 21 p.","ipdsId":"IP-141465","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":446131,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.quascirev.2022.107784","text":"Publisher Index Page"},{"id":435657,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DBH6V7","text":"USGS data release","linkHelpText":"Data release for Seasonality of precipitation in 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Center","active":true,"usgs":true}],"preferred":true,"id":854448,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pigati, Jeffrey S. 0000-0001-5843-6219 jpigati@usgs.gov","orcid":"https://orcid.org/0000-0001-5843-6219","contributorId":201167,"corporation":false,"usgs":true,"family":"Pigati","given":"Jeffrey","email":"jpigati@usgs.gov","middleInitial":"S.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":854449,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reynard, Linda 0000-0001-5732-1532","orcid":"https://orcid.org/0000-0001-5732-1532","contributorId":260328,"corporation":false,"usgs":false,"family":"Reynard","given":"Linda","email":"","affiliations":[{"id":16811,"text":"Harvard University","active":true,"usgs":false}],"preferred":false,"id":854450,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Drewicz, Amanda E.","contributorId":297343,"corporation":false,"usgs":false,"family":"Drewicz","given":"Amanda","email":"","middleInitial":"E.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":854451,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Crevier, Justin","contributorId":297344,"corporation":false,"usgs":false,"family":"Crevier","given":"Justin","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":854452,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Scott, Eric","contributorId":127422,"corporation":false,"usgs":false,"family":"Scott","given":"Eric","email":"","affiliations":[],"preferred":false,"id":854453,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70237484,"text":"sir20225095 - 2022 - Updated annual and semimonthly streamflow statistics for Wild and Scenic Rivers, Owyhee Canyonlands Wilderness, southwestern Idaho, 2021","interactions":[],"lastModifiedDate":"2024-05-07T20:58:03.278223","indexId":"sir20225095","displayToPublicDate":"2022-10-12T10:35:13","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5095","displayTitle":"Updated Annual and Semimonthly Streamflow Statistics for Wild and Scenic Rivers, Owyhee Canyonlands Wilderness, Southwestern Idaho, 2021","title":"Updated annual and semimonthly streamflow statistics for Wild and Scenic Rivers, Owyhee Canyonlands Wilderness, southwestern Idaho, 2021","docAbstract":"The U.S. Geological Survey, in cooperation with the Bureau of Land Management (BLM), continued streamflow data collection in water years 2013–21 to update daily streamflow regressions and annual and semimonthly streamflow statistics initially developed in 2012 for streams designated as “wild,” “scenic,” or “recreational” under the National Wild and Scenic Rivers System in the Owyhee Canyonlands Wilderness in southwestern Idaho. To sustain “outstanding remarkable values” in the Owyhee Canyonlands Wilderness, BLM determined that maintaining specific streamflow conditions in rivers was important for sustaining ecological health, recreational opportunities, and water demands for stock water and irrigation in a region with increased pressure from upstream land development. Streamflow statistics previously developed using regional regressions based on limited number of streamgages and generalized basin characteristics were determined to inaccurately represent hydrologic characteristics in the Owyhee Canyonlands Wilderness.
In this study, updated streamflow regressions and statistics are provided for 11 partial-record sites in the Owyhee Canyonlands Wilderness using 311 additional streamflow measurements. A partial-record Maintenance of Variance Extension, Type 1 (MOVE.1) streamflow regression method was used to relate discrete streamflow measurements collected at partial-record sites with daily mean streamflow at nearby index sites. The updated regressions were used to estimate a synthetic daily mean streamflow record at each partial-record site for the period of record of the selected index site. The computed synthetic streamflow record was then used to determine annual and semimonthly streamflow statistics at each partial-record site. Annual bankfull streamflow statistics were calculated at each partial-record site using the computed bankfull streamflow at the selected index site and the updated streamflow regression.
Additional streamflow measurements representing a larger range of hydrologic conditions since 2012, reevaluation of index site selection, and updated regression techniques improved streamflow statistic estimates in the Owyhee Canyonlands Wilderness. Regression performance was evaluated based on the coefficient of determination (R2) between the partial-record and index sites, percent bias, and similarity of basin characteristics between the selected index site and the partial-record site. Generally, the updated regressions performed well for partial-record sites with an index site located upstream or downstream on the same stream. Regression performance was degraded and less robust for index sites located farther away from the corresponding partial-record site. Additional streamflow measurements at partial-record sites with few measurements over a small range in hydrologic conditions could improve regression performance and reduce prediction intervals. Furthermore, additional index sites in the Owyhee Canyonlands Wilderness could improve the updated streamflow regressions and statistics.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225095","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Dudunake, T.J., and Ducar, S.D., 2022, Updated annual and semimonthly streamflow statistics for Wild and Scenic Rivers, Owyhee Canyonlands Wilderness, southwestern Idaho, 2021 (ver. 1.1, May 2024): U.S. Geological Survey Scientific Investigations Report 2022–5095, 31 p., https://doi.org/10.3133/sir20225095.","productDescription":"Report: viii, 31 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-128129","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":408220,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5095/sir20225095.XML"},{"id":408218,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XSJA24","text":"USGS data release","description":"USGS data release","linkHelpText":"Streamflow regressions and annual and semimonthly exceedance probability statistics for Wild and Scenic Rivers, Owyhee Canyonlands Wilderness, Idaho"},{"id":408217,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5095/sir20225095.pdf","text":"Report","size":"5.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5095"},{"id":408221,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225095/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5095"},{"id":408219,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5095/images"},{"id":428468,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2022/5095/versionHistory.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"},"description":"Version History"},{"id":408216,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5095/coverthb2.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Owyhee Canyonlands Wilderness","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.02636718749999,\n 42.00848901572399\n ],\n [\n -114.14794921875,\n 42.00848901572399\n ],\n [\n -114.14794921875,\n 43.50872101129684\n ],\n [\n -117.02636718749999,\n 43.50872101129684\n ],\n [\n -117.02636718749999,\n 42.00848901572399\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702-4520
Aim: Anticipating when and where changes in species' demographic rates will lead to range shifts in response to changing climate remains a major challenge. Despite evidence of increasing mortality in dry forests across the globe in response to drought and warming temperatures, the overall impacts on the distribution of dry forests are largely unknown because we lack comparable large-scale data on tree recruitment rates. Here, our aim was to develop range-wide population models for dry forest tree species (pinyon pine and juniper), quantifying both mortality and recruitment, to better understand where and under what conditions species range contractions are occurring.
Location: Western United States.
Major taxa studied: Two pinyon pine (Pinus spp.) and three juniper (Juniperus spp.) species.
Methods: We developed range-wide demographic models for five species using forest inventory data from across the western United States and estimated population trends and climate vulnerability.
Results: We find that four of the five species are declining in parts of their range, with Pinus edulis having the largest proportion of populations declining (24%). Population vulnerability increases with aridity and temperature, with up to ~50% of populations declining in the warmest and driest conditions. Mortality and recruitment were both essential to explaining where populations are declining.
Main conclusions: Our results suggest that dry forest species are undergoing an active range shift driven by both changing recruitment and mortality, and that increasing temperatures and drought threaten the long-term viability of many of these species in their current range. While four of the five species examined were experiencing some declines, P. edulis is currently most vulnerable. Management actions such as reducing tree density may be able to mitigate some of these impacts. The framework we present to estimate range-wide demographic rates can be applied to other species to determine where range contractions are most likely.
","language":"English","publisher":"Wiley","doi":"10.1111/geb.13582","usgsCitation":"Shriver, R., Yackulic, C., Bell, D.M., and Bradford, J., 2022, Dry forest decline is driven by both declining recruitment and increasing mortality in response to warm, dry conditions: Global Ecology and Biogeography, v. 31, no. 11, p. 2259-2269, https://doi.org/10.1111/geb.13582.","productDescription":"11 p.","startPage":"2259","endPage":"2269","ipdsId":"IP-143036","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":435659,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FIGKFM","text":"USGS data release","linkHelpText":"Pinyon-juniper basal area, climate and demographics data from National Forest Inventory plots and projected under future density and climate conditions"},{"id":408210,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Idaho, Kansas, Montana, Nebraska, New Mexico, North Dakota, Oklahoma, South Dakota, Texas, Utah, Washington, Wyoming","otherGeospatial":"Great Basin, Rocky Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.5751953125,\n 49.03786794532644\n ],\n [\n -119.64111328125,\n 48.38544219115483\n ],\n [\n -118.63037109375,\n 47.79839667295524\n ],\n [\n -117.44384765625,\n 47.78363463526376\n ],\n [\n -116.806640625,\n 47.14489748555398\n ],\n [\n -117.02636718749999,\n 46.118941506107056\n ],\n [\n -116.56494140625001,\n 45.69083283645816\n ],\n [\n -116.91650390625,\n 44.91813929958515\n ],\n [\n -117.1142578125,\n 44.69989765840318\n ],\n [\n -117.18017578125,\n 44.29240108529005\n ],\n [\n -116.45507812500001,\n 43.77109381775651\n ],\n [\n -115.59814453125001,\n 43.18114705939968\n ],\n [\n -112.96142578125,\n 42.13082130188811\n ],\n [\n -111.20361328125,\n 42.032974332441405\n ],\n [\n -111.29150390625,\n 40.730608477796636\n ],\n [\n -112.08251953125,\n 40.111688665595956\n ],\n [\n -112.30224609374999,\n 39.01064750994083\n ],\n [\n -112.54394531249999,\n 38.13455657705411\n ],\n [\n -111.46728515624999,\n 37.996162679728116\n ],\n [\n -110.23681640625,\n 38.993572058209466\n ],\n [\n -110.06103515625,\n 40.43022363450862\n ],\n [\n -108.984375,\n 40.38002840251183\n ],\n [\n -108.78662109375,\n 38.94232097947902\n ],\n [\n -108.19335937499999,\n 37.33522435930639\n ],\n [\n -106.72119140625,\n 37.00255267215955\n ],\n [\n -106.67724609375,\n 36.54494944148322\n ],\n [\n -106.94091796875,\n 35.746512259918504\n ],\n [\n -106.61132812499999,\n 34.831841149828655\n ],\n [\n -106.3916015625,\n 33.669496972795535\n ],\n [\n -106.6552734375,\n 33.119150226768866\n ],\n [\n -106.5673828125,\n 31.50362930577303\n ],\n [\n -105.16113281249999,\n 30.675715404167743\n ],\n [\n -104.6337890625,\n 29.458731185355344\n ],\n [\n -103.0517578125,\n 28.806173508854776\n ],\n [\n -102.26074218749999,\n 29.649868677972304\n ],\n [\n -101.42578124999999,\n 29.649868677972304\n ],\n [\n -99.36035156249999,\n 32.65787573695528\n ],\n [\n -97.6025390625,\n 33.7243396617476\n ],\n [\n -97.2509765625,\n 34.84987503195418\n ],\n [\n -96.8994140625,\n 37.23032838760387\n ],\n [\n -96.591796875,\n 38.89103282648846\n ],\n [\n -96.94335937499999,\n 41.27780646738183\n ],\n [\n -96.8994140625,\n 42.4234565179383\n ],\n [\n -98.5693359375,\n 43.54854811091286\n ],\n [\n -98.7890625,\n 44.37098696297173\n ],\n [\n -98.87695312499999,\n 47.39834920035926\n ],\n [\n -100.37109375,\n 47.81315451752768\n ],\n [\n -101.9970703125,\n 49.03786794532644\n ],\n [\n -119.5751953125,\n 49.03786794532644\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"31","issue":"11","noUsgsAuthors":false,"publicationDate":"2022-08-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Shriver, Robert K.","contributorId":297511,"corporation":false,"usgs":false,"family":"Shriver","given":"Robert K.","affiliations":[{"id":64419,"text":"Department of Natural Resources and Environmental Science, University of Nevada, Reno; Ecology, Evolution, and Conservation Biology Graduate Program, University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":854333,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yackulic, Charles B. 0000-0001-9661-0724","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":218825,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":854334,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bell, David M.","contributorId":191003,"corporation":false,"usgs":false,"family":"Bell","given":"David","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":854335,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bradford, John B. 0000-0001-9257-6303","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":219257,"corporation":false,"usgs":true,"family":"Bradford","given":"John B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":854336,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70237391,"text":"70237391 - 2022 - An evaluation of the reliability of plumage characters for sexing adult Ruddy Turnstones Arenaria interpres morinella during northward passage in eastern North America","interactions":[],"lastModifiedDate":"2022-10-12T13:40:56.735739","indexId":"70237391","displayToPublicDate":"2022-10-12T08:20:05","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5557,"text":"Wader Study","active":true,"publicationSubtype":{"id":10}},"displayTitle":"An evaluation of the reliability of plumage characters for sexing adult Ruddy Turnstones Arenaria interpres morinella during northward passage in eastern North America","title":"An evaluation of the reliability of plumage characters for sexing adult Ruddy Turnstones Arenaria interpres morinella during northward passage in eastern North America","docAbstract":"We used two datasets to investigate the reliability of plumage for sexing adult Ruddy Turnstones Arenaria interpres of the morinella subspecies during May and early June in Delaware Bay, on the Mid-Atlantic Coast of the United States (39.1202°N, 75.2479°W). We first examined 23 years of data on the capture and recapture of 1,818 individual Ruddy Turnstones to assess the consistency of observers with varying levels of expertise in assigning sex using plumage criteria. Among birds recaptured once, the sex recorded for about 10% differed between captures. This increased to about 16% among birds recaptured more than once. Significantly more birds sexed as females early in the season (during 1–12 May) were later sexed as males than vice versa. This suggests that early-season captures may include birds still in non- (or partial) breeding plumage, which can be confused with female breeding plumage. Second, we compared plumage-based and genetic assessments of sex for 66 Ruddy Turnstones captured in Delaware Bay on 29 May 2016 and 19 May 2017; these individuals were sexed in the hand by an expert on shorebird plumages. Plumage-based and molecular assessments differed in only one case. This suggests that fewer birds will be wrongly sexed on plumage if more care is taken and better instruction is given to observers (including how to distinguish non- breeding plumage from female breeding plumage). We suggest simple procedures to reduce field-sexing errors for Ruddy Turnstones based on plumage.
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