Centered on Fort Morgan, Colorado, this study is intended to build from previous work by adding a three-dimensional (3D) view of the subsurface to better understand the depositional history of Quaternary deposits. A 1:100,000 scale geologic map was made by combining previous geologic maps, regional soil maps, and recent field investigations. In addition to the geologic mapping, drill hole lithologic data from water wells and oil and gas exploration were compiled and lithologic units simplified to best represent the stratigraphy of the Quaternary deposits. From these subsurface data, a 3D subsurface model was constructed, trimmed at the surface by a digital elevation model, and a bedrock surface foundation gridded from drill hole data was added. The surface of the 3D model was then compared visually to the surficial geologic map. Cross sections were constructed from the 3D model and compared to site-specific drilling that was done as part of this project. Finally, the model was examined in detail to reconstruct the depositional history of the subsurface alluvial and eolian units. Alluvial and fluvial drainage basins exposed in the subsurface have a greater areal extent than the present-day narrow drainages. Older eolian sand in the subsurface tends to be interbedded with loess indicating coeval deposition. Holocene sand, both eroded from bedrock exposed at the surface north of the study area and reworked from the South Platte River, buries most of the interbedded older sand and loess.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20255052","usgsCitation":"Taylor, E.M., Berry, M.E., Mahan, S.A., and Havens, J.C., 2026, Reconstructing the Quaternary depositional history using geologic mapping and three-dimensional modeling of the subsurface near Fort Morgan, northeastern Colorado: U.S. Geological Survey Scientific Investigations Report 2025–5052, 48 p., https://doi.org/10.3133/sir20255052.","productDescription":"Report: iv, 48 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-095650","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":500655,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119276.htm","linkFileType":{"id":5,"text":"html"}},{"id":500266,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5052/sir20255052.pdf","text":"Report","size":"60.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5052"},{"id":500265,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5052/coverthb.jpg"},{"id":500267,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13KTS2B","text":"USGS data release","linkHelpText":"Luminescence data for: Reconstructing the Quaternary depositional history using geologic mapping and a 3D model of the subsurface in the vicinity of Fort Morgan, Eastern Colorado"},{"id":500268,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AQ72FB","text":"USGS data release","linkHelpText":"Digital drillhole lithologic data and a radiocarbon age -- data supporting interpretation of Quaternary depositional history in the vicinity of Fort Morgan, Eastern Colorado"}],"country":"United States","state":"Colorado","city":"Fort Morgan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -103.5,\n 40.5\n ],\n [\n -104.5,\n 40.5\n ],\n [\n -104.5,\n 40\n ],\n [\n -103.5,\n 40\n ],\n [\n -103.5,\n 40.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Geosciences and Environmental Change Science Center
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Box 25046, MS-980
Denver, CO 80225-0046
Hurricanes drive diverse estuarine phytoplankton responses and can trigger cascading ecological and physicochemical impacts. Capturing these short-term dynamics requires high spatiotemporal resolution. Here, we applied a globally-applicable coastal ocean color algorithm, Variational Autoencoder (VAE), to Sentinel-2 MSI imagery for chlorophyll-a (Chl-a) estimation and validated its strong performance across the northern Gulf coast of America (GoA) estuaries, including Galveston Bay (TX), Barataria-Terrebonne Estuary (LA), Apalachicola Estuary (FL) and Tampa Bay (FL). The test set showed strong performance (MAE: 1.44 mg m−3; RMSE: 17.7 mg m−3; slope: 0.86; median symmetric accuracy: 30.33%). The validated VAE was then applied to 76 Sentinel-2 MSI images to assess phytoplankton biomass responses to hurricanes Harvey (2017), Michael (2018), Ida (2021), Francine (2024), Helene (2024), and Milton (2024) in the GoA estuaries. Results showed that hurricane disturbances on Chl-a typically lasted 3–5 weeks. Estuarine waters west (left) of hurricane tracks showed a rapid decline in Chl-a (∼5 mg m−3) due to elevated turbidity from heavy rainfall, and wind-driven flushing in the estuary, followed by a rebound over about two weeks, with Chl-a increasing approximately 10–15 mg m−3 above pre-storm levels. In contrast, right-side waters showed a slower response, likely from oligotrophic seawater intrusion driven by the hurricane's counterclockwise rotation. Post-storm observations showed increased freshwater phytoplankton like chlorophytes and cyanobacteria dominating estuaries, while shelf-waters exhibited elevated dinoflagellates (e.g., Karenia brevis bloom after Hurricane Milton). These results highlight the spatial heterogeneity of hurricane impacts on estuarine phytoplankton dynamics, which may trigger cascading effects on biogeochemical cycling and food webs, potentially prolonging ecosystem recovery.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2025JC023274","usgsCitation":"Li, J., Liu, B., Lou, J., Yuan, X., D'Sa, E.J., Baustian, M.M., La Peyre, M., Freeman, A., Martins, V.S., and Habib, E., 2026, Short-term estuarine phytoplankton dynamics in response to hurricanes along the Gulf Coast of America: A Variational Autoencoder (VAE) approach with satellite and bio-optical observations: JGR Oceans, v. 131, no. 3, e2025JC023274, 24 p., https://doi.org/10.1029/2025JC023274.","productDescription":"e2025JC023274, 24 p.","ipdsId":"IP-179432","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":501670,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2025jc023274","text":"Publisher Index 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Delaware","active":true,"usgs":false}],"preferred":false,"id":957597,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"D'Sa, Eurico J.","contributorId":367735,"corporation":false,"usgs":false,"family":"D'Sa","given":"Eurico","middleInitial":"J.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":957598,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Baustian, Melissa Millman 0000-0003-2467-2533","orcid":"https://orcid.org/0000-0003-2467-2533","contributorId":304015,"corporation":false,"usgs":true,"family":"Baustian","given":"Melissa","email":"","middleInitial":"Millman","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":957599,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"La Peyre, Megan 0000-0001-9936-2252 mlapeyre@usgs.gov","orcid":"https://orcid.org/0000-0001-9936-2252","contributorId":79375,"corporation":false,"usgs":true,"family":"La Peyre","given":"Megan","email":"mlapeyre@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":957600,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Freeman, Angelina","contributorId":223755,"corporation":false,"usgs":false,"family":"Freeman","given":"Angelina","affiliations":[{"id":40763,"text":"Coastal Protection and Restoration Authority","active":true,"usgs":false}],"preferred":false,"id":957601,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Martins, Vitor S.","contributorId":367736,"corporation":false,"usgs":false,"family":"Martins","given":"Vitor","middleInitial":"S.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":957602,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Habib, Emad","contributorId":367737,"corporation":false,"usgs":false,"family":"Habib","given":"Emad","affiliations":[{"id":7155,"text":"University of Louisiana at Lafayette","active":true,"usgs":false}],"preferred":false,"id":957603,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70273903,"text":"sir20265116 - 2026 - Erosion potential and flood vulnerability of streams and stream crossings at Acadia National Park, Maine","interactions":[],"lastModifiedDate":"2026-05-08T14:34:40.404083","indexId":"sir20265116","displayToPublicDate":"2026-02-26T09:30:00","publicationYear":"2026","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2026-5116","displayTitle":"Erosion Potential and Flood Vulnerability of Streams and Stream Crossings at Acadia National Park, Maine","title":"Erosion potential and flood vulnerability of streams and stream crossings at Acadia National Park, Maine","docAbstract":"Acadia National Park has had increases in the frequency and magnitude of precipitation in recent years, leading to increased flood flows, stream erosion, and costly infrastructure damage. To improve infrastructure management in a changing climate, the U.S. Geological Survey, in cooperation with the National Park Service, has developed multiple datasets that can help natural resource managers identify stream reaches and stream crossings that have the highest potential for erosion and flood damage within Acadia National Park. To develop these datasets, we first created a lidar-derived hydrography based on a 1-meter digital elevation model and then estimated peak flows at stream crossings and along the stream network using regional regression equations for Maine. We assessed the erosion potential of stream reaches by computing channel morphologic and hydrologic metrics associated with erosive power, such as stream steepness, topographic openness, and percent storage in the contributing watershed. Stream crossing flood vulnerability was assessed by comparing estimated peak flows to stream crossing conveyance capacities. Our results indicate that stream reaches in the headwaters of the Acadia National Park highlands such as Sargent, Penobscot, and Cadillac Mountain, have the highest erosion potential and generally coincide with reaches that have had erosion and infrastructure damage in the past. Stream crossings with the highest flood vulnerability are distributed throughout Mount Desert Island and Acadia National Park, especially south of Jordan Pond, north of Sargent Mountain, and surrounding Eagle Lake. Over a quarter of the total stream crossings have insufficient information to compute flood vulnerability and are often on the parts of the stream with the highest potential for erosion. The datasets allow users to identify stream reaches with the highest erosion potential, stream crossings that are most vulnerable to flood damage, and to highlight areas where supplemental field assessments could most effectively be completed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20265116","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Armstrong, I.P., McCallister, M.A., Hyslop, K.M., and Benthem, A.J., 2026, Erosion potential and flood vulnerability of streams and stream crossings at Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2026–5116, 21 p., https://doi.org/10.3133/sir20265116.","productDescription":"Report: vii, 21 p.; Data Release","numberOfPages":"21","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-178032","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":499817,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2026/5116/coverthb.jpg"},{"id":499818,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2026/5116/sir20265116.pdf","size":"7.83 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2026-5116 PDF"},{"id":499819,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20265116/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2026-5116 HTML"},{"id":499820,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2026/5116/sir20265116.xml","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2026-5116 XML"},{"id":500656,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119275.htm","linkFileType":{"id":5,"text":"html"}},{"id":499821,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2026/5116/images/"},{"id":499822,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1EHZNHN","text":"USGS data release","linkHelpText":"Data for an erosion potential and flood vulnerability assessment of streams and stream crossings at Acadia National Park, Maine"},{"id":500517,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://geonarrative.usgs.gov/acadiaerosionfloodvulnerability/","text":"Interactive dashboard","linkHelpText":"- Erosion Potential and Flood Vulnerability of Streams and Stream Crossings at Acadia National Park"}],"country":"United States","state":"Maine","otherGeospatial":"Acadia National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -68.45003175798666,\n 44.44178922865794\n ],\n [\n -68.45003175798666,\n 44.21621316604151\n ],\n [\n -68.13514216440173,\n 44.21621316604151\n ],\n [\n -68.13514216440173,\n 44.44178922865794\n ],\n [\n -68.45003175798666,\n 44.44178922865794\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Rd.
Northborough, Massachusetts 01532
The U.S. Geological Survey, in cooperation with the National Park Service, has developed multiple datasets that can help natural resource managers identify stream reaches with the highest potential for erosion and stream crossings most vulnerable to flood damage within Acadia National Park. These datasets allow users to identify areas where supplemental field assessments could be most effectively completed.
","publicationDate":"2026-02-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Armstrong, Ian P. 0000-0002-8239-8029","orcid":"https://orcid.org/0000-0002-8239-8029","contributorId":344363,"corporation":false,"usgs":true,"family":"Armstrong","given":"Ian","email":"","middleInitial":"P.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":955710,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCallister, Meghan A. 0000-0001-8814-7725","orcid":"https://orcid.org/0000-0001-8814-7725","contributorId":358213,"corporation":false,"usgs":true,"family":"McCallister","given":"Meghan","middleInitial":"A.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":955711,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hyslop, Kristina M. 0009-0001-2525-5574","orcid":"https://orcid.org/0009-0001-2525-5574","contributorId":334465,"corporation":false,"usgs":true,"family":"Hyslop","given":"Kristina","middleInitial":"M.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":955712,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Benthem, Adam J. 0000-0003-2372-0281","orcid":"https://orcid.org/0000-0003-2372-0281","contributorId":220000,"corporation":false,"usgs":true,"family":"Benthem","given":"Adam","middleInitial":"J.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":955713,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70275084,"text":"70275084 - 2026 - Towards global mapping of dynamic surface water extents using Sentinel-1 SAR data","interactions":[],"lastModifiedDate":"2026-04-15T15:02:05.992722","indexId":"70275084","displayToPublicDate":"2026-02-26T07:54:26","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Towards global mapping of dynamic surface water extents using Sentinel-1 SAR data","docAbstract":"Additional case studies demonstrate the algorithm's ability to handle surface water extent in sand-dominated deserts, to track seasonal amplitude in Folsom Lake (California), drought-induced loss in Cerro Prieto Reservoir (Mexico), and rapid filling of the Grand Ethiopian Renaissance Dam. These results show that the method scales across local to global domains and maintains high accuracy, providing a practical tool for near-real-time monitoring of floods, droughts, and water-resource management. Because the approach is sensor-agnostic, it can be ported to forthcoming L- and S-band missions such as NASA-ISRO synthetic aperture radar (NISAR), broadening its applicability to future hydrologic observations.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2026.115326","usgsCitation":"Jung, J., Fattahi, H., Jeong, S., Bonnema, M.G., Jones, J.W., Bekaert, D., Chan, S.K., and Handweger, A.L., 2026, Towards global mapping of dynamic surface water extents using Sentinel-1 SAR data: Remote Sensing of Environment, v. 337, 115326, 21 p., https://doi.org/10.1016/j.rse.2026.115326.","productDescription":"115326, 21 p.","ipdsId":"IP-183308","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":503010,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2026.115326","text":"Publisher Index Page"},{"id":502816,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"337","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jung, Jungkyo","contributorId":369929,"corporation":false,"usgs":false,"family":"Jung","given":"Jungkyo","affiliations":[{"id":64090,"text":"NASA Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":959401,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fattahi, Heresh","contributorId":292160,"corporation":false,"usgs":false,"family":"Fattahi","given":"Heresh","email":"","affiliations":[],"preferred":false,"id":959402,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jeong, Seongsu","contributorId":369930,"corporation":false,"usgs":false,"family":"Jeong","given":"Seongsu","affiliations":[{"id":64090,"text":"NASA Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":959403,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bonnema, Matthew G.","contributorId":369931,"corporation":false,"usgs":false,"family":"Bonnema","given":"Matthew","middleInitial":"G.","affiliations":[{"id":64090,"text":"NASA Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":959404,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jones, John W. 0000-0001-6117-3691 jwjones@usgs.gov","orcid":"https://orcid.org/0000-0001-6117-3691","contributorId":2220,"corporation":false,"usgs":true,"family":"Jones","given":"John","email":"jwjones@usgs.gov","middleInitial":"W.","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":959405,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bekaert, David","contributorId":267754,"corporation":false,"usgs":false,"family":"Bekaert","given":"David","affiliations":[{"id":13294,"text":"Woods Hole Oceanographic Institute","active":true,"usgs":false}],"preferred":false,"id":959406,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Chan, Steven K.","contributorId":369933,"corporation":false,"usgs":false,"family":"Chan","given":"Steven","middleInitial":"K.","affiliations":[{"id":64090,"text":"NASA Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":959407,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Handweger, Alexander L.","contributorId":369934,"corporation":false,"usgs":false,"family":"Handweger","given":"Alexander","middleInitial":"L.","affiliations":[{"id":64090,"text":"NASA Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":959408,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70273923,"text":"sir20265120 - 2026 - Methods for estimating selected streamflow statistics at ungaged sites in Wyoming based on data through water year 2021","interactions":[],"lastModifiedDate":"2026-04-10T15:07:21.627462","indexId":"sir20265120","displayToPublicDate":"2026-02-26T07:11:17","publicationYear":"2026","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2026-5120","displayTitle":"Methods for Estimating Selected Streamflow Statistics at Ungaged Sites in Wyoming Based on Data Through Water Year 2021","title":"Methods for estimating selected streamflow statistics at ungaged sites in Wyoming based on data through water year 2021","docAbstract":"The U.S. Geological Survey, in cooperation with the Wyoming Water Development Office, developed regional regression equations based on basin characteristics and streamflow statistics for streamgages through water year 2021 (October 1, 2020, to September 30, 2021). The regression equations allow estimates of mean annual maximum, mean annual, mean seasonal, and mean monthly streamflows; frequency statistics for the 7-day mean low flows with 2-year and 10-year recurrence intervals, 14- and 30-day mean low flows with 5-year recurrence intervals, and 60- and 1-day mean high flow with 2-year and 5-year recurrence intervals, respectively; and the 0.1-, 0.2-, 0.5-, 1-, 2-, 4-, 5-, 10-, 20-, 25-, 30-, 50-, 60-, 70-, 75-, 80-, 90-, 95-, 98-, and 99-percent durations for annual streamflows and 0.1-, 0.5-, 10-, 15-, 20-, 25-, 30-, 40-, 50-, 60-, 70-, 75-, 80-, 85-, 90-, 95-, and 99-percent durations for monthly streamflows for most months for ungaged locations in Wyoming that are largely unaltered by diversions or upstream reservoirs.
Regression equations were developed for 243 streamflow statistics. Best-subset selection was used to assess explanatory variables for respective streamflow statistics. Exploratory data analyses determined that, of the 81 basin characteristics evaluated as potential explanatory variables, characteristics such as drainage area and precipitation often produced models with the highest adjusted coefficient of determination and lowest mean squared error, as determined in the best-subset selection. To address heteroskedasticity of model residuals, model variables were regionalized using fixed-effects models; the percentages of the streamgage basins in selected ecoregions were defined as interaction terms, which represent the model slope for specific ecoregions. Most models were determined to be statistically significant for probability values less than or equal to 0.1 for one or more regional explanatory variables. The final regional regression equations defined in this report are available for use in the U.S. Geological Survey’s StreamStats web application at https://streamstats.usgs.gov/ss/.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20265120","collaboration":"Prepared in cooperation with the Wyoming Water Development Office","usgsCitation":"Taylor, N.J., and Sando, R., 2026, Methods for estimating selected streamflow statistics at ungaged sites in Wyoming based on data through water year 2021: U.S. Geological Survey Scientific Investigations Report 2026–5120, 38 p., https://doi.org/10.3133/sir20265120.","productDescription":"Report: vii, 38 p.; 1 Linked Appendix Table; Data Release; Dataset","numberOfPages":"50","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-179497","costCenters":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"links":[{"id":500115,"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":500657,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119274.htm","linkFileType":{"id":5,"text":"html"}},{"id":500117,"rank":8,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20265120/full"},{"id":500114,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P14WLVAH","text":"USGS data release","linkHelpText":"Regression equations for selected streamflow statistics based on data through water year 2021 in and near Wyoming"},{"id":500113,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2026/5120/downloads/","text":"Table 1.1","size":"60 KB","linkFileType":{"id":3,"text":"xlsx"}},{"id":500112,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2026/5120/images/"},{"id":500109,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2026/5120/coverthb.jpg"},{"id":500110,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2026/5120/sir20265120.pdf","text":"Report","size":"7.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2026-5120"},{"id":500111,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2026/5120/sir20265120.XML"}],"country":"United States","state":"Colorado, Idaho, Montana, North Dakota, South Dakota, Utah, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -113.82002110650585,\n 46.421867179561445\n ],\n [\n -113.82002110650585,\n 39.89961451938157\n ],\n [\n -103.32595673094282,\n 39.89961451938157\n ],\n [\n -103.32595673094282,\n 46.421867179561445\n ],\n [\n -113.82002110650585,\n 46.421867179561445\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
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Magnitude and frequency of annual peak streamflows were computed for 813 streamgages on rural, unregulated streams with annual peak streamflow data from 1791 through the 2021 water years in and near Virginia and West Virginia. The study was done in cooperation with the Federal Emergency Management Agency, the West Virginia Department of Transportation, and the Virginia Department of Transportation.
Regression equations were developed for estimating flood frequency and magnitude. Twelve regions with homogeneous flood characteristics were identified. Generalized least squares regression equations relating logarithmic-transformed drainage area and peak streamflow were developed for the 0.5, 0.2, 0.1, 0.04, 0.02, 0.01, 0.005, and 0.002 annual exceedance probabilities (AEPs). Drainage area was the only significant variable for all equations. The range of drainage areas used to develop the equations differed for each region; the smallest drainage area in any region was 0.21 square miles (mi2) and the largest drainage area in any region is 2,966 mi2. Pseudo coefficient of determination (pseudo-R2) values for regression equations ranged from 0.481 to 0.995 for all regions and AEPs. Performance metrics and diagnostic plots indicated that equations for 11 of the 12 regions showed generally good performance, with pseudo-R2 values ranging from 0.762 to 0.968 for the 0.01 AEP.
The overall average change in at-site 0.01 AEP annual peak streamflows at individual streamgages was 0.5 percent compared to the most recent 2011 Virginia study and 2.3 percent compared to the most recent 2010 West Virginia study. Changes from the previous studies for estimates from regional equations for the 0.01 AEP, solved specifically for a 50 mi2 basin, ranged from a 30 percent increase to a 45 percent decrease in areas where the previous regions overlapped with the current regions by 750 mi2 or more.
New regional skews were developed using Bayesian weighted least-squares/Bayesian generalized least-squares regression for two skew regions that included the study area. A constant regional skew of 0.50 was computed for streams in Virginia, West Virginia, and Maryland that drain to the Atlantic Ocean. A constant regional skew of 0.048 was computed for streams that drain to the Gulf of America, including streams in Kentucky and Tennessee, most of West Virginia, far southwestern Virginia, and part of western Maryland.
About 12 percent of the 418 streamgages with 30 or more gaged peaks had statistically significant (p-value [significance level] less than or equal to 0.05) trends, with 40 of these exhibiting positive trends and 11 exhibiting negative trends. Streamgages with 30 percent or greater development were excluded from regression analyses.
A regulation index was developed that accounted for storage and drainage area of dams and drainage area at the streamgage; a value of 0.0040 or more for the regulation index indicates regulated peak streamflow. Frequency analyses were done at 86 streamgages on regulated streams.
Regression procedures developed in this study are applicable only to rural, unregulated streams within Virginia and West Virginia with drainage basins that (1) are within the range of drainage areas used to develop the equations for each region, (2) included less than 30 percent of developed area, and (3) had a regulation index less than 0.0040.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255110","isbn":"978-1-4113-4656-7","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency, the West Virginia Department of Transportation, and the Virginia Department of Transportation","usgsCitation":"Messinger, T., Duda, J.M., Wagner, D.M., O'Shea, P.S., Scott, J.D., and Kandel, C., 2026, Estimation of magnitude and frequency of floods for rural, unregulated streams in and near Virginia and West Virginia: U.S. Geological Survey Scientific Investigations Report 2025–5110, 85 p., https://doi.org/10.3133/sir20255110.","productDescription":"Report: vii, 85 p.; Data Release","numberOfPages":"85","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-169653","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":500658,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119273.htm","linkFileType":{"id":5,"text":"html"}},{"id":500179,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RBZ8OJ","text":"USGS data release","linkHelpText":"Data in support of estimation of magnitude and frequency of floods for rural, unregulated streams in and near Virginia and West Virginia"},{"id":500174,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5110/coverthb.jpg"},{"id":500175,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5110/sir20255110.pdf","size":"34.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5110 PDF"},{"id":500176,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255110/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5110 HTML"},{"id":500177,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5110/sir20255110.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2025-5110 XML"},{"id":500178,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5110/images/"}],"country":"United States","state":"Kentucky, Maryland, North Carolina, Ohio, Pennsylvania, Tennessee, Virginia, West Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -84,\n 41\n ],\n [\n -84,\n 35\n ],\n [\n -75,\n 35\n ],\n [\n -75,\n 41\n ],\n [\n -84,\n 41\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, Virginia 23228
The Lower Eastern Shore Tributary Summary outlines change over time according to a suite of monitored tidal water quality parameters and associated potential drivers of those trends for the period 1985 – 2023, and provides a brief description of the current state of knowledge explaining these observed changes. Water quality parameters described include surface (above pycnocline) total nitrogen (TN), surface total phosphorus (TP), surface water temperature (WTEMP), spring (March-May) and summer (July-September) surface chlorophyll a, summer bottom (below pycnocline) dissolved oxygen (DO) concentrations, and Secchi disk depth (a measure of water clarity). Results for annual bottom TP, bottom TN, surface ortho-phosphate (PO4), surface dissolved inorganic nitrogen (DIN), surface total suspended solids (TSS), and summer surface DO concentrations are provided in an Appendix B. Drivers discussed include physiographic watershed characteristics, changes in TN, TP, and sediment loads from the watershed to tidal waters, expected effects of changing land use, and implementation of nutrient management and natural resource conservation practices. Factors internal to estuarine waters that also play a role as drivers are described including biogeochemical processes, physical forces such as winddriven mixing of the water column and increase in rainfall intensity and volume, and biological factors such as phytoplankton biomass and the presence of submersed aquatic vegetation. Continuing to track water quality response and investigating these influencing factors are important steps to understanding water quality patterns and changes in the Lower Eastern Shore. The intended audiences for this report include, but are not limited to, 1) technical managers within jurisdictions who use tidal water quality to inform management decisions, 2) local watershed organizations that are trying to understand these analyses and working to connect them to their local area(s), and 3) federal, state, and academic researchers. Figure 1 presents a conceptual model highlighting these intended audiences. The Tributary Summary documents are sources of readily available background for change over time in tidal water quality observed with monitoring data. They help answer questions related to water quality, show how landscape factors drive water-quality changes over time, provide support for management decisions that may alter water quality trends and living resources conditions, and highlight where there may be information or knowledge gaps.
","language":"English","publisher":"Chesapeake Bay Program","usgsCitation":"Sullivan, B.M., Gootman, K.S., Duran, G., Smith, E., Karrh, R., Johnson, C., Mason, C.A., Perry, E., Bhatt, G., Keisman, J.L., Webber, J.S., Harcum, J., Lane, M., Devereux, O., Zhang, Q., Murphy, R., Butler, T., Van Note, V., and Wei, Z., 2026, Lower Eastern Shore Tributary summary: A summary of trends in tidal water quality and associated factors, 1985-2023, 82 p.","productDescription":"82 p.","ipdsId":"IP-179870","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":500535,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.chesapeakebay.net/projects/tributary-summaries1"},{"id":504870,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, Virginia","otherGeospatial":"lower eastern shore","geographicExtents":"{\n \"type\": 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Rebecca","contributorId":331418,"corporation":false,"usgs":false,"family":"Murphy","given":"Rebecca","affiliations":[{"id":79204,"text":"UMCES","active":true,"usgs":false}],"preferred":false,"id":962156,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Butler, Tom","contributorId":331422,"corporation":false,"usgs":false,"family":"Butler","given":"Tom","email":"","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":962157,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Van Note, Vanessa","contributorId":331423,"corporation":false,"usgs":false,"family":"Van Note","given":"Vanessa","email":"","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":962158,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Wei, Zhaoying","contributorId":331424,"corporation":false,"usgs":false,"family":"Wei","given":"Zhaoying","affiliations":[{"id":79204,"text":"UMCES","active":true,"usgs":false}],"preferred":false,"id":962159,"contributorType":{"id":1,"text":"Authors"},"rank":19}]}} ,{"id":70274128,"text":"70274128 - 2026 - Decadal trends in the quality of groundwater used for public drinking-water supply in California, 2004–2023, California groundwater ambient monitoring and assessment program, priority basin project","interactions":[],"lastModifiedDate":"2026-02-26T16:50:19.952587","indexId":"70274128","displayToPublicDate":"2026-02-25T10:43:52","publicationYear":"2026","noYear":false,"publicationType":{"id":27,"text":"Preprint"},"publicationSubtype":{"id":32,"text":"Preprint"},"title":"Decadal trends in the quality of groundwater used for public drinking-water supply in California, 2004–2023, California groundwater ambient monitoring and assessment program, priority basin project","docAbstract":"This study provides a comprehensive assessment of decadal changes in the quality of groundwater used for public drinking-water supply at 444 monitoring sites across California during 2004–2023. We assessed decadal step trends in groundwater quality for 145 water-quality constituents and geochemical indicators statewide and across geographic and land-use based network groups. We evaluated the statistical significance of directional changes (predominant increase or decrease of constituent concentrations) and the magnitude of those changes across all network groups.
Uranium showed the most widespread directional and high-magnitude increases of all constituents with regulatory benchmarks statewide, particularly in the agriculture-dominated Central Valley as well as urban- and desert-dominated regions of Southern California. Fluoride and perchlorate showed the most widespread directional and high-magnitude decreases of all constituents with regulatory benchmarks statewide, which were also most pronounced in Southern California. Although arsenic and nitrate did not often register significant directional changes across network groups, they showed widespread, high-magnitude changes in both directions (increase and decrease) at levels often exceeding 10 percent of respective regulatory benchmarks statewide. Triazine herbicides (atrazine and simazine) and the gasoline oxygenate methyl tert-butyl ether (MTBE) showed significant directional decreases statewide, but not at levels considered to be of high magnitude compared to respective regulatory benchmarks.
We observed significant directional and high-magnitude increases of total dissolved solids (TDS) statewide, which were most pronounced in agricultural areas. Analysis of explanatory geochemical indicators indicated that prevalent statewide increases of alkalinity and calcium were the predominant components of the observed statewide increases in TDS by mass. Widespread increases in groundwater alkalinity and calcium across agricultural and urban areas may be related, in part, to warm-season irrigation and other anthropogenic factors that have shifted soil weathering dynamics over the long term. Increasing alkalinity concentrations were related to increasing uranium concentrations, particularly in areas with aquifer materials derived from granitic rocks. Conversely, increasing calcium concentrations were related to decreasing fluoride concentrations, particularly in areas where fluoride occurred naturally at elevated concentrations. Decrease of perchlorate, triazine herbicides, and MTBE are likely related to decreased anthropogenic source inputs over time and natural attenuation in aquifers.
Portions of Underwood Creek in Milwaukee County, Wisconsin were reconstructed beginning in 2010 to allow for improved fish habitat and better management of streamflow during storm events. Four reaches of Underwood Creek were sampled in April 2021 for fish abundance by species to evaluate the status of fish communities after reconstruction efforts were completed. A total of 25 fish species were collected during the April 2021 sampling events. Reach D, a recently restored reach, contained the most fish species (14) and individuals (391). White suckers (Catostomus commersonii) were present in three of four reaches, fulfilling one of the success metrics outlined in the Underwood Creek restoration plan. Another success metric, collection of young of year northern pike (Esox lucius), was not met in this sampling event. However, spawning steelhead (Oncorhynchus mykiss) were observed in several reaches, indicating that reconstruction allowed for suitable habitat and passage for some migratory fish.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20261060","collaboration":"Prepared in cooperation with Milwaukee Metropolitan Sewerage District","usgsCitation":"Bell, A.H., LaFond-Hudson, S., Stefaniak, O.M., Romano, J.T., and Sullivan, D.J., 2026, Summary of fish communities in Underwood Creek, Milwaukee, Wisconsin, April 2021: U.S. Geological Survey Open-File Report 2026–1060, 17 p., https://doi.org/10.3133/ofr20261060.","productDescription":"Report: vi, 17 p.; Data Release; Dataset","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-163980","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":500375,"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":500374,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13FXCII","text":"USGS data release","linkHelpText":"Fish community data for rivers and streams in the Milwaukee, Wisconsin, area"},{"id":500370,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2026/1060/ofr20261060.pdf","text":"Report","size":"1.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2026-1060"},{"id":500369,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2026/1060/coverthb.jpg"},{"id":500372,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2026/1060/images/"},{"id":500373,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20261060/full"},{"id":500371,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2026/1060/ofr20261060.XML"}],"country":"United States","state":"Wisconsin","city":"Milwaukee","otherGeospatial":"Underwood Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.133333,\n 43.1\n ],\n [\n -88.133333,\n 43\n ],\n [\n -88.033333,\n 43\n ],\n [\n -88.033333,\n 43.1\n ],\n [\n -88.133333,\n 43.1\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
Tree islands are patchy upland forested habitats in Florida's Everglades that face degradation and disappearance due to altered hydrologic patterns. The U.S. Geological Survey coordinated with the Miccosukee Tribe of Indians of Florida and the Seminole Tribe of Florida to co-develop a decision-support tool based on tree-island hydrologic conditions. Everglades managers can use this tool to help with restoration planning and water operations decisions that affect tree-island conditions. After a series of organized workshops and meetings, a list of hydrologic metrics was selected as indicators of tree-island health, including hydroperiod, number of days since last dry, and maximum water depth at the head of the island. As a result, a web application tool, called ETree, has been developed and is publicly available online. This web application provides data on daily metrics for the current Everglades water year and annual summaries for past years, beginning in 2000.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2026.114640","usgsCitation":"Haider, S.M., van der Heiden, C., Bozas, M., and Romañach, S.S., 2026, A tool to monitor hydrologic conditions on tree islands in the Everglades: Ecological Indicators, v. 183, 114640, 7 p., https://doi.org/10.1016/j.ecolind.2026.114640.","productDescription":"114640, 7 p.","ipdsId":"IP-175495","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":500612,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2026.114640","text":"Publisher Index Page"},{"id":500509,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.08373270516965,\n 26.65815841176284\n ],\n [\n -82.08373270516965,\n 25.02905745196128\n ],\n [\n -80.69833784238166,\n 25.02905745196128\n ],\n [\n -80.69833784238166,\n 26.65815841176284\n ],\n [\n -82.08373270516965,\n 26.65815841176284\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"183","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Haider, Saira M. 0000-0001-9306-3454","orcid":"https://orcid.org/0000-0001-9306-3454","contributorId":206253,"corporation":false,"usgs":true,"family":"Haider","given":"Saira","middleInitial":"M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":956505,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"van der Heiden, Craig","contributorId":366978,"corporation":false,"usgs":false,"family":"van der Heiden","given":"Craig","affiliations":[{"id":87517,"text":"Seminole Tribe of Florida","active":true,"usgs":false}],"preferred":false,"id":956506,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bozas, Marcel","contributorId":366979,"corporation":false,"usgs":false,"family":"Bozas","given":"Marcel","affiliations":[{"id":87518,"text":"Miccosukee Tribe of Indians of Florida","active":true,"usgs":false}],"preferred":false,"id":956507,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Romañach, Stephanie S. 0000-0003-0271-7825","orcid":"https://orcid.org/0000-0003-0271-7825","contributorId":213745,"corporation":false,"usgs":true,"family":"Romañach","given":"Stephanie","middleInitial":"S.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":956508,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70275235,"text":"70275235 - 2026 - Snow simulations predict future changes in rain-on-snow events across the upper Gallatin River watershed, a Greater Yellowstone Ecosystem headwater system","interactions":[],"lastModifiedDate":"2026-04-23T15:02:12.132201","indexId":"70275235","displayToPublicDate":"2026-02-24T07:55:13","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3823,"text":"Journal of Hydrology: Regional Studies","active":true,"publicationSubtype":{"id":10}},"title":"Snow simulations predict future changes in rain-on-snow events across the upper Gallatin River watershed, a Greater Yellowstone Ecosystem headwater system","docAbstract":"Study region: The upper Gallatin River watershed, an alpine headwater system in the Greater Yellowstone Ecosystem, in Wyoming and Montana.
Study focus: As global and regional air temperatures rise, mountain headwaters across the Greater Yellowstone Ecosystem (GYE) are projected to see more precipitation falling as rain. While the hydrologic effects of this snow-to-rain transition depends on a variety of factors, it can lead to an increased occurrence of rain-on-snow (RoS) events. To investigate these changes, we used high-resolution (30 m) SnowModel simulations of the upper Gallatin River watershed. Simulations were run for 2001-2013 using two scenarios: (1) historical meteorology as control and (2) pseudo global warming (PGW) where control air temperature and precipitation conditions were perturbed to represent mean end-of-century conditions under a high-emissions scenario.
New hydrological insights for the region: SnowModel outputs show that changes in PGW precipitation and snow accumulation varied with elevation. Warmer air temperatures at low elevations (< 2,500 m) led to less snow accumulation and less precipitation falling as snow. Colder baseline air temperatures for elevations above 2,500 meters (m) resulted in minor reductions in winter snowfall fraction. For PGW simulations, spring (April-June) months were rainier, and elevations above 2,500 m experienced more RoS events. Snowpacks between 2,500-3,100 m generated more snowmelt during RoS events, which was reflected in the watershed average. More high-intensity melt events can affect aquatic habitat, water quality, and the accuracy of streamflow forecasts across the region.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ejrh.2026.103253","usgsCitation":"Newcomb, S.K., Barnhart, T., Heldmyer, A.J., and Storb, M.B., 2026, Snow simulations predict future changes in rain-on-snow events across the upper Gallatin River watershed, a Greater Yellowstone Ecosystem headwater system: Journal of Hydrology: Regional Studies, no. 64, 103253, 15 p., https://doi.org/10.1016/j.ejrh.2026.103253.","productDescription":"103253, 15 p.","ipdsId":"IP-175750","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":503450,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ejrh.2026.103253","text":"Publisher Index Page"},{"id":503345,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, Wyoming","otherGeospatial":"Gallatin River watershed, Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.1040556758394,\n 45.775196944106\n ],\n [\n -112.01972949762403,\n 44.55598521200966\n ],\n [\n -111.37153075418532,\n 44.6564938423231\n ],\n [\n -110.83513931044746,\n 44.25694322488387\n ],\n [\n -109.64451930770404,\n 44.03050575033751\n ],\n [\n -109.86085331075995,\n 44.82919751982631\n ],\n [\n -110.74738180936852,\n 45.88093570749882\n ],\n [\n -112.1040556758394,\n 45.775196944106\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","issue":"64","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Newcomb, Sarah Katherine 0000-0002-7832-5089","orcid":"https://orcid.org/0000-0002-7832-5089","contributorId":370359,"corporation":false,"usgs":true,"family":"Newcomb","given":"Sarah","middleInitial":"Katherine","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":960200,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":960201,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Heldmyer, Aaron Joseph 0000-0001-8608-4927","orcid":"https://orcid.org/0000-0001-8608-4927","contributorId":302944,"corporation":false,"usgs":true,"family":"Heldmyer","given":"Aaron","email":"","middleInitial":"Joseph","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":960202,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Storb, Meryl Biesiot 0000-0002-4346-5022","orcid":"https://orcid.org/0000-0002-4346-5022","contributorId":305621,"corporation":false,"usgs":true,"family":"Storb","given":"Meryl","email":"","middleInitial":"Biesiot","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":960203,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70274096,"text":"70274096 - 2026 - Demonstration, validation, and application of hyperspectral microscopy for the collection of cyanobacterial spectral signatures","interactions":[],"lastModifiedDate":"2026-02-26T14:20:20.159526","indexId":"70274096","displayToPublicDate":"2026-02-24T07:50:20","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7183,"text":"Limnology and Oceanography Methods","active":true,"publicationSubtype":{"id":10}},"title":"Demonstration, validation, and application of hyperspectral microscopy for the collection of cyanobacterial spectral signatures","docAbstract":"Cyanobacterial and other algal blooms are an environmental concern in waterbodies worldwide. While these blooms are a nuisance for recreational activities, they can also be harmful to human and wildlife health when the algae produce and release toxins. Algal community composition can be monitored and analyzed by acquiring hyperspectral images that provide information on various photosynthetic and accessory pigments. Validated, traceable measurements are needed to compare data collected by different hyperspectral instruments. In this proof-of-concept study, we detail the development and validation of a custom hyperspectral microscopy imaging system and assess whether this technology can differentiate between cyanobacteria genera based on differences in their reflectance characteristics. As not all cyanobacteria produce toxins, the ability to distinguish among taxa could be used to identify potential toxin-producers and guide field sampling and further research. Spectral characterization of these taxa contributes to remote sensing efforts to characterize and identify cyanobacterial genera at larger spatial scales.
","language":"English","publisher":"Association for the Sciences of Limnology and Oceanography","doi":"10.1002/lom3.70038","usgsCitation":"Hall, N.C., Mumford, A.C., Goldfain, A.M., Allen, D.W., Slonecker, E., Shtabnoy, A., Legleiter, C.J., Spaulding, S.A., 2026, Demonstration, validation, and application of hyperspectral microscopy for the collection of cyanobacterial spectral signatures: Limnology and Oceanography Methods, e70038, 12 p., https://doi.org/10.1002/lom3.70038.","productDescription":"e70038, 12 p.","ipdsId":"IP-133686","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":501074,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/lom3.70038","text":"Publisher Index Page"},{"id":500527,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://aslopubs.onlinelibrary.wiley.com/doi/10.1002/lom3.70038","linkFileType":{"id":5,"text":"html"}},{"id":500505,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Upper Klamath Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.10179264385346,\n 42.48654098074368\n ],\n [\n -122.06572558931417,\n 42.407712468749764\n ],\n [\n -121.92441072839725,\n 42.280845617093135\n ],\n [\n -121.79136441818135,\n 42.21342761218082\n ],\n [\n -121.79230665775252,\n 42.39519175138423\n ],\n [\n -121.93695412023108,\n 42.506365647351515\n ],\n [\n -122.03369521935683,\n 42.50459749688622\n ],\n [\n -122.10179264385346,\n 42.48654098074368\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Online First","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hall, Natalie C. 0000-0002-6448-162X nhall@usgs.gov","orcid":"https://orcid.org/0000-0002-6448-162X","contributorId":223255,"corporation":false,"usgs":true,"family":"Hall","given":"Natalie","email":"nhall@usgs.gov","middleInitial":"C.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":956516,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mumford, Adam C. 0000-0002-8082-8910 amumford@usgs.gov","orcid":"https://orcid.org/0000-0002-8082-8910","contributorId":171791,"corporation":false,"usgs":true,"family":"Mumford","given":"Adam","email":"amumford@usgs.gov","middleInitial":"C.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":956517,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Goldfain, Aaron M. 0000-0002-4119-3983","orcid":"https://orcid.org/0000-0002-4119-3983","contributorId":366980,"corporation":false,"usgs":false,"family":"Goldfain","given":"Aaron","middleInitial":"M.","affiliations":[{"id":25356,"text":"National Institute of Standards and Technology","active":true,"usgs":false}],"preferred":false,"id":956518,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Allen, David W. 0000-0001-8299-7956","orcid":"https://orcid.org/0000-0001-8299-7956","contributorId":366981,"corporation":false,"usgs":false,"family":"Allen","given":"David","middleInitial":"W.","affiliations":[{"id":25356,"text":"National Institute of Standards and Technology","active":true,"usgs":false}],"preferred":false,"id":956519,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Slonecker, E. Terrence 0000-0002-5793-0503","orcid":"https://orcid.org/0000-0002-5793-0503","contributorId":335606,"corporation":false,"usgs":false,"family":"Slonecker","given":"E. 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The effects of climate change on Atlantic sturgeon are largely unknown, but it may exacerbate these water quality issues. We used a 20-year dataset from the Altamaha River estuary, Georgia, USA to fit negative binomial mixed-effects models describing the relationship between water quality and catch per net hour of juvenile Atlantic sturgeon. Water temperature and DO were significant positive predictors of catch; salinity and sampling year were significant negative predictors. The interaction between temperature and DO was also significant. Water temperature, salinity, and year were significant in explaining variability in catch. Our modeling results suggest that response to water quality depends on fish age. Next, we used global climate projections to construct future climate scenarios incorporating warming water and increased salinity. By coupling these predictions with catch models, we forecast juvenile Atlantic sturgeon catch as a proxy for distribution. Water temperature increases of 1–5 °C led to predicted catch increases of 5–24%, although this result may be influenced by aggregation behavior or sampling limitations at high temperatures. Salinity increases of 1–2 ppt led to 9–17% decreases in catch, suggesting that saltwater intrusion may limit future Atlantic sturgeon estuarine habitat availability. Our study combines a long-term dataset with a robust statistical modeling approach to offer some of the first insights into future climate change effects on juvenile Atlantic sturgeon’s southern nursery habitats.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s10641-026-01818-8","usgsCitation":"Kleinhans, M., Nibbelink, N., Irwin, B., Wenger, S., and Fox, A.G., 2026, Climate change and water quality influence on juvenile Atlantic sturgeon aggregation in the Altamaha River, Georgia: Environmental Biology of Fishes, v. 109, 49, 20 p., https://doi.org/10.1007/s10641-026-01818-8.","productDescription":"49, 20 p.","ipdsId":"IP-176138","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":502064,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10641-026-01818-8","text":"Publisher Index Page"},{"id":501976,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia","otherGeospatial":"Altamaha River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.92561023859767,\n 31.34995779368927\n ],\n [\n -81.92561023859767,\n 30.940577592818528\n ],\n [\n -81.32085660186459,\n 30.940577592818528\n ],\n [\n -81.32085660186459,\n 31.34995779368927\n ],\n [\n -81.92561023859767,\n 31.34995779368927\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"109","noUsgsAuthors":false,"publicationDate":"2026-02-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Kleinhans, Maxwell","contributorId":369036,"corporation":false,"usgs":false,"family":"Kleinhans","given":"Maxwell","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":958338,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nibbelink, Nathan","contributorId":369037,"corporation":false,"usgs":false,"family":"Nibbelink","given":"Nathan","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":958339,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Irwin, Brian J. 0000-0002-0666-2641","orcid":"https://orcid.org/0000-0002-0666-2641","contributorId":280043,"corporation":false,"usgs":true,"family":"Irwin","given":"Brian J.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":958340,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wenger, Seth","contributorId":261384,"corporation":false,"usgs":false,"family":"Wenger","given":"Seth","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":958341,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fox, Adam G.","contributorId":179021,"corporation":false,"usgs":false,"family":"Fox","given":"Adam","middleInitial":"G.","affiliations":[],"preferred":false,"id":958342,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70273967,"text":"tm8D3 - 2026 - Design and function of the Autonomous Benthic Imaging and Surveying System (ABISS) for remote sensing of lake and seabed environments","interactions":[],"lastModifiedDate":"2026-04-10T15:02:24.25022","indexId":"tm8D3","displayToPublicDate":"2026-02-23T13:24:26","publicationYear":"2026","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"8-D3","displayTitle":"Design and Function of the Autonomous Benthic Imaging and Surveying System (ABISS) for Remote Sensing of Lake and Seabed Environments","title":"Design and function of the Autonomous Benthic Imaging and Surveying System (ABISS) for remote sensing of lake and seabed environments","docAbstract":"Lake and seabed environments are home to fisheries and other biota that are important to ecosystems and economies, yet these environments and the species that use them are difficult to accurately assess and monitor. Traditional benthic survey techniques, like bottom trawling used by the U.S. Geological Survey, are limited by substrate constraints, poor spatial resolution and precision, and operational depth limits, hindering accurate assessment of benthic species and habitats. In response to these limitations, the U.S. Geological Survey developed the Autonomous Benthic Imaging and Surveying System, a camera system integrated into underwater vehicles, to capture high-resolution images of the lakebed. The system uses color and stereo cameras to collect imagery, which can be analyzed using computational methods to detect organisms and (or) characterize habitat features, such as geologic substrate types. The system has been integrated into autonomous underwater vehicles and into an underwater housing used by self-contained underwater breathing apparatus (SCUBA) divers. Although the engineering of the system was motivated by the need for data collection in the Great Lakes, it has potential to collect high quality data in any aqueous setting with sufficient water clarity and safe operating conditions. The Autonomous Benthic Imaging and Surveying System can operate across diverse depths and light conditions to map and quantify ecological patterns that were difficult or impossible to assess using traditional methods. The Autonomous Benthic Imaging and Surveying System offers the potential for accurate and precise monitoring and assessment of native benthic biota, invasive species, and habitat, potentially providing natural resource managers with improved information to support decision making about benthic resource management.
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Existing reservoir management frameworks traditionally consider historical (predam) flow conditions to deliver environmental flows. Such frameworks may not be feasible because current demand and/or climate could be different from predam conditions. Hence, we developed a multireservoir framework that explicitly considers both human water demands and environmental flow requirements to minimize deviations under current hydroclimatic conditions and demand patterns. The multireservoir framework, Generalized Reservoir Analyses using Probabilistic Streamflow (GRAPS), was modified and implemented to solve the problem of minimizing the flow deviations using feasible sequential quadratic programming for three reservoirs in the Chattahoochee River Basin, Southeastern United States, which is known for its imperiled native biodiversity and productive estuarine ecosystem. Our results show that downstream reservoirs in the cascade system are less influenced by upstream reservoirs’ regulation because the downstream reservoirs receive a significant amount of natural flows. By comparing the average wavelet power spectrum at different periodicities between natural flows and downstream releases, we found that the current release policy and modified releases resulted in highly altered flows under shorter periodicities (e.g., less than 2 months) but synchronized flow variance between natural flow and downstream releases at longer periodicities (e.g., greater than 3 years). This framework of linking the multireservoir allocation model through the time–frequency analysis using wavelet power spectrum could not only advance sustainable water management policies to meet water for human and environmental needs but can also add additional value in meeting the downstream environmental demand at desired periodicities.
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While terrestrially focused applications of remote sensing have empowered wide adoption for research and management, remote sensing of inland aquatic ecosystems remains comparably nascent. This divergence, in part, stems from the lack of standardized, accessible, and near real-time remotely sensed surface reflectance, atmospherically corrected for aquatic environments. To date, surface reflectance products at national scales and with minimal latency are typically designed exclusively for terrestrial environments. Rectifying this situation can be accomplished by applying aquatic-focused atmospheric correction algorithms independent of those used for terrestrial ecosystems. As a first step to filling this data gap, we present the first national scale, dynamically updated, analysis-ready, aquatic reflectance dataset for inland water derived from Sentinel-2 for the conterminous United States.
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Previous research has documented adverse effects of mineral extraction on crayfish. Here, we characterize potential risks of mining-derived waterborne nickel (Ni) to crayfish by documenting the effects of dissolved Ni on growth and food consumption of juvenile virile crayfish (Faxonius virilis) in a 28-day chronic laboratory exposure. Nominal Ni concentrations ranged from 31.25 to 500 micrograms per liter (µg/L; pH = 7.96 ± 0.20, hardness = 150 ± 1 milligrams per liter as calcium carbonate). Crayfish survival, carapace length, and wet weight were measured. After 28 days of exposure, a 24-h feeding trial was performed to determine differences in food consumption. During the growth trial, 99% of crayfish survived. Change in wet weight and final wet weight were the most sensitive endpoints, with 20% effect concentrations of 24.8 and 22.6 µg/L Ni, respectively. Crayfish exposed to an average of 438 µg/L Ni consumed 41% less, and weighed 65.1% less, than control crayfish. These results suggest chronic, sublethal exposure to waterborne Ni may have negative effects on crayfish growth. Reduced growth and consumption rates in crayfish could have wide-ranging consequences throughout aquatic ecosystems since crayfish are consumers, prey, keystone trophic regulators, and ecosystem engineers. Finally, these results could inform bioenergetics and may be coupled with population models to predict potential changes in population sizes of native and invasive crayfishes.
","language":"English","publisher":"Springer","doi":"10.1007/s10646-026-03036-5","usgsCitation":"Moore, A.P., Wildhaber, M.L., Beaman, Z.D., Bennett, K.R., Ditter, K.K., Cleveland, D.M., Blanton, J., and Grant, T.J., 2026, Chronic exposure to waterborne nickel significantly reduced growth of juvenile crayfish (Faxonius virilis): Ecotoxicology, v. 35, 64, https://doi.org/10.1007/s10646-026-03036-5.","productDescription":"64","ipdsId":"IP-179855","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":501386,"rank":4,"type":{"id":42,"text":"Open Access USGS Document"},"url":"https://pubs.usgs.gov/publication/70274234/full"},{"id":501385,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/ja/70274234/images/"},{"id":501384,"rank":2,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/ja/70274234/70274234.XML"},{"id":501228,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"35","noUsgsAuthors":false,"publicationDate":"2026-02-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Moore, Adrian Parr 0000-0001-9277-6399","orcid":"https://orcid.org/0000-0001-9277-6399","contributorId":298590,"corporation":false,"usgs":true,"family":"Moore","given":"Adrian","email":"","middleInitial":"Parr","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":957109,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wildhaber, Mark L. 0000-0002-6538-9083 mwildhaber@usgs.gov","orcid":"https://orcid.org/0000-0002-6538-9083","contributorId":1386,"corporation":false,"usgs":true,"family":"Wildhaber","given":"Mark","email":"mwildhaber@usgs.gov","middleInitial":"L.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":957110,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beaman, Zachary D 0000-0001-9649-1585","orcid":"https://orcid.org/0000-0001-9649-1585","contributorId":312457,"corporation":false,"usgs":true,"family":"Beaman","given":"Zachary","email":"","middleInitial":"D","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":957111,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bennett, Kendell Ray 0000-0001-6081-7002","orcid":"https://orcid.org/0000-0001-6081-7002","contributorId":334116,"corporation":false,"usgs":true,"family":"Bennett","given":"Kendell","email":"","middleInitial":"Ray","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":957112,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ditter, Karlie K 0000-0001-8970-2022","orcid":"https://orcid.org/0000-0001-8970-2022","contributorId":312455,"corporation":false,"usgs":true,"family":"Ditter","given":"Karlie","email":"","middleInitial":"K","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":957113,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cleveland, Danielle M. 0000-0003-3880-4584 dcleveland@usgs.gov","orcid":"https://orcid.org/0000-0003-3880-4584","contributorId":187471,"corporation":false,"usgs":true,"family":"Cleveland","given":"Danielle","email":"dcleveland@usgs.gov","middleInitial":"M.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":957114,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Blanton, J.","contributorId":89345,"corporation":false,"usgs":true,"family":"Blanton","given":"J.","email":"","affiliations":[],"preferred":false,"id":957115,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Grant, Tyler J.","contributorId":149938,"corporation":false,"usgs":false,"family":"Grant","given":"Tyler","email":"","middleInitial":"J.","affiliations":[{"id":17858,"text":"Iowa State U, Ames, IA","active":true,"usgs":false}],"preferred":false,"id":957116,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70276301,"text":"70276301 - 2026 - Diverse cyanopeptides follow distinct temporal succession patterns in freshwater harmful algal blooms","interactions":[],"lastModifiedDate":"2026-05-27T14:21:48.500422","indexId":"70276301","displayToPublicDate":"2026-02-19T09:09:16","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3563,"text":"The ISME Journal","active":true,"publicationSubtype":{"id":10}},"title":"Diverse cyanopeptides follow distinct temporal succession patterns in freshwater harmful algal blooms","docAbstract":"Toxic cyanobacterial harmful algal blooms (cyanoHABs) threaten freshwater resources globally and are intensifying with increasing eutrophication. Bloom toxicity is strongly influenced by intraspecific variation in the biosynthetic repertoires of toxic cyanobacteria, yet few studies examine the diversity of cyanobacterial cyanopeptides beyond hepatotoxic microcystins. To understand the dynamics and drivers of cyanopeptide diversity in cyanoHABs, we analyzed temporal patterns of cyanobacteria, metabolites, and their biosynthetic gene clusters (BGCs) in western Lake Erie using a 7-year time series (2016–2022) of metagenomic and metabolomic data. Our findings demonstrate that shifts from Microcystis to Dolichospermum occur later in the bloom season, coinciding with lower temperatures. Modules of co-varying BGCs (biosynthesis modules) from these genera were identified with hierarchical clustering, with uncharacterized BGCs among the most abundant. Biosynthesis modules rich in nonribosomal peptide synthetases (NRPS) peaked in early August, coinciding with elevated levels of inorganic nitrogen, warmer temperatures, and high Microcystis abundance. In contrast, modules rich in polyketide synthases (PKS) and ribosomally synthesized and post-translationally modified peptides (RiPPs) peaked following the Microcystis maximum in mid-August. Metabolomic analyses confirmed that metabolites followed shared seasonal patterns with their associated biosynthesis modules, forming three phases characterized by (i) microcystins, (ii) anabaenopeptins and aeruginosins, and (iii) aerucyclamides. These phases co-varied with bottom-up and top-down pressures, with later phases coinciding with increased microbially processed organic nitrogen and reduced detection of grazers. This study demonstrates consistent seasonal patterns of cyanobacterial metabolite succession and co-occurrence beyond microcystins, suggesting tradeoffs between biosynthetic resource demands and ecological controls.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/ismejo/wrag026","usgsCitation":"Hart, L.N., Errera, R., Godwin, C., Loftin, K., Laughrey, Z.R., Katona, L.R., Johnson, E.C., Cory, R.M., Kiledal, E.A., Den Uyl, P., Kharbush, J.J., Sherman, D.H., and Dick, G.J., 2026, Diverse cyanopeptides follow distinct temporal succession patterns in freshwater harmful algal blooms: The ISME Journal, v. 20, no. 1, wrag026, 16 p., https://doi.org/10.1093/ismejo/wrag026.","productDescription":"wrag026, 16 p.","ipdsId":"IP-181486","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":84311,"text":"Central Plains Water Science Center","active":true,"usgs":true}],"links":[{"id":504812,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/ismejo/wrag026","text":"Publisher Index Page"},{"id":504732,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Ohio","otherGeospatial":"Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.6915678,\n 42.09434060711348\n ],\n [\n -83.62833511158065,\n 42.09434060711348\n ],\n [\n -83.62833511158065,\n 41.388846\n ],\n [\n -82.6915678,\n 41.388846\n ],\n [\n -82.6915678,\n 42.09434060711348\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"20","issue":"1","noUsgsAuthors":false,"publicationDate":"2026-02-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Hart, Lauren N.","contributorId":371563,"corporation":false,"usgs":false,"family":"Hart","given":"Lauren","middleInitial":"N.","affiliations":[{"id":37387,"text":"University of Michigan","active":true,"usgs":false}],"preferred":false,"id":962024,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Errera, Reagan","contributorId":371564,"corporation":false,"usgs":false,"family":"Errera","given":"Reagan","affiliations":[{"id":38436,"text":"National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":962025,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Godwin, Casey","contributorId":371565,"corporation":false,"usgs":false,"family":"Godwin","given":"Casey","affiliations":[{"id":37387,"text":"University of Michigan","active":true,"usgs":false}],"preferred":false,"id":962026,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Loftin, Keith 0000-0001-5291-876X kloftin@usgs.gov","orcid":"https://orcid.org/0000-0001-5291-876X","contributorId":221958,"corporation":false,"usgs":true,"family":"Loftin","given":"Keith","email":"kloftin@usgs.gov","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":962027,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Laughrey, Zachary R. 0000-0002-7630-2078 zlaughrey@usgs.gov","orcid":"https://orcid.org/0000-0002-7630-2078","contributorId":198516,"corporation":false,"usgs":true,"family":"Laughrey","given":"Zachary","email":"zlaughrey@usgs.gov","middleInitial":"R.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":962028,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Katona, Leon R. 0000-0001-5323-1871","orcid":"https://orcid.org/0000-0001-5323-1871","contributorId":331458,"corporation":false,"usgs":true,"family":"Katona","given":"Leon","email":"","middleInitial":"R.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":962029,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Johnson, Emma C.","contributorId":371566,"corporation":false,"usgs":false,"family":"Johnson","given":"Emma","middleInitial":"C.","affiliations":[{"id":37387,"text":"University of Michigan","active":true,"usgs":false}],"preferred":false,"id":962030,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Cory, Rose M.","contributorId":371567,"corporation":false,"usgs":false,"family":"Cory","given":"Rose","middleInitial":"M.","affiliations":[{"id":37387,"text":"University of Michigan","active":true,"usgs":false}],"preferred":false,"id":962031,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kiledal, E. 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Mountain front recharge from the surrounding Sierra Nevada is the main source of natural recharge to the valley. Increased urbanization, agricultural development, and groundwater pumping during recent decades put IWV in a state of critical overdraft. The U.S. Geological Survey Basin Characterization Model, version 8 (BCMv8) was used to evaluate historical and future climate and hydrologic conditions in IWV. The BCMv8 estimated natural recharge in IWV at 10.7 million cubic meters (Mm3) per year for the period from 1981 to 2010. Future patterns of water balance variables using three future climate scenarios, hot-wet, hot-dry, and warm-moderately wet, were calculated for mid-century (2040–69) and end-of-century (2070–99) periods. Results for both wet models projected an increase in recharge in both periods, whereas the hot-dry model projected a decrease in recharge in both periods. All models reported a large increase in seasonal variability in recharge, indicating more future availability and frequent occurrences of drought years. All climate scenarios projected an increase in climatic water deficit in both periods. These increases in irrigation demand and variability of water supply highlight the importance of strategic management planning for the sustainability of water resources in IWV.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20265114","collaboration":"Prepared in cooperation with Kern County, California","programNote":"Water Availability and Use Science Program","usgsCitation":"Saleh, D., Flint, L., and Stern, M., 2026, Assessing natural recharge in Indian Wells Valley, California—A Basin Characterization Model case study (ver. 1.1, March 2026): U.S. Geological Survey Scientific Investigations Report 2026–5114, 34 p., https://doi.org/10.3133/sir20265114.","productDescription":"vi, 34 p.","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-104255","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":501283,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119214.htm","linkFileType":{"id":5,"text":"html"}},{"id":500366,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2026/5114/coverthb2.jpg"},{"id":501280,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2026/5114/sir20265114.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2026-5114 XML"},{"id":501279,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20265114/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2026-5114 HTML"},{"id":501278,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2026/5114/sir20265114.pdf","text":"Report","size":"4.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2026-5114 PDF"},{"id":501281,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2026/5114/images"},{"id":501282,"rank":6,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2026/5114/versionHist.txt","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2026-5114 Version History"}],"country":"United States","state":"California","otherGeospatial":"Indian Wells Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.5,\n 36.5\n ],\n [\n -118.5,\n 35\n ],\n [\n -117,\n 35\n ],\n [\n -117,\n 36.5\n ],\n [\n -118.5,\n 36.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: February 18, 2026; Version 1.1: March 18, 2026","contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Monitoring suspended-sediment concentration (SSC) is essential to better understand how sediment transport could adversely affect water availability for human communities and ecosystems. Aquatic remote sensing methods are increasingly utilized to estimate SSC and turbidity in rivers; however, an evaluation of their quantitative performance is limited. This study evaluates the performance of three multispectral sensors, which vary in resolution and ease of deployment, to estimate turbidity in the Colorado River: the Multispectral Instrument (MSI) on board the European Space Agency’s Sentinel-2 satellite, an industrial-grade 10-band dual camera system mounted on a cable car, and a consumer-grade 6-band dual camera system positioned on the riverbank. We use multivariate linear regression to compare in situ turbidity measurements with concurrent spectral reflectance data from each sensor. Models for all three sensors selected similar spectral information and resulted in mean errors <35% in predicting turbidity. A cross-sensor comparison showed that little accuracy is lost when applying models developed for satellite-based systems to ground-based systems, and vice versa. Transferability of satellite-based models to ground-based systems could support continuous water-quality monitoring between satellite overpasses and avoid issues associated with cloud interference. Conversely, continuously operating ground-based systems could be used to rapidly establish datasets and models for application in satellite imagery, thus accelerating remote sensing applications. The encouraging performance of the consumer-grade system indicates that SSC could be monitored for low cost.
","language":"English","publisher":"MDPI","doi":"10.3390/rs18040638","usgsCitation":"Day, N.K., King, T.V., and Mosbrucker, A.R., 2026, A comparison of non-contact methods for measuring turbidity in the Colorado River: Remote Sensing, v. 18, no. 4, 638, 26 p., https://doi.org/10.3390/rs18040638.","productDescription":"638, 26 p.","ipdsId":"IP-177709","costCenters":[{"id":157,"text":"Cascades Volcano Observatory","active":false,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":500256,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs18040638","text":"Publisher Index Page"},{"id":500184,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","city":"Cameo","otherGeospatial":"Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108.22,\n 39.26\n ],\n [\n -108.5833,\n 39.26\n ],\n [\n -108.5833,\n 39\n ],\n [\n -108.22,\n 39\n ],\n [\n -108.22,\n 39.26\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","issue":"4","noUsgsAuthors":false,"publicationDate":"2026-02-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Day, Natalie K. 0000-0002-8768-5705","orcid":"https://orcid.org/0000-0002-8768-5705","contributorId":207302,"corporation":false,"usgs":true,"family":"Day","given":"Natalie","middleInitial":"K.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":955899,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"King, Tyler V. 0000-0002-5785-3077","orcid":"https://orcid.org/0000-0002-5785-3077","contributorId":292424,"corporation":false,"usgs":true,"family":"King","given":"Tyler","middleInitial":"V.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":955900,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mosbrucker, Adam R. 0000-0003-0298-0324 amosbrucker@usgs.gov","orcid":"https://orcid.org/0000-0003-0298-0324","contributorId":4968,"corporation":false,"usgs":true,"family":"Mosbrucker","given":"Adam","email":"amosbrucker@usgs.gov","middleInitial":"R.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":955901,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70273925,"text":"sir20255113 - 2026 - Treatability study to evaluate bioremediation of trichloroethene at Site K, former Twin Cities Army Ammunition Plant, Arden Hills, Minnesota, 2020–22","interactions":[],"lastModifiedDate":"2026-04-10T15:25:55.929581","indexId":"sir20255113","displayToPublicDate":"2026-02-18T08:45:00","publicationYear":"2026","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-5113","displayTitle":"Treatability Study to Evaluate Bioremediation of Trichloroethene at Site K, Former Twin Cities Army Ammunition Plant, Arden Hills, Minnesota, 2020–22","title":"Treatability study to evaluate bioremediation of trichloroethene at Site K, former Twin Cities Army Ammunition Plant, Arden Hills, Minnesota, 2020–22","docAbstract":"Chlorinated solvents, including trichloroethene (TCE) and other chlorinated volatile organic compounds (cVOCs), are widespread contaminants that can be treated by bioremediation approaches that enhance anaerobic reductive dechlorination. Reductive dechlorination can be enhanced either through the addition of an electron donor (biostimulation) or the addition of a known dechlorinating culture (bioaugmentation) along with an electron donor. Although bioremediation has been applied at many TCE-contaminated groundwater sites, application in source zones at sites where residual dense nonaqueous phase liquid (DNAPL) is present is more limited. In this study, laboratory and field treatability tests were completed to evaluate the potential application of anaerobic bioremediation for a shallow groundwater plume containing TCE in a perched alluvial aquifer at Site K, former Twin Cities Army Ammunition Plant, Arden Hills, Minnesota, which was on the National Priorities List as the New Brighton/Arden Hills Superfund site until 2019. In addition to the presence of residual DNAPL at the site, temporal variability in groundwater flow directions and input of oxygenated recharge were possible complicating factors for the application of enhanced anaerobic biodegradation in the shallow plume. The Site K plume extends beneath the footprint of Building 103, which was demolished in 2006, and soil excavations to a maximum depth of 6 feet (ft) below ground surface in 2014 were known to leave some deeper contaminated soil in place in the TCE source area. Groundwater treatment at the site, formalized as part of the 1997 Record of Decision, has been in operation since 1986 and consists of an extraction trench at the downgradient edge of the plume to collect groundwater, which is then pumped to an on-site air stripper. Groundwater concentrations in the plume have been relatively stable since treatment began, indicating a continued source of TCE in the aquifer. The desire for a destructive remedy that would enhance the removal of cVOCs in the aquifer at Site K and shorten the remediation timeframe led the U.S. Army to request that the U.S. Geological Survey conduct a groundwater treatability study to assess bioremediation. This report describes the U.S. Geological Survey bioremediation treatability study conducted during 2020–22, including pre-design site characterization to assist in formulating the bioremediation approach, laboratory experiments to support the design of the field pilot test, and implementation and 1-year performance monitoring results for the pilot test.
Pre-design site characterization included the collection of soil cores for cVOC analysis and lithologic descriptions and the re-installment of three wells to obtain hydrologic measurements and initial groundwater chemistry. Relatively flat head gradients were measured at the site, and substantial decreases in water-level elevations occurred from spring to summer (May–July 2021). Continuous water-level monitoring indicated a rapid response to precipitation. Groundwater flow velocities were consistently less than 0.5 foot per day, and the pilot bioremediation test was therefore designed with short lateral distances (about 5 ft) between injection and individual monitoring points. Soil analyses confirmed that high volatile organic compound contamination was left in place in the source area. The highest concentrations were near or in clay at the base of the perched aquifer. Concentrations of cVOCs measured in the replaced wells were consistent with historical data and had a maximum TCE concentration of 57,700 micrograms per liter (μg/L), indicative of nearby residual DNAPL based on the general rule of observed concentrations exceeding 1 percent of solubility. The primary TCE daughter product detected was 1,2-cis-dichloroethene (cisDCE), which indicated limited reductive dechlorination in the plume. Groundwater in both the source and downgradient areas was relatively reducing during the pre-design characterization, particularly in the source area where methane concentrations greater than 400 μg/L were measured.
Initial laboratory tests conducted using native aquifer microorganisms from the three replacement wells showed that anaerobic TCE biodegradation rates were low when biostimulated with the addition of sodium lactate as an electron donor, also known as a carbon donor, and resulted in the production of only cisDCE. Addition of a known dechlorinating culture, WBC-2, however, resulted in rapid biodegradation and production of ethene, verifying complete reductive dechlorination of TCE. Microcosms constructed with aquifer soil collected from the site were used to evaluate other electron donors besides lactate to support reductive dechlorination by WBC-2, including corn syrup as an alternative fast-release compound and whey, soy-based vegetable oil, and 3-D Microemulsion (Regenesis, San Clemente, California) as slow-release compounds. First-order rate constants for total organic chlorine removal in these WBC-2 amended microcosms were greatest with either lactate or vegetable oil as the donor, ranging between 0.061 and 0.047 per day or corresponding half-lives of 11–15 days. Testing of commercial products in other WBC-2-bioaugmented microcosms led to selection for the field pilot test of an emulsified vegetable oil product that also contained some sodium lactate as a fast-release donor. Delaying the addition of WBC-2 relative to the donor in the microcosms resulted in the most rapid overall biodegradation rates.
The selected design for the pilot test utilized three separate test plots, each about 30-ft wide and 60-ft long: plots GS1 and GS2 in the source area of the plume and plot GS3 in the downgradient area of the plume near the excavation trench. Each test plot had one injection well, one monitoring well upgradient from the injection point, and 12 surrounding monitoring wells in a grid to capture variable groundwater flow directions. Donor injections, which included a bromide tracer, were completed in October 2021, immediately following baseline sampling, and the WBC-2 culture was injected about 40 days later, between November 30 and December 2, 2021. Performance monitoring conducted until December 2022 included hydrologic measurements and analyses of cVOCs, redox-sensitive constituents, dissolved organic carbon, bromide, volatile fatty acids, compound-specific carbon isotopes, and microbial communities.
The biogeochemical data collected during the pilot tests in the three treatment plots showed that enhanced, complete reductive dechlorination of cVOCs in the groundwater was achieved in the GS1 and GS3 plots. In contrast, evidence of distribution of the injected amendments and subsequent biodegradation was limited in GS2, which was in an area of more heterogeneous soil lithology and low water table elevations. The molar composition of volatile organic compounds in the GS1 and GS3 plots was dominated by ethene in wells that were reached by the injected amendments by the end of the monitoring period. In the GS1 and GS3 plots, similar patterns were observed of cVOC concentrations decreasing to near detection levels, or below, at some wells sampled in July and October 2022, whereas ethene became dominant and indicated sustained complete reductive dechlorination. Baseline cVOC concentrations were more than a factor of 10 higher in the groundwater in the GS1 plot than in GS3, but no apparent inhibition of complete dechlorination occurred. As expected from the initial pre-design site data and the laboratory experiments, enhanced dissolution of residual DNAPL coupled to biodegradation was evident in the GS1 plot, where a marked increase in dichloroethene (DCE) above the initial baseline and upgradient TCE and DCE concentrations occurred. DCE concentrations subsequently declined where DNAPL dissolution was evident, concurrent with production of vinyl chloride and then predominantly ethene. Thus, overall biodegradation rates outpaced the DNAPL dissolution and desorption and DCE production in the source area. This success in complete degradation to predominantly ethene was achieved even in areas where the DCE concentrations reached a maximum of about 30,000 μg/L. Compound specific isotope analysis of carbon in TCE, cisDCE, trans-1,2-dichloroethene, and vinyl chloride was conducted to provide another line of evidence of the occurrence and extent of anaerobic biodegradation. Along a flow path in each plot that was affected by the injected amendments, carbon isotopes in the TCE and daughter cVOCs in the groundwater became isotopically heavier, indicating biodegradation.
Enhanced biodegradation rates calculated from the field tests in GS1 and GS3 showed half-lives of 36.9–75.3 days for DCE degradation and 9.48–38.5 days for ethene production. Notably, these ethene production rates calculated from the field tests are consistent with the results of WBC-2-bioaugmented microcosms amended with either lactate or vegetable oil, which had half-lives for total organic chlorine removal that ranged from 11 to 15 days. These rates indicated rapid enhanced biodegradation, which is promising for application of a full-scale bioremediation remedy. Ultimately, however, the mass of residual or sorbed TCE in the aquifer that remains accessible for dissolution and biodegradation would likely control the time required for a full-scale bioremediation effort to achieve performance goals for TCE and cisDCE specified in the Record of Decision for Site K.
The field pilot tests showed that the relatively low hydraulic head gradients and temporal changes in groundwater flow directions in the shallow aquifer would add complexity to a full-scale bioremediation effort. The radius of influence (ROI) at GS1 and GS3 (16.3 ft and 12.7 ft, respectively) were close to the design ROI of 15 ft. The estimated ROI at GS2 was about four times the design ROI, but may be less reliable at this location owing to groundwater flow direction. In addition, the low temperatures following WBC-2 injection in late November to early December 2021, in combination with the low hydraulic head gradients, were probably major factors in the delay observed before the onset of enhanced biodegradation following injection of the culture. Additional test injections could be beneficial to optimize the timing of donor and culture injections with the variable temperatures and hydraulic head in the shallow aquifer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255113","collaboration":"Prepared in cooperation with U.S. Army Environmental Command","usgsCitation":"Lorah, M.M., Majcher, E.H., Mumford, A.C., Foss, E.P., Needham, T.P., Psoras, A.W., Livdahl, C.T., Trost, J.J., Berg, A.M., Polite, B.F., Akob, D.M., and Cozzarelli, I.M., 2026, Treatability study to evaluate bioremediation of trichloroethene at Site K, former Twin Cities Army Ammunition Plant, Arden Hills, Minnesota, 2020–22: U.S. Geological Survey Scientific Investigations Report 2025–5113, 88 p., https://doi.org/10.3133/sir20255113.","productDescription":"Report: xii, 88 p.; Data Release","numberOfPages":"88","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-175852","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":500105,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5113/images/"},{"id":500361,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119213.htm","linkFileType":{"id":5,"text":"html"}},{"id":500106,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13QTBR7","text":"USGS data release","linkHelpText":"Former Twin Cities Army Ammunition Site K treatability test data including various field measurements, laboratory tests and degradation constituents in the bioremediation of trichloroethylene and dichloroethylene, Arden Hills, Minnesota 2020–2022"},{"id":500104,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5113/sir20255113.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2025-5113 XML"},{"id":500103,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255113/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5113 HTML"},{"id":500102,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5113/sir20255113.pdf","size":"6.92 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5113 PDF"},{"id":500101,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5113/coverthb.jpg"}],"country":"United States","state":"Minnesota","county":"Ramsey County","city":"Arden Hills","otherGeospatial":"Site K, former Twin Cities Army Ammunition Plant","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.17794646411902,\n 45.1090420800339\n ],\n [\n -93.17794646411902,\n 45.08000250215488\n ],\n [\n -93.14480906199879,\n 45.08000250215488\n ],\n [\n -93.14480906199879,\n 45.1090420800339\n ],\n [\n -93.17794646411902,\n 45.1090420800339\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Maryland-Delaware-D.C. Water Science Center
U.S. Geological Survey
5522 Research Park Drive
Catonsville, MD 21228
The Puyallup River drains a 990 square mile watershed in western Washington, with headwaters on the glacier-covered flanks of Mount Rainier. Major tributaries include the White, Carbon, and Mowich Rivers. In the levee-confined reaches of the lower watershed, loss of flood conveyance due to sand and gravel deposition has been a chronic issue. Over much of the 20th century, flood conveyance was maintained through sediment removal, but this practice ended in the late 1990s. Flood hazard management activities since the 1990s have primarily involved levee removal or setback projects. Assessments of 1984-2009 repeat cross sections suggested that sediment deposition rates were particularly high in reaches with recent levee setbacks. However, there have been no assessments of recent deposition rates since the 2009 surveys. There are also concerns that intensifying flood hydrology or increased sediment delivery from Mount Rainier may exacerbate deposition. However, assessment of those risks has been hindered by limited understanding of watershed-scale sediment delivery and routing, particularly for coarse sand and gravel.
The U.S. Geological Survey, in cooperation with Pierce County, initiated this study to improve understanding of sediment deposition in the lower Puyallup River watershed. This work is primarily based on differencing of multiple aerial lidar datasets collected during 2002–2022, supplemented by early 1990 photogrammetric elevation datasets, geomorphic assessments of streamgage data, historical topographic surveys from 1907, and previously collected sediment transport measurements. Analyses cover the Puyallup, Carbon, and Mowich Rivers, but do not include the White River.
During 2004–2020, repeat aerial lidar indicates that 1.3 ± 0.3 million yd3 of sediment accumulated in the lower 20 valley miles (VMs) of the Puyallup River, averaging 80,000 ± 20,000 cubic yards per year (yd3/yr). Deposition was observed during both 2004–11 and 2011–20 lidar differencing intervals. This continued a long-term depositional trend that extends back to at least 1977. From 2004 to 2011, deposition rates along the Soldiers Home levee setback reach, the only setback project downstream of VM 20 completed prior to 2011, were approximately four times higher than in adjacent unmodified reaches. From 2011 to 2020, two additional setback projects were completed; volumetric deposition rates over all three setback reaches were similar to adjacent unmodified reaches, suggesting elevated setback deposition in the 2004–11 interval may have been influenced by an extreme flood in November 2006. These levee setback projects increased the local cross-sectional area of the floodway, used as a rough proxy for relative flood conveyance, by 50 to 200 percent above 2004 conditions. If deposition continued at recent rates, cross-sectional area over the levee setback reaches would be reduced back to 2004 values by 2050-90.
Deposition also occurred over the lower six VMs of the Carbon River during 2004–20, though volumes (0.15 ± 0.09 million yd3) were an order of magnitude lower than along the Puyallup River. Relatively lower deposition rates in the Carbon River are most likely the combined result of modestly lower incoming sediment loads, modestly steeper channel slope, and the additional sediment transport capacity provided by two large non-glacial tributaries that enter the Carbon River near VM 5.
Upstream of the depositional reaches described above, 2002–22 sediment storage trends along the Puyallup, Carbon, and Mowich Rivers were predominately negative (net erosion) up to the Mount Rainier National Park boundary. Net erosion was the result of bank and bluff erosion exceeding deposition across wetted channel and bare gravel areas, as opposed to uniform vertical downcutting. Net erosion along these river valleys delivered 3.4 ± 0.6 million yd3 to the river system, equivalent to 190,000 ± 35,000 yd3/yr. Most of that volume was supplied by erosion of relatively low (4–10 ft) surfaces along the Puyallup and Mowich Rivers and tall (300 ft) glacial bluffs along the lower Carbon River. Substantial aggradation from 1984 to 2009 reported by Czuba and others (2010) along reaches of the Puyallup River (VM 19–22) where levee confinement has recently been removed was most likely an artifact of methodologic bias.
The Puyallup, Mowich, and Carbon Rivers drain five distinct glaciated watersheds on the flanks of Mount Rainier, four of which were assessed in this study. All four watersheds were impacted by an extreme November 2006 rainstorm. Between 2002 and 2008, debris flows occurred in all four headwater areas, collectively eroding at least 2.1 million yd3 of sediment. These debris flows formed distinct deposits one to two miles downstream of source areas, depositing 30-50 percent of the material eroded upstream. From 2008 to 2022, no headwater debris flows were observed and overall rates of geomorphic change in the headwaters were low. Rivers eroded into debris flow deposits emplaced over the 2002–08 interval, but re-deposited equivalent volumes of material within a half mile downstream.
Stage-discharge relations at five streamgages on upland rivers draining Mount Rainier show either net channel incision or dynamic variability with no long-term trend over the past 60–100 years. Observations of pervasive river valley erosion and stable or incising trends at long-term streamgages in the upper watershed do not support prior claims of widespread and accelerating aggradation of upland rivers draining Mount Rainier.
Erosion and deposition volumes estimated in this report were combined with sediment transport estimates from limited suspended sediment and bedload measurements, estimates of sub-glacial erosion rates, and sediment delivery from non-glacial tributaries to construct watershed-scale sediment budgets for the Puyallup River watershed. During 2004–20, the estimated sediment load entering the depositional lowlands was well balanced by estimated inputs from, in order of relative magnitude, subglacial erosion (33–60 percent of total sediment load), erosion along the major river valleys (25–45 percent), erosion in recently deglaciated headwater areas (7–17 percent) and non-glacial tributaries (3–9 percent). These results are specific to the study period and represent total sediment loads, most of which is fine material carried in suspension. The relative sourcing of sand and gravel may be different than implied by this sediment budget.
Downstream of VM 12, comparison of 1907 and 2009 channel surveys show net lowering of the channel thalweg of 4–12 ft. A long-term gage near VM 22 shows lowering of 4–5 ft through the 1960s. Lowering at both locations was inferred to be a channel response to the substantial straightening, and so steepening, of the river during major phases of levee construction through the early and mid-20th century.
Application of a simple empirical bedload-discharge power-law relation to an ensemble of model-estimated daily mean discharge records in the lower Puyallup River between 1977 and 2100 projects that annual bedload transport capacity in the lower Puyallup River will increase by 20–60 percent by the middle of the 21st century. Actual changes in bedload transport and deposition rates will depend on concurrent changes in sediment supply and local hydraulics governing deposition.
This report presents several key conclusions. First, the persistence and spatial patterns of sand and gravel deposition along the lower Puyallup River support prior claims that deposition is fundamentally caused by decreases in channel slope moving downstream. Given this underlying cause and the abundance of sand and gravel available to be transported downstream, deposition is likely to continue for the foreseeable future. Second, despite continued sediment deposition, recent levee setback projects in the lower Puyallup River will likely provide several decades of flood conveyance benefits relative to a no-action alternative. Third, while the rivers linking Mount Rainier to the Puget Sound lowlands have often been discussed as conduits that either pass or accumulate sediment from Mount Rainier, observations from 2002–22 show these river valleys acting as substantial sediment sources, delivering three times more sediment than recently deglaciated headwater areas on Mount Rainier. While the persistence and underlying cause of recent river valley erosion remain unknown, sediment storage dynamics along these river valleys are likely to be a major control on sand and gravel delivery to the lower watershed.
Little is understood of lake browning (due to increased dissolved organic carbon; DOC) in large lakes such as the Laurentian Great Lakes. Lake browning can alter whole lake ecosystems, including decreasing exposure to damaging ultraviolet radiation (UV-B) which is strongly and selectively attenuated by DOC more so than photosynthetically active radiation (PAR). We compared the changes in UV-B and PAR transparency to DOC data collected during the ice-free seasons from 62 nearshore sites in four of the five Great Lakes from 2002 to 2022 using linear mixed effects regression models based on backwards selected Bayesian information criteria. Regionally, DOC significantly increased from 2002 to 2022 by 0.5% per year on average. DOC strongly and inversely explained the variability of UV-B and PAR transparencies, as did seasons and offshore influence on these habitats. We provide regional evidence of lake browning within the nearshore habitats of the Great Lakes as a strong contrast to the well-documented increased offshore water transparency associated with the spread of invasive dreissenid mussels.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2024-0407","usgsCitation":"Berry, N., Bunnell, D.B., Fisher, T., Overholt, E., Mette, E., Howell, T., and Williamson, C.E., 2026, Decreased water transparency of nearshore Laurentian Great Lakes habitats is driven by increased dissolved organic carbon.: Canadian Journal of Fisheries and Aquatic Sciences, v. 83, p. 1-9, https://doi.org/10.1139/cjfas-2024-0407.","productDescription":"9 p.","startPage":"1","endPage":"9","ipdsId":"IP-170502","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":500258,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1139/cjfas-2024-0407","text":"Publisher Index Page"},{"id":500189,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Great Lakes","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.84751857840234,\n 49.471311017762645\n ],\n [\n -92.81669615927237,\n 46.920316633964475\n ],\n [\n -91.56012545582533,\n 46.18295137477036\n ],\n [\n -87.0507208161673,\n 46.41933841908485\n ],\n [\n -88.74219710454639,\n 43.914354385501326\n ],\n [\n -87.95935986459418,\n 41.59912997740622\n ],\n [\n -83.9720223229704,\n 41.37139907143079\n ],\n [\n -81.15069356336562,\n 40.86644441718417\n ],\n [\n -78.58068940433992,\n 42.033414801353516\n ],\n [\n -75.56437638284248,\n 43.19976943418763\n ],\n [\n -76.07811679933005,\n 44.5165607788128\n ],\n [\n -79.06374555874787,\n 44.2536538155133\n ],\n [\n -79.2312465353583,\n 46.384270370895024\n ],\n [\n -83.12440166219844,\n 46.77820657483097\n ],\n [\n -84.46202674663817,\n 48.73561539687698\n ],\n [\n -88.84751857840234,\n 49.471311017762645\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"83","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Berry, Nicole Lynn 0000-0002-7889-197X","orcid":"https://orcid.org/0000-0002-7889-197X","contributorId":347450,"corporation":false,"usgs":true,"family":"Berry","given":"Nicole Lynn","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":955877,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bunnell, David B. 0000-0003-3521-7747","orcid":"https://orcid.org/0000-0003-3521-7747","contributorId":216540,"corporation":false,"usgs":true,"family":"Bunnell","given":"David","middleInitial":"B.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":955878,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fisher, Thomas J. 0000-0001-5885-7646","orcid":"https://orcid.org/0000-0001-5885-7646","contributorId":347464,"corporation":false,"usgs":false,"family":"Fisher","given":"Thomas J.","affiliations":[{"id":16608,"text":"Miami University","active":true,"usgs":false}],"preferred":false,"id":955879,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Overholt, Erin P. 0000-0001-9078-7086","orcid":"https://orcid.org/0000-0001-9078-7086","contributorId":347452,"corporation":false,"usgs":false,"family":"Overholt","given":"Erin P.","affiliations":[{"id":16608,"text":"Miami University","active":true,"usgs":false}],"preferred":false,"id":955880,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mette, Elizabeth M. 0009-0007-9622-1260","orcid":"https://orcid.org/0009-0007-9622-1260","contributorId":347466,"corporation":false,"usgs":false,"family":"Mette","given":"Elizabeth M.","affiliations":[{"id":16608,"text":"Miami University","active":true,"usgs":false}],"preferred":false,"id":955881,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Howell, Todd","contributorId":294685,"corporation":false,"usgs":false,"family":"Howell","given":"Todd","affiliations":[{"id":63627,"text":"Ontario Ministry of Environment and Climate Change","active":true,"usgs":false}],"preferred":false,"id":955882,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Williamson, Craig E. 0000-0001-7350-1912","orcid":"https://orcid.org/0000-0001-7350-1912","contributorId":347472,"corporation":false,"usgs":false,"family":"Williamson","given":"Craig","middleInitial":"E.","affiliations":[{"id":16608,"text":"Miami University","active":true,"usgs":false}],"preferred":false,"id":955883,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70274186,"text":"70274186 - 2026 - A targeted approach for mapping groundwater discharge to surface water and fish thermal refuge in four Lake Ontario tributaries","interactions":[],"lastModifiedDate":"2026-03-09T15:01:06.104632","indexId":"70274186","displayToPublicDate":"2026-02-17T15:04:37","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7176,"text":"Hydrologic Processes","active":true,"publicationSubtype":{"id":10}},"title":"A targeted approach for mapping groundwater discharge to surface water and fish thermal refuge in four Lake Ontario tributaries","docAbstract":"The duration, magnitude, and frequency of heatwaves are predicted to increase in the coming decades, a combination that can reduce the survival of many fish species. Across the world, there is broad interest in identifying thermal refuge for heat-intolerant fish species and exploring opportunities to enhance or protect these areas. Because deeper groundwater maintains a relatively constant temperature, groundwater-influenced areas along streams can provide cool-water refuge for fish during periods of extreme heat. A targeted approach was developed for identifying existing cold-water zones and areas of substantial groundwater discharge in four high priority Lake Ontario tributaries. Our approach included: (1) predicting where groundwater discharge is most likely with a simple geospatial model and (2) using model predictions to select field sites for intensive high-resolution study, including ground-based mapping of groundwater features (springs, seeps, tributaries) as well as drone-based optical and thermal infrared surveys. Results from field sites were used to both verify model performance and map different types and aerial extents of thermal anomalies. Geospatial modelling successfully predicted regions of widespread groundwater upwelling, later verified and mapped by field and drone surveys. Comparison of model and field survey results further highlighted specific geospatial layers, such as soil/bedrock types and topographic wetness index, as being particularly useful for predicting groundwater influence on streams in the study area. In addition, a comparison of geospatial model results with a model of fish abundances along the studied streams showed significant positive correlations for many heat-intolerant fish species over a wide geographic area. The approach developed in this study can be applied to other watersheds to highlight areas of probable groundwater discharge and could be used by fishery and water resource managers to support cold-water fish habitat management decision-making and resource conservation.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.70459","usgsCitation":"Woda, J., Terry, N., Kelley, D.J., Finkelstein, J., Gazoorian, C.L., and McKenna, J., 2026, A targeted approach for mapping groundwater discharge to surface water and fish thermal refuge in four Lake Ontario tributaries: Hydrologic Processes, v. 40, e70459, 16 p., https://doi.org/10.1002/hyp.70459.","productDescription":"e70459, 16 p.","ipdsId":"IP-176833","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":501102,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.70459","text":"Publisher Index Page"},{"id":500774,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"New York","otherGeospatial":"Lake Ontario","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.34397142741707,\n 44.03795020350384\n ],\n [\n -80.34397142741707,\n 42.83906785974037\n ],\n [\n -75.35823758327766,\n 42.83906785974037\n ],\n [\n -75.35823758327766,\n 44.03795020350384\n ],\n [\n -80.34397142741707,\n 44.03795020350384\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"40","noUsgsAuthors":false,"publicationDate":"2026-03-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Woda, Joshua C. 0000-0002-2932-8013","orcid":"https://orcid.org/0000-0002-2932-8013","contributorId":290172,"corporation":false,"usgs":true,"family":"Woda","given":"Joshua","middleInitial":"C.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":956839,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Terry, Neil C. 0000-0002-3965-340X nterry@usgs.gov","orcid":"https://orcid.org/0000-0002-3965-340X","contributorId":192554,"corporation":false,"usgs":true,"family":"Terry","given":"Neil","email":"nterry@usgs.gov","middleInitial":"C.","affiliations":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":956840,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kelley, David J 0000-0002-0143-0956","orcid":"https://orcid.org/0000-0002-0143-0956","contributorId":367137,"corporation":false,"usgs":true,"family":"Kelley","given":"David","middleInitial":"J","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":956841,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Finkelstein, Jason S. 0000-0002-7496-7236","orcid":"https://orcid.org/0000-0002-7496-7236","contributorId":202452,"corporation":false,"usgs":true,"family":"Finkelstein","given":"Jason S.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":956842,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gazoorian, Christopher L. 0000-0002-5408-6212 cgazoori@usgs.gov","orcid":"https://orcid.org/0000-0002-5408-6212","contributorId":2929,"corporation":false,"usgs":true,"family":"Gazoorian","given":"Christopher","email":"cgazoori@usgs.gov","middleInitial":"L.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":956843,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McKenna, James E. Jr. 0000-0002-1428-7597 jemckenna@usgs.gov","orcid":"https://orcid.org/0000-0002-1428-7597","contributorId":190798,"corporation":false,"usgs":true,"family":"McKenna","given":"James E.","suffix":"Jr.","email":"jemckenna@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":956844,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}