Operation of large, multipurpose dams within the Middle Fork Willamette River Basin, Oregon, including the Fall Creek sub-basin, have disrupted natural streamflow and sediment transport regimes and fish passage along the river corridors. Documenting channel morphology, including channel planform, landforms, vegetation cover, and river channel elevations at multiple points in time spanning the 20th and early 21st centuries, is useful for characterizing net changes occurring in response to construction and operation of these dams. The U.S. Geological Survey assessed historical channel changes that occurred within the past century in response to the construction and operation of flood-control dams by evaluating planimetric datasets (from 1926 plan and profile surveys and 1936 and 2016 aerial photographs) and elevation datasets (from 1926 plan and profile surveys and 2015 light detection and ranging [lidar]). This study specifically focuses on the lower 27.3 kilometers (km) of the Middle Fork Willamette River and the lower 11.5 km of Fall Creek, or the reaches downstream from the U.S. Army Corps of Engineers Dexter Dam and Fall Creek Dam, to the confluence with Coast Fork Willamette River. Altogether, compilation and evaluation of datasets for Fall Creek and the Middle Fork Willamette River downstream from the dams provide a foundation for understanding:
Findings from this study can be used to provide historical and geomorphic context for geomorphic responses to deep reservoir drawdowns on Fall Creek Lake that mobilize reservoir sediment downstream and informing other restoration and river-management activities; this report summarizes one component of a larger research effort to document the magnitude and spatial distribution of geomorphic responses to sediment releases from draining Fall Creek Lake.
As of 2016, the modern Fall Creek flows through a narrow, semi-alluvial channel that efficiently conveys water and sediment at typical streamflows downstream from Fall Creek Dam. This channel planform, including the positions and distributions of bars and secondary water features (side channels, alcoves, and ponds), generally reflects pre-dam conditions in 1936, suggesting relatively modest morphological adjustments resulted from reductions in sediment supply and alterations to peak streamflow after dam construction. The most substantial morphologic change detected over this period was a reduction in unvegetated gravel bars.
As of 2016, the modern Middle Fork Willamette River is a large, gravel-bed river that, despite substantial transformations in channel morphology and reduction in lateral dynamism following the construction of multiple upstream dams, remains a dominantly alluvial river. Prior to dam construction in 1926 and 1936, the reaches of the Middle Fork Willamette River downstream from Dexter Dam were laterally active with multi-thread and single-thread channels flanked by large, shifting gravel bars. Since streamflow regulation and other channel modifications in the mid-20th century, these reaches have become less laterally active and encompass a narrower floodplain corridor as abundant former gravel bars were converted to low-elevation floodplains colonized by young, dense forests. The Middle Fork Willamette River downstream from Dexter Dam has remained mostly vertically stable between 1926 and 2015, although localized segments possibly decreased in elevation as much as 2.3 meters.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235048","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Keith, M.K., Wallick, J.R., Gordon, G.W., and Bervid, H.D., 2023, Historical changes to channel planform and bed elevations downstream from dams along Fall Creek and Middle Fork Willamette River, Oregon, 1926–2016: U.S. Geological Survey Scientific Investigations Report 2023–5048, 34 p., https://doi.org/10.3133/sir20235048.","productDescription":"Report: viii, 34 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-136568","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":500925,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114772.htm","linkFileType":{"id":5,"text":"html"}},{"id":417882,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5048/sir20235048.XML"},{"id":417881,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5048/images"},{"id":417880,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9THIZD6","text":"USGS data release","description":"USGS data release.","linkHelpText":"Fall Creek and Middle Fork Willamette Geomorphic Mapping Geodatabase"},{"id":417877,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5048/coverthb.jpg"},{"id":417878,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5048/sir20235048.pdf","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Oregon","otherGeospatial":"Fall Creek, Middle Fork Willamette River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123,\n 44\n ],\n [\n -123,\n 43.916667\n ],\n [\n -122.75,\n 43.916667\n ],\n [\n -122.75,\n 44\n ],\n [\n -123,\n 44\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
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Large volume rhyolitic ignimbrite volcanism is a significant contributor to the evolving crust. The introduction of high-silica material into the upper crust, differentiation within the middle crust, and partial melting in the lower crust contributes to geochemical and isotopic evolution of the crust. Developing accurate models for the genetic evolution of these events is dependent upon geochronology to determine rates of magmatic processes as model constraints. We present new zircon high-precision CA-ID-TIMS U-Pb geochronology and MC-ICPMS Hf isotope geochemistry for four ignimbrites from the nested caldera complex near Socorro, New Mexico (USA), within the Mogollon-Datil volcanic field. In agreement with past 40Ar-39Ar data, interpretations of new U-Pb data indicate eruptions from the Socorro caldera cluster were pulsed. These pulses were intermittently spaced, and a volcanic hiatus following the Hells Mesa Tuff at 33.442 ± 0.015 Ma was interrupted by four successive eruptions, beginning with the La Jencia Tuff at 29.158 ± 0.025 Ma and finishing with the South Canyon Tuff at 28.066 ± 0.021 Ma. Zircon age spectra became more protracted with each eruption, exhibiting age dispersions ranging from 0.347 Myr in the Hells Mesa Tuff to 4.502 Myr in the South Canyon Tuff. The increased dispersion is paralleled by an increase in the proportion of normally discordant grains, indicative of xenocryst incorporation. These protracted age spectra are not necessarily a function of thermal maturation in the middle to upper crust due to long-lived magma chambers. Rather, they are likely the result of increased melting of zircon-bearing lower crust due to deep thermal maturation from repeated juvenile magma injections based on the incorporation of zircon material at the melt source. In contrast, the Hf isotope record is volumetrically dominated by autocrystic zircon domains and becomes more radiogenic through time, recording juvenile replenishment of the lower crust during progressive melting. Together, these data record the protracted evolution of the lower crust sampled by ignimbrites, lend insight into that evolution, and emphasize the need for detailed interpretation of high-precision datasets to advance volcanic models.
Research Highlight: Davis, C. L., Walls, S. C., Barichivich, W. J., Brown, M. E., & Miller, D. A. (2022). Disentangling direct and indirect effects of extreme events on coastal wetland communities. Journal of Animal Ecology, https://doi.org/10.1111/1365-2656.13874. Catastrophic events such as floods, hurricanes, winter storms, droughts and wildfires increasingly touch our lives either directly or indirectly. These events draw our attention to the seriousness of changes in climate not only to human well-being but also to the integrity of ecological systems upon which we depend. Understanding the impacts of extreme events on ecological systems requires the ability to characterize the cascading effects of environmental changes on the environments in which organisms live and the altered biological interactions produced. This scientific ambition represents no small challenge for the study of animal communities, which are typically difficult to census as well as dynamic in time and space. Davis et al. (2022) in a recent study in the Journal of Animal Ecology examined the amphibian and fish communities found in depressional coastal wetlands to better understand how they respond to major rainfall and flooding events. Data from the U.S. Geological Survey's Amphibian Research and Monitoring Initiative provided an 8-year record of observations as well as environmental measurements. For this study, the authors integrated techniques for assessing the dynamics of animal populations with a Bayesian implementation of structural equation modelling. Using their integrated methodological approach permitted the authors to reveal the direct and indirect effects of extreme weather events on co-occurring amphibian and fish communities while accounting for observational uncertainty and temporal variation in population-level processes. Their findings indicate that the most prominent effects of flooding on the amphibian community were caused by changes in the fish community that led to increased predation and resource competition. In their conclusions, the authors emphasize the importance of understanding networks of abiotic and biotic effects if we are to predict and mitigate the influence of extreme weather events.
This report addresses system characterization of the BlackSky Global satellites and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
The BlackSky Global satellites are three-band multispectral imagers (red, green, and blue multispectral bands plus a panchromatic band) with a 0.8- to 0.9-meter (m) pixel ground sample distance for the assessed satellites. BlackSky Global satellites 9 and 12–17 were launched in March and December 2021, respectively, into a Sun-synchronous orbit of 430–450 kilometers with an inclination of 42–53 degrees and a swath width of 6 kilometers at nadir. Each Global satellite has an expected lifetime of about 3 years. More information on the BlackSky Global satellites is available in the “Land Remote Sensing Satellites Online Compendium” (https://calval.cr.usgs.gov/apps/compendium) and from BlackSky at Real-Time Space-Based Intelligence (blacksky.com)
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior) and spatial performances. Results of these analyses indicate that the assessed BlackSky Global satellites have an interior geometric performance in the range of −0.011 m (−0.012 pixel) to 0.007 m (0.008 pixel) in easting and −0.018 m (−0.020 pixel) to 0.012 m (0.013 pixel) in northing in band-to-band registration; an exterior geometric performance using ground control points of 8.0-m circular error (95-percent certainty) for orthorectified products and 10.7- to 17.4-m circular error (95-percent certainty) for nonorthorectified products, depending on the geolocation metadata used; and a spatial performance in the range of 1.70 to 2.43 pixels for full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.032 to 0.084.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030O","usgsCitation":"Vrabel, J.C., Anderson, C., Bresnahan, P.C., Christopherson, J.B., Clauson, J., Kim, M., Ryan, R.E., and Sampath, A., 2023, System characterization report on the BlackSky Global multispectral sensor (ver. 1.1, April 2024), chap. O of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 23 p., https://doi.org/10.3133/ofr20211030O.","productDescription":"Report: v, 23 p., Version History","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-150816","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":428109,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2021/1030/o/versionHist.txt","text":"Version History","size":"1.33 kB","linkFileType":{"id":2,"text":"txt"}},{"id":417822,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1030/o/ofr20211030o.XML"},{"id":417821,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/o/ofr20211030o.pdf","text":"Report","size":"3.71 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1030–O"},{"id":417820,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/o/coverthb2.jpg"}],"edition":"Version 1.0: June 6, 2023; Version 1.1: April 29, 2024","contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Elevation data are critical for assessments of coastal hazards, including sea-level rise (SLR), flooding, storm surge, tsunami impacts, and wave run-up. Previous research has demonstrated that the quality of data used in elevation-based hazard assessments must be well documented and applied properly to assess potential impacts. Global digital elevation models (DEMs), at 30- to 90-meter resolution, have been used extensively to map and characterize coastal environments and the at-risk resources (population and built structures) contained therein. The inherent absolute vertical accuracy of global DEMs precludes their usefulness for assessing exposure to fine increments (< 1 meter) of coastal inundation at high confidence levels. However, global DEMs are highly suitable for delineation of the global low elevation coastal zone (LECZ) (elevation < 10 meters). An accuracy evaluation of global DEMs over the United States has been conducted to quantify their performance in correctly mapping the LECZ, namely in terms of vertical uncertainty and corresponding confidence levels for several representations of the coastal zone. The evaluation approach includes comparison of the DEMs with an extensive set of high-accuracy geodetic control points as the independent reference data covering a variety of coastal relief settings. The 1-arc-second (30-meter) global DEMs evaluated include ALOS World 3D, ASTER GDEM, Copernicus, FABDEM, and NASADEM, and the 3-arc-second (90-meter) global DEMs include CoastalDEM, Copernicus, MERIT, and TanDEM-X. Additionally, lower resolution (1-kilometer) global DEMs were also assessed, namely the Global Lidar Lowland DTM (derived from ICESat-2) and the GEDI 1-km DEM. The results of the accuracy characterization show that FABDEM performs the best (minimal vertical bias and lowest vertical root mean square error) for high-confidence mapping of the LECZ. Among 90-m DEMs, CoastalDEM performs best, although the differences across datasets are minimal. The results also demonstrate the importance of rigorously accounting for elevation uncertainty when applying global DEMs for coastal mapping applications.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Geomorphometry 2023 proceedings","largerWorkSubtype":{"id":15,"text":"Monograph"},"conferenceTitle":"Geomorphometry 2023","conferenceDate":"July 10-14, 2023","conferenceLocation":"Iasi, Romania","language":"English","publisher":"International Society for Geomorphometry","doi":"10.5281/zenodo.8011577","usgsCitation":"Gesch, D.B., 2023, Assessing global elevation models for mapping the low elevation coastal zone, in Geomorphometry 2023 proceedings, Iasi, Romania, July 10-14, 2023, 4 p., https://doi.org/10.5281/zenodo.8011577.","productDescription":"4 p.","ipdsId":"IP-152258","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":419309,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gesch, Dean B. 0000-0002-8992-4933 gesch@usgs.gov","orcid":"https://orcid.org/0000-0002-8992-4933","contributorId":2956,"corporation":false,"usgs":true,"family":"Gesch","given":"Dean","email":"gesch@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":878927,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70244187,"text":"70244187 - 2023 - Combining field observations and high-resolution numerical modeling to demonstrate the effect of coral reef roughness on turbulence and its implications for reef restoration design","interactions":[],"lastModifiedDate":"2023-06-07T14:17:43.502323","indexId":"70244187","displayToPublicDate":"2023-06-06T09:13:59","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1262,"text":"Coastal Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Combining field observations and high-resolution numerical modeling to demonstrate the effect of coral reef roughness on turbulence and its implications for reef restoration design","docAbstract":"Coral reefs are effective natural barriers that protect adjacent coastal communities from hazards such as erosion and storm-induced flooding. However, the degradation of coral reefs compromises their ability to protect against these hazards, making degraded reefs a target for restoration. There have been limited field and numerical modeling studies conducted to understand how an increase in coral reef roughness, as would occur due to restoration, can affect wave energy dissipation for a range of real-world wave and water level conditions. To address this knowledge gap, field measurements were collected over adjacent low-roughness and high-roughness reefs off Molokaʻi, Hawaiʻi, USA, subjected to the same oceanographic forcing. Those field data were then used to calibrate and validate OpenFOAM computational fluid dynamics models of the reef. These calibrated models were then used to explore energy dissipation for a range of wave conditions based on measurements from a suite of existing datasets and values from the literature. In general, wave dissipation scales with incident wave conditions, where greater dissipation occurred for shallow depths and shorter-period waves. This tendency for short-period waves to be more readily attenuated is supported by wave energy dissipation factors in the range of 0.1–5, which decline with increasing wave period. Near-bed turbulent kinetic energy dissipation also scales with incident wave conditions, where the greatest difference in dissipation between low and high relief cases occurs for short wave periods. Turbulence becomes less affected by bottom roughness as the wave period increases. Based on this study, wave attenuation and turbulent energy dissipation could be enhanced by 0.5–1 order of magnitude (45% per across-shore meter) if the seabed roughness at the field site were increased by 13%, an achievable goal in coral reef restoration.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coastaleng.2023.104331","usgsCitation":"Norris, B.K., Storlazzi, C.D., Pomeroy, A.W., Rosenberger, K.J., Logan, J.B., and Cheriton, O.M., 2023, Combining field observations and high-resolution numerical modeling to demonstrate the effect of coral reef roughness on turbulence and its implications for reef restoration design: Coastal Engineering, v. 184, 104331, 18 p., https://doi.org/10.1016/j.coastaleng.2023.104331.","productDescription":"104331, 18 p.","ipdsId":"IP-137643","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":443176,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.coastaleng.2023.104331","text":"Publisher Index Page"},{"id":435295,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P933TO2Q","text":"USGS data release","linkHelpText":"OpenFOAM models of low- and high-relief sites from the coral reef flat off Waiakane, Molokai, Hawaii"},{"id":435294,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HNLI7Y","text":"USGS data release","linkHelpText":"3D bathymetric surfaces of low- and high-relief sites from the coral reef flat off Waiakane, Molokai"},{"id":435293,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XZT1FK","text":"USGS data release","linkHelpText":"Aerial imagery and structure-from-motion-derived shallow water bathymetry from a UAS survey of the coral reef off Waiakane, Molokai, Hawaii, June 2018"},{"id":417912,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"184","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Norris, Benjamin K 0000-0002-9133-5935","orcid":"https://orcid.org/0000-0002-9133-5935","contributorId":306089,"corporation":false,"usgs":true,"family":"Norris","given":"Benjamin","email":"","middleInitial":"K","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":874818,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Storlazzi, Curt D. 0000-0001-8057-4490","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":213610,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","middleInitial":"D.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":874819,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pomeroy, Andrew W. M.","contributorId":304433,"corporation":false,"usgs":false,"family":"Pomeroy","given":"Andrew","email":"","middleInitial":"W. M.","affiliations":[{"id":13336,"text":"University of Melbourne","active":true,"usgs":false}],"preferred":false,"id":874820,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rosenberger, Kurt J. 0000-0002-5185-5776 krosenberger@usgs.gov","orcid":"https://orcid.org/0000-0002-5185-5776","contributorId":140453,"corporation":false,"usgs":true,"family":"Rosenberger","given":"Kurt","email":"krosenberger@usgs.gov","middleInitial":"J.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":874821,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Logan, Joshua B. 0000-0002-6191-4119 jlogan@usgs.gov","orcid":"https://orcid.org/0000-0002-6191-4119","contributorId":2335,"corporation":false,"usgs":true,"family":"Logan","given":"Joshua","email":"jlogan@usgs.gov","middleInitial":"B.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":874822,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cheriton, Olivia M. 0000-0003-3011-9136","orcid":"https://orcid.org/0000-0003-3011-9136","contributorId":204459,"corporation":false,"usgs":true,"family":"Cheriton","given":"Olivia","middleInitial":"M.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":874823,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70248836,"text":"70248836 - 2023 - Progress in reducing nutrient and sediment loads to Chesapeake Bay: Three decades of monitoring data and implications for restoring complex ecosystems","interactions":[],"lastModifiedDate":"2023-09-22T12:09:23.21022","indexId":"70248836","displayToPublicDate":"2023-06-06T07:06:00","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5067,"text":"WIREs Water","active":true,"publicationSubtype":{"id":10}},"title":"Progress in reducing nutrient and sediment loads to Chesapeake Bay: Three decades of monitoring data and implications for restoring complex ecosystems","docAbstract":"For over three decades, Chesapeake Bay (USA) has been the focal point of a coordinated restoration strategy implemented through a partnership of governmental and nongovernmental entities, which has been a classical model for coastal restoration worldwide. This synthesis aims to provide resource managers and estuarine scientists with a clearer perspective of the magnitude of changes in water quality within the Bay watershed, including nitrogen (N), phosphorus (P), and sediment for the River Input Monitoring (RIM) watershed and the unmonitored below-RIM watershed. The flow-normalized N load from the RIM watershed has declined in the period of 1985–2017, but P and sediment loads have lacked progress. Reductions of riverine N are largely driven by reductions of point sources and atmospheric deposition. Future reductions will require significant progress in managing agricultural nonpoint sources. The below-RIM watershed, which comprises a disproportionately high fraction of inputs to the Bay, has shown long-term declines in major sources, including point sources (N and P), atmospheric deposition (N), manure (N and P) and fertilizer (P), based on a combination of monitoring and modeling assessments. To date, the Bay cleanup efforts have achieved some progress toward reducing nutrients from the watershed, which have resulted in improving water quality in the estuary. However, further reductions are critical to achieve the Chesapeake Bay Total Maximum Daily Load goals, and emerging challenges due to Conowingo Reservoir, legacy nutrients, climate change, and population growth should be considered. Continued monitoring, modeling, and assessment are critically important for informing the restoration of this complex ecosystem.
","language":"English","publisher":"Wiley","doi":"10.1002/wat2.1671","usgsCitation":"Zhang, Q., Blomquist, J.D., Fanelli, R., Keisman, J.L., Moyer, D.L., and Langland, M.J., 2023, Progress in reducing nutrient and sediment loads to Chesapeake Bay: Three decades of monitoring data and implications for restoring complex ecosystems: WIREs Water, v. 15, no. 5, e1671, 20 p., https://doi.org/10.1002/wat2.1671.","productDescription":"e1671, 20 p.","ipdsId":"IP-134733","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":443182,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wat2.1671","text":"Publisher Index Page"},{"id":421065,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Chesapeake Bay 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0000-0001-6808-9193","orcid":"https://orcid.org/0000-0001-6808-9193","contributorId":274827,"corporation":false,"usgs":true,"family":"Keisman","given":"Jennifer","email":"","middleInitial":"L.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":883840,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moyer, Douglas L. 0000-0001-6330-478X dlmoyer@usgs.gov","orcid":"https://orcid.org/0000-0001-6330-478X","contributorId":174389,"corporation":false,"usgs":true,"family":"Moyer","given":"Douglas","email":"dlmoyer@usgs.gov","middleInitial":"L.","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"preferred":true,"id":883841,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Langland, Michael J. 0000-0002-8350-8779","orcid":"https://orcid.org/0000-0002-8350-8779","contributorId":330001,"corporation":false,"usgs":false,"family":"Langland","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":12443,"text":"U.S. Geological Survey (retired)","active":true,"usgs":false}],"preferred":false,"id":883842,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70244153,"text":"ofr20231028 - 2023 - Analysis of aquifer framework and properties, North Magee Street well field, Southampton, New York","interactions":[],"lastModifiedDate":"2026-02-11T21:05:56.661721","indexId":"ofr20231028","displayToPublicDate":"2023-06-05T16:25:00","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-1028","displayTitle":"Analysis of Aquifer Framework and Properties, North Magee Street Well Field, Southampton, New York","title":"Analysis of aquifer framework and properties, North Magee Street well field, Southampton, New York","docAbstract":"The U.S. Geological Survey, in cooperation with the Suffolk County Water Authority, evaluated the groundwater-flow characteristics and aquifer properties of the North Magee Street well field north of the village of Southampton, New York. Characteristics and properties included groundwater-flow direction, potential groundwater-contributing areas to the well field production wells, and aquifer transmissivity and storage. The groundwater flow and aquifer properties were also evaluated to allow Suffolk County Water Authority to better assess the potential source of dissolved halocarbons (refrigerants, such as chlorofluorocarbons).
The well field production wells are screened in the upper glacial aquifer and an observation well is screened in the Magothy aquifer. Based on depth and available logs, groundwater from wells screened in the upper glacial aquifer was classified as under water-table (unconfined) conditions, and groundwater from wells screened in the Magothy aquifer was classified as being under semiconfined conditions.
Groundwater flows radially to the well field during production and in a northwesterly direction under the effect of the regional flow regime. A previously published particle tracking analysis identified the following recharge contributing areas nearby the well field: (1) contributing areas to surface-water bodies of the Peconic Estuary, (2) contributing areas to surface-water bodies of the South Shore Estuary Reserve, (3) a contributing area to the Atlantic Ocean, and (4) a contributing area to another Suffolk County Water Authority well field. Five other pumping well contributing areas were identified within the study area, including those of various wells pumped for golf-course irrigation.
Analysis of drawdown and recovery data collected during the multiple-well aquifer test, through the application of a Neuman analytical model, provided estimates of upper glacial aquifer characteristics and properties. Inclusion of lateral aquifer boundaries was not necessary for the analysis to result in satisfactory matches with the observed water-level responses. Aquifer transmissivity was estimated to be 170,000 feet squared per day. Storativity was estimated to be 0.02 (dimensionless), and specific yield was estimated to be 0.08 (dimensionless), consistent with the inferred degree of confinement and well field characteristics.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231028","collaboration":"Prepared in cooperation with the Suffolk County Water Authority","usgsCitation":"Misut, P.E., 2023, Analysis of aquifer framework and properties, North Magee Street well field, Southampton, New York: U.S. Geological Survey Open-File Report 2023–1028, 14 p., https://doi.org/10.3133/ofr20231028.","productDescription":"Report: iv, 14 p.; Dataset","numberOfPages":"14","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-124210","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":499775,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114761.htm","linkFileType":{"id":5,"text":"html"}},{"id":417746,"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":417745,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1028/images/"},{"id":417744,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1028/ofr20231028.XML"},{"id":417743,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231028/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2023-1028"},{"id":417742,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1028/ofr20231028.pdf","text":"Report","size":"2.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1028"},{"id":417741,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1028/coverthb.jpg"}],"country":"United States","state":"New York","city":"Southampton","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -72.43897301957882,\n 40.91839299249426\n ],\n [\n -72.43897301957882,\n 40.87699855750361\n ],\n [\n -72.37328061868494,\n 40.87699855750361\n ],\n [\n -72.37328061868494,\n 40.91839299249426\n ],\n [\n -72.43897301957882,\n 40.91839299249426\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Biogeographic history can set initial conditions for vegetation community assemblages that determine their climate responses at broad extents that land surface models attempt to forecast. Numerous studies have indicated that evolutionarily conserved biochemical, structural, and other functional attributes of plant species are captured in visible-to-short wavelength infrared, 400 to 2,500 nm, reflectance properties of vegetation. Here, we present a remotely sensed phylogenetic clustering and an evolutionary framework to accommodate spectra, distributions, and traits. Spectral properties evolutionarily conserved in plants provide the opportunity to spatially aggregate species into lineages (interpreted as “lineage functional types” or LFT) with improved classification accuracy. In this study, we use Airborne Visible/Infrared Imaging Spectrometer data from the 2013 Hyperspectral Infrared Imager campaign over the southern Sierra Nevada, California flight box, to investigate the potential for incorporating evolutionary thinking into landcover classification. We link the airborne hyperspectral data with vegetation plot data from 1372 surveys and a phylogeny representing 1,572 species. Despite temporal and spatial differences in our training data, we classified plant lineages with moderate reliability (Kappa = 0.76) and overall classification accuracy of 80.9%. We present an assessment of classification error and detail study limitations to facilitate future LFT development. This work demonstrates that lineage-based methods may be a promising way to leverage the new-generation high-resolution and high return-interval hyperspectral data planned for the forthcoming satellite missions with sparsely sampled existing ground-based ecological data.
","language":"English","publisher":"National Academy of Sciences","doi":"10.1073/pnas.2215533120","usgsCitation":"Griffith, D.M., Byrd, K.B., Anderegg, L., Allen, E., Gatziolis, D., Roberts, D.A., Yacoub, R., and Nemani, R., 2023, Capturing patterns of evolutionary relatedness with reflectance spectra to model and monitor biodiversity: Proceedings of the Natural Academy of Sciences, v. 120, no. 24, e2215533120, 8 p., https://doi.org/10.1073/pnas.2215533120.","productDescription":"e2215533120, 8 p.","ipdsId":"IP-133705","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":443193,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://escholarship.org/uc/item/57x530fd","text":"Publisher Index Page"},{"id":417780,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"120","issue":"24","noUsgsAuthors":false,"publicationDate":"2023-06-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Griffith, Daniel Mark 0000-0001-7463-4004","orcid":"https://orcid.org/0000-0001-7463-4004","contributorId":271033,"corporation":false,"usgs":true,"family":"Griffith","given":"Daniel","email":"","middleInitial":"Mark","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":873558,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Byrd, Kristin B. 0000-0002-5725-7486 kbyrd@usgs.gov","orcid":"https://orcid.org/0000-0002-5725-7486","contributorId":3814,"corporation":false,"usgs":true,"family":"Byrd","given":"Kristin","email":"kbyrd@usgs.gov","middleInitial":"B.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":873559,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderegg, Lee","contributorId":305688,"corporation":false,"usgs":false,"family":"Anderegg","given":"Lee","email":"","affiliations":[{"id":66268,"text":"Department of Ecology, Evolution & Marine Biology, University of California Santa Barbara, Santa Barbara, CA 93106","active":true,"usgs":false}],"preferred":false,"id":873560,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Allen, Elijah","contributorId":305689,"corporation":false,"usgs":false,"family":"Allen","given":"Elijah","email":"","affiliations":[{"id":65456,"text":"Shonto Chapter, Diné (Navajo) Nation","active":true,"usgs":false}],"preferred":false,"id":873561,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gatziolis, Demetrios","contributorId":305690,"corporation":false,"usgs":false,"family":"Gatziolis","given":"Demetrios","email":"","affiliations":[{"id":66269,"text":"USDA Forest Service, PNW Research Station, Portland, OR 97205","active":true,"usgs":false}],"preferred":false,"id":873562,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Roberts, Dar A.","contributorId":100503,"corporation":false,"usgs":false,"family":"Roberts","given":"Dar","email":"","middleInitial":"A.","affiliations":[{"id":12804,"text":"Univ. of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":873563,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Yacoub, Rosie","contributorId":305691,"corporation":false,"usgs":false,"family":"Yacoub","given":"Rosie","email":"","affiliations":[{"id":66271,"text":"California Dept. of Fish and Wildlife, Vegetation Classification and Mapping Program, Sacramento, CA 95811","active":true,"usgs":false}],"preferred":false,"id":873564,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Nemani, Ramakrishna","contributorId":305692,"corporation":false,"usgs":false,"family":"Nemani","given":"Ramakrishna","affiliations":[{"id":66273,"text":"NASA Ames Research Center, Moffett Field, CA, 94035","active":true,"usgs":false}],"preferred":false,"id":873565,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70249738,"text":"70249738 - 2023 - Geographic isolation reduces genetic diversity of a wide-ranging terrestrial vertebrate, Canis lupus","interactions":[],"lastModifiedDate":"2023-10-26T12:11:35.260827","indexId":"70249738","displayToPublicDate":"2023-06-04T07:05:59","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Geographic isolation reduces genetic diversity of a wide-ranging terrestrial vertebrate, Canis lupus","docAbstract":"Genetic diversity is theorized to decrease in populations closer to a species' range edge, where habitat may be suboptimal. Generalist species capable of long-range dispersal may maintain sufficient gene flow to counteract this, though the presence of significant barriers to dispersal (e.g., large water bodies, human-dominated landscapes) may still lead to, and exacerbate, the edge effect. We used microsatellite data for 2421 gray wolves (Canis lupus) from 24 subpopulations (groups) to model how allelic richness and expected heterozygosity varied with mainland–island position and two measures of range edge (latitude and distance from range center) across >7.3 million km2 of northern North America. We expected low genetic diversity both at high latitudes, due to harsh environmental conditions, and on islands, but no change in diversity with distance to the range center due to the species' exceptional dispersal ability and favorable conditions in far eastern and western habitats. We found that allelic richness and expected heterozygosity of island groups were measurably less than that of mainland groups, and that these differences increased with the island's distance to the species' range center in the study area. Our results demonstrate how multiple axes of geographic isolation (distance from range center and island habitation) can act synergistically to erode the genetic diversity of wide-ranging terrestrial vertebrate populations despite the counteracting influence of long-range dispersal ability. These findings emphasize how geographic isolation is a potential threat to the genetic diversity and viability of terrestrial vertebrate populations even among species capable of long-range dispersal.
The establishment of invasive dreissenid mussels Dreissena polymorpha and Dreissena rostriformis bugensis in the Laurentian Great Lakes has affected multiple aspects of the ecosystem. However, the effects of their larvae (veligers) on lower trophic levels are relatively unknown. Previous research has documented that some larval fishes consume veligers, but it is unclear if they select for veligers. To assess the role of veligers in larval fish diets in Lake Huron, we examined the diets of larval burbot Lota lota, rainbow smelt Osmerus mordax, and Coregonus spp., mainly bloater Coregonus hoyi, sampled in July of 2017. Preference for available zooplankton prey was evaluated using Vanderploeg and Scavia’s E*. Results indicated that veligers were on average avoided by large larval burbot, rainbow smelt, and coregonines but were sometimes preferred by small (< 7 mm) and medium-sized (7–10 mm) larval burbot. A mixed model analyzing factors contributing to veliger preference by larval burbot indicated that greater environmental zooplankton prey size is associated with more positive preference for veligers. Thus, veligers may be important for gape-limited larval fish. We also found that, on average, larval burbot and coregonines consumed larger veligers than those sampled in the environment. Overall, consideration of larval fishes’ ability to exploit veligers could help managers to understand the role of dreissenid mussels in Great Lakes food webs.
The Great Dismal Swamp wetland, spanning >400 km2 along the Virginia and North Carolina border, was shaped by a complex combination of geomorphic, climatic, and anthropogenic forcings during the last 14,000 years. Pollen, macrofossils, charcoal, and physical properties from sediment cores at seven sites provide a detailed record of the spatial heterogeneity of the wetland and the roles played by natural hydrologic variability, wildfire, and human modification of drainage in shaping vegetation and habitats. Cold-temperate forests occupied regional uplands from at least 13.5–10.3 cal ka BP. Marshes dominated by grasses and other herbaceous taxa began developing along low-elevation streams as early as 10.3 cal ka BP, resulting in accumulation of organic silts. Long-hydroperiod, peat accumulating marshes, with abundant floating aquatic plants, developed as early as 9.6 cal ka BP, as rapid rates of sea-level rise elevated the water table and facilitated wetland development and peat accumulation along stream courses. By the mid-Holocene (c. 7–6.5 cal ka BP), when local sea-level rise began slowing and reached about 12–15 m below present, shorter hydroperiod, peat-accumulating marshes dominated the landscape, with increased wildfire activity. Great Dismal Swamp vegetation shifted from marshes to peat-accumulating forested wetlands by c. 3.7 cal ka BP; these were dominated by varying combinations of Nyssa (tupelo), Taxodium (cypress), and Chamaecyparis thyoides (Atlantic white cedar). Wildfires were infrequent during this time, and the forested wetlands persisted, with minor compositional changes related to climate-driven fluctuations in stream flow, until colonial ditching and logging began in the swamp during the late 18th century. These activities decreased cypress and cedar populations, and, by the mid-20th century, expanded ditching resulted in even drier conditions and expansion of maple-gum (dominated by Acer and Liquidambar), and pine-pocosin (dominated by Pinus) forests. The distribution of these forests differs from that of the late Holocene and represents a fundamental shift in hydrology, peat structure, vegetation, and fire regime due to landscape alterations of the last few centuries.
Explosive eruptions expel volcanic gases and particles at high pressures and velocities. Within this multiphase fluid, small ash particles affect the flow dynamics, impacting mixing, entrainment, turbulence, and aggregation. To examine the role of turbulent particle behavior, we conducted an analogue experiment using a particle-laden jet. We used compressed air as the carrier fluid, considering turbulent conditions at Reynolds numbers from approximately 5,000 to 20,000. Two different particles were examined: 14-μm diameter solid nickel spheres and 13-μm diameter hollow glass spheres. These resulted in Stokes numbers between 1 and 35 based on the convective scale. The particle mass percentage in the mixture is varied from 0.3% to more than 20%. Based on a 1-D volcanic plume model, these Stokes numbers and mass loadings corresponded to millimeter-scale particle diameters at heights of 4–8 km above the vent during large, sustained eruptions. Through particle image velocimetry, we measured the mean flow behavior and the turbulence statistics in the near-exit region, primarily focusing on the dispersed phase. We show that the flow behavior is dominated by the particle inertia, with high Stokes numbers reducing the entrainment by more than 40%. When applied to volcanic plumes, these results suggest that high-density particles can greatly increase the probability of column collapse.
Between 2015 and 2019, the U.S. Geological Survey (USGS) studied concerns related to projected increases in demand for groundwater, in collaboration with municipal water providers and county managers within the study area, Aiken County and part of Lexington County, South Carolina. A three-dimensional (3D), numerical groundwater-flow model of the Atlantic Coastal Plain (ACP) aquifers, confining units, and the underlying bedrock aquifer in the study area was constructed using the USGS software program MODFLOW–NWT in conjunction with a groundwater-recharge model using the Soil-Water-Balance (SWB) model. Water budgets for dry (2012) and wet (2015) year conditions, future (2017–2065) groundwater-demand scenarios based on general circulation models (GCMs) of future climates, and future agricultural irrigation demands were simulated. Overall, the GCMs projected increased recharge rates. Simulation of projected increased demand on groundwater by agriculture irrigation indicated little drawdown in the study area.
Groundwater-quality samples were collected from representative public-supply wells (PSWs) and analyzed in the field and laboratory. In general, the groundwater in the ACP aquifers is acidic, dilute, and oxic. Conversely, groundwater in the bedrock aquifer was of neutral pH, mineralized, and anoxic. Total-radium concentrations across all PSWs ranged from 0.55 to 6.69 picocuries per liter (pCi/L). Groundwater from some PSWs contained detectable but low concentrations of commonly and historically used volatile organic compounds, such as chloroform, methyl tert-butyl ether (MTBE), cis-1,2-dichloroethylene (cis-1,2-DCE), 1,1-dichloroethane (1,1-DCA), and 1,1-dichloroethylene (1,1-DCE). The stable isotopes of groundwater sampled from all wells indicate the possibility that groundwater from the bedrock aquifer may discharge into the ACP. Finally, groundwater age-dating results and MODPATH simulations indicate recharge between the 1950s and 1980s for PSWs in the ACP and recharge between the 1940s and 1950s for PSWs in bedrock. Maximum groundwater-flow pathways ranged from 270 to 7,470 feet, with the longest simulated-flow pathway for wells pumped at higher rates.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225036","collaboration":"Prepared in cooperation with Aiken County, City of Aiken, Breezy Hill Water and Sewer Company, Inc., Gilbert-Summit Rural Water District, and Montmorenci-Couchton Water & Sewer District, Inc.","programNote":"Water Availability and Use Science Program","usgsCitation":"Campbell, B.G., and Landmeyer, J.E., 2023, Groundwater availability, geochemistry, and flow pathways to public-supply wells in the Atlantic Coastal Plain and bedrock aquifers, Aiken County and part of Lexington County, South Carolina, 2015–2019: U.S. Geological Survey Scientific Investigations Report 2022–5036, 117 p., https://doi.org/10.3133/sir20225036.","productDescription":"Report: xiv, 117 p.; Data Release","numberOfPages":"117","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-111003","costCenters":[{"id":13634,"text":"South Atlantic Water Science 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South Atlantic Water Science Center
U.S. Geological Survey
1770 Corporate Drive, Suite 500
Norcross, GA 30093
The sagebrush biome within the western United States has been reshaped by disturbances, management, and changing environmental conditions. As a result, sagebrush cover and configuration have varied over space and time, influencing processes and species that rely on contiguous, connected sagebrush. Previous studies have documented changes in sagebrush cover, but we know little about how the connectivity of sagebrush has changed over time and across the sagebrush biome. We investigated temporal connectivity patterns for sagebrush using a time series (1985–2020) of fractional sagebrush cover and used an omnidirectional circuit algorithm to assess the density of connections among areas with abundant sagebrush. By comparing connectivity patterns over time, we found that most of the biome experienced moderate change; the amount and type of change varied spatially, indicating that areas differ in the trend direction and magnitude of change. Two different types of designated areas of conservation and management interest had relatively high proportions of stable, high-connectivity patterns over time and stable connectivity trends on average. These results provide ecological information on sagebrush connectivity persistence across spatial and temporal scales that can support targeted actions to address changing structural connectivity and to maintain functioning, connected ecosystems.
","language":"English","publisher":"MDPI","doi":"10.3390/land12061176","usgsCitation":"Buchholtz, E.K., O’Donnell, M.S., Heinrichs, J., and Aldridge, C.L., 2023, Temporal patterns of structural sagebrush connectivity from 1985 to 2020: Land, v. 12, no. 6, 1176, 13 p., https://doi.org/10.3390/land12061176.","productDescription":"1176, 13 p.","ipdsId":"IP-149528","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":443212,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/land12061176","text":"Publisher Index Page"},{"id":435296,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ED3OHH","text":"USGS data release","linkHelpText":"Sagebrush structural connectivity yearly and temporal trends based on RCMAP sagebrush products, biome-wide from 1985 to 2020"},{"id":420238,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120.96080474168178,\n 38.705700643079126\n ],\n [\n -115.78518256667087,\n 34.921896948006165\n ],\n [\n -114.61161113478016,\n 36.338264497018244\n ],\n [\n -113.767664676413,\n 35.05005751349394\n ],\n [\n -112.65446218611822,\n 34.49061043886182\n ],\n [\n -112.06315236527561,\n 35.73094134678898\n ],\n [\n -109.54598209325795,\n 34.19549893805224\n ],\n [\n -106.89665758360098,\n 32.69494633725995\n ],\n [\n -104.71617700706355,\n 36.11077455150861\n ],\n [\n -104.55021627544596,\n 39.39791391643246\n ],\n [\n -102.74204247363139,\n 41.178395117310885\n ],\n [\n -101.91586719976846,\n 44.15607703673757\n ],\n [\n -102.57953583556366,\n 47.8714554984052\n ],\n [\n -104.41540160422653,\n 47.87078532552721\n ],\n [\n -105.32574816853759,\n 47.678443090909866\n ],\n [\n -105.58191871342933,\n 48.88758293756308\n ],\n [\n -111.59184312773306,\n 49.007633669026006\n ],\n [\n -111.43229696003331,\n 46.72141563224213\n ],\n [\n -113.85176301374159,\n 49.08872019311082\n ],\n [\n -115.1516486551431,\n 46.36810451906729\n ],\n [\n -115.50149965627025,\n 44.04779080867547\n ],\n [\n -118.81497419811586,\n 48.948530483008625\n ],\n [\n -120.40779607271287,\n 48.96193708211112\n ],\n [\n -121.56614731481,\n 44.386012657400386\n ],\n [\n -122.69235641752775,\n 42.28566289188129\n ],\n [\n -122.1452674395685,\n 39.40569110412574\n ],\n [\n -120.96080474168178,\n 38.705700643079126\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","issue":"6","noUsgsAuthors":false,"publicationDate":"2023-06-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Buchholtz, Erin K. 0000-0002-1985-9531","orcid":"https://orcid.org/0000-0002-1985-9531","contributorId":300162,"corporation":false,"usgs":true,"family":"Buchholtz","given":"Erin","middleInitial":"K.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":881230,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O’Donnell, Michael S. 0000-0002-3488-003X odonnellm@usgs.gov","orcid":"https://orcid.org/0000-0002-3488-003X","contributorId":140876,"corporation":false,"usgs":true,"family":"O’Donnell","given":"Michael","email":"odonnellm@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":881231,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Heinrichs, Julie A. 0000-0001-7733-5034","orcid":"https://orcid.org/0000-0001-7733-5034","contributorId":240888,"corporation":false,"usgs":false,"family":"Heinrichs","given":"Julie A.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":881232,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":881233,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70244058,"text":"ofr20231023 - 2023 - Calibration of the Trinity River Stream Salmonid Simulator (S3) with extension to the Klamath River, California, 2006–17","interactions":[],"lastModifiedDate":"2023-09-18T19:50:01.76388","indexId":"ofr20231023","displayToPublicDate":"2023-06-02T06:56:34","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-1023","displayTitle":"Calibration of the Trinity River Stream Salmonid Simulator (S3) with Extension to the Klamath River, California, 2006–17","title":"Calibration of the Trinity River Stream Salmonid Simulator (S3) with extension to the Klamath River, California, 2006–17","docAbstract":"The Trinity River is managed in two sections: (1) the upper 64-kilometer (km) “restoration reach” downstream from Lewiston Dam and (2) the 120-km lower Trinity River downstream from the restoration reach. The Stream Salmonid Simulator (S3) has been previously constructed and calibrated for the restoration reach. In this report, we extended and parameterized S3 for the 120-km section of the lower Trinity River to the confluence with the Klamath River and then to the Pacific Ocean in northern California.
S3 is a deterministic life-stage structured-population model that tracks daily growth, movement, and survival of juvenile salmon. A key theme of the model is that river discharge affects habitat availability and capacity, which in turn drives density-dependent population dynamics. To explicitly link population dynamics to habitat quality and quantity, the river environment is constructed as a one-dimensional series of linked habitat units, each of which has an associated daily timeseries of discharge, water temperature, and useable habitat area or carrying capacity. In turn, the physical characteristics of each habitat unit and the number of fish occupying each unit drive (1) survival and growth within each habitat unit and (2) movement of fish among habitat units.
The physical template of the Trinity River was formed by classifying the river into 910 meso-habitat units that were designated into runs, riffles, or pools. For each habitat unit, we developed a timeseries of daily discharge, water temperature, amount of available spawning habitat, and fry and parr carrying capacity. Capacity timeseries were constructed using state-of-the-art models of spatially explicit hydrodynamics and quantitative fish habitat relationships developed for the Trinity River. These variables were then used to drive population dynamics such as egg maturation and survival, and in turn, juvenile movement, growth, and survival.
We estimated key movement and survival parameters by calibrating the model to 12 years (2007–18) of weekly juvenile abundance estimates from two rotary screw traps: (1) the Pear Tree trap near the downstream end of the restoration reach and (2) the Willow Creek trap site is about 40.2 km upriver from the Trinity River’s confluence with the Klamath River. The calibration consisted of replicating historical conditions as closely as possible (for example: flow, temperature, spawner abundance, spawning location and timing, and hatchery releases), and then running the model to predict weekly abundance passing the trap location. We also evaluated four alternative model structures that included either no density-dependence, density-independent movement and survival, density-dependent survival, or density-dependent movement. Akaike information criterion model selection was used to evaluate the strength of evidence for alternative model structures to simulate the observed abundance estimates.
Model selection supported the conclusion that the fully density-dependent model and density-dependent survival model was better supported by the data than the no density-dependence or density-dependent movement model. Because density-dependent movement was favored in past evaluations, we focus on the results from the fully density-dependent model. Parameter estimates from this model indicated that fry were less likely than parr to move downstream and that fry moved slower. Fry had a lower daily survival probability than parr. In contrast, hatchery fish had the highest probability of movement and the lowest daily survival probability.
Fitting the model to both traps individually enabled us to independently compare the fit and performance of S3 at simulating fish abundance, timing, and growth of juvenile salmon in the upper restoration reach and lower Trinity River. We obtained a better fit to the data at the Willow Creek trap site than we obtained at the Pear Tree trap site, regardless of whether we fit the model to the abundances at the Pear Tree trap or Willow Creek trap. This better fit was surprising given that the S3 input data for the upper restoration reach required fewer assumptions than fitting to the Willow Creek trap site that is farther down river. Fitting S3 to weekly abundances at the Willow Creek trap site required making assumptions about (1) extrapolating capacity-flow relationships to unmeasured habitat units; (2) spatially allocating spawners within the lower Trinity River; and (3) approximating the abundance, timing, and size of juveniles entering from tributaries. The model provided better fit to the data at the Willow Creek trap site. In the weekly abundance estimates, in relation to the S3 simulated abundances, several migration years’ (2011, 2015–17) weekly abundance estimates appeared truncated and were near or at peak annual abundances in January, suggesting that a large fraction of juveniles was migrating as early as December at the Pear Tree trap site. Some early life dynamics may not be currently incorporated into S3. For example, the estimation of abundance at the Pear Tree trap may be biased because of size selectivity. Knowing about selectivity at the Pear Tree trap could greatly improve S3’s ability to predict weekly and peak abundances each year.
Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
Arctic-boreal wetlands, important ecosystems for biodiversity and ecological services, are experiencing hydrological changes including permafrost thaw, earlier snowmelt, and increased wildfire susceptibility. These changes are affecting wetland productivity, species diversity, and biogeochemical cycles. However, given the diverse forms and structures of wetland vegetation communities, traditional wetland maps generated from lower spatial and spectral resolution satellite imagery lack community-level vegetation classification and miss spatially complex patterns. In this study, we built a cloud-based workflow to map wetland vegetation community of the Peace-Athabasca Delta (PAD), Canada, by leveraging high-resolution (5-m) airborne multi-sensor datasets, namely NASA's Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), and a historical LiDAR archive. Validation of our classifications using ground references indicates that classifications derived from AVIRIS-NG have higher accuracies (≥87.9%) than either UAVSAR (65.6%) or LiDAR (75.9%) for mapping wetland vegetation communities. We also show improved classification accuracy when combining information from multiple sensors. In particular, incorporating AVIRIS-NG and UAVSAR datasets substantially reduced omission errors of wet graminoid and wet shrub classes from 29.6% to 20.5% and from 10.8% to 7.5%, respectively. Combining AVIRIS-NG and LiDAR datasets further improves overall accuracy (+2.2%) for most classifications, especially emergent vegetation, wet graminoid, and wet shrub. The best performing model, using features derived from all three sensors, achieved an overall accuracy of 93.5%. The framework established here can be used to leverage extensive airborne AVIRIS-NG and UAVSAR datasets collected across Alaska and northwest Canada to understand the spatial distribution of Arctic-Boreal wetland vegetation communities.
Geological maps are powerful models for visualizing the complex distribution of rock types through space and time. However, the descriptive information that forms the basis for a preferred map interpretation is typically stored in geological map databases as unstructured text data that are difficult to use in practice. Herein we apply natural language processing (NLP) to geoscientific text data from Canada, the U.S., and Australia to address that knowledge gap. First, rock descriptions, geological ages, lithostratigraphic and lithodemic information, and other long-form text data are translated to numerical vectors, i.e., a word embedding, using a geoscience language model. Network analysis of word associations, nearest neighbors, and principal component analysis are then used to extract meaningful semantic relationships between rock types. We further demonstrate using simple Naive Bayes classifiers and the area under receiver operating characteristics plots (AUC) how word vectors can be used to: (1) predict the locations of “pegmatitic” (AUC = 0.962) and “alkalic” (AUC = 0.938) rocks; (2) predict mineral potential for Mississippi-Valley-type (AUC = 0.868) and clastic-dominated (AUC = 0.809) Zn-Pb deposits; and (3) search geoscientific text data for analogues of the giant Mount Isa clastic-dominated Zn-Pb deposit using the cosine similarities between word vectors. This form of semantic search is a promising NLP approach for assessing mineral potential with limited training data. Overall, the results highlight how geoscience language models and NLP can be used to extract new knowledge from unstructured text data and reduce the mineral exploration search space for critical raw materials.
Amplifications of seismic waves traveling upward through a continuous, interface‐free velocity profile are consistently smaller when computed using the square‐root‐impedance (SRI) method than when computed using full‐resonance (FR) calculations. This was found for a wide range of velocity profiles. For realistic profiles, for which the gradient of velocity decreases with depth, the differences are not large, with the ratio of FR/SRI amplifications ranging from about 1.05 to 1.3. Comparisons of the amplifications from a continuous velocity profile with those from approximations to that profile using a stack of constant‐velocity layers give some support to the hypothesis that the difference between FR and SRI amplifications for gradient profiles is because the former is controlled by the ratio of seismic impedances, whereas the latter is based on the square root of the seismic impedance ratios. This implies that gradient profiles will always have FR amplifications greater than SRI amplifications. A model‐independent, easy‐to‐implement modification of the SRI amplifications is proposed that shows promise in bringing the SRI amplifications closer to the FR amplifications.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120220197","usgsCitation":"Boore, D., and Abrahamson, N., 2023, On the ratio of full‐resonance to square‐root‐impedance amplifications for shear‐wave velocity profiles that are a continuous function of depth: Bulletin of the Seismological Society of America, v. 113, no. 3, p. 1192-1207, https://doi.org/10.1785/0120220197.","productDescription":"16 p.","startPage":"1192","endPage":"1207","ipdsId":"IP-145432","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":481876,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"113","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Boore, David 0000-0002-8605-9673 boore@usgs.gov","orcid":"https://orcid.org/0000-0002-8605-9673","contributorId":140502,"corporation":false,"usgs":true,"family":"Boore","given":"David","email":"boore@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":926870,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Abrahamson, Norm A","contributorId":195307,"corporation":false,"usgs":false,"family":"Abrahamson","given":"Norm A","affiliations":[],"preferred":false,"id":926871,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70241067,"text":"sir20215142 - 2023 - Groundwater residence times in glacial aquifers—A new general simulation-model approach compared to conventional inset models","interactions":[],"lastModifiedDate":"2026-02-23T18:29:12.962569","indexId":"sir20215142","displayToPublicDate":"2023-06-01T13:55:00","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5142","displayTitle":"Groundwater Residence Times in Glacial Aquifers—A New General Simulation-Model Approach Compared to Conventional Inset Models","title":"Groundwater residence times in glacial aquifers—A new general simulation-model approach compared to conventional inset models","docAbstract":"Groundwater is important as a drinking-water source and for maintaining base flow in rivers, streams, and lakes. Groundwater quality can be predicted, in part, by its residence time in the subsurface, but the residence-time distribution cannot be measured directly and must be inferred from models. This report compares residence-time distributions from four areas where groundwater flow and travel time were simulated with conventional simulation-inset models (IMs) and with a new automated model-construction method called general simulation models (GSMs). The comparison provides an opportunity to explore controls on travel time and improve the methods used in the creation of GSMs. These models can be useful for three main-use cases: (1) rapid testing of relationships that govern groundwater flow and age, (2) generation of consistent examples for training a machine-learning metamodel, and (3) serving as a starting point for more detailed models.
Comparison of the GSMs to IMs indicated a qualified pattern of agreement for residence-time distributions as indicated by the Nash-Sutcliffe efficiency and Spearman’s correlation coefficient. The agreement was best for the median values of the simulated residence times in young fractions of groundwater (defined as the fractions of groundwater in samples less than 65 years old) at the scale of the eight-digit hydrologic-unit code. Generally, the median values of the young fractions in the IMs were correlated with the median values from the GSMs. The relative trends across the four areas also were similar for the other residence-time metrics. The medians of residence-time metrics at finer scales show a fair degree of scatter. The GSM results compared most poorly for median travel times in the older fraction of groundwater (older than 65 years).
The GSM approach is intended as a flexible framework for developing models that can be useful individually as screening tools or collectively to support projects in statistical learning. Although one set of GSM algorithms was presented here, the approach can accommodate many types of data and also different categories of prior information. Comparison of GSMs and IMs suggests ways in which the GSMs, while remaining easy to construct and calibrate, can be improved for estimating groundwater travel times. IMs do not yield exact travel times, and matching GSMs to IMs does not guarantee an improvement; however, IMs provide a convenient benchmark against which to explore relations between physical characteristics of watersheds and the distribution of travel times within them.
This effort was undertaken as part of the National Water Quality Program of the U.S. Geological Survey to assist in determining the susceptibility of groundwater in glacial aquifers to a variety of natural and anthropogenic contaminants.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215142","programNote":"National Water Quality Program","usgsCitation":"Starn, J.J., Kauffman, L.J., and Feinstein, D.T., 2023, Groundwater residence times in glacial aquifers—A new general simulation-model approach compared to conventional inset models: U.S. Geological Survey Scientific Investigations Report 2021–5142, 37 p., https://doi.org/10.3133/sir20215142.","productDescription":"Report: v, 37 p.; Data Release","numberOfPages":"37","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-112499","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":500447,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114759.htm","linkFileType":{"id":5,"text":"html"}},{"id":413862,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5142/images/"},{"id":413858,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5142/coverthb.jpg"},{"id":413859,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5142/sir20215142.pdf","text":"Report","size":"6.69 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5142"},{"id":413861,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5142/sir20215142.XML"},{"id":413863,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HS83JL","text":"USGS data release","linkHelpText":"MODPATH-NWT and MODPATH6 models used to compare a new general simulation model approach with a conventional inset model approach for groundwater residence time in glacial aquifers"},{"id":413860,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20215142/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2021-5142"}],"country":"United States","state":"Illinois, Indiana, Michigan, Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -84.5,\n 46\n ],\n [\n -89,\n 46\n ],\n [\n -89,\n 41.5\n ],\n [\n -84.5,\n 41.5\n ],\n [\n -84.5,\n 46\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Digital flood-inundation maps for an 8-mile reach of Papillion Creek near Offutt Air Force Base, Nebraska, were created by the U.S. Geological Survey (USGS) in cooperation with the U.S. Air Force, Offutt Air Force Base. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Program website at https://www.usgs.gov/mission-areas/water-resources/science/flood-inundation-mapping-fim-program, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgages Papillion Creek at Fort Crook, Nebr. (station 06610795), and Papillion Creek at Harlan Lewis Road near La Platte, Nebr. (station 06610798). Near-real-time stages at these streamgages may be obtained from the USGS National Water Information System database at https://doi.org/10.5066/F7P55KJN or from the National Weather Service Advanced Hydrologic Prediction Service at https://water.weather.gov/ahps/.
Flood profiles were computed for the 8-mile stream reach by means of a one-dimensional step-backwater model. The model was calibrated by adjusting roughness coefficients to best represent the current (2022) stage-streamflow relation at the Papillion Creek at Fort Crook (station 06610795) streamgage.
The hydraulic model then was used to compute water-surface profiles for 157 scenarios using a combination of stage values in 1-foot (ft) stage intervals that ranged from 27 to 39 ft at the Papillion Creek at Fort Crook (station 06610795) streamgage and from 13.9 to 30.9 ft at the Papillion Creek at Harlan Lewis Road near La Platte (station 06610798) streamgage, as referenced to the local datums. The simulated water-surface profiles then were combined by a geographic information system with a digital elevation model, which had a 3.281-ft grid to delineate the area flooded and water depths at each stage. The availability of these flood-inundation maps, along with information regarding current stage from the USGS streamgages, can provide emergency management personnel and residents with information that is critical for flood response activities and postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235054","collaboration":"Prepared in cooperation with the U.S. Air Force, Offutt Air Force Base","usgsCitation":"Strauch, K.R., and Hobza, C.M., 2023, Flood-inundation maps for an 8-mile reach of Papillion Creek near Offutt Air Force Base, Nebraska, 2022: U.S. Geological Survey Scientific Investigations Report 2023–5054, 12 p., https://doi.org/10.3133/sir20235054.","productDescription":"Report: vi, 12 p.; Data Release; Dataset","numberOfPages":"22","onlineOnly":"Y","ipdsId":"IP-135851","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":417490,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5054/images"},{"id":417652,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235054/full"},{"id":417492,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XQIXMN","text":"USGS data release","linkHelpText":"Flood inundation geospatial datasets for Papillion Creek near Offutt Air Force Base, Nebraska"},{"id":417489,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5054/sir20235054.XML"},{"id":417491,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":417486,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5054/sir20235054.pdf","text":"Report","size":"3.29 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5054"},{"id":417485,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5054/coverthb.jpg"},{"id":500933,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114763.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Nebraska","otherGeospatial":"Offutt Air Force Base, Papillion Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95.966667,\n 41.15\n ],\n [\n -95.966667,\n 41.05\n ],\n [\n -95.8667,\n 41.05\n ],\n [\n -95.8667,\n 41.15\n ],\n [\n -95.966667,\n 41.15\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512
Many coastal estuaries have experienced declines in pH over the past few decades due to coastal acidification. However, mean monthly water column pH values (collected during daylight hours) have increased in Tampa Bay, Florida over recent decades concurrent with seagrass recovery. We measured changes in carbonate system and water quality variables in Tampa Bay and the near-coastal Gulf of Mexico environment to quantify diurnal to seasonal trends, drivers, and controls of carbonate chemistry; identify exposure periods to low pH conditions; and to examine the potential for seagrasses to buffer acidification in Tampa Bay. Autonomous sensor packages deployed in Tampa Bay and the Gulf of Mexico from December 2017 to June 2020 recorded hourly measurements of seawater temperature, salinity, pressure, pHT (total scale), carbon dioxide (pCO2), dissolved oxygen (DO), and photosynthetically active radiation. Results indicated strong temperature and biological influence on DO, pHT, and pCO2 in Tampa Bay during the dry season, and only weak to moderate correlation of these variables with temperature and salinity during the wet season. Strong influence from biological processes during the wet season was coincident with spring-to-summer periods of maximum seagrass growth rates. Gulf of Mexico results indicated higher pHT and DO, and lower pCO2 than in Tampa Bay, with similar but attenuated seasonal variation. Results suggest potential benefits from seagrass photosynthesis increasing pHT, DO, and decreasing pCO2 in Tampa Bay, and delivery of high pHT, low pCO2 Gulf of Mexico water to Tampa Bay during flood tides. Approximately 30% of pHT and pCO2 data records collected in Tampa Bay were below pHT 7.900 and above pCO2 of 600 μatm, primarily during the wet season, indicating potential for dissolution of carbonate sediments that may also help buffer acidification conditions in Tampa.
","language":"English","publisher":"Florida Academy of Sciences","usgsCitation":"Yates, K.K., Moore, C., Lemon, M.K., Moyer, R.P., Tomasko, D.A., Masserini, R., and Sherwood, E.T., 2023, Coastal acidification trends and controls in a subtropical estuary, Tampa Bay, Florida USA: Florida Scientist, v. 86, no. 2, p. 214-228.","productDescription":"15 p.","startPage":"214","endPage":"228","ipdsId":"IP-122626","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":420720,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Gulf of Mexico, Tampa Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.34603301937987,\n 28.090560342743913\n ],\n [\n -83.31697141493213,\n 28.090560342743913\n ],\n [\n -83.31697141493213,\n 27.425867242036304\n ],\n [\n -82.34603301937987,\n 27.425867242036304\n ],\n [\n -82.34603301937987,\n 28.090560342743913\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"86","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Yates, Kimberly K. 0000-0001-8764-0358","orcid":"https://orcid.org/0000-0001-8764-0358","contributorId":214349,"corporation":false,"usgs":true,"family":"Yates","given":"Kimberly","email":"","middleInitial":"K.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":882816,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moore, Christopher 0000-0003-3210-4878 csmoore@usgs.gov","orcid":"https://orcid.org/0000-0003-3210-4878","contributorId":149727,"corporation":false,"usgs":true,"family":"Moore","given":"Christopher","email":"csmoore@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":882884,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lemon, Mitchell K","contributorId":329645,"corporation":false,"usgs":false,"family":"Lemon","given":"Mitchell","email":"","middleInitial":"K","affiliations":[{"id":33877,"text":"CNTS","active":true,"usgs":false}],"preferred":false,"id":882885,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Moyer, Ryan P.","contributorId":198993,"corporation":false,"usgs":false,"family":"Moyer","given":"Ryan","email":"","middleInitial":"P.","affiliations":[{"id":13560,"text":"Florida Fish and Wildlife Conservation Commission, Eustis, FL","active":true,"usgs":false}],"preferred":false,"id":882817,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tomasko, David A.","contributorId":172728,"corporation":false,"usgs":false,"family":"Tomasko","given":"David","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":882818,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Masserini, R. 0000-0002-2841-1819","orcid":"https://orcid.org/0000-0002-2841-1819","contributorId":329644,"corporation":false,"usgs":false,"family":"Masserini","given":"R.","email":"","affiliations":[{"id":78677,"text":"University of Tampa","active":true,"usgs":false}],"preferred":false,"id":882819,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sherwood, Edward T. 0000-0001-5330-302X","orcid":"https://orcid.org/0000-0001-5330-302X","contributorId":150472,"corporation":false,"usgs":false,"family":"Sherwood","given":"Edward","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":882820,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70260395,"text":"70260395 - 2023 - Modeling, mapping, and measuring the risk of freshwater invasive species across Alaska","interactions":[],"lastModifiedDate":"2024-10-31T13:49:21.623893","indexId":"70260395","displayToPublicDate":"2023-06-01T08:39:58","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"title":"Modeling, mapping, and measuring the risk of freshwater invasive species across Alaska","docAbstract":"Freshwater ecosystems of the Alaskan Arctic and Subarctic provide resources that are culturally, ecologically, and economically invaluable. Presently, these regions are relatively free of the impacts from invasive species compared to southern latitudes. To date, there have been relatively few verified introductions of aquatic invasive species (AIS) to freshwater ecosystems in Alaska. The expanding list and distribution of AIS has led to significant negative ecological and economic impacts (e.g., waterweed Elodea nuttalli; E. canadensis and northern pike Esox Lucius introduced outside its native range in Alaska). Escalating human activity across Alaskan lands and waters, coupled with rapidly shifting environmental conditions, increases the potential for new species introductions and subsequent establishment. Creating a proactive framework for well-informed decision-making and action can improve the effectiveness of prevention efforts and bolster decision support tools that help resource managers direct limited resources. Prioritizing AIS that may be introduced and become established, as well as the locations at highest risk of invasion, is foundational to building a proactive invasive species management framework in Alaska.
This project sought to identify and prioritize AIS known to be invasive in the contiguous United States, evaluate current and future habitat suitability for AIS in Alaska, and assess potential for AIS to be transported to habitats across Alaska, utilizing similar assessment methods as implemented for Bering Sea marine invasive species and non-native plants in Alaska. To accomplish this goal, the objectives of the project were to: 1) develop a formal ranked list of potential AIS to freshwater systems of Alaska; 2) assess the level of establishment risk for potential AIS by developing habitat suitability models for waterbodies across Alaska; and 3), identify potential pathways and specific vectors for high-risk AIS to invade Alaska and develop a framework for how vector analysis will be completed to understand transport risk. Overall, our goal is horizon scanning which is defined by Roy et al. (2019) as “a systematic examination of potential threats and opportunities, within a given context, and likely future developments, which are at the margin of current thinking and planning.” The scans include pathway analyses and risk screening of species present at pathway origin points, with a focus on identifying species at high risk of being introduced, becoming established, spreading, and causing harm.
We refined a list of 28 AIS from a list of hundreds based on characterizations of species’ invasiveness and species’ proximity to Alaska (USGS 2020; GBIF 2022). Next, we evaluated the relative invasiveness of individual species to create an initial AIS ranking. We sought to characterize habitat suitability of AIS by selecting variables that were continental in scale, covering North America to include Alaska as well as the lower 48 states comparing natural discharge, sub-basin average terrain slope (degrees), average silt fraction, average organic carbon, lithological class, and human footprint in sub-basin in 2009. We estimated AIS habitat suitability across the entire state of Alaska using the physiological tolerances of the AIS (Appendix 2). We also evaluated pathways and vectors for the introduction of AIS (Appendix 2). Many pathways and vectors considered did not meet the criteria for Alaska or freshwater systems.
Of the 28 ranked species that we categorized as very high, high, and moderate levels of invasiveness; all three risk groups included fish and mollusks (Appendix 2). One commonality of the very high-invasiveness-ranked species was the availability of Ecological Risk Screening Summary documents (USFWS, 2022) produced by U.S. Fish and Wildlife Service (USFWS), except for the goldfish (Carassius auratus) and the New Zealand mudsnail (Potamopyrgus antipodarum). The Ecological Risk Screening Summary is now available for New Zealand mudsnails. In general, fish species often ranked very high or high in invasiveness and included sportfish and aquarium fish, suggesting the importance of pathways such as aquarium trade, fishing industry, intentional (but illegal) introductions of sportfishes and aquarium fishes for establishment. The technique we used for habitat suitability models necessitated aquatic environmental datasets that were continental in scale, which was often interpolated from very coarse resolution source data layers, particularly in Alaska. Better spatial data representing aquatic environments would likely improve this approach. While the lack of introductions in Alaska and nearby provinces and states is encouraging, the lack of occurrence data for the focal species also created complications for habitat suitability modeling. Despite the challenges, the habitat suitability models indicated limited suitability for warmwater species while some species, such as Brook trout (Salvelinus fontinalis), have high habitat suitability across Alaska no matter what threshold approach is taken. Some environmental predictors were more important than others. Specifically, the most important predictor variable, ‘frost free days,’ was critical for 15 out of 28 species as expected due to harsh winter conditions in Arctic and Subarctic regions. The second most important predictor was ‘subbasin land surface runoff’, a variable that indicates the amount of discharge and runoff, while the third most important predictor was ‘snow cover’ another indication of winter conditions.
Overall, the ability to understand the effect of future climate scenarios on the establishment of AIS was challenging. A detailed dataset of freshwater temperatures and water chemistry (e.g., pH, calcium) would greatly improve the ability to predict invasiveness of freshwater species to Alaska’s ecosystems on a regional basis. Future studies may benefit from a more focused geographic scope examining a group of subbasins or a regional basin rather than the entire state. These drainages could be selected based upon the mostly likely locations of introduction pathways. The two most prevalent pathway risks for AIS are in-state transfer and stowaways/contaminants. Although there are examples of introductions from other pathways, the risk is somewhat mitigated by Alaska’s climate and regulations. However, variable application of protocols for inspection and cleaning of fishing gear, watercraft, and other similar items while traveling into Alaska as well as transferring from waterbody to waterbody within the state creates a substantial risk in introducing invasive species. We plot cumulative invasive vulnerability for all subbasins and for the top 10% of subbasins (Appendix 3).
","language":"English","publisher":"Alaska Center for Conservation Science, University of Alaska Anchorage","usgsCitation":"Geist, M., Jarnevich, C.S., Steer, A., Osnas, J., Carey, M.P., Martin, A., Davis, T., and Kelty, R., 2023, Modeling, mapping, and measuring the risk of freshwater invasive species across Alaska, 236 p.","productDescription":"236 p.","ipdsId":"IP-140719","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":463468,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://accs.uaa.alaska.edu/publications/"},{"id":463483,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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in coastal marine environments. Within coastal watersheds, environmental managers are particularly interested in climate impacts on terrestrial processes, which may undermine the efficacy of management actions designed to reduce eutrophication and consequent low-oxygen conditions in receiving coastal waters. However, substantial uncertainty accompanies the application of Earth system model (ESM) projections to a regional modeling framework when quantifying future changes to estuarine hypoxia due to climate change. In this study, two downscaling methods are applied to multiple ESMs and used to force two independent watershed models for Chesapeake Bay, a large coastal-plain estuary of the eastern United States. The projected watershed changes are then used to force a coupled 3-D hydrodynamic–biogeochemical estuarine model to project climate impacts on hypoxia, with particular emphasis on projection uncertainties. Results indicate that all three factors (ESM, downscaling method, and watershed model) are found to contribute substantially to the uncertainty associated with future hypoxia, with the choice of ESM being the largest contributor. Overall, in the absence of management actions, there is a high likelihood that climate change impacts on the watershed will expand low-oxygen conditions by 2050 relative to a 1990s baseline period; however, the projected increase in hypoxia is quite small (4 %) because only climate-induced changes in watershed inputs are considered and not those on the estuary itself. Results also demonstrate that the attainment of established nutrient reduction targets will reduce annual hypoxia by about 50 % compared to the 1990s. Given these estimates, it is virtually certain that fully implemented management actions reducing excess nutrient loadings will outweigh hypoxia increases driven by climate-induced changes in terrestrial runoff.
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