The New River Gorge National Park and Preserve in West Virginia receives more than 1 million visitors each year, many of whom come to enjoy the New River, which is known for its whitewater recreation. However, most of the tributaries within the New River Gorge are impaired by fecal-coliform bacteria, which are at concentrations that may exceed recreational-contact standards, posing a potential health risk to the public and, therefore, creating a need to better understand the spatial and temporal distribution of fecal-coliform bacteria and to communicate this information to park visitors.
Concentrations of Escherichia coli, a species of fecal-coliform bacteria, were monitored in the New River and selected tributaries from October 2021 through September 2023, with emphasis placed on the primary recreational-contact season from May through October. Composite and cross-sectional water samples were taken from three U.S. Geological Survey (USGS) monitoring locations: the New River at Highway 41 at Prince, West Virginia (USGS 03184905), New River at Thurmond, West Virginia (USGS 03185400; hereafter, Thurmond), and New River at Fayette, West Virginia (USGS 03186000). Periodic longitudinal transects included water samples collected below seven major tributaries of the New River within the gorge. Water-quality parameters, including water temperature, pH, specific conductance, dissolved oxygen, and turbidity, were recorded with each E. coli water sample.
During the 2 years of sampling, E. coli concentrations in samples collected from the New River ranged from less than 1 to 1,100 most probable number (MPN) per 100 milliliters (MPN/100 mL). The recreational-contact standard, which is based on the U.S. Environmental Protection Agency 90th-percentile statistical threshold value for E. coli concentrations (320 MPN/100 mL), was exceeded in 11 of the 110 samples collected from the New River during this study. Water-quality parameter measurements and E. coli concentrations in collected samples were generally consistent among USGS monitoring locations throughout the New River Gorge; however, storm events created notable exceptions because they increased tributary streamflow and E. coli concentrations in samples, particularly at the New River below Piney Creek at McCreery, West Virginia (USGS 03185208), and New River Below Arbuckle Creek at Thurmond, West Virginia (USGS 03185440), monitoring locations. Escherichia coli concentrations of cross-sectional samples tended to be consistent across the New River, except for a few nearshore samples.
Sample E. coli concentrations and corresponding measurements of continuous water-quality parameters, streamflow, and precipitation data from Thurmond and the Piney Creek at Raleigh, West Virginia (USGS 03185000; tributary to the New River) monitoring locations were evaluated for use in a near-real-time E. coli surrogate model. The antecedent mean 24-hour turbidity at Thurmond was selected as the best variable for a simple linear regression surrogate model for the log10 E. coli concentration in the New River and had an adjusted coefficient of determination of 0.556 and a p-value of less than 0.001. The regression equation surrogate model suggests that the recreational-contact standard is exceeded when the antecedent mean 24-hour turbidity at Thurmond is 23.6 formazin nephelometric units or higher (with a 95-percent confidence interval of 19.4–30.7 formazin nephelometric units). Evaluated against a turbidity duration curve, this standard is exceeded 7.5 percent of the time at Thurmond. This surrogate model could help New River Gorge National Park and Preserve staff provide near-real-time information about E. coli concentrations and related recreational-contact risks to the public.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20265025","programNote":"U.S. Geological Survey—National Park Service Water-Quality Partnership","usgsCitation":"Kearns, M.R., and Chambers, D.B., 2026, Estimation, distribution, and development of a surrogate model for Escherichia Coli in the New River, New River Gorge National Park and Preserve, West Virginia, 2021–23: U.S. Geological Survey Scientific Investigations Report 2026–5025, 32 p., https://doi.org/10.3133/sir20265025.","productDescription":"Report: viii, 32 p.","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-182032","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":505267,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2026/5025/images/"},{"id":505266,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2026/5025/sir20265025.XML","description":"SIR 2026-5025 XML"},{"id":505265,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20265025/full","description":"SIR 2026-5025 HTML"},{"id":505264,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2026/5025/sir20265025.pdf","text":"Report","size":"5.09 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2026-5025 PDF"},{"id":505263,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2026/5025/coverthb.jpg"}],"contact":"Director, Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, Virginia 23228
The planning, permitting, and design of stormwater-management projects require estimates of the depths of extreme precipitation for current and future events with specified durations and return periods. In this project, precipitation data from six downscaled climate datasets were used to determine changes in precipitation depth-duration-frequency curves from the period 1966–2005 to the periods 2020–59 and 2050–89. The downscaled climate datasets are from the Coupled Model Intercomparison Project Phases 5 and 6 and include (1) Coordinated Regional Downscaling Experiment (CORDEX), (2) Localized Constructed Analogs (LOCA), (3) Multivariate Adaptive Constructed Analogs (MACA), (4) Jupiter Intelligence Weather Research and Forecasting model (JupiterWRF), (5) LOCA version 2 (LOCA2), and (6) National Aeronautics and Space Administration Earth Exchange Global Daily Downscaled Projections (NEX-GDDP). Change factors—multiplicative changes in expected extreme precipitation magnitude from a historical to future period—were computed for grid cells containing National Oceanic and Atmospheric Administration Atlas 14 stations in Florida. Change factors for specific durations and return periods were developed to scale the National Oceanic and Atmospheric Administration Atlas 14 historical depth-duration-frequency values to the periods 2020–59 and 2050–89 on the basis of changes in extreme precipitation derived from six downscaled climate datasets.
Overall, a large variation in change factors across downscaled climate datasets was found, with change factors generally being greater than 1 and increasing with return period. In general, median change factors were found to range within 1.01–1.58 for 2020–59 and 1.01–1.63 for 2050–89, depending on the downscaled climate dataset, region, duration, and return period, indicating a projected overall increase in future extreme-precipitation events. When data from all datasets are considered together, median change factors range within 1.04–1.18 for the period 2020–59 and within 1.04–1.23 for the period 2050–89, depending on the region, duration, and return period. Spatial patterns in median change factors were found to vary by dataset.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255111","issn":"2328-0328","collaboration":"Prepared in cooperation with the Florida Flood Hub for Applied Research and Innovation","usgsCitation":"Irizarry-Ortiz, M.M., 2026, Development of projected depth-duration-frequency curves for precipitation in Florida, 2020–59 and 2050–89: U.S. Geological Survey Scientific Investigations Report 2025–5111, 75 p., https://doi.org/10.3133/sir20255111.","productDescription":"Report: xii, 75 p.; Data Release","numberOfPages":"92","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-168289","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":504924,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Q3LEIL","text":"USGS Data Release","linkHelpText":"- Change factors to derive projected future precipitation depth-duration-frequency (DDF) curves at 242 National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in Florida (ver. 3.0, August 2025)"},{"id":504923,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255111/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5111 HTML"},{"id":504922,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5111/sir20255111.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2025-5111 XML"},{"id":504921,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5111/sir20255111.pdf","size":"21.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5111"},{"id":504919,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5111/coverthb.jpg"},{"id":504920,"rank":2,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5111/images"}],"country":"United 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\"}}]}","contact":"Quantifying and projecting the downstream benefits of water stored in lakes and wetlands (SWstorage) requires watershed hydrologic models, which often parameterize surface water storage in topographic depressions using static digital elevation model (DEM) data. Calibration and validation of modeled SWstorage dynamics using external data sets is uncommon, particularly across major river basins, with model calibration typically focused on observed discharge. Here, we develop and assess a novel remote sensing-based (RS) SWstorage data set (Sentinel-1 and Sentinel-2) for verifying simulated SWstorage estimates from a Soil and Water Assessment Tool (SWAT) model of the Upper Mississippi River Basin (UMRB; ∼440,000 km2). Our results suggest that static DEM-based parameterization as well as model calibration based solely on discharge do not adequately capture spatial and temporal SWstorage dynamics in the UMRB. Mean SWstorage as estimated by SWAT was 74% ± 122% (mean ± standard deviation) higher than RS SWstorage, where SWstorage in SWAT was underestimated in wetland-rich subbasins and overestimated in agricultural, tile-drained subbasins. Time series of SWAT SWstorage and RS SWstorage were positively correlated in only 38.8% of subbasins. As RS SWstorage is also vulnerable to error, storage estimates were compared to bathymetric data in select small wetlands. While uncertainty remains in the conversion from extent to storage for RS SWstorage, the method and data set presented here are a promising option for improved parameterization and calibration of SWstorage processes in SWAT and other process-based hydrologic models. Further consideration of these storage processes can potentially improve the accuracy of simulated streamflow in wetland-rich model domains.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2025WR040206","usgsCitation":"Dolan, W., Vanderhoof, M.K., Christensen, J.R., Golden, H.E., Lane, C.R., Rajib, A., Keenan, W., Zheng, Q., and Khare, A., 2026, Remotely sensed surface water storage shows distinct patterns from SWAT-simulated data: Water Resources Research, v. 62, no. 6, e2025WR040206, 22 p., https://doi.org/10.1029/2025WR040206.","productDescription":"e2025WR040206, 22 p.","ipdsId":"IP-175550","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":505481,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2025wr040206","text":"Publisher Index Page"},{"id":505244,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Indiana, Iowa, Minnesota, Missouri, South Dakota, Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.9088108,\n 44.6284877\n ],\n [\n -86.7385234,\n 41.587393\n ],\n [\n -89.9700712,\n 38.3215153\n ],\n [\n -95.2047886,\n 42.7793981\n ],\n [\n -97.7322805,\n 45.1743925\n ],\n [\n -95.1189109,\n 47.5157703\n ],\n [\n -93.9118523,\n 46.800896\n ],\n [\n -89.1539577,\n 44.8972483\n ],\n [\n -91.2125251,\n 43.1136036\n ],\n [\n -91.4028634,\n 43.1633275\n ],\n [\n -91.4813002,\n 43.3107314\n ],\n [\n -95.9088108,\n 44.6284877\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"62","issue":"6","noUsgsAuthors":false,"publicationDate":"2026-06-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Dolan, Wayana 0000-0001-8405-4302","orcid":"https://orcid.org/0000-0001-8405-4302","contributorId":354442,"corporation":false,"usgs":true,"family":"Dolan","given":"Wayana","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":962658,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vanderhoof, Melanie K. 0000-0002-0101-5533 mvanderhoof@usgs.gov","orcid":"https://orcid.org/0000-0002-0101-5533","contributorId":168395,"corporation":false,"usgs":true,"family":"Vanderhoof","given":"Melanie","email":"mvanderhoof@usgs.gov","middleInitial":"K.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":962659,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Christensen, Jay R. 0000-0003-4961-6132","orcid":"https://orcid.org/0000-0003-4961-6132","contributorId":372019,"corporation":false,"usgs":false,"family":"Christensen","given":"Jay","middleInitial":"R.","affiliations":[{"id":88243,"text":"EPA Office of Research and Development","active":true,"usgs":false}],"preferred":false,"id":962660,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Golden, Heather E.","contributorId":364787,"corporation":false,"usgs":false,"family":"Golden","given":"Heather","middleInitial":"E.","affiliations":[{"id":13226,"text":"U.S. Environmental Protection Agency, Office of Research and Development","active":true,"usgs":false}],"preferred":false,"id":962661,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lane, Charles R. 0000-0003-0066-8919","orcid":"https://orcid.org/0000-0003-0066-8919","contributorId":372020,"corporation":false,"usgs":false,"family":"Lane","given":"Charles","middleInitial":"R.","affiliations":[{"id":88243,"text":"EPA Office of Research and Development","active":true,"usgs":false}],"preferred":false,"id":962662,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rajib, Adnan","contributorId":365158,"corporation":false,"usgs":false,"family":"Rajib","given":"Adnan","affiliations":[{"id":50034,"text":"University of Texas, Arlington","active":true,"usgs":false}],"preferred":false,"id":962663,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Keenan, William","contributorId":365156,"corporation":false,"usgs":false,"family":"Keenan","given":"William","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":962664,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Zheng, Qianjin 0000-0002-9535-472X","orcid":"https://orcid.org/0000-0002-9535-472X","contributorId":372022,"corporation":false,"usgs":false,"family":"Zheng","given":"Qianjin","affiliations":[{"id":12734,"text":"University of Texas at Arlington","active":true,"usgs":false}],"preferred":false,"id":962665,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Khare, Arushi","contributorId":366982,"corporation":false,"usgs":false,"family":"Khare","given":"Arushi","affiliations":[{"id":50034,"text":"University of Texas, Arlington","active":true,"usgs":false}],"preferred":false,"id":962666,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70276356,"text":"pp1890R - 2026 - High-resolution magnetic survey using an unoccupied aerial vehicle to constrain buried lava flow geometry, volume, and eruptive history of Little Cones, Crater Flat, Nevada","interactions":[{"subject":{"id":70276356,"text":"pp1890R - 2026 - High-resolution magnetic survey using an unoccupied aerial vehicle to constrain buried lava flow geometry, volume, and eruptive history of Little Cones, Crater Flat, Nevada","indexId":"pp1890R","publicationYear":"2026","noYear":false,"chapter":"R","displayTitle":"High-Resolution Magnetic Survey Using an Unoccupied Aerial Vehicle to Constrain Buried Lava Flow Geometry, Volume, and Eruptive History of Little Cones, Crater Flat, Nevada","title":"High-resolution magnetic survey using an unoccupied aerial vehicle to constrain buried lava flow geometry, volume, and eruptive history of Little Cones, Crater Flat, Nevada"},"predicate":"IS_PART_OF","object":{"id":70259456,"text":"pp1890 - 2024 - Distributed volcanism—Characteristics, processes, and hazards","indexId":"pp1890","publicationYear":"2024","noYear":false,"title":"Distributed volcanism—Characteristics, processes, and hazards"},"id":1}],"isPartOf":{"id":70259456,"text":"pp1890 - 2024 - Distributed volcanism—Characteristics, processes, and hazards","indexId":"pp1890","publicationYear":"2024","noYear":false,"title":"Distributed volcanism—Characteristics, processes, and hazards"},"lastModifiedDate":"2026-06-08T17:27:26.618433","indexId":"pp1890R","displayToPublicDate":"2026-06-05T14:42:00","publicationYear":"2026","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1890","chapter":"R","displayTitle":"High-Resolution Magnetic Survey Using an Unoccupied Aerial Vehicle to Constrain Buried Lava Flow Geometry, Volume, and Eruptive History of Little Cones, Crater Flat, Nevada","title":"High-resolution magnetic survey using an unoccupied aerial vehicle to constrain buried lava flow geometry, volume, and eruptive history of Little Cones, Crater Flat, Nevada","docAbstract":"Magnetic surveys are an important tool used to augment geologic mapping in distributed volcanic fields. Using magnetic anomalies, it is possible to model the geometry of shallowly buried volcanic features, such as conduits, sills, and lava flows. This subsurface mapping is important for understanding eruption dynamics and emplacement of lava flows, and it sometimes reveals buried volcanoes no longer visible at the surface. These data are critical to better interpret the numbers, styles, and magnitudes of eruptions in distributed volcanic fields and their associated volcanic hazards. New advances in unoccupied aerial vehicles (UAVs) offer an attractive middle range of resolution and aerial coverage between ground-based magnetic surveys and aeromagnetic surveys.
Here, we present the results of a UAV fluxgate magnetic survey of the Little Cones, Nevada, scoria cones, which have been the target of previous ground and aeromagnetic surveys. The magnetic anomalies at Little Cones are of interest because the surrounding alluvium conceals lava flows that erupted from Little Cones, making it very difficult to understand the volume and morphology of lava flows from geologic mapping alone. Nonlinear inversion of UAV-collected magnetic data were used to model the thickness and morphology of buried Little Cones’ lava flows with higher precision than achieved previously. The sequence of events and calculated flow characteristics are then interpreted. The total volume of Little Cones, including concealed lava flows, is approximately 0.016 cubic kilometer, and the initial sheet flow erupted in less than 24 hours. The findings presented herein demonstrate that UAV-based magnetic surveys are a reliable method of data collection and an efficient alternative to other survey methods, facilitating development of a three-dimensional perspective of distributed volcanic fields.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1890R","usgsCitation":"Van Alphen, R., Rodgers, M., Malservisi, R., Connor, C.B., Bakowski, R., and Berkey, T., 2026, High-resolution magnetic survey using an unoccupied aerial vehicle to constrain buried lava flow geometry, volume, and eruptive history of\nLittle Cones, Crater Flat, Nevada, chap. R of Poland, M.P., Ort, M.H., Stovall, W.K., Vaughan, R.G., Connor, C.B., and Rumpf, M.E., eds., Distributed volcanism—Characteristics, processes, and hazards: U.S. Geological Survey\nProfessional Paper 1890, 28 p., https://doi.org/10.3133/pp1890R.","productDescription":"Report: vii, 28 p.; Data Release","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-156146","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":505174,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119484.htm","linkFileType":{"id":5,"text":"html"}},{"id":504935,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.17632/zdcsk3rz9z.1","text":"Data release","linkHelpText":"Data and codes utilized for the study of the lava flow of Little Cones, Nevada, USA, using UAV magnetic data (ver. 1)"},{"id":504931,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1890/r/pp1890R.pdf","text":"Report","size":"5.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Professional Paper 1890-R PDF"},{"id":504930,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1890/r/coverthb.jpg"},{"id":504933,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/pp/1890/r/pp1890R.XML","linkFileType":{"id":8,"text":"xml"},"description":"Professional Paper 1890-R XML"},{"id":504932,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/pp1890R/full","linkFileType":{"id":5,"text":"html"},"description":"Professional Paper 1890-R HTML"},{"id":504934,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/pp/1890/r/images"}],"country":"United States","state":"Nevada","otherGeospatial":"Crater Flat","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.663,\n 36.92\n ],\n [\n -116.44,\n 36.92\n ],\n [\n -116.44,\n 36.684\n ],\n [\n -116.663,\n 36.684\n ],\n [\n -116.663,\n 36.92\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Volcano Science Center
U.S. Geological Survey
1300 SE Cardinal Court Bldg. 10
Vancouver, WA 98683
Quantitative mineral resource assessments of potential undiscovered deposits can inform future mineral supply scenarios, but their accuracy is conditional on building robust grade and tonnage models of known deposits. This study presents an up-to-date global compilation and analysis of recently discovered and original, in-situ pegmatite-hosted Li, Cs, and Rb resources prior to historic production. Our analysis yields a median tonnage of 21.2 million tons (Mt) and grade of 1.12% Li2O, respectively, for global Li pegmatite deposits (n = 73). The grades and tonnages of Li pegmatite resources vary depending on the age of the bedrock host domain, pegmatite crystallization age, and primary ore mineralogy. Lithium pegmatite resources hosted in Archean to transitional Archean-Paleoproterozoic domains have the largest median tonnage (29.8 Mt; n = 38), and those hosted in Paleoproterozoic to Mesoproterozoic domains have smaller median tonnages (6.5 Mt; n = 16). Cesium deposits where pollucite is the primary ore mineral have a bimodal grade distribution, with modes of 2.40 and 0.035 wt% Cs2O for high- and low-grade deposits, respectively, while Rb deposits are more unimodal with a median grade of 0.247 wt% Rb2O. Pegmatite-hosted Cs and Rb resources have median tonnages of 7.6 and 6.3 Mt, respectively. Covariation between ore mineralogy and the degree of crustal enrichment in pegmatite-hosted deposits is diagnostic of petrogenetic differences, including melt source characteristics, magma evolution, or variable degrees of volatile solubility. The Li pegmatite compilation is suitable for fitting robust numerical models to support quantitative assessments. More well-defined Rb and Cs pegmatite resources are required for quantitative assessments, but these data provide useful information about original in-place resources for framing supply discussions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.oregeorev.2026.107360","usgsCitation":"Rosera, J.M., McCaffrey, D.M., and Wintzer, N.E., 2026, Global pegmatite-hosted lithium, cesium, and rubidium resources: A dataset for grade and tonnage modeling: Ore Geology Reviews, v. 194, 107360, 16 p., https://doi.org/10.1016/j.oregeorev.2026.107360.","productDescription":"107360, 16 p.","ipdsId":"IP-184418","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":505460,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.oregeorev.2026.107360","text":"Publisher Index Page"},{"id":505302,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13YWFSF","text":"USGS data release","linkHelpText":"Grade and tonnage data for lithium, cesium, and rubidium pegmatite deposits (ver. 2.0, June 2026)"},{"id":505087,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"194","noUsgsAuthors":false,"publicationDate":"2026-06-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Rosera, Joshua Mark 0000-0003-3807-5000","orcid":"https://orcid.org/0000-0003-3807-5000","contributorId":270284,"corporation":false,"usgs":true,"family":"Rosera","given":"Joshua","email":"","middleInitial":"Mark","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":962405,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCaffrey, Dalton M. 0000-0002-2539-4865","orcid":"https://orcid.org/0000-0002-2539-4865","contributorId":298840,"corporation":false,"usgs":true,"family":"McCaffrey","given":"Dalton","middleInitial":"M.","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":962406,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wintzer, Niki E. 0000-0003-3085-435X nwintzer@usgs.gov","orcid":"https://orcid.org/0000-0003-3085-435X","contributorId":5297,"corporation":false,"usgs":true,"family":"Wintzer","given":"Niki","email":"nwintzer@usgs.gov","middleInitial":"E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":962407,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70276589,"text":"70276589 - 2026 - Fifty years of riverine harmful algal bloom modeling: A global synthesis of approaches, challenges, and opportunities","interactions":[],"lastModifiedDate":"2026-06-10T14:57:16.353175","indexId":"70276589","displayToPublicDate":"2026-06-02T07:50:28","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3716,"text":"Water Research","onlineIssn":"1879-2448","printIssn":"0043-1354","active":true,"publicationSubtype":{"id":10}},"title":"Fifty years of riverine harmful algal bloom modeling: A global synthesis of approaches, challenges, and opportunities","docAbstract":"This systematic literature review critically examines 162 articles on harmful algal bloom (HAB) modeling in riverine systems to uncover persistent gaps, redefine critical challenges, and propose trackable opportunities to advance future modeling efforts. Articles largely focused on site-specific applications (93%) across more than 80 rivers worldwide. Most modeled systems were large, eutrophic rivers with flow modifications or obstructions. Geographic clustering of modeled systems was pronounced, with South Korea accounting for 26% of articles, followed by Europe (25%), United States (21%), and China (12%). Modeling approaches were led by process-based models (59%), though use of data-driven models (37%) increased over time, reflecting advances in computing and monitoring technology. Modeling endpoints varied widely across the articles with many focused on gross measures of algal abundance and fewer representing more refined endpoints like algal toxins or community composition. Furthermore, inconsistent units and taxonomic resolution hindered comparability between models. Datasets used for model development and calibration typically spanned 5 years, with weekly to monthly sampling at 1–10 sites, though durations and site counts were positively skewed. Quantitative metrics of model skill were often absent and included a diverse set of metrics when reported. Across all models, nutrients, light availability, streamflow, algal physiological processes, and water temperature emerged as key predictors, though algal processes were rarely incorporated in data-driven models. Scenario analyses primarily were conducted with process-based models and addressed flow management, whereas forecasting applications were less common and typically used data-driven models. After almost 50 years of riverine HAB modeling, persistent challenges include underrepresentation of benthic habitats, neglect of side-channel and backwater influences, insufficient documentation of river features, and weak linkages between modeled endpoints and potential harms. Addressing these gaps through reporting of contextual information, models from other aquatic settings, benchmark datasets, and community-driven tools could advance riverine HAB modeling towards increased transferability and ultimately operational forecasts.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.watres.2026.126240","usgsCitation":"Murphy, J.C., Gorney, R.M., Lucas, L.V., Zwart, J.A., and Graham, J.L., 2026, Fifty years of riverine harmful algal bloom modeling: A global synthesis of approaches, challenges, and opportunities: Water Research, v. 303, 126240, 17 p., https://doi.org/10.1016/j.watres.2026.126240.","productDescription":"126240, 17 p.","ipdsId":"IP-183599","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":505494,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.watres.2026.126240","text":"Publisher Index Page"},{"id":505269,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"303","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":962742,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gorney, Rebecca M. 0000-0003-4406-261X","orcid":"https://orcid.org/0000-0003-4406-261X","contributorId":317259,"corporation":false,"usgs":true,"family":"Gorney","given":"Rebecca","middleInitial":"M.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":962743,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lucas, Lisa V. 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","middleInitial":"V.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":962744,"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":962745,"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":962746,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70276451,"text":"70276451 - 2026 - Trends in subdaily to daily rainfall in Florida, 1990–2022","interactions":[],"lastModifiedDate":"2026-06-05T14:20:31.360639","indexId":"70276451","displayToPublicDate":"2026-06-01T09:17:40","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2344,"text":"Journal of Hydrometeorology","active":true,"publicationSubtype":{"id":10}},"title":"Trends in subdaily to daily rainfall in Florida, 1990–2022","docAbstract":"Changing rainfall patterns and intensifying rainfall extremes affect urban infrastructure and can increase flash-flood risk. Understanding how climate change has altered rainfall can support state and local agencies as they adapt and build resiliency. In this study, rainfall data from 23 weather stations in Florida were used to examine temporal and spatial trends over the period 1990–2022. Subdaily to daily rainfall events of durations 1, 2, 3, 6, 12, and 24 h were examined. A variety of statistical methods were applied to examine annual and seasonal trends, including quantile regression, extreme value analysis, run theory using the Mann–Kendall test, Sen–Theil slope, and Poisson and negative binomial tests, and threshold exceedance rates using generalized additive models. Using subdaily rainfall data posed challenges, including equipment failures, limited documentation of the quality assurance and control process, and potential measurement interferences. Results indicated that over 1990–2022, there was a decrease in hourly rainfall extremes but an increase at moderate quantiles. Overall, the number of rainfall events increased, particularly at shorter durations, but the mean total rainfall per event decreased. Additionally, the annual number of daily rainfall extremes showed more decreases than increases.
","language":"English","publisher":"American Meteorological Society","doi":"10.1175/JHM-D-25-0112.1","usgsCitation":"Haider, S., Irizarry-Ortiz, M.M., Obeysekera, J.T., Maran, A.C., Solaiman, T., and Johnston, B.D., 2026, Trends in subdaily to daily rainfall in Florida, 1990–2022: Journal of Hydrometeorology, v. 27, no. 6, p. 847-865, https://doi.org/10.1175/JHM-D-25-0112.1.","productDescription":"19 p.","startPage":"847","endPage":"865","ipdsId":"IP-175860","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":505091,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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0000-0002-7038-1668","orcid":"https://orcid.org/0000-0002-7038-1668","contributorId":371820,"corporation":false,"usgs":false,"family":"Obeysekera","given":"Jayantha","middleInitial":"T.","affiliations":[{"id":7017,"text":"Florida International University","active":true,"usgs":false}],"preferred":false,"id":962410,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Maran, Ana C.","contributorId":371821,"corporation":false,"usgs":false,"family":"Maran","given":"Ana","middleInitial":"C.","affiliations":[{"id":7036,"text":"South Florida Water Management District","active":true,"usgs":false}],"preferred":false,"id":962411,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Solaiman, Tarana","contributorId":371822,"corporation":false,"usgs":false,"family":"Solaiman","given":"Tarana","affiliations":[{"id":7036,"text":"South Florida Water Management 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We developed a transferable workflow to evaluate geothermal district systems that pair ground heat exchangers with seasonal underground thermal energy storage. Using standardized hourly loads for seven commercial buildings and a uniform cost framework, we simulated ten U.S. cities with a physics-based ground heat exchanger model, subsurface storage simulations, and economic assessment to isolate the roles of climate and hydrogeology. In cooling-dominated cities, underground thermal energy storage supplied the majority of annual cooling, cutting electricity use and summer peaks substantially while achieving levelized costs comparable to or below conventional chiller-boiler plants. In cooler climates, the storage share shrunk, required borefield size and costs rose, and levelized cost of energy increased nearly linearly with declining underground thermal energy storage fraction, indicating storage fraction as the primary economic lever. Sensitivity analysis showed capital risk dominated by borefield drilling and surface heating, ventilation, and air-conditioning and piping, with underground thermal energy storage costs secondary. This workflow provides a transparent foundation for site-specific design and screening of next-generation geothermal district energy systems.
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tomography","interactions":[],"lastModifiedDate":"2026-06-11T14:11:59.66026","indexId":"70276609","displayToPublicDate":"2026-05-29T09:06:00","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":18010,"text":"JGR Machine Learning and Computation","active":true,"publicationSubtype":{"id":10}},"title":"Localization of spatiotemporally heterogeneous subsurface flows using autoencoder-based deep learning framework for time-lapse self-potential tomography","docAbstract":"Self-potential (SP) monitoring has emerged as a valuable method for characterizing subsurface hydrogeological features and processes due to its sensitivity to fluid-induced electrokinetic effects. Despite advancements in SP inversion, challenges remain in imaging groundwater dynamics from SP activities due to complex hydrological settings and transient noise. In this study, a deep learning autoencoder (AE)-based framework is proposed for the spatiotemporal localization of subsurface fluid movement from time-lapse SP tomography. Temporal segments of time-lapse numerical inversions were first derived from long-term SP monitoring conducted from a floodplain site in Oak Ridge, Tennessee, known for active hyporheic exchange. Subsequently, AE models based on vision transformer (ViT), convolutional long short-term memory (ConvLSTM), convolutional neural network, and temporal convolutional network were individually trained and compared on the SP tomography segments for reconstruction performance. Finally, the reconstruction error over time serves as an anomaly score to identify moments of active SP variation, whereas spatial distributions of errors within these moments are analyzed to image and localize regions associated with anomalous subsurface fluid movement. The results demonstrate that ConvLSTM- and ViT-AE are most capable for the localization task with contrasting error distributions and consistent delineation of anomalies. Applying the method to both SP arrays parallel and perpendicular to the stream produced consistent anomaly zones near a fault or karst feature, validating the robustness and generalization of the approach. These results demonstrate the potential of the proposed framework as a scalable and interpretable tool for spatiotemporal analysis of subsurface flow dynamics in complex hydrogeological systems.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2025JH001208","usgsCitation":"Yin, H., Ikard, S., Rucker, D.F., Brooks, S.C., Dai, Z., Soltanian, M.R., and Carroll, K.C., 2026, Localization of spatiotemporally heterogeneous subsurface flows using autoencoder-based deep learning framework for time-lapse self-potential tomography: JGR Machine Learning and Computation, v. 3, no. 3, e2025JH001208, 18 p., https://doi.org/10.1029/2025JH001208.","productDescription":"e2025JH001208, 18 p.","ipdsId":"IP-181872","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":505499,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2025jh001208","text":"Publisher Index Page"},{"id":505401,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Tennessee","city":"Oak Ridge","otherGeospatial":"East Fork Poplar Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.23833522694086,\n 36.016061123798806\n ],\n [\n -84.39831073419363,\n 36.016061123798806\n ],\n [\n -84.39831073419363,\n 35.94029924736503\n ],\n [\n -84.23833522694086,\n 35.94029924736503\n ],\n [\n -84.23833522694086,\n 36.016061123798806\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"3","issue":"3","noUsgsAuthors":false,"publicationDate":"2026-05-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Yin, Huichao 0000-0001-6172-5580","orcid":"https://orcid.org/0000-0001-6172-5580","contributorId":366938,"corporation":false,"usgs":false,"family":"Yin","given":"Huichao","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":962818,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ikard, Scott 0000-0002-8304-4935","orcid":"https://orcid.org/0000-0002-8304-4935","contributorId":201775,"corporation":false,"usgs":true,"family":"Ikard","given":"Scott","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":962819,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rucker, Dale F. 0000-0002-8930-2747","orcid":"https://orcid.org/0000-0002-8930-2747","contributorId":294463,"corporation":false,"usgs":false,"family":"Rucker","given":"Dale","email":"","middleInitial":"F.","affiliations":[{"id":63573,"text":"hydroGEOPHYSICS, Inc.","active":true,"usgs":false}],"preferred":false,"id":962820,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brooks, Scott C. 0000-0002-8437-9788","orcid":"https://orcid.org/0000-0002-8437-9788","contributorId":294464,"corporation":false,"usgs":false,"family":"Brooks","given":"Scott","email":"","middleInitial":"C.","affiliations":[{"id":37070,"text":"Oak Ridge National Laboratory","active":true,"usgs":false}],"preferred":false,"id":962821,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dai, Zhenxue 0000-0002-0805-7621","orcid":"https://orcid.org/0000-0002-0805-7621","contributorId":366941,"corporation":false,"usgs":false,"family":"Dai","given":"Zhenxue","affiliations":[{"id":87510,"text":"Jilin University","active":true,"usgs":false}],"preferred":false,"id":962822,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Soltanian, Mohamad Reza 0000-0002-5126-0668","orcid":"https://orcid.org/0000-0002-5126-0668","contributorId":372147,"corporation":false,"usgs":false,"family":"Soltanian","given":"Mohamad","middleInitial":"Reza","affiliations":[{"id":7159,"text":"University of Cincinnati","active":true,"usgs":false}],"preferred":false,"id":962823,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Carroll, Kenneth C. 0000-0003-2097-9589","orcid":"https://orcid.org/0000-0003-2097-9589","contributorId":247827,"corporation":false,"usgs":false,"family":"Carroll","given":"Kenneth","email":"","middleInitial":"C.","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":962824,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70276608,"text":"70276608 - 2026 - Improving offshore 3D splay fault geometries and slip histories using seismic data reprocessing and structural modeling","interactions":[],"lastModifiedDate":"2026-06-11T13:57:08.223012","indexId":"70276608","displayToPublicDate":"2026-05-29T08:54:29","publicationYear":"2026","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"seriesTitle":{"id":23312,"text":"Final Report","active":true,"publicationSubtype":{"id":3}},"title":"Improving offshore 3D splay fault geometries and slip histories using seismic data reprocessing and structural modeling","docAbstract":"The goal of this project as written in the CRESCENT seed grant proposal was as follows: 1) reprocess selected profiles along strike from 45° to 48°N from the CASIE21 crustal-scale seismic data to obtain higher-resolution and higher-quality imaging of the uppermost 1-2 km of the accretionary wedge; 2) convert high-resolution USGS sparker seismic data from the time to depth domain to constrain near-surface fault geometries; 3) use kinematic modeling in the MOVE software to derive individual fault slip rates and per-event-displacements; and 4) work with the CFM group to create updated 3D models of identified faults based on the new data sources.
At this stage, we have begun the reprocessing of the CASIE21 seismic reflection data (Carbotte et al., 2023). The time-migration reprocessing of the CASIE21 dataset has improved imaging of the near-surface structure by incorporating usable frequencies up to ~220 Hz, in contrast to the ~50 Hz maximum usable content in the currently available pre-stack depth migrated (PSDM) profiles (Fig. 1C). An example of the image quality gained through reprocessing is shown in Fig. 1, where the dominant wavelength of each reflector is reduced to ~10 m (Fig. 1B) from ~20 m in the existing PSDM product (Fig. 1A). The reprocessing work is still currently in progress. While the imaging in the shallow section has been much improved, reprocessing below the first multiple is still needed to create a seamless image from the plate boundary to the surface.
","language":"English","publisher":"Cascadia Region Earthquake Science Center","doi":"10.5281/zenodo.20512959","usgsCitation":"Ledeczi, A.M., Miller, N.C., Tobin, H.J., and Condit, C.B., 2026, Improving offshore 3D splay fault geometries and slip histories using seismic data reprocessing and structural modeling: Final Report, 5 p., https://doi.org/10.5281/zenodo.20512959.","productDescription":"5 p.","ipdsId":"IP-190306","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":505393,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ledeczi, Anna M.","contributorId":372144,"corporation":false,"usgs":false,"family":"Ledeczi","given":"Anna","middleInitial":"M.","affiliations":[{"id":88268,"text":"Department of Earth & Space Sciences, University of Washington, Seattle","active":true,"usgs":false}],"preferred":false,"id":962814,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, Nathaniel C. 0000-0003-3271-2929 ncmiller@usgs.gov","orcid":"https://orcid.org/0000-0003-3271-2929","contributorId":174592,"corporation":false,"usgs":true,"family":"Miller","given":"Nathaniel","email":"ncmiller@usgs.gov","middleInitial":"C.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":962815,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tobin, Harold J.","contributorId":372145,"corporation":false,"usgs":false,"family":"Tobin","given":"Harold","middleInitial":"J.","affiliations":[{"id":88268,"text":"Department of Earth & Space Sciences, University of Washington, Seattle","active":true,"usgs":false}],"preferred":false,"id":962816,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Condit, Cailey B.","contributorId":372146,"corporation":false,"usgs":false,"family":"Condit","given":"Cailey","middleInitial":"B.","affiliations":[{"id":88268,"text":"Department of Earth & Space Sciences, University of Washington, Seattle","active":true,"usgs":false}],"preferred":false,"id":962817,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70276457,"text":"70276457 - 2026 - Future water constraints on United States lithium mining under climate change","interactions":[],"lastModifiedDate":"2026-06-05T13:48:19.979755","indexId":"70276457","displayToPublicDate":"2026-05-28T08:43:26","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8956,"text":"Communications Earth & Environment","active":true,"publicationSubtype":{"id":10}},"title":"Future water constraints on United States lithium mining under climate change","docAbstract":"Lithium is necessary for low-carbon technologies that combat climate change, but lithium extraction is water-intensive. Changes in temperature and precipitation arising from climate change are altering water distribution, which could further strain supplies for new mines and industry, farms, and households. Here we explored how climate change, water use, and mining siting could impact lithium mining in the United States. We analyzed whether there would be sufficient water available to support the single existing and 22 proposed U.S. lithium mines at mid-century under four socioeconomic-climate scenarios and five climate models. Though dependent on socioeconomic-climate scenario, climate model, and lithium deposit type, available water supply in most subbasins would likely be unable to support new mines’ water demands, or even non-mining water demands from other sectors. Water scarcity could hinder the ability of the United States to produce enough lithium to meet domestic demand thereby necessitating higher imports.
","language":"English","publisher":"Nature","doi":"10.1038/s43247-026-03643-4","usgsCitation":"Trost, J.N., Nassar, N.T., and Dunn, J.B., 2026, Future water constraints on United States lithium mining under climate change: Communications Earth & Environment, https://doi.org/10.1038/s43247-026-03643-4.","ipdsId":"IP-182933","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":505458,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s43247-026-03643-4","text":"Publisher Index Page"},{"id":505085,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"conterminous United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n [\n [\n [\n -94.81758,\n 49.38905\n ],\n [\n 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nnassar@usgs.gov","orcid":"https://orcid.org/0000-0001-8758-9732","contributorId":197864,"corporation":false,"usgs":true,"family":"Nassar","given":"Nedal","email":"nnassar@usgs.gov","middleInitial":"T.","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":962434,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dunn, Jennifer B. 0000-0002-2065-5106","orcid":"https://orcid.org/0000-0002-2065-5106","contributorId":371832,"corporation":false,"usgs":false,"family":"Dunn","given":"Jennifer","middleInitial":"B.","affiliations":[{"id":25254,"text":"Northwestern University","active":true,"usgs":false}],"preferred":false,"id":962435,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70276357,"text":"70276357 - 2026 - Moment magnitude for small earthquakes in the Delaware basin of west Texas and southeast New Mexico, USA","interactions":[],"lastModifiedDate":"2026-06-02T14:53:34.270133","indexId":"70276357","displayToPublicDate":"2026-05-27T07:42:42","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Moment magnitude for small earthquakes in the Delaware basin of west Texas and southeast New Mexico, USA","docAbstract":"The Delaware Basin region of west Texas and southeast New Mexico has become one of the most prolific regions of seismic activity in the continental United States due to widespread hydraulic fracturing and wastewater disposal injection. In response to the increased number of earthquakes in this region, rapid and accurate characterization of earthquake sources is necessary to understand the evolution of seismic activity and level of seismic hazard associated with these earthquakes. This study re-evaluates earthquake magnitudes, estimating moment magnitude (MW) for small earthquakes in the Delaware Basin using 1) moment-rate spectra derived from S-wave coda envelopes, and 2) a relative magnitude method that relies exclusively on the ratio of waveform amplitudes between highly correlated waveform pairs. The coda-envelope method produces accurate MW estimates for small earthquakes (M 1.5 – 3) that are consistent with independent, waveform modeled moment magnitudes for events with MW > 3. Using the relative amplitudes method to extend these MW magnitudes to many other events, we successfully provide relative moment magnitude (MW,rel) values for 81% of the Texas Seismological Network catalog in the Delaware Basin region, and 45% of the USGS Induced Seismicity Project’s catalog of events in southeast New Mexico. The adoption and integration of the calibrated MW,rel method with current magnitude estimation methods offers valuable insights into the relationships between local and moment magnitude and will contribute to improved characterization of widespread induced seismicity.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220250246","usgsCitation":"Gable, S., Huang, Y., Shelly, D.R., and Rubinstein, J.L., 2026, Moment magnitude for small earthquakes in the Delaware basin of west Texas and southeast New Mexico, USA: Seismological Research Letters, 13 p., https://doi.org/10.1785/0220250246.","productDescription":"13 p.","ipdsId":"IP-183105","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":505047,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1785/0220250246","text":"Publisher Index Page"},{"id":504950,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico, Texas","otherGeospatial":"southeast New Mexico, west Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.50900660650426,\n 32.93869833342073\n ],\n [\n -102.78073180819358,\n 32.861136118194196\n ],\n [\n -102.84970324032894,\n 30.88089004482086\n ],\n [\n -106.51434494246497,\n 30.96526183674557\n ],\n [\n -106.50900660650426,\n 32.93869833342073\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Online First","noUsgsAuthors":false,"publicationDate":"2026-05-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Gable, Sydney","contributorId":371633,"corporation":false,"usgs":false,"family":"Gable","given":"Sydney","affiliations":[{"id":37387,"text":"University of Michigan","active":true,"usgs":false}],"preferred":false,"id":962204,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Huang, Yihe","contributorId":276214,"corporation":false,"usgs":false,"family":"Huang","given":"Yihe","email":"","affiliations":[{"id":56937,"text":"Univ Michigan","active":true,"usgs":false}],"preferred":false,"id":962205,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shelly, David R. 0000-0003-2783-5158 dshelly@usgs.gov","orcid":"https://orcid.org/0000-0003-2783-5158","contributorId":206750,"corporation":false,"usgs":true,"family":"Shelly","given":"David","email":"dshelly@usgs.gov","middleInitial":"R.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":962206,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rubinstein, Justin L. 0000-0003-1274-6785","orcid":"https://orcid.org/0000-0003-1274-6785","contributorId":215341,"corporation":false,"usgs":true,"family":"Rubinstein","given":"Justin","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":962207,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70275747,"text":"sir20265010 - 2026 - Continuous and high-resolution longitudinal profiles of the water surface and riverbed elevation for 282 miles of the Colorado River from Lees Ferry to Pearce Ferry, Arizona, 2021","interactions":[],"lastModifiedDate":"2026-05-26T18:25:26.706081","indexId":"sir20265010","displayToPublicDate":"2026-05-26T10:00:00","publicationYear":"2026","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2026-5010","displayTitle":"Continuous and High-Resolution Longitudinal Profiles of the Water Surface and Riverbed Elevation for 282 Miles of the Colorado River From Lees Ferry To Pearce Ferry, Arizona, 2021","title":"Continuous and high-resolution longitudinal profiles of the water surface and riverbed elevation for 282 miles of the Colorado River from Lees Ferry to Pearce Ferry, Arizona, 2021","docAbstract":"Longitudinal profiles of water surface and riverbed elevations capture key geomorphic characteristics that can be affected by water infrastructure and natural processes. Continuous water surface profiles of the Colorado River in Grand Canyon, a river influenced by two of the largest dams in the United States, have been measured infrequently. The water surface profile was first measured in 1923, 13 years before the completion of Hoover Dam, which impounded water into western Grand Canyon, and 40 years before the completion of Glen Canyon Dam, which affected streamflow and sediment supply for all of Grand Canyon. The water surface profile was next measured in 2000, 37 years after the completion of Glen Canyon Dam, although this profile did not include the segment affected by Hoover Dam. A continuous profile of riverbed elevations has never been published. Here, we present the first complete, coupled water surface and riverbed elevation profiles, collected in 2021 during a period of steady releases from Glen Canyon Dam. The profiles were constructed from positions and elevations measured by boat-based global navigation satellite systems and from bathymetry collected by multibeam sonar. Data collected by boat were supplemented by data from a photogrammetry-derived digital surface model that was created from concurrently collected aerial images. Independent measurements made by conventional total stations referenced to a common geodetic control network were used to evaluate accuracy of all measurements. The final water surface and riverbed elevation profiles improved the accuracy and precision reported for previous profiles. In this study, the mean absolute vertical accuracy of water surface elevations was 0.07 meter for 85 percent of river miles and 0.19 meter for 11 percent of river miles. For the remaining 4 percent of river miles, water surface elevations were interpolated between measured values. The profiles reported herein can be used for current assessment of Colorado River geomorphic conditions, quantification of changes in the river over time, and predictive modeling of river resources for potential future management scenarios.quantification of changes in the river over time, and predictive modeling of river resources for potential future management scenarios.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20265010","usgsCitation":"Sartain, S.L., Kaplinski, M.A., Kohl, K., Chapman, K.A., Bransky, N.D., Sankey, J.B., and Grams, P.E., 2026, Continuous and high-resolution longitudinal profiles of the water surface and riverbed elevation for 282 miles of the Colorado River from Lees Ferry to Pearce Ferry, Arizona, 2021: U.S. Geological Survey Scientific Investigations Report 2026–5010, 40 p., https://doi.org/10.3133/sir20265010.","productDescription":"Report: vii, 40 p.; Data Release","numberOfPages":"40","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-179784","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":504710,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119446.htm","linkFileType":{"id":5,"text":"html"}},{"id":504453,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2026/5010/sir20265010.pdf","text":"Report","size":"6.81 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2026-5010 PDF"},{"id":504457,"rank":2,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20265010/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2026-5010 HTML"},{"id":504458,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2026/5010/sir20265010.XML","description":"SIR 2026-5010 XML"},{"id":504459,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2026/5010/images"},{"id":504460,"rank":5,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2026/5010/coverthb.jpg"},{"id":504461,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P135FNFM","text":"USGS data release","linkHelpText":"Continuous and high-resolution profiles of the water surface and riverbed elevation for 282 miles of the Colorado River from Lees Ferry to Pearce Ferry, AZ, 2021—Data"}],"country":"United States","state":"Arizona","otherGeospatial":"Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.415697136473,\n 36.90189262731032\n ],\n [\n -114.01161104871596,\n 36.90189262731032\n ],\n [\n -114.01161104871596,\n 35.51758910449131\n ],\n [\n -111.415697136473,\n 35.51758910449131\n ],\n [\n -111.415697136473,\n 36.90189262731032\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Southwest Biological Science Center
U.S. Geological Survey
2255 N. Gemini Drive
Flagstaff, AZ 86001
We measured the elevation of 282 miles of the water surface and riverbed of the Colorado River in Grand Canyon, from Lees Ferry, Arizona, to Pearce Ferry, Ariz. We collected water surface and riverbed elevations during a period of steady releases from Glen Canyon Dam in 2021. We used multiple, concurrent methods to measure the elevation of the water surface and assessed error for each measurement method to use the most accurate data possible in the final elevation profile. The final water surface profile is measured to the centimeter every river hundredth mile, with vertical uncertainty less than or equal to 0.07 meter for 85 percent of the river and less than or equal to 0.19 meter for the remainder of the river. We collected bathymetry of the river centerline everywhere possible, which did not include rapids and shallow areas. This study is the third measurement of a complete water surface profile; the first was collected in 1923, 40 years before Glen Canyon Dam was completed, and the second was collected in 2000, 37 years after Glen Canyon Dam was completed. A continuous riverbed profile had not been collected previously.
","publicationDate":"2026-05-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Sartain, Shannon L. 0000-0003-2395-6825","orcid":"https://orcid.org/0000-0003-2395-6825","contributorId":290222,"corporation":false,"usgs":true,"family":"Sartain","given":"Shannon","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":961627,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kaplinski, Matthew A. 0000-0001-6232-8325","orcid":"https://orcid.org/0000-0001-6232-8325","contributorId":333646,"corporation":false,"usgs":true,"family":"Kaplinski","given":"Matthew","email":"","middleInitial":"A.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":961628,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kohl, Keith 0000-0001-6812-0373","orcid":"https://orcid.org/0000-0001-6812-0373","contributorId":371349,"corporation":false,"usgs":false,"family":"Kohl","given":"Keith","affiliations":[{"id":88119,"text":"NOAA, National Geodetic Survey, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":961629,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chapman, Katherine A. 0009-0009-1806-6474 kchapman@usgs.gov","orcid":"https://orcid.org/0009-0009-1806-6474","contributorId":345014,"corporation":false,"usgs":true,"family":"Chapman","given":"Katherine","email":"kchapman@usgs.gov","middleInitial":"A.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":961630,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bransky, Nathaniel D. 0000-0003-3113-7491","orcid":"https://orcid.org/0000-0003-3113-7491","contributorId":305709,"corporation":false,"usgs":true,"family":"Bransky","given":"Nathaniel","middleInitial":"D.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":961631,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sankey, Joel B. 0000-0003-3150-4992","orcid":"https://orcid.org/0000-0003-3150-4992","contributorId":261248,"corporation":false,"usgs":true,"family":"Sankey","given":"Joel B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":961632,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Grams, Paul E. 0000-0002-0873-0708","orcid":"https://orcid.org/0000-0002-0873-0708","contributorId":212943,"corporation":false,"usgs":true,"family":"Grams","given":"Paul","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":961633,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70276636,"text":"70276636 - 2026 - From start to stop: Simple methods for mapping susceptibility to landslide runout and debris-flow inundation","interactions":[],"lastModifiedDate":"2026-06-12T14:17:21.228806","indexId":"70276636","displayToPublicDate":"2026-05-26T09:00:54","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2604,"text":"Landslides","active":true,"publicationSubtype":{"id":10}},"title":"From start to stop: Simple methods for mapping susceptibility to landslide runout and debris-flow inundation","docAbstract":"Landslide runout and debris-flow inundation can disrupt areas well beyond their initial sources, causing widespread damage and extensive fatalities. Understanding where they start and how far they might travel is essential in many locations worldwide. However, most landslide susceptibility maps focus on initiation areas and fail to incorporate runout areas. Furthermore, hazards vary greatly with the degree of landslide mobility, and debris flows can grow as they travel beyond their initial source. Here, we use the USGS Grfin Tools software suite to map susceptible areas from start to stop in the Federated States of Micronesia where landslide and debris-flows runout onto gentle ground is a deadly threat. The DEM-based models in Grfin Tools use simple, empirical, and well-documented approaches that require minimal parameters and, thus, can be used in areas that lack detailed physical properties. We illustrate methods to estimate the required parameters using direct landslide observations, and we suggest alternate methods if direct observations are absent. Our simple parameter estimations in the Federated States of Micronesia successfully predict areas that match past landslide runout and debris-flow inundation, as well as zones with no inundation. In addition to creating preliminary regional assessments, Grfin Tools can be used for comparing multiple scenarios and/or identifying areas for further investigations.
","language":"English","publisher":"Springer","doi":"10.1007/s10346-026-02751-8","usgsCitation":"Reid, M.E., Cerovski-Darriau, C., Brien, D.L., Leb, I., and Cyr, A.J., 2026, From start to stop: Simple methods for mapping susceptibility to landslide runout and debris-flow inundation: Landslides, https://doi.org/10.1007/s10346-026-02751-8.","ipdsId":"IP-180986","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":505549,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Federated States of Micronesia","otherGeospatial":"Chuuk, Kosrae, Pohnpei","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 163.05349206922193,\n 5.386585845343674\n ],\n [\n 162.8882457651913,\n 5.386585845343674\n ],\n [\n 162.8882457651913,\n 5.249999335212365\n ],\n [\n 163.05349206922193,\n 5.249999335212365\n ],\n [\n 163.05349206922193,\n 5.386585845343674\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 158.10270329195367,\n 7.03005149396914\n ],\n [\n 158.38032341179485,\n 7.03005149396914\n ],\n [\n 158.38032341179485,\n 6.7620431146946345\n ],\n [\n 158.10270329195367,\n 6.7620431146946345\n ],\n [\n 158.10270329195367,\n 7.03005149396914\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 151.96242420771364,\n 7.49357812591748\n ],\n [\n 151.50337875009063,\n 7.49357812591748\n ],\n [\n 151.50337875009063,\n 7.22201647102294\n ],\n [\n 151.96242420771364,\n 7.22201647102294\n ],\n [\n 151.96242420771364,\n 7.49357812591748\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Online First","noUsgsAuthors":false,"publicationDate":"2026-05-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Reid, Mark E. 0000-0002-5595-1503 mreid@usgs.gov","orcid":"https://orcid.org/0000-0002-5595-1503","contributorId":1167,"corporation":false,"usgs":true,"family":"Reid","given":"Mark","email":"mreid@usgs.gov","middleInitial":"E.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":962918,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cerovski-Darriau, Corina 0000-0002-0543-0902","orcid":"https://orcid.org/0000-0002-0543-0902","contributorId":221159,"corporation":false,"usgs":true,"family":"Cerovski-Darriau","given":"Corina","email":"","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":962919,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brien, Dianne L. 0000-0003-3227-7963 dbrien@usgs.gov","orcid":"https://orcid.org/0000-0003-3227-7963","contributorId":229851,"corporation":false,"usgs":true,"family":"Brien","given":"Dianne","email":"dbrien@usgs.gov","middleInitial":"L.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":962920,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Leb, Isaac Henry 0009-0000-7643-6941","orcid":"https://orcid.org/0009-0000-7643-6941","contributorId":339798,"corporation":false,"usgs":true,"family":"Leb","given":"Isaac Henry","affiliations":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true}],"preferred":true,"id":962921,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cyr, Andrew J. 0000-0003-2293-5395 acyr@usgs.gov","orcid":"https://orcid.org/0000-0003-2293-5395","contributorId":3539,"corporation":false,"usgs":true,"family":"Cyr","given":"Andrew","email":"acyr@usgs.gov","middleInitial":"J.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":962922,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70276574,"text":"70276574 - 2026 - Putting weight to work: A review and examples of weight-based indicators in freshwater fish stock assessment","interactions":[],"lastModifiedDate":"2026-06-09T16:17:19.51464","indexId":"70276574","displayToPublicDate":"2026-05-23T09:14:14","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Putting weight to work: A review and examples of weight-based indicators in freshwater fish stock assessment","docAbstract":"Despite being a direct measure of biomass and central to fisheries management, weight-based metrics remain underutilized in freshwater fish stock assessment. Here, we present a concise review of the application of weight in evaluating freshwater fish populations. We examine the historical use of weighing, assess how weight is applied across subdisciplines of freshwater fish science, contrast weight- and length-based approaches, and identify biases in their application. We then synthesize weight-based metrics, indices, and models within four broad categories—population and community weight structure; condition, growth, and efficiency; reproductive potential and production; and yield and exploitation dynamics—highlighting approaches that inform fish ecology, population and community dynamics, and vital rates. We conclude by identifying key opportunities and methodological innovations needed to expand the effective use of weight-based metrics in freshwater fish conservation and management.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/najfmt/vqag021","usgsCitation":"Miranda, L.E., Angulo-Valencia, M.A., and Fraser, C.E., 2026, Putting weight to work: A review and examples of weight-based indicators in freshwater fish stock assessment: North American Journal of Fisheries Management, vqag021, 27 p., https://doi.org/10.1093/najfmt/vqag021.","productDescription":"vqag021, 27 p.","ipdsId":"IP-184202","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":505242,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"Online First","noUsgsAuthors":false,"publicationDate":"2026-05-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Miranda, Leandro E. 0000-0002-2138-7924 smiranda@usgs.gov","orcid":"https://orcid.org/0000-0002-2138-7924","contributorId":531,"corporation":false,"usgs":true,"family":"Miranda","given":"Leandro","email":"smiranda@usgs.gov","middleInitial":"E.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":962704,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Angulo-Valencia, Mirtha A.","contributorId":372064,"corporation":false,"usgs":false,"family":"Angulo-Valencia","given":"Mirtha","middleInitial":"A.","affiliations":[{"id":88247,"text":"Department of Wildlife and Fisheries, Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":962705,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fraser, Camren E.","contributorId":372065,"corporation":false,"usgs":false,"family":"Fraser","given":"Camren","middleInitial":"E.","affiliations":[{"id":88247,"text":"Department of Wildlife and Fisheries, Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":962706,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70276280,"text":"70276280 - 2026 - Biochar modulates the dynamics of legacy nutrients in enhancing soil health and crop productivity","interactions":[],"lastModifiedDate":"2026-05-26T14:24:39.646272","indexId":"70276280","displayToPublicDate":"2026-05-22T09:17:54","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2596,"text":"Land","active":true,"publicationSubtype":{"id":10}},"title":"Biochar modulates the dynamics of legacy nutrients in enhancing soil health and crop productivity","docAbstract":"Most major crops in agricultural soils exhibit relatively low nutrient use efficiency for nitrogen (N), phosphorus (P), and potassium (K), often necessitating supplemental nutrient inputs to achieve sustainable yields. Furthermore, the increasing use of biowastes such as compost, manure, and biosolids, which frequently have nutrient ratios that do not match crop requirements, has contributed to excessive nutrient inputs and subsequent accumulation in soils. This situation has been further exacerbated by intensive farming practices involving multiple cropping cycles per season. Overuse of nutrients causes them to accumulate in the soil, creating a legacy nutrient pool. The application of biochar as soil amendment is considered a potential strategy to control legacy nutrients dynamics. The current review inspects the possible value of biochar in modulating legacy nutrient reserves in the soil, thereby increasing the bioavailability of nutrients and improving crop yield. This review discusses the search scope and synthesis approaches for the bibliometric methodological component through rigorous screening process (Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA)), focusing on journal articles published in last 20 years that specifically address legacy nutrient management. The significance of the economic and environmental effects of legacy nutrients and the insufficient knowledge of how biochar application influences nutrient dynamics in soil highlight the necessity for additional research to address current gaps.
","language":"English","publisher":"MDPI","doi":"10.3390/land15060896","usgsCitation":"Kumar, M., Bolan, S., Kumar, R., Gupta, J., Chen, D., Wu, H., Stackpoole, S.M., Chandel, N., Mukherjee, S., Chandra Garg, M., Mayilswami, S., Siddique, K.H., and Bolan, N., 2026, Biochar modulates the dynamics of legacy nutrients in enhancing soil health and crop productivity: Land, v. 15, no. 6, 896, 38 p., https://doi.org/10.3390/land15060896.","productDescription":"896, 38 p.","ipdsId":"IP-178871","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":504809,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/land15060896","text":"Publisher Index Page"},{"id":504694,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"6","noUsgsAuthors":false,"publicationDate":"2026-05-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Kumar, Manish 0000-0002-1444-5508","orcid":"https://orcid.org/0000-0002-1444-5508","contributorId":371510,"corporation":false,"usgs":false,"family":"Kumar","given":"Manish","affiliations":[{"id":88165,"text":"Amity Institute of Environmental Sciences (AIES), Amity University Uttar Pradesh (AUUP), Noida, India","active":true,"usgs":false}],"preferred":false,"id":961932,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bolan, Shiv","contributorId":371511,"corporation":false,"usgs":false,"family":"Bolan","given":"Shiv","affiliations":[{"id":88166,"text":"UWA School of Agriculture and Environment, The University of Western Australia, Perth, Western Australia 6009, Australia","active":true,"usgs":false}],"preferred":false,"id":961933,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kumar, Rakesh 0000-0001-7264-5682","orcid":"https://orcid.org/0000-0001-7264-5682","contributorId":371512,"corporation":false,"usgs":false,"family":"Kumar","given":"Rakesh","affiliations":[{"id":88167,"text":"Department of Biosystems Engineering, Auburn University, Auburn, Alabama, USA","active":true,"usgs":false}],"preferred":false,"id":961934,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gupta, Juhi","contributorId":371513,"corporation":false,"usgs":false,"family":"Gupta","given":"Juhi","affiliations":[{"id":88165,"text":"Amity Institute of Environmental Sciences (AIES), Amity University Uttar Pradesh (AUUP), Noida, India","active":true,"usgs":false}],"preferred":false,"id":961935,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chen, Dingjiang","contributorId":371514,"corporation":false,"usgs":false,"family":"Chen","given":"Dingjiang","affiliations":[{"id":88168,"text":"College of Environmental & Resource Sciences, Zhejiang University, Hangzhou 310058, China","active":true,"usgs":false}],"preferred":false,"id":961936,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wu, Hao","contributorId":254382,"corporation":false,"usgs":false,"family":"Wu","given":"Hao","email":"","affiliations":[{"id":25550,"text":"Virginia Polytechnic Institute and State University","active":true,"usgs":false}],"preferred":false,"id":961937,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stackpoole, Sarah M. 0000-0002-5876-4922","orcid":"https://orcid.org/0000-0002-5876-4922","contributorId":211238,"corporation":false,"usgs":true,"family":"Stackpoole","given":"Sarah","email":"","middleInitial":"M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":961938,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Chandel, Nitika","contributorId":371516,"corporation":false,"usgs":false,"family":"Chandel","given":"Nitika","affiliations":[{"id":88171,"text":"School of Agriculture, Shoolini University of Biotechnology and Management Sciences, Solan 173229, India","active":true,"usgs":false}],"preferred":false,"id":961940,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Mukherjee, Santanu","contributorId":371515,"corporation":false,"usgs":false,"family":"Mukherjee","given":"Santanu","affiliations":[{"id":88171,"text":"School of Agriculture, Shoolini University of Biotechnology and Management Sciences, Solan 173229, India","active":true,"usgs":false}],"preferred":false,"id":961939,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Chandra Garg, Manoj","contributorId":371532,"corporation":false,"usgs":false,"family":"Chandra Garg","given":"Manoj","affiliations":[],"preferred":false,"id":961962,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Mayilswami, Srinithi 0000-0002-9480-4522","orcid":"https://orcid.org/0000-0002-9480-4522","contributorId":371517,"corporation":false,"usgs":false,"family":"Mayilswami","given":"Srinithi","affiliations":[{"id":88172,"text":"Practical Environmental technologies Pvt Ltd, Site no. 40/41, Super Garden extension, Vadavalli, Coimbatore, Tamil Nadu, India - 641041","active":true,"usgs":false}],"preferred":false,"id":961941,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Siddique, Kadambot H. 0000-0001-6097-4235","orcid":"https://orcid.org/0000-0001-6097-4235","contributorId":371518,"corporation":false,"usgs":false,"family":"Siddique","given":"Kadambot","middleInitial":"H.","affiliations":[{"id":88173,"text":"The UWA Institute of Agriculture, The University of Western Australia, Perth, Western Australia 6009, Australia","active":true,"usgs":false}],"preferred":false,"id":961942,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Bolan, Nanthi 0000-0003-2056-1692","orcid":"https://orcid.org/0000-0003-2056-1692","contributorId":371519,"corporation":false,"usgs":false,"family":"Bolan","given":"Nanthi","affiliations":[{"id":88166,"text":"UWA School of Agriculture and Environment, The University of Western Australia, Perth, Western Australia 6009, Australia","active":true,"usgs":false}],"preferred":false,"id":961943,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70276302,"text":"70276302 - 2026 - Waves, watersheds, and sediment in a coral reef embayment: Towards parsimonious models of accumulation and composition","interactions":[],"lastModifiedDate":"2026-05-27T14:03:54.905785","indexId":"70276302","displayToPublicDate":"2026-05-22T08:55:10","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1587,"text":"Estuarine, Coastal and Shelf Science","active":true,"publicationSubtype":{"id":10}},"title":"Waves, watersheds, and sediment in a coral reef embayment: Towards parsimonious models of accumulation and composition","docAbstract":"High sedimentation rates can damage coral reef ecosystems. Sedimentation rates are controlled by both sediment loads from watersheds and resuspension by waves and associated circulation patterns, but the outcomes are system specific and difficult to predict. The percent terrigenous (non-organic and non-carbonaceous) material in sediment is also often used as an indicator of watershed influence, but its dynamics are poorly understood. Sediment accumulation rates, particle size, and percent terrigenous were monitored quasi-monthly for one year (March 2014-April 2015) at nine sites in a coral reef-fringed embayment in American Samoa, where an aggregate quarry had increased sediment loads to the coast but mitigation reduced loads during the monitored period. Gross and net sediment accumulation rates were measured using sediment traps and SedPods (pods), respectively. Gross accumulation rates exceeded thresholds for impacts on coral health during at least one collection period at most sites, with more exceedances on the northern reef where water residence times and sediment availability are higher and corals show signs of sediment stress. Percent terrigenous of coarse sediment was higher in the traps and pods compared with the surrounding benthic sediment, indicating that some of the terrigenous sediment was advected through the bay without accumulating on the reef. The 95th percentile of hourly wave energy density (E95) taken from a global wave model (WaveWatch 3) was the best predictor of gross accumulation rates of both total and carbonate sediment in a log-log regression at most (n = 6) sites (R2 range 0.72-0.92), indicating a strong role of resuspension of benthic sediment. Gross accumulation rates of terrigenous sediment were not correlated with E95 and only correlated with SSY at the site nearest the stream mouth, indicating that most terrigenous sediment was not from resuspended benthic material but rather from a consistent watershed source. Percent terrigenous decreased with increasing wave energy due to high accumulation rates of carbonates during periods of high wave energy. Detection of the impact of sediment mitigation at the quarry on sediment accumulation was complicated by low wave energy in the period following mitigation. The use of gross accumulation rates and percent terrigenous as indicators of the magnitude and sources of sediment accumulation over time needs to account for wave-induced resuspension, which can be modelled with a simple power function using inputs from a global wave model.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecss.2026.109952","usgsCitation":"Biggs, T., Messina, A., and Storlazzi, C.D., 2026, Waves, watersheds, and sediment in a coral reef embayment: Towards parsimonious models of accumulation and composition: Estuarine, Coastal and Shelf Science, no. 339, 109952, 16 p., https://doi.org/10.1016/j.ecss.2026.109952.","productDescription":"109952, 16 p.","ipdsId":"IP-176787","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":504811,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecss.2026.109952","text":"Publisher Index Page"},{"id":504731,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"American Samoa, Faga'alu Bay, Tutuila Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -170.686,\n -14.286\n ],\n [\n -170.674,\n -14.286\n ],\n [\n -170.674,\n -14.296\n ],\n [\n -170.686,\n -14.296\n ],\n [\n -170.686,\n -14.286\n ]\n ]\n ]\n }\n }\n ]\n}","issue":"339","noUsgsAuthors":false,"publicationDate":"2026-05-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Biggs, Trent","contributorId":208268,"corporation":false,"usgs":false,"family":"Biggs","given":"Trent","affiliations":[],"preferred":false,"id":962036,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Messina, Alex","contributorId":174670,"corporation":false,"usgs":false,"family":"Messina","given":"Alex","email":"","affiliations":[],"preferred":false,"id":962037,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Storlazzi, Curt D. 0000-0001-8057-4490","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":213610,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","middleInitial":"D.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":962038,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70276333,"text":"70276333 - 2026 - High-resolution transboundary vegetation community maps of the Sonoran and Mojave Desert ecoregion to support critical landscape conservation planning and habitat management needs","interactions":[],"lastModifiedDate":"2026-05-29T14:04:44.858669","indexId":"70276333","displayToPublicDate":"2026-05-22T08:54:15","publicationYear":"2026","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5883,"text":"Cooperator Report","active":true,"publicationSubtype":{"id":1}},"title":"High-resolution transboundary vegetation community maps of the Sonoran and Mojave Desert ecoregion to support critical landscape conservation planning and habitat management needs","docAbstract":"We produced a 30-m resolution binational land cover map of Bird Conservation Region 33 (BCR 33) for the U.S. North American Bird Conservation Initiative. The region covers large portions of the Sonoran and Mojave Deserts. The map can support the U.S. Fish and Wildlife Service (FWS) Migratory Bird Program’s recovery planning efforts and constitutes the first known binational land cover dataset spanning sections of the United States–Mexico border and using a consistent classification system for both countries. The mapped region includes 152 distinct land cover classes, covering a total area of 38,421,453 ha (148,345 mi2), of which 13,148,345 ha (52,706 mi2) are located in Mexico and 24,770,640 ha (95,639 mi2) in the United States.
We primarily used Landsat 8 (OLI) imagery, supplemented by limited ground surveys from two field campaigns, drone-based aerial data, and existing vegetation classification frameworks from both countries. The classification applied a data-fusion approach integrating 30-m Landsat 8 imagery, decadal phenology metrics from vegetation indices, and a random forest model trained mainly with datasets from a comprehensive national mapping project from the U.S. Geological Survey (USGS) GAP Analysis Project (GAP) and federal wildland fire agencies’ Landscape Fire and Resource Management Planning Tools (LANDFIRE) (GAP/LANDFIRE) [United States side] and the National Institute of Statistics and Geography (INEGI) [Mexico side] as well as land cover maps and opportunistic open-access and field observations.
Mapping of the full BCR 33 region was carried out in two phases: 1) Phase I, the prototype map, covered a smaller portion of the transboundary area and identified 31 land cover classes, and 2) Phase II, the full BCR 33 map (refer to Figure 1), which resulted in 152 land cover classes. Using a Random Forest classifier, we achieved an overall prediction accuracy of 92% for the Phase I map and 87% for the Phase II full region map. This slight decrease can be attributed to working on a larger, more complex area with a greater number of land cover classes. No formal validation was conducted, aside from using a subset of the collected field observations and training data to assess model performance during and after training. The training sites were further verified using Google Earth (Google, 2026) imagery. Two undergraduate students who worked for over a year visually inspected imagery and open access public images to confirm each training site during model training using in-house developed, online, visual tools. A portion of this field training data was reserved for model validation, and the corresponding results are to be presented in later sections.
The project developed an end-to-end, medium- and fine-resolution remote sensing–based data fusion mapping approach. This effort produced a map (Nagler et al., 2025) and the online tools to support a dynamic, live, online map for visualizing the transboundary vegetation communities in BCR 33. The toolset is currently hosted by the University of Arizona (UofA) Vegetation Index and Phenology (VIP) Lab to support FWS partners (https://vip.arizona.edu/viplab_data_explorer?LCM_BCR33). The online map is designed to allow rapid updates using new training, validation, or correction data, making it dynamic and maintainable.
The approach we took established a framework for rapid updating and correction of land cover maps, as the model can be quickly retrained with new field observations, updated training data, or other sources. This enables dynamic mapping and change detection of the region’s vegetation. This framework is an advance in data fusion and crowdsourced mapping of complex, vulnerable regions, providing support to regional stakeholders and the wider user community.
This transboundary map can inform the protection, conservation, and restoration of vegetation, habitat, and ecosystems, particularly for threatened and endangered species across the two nations using consistent and harmonized binational mapping systems. Beyond supporting land management decisions and stakeholders in the transboundary desert ecoregions, this BCR 33 mapping effort establishes a foundation for future rapid, low-cost, cross-border land cover mapping that can benefit and advance ecosystem management.
","language":"English","publisher":"U.S. Fish and Wildlife Service","usgsCitation":"Nagler, P.L., Duberstein, J., Broska, J., Didan, K., and Traphagen, M.B., 2026, High-resolution transboundary vegetation community maps of the Sonoran and Mojave Desert ecoregion to support critical landscape conservation planning and habitat management needs: Cooperator Report, Report: 79 p.; Appendix.","productDescription":"Report: 79 p.; Appendix","ipdsId":"IP-170033","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":504866,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":504862,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://iris.fws.gov/APPS/ServCat/Reference/Profile/178257"}],"country":"Mexico, United States","state":"Arizona, Baja California, California, Nevada, Sinaloa, Sonora","otherGeospatial":"Sonoran and Mojave Desert ecoregion","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.6219144,\n 24.5008461\n ],\n [\n -108.3403468,\n 26.2416244\n ],\n [\n -109.9404918,\n 28.3885241\n ],\n [\n -110.7895483,\n 33.3745204\n ],\n [\n -114.3490544,\n 37.6779633\n ],\n [\n -118.0391846,\n 38.0389224\n ],\n [\n -118.8882411,\n 35.5285396\n ],\n [\n -115.7206072,\n 32.1940187\n ],\n [\n -115.0674868,\n 30.2674399\n ],\n [\n -114.7409266,\n 30.2674399\n ],\n [\n -114.6429586,\n 31.6118369\n ],\n [\n -113.597966,\n 31.2775142\n ],\n [\n -113.2060937,\n 30.8299019\n ],\n [\n -112.8142215,\n 29.7584522\n ],\n [\n -111.6059488,\n 28.3885241\n ],\n [\n -111.0507964,\n 27.9278682\n ],\n [\n -110.4979306,\n 27.216354\n ],\n [\n -109.8448102,\n 26.7215756\n ],\n [\n -109.2570019,\n 26.3417564\n ],\n [\n -109.3549699,\n 25.7843897\n ],\n [\n -107.6219144,\n 24.5008461\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2026-05-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Nagler, Pamela L 0000-0003-0674-103X","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":363785,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","middleInitial":"L","affiliations":[],"preferred":true,"id":962127,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duberstein, Jennie N.","contributorId":342635,"corporation":false,"usgs":false,"family":"Duberstein","given":"Jennie N.","affiliations":[{"id":40296,"text":"United States Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":962128,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Broska, James","contributorId":371614,"corporation":false,"usgs":false,"family":"Broska","given":"James","affiliations":[{"id":88192,"text":"Assistant Regional Director, Science Applications, Albuquerque, NM","active":true,"usgs":false}],"preferred":false,"id":962129,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Didan, Kamel","contributorId":292780,"corporation":false,"usgs":false,"family":"Didan","given":"Kamel","affiliations":[{"id":62999,"text":"Biosystems Engineering, University of Arizona, Tucson, AZ, 85721 USA","active":true,"usgs":false}],"preferred":false,"id":962131,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Traphagen, Myles B.","contributorId":299076,"corporation":false,"usgs":false,"family":"Traphagen","given":"Myles","email":"","middleInitial":"B.","affiliations":[{"id":64759,"text":"Wildlands Network","active":true,"usgs":false}],"preferred":false,"id":962130,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70276490,"text":"70276490 - 2026 - Patterns of rift basin development and the fidelity of the subsidence record: Insights through Bayesian modeling of rapid tectonic subsidence in a Rio Grande rift basin, Socorro, NM, U.S.A","interactions":[],"lastModifiedDate":"2026-06-08T15:15:42.270728","indexId":"70276490","displayToPublicDate":"2026-05-22T08:08:27","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1427,"text":"Earth and Planetary Science Letters","active":true,"publicationSubtype":{"id":10}},"title":"Patterns of rift basin development and the fidelity of the subsidence record: Insights through Bayesian modeling of rapid tectonic subsidence in a Rio Grande rift basin, Socorro, NM, U.S.A","docAbstract":"","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2026.120097","usgsCitation":"Smith, T.M., Gaynor, S.P., Keller, B.C., Curry, M.E., Schoene, B., and Lapen, T.J., 2026, Patterns of rift basin development and the fidelity of the subsidence record: Insights through Bayesian modeling of rapid tectonic subsidence in a Rio Grande rift basin, Socorro, NM, U.S.A: Earth and Planetary Science Letters, v. 689, 120097, 16 p., https://doi.org/10.1016/j.epsl.2026.120097.","productDescription":"120097, 16 p.","ipdsId":"IP-182398","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":505165,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","city":"Socorro","otherGeospatial":"Magdalena Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.29774870594973,\n 34.1308536513548\n ],\n [\n -107.04756637747504,\n 34.1308536513548\n ],\n [\n -107.04756637747504,\n 33.87406748593881\n ],\n [\n -107.29774870594973,\n 33.87406748593881\n ],\n [\n -107.29774870594973,\n 34.1308536513548\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"689","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Tyson Michael 0000-0003-2834-3526","orcid":"https://orcid.org/0000-0003-2834-3526","contributorId":330276,"corporation":false,"usgs":true,"family":"Smith","given":"Tyson","email":"","middleInitial":"Michael","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":962490,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gaynor, Sean Patrick 0000-0002-8353-511X","orcid":"https://orcid.org/0000-0002-8353-511X","contributorId":346264,"corporation":false,"usgs":true,"family":"Gaynor","given":"Sean","email":"","middleInitial":"Patrick","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":962491,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Keller, Brenhin C.","contributorId":371867,"corporation":false,"usgs":false,"family":"Keller","given":"Brenhin","middleInitial":"C.","affiliations":[{"id":88229,"text":"Dartmouth College, Department of Earth Science","active":true,"usgs":false}],"preferred":false,"id":962492,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Curry, Magdalena E.","contributorId":371868,"corporation":false,"usgs":false,"family":"Curry","given":"Magdalena","middleInitial":"E.","affiliations":[{"id":88230,"text":"North Carolina State University, Marine, Earth and Atmospheric Sciences","active":true,"usgs":false}],"preferred":false,"id":962493,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schoene, Blair","contributorId":353005,"corporation":false,"usgs":false,"family":"Schoene","given":"Blair","affiliations":[{"id":6644,"text":"Princeton University","active":true,"usgs":false}],"preferred":false,"id":962494,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lapen, Tom J.","contributorId":371872,"corporation":false,"usgs":false,"family":"Lapen","given":"Tom","middleInitial":"J.","affiliations":[{"id":88233,"text":"University of Houston, Department of Earth and Atmospheric Sciences","active":true,"usgs":false}],"preferred":false,"id":962495,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70276298,"text":"70276298 - 2026 - Indicators of mercury concentration in Lake Trout: Can fish location and appearance provide information to anglers to reduce their exposure?","interactions":[],"lastModifiedDate":"2026-06-02T15:16:15.230031","indexId":"70276298","displayToPublicDate":"2026-05-21T09:34:12","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Indicators of mercury concentration in Lake Trout: Can fish location and appearance provide information to anglers to reduce their exposure?","docAbstract":"
People are exposed to mercury (Hg) through the consumption of fish. State and federal governments provide broad, often-generalized food safety guidance to reduce exposure; however, numerous rural fishing areas lack testing and location- or species-specific guidance. The aim of this study was to provide tangible, visible, or easily measured characteristics of Lake Trout Salvelinus namaycush that could convey information on Hg exposure to people harvesting and consuming fish where no location-specific guidance exists.
We investigated potential indicators of Lake Trout total Hg (THg) concentrations in muscle across 10 lakes in Alaska's national parks. Potential indicators, including lake, lake zone (i.e., littoral, pelagic, profundal), fish length, head size, body condition, and general appearance, were evaluated by competing linear mixed-effects models.
Lake Trout THg concentrations ranged widely from 22 to 1,306 ng/g wet weight. Much of the variation (48%) in THg concentrations was attributed to differences among individual lakes, but the interaction of the fish's lake zone, body length, and head size accounted for an additional 21%. Predicted THg concentrations increased with Lake Trout length and head : body proportion, but the rate of THg concentration increase with length varied by head : body proportion and lake zone.
Given the overwhelming evidence of high lake-to-lake variability in Lake Trout THg concentrations, we find support for use of lake-specific guidance when data are available. When lake-specific THg concentrations are not available, the best potential way to reduce exposure is to harvest and consume Lake Trout with mean predicted THg concentrations that are within state and federal safe consumption guidelines. This included Lake Trout from surface waters (i.e., pelagic or littoral zone) that are ≤70 cm in length; if harvesting fish from deep waters (i.e., profundal zone), lower THg concentrations were found in Lake Trout with heads ≤25% of their body length. The indicators—lake zone, length, and head size—of Lake Trout THg concentrations can provide harvesters with additional information in the absence of data for specific lakes.
Translocations have been widely used to restore and conserve bighorn sheep (Ovis canadensis) populations in North America. Some translocations have been successful, but many populations remain small and genetically isolated. Population structure can influence the viability and long-term success of reintroductions. Social ungulates often function as interconnected subpopulations (metapopulations); however, few studies evaluate subpopulation sizes, connectivity, and genetic diversity within metapopulations. To address this gap, we conducted a comprehensive study of a reintroduced Rocky Mountain bighorn sheep (Ovis canadensis canadensis) population in Dinosaur National Monument in Colorado and Utah, USA, between 2006–2020. We analyzed global positioning system (GPS) radio-collar data, genetic samples, and results of health testing to evaluate abundance, distribution, genetic structure and diversity, habitat use, movement and connectivity, and presence of or exposure to respiratory pathogens. We integrated these analyses to evaluate the outcomes of a reintroduction effort that began in 1952, over 70 years ago, and to inform management decisions in Dinosaur National Monument. We also provide a framework for evaluating metapopulation processes, including a non-invasive approach that links genetic structure with Bayesian spatial capture-recapture analyses to estimate subpopulation sizes. Despite models indicating continuous suitable habitat, we found a spatially structured population with at least 4 subpopulations with constrained connectivity. Evidence from step selection and density analyses suggested that movement among subpopulations may be limited by semi-permeable barriers including rivers and human disturbance, which could contribute to maintenance of spatial structure over time. In 2006, antibody to Mycoplasma ovipneumoniae was detected in all geographically and genetically distinct subpopulations. Widespread clinical signs of disease and a confirmed exposure to M. ovipneumoniae in 2019 indicate a long-term disease challenge. Proximity to domestic sheep creates repeated opportunities for introduction of new M. ovipneumoniae strains. We estimated abundance in 2019 at 109 (95% CrI = 87–133), composed of subpopulations ranging from 18–39 animals (95% CrIs from 11–50). Genetic diversity was relatively high compared to other reintroduced and native Rocky Mountain bighorn sheep populations, which is likely a consequence of multiple translocations from different sources. Three of 4 subpopulation centers generally aligned with the locations of original translocation release sites. Persistence in the presence of pathogens may be facilitated by metapopulation structure and moderately high genetic diversity. Conversely, metapopulation structure can also facilitate pathogen persistence. Our approach offers a path to advance understanding of the population ecology of reintroduced bighorn sheep and can inform effective conservation and management of their populations.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wmon.70011","usgsCitation":"Carroll, S., Flesch, E.P., Scoresby, S., Spencer, E., Crowhurst, R.S., Epps, C.W., Galloway, N., Janousek, W.M., and Graves, T., 2026, Ecology of reintroduced Rocky Mountain bighorn sheep in Dinosaur National Monument: Wildlife Monographs, https://doi.org/10.1002/wmon.70011.","ipdsId":"IP-170036","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":504813,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wmon.70011","text":"Publisher Index Page"},{"id":504734,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Wyoming","otherGeospatial":"Dinosaur National Monument","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.68629167900906,\n 43.55694885042547\n ],\n [\n -104.42518255194071,\n 43.55694885042547\n ],\n [\n -104.42518255194071,\n 38.60536698151091\n ],\n [\n -110.68629167900906,\n 38.60536698151091\n ],\n [\n -110.68629167900906,\n 43.55694885042547\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Online First","noUsgsAuthors":false,"publicationDate":"2026-05-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Carroll, Sarah Louise 0000-0002-5391-7627","orcid":"https://orcid.org/0000-0002-5391-7627","contributorId":352227,"corporation":false,"usgs":true,"family":"Carroll","given":"Sarah Louise","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":961989,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flesch, Elizabeth P 0000-0002-7592-8124","orcid":"https://orcid.org/0000-0002-7592-8124","contributorId":222685,"corporation":false,"usgs":false,"family":"Flesch","given":"Elizabeth","email":"","middleInitial":"P","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":961990,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Scoresby, Salix","contributorId":352228,"corporation":false,"usgs":false,"family":"Scoresby","given":"Salix","affiliations":[{"id":84134,"text":"Contractor, USGS (Northern Arizona University)","active":true,"usgs":false}],"preferred":false,"id":961991,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Spencer, Emily","contributorId":292165,"corporation":false,"usgs":false,"family":"Spencer","given":"Emily","email":"","affiliations":[],"preferred":false,"id":961992,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Crowhurst, Rachel S.","contributorId":198153,"corporation":false,"usgs":false,"family":"Crowhurst","given":"Rachel","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":961993,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Epps, Clinton W.","contributorId":359530,"corporation":false,"usgs":false,"family":"Epps","given":"Clinton","middleInitial":"W.","affiliations":[{"id":85841,"text":"Department of Fisheries, Wildlife, and Conservation Sciences, Oregon State University, Nash Hall Room 104, Corvallis, OR, 97331, USA","active":true,"usgs":false}],"preferred":false,"id":961994,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Galloway, Nathan L.","contributorId":271191,"corporation":false,"usgs":false,"family":"Galloway","given":"Nathan L.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":961995,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Janousek, William Michael 0000-0003-3978-1775","orcid":"https://orcid.org/0000-0003-3978-1775","contributorId":237980,"corporation":false,"usgs":true,"family":"Janousek","given":"William","email":"","middleInitial":"Michael","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":961996,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Graves, Tabitha A. 0000-0001-5145-2400","orcid":"https://orcid.org/0000-0001-5145-2400","contributorId":202084,"corporation":false,"usgs":true,"family":"Graves","given":"Tabitha A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":961997,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70276282,"text":"70276282 - 2026 - Modeling the seasonality of wind-driven hydrocarbon waves in Titan’s polar lakes","interactions":[],"lastModifiedDate":"2026-05-27T13:31:02.903319","indexId":"70276282","displayToPublicDate":"2026-05-21T09:25:26","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9967,"text":"JGR Planets","active":true,"publicationSubtype":{"id":10}},"title":"Modeling the seasonality of wind-driven hydrocarbon waves in Titan’s polar lakes","docAbstract":"Titan, the only body in the solar system aside from Earth with standing liquids on its surface, has polar hydrocarbon lakes and seas. As Titan’s atmosphere generates light winds, there should be waves on the surface of these lakes and seas, yet, direct wave observations are scant. We introduce and use PlanetWaves, an open source 4D spectral wave model, to study Titan’s waves and create seasonal maps of wave shape and propagation on Ontario Lacus and Ligeia Mare. Titan’s modeled waves grow up to 30 times larger than terrestrial waves for the same wind speed, are seasonally present and are largest in the spring and summer when winds are strongest. Average daily winds almost never exceed the wave generation threshold of 0.5–0.7 m/s. Average storm winds (∼1.5 m/s) generate waves 15–48 cm in height with a period ranging 6–10.5 s while maximum storm winds (∼4 m/s) generate waves 2.7–3.2 m in height with a period up to 32 s. Titan’s waves become fetch-independent at ∼40 km for average storm winds occurring ∼1% of a Titan year and ∼100 kilometers for maximum storm winds occurring 2-3 times per Titan decade. On Ontario Lacus, storm winds blow nearly parallel to the eastern shore, potentially driving wave modification of the smooth eastern shoreline. On Ligeia Mare, waves rarely propagate toward a hypothesized wave modified shoreline suggesting that another process, such as tectonics, may contribute to a straight shoreline morphology.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2026JE009693","usgsCitation":"Detelich, C.E., Schneck, U.G., Hayes, A.G., Curcic, M., Palermo, R.E., Ashton, A.D., Perron, J.T., Lora, J.M., and Steckloff, J., 2026, Modeling the seasonality of wind-driven hydrocarbon waves in Titan’s polar lakes: JGR Planets, v. 131, no. 6, e2026JE009693, 26 p., https://doi.org/10.1029/2026JE009693.","productDescription":"e2026JE009693, 26 p.","ipdsId":"IP-185621","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":504810,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2026je009693","text":"Publisher Index Page"},{"id":504695,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Titan","volume":"131","issue":"6","noUsgsAuthors":false,"publicationDate":"2026-05-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Detelich, Charlene E.","contributorId":371520,"corporation":false,"usgs":false,"family":"Detelich","given":"Charlene","middleInitial":"E.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":961946,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schneck, Una G.","contributorId":371521,"corporation":false,"usgs":false,"family":"Schneck","given":"Una","middleInitial":"G.","affiliations":[{"id":12444,"text":"Massachusetts Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":961947,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hayes, Alexander G.","contributorId":371522,"corporation":false,"usgs":false,"family":"Hayes","given":"Alexander","middleInitial":"G.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":961948,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Curcic, Milan","contributorId":371523,"corporation":false,"usgs":false,"family":"Curcic","given":"Milan","affiliations":[{"id":5112,"text":"University of Miami","active":true,"usgs":false}],"preferred":false,"id":961949,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Palermo, Rose Elizabeth 0000-0002-7438-361X","orcid":"https://orcid.org/0000-0002-7438-361X","contributorId":300046,"corporation":false,"usgs":true,"family":"Palermo","given":"Rose","email":"","middleInitial":"Elizabeth","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":961950,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ashton, Andrew D.","contributorId":371524,"corporation":false,"usgs":false,"family":"Ashton","given":"Andrew","middleInitial":"D.","affiliations":[{"id":36711,"text":"Woods Hole Oceanographic Institution","active":true,"usgs":false}],"preferred":false,"id":961951,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Perron, J. Taylor","contributorId":371526,"corporation":false,"usgs":false,"family":"Perron","given":"J.","middleInitial":"Taylor","affiliations":[{"id":12444,"text":"Massachusetts Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":961953,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lora, Juan M.","contributorId":371525,"corporation":false,"usgs":false,"family":"Lora","given":"Juan","middleInitial":"M.","affiliations":[{"id":37550,"text":"Yale University","active":true,"usgs":false}],"preferred":false,"id":961952,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Steckloff, Jordan","contributorId":371527,"corporation":false,"usgs":false,"family":"Steckloff","given":"Jordan","affiliations":[{"id":13179,"text":"Planetary Science Institute","active":true,"usgs":false}],"preferred":false,"id":961954,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70275769,"text":"fs20263010 - 2026 - The Great Lakes Geologic Mapping Coalition—Working collaboratively to understand the geology of the Great Lakes Region","interactions":[],"lastModifiedDate":"2026-05-26T18:37:48.030145","indexId":"fs20263010","displayToPublicDate":"2026-05-20T13:25:18","publicationYear":"2026","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2026-3010","displayTitle":"The Great Lakes Geologic Mapping Coalition—Working Collaboratively to Understand the Geology of the Great Lakes Region","title":"The Great Lakes Geologic Mapping Coalition—Working collaboratively to understand the geology of the Great Lakes Region","docAbstract":"The Great Lakes Geologic Mapping Coalition (GLGMC), commonly referred to as the “Coalition,” is a partnership between the U.S. Geological Survey (USGS), the U.S. States of Illinois, Indiana, Michigan, Minnesota, New York, Ohio, Pennsylvania, and Wisconsin and the Canadian province of Ontario. The member States receive funding for geologic mapping work from the USGS National Cooperative Geologic Mapping Program (NCGMP), whereas Ontario participates as a nonfunded partner. The mission of the GLGMC is to produce three-dimensional (3D) geologic maps that depict unconsolidated sediments and near-surface bedrock in the Great Lakes region of North America. Geologic maps are the basis of most earth science investigations and help support resource exploration (energy, minerals, groundwater), natural hazard mitigation, infrastructure development, and land-use planning, all of which can be used to advance economic development and strengthen national security in the Great Lakes region.
During the last few million years, the Great Lakes region has experienced repeated glacial advances and retreats, leaving behind extensive sediments, abundant natural resources, and widespread effects on the underlying bedrock geology (Swezey and others, 2022). Linked by shared histories of past glaciations, industrial agriculture, and legacy automotive, coal, steel, and manufacturing industries, the GLGMC member States collaborate to improve the understanding of the 3D distribution of the sediments overlying the region’s bedrock (fig. 1). Developing a comprehensive subsurface 3D framework of this glaciated terrain can provide earth science data to policymakers at all levels. These insights facilitate informed decisions on the exploration, use, and protection of vital resources, such as critical minerals, industrial materials, and aquifers, thereby supporting economic prosperity and the well-being of the citizens of this region.
Since its inception in 1998, the Coalition has completed more than 100 geologic mapping projects across the Great Lakes region. Each project aims to deliver geologic maps, 3D datasets, and other information that improves understanding of the geology of the Great Lakes region, with an emphasis on economic and water resources. Key deliverables include 3D geologic maps and models typically portraying sediment thickness, often derived from top-of-bedrock and borehole data. These products are developed through a combination of fieldwork, subsurface modeling, and the collection and analysis of rock and sediment cores.
To support Coalition goals, member States collaborate with scientists working on related STATEMAP, EDMAP, and FEDMAP projects. Coalition scientists also engage with Tribal Nations in the Great Lakes region to ensure that Tribal interests pertaining to Coalition work are addressed. Through this collaboration, the Coalition unites the efforts of State, Federal, and Tribal Nation stakeholders to advance geologic data production and enhance understanding of the geologic resources of the Great Lakes region.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20263010","issn":"2327-6932","programNote":"National Cooperative Geologic Mapping Program","usgsCitation":"Lopez, B., Shelton, J.L., Marketti, M., Ritzel, K., and Graham, B.L., 2026, The Great Lakes Geologic Mapping Coalition—Working collaboratively to understand the geology of the Great Lakes Region: U.S. Geological Survey Fact Sheet 2026–3010, 4 p., https://doi.org/10.3100/fs20263010.","productDescription":"4 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-182781","costCenters":[{"id":64806,"text":"National Cooperative Geologic Mapping","active":true,"usgs":true}],"links":[{"id":504712,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119447.htm","linkFileType":{"id":5,"text":"html"}},{"id":504509,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2026/3010/images"},{"id":504506,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2026/3010/coverthb.jpg"},{"id":504507,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20263010/full","linkFileType":{"id":5,"text":"html"},"description":"FS 2026-3010 HTML"},{"id":504508,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2026/3010/fs20263010.XML","description":"FS 2026-3010 XML"},{"id":504520,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2026/3010/fs20263010.pdf","text":"Report","size":"34 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2026-3010 PDF"}],"country":"Canada, United States","state":"Illinois, Indiana, Michigan, Minnesota, New York, Ohio, Pennsylvania, Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.02049880324554,\n 50.19476423072376\n ],\n [\n -74.32998999345827,\n 50.19476423072376\n ],\n [\n -74.32998999345827,\n 39.549260024659674\n ],\n [\n -94.02049880324554,\n 39.549260024659674\n ],\n [\n -94.02049880324554,\n 50.19476423072376\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Program Officer, National Cooperative Geologic Mapping Program
U.S. Geological Survey
12201 Sunrise Valley Drive, Mail Stop 913
Reston, VA 20192
Major waterways in the City of Roanoke (City) have failed to meet Virginia’s aquatic life designated use since 1996. Segments of the upper Roanoke River lack healthy benthic macroinvertebrate communities which prompted a total maximum daily load (TMDL) study by the Virginia Department of Environmental Quality (VDEQ) to identify the most probable stressor(s) causing the impairment. Excess fine sediment was identified as the most probable stressor impairing benthic macroinvertebrates on portions of the Roanoke River in 2006, and a watershed implementation plan published in 2016 required communities within the impaired watershed to implement projects that would reduce the load of fine sediment entering the Roanoke River. Additional benthic macroinvertebrate sampling and stream habitat assessments along the Roanoke River and Tinker Creek (a tributary to the Roanoke River that flows through the City) revealed continued impaired conditions, and subsequent stressor identification analysis was completed in 2023. Samples collected downstream of the City on the Roanoke River and Tinker Creek generally showed more impaired conditions relative to samples collected at locations upstream of the City. Based on this evaluation, sediment and sediment-bound polychlorinated biphenyls (PCBs) were identified as probable stressors while specific conductance, total nitrogen, and sediment metals were possible stressors in Tinker Creek; however, only a sediment TMDL target was identified to address impaired benthic macroinvertebrate communities. In the Roanoke River upstream of the Niagara Dam, sediment and total phosphorus were identified as probable stressors, sediment polycyclic aromatic hydrocarbons and sediment PCB were considered possible stressors; however, the TMDL target was only for total phosphorus.
The City partnered with the U.S. Geological Survey (USGS) in 2016 to continuously monitor water quality and streamflow conditions on a major tributary of Tinker Creek, Lick Run, and by 2020, four similar monitoring stations were installed on the Roanoke River and Tinker Creek near the locations of benthic macroinvertebrate sampling. Monitored parameters included streamflow and/or gage height (water level), water temperature, pH, dissolved oxygen, specific conductance, and turbidity. Turbidity is a measure of the relative clarity of the water and was previously used to model suspended-sediment concentrations at the monitoring stations. The City also contracted Kirk Environmental, LLP (KE) to collect benthic macroinvertebrate samples and stream habitat assessments near the locations of the water-quality monitoring stations. Identified benthic macroinvertebrates were used to calculate the Virginia Stream Condition Index (SCI), a multi-metric index composed of eight biological attributes that represent elements of the structure and function of the benthic macroinvertebrate community that measure diversity, composition, and tolerance to pollution.
Study objective: In this report, benthic macroinvertebrate samples and stream habitat assessment scores collected at four locations on the Roanoke River and Tinker Creek by KE and the VDEQ between 2020 and 2023 were compared to measured water-quality and streamflow conditions prior to sampling to evaluate patterns between benthic macroinvertebrate health, water quality, and hydrology.
","language":"English","publisher":"Virginia Tech","usgsCitation":"Miller, S.A., Aguilar, M.F., Helsley, L., and Entrekin, S., 2026, Factors affecting benthic macroinvertebrate health in the City of Roanoke, Virginia, 2020–2023, vi, 139 p.","productDescription":"vi, 139 p.","ipdsId":"IP-182640","costCenters":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"links":[{"id":504682,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://hdl.handle.net/10919/143118"},{"id":504867,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","city":"Roanoke","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.13499498922846,\n 37.407717247099555\n ],\n [\n -79.84673314727793,\n 37.407717247099555\n ],\n [\n -79.84673314727793,\n 37.18395942419494\n ],\n [\n -80.13499498922846,\n 37.18395942419494\n ],\n [\n -80.13499498922846,\n 37.407717247099555\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2026-05-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Miller, Samuel Adam 0000-0003-4225-1601","orcid":"https://orcid.org/0000-0003-4225-1601","contributorId":333495,"corporation":false,"usgs":true,"family":"Miller","given":"Samuel","email":"","middleInitial":"Adam","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"preferred":true,"id":961907,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Aguilar, Marcus F 0000-0002-4431-9596","orcid":"https://orcid.org/0000-0002-4431-9596","contributorId":333497,"corporation":false,"usgs":false,"family":"Aguilar","given":"Marcus","email":"","middleInitial":"F","affiliations":[{"id":79901,"text":"City of Roanoke","active":true,"usgs":false}],"preferred":false,"id":961908,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Helsley, Logan 0009-0000-6496-8617","orcid":"https://orcid.org/0009-0000-6496-8617","contributorId":371497,"corporation":false,"usgs":false,"family":"Helsley","given":"Logan","affiliations":[{"id":88163,"text":"City of Roanoke, Department of Public Works","active":true,"usgs":false}],"preferred":false,"id":961909,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Entrekin, Sally 0000-0002-8276-7832","orcid":"https://orcid.org/0000-0002-8276-7832","contributorId":332044,"corporation":false,"usgs":false,"family":"Entrekin","given":"Sally","email":"","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":961910,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70276549,"text":"70276549 - 2026 - Patterns of recent brook trout invasion in bull trout streams in relation to habitat, source connectivity, biotic resistance, and disturbance","interactions":[],"lastModifiedDate":"2026-06-09T14:49:51.520798","indexId":"70276549","displayToPublicDate":"2026-05-20T07:44:17","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Patterns of recent brook trout invasion in bull trout streams in relation to habitat, source connectivity, biotic resistance, and disturbance","docAbstract":"Anticipating biological invasions by nonnative species is critical to effective conservation. Nonnative brook trout Salvelinus fontinalis represents one of the most widespread threats to native bull trout Salvelinus confluentus, but the factors allowing or preventing ongoing range expansions are poorly understood. We addressed this uncertainty by resampling 221 survey locations in bull trout streams in Idaho and relating shifts in brook trout occupancy to four controls on biological invasion (habitat suitability, source connectivity, disturbance, and biotic resistance to invasion). Brook trout detections increased substantially between the historical period (58 sites) and contemporary period (94 sites). Site colonizations were positively associated with water temperature and negatively associated with landscape resistance metrics (i.e., highest streamflow and gradient between a site and the nearest source) in all top models. In contrast, there was weak support for a positive association with wildfire and limited support for hydrologic distance and biotic resistance metrics. Brook trout invasions in bull trout habitat are ongoing, limited by cold temperatures, and highly influenced by dispersal barriers that may not inhibit more mobile native salmonids.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2025-0293","usgsCitation":"Voss, N.S., Bowersox, B.J., Nolfi, D.C., and Quist, M., 2026, Patterns of recent brook trout invasion in bull trout streams in relation to habitat, source connectivity, biotic resistance, and disturbance: Canadian Journal of Fisheries and Aquatic Sciences, v. 83, p. 1-15, https://doi.org/10.1139/cjfas-2025-0293.","productDescription":"15 p.","startPage":"1","endPage":"15","ipdsId":"IP-182310","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":505469,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1139/cjfas-2025-0293","text":"Publisher Index Page"},{"id":505230,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.0536395,\n 46.2005382\n ],\n [\n -114.3310051,\n 45.9019559\n ],\n [\n -111.934915,\n 43.5348611\n ],\n [\n -117.0872419,\n 43.6416123\n ],\n [\n -117.1995296,\n 44.8428707\n ],\n [\n -117.0536395,\n 46.2005382\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"83","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Voss, Nicholas S.","contributorId":241654,"corporation":false,"usgs":false,"family":"Voss","given":"Nicholas","middleInitial":"S.","affiliations":[{"id":48382,"text":"KBR, Albuquerque Seismological Laboratory","active":true,"usgs":false}],"preferred":false,"id":962645,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bowersox, Brett J.","contributorId":265299,"corporation":false,"usgs":false,"family":"Bowersox","given":"Brett","email":"","middleInitial":"J.","affiliations":[{"id":36224,"text":"Idaho Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":962646,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nolfi, Daniel C.","contributorId":248446,"corporation":false,"usgs":false,"family":"Nolfi","given":"Daniel","email":"","middleInitial":"C.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":962647,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Quist, Michael C. 0000-0001-8268-1839","orcid":"https://orcid.org/0000-0001-8268-1839","contributorId":272016,"corporation":false,"usgs":true,"family":"Quist","given":"Michael C.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":962648,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}