A Decree of the Supreme Court of the United States, entered June 7, 1954, established the position of Delaware River Master within the U.S. Geological Survey. In addition, the Decree authorizes diversion of water from the Delaware River Basin and requires compensating releases from certain reservoirs, owned by New York City, to be made under the supervision and direction of the River Master. The Decree stipulates that the River Master will furnish reports to the Court, not less frequently than annually. This report is the 60th annual report of the River Master of the Delaware River. It covers the 2013 River Master report year, the period from December 1, 2012 to November 30, 2013.
During the report year, precipitation in the upper Delaware River Basin was 44.50 inches or 100 percent of the long-term average. Combined storage in the Pepacton, Cannonsville, and Neversink Reservoirs remained high until October 2013 when it decreased below 80 percent combined capacity. The lowest combined storage of the report year was 70.2 percent of combined capacity on November 26, 2013. Delaware River Master operations during the year were conducted as stipulated by the Decree and the Flexible Flow Management Program.
Diversions from the Delaware River Basin by New York City and New Jersey were in full compliance with the Decree. Reservoir releases were made as directed by the River Master at rates designed to meet the Montague flow objective for the Delaware River at the Montague, New Jersey streamgage on 71 days during the report year. Interim Excess Release Quantity and conservation releases, designed to relieve thermal stress and protect the fishery and aquatic habitat in the tailwaters of the reservoirs, were also made during the report year. An agreement was signed on July 16, 2013 to temporarily increase releases to provide thermal protection below Cannonsville Reservoir.
The quality of water in the Delaware River estuary between streamgages at Trenton, New Jersey, and Reedy Island Jetty, Delaware, was monitored at several locations. Data on water temperature, specific conductance, dissolved oxygen, and pH were collected continuously by electronic instruments at four sites.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221068","isbn":"978-1-4113-4486-0","usgsCitation":"DiFrenna, V.J., Andrews, W.J., Russell, K.L., Norris, J.M., and Mason, R.R., Jr., 2022, Report of the River Master of the Delaware River for the period December 1, 2012–November 30, 2013: U.S. Geological Survey Open-File Report 2022–1068, 99 p., https://doi.org/10.3133/ofr20221068.","productDescription":"xii, 99 p.","numberOfPages":"99","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-123834","costCenters":[{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true}],"links":[{"id":501821,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113786.htm","linkFileType":{"id":5,"text":"html"}},{"id":408577,"rank":4,"type":{"id":31,"text":"Publication 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Office of the Delaware River Master
U.S. Geological Survey
120 Route 209 South
Milford, PA 18337
The Chesapeake Bay, along the mid-Atlantic coast of North America, is the largest estuary in the United States and provides critical habitat for wildlife. In contrast to point and non-point source release of pesticides, metals, and industrial, personal care and household use chemicals on biota in this watershed, there has only been scant attention to potential exposure and effects of algal toxins on wildlife in the Chesapeake Bay region. As background, we first review the scientific literature on algal toxins and harmful algal bloom (HAB) events in various regions of the world that principally affected birds, and to a lesser degree other wildlife. To examine the situation for the Chesapeake, we compiled information from government reports and databases summarizing wildlife mortality events for 2000 through 2020 that were associated with potentially toxic algae and HAB events. Summary findings indicate that there have been few wildlife mortality incidents definitively linked to HABs, other mortality events that were suspected to be related to HABs, and more instances in which HABs may have indirectly contributed to or occurred coincident with wildlife mortality. The dominant toxins found in the Chesapeake Bay drainage that could potentially affect wildlife are microcystins, with concentrations in water approaching or exceeding human-based thresholds for ceasing recreational use and drinking water at a number of locations. As an increasing trend in HAB events in the U.S. and in the Chesapeake Bay have been reported, additional information on HAB toxin exposure routes, comparative sensitivity among species, consequences of sublethal exposure, and better diagnostic and risk criteria would greatly assist in predicting algal toxin hazard and risks to wildlife.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.hal.2022.102319","usgsCitation":"Rattner, B., Wazniak, C.E., Lankton, J.S., McGowan, P.C., Drovetski, S.V., and Egerton, T.A., 2022, Review of harmful algal blooms effects on birds with implications for avian wildlife in the Chesapeake Bay region: Harmful Algae, v. 120, 102319, 20 p.; Data Release, https://doi.org/10.1016/j.hal.2022.102319.","productDescription":"102319, 20 p.; Data Release","ipdsId":"IP-138638","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":446019,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.hal.2022.102319","text":"Publisher Index Page"},{"id":408805,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":418367,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ULAM7G","text":"Wildlife mortality events in counties surrounding the Chesapeake Bay recorded in the Wildlife Health Information Sharing Partnership Event Reporting System (WHISPers) from 2000-2020"}],"country":"United States","state":"Maryland, Virginia","otherGeospatial":"Chesapeake Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -77.18954322170708,\n 37.53524166994633\n ],\n [\n -77.44783397911205,\n 37.364931581898986\n ],\n [\n -76.73341273522277,\n 36.72453067388703\n ],\n [\n -76.21133567238093,\n 36.76416355102856\n ],\n [\n -75.98601820315514,\n 37.06311061060744\n ],\n [\n -75.93655827088573,\n 37.308289271077044\n ],\n [\n -75.41997675607354,\n 37.95675381093146\n ],\n [\n -75.69475415756958,\n 38.48348716740804\n ],\n [\n -75.77718737801817,\n 39.59326365864615\n ],\n [\n -76.0464692314839,\n 39.66521791944069\n ],\n [\n -77.45332952714315,\n 38.82253108298832\n ],\n [\n -77.40386959487374,\n 38.276702908455235\n ],\n [\n -77.18954322170708,\n 37.53524166994633\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"120","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rattner, Barnett A. 0000-0003-3676-2843 brattner@usgs.gov","orcid":"https://orcid.org/0000-0003-3676-2843","contributorId":298580,"corporation":false,"usgs":true,"family":"Rattner","given":"Barnett A.","email":"brattner@usgs.gov","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":855925,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wazniak, Catherine E.","contributorId":298582,"corporation":false,"usgs":false,"family":"Wazniak","given":"Catherine","email":"","middleInitial":"E.","affiliations":[{"id":33964,"text":"Maryland Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":855989,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lankton, Julia S. 0000-0002-6843-4388 jlankton@usgs.gov","orcid":"https://orcid.org/0000-0002-6843-4388","contributorId":5888,"corporation":false,"usgs":true,"family":"Lankton","given":"Julia","email":"jlankton@usgs.gov","middleInitial":"S.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":855990,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McGowan, Peter C.","contributorId":13867,"corporation":false,"usgs":false,"family":"McGowan","given":"Peter","email":"","middleInitial":"C.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":855991,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Drovetski, Sergei V. 0000-0002-1832-5597","orcid":"https://orcid.org/0000-0002-1832-5597","contributorId":229520,"corporation":false,"usgs":true,"family":"Drovetski","given":"Sergei","middleInitial":"V.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":855992,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Egerton, Todd A.","contributorId":298583,"corporation":false,"usgs":false,"family":"Egerton","given":"Todd","email":"","middleInitial":"A.","affiliations":[{"id":64620,"text":"Virginia Department of Health","active":true,"usgs":false}],"preferred":false,"id":855993,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70249889,"text":"70249889 - 2022 - Largest recent impact craters on Mars: Orbital imaging and surface seismic co-investigation","interactions":[],"lastModifiedDate":"2023-11-04T13:24:14.694271","indexId":"70249889","displayToPublicDate":"2022-10-27T08:15:18","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3338,"text":"Science","active":true,"publicationSubtype":{"id":10}},"title":"Largest recent impact craters on Mars: Orbital imaging and surface seismic co-investigation","docAbstract":"Two 130+ meter diameter impact craters formed on Mars during the later half of 2021. These are the two largest fresh impact craters discovered by the Mars Reconnaissance Orbiter since operations started 16 years ago. The impacts created two of the largest seismic events (magnitudes greater than 4) recorded by InSight during its three year mission. The combination of orbital imagery and seismic ground motion enables the investigation of subsurface and atmospheric energy partitioning of the impact process on a planet with a thin atmosphere and the first direct test of Martian deep-interior seismic models with known event distances.The impact at 35°N excavated blocks of water ice, which is the lowest latitude ice has been directly observed on Mars.","language":"English","publisher":"American Association for the Advancement of Science","doi":"10.1126/science.abq7704","usgsCitation":"Posiolova, L., Lognonne, P., Banerdt, W., Clinton, J., Collins, G., Kawamura, T., Ceylan, S., Daubar, I.J., Fernando, B., Froment, M., Giardini, D., Malin, M.C., Miljkovic, K., Stahler, S.C., Xue, Z.G., Banks, M.E., Beucler, E., Cantor, B., Charalambous, C., Dahmen, N., Davis, P.W., Duran, C.M., Drilleau, M., Dundas, C., Euchner, F., Garcia, R., Golombek, M.P., Horleston, A., Keegan, C., Khan, A.S., Kim, D., Larmat, C., Lorenz, R.D., Margerin, L., Menina, S., Panning, M., Pardo, C., Perrin, C., Pike, W., Plasman, M., Rajsic, A., Rolland, L., Rougier, E., Speth, G., Spiga, A., Stott, A.E., Susko, D., Teanby, N., Valeh, A., Werynski, A., Wojcicka, N., and Zenhausern, G., 2022, Largest recent impact craters on Mars: Orbital imaging and surface seismic co-investigation: Science, v. 378, no. 6618, p. 412-417, https://doi.org/10.1126/science.abq7704.","productDescription":"6 p.","startPage":"412","endPage":"417","ipdsId":"IP-141384","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":446025,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://research-information.bris.ac.uk/en/publications/588e1d41-1d7e-4bb5-83dd-c431c832594c","text":"External 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Prior work has found that this lakebed/playa contributes metals-laden dust to snow in the Wasatch and Uinta Mountains. Dust and industrial particulate pollution are also delivered to communities along the Wasatch Front, but their sources, compositions, and fluxes are poorly characterized. In this study, we analyzed the dust deposited in 18 passive samplers positioned near the GSL, in cities in and near the Salt Lake Valley for total dust flux, the <63 µm dust fraction, 87Sr/86Sr, and trace element geochemistry. We compared spatial patterns in metal flux and abundance with community-level socioeconomic metrics. We observed the highest dust fluxes at sites near the GSL playa. Within the urban corridor, 87Sr/86Sr and trace element relative abundances suggest that most of the dust to which people are regularly exposed may be fugitive dust from local soil materials. The trace metal content of dust deposited along the Wasatch Front exceeded Environmental Protection Agency screening levels and exhibited enrichment relative to both the upper continental crust and the dust collected adjacent to GSL. Sources of metals to dust deposited along the Wasatch Front may include industrial activities like mining, oil refining, as well as past historical pesticide and herbicide applications. Arsenic and vanadium indicated a statistically significant positive correlation with income, whereas lead, thallium, and nickel exhibited higher concentrations in the least wealthy and least white neighborhoods.
The U.S. Army Corps of Engineers (USACE), Southwestern Division, Little Rock District Civil Works program has a mission to maintain cohesion between physical and naturally developed environments. The USACE authorized the development of an off-channel harbor (hereinafter referred to as the “proposed slack water harbor”) along the McClellan-Kerr Arkansas River Navigation System at river mile 202.6, and an initial evaluation of shoreline stability and adjacent land near the proposed harbor was considered essential in establishing a baseline for potential effects and future monitoring. In October 2021, the U.S. Geological Survey, in cooperation with the USACE, completed high-resolution bathymetric (underwater elevation) and topographic surveys of the Arkansas River and a quarry at the location of the proposed slack water harbor near Dardanelle, Arkansas, using a combination of multibeam sound navigation and ranging (sonar) and high-resolution, low-altitude aerial light detection and ranging (lidar) data to provide data and analysis needed for as-built information and future monitoring of river shoreline and floodplain management and maintenance.
Bathymetric data were collected using a high-resolution multibeam mapping system, which consists of a multibeam echosounder and an inertial navigation system mounted on a marine survey vessel. Data were collected as the vessel traversed the river and quarry along overlapping survey lines distributed throughout the areas.
Topographic data were collected as a lidar point cloud using an unmanned aircraft system (UAS) with a YellowScan Vx20–100 lidar payload, which consists of the lidar scanner and an inertial navigation system. The lidar point cloud data were collected as the UAS followed two sets of parallel transect lines, oriented perpendicular to each other (nominally north to south and east to west) on separate flights. The bathymetric and UAS topographic datasets were combined with topographic data extracted from publicly available aerial lidar data collected in 2014 to create a multisource point cloud classified as “ground” (code 2) according to the American Society for Photogrammetry and Remote Sensing standard lidar point classes in the proposed harbor area and surroundings, from which topographic contours were derived.
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The presence of year-round surface water in streams (i.e., streamflow permanence) is an important factor for identifying aquatic habitat availability, determining the regulatory status of streams, managing land use change, allocating water resources, and designing scientific studies. However, accurate, high resolution, and dynamic prediction of streamflow permanence that accounts for year-to-year variability at a regional extent is a major gap in modeling capability. Herein, we expand and adapt the U.S. Geological Survey (USGS) PRObability of Streamflow PERmanence (PROSPER) model from its original implementation in the Pacific Northwest (PROSPERPNW) to the upper Missouri River basin (PROSPERUM), a geographical region that includes mountain and prairie ecosystems of the northern United States. PROSPERUM is an empirical model used to estimate the probability that a stream channel has year-round flow in response to climatic conditions (monthly and annual) and static physiographic predictor variables of the upstream basin. The structure and approach of PROSPERUM are generally consistent with the PROSPERPNW model but include improved spatial resolution (10 m) and a longer modeling period. Average model accuracy was 81 %. Drainage area, upstream proportion as wetlands, and upstream proportion as developed land cover were the most important predictor variables. The PROSPERUM model identifies decreases in streamflow permanence during climatically drier years, although there is variability in the magnitude across basins highlighting geographically varying sensitivity to drought. Variability in the response of perennial streams to drought conditions among basins in the study area was also observed.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.hydroa.2022.100138","usgsCitation":"Sando, R., Jaeger, K.L., Farmer, W., Barnhart, T., McShane, R., Welborn, T.L., Kaiser, K.E., Hafen, K., Blasch, K.W., York, B.C., and Shallcross, A., 2022, Predictions and drivers of sub-reach-scale annual streamflow permanence for the upper Missouri River basin: 1989-2018: Journal of Hydrology X, v. 17, 100138, 22 p., https://doi.org/10.1016/j.hydroa.2022.100138.","productDescription":"100138, 22 p.","ipdsId":"IP-137870","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":446045,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.hydroa.2022.100138","text":"Publisher Index Page"},{"id":413911,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, North Dakota, South Dakota, Wyoming","otherGeospatial":"upper Missouri River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -115.06578880812711,\n 48.98607018582902\n ],\n [\n -114.41405308204301,\n 46.6259860103564\n ],\n [\n -114.45505714000817,\n 45.56604735512229\n ],\n [\n -114.1280129319525,\n 45.6810748968652\n ],\n [\n -113.43692249781836,\n 44.85861341197983\n ],\n [\n -112.98504047311934,\n 44.442264035596594\n ],\n [\n -111.79851411576917,\n 44.50526246095063\n ],\n [\n -111.2396387557593,\n 44.90171571207168\n ],\n [\n -110.61806405817302,\n 42.14074973473086\n ],\n [\n -105.77175988800175,\n 41.952647712608155\n ],\n [\n -104.56426824820389,\n 42.95942508508247\n ],\n [\n -103.25615692243574,\n 43.83191953044022\n ],\n [\n -101.00144324324455,\n 44.44211500891038\n ],\n [\n -100.09948505313812,\n 44.838575527202494\n ],\n [\n -99.6320641494822,\n 46.96241959544966\n ],\n [\n -99.99744313272754,\n 48.133167378584716\n ],\n [\n -102.2714095903581,\n 48.758327670163794\n ],\n [\n -107.84693201879426,\n 48.8300878096519\n ],\n [\n -115.06578880812711,\n 48.98607018582902\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"17","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sando, Roy 0000-0003-0704-6258","orcid":"https://orcid.org/0000-0003-0704-6258","contributorId":3874,"corporation":false,"usgs":true,"family":"Sando","given":"Roy","email":"","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":true,"id":865992,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jaeger, Kristin L. 0000-0002-1209-8506","orcid":"https://orcid.org/0000-0002-1209-8506","contributorId":206935,"corporation":false,"usgs":true,"family":"Jaeger","given":"Kristin","middleInitial":"L.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":865993,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Farmer, William H. 0000-0002-2865-2196","orcid":"https://orcid.org/0000-0002-2865-2196","contributorId":223181,"corporation":false,"usgs":true,"family":"Farmer","given":"William H.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":865994,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barnhart, Theodore B. 0000-0002-9682-3217","orcid":"https://orcid.org/0000-0002-9682-3217","contributorId":202558,"corporation":false,"usgs":true,"family":"Barnhart","given":"Theodore B.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":865995,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McShane, Ryan R. 0000-0002-3128-0039","orcid":"https://orcid.org/0000-0002-3128-0039","contributorId":219009,"corporation":false,"usgs":true,"family":"McShane","given":"Ryan R.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":865996,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Welborn, Toby L. 0000-0003-4839-2405 tlwelbor@usgs.gov","orcid":"https://orcid.org/0000-0003-4839-2405","contributorId":2295,"corporation":false,"usgs":true,"family":"Welborn","given":"Toby","email":"tlwelbor@usgs.gov","middleInitial":"L.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":865997,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kaiser, Kendra E. 0000-0003-1773-6236","orcid":"https://orcid.org/0000-0003-1773-6236","contributorId":211475,"corporation":false,"usgs":false,"family":"Kaiser","given":"Kendra","email":"","middleInitial":"E.","affiliations":[{"id":38255,"text":"Boise State Unviersity","active":true,"usgs":false}],"preferred":false,"id":865998,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hafen, Konrad 0000-0002-1451-362X","orcid":"https://orcid.org/0000-0002-1451-362X","contributorId":215959,"corporation":false,"usgs":true,"family":"Hafen","given":"Konrad","email":"","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":865999,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Blasch, Kyle W. 0000-0002-0590-0724 kblasch@usgs.gov","orcid":"https://orcid.org/0000-0002-0590-0724","contributorId":1631,"corporation":false,"usgs":true,"family":"Blasch","given":"Kyle","email":"kblasch@usgs.gov","middleInitial":"W.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":866046,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"York, Benjamin C. 0000-0002-3449-3574 byork@usgs.gov","orcid":"https://orcid.org/0000-0002-3449-3574","contributorId":213613,"corporation":false,"usgs":true,"family":"York","given":"Benjamin","email":"byork@usgs.gov","middleInitial":"C.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":866047,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Shallcross, Alden","contributorId":302945,"corporation":false,"usgs":false,"family":"Shallcross","given":"Alden","email":"","affiliations":[{"id":37086,"text":"U.S. Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":866048,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70237809,"text":"fs20223077 - 2022 - A multiscale approach for monitoring groundwater discharge to headwater streams by the U.S. Geological Survey Next Generation Water Observing System Program—An example from the Neversink Reservoir watershed, New York","interactions":[],"lastModifiedDate":"2026-03-25T16:43:06.391121","indexId":"fs20223077","displayToPublicDate":"2022-10-25T06:15:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-3077","displayTitle":"A Multiscale Approach for Monitoring Groundwater Discharge to Headwater Streams by the U.S. Geological Survey Next Generation Water Observing System Program—An Example From the Neversink Reservoir Watershed, New York","title":"A multiscale approach for monitoring groundwater discharge to headwater streams by the U.S. Geological Survey Next Generation Water Observing System Program—An example from the Neversink Reservoir watershed, New York","docAbstract":"Groundwater-stream connectivity across mountain watersheds is critical for supporting streamflow during dry times and keeping streams cool during warm times, yet U.S. Geological Survey (USGS) stream measurements are often sparse in headwaters. Starting in 2019, the USGS Next Generation Water Observing System Program developed a multiscale methods and technology testbed approach to monitoring groundwater discharge to streams in the Neversink Reservoir watershed in the Catskill Mountains of New York. Groundwater discharge dynamics are complex across space and time because of geographic variability, topography, and preferential groundwater flow patterns, and the monitoring of discharge processes necessitates an innovative approach that includes emerging water tracing methods and enhanced local geologic mapping. This fact sheet describes the multiscale monitoring approach applied in the Neversink Reservoir watershed and specifically how the varied data types are complimentary in understanding groundwater-stream connectivity, with elements transferable to other mountain watersheds.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223077","usgsCitation":"Briggs, M.A., Gazoorian, C.L., Doctor, D.H., and Burns, D.A., 2022, A multiscale approach for monitoring groundwater discharge to headwater streams by the U.S. Geological Survey Next Generation Water Observing System Program—An example from the Neversink Reservoir watershed, New York: U.S. Geological Survey Fact Sheet 2022–3077, 6 p., https://doi.org/10.3133/fs20223077.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-140810","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":501511,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113787.htm","linkFileType":{"id":5,"text":"html"}},{"id":435646,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9R3TYOZ","text":"USGS data release","linkHelpText":"Stream Temperature, Dissolved Radon, and Stable Water Isotope Data Collected along Headwater Streams in the Upper Neversink River Watershed, NY, USA (ver. 2.0, April 2023)"},{"id":408663,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3077/images/"},{"id":408662,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3077/fs20223077.XML"},{"id":408661,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223077/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2022-3077"},{"id":408660,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3077/fs20223077.pdf","text":"Report","size":"10.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022-3077"},{"id":408659,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3077/coverthb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Neversink Reservoir Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.20847061475345,\n 42.188348269216135\n ],\n [\n -74.59712682347332,\n 42.248700527889696\n ],\n [\n -75.01085762630467,\n 41.923046154032335\n ],\n [\n -74.86354438590236,\n 41.71281459504877\n ],\n [\n -74.42317182682854,\n 41.88105505768368\n ],\n [\n -74.19906764196153,\n 42.012767732647546\n ],\n [\n -74.20847061475345,\n 42.188348269216135\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Program Manager, Next Generation Water Observing System
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Despite significant investigation of fly ash spills and mineralogical controls on the release of potentially toxic elements (PTEs) from fly ash, interactions with the surficial environment remain relatively poorly understood. We conducted 90-day batch leaching studies with paired analysis of supernatant and solid-phase mineralogy to assess the elemental release and transformation of fly ash upon reaction with aquatic media (18 MΩ cm−1 water and simulated rainwater). The fly ash in this study, collected from the University of Alaska Fairbanks stoker-boiler power plant, is high in unburned carbon (~20% LOI) and highly enriched in several PTEs relative to the upper continental crust. Supernatant concentrations of oxyanion-forming elements (e.g., As, Se, Mo, Sb) remained relatively low and constant, suggesting equilibrium with the solid phase, possibly ettringite [Ca6Al2(SO4)3(OH)12•26H2O], which is known to incorporate and sorb oxyanion-forming PTEs and was identified by X-ray diffraction. Synthetic precipitation leaching procedure (SPLP) results failed to capture important temporal trends. Lead and Ba supernatant concentrations consistently exceeded drinking water standards, as well as others upon exposure to simulated physiological solutions. Seven-day experiments with dissolved organic matter-isolate solutions indicated that for certain elements, liberation was influenced by carbon concentration and/or the identity of the isolate. Overall, this paired approach can serve as a model for future studies, bridging existing gaps between batch leaching and single-element mineralogical, sorption, or speciation studies.
The coastal waters of Cuba are home to a small, endangered population of West Indian manatee, which would benefit from a comprehensive characterization of the population’s genetic variation. We conducted the first genetic assessment of Cuban manatees to determine the extent of the population's genetic structure and characterize the neutral genetic diversity among regions within the archipelago. We genotyped 49 manatees at 18 microsatellite loci, a subset of 27 samples on 1703 single nucleotide polymorphisms (SNPs), and sequenced 59 manatees at the mitochondrial control region. The Cuba manatee population had low nuclear (microsatellites HE = 0.44, and SNP HE = 0.29) and mitochondrial genetic diversity (h = 0.068 and π = 0.00025), and displayed moderate departures from random mating (microsatellite FIS = 0.12, SNP FIS = 0.10). Our results suggest that the western portion of the archipelago undergoes periodic exchange of alleles based on the evidence of shared ancestry and low but significant differentiation. The southeast Guantanamo Bay region and the western portion of the archipelago were more differentiated than southwest and northwest manatees. The genetic distinctiveness observed in the southeast supports its recognition as a demographically independent unit for natural resource management regardless of whether it is due to historical isolation or isolation by distance. Estimates of the regional effective population sizes, with the microsatellite and SNP datasets, were small (all Ne < 60). Subsequent analyses using additional samples could better examine how the observed structure is masking simple isolation by distance patterns or whether ecological or biogeographic forces shape genetic patterns.
Seasonal snow melt dominates the hydrologic budget across a large portion of the globe. Snow accumulation and melt vary over a broad range of spatial scales, preventing accurate extrapolation of sparse in situ observations to watershed scales. The lidar onboard the Ice, Cloud, and land Elevation, Satellite (ICESat-2) was designed for precise mapping of ice sheets and sea ice, and here we assess the feasibility of snow depth-mapping using ICESat-2 data in more complex and rugged mountain landscapes. We explore the utility of ATL08 Land and Vegetation Height and ATL06 Land Ice Height differencing from reference elevation datasets in two end member study sites. We analyze ∼3 years of data for Reynolds Creek Experimental Watershed in Idaho's Owyhee Mountains and Wolverine Glacier in southcentral Alaska's Kenai Mountains. Our analysis reveals decimeter-scale uncertainties in derived snow depth and glacier mass balance at the watershed scale. Both accuracy and precision decrease as slope increases: the magnitudes of the median and median of the absolute deviation of elevation errors (MAD) vary from ∼0.2 m for slopes <5° to >1 m for slopes >20°. For glacierized regions, failure to account for intra- and inter-annual evolution of glacier surface elevations can strongly bias ATL06 elevations, resulting in under-estimation of the mass balance gradient with elevation. Based on these results, we conclude that ATL08 and ATL06 observations are best suited for characterization of watershed-scale snow depth and mass balance gradients over relatively shallow slopes with thick snowpacks. In these regions, ICESat-2 elevation residual-derived snow depth and mass balance transects can provide valuable watershed scale constraints on terrain parameter- and model-derived estimates of snow accumulation and melt.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2022.113307","usgsCitation":"Enderlin, E., Elkin, C., Gendreau, M., Marshall, H., O'Neel, S., McNeil, C., Florentine, C., and Sass, L., 2022, Uncertainty of ICESat-2 ATL06- and ATL08-derived snow depths for glacierized and vegetated mountain regions: Remote Sensing of Environment, v. 283, 113307, 17 p., https://doi.org/10.1016/j.rse.2022.113307.","productDescription":"113307, 17 p.","ipdsId":"IP-141547","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":446058,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2022.113307","text":"Publisher Index Page"},{"id":486323,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F76Q1WHK","text":"USGS data release","linkHelpText":"Point Raw Glaciological Data: Ablation Stake, Snow Pit, and Probed Snow Depth Data on USGS Benchmark Glaciers"},{"id":408693,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska, Idaho","otherGeospatial":"Reynolds Creek Experimental Watershed, Wolverine Glacier","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.9169563546656,\n 43.36296558422342\n ],\n [\n -116.9169563546656,\n 43.1201924666604\n ],\n [\n -116.63496346150279,\n 43.1201924666604\n ],\n [\n -116.63496346150279,\n 43.36296558422342\n ],\n [\n -116.9169563546656,\n 43.36296558422342\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -148.84207609134586,\n 60.42699332401864\n ],\n [\n -148.8772978314237,\n 60.46763185035496\n ],\n [\n -148.92384084509808,\n 60.44126913255184\n ],\n [\n -148.95214402908923,\n 60.43009729404224\n ],\n [\n -148.9219539661653,\n 60.37666770702921\n ],\n [\n -148.9112616522131,\n 60.37542411458642\n ],\n [\n -148.84207609134586,\n 60.42699332401864\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"283","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Enderlin, Ellyn","contributorId":187445,"corporation":false,"usgs":false,"family":"Enderlin","given":"Ellyn","email":"","affiliations":[],"preferred":false,"id":855759,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Elkin, Colten","contributorId":298508,"corporation":false,"usgs":false,"family":"Elkin","given":"Colten","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":855760,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gendreau, Madeline","contributorId":298509,"corporation":false,"usgs":false,"family":"Gendreau","given":"Madeline","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":855761,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Marshall, H. P.","contributorId":298510,"corporation":false,"usgs":false,"family":"Marshall","given":"H. P.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":855762,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"O'Neel, Shad 0000-0002-9185-0144","orcid":"https://orcid.org/0000-0002-9185-0144","contributorId":289666,"corporation":false,"usgs":false,"family":"O'Neel","given":"Shad","affiliations":[{"id":62222,"text":"Cold Regions Research Laboratory","active":true,"usgs":false}],"preferred":false,"id":855763,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McNeil, Christopher J. 0000-0003-4170-0428 cmcneil@usgs.gov","orcid":"https://orcid.org/0000-0003-4170-0428","contributorId":5803,"corporation":false,"usgs":true,"family":"McNeil","given":"Christopher J.","email":"cmcneil@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":855764,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Florentine, Caitlyn 0000-0002-7028-0963","orcid":"https://orcid.org/0000-0002-7028-0963","contributorId":205964,"corporation":false,"usgs":true,"family":"Florentine","given":"Caitlyn","email":"","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":855766,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sass, Louis C. 0000-0003-4677-029X lsass@usgs.gov","orcid":"https://orcid.org/0000-0003-4677-029X","contributorId":3555,"corporation":false,"usgs":true,"family":"Sass","given":"Louis C.","email":"lsass@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":855765,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70237805,"text":"70237805 - 2022 - Quality of groundwater used for public supply in the continental United States: A comprehensive assessment","interactions":[],"lastModifiedDate":"2022-12-15T15:08:18.716393","indexId":"70237805","displayToPublicDate":"2022-10-21T10:00:40","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12792,"text":"Environmental Science & Technology - Water","active":true,"publicationSubtype":{"id":10}},"title":"Quality of groundwater used for public supply in the continental United States: A comprehensive assessment","docAbstract":"The presence of contaminants in a source water can constrain its suitability for drinking. The quality of groundwater used for public supply was assessed in 25 principal aquifers (PAs) that account for 84% of groundwater pumped for public supply in the U.S. (89.6 million people on a proportional basis). Each PA was sampled across its lateral extent using an equal-area grid, typically with 60 wells per PA. Samples were analyzed for 502 constituents, of which 374 had either a regulatory or nonregulatory human health benchmark (HHB). Nationally, elevated concentrations (relative to HHBs) of geogenic constituents have a larger effect than anthropogenic constituents, as indicated by three metrics: detection frequency, 35% versus 8.1%; prevalence (based on area), 41% versus 6.4%; and population potentially affected, 31.2 million versus 7.1 million. Prevalence of any constituent at elevated concentrations was high─40 to 75%─in PAs comprising unconsolidated sediment (eight PAs) and sandstone or interbedded sandstones and carbonates (four PAs) in the West and Central Interior. Prevalence was lower─15 to 35%─in PAs comprising sediment and sedimentary rocks along the Gulf and Atlantic Coasts (four PAs), carbonates distributed across the continental U.S. (seven PAs), and hard rock (two PAs).
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acsestwater.2c00390","usgsCitation":"Belitz, K., Fram, M.S., Lindsey, B.D., Stackelberg, P.E., Bexfield, L.M., Johnson, T., Jurgens, B., Kingsbury, J.A., McMahon, P.B., and Dubrovsky, N.M., 2022, Quality of groundwater used for public supply in the continental United States: A comprehensive assessment: Environmental Science & Technology - Water, v. 2, no. 12, p. 2645-2656, https://doi.org/10.1021/acsestwater.2c00390.","productDescription":"12 p.","startPage":"2645","endPage":"2656","ipdsId":"IP-142535","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes 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]\n}","volume":"2","issue":"12","noUsgsAuthors":false,"publicationDate":"2022-10-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Belitz, Kenneth 0000-0003-4481-2345","orcid":"https://orcid.org/0000-0003-4481-2345","contributorId":201889,"corporation":false,"usgs":true,"family":"Belitz","given":"Kenneth","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":855702,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fram, Miranda S. 0000-0002-6337-059X mfram@usgs.gov","orcid":"https://orcid.org/0000-0002-6337-059X","contributorId":1156,"corporation":false,"usgs":true,"family":"Fram","given":"Miranda","email":"mfram@usgs.gov","middleInitial":"S.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":855703,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lindsey, Bruce D. 0000-0002-7180-4319 blindsey@usgs.gov","orcid":"https://orcid.org/0000-0002-7180-4319","contributorId":175346,"corporation":false,"usgs":true,"family":"Lindsey","given":"Bruce","email":"blindsey@usgs.gov","middleInitial":"D.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment 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0000-0002-7334-9188","orcid":"https://orcid.org/0000-0002-7334-9188","contributorId":201888,"corporation":false,"usgs":true,"family":"Johnson","given":"Tyler D.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":855708,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jurgens, Bryant C. 0000-0002-1572-113X","orcid":"https://orcid.org/0000-0002-1572-113X","contributorId":203409,"corporation":false,"usgs":true,"family":"Jurgens","given":"Bryant","middleInitial":"C.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":855709,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kingsbury, James A. 0000-0003-4985-275X jakingsb@usgs.gov","orcid":"https://orcid.org/0000-0003-4985-275X","contributorId":883,"corporation":false,"usgs":true,"family":"Kingsbury","given":"James","email":"jakingsb@usgs.gov","middleInitial":"A.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":855707,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"McMahon, Peter B. 0000-0001-7452-2379 pmcmahon@usgs.gov","orcid":"https://orcid.org/0000-0001-7452-2379","contributorId":724,"corporation":false,"usgs":true,"family":"McMahon","given":"Peter","email":"pmcmahon@usgs.gov","middleInitial":"B.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":855710,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Dubrovsky, Neil M. 0000-0001-7786-1149 nmdubrov@usgs.gov","orcid":"https://orcid.org/0000-0001-7786-1149","contributorId":1799,"corporation":false,"usgs":true,"family":"Dubrovsky","given":"Neil","email":"nmdubrov@usgs.gov","middleInitial":"M.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":855711,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70238319,"text":"70238319 - 2022 - Avian predation on juvenile and adult Lost River and Shortnose Suckers: An updated multi-predator species evaluation","interactions":[],"lastModifiedDate":"2023-01-18T17:21:42.376606","indexId":"70238319","displayToPublicDate":"2022-10-21T06:42:32","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Avian predation on juvenile and adult Lost River and Shortnose Suckers: An updated multi-predator species evaluation","docAbstract":"Previous research suggests that predation by piscivorous colonial waterbirds may negatively influence the survival of Lost River Suckers (LRS) Deltistes luxatus and Shortnose Suckers (SNS) Chasmistes brevirostris in the Upper Klamath Basin (UKB), USA. However, estimates of predation from past studies, which were based on suckers with PIT tags, represent minimum estimates of sucker mortality because analyses did not account for the proportion of tags that were consumed by birds and deposited beyond their breeding colony. To address this uncertainty, we fed PIT-tagged suckers to American white pelicans Pelecanus erythrorhynchos to estimate deposition probabilities. A hierarchical Bayesian model was then used to estimate predation rates (percentage of available tagged fish that were consumed) on juvenile suckers that were released as part of the Sucker Assisted Rearing Program (SARP) and on wild juvenile and adult LRS and SNS during 2009–2020. Pelican deposition probabilities were estimated to be 0.47 (95% credible interval = 0.36–0.60), indicating that for every 100 tags consumed, 47 tags on average were deposited on breeding colonies by birds. Deposition-corrected estimates of predation rates were approximately two times greater than those previously reported and ranged annually from 4.3% (95% credible interval = 2.9–6.7%) to 8.5% (6.3–12.7%) on SARP juvenile suckers, from 4.3% (0.9–13.2%) to 10.5% (3.8–24.5%) on wild juvenile suckers, and from 0.1% (<0.1–0.3%) to 7.2% (2.8–16.4%) on adult suckers, depending on species and location. Results suggest that predation by colonial waterbirds, although not the original cause of sucker declines, was a substantial source of sucker mortality in some years. Future studies should consider models that jointly estimate both predation and survival and models that include environmental factors that potentially influence sucker susceptibility to avian predators in the UKB.
Here we describe the methods and results for biological characterization of the benthos on a previously unexplored area of central California, USA seafloor. We conducted 40 remotely operated vehicle dives from 371 to 1173 m water depth. Seafloor habitats and megafauna (fish and invertebrates) were documented from 46.8 km of seafloor video footage. Our expanded development and analysis of biotopes from quantitative data allowed us to describe detailed biological communities, along with the physical characteristics and habitat associations within the study area. This method provides a framework for potential monitoring, detection of future environmental change (natural and anthropogenic) and comprehensive comparison to other deep water regions. From 185 h of observational video at 25 sites, nearly 120,000 annotations of organisms, habitat characters, biological detritus and anthropogenic debris were recorded and analyzed. We identified a total of 228 taxa, with 173 of them present on linear quantitative transects. Species richness for transects ranged from 0.04 to 0.28 m-2 (8–55 taxa), with densities ranging from 0.07 to 5.20 ind. m−2. Both were highest on hard substrate with greatest surface area. Densities decreased with depth. Within soft substratum zones was a large field of pockmarks, which are seafloor depressions averaging 175 m in diameter and 5 m in depth. Pockmarks have sometimes been associated with seafloor gas seepage, but here we found no biological evidence of chemosynthetic organisms. No significant differences were found in either density nor species richness at pockmark sites vs. non-pockmark sites. Mud draped greenish-black coarse sand occurred only in low oxygen areas, while hummocky, rugose mud supported somewhat different species than flat mud plains. Seventy percent of the transects occurred inside the oxygen minimum zone. We conclude that high rugosity, slope, and the presence of hard substratum were better predictors of species richness and density than oxygen concentration in this specific study. Abundant biological detritus, in the form of dead and dying pelagic pyrosomes and salps, created a large, presumably ephemeral flux of carbon to the seafloor during the study period.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.dsr.2022.103872","usgsCitation":"Kuhnz, L.A., Gilbane, L., Cochrane, G.R., and Paull, C.K., 2022, Multi-factor biotopes as a method for detailed site characterization in diverse benthic megafaunal communities and habitats in deep-water off Morro Bay, California: Deep Sea Research Part I: Oceanographic Research Papers, v. 190, 103872, 19 p., https://doi.org/10.1016/j.dsr.2022.103872.","productDescription":"103872, 19 p.","ipdsId":"IP-137867","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":446078,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.dsr.2022.103872","text":"Publisher Index Page"},{"id":408743,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Morro Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.66975787964981,\n 35.804510935101575\n ],\n [\n -121.66975787964981,\n 34.864126610922014\n ],\n [\n -120.43438488666736,\n 34.864126610922014\n ],\n [\n -120.43438488666736,\n 35.804510935101575\n ],\n [\n -121.66975787964981,\n 35.804510935101575\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"190","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kuhnz, Linda A. 0000-0002-8359-3803","orcid":"https://orcid.org/0000-0002-8359-3803","contributorId":289638,"corporation":false,"usgs":false,"family":"Kuhnz","given":"Linda","email":"","middleInitial":"A.","affiliations":[{"id":13620,"text":"Monterey Bay Aquarium Research Institute, Moss Landing, California","active":true,"usgs":false}],"preferred":true,"id":855821,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gilbane, Lisa 0000-0001-9170-5388","orcid":"https://orcid.org/0000-0001-9170-5388","contributorId":289639,"corporation":false,"usgs":false,"family":"Gilbane","given":"Lisa","email":"","affiliations":[{"id":20318,"text":"Bureau of Ocean Energy Management","active":true,"usgs":false}],"preferred":true,"id":855822,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cochrane, Guy R. 0000-0002-8094-4583 gcochrane@usgs.gov","orcid":"https://orcid.org/0000-0002-8094-4583","contributorId":2870,"corporation":false,"usgs":true,"family":"Cochrane","given":"Guy","email":"gcochrane@usgs.gov","middleInitial":"R.","affiliations":[{"id":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":855823,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Paull, Charles K. 0000-0001-5940-3443","orcid":"https://orcid.org/0000-0001-5940-3443","contributorId":55825,"corporation":false,"usgs":false,"family":"Paull","given":"Charles","email":"","middleInitial":"K.","affiliations":[{"id":7043,"text":"University of North Carolina","active":true,"usgs":false}],"preferred":true,"id":855824,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70237636,"text":"ofr20221090 - 2022 - Water-quality, bed-sediment, and invertebrate tissue trace-element concentrations for tributaries in the Clark Fork Basin, Montana, October 2019–September 2020","interactions":[],"lastModifiedDate":"2026-03-30T20:41:39.761658","indexId":"ofr20221090","displayToPublicDate":"2022-10-19T12:38:11","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1090","displayTitle":"Water-Quality, Bed-Sediment, and Invertebrate Tissue Trace-Element Concentrations for Tributaries in the Clark Fork Basin, Montana, October 2019–September 2020","title":"Water-quality, bed-sediment, and invertebrate tissue trace-element concentrations for tributaries in the Clark Fork Basin, Montana, October 2019–September 2020","docAbstract":"Water, bed sediment, and invertebrate tissue were sampled in streams from Butte to near Missoula, Montana, as part of a monitoring program in the Clark Fork Basin. The sampling program was completed by the U.S. Geological Survey, in cooperation with the U.S. Environmental Protection Agency, to characterize aquatic resources in the Clark Fork Basin and monitor trace elements associated with historical mining and smelting activities. Sampling sites were on the Clark Fork River and a subset of its tributaries. Water samples were collected periodically at 22 sites from October 2019 through September 2020. Bed-sediment and tissue samples were collected once at 12 sites during July 2020.
Water-quality data included concentrations of major ions, dissolved organic carbon, nitrogen (nitrate plus nitrite), trace elements, and suspended sediment. Daily values of turbidity were determined at four sites. Bed-sediment data included trace-element concentrations in the fine-grained (less than 0.063 millimeter) fraction. Biological data included trace-element concentrations in whole-body tissue of selected aquatic benthic invertebrates. Statistical summaries of water-quality, bed-sediment, and invertebrate tissue trace-element data for sites in the Clark Fork Basin were provided for the period of record: March 1985–September 2020.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221090","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Clark, G.D., Hornberger, M.I., Hepler, E.J., and Heinert, T.L., 2022, Water-quality, bed-sediment, and invertebrate tissue trace-element concentrations for tributaries in the Clark Fork Basin, Montana, October 2019–September 2020: U.S. Geological Survey Open-File Report 2022–1090, 17 p., https://doi.org/10.3133/ofr20221090.","productDescription":"Report: vii, 17 p.; Data Release; Dataset","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-138065","costCenters":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"links":[{"id":501834,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113781.htm","linkFileType":{"id":5,"text":"html"}},{"id":408546,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221090/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":408387,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":408386,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93BP9P8","text":"USGS data release","linkHelpText":"Results of water-quality, bed-sediment, and invertebrate tissue trace-element concentrations for tributaries in the Clark Fork Basin, Montana, October 2019– September 2020"},{"id":408385,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1090/images"},{"id":408384,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1090/ofr20221090.XML"},{"id":408383,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1090/ofr20221090.pdf","text":"Report","size":"0.97 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022–1090"},{"id":408381,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1090/coverthb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Clark Fork Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -115.67714251796224,\n 47.58172589143089\n ],\n [\n -115.67714251796224,\n 45.00795879114483\n ],\n [\n -111.56647259158387,\n 45.00795879114483\n ],\n [\n -111.56647259158387,\n 47.58172589143089\n ],\n [\n -115.67714251796224,\n 47.58172589143089\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
This broad study field focuses on understanding values, perceptions, attitudes, and knowledge of humans and society as they relate to one another and the world around them. Several Social Science branches use various methodologies to conduct research on natural resources and hazards, climate and land-use change, and other related topics and interactions. Social Science discipline examples include anthropology, political science, psychology, sociology, and human geographers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip218","usgsCitation":"Restrepo-Osorio, D.L., 2022, Social Scientist GS–0101: U.S. Geological Survey General Information Product 218, 2 p., https://doi.org/10.3133/gip218.","productDescription":"Postcard: 6.00 x 4.00 inches","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-146141","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":408534,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/218/coverthb.jpg"},{"id":408535,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/218/gip218.pdf","text":"Report","size":"240 kB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 218"}],"contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
Quantifying the marine radiocarbon reservoir effect, offsets (ΔR), and ΔR variability over time is critical to improving dating estimates of marine samples while also providing a proxy of water mass dynamics. In the northeastern Pacific, where no high-resolution time series of ΔR has yet been established, we sampled radiocarbon (14C) from exactly dated growth increments in a multicentennial chronology of the long-lived bivalve, Pacific geoduck (Paneopea generosa) at the Tree Nob site, coastal British Columbia, Canada. Samples were taken at approximately decadal time intervals from 1725 CE to 1920 CE and indicate average ΔR values of 256 ± 22 years (1σ) consistent with existing discrete estimates. Temporal variability in ΔR is small relative to analogous Atlantic records except for an unusually old-water event, 1802–1812. The correlation between ΔR and sea surface temperature (SST) reconstructed from geoduck increment width is weakly significant (r2 = .29, p = .03), indicating warm water is generally old, when the 1802–1812 interval is excluded. This interval contains the oldest (–2.1σ) anomaly, and that is coincident with the coldest (–2.7σ) anomalies of the temperature reconstruction. An additional 32 14C values spanning 1952–1980 were detrended using a northeastern Pacific bomb pulse curve. Significant positive correlations were identified between the detrended 14C data and annual El Niño Southern Oscillation (ENSO) and summer SST such that cooler conditions are associated with older water. Thus, 14C is generally relatively stable with weak, potentially inconsistent associations to climate variables, but capable of infrequent excursions as illustrated by the unusually cold, old-water 1802–1812 interval.
Hydrologic and geophysical components interact to produce streambed scour. This study investigates methods for improving the utility of estimates of hydrologic flow in streams and rivers used when evaluating potential pier scour over the design-life of highway bridges in Virginia. Recent studies of streambed composition identify potential bridge design cost savings when attributes of cohesive soil and weathered rock unique to certain streambeds are considered within the bridge planning design. To achieve potential cost savings, however, attributes and effects of scour forces caused by water movement across the streambed surface must be accurately described and estimated.
This study explores the potential for improving estimates of the hydrologic component, namely hydrologic flow, afforded by empirically based deterministic, probabilistic, and statistical modeling of flows using streamgage data from 10 selected sites in Virginia. Methods are described and tools are provided that may assist with estimating hydrological components of flow duration and potential cumulative stream power for bridge designs in specific settings, and calculation of comprehensive projections of anticipated individual bridge pier scour rates. Examples of hydrologic properties needed to determine the rates of streambed scour are described for sites spanning a range of basin sizes and locations in Virginia. Deterministic, probabilistic, and statistical modeling methods are demonstrated for estimating hydrological components of streambed scour over a bridge design lifespan. Eight tools provide examples of streamflow analysis using daily and instantaneous streamflow data collected at 10 study sites in Virginia. Tool 1 provides a generalized system dynamics model of streamflow and sediment motion that may be used to estimate hydrologic flow over time. Tool 2 illustrates at-a-station hydraulic geometry using methods pioneered by Leopold and others. Tool 3 provides a system dynamics model developed to test the use of Monte-Carlo sampling of instantaneous streamflow measurements to augment and increase precision of site-specific period-of-record daily-flow values useful for driving stream-power and streambed scour estimates. Tool 4 integrates deterministic modeling, maximum likelihood logistic regression, and Monte-Carlo sampling to identify probable hydrologic flows. Tool 5 provides instantaneous flow hydrologic envelope profiles, using measured instantaneous flow data integrated with measured daily-flow value data. Tool 6 provides precise estimates of hydrologic flow over entire data time-series suitable for driving scour simulation models. Tool 7 provides a threshold of flow and probability of time-under-load interactive calculator that allows selection of a desired bridge design lifespan, ranging from 1 to 250 years, and identification of a flow interval of interest. Tool 8 provides a flow-random sampling interactive tool, developed to facilitate easy access to large datasets of randomly sampled flow data measurements from unique locations for purposes of computing and testing future models of bridge pier scour.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225059","collaboration":"Prepared in cooperation with the Virginia Department of Transportation","usgsCitation":"Austin, S.H., 2022, Virginia Bridge Scour Pilot Study—Hydrological Tools: U.S. Geological Survey Scientific Investigations Report 2022–5059, 46 p., https://doi.org/10.3133/sir20225059.","productDescription":"Report: vii, 46 p.; Data Release; Dataset","numberOfPages":"46","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-137495","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":408486,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P957ABZN","text":"USGS data release","linkHelpText":"Virginia bridge scour pilot study streamflow data"},{"id":408487,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the nation"},{"id":408485,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5059/sir20225059.XML"},{"id":408484,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5059/images/"},{"id":408483,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225059/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5059"},{"id":408482,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5059/sir20225059.pdf","text":"Report","size":"9.59 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5059"},{"id":408481,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5059/coverthb.jpg"}],"country":"United States","state":"Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.28857421875,\n 39.554883059924016\n ],\n [\n -80.39794921875,\n 38.18638677411551\n ],\n [\n -80.4638671875,\n 37.52715361723378\n ],\n [\n -77.49755859375,\n 37.59682400108367\n ],\n [\n -77.32177734375,\n 39.53793974517628\n ],\n [\n -78.28857421875,\n 39.554883059924016\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, Virginia 23228
Planning stormwater projects requires estimates of current and future extreme precipitation depths for events with specified return periods and durations. In this study, precipitation data from four downscaled climate datasets are used to determine changes in precipitation depth-duration-frequency curves from the period 1966–2005 to the period 2050–89 primarily on the basis of Representative Concentration Pathways 4.5 and 8.5 emission scenarios from the Coupled Model Intercomparison Project Phase 5. The four downscaled climate datasets are (1) the Coordinated Regional Downscaling Experiment (CORDEX) dataset, (2) the Localized Constructed Analogs (LOCA) dataset, (3) the Multivariate Adaptive Constructed Analogs (MACA) dataset, and (4) the Jupiter Intelligence Weather Research and Forecasting Model (JupiterWRF) dataset. Change factors—multiplicative changes in expected extreme precipitation magnitude from current to future period—were computed for grid cells from the downscaled climate datasets containing National Oceanic and Atmospheric Administration Atlas 14 stations in central and south Florida. Change factors for specific durations and return periods may be used to scale the National Oceanic and Atmospheric Administration Atlas 14 historical depth-duration-frequency values to the period 2050–89 on the basis of changes in extreme precipitation derived from downscaled climate datasets. Model culling was implemented to select downscaled climate models that best captured observed historical patterns of precipitation extremes in central and south Florida.
Overall, a large variation in change factors across downscaled climate datasets was found, with change factors generally greater than one and increasing with return period. In general, median change factors were higher for the south-central Florida climate region (1.05–1.55 depending on downscaled climate dataset, duration, and return period) than for the south Florida climate region (1–1.4 depending on downscaled climate dataset, duration, and return period) when considering best performing models for both areas, indicating a projected overall increase in future extreme precipitation events.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225093","collaboration":"Prepared in cooperation with the South Florida Water Management District","usgsCitation":"Irizarry-Ortiz, M.M., Stamm, J.F., Maran, C., and Obeysekera, J., 2022, Development of projected depth-duration frequency curves (2050–89) for south Florida: U.S. Geological Survey Scientific Investigations Report 2022–5093, 114 p., https://doi.org/10.3133/sir20225093.","productDescription":"Report: xii, 114 p.; 1 Table; Data Release","numberOfPages":"130","onlineOnly":"Y","ipdsId":"IP-134493","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":408474,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P935WRTG","text":"USGS data release","linkHelpText":"Change factors to derive projected future precipitation depth-duration-frequency (DDF) curves at 174 National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in central and south Florida"},{"id":435653,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Q3LEIL","text":"USGS data release","linkHelpText":"Change factors to derive projected future precipitation depth-duration-frequency (DDF) curves at 242 National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in Florida (ver 2.0, May 2024)"},{"id":408853,"rank":8,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225093/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":408472,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5093/sir20225093_table1.1.xlsx","text":"Table 1.1","size":"50.0 kB","linkFileType":{"id":3,"text":"xlsx"}},{"id":408471,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5093/images"},{"id":408470,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5093/sir20225093.XML"},{"id":408469,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5093/sir20225093.pdf","text":"Report","size":"23.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5093"},{"id":408468,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5093/coverthb.jpg"},{"id":408473,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5093/sir20225093_table1.1.csv","text":"Table 1.1","size":"18.6 kB","linkFileType":{"id":7,"text":"csv"}}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.111572265625,\n 24.327076540018634\n ],\n [\n -79.43115234375,\n 24.327076540018634\n ],\n [\n -79.43115234375,\n 28.98892237190413\n ],\n [\n -83.111572265625,\n 28.98892237190413\n ],\n [\n -83.111572265625,\n 24.327076540018634\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
Tellurium-bearing low-sulfidation epithermal Au-Ag deposits are significant producers of gold, silver, and potentially strategic elements if mineral processing methods are optimized for recovery. Although these deposits are generally related to alkaline magmatism, our study documents an unusual occurrence of Te-rich low-sulfidation epithermal systems in the North Heilongjiang Belt in northeast China that is spatially and temporally associated with calc-alkaline magmatism and tectonic extension in a continental arc‐setting.
In the North Heilongjiang Belt, Te-bearing Au-Ag deposits are usually sited in dilatant zones, mostly along extensional NW structures or at their intersections with deep‐seated NE-striking faults such as the Nenjiang-Heihe and Jiayin-Mudanjiang lineaments. Of these deposits, Sandaowanzi is well‐known for its bonanza gold grades. These faults localized andesitic to rhyolitic volcano-plutonic centers that evolved from mantle‐derived mafic melts to intermediate compositions due to differentiation and crustal assimilation as documented by their mineralogy and Sr, Nd, and Pb isotope compositions. During assimilation of country rocks, fluids containing 3He and other volatiles derived from mantle magmas displaced or mixed with external groundwater containing radiogenic 4He derived from country rocks. In this belt, the bulk metal content of Au, Ag and Te was probably introduced by deep mantle‐derived mafic‐intermediate calc‐alkaline intrusions. However, our study does not exclude input of metals leached from underlying metasedimentary rocks and older Te‐bearing Au mineralization. The noble gas, hydrogen, oxygen and lead isotope compositions of fluid inclusions and ore minerals suggest that the deposits formed by mixing between magmatic fluids and convecting meteoric ground waters containing lead leached from surrounding country rocks. At deeper levels, isotopic evidence suggests that Au-Ag-Te (Bi) precipitated with pyrite, quartz, sericite, and carbonate minerals due to mixing with Te-rich fluids. At shallower levels, Au and Ag precipitated contemporaneously with base‐metal sulfides, hydrothermal quartz and sericite during episodic boiling as evidenced by silica morphology and mineral textures.
The relatively uniform spacing of epithermal systems along NW- and NE-trending structures in the North Heilongjiang Belt was used to identify unexplored spaces within this mineral belt that are prospective for concealed low-sulfidation epithermal gold deposits. Although some of the Au-Ag deposits in this belt are enriched in Te, there is a lack of coeval alkaline igneous rocks that are commonly associated with Te-bearing gold deposits elsewhere in the world (e.g., Cripple Creek, Emperor, and Lihir Island).
","language":"English","publisher":"Elsevier","doi":"10.1016/j.oregeorev.2022.105158","usgsCitation":"Gao, S., Hofstra, A.H., Qin, K., and Xue, H., 2022, A case of Te-rich low-sulfidation epithermal Au-Ag deposits in a calc-alkaline magmatic arc, NE China: Ore Geology Reviews, v. 151, 105158, 17 p., https://doi.org/10.1016/j.oregeorev.2022.105158.","productDescription":"105158, 17 p.","ipdsId":"IP-133938","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":446085,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.oregeorev.2022.105158","text":"Publisher Index Page"},{"id":409917,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China","otherGeospatial":"North Heilongjiang Belt","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 125,\n 52\n ],\n [\n 125,\n 47\n ],\n [\n 131,\n 47\n ],\n [\n 131,\n 52\n ],\n [\n 125,\n 52\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"151","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gao, Shen","contributorId":299579,"corporation":false,"usgs":false,"family":"Gao","given":"Shen","email":"","affiliations":[],"preferred":false,"id":858049,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hofstra, Albert H. 0000-0002-2450-1593 ahofstra@usgs.gov","orcid":"https://orcid.org/0000-0002-2450-1593","contributorId":1302,"corporation":false,"usgs":true,"family":"Hofstra","given":"Albert","email":"ahofstra@usgs.gov","middleInitial":"H.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":858050,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Qin, K.","contributorId":299559,"corporation":false,"usgs":false,"family":"Qin","given":"K.","email":"","affiliations":[{"id":64884,"text":"China University of Geosciences-Beijing","active":true,"usgs":false}],"preferred":false,"id":858051,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Xue, H.","contributorId":219152,"corporation":false,"usgs":false,"family":"Xue","given":"H.","email":"","affiliations":[],"preferred":false,"id":858052,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70237893,"text":"70237893 - 2022 - Rapid and gradual permafrost thaw: A tale of two sites","interactions":[],"lastModifiedDate":"2022-10-31T11:50:28.949538","indexId":"70237893","displayToPublicDate":"2022-10-18T06:40:54","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Rapid and gradual permafrost thaw: A tale of two sites","docAbstract":"Warming temperatures and increasing disturbance by wildfire and extreme weather events is driving permafrost change across northern latitudes. The state of permafrost varies widely in space and time, depending on landscape, climate, hydrologic, and ecological factors. Despite its importance, few approaches commonly measure and monitor the changes in deep (>1 m) permafrost conditions with high spatial resolution. Here, we use electrical resistivity tomography surveys along two transects in interior Alaska previously disturbed by wildfire and more recently by warming temperatures and extreme precipitation. Long-term point observations of permafrost depth, temperature, and water content inform geophysical measurements which, in turn, are used to extrapolate interpretations over larger areas and with high spatial fidelity. We contrast gradual loss of recently formed permafrost driven by warmer temperatures and increased snowfall, with rapid permafrost loss driven by changes in air temperature, snow depth, and extreme summer precipitation in 2014.
Often referred to as “mudflows,” debris flows are a type of landslide made up of a rapidly moving mixture of dirt, rocks, trees, and water (and sometimes ash) that start on a hillside and travel downvalley. They can easily overflow channels and severely damage houses, vehicles, or other structures. Areas burned by wildfires are especially susceptible to these hazards, which can be triggered by storms occurring days to years after a fire. Debris flows in burned areas can start on a dry slope after only a few minutes of heavy rainfall—about half an inch an hour or more. They can also threaten unsuspecting areas downstream, as debris flows can travel many miles and affect places that were neither burned nor received any rain. The tips provided in this Fact Sheet can help to keep you safe in a potential debris flow danger area.
","language":"English, Spanish","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223078","collaboration":"Prepared in cooperation with the National Weather Service","usgsCitation":"Sobieszczyk, S., and Kean, J.W., 2022, Postfire debris flow hazards—Tips to keep you safe: U.S. Geological Survey Fact Sheet 2022–3078, 2 p., https://doi.org/10.3133/fs20223078. [In English and Spanish.]","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","ipdsId":"IP-143718","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":408389,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3078/coverthb.jpg"},{"id":408390,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3078/fs20223078.pdf","text":"Report","size":"570 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022–3078"}],"contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
The Navajo (N) aquifer is an extensive aquifer and the primary source of groundwater in the 5,400-square-mile Black Mesa area in northeastern Arizona. Water availability is an important issue in the Black Mesa area because of the arid climate, past industrial water use, and continued water requirements for municipal use by a growing population. Precipitation in the area typically ranges from less than 6 to more than 16 inches per year depending on location.
The U.S. Geological Survey water-monitoring program in the Black Mesa area began in 1971 and provides information about the long-term effects of groundwater withdrawals from the N aquifer for industrial and municipal uses. This report presents results of data collected as part of the monitoring program in the Black Mesa area from calendar year 2019, and additionally uses streamflow statistics from November and December 2018. The monitoring program includes measurements of (1) groundwater withdrawals (pumping), (2) groundwater levels, (3) spring discharge, (4) surface-water discharge, and (5) groundwater chemistry.
In calendar year 2019, total groundwater withdrawals were estimated to be 3,070 acre-feet (acre-ft), industrial withdrawals were 670 acre-ft, and municipal withdrawals were estimated to be 2,400 acre-ft. Total withdrawals during 2019 were about 58 percent less than total withdrawals in 2005 because of Peabody Western Coal Company’s discontinued use of water to transport coal in a coal slurry pipeline after 2005 and cessation of mining operations in 2019.
Water levels measured in 2019 from wells completed in the unconfined areas of the N aquifer within the Black Mesa area showed a decline in 10 of 16 wells when compared with water levels from the prestress period (prior to 1965). The changes in water levels across all 16 wells ranged from +8.2 feet (ft) to −40.0 ft, and the median change was −1.7 ft. Water levels also showed decline in 16 of 18 wells measured in the confined area of the aquifer when compared to the prestress period. The median change for the confined area of the aquifer was −38.8 ft, with changes across all 18 wells ranging from +12.9 ft to −185.0 ft.
Spring flow was measured at four springs in 2019. Flow fluctuated during the period of record for Burro Spring and Pasture Canyon Spring, but a decreasing trend was statistically significant (p<0.05) at Moenkopi School Spring and Unnamed Spring near Dennehotso. Discharge at Burro Spring has remained relatively constant since it was first measured in the 1980s and discharge at Pasture Canyon Spring has fluctuated for the period of record.
Continuous records of surface-water discharge in the Black Mesa area were collected from streamflow-gaging stations at the following sites: Moenkopi Wash at Moenkopi 09401260 (1976 to 2019), Dinnebito Wash near Sand Springs 09401110 (1993 to 2019), Polacca Wash near Second Mesa 09400568 (1994 to 2019), and Pasture Canyon Springs 09401265 (2004 to 2019). Median winter flows (November through February) of each winter were used as an estimate of the amount of groundwater discharge at the above-named sites. For the period of record, the median winter flows have generally remained constant at Polacca Wash and Pasture Canyon Springs, whereas a decreasing trend was indicated at Moenkopi Wash and Dinnebito Wash.
In 2019, water samples collected from four springs and three wells in the Black Mesa area were analyzed for selected chemical constituents. Results from the four springs were compared with previous analyses from the same springs. Concentrations of dissolved solids, chloride, and sulfate increased at Moenkopi School Spring during the more than 30 years of record at that site. Concentrations of dissolved solids, chloride, and sulfate at Pasture Canyon Spring have not varied significantly (p>0.05) since the early 1980s, and there is no increasing or decreasing trend in those data. Concentrations of dissolved solids, chloride, and sulfate at Unnamed Spring near Dennehotso have varied for the period of record, but there is no statistical trend in the data. Concentrations of dissolved solids and chloride at Burro Spring have varied for the period of record, but there is no statistical trend in the data; however, concentrations of sulfate from Burro Spring now show a trend towards lower concentrations. No statistical trend tests were performed for the three wells sampled in 2019 since less historical water-quality data were available for comparison.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221086","collaboration":"Prepared in cooperation with the Navajo Nation and Peabody Western Coal Company","usgsCitation":"Mason, J.P., 2022, Groundwater, surface-water, and water-chemistry data, Black Mesa area, northeastern Arizona—2018–2019: U.S. Geological Survey Open-File Report 2022–1086, 47 p., https://doi.org/10.3133/ofr20221086.","productDescription":"vii, 47 p.","numberOfPages":"47","onlineOnly":"Y","ipdsId":"IP-119897","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":501833,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113767.htm","linkFileType":{"id":5,"text":"html"}},{"id":408436,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20211124","text":"Open-File Report 2021-1124","linkHelpText":"- Groundwater, Surface-Water, and Water-Chemistry Data, Black Mesa Area, Northeastern Arizona—2016–2018"},{"id":408427,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1086/covrthb.jpg"},{"id":408428,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1086/ofr20221086.pdf","text":"Report","size":"11 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2022-1086"}],"country":"United States","state":"Arizona","otherGeospatial":"Black Mesa area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.06054687499999,\n 34.94899072578227\n ],\n [\n -109.390869140625,\n 34.94899072578227\n ],\n [\n -109.390869140625,\n 36.96744946416934\n ],\n [\n -112.06054687499999,\n 36.96744946416934\n ],\n [\n -112.06054687499999,\n 34.94899072578227\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
Pyrethroids, a class of commonly used insecticides, are frequently detected in aquatic environments, including estuaries. The influence that salinity has on organism physiology and the partitioning of hydrophobic chemicals, such as pyrethroids, has driven interest in how toxicity changes in saltwater compared to freshwater. Early life exposures in fish to pyrethroids cause toxicity at environmentally relevant concentrations, which can alter behavior. Behavior is a highly sensitive endpoint that influences overall organism fitness and can be used to detect toxicity of environmentally relevant concentrations of aquatic pollutants. Inland Silversides (Menidia beryllina), a commonly used euryhaline model fish species, were exposed from 5 days post fertilization (~1-day pre-hatch) for 96 h to six pyrethroids: bifenthrin, cyfluthrin, cyhalothrin, cypermethrin, esfenvalerate and permethrin. Exposures were conducted at three salinities relevant to brackish, estuarine habitat (0.5, 2, and 6 PSU) and across 3 concentrations, either 0.1, 1, 10, and/or 100 ng/L, plus a control. After exposure, Inland Silversides underwent a behavioral assay in which larval fish were subjected to a dark and light cycle stimuli to determine behavioral toxicity. Assessment of total distanced moved and thigmotaxis (wall hugging), used to measure hyper/hypoactivity and anxiety like behavior, respectively, demonstrate that even at the lowest concentration of 0.1 ng/L pyrethroids can induce behavioral changes at all salinities. We found that toxicity decreased as salinity increased for all pyrethroids except permethrin. Additionally, we found evidence to suggest that the relationship between log KOW and thigmotaxis is altered between the lower and highest salinities.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2022.159398","usgsCitation":"Hutton, S., Siddiqui, S., Pedersen, E., Markgraf, C., Segarra, A., Hladik, M.L., Connon, R., and Brander, S.M., 2022, Comparative behavioral ecotoxicology of Inland Silverside larvae exposed to pyrethroids across a salinity gradient: Science of the Total Environment, v. 857, no. Part 3, 159398, 12 p., https://doi.org/10.1016/j.scitotenv.2022.159398.","productDescription":"159398, 12 p.","ipdsId":"IP-143936","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":408606,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"857","issue":"Part 3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hutton, Sara","contributorId":298401,"corporation":false,"usgs":false,"family":"Hutton","given":"Sara","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":855525,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Siddiqui, Samreen","contributorId":298402,"corporation":false,"usgs":false,"family":"Siddiqui","given":"Samreen","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":855526,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pedersen, Emily","contributorId":298404,"corporation":false,"usgs":false,"family":"Pedersen","given":"Emily","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":855527,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Markgraf, Christopher","contributorId":298406,"corporation":false,"usgs":false,"family":"Markgraf","given":"Christopher","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":855528,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Segarra, Amelie 0000-0002-0551-0013","orcid":"https://orcid.org/0000-0002-0551-0013","contributorId":251846,"corporation":false,"usgs":false,"family":"Segarra","given":"Amelie","email":"","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":855529,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hladik, Michelle L. 0000-0002-0891-2712","orcid":"https://orcid.org/0000-0002-0891-2712","contributorId":203857,"corporation":false,"usgs":true,"family":"Hladik","given":"Michelle","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":855530,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Connon, Richard E","contributorId":152478,"corporation":false,"usgs":false,"family":"Connon","given":"Richard E","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":855531,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Brander, Susanne M.","contributorId":187546,"corporation":false,"usgs":false,"family":"Brander","given":"Susanne","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":855532,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70237622,"text":"70237622 - 2022 - Drivers of Pb, Sb and As release from spent gunshot in wetlands: Enhancement by organic matter and native microorganisms","interactions":[],"lastModifiedDate":"2022-12-01T16:11:27.245184","indexId":"70237622","displayToPublicDate":"2022-10-17T08:57:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Drivers of Pb, Sb and As release from spent gunshot in wetlands: Enhancement by organic matter and native microorganisms","docAbstract":"In many countries the use of lead-based ammunition is prevalent, and results in exposure and poisoning of waterfowl and other species of birds. In waterfowl hunting areas large quantities of spent shot may be deposited in wetland and terrestrial habitats. These pellets can undergo transformation, which are influenced by various abiotic and biotic factors. In addition to lead (Pb), other elements like antimony (Sb) and arsenic (As) can be leached from Pb shot into the environment. In vitro simulations that included organic matter and microorganisms were utilized to examine elemental leaching from gunshot. We found that leaching efficiency was the greatest in solutions rich in organic matter derived from artificial root exudates (2.69% for Pb, 1.16% for Sb, 1.83% for As), while leaching efficiency was considerably lower in river water (0.04%). In vitro simulations containing native microorganisms also exhibited greater leaching efficiency (0.49% for Pb, 0.52% for Sb, 1.32% for As) than in ultrapure deionized water and river water. Surface alterations in gunshot included the formation of a weathering crust and secondary phases dominated by carbonates. Spent gunshot is a source of Pb, Sb and As in wetlands that could affect aquatic ecosystems.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2022.159121","usgsCitation":"Potysz, A., Binkowski, L.J., Kierczak, J., and Rattner, B.A., 2022, Drivers of Pb, Sb and As release from spent gunshot in wetlands: Enhancement by organic matter and native microorganisms: Science of the Total Environment, v. 857, no. Part 1, 159121, 8 p., https://doi.org/10.1016/j.scitotenv.2022.159121.","productDescription":"159121, 8 p.","ipdsId":"IP-142053","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":446101,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2022.159121","text":"Publisher Index Page"},{"id":408380,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"857","issue":"Part 1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Potysz, Anna","contributorId":297946,"corporation":false,"usgs":false,"family":"Potysz","given":"Anna","email":"","affiliations":[{"id":64460,"text":"University of Wroclaw, Poland","active":true,"usgs":false}],"preferred":false,"id":854703,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Binkowski, Lukasz J.","contributorId":297947,"corporation":false,"usgs":false,"family":"Binkowski","given":"Lukasz","email":"","middleInitial":"J.","affiliations":[{"id":64461,"text":"Pedagogical University of Krakow, Poland","active":true,"usgs":false}],"preferred":false,"id":854704,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kierczak, Jakub","contributorId":297948,"corporation":false,"usgs":false,"family":"Kierczak","given":"Jakub","email":"","affiliations":[{"id":64462,"text":"University of Wroclaw","active":true,"usgs":false}],"preferred":false,"id":854705,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rattner, Barnett A. 0000-0003-3676-2843 brattner@usgs.gov","orcid":"https://orcid.org/0000-0003-3676-2843","contributorId":4142,"corporation":false,"usgs":true,"family":"Rattner","given":"Barnett","email":"brattner@usgs.gov","middleInitial":"A.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":854706,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70237138,"text":"pp1874 - 2022 - Lessons learned from wetlands research at the Cottonwood Lake Study Area, Stutsman County, North Dakota, 1967–2021","interactions":[],"lastModifiedDate":"2026-03-31T21:17:44.416924","indexId":"pp1874","displayToPublicDate":"2022-10-17T08:45:31","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1874","displayTitle":"Lessons Learned from Wetlands Research at the Cottonwood Lake Study Area, Stutsman County, North Dakota, 1967–2021","title":"Lessons learned from wetlands research at the Cottonwood Lake Study Area, Stutsman County, North Dakota, 1967–2021","docAbstract":"Depressional wetlands in the Prairie Pothole Region of North America have a long history of investigation owing to their importance in maintaining migratory-bird populations, especially waterfowl. One area of particularly intensive study is the Cottonwood Lake study area in Stutsman County, North Dakota. Studies at the Cottonwood Lake study area began in 1967 and continue through the present (2022). During this period of scientific discovery, meteorological conditions at the Cottonwood Lake study area varied greatly and included one of the most severe droughts of the 20th century and one of the wettest periods in the past 500 years.
Persistent wet conditions that began in 1993 have contributed to state changes in many of the study area’s larger wetlands to lake-like conditions, whereas the smaller wetlands returned to seasonally ponded conditions during relatively dry years interspersed within the longer-term wet period. Additionally, some nonwetland areas of the study area developed wetland plant, hydrology, and soil characteristics during the 1993-to-present (2022) wet period. The persistently high stages of water in the larger wetlands since 1993 contributed to a buildup of dissolved solids and increases in salinity with time following an initial decrease in salinity caused by the dilution of dissolved solids within a larger volume of water. During 2021, drought conditions similar to the 1988 to 1992 period may develop if conditions persist. However, meteorological changes during the past 30 years have persisted long enough to be considered a change in climate conditions at the study area and, if such wet conditions continue, would represent a change from conditions that occurred in the past two millennia.
During the period of study covered in this report (1967–2021), biotic communities responded in a variety of ways to subtle and marked changes in ponded-water depths, permanence, and salinity among the different wetland types in the study area. This report provides background information on the Cottonwood Lake study area and its context within the Prairie Pothole Region, documents techniques used to quantify environmental conditions and biotic communities, describes major trends that have been observed, presents significant findings as “lessons learned,” discusses recent modeling advances, and highlights key messages to managers. The Wetland Continuum concept was used as a framework to place research findings within an ecological context and to highlight the dynamic nature of prairie-pothole wetland ecosystems.
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U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401