Dry permafrost underlain by ice-cemented permafrost has been reported in several locations in Antarctica. Initially thought to be relic ice, it is now understood that this subsurface ice is in equilibrium with the surface conditions, although it is not in equilibrium with the atmosphere. We use year-round data from University Valley in the Dry Valleys and Elephant Head in the Ellsworth Mountains to investigate the seasonal variations in water vapour flux that control the depth to the ice table under dry permafrost. Our analysis shows that the mean annual water vapour density of the soil surface exceeds the atmospheric value by a factor of up to ~2 due to summer snow. The attenuation and phase shift of the annual temperature cycle with depth result in colder temperatures at the ice table than at the surface of the soil in summer. We conclude that this temperature gradient, combined with the summer snow, provides the flux of water to the ice table necessary to maintain the ice. In University Valley, reducing the snow days by 40% moves the stability depth of the ice table from 42 to 66 cm. Increasing the snow days by 60% shifts the ice table to 17 cm. These variations can explain the observed gradient in the depth to the ice table in University Valley.
","language":"English","publisher":"Cambridge University Press","doi":"10.1017/S0954102025100448","usgsCitation":"McKay, C.P., Marinova, M., Williams, K.E., and Mellon, M., 2025, Summer snow determines the depth to ice-cemented ground under dry permafrost in Antarctica: Antarctic Science, https://doi.org/10.1017/S0954102025100448.","ipdsId":"IP-173704","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":497724,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1017/s0954102025100448","text":"Publisher Index Page"},{"id":497566,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"Online First","noUsgsAuthors":false,"publicationDate":"2025-11-27","publicationStatus":"PW","contributors":{"authors":[{"text":"McKay, C. P.","contributorId":237824,"corporation":false,"usgs":false,"family":"McKay","given":"C.","email":"","middleInitial":"P.","affiliations":[{"id":24796,"text":"NASA Ames Research Center","active":true,"usgs":false}],"preferred":false,"id":952401,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Marinova, M.","contributorId":364258,"corporation":false,"usgs":false,"family":"Marinova","given":"M.","affiliations":[{"id":86775,"text":"M3 Interplanetary Corp.","active":true,"usgs":false}],"preferred":false,"id":952402,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Williams, Kaj E. 0000-0003-1755-1872 kewilliams@usgs.gov","orcid":"https://orcid.org/0000-0003-1755-1872","contributorId":196988,"corporation":false,"usgs":true,"family":"Williams","given":"Kaj","email":"kewilliams@usgs.gov","middleInitial":"E.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":952403,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mellon, M.","contributorId":241722,"corporation":false,"usgs":false,"family":"Mellon","given":"M.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":952404,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70272619,"text":"ofr20251054 - 2025 - Comparisons of shoreline positions from satellite-derived and traditional field- and remote-sensing techniques","interactions":[],"lastModifiedDate":"2026-02-03T16:40:03.569804","indexId":"ofr20251054","displayToPublicDate":"2025-11-26T12:05:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-1054","displayTitle":"Comparisons of Shoreline Positions from Satellite-Derived and Traditional Field- and Remote-Sensing Techniques","title":"Comparisons of shoreline positions from satellite-derived and traditional field- and remote-sensing techniques","docAbstract":"Satellite-derived shorelines (SDS) have the potential to help researchers answer critical coastal science questions and support work to predict coastal change by filling in the spatial and temporal gaps present in current field-based and remote-sensing data collection methods. The U.S. Geological Survey conducted comparison analyses of traditionally sourced shorelines and SDS in diverse coastal landscapes to determine how SDS could be used in ongoing and future work across varied coastal environments and provided some initial findings that could be used for implementation. Using CoastSeg, a browser-based program for SDS detection and mapping, SDS for the period 1984–2023 for multiple locations across the United States were compared to shoreline positions from traditionally sourced shoreline data. In this report, the authors present these comparisons alongside lessons learned and challenges encountered when building SDS workflows in different coastal locations. Results show that individual SDS have larger uncertainty and yet produced similar linear trends to sparser, traditionally sourced shoreline data; because SDS methods provide orders of magnitude more data than traditional shoreline-detection methods, they can be used to evaluate shoreline behaviors. Refining average scalar slopes used in tidal corrections did not result in substantial decreases in uncertainty. Using lessons from this work to outline needs for regional implementation, initial setup time would be considerable, being on the order of weeks. However, once complete, shoreline detections and analyses are fast (on the order of minutes to hours) and achievable using a desktop computer.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251054","programNote":"Coastal and Marine Hazards and Resources Program","usgsCitation":"O’Neill, A.C., Batiste, S.F., Buscombe, D.D., Burgess, J., Doran, K.S., Gibbs, A.E., Henderson, R.E., Heslin, J.L., Janda, C.N., Lundine, M.A., Terrano, J.F., Warrick, J.A., and Weber, K.M., 2025, Comparisons of shoreline positions from satellite-derived and traditional field- and remote-sensing techniques: U.S. Geological Survey Open-File Report 2025–1054, 41 p., https://doi.org/10.3133/ofr20251054.","productDescription":"viii, 41 p.","numberOfPages":"41","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-177965","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":496871,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251054/full","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1054 HTML"},{"id":496869,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1054/coverthb.jpg"},{"id":496873,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1054/images/"},{"id":496872,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1054/ofr20251054.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2025-1054 XML"},{"id":496870,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1054/ofr20251054.pdf","text":"Report","size":"8.29 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1054 PDF"}],"country":"United States","state":"Alaska, Florida, Massachusetts, Washington","otherGeospatial":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -70.23262232723678,\n 42.09708901142952\n ],\n [\n -70.23262232723678,\n 41.7296953201367\n ],\n [\n -69.88869105489118,\n 41.7296953201367\n ],\n [\n -69.88869105489118,\n 42.09708901142952\n ],\n [\n -70.23262232723678,\n 42.09708901142952\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.8586476385848,\n 27.855837758503966\n ],\n [\n -82.8586476385848,\n 27.73142586709062\n ],\n [\n -82.70953873940795,\n 27.73142586709062\n ],\n [\n -82.70953873940795,\n 27.855837758503966\n ],\n [\n -82.8586476385848,\n 27.855837758503966\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.6354762817767,\n 48.15958852423637\n ],\n [\n -123.6354762817767,\n 48.07700409405399\n ],\n [\n -123.49731286568142,\n 48.07700409405399\n ],\n [\n -123.49731286568142,\n 48.15958852423637\n ],\n [\n -123.6354762817767,\n 48.15958852423637\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -143.75227424150037,\n 70.14885784620938\n ],\n [\n -143.75227424150037,\n 70.08259373043822\n ],\n [\n -143.56394337243916,\n 70.08259373043822\n ],\n [\n -143.56394337243916,\n 70.14885784620938\n ],\n [\n -143.75227424150037,\n 70.14885784620938\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -67.26663872487647,\n 18.348796644288143\n ],\n [\n -67.26663872487647,\n 18.327492880601937\n ],\n [\n -67.24442657044659,\n 18.327492880601937\n ],\n [\n -67.24442657044659,\n 18.348796644288143\n ],\n [\n -67.26663872487647,\n 18.348796644288143\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Pacific Coastal and Marine Science Center
U.S. Geological Survey
2885 Mission Street
Santa Cruz, California 95060
Sudden habitat loss associated with environmental disturbance can trigger animals to move from affected to undisturbed areas, where increases in local density may occur. Although pathogen transmission is strongly related to local density, how crowding after habitat loss affects infection dynamics in wild populations remains unclear. Here we conceptualize the Disturbance-Density-Disease hypothesis, which posits that disturbance-induced habitat loss results in increased pathogen prevalence via increases in local density at adjacent, undisturbed patches. We then used data from before, during, and after flooding disturbance to test this hypothesis in boreal toads Anaxyrus boreas boreas co-occurring with the pathogenic fungus Batrachochytrium dendrobatidis (Bd). We collected Bd samples from captured individuals during a 5-year (2015–2019) mark-recapture study of boreal toads (n = 1295) that breed in beaver ponds in western Wyoming, USA. During spring of 2017, an extreme flooding event destroyed several beaver dams, resulting in the loss of breeding habitat. We compared host density and pathogen prevalence pre- and post-disturbance at sites affected versus unaffected by flooding. At affected sites, population density and Bd prevalence increased at adjacent, undisturbed ponds following the sudden loss of habitat. Moreover, neither host density nor Bd prevalence increased at control sites in areas unaffected by flooding. Taken together, our results support hypothesized links between disturbance, adjacent increases in density, and subsequent increases in pathogen prevalence. Our study contributes to a growing body of ecological research leveraging natural experiments to extract insights from extreme disturbance events. By doing so, we demonstrate an important consequence of disturbance beyond proximate habitat loss and introduce a clear conceptual approach (the Disturbance-Density-Disease hypothesis) to understanding how pathogen transmission can be affected by disturbance via alterations to local density.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.70265","usgsCitation":"Barrile, G.M., Chalfoun, A.D., Walters, A.W., Merkle, J.A., 2025, Density as a mechanism linking habitat disturbance to increased pathogen prevalence: Evidence from a natural experiment: Ecology, v. 106, no. 11, e70265, 15 p., https://doi.org/10.1002/ecy.70265.","productDescription":"e70265, 15 p.","ipdsId":"IP-171436","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":500442,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Bridger-Teton National Forest, northern Wyoming Range, Wind River Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.9701288311253,\n 44.01466129417253\n ],\n [\n -110.9701288311253,\n 41.8640098196185\n ],\n [\n -108.35885871498692,\n 41.8640098196185\n ],\n [\n -108.35885871498692,\n 44.01466129417253\n ],\n [\n -110.9701288311253,\n 44.01466129417253\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"106","issue":"11","noUsgsAuthors":false,"publicationDate":"2025-11-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Barrile, Gabriel M.","contributorId":366853,"corporation":false,"usgs":false,"family":"Barrile","given":"Gabriel","middleInitial":"M.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":956316,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chalfoun, Anna D. 0000-0002-0219-6006 achalfoun@usgs.gov","orcid":"https://orcid.org/0000-0002-0219-6006","contributorId":197589,"corporation":false,"usgs":true,"family":"Chalfoun","given":"Anna","email":"achalfoun@usgs.gov","middleInitial":"D.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":956317,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walters, Annika W. 0000-0002-8638-6682 awalters@usgs.gov","orcid":"https://orcid.org/0000-0002-8638-6682","contributorId":4190,"corporation":false,"usgs":true,"family":"Walters","given":"Annika","email":"awalters@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":956318,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Merkle, Jerod A.","contributorId":366854,"corporation":false,"usgs":false,"family":"Merkle","given":"Jerod","middleInitial":"A.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":956319,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70272658,"text":"70272658 - 2025 - The acoustic-Doppler current profiler (ADCP): A comprehensive tool for river-reach hydromorphodynamics","interactions":[],"lastModifiedDate":"2025-12-03T17:19:25.259086","indexId":"70272658","displayToPublicDate":"2025-11-26T11:01:15","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":664,"text":"Advances in Water Resources","active":true,"publicationSubtype":{"id":10}},"title":"The acoustic-Doppler current profiler (ADCP): A comprehensive tool for river-reach hydromorphodynamics","docAbstract":"This paper introduces the use of acoustic Doppler current profiler (ADCP) measurements as input for the Acoustic Mapping Velocimetry (AMV) method, a technique for characterizing the dynamics of riverine bedforms. The performance of this new approach, ADCP-AMV, is compared with input from a multibeam echosounder through a field study conducted on the Mississippi River (USA). A virtual ADCP tool has been created to support the ADCP-AMV measurements with optimal data density predictions. To the authors’ knowledge, this is the first time ADCP measurements have been used in conjunction with the AMV dune-tracking method. Subsequently, the paper discusses the coupling of ADCP-AMV measurements with ancillary data extracted from the ADCP. These ancillary data are processed using previously developed protocols to characterize hydrodynamics and the suspended sediment distribution in the water column. This paper emphasizes the capability of ADCPs to characterize open-channel river hydromorphodynamic parameters with high spatiotemporal resolution. Recommendations to accurately and efficiently acquire these multi-variable measurements and derived datasets are discussed.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.advwatres.2025.105180","usgsCitation":"Fleit, G., Muste, M., Baranya, S., Kim, D., Whaling, A., McAlpin, T., and You, H., 2025, The acoustic-Doppler current profiler (ADCP): A comprehensive tool for river-reach hydromorphodynamics: Advances in Water Resources, v. 206, 105180, 15 p., https://doi.org/10.1016/j.advwatres.2025.105180.","productDescription":"105180, 15 p.","ipdsId":"IP-177812","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":497092,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.advwatres.2025.105180","text":"Publisher Index Page"},{"id":497018,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Tennessee","city":"Memphis","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.08,\n 35.13\n ],\n [\n -90.08,\n 35.12\n ],\n [\n -90.07,\n 35.12\n ],\n [\n -90.07,\n 35.13\n ],\n [\n -90.08,\n 35.13\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"206","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fleit, Gábor","contributorId":363187,"corporation":false,"usgs":false,"family":"Fleit","given":"Gábor","affiliations":[{"id":86640,"text":"Research fellow at Budapest University of Technology and Economics","active":true,"usgs":false}],"preferred":false,"id":951233,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Muste, Marian 0000-0002-5975-462X","orcid":"https://orcid.org/0000-0002-5975-462X","contributorId":192136,"corporation":false,"usgs":false,"family":"Muste","given":"Marian","email":"","affiliations":[],"preferred":false,"id":951234,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baranya, Sándor","contributorId":363188,"corporation":false,"usgs":false,"family":"Baranya","given":"Sándor","affiliations":[{"id":86642,"text":"Professor (Associate) at Budapest University of Technology and Economics","active":true,"usgs":false}],"preferred":false,"id":951235,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kim, Dongsu","contributorId":363189,"corporation":false,"usgs":false,"family":"Kim","given":"Dongsu","affiliations":[{"id":86643,"text":"Professor at Dankook University","active":true,"usgs":false}],"preferred":false,"id":951236,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Whaling, Amanda 0000-0003-1375-8323","orcid":"https://orcid.org/0000-0003-1375-8323","contributorId":213953,"corporation":false,"usgs":true,"family":"Whaling","given":"Amanda","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":951237,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McAlpin, Tate","contributorId":363190,"corporation":false,"usgs":false,"family":"McAlpin","given":"Tate","affiliations":[{"id":86644,"text":"Coastal and Hydraulics Laboratory, U.S. Army Engineer Research and Development Center, Vicksburg, MS, USA.","active":true,"usgs":false}],"preferred":false,"id":951238,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"You, Hojun","contributorId":363191,"corporation":false,"usgs":false,"family":"You","given":"Hojun","affiliations":[{"id":86646,"text":"Senior Researcher at K-water Research Institute","active":true,"usgs":false}],"preferred":false,"id":951239,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70272618,"text":"sir20255099 - 2025 - Temporal changes in nutrient concentrations in the Lower Grand River and selected drainage basins, Missouri and Iowa, during the Mississippi River Basin Healthy Watersheds Initiative (2010–23)","interactions":[],"lastModifiedDate":"2026-02-03T16:39:07.258008","indexId":"sir20255099","displayToPublicDate":"2025-11-26T08:25:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-5099","displayTitle":"Temporal Changes in Nutrient Concentrations in the Lower Grand River and Selected Drainage Basins, Missouri and Iowa, During the Mississippi River Basin Healthy Watersheds Initiative (2010–23)","title":"Temporal changes in nutrient concentrations in the Lower Grand River and selected drainage basins, Missouri and Iowa, during the Mississippi River Basin Healthy Watersheds Initiative (2010–23)","docAbstract":"This report describes a cooperative study by the U.S. Geological Survey and Missouri Department of Natural Resources that evaluated temporal changes in total nitrogen (TN) and total phosphorus (TP) concentrations in the Lower Grand River hydrologic unit. The study focused on trends since 2010, when the basin was designated as a priority drainage basin of the Mississippi River Basin Healthy Watersheds Initiative (MRBI). At three local drainage basins within the Lower Grand hydrological unit (MRBI sites), stream nutrient trends were evaluated using flow-adjusted (FA) TN and TP concentrations for water years 2011 through 2023. FATN concentration trends were not statistically significant for any MRBI site. One site (site 2) showed a statistically significant increasing trend in FATP concentration, indicating a possible increase in phosphorus sources in parts of the basin. Overall, streamflow variability appeared to be the dominant factor affecting nutrient concentrations at MRBI sites. At five regional drainage basins, including the Grand River and nearby rivers with data from 1994 through 2023 (long-term sites), annual flow-normalized (FN) TN and TP concentrations were evaluated for trends before (water years 2000–10) and during (water years 2010–23) the MRBI. For water years 2010 through 2023, annual FNTN and FNTP concentrations decreased in the Grand River, as well as in the Nodaway and Chariton Rivers, which were not targeted by the MRBI. The Grand River (site 9) reversed from increasing to decreasing FNTP concentrations after 2010, with a 26-percent reduction. Annual FNTN and FNTP concentrations also decreased at the Missouri River sites. While nutrient reductions in the Grand River may reflect the effects of implemented conservation practices, similar trends in nearby, nontargeted rivers and the absence of strong decreasing trends at MRBI sites suggest that broader regional factors, instead of or in addition to MRBI efforts, may have contributed to nutrient reductions in the Grand River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255099","collaboration":"Prepared in cooperation with the Missouri Department of Natural Resources","usgsCitation":"Kamrath, B.J.W., Lauderback, C.N., and Murphy, J.C., 2025, Temporal changes in nutrient concentrations in the Lower Grand River and selected drainage basins, Missouri and Iowa, during the Mississippi River Basin Healthy Watersheds Initiative (2010–23): U.S. Geological Survey Scientific Investigations Report 2025–5099, 19 p., https://doi.org/10.3133/sir20255099.","productDescription":"Report: vii, 19 p.; 5 Linked Tables; Data Release; Dataset","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-167198","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":497801,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118990.htm"},{"id":496854,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":496853,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13FQ2YN","text":"USGS data release","linkHelpText":"Archive of the load estimation models used in the analyses of temporal changes in nutrient concentrations in the Lower Grand River and selected drainage basins, Missouri and Iowa (2010–23)"},{"id":496855,"rank":8,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255099/full"},{"id":496848,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5099/coverthb.jpg"},{"id":496852,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2025/5099/downloads/","text":"Tables 1.1 to 1.5","linkFileType":{"id":3,"text":"xlsx"}},{"id":496851,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5099/images/"},{"id":496849,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5099/sir20255099.pdf","text":"Report","size":"2.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5099"},{"id":496850,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5099/sir20255099.XML"}],"country":"United States","state":"Iowa, Missouri","otherGeospatial":"Lower Grand River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95.5,\n 41.5\n ],\n [\n -95.5,\n 38.5\n ],\n [\n -91.5,\n 38.5\n ],\n [\n -91.5,\n 41.5\n ],\n [\n -95.5,\n 41.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
The U.S. Geological Survey, in cooperation with the Missouri Department of Natural Resources, estimated total nitrogen and total phosphorus concentrations at three local and five regional monitoring sites in Missouri. Temporal changes in total nitrogen and total phosphorus were quantified to evaluate whether instream nutrient concentrations have changed at local or regional scales. At the local scale sites, total phosphorus concentrations substantially increased at one site, which indicated a possible increase in phosphorus sources in the Lower Grand River hydrologic unit, while total nitrogen concentrations did not change substantially. At the regional site, annual total nitrogen and total phosphorus concentrations generally decreased. The regional decline in stream nutrients paired with the lack of nutrient reduction at the local sites indicated that nutrient reductions in the Grand River may have been driven by regional changes in nutrient export, instead of or in addition to conservation practices implemented as part of the Mississippi River Basin Healthy Watersheds Initiative.
","publicationDate":"2025-11-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Kamrath, Brock J.W. 0000-0001-7118-0537","orcid":"https://orcid.org/0000-0001-7118-0537","contributorId":347859,"corporation":false,"usgs":true,"family":"Kamrath","given":"Brock","middleInitial":"J.W.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":950957,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lauderback, Courtney N. 0000-0002-6975-0331","orcid":"https://orcid.org/0000-0002-6975-0331","contributorId":363041,"corporation":false,"usgs":true,"family":"Lauderback","given":"Courtney","middleInitial":"N.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":950958,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Murphy, Jennifer C. 0000-0002-0881-0919 jmurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-0881-0919","contributorId":4281,"corporation":false,"usgs":true,"family":"Murphy","given":"Jennifer","email":"jmurphy@usgs.gov","middleInitial":"C.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":950959,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70272103,"text":"sir20255062 - 2025 - An evaluation of the effects of different deicing salt application rates on three watersheds in Essex County, New York","interactions":[],"lastModifiedDate":"2026-02-03T16:38:12.480343","indexId":"sir20255062","displayToPublicDate":"2025-11-25T15:50:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-5062","displayTitle":"An Evaluation of the Effects of Different Deicing Salt Application Rates on Three Watersheds in Essex County, New York","title":"An evaluation of the effects of different deicing salt application rates on three watersheds in Essex County, New York","docAbstract":"The U.S. Geological Survey, in cooperation with the New York State Department of Transportation, evaluated the effects of different deicing salt application rates on surface water, groundwater, and highway runoff quality near State highways in northern New York. Three reaches of State highways were tested with different deicing treatments between October 2019 and November 2022: a salt-sand mixture (Treatment A), a salt mixture applied at a lower rate (Treatment B), and a control mixture consistent with typical deicing salt amounts and application rates. Data on pavement conditions and the quality of surface water, highway runoff, and groundwater were collected. Surface electromagnetic data were also collected. Surface-water and groundwater quality downgradient from the State highways were compared with water quality at upgradient locations. The percentage of snow or ice coverage was used to evaluate the effectiveness of the salt applications.
This report provides an overview of the transport of deicing salt. The Treatment B watershed had deicing mixture applied more frequently than other highway reaches, which caused it to have the highest annual total chloride application. Despite differences in chloride application, flow-weighted mean chloride concentrations in highway runoff were comparable across treatments. Chloride concentrations were elevated in surface water and groundwater downgradient from highways relative to chloride concentrations upgradient from highways. A chloride mass balance, calculated for one treatment watershed, indicated that groundwater affected by legacy deicing practices may be contributing additional chloride to surface water. Spatial patterns from electromagnetic surveys show a shallow saline plume alongside the highway in that area.
Differences in winter severity and pavement-surface conditions drove deicing salt applications in the treatment areas. This study found that several factors affect chloride loads in the watersheds, including variable winter conditions, adaptive snow and ice management, legacy management practices, and area-specific aquifer and groundwater conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255062","collaboration":"Prepared in cooperation with the New York State Department of Transportation","usgsCitation":"Gutchess, K., Scavotto, N., Dondero, A., Woda, J., Terry, N., Smith, K., and Williams, J., 2025, An evaluation of the effects of different deicing salt application rates on three watersheds in Essex County, New York: U.S. Geological Survey Scientific Investigations Report 2025–5062, 31 p., https://doi.org/10.3133/sir20255062.","productDescription":"Report: viii, 31 p.; 2 Data Releases","numberOfPages":"31","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-160931","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":497798,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118989.htm"},{"id":496533,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QES2KJ","text":"USGS data release","linkHelpText":"Frequency domain electromagnetic induction (FDEM) geophysical data collected near Lake Placid, NY"},{"id":496532,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13CJ3D9","text":"USGS data release","linkHelpText":"Chloride regression model and mass balance for different deicing salt application rates on three watersheds in Essex County, New York"},{"id":496531,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5062/images/"},{"id":496530,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5062/sir20255062.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2025-5062 XML"},{"id":496529,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255062/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5062 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New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 1280–8349
The central–marginal hypothesis (CMH) predicts reduced genetic diversity and increased differentiation in range-edge populations due to ecological marginality and limited gene flow. Deviations from this pattern, however, can result from historical demographic processes, variation in reproductive strategies or interspecific hybridization. The genus Quercus, known for hybridization and long-distance pollination, offers an excellent model to examine the spatial patterns of genetic diversity, structure and introgression across species distributions. Here, we investigate these dynamics in Quercus bicolor Willd., a widespread eastern North American oak. Using RADseq, we genotyped 142 individuals from 12 sites at the fragmented trailing range edge and nine sites from the range core. To detect introgression, we incorporated reference data from six sympatric white oak species. We reveal extensive introgression, particularly from Q. lyrata Walt., in nearly all southern edge populations, but none in core populations despite sympatry with closely related congeners. Southern populations also showed increased genetic structure and differentiation, but not reduced diversity or increased inbreeding, even when only examining non-admixed individuals. Regression analyses reveal relationships between introgressed ancestry and heterozygosity, inbreeding and differentiation, indicating that introgression may buffer range-edge populations against genetic erosion by introducing novel alleles. Hindcast, current and forecast ecological niche models demonstrate temporally changing degrees of overlap between the geographic range of Q. lyrata and Q. bicolor and suggest higher hybridization potential in the future. These findings offer mixed support for the CMH while underscoring the evolutionary relevance of introgression in shaping genetic landscapes at range margins with significant implications for conservation.
","language":"English","publisher":"Wiley","doi":"10.1111/mec.70185","usgsCitation":"Jesse B. Parker, Sean Hoban, Thompson, L., and Scott E. Schlarbaum, 2025, Evaluating the central–marginal hypothesis: Introgression and genetic variation at the trailing edge of Quercus bicolor: Molecular Ecology, v. 34, no. 24, e70185, 19 p., https://doi.org/10.1111/mec.70185.","productDescription":"e70185, 19 p.","ipdsId":"IP-182841","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":497082,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/mec.70185","text":"Publisher Index Page"},{"id":496986,"rank":2,"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 \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -96.7397874284601,\n 47.69010833548356\n ],\n [\n -96.50515645608941,\n 38.374728664216455\n ],\n [\n -94.46111669832068,\n 35.024323679813264\n ],\n [\n -75.54658793458438,\n 34.796796242513224\n ],\n [\n -73.9846649987043,\n 39.20135867583498\n ],\n [\n -69.38877471060589,\n 41.55065832321056\n ],\n [\n -66.75532196682867,\n 44.51664342888958\n ],\n [\n -66.71186067192609,\n 47.40639899204692\n ],\n [\n -76.96565391885454,\n 44.77224521173778\n ],\n [\n -85.96379061474377,\n 47.170718633297994\n ],\n [\n -96.7397874284601,\n 47.69010833548356\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"34","issue":"24","noUsgsAuthors":false,"publicationDate":"2025-11-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Jesse B. Parker","contributorId":363175,"corporation":false,"usgs":false,"family":"Jesse B. Parker","affiliations":[{"id":63836,"text":"University of Tennessee, Knoxville","active":true,"usgs":false}],"preferred":false,"id":951194,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sean Hoban","contributorId":363177,"corporation":false,"usgs":false,"family":"Sean Hoban","affiliations":[{"id":86637,"text":"Morton Arboretum","active":true,"usgs":false}],"preferred":false,"id":951195,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thompson, Laura 0000-0002-7884-6001","orcid":"https://orcid.org/0000-0002-7884-6001","contributorId":207364,"corporation":false,"usgs":true,"family":"Thompson","given":"Laura","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":951196,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Scott E. Schlarbaum","contributorId":363179,"corporation":false,"usgs":false,"family":"Scott E. Schlarbaum","affiliations":[{"id":63836,"text":"University of Tennessee, Knoxville","active":true,"usgs":false}],"preferred":false,"id":951197,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70273031,"text":"70273031 - 2025 - Spatial occupancy patterns of the endangered northern long‐eared bat in New England","interactions":[],"lastModifiedDate":"2025-12-12T16:38:07.189122","indexId":"70273031","displayToPublicDate":"2025-11-25T09:30:32","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1399,"text":"Diversity and Distributions","active":true,"publicationSubtype":{"id":10}},"title":"Spatial occupancy patterns of the endangered northern long‐eared bat in New England","docAbstract":"Aim
White-nose syndrome has caused severe declines in eastern North American cave bats, leading to the federal listing of the northern long-eared bat (Myotis septentrionalis) as endangered in the United States and Canada. This has heightened the importance of long-term monitoring to inform species status assessments. We employed a combination of long-term repeated and single-season acoustic survey data to assess the regional presence, spatial distribution, occupancy, and detection probability of northern long-eared bats.
Location
New England, United States.
Methods
We analysed acoustic data from 2357 detector sites, aggregated by year, using Bayesian single-species occupancy models. We investigated the influence of habitat characteristics, climatic variables, and year (2015–2022) on occupancy and the effects of weather conditions and survey month (May to August) on detection probability. Spatial random effects were included to address residual spatial autocorrelation, with a 1-km resolution chosen based on significant positive autocorrelation observed in a non-spatial model.
Results
Occupancy was highest on steep, forested hillsides with minimal anthropogenic development, higher in warmer regions, particularly along coastlines and on offshore islands, and declined across survey years. Including a 1-km spatial random effect reduced residual autocorrelation and suggests northern long-eared bats utilise resources at small to medium landscape scales. Detection probability was highest earlier in the maternity season, but declined when monthly precipitation or temperature exceeded average conditions.
Conclusions
Conservation efforts that focus on steep, forested hillsides in warmer regions with low anthropogenic development could be beneficial. Our analysis supports the use of spatial random effects at a 1-km2 scale, highlighting the importance of survey designs that capture ecological variation at species-specific resolutions. Additionally, early-season acoustic surveys conducted during favourable weather conditions may improve monitoring effectiveness. Acoustic sampling and spatial occupancy modelling offer powerful tools for monitoring remnant populations of northern long-eared bats and guiding conservation practices.
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The equations were developed using generalized least-squares regression and flood-frequency and drainage-basin characteristics from 156 streamgages. Flood-frequency analyses were completed using data through the 2023 water year. The drainage-basin characteristics used as explanatory variables in the regression equations are drainage area, percentage of wetland area, and basin-wide mean of the average annual precipitation. The average standard errors of prediction used to estimate flood discharges at the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent AEP with these equations are 34.9, 37.1, 38.2, 41.6, 43.8, 46.0, 49.1, and 53.2 percent, respectively.
Flood discharges at selected AEPs for streamgages were computed using the Expected Moments Algorithm. Techniques used to adjust an AEP discharge computed from a streamgage record with results from the regression equations and to estimate flood discharge at a selected AEP for an ungaged site upstream or downstream from a streamgage using a drainage-area adjustment are both described. The final regression equations and the flood-discharge frequency data used in this study will be available in StreamStats. StreamStats is an internet-based application that provides automated regression-equation solutions for user-selected sites on streams.
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\"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
The Birds, Not Mosquitoes Monitoring and Support Science Working Group detailed methods for monitoring the population response of Hawaiian forest birds during implementation of the Incompatible Insect Technique (IIT) on the islands of Maui and Kauaʻi. The group prioritized methods for measuring the influence of mosquito suppression on populations within IIT treatment and control areas and identified focal species for IIT efficacy monitoring in birds. Three primary metrics were established to assess the impact of IIT on vulnerable species: population demography, density, and geographic range. Each metric can be evaluated using multiple methods. This report reviews those methods, with emphasis on approaches supported by pre-IIT baseline data and compatible with a before-after control-impact (BACI) study design for evaluating population responses over time. Focal avian species were selected based on population size estimates, fecundity, and disease susceptibility. We identified ʻākohekohe (Palmeria dolei), ʻiʻiwi (Drepanis coccinea), Maui ʻalauahio (Paroreomyza montana), Hawaiʻi ʻamakihi (Chlorodrepanis virens), Kauaʻi ʻamakihi (Chlorodrepanis stejnegeri), Kauaʻi ʻelepaio (Chasiempis sclateri), and ʻanianiau (Magumma parva) as focal species for monitoring population level response to disease suppression.
Populations of kiwikiu (Pseudonestor xanthophrys), ʻakikiki (Oreomystis bairdi), akekeʻe (Loxops caeruleirostris), and the ʻiʻiwi population on Kauaʻi may be too small (e.g., <100 individuals) to effectively monitor, and it is unlikely that sufficient data can be collected from these birds to show IIT efficacy in a relatively short time frame (i.e., 5–10 years). Despite the logistical challenges to IIT implementation, there is potential to maintain disease-free status in individual populations of birds. Indeed, the continued existence of these critically endangered species in the wild within or near IIT treatment areas could be considered an accomplishment of IIT, given the current predictions for their extinction in the wild within 5–10 years. Demographic monitoring methods, including territory mapping, nest monitoring, mist-netting, and mark-recapture studies, provide direct evidence of survivorship and reproductive output.
When combined with disease surveillance, these approaches could provide the most robust evidence of increased survivorship and productivity resulting from avian malaria suppression via IIT. However, demographic studies require several years of monitoring to achieve statistically robust BACI comparisons of survivorship and are more difficult to implement relative to other approaches. Given that these field efforts are labor-intensive and heavily reliant on personnel availability and funding, demographic monitoring could be conducted when adequate resources permit.
On both Maui and Kauaʻi, passive acoustic monitoring (PAM) was identified as a priority method for monitoring the range, occupancy, and relative abundance of focal species. Autonomous recording units (ARUs) can record bird vocalizations in remote areas for several months.
Innovative machine learning techniques permit rapid and semi-autonomous identification of most endemic honeycreepers on each island, maximizing sampling efficiencies and minimizing data processing costs. We predict mosquito suppression could support expansion of focal species into areas where disease transmission is currently excluding these species and expect acoustic monitoring data of focal species to reflect these spatial patterns. Additionally, the relative occupancy and call densities can be monitored temporally and spatially to assess the efficacy of IIT for supporting positive growth in vulnerable bird species. It is not yet clear if PAM is more effective than other methods, such as distance sampling, for detecting trends in the densities of rare species. However, the increased detections resulting from the larger sample size per observation point using ARUs will likely improve accuracy in detecting changes in species’ ranges. Collection of during and after treatment data within the BACI design could help to provide critical information to track avian population response, recovery, and potential range expansion related to IIT efforts. Point-transect distance sampling (point-counts) was prioritized as a method for monitoring population densities of focal species. Extensive historical sampling across focal species’ ranges provides a robust baseline for detecting change. These counts provide updated population densities and can be used to assess the distribution of focal species within IIT treatment areas.
However, detecting subtle population changes with traditional distance sampling requires intensive spatial and temporal effort and may be less effective for rare species. To improve resolution, density surface modeling can integrate multiple data sources (e.g., point-counts, PAM, spot-mapping, and resightings) to estimate species-specific densities at finer spatial scales, including within and outside IIT treatment areas. This integrated modeling approach allows for detailed comparisons and may reveal early signs of recovery, including recolonization of formerly occupied sites. A coordinated monitoring strategy can allow managers to evaluate the success of mosquito suppression as a conservation intervention and support adaptive management in the face of emerging challenges.
","language":"English","publisher":"University of Hawai‘i at Hilo","usgsCitation":"Judge, S., Warren, C.C., Navine, A.K., Camp, R.J., Crampton, L.H., Mounce, H.L., Vetter, J., Smith, L., Hart, P.J., Bellinger, M.R., and McClure, K.M., 2025, A monitoring framework to assess forest bird population response to landscape scale mosquito suppression using the Incompatible Insect Technique: Hawaii Cooperative Studies Unit Technical Report HCSU-119, iv, 40 p.","productDescription":"iv, 40 p.","ipdsId":"IP-179519","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":497301,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":497294,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://hdl.handle.net/10790/5402"}],"country":"United States","state":"Hawaii","otherGeospatial":"Alaka'i Plateau, Haleakalā National Park , Waikamoi region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -156.1522358062181,\n 20.73823881977907\n ],\n [\n -156.1522358062181,\n 20.64621454011673\n ],\n [\n -156.0266571558431,\n 20.64621454011673\n ],\n [\n -156.0266571558431,\n 20.73823881977907\n ],\n [\n -156.1522358062181,\n 20.73823881977907\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -156.24921043067422,\n 20.86997751409396\n ],\n [\n -156.24921043067422,\n 20.764987165934627\n ],\n [\n -156.1306083719868,\n 20.764987165934627\n ],\n [\n -156.1306083719868,\n 20.86997751409396\n ],\n [\n -156.24921043067422,\n 20.86997751409396\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -159.60125365576934,\n 22.147325602222963\n ],\n [\n -159.60125365576934,\n 22.042746120889333\n ],\n [\n -159.48161313138613,\n 22.042746120889333\n ],\n [\n -159.48161313138613,\n 22.147325602222963\n ],\n [\n -159.60125365576934,\n 22.147325602222963\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Judge, Seth 0000-0003-3832-3246","orcid":"https://orcid.org/0000-0003-3832-3246","contributorId":189965,"corporation":false,"usgs":false,"family":"Judge","given":"Seth","email":"","affiliations":[],"preferred":false,"id":951876,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Warren, Christopher C","contributorId":264665,"corporation":false,"usgs":false,"family":"Warren","given":"Christopher","email":"","middleInitial":"C","affiliations":[{"id":54533,"text":"Maui Forest Bird Recovery Project, Pacific Cooperative Studies Unit, University of Hawai‘i at Manoa","active":true,"usgs":false}],"preferred":false,"id":951877,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Navine, Amanda K","contributorId":333575,"corporation":false,"usgs":false,"family":"Navine","given":"Amanda","email":"","middleInitial":"K","affiliations":[{"id":37485,"text":"University of Hawai‘i - 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Pacific Islands Fish and Wildlife Office","active":true,"usgs":false}],"preferred":false,"id":951882,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Smith, Lauren K. 0000-0003-1783-715X","orcid":"https://orcid.org/0000-0003-1783-715X","contributorId":353538,"corporation":false,"usgs":false,"family":"Smith","given":"Lauren K.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":951883,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hart, Patrick J.","contributorId":147728,"corporation":false,"usgs":false,"family":"Hart","given":"Patrick","email":"","middleInitial":"J.","affiliations":[{"id":6977,"text":"University of Hawai`i at Hilo","active":true,"usgs":false}],"preferred":false,"id":951884,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Bellinger, Mona Renee 0000-0001-5274-9572","orcid":"https://orcid.org/0000-0001-5274-9572","contributorId":301018,"corporation":false,"usgs":true,"family":"Bellinger","given":"Mona","email":"","middleInitial":"Renee","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":951885,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"McClure, Katherine Maria 0000-0001-8595-7677","orcid":"https://orcid.org/0000-0001-8595-7677","contributorId":332279,"corporation":false,"usgs":true,"family":"McClure","given":"Katherine","email":"","middleInitial":"Maria","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":951886,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70273153,"text":"70273153 - 2025 - Groundwater structures fish growth and production across a riverscape","interactions":[],"lastModifiedDate":"2025-12-17T15:07:26.397254","indexId":"70273153","displayToPublicDate":"2025-11-23T08:59:12","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1696,"text":"Freshwater Biology","active":true,"publicationSubtype":{"id":10}},"title":"Groundwater structures fish growth and production across a riverscape","docAbstract":"Occupancy models have become increasingly popular for species monitoring and assessment, in part, because detection/non-detection data are readily obtained using a variety of methods. Multispecies occupancy models (MSOMs) can yield more accurate parameter estimates than single-species models (SSOMs) with less data through their hierarchical structure, making MSOMs an attractive option when species are hard to detect or when data collection is constrained, leading to sparse datasets. Such constraints may arise from limited sampling resources, but also occur in rare species monitoring or where preliminary results are desired to inform adaptive management. Further, experimental habitat treatments often impose spatial constraints on sampling based on the scale of their implementation. Whether a MSOM outperforms SSOMs depends on the volume of data, characteristics of the ecological community, research goals of a study and how these factors align with modeling assumptions. We performed a simulation study of hypothetical pollinator communities under varying sampling intensities for scenarios in which experimental habitat treatments produced different community-level effects. We fit occupancy models to simulated datasets and assessed model performance. At lower sampling intensities (< 20 spatial replicates and < 4 temporal replicates), MSOM community-level treatment effect estimates were biased. Even at twice this sampling intensity, SSOMs yielded more accurate species-specific effect estimates in treatment effect scenarios with high variance. In some cases, MSOMs can pull species in the tails of distributions too far toward the community mean effect, which risks incorrect conclusions concerning whether treatments help or harm individual species. When quantifying species-specific effects is the main objective, particularly for rarely observed species, SSOMs are more robust to outliers across a range of community response scenarios. Researchers can use this information to inform study design, guide simulation studies and decide whether the higher precision of MSOMs outweighs risks of improperly estimated effects for some species.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.72315","usgsCitation":"Cotterill, G.G., Keinath, D.A., and Graves, T., 2025, When do single-species occupancy models outperform multispecies models?: Ecology and Evolution, v. 15, no. 11, e72315, 14 p., https://doi.org/10.1002/ece3.72315.","productDescription":"e72315, 14 p.","ipdsId":"IP-178046","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":496936,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.72315","text":"Publisher Index Page"},{"id":496899,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"11","noUsgsAuthors":false,"publicationDate":"2025-11-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Cotterill, Gavin G. 0000-0002-1408-778X","orcid":"https://orcid.org/0000-0002-1408-778X","contributorId":346534,"corporation":false,"usgs":true,"family":"Cotterill","given":"Gavin","middleInitial":"G.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":951037,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Keinath, Douglas A.","contributorId":363056,"corporation":false,"usgs":false,"family":"Keinath","given":"Douglas","middleInitial":"A.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":951038,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Graves, Tabitha A. 0000-0001-5145-2400","orcid":"https://orcid.org/0000-0001-5145-2400","contributorId":202084,"corporation":false,"usgs":true,"family":"Graves","given":"Tabitha A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":951039,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70273831,"text":"70273831 - 2025 - Topographic, climatic, and age controls on the reworking of volcanic debris avalanche deposits","interactions":[],"lastModifiedDate":"2026-02-05T15:07:01.492317","indexId":"70273831","displayToPublicDate":"2025-11-23T08:01:52","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"Topographic, climatic, and age controls on the reworking of volcanic debris avalanche deposits","docAbstract":"Volcanic debris avalanches have deposited as much as 1000 km3 of largely unconsolidated material on landscapes and remodeled existing drainage networks. The landscape disturbances created by these events pose severe, cascading downstream sedimentation hazards that can require long-term societal management, as demonstrated by decades of observations and ongoing interventions after the deposition of the 1980 debris avalanche of Mount St. Helens (United States). There, post-emplacement sediment yields caused by deposit erosion remain several times above estimated background yield and lakes impounded by the deposit still pose threats of downstream flooding. Despite the length and quality of measurements of the geomorphic evolution and consequent sediment release at Mount St. Helens, the long-term trajectory of drainage network evolution across, and the associated sediment release from, large volcanic debris avalanches remains uncertain. Observations and modeling at Mount St. Helens, however, indicate channel instability can persist many decades and may persist for centuries to millennia. We examined potential influences on the erosion and preservation of volcanic debris avalanche deposits (VDADs) by mapping valley networks developed on 89 VDADs selected from volcanic arcs across the world and spanning a variety of topographic settings and climate regimes. Using the best available topographic data (1 m lidar to 30 m radar-derived data depending on location) and aerial imagery, we estimated the areas of deposits that have been reworked relative to initial deposit footprints as a proxy for post-emplacement erosion. We found that a primary influence on reworking is the topographic confinement of the VDAD: confined, valley-filling deposits are systematically more reworked than unconfined deposits. There is no apparent relationship between deposit age and reworking for valley-filling deposits, indicating that drainage networks on deposits in confined topographic settings like at Mount St. Helens reform rapidly after emplacement. In contrast, our data indicate that the reworking of unconfined deposits has a monotonic positive relationship with age. This observation agrees with a conceptual model of channel formation at Mount Taranaki (New Zealand), which posits that an unconfined VDAD created a topographic high that initially (e.g., 2–8 ky for the Pungarehu formation at Taranaki) diverted erosion to the deposit margins. We found only a weak to moderate relationship between reworking and modern precipitation regimes, which may reflect differences between modern and paleo-precipitation conditions at many of our study sites. We also found no correlation between the size (surface area or volume) of deposits and the degree of reworking. The work presented here implies that downstream cascading sediment hazards from landscape-resetting processes like VDADs (such as thick, extensive pyroclastic flow deposits) depend on the relief and organization of the surrounding landscape.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s00445-025-01914-0","usgsCitation":"Sweeney, K., and Major, J.J., 2025, Topographic, climatic, and age controls on the reworking of volcanic debris avalanche deposits: Bulletin of Volcanology, v. 87, 115, https://doi.org/10.1007/s00445-025-01914-0.","productDescription":"115","ipdsId":"IP-167440","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":499579,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Mount St. Helens","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.35475363838827,\n 46.30537067138167\n ],\n [\n -122.35475363838827,\n 46.11419720033388\n ],\n [\n -121.99492703337631,\n 46.11419720033388\n ],\n [\n -121.99492703337631,\n 46.30537067138167\n ],\n [\n -122.35475363838827,\n 46.30537067138167\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"87","noUsgsAuthors":false,"publicationDate":"2025-11-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Sweeney, Kristin","contributorId":365989,"corporation":false,"usgs":false,"family":"Sweeney","given":"Kristin","affiliations":[{"id":61798,"text":"University of Portland","active":true,"usgs":false}],"preferred":false,"id":955119,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Major, Jon J. 0000-0003-2449-4466 jjmajor@usgs.gov","orcid":"https://orcid.org/0000-0003-2449-4466","contributorId":439,"corporation":false,"usgs":true,"family":"Major","given":"Jon","email":"jjmajor@usgs.gov","middleInitial":"J.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":955120,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70273755,"text":"70273755 - 2025 - Twenty years (2000-2020) of butterfly monitoring data across the contiguous United States","interactions":[],"lastModifiedDate":"2026-01-28T16:44:37.745111","indexId":"70273755","displayToPublicDate":"2025-11-22T09:37:47","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3907,"text":"Scientific Data","active":true,"publicationSubtype":{"id":10}},"title":"Twenty years (2000-2020) of butterfly monitoring data across the contiguous United States","docAbstract":"We present the most comprehensive, integrated, butterfly monitoring dataset ever assembled for the United States. It contains over 1.2 million count records, from 65,000 surveys, representing over 12.6 million individual butterflies. To compile this dataset, we integrated data and harmonized taxonomy across 19 butterfly monitoring programs in the United States – one national, 13 statewide, and 5 local (e.g. individual county or National Park) in scale. In addition to the data, we also provide the taxonomic dictionary used to crosswalk butterfly taxonomy across programs, and the code used to assemble the integrated dataset. The publication of this dataset will inspire new analyses of butterfly population trends and drivers that help to identify solutions to the biodiversity crisis.
","language":"English","publisher":"Springer Nature","doi":"10.1038/s41597-025-05513-8","usgsCitation":"Henry, E.H., Edwards, C., Shirey, V., Pippen, J.S., Waetjen, D., Forister, M.L., Larsen, E., Schultz, C.B., Michielini, J., Brockman, N., Burls, K., Drum, R., Gatch, M., Glassberg, J., Hamlett, N., Hershcovich, S.V., Le, C., McGaffin, S., Meilinger, J., Richter, L., Rochefort, R., Schelz, C., Shapiro, A.M., Sullivan, K., Taron, D., Thogmartin, W.E., Walker, A., Westphal, A., Wiedmann, J., Wilcockson, I.U., Zaspel, J., and Ries, L., 2025, Twenty years (2000-2020) of butterfly monitoring data across the contiguous United States: Scientific Data, v. 12, 1869, 8 p., https://doi.org/10.1038/s41597-025-05513-8.","productDescription":"1869, 8 p.","ipdsId":"IP-174696","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":499339,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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The number of large-scale solar photovoltaic facilities increased approximately tenfold between 2012 and 2021, with an associated 25-fold increase in cumulative installed capacity. With ambitious decarbonization and renewable energy deployment goals at both the federal and state levels, deployments of large-scale solar photovoltaic facilities will continue apace. This growth is likely to be complex with ripples of impacts felt throughout different aspects of society, and thus accurate solar land use metrics allowing more accurate predictions are of value to policymakers, planners, and other stakeholders in the future photovoltaic build-out. In this paper, we leverage data from the newly released US Large-Scale Solar Photovoltaic Database to examine recent trends in large-scale solar photovoltaic land use. We analyze the relationships between solar array capacity density (W/acre) and a range of facility attributes to better understand the future land requirements of solar capacity expansion over the coming years. Installed capacity was the single strongest determinant of solar array area. However, we found substantial variation in capacity density across facility attributes, including mount type, latitude, urbanicity, time, and prior land use.
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Sydny","contributorId":331485,"corporation":false,"usgs":false,"family":"Fujita","given":"K.","middleInitial":"Sydny","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":951420,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":951421,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Robson, Dana","contributorId":331488,"corporation":false,"usgs":false,"family":"Robson","given":"Dana","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":951422,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rand, Joesph 0000-0002-6398-2473","orcid":"https://orcid.org/0000-0002-6398-2473","contributorId":204880,"corporation":false,"usgs":false,"family":"Rand","given":"Joesph","email":"","affiliations":[{"id":37002,"text":"DOE LBNL","active":true,"usgs":false}],"preferred":false,"id":951423,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ancona, Zachary H. 0000-0001-5430-0218 zancona@usgs.gov","orcid":"https://orcid.org/0000-0001-5430-0218","contributorId":5578,"corporation":false,"usgs":true,"family":"Ancona","given":"Zachary","email":"zancona@usgs.gov","middleInitial":"H.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":951424,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":951425,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"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":951426,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"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":951427,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gu, Jianyu","contributorId":138719,"corporation":false,"usgs":false,"family":"Gu","given":"Jianyu","email":"","affiliations":[{"id":12507,"text":"Department of Natural Resources and the Environment, University of New Hampshire, 56 College Road, Durham, NH 03824, USA","active":true,"usgs":false}],"preferred":false,"id":951428,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Macknick, Jordan","contributorId":363275,"corporation":false,"usgs":false,"family":"Macknick","given":"Jordan","affiliations":[{"id":33782,"text":"National Renewable Energy Laboratory","active":true,"usgs":false}],"preferred":false,"id":951429,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70272190,"text":"pp1890I - 2025 - Spatio-temporal evolution of distributed volcanic fields, case studies—Sierra Chichinautzin and Michoacán-Guanajuato, México","interactions":[{"subject":{"id":70272190,"text":"pp1890I - 2025 - Spatio-temporal evolution of distributed volcanic fields, case studies—Sierra Chichinautzin and Michoacán-Guanajuato, México","indexId":"pp1890I","publicationYear":"2025","noYear":false,"chapter":"I","displayTitle":"Spatio-Temporal Evolution of Distributed Volcanic Fields, Case Studies—Sierra Chichinautzin and Michoacán-Guanajuato, México","title":"Spatio-temporal evolution of distributed volcanic fields, case studies—Sierra Chichinautzin and Michoacán-Guanajuato, México"},"predicate":"IS_PART_OF","object":{"id":70259456,"text":"pp1890 - 2024 - Distributed volcanism—Characteristics, processes, and hazards","indexId":"pp1890","publicationYear":"2024","noYear":false,"title":"Distributed volcanism—Characteristics, processes, and hazards"},"id":1}],"isPartOf":{"id":70259456,"text":"pp1890 - 2024 - Distributed volcanism—Characteristics, processes, and hazards","indexId":"pp1890","publicationYear":"2024","noYear":false,"title":"Distributed volcanism—Characteristics, processes, and hazards"},"lastModifiedDate":"2026-02-03T16:35:11.775005","indexId":"pp1890I","displayToPublicDate":"2025-11-20T11:30:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1890","chapter":"I","displayTitle":"Spatio-Temporal Evolution of Distributed Volcanic Fields, Case Studies—Sierra Chichinautzin and Michoacán-Guanajuato, México","title":"Spatio-temporal evolution of distributed volcanic fields, case studies—Sierra Chichinautzin and Michoacán-Guanajuato, México","docAbstract":"An analysis of 1,375 volcanoes in the Michoacán-Guanajuato (1,148 volcanoes in a 26,200 square-kilometer area) and Sierra Chichinautzin (227 volcanoes in a 3,500 square-kilometer area) volcanic fields in central Mexico identified patterns in the spatial and temporal distribution of past eruptions. A cluster agglomerative hierarchical method and kernel analysis confirmed that the Michoacán-Guanajuato volcanic field comprises four volcanic fields (Valle de Santiago, Uruapan, Apatzingán, and Pátzcuaro volcanic fields) controlled by different fault systems, indicating that it is not a single volcanic field but rather a group of volcanic fields (a “superfield”), each of which has distinct characteristics.
In the Sierra Chichinautzin volcanic field, well-constrained isotopic ages were used to build a model of how the spatial distribution of the eruptions has changed over time. Two new 40Ar/39Ar ages from a locally recognized volcanic feature near the town of El Cantil, herein called El Cantil volcano (1,537±17 kilo-annum [ka]) and the volcanic feature at Cerro el Elefante (herein called El Elefante dome) (1,485±92 ka) belong to the oldest volcanic group identified in the Sierra Chichinautzin volcanic field, confirming the timing of the beginning of monogenetic volcanism in the region. Based on the volcanic groups identified in the Sierra Chichinautzin volcanic field, the youngest volcanism (less than 35 ka) is found only in the central-western sector of the field. Principal component analysis determined the directional trends of feeder dikes only for vents <10 ka in the Sierra Chichinautzin volcanic field. Possible magma migration paths through the crust were identified using seismic data from both volcanic fields using an earthquake catalog from 1973 to 2023, which includes 9,016 earthquakes in the Michoacán-Guanajuato volcanic field and 841 in the Sierra Chichinautzin volcanic field. The spatial distribution of the hypocenters does not highlight any trend that could be associated with superficial movement of magma in the Sierra Chichinautzin volcanic field. In the Michoacán-Guanajuato volcanic field, however, eight seismic swarms since 1997 have been detected. These swarms are interpreted to result from ascending magma. Strengthening monitoring systems and reinforcing mitigation measures to address volcanic hazards and risk are important means of preparing for future eruptions in both regions. Analysis such as those herein provide insights as to where an eruption might occur and may help mitigate volcanic hazards.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1890I","usgsCitation":"Jaimes-Viera, C., Nieto-Torres, A., Martin Del Pozzo, A.L., Germa, A., Connor, C., Ort, M., Layer, P., and Benowitz, J., 2025, Spatio-Temporal Evolution of Distributed Volcanic Fields, Case Studies—Sierra Chichinautzin and Michoacán-Guanajuato, México, chap. I of Poland, M.P., Ort, M.H., Stovall, W.K., Vaughan, G.R., Conner, C.B., and Rumpf, M.E., eds., Distributed volcanism—Characteristics, processes, and hazards: U.S. Geological Survey Professional Paper 1890, 28 p., https://doi.org/10.3133/pp1890I.","productDescription":"Report: v, 28 p.; 3 Tables","onlineOnly":"Y","ipdsId":"IP-157952","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":496700,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/pp/1890/i/pp1890I.XML"},{"id":496699,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/pp/1890/i/images"},{"id":496625,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1890/i/coverthb.jpg"},{"id":496626,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1890/i/pp1890I.pdf","text":"Report","size":"7.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Professional Paper 1890-I"},{"id":496629,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/pp/1890/i/PP1890I_supptable1A.csv","text":"Table 1A","size":"12.0 KB","linkFileType":{"id":7,"text":"csv"},"description":"Professional Paper 1890-I, Table 1A","linkHelpText":"Radiometric (14C and 40Ar/39Ar) ages from volcanoes from Sierra Chichinautzin volcanic field"},{"id":496632,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/pp/1890/i/PP1890I_supptable1B.csv","text":"Table 1B","size":"12.0 KB","linkFileType":{"id":7,"text":"csv"},"description":"Professional Paper 1890-I, Table 1B","linkHelpText":"Estimated and calibrated ages from the volcanoes from Sierra Chichinautzin volcanic field"},{"id":496633,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/pp/1890/i/PP1890I_supptable2.csv","text":"Table 2","size":"80.0 KB","linkFileType":{"id":7,"text":"csv"},"description":"Professional Paper 1890-I, Table 2","linkHelpText":"Location, h/wb ratio and age of main cones in Michoacán-Guanajuato and Sierra Chichinautzin volcanic fields"},{"id":497260,"rank":8,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/pp1890I/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"Professional Paper 1890-I"}],"country":"Mexico","otherGeospatial":"Michoacán-Guanajuato, Sierra Chichinautzin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -99.667,\n 19.333\n ],\n [\n -99.667,\n 18.833\n ],\n [\n -98.5,\n 18.833\n ],\n [\n -98.5,\n 19.333\n ],\n [\n -99.667,\n 19.333\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -104,\n 20.6\n ],\n [\n -104,\n 19\n ],\n [\n -100,\n 19\n ],\n [\n -100,\n 20.6\n ],\n [\n -104,\n 20.6\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Volcano Science Center
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Rare earth elements (REEs) are a scarce but vital resource for our modern economies and lifestyles. Since the late 1990s, China has supplied the vast majority of the world’s refined REEs. Increasing global demand has broadened the search for REE deposits to unconventional places, including the Moon. Although most lunar rocks have very low REE concentrations, Apollo samples showed that one type of lunar rock containing potassium (K), REEs, and phosphorus (P)—known by the acronym KREEP—has high concentrations of REEs. Data from orbiting satellites have identified locations where substantial deposits of KREEP are likely. The viability of mining these deposits depends on the evolution of REE economics, the development of the Earth-Moon infrastructure, and the findings from future lunar mineral exploration missions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20253049","usgsCitation":"Keszthelyi, L.P., Coyan, J.A., Pigue, L.M., Bennett, K.A., and Gabriel, T.S.J., 2025, Rare earth elements on the Moon: U.S. Geological Survey Fact Sheet 2025-3049, 4 p., https://doi.org/10.3133/fs20253049.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","ipdsId":"IP-177188","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":496650,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2025/3049/fs20253049.pdf","text":"Report","size":"9.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2025-3049 PDF"},{"id":496649,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2025/3049/coverthb.jpg"}],"otherGeospatial":"the Moon","contact":"Astrogeology Science Center
U.S. Geological Survey
2255 N. Gemini Dr.
Flagstaff, AZ 86001
Urban stream rehabilitation plans can benefit from knowledge of the landscape setting and vegetative communities that were adjacent to streams prior to urbanization. Downstream to upstream connections of these characteristics can be relevant for native migratory fish species that have a range of preferred spawning habitats. Based on a need for more quantitative data on these potential connections, the U.S. Geological Survey assembled geomorphic characteristics, surficial geology, and pre-Euro-American settlement vegetation for 333 kilometers of stream segments in the Kinnickinnic River and Menomonee River subbasins of the Milwaukee River, Wisconsin. Channel slopes ranged from less than 0.3 percent to greater than 2 percent, covering at least two channel morphology and bedform types spanning low-energy irregular and pool-riffle complexes. Postglacial surficial geology ranged from coarse-grained outwash sand and gravel to lacustrine silt and clay, allowing for a range of stream substrate sizes. Presettlement riparian vegetation was mainly forest, including forested uplands, forested lowlands, and to a lesser extent, conifer-dominated wetlands in headwaters. This resulting framework of geomorphic habitat response units can be used for habitat rehabilitation projects for migratory native fish in other urban Great Lakes tributaries.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251049","collaboration":"Prepared in cooperation with Milwaukee Metropolitan Sewerage District and the University of Wisconsin","usgsCitation":"Fitzpatrick, F.A., Sterner, S.P., Blount, J.D., and Stewart, J.S., 2025, Geomorphic habitat response units for urban stream rehabilitation, Milwaukee, Wisconsin: U.S. Geological Survey Open-File Report 2025–1049, 17 p., https://doi.org/10.3133/ofr20251049.","productDescription":"Report: vi, 17 p.; Data Release","numberOfPages":"17","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-154626","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":496620,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90S2FMB","text":"USGS data release","linkHelpText":"Geomorphic habitat response units attributes for the Wisconsin DNR 24k hydrography flowline network in the Milwaukee River Basin, Wisconsin"},{"id":496619,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1049/ofr20251049.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2025-1049 XML"},{"id":496615,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1049/coverthb.jpg"},{"id":496616,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1049/ofr20251049.pdf","text":"Report","size":"7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025–1049"},{"id":496617,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1049/images"},{"id":496618,"rank":4,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251049/full","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025–1049 HTML"}],"country":"United States","state":"Wisconsin","city":"Milwaukee","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.21,\n 43.3\n ],\n [\n -88.21,\n 42.8\n ],\n [\n -87.8,\n 42.8\n ],\n [\n -87.8,\n 43.3\n ],\n [\n -88.21,\n 43.3\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Dr.
Madison, WI 53726
The U.S. Geological Survey intersected stream network geomorphic characteristics with maps of original pre-Euro-American settlement vegetation, surficial geology, and land-use attributes for the Kinnickinnic River and Menomonee River subbasins of the Milwaukee River Basin in eastern Wisconsin. The resulting framework of geomorphic habitat response units can be used for planning, designing, and evaluating ongoing and future native fish passage and spawning habitat rehabilitation projects in other urban areas where concrete-lined channels are being replaced with more natural counterparts.
","publicationDate":"2025-11-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Fitzpatrick, Faith A. 0000-0002-9748-7075 fafitzpa@usgs.gov","orcid":"https://orcid.org/0000-0002-9748-7075","contributorId":209516,"corporation":false,"usgs":true,"family":"Fitzpatrick","given":"Faith","email":"fafitzpa@usgs.gov","middleInitial":"A.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":950539,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sterner, Shelby P. 0000-0002-3103-7960","orcid":"https://orcid.org/0000-0002-3103-7960","contributorId":292246,"corporation":false,"usgs":true,"family":"Sterner","given":"Shelby","email":"","middleInitial":"P.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":950540,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Blount, James D. 0000-0002-0006-3947 jblount@usgs.gov","orcid":"https://orcid.org/0000-0002-0006-3947","contributorId":200231,"corporation":false,"usgs":true,"family":"Blount","given":"James","email":"jblount@usgs.gov","middleInitial":"D.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":950541,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stewart, Jana S. 0000-0002-8121-1373","orcid":"https://orcid.org/0000-0002-8121-1373","contributorId":211037,"corporation":false,"usgs":true,"family":"Stewart","given":"Jana","middleInitial":"S.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":950542,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70272629,"text":"70272629 - 2025 - MIMAR-Net: Multiscale Inception-based Manhattan Attention Residual Network and its application to underwater image super-resolution","interactions":[],"lastModifiedDate":"2025-11-26T14:12:30.52052","indexId":"70272629","displayToPublicDate":"2025-11-20T08:10:00","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":22985,"text":"Electronics","active":true,"publicationSubtype":{"id":10}},"title":"MIMAR-Net: Multiscale Inception-based Manhattan Attention Residual Network and its application to underwater image super-resolution","docAbstract":"In recent years, Single-Image Super-Resolution (SISR) has gained significant attention in the geoscience and remote sensing community for its potential to improve the resolution of low-quality underwater imagery. This paper introduces MIMAR-Net (Multiscale Inception-based Manhattan Attention Residual Network), a new deep learning architecture designed to increase the spatial resolution of input color images. MIMAR-Net integrates a multiscale inception module, cascaded residue learning, and advanced attention mechanisms, such as the MaSA layer, to capture both local and global contextual information effectively. By utilizing multiscale processing and advanced attention strategies, MIMAR-Net allows us to handle the complexities of underwater environments with precision and robustness. We evaluate the model on three popular underwater image datasets, namely UFO-120, USR-248, and EUVP, and perform extensive comparisons against state-of-the-art methods. Experimental results demonstrate that MIMAR-Net consistently outperforms existing approaches, achieving superior qualitative and quantitative improvements in image quality, making it a reliable solution for underwater image enhancement in various challenging scenarios.
","language":"English","publisher":"MDPI","doi":"10.3390/electronics14224544","usgsCitation":"Zahan, N., Paheding, S., Saleem, A., Havens, T.C., and Esselman, P., 2025, MIMAR-Net: Multiscale Inception-based Manhattan Attention Residual Network and its application to underwater image super-resolution: Electronics, v. 14, no. 22, 4544, 24 p., https://doi.org/10.3390/electronics14224544.","productDescription":"4544, 24 p.","ipdsId":"IP-175539","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":496934,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/electronics14224544","text":"Publisher Index Page"},{"id":496897,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"22","noUsgsAuthors":false,"publicationDate":"2025-11-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Zahan, Nusrat","contributorId":363057,"corporation":false,"usgs":false,"family":"Zahan","given":"Nusrat","affiliations":[{"id":86604,"text":"Fairfield University","active":true,"usgs":false}],"preferred":false,"id":951040,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paheding, Sidike","contributorId":347829,"corporation":false,"usgs":false,"family":"Paheding","given":"Sidike","affiliations":[{"id":16203,"text":"Michigan Technological university","active":true,"usgs":false}],"preferred":false,"id":951041,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Saleem, Ashraf","contributorId":347827,"corporation":false,"usgs":false,"family":"Saleem","given":"Ashraf","affiliations":[{"id":16203,"text":"Michigan Technological university","active":true,"usgs":false}],"preferred":false,"id":951042,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Havens, Timothy C.","contributorId":363058,"corporation":false,"usgs":false,"family":"Havens","given":"Timothy","middleInitial":"C.","affiliations":[{"id":16203,"text":"Michigan Technological university","active":true,"usgs":false}],"preferred":false,"id":951043,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Esselman, Peter C. 0000-0002-0085-903X","orcid":"https://orcid.org/0000-0002-0085-903X","contributorId":204291,"corporation":false,"usgs":true,"family":"Esselman","given":"Peter C.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":951044,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70271405,"text":"sir20255017 - 2025 - Groundwater response to managed aquifer recharge at the Southeast Houghton Artificial Recharge Project in Tucson, Arizona","interactions":[],"lastModifiedDate":"2026-02-03T16:32:26.605397","indexId":"sir20255017","displayToPublicDate":"2025-11-19T11:56:06","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-5017","displayTitle":"Groundwater Response to Managed Aquifer Recharge at the Southeast Houghton Artificial Recharge Project in Tucson, Arizona","title":"Groundwater response to managed aquifer recharge at the Southeast Houghton Artificial Recharge Project in Tucson, Arizona","docAbstract":"Managed aquifer recharge is a widespread practice for storing water in the subsurface as groundwater. At a managed aquifer recharge facility in southern Arizona, groundwater-level and repeat microgravity data were collected to monitor aquifer response. These data were used to inform parameter identification for an unsaturated-zone flow model used to simulate the recharge process. The facility, the Southeast Houghton Artificial Recharge Project (SHARP), consists of 3 surface basins (about 27,600 square meters [6.8 acres] total surface area) where recycled water is distributed in recharge cycles lasting several months, with dry periods in between. During the study period, December 2020–December 2022, Tucson Water (the City of Tucson’s water utility) reported 6.56×106 cubic meters of water (5,320 acre-feet) recharged.
Monitoring included groundwater-level observations at 3 monitoring wells and repeat microgravity measurements at as many as 22 locations (some stations were destroyed between surveys). Six gravity surveys were carried out using absolute- and relative-gravity meters. Large gravity increases, more than 250 microgals, were observed during the first repeat survey, 3.5 months after the start of recharge, but only in the immediate vicinity of the recharge basins. Data show that water moved downward to the water table, and storage changes in the unsaturated zone away from the facility were likely minimal. Gravity decreased at stations more than 1 kilometer from the facility, consistent with regional groundwater-level changes. Groundwater-level increases in wells adjacent to the recharge basins began 2 months after the second repeat gravity survey, and 5.5 months after recharge began.
Unsaturated-zone flow modeling was carried out using software that simulates water movement and parameter estimation. Model calibration was carried out by minimizing an objective function calculated from the differences between simulated and observed groundwater levels, and between simulated and observed repeat microgravity data. Including repeat microgravity data in the objective function reduced the uncertainty in estimated parameter values for saturated hydraulic conductivity and saturated water content. Modeling indicated that the unsaturated zone between the recharge basins and the water table does not become saturated even after 685 days of simulated infiltration. This gradual wetting may account for increasing infiltration rates over time, as hydraulic conductivity increases with increasing water content. Unsaturated-zone water content decreased rapidly between recharge cycles. Model-simulated groundwater mounding extended about 1 kilometer from the center of SHARP after the 685-day period following the onset of recharge.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255017","collaboration":"Prepared in cooperation with Tucson Water","programNote":"Water Availability and Use Program","usgsCitation":"Wildermuth, L.M., Kennedy, J.R., and Conrad, J.L., 2025, Groundwater response to managed aquifer recharge at the Southeast Houghton Artificial Recharge Project in Tucson, Arizona: U.S. Geological Survey Scientific Investigations\nReport 2025–5017, 38 p., https://doi.org/10.3133/sir20255017.","productDescription":"Report: v, 38 p.; Data Release","numberOfPages":"38","onlineOnly":"Y","ipdsId":"IP-152298","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":497795,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118986.htm"},{"id":495375,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9E19SSK","text":"USGS data release","description":"Landrum, M.T., 2021, Repeat microgravity data from South Houghton Area Recharge Project, Tucson, Arizona, 2020-2022 (ver. 2.0, August 2024): U.S. Geological Survey data release, https://doi.org/10.5066/P9E19SSK.","linkHelpText":"Repeat microgravity data from South Houghton Area Recharge Project, Tucson, Arizona, 2020-2022 (ver. 2.0, August 2024)"},{"id":495371,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5017/sir20255017.pdf","text":"Report","size":"35 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5017 PDF"},{"id":495370,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5017/coverthb.jpg"},{"id":495372,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255017/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5017 HTML"},{"id":495374,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5017/images"},{"id":495373,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5017/sir20255017.XML","description":"SIR 2025-5017 XML"}],"country":"United States","state":"Arizona","city":"Tucson","otherGeospatial":"Southeast Houghton Artificial Recharge Project","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.758333,\n 32.159722\n ],\n [\n -110.758333,\n 32.141667\n ],\n [\n -110.791667,\n 32.141667\n ],\n [\n -110.791667,\n 32.159722\n ],\n [\n -110.758333,\n 32.159722\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
Landscapes encompass both aquatic and terrestrial ecosystems that experience the same climate but may respond to climate in divergent ways. For example, the time lag between seasonal dry-down of terrestrial soil moisture and decline in streamflow has important implications for species and ecosystem processes across the aquatic–terrestrial interface. How these lags between aquatic and terrestrial hydrology vary with climate and spatial location within watersheds remains largely unexplored. Here, we examine seasonal patterns of aquatic–terrestrial dry-down across seven watersheds in the northwestern USA, spanning a wide range of aridity. We compared daily streamflow data from USGS gages at watershed outlets with simulated daily soil moisture (1979–2020) from multiple locations within each watershed. In all watersheds, annual dry cycles progressed sequentially through the following features: evapotranspiration, precipitation, shallow soil moisture, deep soil moisture, and finally streamflow. Seasonal streamflow minima lagged behind soil moisture minima for shorter durations in more arid watersheds and drier years. Within watersheds, lag times varied spatially due to interactions between elevation and aridity, with short lags in low-elevation soils near streams in arid watersheds and longer lags in less arid watersheds. Collectively, these results indicate shorter lags between seasonal aquatic and terrestrial dry periods in drier watersheds and years, and show that these tighter linkages are spatially aggregated in drier watersheds. The co-occurrence of seasonally dry conditions in both aquatic and terrestrial systems under increasing aridification is likely to intensify stressors on ecosystems and services. Recognizing these patterns may be critical for predicting ecosystem vulnerabilities and informing adaptation strategies to mitigate the impacts of seasonally dry conditions.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.70413","usgsCitation":"Butterfield, B.J., Schlaepfer, D.R., Al-Chokhachy, R., Dunham, J., Groom, J.D., Muhlfeld, C.C., Torgersen, C.E., and Bradford, J., 2025, Aridity reduces lag times between aquatic and terrestrial dry-down among watersheds and across years in the northwest US: Ecosphere, v. 16, no. 11, e70413, 14 p., https://doi.org/10.1002/ecs2.70413.","productDescription":"e70413, 14 p.","ipdsId":"IP-176106","costCenters":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":49226,"text":"Northwest Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":496924,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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0000-0002-2136-5098","orcid":"https://orcid.org/0000-0002-2136-5098","contributorId":211560,"corporation":false,"usgs":true,"family":"Al-Chokhachy","given":"Robert","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":950820,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dunham, Jason 0000-0002-6268-0633","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":220078,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":950821,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Groom, Jeremiah D.","contributorId":362928,"corporation":false,"usgs":false,"family":"Groom","given":"Jeremiah","middleInitial":"D.","affiliations":[{"id":86575,"text":"Groom Analytics LLC, 1975 SE Crystal Lake Dr., Unit 173, Corvallis, OR 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Mississippi","interactions":[],"lastModifiedDate":"2026-02-03T16:31:45.919091","indexId":"sir20255055","displayToPublicDate":"2025-11-18T12:06:15","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-5055","displayTitle":"An Inset Groundwater-Flow Model to Evaluate the Effects of Layering Configuration on Model Calibration and Assess Managed Aquifer Recharge near Shellmound, Mississippi","title":"An inset groundwater-flow model to evaluate the effects of layering configuration on model calibration and assess managed aquifer recharge near Shellmound, Mississippi","docAbstract":"The U.S. Geological Survey has developed a high-resolution inset groundwater-flow model in the Mississippi Delta as part of an interdisciplinary collaboration coordinated by the Mississippi Alluvial Plain project to provide a tool that stakeholders can use to support water-resource management decisions. Groundwater withdrawals from the Mississippi River Valley alluvial (MRVA) aquifer have been vital to support agricultural production in the region, but substantial groundwater-level declines near Shellmound, Mississippi, have caused concerns for long-term sustainability of the aquifer. To better understand the subsurface and try to mitigate the long-term groundwater-level declines, stakeholders have undertaken actions including a Groundwater Transfer and Injection Pilot (GTIP) project using a riverbank filtration-based managed aquifer recharge approach. The pilot project consisted of extracting groundwater near the Tallahatchie River and reinjecting it into the aquifer 3 kilometers west where water levels have substantially declined. A high-resolution airborne electromagnetic (AEM) survey was also completed to collect electrical resistivity data to support the GTIP project and the development of the groundwater model.
The inset groundwater-flow model was developed to (1) integrate the AEM data into the optimal layering configuration of the MRVA aquifer that the available observation data can support through calibration, and (2) assess the potential effect of the GTIP project on the groundwater levels. The AEM data were processed into three different layering configurations leading to the development of model A (18 layers), model B (16 layers), and model C (8 layers), all at a 100- x 100-meter cell spatial resolution using the U.S. Geological Survey modular finite-difference flow model 6 code with Newton-Raphson formulation. The model development process integrated recent advances in modeling, such as the incorporation of AEM data, the use of outputs from the soil-water-balance (SWB) model, and the Aquaculture and Irrigation Water-Use Model, and was facilitated by robust automation using the open-source python packages Modflow-setup and SFRmaker. Using Parameter Estimation ++ Iterative Ensemble Smoother, the three numerical groundwater-flow models (models A, B, and C) were calibrated against a set of observations, which included aquifer groundwater levels, streamflows, stream stage, and aquifer transmissivity. Results indicate that the detailed representation of MRVA aquifer layers in model A produced the best calibrated model by history matching, and the integration of data representing surficial connectivity played a key role in improving groundwater recharge and enhancing the ability of the model to match groundwater levels in the cone of depression. A forecast model simulated the managed aquifer recharge approach, and the results indicated that, given average irrigation and recharge conditions (2010–15), the GTIP project has the potential to induce groundwater-level increases of as much as 3 meters around the injection site, but a sustained increase would require repetition in subsequent years of water transfer at 2022 rates or above.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255055","collaboration":"Prepared in cooperation with U.S. Department of Agriculture Agricultural Research Service and the Mississippi Department of Environmental Quality","programNote":"Water Availability and Use Science Program","usgsCitation":"Guira, M., Traylor, J.P., Leaf, A.T., and Weisser, A.R., 2025, An inset groundwater-flow model to evaluate the effects of layering configuration on model calibration and assess managed aquifer recharge near Shellmound, Mississippi: U.S. Geological Survey Scientific Investigations Report 2025–5055, 134 p., https://doi.org/10.3133/sir20255055.","productDescription":"Report: ix, 134 p.; 3 Figures: 11.00 x 8.50 inches; Data Release; Dataset","numberOfPages":"148","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-154357","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":84311,"text":"Central Plains Water Science Center","active":true,"usgs":true}],"links":[{"id":497793,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118974.htm"},{"id":495719,"rank":8,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2025/5055/downloads/","text":"Layered figures","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"Downloadable layered PDF files for figures 11, 12, and 13"},{"id":495626,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13DWA86","text":"USGS data release","linkHelpText":"Inset models used to evaluate the effects of layering configuration on model calibration from 1900 to 2018, and assess managed aquifer recharge near Shellmound, Mississippi, from 2019 to 2050"},{"id":495670,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255055/full"},{"id":495623,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5055/sir20255055.XML"},{"id":495622,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5055/sir20255055.pdf","text":"Report","size":"40 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5055"},{"id":495625,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":495624,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5055/images/"},{"id":495621,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5055/coverthb.jpg"}],"country":"United States","state":"Mississippi","city":"Shellmound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.55,\n 33.8\n ],\n [\n -90.55,\n 33.5\n ],\n [\n -90.1667,\n 33.5\n ],\n [\n -90.1667,\n 33.8\n ],\n [\n -90.55,\n 33.8\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512
The use of Uncrewed Aerial Systems (UASs) for remote sensing applications has increased significantly in recent years due to their low cost, operational flexibility, and rapid advancements in sensor technologies. In many cases, UAS platforms are considered viable alternatives to conventional satellite and crewed airborne platforms, offering very high spatial, spectral, and temporal resolution data. However, the radiometric quality of UAS-acquired data has not received equivalent attention, particularly with respect to absolute calibration. In this study, we (1) evaluate the absolute radiometric performance of two commonly used UAS sensors: the Headwall Nano-Hyperspec hyperspectral sensor and the MicaSense RedEdge-MX Dual Camera multispectral system; (2) assess the effectiveness of the Empirical Line Method (ELM) in improving the radiometric accuracy of reflectance products generated by these sensors; and (3) investigate the influence of calibration target characteristics—including size, material type, reflectance intensity, and quantity—on the performance of ELM for UAS data. A field campaign was conducted jointly by the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and the USGS National Uncrewed Systems Office (NUSO) from 15 to 18 July 2023, at the USGS EROS Ground Validation Radiometer (GVR) site in Sioux Falls, South Dakota, USA, over a 160 m × 160 m vegetated area. Absolute calibration accuracy was evaluated by comparing UAS sensor-derived reflectance to in situ measurements of the site. Results indicate that the Headwall Nano-Hyperspec and MicaSense sensors underestimated reflectance by approximately 0.05 and 0.015 reflectance units, respectively. While the MicaSense sensor demonstrated better inherent radiometric accuracy, it exhibited saturation over bright targets due to limitations in its automatic gain and exposure settings. Application of the ELM using just two calibration targets reduced discrepancies to within 0.005 reflectance units. Reflectance products generated using various target materials—such as felt, melamine, or commercially available validation targets—showed comparable agreement with in situ measurements when used with the Nano-Hyperspec sensor. Furthermore, increasing the number of calibration targets beyond two did not yield measurable improvements in calibration accuracy. At a flight altitude of 200 ft above ground level (AGL), a target size of 0.6 m × 0.6 m or larger was sufficient to provide pure pixels for ELM implementation, whereas smaller targets (e.g., 0.3 m × 0.3 m) posed challenges in isolating pure pixels. Overall, the standard manufacturer-recommended calibration procedures were insufficient for achieving high radiometric accuracy with the tested sensors, which may restrict their applicability in scenarios requiring greater accuracy and precision. The use of the ELM significantly improved data quality, enhancing the reliability and applicability of UAS-based remote sensing in contexts requiring high precision and accuracy.
","language":"English","publisher":"MDPI","doi":"10.3390/rs17223738","usgsCitation":"Shrestha, M., Scholl, V.M., Sampath, A., Irwin, J., Kropuenske, T., Adams, J., Burgess, M.A., and Brady, L.R., 2025, Absolute radiometric calibration evaluation of Uncrewed Aerial System (UAS) Headwall and MicaSense sensors and improving data quality using the Empirical Line Method: Remote Sensing, v. 17, no. 22, 3738, 29 p., https://doi.org/10.3390/rs17223738.","productDescription":"3738, 29 p.","ipdsId":"IP-178436","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":497081,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs17223738","text":"Publisher Index Page"},{"id":496979,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"17","issue":"22","noUsgsAuthors":false,"publicationDate":"2025-11-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Shrestha, Mahesh 0000-0002-8368-6399 mshrestha@contractor.usgs.gov","orcid":"https://orcid.org/0000-0002-8368-6399","contributorId":259303,"corporation":false,"usgs":false,"family":"Shrestha","given":"Mahesh","email":"mshrestha@contractor.usgs.gov","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":true,"id":951160,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Scholl, Victoria Mary 0000-0002-2085-1449","orcid":"https://orcid.org/0000-0002-2085-1449","contributorId":295713,"corporation":false,"usgs":true,"family":"Scholl","given":"Victoria","email":"","middleInitial":"Mary","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":951161,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sampath, Aparajithan 0000-0002-6922-4913","orcid":"https://orcid.org/0000-0002-6922-4913","contributorId":222486,"corporation":false,"usgs":false,"family":"Sampath","given":"Aparajithan","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":false,"id":951162,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Irwin, Jeffrey 0000-0001-5828-0787 jrirwin@usgs.gov","orcid":"https://orcid.org/0000-0001-5828-0787","contributorId":222485,"corporation":false,"usgs":true,"family":"Irwin","given":"Jeffrey","email":"jrirwin@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":951163,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kropuenske, Travis 0000-0002-3269-4225","orcid":"https://orcid.org/0000-0002-3269-4225","contributorId":331816,"corporation":false,"usgs":false,"family":"Kropuenske","given":"Travis","email":"","affiliations":[{"id":53079,"text":"KBR, contractor to U.S. Geological Survey","active":true,"usgs":false}],"preferred":false,"id":951164,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Adams, Josip 0000-0001-8470-4141","orcid":"https://orcid.org/0000-0001-8470-4141","contributorId":217936,"corporation":false,"usgs":true,"family":"Adams","given":"Josip","email":"","affiliations":[{"id":5078,"text":"Southwest Regional Director's Office","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":951165,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Burgess, Matthew Alexander 0000-0003-3487-4972 mburgess@usgs.gov","orcid":"https://orcid.org/0000-0003-3487-4972","contributorId":225090,"corporation":false,"usgs":true,"family":"Burgess","given":"Matthew","email":"mburgess@usgs.gov","middleInitial":"Alexander","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":951166,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Brady, Lance R","contributorId":363145,"corporation":false,"usgs":false,"family":"Brady","given":"Lance","middleInitial":"R","affiliations":[{"id":86626,"text":"BLM (former USGS employee)","active":true,"usgs":false}],"preferred":false,"id":951167,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70272577,"text":"70272577 - 2025 - Performance analysis of oil recovery and CO2 retention in a greenfield residual oil zone: CO2-EOR in Tall Cotton Field (Permian Basin, West Texas, USA)","interactions":[],"lastModifiedDate":"2025-11-24T16:11:13.736607","indexId":"70272577","displayToPublicDate":"2025-11-15T09:01:56","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":22979,"text":"Carbon Capture Science and Technology","active":true,"publicationSubtype":{"id":10}},"title":"Performance analysis of oil recovery and CO2 retention in a greenfield residual oil zone: CO2-EOR in Tall Cotton Field (Permian Basin, West Texas, USA)","docAbstract":"Residual oil zones (ROZs) can offer significant oil resources via enhanced oil recovery (EOR) as well as subsurface carbon dioxide (CO2) retention during injection. If injected CO2 is anthropogenic, the ROZs can offer a substantial geologic storage potential. The ROZs below the oil/water contact (OWC) of main pay zones (MPZ) in conventional reservoirs or brownfields, are more commonly developed for CO2 injection and oil production and reported in the literature. However, CO2-EOR in greenfield ROZs, reservoirs without a MPZ present, have rarely been developed for CO2-EOR operation. The Tall Cotton Field of West Texas, Permian Basin, which started production in 2015 (Phase 1) and expanded in 2017 (Phase 2) from the San Andres Limestone, is one of the first examples of greenfield ROZs developed for EOR by injecting CO2.
This paper analyses EOR and CO2 retention performance of Tall Cotton Field using allocated injection and production data from inverted 5-spot well patterns of Phase-1 and -2 developments. Production and injection data allocated to each of the 28 identified patterns (nine 20-acre patterns for Phase-1, three 20-acre and sixteen 10-acre patterns for Phase-2) were analyzed for historical and forecasted oil recovery using ratio-trend decline analysis, and for CO2 retention performance of the patterns. The allocated data were further used to calculate injected reservoir pore volume and void replacement ratios (VRR) for the analysis period. Quantitative results indicated that oil recovery factors of the 5-spot patterns varied between 4–10 %, and 5–30 % between the end of injection and the forecast periods, respectively. Storage of CO2, on the other hand, increased to a mean value of ∼7130 MMscf per pattern in Phase-1 and to a mean storage of 3700 MMscf per pattern in Phase-2 until the end of injection, followed by a decline after the end of injection and into the forecast period. Resulting CO2 utilization factors ∼6–50 Mscf/bbl were estimated at the end of injection. Overall, presented results suggested that developing greenfield ROZs for CO2-EOR can be as promising as brownfield ROZs and mature MPZs for EOR and underground storage of injected CO2. For Tall Cotton Field, results suggest that Phase-2 patterns generally outperformed Phase-1 for oil recovery factors, while Phase-1 performed better in CO2 retention performance metrics. This is the first study in the literature that reports a detailed CO2-EOR performance analysis of a greenfield ROZ in the Permian Basin, which can potentially allow for comparison with MPZs and brownfield ROZs.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ccst.2025.100544","usgsCitation":"Karacan, C.O., 2025, Performance analysis of oil recovery and CO2 retention in a greenfield residual oil zone: CO2-EOR in Tall Cotton Field (Permian Basin, West Texas, USA): Carbon Capture Science and Technology, v. 17, 100544, 14 p., https://doi.org/10.1016/j.ccst.2025.100544.","productDescription":"100544, 14 p.","ipdsId":"IP-179246","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":496930,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ccst.2025.100544","text":"Publisher Index Page"},{"id":496830,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","county":"Gaines County","otherGeospatial":"Tall Cotton Field","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -103.04678856680947,\n 33.3881629621319\n ],\n [\n -103.04678856680947,\n 31.601101499990648\n ],\n [\n -101.42024271710294,\n 31.601101499990648\n ],\n [\n -101.42024271710294,\n 33.3881629621319\n ],\n [\n -103.04678856680947,\n 33.3881629621319\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"17","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Karacan, C. Ozgen 0000-0002-0947-8241","orcid":"https://orcid.org/0000-0002-0947-8241","contributorId":201991,"corporation":false,"usgs":true,"family":"Karacan","given":"C.","email":"","middleInitial":"Ozgen","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":950843,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ]}