Viruses are the most abundant and diverse organisms on Earth, though only a small portion cause disease. Understanding viral diversity is key to understanding and predicting pathogen emergence and zoonotic spillover. Here, we use meta-transcriptomic sequencing to examine the viral communities in the ileum of 25 Northern Mockingbirds (Mimus polyglottos) from various locations across Texas. We assembled high-quality genomes of 43 viral species (40 species identified to 13 families, one to kingdom, and two to realm), 38 of which were novel. They tentatively represent avian- (n = 3), arthropod- (n = 21), plant- (n = 5) and fungi- (n = 4) associated, or other (n = 10) viruses. The arthropod-associated Dicistroviridae family was the most dominant, comprising known and potentially new species. Of potential epidemiological importance were three novel and avian-associated viruses: members of the families Hepeviridae and Picornaviridae, and a new Matryoshka RNA virus. The Matryoshka RNA virus 8 (MaRNAV-8) is sister to other Matryoshka RNA viruses, and its co-occurrence with haemosporida further supports the nested virus-parasite-vector-vertebrate host relationship of this group of viruses, with potential implications for parasite evolution, fitness and load and vector competence. The Picornaviridae virus is a member of an avian hepatovirus clade, found nested within a clade containing both the mammalian pathogens Hepatovirus A – I and the avian Tremovirus pathogens, suggestive of a newly discovered pathogen of Northern Mockingbird. Although the recovered Hepeviridae virus is of unknown pathology, its family members include the Hepatitis E viruses. With the great diversity and novelty described from ileal viromes, discriminating potential pathogens and commensal microbiota from viruses associated with food items remains challenging. A deeper understanding of virus transmission and the risk of potential zoonosis can be enhanced by tracking viruses through the food web and via inter-specific and predator-prey interactions, particular in areas subject to land-use change, where human-wildlife interactions are increased and the risks from emerging pathogens of veterinary and medical importance are more pronounced.
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This report describes the data collected through two efforts conducted in the Cooper Landing, Alaska, study area: (1) parcel-level rapid wildfire risk assessments performed by trained assessors and (2) homeowner surveys in which respondents provided self-assessments of their parcel-level wildfire risk. This project was undertaken to support the Kenai Peninsula Borough Office of Emergency Management and Cooper Landing Emergency Services to inform decisions about wildfire adaptation. The household surveys explored the social dimensions of wildfire, including understanding of wildfire risk, outreach or communication preferences, mitigation and evacuation preparedness activities, and perceptions of community risk reduction strategies. Overall, the study indicated a community that was engaged in preparing for and mitigating the risk of wildfire yet had more that could be done to reduce its risk.
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2026 - Abandonment of the Upper Devonian Greenland Gap Group and Scherr Formation and revision of the Upper Devonian Brallier and Foreknobs Formations in the central Valley and Ridge Province","interactions":[],"lastModifiedDate":"2026-04-02T16:08:57.895011","indexId":"70274628","displayToPublicDate":"2026-03-01T09:00:45","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3481,"text":"Stratigraphy","active":true,"publicationSubtype":{"id":10}},"title":"Abandonment of the Upper Devonian Greenland Gap Group and Scherr Formation and revision of the Upper Devonian Brallier and Foreknobs Formations in the central Valley and Ridge Province","docAbstract":"This study revises the lithostratigraphic framework of the Upper Devonian interval traditionally assigned to the Greenland Gap Group across the central Appalachian Valley and Ridge Province. The work aims to modernize and standardize lithostratigraphic nomenclature, establish a new reference section and demonstrate how the revised stratigraphy improves edge-matching of 1:24,000 scale geologic maps and supports compilation mapping at scales of 1:100,000 and larger. The revision eliminates the names Greenland Gap Group, Scherr Formation and the Minnehaha Springs Member of the Scherr Formation; reassigns all strata previously designated as Scherr Formation by Dennison (1970) to the upper Brallier Formation; and abandons the basal Mallow Member of the Foreknobs Formation, placing its strata within the upper Brallier as originally defined by Butts (1918).\n\nThe contact between the Brallier and Foreknobs formations is placed at the base of the first mappable, ridge-forming package of fine- to coarse-grained, cross-bedded, sandstone beds, often containing rounded quartz pebbles with minor interbeds of shale and siltstone. This contact may be gradational in places but, even in absence of good exposure, can usually be distinguished topographically in recently produced lidar-derived imagery as having elevated relief due to the presence of more resistant, compositionally mature coarse-grained sandstone-rich strata. Applying this criterion for mapping the contact between the Brallier and Foreknobs formations has resulted in reconciliation of mismatches of geologic contacts along several 7.5-minute quadrangle boundaries in the states of Virginia, West Virginia, Maryland and Pennsylvania. A new reference section at Baker, West Virginia showcases the contacts between the Harrell Shale, Brallier Formation, Foreknobs Formation and Hampshire Formation. A digital outcrop model of the reference section is provided for future preservation.","language":"English","publisher":"Micropaleontology Press","doi":"10.29041/strat.23.1.03","usgsCitation":"Pitts, A.D., and Doctor, D.H., 2026, Abandonment of the Upper Devonian Greenland Gap Group and Scherr Formation and revision of the Upper Devonian Brallier and Foreknobs Formations in the central Valley and Ridge Province: Stratigraphy, v. 23, no. 1, p. 31-44, https://doi.org/10.29041/strat.23.1.03.","productDescription":"14 p.","startPage":"31","endPage":"44","ipdsId":"IP-153186","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":502010,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, Pennsylvania, Virginia, West Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.5837839625223,\n 41.90737349195368\n ],\n [\n -80.97986900952827,\n 39.8912761703621\n ],\n [\n -82.00598531940697,\n 38.93662885204664\n ],\n [\n -82.63094418286235,\n 38.26806111836963\n ],\n [\n -82.14502301832408,\n 37.467676901275595\n ],\n [\n -83.57714608050935,\n 36.57298398456588\n ],\n [\n -75.54432999451647,\n 36.46466032532334\n ],\n [\n -75.54432999451647,\n 41.90737349195368\n ],\n [\n -80.5837839625223,\n 41.90737349195368\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"23","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Pitts, Alan D. 0000-0002-9661-4917","orcid":"https://orcid.org/0000-0002-9661-4917","contributorId":350522,"corporation":false,"usgs":true,"family":"Pitts","given":"Alan","middleInitial":"D.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":958490,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Doctor, Daniel H. 0000-0002-8338-9722 dhdoctor@usgs.gov","orcid":"https://orcid.org/0000-0002-8338-9722","contributorId":2037,"corporation":false,"usgs":true,"family":"Doctor","given":"Daniel","email":"dhdoctor@usgs.gov","middleInitial":"H.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":958491,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70275044,"text":"70275044 - 2026 - Open-source gravity reduction workflows for geothermal resource assessment","interactions":[],"lastModifiedDate":"2026-04-13T13:52:22.999434","indexId":"70275044","displayToPublicDate":"2026-03-01T08:43:12","publicationYear":"2026","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Open-source gravity reduction workflows for geothermal resource assessment","docAbstract":"Potential-field geophysical data such as gravity can enhance understanding of geothermal resources at all stages of the resource life cycle, including assessment, exploration, development, and monitoring, and at multiple scales, from the reservoir scale to regional scale. However, to make gravity data useful for geothermal resource characterization, several processing steps are required to isolate the effects of density variations in the Earth’s crust to enable the identification of structural features associated with geothermal resources. Although this process is well-established, standard computational implementations for processing gravity data that are FAIR (Findable, Accessible, Interoperable, and Reproduceable) are still lacking. This paper details ongoing efforts at the U.S. Geological Survey (USGS) to develop a standard set of open-source Python tools for gravity data reduction that align with the FAIR principles. This workflow makes use of existing open-source tools for geophysical data processing with the goal of maximizing opportunities for rapid improvements, interoperability, and adaptability to other types of geophysical data.
","conferenceTitle":"51st Stanford Geothermal Workshop","conferenceDate":"February 9-11, 2026","conferenceLocation":"Stanford, CA","language":"English","publisher":"Stanford University","usgsCitation":"Cronkite-Ratcliff, C., 2026, Open-source gravity reduction workflows for geothermal resource assessment, 51st Stanford Geothermal Workshop, Stanford, CA, February 9-11, 2026, 6 p.","productDescription":"6 p.","ipdsId":"IP-186091","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":502738,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":502737,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pangea.stanford.edu/ERE/db/IGAstandard/record_detail.php?id=38271"}],"noUsgsAuthors":false,"publicationDate":"2026-03-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Cronkite-Ratcliff, Collin 0000-0001-5485-3832 ccronkite-ratcliff@usgs.gov","orcid":"https://orcid.org/0000-0001-5485-3832","contributorId":203951,"corporation":false,"usgs":true,"family":"Cronkite-Ratcliff","given":"Collin","email":"ccronkite-ratcliff@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":959294,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70274291,"text":"70274291 - 2026 - Living with wildfire in Nikiski, Kenai Peninsula Borough, Alaska: 2023 Data report","interactions":[],"lastModifiedDate":"2026-03-24T13:40:49.401301","indexId":"70274291","displayToPublicDate":"2026-03-01T08:34:44","publicationYear":"2026","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":72,"text":"Research Note","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"RMRS-RN-110","title":"Living with wildfire in Nikiski, Kenai Peninsula Borough, Alaska: 2023 Data report","docAbstract":"Homeowner wildfire risk mitigation and preparedness are important components of community wildfire readiness. This report describes the data collected through two efforts conducted in the Nikiski, Alaska, study area: (1) parcel-level rapid wildfire risk assessments performed by trained assessors and (2) homeowner surveys in which respondents provided self-assessments of their parcel-level wildfire risk. This project was undertaken to support the Kenai Peninsula Borough Office of Emergency Management, and the Nikiski Fire Service Area to inform decisions about wildfire adaptation. The household surveys explored the social dimensions of wildfire, including understanding of wildfire risk, outreach or communication preferences, mitigation and evacuation preparedness activities, and perceptions of community risk reduction strategies. The results provide evidence that despite some results seeming to suggest lower wildfire risk perceptions, respondents indicate taking action to reduce risk on their properties, support fuels management on adjacent public lands, and express interest in opportunities to undertake both mitigation and emergency preparedness activities to reduce risks associated with the threat of wildfire.
","language":"English","publisher":"U.S. Department of Agriculture, Forest Service","doi":"10.2737/RMRS-RN-110","usgsCitation":"Donovan, C., Wittenbrink, S., Brenkert-Smith, H., Kuehn, J., Ahlberg, B., Champ, P.A., Barth, C.M., Meldrum, J., Wagner, C., and Taniguchi, C., 2026, Living with wildfire in Nikiski, Kenai Peninsula Borough, Alaska: 2023 Data report: Research Note RMRS-RN-110, 151 p., https://doi.org/10.2737/RMRS-RN-110.","productDescription":"151 p.","ipdsId":"IP-179901","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":501440,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":501439,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://research.fs.usda.gov/treesearch/80226"}],"country":"United States","state":"Alaska","city":"Nikiski","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -151.1675112339671,\n 60.78632313300909\n ],\n [\n -151.42365967505256,\n 60.78632313300909\n ],\n [\n -151.42365967505256,\n 60.607918518507574\n ],\n [\n -151.1675112339671,\n 60.607918518507574\n ],\n [\n -151.1675112339671,\n 60.78632313300909\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2026-03-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Donovan, Colleen","contributorId":240586,"corporation":false,"usgs":false,"family":"Donovan","given":"Colleen","email":"","affiliations":[{"id":48103,"text":"Wildfire Research (WiRē) Center","active":true,"usgs":false}],"preferred":false,"id":957636,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wittenbrink, Suzanne","contributorId":333353,"corporation":false,"usgs":false,"family":"Wittenbrink","given":"Suzanne","email":"","affiliations":[{"id":48103,"text":"Wildfire Research (WiRē) Center","active":true,"usgs":false}],"preferred":false,"id":957637,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brenkert-Smith, Hannah 0000-0001-6117-8863","orcid":"https://orcid.org/0000-0001-6117-8863","contributorId":195485,"corporation":false,"usgs":false,"family":"Brenkert-Smith","given":"Hannah","email":"","affiliations":[],"preferred":false,"id":957638,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kuehn, Josh","contributorId":269634,"corporation":false,"usgs":false,"family":"Kuehn","given":"Josh","email":"","affiliations":[{"id":56021,"text":"Colorado State Forest Service","active":true,"usgs":false}],"preferred":false,"id":957639,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ahlberg, Brenda","contributorId":367752,"corporation":false,"usgs":false,"family":"Ahlberg","given":"Brenda","affiliations":[{"id":87597,"text":"Office of Emergency Management, Kenai Peninsula Borough, Alaska","active":true,"usgs":false}],"preferred":false,"id":957640,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Champ, Patricia A. 0000-0003-1917-883X","orcid":"https://orcid.org/0000-0003-1917-883X","contributorId":360956,"corporation":false,"usgs":false,"family":"Champ","given":"Patricia","middleInitial":"A.","affiliations":[{"id":86128,"text":"U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station","active":true,"usgs":false}],"preferred":false,"id":957641,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Barth, Christopher M.","contributorId":367753,"corporation":false,"usgs":false,"family":"Barth","given":"Christopher","middleInitial":"M.","affiliations":[{"id":86132,"text":"U.S. Department of Agriculture, Forest Service, Washington Office","active":true,"usgs":false}],"preferred":false,"id":957642,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Meldrum, James R. 0000-0001-5250-3759 jmeldrum@usgs.gov","orcid":"https://orcid.org/0000-0001-5250-3759","contributorId":195484,"corporation":false,"usgs":true,"family":"Meldrum","given":"James","email":"jmeldrum@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":957643,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Wagner, Carolyn","contributorId":240587,"corporation":false,"usgs":false,"family":"Wagner","given":"Carolyn","affiliations":[{"id":48103,"text":"Wildfire Research (WiRē) Center","active":true,"usgs":false}],"preferred":false,"id":957644,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Taniguchi, Christine","contributorId":355605,"corporation":false,"usgs":false,"family":"Taniguchi","given":"Christine","affiliations":[{"id":48103,"text":"Wildfire Research (WiRē) Center","active":true,"usgs":false}],"preferred":false,"id":957645,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70274724,"text":"70274724 - 2026 - Report 17—Revisions to the articles of organization and procedure of the Commission on Stratigraphic Nomenclature of the Americas","interactions":[],"lastModifiedDate":"2026-04-08T14:50:57.563351","indexId":"70274724","displayToPublicDate":"2026-03-01T07:45:24","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3481,"text":"Stratigraphy","active":true,"publicationSubtype":{"id":10}},"title":"Report 17—Revisions to the articles of organization and procedure of the Commission on Stratigraphic Nomenclature of the Americas","docAbstract":"Several revisions to the Articles of Organization and Procedure for the North American Commission on Stratigraphic Nomenclature have been adopted following the 75th, 79th, and 80th annual meetings of the Commission in 2020, 2024 and 2025, respectively. Of these, the most substantial change was revision of Article III regarding membership composition of the Commission and the addition of new member organizations from Central and South America and the Caribbean region. As a result, the Commission also voted to change its name to be more inclusive of the expanding membership, with approval of the name: Commission on Stratigraphic Nomenclature of the Americas. A corresponding change to the Commission’s fundamental publication was also approved at the 2025 meeting as the Stratigraphic Code of the Americas (formerly the North American Stratigraphic Code). Additional revisions to the Articles include those aimed at ensuring gender neutrality of titles, permission to hold virtual meetings, and processes for designating new Commissioners. Article V was also modified to define quorum for meetings and the nature of a two-thirds majority vote, with the process for making amendments to the Articles modified to specify that a two-thirds majority vote is required. These revisions are reflected in the following bylaws, which otherwise are as adopted by the Commission under its former name (North American Commission
on Stratigraphic Nomenclature) at its 62nd annual meeting in 2007 amended by mail ballot, and published by Owen et al. (2009). The amended bylaws printed below became effective at the close of the 80th annual meeting in 2025 and supersede all previous versions (Moore 1947; Hutchinson 1953; Owen et al. 1985; 2009)
Retrieving the phytoplankton absorption coefficient (aphy; m−1), one of the most spectrally rich inherent optical properties, remains challenging in optically complex coastal waters worldwide. Leveraging NASA's new hyperspectral mission, PACE, we introduce Hyper-MoE-VAE, a deep-learning architecture that integrates a Mixture-of-Experts with a Variational Autoencoder to retrieve high-dimensional aphy and subsequent estimation of phytoplankton community composition (PCC) from PACE-OCI hyperspectral remote sensing reflectance (Rrs). Pre-trained on global hyperspectral bio-optical datasets and fine-tuned using regional field Rrs–aphy pairings from inland– estuarine–coastal waters, Hyper-MoE-VAE demonstrated strong transferability and effective adaptation across regions. Validation with in-situ Rrs showed accurate aphy retrievals in Lake Erie (NRMSE = 0.12, ε = 17.10), Lake Pontchartrain (NRMSE = 0.11, ε = 37.12), and the Barataria–Terrebonne Estuary (NRMSE = 0.14, ε = 38.89). Using same-day PACE-OCI Level 2 Rrs, the model achieved comparable performance in Lake Erie (NRMSE = 0.19, ε = 55.19), Lake Pontchartrain (NRMSE = 0.14, ε = 51.39), and the Barataria–Terrebonne Estuary (NRMSE = 0.17, ε = 47.92). Hyper-MoE-VAE derived PACE-OCI hyperspectral aphy was further decomposed against mass-specific absorption spectra to estimate group-specific contributions to total chlorophyll a. The resulting PCC showed strong agreement with HPLC–CHEMTAX in Lake Erie (R2= 0.692) and Gulf estuarine–coastal systems (R2 = 0.732). Monte Carlo noise experiments further revealed group-dependent sensitivities, with diatoms and dinoflagellates showing moderate susceptibility to noise, while cyanobacteria and cryptophytes exhibited narrow uncertainty distributions. These results demonstrate Hyper-MoE-VAE's capability for regional, operational water-quality monitoring with PACE-OCI and its adaptability to current and future hyperspectral missions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2026.115327","usgsCitation":"Bai, X., Liu, B., Li, J., Xiong, Y., D'Sa, E.J., Baustian, M.M., Zhang, X., Grunert, B.K., Emeghiebo, C.O., Glasspie, C., and Yuan, X., 2026, Hyperspectral retrieval of phytoplankton absorption and community composition from NASA’s PACE-OCI in estuarine–coastal waters using a hybrid framework combining mixture-of-experts and Variational Autoencoder: Remote Sensing of Environment, v. 337, 115327, 21 p., https://doi.org/10.1016/j.rse.2026.115327.","productDescription":"115327, 21 p.","ipdsId":"IP-183464","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":501687,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2026.115327","text":"Publisher Index 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gradient","interactions":[],"lastModifiedDate":"2026-03-24T14:10:42.104231","indexId":"70274263","displayToPublicDate":"2026-02-28T09:05:05","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9326,"text":"JGR Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Mercury cycling across a U.S. semi-arid mountain ecosystem elevation gradient","docAbstract":"Mountains comprise ∼30% of the Earth's surface, but mercury (Hg) cycling in these regions remains understudied, particularly in the semi-arid western U.S. where strong climatic and ecological gradients in mountainous landscapes influence Hg deposition, retention, and bioaccumulation. In this study, we quantified growing season inputs, storage, and bioaccumulation of Hg along a ∼2,000 m elevation gradient in the Colorado Rocky Mountains, spanning the plains to the alpine. We measured Hg in atmospheric deposition, vegetation, soil, and 12-day-old chickadees. Accounting for percent canopy cover, open precipitation was the largest component of atmospheric deposition at all elevations, followed by throughfall and litterfall fluxes. Atmospheric Hg fluxes peaked at mid-elevations, likely due to cloud-cap dynamics and denser canopy cover. Total gaseous Hg and precipitation fluxes were highest at low elevations, likely reflecting local emissions and meteorological pooling. Surface soil Hg storage was more strongly predicted by organic matter content (R2 = 0.49; p < 0.01) and water retention (R2 = 0.45; p < 0.01) than by elevation (R2 = 0.21; p < 0.05). Alpine soils (66.3 ± 25.3 ng g−1) had significantly higher total Hg concentrations than lower elevations (<41.0 ± 12.7 ng g−1; p < 0.01), likely reflecting slower organic matter turnover. Soils on north-facing slopes also retained significantly higher pools of Hg in surface soils compared with south- and east-facing slopes. Vegetation Hg pools were greatest in the alpine region, likely due to long-lived plant species. Methylmercury (MeHg) concentrations in chickadee feathers peaked at mid-elevations (205 ± 155 ng g−1), corresponding to higher ecosystem Hg inputs via throughfall. Our results show that deposition, canopy cover, and meteorological conditions—not elevation alone—predict Hg retention and bioaccumulation.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2025JG009556","usgsCitation":"Miller, H.R., Janssen, S., Taylor, S.A., Gerson, J.R., McIntosh, T.L., and Hinckley, E.S., 2026, Mercury cycling across a U.S. semi-arid mountain ecosystem elevation gradient: JGR Biogeosciences, v. 131, no. 3, e2025JG009556, 19 p., https://doi.org/10.1029/2025JG009556.","productDescription":"e2025JG009556, 19 p.","ipdsId":"IP-177248","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":501443,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"headwaters of the Boulder Creek Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105.58,\n 40.06\n ],\n [\n -105.58,\n 39.98\n ],\n [\n -105.27,\n 39.98\n ],\n [\n -105.27,\n 40.06\n ],\n [\n -105.58,\n 40.06\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"131","issue":"3","noUsgsAuthors":false,"publicationDate":"2026-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Miller, Hannah R.","contributorId":367690,"corporation":false,"usgs":false,"family":"Miller","given":"Hannah","middleInitial":"R.","affiliations":[{"id":16144,"text":"University of Colorado-Boulder","active":true,"usgs":false}],"preferred":false,"id":957444,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Janssen, Sarah E. 0000-0003-4432-3154","orcid":"https://orcid.org/0000-0003-4432-3154","contributorId":210991,"corporation":false,"usgs":true,"family":"Janssen","given":"Sarah E.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":957445,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Taylor, Scott A.","contributorId":367691,"corporation":false,"usgs":false,"family":"Taylor","given":"Scott","middleInitial":"A.","affiliations":[{"id":16144,"text":"University of Colorado-Boulder","active":true,"usgs":false}],"preferred":false,"id":957446,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gerson, Jacqueline R.","contributorId":367692,"corporation":false,"usgs":false,"family":"Gerson","given":"Jacqueline","middleInitial":"R.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":957447,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McIntosh, Tyler L.","contributorId":367693,"corporation":false,"usgs":false,"family":"McIntosh","given":"Tyler","middleInitial":"L.","affiliations":[{"id":16144,"text":"University of Colorado-Boulder","active":true,"usgs":false}],"preferred":false,"id":957448,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hinckley, Eve-Lyn S.","contributorId":367694,"corporation":false,"usgs":false,"family":"Hinckley","given":"Eve-Lyn","middleInitial":"S.","affiliations":[{"id":16144,"text":"University of Colorado-Boulder","active":true,"usgs":false}],"preferred":false,"id":957449,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70274197,"text":"70274197 - 2026 - Terrestrial ecosystem response to changing temperature and seasonality in the Paleocene-Eocene Thermal Maximum: Shallow marine records from the Salisbury Embayment, USA","interactions":[],"lastModifiedDate":"2026-03-10T13:41:31.319054","indexId":"70274197","displayToPublicDate":"2026-02-28T08:12:37","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5790,"text":"Paleoceanography and Paleoclimatology","active":true,"publicationSubtype":{"id":10}},"title":"Terrestrial ecosystem response to changing temperature and seasonality in the Paleocene-Eocene Thermal Maximum: Shallow marine records from the Salisbury Embayment, USA","docAbstract":"The Paleocene-Eocene thermal maximum (PETM, ∼56 Ma) is marked by a massive and rapid rise in atmospheric CO2 and ∼5°C of global warming. It is globally characterized by a negative carbon isotope excursion (CIE), and, at least locally, is preceded by a pre-onset excursion (POE). We present palynological and bioclimatic analyses from stratigraphically expanded marginal marine sediment sections from the eastern United States. Late Paleocene forests were dominated by needle-leaved gymnosperms and broad-leaved angiosperms characteristic of warm climates. The POE is marked by a minor expansion of angiosperms and pteridophytes, warmer winters, and altered seasonal precipitation, followed by a return to pre-POE conditions. Increased terrestrial palynomorph concentrations before the CIE are suggestive of increased fluvial discharge before the PETM. Early PETM assemblages are characterized by dominance of ferns, loss of conifers, and expansion of broad-leaved angiosperm forests. Bioclimatic analyses indicate warmer mean atmospheric temperatures in early PETM time, driven primarily by winter warming of ∼3°C. A shift in seasonality, associated with increased severity of storms and floods that scoured the late Paleocene floodplain, facilitated establishment of riparian fern communities at the CIE onset. These flooding events persisted through the early part of the PETM and were severe enough to transport Westphalian-age (Middle Pennsylvanian) reworked material from the central Appalachian Basin and flush large amounts of terrestrial material and carbon onto the continental shelf, resulting in decreased salinity, increased productivity, and water-column stratification.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2025PA005278","usgsCitation":"Willard, D., Nelissen, M., Sluijs, A., Brinkhuis, H., Reichgelt, T., Robinson, M., and Self-Trail, J., 2026, Terrestrial ecosystem response to changing temperature and seasonality in the Paleocene-Eocene Thermal Maximum: Shallow marine records from the Salisbury Embayment, USA: Paleoceanography and Paleoclimatology, v. 41, no. 3, e2025PA005278, 19 p., https://doi.org/10.1029/2025PA005278.","productDescription":"e2025PA005278, 19 p.","ipdsId":"IP-171569","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":501095,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2025pa005278","text":"Publisher Index Page"},{"id":500858,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, Maryland, New Jersey, Virginia","otherGeospatial":"Salisbury Embayment","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74,\n 40\n ],\n [\n -77,\n 40\n ],\n [\n -77,\n 37\n ],\n [\n -74,\n 37\n ],\n [\n -74,\n 40\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"41","issue":"3","noUsgsAuthors":false,"publicationDate":"2026-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Willard, Debra A. 0000-0003-4878-0942","orcid":"https://orcid.org/0000-0003-4878-0942","contributorId":269840,"corporation":false,"usgs":true,"family":"Willard","given":"Debra A.","affiliations":[],"preferred":true,"id":956904,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nelissen, Mei","contributorId":362170,"corporation":false,"usgs":false,"family":"Nelissen","given":"Mei","affiliations":[{"id":36885,"text":"Utrecht University","active":true,"usgs":false}],"preferred":false,"id":956905,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sluijs, Appy","contributorId":215371,"corporation":false,"usgs":false,"family":"Sluijs","given":"Appy","email":"","affiliations":[{"id":36885,"text":"Utrecht University","active":true,"usgs":false}],"preferred":false,"id":956906,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brinkhuis, Henk","contributorId":328591,"corporation":false,"usgs":false,"family":"Brinkhuis","given":"Henk","affiliations":[{"id":36885,"text":"Utrecht University","active":true,"usgs":false}],"preferred":false,"id":956907,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reichgelt, Tammo","contributorId":215367,"corporation":false,"usgs":false,"family":"Reichgelt","given":"Tammo","email":"","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":956908,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Robinson, Marci M. 0000-0002-9200-4097","orcid":"https://orcid.org/0000-0002-9200-4097","contributorId":261664,"corporation":false,"usgs":true,"family":"Robinson","given":"Marci M.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":956909,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Self-Trail, Jean 0000-0002-3018-4985 jstrail@usgs.gov","orcid":"https://orcid.org/0000-0002-3018-4985","contributorId":147370,"corporation":false,"usgs":true,"family":"Self-Trail","given":"Jean","email":"jstrail@usgs.gov","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":956910,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70274266,"text":"70274266 - 2026 - Extreme precipitation variability and soil texture controls on water-table response","interactions":[],"lastModifiedDate":"2026-03-24T16:31:28.917628","indexId":"70274266","displayToPublicDate":"2026-02-27T09:28:04","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":"Extreme precipitation variability and soil texture controls on water-table response","docAbstract":"Extreme precipitation events (EPEs), a key class of hydrometeorological extremes, are intensifying globally under climate change; however, their effects on water-table dynamics across varying soil textures remain poorly understood. To better understand the impacts of EPEs, we conducted one-dimensional modeling to evaluate water-table response time, displacement, recession time, and total recharge under EPEs of 0.20 m, 0.40 m, and 0.60 m amounts, applied over 1-, 7-, and 20-day durations across twelve soil textures. The results show that coarse soils (i.e., sand) respond within days, while fine soils (i.e., clay) may take over 200 days. Water-table displacement ranged from 0.30 to 1.64 m and increased with EPE magnitude. The time it took for water tables to recede ranged from 1.2 to 3.0 years. A first-order estimate of total possible recharge, calculated from porosity and displacement, ranged from 17% (clay) to 97% (sand), averaging ~63% across soil textures. These findings highlight that recharge is primarily governed by EPE magnitude and soil properties, not event duration. This modeling effort provides new insight into how soil texture modulates groundwater response to extreme precipitation, informing future water budget and resilience assessments.
","language":"English","publisher":"MDPI","doi":"10.3390/w18050587","usgsCitation":"Corona, C.R., Ge, S., Anderson, S.P., and Dickinson, J.E., 2026, Extreme precipitation variability and soil texture controls on water-table response: Water, v. 18, no. 5, 587, 20 p., https://doi.org/10.3390/w18050587.","productDescription":"587, 20 p.","ipdsId":"IP-160684","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":501680,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w18050587","text":"Publisher Index Page"},{"id":501472,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"18","issue":"5","noUsgsAuthors":false,"publicationDate":"2026-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Corona, Claudia R.","contributorId":152548,"corporation":false,"usgs":false,"family":"Corona","given":"Claudia","middleInitial":"R.","affiliations":[{"id":6690,"text":"San Francisco State University","active":true,"usgs":false}],"preferred":false,"id":957469,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ge, Shemin","contributorId":203465,"corporation":false,"usgs":false,"family":"Ge","given":"Shemin","email":"","affiliations":[{"id":36627,"text":"University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":957470,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, Suzanne P. 0000-0002-6796-6649","orcid":"https://orcid.org/0000-0002-6796-6649","contributorId":172732,"corporation":false,"usgs":false,"family":"Anderson","given":"Suzanne","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":957471,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dickinson, Jesse E. 0000-0002-0048-0839 jdickins@usgs.gov","orcid":"https://orcid.org/0000-0002-0048-0839","contributorId":152545,"corporation":false,"usgs":true,"family":"Dickinson","given":"Jesse","email":"jdickins@usgs.gov","middleInitial":"E.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":957472,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70274150,"text":"70274150 - 2026 - Urbanization alters riverine fluorescent dissolved organic matter characteristics in a forested city – metropolitan Atlanta, Georgia (USA)","interactions":[],"lastModifiedDate":"2026-03-02T14:49:19.406602","indexId":"70274150","displayToPublicDate":"2026-02-27T08:39:47","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1561,"text":"Environmental Research","active":true,"publicationSubtype":{"id":10}},"title":"Urbanization alters riverine fluorescent dissolved organic matter characteristics in a forested city – metropolitan Atlanta, Georgia (USA)","docAbstract":"Streams and rivers in urban watersheds are predicted to export more bioreactive, autochthonous dissolved organic matter (DOM) relative to forested watersheds. However, the spatial and temporal variations of DOM quality in forested urban watersheds remain uncertain, and their relationships with socioeconomic conditions, biological characteristics, and the built environment are understudied. We measured optical properties of fluorescent DOM (FDOM) in 93 streams spanning a gradient of land-use and land cover during four seasons in metropolitan Atlanta, Georgia, USA. Streamwater FDOM was dominated by humic substances from anthropogenic (41%) and terrestrial origin (41.5%). Impervious surface cover was the strongest predictor, which was positively correlated with anthropogenically- and autochthonously-derived FDOM. Overwater canopy cover was positively associated with autochthonous FDOM, and housing age increased diagenetic FDOM. FDOM was more proteinaceous during low-flow conditions (fall, winter), and more allochthonous humic-like FDOM was detected during periods of higher flows (spring, summer). Interestingly, wastewater-related FDOM proxies were highest during low flows, suggesting that sewer exfiltration is a pervasive source and is diluted by other inputs during high flows. Overall, seasonal patterns in FDOM quality were associated with changes in hydrology, and FDOM was primarily humic throughout the year, a pattern likely driven by ubiquitous forest canopy cover. Our results highlight the importance of urban forests in mediating aquatic carbon cycling and provide a template for future studies that integrate sociodemographic and infrastructure information into studies of watershed biogeochemistry, especially in regions undergoing rapid, intense, and localized urban development.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envres.2026.124085","usgsCitation":"Chen, S., Hale, R., Hopkins, K.G., Ortiz Muñoz, L., Kominoski, J., Ledford, S., and Capps, K., 2026, Urbanization alters riverine fluorescent dissolved organic matter characteristics in a forested city – metropolitan Atlanta, Georgia (USA): Environmental Research, v. 297, 124085, 15 p., https://doi.org/10.1016/j.envres.2026.124085.","productDescription":"124085, 15 p.","ipdsId":"IP-183715","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":500667,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia","city":"Atlanta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -84.1667,\n 34\n ],\n [\n -84.7,\n 34\n ],\n [\n -84.7,\n 33.5\n ],\n [\n -84.1667,\n 33.5\n ],\n [\n -84.1667,\n 34\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"297","noUsgsAuthors":false,"publicationDate":"2026-02-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Chen, Shuo","contributorId":343806,"corporation":false,"usgs":false,"family":"Chen","given":"Shuo","affiliations":[{"id":13510,"text":"Smithsonian Environmental Research Center","active":true,"usgs":false}],"preferred":false,"id":956692,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hale, Rebecca","contributorId":348368,"corporation":false,"usgs":false,"family":"Hale","given":"Rebecca","affiliations":[{"id":38154,"text":"Idaho State University","active":true,"usgs":false}],"preferred":false,"id":956693,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hopkins, Kristina G. 0000-0003-1699-9384 khopkins@usgs.gov","orcid":"https://orcid.org/0000-0003-1699-9384","contributorId":195604,"corporation":false,"usgs":true,"family":"Hopkins","given":"Kristina","email":"khopkins@usgs.gov","middleInitial":"G.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":956694,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ortiz Muñoz, Liz","contributorId":343807,"corporation":false,"usgs":false,"family":"Ortiz Muñoz","given":"Liz","affiliations":[{"id":7017,"text":"Florida International University","active":true,"usgs":false}],"preferred":false,"id":956695,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kominoski, John","contributorId":298258,"corporation":false,"usgs":false,"family":"Kominoski","given":"John","affiliations":[{"id":7017,"text":"Florida International University","active":true,"usgs":false}],"preferred":false,"id":956696,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ledford, Sarah","contributorId":300624,"corporation":false,"usgs":false,"family":"Ledford","given":"Sarah","email":"","affiliations":[{"id":52554,"text":"Georgia State University","active":true,"usgs":false}],"preferred":false,"id":956697,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Capps, Krista A.","contributorId":270490,"corporation":false,"usgs":false,"family":"Capps","given":"Krista A.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":956698,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70274269,"text":"70274269 - 2026 - Bird guilds exhibit varied responses to floodplain forest restoration in the Colorado River delta, Mexico","interactions":[],"lastModifiedDate":"2026-03-24T14:48:28.344876","indexId":"70274269","displayToPublicDate":"2026-02-27T07:38:28","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2183,"text":"Journal of Arid Environments","active":true,"publicationSubtype":{"id":10}},"title":"Bird guilds exhibit varied responses to floodplain forest restoration in the Colorado River delta, Mexico","docAbstract":"Grouping species into guilds can be useful to inform management decisions locally and at broader scales because guilds lack species-specificity. We investigated the response of five breeding bird guilds to riparian habitat restoration in the arid Colorado River delta, based on two decades of bird detections (2002–2021) at 230 bird count stations across 7 routes in actively revegetated (“restored”) sites, and 20 routes in non-actively revegetated (“control”) sites. We used guilds based on habitat associations. We also described changes in vegetation and explored their influence on bird species detections and guild dynamics. Riparian forest bird specialists responded positively to active revegetation, but this positive response was delayed and weaker in a river reach where restoration began later and featured less typical riparian vegetation. Birds associated with wetland habitat showed a positive response to restoration in the wettest reach, which had a baseline of high abundance of wetland birds in control sites and relatively abundant macrophyte cover. Conversely, the abundance of desert scrub bird specialists was highest in the driest and least vegetated restored reach. Generalists only exhibited decreased detections in the wettest restored reach. All this occurred while declines of riparian forest, wetland, desert scrub, and generalist bird species observed over a decade prior to restoration had stabilized in control sites. Detections of birds associated with agricultural fields increased in the study area, irrespective of restoration efforts. Our study demonstrates that the choice of bird guilds as ecological indicators can significantly influence the interpretation of restoration outcomes.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jaridenv.2026.105558","usgsCitation":"González-Sargas, E., Meehan, T.D., Hinojosa-Huerta, O., Villagomez-Palma, S., Dodge, C., Gómez-Sapiens, M., Nagler, P.L., and Shafroth, P., 2026, Bird guilds exhibit varied responses to floodplain forest restoration in the Colorado River delta, Mexico: Journal of Arid Environments, v. 234, 105558, 13 p., https://doi.org/10.1016/j.jaridenv.2026.105558.","productDescription":"105558, 13 p.","ipdsId":"IP-167498","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":501446,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","state":"Arizona","city":"Yuma","otherGeospatial":"Baja California, Colorado River delta, Sonora","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -115.81211693979633,\n 32.68547653091534\n ],\n [\n -115.86774708609713,\n 32.31307314052428\n ],\n [\n -114.87086666471464,\n 31.381309169456074\n ],\n [\n -114.53201791755214,\n 31.615208177200756\n ],\n [\n -114.74741121436266,\n 32.276188738917746\n ],\n [\n -114.87424214545963,\n 32.73959839855473\n ],\n [\n -115.81211693979633,\n 32.68547653091534\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"234","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"González-Sargas, Eduardo","contributorId":349720,"corporation":false,"usgs":false,"family":"González-Sargas","given":"Eduardo","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":957484,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Meehan, Timothy D.","contributorId":367699,"corporation":false,"usgs":false,"family":"Meehan","given":"Timothy","middleInitial":"D.","affiliations":[{"id":27800,"text":"National Audubon Society","active":true,"usgs":false}],"preferred":false,"id":957485,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hinojosa-Huerta, Osvel","contributorId":167198,"corporation":false,"usgs":false,"family":"Hinojosa-Huerta","given":"Osvel","affiliations":[{"id":24640,"text":"Pronatura Noroeste","active":true,"usgs":false}],"preferred":false,"id":957486,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Villagomez-Palma, Stefanny","contributorId":334579,"corporation":false,"usgs":false,"family":"Villagomez-Palma","given":"Stefanny","email":"","affiliations":[{"id":80193,"text":"Pronatura Noroeste, Cjon. 16 de Septiembre St, San Luis Rio Colorado, Sonora, 83440, México","active":true,"usgs":false}],"preferred":false,"id":957487,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dodge, Christopher","contributorId":339758,"corporation":false,"usgs":false,"family":"Dodge","given":"Christopher","email":"","affiliations":[{"id":6736,"text":"Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":957488,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gómez-Sapiens, Martha","contributorId":292779,"corporation":false,"usgs":false,"family":"Gómez-Sapiens","given":"Martha","affiliations":[{"id":62998,"text":"Department of Geosciences, University of Arizona, Tucson, AZ 85721, USA","active":true,"usgs":false}],"preferred":false,"id":957489,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Nagler, Pamela L. 0000-0003-0674-103X","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":363777,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","middleInitial":"L.","affiliations":[],"preferred":true,"id":957490,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Shafroth, Patrick B. 0000-0002-6064-871X","orcid":"https://orcid.org/0000-0002-6064-871X","contributorId":247484,"corporation":false,"usgs":true,"family":"Shafroth","given":"Patrick B.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":957491,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70274109,"text":"sir20265118 - 2026 - Groundwater budget for the Mountain Home area, southern Idaho, 2022–23","interactions":[],"lastModifiedDate":"2026-02-27T21:32:48.272408","indexId":"sir20265118","displayToPublicDate":"2026-02-26T15:10:00","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":"2026-5118","displayTitle":"Groundwater Budget for the Mountain Home Area, Southern Idaho, 2022–23","title":"Groundwater budget for the Mountain Home area, southern Idaho, 2022–23","docAbstract":"The U.S. Geological Survey, with funding from the Idaho Department of Water Resources, developed a groundwater budget for the Mountain Home area in southern Idaho for irrigation year 2023 (November 1, 2022–October 31, 2023). This study focused on the water balance across the Cinder Cone Butte Critical Groundwater Area (CGWA), Mountain Home Groundwater Management Area (GWMA), and the rest of the study area (RoSA), compiling data from various sources, including precipitation records, groundwater level measurements, metered groundwater pumpage data, surface water diversions and evapotranspiration (ET) estimates derived from remote sensing satellite imagery, and ground-based reference data. Key inflow components included recharge from applied surface water irrigation (which incorporates incidental recharge from irrigation practices and conveyance losses), estimated tributary streamflow, and estimated mountain block recharge. The key outflow components were groundwater pumpage for irrigation, municipal, industrial, and domestic uses, and ET. Recharge from applied irrigation and mountain block recharge were the largest inflows, and groundwater pumpage for irrigation was the largest outflow.
The CGWA had a positive groundwater budget residual of 2,170 acre-feet (acre-ft), which contrasts with observed long-term groundwater level declines and historical trends of storage depletion. This positive residual is likely associated with unquantified outflows, including lateral groundwater flow out of the subregion, or other complexities, such as overestimated tributary contributions relative to the actual recharge for the 2023 water budget. The GWMA exhibited a positive residual of 56,563 acre-ft, primarily owing to recharge from applied surface water irrigation and areal recharge during a wetter-than-average year, which allowed irrigation entities to deliver more water from in-basin and out-of-basin reservoirs. The RoSA showed a large positive residual of 124,933 acre-ft. The interpretation of these positive residuals must account for significant uncertainties, including estimations of areal recharge, tributary streamflow (particularly losses and diversions), ET, the volume of surface water loss to the Snake River, lateral groundwater flows between subregions and across study area boundaries, and the unquantified groundwater discharge to the Snake River. These uncertainties, in combination with the complex hydrogeologic controls on water movement and limitations of remotely sensed data, directly affect the accuracy of water availability assessments.
Future data collection efforts would help reduce these uncertainties and support water resource management decisions in the Mountain Home area. Key efforts could include installing additional streamflow gaging stations (particularly to quantify tributary losses and gains and surface water losses to the Snake River), improving groundwater pumpage metering, and validating remotely sensed ET data with ground-based measurements. Furthermore, to better quantify unrepresented or highly uncertain fluxes, focused investigations on groundwater discharge to the Snake River, lateral groundwater flows between subregions and across study area boundaries, and a more robust determination of the actual influence and volume of mountain block recharge would help refine future water availability assessments for the Mountain Home area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20265118","collaboration":"Prepared in cooperation with the Idaho Department of Water Resources","usgsCitation":"Thomas, P.M., 2026, Groundwater budget for the Mountain Home area, southern Idaho, 2022–23: U.S. Geological Survey Scientific Investigations Report 2026–5118, 41 p., https://doi.org/10.3133/sir20265118.","productDescription":"Report: ix, 41 p.; Data Release","numberOfPages":"56","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-140358","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":500654,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119277.htm","linkFileType":{"id":5,"text":"html"}},{"id":500532,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13ZG67D","text":"USGS data release","linkHelpText":"Supporting data for 2022–2023 groundwater budget for the Mountain Home area, southern Idaho"},{"id":500531,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2026/5118/images/"},{"id":500530,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2026/5118/sir20265118.XML"},{"id":500528,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2026/5118/coverthb.jpg"},{"id":500529,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2026/5118/sir20265118.pdf","text":"Report","size":"4.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2026-5118"},{"id":500533,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20265118/full"}],"country":"United States","state":"Idaho","otherGeospatial":"Mountain Home area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.5,\n 43.5\n ],\n [\n -116.5,\n 42.833\n ],\n [\n -115.0833,\n 42.833\n ],\n [\n -115.0833,\n 43.5\n ],\n [\n -116.5,\n 43.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
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230 Collins Rd
Boise, Idaho 83702-4520
Intersections of drainage networks and road networks represent a critical nexus between natural waterways and human infrastructure. Managing these systems involves decisions related to management of infrastructure, hydrologic and geomorphic processes, and ecological connectivity. Interactions among these systems influence multiple values, including the intactness of transportation networks, public safety, water quality, and ecosystem function that collectively amount to billions of dollars. Despite the importance of road-stream crossings, there are countless gaps in knowing where and what they are. These gaps limit the degree to which managers can inventory and assess stream and road networks to inform decisions. To address this first-level need, we developed RoadxStr (road-stream crossings): a survey tool that effectively characterizes road-stream crossings across the full stream and drainage network. This document describes the RoadxStr Field Form, available within a mobile application, which is designed for rapid and standardized data collection involving assessment of a road-stream crossing, including the road, crossing structure(s), and the nearby hydrologic channel. This document provides instructions on how to (1) access and download the RoadxStr Field Form within the mobile application service and (2) use and complete a RoadxStr Field Form survey.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm18B1","collaboration":"Prepared in cooperation with the Bureau of Land Management and U.S. Forest Service","usgsCitation":"Heaston, E., Winter, S., Bauer, S., Ronningen, T., and Dunham, J., 2026, RoadxStr user’s guide—For collection of road-stream crossing assessment field observations: U.S. Geological Survey Techniques and Methods, book 18, chap. B1, 32 p., https://doi.org/10.3133/tm18B1.","productDescription":"vii, 32 p.","numberOfPages":"44","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-176750","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":500545,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/18/b1/coverthb.jpg"},{"id":500546,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/18/b1/tm18B1.pdf","text":"Report","size":"15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 18-B1"},{"id":500547,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/tm/18/b1/tm18B1.XML"},{"id":500549,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/tm18B1/full"},{"id":500548,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/tm/18/b1/images/"}],"contact":"Director, Forest and Rangeland Ecosystem Science Center Corvallis Research Group
3200 SW Jefferson Way
Corvallis, OR 97331
fresc_outreach@usgs.gov
Centered on Fort Morgan, Colorado, this study is intended to build from previous work by adding a three-dimensional (3D) view of the subsurface to better understand the depositional history of Quaternary deposits. A 1:100,000 scale geologic map was made by combining previous geologic maps, regional soil maps, and recent field investigations. In addition to the geologic mapping, drill hole lithologic data from water wells and oil and gas exploration were compiled and lithologic units simplified to best represent the stratigraphy of the Quaternary deposits. From these subsurface data, a 3D subsurface model was constructed, trimmed at the surface by a digital elevation model, and a bedrock surface foundation gridded from drill hole data was added. The surface of the 3D model was then compared visually to the surficial geologic map. Cross sections were constructed from the 3D model and compared to site-specific drilling that was done as part of this project. Finally, the model was examined in detail to reconstruct the depositional history of the subsurface alluvial and eolian units. Alluvial and fluvial drainage basins exposed in the subsurface have a greater areal extent than the present-day narrow drainages. Older eolian sand in the subsurface tends to be interbedded with loess indicating coeval deposition. Holocene sand, both eroded from bedrock exposed at the surface north of the study area and reworked from the South Platte River, buries most of the interbedded older sand and loess.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20255052","usgsCitation":"Taylor, E.M., Berry, M.E., Mahan, S.A., and Havens, J.C., 2026, Reconstructing the Quaternary depositional history using geologic mapping and three-dimensional modeling of the subsurface near Fort Morgan, northeastern Colorado: U.S. Geological Survey Scientific Investigations Report 2025–5052, 48 p., https://doi.org/10.3133/sir20255052.","productDescription":"Report: iv, 48 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-095650","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":500655,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119276.htm","linkFileType":{"id":5,"text":"html"}},{"id":500266,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5052/sir20255052.pdf","text":"Report","size":"60.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5052"},{"id":500265,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5052/coverthb.jpg"},{"id":500267,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13KTS2B","text":"USGS data release","linkHelpText":"Luminescence data for: Reconstructing the Quaternary depositional history using geologic mapping and a 3D model of the subsurface in the vicinity of Fort Morgan, Eastern Colorado"},{"id":500268,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AQ72FB","text":"USGS data release","linkHelpText":"Digital drillhole lithologic data and a radiocarbon age -- data supporting interpretation of Quaternary depositional history in the vicinity of Fort Morgan, Eastern Colorado"}],"country":"United States","state":"Colorado","city":"Fort Morgan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -103.5,\n 40.5\n ],\n [\n -104.5,\n 40.5\n ],\n [\n -104.5,\n 40\n ],\n [\n -103.5,\n 40\n ],\n [\n -103.5,\n 40.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Geosciences and Environmental Change Science Center
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Box 25046, MS-980
Denver, CO 80225-0046
The eastern box turtle (Terrapene carolina carolina) is a long-lived terrestrial turtle species distributed throughout the eastern United States that has experienced widespread population decline. Many eastern box turtle populations are persisting as remanent populations in small, fragmented urban green spaces. We investigated the movement and resource selection of eastern box turtles within a mid-Atlantic region urban forest in the eastern United States. We used a combination of turtle occurrence data (via visual encounter surveys) and radio telemetry to create resource selection functions. Additionally, we applied a simulation modeling approach and modeled activity areas via dynamic Brownian Bridge Movement Models to quantify interactions between turtles and roads or trails. We also used these models to determine the propensity for turtles to move outside of the managed urban forest boundary and into surrounding development. We observed that turtles selected for deciduous forest patches and avoided roads and trails despite the urban forest having very little available areas where anthropogenic features could be avoided. We also demonstrated observed (and probable) movements outside of the urban forest boundary. Although eastern box turtles are persisting within the urban green space we examined, our work determined that interactions with roads and trails, and movements outside of protected boundaries into developed areas present challenges to individuals navigating the urban forest.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s11252-026-01938-0","usgsCitation":"Jones, M.D., Ferebee, K.B., Ford, W., and Hunter, E.A., 2026, Boxed in or branching out? Movement and resource selection of eastern box turtles (Terrapene carolina carolina) in an urban green space: Urban Ecosystems, v. 29, 72, 14 p., https://doi.org/10.1007/s11252-026-01938-0.","productDescription":"72, 14 p.","ipdsId":"IP-180260","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":502096,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s11252-026-01938-0","text":"Publisher Index Page"},{"id":502028,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"eastern United States, mid-Atlantic region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.51749069483542,\n 39.74954311636529\n ],\n [\n -80.30490645674448,\n 33.87108405455136\n ],\n [\n -77.15471518629862,\n 32.58298528230786\n ],\n [\n -73.67400827908685,\n 39.35915324575973\n ],\n [\n -76.51749069483542,\n 39.74954311636529\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"29","noUsgsAuthors":false,"publicationDate":"2026-02-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Jones, Max D.","contributorId":369034,"corporation":false,"usgs":false,"family":"Jones","given":"Max","middleInitial":"D.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":958334,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ferebee, Kenneth B.","contributorId":369035,"corporation":false,"usgs":false,"family":"Ferebee","given":"Kenneth","middleInitial":"B.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":958335,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":958336,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hunter, Elizabeth Ann 0000-0003-4710-167X","orcid":"https://orcid.org/0000-0003-4710-167X","contributorId":288535,"corporation":false,"usgs":true,"family":"Hunter","given":"Elizabeth","email":"","middleInitial":"Ann","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":958337,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70274283,"text":"70274283 - 2026 - Short-term estuarine phytoplankton dynamics in response to hurricanes along the Gulf Coast of America: A Variational Autoencoder (VAE) approach with satellite and bio-optical observations","interactions":[],"lastModifiedDate":"2026-03-24T14:57:45.149791","indexId":"70274283","displayToPublicDate":"2026-02-26T09:52:46","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7159,"text":"JGR Oceans","active":true,"publicationSubtype":{"id":10}},"title":"Short-term estuarine phytoplankton dynamics in response to hurricanes along the Gulf Coast of America: A Variational Autoencoder (VAE) approach with satellite and bio-optical observations","docAbstract":"Hurricanes drive diverse estuarine phytoplankton responses and can trigger cascading ecological and physicochemical impacts. Capturing these short-term dynamics requires high spatiotemporal resolution. Here, we applied a globally-applicable coastal ocean color algorithm, Variational Autoencoder (VAE), to Sentinel-2 MSI imagery for chlorophyll-a (Chl-a) estimation and validated its strong performance across the northern Gulf coast of America (GoA) estuaries, including Galveston Bay (TX), Barataria-Terrebonne Estuary (LA), Apalachicola Estuary (FL) and Tampa Bay (FL). The test set showed strong performance (MAE: 1.44 mg m−3; RMSE: 17.7 mg m−3; slope: 0.86; median symmetric accuracy: 30.33%). The validated VAE was then applied to 76 Sentinel-2 MSI images to assess phytoplankton biomass responses to hurricanes Harvey (2017), Michael (2018), Ida (2021), Francine (2024), Helene (2024), and Milton (2024) in the GoA estuaries. Results showed that hurricane disturbances on Chl-a typically lasted 3–5 weeks. Estuarine waters west (left) of hurricane tracks showed a rapid decline in Chl-a (∼5 mg m−3) due to elevated turbidity from heavy rainfall, and wind-driven flushing in the estuary, followed by a rebound over about two weeks, with Chl-a increasing approximately 10–15 mg m−3 above pre-storm levels. In contrast, right-side waters showed a slower response, likely from oligotrophic seawater intrusion driven by the hurricane's counterclockwise rotation. Post-storm observations showed increased freshwater phytoplankton like chlorophytes and cyanobacteria dominating estuaries, while shelf-waters exhibited elevated dinoflagellates (e.g., Karenia brevis bloom after Hurricane Milton). These results highlight the spatial heterogeneity of hurricane impacts on estuarine phytoplankton dynamics, which may trigger cascading effects on biogeochemical cycling and food webs, potentially prolonging ecosystem recovery.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2025JC023274","usgsCitation":"Li, J., Liu, B., Lou, J., Yuan, X., D'Sa, E.J., Baustian, M.M., La Peyre, M., Freeman, A., Martins, V.S., and Habib, E., 2026, Short-term estuarine phytoplankton dynamics in response to hurricanes along the Gulf Coast of America: A Variational Autoencoder (VAE) approach with satellite and bio-optical observations: JGR Oceans, v. 131, no. 3, e2025JC023274, 24 p., https://doi.org/10.1029/2025JC023274.","productDescription":"e2025JC023274, 24 p.","ipdsId":"IP-179432","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":501670,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2025jc023274","text":"Publisher Index Page"},{"id":501448,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Florida, Louisiana, Mississippi, Texas","otherGeospatial":"Gulf Coast of America","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.47819206412116,\n 26.186734663282863\n ],\n [\n -81.47819206412116,\n 30.987444570659832\n ],\n [\n -98.57147947412544,\n 30.987444570659832\n ],\n [\n -98.57147947412544,\n 26.186734663282863\n ],\n [\n -81.47819206412116,\n 26.186734663282863\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"131","issue":"3","noUsgsAuthors":false,"publicationDate":"2026-02-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Li, Jiang","contributorId":167428,"corporation":false,"usgs":false,"family":"Li","given":"Jiang","email":"","affiliations":[],"preferred":false,"id":957594,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Liu, Bingqing","contributorId":304014,"corporation":false,"usgs":false,"family":"Liu","given":"Bingqing","email":"","affiliations":[{"id":13499,"text":"The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":957595,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lou, Jiadong","contributorId":367733,"corporation":false,"usgs":false,"family":"Lou","given":"Jiadong","affiliations":[{"id":13359,"text":"University of Delaware","active":true,"usgs":false}],"preferred":false,"id":957596,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yuan, Xu","contributorId":367734,"corporation":false,"usgs":false,"family":"Yuan","given":"Xu","affiliations":[{"id":13359,"text":"University of Delaware","active":true,"usgs":false}],"preferred":false,"id":957597,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"D'Sa, Eurico J.","contributorId":367735,"corporation":false,"usgs":false,"family":"D'Sa","given":"Eurico","middleInitial":"J.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":957598,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Baustian, Melissa Millman 0000-0003-2467-2533","orcid":"https://orcid.org/0000-0003-2467-2533","contributorId":304015,"corporation":false,"usgs":true,"family":"Baustian","given":"Melissa","email":"","middleInitial":"Millman","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":957599,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"La Peyre, Megan 0000-0001-9936-2252 mlapeyre@usgs.gov","orcid":"https://orcid.org/0000-0001-9936-2252","contributorId":79375,"corporation":false,"usgs":true,"family":"La Peyre","given":"Megan","email":"mlapeyre@usgs.gov","affiliations":[{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":957600,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Freeman, Angelina","contributorId":223755,"corporation":false,"usgs":false,"family":"Freeman","given":"Angelina","affiliations":[{"id":40763,"text":"Coastal Protection and Restoration Authority","active":true,"usgs":false}],"preferred":false,"id":957601,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Martins, Vitor S.","contributorId":367736,"corporation":false,"usgs":false,"family":"Martins","given":"Vitor","middleInitial":"S.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":957602,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Habib, Emad","contributorId":367737,"corporation":false,"usgs":false,"family":"Habib","given":"Emad","affiliations":[{"id":7155,"text":"University of Louisiana at Lafayette","active":true,"usgs":false}],"preferred":false,"id":957603,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70273903,"text":"sir20265116 - 2026 - Erosion potential and flood vulnerability of streams and stream crossings at Acadia National Park, Maine","interactions":[],"lastModifiedDate":"2026-04-10T15:20:24.005929","indexId":"sir20265116","displayToPublicDate":"2026-02-26T09:30:00","publicationYear":"2026","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2026-5116","displayTitle":"Erosion Potential and Flood Vulnerability of Streams and Stream Crossings at Acadia National Park, Maine","title":"Erosion potential and flood vulnerability of streams and stream crossings at Acadia National Park, Maine","docAbstract":"Acadia National Park has had increases in the frequency and magnitude of precipitation in recent years, leading to increased flood flows, stream erosion, and costly infrastructure damage. To improve infrastructure management in a changing climate, the U.S. Geological Survey, in cooperation with the National Park Service, has developed multiple datasets that can help natural resource managers identify stream reaches and stream crossings that have the highest potential for erosion and flood damage within Acadia National Park. To develop these datasets, we first created a lidar-derived hydrography based on a 1-meter digital elevation model and then estimated peak flows at stream crossings and along the stream network using regional regression equations for Maine. We assessed the erosion potential of stream reaches by computing channel morphologic and hydrologic metrics associated with erosive power, such as stream steepness, topographic openness, and percent storage in the contributing watershed. Stream crossing flood vulnerability was assessed by comparing estimated peak flows to stream crossing conveyance capacities. Our results indicate that stream reaches in the headwaters of the Acadia National Park highlands such as Sargent, Penobscot, and Cadillac Mountain, have the highest erosion potential and generally coincide with reaches that have had erosion and infrastructure damage in the past. Stream crossings with the highest flood vulnerability are distributed throughout Mount Desert Island and Acadia National Park, especially south of Jordan Pond, north of Sargent Mountain, and surrounding Eagle Lake. Over a quarter of the total stream crossings have insufficient information to compute flood vulnerability and are often on the parts of the stream with the highest potential for erosion. The datasets allow users to identify stream reaches with the highest erosion potential, stream crossings that are most vulnerable to flood damage, and to highlight areas where supplemental field assessments could most effectively be completed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20265116","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Armstrong, I.P., McCallister, M.A., Hyslop, K.M., and Benthem, A.J., 2026, Erosion potential and flood vulnerability of streams and stream crossings at Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2026–5116, 21 p., https://doi.org/10.3133/sir20265116.","productDescription":"Report: vii, 21 p.; Data Release","numberOfPages":"21","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-178032","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":500752,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13Y2RY2","text":"USGS data release","linkHelpText":"Data for an Erosion and Flood Vulnerability Assessment of Streams and Stream Crossings at Acadia National Park, Maine"},{"id":500517,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://geonarrative.usgs.gov/acadiaerosionfloodvulnerability/","text":"Interactive dashboard","linkHelpText":"- Erosion Potential and Flood Vulnerability of Streams and Stream Crossings at Acadia National Park"},{"id":499822,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1EHZNHN","text":"USGS data release","linkHelpText":"Data for an erosion potential and flood vulnerability assessment of streams and stream crossings at Acadia National Park, Maine"},{"id":499821,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2026/5116/images/"},{"id":500656,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119275.htm","linkFileType":{"id":5,"text":"html"}},{"id":499820,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2026/5116/sir20265116.xml","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2026-5116 XML"},{"id":499819,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20265116/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2026-5116 HTML"},{"id":499818,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2026/5116/sir20265116.pdf","size":"7.83 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2026-5116 PDF"},{"id":499817,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2026/5116/coverthb.jpg"}],"country":"United States","state":"Maine","otherGeospatial":"Acadia National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -68.45003175798666,\n 44.44178922865794\n ],\n [\n -68.45003175798666,\n 44.21621316604151\n ],\n [\n -68.13514216440173,\n 44.21621316604151\n ],\n [\n -68.13514216440173,\n 44.44178922865794\n ],\n [\n -68.45003175798666,\n 44.44178922865794\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Rd.
Northborough, Massachusetts 01532
The U.S. Geological Survey, in cooperation with the National Park Service, has developed multiple datasets that can help natural resource managers identify stream reaches with the highest potential for erosion and stream crossings most vulnerable to flood damage within Acadia National Park. These datasets allow users to identify areas where supplemental field assessments could be most effectively completed.
","publicationDate":"2026-02-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Armstrong, Ian P. 0000-0002-8239-8029","orcid":"https://orcid.org/0000-0002-8239-8029","contributorId":344363,"corporation":false,"usgs":true,"family":"Armstrong","given":"Ian","email":"","middleInitial":"P.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":955710,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCallister, Meghan A. 0000-0001-8814-7725","orcid":"https://orcid.org/0000-0001-8814-7725","contributorId":358213,"corporation":false,"usgs":true,"family":"McCallister","given":"Meghan","middleInitial":"A.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":955711,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hyslop, Kristina M. 0009-0001-2525-5574","orcid":"https://orcid.org/0009-0001-2525-5574","contributorId":334465,"corporation":false,"usgs":true,"family":"Hyslop","given":"Kristina","middleInitial":"M.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":955712,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Benthem, Adam J. 0000-0003-2372-0281","orcid":"https://orcid.org/0000-0003-2372-0281","contributorId":220000,"corporation":false,"usgs":true,"family":"Benthem","given":"Adam","middleInitial":"J.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":955713,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70275084,"text":"70275084 - 2026 - Towards global mapping of dynamic surface water extents using Sentinel-1 SAR data","interactions":[],"lastModifiedDate":"2026-04-15T15:02:05.992722","indexId":"70275084","displayToPublicDate":"2026-02-26T07:54:26","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Towards global mapping of dynamic surface water extents using Sentinel-1 SAR data","docAbstract":"Additional case studies demonstrate the algorithm's ability to handle surface water extent in sand-dominated deserts, to track seasonal amplitude in Folsom Lake (California), drought-induced loss in Cerro Prieto Reservoir (Mexico), and rapid filling of the Grand Ethiopian Renaissance Dam. These results show that the method scales across local to global domains and maintains high accuracy, providing a practical tool for near-real-time monitoring of floods, droughts, and water-resource management. Because the approach is sensor-agnostic, it can be ported to forthcoming L- and S-band missions such as NASA-ISRO synthetic aperture radar (NISAR), broadening its applicability to future hydrologic observations.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2026.115326","usgsCitation":"Jung, J., Fattahi, H., Jeong, S., Bonnema, M.G., Jones, J.W., Bekaert, D., Chan, S.K., and Handweger, A.L., 2026, Towards global mapping of dynamic surface water extents using Sentinel-1 SAR data: Remote Sensing of Environment, v. 337, 115326, 21 p., https://doi.org/10.1016/j.rse.2026.115326.","productDescription":"115326, 21 p.","ipdsId":"IP-183308","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":502816,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"337","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jung, Jungkyo","contributorId":369929,"corporation":false,"usgs":false,"family":"Jung","given":"Jungkyo","affiliations":[{"id":64090,"text":"NASA Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":959401,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fattahi, Heresh","contributorId":292160,"corporation":false,"usgs":false,"family":"Fattahi","given":"Heresh","email":"","affiliations":[],"preferred":false,"id":959402,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jeong, Seongsu","contributorId":369930,"corporation":false,"usgs":false,"family":"Jeong","given":"Seongsu","affiliations":[{"id":64090,"text":"NASA Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":959403,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bonnema, Matthew G.","contributorId":369931,"corporation":false,"usgs":false,"family":"Bonnema","given":"Matthew","middleInitial":"G.","affiliations":[{"id":64090,"text":"NASA Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":959404,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jones, John W. 0000-0001-6117-3691 jwjones@usgs.gov","orcid":"https://orcid.org/0000-0001-6117-3691","contributorId":2220,"corporation":false,"usgs":true,"family":"Jones","given":"John","email":"jwjones@usgs.gov","middleInitial":"W.","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":959405,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bekaert, David","contributorId":267754,"corporation":false,"usgs":false,"family":"Bekaert","given":"David","affiliations":[{"id":13294,"text":"Woods Hole Oceanographic Institute","active":true,"usgs":false}],"preferred":false,"id":959406,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Chan, Steven K.","contributorId":369933,"corporation":false,"usgs":false,"family":"Chan","given":"Steven","middleInitial":"K.","affiliations":[{"id":64090,"text":"NASA Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":959407,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Handweger, Alexander L.","contributorId":369934,"corporation":false,"usgs":false,"family":"Handweger","given":"Alexander","middleInitial":"L.","affiliations":[{"id":64090,"text":"NASA Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":959408,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70273923,"text":"sir20265120 - 2026 - Methods for estimating selected streamflow statistics at ungaged sites in Wyoming based on data through water year 2021","interactions":[],"lastModifiedDate":"2026-04-10T15:07:21.627462","indexId":"sir20265120","displayToPublicDate":"2026-02-26T07:11:17","publicationYear":"2026","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2026-5120","displayTitle":"Methods for Estimating Selected Streamflow Statistics at Ungaged Sites in Wyoming Based on Data Through Water Year 2021","title":"Methods for estimating selected streamflow statistics at ungaged sites in Wyoming based on data through water year 2021","docAbstract":"The U.S. Geological Survey, in cooperation with the Wyoming Water Development Office, developed regional regression equations based on basin characteristics and streamflow statistics for streamgages through water year 2021 (October 1, 2020, to September 30, 2021). The regression equations allow estimates of mean annual maximum, mean annual, mean seasonal, and mean monthly streamflows; frequency statistics for the 7-day mean low flows with 2-year and 10-year recurrence intervals, 14- and 30-day mean low flows with 5-year recurrence intervals, and 60- and 1-day mean high flow with 2-year and 5-year recurrence intervals, respectively; and the 0.1-, 0.2-, 0.5-, 1-, 2-, 4-, 5-, 10-, 20-, 25-, 30-, 50-, 60-, 70-, 75-, 80-, 90-, 95-, 98-, and 99-percent durations for annual streamflows and 0.1-, 0.5-, 10-, 15-, 20-, 25-, 30-, 40-, 50-, 60-, 70-, 75-, 80-, 85-, 90-, 95-, and 99-percent durations for monthly streamflows for most months for ungaged locations in Wyoming that are largely unaltered by diversions or upstream reservoirs.
Regression equations were developed for 243 streamflow statistics. Best-subset selection was used to assess explanatory variables for respective streamflow statistics. Exploratory data analyses determined that, of the 81 basin characteristics evaluated as potential explanatory variables, characteristics such as drainage area and precipitation often produced models with the highest adjusted coefficient of determination and lowest mean squared error, as determined in the best-subset selection. To address heteroskedasticity of model residuals, model variables were regionalized using fixed-effects models; the percentages of the streamgage basins in selected ecoregions were defined as interaction terms, which represent the model slope for specific ecoregions. Most models were determined to be statistically significant for probability values less than or equal to 0.1 for one or more regional explanatory variables. The final regional regression equations defined in this report are available for use in the U.S. Geological Survey’s StreamStats web application at https://streamstats.usgs.gov/ss/.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20265120","collaboration":"Prepared in cooperation with the Wyoming Water Development Office","usgsCitation":"Taylor, N.J., and Sando, R., 2026, Methods for estimating selected streamflow statistics at ungaged sites in Wyoming based on data through water year 2021: U.S. Geological Survey Scientific Investigations Report 2026–5120, 38 p., https://doi.org/10.3133/sir20265120.","productDescription":"Report: vii, 38 p.; 1 Linked Appendix Table; Data Release; Dataset","numberOfPages":"50","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-179497","costCenters":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"links":[{"id":500115,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":500657,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119274.htm","linkFileType":{"id":5,"text":"html"}},{"id":500117,"rank":8,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20265120/full"},{"id":500114,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P14WLVAH","text":"USGS data release","linkHelpText":"Regression equations for selected streamflow statistics based on data through water year 2021 in and near Wyoming"},{"id":500113,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2026/5120/downloads/","text":"Table 1.1","size":"60 KB","linkFileType":{"id":3,"text":"xlsx"}},{"id":500112,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2026/5120/images/"},{"id":500109,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2026/5120/coverthb.jpg"},{"id":500110,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2026/5120/sir20265120.pdf","text":"Report","size":"7.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2026-5120"},{"id":500111,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2026/5120/sir20265120.XML"}],"country":"United States","state":"Colorado, Idaho, Montana, North Dakota, South Dakota, Utah, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -113.82002110650585,\n 46.421867179561445\n ],\n [\n -113.82002110650585,\n 39.89961451938157\n ],\n [\n -103.32595673094282,\n 39.89961451938157\n ],\n [\n -103.32595673094282,\n 46.421867179561445\n ],\n [\n -113.82002110650585,\n 46.421867179561445\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
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Magnitude and frequency of annual peak streamflows were computed for 813 streamgages on rural, unregulated streams with annual peak streamflow data from 1791 through the 2021 water years in and near Virginia and West Virginia. The study was done in cooperation with the Federal Emergency Management Agency, the West Virginia Department of Transportation, and the Virginia Department of Transportation.
Regression equations were developed for estimating flood frequency and magnitude. Twelve regions with homogeneous flood characteristics were identified. Generalized least squares regression equations relating logarithmic-transformed drainage area and peak streamflow were developed for the 0.5, 0.2, 0.1, 0.04, 0.02, 0.01, 0.005, and 0.002 annual exceedance probabilities (AEPs). Drainage area was the only significant variable for all equations. The range of drainage areas used to develop the equations differed for each region; the smallest drainage area in any region was 0.21 square miles (mi2) and the largest drainage area in any region is 2,966 mi2. Pseudo coefficient of determination (pseudo-R2) values for regression equations ranged from 0.481 to 0.995 for all regions and AEPs. Performance metrics and diagnostic plots indicated that equations for 11 of the 12 regions showed generally good performance, with pseudo-R2 values ranging from 0.762 to 0.968 for the 0.01 AEP.
The overall average change in at-site 0.01 AEP annual peak streamflows at individual streamgages was 0.5 percent compared to the most recent 2011 Virginia study and 2.3 percent compared to the most recent 2010 West Virginia study. Changes from the previous studies for estimates from regional equations for the 0.01 AEP, solved specifically for a 50 mi2 basin, ranged from a 30 percent increase to a 45 percent decrease in areas where the previous regions overlapped with the current regions by 750 mi2 or more.
New regional skews were developed using Bayesian weighted least-squares/Bayesian generalized least-squares regression for two skew regions that included the study area. A constant regional skew of 0.50 was computed for streams in Virginia, West Virginia, and Maryland that drain to the Atlantic Ocean. A constant regional skew of 0.048 was computed for streams that drain to the Gulf of America, including streams in Kentucky and Tennessee, most of West Virginia, far southwestern Virginia, and part of western Maryland.
About 12 percent of the 418 streamgages with 30 or more gaged peaks had statistically significant (p-value [significance level] less than or equal to 0.05) trends, with 40 of these exhibiting positive trends and 11 exhibiting negative trends. Streamgages with 30 percent or greater development were excluded from regression analyses.
A regulation index was developed that accounted for storage and drainage area of dams and drainage area at the streamgage; a value of 0.0040 or more for the regulation index indicates regulated peak streamflow. Frequency analyses were done at 86 streamgages on regulated streams.
Regression procedures developed in this study are applicable only to rural, unregulated streams within Virginia and West Virginia with drainage basins that (1) are within the range of drainage areas used to develop the equations for each region, (2) included less than 30 percent of developed area, and (3) had a regulation index less than 0.0040.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255110","isbn":"978-1-4113-4656-7","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency, the West Virginia Department of Transportation, and the Virginia Department of Transportation","usgsCitation":"Messinger, T., Duda, J.M., Wagner, D.M., O'Shea, P.S., Scott, J.D., and Kandel, C., 2026, Estimation of magnitude and frequency of floods for rural, unregulated streams in and near Virginia and West Virginia: U.S. Geological Survey Scientific Investigations Report 2025–5110, 85 p., https://doi.org/10.3133/sir20255110.","productDescription":"Report: vii, 85 p.; Data Release","numberOfPages":"85","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-169653","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":500658,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119273.htm","linkFileType":{"id":5,"text":"html"}},{"id":500179,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RBZ8OJ","text":"USGS data release","linkHelpText":"Data in support of estimation of magnitude and frequency of floods for rural, unregulated streams in and near Virginia and West Virginia"},{"id":500174,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5110/coverthb.jpg"},{"id":500175,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5110/sir20255110.pdf","size":"34.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5110 PDF"},{"id":500176,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255110/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5110 HTML"},{"id":500177,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5110/sir20255110.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2025-5110 XML"},{"id":500178,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5110/images/"}],"country":"United States","state":"Kentucky, Maryland, North Carolina, Ohio, Pennsylvania, Tennessee, Virginia, West Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -84,\n 41\n ],\n [\n -84,\n 35\n ],\n [\n -75,\n 35\n ],\n [\n -75,\n 41\n ],\n [\n -84,\n 41\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Virginia and West Virginia Water Science Center
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This study provides a comprehensive assessment of decadal changes in the quality of groundwater used for public drinking-water supply at 444 monitoring sites across California during 2004–2023. We assessed decadal step trends in groundwater quality for 145 water-quality constituents and geochemical indicators statewide and across geographic and land-use based network groups. We evaluated the statistical significance of directional changes (predominant increase or decrease of constituent concentrations) and the magnitude of those changes across all network groups.
Uranium showed the most widespread directional and high-magnitude increases of all constituents with regulatory benchmarks statewide, particularly in the agriculture-dominated Central Valley as well as urban- and desert-dominated regions of Southern California. Fluoride and perchlorate showed the most widespread directional and high-magnitude decreases of all constituents with regulatory benchmarks statewide, which were also most pronounced in Southern California. Although arsenic and nitrate did not often register significant directional changes across network groups, they showed widespread, high-magnitude changes in both directions (increase and decrease) at levels often exceeding 10 percent of respective regulatory benchmarks statewide. Triazine herbicides (atrazine and simazine) and the gasoline oxygenate methyl tert-butyl ether (MTBE) showed significant directional decreases statewide, but not at levels considered to be of high magnitude compared to respective regulatory benchmarks.
We observed significant directional and high-magnitude increases of total dissolved solids (TDS) statewide, which were most pronounced in agricultural areas. Analysis of explanatory geochemical indicators indicated that prevalent statewide increases of alkalinity and calcium were the predominant components of the observed statewide increases in TDS by mass. Widespread increases in groundwater alkalinity and calcium across agricultural and urban areas may be related, in part, to warm-season irrigation and other anthropogenic factors that have shifted soil weathering dynamics over the long term. Increasing alkalinity concentrations were related to increasing uranium concentrations, particularly in areas with aquifer materials derived from granitic rocks. Conversely, increasing calcium concentrations were related to decreasing fluoride concentrations, particularly in areas where fluoride occurred naturally at elevated concentrations. Decrease of perchlorate, triazine herbicides, and MTBE are likely related to decreased anthropogenic source inputs over time and natural attenuation in aquifers.
As fragility and risk modeling techniques and computational capabilities evolve, complemented by moving toward more routine and systematic seismic risk assessment of all buildings and critical infrastructure, the authors pose a few critical questions to investigate how the U.S. Geological Survey (USGS) National Seismic Hazard Models (NSHMs) can be used and enhanced further to serve such issues. In this paper, we use three examples from multiple sectors to (1) identify the role of USGS NSHMs in evaluating seismic risks to critical infrastructure, (2) quantify potential impacts from NSHM enhancements (i.e., [i] hazard curves for the vertical component of ground motion, [ii] stochastic event sets, and [iii] maps of probabilistic ground failure hazards), and (3) clarify the feasibility of relevant NSHM improvements. We illuminate that NSHMs are commonly used in location-specific performance assessments, whereas earthquake effects on critical infrastructure can be widespread across large geospatial regions. Further, we found that without the NSHM extensions considered here, risk can be severely underestimated, e.g., neglecting ground failure hazards can underestimate regional loss by a factor of two or more. Although many challenges remain, we developed example prototypes to clarify the feasibility of the NSHM extensions, which can facilitate improved management of risks to critical infrastructure.
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kjaiswal@usgs.gov","orcid":"https://orcid.org/0000-0002-5803-8007","contributorId":149796,"corporation":false,"usgs":true,"family":"Jaiswal","given":"Kishor","email":"kjaiswal@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":958486,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kwong, N. Simon 0000-0003-3017-9585","orcid":"https://orcid.org/0000-0003-3017-9585","contributorId":369125,"corporation":false,"usgs":false,"family":"Kwong","given":"N.","middleInitial":"Simon","affiliations":[{"id":87727,"text":"Senior project engineer, Lettis Consultants International, Inc., Concord, CA 94520","active":true,"usgs":false}],"preferred":false,"id":958487,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70274637,"text":"70274637 - 2026 - Invasive carps versus native fish: A first-pass trait-based index for assessing competition threats.","interactions":[],"lastModifiedDate":"2026-04-02T17:24:54.415981","indexId":"70274637","displayToPublicDate":"2026-02-25T10:17:35","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":18328,"text":"Frontiers in Freshwater Science","active":true,"publicationSubtype":{"id":10}},"title":"Invasive carps versus native fish: A first-pass trait-based index for assessing competition threats.","docAbstract":"Introduction:
Bigheaded carp (Hypophthalmichthys spp.) are invasive fish in the Mississippi River basin. Their rapid proliferation has raised concerns about exploitative competition with native fishes, with consequences that remain incompletely understood. We aimed to identify native species most susceptible to competition based on overlap with bigheaded carp in dietary and habitat traits.
Methods:
We used an established fish traits database to quantify dietary and habitat overlap between bigheaded carp and 100 native fish species. We then integrated dietary and habitat overlap into a composite competition index.
Results:
Dietary similarity with the native assemblage exceeded habitat similarity, suggesting that while competition with some native species may occur, it may often be limited by spatial separation. Dietary and habitat similarity coefficients were not correlated, indicating that strong dietary overlap did not necessarily coincide with similar habitat use (and vice versa). Approximately 20% of species were classified as high competition risk. The highest-risk species included bigmouth buffalo (Ictiobus cyprinellus), threadfin shad (Dorosoma petenense), black redhorse (Moxostoma duquesnii), bluntnose minnow (Pimephales notatus), highfin carpsucker (Carpiodes velifer), and gizzard shad (Dorosoma cepedianum).
Discussion:
Although trait-based predictions have limitations, our results are consistent with empirically documented interactions and provide a rapid, first-pass assessment of potential competitive vulnerability. Dietary overlap, habitat overlap, and the derived competition index offer actionable decision-support for managing potential competition between bigheaded carp and native species. We included ten practical recommendations to translate predictions into conservation and management actions.