Each mineral commodity chapter of the 2026 edition of the U.S. Geological Survey (USGS) Mineral Commodity Summaries (MCS) includes information on events, trends, and issues for each mineral commodity as well as discussions and tabular presentations on domestic industry structure, Government programs, tariffs, 5-year salient statistics, and world production, reserves, and resources. The MCS is the earliest comprehensive source of 2025 mineral production data for the world. More than 90 individual minerals and materials are covered by two-page synopses.
Abbreviations and units of measure and definitions of selected terms used in the report are in Appendix A and Appendix B, respectively. Reserves and resources information is in Appendix C, which includes “Part A—Resource and Reserve Classification for Minerals” and “Part B—Sources of Reserves Data.” A directory of USGS minerals information country specialists and their responsibilities is in Appendix D.
The USGS continually strives to improve the value of its publications to users. Constructive comments and suggestions by readers of the 2026 MCS are welcomed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/mcs2026","isbn":"978-1-4113-4643-7","usgsCitation":"U.S. Geological Survey, 2026, Mineral commodity summaries 2026 (ver. 1.3, May 2026): U.S. Geological Survey, 222 p., https://doi.org/10.3133/mcs2026.","productDescription":"Report: 222 p.; Data Release; Data Visualization","numberOfPages":"222","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-185894","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":499639,"rank":6,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://apps.usgs.gov/critical-minerals/mineral-commodities-2026.html","text":"Data visualization"},{"id":498869,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/periodicals/mcs2026/coverthb2.jpg"},{"id":499534,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/periodicals/mcs2026/mcs2026.pdf","text":"Report","size":"16.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"MCS 2026 PDF"},{"id":499535,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://www.usgs.gov/centers/national-minerals-information-center/mineral-commodity-summaries","text":"Mineral Commodity Summaries Prior to 2026"},{"id":500785,"rank":8,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/periodicals/mcs2026/versionHist.txt","size":"1.86 KB","linkFileType":{"id":2,"text":"txt"}},{"id":499654,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119204.htm","linkFileType":{"id":5,"text":"html"}},{"id":499537,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1WKQ63T","text":"USGS data release","linkHelpText":"Data release for mineral commodity summaries 2026"},{"id":499536,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://www.usgs.gov/centers/national-minerals-information-center/commodity-statistics-and-information","text":"Commodity Statistics and Information"}],"edition":"Version 1.0: February 2026; Version 1.1: March 2026; Version 1.2: April 2026; Version 1.3: May 2026","contact":"Director, National Minerals Information Center
U.S. Geological Survey
12201 Sunrise Valley Drive
988 National Center
Reston, VA 20192
Email: nmicrecordsmgt@usgs.gov
","tableOfContents":"Using Alaskan lake sediment oxygen isotope records (δ18O), which trace the δ18O of precipitation, we establish that abrupt atmospheric shifts occurred during the last deglacial period in the North Pacific-Arctic. The robust lake δ18O chronologies confidently correlate Younger-Dryas (YD) atmospheric adjustments in Alaska with Greenland ice-core records and their seasonal sensitivity are consistent with cooling during winter. In contrast, abrupt δ18O decreases during the late Holocene observed in our records, of similar magnitude as the YD, are best explained by atmospheric modes involving long-distance transport of sub-tropical Pacific moisture. Our sediment cores are among the most reliably dated records yet produced in the circum-Arctic and show that similar decreases in δ18O of winter precipitation during the YD and late Holocene were driven by different atmospheric teleconnections. These results underscore major roles for seasonality and atmospheric patterns in the conceptual understanding of global scale climate oscillations, both past and future.
","language":"English","publisher":"Nature","doi":"10.1038/s41467-026-68841-2","usgsCitation":"Anderson, L., Finney, B.P., and Baxter, W.B., 2026, Shifting winter atmospheric teleconnections to the North Pacific reconcile Younger-Dryas and Holocene δ18O signals: Nature Communications, v. 17, 2287, 11 p., https://doi.org/10.1038/s41467-026-68841-2.","productDescription":"2287, 11 p.","ipdsId":"IP-173518","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":502072,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-026-68841-2","text":"Publisher Index Page"},{"id":501859,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Greenland, United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -154.5838715688011,\n 61.71714669073339\n ],\n [\n -154.5838715688011,\n 58.64385747164533\n ],\n [\n -140.97306433569628,\n 58.64385747164533\n ],\n [\n -140.97306433569628,\n 61.71714669073339\n ],\n [\n -154.5838715688011,\n 61.71714669073339\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -54.47050647646307,\n 76.12487765561002\n ],\n [\n -54.47050647646307,\n 68.64845510387647\n ],\n [\n -23.062966953169052,\n 68.64845510387647\n ],\n [\n -23.062966953169052,\n 76.12487765561002\n ],\n [\n -54.47050647646307,\n 76.12487765561002\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"17","noUsgsAuthors":false,"publicationDate":"2026-02-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Anderson, Lesleigh 0000-0002-5264-089X","orcid":"https://orcid.org/0000-0002-5264-089X","contributorId":368960,"corporation":false,"usgs":true,"family":"Anderson","given":"Lesleigh","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":958185,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Finney, Bruce P.","contributorId":368961,"corporation":false,"usgs":false,"family":"Finney","given":"Bruce","middleInitial":"P.","affiliations":[{"id":38154,"text":"Idaho State University","active":true,"usgs":false}],"preferred":false,"id":958186,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baxter, W. 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Accordingly, questions pertaining to WT end-of-life have risen in importance in policy and practice. Yet, research on the various factors relating to WT decommissioning is relatively sparse. Moreover, the key assumptions underpinning that prior research (e.g., the lifespan of WTs, characteristics of WTs being decommissioned, and whether the site is repowered with new WTs) have never been empirically tested across a large set of decommissioned WTs. Leveraging a uniquely comprehensive and spatially explicit dataset of decommissioned WTs in the United States, this research analyzes spatial, technological, and temporal trends in WT decommissioning and develops a novel predictive model for WT decommissioning. Our analysis pinpoints more than 12,400 WTs that have been fully decommissioned in the United States., the majority of which have been relatively old (> 30 years) and small (< 200 kW). While a WT's age alone is a good predictor of the likelihood of decommissioning, other factors such as the size of the WT and recent performance are also important and significant predictors. Most sites where decommissioning has occurred have seen subsequent repowering, with repowered plants featuring substantially fewer WTs (−86 on average) and higher rated plant capacity (+62 MW on average). Many existing WTs in the U.S. are approaching the end of their expected life with roughly 7500 being 20 or more years old. Findings can help policymakers and stakeholders begin preparing for this potential wave of future decommissioning and repowering.
","language":"English","publisher":"Wiley","doi":"10.1002/we.70099","usgsCitation":"Rand, J., Kramer, L., Hoen, B., Diffendorfer, J., and Garrity, C., 2026, Out with the old: Empirical trends in U.S. land-based wind turbine decommissioning and repowering: Wind Energy, v. 29, no. 3, e70099, 13 p., https://doi.org/10.1002/we.70099.","productDescription":"e70099, 13 p.","ipdsId":"IP-182443","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":499936,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/we.70099","text":"Publisher Index Page"},{"id":499676,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"29","issue":"3","noUsgsAuthors":false,"publicationDate":"2026-02-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Rand, Joseph","contributorId":290223,"corporation":false,"usgs":false,"family":"Rand","given":"Joseph","affiliations":[{"id":41633,"text":"DOE Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":955277,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kramer, Louisa 0000-0002-6776-9768","orcid":"https://orcid.org/0000-0002-6776-9768","contributorId":204878,"corporation":false,"usgs":true,"family":"Kramer","given":"Louisa","email":"","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":955278,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hoen, Ben 0000-0002-9512-5572","orcid":"https://orcid.org/0000-0002-9512-5572","contributorId":204879,"corporation":false,"usgs":false,"family":"Hoen","given":"Ben","email":"","affiliations":[{"id":37001,"text":"DOE Lawrence Berkeley National Labs","active":true,"usgs":false}],"preferred":false,"id":955279,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Diffendorfer, James E. 0000-0003-1093-6948 jediffendorfer@usgs.gov","orcid":"https://orcid.org/0000-0003-1093-6948","contributorId":223504,"corporation":false,"usgs":true,"family":"Diffendorfer","given":"James","email":"jediffendorfer@usgs.gov","middleInitial":"E.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":955280,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Garrity, Christopher 0000-0002-5565-1818 cgarrity@usgs.gov","orcid":"https://orcid.org/0000-0002-5565-1818","contributorId":220994,"corporation":false,"usgs":true,"family":"Garrity","given":"Christopher","email":"cgarrity@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":955281,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70273858,"text":"70273858 - 2026 - Evaluating machine learning approaches to identify and predict oil and gas produced water lithium concentrations","interactions":[],"lastModifiedDate":"2026-02-09T15:28:29.171335","indexId":"70273858","displayToPublicDate":"2026-02-06T08:20:00","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":23297,"text":"Data Science in Science","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating machine learning approaches to identify and predict oil and gas produced water lithium concentrations","docAbstract":"Recently, the demand for battery-grade lithium has substantially increased, largely due to electrification of the transportation sector. The search for new lithium sources has turned to produced waters (frequently brines), a large-volume wastewater by-product of oil and gas extraction. Geochemical analysis indicates the presence of varying concentrations of lithium from produced water samples collected across the United States and represented in the U.S. Geological Survey’s National Produced Water Geochemical Database, as well as mixtures of Marcellus Shale produced water included in the Pennsylvania Department of Environmental Protection’s Oil and Gas Well Waste Reports. We first examined whether the geochemical signature of the lithium-bearing produced waters is sufficiently distinct so that machine learning (ML) can be used to correctly classify samples to the formation of origin. The produced water sample data used to assess classification accuracy were from the Marcellus Shale, Utica Shale and Point Pleasant Formation (Utica), and Smackover Formation oil and gas wells. Further, we evaluated the potential for ML to accurately classify Marcellus Shale produced water spatially (i.e., northeast versus southwest Pennsylvania). We then investigated whether ML algorithms applied to a suite of geochemical concentration data (i.e. Ba, Br, Cl, K, Mg, Sr) may be used to predict the lithium concentration of an unknown sample. Finally, we applied an estimated economic lithium grade cutoff of 150 milligrams per liter (mg/l) and assessed the utility of ML to predict whether a produced water sample would fall above or below the grade cutoff based on the suite of geochemical parameters. Four machine learning algorithms—Random Forest (RF), Gradient Boosting Trees (GBT), Extreme Boosting (XGBoost), and Deep Neural Networks (DNN) were assessed. This study successfully demonstrates that all four machine learning methods can precisely and accurately estimate lithium concentrations and geologic formation classification. The products of this study contribute to the growing body of knowledge aimed at expanding the lithium resource base within the United States.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/26941899.2026.2624195","usgsCitation":"Attanasi, E., McDevitt, B., Freeman, P., and Coburn, T., 2026, Evaluating machine learning approaches to identify and predict oil and gas produced water lithium concentrations: Data Science in Science, v. 5, no. 1, 2624195, 18 p., https://doi.org/10.1080/26941899.2026.2624195.","productDescription":"2624195, 18 p.","ipdsId":"IP-180475","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":499937,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/26941899.2026.2624195","text":"Publisher Index Page"},{"id":499678,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Arkansas, Florida, Georgia, Louisiana, Mississippi, Oklahoma, South Carolina, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -100.73317123367434,\n 35.41758190253161\n ],\n [\n -100.73317123367434,\n 28.707661068137995\n ],\n [\n -83.28804352213777,\n 28.707661068137995\n ],\n [\n -83.28804352213777,\n 35.41758190253161\n ],\n [\n -100.73317123367434,\n 35.41758190253161\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"5","issue":"1","noUsgsAuthors":false,"publicationDate":"2026-02-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Attanasi, Emil 0000-0001-6845-7160 attanasi@usgs.gov","orcid":"https://orcid.org/0000-0001-6845-7160","contributorId":1809,"corporation":false,"usgs":true,"family":"Attanasi","given":"Emil","email":"attanasi@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":955297,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McDevitt, Bonnie 0000-0001-8390-0028","orcid":"https://orcid.org/0000-0001-8390-0028","contributorId":291246,"corporation":false,"usgs":true,"family":"McDevitt","given":"Bonnie","email":"","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":955298,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Freeman, Philip A. 0000-0002-0863-7431","orcid":"https://orcid.org/0000-0002-0863-7431","contributorId":347358,"corporation":false,"usgs":false,"family":"Freeman","given":"Philip A.","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":955299,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Coburn, Timothy 0000-0002-7309-0962","orcid":"https://orcid.org/0000-0002-7309-0962","contributorId":366116,"corporation":false,"usgs":false,"family":"Coburn","given":"Timothy","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":955300,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70273893,"text":"70273893 - 2026 - Abiotic and biotic controls of non-native perennial plant success in drylands","interactions":[],"lastModifiedDate":"2026-03-23T14:56:45.294197","indexId":"70273893","displayToPublicDate":"2026-02-06T08:03:49","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6505,"text":"Nature Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Abiotic and biotic controls of non-native perennial plant success in drylands","docAbstract":"Drivers of non-native plant success in drylands are poorly understood. Here we identify functional differences between dryland native and non-native perennial plants and assess how biotic, abiotic and anthropogenic factors shape the success of the latter. On the basis of plant community and functional trait data from 98 sites across 25 countries, we report a total of 41 non-native plant species at 31 sites. Non-natives tend towards faster growth strategies than natives. Non-native plant richness is higher at sites with greater grazing pressure and under environmental conditions associated with higher soil fertility, decomposition and fungal richness—conditions that tend to occur in less arid regions—and lower where native plant and herbivore richness are greater. Non-native plant cover correlates positively with grazing pressure and negatively with native plant richness. Taken together, our results suggest that non-native plant success in drylands is facilitated when high grazing pressure coincides with elevated resource availability. Such context-dependence of non-native plant success and linkages with native plant and herbivore diversity highlight the need for managing grazing and conserving biodiversity across the world’s drylands.
","language":"English","publisher":"Springer Nature","doi":"10.1038/s41559-025-02971-6","usgsCitation":"Rahmanian, S., Eisenhauer, N., Huang, Y., Hejda, M., Pyšek, P., Feilhauer, H., Eldridge, D.J., Gross, N., Le Bagousse-Pinguet, Y., Saiz, H., Delgado-Baquerizo, M., Berdugo, M., Ochoa, V., Gozalo, B., Asensio, S., Guirado, E., Valencia, E., García-Gómez, M., Gaitán, J.J., Mendoza, B.J., Plaza, C., Díaz-Martínez, P., Martínez-Valderrama, J., Abedi, M., Ahmadian, N., Ahumada, R.J., Amghar, F., Araújo, T., Arroyo, A.I., Ben Salem, F., Blaum, N., Boldbat, E., Boldgiv, B., Bowker, M., van den Brink, L., Bu, C., Canessa, R., Castillo-Monroy, A.P., Castro, H., Castro-Quezada, P., Chaieb, G., Chibani, R., Conceição, A.A., Davila, Y.C., Deák, B., Donoso, D.A., Dougill, A., Espinosa, C.I., Fajardo, A., Farzam, M., Ferrante, D., Franzese, J., Fraser, L.H., Geiger, E.L., Gonzalez, S.L., Montalván, E.G., Hering, R., Marais, E., Hernández, R.M., Hernández-Valdez, S., Hölzel, N., Huber-Sannwald, E., Jadán, O., Jentsch, A., Kindermann, L., Köbel, M., le Roux, P.C., Leder, C.V., Li, X., Liancourt, P., Linstädter, A., Liu, J., Louw, M.A., Maggs-Kölling, G., Makhalanyane, T.P., Issa, O.M., Manzaneda, A.J., Margerie, P., Martin, R., McClaran, M.P., Messeder, J.V., Mora, J.P., Moreno, G., Munson, S.M., Nair, G.R., Nunes, A., Oliva, G., Palpurina, S., Peter, G., Pueyo, Y., Quiroga, E., Reed, S.C., Rey, P.J., Rodríguez, A., Rolo, V., Ruppert, J.C., Salah, A., Sarig, S., Singh, B.K., Swemmer, A.M., Teixido, A.L., Thomas, A.D., Tielbörger, K., Travers, S.K., Valkó, O., Wamiti, W., Wang, D., Wang, L., Wardle, G.M., Wolff, P., Yahdjian, L., Oñatibia, G.R., Yari, R., Zaady, E., Zhang, Y., Zhou, X., and Maestre, F.T., 2026, Abiotic and biotic controls of non-native perennial plant success in drylands: Nature Ecology and Evolution, v. 10, p. 523-535, https://doi.org/10.1038/s41559-025-02971-6.","productDescription":"13 p.","startPage":"523","endPage":"535","ipdsId":"IP-183980","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":499811,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","noUsgsAuthors":false,"publicationDate":"2026-02-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Rahmanian, Soroor","contributorId":366231,"corporation":false,"usgs":false,"family":"Rahmanian","given":"Soroor","affiliations":[{"id":87393,"text":"German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig; Puschstraße 4, 04103 Leipzig, Germany; Institute of Biology, Leipzig University; Puschstraße 4, 04103 Leipzig, Germany","active":true,"usgs":false}],"preferred":false,"id":955510,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eisenhauer, Nico","contributorId":141161,"corporation":false,"usgs":false,"family":"Eisenhauer","given":"Nico","email":"","affiliations":[{"id":13699,"text":"German Centre for Integrative Biodiversity Research, Germany","active":true,"usgs":false}],"preferred":false,"id":955511,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Huang, Yuanyuan","contributorId":366232,"corporation":false,"usgs":false,"family":"Huang","given":"Yuanyuan","affiliations":[{"id":87393,"text":"German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig; Puschstraße 4, 04103 Leipzig, Germany; Institute of Biology, Leipzig University; Puschstraße 4, 04103 Leipzig, Germany","active":true,"usgs":false}],"preferred":false,"id":955512,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hejda, Martin","contributorId":366233,"corporation":false,"usgs":false,"family":"Hejda","given":"Martin","affiliations":[{"id":87394,"text":"Department of Invasion Ecology, Czech Academy of Sciences, Institute of Botany; 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Besides competition for resources, carp may also have disadvantageous nutrition for predators. One biochemical aspect to consider in carp is production of the enzyme thiaminase that cleaves thiamine (vitamin B1), making it unavailable to most vertebrates. The function of thiaminase within prey fishes is unclear, but roughly half of all fishes tested have measurable thiaminase activity. Predators consuming large volumes of prey with high thiaminase activity can develop thiamine deficiency, ultimately leading to offspring mortality. Three invasive carp (black carp, grass carp, and bighead carp) have no published thiaminase data. We tested juvenile (<160 mm standard length) black carp (n = 38), bighead carp (n = 7), grass carp (n = 50), and silver carp (n = 50) for thiaminase activity. All four species had measurable activity; however, grass carp had statistically higher thiaminase activity (p < 0.001; median = 61 nmol/g/min) than the other three species. Silver carp had the second highest thiaminase activity (14 nmol/g/min). Bighead (2.7 nmol/g/min) and black (2.2 nmol/g/min) carp had the lowest thiaminase activity; activities were often near detection limits. Predators with diets heavily composed of grass carp or silver carp may experience thiamine deficiency, though seasonal variation in carp size and other food source availability may determine potential effects of invasive carp predation in Great Lakes food webs.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2026.102751","usgsCitation":"Rowland, F.E., Byrd, C.G., and Kroboth, P., 2026, Thiaminase I activity is high in grass and silver carp, but negligible in bighead and black carp: Journal of Great Lakes Research, v. 52, no. 1, 102751, 8 p., https://doi.org/10.1016/j.jglr.2026.102751.","productDescription":"102751, 8 p.","ipdsId":"IP-183322","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":501233,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/ja/70274235/images"},{"id":501232,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/ja/70274235/70274235.XML"},{"id":501231,"rank":2,"type":{"id":42,"text":"Open Access USGS Document"},"url":"https://pubs.usgs.gov/publication/70274235/full"},{"id":501229,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"52","issue":"1","noUsgsAuthors":false,"publicationDate":"2026-02-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Rowland, Freya Elizabeth 0000-0002-1041-5301","orcid":"https://orcid.org/0000-0002-1041-5301","contributorId":302395,"corporation":false,"usgs":true,"family":"Rowland","given":"Freya","email":"","middleInitial":"Elizabeth","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":957117,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Byrd, Curtis G. 0000-0002-5124-5652","orcid":"https://orcid.org/0000-0002-5124-5652","contributorId":210798,"corporation":false,"usgs":true,"family":"Byrd","given":"Curtis","email":"","middleInitial":"G.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":957118,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kroboth, Patrick 0000-0002-9447-4818","orcid":"https://orcid.org/0000-0002-9447-4818","contributorId":216578,"corporation":false,"usgs":true,"family":"Kroboth","given":"Patrick","email":"","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":957119,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70275576,"text":"70275576 - 2026 - Weather, water, and precipitation","interactions":[],"lastModifiedDate":"2026-05-04T15:43:51.730851","indexId":"70275576","displayToPublicDate":"2026-02-05T10:40:54","publicationYear":"2026","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"12","title":"Weather, water, and precipitation","docAbstract":"No abstract available.
","largerWorkTitle":"Mountain sheep in North America: Biology, ecology, conservation, and management","language":"English","publisher":"CRC Press","usgsCitation":"Cain, J.W., and van de Kerk, M., 2026, Weather, water, and precipitation, chap. 12 of Mountain sheep in North America: Biology, ecology, conservation, and management.","ipdsId":"IP-174390","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":503940,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Krausman, Paul R.","contributorId":356094,"corporation":false,"usgs":false,"family":"Krausman","given":"Paul R.","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":960968,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Jex, William","contributorId":371079,"corporation":false,"usgs":false,"family":"Jex","given":"William","affiliations":[],"preferred":false,"id":960969,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"Cain, James W. III 0000-0003-4743-516X jwcain@usgs.gov","orcid":"https://orcid.org/0000-0003-4743-516X","contributorId":4063,"corporation":false,"usgs":true,"family":"Cain","given":"James","suffix":"III","email":"jwcain@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":960926,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"van de Kerk, Madelon","contributorId":371051,"corporation":false,"usgs":false,"family":"van de Kerk","given":"Madelon","affiliations":[{"id":38118,"text":"Western Colorado University","active":true,"usgs":false}],"preferred":false,"id":960927,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70273385,"text":"sir20255104 - 2026 - Estimating the magnitude and frequency of floods at ungaged locations on urban streams in Tennessee and parts of Alabama, Georgia, Mississippi, North Carolina, and South Carolina, using data through the 2022 water year","interactions":[],"lastModifiedDate":"2026-02-05T22:06:06.078649","indexId":"sir20255104","displayToPublicDate":"2026-02-05T08:41:23","publicationYear":"2026","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-5104","displayTitle":"Estimating the Magnitude and Frequency of Floods at Ungaged Locations on Urban Streams in Tennessee and Parts of Alabama, Georgia, Mississippi, North Carolina, and South Carolina, Using Data Through the 2022 Water Year","title":"Estimating the magnitude and frequency of floods at ungaged locations on urban streams in Tennessee and parts of Alabama, Georgia, Mississippi, North Carolina, and South Carolina, using data through the 2022 water year","docAbstract":"In 2024, the U.S. Geological Survey, in cooperation with the Tennessee Department of Transportation, updated the methods for predicting the magnitude and frequency of floods at ungaged locations on streams in urban areas in Tennessee. The study area included 136 streamgages in urban areas in Tennessee, Mississippi, Alabama, Georgia, South Carolina, and North Carolina that had at least 10 percent developed imperviousness in their basins as indicated by data from the 2011 National Land Cover Database. Regression equations were developed to predict streamflows corresponding to the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities (AEPs) and were incorporated into the StreamStats application. In generalized least-squares regression, the base-10 logarithm of drainage area, the percentages of the streamgage basins in developed land use, and the percentages of the streamgage basins in the Piedmont and Ridge and Valley Level 3 ecoregions were statistically significant in explaining the variability in annual peak streamflows in the study area. Drainage areas ranged from 0.164 to 93.4 square miles, the percentage of the streamgage basins in developed land use ranged from 26 to 100 percent, and the percentage of the streamgage basins in Piedmont and Ridge and Valley Level 3 ecoregions ranged from 0 to 100 percent. Pseudo R-squared values for the regression equations ranged from 0.86, or 86 percent, for the 50- and 20-percent AEPs (2- and 5-year floods) to 0.71, or 71 percent, for the 0.2-percent AEP (500-year flood). The average variance of prediction (in log base-10 units) ranged from 0.023 for the 20- and 10-percent AEPs to 0.05 for the 0.2-percent AEP. The average variance of prediction can be reported as a percentage of the predicted value, known as the standard error of prediction, which ranged from 35.8 percent for the 20-percent AEP (5-year flood) to 55.4 percent for the 0.2-percent AEP (500-year flood). Methods are presented for estimating annual peak streamflows for gaged locations, ungaged locations on gaged streams, and locations on ungaged streams.
Contact Us- USGS Publications Warehouse
Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Digital flood-inundation maps for a 2.7-mile reach of Río Grande De Loíza in Caguas, Puerto Rico, were created by the U.S. Geological Survey. Water-surface profiles were computed for the stream reach by using a one-dimensional, steady-state, step-backwater model. The model was calibrated to the current (2025) stage-streamflow relation (rating curve) for the U.S. Geological Survey streamgage 50055000, Río Grande De Loíza, Puerto Rico. The resulting hydraulic model was then used to compute 16 water-surface profiles for water levels (flood stages) ranging from 19.00 to 34.00 feet at the streamgage; these flood stages range from “moderate flood stage” to above “major flood stage” as defined by the National Weather Service. The 34.00-foot stage exceeds the historical maximum peak stage of 33.20 feet, recorded at the streamgage in 1945. The simulated water-surface profiles were used in combination with a digital elevation model derived from light detection and ranging (lidar) data to map the inundated areas associated with each flood profile.
The flood-inundation maps and the supporting hydraulic model produced by this study can be used by emergency managers and local officials to assess flood-mitigation strategies and to define flood-hazard areas to help protect life and property, to coordinate flood-response activities such as evacuations and road closures, and to aid post-flood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255112","usgsCitation":"Ostheimer, C.J., Torres-Garcia, L.M., and Gomez-Fragoso, J.M., 2026, Flood-inundation maps for Río Grande De Loíza in and near Caguas, Puerto Rico, 2026: U.S. Geological Survey Scientific Investigations Report 2025–5112, 16 p., https://doi.org/10.3133/sir20255112.","productDescription":"Report: vii, 16 p.; Data Release","numberOfPages":"16","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-179822","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":499274,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5112/coverthb.jpg"},{"id":499277,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255112/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5112 HTML"},{"id":499276,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5112/sir20255112.pdf","size":"3.16 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5112 PDF"},{"id":499609,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119202.htm","linkFileType":{"id":5,"text":"html"}},{"id":499280,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1IZ6D5V","text":"USGS data release","linkHelpText":"Geospatial data sets and hydraulic model for Río Grande De Loíza in Caguas, Puerto Rico"},{"id":499279,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5112/images/"},{"id":499278,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5112/sir20255112.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2025-5112 XML"}],"country":"United States","otherGeospatial":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -66.09390482728071,\n 18.33190261334512\n ],\n [\n -66.09390482728071,\n 18.134783495693256\n ],\n [\n -65.97456945279221,\n 18.134783495693256\n ],\n [\n -65.97456945279221,\n 18.33190261334512\n ],\n [\n -66.09390482728071,\n 18.33190261334512\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
6460 Busch Blvd, Suite 100
Columbus, OH 43229-1737
Invasive species within North America, particularly wild pigs (Sus scrofa), pose a serious threat to native ecosystems through both direct and indirect impacts. Wild pigs are a large-bodied omnivorous species native to Eurasia and introduced to North America. Using DNA metabarcoding of fecal samples, we investigated the diet of wild pigs at a bottomland hardwood forest ecosystem within Felsenthal National Wildlife Refuge in southern Arkansas. We found that the wild pig diet was diverse and included at least 74 plant families and 106 genera, dominated by plant matter with Fagaceae (oaks and hickories), Poaceae (grasses), and Asteraceae (asters, sunflowers, and daisies) comprising over 50% of total dietary detections. Hard mast was a primary food source in fall and winter, while herbaceous vegetation peaked in spring. We documented DNA from 23 species of vertebrates in wild pig fecal samples, including mammals, birds, fish, frogs, and turtles. Vertebrate matter was most frequently detected in autumn. Vertebrate consumption constituted ~17% of wild pig diet by relative read abundance. Our results highlighted the potential long-term ecological consequences of wild pig foraging behaviors, particularly competition for critical food resources with native wildlife and impact on forest regeneration through the consumption of mast. Understanding dietary dynamics is crucial for managing wild pig populations and mitigating impacts on vulnerable ecosystems and wildlife.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wsb.70019","usgsCitation":"Wilson, K., Chiavacci, S.J., Roberts, C.P., and DeGregorio, B.A., 2026, Seasonal variation in wild pig (Sus scrofa) diet revealed by DNA metabarcoding: Wildlife Society Bulletin, v. 50, no. 1, e70019, 22 p., https://doi.org/10.1002/wsb.70019.","productDescription":"e70019, 22 p.","ipdsId":"IP-176314","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":502074,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wsb.70019","text":"Publisher Index Page"},{"id":501862,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas","otherGeospatial":"Felsenthal National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92.25663096909476,\n 33.2889631983969\n ],\n [\n -92.25663096909476,\n 33.05047632468441\n ],\n [\n -92.0547025523673,\n 33.05047632468441\n ],\n [\n -92.0547025523673,\n 33.2889631983969\n ],\n [\n -92.25663096909476,\n 33.2889631983969\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"50","issue":"1","noUsgsAuthors":false,"publicationDate":"2026-02-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Wilson, Kenneth","contributorId":341075,"corporation":false,"usgs":false,"family":"Wilson","given":"Kenneth","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":958330,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chiavacci, Scott J. 0000-0003-3579-8377","orcid":"https://orcid.org/0000-0003-3579-8377","contributorId":206161,"corporation":false,"usgs":true,"family":"Chiavacci","given":"Scott","email":"","middleInitial":"J.","affiliations":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"preferred":true,"id":958333,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roberts, Caleb Powell 0000-0002-8716-0423","orcid":"https://orcid.org/0000-0002-8716-0423","contributorId":288567,"corporation":false,"usgs":true,"family":"Roberts","given":"Caleb","email":"","middleInitial":"Powell","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":958332,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DeGregorio, Brett Alexander 0000-0002-5273-049X","orcid":"https://orcid.org/0000-0002-5273-049X","contributorId":243214,"corporation":false,"usgs":true,"family":"DeGregorio","given":"Brett","email":"","middleInitial":"Alexander","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":958331,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70274663,"text":"70274663 - 2026 - Interplay between tectonics and submarine mass transport deposits in Cortes Basin: New high-resolution geophysics in the Outer California Borderland","interactions":[],"lastModifiedDate":"2026-04-03T15:15:07.218513","indexId":"70274663","displayToPublicDate":"2026-02-04T10:03:52","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7501,"text":"JGR Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Interplay between tectonics and submarine mass transport deposits in Cortes Basin: New high-resolution geophysics in the Outer California Borderland","docAbstract":"The Outer California Borderland (OCB) is an active transform plate boundary offshore Southern California, where the relationship between faulting and submarine mass transport deposits (MTDs) remains poorly understood. Onshore paleoseismic data provide high-resolution earthquake records, whereas marine geophysical data capture longer-term histories. Offshore fault systems pose hazards to infrastructure and dense coastal populations, particularly when linked to submarine landslides. We present new high-resolution geophysical data set (cruise SR2303), including bathymetric and CHIRP sub-bottom data integrated with legacy seismic reflection data and chronostratigraphic constraints from ODP Site 1012 to examine Quaternary MTD recurrence and tectonic controls in the Cortes Basin, OCB. Bathymetry shows deformational features, including slide scarps and previously unmapped fault segments with evidence of Holocene activity. CHIRP profiles reveal 10 stacked MTDs in the East Cortes Basin and 8 in the West Cortes Basin, spanning ∼752 ka with an average recurrence of ∼83.6 ± 1 ka. Acoustic imaging shows 7 MTD intervals coinciding with fault offset increments and fault growth suggesting earthquake-triggered mass wasting. A strong association between MTD occurrences and sea-level extremes also supports glacio-eustatic contribution to slope failure. Stratigraphic correlations suggest quasi-synchronous MTDs across the eastern and western areas, likely triggered by larger eathquakes in the Quaternary. Although the identified MTDs occur relatively far from the Southern California coast, they still pose a potential tsunamigenic hazard requiring further assessment. Moreover, if linked to earthquakes along major strike-slip faults, for example, the Ferrelo fault, the MTDs may provide valuable proxies to constrain rupture scenarios and fault connectivity within the understudied OCB.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2025JB032100","usgsCitation":"Fabbrizzi, A., Maloney, J.M., Derosier, B.J., and Keith, B., 2026, Interplay between tectonics and submarine mass transport deposits in Cortes Basin: New high-resolution geophysics in the Outer California Borderland: JGR Solid Earth, v. 131, no. 2, e2025JB032100, 30 p., https://doi.org/10.1029/2025JB032100.","productDescription":"e2025JB032100, 30 p.","ipdsId":"IP-178847","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":502458,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2025jb032100","text":"Publisher Index Page"},{"id":502163,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Cortes Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.25,\n 32.575\n ],\n [\n -119.25,\n 32.575\n ],\n [\n -119.25,\n 31.75\n ],\n [\n -118.25,\n 31.75\n ],\n [\n -118.25,\n 32.575\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"131","issue":"2","noUsgsAuthors":false,"publicationDate":"2026-02-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Fabbrizzi, Andrea 0000-0003-3166-1015","orcid":"https://orcid.org/0000-0003-3166-1015","contributorId":369216,"corporation":false,"usgs":false,"family":"Fabbrizzi","given":"Andrea","affiliations":[{"id":6608,"text":"San Diego State University","active":true,"usgs":false}],"preferred":false,"id":958617,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Maloney, Jillian M. 0000-0001-8223-4676","orcid":"https://orcid.org/0000-0001-8223-4676","contributorId":261208,"corporation":false,"usgs":false,"family":"Maloney","given":"Jillian","email":"","middleInitial":"M.","affiliations":[{"id":6608,"text":"San Diego State University","active":true,"usgs":false}],"preferred":false,"id":958618,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Derosier, Boe Jay 0000-0003-1517-4129","orcid":"https://orcid.org/0000-0003-1517-4129","contributorId":369217,"corporation":false,"usgs":true,"family":"Derosier","given":"Boe","middleInitial":"Jay","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":958619,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Keith, Bradley","contributorId":369218,"corporation":false,"usgs":false,"family":"Keith","given":"Bradley","affiliations":[{"id":6608,"text":"San Diego State University","active":true,"usgs":false}],"preferred":false,"id":958620,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70273876,"text":"70273876 - 2026 - Migration water temperature and heat stress assessments in western Alaska Chinook salmon overlapping the 2019 heatwave","interactions":[],"lastModifiedDate":"2026-02-11T15:25:43.001543","indexId":"70273876","displayToPublicDate":"2026-02-04T09:17:59","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Migration water temperature and heat stress assessments in western Alaska Chinook salmon overlapping the 2019 heatwave","docAbstract":"Chinook salmon population declines span their geographic range with climate hypothesized as a major driver. Concerns of warming freshwater temperatures in their northern range gained urgency during 2019 when a heatwave coincided with premature mortality. This study examined heat stress during the 2019 heatwave compared to subsequent years and described water temperatures in western Alaska to understand the degree to which freshwater temperatures may be a stressor. Heat stress was prevalent among Chinook salmon captured in the 2019 heatwave (Kuskokwim tributaries: 90% in Kwethluk and 63% Takotna river), and variable in subsequent years (∼8% to 60% across Kuskokwim tributaries and Norton Sound rivers). A review of water temperature data indicated that potentially stressful temperatures (≥18 °C) were most common and prolonged in the Yukon River, moderately common and prolonged in the Kuskokwim River, and relatively rare in the Norton Sound region. Water temperatures in 2019 broke several records for overall maximum and frequency of temperatures ≥ 18 °C. Migration water temperatures and heat stress in northern Pacific salmon habitats vary more widely than previously recognized (up to 25 °C).
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2025-0109","usgsCitation":"von Biela, V.R., Regish, A.M., McCormick, S.D., Spaeder, J., Whitworth, K., Leon, J., Gillikin, D., Liller, Z., Ivanoff, R., Bell, J., Larson, S.D., Carey, M.P., and Zimmerman, C.E., 2026, Migration water temperature and heat stress assessments in western Alaska Chinook salmon overlapping the 2019 heatwave: Canadian Journal of Fisheries and Aquatic Sciences, https://doi.org/10.1139/cjfas-2025-0109.","ipdsId":"IP-171279","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true},{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":499750,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"western Alaska","edition":"Online 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The models were used for estimating runoff and quantities of water diverted from the Rio Grande/Rio Bravo del Norte (hereinafter referred to as the “Rio Grande”) to maintain water-surface elevations in the canals and resacas (former distributary channels cut off from the main channel of the Rio Grande). Resacas provide habitat to aquatic species and help reduce the effects of flooding.
Because of the large size of the study area and diversity of hydrologic conditions, the study area was divided into 11 watersheds, and separate hydrologic models were developed for 9 of the watersheds. Six of the nine modeled watersheds are drained mostly by canals (canal watersheds), and three of the modeled watersheds drain to resacas (resaca watersheds). The Hydrological Simulation Program—FORTRAN was selected for modeling the study area watersheds because it is flexible in simulating a wide variety of watershed conditions.
The models were calibrated with streamflow data collected during 2022–23. The calibrated models were used to simulate water budgets (streamflow, evapotranspiration, water-storage volumes, and water diversions and withdrawals) during 2022–23. Model simulations showed that the resaca watersheds required more diversions from the Rio Grande and released less runoff than did the canal watersheds. Management practices maintaining resaca water levels constrained their runoff.
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Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
Contact Us- USGS Publications Warehouse
","tableOfContents":"Understanding the local to regional history of extreme events such as debris flows and floods provides context to plan for and mitigate these hazards to life, property, and infrastructure. The Klamath Mountains of northwestern California have experienced both debris flows and devastating wildfires. Whiskeytown National Recreation Area (WHIS) is at the heart of this range and has a wealth of debris flow–related landforms. Gaining an understanding of prehistoric flows and their relationship with fire or other potential triggers can help mitigate future problems. Optically stimulated luminescence and radiocarbon analyses from sediment and entrained organics in undisturbed facies, including beneath partially buried boulders, establishes a chronology of paleo-events in WHIS. The levee deposits indicate a repetition of debris flows during the latest Holocene, every 125–150 years, since 850 yr. Larger flows occurred, with a record elucidated from debris-flow deposits along Clear Creek, with Middle Holocene ages, ca. 2600 to 5500 yr, most of which have sufficient concentrations of charcoal to indicate origins as postfire debris flows. Deposits at higher elevations show events from the latest Pleistocene ca. 13,000 yr. This geochronology indicates that these are not singular events but are relatively common and inherent to the geomorphic processes shaping this landscape.
","language":"English","publisher":"Cambridge University Press","doi":"10.1017/qua.2025.10064","usgsCitation":"Wood, J., Mahan, S.A., East, A.E., Bilderback, E., Krolczyk, E.T., Rasmussen, B.A., Zyatitsky, K.S., and Hallas, L.(., 2026, Geochronologic data reveal Late Pleistocene to Holocene debris-flow history and wildfire association within Whiskeytown National Recreation Area, Klamath Mountains, northern California: Quaternary Research, 21 p., https://doi.org/10.1017/qua.2025.10064.","productDescription":"21 p.","ipdsId":"IP-176240","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":499931,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1017/qua.2025.10064","text":"Publisher Index Page"},{"id":499584,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Klamath Mountains, Whiskeytown National Recreation Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.63689471598963,\n 40.6731703155468\n ],\n [\n -122.63689471598963,\n 40.56431461436682\n ],\n [\n -122.47862213398994,\n 40.56431461436682\n ],\n [\n -122.47862213398994,\n 40.6731703155468\n ],\n [\n -122.63689471598963,\n 40.6731703155468\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Online First","noUsgsAuthors":false,"publicationDate":"2026-02-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Wood, John R. \"Jack\" 0000-0002-0270-6328","orcid":"https://orcid.org/0000-0002-0270-6328","contributorId":359808,"corporation":false,"usgs":false,"family":"Wood","given":"John R. \"Jack\"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":955111,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mahan, Shannon A. 0000-0001-5214-7774 smahan@usgs.gov","orcid":"https://orcid.org/0000-0001-5214-7774","contributorId":147159,"corporation":false,"usgs":true,"family":"Mahan","given":"Shannon","email":"smahan@usgs.gov","middleInitial":"A.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":955112,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"East, Amy E. 0000-0002-9567-9460 aeast@usgs.gov","orcid":"https://orcid.org/0000-0002-9567-9460","contributorId":219600,"corporation":false,"usgs":true,"family":"East","given":"Amy","email":"aeast@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":955113,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bilderback, Eric Leland 0000-0002-2027-5699","orcid":"https://orcid.org/0000-0002-2027-5699","contributorId":349936,"corporation":false,"usgs":true,"family":"Bilderback","given":"Eric Leland","affiliations":[{"id":78941,"text":"Geologic Hazards Science Center - Landslides / Earthquake Geology","active":true,"usgs":true}],"preferred":true,"id":955114,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Krolczyk, Emma Taylor 0000-0002-7163-4348","orcid":"https://orcid.org/0000-0002-7163-4348","contributorId":291354,"corporation":false,"usgs":true,"family":"Krolczyk","given":"Emma","email":"","middleInitial":"Taylor","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":955115,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rasmussen, Brian A.","contributorId":365987,"corporation":false,"usgs":false,"family":"Rasmussen","given":"Brian","middleInitial":"A.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":955116,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Zyatitsky, Karina S.","contributorId":365988,"corporation":false,"usgs":false,"family":"Zyatitsky","given":"Karina","middleInitial":"S.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":955117,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hallas, Leticia (Contractor) 0009-0004-4071-2456","orcid":"https://orcid.org/0009-0004-4071-2456","contributorId":359806,"corporation":false,"usgs":true,"family":"Hallas","given":"Leticia","middleInitial":"(Contractor)","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":955118,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70273882,"text":"70273882 - 2026 - Ensemble methods for history matching and uncertainty quantification with a watershed model","interactions":[],"lastModifiedDate":"2026-02-11T15:28:04.157861","indexId":"70273882","displayToPublicDate":"2026-02-04T08:23:01","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Ensemble methods for history matching and uncertainty quantification with a watershed model","docAbstract":"History matching of large hydrologic models is challenging due to data sparsity and non-unique process combinations (and associated parameters) that can produce similar model predictions. We develop an ensemble-based history matching (and uncertainty quantification) approach using an iterative ensemble smoother (iES) method for three cutouts of the National Hydrologic Model (NHM) and qualitatively compare the results and performance to the stepwise history matching approach. In the latter approach, subsets of parameters and observations were sequentially calibrated to a diverse range of observations to mitigate non-uniqueness and local minima. In iES, localization simulates the same causal connections between parameters and observations without the need (and computational cost) of sequential history matching steps. iES uses a weighted sum-of-squared-errors objective function which allows differential weighting of multiple data sources. Formal adoption of range observation also pushes results to within ranges of observation values rather than discrete values. Overall, the ensemble approach performs similarly to the stepwise approach. Both approaches performed poorly for the cutout representing a snowmelt-dominated watershed, indicating a structural issue in the process representation of the model. The main advantage of iES is quantification of uncertainty in both the history matching and the predictions of interest.
","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.70086","usgsCitation":"Fienen, M., Long, A.J., Markovich, K.H., Haj, A.E., and Barker, M., 2026, Ensemble methods for history matching and uncertainty quantification with a watershed model: Journal of the American Water Resources Association, v. 62, no. 1, e70086, 18 p., https://doi.org/10.1111/1752-1688.70086.","productDescription":"e70086, 18 p.","ipdsId":"IP-181945","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":499751,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n [\n [\n [\n -94.81758,\n 49.38905\n ],\n [\n -94.64,\n 48.84\n ],\n [\n -94.32914,\n 48.67074\n ],\n [\n -93.63087,\n 48.60926\n ],\n [\n 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world","interactions":[],"lastModifiedDate":"2026-03-23T14:47:40.529718","indexId":"70273852","displayToPublicDate":"2026-02-04T08:07:16","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":23291,"text":"FEBS Letters","active":true,"publicationSubtype":{"id":10}},"title":"A fresh perspective - Advancing fish immunotoxicology in a complex world","docAbstract":"Understanding how environmental changes affect the health of organisms and ecosystems is complex, but recent interdisciplinary advances and the recognition of immune function as a dynamic mediator offer exciting progress. Environmental immunotoxicology in teleost fishes is evolving beyond cataloguing stressors towards a mechanistic, integrative framework that leverages omics, in vivo tracking and cross-disciplinary modelling. However, knowledge gaps in immune mechanisms, toxicokinetics and multi-stressor interactions remain. The present work highlights these gaps, advocating for immune function as both a mechanistic lens and an integrative health indicator. Such a framework can improve predictive risk assessments, management strategies and our understanding of contaminant effects on resilience, disease susceptibility and population viability. While challenges remain, the field is poised for significant growth through collaborative innovation and advancing technology.
","language":"English","publisher":"FEBS Press","doi":"10.1002/1873-3468.70296","usgsCitation":"Smith, C.R., Burattin, L., Iglesias, N.R., Sullivan, R., Rice, C.D., Segner, H., and Tort, L., 2026, A fresh perspective - Advancing fish immunotoxicology in a complex world: FEBS Letters, v. 600, no. 5, p. 572-590, https://doi.org/10.1002/1873-3468.70296.","productDescription":"19 p.","startPage":"572","endPage":"590","ipdsId":"IP-180147","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":499675,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":499935,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/1873-3468.70296","text":"Publisher Index Page"}],"volume":"600","issue":"5","noUsgsAuthors":false,"publicationDate":"2026-02-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Cheyenne R. 0000-0002-7226-1774","orcid":"https://orcid.org/0000-0002-7226-1774","contributorId":219236,"corporation":false,"usgs":true,"family":"Smith","given":"Cheyenne","email":"","middleInitial":"R.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true},{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":true,"id":955256,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burattin, Laura","contributorId":366092,"corporation":false,"usgs":false,"family":"Burattin","given":"Laura","affiliations":[{"id":66158,"text":"University of Namur","active":true,"usgs":false}],"preferred":false,"id":955257,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Iglesias, Nuria Ruiz","contributorId":366093,"corporation":false,"usgs":false,"family":"Iglesias","given":"Nuria","middleInitial":"Ruiz","affiliations":[{"id":87357,"text":"Autonomous University of Barcelona","active":true,"usgs":false}],"preferred":false,"id":955258,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sullivan, Roisin","contributorId":366094,"corporation":false,"usgs":false,"family":"Sullivan","given":"Roisin","affiliations":[{"id":84564,"text":"The University of Sydney","active":true,"usgs":false}],"preferred":false,"id":955259,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rice, Charles D.","contributorId":366095,"corporation":false,"usgs":false,"family":"Rice","given":"Charles","middleInitial":"D.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":955260,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Segner, Helmut","contributorId":366096,"corporation":false,"usgs":false,"family":"Segner","given":"Helmut","affiliations":[{"id":25430,"text":"University of Bern","active":true,"usgs":false}],"preferred":false,"id":955261,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Tort, Lluis","contributorId":169142,"corporation":false,"usgs":false,"family":"Tort","given":"Lluis","email":"","affiliations":[],"preferred":false,"id":955262,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70275340,"text":"70275340 - 2026 - Rupture into slow-slip fault regime during the 2018 Mw 6.9 Island of Hawaiʻi earthquake is followed by modest postseismic slip","interactions":[],"lastModifiedDate":"2026-06-02T15:28:19.704962","indexId":"70275340","displayToPublicDate":"2026-02-03T10:06:13","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Rupture into slow-slip fault regime during the 2018 Mw 6.9 Island of Hawaiʻi earthquake is followed by modest postseismic slip","title":"Rupture into slow-slip fault regime during the 2018 Mw 6.9 Island of Hawaiʻi earthquake is followed by modest postseismic slip","docAbstract":"On 4 May 2018, a Mw 6.9 earthquake occurred on the south flank of Kīlauea, in the midst of an historic event that included a voluminous eruption from Kīlauea’s lower East Rift zone and caldera collapse at its summit. The earthquake was a consequence of both short‐ and long‐term stress buildup due to magmatic activity associated with the eruption and steady flank motion, respectively, and it revealed features of Kīlauea’s décollement fault that can inform understanding of future earthquake activity. We used geodetic data to determine the distributions of slip during the coseismic and postseismic periods and compared these with areas of known fault slip during past earthquakes and slow‐slip events (SSEs). The 2018 earthquake ruptured into an area of the décollement fault that was active during quasi‐regular SSEs that occurred in the two decades prior to 2018 but that have not been observed since. The coseismic slip model indicates that the amount of motion on the décollement fault was several times greater than what typically occurred during SSEs, suggesting that it may take decades for the fault to rebuild stress to the point at which SSEs will occur again. Postseismic afterslip also occurred in an area of the fault known to experience slow slip; however, unlike at other creeping faults, postseismic afterslip was rapid, being largely over within 2–3 days. The rapid nature and small magnitude of the postseismic afterslip may be due to the lack of a viscoelastic relaxation component, which is possibly a result of the shallow dip of the décollement fault not transferring stress efficiently into the lower crust.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120240222","usgsCitation":"Johanson, I.A., Montgomery-Brown, E.K., and Poland, M., 2026, Rupture into slow-slip fault regime during the 2018 Mw 6.9 Island of Hawaiʻi earthquake is followed by modest postseismic slip: Bulletin of the Seismological Society of America, v. 113, no. 3, p. 1023-1035, https://doi.org/10.1785/0120240222.","productDescription":"13 p.","startPage":"1023","endPage":"1035","ipdsId":"IP-169927","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":503676,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"island of Hawaii","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.17989695699225,\n 19.908375170783174\n ],\n [\n -154.7062809263427,\n 19.908375170783174\n ],\n [\n -154.7062809263427,\n 18.965995638681747\n ],\n [\n -156.17989695699225,\n 18.965995638681747\n ],\n [\n -156.17989695699225,\n 19.908375170783174\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"113","issue":"3","noUsgsAuthors":false,"publicationDate":"2026-02-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Johanson, Ingrid A. 0000-0002-6049-2225","orcid":"https://orcid.org/0000-0002-6049-2225","contributorId":215613,"corporation":false,"usgs":true,"family":"Johanson","given":"Ingrid","email":"","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":960624,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Montgomery-Brown, Emily K. 0000-0001-6787-2055","orcid":"https://orcid.org/0000-0001-6787-2055","contributorId":214074,"corporation":false,"usgs":true,"family":"Montgomery-Brown","given":"Emily","email":"","middleInitial":"K.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":960625,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Poland, Michael 0000-0001-5240-6123","orcid":"https://orcid.org/0000-0001-5240-6123","contributorId":49920,"corporation":false,"usgs":true,"family":"Poland","given":"Michael","affiliations":[{"id":336,"text":"Hawaiian Volcano Observatory","active":false,"usgs":true}],"preferred":true,"id":960626,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70273955,"text":"70273955 - 2026 - Large streamflow differences between forested and urbanized watersheds in the energy-limited eastern United States: The role of evapotranspiration and impervious surfaces","interactions":[],"lastModifiedDate":"2026-02-19T15:12:36.147521","indexId":"70273955","displayToPublicDate":"2026-02-03T09:07:45","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Large streamflow differences between forested and urbanized watersheds in the energy-limited eastern United States: The role of evapotranspiration and impervious surfaces","docAbstract":"Urban forests and other green infrastructures have been viewed as part of the “Nature-based Solutions” (NbS) to mitigate emerging urban environmental change. This study focuses on the role of evapotranspiration (ET) in regulating water balances of small watersheds in the eastern United States. We compared streamflow and ET patterns at daily, monthly and annual scales and linked these hydrological variables to the physical properties of 11 paired watersheds dominated by forests (FW) or urban (UW) land covers. The annual precipitation ranged from 1028 mm to 1683 mm and potential ET (PET) from 815 mm to 1450 mm. The mean annual flow/precipitation (Q/P) ratios were 0.26 ± 0.13 and 0.41 ± 0.1 for FW and UW, respectively. Overall, UW had lower annual ET (772 mm in UW vs. 947 mm in FW), but higher mean annual and (∼58% higher), monthly water yield (17%–186% higher), and peakflow rates (up to 100 times higher) than FW. The streamflow differences between FW and UW were most pronounced during the growing season and early winter (June-November). The mean Q/P ratios for 30 large hurricane events (2016–2021) were 0.12 ± 0.11 and 0.38 ± 0.23 for FW and UW, respectively. The flow rates in the dormant season (around December-May) in UW were similar or lower than FW. We developed conceptual models to explain the seasonal and storm event streamflow differences using background climate (PET), ET, and land surface characteristics. Urban NbS designs should factor in strategies that maximize ET while minimizing impervious surfaces enhancing watershed “sponge” and “pump” functions.
","language":"English","publisher":"American Geophysical Union (AGU)","doi":"10.1029/2025WR041340","usgsCitation":"Sun, G., Bian, Z., Khand, K., Caldwell, P.V., Boggs, J., Wang, C., Chen, Y., Liu, N., Zhang, Y., Chen, X., Senay, G., and McNulty, S.G., 2026, Large streamflow differences between forested and urbanized watersheds in the energy-limited eastern United States: The role of evapotranspiration and impervious surfaces: Water Resources Research, v. 62, no. 2, e2025WR041340, 20 p., https://doi.org/10.1029/2025WR041340.","productDescription":"e2025WR041340, 20 p.","ipdsId":"IP-185235","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":500255,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2025wr041340","text":"Publisher Index Page"},{"id":500183,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"eastern United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -68.13910817442056,\n 42.504366194491354\n ],\n [\n -96.89658092237323,\n 42.504366194491354\n ],\n [\n -96.89658092237323,\n 25.74722198798669\n ],\n [\n -68.13910817442056,\n 25.74722198798669\n ],\n [\n -68.13910817442056,\n 42.504366194491354\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"62","issue":"2","noUsgsAuthors":false,"publicationDate":"2026-02-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Sun, G.","contributorId":205528,"corporation":false,"usgs":false,"family":"Sun","given":"G.","email":"","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":955905,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bian, Z.","contributorId":366435,"corporation":false,"usgs":false,"family":"Bian","given":"Z.","affiliations":[{"id":78585,"text":"Nanjing Normal University","active":true,"usgs":false}],"preferred":false,"id":955906,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Khand, K.","contributorId":366436,"corporation":false,"usgs":false,"family":"Khand","given":"K.","affiliations":[{"id":87483,"text":"AFDS, contractor to USGS EROS","active":true,"usgs":false}],"preferred":false,"id":955907,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Caldwell, P. 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Geodetic measurements will play a critical role in determining Europa’s internal structure, rotational state, and tidal deformation. The Geodesy Focus Group (GFG) coordinates cross-instrument efforts to measure Europa’s global shape, rotational parameters, gravity field, and degree-2 tidal Love numbers (k2 and h2). The data provided by the Gravity/Radio Science (G/RS) investigation, Europa Imaging System (EIS), Radar for Europa Assessment and Sounding (REASON), and Europa Ultraviolet Spectrometer (UVS) will be used to infer geodetic constraints on the interior structure and construct a precise cartographic reference system. This combined dataset will provide new constraints on Europa’s tidal response, ice shell thickness, and the properties of its subsurface ocean. The resulting geodetic information will contribute to a deeper understanding of Europa’s internal dynamics and the potential habitability of its ocean.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s11214-025-01250-x","usgsCitation":"Steinbrügge, G., Park, R., Roberts, J., Bland, M.T., Brooks, S.M., Castillo-Rogez, J.C., Cascioli, G., Genova, A., Greathouse, T., Hussmann, H., Kirk, R.L., Magnanini, A., Mazarico, E., Nimmo, F., Park, M.S., Petricca, F., Retherford, K., Schroeder, D., Soderlund, K., Tortora, P., and Zannoni, M., 2026, Geodetic investigations of the Europa Clipper mission: Space Science Reviews, v. 222, 17, 27 p., https://doi.org/10.1007/s11214-025-01250-x.","productDescription":"17, 27 p.","ipdsId":"IP-173933","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":504197,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s11214-025-01250-x","text":"Publisher Index Page"},{"id":503996,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Europa, Jupiter","volume":"222","noUsgsAuthors":false,"publicationDate":"2026-02-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Steinbrügge, G.","contributorId":371106,"corporation":false,"usgs":false,"family":"Steinbrügge","given":"G.","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":961046,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Park, R.S.","contributorId":371107,"corporation":false,"usgs":false,"family":"Park","given":"R.S.","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":961047,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roberts, J.H.","contributorId":371108,"corporation":false,"usgs":false,"family":"Roberts","given":"J.H.","affiliations":[{"id":27117,"text":"Johns Hopkins Applied Physics Laboratory","active":true,"usgs":false}],"preferred":false,"id":961048,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bland, Michael T. 0000-0001-5543-1519 mbland@usgs.gov","orcid":"https://orcid.org/0000-0001-5543-1519","contributorId":146287,"corporation":false,"usgs":true,"family":"Bland","given":"Michael","email":"mbland@usgs.gov","middleInitial":"T.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":961049,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brooks, S. 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Understanding the interaction between seismicity and injection can shed important light on the hydrothermal characteristics of the field. Here, we analyze 15 years of local seismic and daily operational data from the CGF, identifying a strong correlation between short-term increase in seismicity rate and seasonal volumetric and temperature variations in the reinjected fluid. Furthermore, the seismic footprint during peak injection of colder fluids reveals a near-instantaneous response up to 2 km away from the injection well, too rapid for pore pressure diffusion alone. This short-term and distant response is observed to have directional preference, indicating structural or permeability anisotropy within the reservoir. Additionally, the seismic response correlates with the initial volumetric increase of colder fluids, but also with temperature decrease during stable injection periods, suggesting thermal effects alone can play an important role in triggering distant seismicity.
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This invasive grass–fire cycle has severe negative consequences for ecosystems, creating a need to understand how different invasive grass species alter fuel characteristics and fire behavior, as well as effective treatments to control their abundance. To address these needs and increase fire and natural resource management preparedness, we performed a review and meta-analysis of recent (1985 to 2023) scientific literature. We focused on the Intermountain West, USA, where six dominant invasive grass species have already transformed ecosystems, including winter annuals—cheatgrass (Bromus tectorum L.), medusahead [Taeniatherum caput-medusae (L.) Nevski], red brome (Bromus rubens L.), and Mediterranean grass [Schismus arabicus Nees and Schismus barbatus (Loefl. ex L.) Thell]; and summer perennials—buffelgrass [Pennisetum ciliare (L.) Link] and Lehmann’s lovegrass (Eragrostis lehmanniana Nees). Within the 204 selected articles, B. tectorum was the most well-studied species, treatment effectiveness was the most common study type, and more studies addressed fuel accumulation than fire characteristics. While initial reductions in B. tectorum following wildfire were followed by large increases, P. ciliare initially increased and then steadily declined, and other invasive grass species had no significant post-fire changes over time. Chemical treatments were more effective than other treatments for B. tectorum, P. ciliare, and Schismus spp., although T. caput-medusae had a greater reduction with chemical treatments compared with the other species. In many cases, treatment effectiveness was enhanced when treatment types were combined or repeat treatments were conducted. Both B. tectorum and T. caput-medusae increased to pretreatment conditions within 3 and 5 yr, respectively, although there were no detectable trends for other species. Our results provide comprehensive comparisons of the effect of invasive grass species on fuel and fire characteristics and much needed insight on effective strategies for reducing invasive grass impacts to ecosystems.
","language":"English","publisher":"Cambridge University Press","doi":"10.1017/inp.2025.10037","usgsCitation":"Costanzo, S.A., and Munson, S.M., 2026, Invasive grass influences on the fire cycle and treatment effectiveness to control their abundance in the Intermountain West, USA: Invasive Plant Science and Management, v. 19, e9, 14 p., https://doi.org/10.1017/inp.2025.10037.","productDescription":"e9, 14 p.","ipdsId":"IP-180413","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":503781,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1017/inp.2025.10037","text":"Publisher Index Page"},{"id":503623,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Intermountain West","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -97.20191397759243,\n 25.799924192791465\n ],\n [\n -97.08570338392701,\n 27.71184091561038\n ],\n [\n -93.6302752119999,\n 29.556685714308273\n ],\n [\n -94.01839885426105,\n 33.569041394798376\n ],\n [\n -94.4114118449989,\n 33.722907975080744\n ],\n [\n -94.56895248066871,\n 39.022669330750915\n ],\n [\n -96.60465919702102,\n 42.569015980091905\n ],\n [\n -96.46212892791729,\n 43.6378822578892\n ],\n [\n -96.39391822590662,\n 45.338271212362\n ],\n [\n -96.94111957465111,\n 45.62766984448126\n ],\n [\n -96.61784261870793,\n 45.89323403476516\n ],\n [\n -97.17558648749741,\n 48.98246735485887\n ],\n [\n -123.26834630518886,\n 49.03338334571416\n ],\n [\n -123.46280430453601,\n 48.2115191006188\n ],\n [\n -125.1266932544844,\n 48.53825793050066\n ],\n [\n -124.90887642277227,\n 42.71034652981541\n ],\n [\n -124.72623919511037,\n 40.280134924916354\n ],\n [\n -123.58516086013148,\n 38.358363455906925\n ],\n [\n -120.78592614086082,\n 34.150701834353896\n ],\n [\n -117.4406932191169,\n 32.58728711854212\n ],\n [\n -115.08463551733962,\n 32.678820058860225\n ],\n [\n -111.14923026778985,\n 31.34744276818276\n ],\n [\n -108.16055723699336,\n 31.396141648927667\n ],\n [\n -108.25091900170565,\n 31.724764402083153\n ],\n [\n -106.45651564913305,\n 31.72778080722044\n ],\n [\n -105.01577159362975,\n 30.51681285637636\n ],\n [\n -104.44318555219559,\n 29.656464576953027\n ],\n [\n -103.27212932557126,\n 28.98357156768232\n ],\n [\n -102.61396155680703,\n 29.736542062482812\n ],\n [\n -101.46792278734301,\n 29.676484198018315\n ],\n [\n -100.83082980888662,\n 29.22404418236583\n ],\n [\n -100.11024209554432,\n 27.95860988681926\n ],\n [\n -99.63243163801059,\n 27.464923832621068\n ],\n [\n -99.13773657635741,\n 26.387140931185314\n ],\n [\n -97.20191397759243,\n 25.799924192791465\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"19","noUsgsAuthors":false,"publicationDate":"2026-02-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Costanzo, Sarah A. 0000-0003-4137-1548","orcid":"https://orcid.org/0000-0003-4137-1548","contributorId":346108,"corporation":false,"usgs":true,"family":"Costanzo","given":"Sarah","middleInitial":"A.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":960598,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Munson, Seth M. 0000-0002-2736-6374 smunson@usgs.gov","orcid":"https://orcid.org/0000-0002-2736-6374","contributorId":1334,"corporation":false,"usgs":true,"family":"Munson","given":"Seth","email":"smunson@usgs.gov","middleInitial":"M.","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":960599,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70274206,"text":"70274206 - 2026 - Earthquake catalog for the Fairbanks region of central Alaska, 2014–2024, based on waveform cross-correlation","interactions":[],"lastModifiedDate":"2026-03-12T14:13:31.609824","indexId":"70274206","displayToPublicDate":"2026-02-02T09:08:50","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Earthquake catalog for the Fairbanks region of central Alaska, 2014–2024, based on waveform cross-correlation","docAbstract":"The Fairbanks region of central Alaska is part of a broad zone of intraplate crustal deformation, situated north of the Denali fault and north of the ongoing collision and flat‐slab subduction of the Yakutat oceanic plateau. Seismicity in the Fairbanks region occurs both in diffuse areas as well as in well‐defined lineaments, such as the left‐lateral Salcha fault, which hosted the 1937 M8 7.3 earthquake. Starting with the regional seismicity catalog, we perform waveform cross‐correlation, network‐matched filtering, and relative relocation to obtain an enhanced seismicity catalog over the time period 2014–2024. Based on the relocated catalog, we interpret a set of 15 fault segments, including two conjugate faults and two new faults east of the previously documented fault system. Considering the combined seismicity in the Minto and Fairbanks regions, the median depth of seismicity decreases from east (6 km) to west (20 km). Our interpreted faults provide guidance for future tectonic modeling and assessment of seismic hazards in this region.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220250342","usgsCitation":"Sims, N.E., Tape, C., Ruppert, N., and West, M.E., 2026, Earthquake catalog for the Fairbanks region of central Alaska, 2014–2024, based on waveform cross-correlation: Seismological Research Letters, v. 97, no. 2A, p. 877-896, https://doi.org/10.1785/0220250342.","productDescription":"20 p.","startPage":"877","endPage":"896","ipdsId":"IP-184501","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":501098,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1785/0220250342","text":"Publisher Index Page"},{"id":500986,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Fairbanks region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -154,\n 66.5\n ],\n [\n -154,\n 63\n ],\n [\n -144,\n 63\n ],\n [\n -144,\n 66.5\n ],\n [\n -154,\n 66.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"97","issue":"2A","noUsgsAuthors":false,"publicationDate":"2026-02-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Sims, Nealey E.","contributorId":367184,"corporation":false,"usgs":false,"family":"Sims","given":"Nealey","middleInitial":"E.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":956981,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tape, Carl","contributorId":219960,"corporation":false,"usgs":false,"family":"Tape","given":"Carl","email":"","affiliations":[{"id":40098,"text":"Geophysical Institute, 2156 Koyukuk Drive, University of Alaska Fairbanks, Fairbanks, AK 99775","active":true,"usgs":false}],"preferred":false,"id":956982,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ruppert, Natalia A. 0000-0003-0589-1159","orcid":"https://orcid.org/0000-0003-0589-1159","contributorId":351514,"corporation":false,"usgs":true,"family":"Ruppert","given":"Natalia A.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":956983,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"West, Michael E.","contributorId":367185,"corporation":false,"usgs":false,"family":"West","given":"Michael","middleInitial":"E.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":956984,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70273806,"text":"70273806 - 2026 - Origins, evolutions, and future directions of Landsat science products for advancing global inland water and coastal ocean observations","interactions":[],"lastModifiedDate":"2026-02-03T14:43:09.95163","indexId":"70273806","displayToPublicDate":"2026-02-02T08:37:01","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1426,"text":"Earth System Science Data","active":true,"publicationSubtype":{"id":10}},"title":"Origins, evolutions, and future directions of Landsat science products for advancing global inland water and coastal ocean observations","docAbstract":"In April 2020, the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center introduced a Level 2 provisional Aquatic Reflectance (AR) product for the Landsat 8 Operational Land Imager (OLI), marking the initial phase in developing a standardized global product for Landsat-derived surface water measurements. The goal of USGS EROS aquatic product research and development is to prepare for an operational processing architecture for Landsat Collection 3 in the late 2020s that will enable use of quality-controlled data for emerging Landsat aquatic science applications. To achieve this, we released a subset of the Landsat 8/9 provisional AR products (Crawford et al., 2025, https://doi.org/10.5066/P14MBBRM) and examined its general performance through the Science Algorithms to Operations (SATO) framework alongside quantitative assessment using community made inland water data records (GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments, GLORIA) and radiometric coastal validation platforms (NASA’s Ocean Color component of the Aerosol Robotic Network, AERONET-OC). Variability within the validation datasets indicate that the performance of the Landsat 8/9 provisional AR retrieval is highly context-dependent; errors are minimal in optically simple waters (e.g., clear to moderately turbid coastal waters) but increase considerably in optically complex waters where factors such as elevated levels of turbidity, chlorophyll (Chl a) concentrations, or colored dissolved organic matter (CDOM) dominate the water column. Additionally, this paper examines key algorithmic considerations for atmospheric correction, highlighting factors that influence accuracy, scalability, and computational efficiency necessary for collection processing in the operational Landsat Product Generation System (LPGS). This paper is intended to communicate with aquatic scientists, satellite oceanographers, and the broader Earth observation community on the origins, requirements, challenges, successes, and future objectives for operationalizing global AR data products for Landsat satellite missions.
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