The United States Magnetotelluric Array (USMTArray) data set, collected in the years 2006–2024, consists of more than 1,700 long-period magnetotelluric stations covering the entirety of the contiguous United States on a quasi-regular 70 km grid. Funding across multiple federal agencies was critical to sustaining this effort to its completion. Important components of the project included active guidance and participation from the MT community, the open and timely availability of all data, and the application of consistent instrumentation and robust data processing. Together with parallel advancement in the development of publicly available three-dimensional (3D) inversion codes, the USMTArray has revitalized the US magnetotelluric community and increased the visibility of magnetotellurics within the Earth-science community. Taken as a whole, these data are visualized as the National Impedance Map, which, together with a 3D synthesis conductivity model of the nation, reveals the electrical architecture of the contiguous US. USMTArray data are used by researchers worldwide for fundamental and applied studies, including investigations of continental architecture and evolution, estimation of hazards to critical infrastructure due to geomagnetic storms, and assessment of the nation's undiscovered geothermal and mineral resources. We here review the history and development of the project, discuss the challenges and successes in its execution, present the National Impedance Map and synthesis conductivity model, and highlight the breadth of research stemming from this rich data set.
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In 2025, 37 sites in the Wanaque, Ramapo, Pompton, and Passaic River watersheds were sampled in January, March, July, and September under base-flow conditions and a subset of sites was sampled during two rain events. Samples were analyzed for 40 individual PFAS and total organic carbon and a subset of samples was analyzed for 1,4-dioxane and trace elements. Fifteen PFAS were detected at least once, with individual concentrations ranging from 0.42 to 28 nanograms per liter (ng/L; median, 2.8 ng/L). Perfluorooctanoate (PFOA) and perfluorooctane sulfonate (PFOS) were widespread and detected in 100 and 97 percent of the samples, respectively. Concentrations of PFOA and PFOS ranged from 1.2 to 28 ng/L (median, 7.7 ng/L) and from 0.52 to 12 ng/L (median, 3.8 ng/L), respectively. Generally, concentrations were lower in the Wanaque and Ramapo River watersheds compared to the Pompton and Passaic River watersheds. Concentrations of PFOA and PFOS were highest in July and September when flows were low. During rain events, median concentrations of PFOS were elevated compared to those observed under base-flow conditions, indicating potential inputs from non-point sources. To understand potential drivers of PFAS concentrations, land cover and potential PFAS sources were summarized for each sampling site, and an accumulated wastewater model was used to estimate the percentage of wastewater from upstream municipal and industrial sources in all flowlines of the Passaic River Basin. Developed land, the number of potential sources, and the mean-annual accumulated wastewater percentage were highly correlated with PFAS concentrations and Deciduous Forests were negatively related to concentrations. Data provided by this study can be used by water purveyors and resource managers to make treatment and mitigation decisions to minimize PFAS in local surface waters used as drinking-water resources.
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Lawrenceville, NJ 08648
U.S. Geological Survey researchers, in cooperation with the North Jersey District Water Supply Commission, determined that per- and polyfluoroalkyl substances (PFAS) are present in northern New Jersey rivers that are used as drinking-water sources. During their 2025 study, the researchers sampled 37 locations across the Wanaque, Ramapo, Pompton, and Passaic River watersheds. The researchers tested each sample for 40 types of PFAS. Of these, 15 were detected at least once. Two PFAS, perfluorooctanoate (PFOA) and perfluorooctane sulfonate (PFOS), were present in nearly every sample. PFAS concentrations varied by watershed and season. The lowest were detected in the Wanaque and Ramapo River watersheds, and the highest, in the Pompton and Passaic River watersheds. Concentrations of PFOA and PFOS were highest under base-flow conditions in July and September.
","publicationDate":"2026-04-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Schreiner, Molly L. 0000-0001-9306-5564","orcid":"https://orcid.org/0000-0001-9306-5564","contributorId":296363,"corporation":false,"usgs":true,"family":"Schreiner","given":"Molly L.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":960544,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Romanok, Kristin M. 0000-0002-8472-8765 kromanok@usgs.gov","orcid":"https://orcid.org/0000-0002-8472-8765","contributorId":204640,"corporation":false,"usgs":true,"family":"Romanok","given":"Kristin","email":"kromanok@usgs.gov","middleInitial":"M.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":960545,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gray, Jacob T. 0000-0002-9374-0336","orcid":"https://orcid.org/0000-0002-9374-0336","contributorId":330273,"corporation":false,"usgs":true,"family":"Gray","given":"Jacob","middleInitial":"T.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":960546,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brown, Eileen J. 0000-0003-3417-0203 ejbrown@usgs.gov","orcid":"https://orcid.org/0000-0003-3417-0203","contributorId":361968,"corporation":false,"usgs":true,"family":"Brown","given":"Eileen","email":"ejbrown@usgs.gov","middleInitial":"J.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":960547,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Williams, Brianna M. 0000-0003-3389-8251","orcid":"https://orcid.org/0000-0003-3389-8251","contributorId":204714,"corporation":false,"usgs":false,"family":"Williams","given":"Brianna","middleInitial":"M.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":960548,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kneser, Maureen","contributorId":370591,"corporation":false,"usgs":false,"family":"Kneser","given":"Maureen","affiliations":[{"id":88047,"text":"North Jersey District Water Supply Commission","active":true,"usgs":false}],"preferred":false,"id":960549,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Capuzzi, Albert J.","contributorId":370592,"corporation":false,"usgs":false,"family":"Capuzzi","given":"Albert","middleInitial":"J.","affiliations":[{"id":88047,"text":"North Jersey District Water Supply Commission","active":true,"usgs":false}],"preferred":false,"id":960550,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Boerner, Jason","contributorId":370593,"corporation":false,"usgs":false,"family":"Boerner","given":"Jason","affiliations":[{"id":88047,"text":"North Jersey District Water Supply Commission","active":true,"usgs":false}],"preferred":false,"id":960551,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Giunta, Luke","contributorId":370594,"corporation":false,"usgs":false,"family":"Giunta","given":"Luke","affiliations":[{"id":88047,"text":"North Jersey District Water Supply Commission","active":true,"usgs":false}],"preferred":false,"id":960552,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Serillo, Paul","contributorId":370595,"corporation":false,"usgs":false,"family":"Serillo","given":"Paul","affiliations":[{"id":88047,"text":"North Jersey District Water Supply Commission","active":true,"usgs":false}],"preferred":false,"id":960553,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Trainor, John J. 0000-0002-6603-2684 jtrainor@usgs.gov","orcid":"https://orcid.org/0000-0002-6603-2684","contributorId":5408,"corporation":false,"usgs":true,"family":"Trainor","given":"John","email":"jtrainor@usgs.gov","middleInitial":"J.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":960554,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Smalling, Kelly L. 0000-0002-1214-4920","orcid":"https://orcid.org/0000-0002-1214-4920","contributorId":221234,"corporation":false,"usgs":true,"family":"Smalling","given":"Kelly","middleInitial":"L.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":960555,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70275239,"text":"sir20265142 - 2026 - Assessment of long-term trends in streamflow statistics within and near the Mobile Bay and Perdido Bay watersheds, United States, 1950–2022","interactions":[],"lastModifiedDate":"2026-05-01T16:45:34.897463","indexId":"sir20265142","displayToPublicDate":"2026-04-30T10:23:48","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":"2026-5142","displayTitle":"Assessment of Long-Term Trends in Streamflow Statistics Within and Near the Mobile Bay and Perdido Bay Watersheds, United States, 1950–2022","title":"Assessment of long-term trends in streamflow statistics within and near the Mobile Bay and Perdido Bay watersheds, United States, 1950–2022","docAbstract":"The U.S. Geological Survey, in cooperation with the Gulf Coast Ecosystem Restoration Council, assessed monotonic trends for a variety of streamflow statistics for 69 long-term U.S. Geological Survey streamgages within either the Mobile Bay or Perdido Bay watersheds that were active through at least at the end of calendar year 2019. Long-term data were defined for this investigation as having at least 50 years of cumulative record within the period since January 1, 1950, with a requirement for a complete record of streamflow during the 2010s (2010–19). The 69 streamgages have at least 54 years and as many as 73 years of daily mean streamflow data; the median period of record is 72 years; and 15 of the streamgages are identified as “major nodes” on the basis of the criteria described. The occurrence of statistically monotonic significant trends for the 69 streamgages at the 0.05 significance level is spatially shown for six statistics. For the major node streamgages, the study depicts (1) time-series graphics of annual mean, annual harmonic mean, decadal 10th, 50th, and 90th-percentile streamflows, and (2) a variation on Quantile-Kendall plots of Kendall’s tau and streamflow nonexceedance probabilities for each of the 365 days of a year. Trend assessment synthesis shows that, except for a few streamgages with relatively greater counts of statistically significant trends than others, the majority (about 93 percent) of individual trend tests indicate no trend in the streamflow and ecological metrics considered.
Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Contact Us- USGS Publications Warehouse
","tableOfContents":"Pinyon–juniper (PJ) woodlands, one of the most extensive mature and old-growth woodland types in the Western United States, provide critical ecological, cultural, and economic benefits but face increasing threats from climate change, altered disturbance regimes, invasive species, and pests. We developed the PJ Woodland Climate Adaptation Management Menu, a decision support tool designed to guide adaptive, climate-informed management of PJ ecosystems, particularly within the Colorado Plateau ecoregion. The menu was created through an iterative, collaborative process involving literature review, integration of strategies from existing adaptation frameworks, and extensive input from scientists, land managers, and community partners during workshops and focus groups. The menu links specific, evidence-based approaches to each of six broad strategies, including soliciting community input, mitigating disturbance, enhancing and maintaining biodiversity, conserving ecotones, timing actions for optimal outcomes, and accepting climate-driven changes when appropriate. It is intended for use with the Adaptation Workbook to help managers connect local goals and climate vulnerabilities to tailored management tactics. Hypothetical scenarios demonstrate the menu’s application to contrasting PJ woodland conditions, from die-off events to old-growth maintenance. Lessons learned during development underscore the value of early stakeholder engagement, cross-sector collaboration, and balancing diverse ecological objectives. This menu offers a flexible, transferable framework to strengthen climate resilience in PJ woodlands and serves as a model that could improve adaptation planning in other dryland forest ecosystems.
","language":"English","publisher":"MDPI","doi":"10.3390/f17050554","usgsCitation":"Gray, J., Slate, M.L., Ennis, A., Peterson, C., Bradford, J., Noel, A.R., Duniway, M.C., Bishop, T.B., Barrett, I.P., Domschke, C., Humphries, J.T., and Barger, N.N., 2026, Building resilience in dryland ecosystems: A climate adaptation strategy menu for pinyon–juniper woodlands: Forests, v. 17, no. 5, 554, 27 p., https://doi.org/10.3390/f17050554.","productDescription":"554, 27 p.","ipdsId":"IP-184031","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":504168,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/f17050554","text":"Publisher Index Page"},{"id":503930,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"17","issue":"5","noUsgsAuthors":false,"publicationDate":"2026-04-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Gray, Jesse","contributorId":371077,"corporation":false,"usgs":false,"family":"Gray","given":"Jesse","affiliations":[{"id":36627,"text":"University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":960955,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Slate, Mandy L.","contributorId":335942,"corporation":false,"usgs":false,"family":"Slate","given":"Mandy","email":"","middleInitial":"L.","affiliations":[{"id":80588,"text":"Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, CO 80309, USA; Department of Evolution, Ecology, and Organismal Biology, The Ohio State University, Columbus, OH, 43210 USA","active":true,"usgs":false}],"preferred":false,"id":960956,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ennis, Alyson","contributorId":352211,"corporation":false,"usgs":false,"family":"Ennis","given":"Alyson","affiliations":[],"preferred":false,"id":960957,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Peterson, Courtney","contributorId":291807,"corporation":false,"usgs":false,"family":"Peterson","given":"Courtney","email":"","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":960958,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bradford, John B. 0000-0001-9257-6303","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":219257,"corporation":false,"usgs":true,"family":"Bradford","given":"John B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":960959,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Noel, Adam Roy 0000-0002-0891-4005","orcid":"https://orcid.org/0000-0002-0891-4005","contributorId":294761,"corporation":false,"usgs":true,"family":"Noel","given":"Adam","email":"","middleInitial":"Roy","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":960960,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":219284,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":960961,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bishop, Tara B.","contributorId":360618,"corporation":false,"usgs":false,"family":"Bishop","given":"Tara","middleInitial":"B.","affiliations":[{"id":86058,"text":"Utah Valley University, Department of Earth Science, Orem, UT, USA","active":true,"usgs":false}],"preferred":false,"id":960962,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Barrett, Ian P.","contributorId":360597,"corporation":false,"usgs":false,"family":"Barrett","given":"Ian","middleInitial":"P.","affiliations":[{"id":86049,"text":"Bureau of Land Management, National Interagency Fire Center, Boise ID","active":true,"usgs":false}],"preferred":false,"id":960963,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Domschke, Chris","contributorId":267281,"corporation":false,"usgs":false,"family":"Domschke","given":"Chris","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":960964,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Humphries, Joel T.","contributorId":270937,"corporation":false,"usgs":false,"family":"Humphries","given":"Joel","email":"","middleInitial":"T.","affiliations":[{"id":56221,"text":"US Bureau of Land Management, Colorado State Office, Lakewood, CO 80215, USA","active":true,"usgs":false}],"preferred":false,"id":960965,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Barger, Nicole N.","contributorId":196430,"corporation":false,"usgs":false,"family":"Barger","given":"Nicole","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":960966,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70275662,"text":"70275662 - 2026 - Nest site and habitat changes over 15 years in a predicted climate refugium in Beluga, AK, USA, have a positive impact on Hudsonian godwit (Limosa haemastica) nest survival","interactions":[],"lastModifiedDate":"2026-05-07T14:50:17.396624","indexId":"70275662","displayToPublicDate":"2026-04-30T09:44:26","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3093,"text":"Polar Biology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Nest site and habitat changes over 15 years in a predicted climate refugium in Beluga, AK, USA, have a positive impact on Hudsonian godwit (Limosa haemastica) nest survival","title":"Nest site and habitat changes over 15 years in a predicted climate refugium in Beluga, AK, USA, have a positive impact on Hudsonian godwit (Limosa haemastica) nest survival","docAbstract":"Climate change is transforming the Arctic and sub-Arctic at a pace that threatens many taxa with population declines and extinction. However, some habitats–such as muskeg bogs–can serve as climatic refugia and lessen the effects of a changing climate on the species that rely on them. Hudsonian Godwits (Limosa haemastica) are a species of migratory shorebird that utilizes the muskeg bogs of Alaska and Canada to breed. Our study focused on a muskeg bog in Beluga, Alaska, USA to see if it had changed from 2009 to 2023, if the availability of Godwit nests sites in the bogs changed in concert, and if Godwit nest survival was affected by any of these changes. We found that, overall, the bog dried and became more vegetated, with the proportional cover of graminoids, shrubs, and forbs all increasing during our study. Godwit nest sites also changed, with the proportion of shrubs and graminoids around nests increasing over time. Nonetheless, these changes did not negatively impact Godwit nest survival. Instead, nest survival increased ~ 22% during our study period, and we observed no decline in the number of potential nests sites available to Godwits. Taken together, these results suggest that while muskeg bogs are changing, they are also currently acting as climate refugia for Godwits. However, it is unclear for how long muskeg bogs can continue to buffer Godwits and other species from the effects of climate change.
","language":"English","publisher":"Springer","doi":"10.1007/s00300-026-03489-8","usgsCitation":"Smith, E., Swift, R.J., Courtemanche, A., Huang, F., Pelton, M.M., Puleo, L., Simmonds, J., Waller, M., Walton, H., Weissburg, C., Wilde, L.R., and Senner, N.R., 2026, Nest site and habitat changes over 15 years in a predicted climate refugium in Beluga, AK, USA, have a positive impact on Hudsonian godwit (Limosa haemastica) nest survival: Polar Biology, v. 49, 47, https://doi.org/10.1007/s00300-026-03489-8.","productDescription":"47","ipdsId":"IP-182024","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":504087,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","city":"Beluga","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -151.1516898202142,\n 61.23896112663158\n ],\n [\n -150.95964132905272,\n 61.23896112663158\n ],\n [\n -150.95964132905272,\n 61.12105149361793\n ],\n [\n -151.1516898202142,\n 61.12105149361793\n ],\n [\n -151.1516898202142,\n 61.23896112663158\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"49","noUsgsAuthors":false,"publicationDate":"2026-04-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Eden","contributorId":371234,"corporation":false,"usgs":false,"family":"Smith","given":"Eden","affiliations":[{"id":34616,"text":"University of Massachusetts Amherst","active":true,"usgs":false}],"preferred":false,"id":961332,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Swift, Rose J. 0000-0001-7044-6196","orcid":"https://orcid.org/0000-0001-7044-6196","contributorId":212082,"corporation":false,"usgs":true,"family":"Swift","given":"Rose","email":"","middleInitial":"J.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":961333,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Courtemanche, Anna","contributorId":371235,"corporation":false,"usgs":false,"family":"Courtemanche","given":"Anna","affiliations":[{"id":34616,"text":"University of Massachusetts Amherst","active":true,"usgs":false}],"preferred":false,"id":961334,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Huang, Feipeng","contributorId":371089,"corporation":false,"usgs":false,"family":"Huang","given":"Feipeng","affiliations":[{"id":88085,"text":"Department of Environmental Conservation, University of Massachusetts Amherst, Amherst, Massachusetts, USA","active":true,"usgs":false}],"preferred":false,"id":961335,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pelton, Mary Margaret","contributorId":371236,"corporation":false,"usgs":false,"family":"Pelton","given":"Mary","middleInitial":"Margaret","affiliations":[{"id":88108,"text":"Belle W. Baruch Institute for Marine and Coastal Sciences","active":true,"usgs":false}],"preferred":false,"id":961336,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Puleo, Lauren","contributorId":371093,"corporation":false,"usgs":false,"family":"Puleo","given":"Lauren","affiliations":[{"id":88085,"text":"Department of Environmental Conservation, University of Massachusetts Amherst, Amherst, Massachusetts, USA","active":true,"usgs":false}],"preferred":false,"id":961337,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Simmonds, Josiah","contributorId":371237,"corporation":false,"usgs":false,"family":"Simmonds","given":"Josiah","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":961338,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Waller, Matthew","contributorId":371238,"corporation":false,"usgs":false,"family":"Waller","given":"Matthew","affiliations":[{"id":13252,"text":"University of Utah","active":true,"usgs":false}],"preferred":false,"id":961339,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Walton, Hannah","contributorId":371239,"corporation":false,"usgs":false,"family":"Walton","given":"Hannah","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":961340,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Weissburg, Casey","contributorId":371240,"corporation":false,"usgs":false,"family":"Weissburg","given":"Casey","affiliations":[{"id":6709,"text":"University of Colorado, Denver","active":true,"usgs":false}],"preferred":false,"id":961341,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Wilde, Luke R.","contributorId":371241,"corporation":false,"usgs":false,"family":"Wilde","given":"Luke","middleInitial":"R.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":961342,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Senner, Nathan R.","contributorId":371242,"corporation":false,"usgs":false,"family":"Senner","given":"Nathan","middleInitial":"R.","affiliations":[{"id":34616,"text":"University of Massachusetts Amherst","active":true,"usgs":false}],"preferred":false,"id":961343,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70275370,"text":"70275370 - 2026 - Evaluating approximations of river channel shape using a national cross section database","interactions":[],"lastModifiedDate":"2026-05-01T14:00:47.462328","indexId":"70275370","displayToPublicDate":"2026-04-30T08:57:32","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating approximations of river channel shape using a national cross section database","docAbstract":"Many hydrologic applications require basic information on the size and shape of river channels, but measuring cross section (XS) geometry in the field or via remote sensing can be costly and often provides only partial coverage. Given these challenges, we capitalized upon an existing data set of 46,971 XS from gaging stations to evaluate various approximations of channel shape. After screening and pre-processing these data, we fit four model types to each XS, including a new approach that involves Stacking PDFs (probability density functions) to Approximate River Channel Shapes (SPARCS). This framework produced depth estimates that closely matched field measurements, with typical cross-sectional area errors <1% and a median R2 of 0.77 for comparison of observed and predicted depths. SPARCS model parameters can be interpreted in terms of channel characteristics: mean depth, asymmetry, bar convexity, and flatness of the bed. The model performed well for the XS included in the database, which was biased toward straight, uniform channels conducive to operational streamflow measurement. Neither model parameters nor accuracy were dependent on discharge. We also assessed the potential of SPARCS to fill in measurement gaps and found that although the model can help, the accuracy of inferred depths decreased as the observable fraction of the channel decreased. An important limitation of SPARCS is that mid-channel bars or multi-threaded morphologies cannot be produced. Graphical tools can help visualize how model parameters affect simulated river forms. SPARCS could facilitate satellite-based discharge estimation by providing prior information on channel shape.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2025WR041177","usgsCitation":"Legleiter, C.J., and Kinzel, P.J., 2026, Evaluating approximations of river channel shape using a national cross section database: Water Resources Research, v. 62, no. 5, e2025WR041177, 35 p., https://doi.org/10.1029/2025WR041177.","productDescription":"e2025WR041177, 35 p.","ipdsId":"IP-179311","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":504157,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2025wr041177","text":"Publisher Index Page"},{"id":503881,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"62","issue":"5","noUsgsAuthors":false,"publicationDate":"2026-04-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":960758,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kinzel, Paul J. 0000-0002-6076-9730 pjkinzel@usgs.gov","orcid":"https://orcid.org/0000-0002-6076-9730","contributorId":743,"corporation":false,"usgs":true,"family":"Kinzel","given":"Paul","email":"pjkinzel@usgs.gov","middleInitial":"J.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":960759,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70275718,"text":"70275718 - 2026 - Changes in suspended sediment concentration along tidal rivers of the Chesapeake Bay: The tidal freshwater “sediment shadow”","interactions":[],"lastModifiedDate":"2026-05-15T13:15:07.488526","indexId":"70275718","displayToPublicDate":"2026-04-30T08:34:23","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":"Changes in suspended sediment concentration along tidal rivers of the Chesapeake Bay: The tidal freshwater “sediment shadow”","docAbstract":"Transport of terrigenic sediment from nontidal watersheds into estuaries has important impacts on coastal habitat quality, pollutant transport, and resilience to sea-level rise. However, relatively little is known about changes in suspended sediment as nontidal rivers encounter tide, transition into tidal rivers through the tidal freshwater zone (TFZ), and enter saline portions of estuaries. The goal of this paper is to identify spatial and temporal patterns in suspended sediment concentration (SS) changes across tidal and salinity gradients over multiple tidal rivers, using a robust monitoring long-term dataset from the Chesapeake Bay. The multiple TFZs in the Chesapeake Bay consistently have a “sediment shadow” shown by a local spatial minimum in SS compared to upstream nontidal and downgradient oligohaline river reaches. Similarly, freshwater inputs from nontidal rivers have diminishing influence on tidal SS temporal dynamics with distance downstream from the head-of-tide. Therefore, little of the contemporary watershed sediment load is likely transported past the TFZ except during extreme floods when some sediment may be delivered to saline portions of the estuary. Tidal freshwater and brackish portions of the estuary have spatially variable trends in SS over time, both increases and decreases. However, the more saline downstream ends of tidal rivers and the mainstem of the Chesapeake Bay have had a consistent average 25% decline in SS over the past decades. In summary, the presence of “sediment shadows” suggests watershed loads of sediment are currently mostly not transported through the TFZ into the saline estuary, and likely generate sediment deficits for tidal freshwater wetlands.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecss.2026.109931","usgsCitation":"Noe, G.E., Murphy, R., and Krauss, K., 2026, Changes in suspended sediment concentration along tidal rivers of the Chesapeake Bay: The tidal freshwater “sediment shadow”: Estuarine, Coastal and Shelf Science, v. 337, 109931, 14 p., https://doi.org/10.1016/j.ecss.2026.109931.","productDescription":"109931, 14 p.","ipdsId":"IP-178405","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":504324,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":504375,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecss.2026.109931","text":"Publisher Index Page"}],"country":"United States","state":"Delaware, Maryland, Pennsylvania","otherGeospatial":"Chesapeake Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78,\n 40\n ],\n [\n -75.2,\n 40\n ],\n [\n -75.2,\n 37\n ],\n [\n -78,\n 37\n ],\n [\n -78,\n 40\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"337","noUsgsAuthors":false,"publicationDate":"2026-04-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Noe, Gregory E. 0000-0002-6661-2646 gnoe@usgs.gov","orcid":"https://orcid.org/0000-0002-6661-2646","contributorId":139100,"corporation":false,"usgs":true,"family":"Noe","given":"Gregory","email":"gnoe@usgs.gov","middleInitial":"E.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true}],"preferred":true,"id":961522,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Murphy, Rebecca","contributorId":331418,"corporation":false,"usgs":false,"family":"Murphy","given":"Rebecca","affiliations":[{"id":79204,"text":"UMCES","active":true,"usgs":false}],"preferred":false,"id":961523,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Krauss, Ken 0000-0003-2195-0729","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":210857,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":961524,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70275365,"text":"70275365 - 2026 - Socio-ecological impacts of the 2025 Los Angeles urban fires on communities, neighborhoods, and homes","interactions":[],"lastModifiedDate":"2026-05-01T14:59:05.281901","indexId":"70275365","displayToPublicDate":"2026-04-30T07:52:24","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2842,"text":"Nature Communications","active":true,"publicationSubtype":{"id":10}},"title":"Socio-ecological impacts of the 2025 Los Angeles urban fires on communities, neighborhoods, and homes","docAbstract":"Human settlements are increasingly being impacted by urban fires initiated by wildfires. Metrics such as area burned and number of structures destroyed are important, but research often overlooks the socio-ecological complexity of urban fires. We study the impacts of the 2025 Los Angeles fires on two communities at the neighborhood and residential parcel scales. Geospatial analyses and econometric modeling explore the relationships between urban morphology, socio-demographic factors, and home destruction. Here we show that socio-ecological characteristics and scale are key in parsing the dynamics of urban fires. Also, new socio-demographic populations are being affected and urban morphology metrics are more important than vegetation cover. Despite parallels with 19th and early 20th century urban conflagrations, understanding these re-emerging urban fires requires transdisciplinary approaches and unique metrics. Investigating the socio-ecological scales and dynamics of urban fires provides a valuable next step towards understanding and adapting to the risk associated with these disasters.
","language":"English","publisher":"Springer Nature","doi":"10.1038/s41467-026-71376-1","usgsCitation":"Norlen, C.A., Sharma, S., and Escobedo, F.J., 2026, Socio-ecological impacts of the 2025 Los Angeles urban fires on communities, neighborhoods, and homes: Nature Communications, v. 17, 3941, 12 p., https://doi.org/10.1038/s41467-026-71376-1.","productDescription":"3941, 12 p.","ipdsId":"IP-180700","costCenters":[{"id":498,"text":"Office of Land Remote Sensing (Geography)","active":true,"usgs":true}],"links":[{"id":504163,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-026-71376-1","text":"Publisher Index Page"},{"id":503888,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Los Angeles","otherGeospatial":"Eaton, Palisades","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.43544529821688,\n 34.248989931303086\n ],\n [\n -118.43544529821688,\n 33.55012105378101\n ],\n [\n -117.27431517058712,\n 33.55012105378101\n ],\n [\n -117.27431517058712,\n 34.248989931303086\n ],\n [\n -118.43544529821688,\n 34.248989931303086\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"17","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Norlen, Carl August 0000-0003-1363-9930","orcid":"https://orcid.org/0000-0003-1363-9930","contributorId":370758,"corporation":false,"usgs":true,"family":"Norlen","given":"Carl","middleInitial":"August","affiliations":[{"id":498,"text":"Office of Land Remote Sensing (Geography)","active":true,"usgs":true}],"preferred":true,"id":960735,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sharma, Sadikshya","contributorId":370759,"corporation":false,"usgs":false,"family":"Sharma","given":"Sadikshya","affiliations":[{"id":36658,"text":"U.S. Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":960736,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Escobedo, Francisco J.","contributorId":370760,"corporation":false,"usgs":false,"family":"Escobedo","given":"Francisco","middleInitial":"J.","affiliations":[{"id":36658,"text":"U.S. Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":960737,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70275267,"text":"sir20265132 - 2026 - Hydrologic investigation of water level fluctuations at Moreau Lake, Moreau Lake State Park, town of Moreau, New York","interactions":[],"lastModifiedDate":"2026-05-01T16:44:17.159946","indexId":"sir20265132","displayToPublicDate":"2026-04-29T11:00:00","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":"2026-5132","displayTitle":"Hydrologic Investigation of Water Level Fluctuations at Moreau Lake, Moreau Lake State Park, Town of Moreau, New York","title":"Hydrologic investigation of water level fluctuations at Moreau Lake, Moreau Lake State Park, town of Moreau, New York","docAbstract":"The causes of water level fluctuations at Moreau Lake, within Moreau Lake State Park in the town of Moreau, New York, were investigated from 2016 to 2021 after lake water levels dropped between 2015 and 2016, raising concerns about the loss of a shallow swimming area at the park beach. Annual variation in precipitation records from the area did not account for the lake water level decline. Two possible causes for the low lake water levels were investigated: the increase in groundwater withdrawals from new residential development since about 2000 and seasonal changes (nongrowing and growing seasons) in precipitation.
Investigation of the potential effects of nearby groundwater withdrawals required the compilation and collection of well-log data, seismic surveys, and measurements of lake and groundwater levels, field chemical parameters, and water isotopes to define the hydrogeologic system and to estimate water use. The net result of this work was the determination that Moreau Lake is a “flow though” lake with no surface water outlet; groundwater enters the lake on the upgradient side and exits through the downgradient side, however, groundwater does not flow southward from the lake toward nearby groundwater withdrawals from the semiconfined aquifer, and thus groundwater withdrawals were unlikely to have an effect on lake water levels.
Investigation of the historic precipitation records during nongrowing (November through April) and growing (May through October) indicated that (1) nongrowing season precipitation from 2011–12 to 2015–16 was more deficient than any similar period during the past 78 years and (2) since about 2000, nongrowing seasons have been drier overall and growing seasons have been considerably wetter. Initiation of lake water level monitoring in 2016 provided an opportunity to compare seasonal precipitation with seasonal lake water level changes. Nongrowing season lake water levels are very sensitive to precipitation, such that high precipitation (40 percent above the seasonal median) resulted in a 5-foot rise in lake water level. In contrast, the growing season lake water levels are sensitive to dry conditions; for example, deficient rainfall (about 6 percent below the seasonal median) resulted in a decline of lake water levels of about 3.5 feet. However, lake water levels are insensitive to high growing season rainfall inputs (about 10 to 47 percent above the seasonal median); lake water levels consistently declined about by 0.8 feet above this range of seasonal excessive precipitation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20265132","collaboration":"Prepared in cooperation with New York State Department of Parks, Recreation and Historic Preservation","usgsCitation":"Heisig, P.M., 2026, Hydrologic investigation of water level fluctuations at Moreau Lake, Moreau Lake State Park, town of Moreau, New York: U.S. Geological Survey Scientific Investigations Report 2026–5132, 55 p., https://doi.org/10.3133/sir20265132.","productDescription":"Report: viii, 55 p., 3 Data Releases","numberOfPages":"55","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-148237","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":503535,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2026/5132/coverthb.jpg"},{"id":503899,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119387.htm","linkFileType":{"id":5,"text":"html"}},{"id":503541,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9R49VRO","text":"USGS data release","linkHelpText":"Horizontal-to-vertical spectral ratio (HVSR) soundings and depth-to-bedrock data for the Moreau Lake area, town of Moreau, N.Y."},{"id":503540,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K0LSHJ","text":"USGS data release","linkHelpText":"Hydrologic data from the Moreau Lake area, town of Moreau, N.Y."},{"id":503539,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JPZ1R5","text":"USGS data release","linkHelpText":"Geospatial data from the Moreau Lake area, town of Moreau, N.Y."},{"id":503538,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2026/5132/images"},{"id":503537,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2026/5132/sir20265132.XML","description":"SIR 2026-5132 XML"},{"id":503536,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20265132/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2026-5132 HTML"},{"id":503534,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2026/5132/sir20265132.pdf","text":"Report","size":"36.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2026-5132 PDF"}],"country":"United States","state":"New York","otherGeospatial":"Moreau Lake, Moreau Lake State Park, Town of Moreau","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.7367112856191,\n 43.25254327180468\n ],\n [\n -73.67250609715373,\n 43.25254327180468\n ],\n [\n -73.67250609715373,\n 43.20548515764352\n ],\n [\n -73.7367112856191,\n 43.20548515764352\n ],\n [\n -73.7367112856191,\n 43.25254327180468\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180
Understanding the distributions of rare species is necessary to guide monitoring and inform species recovery efforts. The rusty patched bumble bee (RPBB; Bombus affinis, Cresson) is an endangered species with an extant, known distribution centered around urban areas of the Midwestern United States. We tested a novel approach for finding undocumented RPBBs outside of urban centers and estimated the species occurrence at two scales that are relevant to management. We confirmed presence of RPBBs at 54% of the sampled 100 km2 grid cells where the species was previously undocumented, expanding the species’ known distribution by 5700 km2. After accounting for imperfect detection, our occupancy model estimated the number of occupied grid cells was 67 of 105 sampled grids, suggesting our methods were effective for finding undiscovered RPBB sites. Occupancy within 100 km2 grids was positively related to the number of occupied neighboring units but was not related to the area of developed land within 100km2 grid cells or smaller subunits (i.e. 3.14 ha patches or roadside transects). We highlight the utility of our approach for guiding future survey efforts by identifying an additional 145 grid cells where the occupancy status of RPBB is unknown but we predict a relatively high likelihood of RPBB occurrence. Our approach can be extended to find undiscovered RPBB sites in other areas and applied to other bee species where occurrence information is lacking outside of their core distribution.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-026-46861-8","usgsCitation":"Otto, C., Schrage, A.C., Lothspeich, A., Bailey, L., Smith, T., Planman, R., Cardin, J., Ellis, K.S., Dennis, B., and Grundel, R., 2026, A hierarchical approach for finding undiscovered populations of an endangered bumble bee: Scientific Reports, v. 16, 13759, 12 p., https://doi.org/10.1038/s41598-026-46861-8.","productDescription":"13759, 12 p.","ipdsId":"IP-181147","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":504215,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-026-46861-8","text":"Publisher Index Page"},{"id":504089,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Iowa. Minnesota, Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.08828853395633,\n 46.763583773695586\n ],\n [\n -87.05663513249607,\n 46.763583773695586\n ],\n [\n -87.05663513249607,\n 41.46394878090919\n ],\n [\n -95.08828853395633,\n 41.46394878090919\n ],\n [\n -95.08828853395633,\n 46.763583773695586\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","noUsgsAuthors":false,"publicationDate":"2026-04-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Otto, Clint 0000-0002-7582-3525 cotto@usgs.gov","orcid":"https://orcid.org/0000-0002-7582-3525","contributorId":5426,"corporation":false,"usgs":true,"family":"Otto","given":"Clint","email":"cotto@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":961297,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schrage, Alma Christa 0000-0002-7388-6979","orcid":"https://orcid.org/0000-0002-7388-6979","contributorId":363227,"corporation":false,"usgs":true,"family":"Schrage","given":"Alma","middleInitial":"Christa","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":961298,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lothspeich, Audrey Claire 0000-0002-5460-6142","orcid":"https://orcid.org/0000-0002-5460-6142","contributorId":355935,"corporation":false,"usgs":true,"family":"Lothspeich","given":"Audrey Claire","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":961299,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bailey, Larissa L.","contributorId":337882,"corporation":false,"usgs":false,"family":"Bailey","given":"Larissa L.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":961300,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, Tamara","contributorId":351890,"corporation":false,"usgs":false,"family":"Smith","given":"Tamara","affiliations":[{"id":37461,"text":"fws","active":true,"usgs":false}],"preferred":false,"id":961301,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Planman, Robert","contributorId":371218,"corporation":false,"usgs":false,"family":"Planman","given":"Robert","affiliations":[{"id":88106,"text":"WI Bee3","active":true,"usgs":false}],"preferred":false,"id":961302,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cardin, Judy","contributorId":371244,"corporation":false,"usgs":false,"family":"Cardin","given":"Judy","affiliations":[],"preferred":false,"id":961351,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ellis, Kristen S. 0000-0003-2759-3670","orcid":"https://orcid.org/0000-0003-2759-3670","contributorId":251877,"corporation":false,"usgs":true,"family":"Ellis","given":"Kristen","email":"","middleInitial":"S.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":961303,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Dennis, Bethany","contributorId":371245,"corporation":false,"usgs":false,"family":"Dennis","given":"Bethany","affiliations":[],"preferred":false,"id":961352,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Grundel, Ralph 0000-0002-2949-7087 rgrundel@usgs.gov","orcid":"https://orcid.org/0000-0002-2949-7087","contributorId":2444,"corporation":false,"usgs":true,"family":"Grundel","given":"Ralph","email":"rgrundel@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":961304,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70275078,"text":"sir20265129 - 2026 - Shallow hydrogeologic framework of the Tully Valley mudboil area, Onondaga County, New York","interactions":[],"lastModifiedDate":"2026-05-01T16:42:15.609499","indexId":"sir20265129","displayToPublicDate":"2026-04-29T09:32:47","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":"2026-5129","displayTitle":"Shallow Hydrogeologic Framework of the Tully Valley Mudboil Area, Onondaga County, New York","title":"Shallow hydrogeologic framework of the Tully Valley mudboil area, Onondaga County, New York","docAbstract":"Mudboils have been documented in the Tully Valley in southern Onondaga County, New York, since the late 1890s. Sediment-laden water from the mudboils flows into Onondaga Creek, which empties into Onondaga Lake at Syracuse 15 miles to the north. Turbidity from the mudboils has degraded the water quality of Onondaga Creek despite a series of mitigation efforts that began in the early 1990s. Turbidity mitigation actions presently (2025) being considered include creek relocation and offline sediment settling. In support of these proposed actions during 2021–23, the U.S. Geological Survey, in cooperation with the New York State Department of Environmental Conservation, U.S. Environmental Protection Agency, Onondaga Nation, Onondaga Environmental Institute, and Central New York Regional Planning and Development Board, collected and analyzed geologic, hydrologic, geophysical, and geotechnical data to characterize the shallow hydrogeology along four proposed creek-relocation paths and in the proposed offline settling basin area.
The investigation indicated that the four proposed creek-relocation paths, two east of Onondaga Creek and two west of Onondaga Creek, are underlain by sediments including muck, alluvium, mudboil deposits, alluvial-fan sand and gravel, and lacustrine fines. The proposed excavations would penetrate partially to fully saturated conditions: generally, the water table is shallow near the creek and deep on the alluvial fans. The shallowest excavation, about 5 feet below land surface, would be near the creek and primarily in alluvium, and the deepest excavation, as much as 30 feet below land surface, would be in the alluvial-fan deposits. Brackish waters would be penetrated by proposed channel excavations on the eastern side of Onondaga Creek in an area downgradient from a potentially leaking historical salt-exploration borehole and near the main mudboil area. Excavation in these areas likely would provide a continuous source of brackish groundwater to the relocated creek. Proposed channel excavations of muck, soft to very soft lacustrine fines, and mudboil-type sediments in mudboil and suspected mudboil areas would pose an excavation and slope stability challenge and would have the greatest potential to create new mudboils. Proposed channel excavations below the water table on the Rattlesnake Gulf and Rainbow Creek alluvial fans would intercept groundwater and make the constructed streambank susceptible to seepage-induced slope instability. The substantial water-level fluctuation in the sediments of both alluvial fans would aggravate the stability condition. In addition, excavation on the Rattlesnake Gulf alluvial fan would have the potential to affect water-supply springs at the toe of the fan.
The proposed offline settling basin area is in the northern part of the Rattlesnake Gulf alluvial fan. Natural and man-made diversions of Rattlesnake Gulf have resulted in saturated conditions in the general area of the proposed basin. The proposed offline settling basin would be excavated in, and berms would be constructed on, alluvial-fan deposits and lacustrine fines. In the proposed basin area, the alluvial deposits overlying the lacustrine fines are less than 10 feet thick. Excavation, berm construction, and loading of the saturated, soft to very soft lacustrine fines may be problematic and require soil strengthening.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20265129","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation, U.S. Environmental Protection Agency, Onondaga Nation, Onondaga Environmental Institute, and Central New York Regional Planning and Development Board","usgsCitation":"Williams, J.H., Terry, N.C., Kappel, W.M., Heisig, P.M., Glas, R.L., and Woda, J.C., 2026, Shallow hydrogeologic framework of the Tully Valley mudboil area, Onondaga County, New York: U.S. Geological Survey Scientific Investigations Report 2026–5129, 58 p., https://doi.org/10.3133/sir20265129.","productDescription":"Report: ix, 58 p.; Data Release","numberOfPages":"58","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-160204","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":503898,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119386.htm","linkFileType":{"id":5,"text":"html"}},{"id":502792,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2026/5129/images"},{"id":502793,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P149P35X","text":"USGS data release","linkHelpText":"Surface-geophysical, geotechnical, hydraulic-slug test, specific conductance, and geospatial data for shallow hydrogeologic investigations of the Tully Valley mudboil area, Onondaga County, New York"},{"id":502791,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2026/5129/sir20265129.XML","description":"SIR 2026-5129 XML"},{"id":502790,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20265129/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2026-5129 HTML"},{"id":502789,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2026/5129/coverthb2.jpg"},{"id":502786,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2026/5129/sir20265129.pdf","text":"Report","size":"21.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2026-5129 PDF"}],"country":"United States","state":"New York","county":"Onondaga County","otherGeospatial":"Tully Valley Mudboil Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.30484467796983,\n 43.13842576064047\n ],\n [\n -76.30484467796983,\n 42.78187367077939\n ],\n [\n -76.00789969132674,\n 42.78187367077939\n ],\n [\n -76.00789969132674,\n 43.13842576064047\n ],\n [\n -76.30484467796983,\n 43.13842576064047\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Laminated soil carbonate rinds are a Quaternary paleoclimate archive whose isotope composition is linked to soil formation conditions. At Rio Mesa, Utah (USA), we investigated the fidelity of rind records in a river terrace setting by determining the seasonal timing of rind formation and testing for inter-record replication. We infer soil carbonate formed in the spring season, contrasting with our prior inference of summer formation at Teasdale, Utah, ≈200 km distant. This apparent discrepancy occurs because of differences in the timing of the largest annual infiltration (spring vs. summer). At Rio Mesa, modern soil data show that soil carbonate δ13C would have high values (−2 to 2‰ VPDB) regardless of seasonal activity of C3 versus C4 plants because respiration rate is a strong control. We accordingly suggest reassessment of published records interpreting soil carbonate δ13C only via C3 versus C4 plant abundance. Three rind δ13C and δ18O records generally replicated. Intriguingly, rind δ13C may inversely correlate with summer insolation, evidence for global-scale influence on soils. Rind δ18O is not as clearly correlated with published western USA paleoclimate records, potentially due to regional differences in climate and because rinds record soil-specific processes. Our results support the fidelity of the soil carbonate rind paleoarchive and suggest that because rind formation seasonality is intimately tied to infiltration seasonality, spatial transects of rind records might be used to delineate boundaries between areas dominated by spring and summer infiltration, permitting reconstruction of the geographic extent of large-scale hydrologic phenomena such as the North American Monsoon.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2025GC012660","usgsCitation":"Huth, T.E., Cerling, T.E., Marchetti, D.W., Ellwein, A.L., Mahan, S.A., Bowling, D.R., Passey, B.H., Polyak, V.J., and Asmerom, Y., 2026, Springtime formation of laminated soil carbonate rinds and changes in fluvial terrace soils on orbital timescales at Rio Mesa, Utah, USA: Geochemistry, Geophysics, Geosystems, v. 27, no. 5, e2025GC012660, 20 p., https://doi.org/10.1029/2025GC012660.","productDescription":"e2025GC012660, 20 p.","ipdsId":"IP-179258","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":504152,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2025gc012660","text":"Publisher Index Page"},{"id":504055,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13EMTYG","text":"USGS data release","linkHelpText":"Luminescence data for: Springtime Formation of Laminated Soil Carbonate Rinds and Changes in Fluvial Terrace Soils on Orbital Timescales at Rio Mesa, Utah, USA"},{"id":503670,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Rio Mesa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.19947650382221,\n 38.81144940086827\n ],\n [\n -109.15369813510924,\n 38.81144940086827\n ],\n [\n -109.15369813510924,\n 38.77688465318985\n ],\n [\n -109.19947650382221,\n 38.77688465318985\n ],\n [\n -109.19947650382221,\n 38.81144940086827\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"27","issue":"5","noUsgsAuthors":false,"publicationDate":"2026-04-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Huth, Tyler E. 0000-0002-3436-7009","orcid":"https://orcid.org/0000-0002-3436-7009","contributorId":370676,"corporation":false,"usgs":false,"family":"Huth","given":"Tyler","middleInitial":"E.","affiliations":[{"id":37383,"text":"Washington University","active":true,"usgs":false}],"preferred":false,"id":960678,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cerling, Thure E.","contributorId":370677,"corporation":false,"usgs":false,"family":"Cerling","given":"Thure","middleInitial":"E.","affiliations":[{"id":13252,"text":"University of Utah","active":true,"usgs":false}],"preferred":false,"id":960679,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Marchetti, David W.","contributorId":370678,"corporation":false,"usgs":false,"family":"Marchetti","given":"David","middleInitial":"W.","affiliations":[{"id":6693,"text":"Western State Colorado University","active":true,"usgs":false}],"preferred":false,"id":960680,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ellwein, Amy L. 0000-0003-3591-6601","orcid":"https://orcid.org/0000-0003-3591-6601","contributorId":370679,"corporation":false,"usgs":false,"family":"Ellwein","given":"Amy","middleInitial":"L.","affiliations":[{"id":49195,"text":"Rocky Mountain Biological Laboratory","active":true,"usgs":false}],"preferred":false,"id":960681,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mahan, Shannon A. 0000-0001-5214-7774 smahan@usgs.gov","orcid":"https://orcid.org/0000-0001-5214-7774","contributorId":147159,"corporation":false,"usgs":true,"family":"Mahan","given":"Shannon","email":"smahan@usgs.gov","middleInitial":"A.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":960682,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bowling, David R.","contributorId":370684,"corporation":false,"usgs":false,"family":"Bowling","given":"David","middleInitial":"R.","affiliations":[{"id":13252,"text":"University of Utah","active":true,"usgs":false}],"preferred":false,"id":960683,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Passey, Benjamin H.","contributorId":370685,"corporation":false,"usgs":false,"family":"Passey","given":"Benjamin","middleInitial":"H.","affiliations":[{"id":37387,"text":"University of Michigan","active":true,"usgs":false}],"preferred":false,"id":960684,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Polyak, Victor J.","contributorId":370686,"corporation":false,"usgs":false,"family":"Polyak","given":"Victor","middleInitial":"J.","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":960685,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Asmerom, Yemane","contributorId":295388,"corporation":false,"usgs":false,"family":"Asmerom","given":"Yemane","affiliations":[{"id":16658,"text":"UNM","active":true,"usgs":false}],"preferred":false,"id":960686,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70275363,"text":"70275363 - 2026 - Modeling chronic wasting disease transmission risk in mule deer related to habitat characteristics","interactions":[],"lastModifiedDate":"2026-05-01T13:56:46.724745","indexId":"70275363","displayToPublicDate":"2026-04-29T08:52:37","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Modeling chronic wasting disease transmission risk in mule deer related to habitat characteristics","docAbstract":"Chronic wasting disease (CWD) is a prion disease of cervids that spreads to uninfected individuals through direct transmission (contact with infected individuals), vertical transmission (from mother to offspring), or indirect transmission (exposure to contaminated environments). The risk of indirect transmission is unevenly distributed on the landscape, and risk levels are expected to be controlled by patterns of habitat use by infected and uninfected individuals as well as environmental properties that alter the length of time prions remain infectious and available for uptake. Despite evidence from controlled or laboratory studies identifying environmental properties likely to affect patterns of CWD prion locations on the landscape, it remains difficult to connect mechanisms to realized increased or decreased risk of disease transmission, and few studies have attempted to detect patterns of different CWD risk in different environments. Using data from GPS-collared mule deer in Wyoming that were CWD-tested annually, we constructed models predicting annual probability of disease transmission contingent on environmental properties extracted from GPS use points. We compared models that emphasized different pathways of disease transmission by including or excluding sets of covariates that described deer density, habitat selection, and covariates expected to affect prion persistence in the environment. Results indicated that key habitat characteristics often selected by mule deer, such as proximity to secondary roads, were also associated with higher risk of testing positive for CWD, which supports the hypothesis that disease risk was correlated to patterns of habitat use by deer. We also found increased risk associated with spatial properties that were not selected-for by deer, such as areas where topography collects moisture, suggesting that prion retention mechanisms also play a role in risk. Incorporating these spatially-varying risk factors into our understanding of CWD transmission and outbreak progression can support managers in designing data collection and disease management strategies.","language":"English","publisher":"PLoS","doi":"10.1371/journal.pone.0346077","usgsCitation":"Christensen, E.M., Kleist, N.J., Edmunds, D.R., Heinrichs, J., Saher, D., Whipple, A.L., DeVivo, M., and Aldridge, C.L., 2026, Modeling chronic wasting disease transmission risk in mule deer related to habitat characteristics: PLoS ONE, v. 21, no. 4, e0346077, 24 p., https://doi.org/10.1371/journal.pone.0346077.","productDescription":"e0346077, 24 p.","ipdsId":"IP-179327","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":504156,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0346077","text":"Publisher Index Page"},{"id":504056,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1K3QFC8","text":"USGS data release","linkHelpText":"Raster maps of relative risk of chronic wasting disease transmission based on environmental covariates for the South Converse Mule Deer Herd, Converse County, Wyoming"},{"id":503880,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.91552517753678,\n 43.139560980857084\n ],\n [\n -104.18803524946959,\n 43.139560980857084\n ],\n [\n -104.18803524946959,\n 41.60389872904699\n ],\n [\n -106.91552517753678,\n 41.60389872904699\n ],\n [\n -106.91552517753678,\n 43.139560980857084\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"21","issue":"4","noUsgsAuthors":false,"publicationDate":"2026-04-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Christensen, Erica Meta 0000-0002-5635-2502","orcid":"https://orcid.org/0000-0002-5635-2502","contributorId":370740,"corporation":false,"usgs":true,"family":"Christensen","given":"Erica","middleInitial":"Meta","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":960708,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kleist, Nathan J. 0000-0002-2468-4318","orcid":"https://orcid.org/0000-0002-2468-4318","contributorId":260598,"corporation":false,"usgs":true,"family":"Kleist","given":"Nathan","email":"","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":960709,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Edmunds, David R. 0000-0002-5212-8271 dedmunds@usgs.gov","orcid":"https://orcid.org/0000-0002-5212-8271","contributorId":152210,"corporation":false,"usgs":true,"family":"Edmunds","given":"David","email":"dedmunds@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":960710,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Heinrichs, Julie A. 0000-0001-7733-5034","orcid":"https://orcid.org/0000-0001-7733-5034","contributorId":240888,"corporation":false,"usgs":false,"family":"Heinrichs","given":"Julie A.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":960711,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Saher, D. Joanne 0000-0002-2452-2570","orcid":"https://orcid.org/0000-0002-2452-2570","contributorId":288928,"corporation":false,"usgs":false,"family":"Saher","given":"D. 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Leveraging >55,000 reported landslides from across the United States (U.S.), we used circular statistics to quantify landslide seasonality in 67 National Weather Service County Warning Areas (CWAs). We found regional differences in landslide season timing and duration, with transitions between domains variably corresponding to climate class or river basin. We assessed differences in seasonality by movement type for slides, flows, and falls, detecting apparent, but uncertain, differences between slide and fall seasonalities in 27 of 35 (77%) of CWAs with both types reported. In the Pacific Northwest, where long records exist, we found a credible shift toward a later mean landslide season in western Washington from 1990 to 2020, but no trend in western Oregon. Our results can provide emergency planners a resource to assess seasonal landslide probability nationwide.
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","language":"English","publisher":"Interagency Grizzly Bear Study Team","collaboration":"U.S. Fish and Wildlife Service, U.S. Forest Service, U.S. National Park Service, Montana Fish, Wildlife and Parks, Idaho Department of Fish and Game, Wyoming Game and Fish Department, Eastern Shoshone and Northern Arapaho Tribal Fish and Game Department","usgsCitation":"2026, Yellowstone grizzly bear investigations 2024: Annual report of the Interagency Grizzly Bear Study Team: Annual Report, 166 p.","productDescription":"166 p.","ipdsId":"IP-186437","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":504245,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":504244,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://igbconline.org/grizzly-bear-study-team/"}],"country":"United States","state":"Idaho, Montana, Wyoming","otherGeospatial":"Greater Yellowstone Ecosystem","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.5,\n 46\n ],\n [\n -108.5,\n 46\n ],\n [\n -108.5,\n 42\n ],\n [\n -112.5,\n 42\n ],\n [\n -112.5,\n 46\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Gould, Michael J. 0000-0002-9703-4690","orcid":"https://orcid.org/0000-0002-9703-4690","contributorId":316810,"corporation":false,"usgs":true,"family":"Gould","given":"Michael","middleInitial":"J.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":961384,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"van Manen, Frank T. 0000-0001-5340-8489 fvanmanen@usgs.gov","orcid":"https://orcid.org/0000-0001-5340-8489","contributorId":2267,"corporation":false,"usgs":true,"family":"van Manen","given":"Frank","email":"fvanmanen@usgs.gov","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":961385,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Karabensh, Bryn 0000-0002-2052-5256","orcid":"https://orcid.org/0000-0002-2052-5256","contributorId":219113,"corporation":false,"usgs":true,"family":"Karabensh","given":"Bryn","email":"","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":961386,"contributorType":{"id":2,"text":"Editors"},"rank":3}]}} ,{"id":70275326,"text":"70275326 - 2026 - Characterizing the long-term (1981–2023) temperature and precipitation dynamics in the Trans-Mountain regions of Kazakhstan, Central Asia","interactions":[],"lastModifiedDate":"2026-04-29T14:27:38.481172","indexId":"70275326","displayToPublicDate":"2026-04-28T09:15:49","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Characterizing the long-term (1981–2023) temperature and precipitation dynamics in the Trans-Mountain regions of Kazakhstan, Central Asia","docAbstract":"Mountain regions are highly climate-sensitive, yet long-term observational evidence of elevation and seasonal climate dynamics in Central Asia remains limited. This study examines spatiotemporal trends in temperature (Tmean, Tmax, Tmin, and diurnal temperature range [DTR]) and precipitation across Kazakhstan’s transmountain regions using 74 meteorological stations (1981–2023). Data were analyzed using the Mann–Kendall test and Sen’s slope estimator, stratified across six elevation zones from lowlands (<400 m) to high mountains (>1500 m). Results reveal a robust, spatially coherent warming signal across all zones. Annual Tmean increased at a median rate of ~0.30 °C decade−1, peaking at 0.36 °C decade−1 above 1500 m, corresponding to an absolute increase exceeding 1.5 °C. Warming exhibited strong seasonal and diurnal asymmetries. Spring warmed most rapidly, with Tmean increasing >0.60 °C decade−1 (approaching 3 °C total). Winter warming was driven by Tmin increases (up to 0.44 °C decade−1), causing widespread DTR contraction, whereas summer warming was driven by Tmax increases, expanding DTR at higher elevations. Tmin showed the strongest elevation amplification overall. In stark contrast, precipitation trends were weak, spatially heterogeneous, and largely non-significant. Annual changes ranged from −6.63 to +14.35 mm decade−1, with seasonal tendencies indicating modest, non-significant winter/spring wetting and summer drying. Ultimately, the results demonstrate a profound decoupling between strong, elevation-dependent warming and weak precipitation changes. The acute amplification of temperature, particularly during spring and summer at high elevations, has severe implications for snowmelt timing, glacier mass balance, evapotranspiration demand, and long-term water security in Kazakhstan.
","language":"English","publisher":"MDPI","doi":"10.3390/w18091046","usgsCitation":"Duisebek, B., Senay, G.B., Usmanov, T., Kyrgyzbay, K., Sagin, J., Mukanov, Y., Samarkhanov, K., Wang, X., Danierhan, S., and Pan, X., 2026, Characterizing the long-term (1981–2023) temperature and precipitation dynamics in the Trans-Mountain regions of Kazakhstan, Central Asia: Water, v. 18, no. 9, 1046, 26 p., https://doi.org/10.3390/w18091046.","productDescription":"1046, 26 p.","ipdsId":"IP-187896","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":503778,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w18091046","text":"Publisher Index Page"},{"id":503620,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Kazakhstan","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[70.96231,42.26615],[70.38896,42.08131],[69.07003,41.38424],[68.63248,40.66868],[68.2599,40.66232],[67.98586,41.13599],[66.71405,41.16844],[66.51065,41.98764],[66.02339,41.99465],[66.09801,42.99766],[64.90082,43.72808],[63.18579,43.65007],[62.0133,43.50448],[61.05832,44.40582],[60.23997,44.78404],[58.68999,45.50001],[58.50313,45.5868],[55.92892,44.99586],[55.96819,41.30864],[55.45525,41.25986],[54.75535,42.04397],[54.07942,42.32411],[52.94429,42.11603],[52.50246,41.78332],[52.44634,42.02715],[52.69211,42.4439],[52.50143,42.7923],[51.34243,43.13297],[50.89129,44.03103],[50.33913,44.28402],[50.30564,44.60984],[51.2785,44.51485],[51.3169,45.246],[52.16739,45.40839],[53.04088,45.25905],[53.22087,46.23465],[53.04274,46.85301],[52.04202,46.80464],[51.19195,47.0487],[50.03408,46.60899],[49.10116,46.39933],[48.59324,46.56103],[48.69473,47.07563],[48.05725,47.74375],[47.31523,47.71585],[46.46645,48.39415],[47.04367,49.15204],[46.7516,49.35601],[47.54948,50.4547],[48.57784,49.87476],[48.70238,50.60513],[50.76665,51.69276],[52.32872,51.71865],[54.53288,51.02624],[55.71694,50.62172],[56.77796,51.04355],[58.36329,51.06365],[59.64228,50.54544],[59.93281,50.84219],[61.33742,50.79907],[61.588,51.27266],[59.96753,51.96042],[60.92727,52.44755],[60.73999,52.71999],[61.69999,52.98],[60.97807,53.66499],[61.43659,54.00626],[65.17853,54.35423],[65.66688,54.60127],[68.1691,54.97039],[69.06817,55.38525],[70.86527,55.16973],[71.18013,54.13329],[72.22415,54.37666],[73.50852,54.03562],[73.42568,53.48981],[74.38485,53.54686],[76.8911,54.49052],[76.52518,54.177],[77.80092,53.40441],[80.03556,50.86475],[80.56845,51.38834],[81.94599,50.8122],[83.383,51.06918],[83.93511,50.88925],[84.41638,50.3114],[85.11556,50.1173],[85.54127,49.69286],[86.82936,49.82667],[87.35997,49.21498],[86.59878,48.54918],[85.76823,48.45575],[85.72048,47.45297],[85.16429,47.00096],[83.18048,47.33003],[82.45893,45.53965],[81.94707,45.31703],[79.96611,44.91752],[80.86621,43.18036],[80.18015,42.92007],[80.25999,42.35],[79.64365,42.49668],[79.14218,42.85609],[77.65839,42.96069],[76.00035,42.98802],[75.63696,42.8779],[74.21287,43.29834],[73.6453,43.09127],[73.48976,42.50089],[71.84464,42.8454],[71.18628,42.70429],[70.96231,42.26615]]]},\"properties\":{\"name\":\"Kazakhstan\"}}]}","volume":"18","issue":"9","noUsgsAuthors":false,"publicationDate":"2026-04-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Duisebek, Baktybek","contributorId":360498,"corporation":false,"usgs":false,"family":"Duisebek","given":"Baktybek","affiliations":[{"id":86016,"text":"Kazakh British Technical University","active":true,"usgs":false}],"preferred":false,"id":960567,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":166812,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":960568,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Usmanov, Talgat","contributorId":370600,"corporation":false,"usgs":false,"family":"Usmanov","given":"Talgat","affiliations":[{"id":88048,"text":"Kazakh-British Technical University","active":true,"usgs":false}],"preferred":false,"id":960569,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kyrgyzbay, Kudaibergen","contributorId":370601,"corporation":false,"usgs":false,"family":"Kyrgyzbay","given":"Kudaibergen","affiliations":[{"id":88049,"text":"Al-Farabi Kazakh National University, Kazakhstan","active":true,"usgs":false}],"preferred":false,"id":960570,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sagin, Janay","contributorId":360500,"corporation":false,"usgs":false,"family":"Sagin","given":"Janay","affiliations":[{"id":86016,"text":"Kazakh British Technical University","active":true,"usgs":false}],"preferred":false,"id":960571,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mukanov, Yerbolat","contributorId":370602,"corporation":false,"usgs":false,"family":"Mukanov","given":"Yerbolat","affiliations":[{"id":88050,"text":"Gumilyov Eurasian National University, AKazakhstan","active":true,"usgs":false}],"preferred":false,"id":960572,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Samarkhanov, Kanat","contributorId":370637,"corporation":false,"usgs":false,"family":"Samarkhanov","given":"Kanat","affiliations":[],"preferred":false,"id":960603,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wang, Xuejia","contributorId":370638,"corporation":false,"usgs":false,"family":"Wang","given":"Xuejia","affiliations":[],"preferred":false,"id":960573,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Danierhan, Sulitan","contributorId":370603,"corporation":false,"usgs":false,"family":"Danierhan","given":"Sulitan","affiliations":[{"id":88051,"text":"Chinese Academy of Sciences, China","active":true,"usgs":false}],"preferred":false,"id":960574,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Pan, Xiaohui","contributorId":370604,"corporation":false,"usgs":false,"family":"Pan","given":"Xiaohui","affiliations":[{"id":88051,"text":"Chinese Academy of Sciences, China","active":true,"usgs":false}],"preferred":false,"id":960575,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70275316,"text":"70275316 - 2026 - Using structured decision-making to develop a communications strategy for the U.S. Geological Survey Cooperative Research Units Program","interactions":[],"lastModifiedDate":"2026-04-30T13:39:25.408554","indexId":"70275316","displayToPublicDate":"2026-04-28T08:33:07","publicationYear":"2026","noYear":false,"publicationType":{"id":27,"text":"Preprint"},"publicationSubtype":{"id":32,"text":"Preprint"},"seriesTitle":{"id":24021,"text":"Preprints.org","active":true,"publicationSubtype":{"id":32}},"title":"Using structured decision-making to develop a communications strategy for the U.S. Geological Survey Cooperative Research Units Program","docAbstract":"Communication regarding the mission of the U.S. Geological Survey (USGS) Cooperative Research Units Program (CRU) can take many forms, yet clear and concise messaging for various audiences is critical to highlight program accomplishments and increase visibility. Before the work described in this report, CRU did not have a communication strategy; therefore, CRU leadership supported a structured decision-making (SDM) workshop to develop a comprehensive strategy for multiple audiences. The workshop was conducted in November 2024, in Nebraska City, Nebraska. The working group for this SDM process included CRU Program leadership, the CRU Communications Team lead, Unit scientists, a Unit administrative assistant, a representative of the Wildlife Management Institute (WMI), a member of the USGS Ecosystems Mission Area (EMA), Office of Communications and Publishing (OCAP) team, and the team lead for the CRU Program strategic planning process, as well as three facilitators who were also unit scientists as well as experts in SDM. Over the course of a week, the SDM team followed the PrOACT framework which identified the problem, objectives, alternatives, consequences, and tradeoffs to guide us toward a strategy for implementation of a set of actions for CRU communications.\n\nResults of the SDM workshop included the development of a problem statement, an objectives hierarchy, a suite of alternatives that were evaluated using a consequences table and a clear process for assessing tradeoffs among alternative communication actions and strategies. Through the evaluation of consequences of each action or campaign, the team developed both the assessment tool (for the future) and an immediate plan for communication product development and distribution. The consequences table for this problem was meant to be flexible to accommodate changes in CRU thematic priorities and can be easily updated with new objectives, measures, and alternatives. In addition, the weight placed on objectives may change as the Team moves forward; the ranking and scoring system used in the workshop can be easily updated. \n\nOverall, the working group identified three different actions or campaigns—Fact Sheets, Who Are We Campaign, and Alumni Campaign—that scored high in the prototype decision framework. However, the tradeoffs analysis indicated that each action(s) performed better on some objectives than others. The working group identified a need to therefore develop an implementation plan that is composed of individual actions that each target different objectives to potentially create a holistic and feasible communications strategy that performs well for all objectives. In addition, the SDM prototype developed a scalable, objective-based framework for effectively communication of the value and accomplishments of the CRU program.","language":"English","publisher":"Preprints.org","doi":"10.20944/preprints202604.2012.v1","usgsCitation":"Robinson, K.F., Sells, S.N., McGowan, C.P., and Irwin, E.R., 2026, Using structured decision-making to develop a communications strategy for the U.S. Geological Survey Cooperative Research Units Program: Preprints.org, preprint posted April 28, 2026, https://doi.org/10.20944/preprints202604.2012.v1.","productDescription":"26 p.","ipdsId":"IP-177620","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"links":[{"id":504151,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.20944/preprints202604.2012.v1","text":"External Repository"},{"id":503616,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2026-04-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Robinson, Kelly Filer 0000-0001-8109-9492","orcid":"https://orcid.org/0000-0001-8109-9492","contributorId":340631,"corporation":false,"usgs":true,"family":"Robinson","given":"Kelly","email":"","middleInitial":"Filer","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":960556,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sells, Sarah Nelson 0000-0003-4859-7160","orcid":"https://orcid.org/0000-0003-4859-7160","contributorId":302377,"corporation":false,"usgs":true,"family":"Sells","given":"Sarah","email":"","middleInitial":"Nelson","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":960557,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McGowan, Conor P. 0000-0002-7330-9581 cmcgowan@usgs.gov","orcid":"https://orcid.org/0000-0002-7330-9581","contributorId":10145,"corporation":false,"usgs":true,"family":"McGowan","given":"Conor","email":"cmcgowan@usgs.gov","middleInitial":"P.","affiliations":[],"preferred":false,"id":960558,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Irwin, Elise R. 0000-0002-6866-4976 eirwin@usgs.gov","orcid":"https://orcid.org/0000-0002-6866-4976","contributorId":2588,"corporation":false,"usgs":true,"family":"Irwin","given":"Elise","email":"eirwin@usgs.gov","middleInitial":"R.","affiliations":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":960559,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70274589,"text":"70274589 - 2026 - GT-Seq panel development for species identification and parentage analysis of closely related hybridising Scaphirhynchus sturgeons","interactions":[],"lastModifiedDate":"2026-04-01T16:19:38.323977","indexId":"70274589","displayToPublicDate":"2026-04-27T09:07:47","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2776,"text":"Molecular Ecology Resources","active":true,"publicationSubtype":{"id":10}},"title":"GT-Seq panel development for species identification and parentage analysis of closely related hybridising Scaphirhynchus sturgeons","docAbstract":"Hatchery supplementation is vital for conserving dwindling fish populations. Effective augmentation requires distinguishing hatchery-origin from wild individuals and accurately identifying species, particularly in systems where closely related species coexist. Genetic monitoring is key to quantifying genetic differences, but conventional markers do not distinguish hybrids, especially backcrosses. Misidentifying hybrids in hatchery programs compromises wild gene pools because hatchery broodstock contributes to numerous offspring being released into the wild. Here, we present a workflow for developing and evaluating the Genotyping-in-Thousands by sequencing (GT-seq) single nucleotide polymorphism (SNP) panel for North American river sturgeons (Scaphirhynchus spp.). This panel is designed to detect complex hybrid classes and to determine parent-offspring relationships. Our species identification panel (S-loci) contains 155 SNPs selected for high genetic differentiation (FST) between Pallid Sturgeon (S. albus) and Shovelnose Sturgeon (S. platorynchus), and the parentage assignment panel (P-loci) includes 112 SNPs with high heterozygosity within Pallid Sturgeon. Simulation analyses demonstrated that our GT-seq S-loci panel reliably classifies pure species, F1, F2 and backcross hybrids, even with up to 70% missing data. The P-loci panel achieves high-confidence parentage assignment with ≥ 80% typed loci, with performance influenced by the proportion of sampled parents. Overall, the novel Scaphirhynchus GT-seq panel developed in this study represents a robust and efficient tool for detecting hybridisation, assigning parentage and providing critical information for management decisions in ongoing Pallid Sturgeon conservation.
","language":"English","publisher":"Wiley","doi":"10.1111/1755-0998.70124","usgsCitation":"Huang, J., Flamio Jr., R., Campbell, N.R., DeLonay, A.J., Buhman, A.C., and Heist, E.J., 2026, GT-Seq panel development for species identification and parentage analysis of closely related hybridising Scaphirhynchus sturgeons: Molecular Ecology Resources, v. 26, no. 3, e70124, 15 p., https://doi.org/10.1111/1755-0998.70124.","productDescription":"e70124, 15 p.","ipdsId":"IP-182509","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":502047,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1755-0998.70124","text":"Publisher Index Page"},{"id":501950,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"lower Mississippi River, Missouri River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92.23064427153525,\n 39.585048555903796\n ],\n [\n -92.23064427153525,\n 29.23866385061089\n ],\n [\n -89.29970131135747,\n 29.23866385061089\n ],\n [\n -89.29970131135747,\n 39.585048555903796\n ],\n [\n -92.23064427153525,\n 39.585048555903796\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"26","issue":"3","noUsgsAuthors":false,"publicationDate":"2026-03-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Huang, Junman","contributorId":369077,"corporation":false,"usgs":false,"family":"Huang","given":"Junman","affiliations":[{"id":25439,"text":"Southern Illinois University, Carbondale","active":true,"usgs":false}],"preferred":false,"id":958410,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flamio Jr., Richard","contributorId":253126,"corporation":false,"usgs":false,"family":"Flamio Jr.","given":"Richard","affiliations":[{"id":50485,"text":"Department of Zoology, Southern Illinois University Carbondale, Carbondale, IL","active":true,"usgs":false}],"preferred":false,"id":958411,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Campbell, Nathan R.","contributorId":369080,"corporation":false,"usgs":false,"family":"Campbell","given":"Nathan","middleInitial":"R.","affiliations":[{"id":87710,"text":"GTseek, LLC","active":true,"usgs":false}],"preferred":false,"id":958412,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DeLonay, Aaron J. 0000-0002-3752-2799 adelonay@usgs.gov","orcid":"https://orcid.org/0000-0002-3752-2799","contributorId":2725,"corporation":false,"usgs":true,"family":"DeLonay","given":"Aaron","email":"adelonay@usgs.gov","middleInitial":"J.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":958413,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Buhman, Amy C.","contributorId":369081,"corporation":false,"usgs":false,"family":"Buhman","given":"Amy","middleInitial":"C.","affiliations":[{"id":25439,"text":"Southern Illinois University, Carbondale","active":true,"usgs":false}],"preferred":false,"id":958414,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Heist, Edward J.","contributorId":369082,"corporation":false,"usgs":false,"family":"Heist","given":"Edward","middleInitial":"J.","affiliations":[{"id":25439,"text":"Southern Illinois University, Carbondale","active":true,"usgs":false}],"preferred":false,"id":958415,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70275190,"text":"sir20255047 - 2026 - Peak-, mean-, and low-streamflow regional-regression equations for natural streamflow in central and western Colorado, 2019","interactions":[],"lastModifiedDate":"2026-05-18T16:07:47.996983","indexId":"sir20255047","displayToPublicDate":"2026-04-24T13:10:00","publicationYear":"2026","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-5047","displayTitle":"Peak-, Mean-, and Low-Streamflow Regional-Regression Equations for Natural Streamflow in Central and Western Colorado, 2019","title":"Peak-, mean-, and low-streamflow regional-regression equations for natural streamflow in central and western Colorado, 2019","docAbstract":"The U.S. Geological Survey (USGS), in cooperation with the Colorado Department of Transportation, developed peak-, mean-, and low-streamflow regional-regression equations for estimating various statistics for natural streamflow in hydrologic regions of central and western Colorado. The peak-streamflow regression equations were developed using data from 418 streamgages, consisting of 15,202 years of record and a mean of approximately 36 years of record per streamgage. The mean- and low-streamflow regional-regression equations were developed using data from 323 streamgages where daily streamflow data were collected year-round. The annual exceedance-probability discharges for each streamgage were computed using the USGS software program PeakFQ. Mean monthly and 7-day minimum and maximum streamflows were computed using the USGS software program SWToolbox. Streamflow-duration values were computed using an R script. The regional-regression equations were determined using data for the period of record for a given streamgage through water year 2019. Geographic information systems datasets were used to develop 55 basin and 42 climatic characteristics, which were evaluated as candidate explanatory variables in the regression analysis.
For the peak-streamflow regional-regression equations, the study area was divided into four hydrologic regions based on mean basin elevation, including the Plateau (less than 8,014 feet), Mid-Elevation (8,015 feet to 9,492 feet), Sub-Alpine (9,493 feet to 10,490 feet), and Alpine (greater than 10,490 feet) regions. For the peak-streamflow equations, the selection of basin and climatic characteristics was based on the 1-percent annual exceedance-probability discharge for each hydrologic region.
For the mean streamflow, streamflow-duration values, and 7-day minimum and maximum streamflows, the study area was divided into four hydrologic regions based on river basin, including the (1) Colorado-East Slope Headwaters, (2) Green River, (3) Rio Grande, and (4) San Juan-Dolores. For mean streamflows, basin and climatic characteristics were evaluated separately for the annual period and each month for each hydrologic region. Regional regression equations published in this report are available for use in the USGS web-based program StreamStats.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20255047","collaboration":"Prepared in cooperation with the Colorado Department of Transportation","usgsCitation":"Kohn, M.S., Mast, M.A., and Gross, T.A., 2026, Peak-, mean-, and low-streamflow regional-regression equations for natural streamflow in central and western Colorado, 2019: U.S. Geological Survey Scientific Investigations Report 2025–5047, 38 p., https://doi.org/10.3133/sir20255047.","productDescription":"Report: viii, 38 p.; 2 Tables; Data Release","onlineOnly":"Y","ipdsId":"IP-140049","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":503285,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5047/sir20255047.pdf","text":"Report","size":"5.86 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5047"},{"id":504484,"rank":9,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255047/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5047"},{"id":504422,"rank":8,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5047/sir20255047.xml"},{"id":504421,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5047/images"},{"id":503897,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119385.htm","linkFileType":{"id":5,"text":"html"}},{"id":503290,"rank":5,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sir/2025/5047/sir20255047_table1.2.csv","text":"Table 1.2","size":"16.0 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2025-5047 Table 2","linkHelpText":"Basin and climate characteristics evaluated for use in the peak-, mean-, and low-streamflow regional-regression equations in central and western Colorado, 2019"},{"id":503284,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5047/coverthb.jpg"},{"id":503289,"rank":4,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sir/2025/5047/sir20255047_table1.1.csv","text":"Table 1.1","size":"72.0 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2025-5047 Table 1","linkHelpText":"Summary of the streamgages used in the regression analysis of natural streams in central and western Colorado, 2019"},{"id":503286,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Q5AMFV","text":"USGS data release","linkHelpText":"Streamflow data and basin characteristics of natural streams in central and western Colorado, 2019"}],"country":"United States","state":"Colorado","otherGeospatial":"central and western Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.5157587,\n 37.0336449\n ],\n [\n -104.2371905,\n 37.7437419\n ],\n [\n -104.4345097,\n 38.3014981\n ],\n [\n -104.9220039,\n 39.2695668\n ],\n [\n -104.5737937,\n 39.5207192\n ],\n [\n -104.8523603,\n 41.026094\n ],\n [\n -109.0540968,\n 40.9910589\n ],\n [\n -109.0557679,\n 37.0087015\n ],\n [\n -104.5157587,\n 37.0336449\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
Denver, CO 80225
Noise exposure is a potential stressor for free-ranging marine mammals and is often studied in the absence of other environmental factors. Here, a multi-investigator, interdisciplinary effort was undertaken to examine the response of elephant seals to multiple stressors. An integrated physiological and ecological approach was taken, including immunology, stress physiology, toxicology, animal behavior, population biology, and life history theory, to examine the cumulative effects of exposure to multiple stressors in elephant seals. While we measured the response of individual animals, a population response can be predicted by incorporating these results into the long-term data on elephant seal demographics.
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In 2023, the conterminous United States produced 66 million short tons (mst) of met coal, consumed 15.85 mst domestically, exported 51.1 mst, and imported 0.7 mst. Most met coal was produced in the Appalachian Basin, but there are also resources that meet the specifications for met coal in the Western United States.
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U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
Interest in nuclear power for the generation of electricity has risen with the increase in the need for more diverse baseload power, enhanced energy security, and the development of new technologies, such as small modular reactors (SMRs), which could provide power for remote areas, industrial applications, and artificial intelligence (AI) data centers. In 2024, the U.S. Department of Energy received $2.7 billion in congressional funding to bolster the domestic uranium production and nuclear fuel supply chain and address reliance on imports from foreign suppliers. In 2025, the U.S. Government issued several Executive and Secretary’s orders aimed at revitalizing the U.S. nuclear sector. If SMRs are to be as widely deployed in the United States and worldwide as envisioned, demand for uranium (nuclear reactor fuel) will likely increase.
After the Fukushima nuclear accident in 2011, the market spot price of uranium began a decline, followed by a decrease in U.S. and global uranium exploration and mine development expenditures that led to a uranium supply deficit until 2020, when prices started to recover, prompting a resurgence in uranium exploration and development. In January of 2024, the uranium spot price rose to a 17-year high $106 (U.S. dollars) per pound of U3O8 (triuranium oxide, commonly known as “yellowcake”), which is expected to increase uranium exploration, mine development, and uranium production domestically and worldwide.
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U.S. Geological Survey
Building 19, 350 N. Akron Rd.
P.O. Box 158
Moffett Field, CA 94035
Cyanobacterial harmful algal blooms (cyanoHABs) pose risks to human and animal health.
We investigated the relationship between cyanoHABs and asthma or wheeze-related emergency department (ED) visits near three Wisconsin cities (Green Bay, Madison, and Oshkosh) during 2017–2019. CyanoHAB exposure was approximated using the Cyanobacterial Assessment Network remotely sensed satellite indicator of cyanobacterial biomass, a chlorophyl algorithm (ChlBS) aggregated by water-adjacent ZIP Code Tabulation Areas (ZCTA), and distance weighted from the nearest waterbody. Weekly counts of ED visits for asthma or wheeze were aggregated by ZCTA. Poisson generalized linear models estimated the association between the weekly number of ED visits and weekly ChlBS, adjusting for maximum temperature, dewpoint, fine particulate matter (PM2.5), month, and correlation within ZCTA.
During 2017–2019, 7,057 ED visits for asthma or wheeze occurred in the study area (42 ZCTAs). Peaks in ChlBS occurred between June and October, with higher values in Lake Winnebago and Lake Mendota compared to Green Bay. ChlBS was not associated with ED visits for asthma or wheeze (adjusted rate ratio = 1.00, 95% confidence interval = 0.99, 1.00), and the presence of onshore winds did not change this result. Monthly aggregations of ED visits and ChlBS showed a monotonic trend between increasing ChlBS and ED visits during July–September.
This study demonstrates the utility of remote sensing data in environmental health research. Future studies could explore individual-level exposure and outcomes to refine health risks associated with cyanoHABs.
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Washington","interactions":[{"subject":{"id":70275112,"text":"70275112 - 2025 - Development of a two-stage life cycle model to inform the Trap and Haul Program for Coho salmon in the Lewis River, Washington","indexId":"70275112","publicationYear":"2025","noYear":false,"title":"Development of a two-stage life cycle model to inform the Trap and Haul Program for Coho salmon in the Lewis River, Washington"},"predicate":"SUPERSEDED_BY","object":{"id":70275077,"text":"ofr20261004 - 2026 - Development of a two-stage lifecycle model to inform the trap-and-haul program for Oncorhynchus kisutch (coho salmon) in the Lewis River, Washington","indexId":"ofr20261004","publicationYear":"2026","noYear":false,"title":"Development of a two-stage lifecycle model to inform the trap-and-haul program for Oncorhynchus kisutch (coho salmon) in the Lewis River, Washington"},"id":1}],"lastModifiedDate":"2026-04-23T13:56:24.60608","indexId":"ofr20261004","displayToPublicDate":"2026-04-22T14:45:00","publicationYear":"2026","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2026-1004","displayTitle":"Development of a Two-Stage Lifecycle Model to Inform the Trap-and-Haul Program for Oncorhynchus kisutch (Coho Salmon) in the Lewis River, Washington","title":"Development of a two-stage lifecycle model to inform the trap-and-haul program for Oncorhynchus kisutch (coho salmon) in the Lewis River, Washington","docAbstract":"Restoration of salmon populations in the upper Lewis River Basin, Washington, depends on a trap-and-haul program owing to the Lewis River Hydroelectric Project (hereinafter referred to as “Project”) operated by PacifiCorp and Cowlitz Public Utilities District (hereinafter referred to as “Utilities”), which has been a barrier to salmon passage since the 1930s. Thus, sustaining the Oncorhynchus kisutch (Walbaum, 1792; coho salmon) population upstream from the Project currently depends on two fundamental factors: (1) the collection of upstream migrating adult coho salmon at Merwin Dam, the lowermost dam within the Project, and transporting them by truck to spawn above Swift Dam, the uppermost dam within the Project; and (2) the collection of out-migrating juvenile coho salmon at the downstream collection facility at Swift Dam for transport and release below the Project. The reintroduction program began once the downstream collection facility at Swift Dam was commissioned in late 2012, with the first year of transport data being collected in 2013. Over the past decade, the Utilities have been collecting data on juvenile outmigrants and adult fish returns at the dams. The need to construct a lifecycle model for Lewis River anadromous fish was identified by the Lewis River Aquatic Technical Subgroup, with the understanding that many years (more than 15 years) of data collection are needed to adequately measure the lifecycle production of salmon. The U.S. Geological Survey was contracted to develop and apply the model to past data at the Lewis River dams to help inform future data collection and provide a framework that can be updated annually to measure trap-and-haul program performance within a lifecycle context.
Because coho salmon can live as long as 5 years, estimating demographic parameters for coho salmon populations over their lifecycle requires at least 10 or more years of data collection. Over the past decade, PacifiCorp has been collecting data on fish collection efficiency and the numbers of adult and juvenile salmon transported around the Lewis River dams, making this an ideal time to formulate a lifecycle model that can guide future data collection efforts and provide preliminary information to resource managers. The goal of the statistical lifecycle model is to estimate annual production and survival during two critical life-stage transitions: (1) the freshwater production from escapement of adults released upstream from Swift Dam, and the collection of downstream migrating juveniles at the downstream passage facility at Swift Dam; and (2) the smolt-to-adult survival from the time of collection at Swift Dam to their return as adults. We used the Beverton-Holt stock-recruitment model to estimate juvenile production from the number of spawners (Beverton and Holt, 1957). This approach allowed us to test for density dependence at current spawner abundances while estimating annual productivity, defined as the number of juveniles produced per spawner at low spawner abundance. Productivity was then expressed as a function of the number of juveniles collected and transported downstream from the Project. Because juvenile fish collection efficiency (FCE) directly affects the number of juveniles that survive to continue downstream migration, FCE is a primary determinant of fish production. Consequently, the modeling framework is well suited to evaluate the performance of trap-and-haul programs within a lifecycle context.
The objectives of this study were to (1) gather and collate available data on adult and juvenile coho salmon at Merwin and Swift Dams; (2) quantify adult escapement, juvenile abundance, and the age at outmigration and adult return; (3) describe, formulate and fit the integrated population model to the data; and (4) summarize our findings, identify data gaps, and identify opportunities for future studies that could improve model estimation and inference. Our key findings were: (1) over and above the number of spawning females, FCE was the primary factor affecting productivity of coho salmon above Swift Dam; (2) smolt-to-adult return (SAR) rates were relatively high considering that harvest was included in the estimate, averaging about 4.5 percent and ranging as high as 12.9 percent; and (3) juvenile capacity upstream from Swift Dam was difficult to estimate due to the limited range in spawning females over the time series of data, suggesting the model may be improved by collecting data at higher spawner abundances. In addition, by including FCE in the model, we estimated that the median pre-collection productivity, defined as the number of juveniles produced per spawner when FCE=1, was 64 juveniles per spawner. Because the two-stage lifecycle model partitions factors that affect fish production in rivers versus the ocean, the model estimates may help inform fishery managers about the overall role that fish collection at Swift Dam plays in the recovery and sustainability of Lewis River coho salmon. By providing the model with (1) more years of data, (2) higher numbers of spawning females, and (3) data on age at juvenile migration in relation to age at adult return, greater certainty in the estimates of capacity and SAR can be attained. Ultimately, information provided by the model may assist in the evaluation and continued improvement of the current trap-and-haul program to support anadromous fishes in the Lewis River Basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20261004","collaboration":"Prepared in cooperation with PacifiCorp","usgsCitation":"Plumb, J.M., and Perry, R.W., 2026, Development of a two-stage lifecycle model to inform the trap-and-haul program for Oncorhynchus kisutch (coho salmon) in the Lewis River, Washington: U.S. Geological Survey Open-File Report 2026–1004, 24 p., https://doi.org/10.3133/ofr20261004. [Supersedes preprint https://doi.org/10.1101/2025.04.30.651546.]","productDescription":"vii, 24 p.","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-170103","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":502780,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2026/1004/coverthb.jpg"},{"id":502781,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2026/1004/ofr20261004.pdf","size":"5.95 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2026-1004 PDF"},{"id":502782,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20261004/full","linkFileType":{"id":5,"text":"html"},"description":"OFR 2026-1004 HTML"},{"id":502783,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2026/1004/ofr20261004.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2026-1004 XML"},{"id":502784,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2026/1004/images/"}],"country":"United States","state":"Washington","otherGeospatial":"Lewis River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.99926718616892,\n 46.128547340095906\n ],\n [\n -122.8039992422735,\n 46.12907889994935\n ],\n [\n -122.80484882535518,\n 45.8612743686528\n ],\n [\n -122.00013863515652,\n 45.86266272702096\n ],\n [\n -121.99926718616892,\n 46.128547340095906\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
5501-A Cook Underwood Road
Cook, Washington 98605-9717