Seven potential sources of water, consisting of free-flowing discharge from abandoned coal mines at six locations and one abandoned flooded underground coal mine air shaft, were sampled for chemical analysis to assess the quality of the groundwater emanating from the seven mine sources. The six free-flowing mine discharge sources were also assessed for discharge by current-meter measurements on two separate occasions. The U.S. Geological Survey assessed these seven sources to provide information to the City of Gary, West Virginia (W. Va.), and the City of Gary’s consulting engineer with groundwater-quality and flow data to allow them to assess the seven sites as potential alternate sources of water for the City of Gary to augment its existing supply.
For the six sites where discharge could be measured, discharge ranged from a minimum of 0.082 cubic feet per second (ft3/s) to a maximum of 3.685 ft3/s. Of the six sites measured, only two, Harmon Branch at Thorpe, W. Va. (USGS site 372201081303501) and the abandoned public-supply water wells near Havaco, W. Va. (USGS site 372358081344601), had discharge in excess of 1.00 ft3/s. Discharge from the abandoned public supply wells was 3.685 ft3/s on September 20, 2023, and 2.888 ft3/s on October 16, 2023, and discharge from Harmon Branch at Thorpe, W. Va., was 1.049 ft3/s on September 22, 2023, and 1.038 ft3/s on October 17, 2023. Discharge in the abandoned underground mine air shaft (USGS site 372224081340901) could not be assessed, but the air shaft drains an abandoned mine that likely contains water stored in approximately 1.7 square miles (mi2) of abandoned underground coal mines in the Pocahontas No. 3 coal seam, and possibly an additional 0.9 mi2 of leakage from the overlying Pocahontas No. 4 coal seam. Discharge for the six sites measured for the study was measured during a period between September 20 and October 18, 2023, and corresponded to the 12th to the 15th percentile of flow-duration statistics for the Tug Fork downstream of Elkhorn Creek at Welch, W. Va. streamgage (USGS site 03212750).
Water-quality data for the seven sites sampled overall were acceptable with respect to drinking water standards. Of the 203 constituents analyzed, only a few failed to meet applicable U.S. Environmental Protection Agency (EPA) drinking water standards. Iron exceeded the 300 micrograms per liter (μg/L) secondary maximum contaminant level (SMCL) at only 1 of the 7 sites (14.3 percent) sampled. Iron concentrations ranged from a minimum of less than (<) 5.00 μg/L to a maximum of 724 μg/L with a median concentration of 7.62 μg/L. Manganese exceeded the 50.0 μg/L SMCL at 2 of the 7 sites (28.6 percent) sampled. Manganese concentrations ranged from a minimum of 1.93 μg/L to a maximum of 271 μg/L with a median concentration of 4.03 μg/L. No sites sampled exceeded the arsenic maximum contaminant level (MCL) of 10 μg/L. Arsenic concentrations ranged from a minimum of <0.100 μg/L to a maximum of 2.35 μg/L with a median arsenic concentration of 0.200 μg/L. None of the seven sites sampled for selenium for this study exceeded the EPA MCL of 50.0 μg/L. Selenium concentrations ranged from a minimum of <0.050 μg/L to a maximum of 5.26 μg/L with a median concentration of 3.21 μg/L.
All seven sites were sampled for volatile organic compounds (VOCs), semivolatile organic compounds (SVOCs), and polychlorinated biphenyls (PCBs), but most had concentrations below the detection limit. Of the 10 PCB compounds analyzed for the seven sites sampled, none contained detectable concentrations of PCBs or Aroclor compounds. Of the 44 SVOCs analyzed at each of the seven sites sampled, only 1 SVOC, acenaphthene, was detected, at a concentration of 0.02 μg/L. Of the 96 VOCs analyzed, from each of the seven sites sampled, only two were found at detectable concentrations. Trichloromethane was detected only at 1 of the 7 (14.3 percent) sites sampled at a concentration of 0.027 μg/L, and benzene was detected at the same site and 3 additional sites (4 of the 7 sites or 57.1 percent of the sites sampled) at concentrations of 0.028, 0.029, 0.021, and 0.035 μg/L, but none exceeded the EPA MCL for benzene of 5.00 μg/L.
Total coliform bacteria are ubiquitous in the environment, and their presence only suggests the potential for contamination by near-surface processes. Escherichia coli (E. coli) bacteria are derived from either human or animal fecal material and can be an indicator of potential contamination by pathogenic bacteria or viruses. Total coliform bacteria were detected at all 7 sites sampled at concentrations ranging from 17.5 to greater than (>) 2,420 most probable number per 100 mL (MPN/100 mL) of sample, with a median total coliform concentration of 1,553 MPN/100 mL. Escherichia coli bacteria were detected at 4 of the 7 sites sampled at concentrations ranging from <1 to 11.9 MPN/100 mL, with a median E. coli concentration of 5.1 MPN/100 mL.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251037","collaboration":"Prepared in cooperation with the City of Gary, West Virginia","usgsCitation":"Kozar, M.D., and Austin, S.H., 2025, Reconnaissance of potential alternate water supply sources for the City of Gary, West Virginia: U.S. Geological Survey Open-File Report 2025–1037, 27 p., https://doi.org/10.3133/ofr20251037.","productDescription":"Report: viii, 27 p.; Appendix","numberOfPages":"27","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-176784","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":496467,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1037/ofr20251037.pdf","text":"Report","size":"5.71 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1037 PDF"},{"id":496466,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1037/coverthb.jpg"},{"id":497789,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118952.htm"},{"id":496471,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2025/1037/ofr20251037_app2.csv","text":"Appendix 2","size":"222 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Water-Quality Data Collected During the Study"},{"id":496470,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1037/ofr20251037.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2025-1037 XML"},{"id":496469,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1037/images/"},{"id":496468,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251037/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1037 HTML"}],"country":"United States","state":"West Virginia","city":"Gary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.616667,\n 37.433333\n ],\n [\n -81.616667,\n 37.25\n ],\n [\n -81.45,\n 37.25\n ],\n [\n -81.45,\n 37.433333\n ],\n [\n -81.616667,\n 37.433333\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
Occurrences of harmful algal blooms (HABs) in rivers challenge the belief that rivers are not susceptible to HABs because of their short residence times and fluctuating hydrology. Here we present a systematic literature review of predictive and forecasting models for HABs in flowing waters, including rivers, flowing in-stream reservoirs (e.g., run-of-river reservoirs and lock-and-dam systems) and tidal or estuarine systems with riverine processes. The review aimed to understand current and historical modeling approaches for predicting and forecasting river HABs, without restricting to specific taxa, such as cyanobacteria, or modeling endpoints. The review included 162 articles published over nearly 50 years, covering more than 80 rivers worldwide. Eutrophic, non-wadable rivers with in-stream obstruction were commonly modeled, though diverse environmental characteristics were reported. Most articles used algal biomass or chlorophyll as modeling endpoints, with a quarter using novel or unique endpoints. Algal toxins motivated model development in 23% of the articles, however just 5% used algal toxins as an endpoint. Only 6% of the articles modeled benthic HABs; the rest focused on pelagic HABs. There was no standard model used for modeling river HABs. Process-based models were more common (59%) than data-driven approaches (37%), with model formulations ranging from simple to complex, which contrasts with a lake-focused literature review of HAB models that found data-driven models were more common. Models in river settings shared similar input variables as those previously identified for lakes, such as water temperature, nutrients, and light availability. However, streamflow and other transport metrics took prominence in river models compared to lake models. Algal cell physiology (such as growth, predation, and motility) was routinely included as input data or as mathematical formulations in process-based models and these processes were frequently identified as an important predictor by the articles’ authors. Conversely, data-driven models rarely included these processes, instead using predictors related to environmental conditions, such as nutrients, water quality, water temperature, and streamflow. These important proxy predictors have apparent success with modeling overall algal biomass (irrespective of taxa) whereas other factors, such as those related to algal physiology and other biological processes, are likely responsible for more subtle shifts in community composition. These differences highlight the influence of data availability, especially for processes that are difficult, time-consuming, or expensive to measure, on model development and model outcomes, raising questions about the selection of modeling inputs and endpoints. Challenges to advancing river HAB modeling include the lack of site-specific model inputs representing key processes (e.g., photosynthetic parameters and predation rates), overlooked riverine environments like the benthos and side/back-channel areas, lack of information on environmental settings, and poorly reported model performance metrics. This review emphasizes opportunities for advancing river HAB modeling by learning from well-honed estuarine models, supporting current forecasting and operationalization efforts, and developing common datasets for river HAB model development and evaluation.
","language":"English","publisher":"BioRxiv","doi":"10.1101/2025.09.29.679270","usgsCitation":"Murphy, J.C., Gorney, R.M., Lucas, L., Zwart, J.A., and Graham, J.L., 2025, A systematic literature review of forecasting and predictive models of harmful algal blooms in flowing waters: BioRxiv, https://doi.org/10.1101/2025.09.29.679270.","productDescription":"52 p.","ipdsId":"IP-179513","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":496741,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1101/2025.09.29.679270","text":"External Repository"},{"id":496628,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Murphy, Jennifer C. 0000-0002-0881-0919 jmurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-0881-0919","contributorId":4281,"corporation":false,"usgs":true,"family":"Murphy","given":"Jennifer","email":"jmurphy@usgs.gov","middleInitial":"C.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":950534,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gorney, Rebecca Michelle 0000-0003-4406-261X","orcid":"https://orcid.org/0000-0003-4406-261X","contributorId":317259,"corporation":false,"usgs":true,"family":"Gorney","given":"Rebecca","email":"","middleInitial":"Michelle","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":950535,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lucas, Lisa 0000-0001-7797-5517 llucas@usgs.gov","orcid":"https://orcid.org/0000-0001-7797-5517","contributorId":260498,"corporation":false,"usgs":true,"family":"Lucas","given":"Lisa","email":"llucas@usgs.gov","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":950536,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zwart, Jacob Aaron 0000-0002-3870-405X","orcid":"https://orcid.org/0000-0002-3870-405X","contributorId":237809,"corporation":false,"usgs":true,"family":"Zwart","given":"Jacob","email":"","middleInitial":"Aaron","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":950537,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Graham, Jennifer L. 0000-0002-6420-9335 jlgraham@usgs.gov","orcid":"https://orcid.org/0000-0002-6420-9335","contributorId":202923,"corporation":false,"usgs":true,"family":"Graham","given":"Jennifer","email":"jlgraham@usgs.gov","middleInitial":"L.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":950538,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70271982,"text":"sir20255031 - 2025 - User’s guide for the National Hydrography Dataset Plus High Resolution (NHDPlus HR)","interactions":[{"subject":{"id":70206120,"text":"ofr20191096 - 2019 - User's guide for the national hydrography dataset plus (NHDPlus) high resolution","indexId":"ofr20191096","publicationYear":"2019","noYear":false,"displayTitle":"User’s Guide for the National Hydrography Dataset Plus (NHDPlus) High Resolution","title":"User's guide for the national hydrography dataset plus (NHDPlus) high resolution"},"predicate":"SUPERSEDED_BY","object":{"id":70271982,"text":"sir20255031 - 2025 - User’s guide for the National Hydrography Dataset Plus High Resolution (NHDPlus HR)","indexId":"sir20255031","publicationYear":"2025","noYear":false,"title":"User’s guide for the National Hydrography Dataset Plus High Resolution (NHDPlus HR)"},"id":1}],"lastModifiedDate":"2026-02-03T16:23:33.096091","indexId":"sir20255031","displayToPublicDate":"2025-09-30T13:20:00","publicationYear":"2025","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":"2025-5031","displayTitle":"User’s Guide for the National Hydrography Dataset Plus High Resolution (NHDPlus HR)","title":"User’s guide for the National Hydrography Dataset Plus High Resolution (NHDPlus HR)","docAbstract":"The National Hydrography Dataset Plus High Resolution (NHDPlus HR) is a scalable hydrologic geospatial fabric or framework, built from (1) the High Resolution (1:24,000-scale or better) National Hydrography Dataset (NHD), (2) nationally complete Watershed Boundary Dataset (WBD), and (3) 1/3-arc-second 3D Elevation Program (3DEP) digital elevation model (DEM) data (at a 10-meter ground spacing; or 5-meter 3DEP DEM in Alaska only). The NHDPlus HR provides a modeling and assessment framework at a local 1:24,000 scale, while nesting seamlessly into the national context.
NHDPlus HR is modeled after the highly successful NHDPlus version 2 (NHDPlusV2). Like NHDPlusV2, the NHDPlus HR includes data for a nationally seamless network of stream reaches, elevation-based catchment areas, flow surfaces, and value-added attributes that enhance stream-network navigation, analysis, and data display. However, NHDPlus HR provides much greater spatial detail than NHDPlusV2, while NHDPlusV2 is, at present, more complete in its attribution of additions, removals, and diversions, as well as stream connectivity. This user’s guide is intended to provide necessary information and guidance in the use of NHDPlus HR data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255031","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","programNote":"National Geospatial Program","usgsCitation":"Moore, R.B., McKay, L.D., Rea, A.H., Bondelid, T.R., Price, C.V., Dewald, T.G., and Hayes, L., 2025, User’s guide for the National Hydrography Dataset Plus High Resolution (NHDPlus HR): U.S. Geological Survey Scientific Investigations Report 2025–5031, 78 p., https://doi.org/10.3133/sir20255031. [Supersedes USGS Open-File Report 2019–1096.]","productDescription":"Report: xiii, 78 p.; 2 Data Releases; Project Site","numberOfPages":"78","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-150034","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":496237,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5031/sir20255031.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2025-5031 XML"},{"id":496238,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5031/images"},{"id":496239,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WFOBQI","text":"USGS data release","linkHelpText":"USGS National Hydrography Dataset Plus High Resolution National Release 1 FileGDB"},{"id":496240,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://apps.nationalmap.gov/downloader/#/","text":"USGS data release","linkHelpText":"The National Map downloader (ver. 2.0)"},{"id":496273,"rank":8,"type":{"id":18,"text":"Project Site"},"url":"https://www.usgs.gov/national-hydrography/nhdplus-high-resolution","text":"NHDPlus High Resolution"},{"id":496236,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255031/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5031 HTML"},{"id":496235,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5031/sir20255031.pdf","text":"Report","size":"9.55 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5031 PDF"},{"id":496234,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5031/coverthb.jpg"}],"contact":"Director, National Geospatial Program
Core Science Systems
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 511
Reston, VA 20192
American beavers (Castor canadensis) are native to the Pacific Northwest, and their populations have increased in many locations after being nearly removed by historical trapping. Beaver dams have well-documented effects on water quality in forested streams, but their effects on water quality in urban streams have not been well characterized. The study documented the water-quality effects of beaver dams and beaver activity in selected urban streams of the Tualatin River Basin in northwestern Oregon. Variations in water quality upstream, downstream, and within ponded areas behind beaver dams were quantified with continuous measurements of water temperature, specific conductance, dissolved oxygen, and pH from May 2016 to November 2017 in two intensively monitored reaches of urban streams (Fanno and Bronson Creeks). Five other urban stream reaches were monitored upstream and downstream from beaver ponds using water-temperature sensors to document water-temperature changes in additional beaver-affected reaches. Spatial water-quality variations within a beaver pond along Fanno Creek were characterized in more detail on four hot summer afternoons with numerous measurements of temperature and dissolved oxygen. Results from the study were used to document and derive insights from measured patterns in the water-quality data, such as the following:
Director, Oregon Water Science Center
U.S. Geological Survey
601 SW 2nd Avenue, Suite 1950
Portland, Oregon 97204
Growing interest in beaver-assisted restoration in the Tualatin River Basin of northwestern Oregon motivated a series of studies by the U.S. Geological Survey to assess the capacity of the stream network to support beaver dams and to evaluate the effects of beaver dams and ponds on urban streams. This multichapter volume describes the data collection from 2016–17 and the findings of these studies, which were done in partnership with Clean Water Services. Chapter A documents the locations of beaver dams in the Tualatin River Basin and how many beaver dams the stream network could support with existing and improved riparian vegetation. Beaver dam capacity was estimated by modifying existing tools to account for the low gradient of many streams in the Tualatin River Basin. Chapter B describes the effects of beaver dams and ponds on hydrologic and hydraulic responses of storm flows. Hydrologic and hydraulic responses for two urban stream reaches were compared with and without beaver dams and ponds and for a range of streamflow conditions using two-dimensional hydraulic models. Chapter C characterizes the effects of beaver dams and ponds on the transport and deposition of suspended sediment. Continuous turbidity, discrete suspended-sediment samples, and streamflow measurements collected during storms and base-flow periods were used to assess: (1) suspended-sediment loads upstream and downstream from two beaver-affected reaches, and (2) seasonal and longitudinal turbidity patterns. Chapter D describes the effects of beaver dams and ponds on longitudinal, spatial, and seasonal water-quality patterns. Continuous and synoptic water-quality data were collected along urban stream reaches, and net ecosystem production was calculated for two beaver-affected reaches. The findings of these studies illustrate that the effects of beaver dams and ponds on hydrology, hydraulics, suspended-sediment transport and deposition, and water quality are dependent on the characteristics of a stream reach (for example, channel gradient, groundwater exchange, and riparian vegetation) and the characteristics of beaver dams and ponds along that reach. This information can be used to consider the implications of beaver-assisted restoration in the Tualatin River Basin and the effects of beaver dams and ponds in urban streams.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255039","collaboration":"Prepared in cooperation with Clean Water Services","usgsCitation":"Jones, K.L, and Smith, C.D., eds., Beavers in the Tualatin River Basin, northwestern Oregon: U.S. Geological Survey Scientific Investigations Report 2025–5039, https://doi.org/10.3133/sir20255039.","productDescription":"Chapters A-D","onlineOnly":"Y","costCenters":[],"links":[{"id":496209,"rank":2,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://doi.org/10.3133/fs20253022","text":"Fact Sheet 2025-3022","description":"FS 2025-3022","linkHelpText":"- Beaver dams and their effects on urban streams in the Tualatin River Basin, northwestern Oregon"},{"id":496091,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5039/coverthb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Tualatin River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.5,\n 45.75\n ],\n [\n -123.5,\n 45.375\n ],\n [\n -122.5,\n 45.375\n ],\n [\n -122.5,\n 45.75\n ],\n [\n -123.5,\n 45.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
601 SW 2nd Avenue, Suite 1950
Portland, Oregon 97204
Between 1980 and 1986, the U.S. Geological Survey made a series of 1:2,000-scale topographic contour maps from aerial photographic surveys to monitor the eruption. These maps were made for operational purposes and were not intended for publication. Since then, advances in technology made it possible to digitize the original, highly detailed hardcopy maps and derive new digital data elevation models of the surface of the lava dome. These digital elevation models allow for the visualization of the progression of the eruption and reveal the rubbly, chaotic surface of the lava flows and dome. Additionally, these new data help fill gaps in the long-term record of topographic changes that have occurred at the volcano since the May 18, 1980, eruption.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip262","usgsCitation":"Bard, J.A., Friedle, C.M., Bartee, L., Dierker, B.C., Ganick, J.M., Gregory, N.M., Hill, K.R., Klug, J.G., Kruger, A., Mooney, D.T., Morrison, R.T., Rojas, I.I., Rollo, P., Stanton, S.A., Stewart, B., Stuhlmuller, B.E., Zyla, A.D., 2025, Rebuilding a volcano one lava flow at a time—Visualizing the lava dome-building eruption in the crater of Mount St. Helens, 1982–1986: U.S. Geological Survey General Information Product 262, https://doi.org/10.3133/gip262.","productDescription":"1 p.","onlineOnly":"N","ipdsId":"IP-180615","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":496232,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/262/coverthb.jpg"},{"id":496233,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/262/gip262.pdf","text":"Document","size":"33.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 262"},{"id":497787,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118911.htm"}],"country":"United States","state":"Washington","otherGeospatial":"Mount St. Helens","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.38015899040656,\n 46.32565684366381\n ],\n [\n -122.38015899040656,\n 46.080226964619754\n ],\n [\n -122.00665168620951,\n 46.080226964619754\n ],\n [\n -122.00665168620951,\n 46.32565684366381\n ],\n [\n -122.38015899040656,\n 46.32565684366381\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Center Director, Science Analytics and Synthesis Program
U.S. Geological Survey
P.O. Box 25046, Mail Stop 302
Denver, CO 80225
This report describes the techniques and methods for computing the mean-channel velocity and discharge using the entropy-based probability concept (probability concept). The method is an alternative to or augments standard streamgaging methods adopted by the U.S. Geological Survey (USGS). Although sensor technology for measuring the mean velocity and discharge has advanced, standard streamgaging and computational methods have remained relatively unchanged since the USGS established its first streamgage at the Rio Grande at Embudo, New Mexico in 1889.
Standard streamgaging methods rely on integrating velocities and depths measured at multiple verticals at a channel cross section (standard cross section) to compute a discharge. The probability concept computes discharge at a single vertical (y-axis) using the ratio of the mean-channel velocity (mean velocity) and maximum velocity, the measured maximum velocity, and the area as a function of stage at the standard cross section. Proper siting and operation and maintenance are required. If siting is conducted appropriately, the probability concept parameters and the y-axis stationing will be similar for different streamflow conditions. The timing of operation and maintenance visits should be based on hydrologic and meteorologic occurrences and seasonality and should capture low, medium, high, and opportunistic streamflow conditions.
Advantages of the probability concept are the capacity to (1) compute discharge time series immediately after streamgage siting, (2) compute discharge for complex streamflow conditions that cannot be quantified by stage-discharge methods, (3) augment time-series data where gaps exist, and (4) integrate with surface velocity sensors such as Doppler velocity radars and cameras, which are not subject to damage caused by ice, debris, and flood flows. Potential sources of bias in discharge derived from the probability concept include (1) rain, (2) wind, and (3) geomorphologic and hydraulic instabilities. Recommendations to address these biases are provided.
This report guides users through the steps to parameterize the probability concept, process field data, and compute the mean velocity and discharge using the probability concept.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/tm3A26","usgsCitation":"Fulton, J.W., Engel, F.L., Eggleston, J.R., and Chiu, C.-L., 2025, Computing discharge using the entropy-based probability concept: U.S. Geological Survey Techniques and Methods book 3, chap. A26, 66 p., https://doi.org/10.3133/tm3A26.","productDescription":"Report: viii, 66 p.; Appendix","onlineOnly":"Y","ipdsId":"IP-138301","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":496208,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/03/a26/tm3a26.pdf","text":"Report","size":"6.77 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T and M 2-A26"},{"id":496210,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/03/a26/Appendix_3_Wind_Bias.csv","text":"Appendix 3","size":"8.0 KB","linkFileType":{"id":7,"text":"csv"},"description":"T and M 2-A26 Appendix 3","linkHelpText":"Correction for Wind Bias"},{"id":496207,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/03/a26/coverthb.jpg"}],"contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
Denver, CO 80225
This report describes the steps and the theory to compute the speed and flow of water in streams using the probability concept.
","publicationDate":"2025-09-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Fulton, John W, 0000-0002-5335-0720","orcid":"https://orcid.org/0000-0002-5335-0720","contributorId":213630,"corporation":false,"usgs":true,"family":"Fulton","given":"John","middleInitial":"W,","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":949518,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Engel, Frank L. 0000-0002-4253-2625","orcid":"https://orcid.org/0000-0002-4253-2625","contributorId":218208,"corporation":false,"usgs":true,"family":"Engel","given":"Frank","middleInitial":"L.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":949519,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eggleston, Jack R. 0000-0001-6633-3041","orcid":"https://orcid.org/0000-0001-6633-3041","contributorId":204628,"corporation":false,"usgs":true,"family":"Eggleston","given":"Jack R.","affiliations":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"preferred":true,"id":949520,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chiu, Chao-Lin","contributorId":361821,"corporation":false,"usgs":false,"family":"Chiu","given":"Chao-Lin","affiliations":[{"id":86362,"text":"Emeritus - University of Pittsburgh","active":true,"usgs":false}],"preferred":false,"id":949521,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70271715,"text":"sir20255092 - 2025 - Flood-Inundation Maps of the Current and Jacks Fork Rivers including the Ozark National Scenic Riverways, Southeast Missouri, 2023","interactions":[],"lastModifiedDate":"2026-02-03T15:34:29.756536","indexId":"sir20255092","displayToPublicDate":"2025-09-23T12:04:51","publicationYear":"2025","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":"2025-5092","displayTitle":"Flood-inundation maps of the Current and Jacks Fork Rivers including the Ozark National Scenic Riverways, southeast Missouri, 2023","title":"Flood-Inundation Maps of the Current and Jacks Fork Rivers including the Ozark National Scenic Riverways, Southeast Missouri, 2023","docAbstract":"Digital flood-inundation maps for a 131.8-mile reach of the Current River and a 44.6-mile reach of the Jacks Fork River, in southeast Missouri, were created by the U.S. Geological Survey (USGS) in cooperation with the Ozark Foothills Regional Planning Commission and the South Central Ozark Council of Governments. The maps also encompass the 134 miles of the Current and Jacks Fork Rivers within the Ozark National Scenic Riverways, which is the first national park area to protect a river system. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Program website at https://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (hereafter referred to as “stages”) at eight reference USGS streamgages—five on the Current River (USGS station numbers 07064440, 07064533, 07066510, 07067000, and 07068000) and three on the Jacks Fork River (USGS station numbers 07065200, 07065495, and 07066000). Near-real-time stages at these streamgages may be obtained from the USGS National Water Information System at https://doi.org/10.5066/F7P55KJN or the National Weather Service National Water Prediction Service at http://water.noaa.gov/, which also forecasts flood hydrographs at four of these sites (USGS station numbers 07067000, 07068000, 07065495, and 07066000).
Flood profiles were computed for seven of the eight map reaches by means of two-dimensional hydraulic models and the remaining reach by a one-dimensional hydraulic model. The models were calibrated by using stage-streamflow relations or streamflow measurements at the USGS streamgages and from high-flow stage measurements from water-level loggers distributed throughout the reaches.
The hydraulic models were used to compute water-surface profiles for flood stages at 1-foot intervals referenced to the streamgage datums. The profile stages ranged from the National Weather Service “action stage” or near bankfull, to a stage exceeding the highest recorded water level at each streamgage. The simulated water-surface profiles were then combined with a digital elevation model (derived from light detection and ranging data having a nonvegetated vertical accuracy of a maximum 10-centimeter root mean square error) to delineate the area flooded at each water level and the associated water depths.
The availability of these maps, along with information regarding current stage from the USGS streamgage and forecasted high-flow stages from the National Weather Service, will provide emergency management personnel, resource managers, and residents with information that is critical for flood-response activities such as evacuations and road closures, as well as for postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20255092","collaboration":"South Central Ozark Council of Governments, Ozark Foothills Regional Planning Commission","usgsCitation":"Heimann, D.C., High, J.L., Atkinson, A.A., and Rydlund, P.H., Jr., 2025, Flood-inundation maps of the Current and Jacks Fork Rivers including the Ozark National Scenic Riverways, southeast Missouri, 2023: U.S. Geological Survey Scientific Investigations Report 2025–5092, 29 p., https://doi.org/10.3133/sir20255092.","productDescription":"Report: viii, 29 p.; Data Release; Dataset","numberOfPages":"42","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-135288","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":497780,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118892.htm"},{"id":495817,"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":495816,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90H2UQY","text":"USGS data release","linkHelpText":"Hydraulic models and geospatial products associated with flood-inundation mapping of the Current and Jacks Fork Rivers including the Ozark National Scenic Riverways, Southeast Missouri, 2022–25"},{"id":495815,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5092/images"},{"id":495813,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255092/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5092 HTML"},{"id":495809,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5092/sir20255092.pdf","text":"Report","size":"17 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5092"},{"id":495808,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5092/coverthb.jpg"},{"id":495814,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5092/sir20255092.XML","description":"SIR 2025-5092 XML"}],"country":"United States","state":"Missouri","otherGeospatial":"Current River, Jacks Fork River, Ozark National Scenic Riverways","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -91.75,\n 37.5\n ],\n [\n -91.75,\n 36.5\n ],\n [\n -90.75,\n 36.5\n ],\n [\n -90.75,\n 37.5\n ],\n [\n -91.75,\n 37.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
The U.S. Geological Survey created flood-inundation maps that make up a 131.8-mile reach of the Current River, a 44.6-mile reach of the Jacks Fork River, including 134 miles of the Ozark National Scenic Riverways in southeast Missouri. The flood-inundation maps show estimates of the extent and depth of flooding corresponding to selected water levels at eight reference U.S. Geological Survey streamgages—five on the Current River (U.S. Geological Survey station numbers 07064440, 07064533, 07066510, 07067000, and 07068000) and three on the Jacks Fork River (U.S. Geological Survey station numbers 07065200, 07065495, and 07066000).
","publicationDate":"2025-09-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Heimann, David C. 0000-0003-0450-2545 dheimann@usgs.gov","orcid":"https://orcid.org/0000-0003-0450-2545","contributorId":3822,"corporation":false,"usgs":true,"family":"Heimann","given":"David","email":"dheimann@usgs.gov","middleInitial":"C.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":949167,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"High, Jason L. 0009-0009-1031-1439","orcid":"https://orcid.org/0009-0009-1031-1439","contributorId":361676,"corporation":false,"usgs":true,"family":"High","given":"Jason","middleInitial":"L.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":949168,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Atkinson, Allison A. 0009-0001-7572-0729 aatkinson@usgs.gov","orcid":"https://orcid.org/0009-0001-7572-0729","contributorId":330979,"corporation":false,"usgs":true,"family":"Atkinson","given":"Allison","email":"aatkinson@usgs.gov","middleInitial":"A.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":949169,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rydlund, Paul H. Jr. 0000-0001-9461-9944 prydlund@usgs.gov","orcid":"https://orcid.org/0000-0001-9461-9944","contributorId":3840,"corporation":false,"usgs":true,"family":"Rydlund","given":"Paul","suffix":"Jr.","email":"prydlund@usgs.gov","middleInitial":"H.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":949170,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70271679,"text":"sir20255063 - 2025 - Assessment of channel morphology, hydraulics, and bedload transport along the Siletz River, western Oregon","interactions":[],"lastModifiedDate":"2026-02-03T15:31:12.18485","indexId":"sir20255063","displayToPublicDate":"2025-09-22T13:02:49","publicationYear":"2025","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":"2025-5063","displayTitle":"Assessment of Channel Morphology, Hydraulics, and Bedload Transport along the Siletz River, Western Oregon","title":"Assessment of channel morphology, hydraulics, and bedload transport along the Siletz River, western Oregon","docAbstract":"Chinook salmon (Oncorhynchus tshawytscha) and Pacific lamprey (Entosphenus tridentatus) are native, anadromous fish species in the Siletz River Basin, western Oregon, that face many threats to their survival in freshwater and the ocean. The Confederated Tribes of Siletz Indians of Oregon seek to mitigate freshwater threats to Chinook salmon and Pacific lamprey, where possible, with habitat conservation and restoration efforts. This study was conducted to assist the Confederated Tribes of Siletz Indians of Oregon in documenting and understanding the hydrogeomorphic processes shaping present-day habitat conditions and assessing future habitat implications for Chinook salmon and Pacific lamprey along the main-stem Siletz River. As such, this study focused on understanding geomorphic processes and patterns of channel change, including lateral and vertical adjustments in channel position and changes in bed-material sediment (sands, gravels, and cobbles that mantle the channel bed), which collectively determine overall patterns of channel morphology and fluvial habitats. Objective One was to evaluate lateral changes in channel position, vertical changes in bed elevation, and longitudinal patterns in bed-material particle size along the Siletz River using detailed channel maps developed from aerial photographs collected from 1939 to 2016, long-term records of stage and discharge collected by the U.S. Geological Survey (USGS) near the City of Siletz, and sediment particle size data. Objective Two was to assess hydraulic conditions using one- and two-dimensional hydraulic models and transport capacity of bed-material sediment using bedload transport models and sediment particle size data for a range of discharge conditions. Objective Three was to identify potential burrowing habitat for lamprey larvae (PBH) along the Siletz River network and provide insights in local factors influencing PBH along the main-stem Siletz River. The overall findings are synthesized to describe habitat implications for Chinook salmon and Pacific lamprey under present-day and future conditions.
Results of Objective One, an evaluation of changes in channel position and bed elevations and longitudinal patterns in bed-material particle size along the Siletz River, include the following
Results of Objective Two, an evaluation of hydraulic and bedload transport conditions along the Siletz River, include the following
Results of Objective Three, an analysis of PBH for lamprey larvae, include the following
Together, these results suggest that most of the Siletz River between Wildcat Creek and the City of Siletz has had only modest vertical and lateral change between the 1930s and 2010s because of the bedrock in and along the main channel and the river’s relatively high transport capacity relative to bed-material sediment supply. However, localized sections of the Siletz River where the active channel widens, particularly at channel bends, exhibited some change in channel planform and the locations and area of gravel bars. In the future, moderate increases in autumn-winter discharge may not result in substantial changes in coarse gravel bars along the Siletz River but may result in selective transport of finer bed-material sediment (gravel, sands, and silts) that provide spawning habitats for Chinook salmon and Pacific lamprey and burrowing habitats for lamprey larvae. Assuming no substantial changes in bed-material sediment supply, increased bedload transport capacity may cause frequent entrainment of lamprey larvae that are burrowed in coarse sand deposits, suspension and downstream transport of salmon eggs incubating in gravels, and reductions in the areas of spawning gravels for Chinook salmon and Pacific lamprey. Exact implications of current and future discharge conditions for these species along the Siletz River depends on many factors, including sediment supply, local hydraulics, and the timing of flood events relative to fish life stages.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255063","collaboration":"Prepared in cooperation with the Confederated Tribes of Siletz Indians of Oregon","usgsCitation":"Jones, K.L., Keith, M.K., Harden, T.M., White, J.S., van de Wetering, S., and Dunham, J.B., 2025, Assessment of\nchannel morphology, hydraulics, and bedload transport along the Siletz River, western Oregon: U.S. Geological Survey\nScientific Investigations Report 2025–5063, 95 p., https://doi.org/10.3133/sir20255063.","productDescription":"Report: xii, 95 p.; 5 Data Releases","onlineOnly":"Y","ipdsId":"IP-127308","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":496021,"rank":11,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118884.htm","linkFileType":{"id":5,"text":"html"}},{"id":495760,"rank":10,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5063/sir20255063.XML"},{"id":495758,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1489NN8","text":"USGS data release","description":"USGS data release","linkHelpText":"One- and two-dimensional hydraulic models for the Siletz River, Oregon"},{"id":495757,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1N35MQN","text":"USGS data release","description":"USGS data release","linkHelpText":"Water surface elevation data from the Siletz River, 2017–18"},{"id":495751,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5063/coverthb.jpg"},{"id":495752,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5063/sir20255063.pdf","text":"Report","size":"28.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5063"},{"id":495753,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255063/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5063"},{"id":495754,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AWWRA0","text":"USGS data release","description":"USGS data release","linkHelpText":"Active channel mapping for the Siletz River, Oregon, 1939 to 2016"},{"id":495755,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96ZXPP","text":"USGS data release","description":"USGS data release","linkHelpText":"Surficial and subsurface grain-size data for the Siletz River, Oregon, 2017–18"},{"id":495756,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TIADK3","text":"USGS data release","description":"USGS data release","linkHelpText":"Modeled bedload transport capacity for the Siletz River, Oregon"},{"id":495759,"rank":9,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5063/images"}],"country":"United States","state":"Oregon","otherGeospatial":"Siletz River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -124.1,\n 45\n ],\n [\n -124.1,\n 44.333\n ],\n [\n -123.5,\n 44.333\n ],\n [\n -123.5,\n 45\n ],\n [\n -124.1,\n 45\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
601 SW 2nd Avenue, Suite 1950
Portland, OR 97204
The Zuni Salt Lake is located in a maar in west-central New Mexico and contains hypersaline water that has long been used by Native Americans for religious purposes and the collection of salt. There have been several investigations suggesting different sources for the water and salt to the lake. Springs, seeps, and ephemeral streamflow have all been observed to contribute freshwater to the lake, and brackish to hypersaline seeps have been documented along the banks of the lake. This report summarizes the findings of a study that characterizes the lake’s hydrology, its water and salinity sources, and the hydrogeologic conceptual model. Regional groundwater levels indicate that each of the aquifers in the area have the potential to discharge groundwater to the lake. There is also evidence of vertical groundwater flow pathways at the maar that were likely created by the igneous intrusion that fractured the intersecting aquifers. A detailed water budget was constructed from continuous lake stage, precipitation, and evaporation data to estimate the groundwater inflow to the Zuni Salt Lake. It was determined that groundwater inflow to the lake is 441 ±94 acre-feet per year, which composes as much as 77 percent of the total inflows. The high sodium and chloride concentrations measured in two hypersaline samples collected near the lake indicate that the majority of the dissolved solids entering the lake are from a hypersaline groundwater source. The geochemical and isotopic compositions measured in the lake and surrounding features support the interpretation that hypersaline groundwater is the primary source of salts to the lake, which is likely sourced from the older (and deeper) Permian units. The hypersaline groundwater samples collected during this investigation have a unique aqueous chemistry relative to each of the mapped aquifers, and variability in groundwater compositions is interpreted to result from differences in minerology and residence time.
Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
Contact Us- USGS Publications Warehouse
","tableOfContents":"Sedimentation affects many of the aging reservoirs in the United States. Dams and water diversions from rivers have been central elements of infrastructure supporting agricultural irrigation in the arid and semiarid regions of the Western United States for more than a century. The Willwood Irrigation District diversion dam (hereafter referred to as “Willwood Dam”) in Park County, Wyoming, is approximately 12 miles northeast of Cody, Wyo.; has a structural height of 70 feet; and impounds the Shoshone River for diversion into the Willwood Canal. Willwood Dam is part of a larger irrigation scheme supported by water storage in the much larger Buffalo Bill Dam, which is approximately 20 miles upstream. In October 2016, renovation construction activities at Willwood Dam and the Willwood Canal caused an unplanned evacuation of nearly 96,000 cubic yards of fine sediment.
The fine sediment release in 2016 raised concerns that ongoing sediment management at Willwood Dam could impose limits on the long-term health of the aquatic ecosystem and fish populations. The U.S. Geological Survey, in cooperation with Wyoming Department of Environmental Quality and Willwood Work Groups 2 and 3, initiated an investigation of the dynamics of sediment transport in the Shoshone River and selected tributaries between Buffalo Bill Dam and Willwood Dam. The goal of the study was to quantify sediment transport into and out of Willwood Dam on an annual, seasonal, and event basis to better understand the relative quantities of sediment coming from natural sources and human activities on the landscape. The study ran from March 2019 through October 2021 and used observations of streamflow, turbidity, and acoustic backscatter collected at streamgages upstream and downstream from Willwood Dam to quantify suspended-sediment loads into and out of the dam during irrigation and fallow seasons, precipitation-runoff events, and deliberate sediment releases. Each tributary’s relative contribution to the sediment load upstream from Willwood Dam was examined using discrete measurements of suspended-sediment concentration and bedload during irrigation and fallow seasons, precipitation events, and stable conditions.
Analysis of daily precipitation and temperature data indicated that conditions in the study area during the 2019 agricultural year were wetter and colder than period of record normal, and drier and near normal temperatures for the 2020 and 2021 agricultural years. Not all sediment load records between 2019 and 2021 are complete because of rejected observations (outliers), instrument failures or fouling, and instrument removal for calibrations.
Statistical modeling of suspended-sediment concentration using paired values of turbidity and acoustic backscatter produced four models that, after refinement, had coefficients of determination indicating that more than 84 percent of the variance was explained by either turbidity or acoustic backscatter. A system of rules was developed to select the model predictions based on the seasonal operations of Willwood Dam, assumptions about the grain sizes mobilized during these operations, and assumed accuracy of the models at the downstream streamgage (Shoshone River below Willwood Dam, near Ralston, Wyo. [streamgage 06284010]) under different operational conditions. The sediment budget between upstream and downstream estimates of loads was interpreted using the mean predicted values bound by their respective model prediction intervals. When mean predicted loads of one streamgage were contained in the prediction intervals of the other streamgage, and vice-versa, difference in the sediment budget were interpreted as “indeterminate.”
Modeled sediment load balances demonstrated the depositional and erosional behaviors expected from the conceptual model of dam operations whereby sediment tends to accumulate during irrigation seasons when the dam is spilling over the top, and sediment tends to evacuate during the fallow seasons when it is flowing through the sluice gates at the base of the dam. The sediment load calculations using the rules-based model criteria indicated that between 14,200 and 380,000 tons of suspended sediment moved through the Shoshone River around Willwood Dam during the irrigation seasons of 2019, 2020, and 2021; 380,000 tons of suspended sediment were transported during the cool, wet year of 2019, and 14,200 tons of suspended sediment were transported in 2020, which was relatively dry. During fallow seasons 2019, 2020, and 2021, which had fewer complete records, between 1,140 and 106,000 tons of suspended sediment was estimated to have moved through the river.
For all seasons except fallow season 2022, the models estimated that more sediment was released from the dam than entered the dam, but the modeled mean loads at each streamgage were nearly always within the prediction intervals of each other, making the sediment balance indeterminant. Examination of suspended-sediment loads during irrigation seasons indicated that between 65 and 85 percent of fine sediment was transported during annual high flows and storm events, with the remainder transported during steady, lower streamflows. Examination of suspended loads during fallow seasons indicated that deliberate sediment releases through Willwood Dam accounted for between 39 and 67 percent of the total sediment moved during the fallow seasons. Deliberate sediment releases from Willwood Dam had estimated net exports of between 1,360 and 22,400 tons.
Between August 2017 and July 2023, suspended-sediment concentration and bedload sediment samples were collected from 9 tributaries to the Shoshone River during 137 sampling events, including stable and precipitation-runoff conditions. During irrigation season precipitation events, the mean total sediment yields ranged from 0.33 to 9.51 tons per day per square mile; during fallow season precipitation events, the mean total yields ranged from 0.04 to 0.95 ton per day per square mile. The mean total sediment yield per unit area across all samples at each tributary site ranged from 0.26 to 3.08 tons per day per square mile. Bedload was a minor fraction of the total load, constituting a mean of 4 percent across all samples; 3 and 6 percent for events and nonevents, respectively, during irrigation season; and 3 and 1 percent for events and nonevents, respectively, during the fallow season. With the exception of one tributary, Dry Creek, these mean yield values were within the range of watershed-scale background sediment yield values estimated from reservoir surveys and previous suspended-sediment studies.
Imagery from irrigation seasons 2012, 2015, 2017, 2019, and 2022 was used to determine the planimetric backwater extent of the pool area in the Shoshone River behind Willwood Dam to identify any changes in sediment storage. Active river channel widths in the Shoshone River upstream from Willwood Dam were all similar between years except 2015, which was determined to be statistically different from all other years. Bathymetric data taken in the pool behind Willwood Dam during three different surveys between November 2017 and April 2022 indicated no statistically significant differences in bed elevations between the years. Results from the planimetric and bathymetric survey data provide multiple lines of evidence indicating that sediment did not accumulate behind the dam within the error of the methods used.
Examination of how precipitation affects sediment transport in the Shoshone River upstream from Willwood Dam indicated that accumulated rainfall from the natural runoff events captured during the study period varied from a trace to as much as 4.26 inches, with associated predicted suspended-sediment loads varying from 112 to 232,000 tons of suspended sediment. The behavior of the sediment loads relative to accumulated precipitation did not appear to change depending on irrigation or fallow season. A model of suspended-sediment concentrations relative to the 2-day accumulated precipitation indicated that suspended-sediment concentrations in the Shoshone River upstream from Willwood Dam increased exponentially for accumulations of 0.3 inch or more; such storms accounted for 10 percent or less of precipitation events observed during the 1981 to 2018 period of record.
The gaps in records, precision of the instrumentation, and large variation in grain sizes in suspended-sediment mixtures downstream from the dam made closing the sediment budgets for most seasons unattainable. The biggest recent change in sediment storage measured using the planimetric area of deposits behind Willwood Dam took place between 2015 and 2017. The main event between these two measurements was the installation of new Willwood Canal gates in October 2016, which resulted in the large unplanned sediment release. Because the sediment budgets were nearly always indeterminate and the planimetric and bathymetric data indicated little change in the bed and bank material, it is likely that the change in sediment storage behind the dam during the study period was small relative to the precision of the statistical models and other uncertainties.
This body of evidence suggests that, averaged during the 3-year study period, no major changes in storage took place, and that the current operations may be keeping storage at near-equilibrium. This condition could have been initiated because the middle sluice gate has now been operational since 2014, and the sediment release in October 2016 evacuated a large amount of legacy sediment from storage. Although the uncertainties are large, sluicing events allow for controlled releases of sediment that contributed to the near equilibrium conditions observed over an annual basis during this study.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255077","collaboration":"Prepared in cooperation with the Wyoming Department of Environmental Quality","usgsCitation":"Alexander, J.S., Brown, H., Eddy-Miller, C.A., Burckhardt, J., Burckhardt, L., Ellison, C., McIntyre, C., Moger, T., Patterson, L., Tavelli, C., Waterstreet, D., and Williams, M., 2025, Fluvial sediment dynamics in the Shoshone River and tributaries around Willwood Dam, Park County, Wyoming: U.S. Geological Survey Scientific Investigations Report 2025–5077, 70 p., https://doi.org/10.3133/sir20255077.","productDescription":"Report: x, 70 p.; Data Release; Dataset","numberOfPages":"84","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-164415","costCenters":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"links":[{"id":494651,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255077/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5077"},{"id":494674,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":494673,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13VHDRG","text":"USGS data release","linkHelpText":"Shapefiles of digitized backwater extent behind Willwood Dam on the Shoshone River, near Cody, Wyoming, derived from 2012, 2015, 2017, 2019, and 2022 National Agriculture Imagery Program imagery"},{"id":494652,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5077/images"},{"id":494654,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5077/sir20255077.XML","linkFileType":{"id":8,"text":"xml"}},{"id":494650,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5077/sir20255077.pdf","text":"Report","size":"9.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5077"},{"id":494649,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5077/coverthb.jpg"}],"country":"United States","state":"Wyoming","county":"Park County","otherGeospatial":"Shoshone River and tributaries around Willwood Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108.66170194279013,\n 44.80967182289373\n ],\n [\n -109.31267227648169,\n 44.80967182289373\n ],\n [\n -109.31267227648169,\n 44.39309612019585\n ],\n [\n -108.66170194279013,\n 44.39309612019585\n ],\n [\n -108.66170194279013,\n 44.80967182289373\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
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The former Stuart Ranch, now managed by the Bureau of Land Management, is transected by Meadow Valley Wash, where 4,600 feet of perennial stream and adjacent riparian vegetation provide critical habitat for several wildlife and aquatic species protected under the Endangered Species Act. The stream has been altered by prior construction of irrigation diversions, gravel mining, and removal of riparian vegetation, resulting in the loss of instream and riparian vegetation and disconnected floodplains. The stream alteration has also resulted in the loss of native species and increased non-native invasive species and changes in ecological cycles. With the goal of improving habitat extent and quality for native threatened and endangered species, the Bureau of Land Management (BLM) is considering establishing perennial streams through braided side channels by constructing beaver dam analogs, excavating side channel connectors, and grading an irrigation reservoir berm on the floodplain. The U.S. Geological Survey (USGS) provided hydraulic modeling to assist the BLM in evaluating how possible restoration modifications could affect the extent of aquatic, riparian, and other habitat types. Three two-dimensional (2-D) hydraulic models were developed to simulate 2021 conditions (when most of the topographic data were collected), minor restoration modifications (one excavated side channel and a beaver dam analog), and major restoration modifications (three excavated side channels, a beaver dam analog, and an excavated and graded area to remove the irrigation reservoir) to determine streamflow-inundation extents and hydraulic characteristics (depth and velocity) for base flow and various flood (50-, 20-, 10-, 4-, 2-, and 1-percent annual exceedance probability [AEP]) scenarios. An average summer base flow of 0.92 cubic feet per second was estimated based on data from a USGS streamgage in the study area. The 50-, 20-, 10-, 4-, 2-, and 1-percent AEP streamflows were estimated based on a flood-frequency analysis of data from the streamgage. The base flow and AEP floods were combined with surveyed topographic data to create a 2-D unsteady hydraulic model. The hydraulic model was used to simulate the base flow and flood-inundation extents and hydraulic characteristics under 2021 conditions and with two possible restoration modification scenarios. Under 2021 conditions, flow remains in a single channel until the most downstream end of the modeled reach, where flow then expands into slower velocity pools. During floods, streamflow begins to enter the side channels at the 50-percent flood, expands into the east floodplain at 20-percent flood, and flows in the irrigation reservoir at 4-percent flood. Compared to 2021 conditions with no terrain modification, base flow under the possible restoration modifications enters and remains in the side channels, thus increasing the likelihood of expanding riparian habitat. Additionally, during floods under the major restoration modifications, streamflow expands into the modified terrain surrounding the irrigation reservoir at 10-percent AEP, as opposed to 4-percent AEP under 2021 conditions. For all modeled streamflow scenarios, streamflow is deepest in the center of the main and side channels, as well as the downstream pooled areas. Streamflow is fastest in the narrow sections of the channels, especially in the upper 1,220 feet of the modeled reach.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255069","collaboration":"Prepared in cooperation with Bureau of Land Management","programNote":"Water Resources Mission Area","usgsCitation":"Dye, L.A., Morris, C.M., and Childres, H.K., 2025, Streamflow extents and hydraulic characteristics of Meadow Valley Wash at Stuart Ranch, near Rox, Nevada: U.S. Geological Survey Scientific Investigations Report 2025–5069, 24 p., https://doi.org/10.3133/sir20255069.","productDescription":"Report: vi, 24 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-124818","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":494320,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5069/images"},{"id":494319,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96HQ6F7","text":"USGS data release","description":"USGS data release","linkHelpText":"Geospatial data, flood-frequency analysis, and surface-water model archive for streamflow extents and hydraulic characteristics of Meadow Valley Wash at Stuart Ranch, near Rox, Nevada"},{"id":494317,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5069/sir20255069.pdf","text":"Report","size":"11.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5069"},{"id":494316,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5069/coverthb.jpg"},{"id":494318,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255069/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5069"},{"id":494321,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5069/sir20255069.XML"}],"country":"United States","state":"Nevada","city":"Rox","otherGeospatial":"Meadow Valley Wash at Stuart Ranch","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.6611,\n 36.84\n ],\n [\n -114.6611,\n 36.8278\n ],\n [\n -114.65,\n 36.8278\n ],\n [\n -114.65,\n 36.84\n ],\n [\n -114.6611,\n 36.84\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Nevada Water Science Center
U.S. Geological Survey
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Aeromagnetic and gravity surveys of the Skaergaard intrusion in East Greenland were carried out in July–August 1971 as part of a grant to the University of Oregon Center for Volcanology to refine the models of crystallization and differentiation of the intrusion, specifically to test whether the intrusion is underlain by dense rocks of a reservoir 20 kilometers (km) thick (referred to as a “hidden zone”). The Skaergaard intrusion is a source of platinum group elements that are critical mineral resources for many technologies, and because no new data have been collected these legacy datasets remain a valuable asset. The total-intensity aeromagnetic survey was flown in early July 1971 with a proton precession magnetometer at a constant barometric altitude of 1.5 km (5,000 feet) with a nominal line spacing of 1 km. Two gravimeters were used to acquire 168 stations of which 86 were at known altitudes (mainly sea level) and 82 had altitudes measured by altimetry in late July–August 1971. Finally, a north-south ground vertical-intensity magnetic traverse was completed across the intrusion together with collection of oriented hand specimens. The hand specimens were measured for remnant magnetization and density, along with density measurements of more specimens collected by expedition geologists for other purposes.
The intrusion is composed of layered gabbro with extensive crystal fractionation that is dense and strongly reversely polarized. After terrain correction and standard Bouguer gravity reduction, the gravity anomaly dataset was corrected for all rock above sea level using the density measurements of the various zones of the intrusion and the topographic and geologic maps (variable density Bouguer gravity reduction).
A large regional gradient in the gravity anomaly data was removed using orthogonal polynomial fitting to the gridded data. The zonal volumes of rock below sea level were calculated from the dipping polygonal layer gravity model of the intrusion below sea level and combined with elliptic cross–section cylinders for the various zones above sea level to approximate the original zonal volumes of the intrusion. The residual gravity anomaly of 18–20 milligals (mGal) was only about half of the expected anomaly if a large hidden zone proposed from petrologic considerations were present, and both two-dimensional and three-dimensional models imply that the exposed series of intrusion zones explain the gravity anomaly by their down-dip extension below sea level together with a small hidden-zone volume. A three-dimensional model of the exposed rocks and their down-dip extension below sea level also can account for the aeromagnetic anomaly with little or no requirement for hidden-zone rock. The middle and upper zone units of the intrusion contain the most magnetite and account for most of the aeromagnetic anomaly.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251030","programNote":"Mineral Resources Program","usgsCitation":"Gettings, M.E., 2025, Gravity and magnetic surveys of the Skaergaard intrusion, East Greenland: U.S. Geological Survey Open-File Report 2025–1030, 43 p., https://doi.org/10.3133/ofr20251030.","productDescription":"Report: ix, 43 p.; Data Release","numberOfPages":"43","onlineOnly":"Y","ipdsId":"IP-126792","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":494352,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91OVG7G","text":"USGS data release","description":"Gettings, M.E., and Parks, H.L., 2025, Aeromagnetic and gravity surveys of the Skaergaard intrusion in East Greenland, 1971: U.S. Geological Survey data release, https://doi.org/10.5066/P91OVG7G.","linkHelpText":"Aeromagnetic and gravity surveys of the Skaergaard intrusion in East Greenland, 1971"},{"id":494347,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1030/coverthb.jpg"},{"id":494348,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1030/ofr20251030.pdf","text":"Report","size":"6.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1030 PDF"},{"id":494349,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251030/full","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1030 HTML"},{"id":494350,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1030/ofr20251030.XML","description":"OFR 2025-1030 XML"},{"id":494351,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1030/images"}],"country":"Greenland","otherGeospatial":"Skaergaard intrusion","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -32.1667,\n 68.3\n ],\n [\n -32.1677,\n 68\n ],\n [\n -31.1667,\n 68\n ],\n [\n -31.1667,\n 68.3\n ],\n [\n -32.1667,\n 68.3\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Geology, Minerals, Energy, and Geophysics Science Center
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Freshwater streams and rivers are recognized as vital habitats within the Chesapeake Bay watershed, which has been undergoing extensive restoration efforts for more than 30 years. Resource managers need to understand stream and river condition and how these conditions are changing over time to determine whether regional long-term restoration and conservation goals are being met. The objective of this report was to document the spatial and temporal variability of conditions for seven indicators of river and stream health across the nontidal Chesapeake Bay watershed. The framework for the U.S. Geological Survey’s Nontidal Network (NTN), a network of more than 100 nutrient and suspended sediment monitoring locations, was extended to assess conditions for six additional indicators of stream health: temperature, salinity, toxic contaminants, streamflow, hydromorphology, and biological aquatic communities. For each indicator, the latest available data from multiple sources were compiled and harmonized, and key metrics were identified to describe indicator conditions across space and time. A status condition was defined for each indicator to describe overall spatial variability in recent condition, and trend analyses were used to describe changes in each indicator metric over time. The analysis revealed clear differences in spatial and temporal data coverage across the seven indicators, so individual indicator trend analyses were not constrained to a common time interval. However, a status snapshot was conducted across all indicators for the 2015–17 period to simultaneously explore spatial variability across all indicators. The status snapshot highlighted general degraded conditions across multiple indicators in large metropolitan regions, such as the Baltimore–Washington, D.C., metropolitan area. Regression analysis between indicator status metrics and major land cover for the sites suggest urbanization as a potential driver of degraded conditions for many of the indicator metrics, including total phosphorus, salinity, temperature, high-flow frequency, and metrics of habitat and biological assemblage quality. A final analysis exploring the spatial representation of each indicator network showed that some indicator monitoring networks did not cover certain settings, such as small watersheds. These results provided an initial assessment of stream health status and trends and will continue to be leveraged to describe conditions across the Chesapeake Bay watershed to help inform local and regional management decisions. These results also highlighted the need for improved coordination among monitoring organizations to support long-term multi-indicator monitoring and assessment.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255072","usgsCitation":"Boyle, L.J., Austin, S.H., Cashman, M.J., Clifton, Z.J., Clune, J.W., Colgin, J.E., Elliott, K.E.M., Fanelli, R.M., Foss, E.P., Hitt, N.P., Hittle, E.A., Howe, C.M., Majcher, E.H., Maloney, K.O., Mason, C.A., Metes, M.J., Moyer, D.L., Needham, T.P., Rogers, K.M., Thompson, J.J., Yang, G., and Zimmerman, T.M., 2025, Tracking status and trends in seven key indicators of river and stream condition in the Chesapeake Bay watershed: U.S. Geological Survey Scientific Investigations Report 2025–5072, 104 p., https://doi.org/10.3133/sir20255072.","productDescription":"Report: x, 104 p.; Data Release","numberOfPages":"104","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-171508","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":494176,"rank":5,"type":{"id":34,"text":"Image 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U.S. Geological Survey
1730 East Parham Road
Richmond, Virginia 23228
In 2021, the U.S. Geological Survey (USGS) published Circular 1490 titled, “Integrated Science for the Study of Perfluoroalkyl and Polyfluoroalkyl Substances (PFAS) in the Environment: A Strategic Science Vision for the U.S. Geological Survey” (Tokranov and others, 2021). Circular 1490 was created to be a resource for USGS scientists prioritizing and planning research related to per- and polyfluoroalkyl substances (PFAS) and to be a guide for developing partnerships with other scientists, State and Federal agencies, and stakeholders engaged in PFAS research and management and mitigation of the environmental and human-health effects of PFAS. This USGS PFAS Strategic Science Vision document was intended to be the foundation for a “living strategic vision,” periodically providing updates on the state of USGS PFAS research, emerging PFAS data gaps and needs, and progress on interagency and stakeholder PFAS partnerships and priorities. To meet this objective, the USGS planned to host an Interagency and Stakeholder PFAS Workshop every 2–3 years.
During September 10–12, 2024, the USGS hosted the first Interagency and Stakeholder PFAS Workshop in Reston, Virginia. The Workshop brought together experts from other Federal agencies (U.S. Environmental Protection Agency, National Institute of Environmental Health Sciences, Food and Drug Administration, Department of Defense [Air Force, Army]), State agencies (Washington Fish and Wildlife, Virginia Department of Transportation), and academia (Harvard University, University of Maryland) to address key challenges relating to the measurement and modeling of PFAS and the implications for environmental health. Participants engaged in in-depth discussions centered around six pivotal topics related to PFAS: (1) sampling protocols, methods and interpretation; (2) environmental sources, source apportionment, and occurrence; (3) environmental fate and transport; (4) human and wildlife exposure routes and risk; (5) bioconcentration, bioaccumulation, and biomagnification; and (6) ecotoxicology and effects. Each topic had three breakout sessions.
A recurrent theme of workshop discussions was how data on a nationwide scale for PFAS occurrence in various environmental matrices, including air, water, food crops, biota, soil, and streambed sediment could help to advance scientific understanding. Participants noted significant geospatial data gaps, particularly in the midwestern and southern United States and the Pacific Northwest. PFAS data collection tends to be more robust along the eastern seaboard and in California.
Participants stressed how enhancing the integration of large and small datasets across various agencies could help to support national scale understanding of PFAS. To address these gaps, attendees suggested leveraging datasets from Federal entities like the USGS and the U.S. Department of Defense, State agencies, and municipal utility services to develop predictive contaminant detection and transport models. Improved coordination between water quality programs and USGS research could help to facilitate access to valuable data, leading to comprehensive databases that inform PFAS point (wastewater treatment plants and landfills) and nonpoint (runoff from land, atmospheric deposition, food packaging) sources, environmental transport mechanisms, environmental detection and concentrations, potential exposure routes, and health effects on different biota, including humans. A specific request was made to develop a map demarking the depth of modern (1953 or later) groundwater, which is susceptible to surface-derived anthropogenic (that is, human-made) contamination, based on tritium-age dating. Emphasis was placed on incorporation of hydrology, groundwater flow paths, groundwater–surface water interactions, and landscape factors in predictive statistical models as a step to improve contaminant source identification and tracking.
Molecular fingerprinting approaches garnered attention as techniques to link specific PFAS mixtures detected in a sample to environmental sources and levels in biota (Dávila-Santiago and others, 2022). Integrating data from abiotic (that is, water, soil, and air) and biotic (that is, living organisms) systems identified as a research opportunity. For example, understanding the composition of soils and sediments, which include a mixture of mineral, plant, and animal components, could advance understanding of exposure pathways.
The discussions highlighted opportunities to explore and understand the potential redistribution and biotic exposures of PFAS from biosolid and wastewater treatment plant effluent land application practices, in addition to atmospheric releases and discharges from landfill and wastewater treatment plants. Participants identified research gaps surrounding how these sources may contribute to contamination and may affect surrounding ecosystems, including a better definition of anthropogenic background concentrations.
Moving forward, the collection of co-occurrence data was noted as a means to improve understanding of complex mixtures and to leverage companion modeling efforts focused on areas with high and low contamination levels to identify areas of concern and unaffected resources. Participants emphasized how centralized USGS databases and the establishment of sample-metadata archives can help to ensure that samples are preserved and accessible for future research.
In conclusion, the workshop participants identified opportunities to bridge data gaps and improve measurement techniques, modeling frameworks, databases, and communication, to enhance the understanding of PFAS and their effects on environmental and human health. Upon completion of the workshop, participants indicated an interest in developing strategic data collection, modeling, and analytical approaches to address these challenges.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251044","programNote":"Environmental Health Program","usgsCitation":"Iwanowicz, D.D., Beisner, K.R., Bradley, P.M., Bright, P.R., Brown, J.B., Churchill, C.J., Gordon, S.E., Karouna, N.K., Kolpin, D.W., Lambert, R.B., Pulster, E.L., Shively, R.S., Smalling, K., Steevens, J.A., and Tokranov, A.K., 2025, Insights and strategic opportunities from the USGS 2024 Per- and Polyfluoroalkyl Substances (PFAS) Interagency Workshop—Addendum I of Circular 1490: U.S. Geological Survey Open-File Report 2025–1044, 10 p., https://doi.org/10.3133/ofr20251044.","productDescription":"iii, 10 p.","numberOfPages":"10","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-177608","costCenters":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"links":[{"id":493438,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1044/coverthb.jpg"},{"id":493439,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1044/ofr20251044.pdf","text":"Report","size":"2.64 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1044 PDF"},{"id":493440,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251044/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1044 HTML"},{"id":493442,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1044/images/"},{"id":493441,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1044/ofr20251044.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2025-1044 XML"}],"contact":"Director, Ecosystems Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
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Models to estimate low-streamflow statistics at ungaged locations in New York, excluding Long Island and including hydrologically connected basins from bordering States, were developed for the first time by the U.S. Geological Survey, in cooperation with the New York State Department of Environmental Conservation. A total of 224 basin characteristics were developed for 213 unaltered streamgages (locations where the human effects on streamflow were limited), across the following categories: basin geometry, climate, land cover, soils, surficial geology, and other characteristics. The basins with unaltered streamgages were evaluated for potential redundancy, and streamgages in close proximity and with similar drainage areas were flagged and removed from the testing and cross-validation datasets to prevent data leaking from the training dataset to the testing dataset.
Random forest regression models were created by using basin characteristics as predictor variables and by developing a workflow to train, tune, and test the model. Models were developed to estimate the ungaged lowest annual 7-day and 30-day average streamflow that occurs (on average) once every 10 years (7Q10 and 30Q10). The top four basin characteristics used for the 7Q10 and 30Q10 models were drainage area, total stream length, perimeter of the basin, and length of the longest flow path. Results for the 7Q10 and 30Q10 models had coefficients of determination (R2) of 0.796 and 0.853, respectively. The output model results were bias-corrected for ungaged locations across New York and are available within the interactive StreamStats tool.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255060","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Stagnitta, T.J., Woda, J.C., and Graziano, A.P., 2025, Random forest regression models for estimating low-streamflow statistics at ungaged locations in New York, excluding Long Island: U.S. Geological Survey Scientific Investigations Report 2025–5060, 23 p., https://doi.org/10.3133/sir20255060.","productDescription":"Report: v, 23 p.; 2 Data Releases","numberOfPages":"23","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-167540","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":492989,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://streamstats.usgs.gov/ss/","text":"StreamStats"},{"id":492988,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20245055","text":"Scientific Investigations Report 2024–5055","linkHelpText":"- Low-Flow Statistics for Selected Streams in New York, Excluding Long Island"},{"id":492987,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P146MTRS","text":"USGS data release","linkHelpText":"Random forest regression model archive for estimating low-streamflow statistics at ungaged locations in New York, excluding Long Island"},{"id":492986,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NOM6FR","text":"USGS data release","linkHelpText":"Low-flow statistics for New York State, excluding Long Island, computed through March 2022"},{"id":492985,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5060/images/"},{"id":492984,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5060/sir20255060.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2025-5060 XML"},{"id":492983,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255060/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5060 HTML"},{"id":492982,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5060/sir20255060.pdf","text":"Report","size":"8.99 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5060 PDF"},{"id":492981,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5060/coverthb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"New York excluding Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.8668360740237,\n 40.82174116460561\n ],\n [\n -73.64101370770591,\n 40.97341618058218\n ],\n [\n -73.641675229166,\n 41.369110620239354\n ],\n [\n -73.44442715633755,\n 41.42737341824312\n ],\n [\n -73.23400688811246,\n 42.735562128301694\n ],\n [\n -73.28689303884832,\n 45.063167069767246\n ],\n [\n -74.92263898531354,\n 45.049241271408505\n ],\n [\n -76.59790269295956,\n 44.152600935032865\n ],\n [\n -76.27472597825448,\n 43.63979148650773\n ],\n [\n -76.9594903941915,\n 43.33322253590592\n ],\n [\n -78.50182350844328,\n 43.459456447836146\n ],\n [\n -79.39789785747713,\n 43.19859290777666\n ],\n [\n -78.90328998597391,\n 42.86522759603616\n ],\n [\n -79.82520290314585,\n 42.24436339597355\n ],\n [\n -79.76467975802953,\n 41.964093469327736\n ],\n [\n -75.37056382699097,\n 42.00382656329654\n ],\n [\n -74.98586389700988,\n 41.54435731278656\n ],\n [\n -73.999903714982,\n 41.001270702844295\n ],\n [\n -73.96336292917533,\n 40.82357491793678\n ],\n [\n -73.8668360740237,\n 40.82174116460561\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
We review how ‘abrupt thaw’ has been used in published studies, compare these definitions to abrupt processes in other Earth science disciplines, and provide a definitive framework for how abrupt thaw should be used in the context of permafrost science.
We address several aspects of permafrost systems necessary for abrupt thaw to occur and propose a framework for classifying permafrost processes as abrupt thaw in the future. Based on a literature review and our collective expertise, we propose that abrupt thaw refers to thaw processes that lead to a substantial persistent environmental change within a few decades. Abrupt thaw typically occurs in ice-rich permafrost but may be initiated in ice-poor permafrost by external factors such as hydrologic change (i.e., increased streamflow, soil moisture fluctuations, altered groundwater recharge) or wildfire.
Permafrost thaw alters greenhouse gas emissions, soil and vegetation properties, and hydrologic flow, threatening infrastructure and the cultures and livelihoods of northern communities. The term ‘abrupt thaw’ has emerged in scientific discourse over the past two decades to differentiate processes that rapidly impact large depths of permafrost, such as thermokarst, from more gradual, top-down thaw processes that impact centimeters of near-surface permafrost over years to decades. However, there has been no formal definition for abrupt thaw and its use in the scientific literature has varied considerably. Our standardized definition of abrupt thaw offers a path forward to better understand drivers and patterns of abrupt thaw and its consequences for global greenhouse gas budgets, impacts to infrastructure and land-use, and Arctic policy- and decision-making.
","language":"English","publisher":"Springer","doi":"10.1007/s40641-025-00204-3","usgsCitation":"Webb, H., Fuchs, M., Abbott, B.W., Douglas, T.A., Elder, C.D., Ernakovich, J.G., Euskirchen, E., Göckede, M., Grosse, G., Hugelius, G., Jones, M.C., Koven, C., Kropp, H., Lathrop, E., Li, W., Loranty, M.M., Natali, S.M., Olefeldt, D., Christina Schädel, Schuur, E.A., Sonnentag, O., Strauss, J., Virkkala, A., and Merritt R. Turetsky, 2025, A review of abrupt permafrost thaw: Definitions, usage, and a proposed conceptual framework: Current Climate Change Reports, v. 11, 7, 15 p., https://doi.org/10.1007/s40641-025-00204-3.","productDescription":"7, 15 p.","ipdsId":"IP-178954","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":499934,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s40641-025-00204-3","text":"Publisher Index Page"},{"id":499649,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","noUsgsAuthors":false,"publicationDate":"2025-07-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Webb, Hailey","contributorId":366038,"corporation":false,"usgs":false,"family":"Webb","given":"Hailey","affiliations":[{"id":87335,"text":"Renewable and Sustainable Energy Institute, University of Colorado Boulder, Boulder, CO USA; 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In addition, regression equations were developed for monthly and seasonal flow durations, ranging from 25 to 99 percent for aquatic biological processes of salmonid spawning (November), overwinter (December–February), clupeid spawning (May), resident spawning (June), and rearing and growth (July–October) periods, and for flow durations ranging from 1 to 99 percent for the habitat forming (March–April) period. Statistics were derived from daily mean streamflow data collected from streamgages with at least 10 years of data through water year 2022 in southern New England and eastern New York.
Forty streamgages in Connecticut and adjacent areas of neighboring States were used in the regression analysis. Regression methods of weighted least squares and generalized least squares were used to derive the final coefficients and measures of uncertainty for the regression equations. The equations used to estimate selected streamflow statistics were developed by relating the flow statistics to different basin characteristics (physical, land cover, and climatic) at the 40 streamgages. Nine basin characteristics served as the explanatory variables in the statewide regression equations: drainage area, percentage of area with coarse-grained stratified deposits, stream density, mean basin slope, mean basin elevation, percentage of area with hydrologic soil group A, mean monthly precipitation for November, mean seasonal precipitation in the winter (December, January, and February), and mean annual temperature. The root mean square error of the 47 equations ranged from 7.9 to 121.9 percent, with an average of 27.9 percent. The equations estimate flows most accurately near the mean (50-percent flow duration), become less accurate for low flows, and are the least accurate for extreme low flows. The root mean square error for the 50-percent flow duration is 15.1 percent, with an average of 17.6 percent across the six periods. The extreme low flow statistics of 7-day, 10-year low-flow frequency, 99-percent flow duration, and 99-percent rearing and growth period flow durations have root mean square errors of 121.9, 105.1, and 121.9 percent, respectively. The adjusted coefficient of determination of the 47 equations ranged from 73.4 to 99.5 percent, with an average of 95.1 percent.
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\"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
The U.S. Geological Survey, the Connecticut Department of Energy and Environmental Protection, and the Connecticut Department of Transportation collaboratively updated flow statistics for 118 streamgages and developed 47 regression equations to estimate key flow statistics in Connecticut. These included various flow durations and low-flow frequencies, as well as mean flow statistics for specific aquatic biological processes. The analysis used daily mean streamflow data from 40 streamgages with at least 10 years of data and incorporated basin characteristics such as drainage area and precipitation. The equations were most accurate near the mean flow (50-percent flow duration), with an average root mean square error of 27.9 percent, while accuracy decreased for low and extreme low flows. The adjusted coefficient of determination ranged from 73.4 to 99.5 percent, averaging 95.1 percent.
","publicationDate":"2025-07-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Ahearn, Elizabeth A. 0000-0002-5633-2640 eaahearn@usgs.gov","orcid":"https://orcid.org/0000-0002-5633-2640","contributorId":194658,"corporation":false,"usgs":true,"family":"Ahearn","given":"Elizabeth","email":"eaahearn@usgs.gov","middleInitial":"A.","affiliations":[{"id":377,"text":"Massachusetts-Rhode Island Water Science Center","active":false,"usgs":true},{"id":196,"text":"Connecticut Water Science Center","active":true,"usgs":true}],"preferred":false,"id":942790,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bent, Gardner C. 0000-0002-5085-3146","orcid":"https://orcid.org/0000-0002-5085-3146","contributorId":205226,"corporation":false,"usgs":true,"family":"Bent","given":"Gardner C.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":942791,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70269291,"text":"sir20255056 - 2025 - Selected special conditions affecting peak streamflow and extreme floods in Alaska through water year 2022","interactions":[],"lastModifiedDate":"2026-02-03T14:28:42.124178","indexId":"sir20255056","displayToPublicDate":"2025-07-17T14:22:08","publicationYear":"2025","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":"2025-5056","displayTitle":"Selected Special Conditions Affecting Peak Streamflow and Extreme Floods in Alaska Through Water Year 2022","title":"Selected special conditions affecting peak streamflow and extreme floods in Alaska through water year 2022","docAbstract":"The U.S. Geological Survey, in cooperation with the Alaska Department of Transportation and Public Facilities, inventoried selected special conditions for annual peak flows and identified extreme floods at streamgages in Alaska through water year 2022 to facilitate hydrologic analysis. Special conditions identified from U.S. Geological Survey gaging records and basin characteristics included regulation and diversion, urbanization, indeterminate drainage areas, drainage areas less than the minimum used in regional analyses, glacial lake outburst floods, other outburst floods, and snowmelt floods. For peak flows that occurred during calendar years 1980–2019, an atmospheric river dataset was used to identify atmospheric river presence or absence on the dates peak flows occurred. Extreme floods (defined as peak flows exceeding the 1-percent annual exceedance probability flood magnitude or an empirical measure of relative magnitude using Creager’s coefficient C) were identified and associated with flood-generating mechanisms using the other inventoried special conditions and other information.
The gaging record contained glacial lake outburst floods at 15 streamgages and other types of outburst floods at 10 streamgages. Non-outburst peak flows in Alaska resulted from a mixture of rainfall and melt-based flood-generating mechanisms in all but the most rain-dominated seasonal flow regime. Melt-based flood-generating mechanisms included snowmelt, high-elevation snow and ice melt, or rain-on-snow events. Atmospheric rivers were common in Alaska and conterminous basins in Canada, occurring in that region on 67 percent of the days in the calendar year 1980–2019 period. Atmospheric rivers were more common on the days of peak flows and even more common on the days of non-outburst extreme floods. The percentage of days when an atmospheric river was present increased to 78 percent for the days of peak flows in that period and to 83 percent for the days of non-outburst extreme floods in that period. Of 149 extreme floods in the gaging record, 38 were generated by outburst floods. Of the non-outburst extreme floods, 72 percent were generated by rainfall and 26 percent were generated by melt-based processes or a combination of rainfall and melt-based processes. Flood-generating mechanisms could not be determined for the final 2 percent of the non-outburst extreme floods because the month and day of the peak flows were unknown and no other information was available. Secondary factors strongly associated with extreme floods included antecedent rain and streamflow conditions and warm storm conditions that produced rain instead of snow or generated snowmelt.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255056","collaboration":"Prepared in cooperation with Alaska Department of Transportation and Public Facilities","usgsCitation":"Curran, J.H., 2025, Selected special conditions affecting peak streamflow and extreme floods in Alaska through water year 2022: U.S. Geological Survey Scientific Investigations Report 2025–5056, 41 p., https://doi.org/10.3133/sir20255056.","productDescription":"Report: viii, 41 p.; 5 Data Releases","onlineOnly":"Y","ipdsId":"IP-169678","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":499049,"rank":11,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118692.htm","linkFileType":{"id":5,"text":"html"}},{"id":492464,"rank":10,"type":{"id":31,"text":"Publication 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Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska, 99508
The longitudinal propagation of water-quality and ecological impairments in rivers during and after wildfires remain poorly understood. In Northern California, the 2022 McKinney Fire burned 243 km2 of the Klamath National Forest, with 83% of the burned area classified as moderate to high severity. During the active wildfire, a high-intensity monsoonal rain event triggered sediment-laden flooding and runoff-initiated debris flows, causing extreme water-quality impairments and a 95 km fish kill zone along the main-stem Klamath River. This rain-on-wildfire event produced a flood wave that outpaced a sediment pulse, diminishing the dilution effect of the floodwaters. A network of high-frequency water-quality sensors recorded water-quality impairments that propagated 296 km downstream. Impairments at the nearest monitoring station, situated 71 km downstream from the fire perimeter, included dissolved oxygen sags to zero (anoxia) for 5.25 h, turbidity spikes exceeding 1000 FNU, a doubling of specific conductance from 175 to 415 µS/cm (at 25 °C), and pH anomalies of 0.5 units from 7.8 to 7.3. This novel rain-on-wildfire event triggered the first flush of fire-scar material during an active wildfire, resulting in water-quality impairments unprecedented in the historical monitoring data for the river spanning 2012 to 2022. This study provides new insights into the potential role of rain-on-wildfire events in generating extreme downstream water-quality and ecological impairments in a more fire-prone future.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-025-08179-9","usgsCitation":"Curtis, J., Johnson, G., Cahill, J., Genzoli, L., Dahm, C., Schenk, L.N., and Oberholzer, J., 2025, 2022 McKinney rain-on-wildfire event, dissolved oxygen sags, and a fish kill on the Klamath River, California: Scientific Reports, v. 15, 24668, 14 p., https://doi.org/10.1038/s41598-025-08179-9.","productDescription":"24668, 14 p.","ipdsId":"IP-161626","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":493309,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-025-08179-9","text":"Publisher Index Page"},{"id":492907,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.18203511324225,\n 42.00661546335127\n ],\n [\n -122.63359944859491,\n 42.00661546335127\n ],\n [\n -122.63359944859491,\n 41.86910985623359\n ],\n [\n -122.18203511324225,\n 41.86910985623359\n ],\n [\n -122.18203511324225,\n 42.00661546335127\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","noUsgsAuthors":false,"publicationDate":"2025-07-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Curtis, Jennifer 0000-0001-7766-994X","orcid":"https://orcid.org/0000-0001-7766-994X","contributorId":212727,"corporation":false,"usgs":true,"family":"Curtis","given":"Jennifer","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":943996,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Grant 0009-0003-9549-2713","orcid":"https://orcid.org/0009-0003-9549-2713","contributorId":358610,"corporation":false,"usgs":false,"family":"Johnson","given":"Grant","affiliations":[{"id":80103,"text":"Karuk Tribe","active":true,"usgs":false}],"preferred":false,"id":943997,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cahill, Josh 0009-0008-0811-3305","orcid":"https://orcid.org/0009-0008-0811-3305","contributorId":358613,"corporation":false,"usgs":false,"family":"Cahill","given":"Josh","affiliations":[{"id":38097,"text":"Yurok Tribe","active":true,"usgs":false}],"preferred":false,"id":943998,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Genzoli, Laurel 0000-0001-5660-7627","orcid":"https://orcid.org/0000-0001-5660-7627","contributorId":358616,"corporation":false,"usgs":false,"family":"Genzoli","given":"Laurel","affiliations":[{"id":28239,"text":"Univ of Montana","active":true,"usgs":false}],"preferred":false,"id":943999,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dahm, Clifford 0000-0003-0191-6830","orcid":"https://orcid.org/0000-0003-0191-6830","contributorId":358619,"corporation":false,"usgs":false,"family":"Dahm","given":"Clifford","affiliations":[{"id":35754,"text":"Univ of New Mexico","active":true,"usgs":false}],"preferred":false,"id":944000,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schenk, Liam N. 0000-0002-2491-0813 lschenk@usgs.gov","orcid":"https://orcid.org/0000-0002-2491-0813","contributorId":4273,"corporation":false,"usgs":true,"family":"Schenk","given":"Liam","email":"lschenk@usgs.gov","middleInitial":"N.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":944001,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Oberholzer, John 0009-0005-9164-0330","orcid":"https://orcid.org/0009-0005-9164-0330","contributorId":358622,"corporation":false,"usgs":false,"family":"Oberholzer","given":"John","affiliations":[{"id":80103,"text":"Karuk Tribe","active":true,"usgs":false}],"preferred":false,"id":944002,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70268792,"text":"sir20255049 - 2025 - Completion summary for monitor wells NRF-17 and NRF-18 at the Naval Reactors Facility, Idaho National Laboratory, Idaho","interactions":[],"lastModifiedDate":"2026-01-26T19:27:33.044408","indexId":"sir20255049","displayToPublicDate":"2025-07-03T07:22:30","publicationYear":"2025","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":"2025-5049","displayTitle":"Completion Summary for Monitor Wells NRF-17 and NRF-18 at the Naval Reactors Facility, Idaho National Laboratory, Idaho","title":"Completion summary for monitor wells NRF-17 and NRF-18 at the Naval Reactors Facility, Idaho National Laboratory, Idaho","docAbstract":"The U.S. Geological Survey (USGS)—in cooperation with the U.S. Department of Energy (DOE) for the Naval Reactors Laboratory Field Office that supports operations for the Naval Reactors Facility (NRF) located at the Idaho National Laboratory (INL)—drilled and constructed well NRF-17 (formerly borehole USGS 151) and well NRF-18 (formerly borehole USGS 152) for stratigraphic framework analyses and water-quality monitoring at the Idaho National Laboratory (INL) near the NRF, in southeastern Idaho. Borehole USGS 151 was continuously cored from about 48 to 1,070 feet (ft) below land surface (BLS); rotary drilled from approximately 1,070 to 1,720 ft BLS; and re-drilled to complete construction as a monitor well NRF-17, completed to 461 ft BLS. Borehole USGS 152 was continuously cored from approximately 19 to 1,259 ft BLS; rotary drilled from approximately 1,259 to 1,630 ft BLS; and re-drilled to complete construction as a monitor well NRF-18, completed to 450 ft BLS.
Geophysical data were examined with photographed core material to record lithologic descriptions and to suggest zones where groundwater flow was anticipated. Basalt flows varied from highly fractured to dense, with high-to-low vesiculation. Well NRF-17 generally was constructed in mostly dense basalt (greater than 75 percent), and well NRF-18 was constructed in primarily fractured and (or) vesicular basalt. In well NRF-17, the well capacity is directly affected by the limited amount of fractured basalt, which serves as the primary pathway for groundwater. This effect was observed during the pumping test conducted after the well's final construction.
Single-well aquifer tests were done at wells NRF-17 and NRF-18 to provide estimates of transmissivity and hydraulic conductivity after final well construction and initial well development. Estimated values of transmissivity and hydraulic conductivity for well NRF-17 were 8.81 feet squared per day (ft2/d) and 1.04×10-2 feet per day (ft/d), respectively. Estimated values of transmissivity and hydraulic conductivity for well NRF-18 were 4.77×103 ft2/d and 5.61 ft/d, respectively. The NRF-17 pump test resulted in 19.41 ft of measured drawdown at a sustained average pumping rate of 3.3 gallons per minute (gal/min). The NRF-18 pump test resulted in 0.55 ft of measured drawdown at a sustained average pumping rate of 31.0 gal/min.
Water-quality samples collected from the two wells were analyzed for cations, anions, metals, nutrients, volatile organic compounds, stable isotopes, and radionuclides. Water samples for select inorganic constituents showed concentrations consistent with signatures from tributary valley groundwater with influences from ephemeral surface-water recharge from the Big Lost River. Water-quality samples analyzed for stable isotopes of oxygen and hydrogen are consistent with signatures from tributary valley groundwater and surface-water recharge inputs to the aquifer. No measured water-quality results were greater than their respective maximum contaminant levels for public drinking-water supplies. Inorganic and nutrient water-quality results for well NRF-17 and well NRF-18 suggest the groundwater in this area is potentially affected by industrial wastewater disposal.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255049","collaboration":"Prepared in cooperation with the U.S. Department of Energy","programNote":"DOE/ID-22264","usgsCitation":"Twining, B.V., Treinen, K.C., and Zingre, J.A., 2025, Completion summary for monitor wells NRF-17 and NRF-18 at the Naval Reactors Facility, Idaho National Laboratory, Idaho: U.S. Geological Survey Scientific Investigations Report\n2025–5049, 37 p., https://doi.org/10.3133/sir20255049.","productDescription":"Report: vii, 37 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-159224","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":491691,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5049/images"},{"id":491690,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13URUXF","text":"USGS data release","description":"USGS data release","linkHelpText":"Single-well aquifer test data from wells NRF-17 and NRF-18, Idaho National Laboratory, Idaho"},{"id":499046,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118682.htm","linkFileType":{"id":5,"text":"html"}},{"id":491692,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5049/sir20255049.XML"},{"id":491688,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5049/sir20255049.pdf","size":"3.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5049"},{"id":491687,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5049/coverthb.jpg"},{"id":491689,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255049/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5049"}],"country":"United States","state":"Idaho","otherGeospatial":"Idaho National Laboratory, Naval Reactors Facility","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.5,\n 44\n ],\n [\n -113.5,\n 44\n ],\n [\n -113.5,\n 43.25\n ],\n [\n -112.5,\n 43.25\n ],\n [\n -112.5,\n 44\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Rd
Boise, Idaho 83702-4520
This report summarizes results from boat-based, high-resolution water-quality mapping surveys completed before, during, and after upgrades to the EchoWater Resource Recovery Facility (EchoWater Facility), the regional wastewater facility for the City of Sacramento and surrounding areas, near Elk Grove, California. Surveys were completed in the tidal aquatic environments of the Sacramento–San Joaquin Delta (Delta) in spring (May or June) 2018, 2020, 2021, and 2022. In each survey, a suite of in situ sensors were used to continuously (one measurement per second) measure water-quality conditions, nutrients, phytoplankton abundance, and species composition. In addition to in situ data collection, discrete water samples were collected about every 2 miles while underway for determination of phosphate, ammonium, and nitrate concentration. The boat stopped at about 30 locations to collect discrete samples for a suite of additional analytes, including phytoplankton enumeration. The four surveys represent snapshots in time across different phases of the EchoWater Facility Biological Nutrient Reduction (BNR) upgrade. The May 2018 survey represents conditions before the upgrade. The second survey (June 2020) represents conditions after implementation of the Nitrifying Sidestream Treatment. The third survey (May 2021) was completed immediately after the completion of the BNR upgrade and represents a transitional period, and the final survey (May 2022) represents post-upgrade conditions.
Relevant hydrologic and climatic context such as water-year type, X2 position (the distance from the Golden Gate Bridge to the point upstream where bottom salinity is 2 parts per thousand; Jassby and others, 1995), water export to import ratio, and management actions like the Delta Cross Channel gate operations are presented for each survey so they may be considered in comparisons among surveys. Differences in water-quality parameters, like turbidity, temperature, salinity, pH, and dissolved oxygen (DO) improve understanding of nutrient cycling and phytoplankton dynamics. Because the Delta is a complex system, we divided the study area into hydrologic zones to better examine general trends and obtain a broadscale view of differences among the 4 study years. Results are presented for each survey and parameter using box plots to compare the different hydrologic zones. We also present each parameter using contour maps by survey to display gradients across the system.
The most evident change to water quality in the Delta across surveys is related to the EchoWater Facility BNR upgrade, which included nitrification and denitrification processes. Through this upgrade, effluent ammonium (NH4+) concentrations were reduced by more than 95 percent (from about 2,000 micromolars [μM] to below the reporting limit of 35 μM), and nitrate (NO3−) concentrations increased from near zero to about 500 μM; therefore, the concentration of dissolved inorganic nitrogen (DIN; the sum of NH4+ and NO3−) in the effluent was reduced by about 75 percent between May 2018 and May 2022. The BNR upgrade resulted in a reduction in NH4+ concentrations in aquatic habitats immediately below the facility, designated as the “north Delta tidal transition zone” (Bergamaschi and others, 2024), from about 30 μM pre-upgrade to near zero during the 2022 spring survey, whereas effluent NO3− increased from median concentrations of about 7 μM to about 15 μM. Because of the reduced effluent nitrogen loads and variability in Sacramento River nitrogen loads from upstream sources, DIN concentrations in the north Delta tidal transition zone decreased from a median of 53.3 μM in 2018 to 35.3 μM in 2020, 20.7 μM in 2021, and 11.3 μM in 2022 during the spring surveys.
The changes in DIN concentration and form observed in the north Delta tidal transition zone after the EchoWater Facility upgrade extended downstream but were rapidly altered by hydrologic mixing, biogeochemical processes, and other nutrient source inputs. Most of the Delta indicated near-zero concentrations of NH4+ 1 year after the completion of the EchoWater Facility upgrades represented by the 2022 survey. Exceptions to this finding were observed in the San Joaquin River near Stockton and in Suisun Bay, indicating there are NH4+ inputs to these locations from other sources (for example, Stockton Regional Wastewater Control Facility and Central Contra Costs Sanitary District wastewater treatment plants or agricultural and urban runoff).
Although there was an increase in NO3− concentrations in the north Delta tidal transition zone after the upgrade, increases in NO3− in other zones were not apparent, presumably because nitrification of effluent derived ammonium was no longer a source of NO3−. Concentrations of DIN in many Delta zones were lower in 2022 compared to 2018 and 2020, with concentrations near or below what is considered potentially nitrogen limiting conditions for phytoplankton growth in the North Delta tidal transition zone and the Cache Slough complex channel system. Unrelated to the EchoWater Facility upgrade, NO3− and therefore DIN concentrations increased in the San Joaquin River near Stockton and in adjacent water bodies by survey date (likely associated with increasing drought conditions). The Mokelumne River had low DIN concentrations, except in 2018 when the Delta Cross Channel was open, which allowed nutrient-rich Sacramento River water to flow into this section of the river. Data from these surveys also support the hypothesis that nutrient drawdown during phytoplankton blooms may create localized nitrogen limiting conditions.
The BNR upgrade resulted in lower effluent phosphate (PO43−) concentrations, which lowered PO43− concentrations in some zones of the Delta during the four spring surveys; however, PO43− concentrations throughout the Delta remained above 0.3 μM, indicating that primary productivity was not limited by phosphorous availability. DIN and PO43− decreased after the upgrade in many areas of the Delta, and the DIN to dissolved inorganic phosphorus (DIN:DIP) ratio remained similar to pre-upgrade conditions and was often below the Redfield Ratio of 16, indicating nitrogen is more likely to limit phytoplankton growth than phosphorous. Inputs of dissolved organic carbon (DOC) from the EchoWater Facility are a minor source of this constituent to the Delta, so the upgrade had little to no effect on DOC concentrations across the Delta.
Because phytoplankton abundance and species composition in the Delta are shaped by multiple factors other than nutrients (for example, light availability, temperature, salinity, and predation), it is important to consider these factors (as well as long-term monitoring) in addition to the EchoWater Facility upgrade. Although phytoplankton populations were low across much of the Delta during the spring surveys, several localized phytoplankton blooms (defined here as greater than 15 micrograms per liter [μg/L] of chlorophyll) provide insight into conditions that may favor the growth of beneficial and harmful species.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255035","collaboration":"Prepared in cooperation with the Delta Regional Monitoring Program","programNote":"Water Resources Mission Area—Water Availability and Use Science Program","usgsCitation":"Richardson, E., Kraus, T., O’Donnell, K., Soto-Perez, J., Sturgeon, C., Stumpner, E., and Bergamaschi, B., 2025, Assessing spatial variability of nutrients, phytoplankton, and related water-quality constituents in the California\nSacramento–San Joaquin Delta at the landscape scale—Comparison of four (2018, 2020, 2021, 2022) spring high-resolution mapping surveys: U.S. Geological Survey Scientific Investigations Report 2025–5035, 78 p.,\nhttps://doi.org/10.3133/sir20255035.","productDescription":"Report: x, 78 p.; 3 Data Releases","onlineOnly":"Y","ipdsId":"IP-151343","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":491472,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5035/images"},{"id":491473,"rank":8,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5035/sir20255035.XML"},{"id":491469,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FQEUAL","text":"USGS data release","description":"USGS data release","linkHelpText":"Assessing spatial variability of nutrients and related water quality constituents in the California Sacramento–San Joaquin Delta at the landscape scale—2018 High resolution mapping surveys (ver. 2.0, October 2023)"},{"id":491468,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255035/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5035"},{"id":491467,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5035/sir20255035.pdf","text":"Report","size":"41.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5035"},{"id":491466,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5035/coverthb.jpg"},{"id":491471,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QULEAT","text":"USGS data release","description":"USGS data release","linkHelpText":"Assessing spatial variability of nutrients, phytoplankton, and related water quality constituents in the California Sacramento–San Joaquin Delta at the landscape scale—2022 High resolution mapping surveys"},{"id":491470,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90VYUBX","text":"USGS data release","description":"USGS data release","linkHelpText":"Assessing spatial variability of nutrients, phytoplankton, and related water-quality constituents in the California Sacramento–San Joaquin Delta at the landscape scale—2020–2021 high-resolution mapping surveys"}],"country":"United States","state":"Callifornia","otherGeospatial":"Sacramento–San Joaquin Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121,\n 38.5833\n ],\n [\n -122.1667,\n 38.5833\n ],\n [\n -122.1667,\n 37.5\n ],\n [\n -121,\n 37.5\n ],\n [\n -121,\n 38.5833\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
The Wilcox Oil Company Superfund site (hereinafter referred to as “the site”) was formerly an oil refinery northeast of Bristow in Creek County, Oklahoma. Historical refinery operations contaminated the soil, surface water, streambed sediments, alluvium, and groundwater with refined and stored products at the site. The Wilcox and Lorraine process areas are where the highest concentrations of volatile organic compounds, semivolatile organic compounds, polycyclic aromatic hydrocarbons, and trace elements (including metals) (collectively hereinafter referred to as “contaminants”) were measured in a local shallow perched groundwater system within the alluvium (hereinafter referred to as the “alluvial aquifer”) at the site during previous site assessments. In order to understand the potential migration of contaminants through the soil and groundwater in these areas, the U.S. Geological Survey, in cooperation with the U.S. Environmental Protection Agency, investigated aquifer characteristics of the alluvial aquifer in the Wilcox and Lorraine process areas of the site to (1) document hydraulic conductivity and other aquifer characteristics of the alluvial aquifer that govern contaminant fate and transport, (2) describe the geospatial extent and concentration of the contaminants in the alluvial aquifer in the Wilcox and Lorraine process areas, and (3) describe the geochemical controls pertaining to oxidation and reduction governing the fate and transport and the degradation potential of contaminants in the groundwater. Various data were compiled and collected to evaluate the aquifer characteristics at the site including the hydrogeologic framework, groundwater-flow system, geochemistry, and hydraulic properties of the aquifer. A total of 20 new (2022) groundwater monitoring wells were installed at the site to collect data used to supplement groundwater-level altitude and groundwater-quality data collected from older, existing groundwater monitoring wells and piezometers. Data compiled and collected for the study were used to evaluate the characteristics of the alluvial aquifer at the site. These aquifer characteristics are defined by the hydrogeologic framework, groundwater-flow system, geochemistry, and hydraulic properties of the aquifer.
Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
Contact Us- USGS Publications Warehouse
","tableOfContents":"The U.S. Geological Survey, in cooperation with the U.S. Department of Energy, used paleomagnetic data from 22 coreholes to construct 3 fence diagrams of subsurface basalt flows in the southern part of the Idaho National Laboratory. These diagrams provide comprehensive descriptions of the horizontal and vertical distribution of basalt flows and sediment layers beneath the surface, aiding geological studies and contributing valuable data to numerical models of groundwater flow and contaminant transport. The correlations established though these diagrams include spatial correlations between basalt flows found in multiple coreholes. Correlations were identified by matching average paleomagnetic inclinations and confirming or denying these correlations using petrology, geochemistry and radiometric ages.
The fence diagrams aid in identifying potential locations of subsurface vents, volcanic vents that have been buried by more recent volcanic activity, associated to subsurface basalt flows. By tracing the subsurface flows and analyzing where the greatest thickness occurs, the locations of buried vents can be inferred. Some subsurface flows exhibit correlations across several coreholes and may indicate yet unidentified surface or buried vents, thereby enhancing our understanding of the volcanic history and subsurface geology of the region.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255020","collaboration":"Prepared in cooperation with the U.S. Department of Energy","programNote":"DOE/ID-22263","usgsCitation":"Hodges, M.K.V., Trcka, A.R., and Champion, D.E., 2025, Paleomagnetic correlation of surface and subsurface basalt flows in the central and southwestern part of the Idaho National Laboratory, Idaho: U.S. Geological Survey Scientific Investigations Report 2025–5020, 38 p., 1 pl., https://doi.org/10.3133/sir20255020.","productDescription":"Report: vi, 38 p.; 1 Plate: 50.00 x 32.00 inches; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-107892","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":489517,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255020/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5020"},{"id":489516,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5020/sir20255020.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5020"},{"id":489515,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5020/coverthb.jpg"},{"id":489518,"rank":4,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2025/5020/sir20255020_plate1.pdf","text":"Plate 1","size":"476 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5020 Plate 1","linkHelpText":"- Subsurface stratigraphic fence diagrams interpreted from paleomagnetic inclination data from coreholes in the southern part of the Idaho National Laboratory, Idaho, pl. 1"},{"id":489519,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LTUTU8","text":"USGS data release","description":"USGS data release","linkHelpText":"Paleomagnetic inclination data collected from Coreholes EREF-GW-1, STF-PIE-AQ-02, TAN 2336, USGS 138, USGS 139, USGS 142, USGS 143, USGS 144, USGS 145, USGS 147, and USGS 148A, located at and near the Idaho National Laboratory, Idaho"},{"id":489520,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5020/images"},{"id":489521,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5020/sir20255020.XML"},{"id":494133,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118634.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Idaho","otherGeospatial":"Idaho National Laboratory","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.5,\n 43.75\n ],\n [\n -113.125,\n 43.75\n ],\n [\n -113.125,\n 43.26602031163614\n ],\n [\n -112.5,\n 43.26602031163614\n ],\n [\n -112.5,\n 43.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Rd
Boise, Idaho 83702-4520