The Big River is a tributary to the Meramec River in south-central Missouri. It drains an area that has been historically one of the largest lead producers in the world, and associated mine wastes have contaminated sediments in much of the river corridor. This study investigated hydraulic conditions in four study reaches to evaluate the potential contribution of physical habitat dynamics to mechanical and physiological stress on native mussel populations. We quantified hydraulic conditions and relative bed stability in previously identified and delineated mussel habitats (MHs) and in the surrounding reaches to refine understanding of the reach-scale (about 1 kilometer) hydraulic characteristics that affect the distribution of mussel aggregations in the river. Two-dimensional hydrodynamic models were compiled for discharge scenarios from base flow (90-percent flow exceedance) to the approximate bankfull discharge (2-year mean return interval peak flow) for the reaches. Discharge, velocity, and water-surface elevation data were collected at all four study reaches at various discharges to calibrate the models across a range of discharges. Shields values to predict incipient motion of the substrate were computed for the MHs and surrounding reaches using bed-surface sediment data collected during this study and previous studies.
The distributions of hydraulic values at the range of simulated discharge scenarios were significantly different among the MHs. Depth values in the MHs ranged from 0.03 to 5.7 meters, with parts remaining dry at some lower flow scenarios (for example, 90- and 50-percent flow exceedance). MH velocities and bed shear stresses (shear stresses) reached 3.1 meters per second and 31 newtons per square meter, respectively. Through the range of simulated discharges, velocity and shear stress within the MHs were limited by reach-scale hydraulic behavior.
Our calculations predicted sand mobility within at least 50 percent of the wetted area of all four MHs for discharges from the 50-percent exceedance flow to the approximate bankfull discharge, whereas 50th-percentile (median) particle size fraction mobility was only predicted within a small area of one of the MHs at the 2-year peak discharge. These results indicate that finer size fractions are mobile within the four MHs, but the larger framework grains of the substrate are predominantly stable at the most frequent discharges.
Our results indicate that suitable mussel habitat on the Big River cannot be identified within a narrow range of velocities, depths, and shear stresses. However, the consistent patterns of sediment mobility and the slow increase of hydraulic forces with increasing discharge within all the MHs indicate that flushing flows at low discharges and coarse sediment stability at higher discharges are important for habitat suitability in the Big River. These patterns of sediment mobility are comparable among the robust and depauperate MHs, indicating that the depauperate beds are likely not impaired by bed instability or siltation. Coarse sediment stability up to bankfull discharges further indicates that bed instability is not widespread in these modeled reaches and is likely not related to the spatial distribution of mussels in these locations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225002","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Roberts, M.O., Jacobson, R.B., and Erwin, S.O., 2022, Hydraulics of freshwater mussel habitat in select reaches of the Big River, Missouri: U.S. Geological Survey Scientific Investigations Report 2022–5002, 49 p., https://doi.org/10.3133/sir20225002.","productDescription":"Report: viii, 49 p.; Data Release; Dataset","numberOfPages":"62","onlineOnly":"Y","ipdsId":"IP-122009","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":399191,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5002/coverthb.jpg"},{"id":399688,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225002/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5002"},{"id":399192,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5002/sir20225002.pdf","text":"Report","size":"12.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5002"},{"id":399193,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5002/sir20225002.XML"},{"id":399194,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5002/images"},{"id":399195,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K3ENAX","text":"USGS data release","linkHelpText":"Hydraulic measurements from select reaches of the Big River, Missouri"},{"id":399196,"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":502289,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112957.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Missouri","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.25244140624999,\n 37.63163475580645\n ],\n [\n -90.32958984375,\n 37.63163475580645\n ],\n [\n -90.32958984375,\n 38.53097889440026\n ],\n [\n -91.25244140624999,\n 38.53097889440026\n ],\n [\n -91.25244140624999,\n 37.63163475580645\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Columbia Environmental Research Center
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The North Brawley Geothermal Field, located within the Brawley Seismic Zone of Southern California, presents a case study for understanding seismic hazards linked to fluid injection and geothermal energy extraction. An earthquake swarm near the geothermal field in 2012 included two earthquakes with magnitudes greater than 5 and was potentially preceded by a years-long aseismic slip transient. To better understand ground deformation around the geothermal field, including its evolution with time and its physical mechanisms, we analyze deformation before, during, and after the swarm using ground- and satellite-based geodetic techniques between 2009 and 2019. We integrate observations from GNSS, Sentinel-1, TerraSAR-X, UAVSAR, and leveling surveys into a single deformation history. Modeling of this new collection of observations at the North Brawley Geothermal Field provides evidence for 80% more pre-swarm aseismic slip than previously recognized from 2009 to 2012. During the 2012 Brawley swarm, our geodetic slip inversions closely match the results of seismic waveform inversions from the swarm events. After the 2012 swarm, surface deformation is dominated by poroelastic deformation of a shallow fluid reservoir at <1 km depth rather than fault slip. The deformation history and seismicity catalogs at North Brawley suggest a cessation of fault-related slip during the ∼7 years after the 2012 earthquake swarm.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021JB023335","usgsCitation":"Materna, K.Z., Barbour, A.J., Jiang, J., and Eneva, M., 2022, Detection of aseismic slip and poroelastic reservoir deformation at the North Brawley Geothermal Field from 2009 to 2019: Journal of Geophysical Research Solid Earth, v. 127, no. 5, e2021JB023335, 19 p., https://doi.org/10.1029/2021JB023335.","productDescription":"e2021JB023335, 19 p.","ipdsId":"IP-125755","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":406832,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"North Brawley Geothermal Field","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.875,\n 32.875\n ],\n [\n -115.25,\n 32.875\n ],\n [\n -115.25,\n 33.25\n ],\n [\n -115.875,\n 33.25\n ],\n [\n -115.875,\n 32.875\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"127","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-05-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Materna, Kathryn Zerbe 0000-0002-6687-980X","orcid":"https://orcid.org/0000-0002-6687-980X","contributorId":261337,"corporation":false,"usgs":true,"family":"Materna","given":"Kathryn","email":"","middleInitial":"Zerbe","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":851918,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barbour, Andrew J. 0000-0002-6890-2452","orcid":"https://orcid.org/0000-0002-6890-2452","contributorId":215339,"corporation":false,"usgs":true,"family":"Barbour","given":"Andrew","middleInitial":"J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":851919,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jiang, Junle","contributorId":206383,"corporation":false,"usgs":false,"family":"Jiang","given":"Junle","email":"","affiliations":[{"id":16619,"text":"UCSD","active":true,"usgs":false}],"preferred":false,"id":851920,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Eneva, Mariana","contributorId":167022,"corporation":false,"usgs":false,"family":"Eneva","given":"Mariana","email":"","affiliations":[{"id":24596,"text":"Imageair Inc.","active":true,"usgs":false}],"preferred":false,"id":851921,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70230856,"text":"70230856 - 2022 - Detection and characterization of coastal tidal wetland change in the northeastern US using Landsat time series","interactions":[],"lastModifiedDate":"2022-04-27T11:42:39.21789","indexId":"70230856","displayToPublicDate":"2022-04-26T06:39:47","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Detection and characterization of coastal tidal wetland change in the northeastern US using Landsat time series","docAbstract":"Coastal tidal wetlands are highly altered ecosystems exposed to substantial risk due to widespread and frequent land-use change coupled with sea-level rise, leading to disrupted hydrologic and ecologic functions and ultimately, significant reduction in climate resiliency. Knowing where and when the changes have occurred, and the nature of those changes, is important for coastal communities and natural resource management. Large-scale mapping of coastal tidal wetland changes is extremely difficult due to their inherent dynamic nature. To bridge this gap, we developed an automated algorithm for DEtection and Characterization of cOastal tiDal wEtlands change (DECODE) using dense Landsat time series. DECODE consists of three elements, including spectral break detection, land cover classification and change characterization. DECODE assembles all available Landsat observations and introduces a water level regressor for each pixel to flag the spectral breaks and estimate harmonic time-series models for the divided temporal segments. Each temporal segment is classified (e.g., vegetated wetlands, open water, and others – including unvegetated areas and uplands) based on the phenological characteristics and the synthetic surface reflectance values calculated from the harmonic model coefficients, as well as a generic rule-based classification system. This harmonic model-based approach has the advantage of not needing the acquisition of satellite images at optimal conditions (i.e., low tide status) to avoid underestimating coastal vegetation caused by the tidal fluctuation. At the same time, DECODE can also characterize different kinds of changes including land cover change and condition change (i.e., land cover modification without conversion). We used DECODE to track status of coastal tidal wetlands in the northeastern United States from 1986 to 2020. The overall accuracy of land cover classification and change detection is approximately 95.8% and 99.8%, respectively. The vegetated wetlands and open water were mapped with user's accuracy of 94.6% and 99.0%, and producer's accuracy of 98.1% and 93.5%, respectively. The cover change and condition change were mapped with user's accuracy of 68.0% and 80.0%, and producer's accuracy of 80.5% and 97.1%, respectively. Approximately 3283 km2 of the coastal landscape within our study area in the northeastern United States changed at least once (12% of the study area), and condition changes were the dominant change type (84.3%). Vegetated coastal tidal wetland decreased consistently (~2.6 km2 per year) in the past 35 years, largely due to conversion to open water in the context of sea-level rise.
National and global greenhouse gas (GHG) budgets are continually being refined as data become available. Primary sources of the potent GHG nitrous oxide (N2O) include agricultural soil management and burning of fossil fuels, but comprehensive N2O budgets also incorporate less prominent factors such as wetlands. Freshwater wetland GHG flux estimates, however, have high uncertainty, and wetlands have been identified as both sources and sinks. Here, we analyzed a regional database of >26,000 N2O chamber flux measurements sampled across >150 wetlands from the Prairie Pothole Region (PPR) in the Great Plains of North America. Our goal was to identify important land use and hydrologic drivers of N2O flux to help reduce uncertainty in N2O models, and to incorporate these drivers into an upscaled estimate of wetland N2O emissions from the U.S. portion of the PPR. Within individual wetlands, exposed soils with no standing water, such as along wetland edges, were hotspots that accounted for greater than 90% of wetland N2O emissions. In contrast wet (i.e., ponded) areas had minimal or negative N2O flux. N2O flux from wetlands nested within croplands (16.3–17.3 μg N2O m−2 hr−1) was, in some instances, nearly double that from wetlands within grasslands (9.2–14.4 μg N2O m−2 h−1). We estimated that seasonal N2O flux from PPR wetlands equated to roughly 0.2% (1.04 Tg CO2 equivalents) of the U.S. N2O budget (c. 2019). Overall, even though PPR wetlands are a small net source of N2O to the atmosphere, their emissions are negligible relative to agricultural soil management. Policy and management to restore wetland hydrology and surrounding uplands from cropland to grasslands can reduce landscape N2O fluxes. Future activities focused on wetland N2O flux would benefit from inclusion of adjacent land use and hydrologic factors, as well as from incorporation of temporally dynamic ponded wetland areas.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.agrformet.2022.108968","usgsCitation":"Tangen, B., and Bansal, S., 2022, Prairie wetlands as sources or sinks of nitrous oxide: Effects of land use and hydrology: Agricultural and Forest Meteorology, v. 320, 108968, 10 p., https://doi.org/10.1016/j.agrformet.2022.108968.","productDescription":"108968, 10 p.","ipdsId":"IP-134939","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":399665,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Iowa, Minnesota, Montana, North Dakota, South Dakota","otherGeospatial":"Prairie Potholes Region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.9296875,\n 48.86471476180277\n ],\n [\n -101.162109375,\n 47.57652571374621\n ],\n [\n -100.283203125,\n 45.706179285330855\n ],\n [\n -100.72265625,\n 44.653024159812\n ],\n [\n -99.755859375,\n 43.83452678223682\n ],\n [\n -97.119140625,\n 43.068887774169625\n ],\n [\n -96.767578125,\n 43.96119063892024\n ],\n [\n -95.625,\n 43.32517767999296\n ],\n [\n -94.306640625,\n 41.77131167976407\n ],\n [\n -92.724609375,\n 42.293564192170095\n ],\n [\n -93.07617187499999,\n 44.213709909702054\n ],\n [\n -97.20703125,\n 48.22467264956519\n ],\n [\n -98.7890625,\n 48.980216985374994\n ],\n [\n -107.9296875,\n 48.86471476180277\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"320","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Tangen, Brian 0000-0001-5157-9882 btangen@usgs.gov","orcid":"https://orcid.org/0000-0001-5157-9882","contributorId":167277,"corporation":false,"usgs":true,"family":"Tangen","given":"Brian","email":"btangen@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":841430,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bansal, Sheel 0000-0003-1233-1707 sbansal@usgs.gov","orcid":"https://orcid.org/0000-0003-1233-1707","contributorId":167295,"corporation":false,"usgs":true,"family":"Bansal","given":"Sheel","email":"sbansal@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":841431,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70236256,"text":"70236256 - 2022 - Assessing placement bias of the global river gauge network","interactions":[],"lastModifiedDate":"2022-08-31T13:33:17.808542","indexId":"70236256","displayToPublicDate":"2022-04-25T08:20:31","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5791,"text":"Nature Sustainability","active":true,"publicationSubtype":{"id":10}},"title":"Assessing placement bias of the global river gauge network","docAbstract":"Knowing where and when rivers flow is paramount to managing freshwater ecosystems. Yet stream gauging stations are distributed sparsely across rivers globally and may not capture the diversity of fluvial network properties and anthropogenic influences. Here we evaluate the placement bias of a global stream gauge dataset on its representation of socioecological, hydrologic, climatic and physiographic diversity of rivers. We find that gauges are located disproportionally in large, perennial rivers draining more human-occupied watersheds. Gauges are sparsely distributed in protected areas and rivers characterized by non-perennial flow regimes, both of which are critical to freshwater conservation and water security concerns. Disparities between the geography of the global gauging network and the broad diversity of streams and rivers weakens our ability to understand critical hydrologic processes and make informed water-management and policy decisions. Our findings underscore the need to address current gauge placement biases by investing in and prioritizing the installation of new gauging stations, embracing alternative water-monitoring strategies, advancing innovation in hydrologic modelling, and increasing accessibility of local and regional gauging data to support human responses to water challenges, both today and in the future.
","language":"English","publisher":"Nature Publications","doi":"10.1038/s41893-022-00873-0","usgsCitation":"Krabbenhoft, C., Allen, G.H., Lin, P., Godsey, S., Allen, D., Burrows, R., DelVecchia, A., Fritz, K.M., Shanafield, M., Burgin, A.J., Zimmer, M., Datry, T., Dodds, W., Jones, C., Mimms, M., Franklin, C., Hammond, J., Zipper, S., Ward, A.S., Costigan, K., Beck, H., and Olden, J., 2022, Assessing placement bias of the global river gauge network: Nature Sustainability, v. 5, p. 586-592, https://doi.org/10.1038/s41893-022-00873-0.","productDescription":"7 p.","startPage":"586","endPage":"592","ipdsId":"IP-130183","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":448023,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1038/s41893-022-00873-0","text":"External 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Geography, Texas A&M University, College Station, TX, 77843","active":true,"usgs":false}],"preferred":false,"id":850340,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lin, Peirong","contributorId":295975,"corporation":false,"usgs":false,"family":"Lin","given":"Peirong","affiliations":[{"id":6644,"text":"Princeton University","active":true,"usgs":false}],"preferred":false,"id":850342,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Godsey, Sarah E","contributorId":223120,"corporation":false,"usgs":false,"family":"Godsey","given":"Sarah E","affiliations":[{"id":38154,"text":"Idaho State University","active":true,"usgs":false}],"preferred":false,"id":850343,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Allen, Daniel C. 0000-0002-0451-0564","orcid":"https://orcid.org/0000-0002-0451-0564","contributorId":225169,"corporation":false,"usgs":false,"family":"Allen","given":"Daniel","middleInitial":"C.","affiliations":[{"id":41064,"text":"Department of Biology, University of Oklahoma, Norman OK, 73019","active":true,"usgs":false}],"preferred":false,"id":850351,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Burrows, Ryan","contributorId":295995,"corporation":false,"usgs":false,"family":"Burrows","given":"Ryan","affiliations":[{"id":13336,"text":"University of Melbourne","active":true,"usgs":false}],"preferred":false,"id":850357,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"DelVecchia, Amanda 0000-0003-4252-5991","orcid":"https://orcid.org/0000-0003-4252-5991","contributorId":225165,"corporation":false,"usgs":false,"family":"DelVecchia","given":"Amanda","email":"","affiliations":[{"id":41061,"text":"Flathead Lake Biological Station, University of Montana, Polson, MT 59860","active":true,"usgs":false}],"preferred":false,"id":850361,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fritz, Ken M. 0000-0002-3831-2531","orcid":"https://orcid.org/0000-0002-3831-2531","contributorId":203959,"corporation":false,"usgs":false,"family":"Fritz","given":"Ken","email":"","middleInitial":"M.","affiliations":[{"id":36773,"text":"USEPA NERL","active":true,"usgs":false}],"preferred":false,"id":850345,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Shanafield, Margaret","contributorId":106772,"corporation":false,"usgs":true,"family":"Shanafield","given":"Margaret","affiliations":[],"preferred":false,"id":850344,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Burgin, Amy J. 0000-0001-8489-4002","orcid":"https://orcid.org/0000-0001-8489-4002","contributorId":296009,"corporation":false,"usgs":false,"family":"Burgin","given":"Amy","email":"","middleInitial":"J.","affiliations":[{"id":6773,"text":"University of Kansas","active":true,"usgs":false}],"preferred":false,"id":850356,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Zimmer, 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However, accurately sampling drift across a wide range of invertebrate sizes is difficult because small invertebrates slip through coarse-mesh drift nets, and fine mesh clogs more easily, which reduces filtration efficiency and measurement accuracy. To avoid this limiting tradeoff, we developed a gas-powered drift pump which pours 20 m3/hour of river water through nested 80- and 750-m nets suspended in the air, and we tested it against a conventional 250-m drift net during low and high flows in a clearwater Alaskan river. The drift pump detected a geometric mean drift concentration of 467 invertebrates m-3 and maximum of 5637 m-3, eleven times the mean concentration of 42 m-3 from the drift net. Invertebrates 3 mm length, primarily chironomids, comprised the entire difference. Studies in which the drift of 0.5 – 3 mm invertebrates might be relevant, such as foraging models investigating the growth of juvenile drift-feeding fishes, should consider using similar methods to quantify small invertebrate drift, lest they underestimate it by an order of magnitude.","language":"English","publisher":"Springer","doi":"10.1007/s10750-022-04849-1","usgsCitation":"Neuswanger, J., Schoen, E.R., Wipfli, M.S., Volk, C.J., and Savereide, J.W., 2022, A suction pump sampler for invertebrate drift detects exceptionally high concentrations of small invertebrates that drift nets miss: Hydrobiologia, v. 849, p. 2077-2089, https://doi.org/10.1007/s10750-022-04849-1.","productDescription":"13 p.","startPage":"2077","endPage":"2089","ipdsId":"IP-132981","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":429923,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Chena River","volume":"849","noUsgsAuthors":false,"publicationDate":"2022-04-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Neuswanger, Jason R.","contributorId":337745,"corporation":false,"usgs":false,"family":"Neuswanger","given":"Jason R.","affiliations":[{"id":81040,"text":"South Fork Research, Inc","active":true,"usgs":false}],"preferred":false,"id":902649,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schoen, Erik R.","contributorId":184107,"corporation":false,"usgs":false,"family":"Schoen","given":"Erik","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":902650,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wipfli, Mark S. 0000-0002-4856-6068 mwipfli@usgs.gov","orcid":"https://orcid.org/0000-0002-4856-6068","contributorId":1425,"corporation":false,"usgs":true,"family":"Wipfli","given":"Mark","email":"mwipfli@usgs.gov","middleInitial":"S.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":902648,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Volk, Carol J.","contributorId":337746,"corporation":false,"usgs":false,"family":"Volk","given":"Carol","email":"","middleInitial":"J.","affiliations":[{"id":81040,"text":"South Fork Research, Inc","active":true,"usgs":false}],"preferred":false,"id":902651,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Savereide, James W.","contributorId":204591,"corporation":false,"usgs":false,"family":"Savereide","given":"James","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":902652,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70261243,"text":"70261243 - 2022 - Complex magmatic-tectonic interactions during the 2020 Makushin Volcano, Alaska, earthquake swarm","interactions":[],"lastModifiedDate":"2024-12-03T14:58:21.352545","indexId":"70261243","displayToPublicDate":"2022-04-22T08:51:14","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1427,"text":"Earth and Planetary Science Letters","active":true,"publicationSubtype":{"id":10}},"title":"Complex magmatic-tectonic interactions during the 2020 Makushin Volcano, Alaska, earthquake swarm","docAbstract":"On June 15, 2020, at 21:16 UTC, a locally-felt earthquake of magnitude 4.2 struck Unalaska Island, Alaska, ∼15 km west of the town of Unalaska and the large fishing port of Dutch Harbor. The event was followed by a M4.1 earthquake at 00:34 UTC and several M3+ aftershocks, initiating a prolific sequence with hundreds of earthquakes recorded into late December. The earthquakes all locate about 12 km southeast of the summit of Makushin Volcano at 7 to 10 km depth. To date, no eruptive activity or other surface changes have been observed at the volcano in webcam images, GPS or InSAR. Seismic bursts close to volcanoes are often associated with the onset of unrest that can lead to eruption. However, determining whether seismicity reflects magmatic rather than tectonic stresses is often challenging, although critical for hazard assessments and risk management strategies. To investigate the triggering mechanisms of the recent Makushin seismicity, we integrate information from space-time patterns of the earthquake hypocenters with their fault-plane solutions. We relocate the swarm events using double-difference relocation techniques and a 3D velocity model and find that the earthquakes, although they seem to follow two predominant orientations (NW-SE and SW-NE), do not show clear clustering into preferred alignments. Similarly, we do not observe pronounced migration in time and space. Fault-plane solutions (FPS) for all but one M2.5+ earthquakes have P-axis orientations consistent with subhorizontal NW-SE oriented regional maximum compression, whereas many of the lower-magnitude earthquakes have P-axes perpendicular to regional maximum compression. This provides evidence for the presence of a local stress field likely induced by magma intrusion. Results from Coulomb stress modeling are also consistent with dike inflation modulated by stresses induced by the M4+ earthquakes. The seismic swarm is thus likely linked to a superposition of driving stresses from both magmatic and tectonic processes on pre-existing faults. The case of the 2020 Makushin swarm, with its unusual characteristics, challenges traditional swarm classification schemes and suggests that a reconsideration of the definition of seismic swarms as having the maximum magnitude event in the middle of the swarm is warranted.
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As part of a study of sandhill cranes Antigone canadensis in the zone of contact between breeding distributions of the Eastern Population and Midcontinent Population in Minnesota, we monitored first summer survival of 34 sandhill cranes (hereafter colts) by using very-high-frequency and global positioning system–global system for mobile communications transmitters. We estimated daily survival probabilities from 19 to 120 d posthatch by using a generalized linear model accounting for interval censoring, resulting in an estimated period survival rate of 0.52 (90% CI, 0.36–0.71) over summer (100 d). Estimated daily probabilities of survival increased as colts became older and fledged (at 70–75 d posthatch), when they presumably became less vulnerable to predation. Causes of mortality were mostly unknown aside from one case of a collision with a vehicle. There is a scarcity of published colt survival rate estimates for sandhill cranes, and what is available varies widely by study site. Region-specific sandhill crane colt survival rate estimates can inform future management efforts and inform population dynamics research and overall natural history knowledge of sandhill cranes.
","language":"English","publisher":"Allen Press","doi":"10.3996/jfwm-21-097","usgsCitation":"Severud, W., Wolfson, D., Fieberg, J., and Andersen, D.E., 2022, Sandhill crane colt survival in Minnesota: Journal of Fish and Wildlife Management, v. 13, no. 2, p. 494-501, https://doi.org/10.3996/jfwm-21-097.","productDescription":"8 p.","startPage":"494","endPage":"501","ipdsId":"IP-135672","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481088,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-21-097","text":"Publisher Index Page"},{"id":480919,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","county":"Aitkin County, Becker County, Cass County, Clearwater County, Mahnomen County, Todd 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William J.","contributorId":348747,"corporation":false,"usgs":false,"family":"Severud","given":"William J.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":923739,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wolfson, David","contributorId":348748,"corporation":false,"usgs":false,"family":"Wolfson","given":"David","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":923740,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fieberg, John","contributorId":348749,"corporation":false,"usgs":false,"family":"Fieberg","given":"John","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":923741,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Andersen, David E. 0000-0001-9535-3404 dea@usgs.gov","orcid":"https://orcid.org/0000-0001-9535-3404","contributorId":199408,"corporation":false,"usgs":true,"family":"Andersen","given":"David","email":"dea@usgs.gov","middleInitial":"E.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923738,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70231525,"text":"70231525 - 2022 - Biogeochemical and ecosystem properties in three adjacent semiarid grasslands are resistant to nitrogen deposition but sensitive to edaphic variability","interactions":[],"lastModifiedDate":"2022-08-02T14:21:41.486398","indexId":"70231525","displayToPublicDate":"2022-04-21T08:43:45","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2242,"text":"Journal of Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Biogeochemical and ecosystem properties in three adjacent semiarid grasslands are resistant to nitrogen deposition but sensitive to edaphic variability","docAbstract":"Preventing wildlife disease outbreaks is a priority for natural resource agencies, and management decisions can be urgent, especially in epidemic circumstances. With the emergence of SARS-CoV-2, wildlife agencies were concerned whether the activities they authorize might increase the risk of viral transmission from humans to North American bats, but had a limited amount of time in which to make decisions. We describe how decision analysis provides a powerful framework to analyze and reanalyze complex natural resource management problems as knowledge evolves. Coupled with expert judgment and avenues for the rapid release of information, risk assessment can provide timely scientific information for evolving decisions. In April 2020, the first rapid risk assessment was conducted to evaluate the risk of transmission of SARS-CoV-2 from humans to North American bats. Based on the best available information and relying heavily on expert judgment, the risk assessment found a small possibility of transmission during summer work activities. Following that assessment, additional knowledge and data emerged, such as bat viral challenge studies, that further elucidated the risks of human-to-bat transmission and culminated in a second risk assessment in the fall of 2020. We updated the first SARS-CoV-2 risk assessment with new management alternatives and new estimates of little brown bat (Myotis lucifugus) susceptibility, using findings from the fall 2020 assessment and other empirical studies. We found that new knowledge led to an 88% decrease in the median number of bats estimated to be infected per 1,000 encountered when compared to earlier results. The use of facemasks during, or a negative COVID-19 test or vaccination prior to, bat encounters further reduced those risks. Using a combination of decision analysis, expert judgment, rapid risk assessment, and efficient modes of information distribution, we provided timely science-based support to decision makers for summer bat work in North America.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wsb.1262","usgsCitation":"Cook, J.D., Campbell Grant, E.H., Coleman, J., Sleeman, J.M., and Runge, M.C., 2022, Evaluating the risk of SARS-CoV-2 transmission to bats in the context of wildlife research, rehabilitation, and control: Wildlife Society Bulletin, v. 46, no. 3, e1262, 16 p., https://doi.org/10.1002/wsb.1262.","productDescription":"e1262, 16 p.","ipdsId":"IP-129889","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":489846,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9111074","text":"External Repository"},{"id":400278,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"46","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-04-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Cook, Jonathan D. 0000-0001-7000-8727","orcid":"https://orcid.org/0000-0001-7000-8727","contributorId":291411,"corporation":false,"usgs":true,"family":"Cook","given":"Jonathan","middleInitial":"D.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":842321,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Campbell Grant, Evan H. 0000-0003-4401-6496 ehgrant@usgs.gov","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":150443,"corporation":false,"usgs":true,"family":"Campbell Grant","given":"Evan","email":"ehgrant@usgs.gov","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":842322,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coleman, Jeremy T. H.","contributorId":291412,"corporation":false,"usgs":false,"family":"Coleman","given":"Jeremy T. H.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":842323,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sleeman, Jonathan M. 0000-0002-9910-6125 jsleeman@usgs.gov","orcid":"https://orcid.org/0000-0002-9910-6125","contributorId":128,"corporation":false,"usgs":true,"family":"Sleeman","given":"Jonathan","email":"jsleeman@usgs.gov","middleInitial":"M.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":82110,"text":"Midcontinent Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":842324,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":842325,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70235714,"text":"70235714 - 2022 - Central Andean (28–34°S) flood record 0–25 ka from Salinas del Bebedero, Argentina","interactions":[],"lastModifiedDate":"2022-09-27T16:56:37.687376","indexId":"70235714","displayToPublicDate":"2022-04-20T06:54:16","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3218,"text":"Quaternary Research","active":true,"publicationSubtype":{"id":10}},"title":"Central Andean (28–34°S) flood record 0–25 ka from Salinas del Bebedero, Argentina","docAbstract":"The Salinas del Bebedero occupies an isolated basin in the foreland of central Argentina at 33°S and was flooded repeatedly over past 25 ka. Isotopic evidence demonstrates that this flooding was due to overflow of the nearby Río Desaguadero with waters derived from the distant (≥300 km) central Andes between 28–34°S. Stratigraphic and shoreline evidence shows that floods occurred most frequently between 14.4 and 15.7 ka, followed by lesser events between 14.3 to 11.4 ka, and during the late Holocene from 2.6 to ca. 0.2 ka. Hydraulic modeling (2D HEC-RAS) shows that these floods could have originated from repeated subglacial drainage or sudden outbursts with a volume of >100 × 106 m3 and a peak discharge of >1,000 m3 s-1 each. The absence of flood deposits from 11 to 3 ka points to exceptionally dry and virtually ice-free conditions in the Andes between 28–34°S. The floods were probably caused by major rainfall or dammed-lake outbursts clustered largely during wet pluvial periods in the otherwise moisture-limited central Andes and Atacama Desert, such as when the Intertropical Convergence Zone was shifted southward. These include Central Andean pluvial events (CAPE) I (17–14.5 ka) and II (12.5–9 ka), and the Neoglacial/Formative archeological period 2500 ka to near-present.
pyGSFLOW is a python package designed to create new GSFLOW integrated hydrologic models, read existing models, edit model input data, run GSFLOW models, process output, and visualize model data.
","language":"English","publisher":"Journal of Open Source Software","doi":"10.21105/joss.03852","usgsCitation":"Larsen, J., Alzraiee, A.H., and Niswonger, R.G., 2022, Integrated hydrologic model development and postprocessing for GSFLOW using pyGSFLOW: Journal of Open Source Software, v. 7, no. 7, 3852, 5 p., https://doi.org/10.21105/joss.03852.","productDescription":"3852, 5 p.","ipdsId":"IP-128406","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":448080,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.21105/joss.03852","text":"Publisher Index Page"},{"id":435870,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NPZ5AD","text":"USGS data release","linkHelpText":"pyGSFLOW v1.0.0"},{"id":399570,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"7","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Larsen, Joshua 0000-0002-1218-800X jlarsen@usgs.gov","orcid":"https://orcid.org/0000-0002-1218-800X","contributorId":272403,"corporation":false,"usgs":true,"family":"Larsen","given":"Joshua","email":"jlarsen@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":841282,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Alzraiee, Ayman H. 0000-0001-7576-3449","orcid":"https://orcid.org/0000-0001-7576-3449","contributorId":272120,"corporation":false,"usgs":true,"family":"Alzraiee","given":"Ayman","email":"","middleInitial":"H.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":841283,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Niswonger, Richard G. 0000-0001-6397-2403 rniswon@usgs.gov","orcid":"https://orcid.org/0000-0001-6397-2403","contributorId":197892,"corporation":false,"usgs":true,"family":"Niswonger","given":"Richard","email":"rniswon@usgs.gov","middleInitial":"G.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":841284,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70242816,"text":"70242816 - 2022 - The economic effects of the HayWired Scenario using the association of Bay Area governments regional growth forecast—A focus on network disruption and resilience","interactions":[],"lastModifiedDate":"2024-10-28T16:53:30.588929","indexId":"70242816","displayToPublicDate":"2022-04-19T06:51:31","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"The economic effects of the HayWired Scenario using the association of Bay Area governments regional growth forecast—A focus on network disruption and resilience","docAbstract":"This paper describes how impacts to infrastructure networks within the San Francisco Bay Area may exacerbate the effects of building damage and how policies addressing these networks can improve resilience before and after the earthquake. The analysis uses existing modeling techniques that underlie the Association of Bay Area Government’s (ABAG) 2015 regional economic forecast of the San Francisco Bay region, California to estimate how a moment magnitude (MW) 7.0 earthquake scenario along the Hayward Fault, HayWired, would change the trajectory of that forecast. The ABAG forecast released in January 2015 is built on the framework of a Regional Economic Models, Inc. (REMI) model for the San Francisco Bay region and projects growth in the bay area through 2040. Using the simulation tools in the REMI model, the analysis applies the direct output losses flowing from building damages from the HayWired scenario (estimated using the FEMA Hazus model) to ABAG’s economic and demographic 2015 regional forecast. Also the analysis estimates direct, indirect, and induced effects on gross regional product (GRP), employment and population, and also highlights the effects of the physical infrastructure damage to roads, bridges, and rail to the region’s economy. Communications infrastructure, if resilient or restored, can help counteract the losses generated by building and transportation network damage. The REMI model results show that in the first year, employment would drop by almost half a million jobs, whereas GRP would decline by 8 percent. The two counties near the epicenter of the earthquake would have greater losses, of 15 percent in jobs and 13 percent in GRP. Counties with less physical damage may still have economic slowdowns due to transportation disruption. Much of the economy could recover within a few years, but a full return to the projected trajectory could take more than five years for the region and closer to a decade for the most severely affected counties. Recovery and rebuilding investments will be crucial to repairing the economic base of the region and returning it to its projected growth trajectory. State and local policies, as well as business and personal preparedness and employer flexibility in allowing remote work can reduce the length and severity of effects. Furthermore, sensitivity analyses using the model identify some critical factors that would lead to different levels of change. For example, a shortage of construction workers could result in a deeper, longer recession as rebuilding is postponed. Should major technology employers decide to relocate substantial portions of operations or expand outside of the region, the recovery period from the earthquake induced recession could stretch to six or seven years and the region’s trajectory could be permanently damped relative to the ABAG 2015 forecast for 2040.
Genetic analysis of non-invasively obtained samples is an increasingly affordable option for many wildlife studies, but it has remained difficult to obtain high-quality samples from many species. We modified 8” Belisle foot snares (Belisle Enterprises, Quebec, Canada) to non-invasively obtain mountain lion (Puma concolor) hair samples in unbaited trail sets. We deployed 22 hair traps, monitored by remote cameras, at 66 locations for 1618 active trap nights (x̄= 24.5 nights, SD = 7.2 nights). Photos indicated 20 instances of mountain lions passing within 2 m of a hair trap and we collected 7 mountain lion hair samples, which averaged >20 hairs/sample. All samples contained hair with visible roots and were identifiable to species; 6 of the 7 (85.7%) yielded sufficient DNA for individual identification. We attributed failure to obtain samples to 3 primary causes: individual trap saturation (2 instances), trap failure (2 instances), and non-trigger events (9 instances). Black bears (Ursus americanus) and heavy rains were the primary sources of disturbance to hair trap sets, contributing to individual trap saturation and trap failure. We speculate that low trigger rates were associated with pan tension having been set too high in the first month of the study, as well as disturbance of hair traps or leading foot placements by nontarget species. We discuss strategies to increase hair sample collection rates, including seasonal use of hair traps, more selective placement on the landscape, and altering physical attributes of the hair traps. Taking these strategies and the quality of hair samples collected into account, we believe hair traps are a viable tool for noninvasively collecting genetic material for individual identification of mountain lions and other elusive species. These data can be applied to studies of habitat connectivity, breeding success and relatedness, population density, metapopulation structure, or any others in which a bank of individual genotypes are useful.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wsb.1257","usgsCitation":"Rossettie, T., Perry, T., and Cain, J.W., 2022, Noninvasive sampling of mountain lion hair using modified foothold traps: Wildlife Society Bulletin, v. 46, no. 1, e1257, 13 p., https://doi.org/10.1002/wsb.1257.","productDescription":"e1257, 13 p.","ipdsId":"IP-119182","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":486524,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","county":"Sierra County","otherGeospatial":"Black Range Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108.48798824240478,\n 33.36187929179046\n ],\n [\n -108.48798824240478,\n 32.91656124812863\n ],\n [\n -107.58956320668692,\n 32.91656124812863\n ],\n [\n -107.58956320668692,\n 33.36187929179046\n ],\n [\n -108.48798824240478,\n 33.36187929179046\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"46","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-04-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Rossettie, Tricia S.","contributorId":355783,"corporation":false,"usgs":false,"family":"Rossettie","given":"Tricia S.","affiliations":[{"id":27575,"text":"NMSU","active":true,"usgs":false}],"preferred":false,"id":938152,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Perry, Travis W.","contributorId":355784,"corporation":false,"usgs":false,"family":"Perry","given":"Travis W.","affiliations":[{"id":84836,"text":"fu","active":true,"usgs":false}],"preferred":false,"id":938153,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cain, James W. III 0000-0003-4743-516X jwcain@usgs.gov","orcid":"https://orcid.org/0000-0003-4743-516X","contributorId":4063,"corporation":false,"usgs":true,"family":"Cain","given":"James","suffix":"III","email":"jwcain@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":938151,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70230602,"text":"ofr20211030K - 2022 - System characterization report on PRecursore IperSpettrale della Missione Applicativa (PRISMA)","interactions":[{"subject":{"id":70230602,"text":"ofr20211030K - 2022 - System characterization report on PRecursore IperSpettrale della Missione Applicativa (PRISMA)","indexId":"ofr20211030K","publicationYear":"2022","noYear":false,"chapter":"K","displayTitle":"System Characterization Report on PRecursore IperSpettrale della Missione Applicativa (PRISMA)","title":"System characterization report on PRecursore IperSpettrale della Missione Applicativa (PRISMA)"},"predicate":"IS_PART_OF","object":{"id":70221266,"text":"ofr20211030 - 2021 - System characterization of Earth observation sensors","indexId":"ofr20211030","publicationYear":"2021","noYear":false,"title":"System characterization of Earth observation sensors"},"id":1}],"isPartOf":{"id":70221266,"text":"ofr20211030 - 2021 - System characterization of Earth observation sensors","indexId":"ofr20211030","publicationYear":"2021","noYear":false,"title":"System characterization of Earth observation sensors"},"lastModifiedDate":"2022-04-19T10:54:07.62676","indexId":"ofr20211030K","displayToPublicDate":"2022-04-18T15:29:12","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1030","chapter":"K","displayTitle":"System Characterization Report on PRecursore IperSpettrale della Missione Applicativa (PRISMA)","title":"System characterization report on PRecursore IperSpettrale della Missione Applicativa (PRISMA)","docAbstract":"This report addresses system characterization of the Italian Space Agency’s PRecursore IperSpettrale della Missione Applicativa (PRISMA) 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 Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (band to band and image to image), radiometric, and spatial performances. Results of these analyses indicate that PRISMA has a band-to-band geometric performance in the range of −0.046 to 0.040 pixel; an image-to-image geometric performance (relative to the Landsat 8 Operational Land Imager) in the range of −60.791 meters (m; −2.03 pixels) to 299.541 m (9.98 pixels); a radiometric performance in the range of −0.037 to −0.001 in offset and 1.026 to 1.274 in slope; and a spatial performance with a relative edge response in the range of 0.56 to 0.63, full width at half maximum in the range of 1.84 to 1.97 pixels, and a modulation transfer function at a Nyquist frequency in the range of 0.054 to 0.096. Regarding fairly large geometric accuracy, the following explanation is provided to help the reader. The geometric accuracy required for PRISMA is a 200-m circular error at 90 percent (CE90) without ground control points (GCPs), a 15-m CE90 using GCPs is documented in the PRISMA mission overview (Agenzia Spaziale Italiana, 2021). The PRISMA images used for the current system characterization were georeferenced without using any GCPs; thus, the 200-m geometric accuracy requirement is applied. Beginning in 2022, a worldwide GCP database will be used in the PRISMA product processing chain, which will improve georeferencing accuracy to meet the 15-m CE90 requirement.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030K","usgsCitation":"Kim, M., Park, S., Anderson, C., and Stensaas, G.L., 2022, System characterization report on PRecursore IperSpettrale della Missione Applicativa (PRISMA), chap. K of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 28 p., https://doi.org/10.3133/ofr20211030K.","productDescription":"iv, 28 p.","numberOfPages":"36","onlineOnly":"Y","ipdsId":"IP-129829","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":398958,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/k/coverthb.jpg"},{"id":398959,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/k/ofr20211030k.pdf","text":"Report","size":"14.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1030-K"},{"id":398960,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1030/k/ofr20211030k.XML"},{"id":398961,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1030/k/images"}],"contact":"Director, Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
The Willamette River is home to at least 69 species of fish, 33 of which are native, including Chinook salmon (Oncorhynchus tshawytscha) and steelhead (Oncorhynchus mykiss). These fish need suitable hydraulic conditions, such as water depth and velocity, to fulfill various stages of their life. Hydraulic conditions are driven by interactions between channel morphology and streamflow, which throughout the Willamette River are strongly influenced by the operation of flood-control dams in upstream tributaries. To assess how streamflow management at these dams affects downstream fish habitat, the U.S. Geological Survey has developed high-resolution bathymetric datasets to support the development of two-dimensional hydraulic models. The datasets were created by combining data collected by airborne topo-bathymetric Light Detection and Ranging with boat-based sonar to create a seamless modeling surface over which a computational mesh with a resolution of roughly 5 by 5 meters was overlaid using the U.S. Army Corps of Engineers Hydraulic Engineering Center’s River Analysis System 5.0.7 hydraulic modeling software. Models were developed for about 200 river kilometers, separated into five modeling reaches, and hydraulic conditions were simulated at flows ranging from extremely low values to annual peak flows. Results of the simulations highlight distinct patterns of inundation extents, water depths, and velocities that vary longitudinally along the Willamette River. In the two farthest upstream model reaches, from Eugene to Corvallis, the river is slower, shallower, and inundates more area at similar seasonal flows than in reaches downstream from Corvallis, where the river generally is deeper and faster. These findings align with previous geomorphic analysis of the Willamette River showing the upper reaches of the river to be geomorphically more dynamic compared to the largely single-thread channel farther downstream. Results of simulations made with these hydraulic models can be used to drive fish-habitat models to further inform flow-management decisions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225025","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"White, J.S., and Wallick, J.R., 2022, Development of continuous bathymetry and two-dimensional hydraulic models for the Willamette River, Oregon: U.S. Geological Survey Scientific Investigations Report 2022–5025, 67 p., https://doi.org/10.3133/sir20225025.","productDescription":"viii, 67 p.","onlineOnly":"Y","ipdsId":"IP-112990","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":435872,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NB0KUT","text":"USGS data release","linkHelpText":"Two-dimensional HEC-RAS models and topo-bathymetric datasets for the Willamette River, Oregon"},{"id":435871,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92TTY4R","text":"USGS data release","linkHelpText":"Single-beam Echosounder Bathymetry of the Willamette River, Oregon 2015-2018"},{"id":398793,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5025/coverthb.jpg"},{"id":502381,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112938.htm","linkFileType":{"id":5,"text":"html"}},{"id":398796,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5025/sir20225025.XML"},{"id":398795,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5025/images"},{"id":398794,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5025/sir20225025.pdf","text":"Report","size":"20.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5025"}],"country":"United States","state":"Oregon","otherGeospatial":"Willamette River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.59619140625001,\n 43.94537239244209\n ],\n [\n -121.904296875,\n 43.94537239244209\n ],\n [\n -121.904296875,\n 45.521743896993634\n ],\n [\n -123.59619140625001,\n 45.521743896993634\n ],\n [\n -123.59619140625001,\n 43.94537239244209\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
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The conceptual models of groundwater flow outlined herein synthesize what is known and hypothesized about the groundwater-flow systems that discharge to the Grand Canyon of Arizona. These models interpret the hydrogeologic characteristics and hydrologic dynamics of the physical systems into a framework for understanding key aspects of the physical systems as they relate to groundwater flow and contaminant transport. This report describes five individual groundwater-flow systems draining to the Grand Canyon: Kaibab, Uinkaret-Kanab, Marble-Shinumo, Cataract, and Blue Spring. These systems are present in the saturated parts of the lower Paleozoic carbonate section exposed on the walls of the Grand Canyon; specifically, the Mississippian Redwall Limestone down through the Cambrian Muav Limestone of Tonto Group. Together, the systems described in this report compose the regional groundwater-flow system. Local to subregional flow systems in the sedimentary units of the overlying Permian section could provide transport pathways from the land surface to the regional flow system. Despite the potential importance of the local systems, the focus of this report is on the systems present in the lower Paleozoic section because all major springs in the Grand Canyon discharge from those units.
The most important hydrogeologic characteristics include system boundaries imposed by major tectonic structures, and the degree to which karstification influences the magnitude and direction of flow in each system. Important hydrologic dynamics include locations and rates of potential groundwater recharge, vertical pathways to the regional aquifer, and the locations, magnitude, geochemical signature, and hydrostratigraphic setting of groundwater discharge from springs. Unknown properties or conditions that represent the greatest uncertainties in our current understanding of the regional groundwater-flow system are identified for additional consideration.
Groundwater data are sparse owing to geographic remoteness and extreme depth to water throughout much of the study area. This paucity of information was diminished with the development of a structural contour map of the top and bottom surfaces of the regional aquifer, and a Soil-Water-Balance model that produces spatial distributions of rates of potential recharge. Investigation of the five groundwater-flow systems reveals important, though mostly qualitative, characteristics controlling the rates and directions of groundwater flow. Karstification has produced dissolution-enhanced conduit flow pathways to various degrees in each of the systems. Parts of each system exhibit relative structural uplift or downdropping of the hydrostratigraphic units of the regional aquifer, with some uplifted sections dipping inward toward the Grand Canyon and others dipping outward. The Kaibab groundwater system is archetypical of an uplifted, inward-dipping karst system, whereas the Blue Spring groundwater system and most of the Cataract groundwater system are representative instances of a downdropped or basin karst system. The Uinkaret-Kanab groundwater-flow system is structurally similar to the basin karst systems but karstification has not progressed to nearly the same degree. The Marble-Shinumo groundwater system does not fall cleanly into either category and its boundaries are the most uncertain of all the groundwater systems.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225037","usgsCitation":"Knight, J.E., and Huntoon, P.W., 2022, Conceptual models of groundwater flow in the Grand Canyon region, Arizona: U.S. Geological Survey Scientific Investigation Report 2022–5037, 51 p., https://doi.org/10.3133/sir20225037.","productDescription":"Report: vi, 51 p.; Data Release","numberOfPages":"51","onlineOnly":"Y","ipdsId":"IP-097904","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":502392,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112937.htm","linkFileType":{"id":5,"text":"html"}},{"id":398737,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FQ7BSY","text":"Soil-Water-Balance (SWB) model archive used to simulate potential mean annual recharge in the Grand Canyon region, Arizona","description":"Knight, J.E., and Jones, C.J., 2022, Soil-Water-Balance (SWB) model archive used to simulate potential mean annual recharge in the Grand Canyon region, Arizona: U.S. Geological Survey data release, https://doi.org/10.5066/P9FQ7BSY."},{"id":398739,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5037/sir20225037.pdf","text":"Report","size":"24 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":398738,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5037/covrthb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.64257812499999,\n 34.79576153473033\n ],\n [\n -110.58837890625,\n 34.79576153473033\n ],\n [\n -110.58837890625,\n 36.96744946416934\n ],\n [\n -113.64257812499999,\n 36.96744946416934\n ],\n [\n -113.64257812499999,\n 34.79576153473033\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
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Prescribed burns and understory thinnings are forest management practices aimed at reducing fuel loads to lessen wildfire threat in the Southern Appalachians, USA. Spiders play a critical role in forest ecosystems by controlling insect populations and providing an important food source for vertebrates. We used pitfall and colored pan traps to investigate how abundance, species richness, and diversity of spiders differed among three fuel reduction treatments administered repeatedly over a 15-year period and untreated controls. Additionally, we examined how spiders responded to one round (before and after) of fuel reduction treatments. We established treatments within the 15-year period as follows: mechanical understory removal (twice; M), prescribed burning (four times; B), mechanical understory removal followed one year later by high-severity prescribed burns and three subsequent burns (MB), and untreated controls (C). Our study period (2014–2016) occurred after multiple prescribed burns and two rounds of mechanical understory removal had occurred. Salticidae and Lycosidae were the two most commonly collected spider families in Southern Appalachian hardwood forests. Generally, we found increased spider abundances within all fuel-reduction treatments compared to controls. Individual spider families and species showed variable responses to treatments, but abundance of several spider families was greater in one or more fuel-reduction treatments than in controls. Additionally, abundance of several spider families and hunting/web building guilds (webs built for hunting purposes or defense) exhibited yearly differences to the last round of fuel-reduction treatments. Overall, our results suggest that changes in the overstory and understory of a forest are important drivers of regional spider abundance and assemblages, and forest management practices that modify forest structure can dramatically alter spider abundance and richness, usually in a positive manner.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2022.120127","usgsCitation":"Campbell, J., Grodsky, S.M., Milne, M., Viguiera, P., Viguiera, C., Stern, E., and Greenberg, C., 2022, Prescribed fire and other fuel-reduction treatments alter ground spider assemblages in a Southern Appalachian hardwood forest: Forest Ecology and Management, v. 510, 120127, 9 p., https://doi.org/10.1016/j.foreco.2022.120127.","productDescription":"120127, 9 p.","ipdsId":"IP-128340","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481089,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.foreco.2022.120127","text":"Publisher Index Page"},{"id":480823,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","county":"Polk County","otherGeospatial":"Green River Game Land","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-82.3541,35.1926],[-82.3507,35.2285],[-82.3597,35.2288],[-82.3591,35.2452],[-82.3495,35.2444],[-82.3462,35.2836],[-82.3294,35.3075],[-82.3183,35.3113],[-82.2784,35.3734],[-82.2636,35.3869],[-82.2615,35.3946],[-82.2477,35.4021],[-82.2403,35.4032],[-82.2283,35.3975],[-82.2181,35.3964],[-82.2098,35.4006],[-82.2059,35.4034],[-82.2008,35.4035],[-82.1945,35.3986],[-82.1872,35.3992],[-82.1738,35.4035],[-82.1637,35.4087],[-82.1507,35.4071],[-82.1392,35.3978],[-82.129,35.3975],[-82.1077,35.3807],[-82.0955,35.3682],[-82.0795,35.3421],[-82.0732,35.3386],[-82.0602,35.3352],[-82.04,35.3183],[-82.0341,35.3098],[-82.0311,35.3021],[-82.0214,35.2986],[-81.9885,35.2679],[-81.9736,35.2586],[-81.9667,35.251],[-81.9636,35.2388],[-81.9713,35.2123],[-81.9732,35.1959],[-81.9713,35.1876],[-82.1521,35.1942],[-82.1554,35.1943],[-82.2163,35.1959],[-82.2861,35.198],[-82.2889,35.1975],[-82.2923,35.1969],[-82.2945,35.1965],[-82.2967,35.1951],[-82.2984,35.1945],[-82.3001,35.194],[-82.3046,35.1926],[-82.3068,35.1922],[-82.3096,35.1916],[-82.3112,35.1912],[-82.313,35.1902],[-82.3157,35.1888],[-82.3186,35.1874],[-82.3219,35.1869],[-82.3246,35.1868],[-82.3281,35.1869],[-82.3297,35.1876],[-82.3303,35.1879],[-82.3326,35.1889],[-82.3354,35.1898],[-82.3377,35.1902],[-82.3406,35.1896],[-82.3433,35.1892],[-82.3456,35.1894],[-82.3489,35.1899],[-82.3501,35.1908],[-82.3518,35.1917],[-82.3541,35.1926]]]},\"properties\":{\"name\":\"Polk\",\"state\":\"NC\"}}]}","volume":"510","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Campbell, Joshua W.","contributorId":349587,"corporation":false,"usgs":false,"family":"Campbell","given":"Joshua W.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":924496,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grodsky, Steven Mark 0000-0003-0846-7230","orcid":"https://orcid.org/0000-0003-0846-7230","contributorId":328517,"corporation":false,"usgs":true,"family":"Grodsky","given":"Steven","email":"","middleInitial":"Mark","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":924495,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Milne, Marc","contributorId":349588,"corporation":false,"usgs":false,"family":"Milne","given":"Marc","affiliations":[{"id":79086,"text":"University of Indianapolis","active":true,"usgs":false}],"preferred":false,"id":924497,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Viguiera, Patrick","contributorId":349591,"corporation":false,"usgs":false,"family":"Viguiera","given":"Patrick","affiliations":[{"id":83493,"text":"High Point University","active":true,"usgs":false}],"preferred":false,"id":924498,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Viguiera, Cynthia C.","contributorId":349592,"corporation":false,"usgs":false,"family":"Viguiera","given":"Cynthia C.","affiliations":[{"id":83493,"text":"High Point University","active":true,"usgs":false}],"preferred":false,"id":924499,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stern, Emily","contributorId":349594,"corporation":false,"usgs":false,"family":"Stern","given":"Emily","affiliations":[{"id":79086,"text":"University of Indianapolis","active":true,"usgs":false}],"preferred":false,"id":924500,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Greenberg, Cathryn H.","contributorId":349596,"corporation":false,"usgs":false,"family":"Greenberg","given":"Cathryn H.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":924501,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70230029,"text":"sir20215101 - 2022 - Aquatic-life criteria compared to concentrations of cadmium, copper, lead, and zinc in streams near Fort Polk Military Reservation, Louisiana, December 2015–August 2016","interactions":[],"lastModifiedDate":"2026-04-02T19:40:54.308381","indexId":"sir20215101","displayToPublicDate":"2022-04-14T08:38:54","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5101","displayTitle":"Aquatic-Life Criteria Compared to Concentrations of Cadmium, Copper, Lead, and Zinc in Streams near Fort Polk Military Reservation, Louisiana, December 2015–August 2016","title":"Aquatic-life criteria compared to concentrations of cadmium, copper, lead, and zinc in streams near Fort Polk Military Reservation, Louisiana, December 2015–August 2016","docAbstract":"The primary focus of this study was to document cadmium, copper, lead, and zinc concentrations in selected streams near the U.S. Army Joint Readiness Training Center (JRTC) and Fort Polk Military Reservation and to compare those values to Federal and State aquatic-life criteria guidelines. The acute aquatic-life criteria used for this study are as follows: the U.S. Environmental Protection Agency (EPA) aquatic-life criterion maximum concentration (CMC) based on hardness, the EPA CMC for copper based on the biotic ligand model (BLM), and the Louisiana Department of Environmental Quality (LDEQ) acute aquatic-life criteria based on hardness. The chronic aquatic-life criteria used for this study are as follows: the EPA aquatic-life criterion continuous concentration (CCC) based on hardness, the EPA CCC for copper based on the BLM, and the LDEQ chronic aquatic-life criteria based on hardness.
Cadmium was detected in one stream-water sample collected near the Peason Ridge training area, hereinafter referred to as Peason Ridge, and one stream-water sample collected near North and South Fort Polk, hereinafter referred to as the Main Post. A cadmium concentration of an estimated (E) 0.48 microgram per liter (μg/L) in a stream-water sample collected during high stage near Peason Ridge exceeded the EPA CMC of 0.10 μg/L. A second cadmium concentration of E0.33 μg/L in a stream-water sample collected during low stage exceeded the EPA CMC of 0.22 μg/L, and a 4-day average cadmium concentration of E0.16 μg/L exceeded the EPA CCC of 0.14 μg/L.
Copper was detected in 34 stream-water samples collected near Peason Ridge and 22 stream-water samples collected near the Main Post. The EPA acute criteria for copper were exceeded 17 times in stream-water samples collected near Peason Ridge and 19 times in stream-water samples collected near the Main Post. The EPA chronic criteria for copper were exceeded five times in stream-water samples collected near Peason Ridge and seven times in stream-water samples collected near the Main Post.
Lead was detected in 31 stream-water samples collected near Peason Ridge and 16 stream-water samples collected near the Main Post. A concentration of 6.0 μg/L in a stream-water sample collected during high stage at site 2 near Peason Ridge exceeded the EPA CMC of 5.5 μg/L, and a concentration of 4.1 μg/L in a stream-water sample collected during high stage at site 4 near the Main Post exceeded the EPA CMC of 2.9 μg/L. The EPA chronic criteria for lead were exceeded nine times in stream-water samples collected near Peason Ridge and three times in stream-water samples collected near the Main Post. The LDEQ chronic criteria were exceeded two times in stream-water samples near Peason Ridge and none near the Main Post.
Zinc was detected in 35 stream-water samples collected near Peason Ridge and 17 stream-water samples collected near the Main Post. A concentration of 100 μg/L in a stream-water sample collected at site 3 near Peason Ridge exceeded the EPA CMC of 8.9 μg/L and the LDEQ acute aquatic-life criteria of 36 μg/L. One 4-day average zinc concentration, E28 μg/L for stream-water samples collected from site 3 near Peason Ridge, exceeded the EPA CCC of 8.2 μg/L; however, no concentrations of zinc exceeded the LDEQ chronic aquatic-life criteria near Peason Ridge or the Main Post.
The presence of copper, lead, and zinc at concentrations above the calculated acute or chronic aquatic-life criteria for some stream-water samples collected in relatively pristine streams near Peason Ridge and the Main Post indicates that these waters are susceptible to elevated trace element concentrations likely because of low ionic strength and hardness.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215101","collaboration":"Prepared in cooperation with the U.S. Army Joint Readiness Training Center and the Fort Polk Military Reservation","usgsCitation":"Tollett, R.W., 2022, Aquatic-life criteria compared to concentrations of cadmium, copper, lead, and zinc in streams near Fort Polk Military Reservation, Louisiana, December 2015–August 2016: U.S. Geological Survey Scientific Investigations Report 2021–5101, 40 p., https://doi.org/10.3133/sir20215101.","productDescription":"Report: viii, 40 p.; Data Release; Dataset","numberOfPages":"52","onlineOnly":"Y","ipdsId":"IP-106720","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":397567,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5101/sir20215101.XML"},{"id":397566,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5101/sir20215101.pdf","text":"Report","size":"4.37 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":502115,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112935.htm","linkFileType":{"id":5,"text":"html"}},{"id":397571,"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":397570,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F74M93FJ","text":"USGS data release","linkHelpText":"Water-quality and grain-size data collected at three sites near the Peason Ridge training area and two sites near the Main Post at the Joint Readiness Training Center and Fort Polk, 2015–2016"},{"id":397568,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5101/images"},{"id":397565,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5101/coverthb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Fort Polk Military Reservation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.39340209960938,\n 30.9187201197222\n ],\n [\n -92.58865356445312,\n 30.9187201197222\n ],\n [\n -92.58865356445312,\n 31.431006719178512\n ],\n [\n -93.39340209960938,\n 31.431006719178512\n ],\n [\n -93.39340209960938,\n 30.9187201197222\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
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640 Grassmere Park, Suite 100
Nashville, TN 37211
The Global Seismographic Network (GSN)—a global network of ≈150 very broadband stations—is used by researchers to study the free oscillations of the Earth (≈0.3–10 mHz) following large earthquakes. Normal‐mode observations can provide information about the radial density and anisotropic velocity structure of the Earth (including near the core–mantle boundary), but only when signal‐to‐noise ratios at very low frequencies are sufficiently high. Most normal‐mode observations in the past three decades have been made using Streckeisen STS‐1 vault seismometers. However, these sensors are no longer being manufactured or serviced. Candidate replacement sensors, the Streckeisen STS‐6 and the Nanometrics T‐360GSN, have been recently installed in boreholes, postholes, and vaults at several GSN stations and GSN testbeds. In this study, we examine normal‐mode spectra following three
Ceratonova shasta is a myxozoan parasite endemic to the Pacific Northwest of North America that is linked to low survival rates of juvenile salmonids in some watersheds such as the Klamath River basin. The density of C. shasta actinospores in the water column is typically highest in the spring (March–June), and directly influences infection rates for outmigrating juvenile salmonids. Current management approaches require quantities of C. shasta density to assess disease risk and estimate survival of juvenile salmonids. Therefore, we developed a model to simulate the density of waterborne C. shasta actinospores using a mechanistic framework based on abiotic drivers and informed by empirical data. The model quantified factors that describe the key features of parasite abundance during the period of juvenile salmon outmigration, including the week of initial detection (onset), seasonal pattern of spore density, and peak density of C. shasta. Spore onset was simulated by a bio-physical degree-day model using the timing of adult salmon spawning and accumulation of thermal units for parasite development. Normalized spore density was simulated by a quadratic regression model based on a parabolic thermal response with river water temperature. Peak spore density was simulated based on retained explanatory variables in a generalized linear model that included the prevalence of infection in hatchery-origin Chinook juveniles the previous year and the occurrence of flushing flows (≥171 m3/s). The final model performed well, closely matched the initial detections (onset) of spores, and explained inter-annual variations for most water years. Our C. shasta model has direct applications as a management tool to assess the impact of proposed flow regimes on the parasite, and it can be used for projecting the effects of alternative water management scenarios on disease-induced mortality of juvenile salmonids such as with an altered water temperature regime or with dam removal.