{"pageNumber":"26","pageRowStart":"625","pageSize":"25","recordCount":40769,"records":[{"id":70268866,"text":"70268866 - 2025 - Isotopic niche plasticity of American alligators within the southern Everglades","interactions":[],"lastModifiedDate":"2025-07-09T15:07:58.020309","indexId":"70268866","displayToPublicDate":"2025-06-27T08:03:47","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Isotopic niche plasticity of American alligators within the southern Everglades","docAbstract":"<p><span>Hydrologic alterations within the Everglades have degraded American alligator (</span><i>Alligator mississippiensis</i><span>) habitat, reduced prey base, and increased physiological stress. Alligator body condition declined across many management areas from 2000 through 2014, prompting us to investigate the relationship between their intraspecific isotopic niche dynamics and body condition. Alligators within the estuary had a larger niche driven by a wider range in stable carbon isotope ratios than those sampled in freshwater habitats. Spatially, model predictability was higher at the smaller scale, reflecting the variability in basal sources and biochemistry among capture sites. Male niches were often larger than those of females, driven by wider ranges of δ</span><sup>13</sup><span>C values, suggesting that they differ in their proportional use of habitats and or resources. However, the similar ranges of δ</span><sup>15</sup><span>N values indicated both sexes foraged within the same trophic level. Furthermore, while not significantly different, large alligators often had a larger niche with elevated δ</span><sup>15</sup><span>N values compared to medium-sized alligators. Although alligators utilize similar stable carbon and nitrogen isotope pools through time, there was considerable temporal variability. These temporal variations in alligators’ isotopic niche were likely influenced by seasonal hydrologic fluctuations within each site, with their niches often being larger in the spring captures than the fall captures. Alligators’ body condition estimates were correlated with intraspecific niche characteristics, including the mean centroid distance between sexes and the interaction between male and female niche size and overlap, within a site, capture period, and year. The variability in intraspecific niche dynamics, landscape heterogeneity, and dynamic hydrology are considerations for designing sustainable management strategies to conserve and enhance alligator populations within the Everglades landscape.</span></p>","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0326148","usgsCitation":"Denton, M., Cherkiss, M., Mazzotti, F.J., Brandt, L.A., Godfrey, S.T., Johnson, D., and Hart, K., 2025, Isotopic niche plasticity of American alligators within the southern Everglades: PLoS ONE, v. 20, no. 6, e0326148, 29 p., https://doi.org/10.1371/journal.pone.0326148.","productDescription":"e0326148, 29 p.","ipdsId":"IP-152063","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":492082,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0326148","text":"Publisher Index Page"},{"id":491899,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"southern Everglades","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -82.15227880692397,\n              26.64313806276658\n            ],\n            [\n              -82.15227880692397,\n              25.088643124435762\n            ],\n            [\n              -79.51780855484174,\n              25.088643124435762\n            ],\n            [\n              -79.51780855484174,\n              26.64313806276658\n            ],\n            [\n              -82.15227880692397,\n              26.64313806276658\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"20","issue":"6","noUsgsAuthors":false,"publicationDate":"2025-06-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Denton, Mathew 0000-0002-1024-3722","orcid":"https://orcid.org/0000-0002-1024-3722","contributorId":210504,"corporation":false,"usgs":true,"family":"Denton","given":"Mathew","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":942428,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cherkiss, Michael 0000-0002-7802-6791","orcid":"https://orcid.org/0000-0002-7802-6791","contributorId":222180,"corporation":false,"usgs":true,"family":"Cherkiss","given":"Michael","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":942429,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mazzotti, Frank J.","contributorId":146647,"corporation":false,"usgs":false,"family":"Mazzotti","given":"Frank","email":"","middleInitial":"J.","affiliations":[{"id":12557,"text":"University of Florida, FLREC","active":true,"usgs":false}],"preferred":false,"id":942430,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brandt, Laura A.","contributorId":146646,"corporation":false,"usgs":false,"family":"Brandt","given":"Laura","email":"","middleInitial":"A.","affiliations":[{"id":6927,"text":"USFWS, National Wildlife Refuge System","active":true,"usgs":false}],"preferred":false,"id":942431,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Godfrey, Sidney T.","contributorId":302877,"corporation":false,"usgs":false,"family":"Godfrey","given":"Sidney","email":"","middleInitial":"T.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":942432,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Johnson, Darren 0000-0002-0502-6045","orcid":"https://orcid.org/0000-0002-0502-6045","contributorId":203921,"corporation":false,"usgs":true,"family":"Johnson","given":"Darren","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":942433,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hart, Kristen 0000-0002-5257-7974","orcid":"https://orcid.org/0000-0002-5257-7974","contributorId":222407,"corporation":false,"usgs":true,"family":"Hart","given":"Kristen","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":942434,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70269513,"text":"70269513 - 2025 - Avian navigation: Comparing the olfactory navigational “map” and the infrasound direction-finding hypotheses to aeronautics","interactions":[],"lastModifiedDate":"2025-11-20T16:42:10.638075","indexId":"70269513","displayToPublicDate":"2025-06-27T07:45:44","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2225,"text":"Journal of Comparative Physiology A","active":true,"publicationSubtype":{"id":10}},"title":"Avian navigation: Comparing the olfactory navigational “map” and the infrasound direction-finding hypotheses to aeronautics","docAbstract":"Animal navigation has long been a fascinating but bewildering subject. Humans and animals might well share similar navigational strategies because they developed within the same physical environments. A “map-and-compass” model has been proposed to explain the two-step avian navigational process, but the “map” step has remained elusive. Although scalar values from bicoordinate geomagnetic or atmospheric olfactory gradients have been considered foundational to the avian map, neither has proved convincing engendering decades of controversy. The olfactory map, and an alternative infrasound direction-finding (IDF) hypothesis, are discussed in this review. The olfactory map hypothesis currently requires extensive stable gradients of trace-odor ratios, but such gradients are highly unlikely within a turbulent and rapidly mixed lower atmosphere. The IDF hypothesis, on the other hand, postulates a two-step navigational model analogous to the maritime and aeronautical radio direction-finding technique. This review was also written to encourage further investigation, and direct testing, of the acoustic navigational process. The IDF hypothesis, at present, appears the better explanation of observed avian navigational behavior and accuracy within the atmosphere’s physical environment.","language":"English","publisher":"Springer Nature","doi":"10.1007/s00359-025-01748-3","usgsCitation":"Hagstrum, J.T., 2025, Avian navigation: Comparing the olfactory navigational “map” and the infrasound direction-finding hypotheses to aeronautics: Journal of Comparative Physiology A, v. 211, p. 603-616, https://doi.org/10.1007/s00359-025-01748-3.","productDescription":"14 p.","startPage":"603","endPage":"616","ipdsId":"IP-176023","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":492832,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"211","noUsgsAuthors":false,"publicationDate":"2025-06-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Hagstrum, Jonathan T. 0000-0002-0689-280X jhag@usgs.gov","orcid":"https://orcid.org/0000-0002-0689-280X","contributorId":3474,"corporation":false,"usgs":true,"family":"Hagstrum","given":"Jonathan","email":"jhag@usgs.gov","middleInitial":"T.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":943926,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70268445,"text":"sir20245134 - 2025 - Assessment and validation of depressions in digital elevation models from multiple elevation data sources and delineation of depressions, sinking streams, and their watersheds in Tennessee and parts of Kentucky, Virginia, North Carolina, Georgia, Alabama, and Mississippi","interactions":[],"lastModifiedDate":"2025-08-14T19:40:56.797048","indexId":"sir20245134","displayToPublicDate":"2025-06-26T13:45:32","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":"2024-5134","displayTitle":"Assessment and Validation of Depressions in Digital Elevation Models From Multiple Elevation Data Sources and Delineation of Depressions, Sinking Streams, and Their Watersheds in Tennessee and Parts of Kentucky, Virginia, North Carolina, Georgia, Alabama, and Mississippi","title":"Assessment and validation of depressions in digital elevation models from multiple elevation data sources and delineation of depressions, sinking streams, and their watersheds in Tennessee and parts of Kentucky, Virginia, North Carolina, Georgia, Alabama, and Mississippi","docAbstract":"<p>Closed depressions and sinking streams in karst landscapes pose difficulties for water-resources management, in the construction of roads and other public works, and in hydrologic and hydrogeomorphic analyses. Digital elevation models (DEMs) can be used to identify the location and determine the size and shape of closed depressions, but separating artificial depressions due to error from real depressions in DEMs can be difficult. Artificial depressions in the DEMs can result from errors that were inherited from limitations in the source data, the interpolation of the elevation data into a grid of values, or horizontal and vertical accuracy of the elevation data. Because the source dataset used to derive DEMs is only a model of the true landscape, field verification is necessary to separate artificial depressions from real ones in DEMs. DEM analysis alone can only be used to determine whether a depression is likely or unlikely to exist in the landscape.</p><p>The U.S. Geological Survey has applied methods to delineate depressions, sinking streams, and their watersheds by using DEMs derived from two sources of elevation data within karst areas of Tennessee and parts of surrounding States. Preliminary depressions, which include all depressions before separating the likely depressions from the unlikely depressions, were delineated from the DEMs with 30- by 30-foot cells derived from each elevation data source. The characteristics of these preliminary depressions were compared to occurrence probabilities for depressions derived from numerical error propagation tests in 10 test areas across the study area and to topographic-contour source data within a 17,739-square-mile test area in middle Tennessee and northern Alabama. The comparison was conducted to determine depression characteristics that, when combined with depression-proximity filters, could be used to separate unlikely from likely depressions. Preliminary depressions were examined in the field at 91 sites in Tennessee, and field observations were compared to digital determinations of unlikely and likely depressions.</p><p>The density and size of depressions derived from each elevation dataset were compared within eight karst regions in the study area. Depressions and their watersheds were compiled from each elevation dataset. Sinking streams derived from the National Hydrography Dataset and their watersheds also were compiled for the study area.<br></p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245134","issn":"2328-0328","collaboration":"Prepared in cooperation with the Tennessee Department of Transportation","usgsCitation":"Ladd, D.E., and Carmichael, J.K., 2025, Assessment and validation of depressions in digital elevation models from multiple elevation data sources and delineation of depressions, sinking streams, and their watersheds in Tennessee and parts of Kentucky, Virginia, North Carolina, Georgia, Alabama, and Mississippi: U.S. Geological Survey Scientific Investigations Report 2024–5134, 44 p., https://doi.org/10.3133/sir20245134.","productDescription":"Report: viii, 44 p.; Data Release","numberOfPages":"56","onlineOnly":"Y","ipdsId":"IP-045246","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":491319,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F74F1PZJ","text":"USGS Data Release","linkHelpText":"- Geospatial dataset of depressions, sinking streams, and associated watersheds in karst areas of Tennessee and parts of surrounding States"},{"id":491318,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245134/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5134 HTML"},{"id":491317,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5134/sir20245134.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2024-5134 XML"},{"id":491316,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5134/sir20245134.pdf","size":"4.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5134"},{"id":494148,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118671.htm","linkFileType":{"id":5,"text":"html"}},{"id":491315,"rank":2,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5134/images"},{"id":491314,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5134/coverthb.jpg"}],"country":"United States","state":"Alabama, Georgia, Kentucky, Mississippi, North Carolina, Tennessee, Virginia","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -88.69263791265882,\n              35.14686343769149\n            ],\n            [\n              -87.98527439570057,\n              33.891319067917465\n            ],\n            [\n              -85.46652629367523,\n              33.88855601482584\n            ],\n            [\n              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data-mce-href=\"https://www.usgs.gov/centers/lmg-water/\" href=\"https://www.usgs.gov/centers/lmg-water/\">Lower Mississippi-Gulf Water Science Center</a><br>U.S. Geological Survey<br>640 Grassmere Park, Suite 100<br>Nashville, TN 37211<br></p><p><a id=\"LPlnkOWAb30f03cb-e6c0-c412-988f-235c353ce0b0\" class=\"OWAAutoLink\" href=\"https://pubs.usgs.gov/contact\" data-auth=\"NotApplicable\" data-olk-copy-source=\"MailCompose\" data-mce-href=\"../contact\">Contact Us- USGS Publications Warehouse</a></p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods of Study</li><li>Results and Discussion</li><li>Summary</li><li>References Cited</li><li>Appendix 1</li></ul>","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2025-06-26","noUsgsAuthors":false,"publicationDate":"2025-06-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Ladd, David E. 0000-0002-9247-7839 deladd@usgs.gov","orcid":"https://orcid.org/0000-0002-9247-7839","contributorId":1646,"corporation":false,"usgs":true,"family":"Ladd","given":"David","email":"deladd@usgs.gov","middleInitial":"E.","affiliations":[{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true}],"preferred":true,"id":941322,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carmichael, John K. 0000-0003-1099-841X jkcarmic@usgs.gov","orcid":"https://orcid.org/0000-0003-1099-841X","contributorId":4554,"corporation":false,"usgs":true,"family":"Carmichael","given":"John","email":"jkcarmic@usgs.gov","middleInitial":"K.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":941323,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70268817,"text":"70268817 - 2025 - In situ, modeled, and earth observation monitoring of surface water availability in West African rangelands","interactions":[],"lastModifiedDate":"2025-07-08T15:28:17.235284","indexId":"70268817","displayToPublicDate":"2025-06-26T10:21:12","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7170,"text":"Frontiers in Water","active":true,"publicationSubtype":{"id":10}},"title":"In situ, modeled, and earth observation monitoring of surface water availability in West African rangelands","docAbstract":"<p class=\"mb15\"><strong>Introduction:</strong><span>&nbsp;</span>Rangeland ponds are vital to the livelihoods of pastoral and agropastoral communities in Africa, providing an important source of water for livestock. However, sparse instrumentation across much of Africa makes it extremely challenging to monitor surface water availability in these areas. Model estimates of surface water, for example, as used by the Famine Early Warning Systems Network (FEWS NET) Water Point Viewer, are one of the few operational tools available to monitor surface water stress across pastoral areas of the Sahel and East Africa.</p><p class=\"mb15\"><strong>Methods:</strong><span>&nbsp;</span>Water availability data from these models are difficult to validate. New methods using satellite data to classify surface water provide an opportunity to assess the performance of these tools. This study compares water availability estimates derived from Landsat and Sentinel 1 satellite imagery to<span>&nbsp;</span><i>in situ</i><span>&nbsp;</span>observations and model simulations of water availability in 22 ephemeral ponds located in the Ferlo region of Senegal.</p><p class=\"mb0\"><strong>Results and discussion:</strong><span>&nbsp;</span>The Active-Passive Water Classification (APWC) algorithm detected surface water at each location. Over 2022 and 2023, water was detected in pond locations annually at a frequency of 68.2% for all ponds and at a frequency of 43.8% for ponds with a surface area &lt;10,000 square meters (m<sup>2</sup>). The APWC results outperform global and continental surface water datasets in the Ferlo region. Seasonal water availability was captured in 12 ponds over the 2022 and 2023 seasons. The 12 locations can function as sentinel ponds to monitor local water availability. Study results demonstrate the viability of satellite methods to assess water availability in the region, as well as the challenges to using satellite-based methods to estimate water availability in small ponds.</p>","language":"English","publisher":"Frontiers Media","doi":"10.3389/frwa.2025.1320010","usgsCitation":"Slinski, K., Senay, G.B., Adoum, A., Shukla, S., McNally, A., Rowland, J., Fillol, E., Yatheendradas, S., Funk, C., Hoell, A., and Jasinski, M., 2025, In situ, modeled, and earth observation monitoring of surface water availability in West African rangelands: Frontiers in Water, v. 7, 1320010, 17 p., https://doi.org/10.3389/frwa.2025.1320010.","productDescription":"1320010, 17 p.","ipdsId":"IP-162900","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":492054,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/frwa.2025.1320010","text":"Publisher Index Page"},{"id":491802,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Senegal","otherGeospatial":"Ferlo Region","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -16.125,\n              16.5\n            ],\n            [\n              -16.125,\n              14.6667\n            ],\n            [\n              -14,\n              14.6667\n            ],\n            [\n              -14,\n              16.5\n            ],\n            [\n              -16.125,\n              16.5\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"7","noUsgsAuthors":false,"publicationDate":"2025-06-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Slinski, Kimberly","contributorId":337030,"corporation":false,"usgs":false,"family":"Slinski","given":"Kimberly","email":"","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":942089,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":942090,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Adoum, Alkhalil","contributorId":357639,"corporation":false,"usgs":false,"family":"Adoum","given":"Alkhalil","affiliations":[{"id":85483,"text":"University of California, Climate Hazards Center, Santa Barbara, CA, USA","active":true,"usgs":false}],"preferred":false,"id":942091,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shukla, Shraddhanand","contributorId":224784,"corporation":false,"usgs":false,"family":"Shukla","given":"Shraddhanand","affiliations":[{"id":13549,"text":"UC Santa Barbara Climate Hazards Group","active":true,"usgs":false}],"preferred":false,"id":942092,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McNally, Amy","contributorId":331306,"corporation":false,"usgs":false,"family":"McNally","given":"Amy","affiliations":[{"id":79185,"text":"NASA Goddard Space Flight Center/SAIC","active":true,"usgs":false}],"preferred":false,"id":942093,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rowland, James 0000-0003-4837-3511 rowland@usgs.gov","orcid":"https://orcid.org/0000-0003-4837-3511","contributorId":145846,"corporation":false,"usgs":true,"family":"Rowland","given":"James","email":"rowland@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":942094,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fillol, Erwan","contributorId":357640,"corporation":false,"usgs":false,"family":"Fillol","given":"Erwan","affiliations":[{"id":85484,"text":"Action Contre la Faim, Regional Office for West & Central Africa, Dakar, Senegal","active":true,"usgs":false}],"preferred":false,"id":942095,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Yatheendradas, Soni","contributorId":217737,"corporation":false,"usgs":false,"family":"Yatheendradas","given":"Soni","email":"","affiliations":[{"id":39690,"text":"University of Maryland; NASA GSFC","active":true,"usgs":false}],"preferred":false,"id":942096,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Funk, Chris","contributorId":302160,"corporation":false,"usgs":false,"family":"Funk","given":"Chris","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":942097,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hoell, Andrew","contributorId":331301,"corporation":false,"usgs":false,"family":"Hoell","given":"Andrew","affiliations":[{"id":79182,"text":"NOAA ESRL","active":true,"usgs":false}],"preferred":false,"id":942098,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Jasinski, Michael","contributorId":357641,"corporation":false,"usgs":false,"family":"Jasinski","given":"Michael","affiliations":[{"id":85485,"text":"NASA, Goddard Space Flight Center Department, Greenbelt, MD, USA","active":true,"usgs":false}],"preferred":false,"id":942099,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}}
,{"id":70270650,"text":"70270650 - 2025 - Hybridization and asymmetrical introgression between the vulnerable Gray‐Headed Chickadee and a more abundant congener, the Boreal Chickadee: Implications for conservation","interactions":[],"lastModifiedDate":"2025-08-22T17:01:30.875265","indexId":"70270650","displayToPublicDate":"2025-06-26T09:54:49","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Hybridization and asymmetrical introgression between the vulnerable Gray‐Headed Chickadee and a more abundant congener, the Boreal Chickadee: Implications for conservation","docAbstract":"<p><span>Hybridization is a common process among bird species that can precipitate a mix of positive or negative species outcomes. Particularly for rare populations, detrimental effects of hybridization on demographic growth rates and genetic integrity are of serious concern. In Alaska and a small region of northwestern Canada, the endemic subspecies of Gray-headed Chickadee (</span><i>Poecile cinctus lathami</i><span>) has declined in recent decades from being locally common to being extremely rare. The more widespread Boreal Chickadee (</span><i>P. hudsonicus</i><span>) has become increasingly abundant in areas of sympatry. These changes in abundance may have led to hybridization between Gray-headed Chickadees and Boreal Chickadees. We used a series of analyses to test for signatures of introgression at mitochondrial DNA and nuclear DNA using historical museum samples of both species collected between 1875 and 1979 as well as contemporary Boreal Chickadee samples. In addition, we modeled Gray-headed Chickadee and Boreal Chickadee demographic histories to better understand patterns of effective population size changes and gene flow over time. Introgression of Gray-headed Chickadee nuclear DNA was detected in contemporary and historical Boreal Chickadee populations, and two first-generation hybrid backcrosses were observed in the historical Boreal Chickadee samples. Lack of mitochondrial DNA introgression or backcrossing into the Gray-headed Chickadee historical samples may be an artifact of mate scarcity during the period before local abundances of Boreal Chickadee exceeded Gray-headed Chickadees. Demographic modeling with nuclear loci estimated a low level of symmetric gene flow between Gray-headed Chickadees and Boreal Chickadees since the time of divergence. Our study suggests that hybridization may be linked to Gray-headed Chickadee declines and represents a case study of how museum collections can be used to infer introgression in a population too scarce to directly investigate.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/ece3.71673","usgsCitation":"Armstrong, M., Wilson, R.E., Johnson, J.A., Booms, T.L., Gesmundo, C., Pohlen, Z.M., Leonard, P., and Sonsthagen, S.A., 2025, Hybridization and asymmetrical introgression between the vulnerable Gray‐Headed Chickadee and a more abundant congener, the Boreal Chickadee: Implications for conservation: Ecology and Evolution, v. 15, no. 7, e71673, 24 p., https://doi.org/10.1002/ece3.71673.","productDescription":"e71673, 24 p.","ipdsId":"IP-174990","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":495049,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index 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Lincoln","active":true,"usgs":false}],"preferred":false,"id":946763,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wilson, Robert E. 0000-0003-1800-0183","orcid":"https://orcid.org/0000-0003-1800-0183","contributorId":360072,"corporation":false,"usgs":false,"family":"Wilson","given":"Robert","middleInitial":"E.","affiliations":[{"id":16587,"text":"University of Nebraska Lincoln","active":true,"usgs":false}],"preferred":false,"id":946764,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, James A.","contributorId":360074,"corporation":false,"usgs":false,"family":"Johnson","given":"James","middleInitial":"A.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":946765,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Booms, Travis 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,{"id":70269406,"text":"70269406 - 2025 - A wavier polar jet stream contributed to the mid-20th century winter warming hole in the United States","interactions":[],"lastModifiedDate":"2025-07-22T14:45:49.453559","indexId":"70269406","displayToPublicDate":"2025-06-26T09:40:39","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7751,"text":"AGU Advances","active":true,"publicationSubtype":{"id":10}},"title":"A wavier polar jet stream contributed to the mid-20th century winter warming hole in the United States","docAbstract":"<p><span>Winter waves in the polar jet stream are associated with extreme cold outbreaks and can modulate longer-term winter temperature trends in the mid-latitudes. Recent research has highlighted a positive trend in jet stream waviness from 1990 to 2010, with a hypothesized connection to Arctic amplification of anthropogenic warming. However, an increase in jet stream waviness has also been hypothesized to contribute to the winter “warming hole” (WH) in eastern North America, a cooling phenomenon from 1958–1988, beginning several decades prior to the recent waviness trend. These potentially conflicting hypotheses highlight the uncertainty of long-term jet stream waviness variability prior to the satellite era (1979–present). Here we develop a new record of wintertime jet stream waviness spanning 1901–2023 based on self-organizing maps and nine different temperature and reanalysis data sets with the dual purpose of (a) understanding the historical variability of polar jet stream waviness in the eastern United States, and (b) quantifying the impact of jet stream waviness on WH-era surface temperatures. Our analysis reveals elevated jet stream waviness in the 1960s–1980s that surpassed modern waviness levels, and we find that jet stream waviness contributed to two-thirds of winter WH cooling beginning in 1958. These results are consistent with a strong connection between temperature trends in the eastern U.S. and jet stream troughing but indicate that additional mechanisms also contributed to the WH. Our analysis further highlights that recent increases in jet stream waviness are well within the range of early to mid-20th century variability, prior to the emergence of Arctic amplification.</span></p>","language":"English","publisher":"Wiley","doi":"10.1029/2024AV001399","usgsCitation":"Chalif, J., Osterberg, E., and Partridge, T.F., 2025, A wavier polar jet stream contributed to the mid-20th century winter warming hole in the United States: AGU Advances, v. 6, no. 3, e2024AV001399, 16 p., https://doi.org/10.1029/2024AV001399.","productDescription":"e2024AV001399, 16 p.","ipdsId":"IP-166144","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":492878,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024av001399","text":"Publisher Index Page"},{"id":492730,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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,{"id":70268501,"text":"70268501 - 2025 - Permafrost–wildfire interactions: active layer thickness estimates for paired burned and unburned sites in northern high latitudes","interactions":[],"lastModifiedDate":"2025-06-27T14:34:16.141056","indexId":"70268501","displayToPublicDate":"2025-06-26T09:22:07","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1426,"text":"Earth System Science Data","active":true,"publicationSubtype":{"id":10}},"title":"Permafrost–wildfire interactions: active layer thickness estimates for paired burned and unburned sites in northern high latitudes","docAbstract":"<p><span>As the northern high-latitude permafrost zone experiences accelerated warming, permafrost has become vulnerable to widespread thaw. Simultaneously, wildfire activity across northern boreal forest and Arctic/subarctic tundra regions impacts permafrost stability through the combustion of insulating organic matter, vegetation, and post-fire changes in albedo. Efforts to synthesis the impacts of wildfire on permafrost are limited and are typically reliant on antecedent pre-fire conditions. To address this, we created the FireALT dataset by soliciting data contributions that included thaw depth measurements, site conditions, and fire event details with paired measurements at environmentally comparable burned and unburned sites. The solicitation resulted in 52 466&nbsp;thaw depth measurements from 18&nbsp;contributors across North America and Russia. Because thaw depths were taken at various times throughout the thawing season, we also estimated end-of-season active layer thickness&nbsp;(ALT) for each measurement using a modified version of the Stefan equation. Here, we describe our methods for collecting and quality-checking the data, estimating ALT, the data structure, strengths and limitations, and future research opportunities. The final dataset includes 48 669&nbsp;ALT estimates with 32&nbsp;attributes across 9446&nbsp;plots and 157&nbsp;burned–unburned pairs spanning Canada, Russia, and the United States. The data span fire events from&nbsp;1900 to&nbsp;2022 with measurements collected from&nbsp;2001 to&nbsp;2023. The time since fire ranges from 0 to 114&nbsp;years. The FireALT dataset addresses a key challenge: the ability to assess impacts of wildfire on ALT when measurements are taken at various times throughout the thaw season depending on the time of field campaigns (typically June through August) by estimating ALT at the end-of-season maximum. This dataset can be used to address understudied research areas, particularly algorithm development, calibration, and validation for evolving process-based models as well as extrapolating across space and time, which could elucidate permafrost–wildfire interactions under accelerated warming across the high-northern-latitude permafrost zone. The FireALT dataset is available through the Arctic Data Center (</span><a href=\"https://doi.org/10.18739/A2RN3092P\" data-mce-href=\"https://doi.org/10.18739/A2RN3092P\">https://doi.org/10.18739/A2RN3092P</a><span>, Talucci et al., 2024).</span></p>","language":"English","publisher":"Copernicus Publications","doi":"10.5194/essd-17-2887-2025","usgsCitation":"Talucci, A., Loranty, M., Holloway, J., Rogers, B.M., Alexander, H.D., Baillargeon, N., Baltzer, J.L., Berner, L., Breen, A., Brodt, L., Buma, B., Dean, J., Delcourt, C., Diaz, L., Dieleman, C., Douglas, T.A., Frost, G., Gaglioti, B., Hewitt, R.E., Hollingsworth, T., Jorenson, M., Lara, M.J., Loehman, R.A., Mack, M.C., Manies, K.L., Minions, C., Natali, S., O’Donnell, J.A., Olefeldt, D., Paulson, A., Rocha, A., Saperstein, L., Shestakova, T., Sistla, S., Sizov, O., Soromotin, A., Turetksy, M., Veraverbeke, S., and Walvoord, M.A., 2025, Permafrost–wildfire interactions: active layer thickness estimates for paired burned and unburned sites in northern high latitudes: Earth System Science Data, no. 17, p. 2887-2909, https://doi.org/10.5194/essd-17-2887-2025.","productDescription":"23 p.","startPage":"2887","endPage":"2909","ipdsId":"IP-172058","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":491716,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/essd-17-2887-2025","text":"Publisher Index Page"},{"id":491526,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, Russia, United States","otherGeospatial":"Arctic","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      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,{"id":70268271,"text":"sir20255043 - 2025 - Hydrogeology, water budget, and simulated groundwater availability in the Salt Fork Arkansas River and Chikaskia River alluvial aquifers, northern Oklahoma, 1980–2020","interactions":[],"lastModifiedDate":"2026-01-26T19:24:17.680372","indexId":"sir20255043","displayToPublicDate":"2025-06-25T12:32:42","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":"2025-5043","displayTitle":"Hydrogeology, Water Budget, and Simulated Groundwater Availability in the Salt Fork Arkansas River and Chikaskia River Alluvial Aquifers, Northern Oklahoma, 1980–2020","title":"Hydrogeology, water budget, and simulated groundwater availability in the Salt Fork Arkansas River and Chikaskia River alluvial aquifers, northern Oklahoma, 1980–2020","docAbstract":"<p>The 1973 Oklahoma Groundwater Law (Oklahoma Statute §82–1020.5) requires that the Oklahoma Water Resources Board conduct hydrologic investigations of the State’s aquifers to determine the maximum annual yield for each groundwater basin. The U.S. Geological Survey, in cooperation with the Oklahoma Water Resources Board, conducted an updated hydrologic investigation of the Salt Fork Arkansas River and Chikaskia River alluvial aquifers in northern Oklahoma for the study period spanning 1980–2020 and evaluated the simulated effects of potential groundwater withdrawals on groundwater flow and availability in the Salt Fork Arkansas River alluvial aquifer. A hydrogeologic framework and conceptual model were developed to guide the development of a numerical model.</p><p>Three groundwater-availability scenarios were evaluated by using the calibrated numerical model, which was focused on the Salt Fork Arkansas River alluvial aquifer. These scenarios were used to (1) estimate equal-proportionate-share groundwater withdrawal rates, (2) quantify the potential effects of projected well withdrawals on groundwater storage over a 50-year period, and (3) simulate the potential effects of a hypothetical 10-year drought. The 20-, 40-, and 50-year equal-proportionate-share groundwater withdrawal rates for the Salt Fork Arkansas River alluvial aquifer under normal recharge conditions were about 0.63, 0.58, and 0.57 acre-foot per acre per year, respectively. Projected 50-year groundwater withdrawal scenarios were used to simulate the effects of modified well withdrawal rates. Because well withdrawals were less than 2 percent of the calibrated numerical-model water budget, changes to the well groundwater withdrawal rates had little effect on simulated Salt Fork Arkansas River base flows and groundwater storage in the Salt Fork Arkansas River alluvial aquifer. A hypothetical 10-year drought scenario was used to simulate the potential effects of a prolonged period of reduced recharge on groundwater storage. Groundwater storage at the end of the hypothetical drought period was 14.5 percent less than the groundwater storage of the calibrated numerical model without the simulated drought.<br></p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255043","issn":"2328-0328","collaboration":"Prepared in cooperation with the Oklahoma Water Resources Board","usgsCitation":"Gammill, N.C., and Smith, S.J., 2025, Hydrogeology, water budget, and simulated groundwater availability in the Salt Fork Arkansas River and Chikaskia River alluvial aquifers, northern Oklahoma, 1980–2020: U.S. Geological Survey Scientific Investigations Report 2025–5043, 111 p., https://doi.org/10.3133/sir20255043.","productDescription":"Report: xii, 111 p.; 1 Figure: 11.00 x 17.00 inches: 2 Data Releases","numberOfPages":"128","onlineOnly":"Y","ipdsId":"IP-140395","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":490957,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS Data Release","linkHelpText":"- U.S. Geological Survey National Water Information System database"},{"id":490956,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1KASBTM","text":"USGS Data Release","linkHelpText":"- MODFLOW-NWT model used in hydrogeology and simulated groundwater availability in the Salt Fork Arkansas River aquifer, northern Oklahoma, 1980–2020"},{"id":490955,"rank":5,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2025/5043/sir20255043_fig14.pdf","text":"Figure 14, 11\" X 17\"","size":"9.53 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5043 figure 14"},{"id":490954,"rank":4,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255043/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5043 HTML"},{"id":490953,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5043/sir20255043.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2025-5043 XML"},{"id":490952,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5043/sir20255043.pdf","size":"29.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5043"},{"id":490951,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5043/coverthb.jpg"},{"id":499044,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118659.htm","linkFileType":{"id":5,"text":"html"}},{"id":491120,"rank":8,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5043/images"}],"country":"United States","state":"Oklahoma","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -99,\n              37\n            ],\n            [\n              -99,\n              36.4167\n            ],\n            [\n              -97,\n              36.4167\n            ],\n            [\n              -97,\n              37\n            ],\n            [\n              -99,\n              37\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p>Director, <a data-mce-href=\"https://www.usgs.gov/centers/ot-water\" href=\"https://www.usgs.gov/centers/ot-water\">Oklahoma-Texas Water Science Center</a><br>U.S. Geological Survey<br>1505 Ferguson Lane<br>Austin, TX 78754–4501<br></p><p><a id=\"LPlnkOWAb30f03cb-e6c0-c412-988f-235c353ce0b0\" class=\"OWAAutoLink\" href=\"https://pubs.usgs.gov/contact\" data-auth=\"NotApplicable\" data-olk-copy-source=\"MailCompose\" data-mce-href=\"../contact\">Contact Us- USGS Publications Warehouse</a></p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Hydrogeology of the Salt Fork Arkansas River and Chikaskia River Aquifers and Surrounding Units</li><li>Hydrogeologic Framework</li><li>Conceptual Groundwater-Flow Model and Water Budget</li><li>Numerical Groundwater-Flow Model</li><li>Groundwater-Availability Scenarios</li><li>Model Limitations</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2025-06-25","noUsgsAuthors":false,"publicationDate":"2025-06-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Gammill, Nicole C. 0000-0003-3037-2668","orcid":"https://orcid.org/0000-0003-3037-2668","contributorId":328664,"corporation":false,"usgs":false,"family":"Gammill","given":"Nicole C.","affiliations":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"preferred":false,"id":940666,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, S. Jerrod 0000-0002-9379-8167 sjsmith@usgs.gov","orcid":"https://orcid.org/0000-0002-9379-8167","contributorId":981,"corporation":false,"usgs":true,"family":"Smith","given":"S.","email":"sjsmith@usgs.gov","middleInitial":"Jerrod","affiliations":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"preferred":true,"id":940667,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70274687,"text":"70274687 - 2025 - Karhunen–Loève deep learning method for surrogate modeling and approximate Bayesian parameter estimation","interactions":[],"lastModifiedDate":"2026-04-06T14:26:32.391134","indexId":"70274687","displayToPublicDate":"2025-06-25T09:20:36","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":"Karhunen–Loève deep learning method for surrogate modeling and approximate Bayesian parameter estimation","docAbstract":"<div id=\"d1e1559\" class=\"u-margin-s-bottom\">We evaluate the performance of the Karhunen–Loève<span>&nbsp;</span>Deep Neural Network<span>&nbsp;</span>(KL-DNN) framework for surrogate modeling and approximate Bayesian parameter estimation in partial differential equation models. In the surrogate model, the Karhunen–Loève (KL) expansions are used for the dimensionality reduction of the number of unknown parameters and variables, and a deep neural network is employed to relate the reduced space of parameters to that of the state variables. The KL-DNN surrogate model is used to formulate a maximum-a-posteriori-like least-squares problem, which is randomized to draw samples of the posterior distribution of the parameters.</div><div class=\"u-margin-s-bottom\"><br data-mce-bogus=\"1\"></div><div id=\"d1e1561\" class=\"u-margin-s-bottom\">We test the proposed framework for a hypothetical<span>&nbsp;</span>unconfined aquifer<span>&nbsp;via comparison with the forward MODFLOW and inverse PEST++ iterative ensemble smoother (IES) solutions as well as the state-of-the-art Fourier neural operator (FNO) and deep operator networks (DeepONets) operator learning surrogate models. Our results show that the KL-DNN surrogate model outperforms FNO and DeepONet for forward predictions. For solving&nbsp;inverse problems, the randomized algorithm provides the same or more accurate Bayesian predictions of the parameters than IES as evidenced by the higher log predictive probability of both the estimated parameter field and the forecast hydraulic head. The posterior mean obtained from the randomized algorithm is closer to the reference parameter field than that obtained with FNO as the maximum a posteriori estimate.</span></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.advwatres.2025.105024","usgsCitation":"Wang, Y., Zong, Y., McCreight, J.L., Hughes, J.D., Fienen, M., and Tartakovsky, A., 2025, Karhunen–Loève deep learning method for surrogate modeling and approximate Bayesian parameter estimation: Advances in Water Resources, v. 203, 105024, 16 p., https://doi.org/10.1016/j.advwatres.2025.105024.","productDescription":"105024, 16 p.","ipdsId":"IP-168049","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":502201,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"203","noUsgsAuthors":false,"publicationDate":"2025-06-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Wang, Yuanzhe 0000-0001-9206-6573","orcid":"https://orcid.org/0000-0001-9206-6573","contributorId":369260,"corporation":false,"usgs":false,"family":"Wang","given":"Yuanzhe","affiliations":[{"id":36403,"text":"University of Illinois","active":true,"usgs":false}],"preferred":false,"id":958697,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zong, Yifei 0000-0001-5921-2292","orcid":"https://orcid.org/0000-0001-5921-2292","contributorId":369261,"corporation":false,"usgs":false,"family":"Zong","given":"Yifei","affiliations":[{"id":36403,"text":"University of Illinois","active":true,"usgs":false}],"preferred":false,"id":958698,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCreight, James Lucian 0000-0001-6018-425X","orcid":"https://orcid.org/0000-0001-6018-425X","contributorId":369262,"corporation":false,"usgs":true,"family":"McCreight","given":"James","middleInitial":"Lucian","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":958699,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hughes, Joseph D. 0000-0003-1311-2354 jdhughes@usgs.gov","orcid":"https://orcid.org/0000-0003-1311-2354","contributorId":2492,"corporation":false,"usgs":true,"family":"Hughes","given":"Joseph","email":"jdhughes@usgs.gov","middleInitial":"D.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":958700,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fienen, Michael N. 0000-0002-7756-4651","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":245632,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael N.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":958701,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tartakovsky, Alexandre 0000-0003-2375-318X","orcid":"https://orcid.org/0000-0003-2375-318X","contributorId":317072,"corporation":false,"usgs":false,"family":"Tartakovsky","given":"Alexandre","email":"","affiliations":[{"id":68930,"text":"Civil and Environmental Engineering, University of Illinois, Urbana Champaign, IL, USA","active":true,"usgs":false}],"preferred":false,"id":958702,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70268478,"text":"70268478 - 2025 - Glaciers in Western Canada-conterminous US and Switzerland experience unprecedented mass loss over the last four years (2021–2024)","interactions":[],"lastModifiedDate":"2025-06-27T14:01:50.800891","indexId":"70268478","displayToPublicDate":"2025-06-25T08:57:05","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Glaciers in Western Canada-conterminous US and Switzerland experience unprecedented mass loss over the last four years (2021–2024)","docAbstract":"<p><span>Over the period 2021–2024, glaciers in Western Canada and the conterminous US (WCAN-US), and Switzerland respectively lost mass at rates of 22.2&nbsp;±&nbsp;9.0 and 1.5&nbsp;±&nbsp;0.3&nbsp;Gt&nbsp;yr</span><sup>−1</sup><span>&nbsp;representing a twofold increase in mass loss compared to the period 2010–2020. Since 2020, total ice volume was depleted by 12% (WCAN-US) and 13% (Switzerland). Meteorological conditions that favored high rates of mass loss included low winter snow accumulation, early-season heat waves, and prolonged warm, dry conditions. High transient snow lines, and impurity loading due to wildfires (WCAN-US) or Saharan dust (Switzerland) darkened glaciers and thereby increased mass loss via greater absorbed shortwave radiation available for melt. This ice-albedo feedback will lead to continued high rates of thinning unless recently exposed dark ice and firn at high elevations is buried by seasonal snowfall. Physical models that simulate impurity deposition and movement through firn and ice are needed to improve future projections of glacier mass change.</span></p>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2025GL115235","usgsCitation":"Menounos, B., Huss, M., Marshall, S., Ednie, M., Florentine, C., and Hartl, L., 2025, Glaciers in Western Canada-conterminous US and Switzerland experience unprecedented mass loss over the last four years (2021–2024): Geophysical Research Letters, v. 52, no. 12, e2025GL115235, 10 p., https://doi.org/10.1029/2025GL115235.","productDescription":"e2025GL115235, 10 p.","ipdsId":"IP-168264","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":491714,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2025gl115235","text":"Publisher Index Page"},{"id":491524,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, Switzerland, United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -135.99568879770823,\n              54.91892016736358\n            ],\n            [\n              -135.99568879770823,\n              46.622431752452655\n            ],\n            [\n              -111.83646544244147,\n              46.622431752452655\n            ],\n            [\n              -111.83646544244147,\n              54.91892016736358\n            ],\n            [\n              -135.99568879770823,\n              54.91892016736358\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    },\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              5,\n              48\n            ],\n            [\n              5,\n              45.5\n            ],\n            [\n              12,\n              45.5\n            ],\n            [\n              12,\n              48\n            ],\n            [\n              5,\n              48\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"52","issue":"12","noUsgsAuthors":false,"publicationDate":"2025-06-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Menounos, Brian","contributorId":225514,"corporation":false,"usgs":false,"family":"Menounos","given":"Brian","email":"","affiliations":[{"id":41154,"text":"Geography Program and Natural Resources and Environmental Studies Institute, University of Northern British Columbia","active":true,"usgs":false}],"preferred":false,"id":941479,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Huss, Matthias","contributorId":342088,"corporation":false,"usgs":false,"family":"Huss","given":"Matthias","affiliations":[],"preferred":false,"id":941480,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Marshall, Shawn","contributorId":357456,"corporation":false,"usgs":false,"family":"Marshall","given":"Shawn","affiliations":[{"id":36681,"text":"Environment and Climate Change Canada","active":true,"usgs":false}],"preferred":false,"id":941481,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ednie, Mark","contributorId":357457,"corporation":false,"usgs":false,"family":"Ednie","given":"Mark","affiliations":[{"id":13092,"text":"Geological Survey of Canada","active":true,"usgs":false}],"preferred":false,"id":941482,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Florentine, Caitlyn 0000-0002-7028-0963","orcid":"https://orcid.org/0000-0002-7028-0963","contributorId":205964,"corporation":false,"usgs":true,"family":"Florentine","given":"Caitlyn","email":"","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":941483,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hartl, Lea","contributorId":347731,"corporation":false,"usgs":false,"family":"Hartl","given":"Lea","affiliations":[{"id":82428,"text":"Austrian Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":941484,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70269968,"text":"70269968 - 2025 - Parasite‐mediated competition limits dominant cervid competitor","interactions":[],"lastModifiedDate":"2025-08-07T15:41:00.415168","indexId":"70269968","displayToPublicDate":"2025-06-25T08:35:52","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1466,"text":"Ecology Letters","active":true,"publicationSubtype":{"id":10}},"title":"Parasite‐mediated competition limits dominant cervid competitor","docAbstract":"<p><span>Species interactions structure ecological communities through direct and indirect pathways with ecosystem-wide implications. Despite mounting interest in the importance of indirect interactions, empirical evidence remains limited. Here, we demonstrate the critical role of parasite-mediated competition in driving community outcomes in a multi-species system of conservation and management concern. We leveraged 2 years of detection/non-detection data of moose (</span><i>Alces alces</i><span>) and white-tailed deer (</span><i>Odocoileus virginianus</i><span>) and parasite loads in faecal samples within a hierarchical abundance-mediated interaction model to test hypotheses regarding interactions between these cervids and their shared parasites (</span><i>Parelaphostrongylus tenuis</i><span>,&nbsp;</span><i>Fascioloides magna</i><span>). We demonstrate that moose occupancy was limited by parasite-mediated competition, with no evidence of population-level effects of direct competitive interactions between moose and white-tailed deer. Such evidence of the importance of indirect interactions and resulting community outcomes is critical for species conservation and managing range contractions due to increasing pressures from habitat loss, disease and climate change.</span></p>","language":"English","publisher":"Wiley","doi":"10.1111/ele.70159","usgsCitation":"Grauer, J., Twining, J., Lejeune, M., Frair, J., Schuler, K., Kramer, D., and Fuller, A.K., 2025, Parasite‐mediated competition limits dominant cervid competitor: Ecology Letters, v. 28, no. 6, e70159, 11 p., https://doi.org/10.1111/ele.70159.","productDescription":"e70159, 11 p.","ipdsId":"IP-171932","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":493800,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ele.70159","text":"Publisher Index Page"},{"id":493718,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Adirondack Park","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -74.86450930313202,\n              44.87977988441\n            ],\n            [\n              -74.86450930313202,\n              44.084393619454204\n            ],\n            [\n              -73.3992247689462,\n              44.084393619454204\n            ],\n            [\n              -73.3992247689462,\n              44.87977988441\n            ],\n            [\n              -74.86450930313202,\n              44.87977988441\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"28","issue":"6","noUsgsAuthors":false,"publicationDate":"2025-06-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Grauer, Jennifer A.","contributorId":359241,"corporation":false,"usgs":false,"family":"Grauer","given":"Jennifer A.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":945071,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Twining, Joshua P.","contributorId":349314,"corporation":false,"usgs":false,"family":"Twining","given":"Joshua P.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":945072,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lejeune, Manigandan","contributorId":359243,"corporation":false,"usgs":false,"family":"Lejeune","given":"Manigandan","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":945073,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Frair, Jacqueline L.","contributorId":342845,"corporation":false,"usgs":false,"family":"Frair","given":"Jacqueline L.","affiliations":[{"id":48981,"text":"State University of New York","active":true,"usgs":false}],"preferred":false,"id":945074,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schuler, Krysten L.","contributorId":342869,"corporation":false,"usgs":false,"family":"Schuler","given":"Krysten L.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":945075,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kramer, David W.","contributorId":359247,"corporation":false,"usgs":false,"family":"Kramer","given":"David W.","affiliations":[{"id":37519,"text":"SUNY College of Environmental Science and Forestry","active":true,"usgs":false}],"preferred":false,"id":945076,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fuller, Angela K. 0000-0002-9247-7468 afuller@usgs.gov","orcid":"https://orcid.org/0000-0002-9247-7468","contributorId":3984,"corporation":false,"usgs":true,"family":"Fuller","given":"Angela","email":"afuller@usgs.gov","middleInitial":"K.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":945077,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70268819,"text":"70268819 - 2025 - Network of networks: Time series clustering of AmeriFlux sites","interactions":[],"lastModifiedDate":"2025-07-08T14:25:01.510441","indexId":"70268819","displayToPublicDate":"2025-06-24T09:10:47","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":681,"text":"Agricultural and Forest Meteorology","active":true,"publicationSubtype":{"id":10}},"title":"Network of networks: Time series clustering of AmeriFlux sites","docAbstract":"<p><span>Environmental observation networks, such as AmeriFlux, are foundational for monitoring ecosystem response to climate change, management practices, and natural disturbances; however, their effectiveness depends on their representativeness for the regions or continents. We proposed an empirical, time series approach to quantify the similarity of ecosystem fluxes across AmeriFlux sites. We extracted the diel and seasonal characteristics (i.e., amplitudes, phases) from carbon dioxide, water vapor, energy, and momentum fluxes, which reflect the effects of climate, plant phenology, and ecophysiology on the observations, and explored the potential aggregations of AmeriFlux sites through hierarchical clustering. While net radiation and temperature showed latitudinal clustering as expected, flux variables revealed a more uneven clustering with many small (number of sites &lt; 5), unique groups and a few large (&gt; 100) to intermediate (15–70) groups, highlighting the significant ecological regulations of ecosystem fluxes. Many identified unique groups were from under-sampled ecoregions and biome types of the International Geosphere-Biosphere Programme (IGBP), with distinct flux dynamics compared to the rest of the network. At the finer spatial scale, local topography, disturbance, management, edaphic, and hydrological regimes further enlarge the difference in flux dynamics within the groups. Nonetheless, our clustering approach is a data-driven method to interpret the AmeriFlux network, informing future cross-site syntheses, upscaling, and model-data benchmarking research. Finally, we highlighted the unique and underrepresented sites in the AmeriFlux network, which were found mainly in Hawaii and Latin America, mountains, and at under-sampled IGBP types (e.g., urban, open water), motivating the incorporation of new/unregistered sites from these groups.</span></p>","language":"English","publisher":"Elsevier B.V.","doi":"10.1016/j.agrformet.2025.110686","usgsCitation":"Reed, D., Chu, H., Peter, B.G., Chen, J., Abraha, M., Amiro, B., Anderson, R.G., Arain, M., Arruda, P., Barron-Gafford, G.A., Bernacchi, C., Beverly, D., Biraud, S., Black, T.A., Blanken, P.D., Bohrer, G., Bowler, R., Bowling, D., Bret-Harte, M., Bretfeld, M., Brunsell, N., Bullock, S., Celis, G., Chen, X., Classen, A., Cook, D., Cueva, A., Dalmagro, H.J., Davis, K.J., Desai, A., Duff, A., Dunn, A., Durden, D., Edgar, C.W., Euskirchen, E., Bracho, R., Ewers, B.E., Flanagan, L.B., Florian, C.R., Foord, V., Forbrich, I., Forsythe, B., Frank, J., Garatuza-Payan, J., Goslee, S., Gough, C.M., Green, M.B., Griffis, T., Helbig, M., Hill, A., Hinkle, R., Horne, J., Humphreys, E., Ikawa, H., Iwahana, G., Jassal, R., Johnson, B.K., Johnson, M.S., Kannenberg, S., Kelsey, E., King, J., Knowles, J.F., Knox, S., Kobayashi, H., Kolb, T., Kolka, R., Krauss, K., Kutzbach, L., Lamb, B.T., Law, B.E., Lee, S., Lee, X., Liu, H., Loescher, H.W., Malone, S.L., Matamala, R., Mauritz, M., Metzger, S., Meyer, G., Mitra, B., Munger, J., Nesic, Z., Noormets, A., O'Halloran, T., O'Keeffe, P., Oberbauer, S.F., Oechel, W., Oikawa, P., Olivas, P., Ouimette, A., Pastorello, G., Perez-Quezada, J., Phillips, C., Posse, G., Qu, B., Quinton, W.L., Reba, M.L., Richardson, A.D., Picasso, V., Rocha, A., Rodriguez, J., Ruzol, R., Saleska, S., Scott, R.L., Schreiner-McGraw, A.P., Schuur, E., Silveira, M., Sonnentag, O., Spittlehouse, D., Staebler, R., Starr, G., Staudhammer, C., Still, C., Sturtevant, C., Sullivan, R., Suyker, A., Trejo, D., Ueyama, M., Vargas, R., Viner, B., Vivoni, E.R., Wang, D., Ward, E.J., Wiesner, S., Windham-Myers, L., Yannick, D., Yepez, E., Zenone, T., Zhao, J., and Zona, D., 2025, Network of networks: Time series clustering of AmeriFlux sites: Agricultural and Forest Meteorology, v. 372, 110686, 18 p., https://doi.org/10.1016/j.agrformet.2025.110686.","productDescription":"110686, 18 p.","ipdsId":"IP-167572","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":37277,"text":"WMA - 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Obregon, Sonora, Mexico 85000","active":true,"usgs":false}],"preferred":false,"id":942234,"contributorType":{"id":1,"text":"Authors"},"rank":127},{"text":"Zenone, Terenzio","contributorId":357698,"corporation":false,"usgs":false,"family":"Zenone","given":"Terenzio","affiliations":[{"id":85536,"text":"Institute of Research on Terrestrial Ecosystems (IRET) National Research Council (CNR), Via Castellino 111, 80131 Napoli, Italy","active":true,"usgs":false}],"preferred":false,"id":942235,"contributorType":{"id":1,"text":"Authors"},"rank":128},{"text":"Zhao, Junbin","contributorId":357699,"corporation":false,"usgs":false,"family":"Zhao","given":"Junbin","affiliations":[{"id":85537,"text":"Department of Biogeochemistry and Soil Quality, Norwegian Institute of Bioeconomy Research, Ås, 1433, Norway","active":true,"usgs":false}],"preferred":false,"id":942236,"contributorType":{"id":1,"text":"Authors"},"rank":129},{"text":"Zona, Donatella","contributorId":330087,"corporation":false,"usgs":false,"family":"Zona","given":"Donatella","affiliations":[{"id":78805,"text":"University of Sheffield, Western Bank, Sheffield, S10 2TN, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":942237,"contributorType":{"id":1,"text":"Authors"},"rank":130}]}}
,{"id":70268358,"text":"ofr20251033 - 2025 - Select elements of concern in surface water of three hydrologic basins (Delaware River, Illinois River, and Upper Colorado River)—Data screening for the development of spatial and temporal models","interactions":[],"lastModifiedDate":"2025-06-24T13:43:22.987262","indexId":"ofr20251033","displayToPublicDate":"2025-06-23T14:10:00","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":"2025-1033","displayTitle":"Select Elements of Concern in Surface Water of Three Hydrologic Basins (Delaware River, Illinois River, and Upper Colorado River)—Data Screening for the Development of Spatial and Temporal Models","title":"Select elements of concern in surface water of three hydrologic basins (Delaware River, Illinois River, and Upper Colorado River)—Data screening for the development of spatial and temporal models","docAbstract":"<p>The report focuses on the screening of previously published concentration data associated with 12 elements of concern (aluminum, arsenic, cadmium, chromium, copper, iron, mercury, manganese, lead, selenium, uranium, and zinc) measured in stream surface waters of three hydrologic basins (Delaware River Basin, Illinois River Basin, and the Upper Colorado River Basin). The purpose of this analysis is to determine what subsets of the original dataset (containing more than 1,500,000 observations) may be most suitable for each of two types of modeling efforts. The first type of modeling envisions a machine learning approach to determine which geospatial attributes are most significant in describing the spatial distribution of elemental concentrations within a basin. The second type of modeling envisions a stepwise regression approach to develop multivariable models that can be used to determine high resolution time-series estimates of elemental concentrations or loads at discrete U.S. Geological Survey real-time stream surface water sites. These site-specific temporal models are based on continuous measurements of available discharge and (or) in situ sensor data (temperature, pH, turbidity, dissolved oxygen, specific conductance, and (or) fluorescent dissolved organic matter) as the explanatory variables. The data screening for both model types considered historical trends in analytical methods and detection quantitation limits, the extent of censored data, data density, and environmental relevance with respect to three U.S. Environmental Protection Agency water quality thresholds (drinking water guidelines, human health criteria, and aquatic life criteria). The result of this analysis was the production of a final list of potential models deemed suitable for further development based upon the data exclusion (or inclusion) scheme developed herein for each model type. In both cases, the final models included mostly the three crustal elements (iron, manganese, and aluminum) that are found at comparatively high concentrations in surface water, whereas most of the more pernicious elements were excluded from the final model lists owing to various data limitations. The one exception to this was arsenic, for which the existing data were sufficient at three U.S. Geological Survey real-time sites for potential further development of time-series models.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251033","programNote":"Water Quality Program","usgsCitation":"Marvin-DiPasquale, M.C., McCleskey, R.B., Sullivan, S.L., Root, J.C., Seawolf, S.M., Ransom, K.M., Wherry, S.A., Kakouros, E., and Baesman, S., 2025, Select elements of concern in surface water of three hydrologic basins (Delaware River, Illinois River, and Upper Colorado River)—Data screening for the development of spatial and temporal models: U.S. Geological Survey Open-File Report 2025–1033, 25 p., https://doi.org/10.3133/ofr20251033.","productDescription":"Report: v, 25 p.; 2 Data Releases","numberOfPages":"25","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-151463","costCenters":[{"id":37277,"text":"WMA - Earth 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href=\"https://pubs.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Section I: Data Distribution by Element, Fraction and Hydrologic Basin</li><li>Section II: Analytical Methods and Detection Quantitation Limits</li><li>Section III. Analysis of Censored Data</li><li>Section IV: Median EoC Concentrations by Catchment</li><li>Section V: Decision Tree for Geospatial—Machine Learning Models</li><li>Section VI: Analysis of EoC Concentration Data at USGS Real-Time Sites</li><li>Section VII: Ongoing Modeling Efforts</li><li>Summary</li><li>Acknowledgments</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"publishedDate":"2025-06-23","noUsgsAuthors":false,"publicationDate":"2025-06-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Marvin-DiPasquale, Mark C. 0000-0002-8186-9167 mmarvin@usgs.gov","orcid":"https://orcid.org/0000-0002-8186-9167","contributorId":1485,"corporation":false,"usgs":true,"family":"Marvin-DiPasquale","given":"Mark","email":"mmarvin@usgs.gov","middleInitial":"C.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":941074,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCleskey, R. Blaine 0000-0002-2521-8052","orcid":"https://orcid.org/0000-0002-2521-8052","contributorId":205663,"corporation":false,"usgs":true,"family":"McCleskey","given":"R. Blaine","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":941075,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sullivan, Samantha L. 0000-0002-9462-0029","orcid":"https://orcid.org/0000-0002-9462-0029","contributorId":205316,"corporation":false,"usgs":true,"family":"Sullivan","given":"Samantha","email":"","middleInitial":"L.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":941076,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Root, Jonathan Casey 0000-0003-0537-4418","orcid":"https://orcid.org/0000-0003-0537-4418","contributorId":223107,"corporation":false,"usgs":true,"family":"Root","given":"Jonathan","email":"","middleInitial":"Casey","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":941077,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Seawolf, Serena M. 0000-0002-9254-4173","orcid":"https://orcid.org/0000-0002-9254-4173","contributorId":305711,"corporation":false,"usgs":true,"family":"Seawolf","given":"Serena M.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":941078,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ransom, Katherine M. 0000-0001-6195-7699","orcid":"https://orcid.org/0000-0001-6195-7699","contributorId":192230,"corporation":false,"usgs":false,"family":"Ransom","given":"Katherine","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":941079,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wherry, Susan 0000-0002-6749-8697 swherry@usgs.gov","orcid":"https://orcid.org/0000-0002-6749-8697","contributorId":140159,"corporation":false,"usgs":true,"family":"Wherry","given":"Susan","email":"swherry@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":941080,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kakouros, Evangelos 0000-0002-4778-4039 kakouros@usgs.gov","orcid":"https://orcid.org/0000-0002-4778-4039","contributorId":2587,"corporation":false,"usgs":true,"family":"Kakouros","given":"Evangelos","email":"kakouros@usgs.gov","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":941081,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Baesman, Shaun 0000-0003-0741-8269 sbaesman@usgs.gov","orcid":"https://orcid.org/0000-0003-0741-8269","contributorId":3478,"corporation":false,"usgs":true,"family":"Baesman","given":"Shaun","email":"sbaesman@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":941082,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70268459,"text":"70268459 - 2025 - Widespread thiamine deficiency in California salmon linked to an anchovy-dominated marine prey base","interactions":[],"lastModifiedDate":"2025-06-26T16:16:28.64751","indexId":"70268459","displayToPublicDate":"2025-06-23T11:05:17","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3164,"text":"Proceedings of the National Academy of Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Widespread thiamine deficiency in California salmon linked to an anchovy-dominated marine prey base","docAbstract":"<p><span>Thiamine (vitamin B</span><sub>1</sub><span>) deficiency in marine systems is a globally significant threat to marine life. In 2020, newly hatched Chinook salmon (</span><i>Oncorhynchus tshawytscha</i><span>) fry in California’s Central Valley (CCV) hatcheries swam in corkscrew patterns and died at unusually high rates due to a lack of this essential vitamin. We subsequently investigated the impacts and causes of thiamine deficiency in California’s anadromous salmonids. Our laboratory studies defined the relationship between thiamine concentrations in Chinook salmon eggs and early life-stage survival in offspring; we used these data to develop a model that estimated 26 to 48% thiamine-dependent fry mortality across consecutive years (2020–2021) for winter-run Chinook salmon. We established an egg surveillance effort that found widespread thiamine deficiency in CCV Chinook salmon in 2020 and 2021, and emerging thiamine deficiency in Klamath River and Trinity River coho salmon (</span><i>Oncorhynchus kisutch</i><span>) in 2021. We determined that thiamine injections into adults raised egg thiamine concentrations above levels found to impact early life-stage survival and swimming behavior. Ocean surveys, prey nutrition, salmon gut contents, and stable isotope data link thiamine deficiency to an ocean diet dominated by a booming population of northern anchovy (</span><i>Engraulis mordax</i><span>). This forage fish had low thiamine, high lipid, and high thiaminase activity levels consistent with both a thiaminase and oxidative stress hypothesis for causing thiamine deficiency in California salmon. Our research suggests California’s already stressed anadromous salmonids will continue to be impacted by thiamine deficiency as long as their ocean forage base and diet are dominated by northern anchovy.</span></p>","language":"English","publisher":"National Academy of Sciences","doi":"10.1073/pnas.2426011122","usgsCitation":"Mantua, N., Bell, H.M., Todgham, A.E., Daniels, M.E., Rinchard, J., Ludwig, J.R., Field, J., Lindley, S., Rowland, F.E., Richter, C.A., Walters, D., Finney, B., Haskell, A., Tillitt, D., Honeyfield, D.C., Lipscomb, T.N., Kwak, K., Kindopp, J., Cocherell, D.E., Ward, A., Williams, T.H., Harding, J., Fangue, N., Jeffres, C., Ruiz-Cooley, R., Litvin, S., Foott, S., Adkison, M., Kormos, B., Harte, P., Colwell, F.S., Suffridge, C., Shannon, K., Cranford, A., Ambrose, C., Reed, A.N., and Johnson, R.C., 2025, Widespread thiamine deficiency in California salmon linked to an anchovy-dominated marine prey base: Proceedings of the National Academy of Sciences, v. 122, no. 26, e2426011122, 12 p., https://doi.org/10.1073/pnas.2426011122.","productDescription":"e2426011122, 12 p.","ipdsId":"IP-168561","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":492035,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/12232615","text":"External Repository"},{"id":491392,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"122","issue":"26","noUsgsAuthors":false,"publicationDate":"2025-06-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Mantua, 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Center","active":true,"usgs":true}],"preferred":true,"id":941358,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Finney, Bruce P.","contributorId":267775,"corporation":false,"usgs":false,"family":"Finney","given":"Bruce P.","affiliations":[{"id":38154,"text":"Idaho State University","active":true,"usgs":false}],"preferred":false,"id":941367,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Haskell, Anne R. Distajo","contributorId":357444,"corporation":false,"usgs":false,"family":"Haskell","given":"Anne R. Distajo","affiliations":[],"preferred":false,"id":941415,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Tillitt, Donald 0000-0002-8278-3955","orcid":"https://orcid.org/0000-0002-8278-3955","contributorId":301911,"corporation":false,"usgs":false,"family":"Tillitt","given":"Donald","affiliations":[{"id":12545,"text":"USGS retired","active":true,"usgs":false}],"preferred":false,"id":941343,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Honeyfield, Dale C. 0000-0003-3034-2047 honeyfie@usgs.gov","orcid":"https://orcid.org/0000-0003-3034-2047","contributorId":2774,"corporation":false,"usgs":true,"family":"Honeyfield","given":"Dale","email":"honeyfie@usgs.gov","middleInitial":"C.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":941344,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Lipscomb, Taylor N.","contributorId":298023,"corporation":false,"usgs":false,"family":"Lipscomb","given":"Taylor","email":"","middleInitial":"N.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":941345,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Kwak, Kevin","contributorId":357392,"corporation":false,"usgs":false,"family":"Kwak","given":"Kevin","affiliations":[{"id":12939,"text":"California Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":941360,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Kindopp, Jason","contributorId":357388,"corporation":false,"usgs":false,"family":"Kindopp","given":"Jason","affiliations":[{"id":37342,"text":"California Department of Water Resources","active":true,"usgs":false}],"preferred":false,"id":941354,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Cocherell, Dennis E.","contributorId":174644,"corporation":false,"usgs":false,"family":"Cocherell","given":"Dennis","email":"","middleInitial":"E.","affiliations":[{"id":13461,"text":"U.C. 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Davis","active":true,"usgs":false}],"preferred":false,"id":941363,"contributorType":{"id":1,"text":"Authors"},"rank":30},{"text":"Colwell, Frederick S.","contributorId":140946,"corporation":false,"usgs":false,"family":"Colwell","given":"Frederick","email":"","middleInitial":"S.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":941364,"contributorType":{"id":1,"text":"Authors"},"rank":31},{"text":"Suffridge, Christopher P.","contributorId":357400,"corporation":false,"usgs":false,"family":"Suffridge","given":"Christopher P.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":941365,"contributorType":{"id":1,"text":"Authors"},"rank":32},{"text":"Shannon, Kelly","contributorId":329636,"corporation":false,"usgs":false,"family":"Shannon","given":"Kelly","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":941366,"contributorType":{"id":1,"text":"Authors"},"rank":33},{"text":"Cranford, Amanda","contributorId":357405,"corporation":false,"usgs":false,"family":"Cranford","given":"Amanda","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":941371,"contributorType":{"id":1,"text":"Authors"},"rank":34},{"text":"Ambrose, Charlotte","contributorId":357406,"corporation":false,"usgs":false,"family":"Ambrose","given":"Charlotte","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":941372,"contributorType":{"id":1,"text":"Authors"},"rank":35},{"text":"Reed, Aimee N.","contributorId":302394,"corporation":false,"usgs":false,"family":"Reed","given":"Aimee","email":"","middleInitial":"N.","affiliations":[{"id":36223,"text":"Oregon Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":941373,"contributorType":{"id":1,"text":"Authors"},"rank":36},{"text":"Johnson, Rachel C.","contributorId":196877,"corporation":false,"usgs":false,"family":"Johnson","given":"Rachel","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":941374,"contributorType":{"id":1,"text":"Authors"},"rank":37}]}}
,{"id":70272025,"text":"70272025 - 2025 - Automated methods for processing camera trap video data for distance sampling","interactions":[],"lastModifiedDate":"2025-11-13T16:59:28.553199","indexId":"70272025","displayToPublicDate":"2025-06-23T09:52:49","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2984,"text":"Pacific Conservation Biology","active":true,"publicationSubtype":{"id":10}},"title":"Automated methods for processing camera trap video data for distance sampling","docAbstract":"<p>Context</p><p><span>Population monitoring is an essential need for tracking biodiversity and judging efficacy of conservation management actions, both globally and in the Pacific. However, population monitoring efforts are often temporally inconsistent and limited to small scales. Motion-activated cameras (‘camera traps’)&nbsp;offer a way to cost-effectively monitor populations, but they also generate large amounts of data that are time intensive to process.</span></p><p><span>Aims</span></p><p><span>To develop an automated pipeline for processing videos of ungulates (Philippine deer,&nbsp;<i>Rusa marianna</i>;&nbsp;and pigs,&nbsp;<i>Sus scrofa</i>) on Andersen Air Force Base in Guam.</span></p><p><span>Methods</span></p><p><span>We processed camera videos with a machine learning model for object detection and classification. To estimate density using distance sampling methods, we used a separate machine learning model to estimate the distance of target animals from the camera. We compared density estimates generated using manual versus automated methods and assessed accuracy and processing time saved.</span></p><p><span>Key results</span></p><p><span>The object detection and classification model achieved an overall accuracy &gt;80% and F1 score ≥0.9 and saved 36.9&nbsp;h of processing time. The automated distance estimation was fairly accurate, with a 1.1&nbsp;m (±1.4&nbsp;m) difference from manual distance estimates, and saved 16.8&nbsp;h of processing time. Density estimates did not differ substantially between manual and automated distance estimation.</span></p><p><span>Conclusions</span></p><p><span>Machine learning models accurately processed camera videos, allowing efficient estimates of density from camera data.</span></p><p><span>Implications</span></p><p><span>Further adoption of motion-activated cameras coupled with automated processing could lead to continuous, large-scale monitoring of populations, helping to understand and address changes in biodiversity.</span></p>","language":"English","publisher":"CSIRO Publishing","doi":"10.1071/PC25008","usgsCitation":"Bak, T., Camp, R.J., Burt, M.D., and Vogt, S., 2025, Automated methods for processing camera trap video data for distance sampling: Pacific Conservation Biology, v. 31, no. 4, PC25008, 11 p., https://doi.org/10.1071/PC25008.","productDescription":"PC25008, 11 p.","ipdsId":"IP-166069","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":496425,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1071/pc25008","text":"Publisher Index Page"},{"id":496411,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Guam","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              144.8850934540036,\n              13.611360165979548\n            ],\n            [\n              144.8850934540036,\n              13.529004288552699\n            ],\n            [\n              144.95944459854542,\n              13.529004288552699\n            ],\n            [\n              144.95944459854542,\n              13.611360165979548\n            ],\n            [\n              144.8850934540036,\n              13.611360165979548\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"31","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-06-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Bak, Trevor","contributorId":292157,"corporation":false,"usgs":false,"family":"Bak","given":"Trevor","affiliations":[{"id":13341,"text":"Hawai‘i Cooperative Studies Unit, University of Hawai‘i at Hilo","active":true,"usgs":false}],"preferred":false,"id":949759,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Camp, Richard J. 0000-0001-7008-923X rick_camp@usgs.gov","orcid":"https://orcid.org/0000-0001-7008-923X","contributorId":189964,"corporation":false,"usgs":true,"family":"Camp","given":"Richard","email":"rick_camp@usgs.gov","middleInitial":"J.","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true},{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":949760,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Burt, Matthew D.","contributorId":361976,"corporation":false,"usgs":false,"family":"Burt","given":"Matthew","middleInitial":"D.","affiliations":[{"id":84860,"text":"Naval Facilities Marianas","active":true,"usgs":false}],"preferred":false,"id":949761,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vogt, Scott","contributorId":355926,"corporation":false,"usgs":false,"family":"Vogt","given":"Scott","affiliations":[{"id":84860,"text":"Naval Facilities Marianas","active":true,"usgs":false}],"preferred":false,"id":949762,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70269057,"text":"70269057 - 2025 - Leveraging wildfire to augment forest management and amplify forest resilience","interactions":[],"lastModifiedDate":"2025-07-15T14:16:45.890932","indexId":"70269057","displayToPublicDate":"2025-06-22T09:12:45","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Leveraging wildfire to augment forest management and amplify forest resilience","docAbstract":"<p><span>Successive catastrophic wildfire seasons in western North America have escalated the urgency around reducing fire risk to communities and ecosystems. In historically frequent-fire forests, fuel buildup as a result of fire exclusion is contributing to increased fire severity. The probability of high-severity fire can be reduced by active forest management that reduces fuels, prompting federal and state agencies to commit significant resources to increase the pace and scale of fuel reduction treatments. However, lower severity areas of wildfires also have the potential to act as “treatments,” and even catastrophic fires with large areas of high severity can still have substantial areas of lower severity fire that may be improving forest conditions locally. We quantified active management and wildfire severity across yellow pine and mixed conifer (YPMC) forests in the Sierra Nevada of California over a 22-year period (2001–2022). We did not detect increases in the area treated through time, but the area of beneficial wildfire (low to moderate severity) increased substantially, exceeding active treatment area in 8 of 22 years. Overall, beneficial wildfire treated ~17% more area than all treatments combined, and roughly four times more area than fire-related treatments alone. We then used disturbance history to evaluate resistance to high-severity wildfire and forest loss across the YPMC range. Of the 2.3 million ha YPMC of forests in 2001, 20% lost mature forests due to high-severity fire by 2022, which is nearly half of all YPMC area burned. Most of the landscape (47%) remains at risk of high-severity fire because it had no restorative disturbances, but 33% of the study area has some level of resistance to high-severity wildfire. In these areas, resistance will need to be enhanced and maintained over time via active management or managed wildfire, but these treatment needs will likely outpace capacity even under optimistic implementation scenarios. Given limited resources for implementing active management and the likelihood of a more fiery future, incorporating beneficial wildfire into landscape-level treatment planning has the potential to amplify the impact of active management treatments.</span></p>","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.70306","usgsCitation":"Shive, K., Knight, C.A., Steel, Z.L., Stanley, C., and Wilson, K., 2025, Leveraging wildfire to augment forest management and amplify forest resilience: Ecosphere, v. 16, no. 6, e70306, 23 p., https://doi.org/10.1002/ecs2.70306.","productDescription":"e70306, 23 p.","ipdsId":"IP-171515","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":492491,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.70306","text":"Publisher Index Page"},{"id":492239,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Nevada","otherGeospatial":"Sierra Nevada mountains","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -117.49191600648966,\n              35.65099754384805\n            ],\n            [\n              -118.41892399844924,\n              37.519967279694015\n            ],\n            [\n              -119.44265195907964,\n              39.671467955184795\n            ],\n            [\n              -120.0832792618448,\n              40.51478566722406\n            ],\n            [\n              -122.12439984049409,\n              40.20490455276678\n            ],\n            [\n              -121.63327673827555,\n              39.70452920126846\n            ],\n            [\n              -121.05326397512911,\n              38.48323730930633\n            ],\n            [\n              -118.5302083539183,\n              35.15085062254475\n            ],\n            [\n              -117.49191600648966,\n              35.65099754384805\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"16","issue":"6","noUsgsAuthors":false,"publicationDate":"2025-06-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Shive, Kristen I. 0000-0002-5633-2528","orcid":"https://orcid.org/0000-0002-5633-2528","contributorId":352132,"corporation":false,"usgs":false,"family":"Shive","given":"Kristen I.","affiliations":[{"id":84117,"text":"University of California Cooperative Extension and Department of Environmental Science","active":true,"usgs":false}],"preferred":false,"id":943173,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Knight, Clarke Alexandra 0000-0003-0002-6959","orcid":"https://orcid.org/0000-0003-0002-6959","contributorId":288487,"corporation":false,"usgs":true,"family":"Knight","given":"Clarke","email":"","middleInitial":"Alexandra","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":943174,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Steel, Zachary L 0000-0002-1659-3141","orcid":"https://orcid.org/0000-0002-1659-3141","contributorId":329821,"corporation":false,"usgs":false,"family":"Steel","given":"Zachary","email":"","middleInitial":"L","affiliations":[{"id":6643,"text":"University of California - Berkeley","active":true,"usgs":false}],"preferred":false,"id":943175,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stanley, Charlotte K. 0000-0002-5019-4427","orcid":"https://orcid.org/0000-0002-5019-4427","contributorId":358047,"corporation":false,"usgs":false,"family":"Stanley","given":"Charlotte K.","affiliations":[{"id":85576,"text":"The Nature Conservancy, San Francisco, California","active":true,"usgs":false}],"preferred":false,"id":943176,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wilson, Kristen N. 0000-0003-4769-2086","orcid":"https://orcid.org/0000-0003-4769-2086","contributorId":358048,"corporation":false,"usgs":false,"family":"Wilson","given":"Kristen N.","affiliations":[{"id":85576,"text":"The Nature Conservancy, San Francisco, California","active":true,"usgs":false}],"preferred":false,"id":943177,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70271508,"text":"70271508 - 2025 - Impact of gas/liquid phase change of CO2 during injection for sequestration","interactions":[],"lastModifiedDate":"2025-09-18T15:18:17.86038","indexId":"70271508","displayToPublicDate":"2025-06-20T09:58:59","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":22362,"text":"Journal of the Mechanics and Physics of Solids","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Impact of gas/liquid phase change of CO<sub>2</sub>  during injection for sequestration","title":"Impact of gas/liquid phase change of CO2 during injection for sequestration","docAbstract":"<div id=\"d1e765\" class=\"u-margin-s-bottom\"><span>CO<sub>2</sub> </span>sequestration in deep saline formations is an effective and important process to control the rapid rise in CO<sub>2</sub><span>&nbsp;</span>emissions. The process of injecting CO<sub>2</sub><span>&nbsp;</span>requires reliable predictions of the stress in the formation and the fluid pressure distributions – particularly since monitoring of the CO<sub>2</sub><span> migration is difficult – to mitigate leakage, prevent&nbsp;induced seismicity, and analyze&nbsp;wellbore&nbsp;stability. A key aspect of CO<sub>2</sub></span><span>&nbsp;</span>is the gas–liquid phase transition at the temperatures and pressures of relevance to leakage and sequestration, which has been recognized as being critical for accurate predictions but has been challenging to model without<span>&nbsp;</span><i>ad hoc</i><span>&nbsp;</span>empiricisms.</div><div id=\"d1e811\" class=\"u-margin-s-bottom\">This paper presents a robust multiphase thermodynamics-based poromechanics model to capture the complex phase transition behavior of CO<sub>2</sub><span> and predict the stress and pressure distribution under super- and sub- critical conditions during the&nbsp;injection process. A&nbsp;finite element implementation&nbsp;of the model is applied to analyze the behavior of a multiphase porous system with CO<sub>2</sub></span><span>&nbsp;</span>as it displaces the fluid brine phase. We find that if CO<sub>2</sub><span>&nbsp;</span>undergoes a phase transition in the geologic reservoir, the spatial variation of the density is significantly affected, and the migration mobility of CO<sub>2</sub><span>&nbsp;</span>decreases in the reservoir. A key feature of our approach is that we do not<span>&nbsp;</span><i>a priori</i><span>&nbsp;</span>assume the location of the CO<sub>2</sub><span>&nbsp;</span>gas/liquid interface – or even if it occurs at all – but rather, this is a prediction of the model, along with the spatial variation of the phase of CO<sub>2</sub><span>&nbsp;</span>and the change of the saturation profile due to the phase change.</div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.jmps.2025.106232","usgsCitation":"Karimi, M., Cochran, E.S., Massoudi, M., Walkington, N., Pozzi, M., and Dayal, K., 2025, Impact of gas/liquid phase change of CO2 during injection for sequestration: Journal of the Mechanics and Physics of Solids, v. 203, 106232, 14 p., https://doi.org/10.1016/j.jmps.2025.106232.","productDescription":"106232, 14 p.","ipdsId":"IP-153941","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":495746,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jmps.2025.106232","text":"Publisher Index Page"},{"id":495712,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"203","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Karimi, M.","contributorId":361549,"corporation":false,"usgs":false,"family":"Karimi","given":"M.","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":948979,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cochran, Elizabeth S. 0000-0003-2485-4484 ecochran@usgs.gov","orcid":"https://orcid.org/0000-0003-2485-4484","contributorId":2025,"corporation":false,"usgs":true,"family":"Cochran","given":"Elizabeth","email":"ecochran@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":948980,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Massoudi, Mehrdad","contributorId":361550,"corporation":false,"usgs":false,"family":"Massoudi","given":"Mehrdad","affiliations":[{"id":64933,"text":"National Energy Technology Laboratory","active":true,"usgs":false}],"preferred":false,"id":948981,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Walkington, Noel","contributorId":361552,"corporation":false,"usgs":false,"family":"Walkington","given":"Noel","affiliations":[{"id":12943,"text":"Carnegie Mellon University","active":true,"usgs":false}],"preferred":false,"id":948982,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pozzi, Matteo","contributorId":361553,"corporation":false,"usgs":false,"family":"Pozzi","given":"Matteo","affiliations":[{"id":12943,"text":"Carnegie Mellon University","active":true,"usgs":false}],"preferred":false,"id":948983,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dayal, Kaushik","contributorId":361555,"corporation":false,"usgs":false,"family":"Dayal","given":"Kaushik","affiliations":[{"id":12943,"text":"Carnegie Mellon University","active":true,"usgs":false}],"preferred":false,"id":948984,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70273158,"text":"70273158 - 2025 - Multiscale framework for assessing land cover change on barrier islands from extreme storms and restoration","interactions":[],"lastModifiedDate":"2025-12-17T16:03:03.075808","indexId":"70273158","displayToPublicDate":"2025-06-20T09:54:40","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2220,"text":"Journal of Coastal Research","active":true,"publicationSubtype":{"id":10}},"title":"Multiscale framework for assessing land cover change on barrier islands from extreme storms and restoration","docAbstract":"<p><span>Often found along the estuarine-marine interface, barrier islands and mainland coastal zones are shaped by tides, currents, extreme storms, and relative sea-level rise. These systems provide ecosystem services such as storm surge and wave attenuation, erosion protection to inland areas, habitat for fish and wildlife, recreation, and tourism. Given the importance of these ecosystems coupled with their dynamic nature, information on how these coastal systems are changing can help to inform natural resource management. Remote sensing advancements have led to an abundance of data for monitoring change in coastal settings. This study developed a multiscale framework that can provide trajectory information from screening-level analyses by using existing or custom moderate spatial resolution land cover maps. Using the north-central Gulf Coast as a case study, the trajectory of land cover area for barrier islands and mainland coastal zones was assessed using several geospatial data sets, including: (1) long-term moderate-resolution remote sensing products with an annual (or more frequent) temporal frequency; (2) a restoration database (</span><i>e.g.</i><span>, beach/dune restoration, sediment placement, and dune enhancement); and (3) a tropical storm database. Due to the coarser spatial resolution of data sets used for screening-level analyses, detailed or application-specific analyses are often needed to reduce uncertainty in smaller changes that may not be captured. These may include land cover change analyses (</span><i>i.e.</i><span>&nbsp;this study), periodic land cover maps with higher spatial resolution and more detailed land cover classes, or elevation-related analyses (</span><i>e.g.</i><span>, dune change or inundation change). Using this framework, abrupt changes in land cover on Dauphin Island, Alabama, resulting from extreme storms were detected using moderate spatial resolution screening-level data, while restoration impact analyses may require higher resolution data. Further, land cover change analyses that incorporate change allocation provide robust information for understanding land cover change in dynamic coastal settings.</span></p>","language":"English","publisher":"Coastal Education and Research Foundation, Inc.","doi":"10.2112/JCOASTRES-D-24-00084.1","usgsCitation":"Enwright, N., Dalyander, P.S., Stuht, C.M., Han, M., Palmsten, M.L., Davenport, T.M., Kingwill, C.J., Steyer, G., and La Peyre, M., 2025, Multiscale framework for assessing land cover change on barrier islands from extreme storms and restoration: Journal of Coastal Research, v. 41, no. 6, p. 1029-1042, https://doi.org/10.2112/JCOASTRES-D-24-00084.1.","productDescription":"14 p.","startPage":"1029","endPage":"1042","ipdsId":"IP-172879","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":497643,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Florida","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -88.40937976247565,\n              30.743725811842268\n            ],\n            [\n              -88.40937976247565,\n              29.56478712694208\n            ],\n            [\n              -83.91845836432758,\n              29.56478712694208\n            ],\n            [\n              -83.91845836432758,\n              30.743725811842268\n            ],\n            [\n              -88.40937976247565,\n              30.743725811842268\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"41","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Enwright, Nicholas 0000-0002-7887-3261","orcid":"https://orcid.org/0000-0002-7887-3261","contributorId":214839,"corporation":false,"usgs":true,"family":"Enwright","given":"Nicholas","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":952525,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dalyander, P. Soupy","contributorId":364329,"corporation":false,"usgs":false,"family":"Dalyander","given":"P.","middleInitial":"Soupy","affiliations":[{"id":81504,"text":"The Water Institute","active":true,"usgs":false}],"preferred":false,"id":952526,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stuht, Casey M.","contributorId":364330,"corporation":false,"usgs":false,"family":"Stuht","given":"Casey","middleInitial":"M.","affiliations":[{"id":83764,"text":"Cherokee Nation System Solutions, contracted to the U.S. Geological Survey","active":true,"usgs":false}],"preferred":false,"id":952527,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Han, Minoo 0000-0002-6009-602X","orcid":"https://orcid.org/0000-0002-6009-602X","contributorId":332099,"corporation":false,"usgs":false,"family":"Han","given":"Minoo","email":"","affiliations":[{"id":79381,"text":"Han Consulting contracted to U.S. Geological Survey","active":true,"usgs":false}],"preferred":false,"id":952528,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Palmsten, Margaret L. 0000-0002-6424-2338","orcid":"https://orcid.org/0000-0002-6424-2338","contributorId":239955,"corporation":false,"usgs":true,"family":"Palmsten","given":"Margaret","email":"","middleInitial":"L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":952529,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Davenport, Theresa M.","contributorId":364331,"corporation":false,"usgs":false,"family":"Davenport","given":"Theresa","middleInitial":"M.","affiliations":[{"id":86808,"text":"Louisiana State University, School of Renewable Natural Resources","active":true,"usgs":false}],"preferred":false,"id":952530,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kingwill, Christopher J.","contributorId":364332,"corporation":false,"usgs":false,"family":"Kingwill","given":"Christopher","middleInitial":"J.","affiliations":[{"id":83764,"text":"Cherokee Nation System Solutions, contracted to the U.S. Geological Survey","active":true,"usgs":false}],"preferred":false,"id":952531,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Steyer, Gregory 0000-0001-7231-0110","orcid":"https://orcid.org/0000-0001-7231-0110","contributorId":218813,"corporation":false,"usgs":true,"family":"Steyer","given":"Gregory","affiliations":[{"id":5064,"text":"Southeast Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":952532,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"La Peyre, Megan 0000-0001-9936-2252 mlapeyre@usgs.gov","orcid":"https://orcid.org/0000-0001-9936-2252","contributorId":79375,"corporation":false,"usgs":true,"family":"La Peyre","given":"Megan","email":"mlapeyre@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":952533,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70268398,"text":"70268398 - 2025 - Numerical simulation of sound-side barrier-island inundation and breaching during Hurricane Dorian (2019)","interactions":[],"lastModifiedDate":"2025-06-25T14:38:37.180303","indexId":"70268398","displayToPublicDate":"2025-06-20T09:31:40","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7357,"text":"JGR Earth Surface","active":true,"publicationSubtype":{"id":10}},"title":"Numerical simulation of sound-side barrier-island inundation and breaching during Hurricane Dorian (2019)","docAbstract":"<p><span>Hurricane-induced morphological changes and associated community hazards along sandy, barrier-island coastlines have been studied primarily from the perspective of ocean-side attack by storm-driven ocean surge and large waves. Thus, our understanding of long-term barrier island morphological change focuses on beach erosion, overwash, and inlet formation. In contrast, outwash events with inundation from the sound side, such as one that occurred in Cape Lookout National Seashore, North Carolina, USA during Hurricane Dorian (September 2019), are understudied. Studying such events can improve understanding of barrier island response and stability for a broader range of conditions. Here, we model the hydrodynamics and morphological evolution of a barrier island using a coupled wave-current-sediment transport modeling system. Wind-driven surge in Pamlico Sound led to overtopping from the sound side, which eroded outwash channels and transported sediment seaward into the nearshore. Simulations reproduce the channel features observed with aerial imagery and provide information not available from the remote-sensing observations, including channel depths (&gt;2&nbsp;m) and the fate of the eroded sand. We found that &gt;99% of the eroded sand was deposited in the nearshore, within 1,000&nbsp;m of the shoreline in depths &lt;10&nbsp;m, suggesting that the deposited sediment remains available for littoral transport and beach recovery. Simulations with combinations of coarse or fine sediment and vegetated or unvegetated landcover indicate that channel position did not vary with grain size or vegetation, while volume of erosion and channel morphology were more responsive to variations in grain size and less responsive to presence of vegetation.</span></p>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2025JF008309","usgsCitation":"Warner, J.C., Sherwood, C.R., Hegermiller, C., Defne, Z., Zambon, J., He, R., Xue, G., Bao, D., Yin, D., and Moulton, M., 2025, Numerical simulation of sound-side barrier-island inundation and breaching during Hurricane Dorian (2019): JGR Earth Surface, v. 130, no. 6, e2025JF008309, 23 p., https://doi.org/10.1029/2025JF008309.","productDescription":"e2025JF008309, 23 p.","ipdsId":"IP-170654","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":491441,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2025jf008309","text":"Publisher Index Page"},{"id":491278,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","otherGeospatial":"Outer Banks","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -76.5,\n              35.25\n            ],\n            [\n              -76.5,\n              34.75\n            ],\n            [\n              -75.5,\n              34.75\n            ],\n            [\n              -75.5,\n              35.25\n            ],\n            [\n              -76.5,\n              35.25\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"130","issue":"6","noUsgsAuthors":false,"publicationDate":"2025-06-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Warner, John C. 0000-0002-3734-8903 jcwarner@usgs.gov","orcid":"https://orcid.org/0000-0002-3734-8903","contributorId":258015,"corporation":false,"usgs":true,"family":"Warner","given":"John","email":"jcwarner@usgs.gov","middleInitial":"C.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":941217,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sherwood, Christopher R. 0000-0001-6135-3553 csherwood@usgs.gov","orcid":"https://orcid.org/0000-0001-6135-3553","contributorId":2866,"corporation":false,"usgs":true,"family":"Sherwood","given":"Christopher","email":"csherwood@usgs.gov","middleInitial":"R.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":941218,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hegermiller, Christie A.","contributorId":357332,"corporation":false,"usgs":false,"family":"Hegermiller","given":"Christie A.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":941219,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Defne, Zafer 0000-0003-4544-4310 zdefne@usgs.gov","orcid":"https://orcid.org/0000-0003-4544-4310","contributorId":5520,"corporation":false,"usgs":true,"family":"Defne","given":"Zafer","email":"zdefne@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":941220,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zambon, Joseph B.","contributorId":336620,"corporation":false,"usgs":false,"family":"Zambon","given":"Joseph B.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":941221,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"He, Ruoying 0000-0001-6158-2292","orcid":"https://orcid.org/0000-0001-6158-2292","contributorId":202189,"corporation":false,"usgs":false,"family":"He","given":"Ruoying","email":"","affiliations":[],"preferred":false,"id":941222,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Xue, George","contributorId":294533,"corporation":false,"usgs":false,"family":"Xue","given":"George","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":941223,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bao, Daoyang","contributorId":294534,"corporation":false,"usgs":false,"family":"Bao","given":"Daoyang","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":941224,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Yin, Dongxiao","contributorId":294535,"corporation":false,"usgs":false,"family":"Yin","given":"Dongxiao","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":941225,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Moulton, Melissa","contributorId":305679,"corporation":false,"usgs":false,"family":"Moulton","given":"Melissa","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":941226,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70274598,"text":"70274598 - 2025 - Near-surface geophysics: Environmental applications","interactions":[],"lastModifiedDate":"2026-04-01T13:53:22.33473","indexId":"70274598","displayToPublicDate":"2025-06-20T08:47:58","publicationYear":"2025","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Near-surface geophysics: Environmental applications","docAbstract":"The field of geophysics encompasses a broad and diverse compilation of methodologies that employs principles of physics to characterize properties of earth materials within the subsurface. While geophysical methods have a long history in resource exploration and studies of Earth’s interior, the subdiscipline of “near-surface geophysics” has evolved in recent decades for examination of the shallow, near-surface environment for a range of purposes ranging from archaeological or forensic investigations to assessment of geologic, hydrologic, biologic, and geochemical properties and processes. “Environmental geophysics” are near-surface geophysical studies and methods that focus on understanding natural systems (e.g., watershed hydrology, groundwater–surface water connections, biophysical processes) as well as research pertaining to anthropogenic impacts and land management, (e.g., contamination and remediation, saltwater intrusion, agricultural practices). This field can be further subdivided into subdisciplines focused on specific topics and applications, such as water resources and hydrology (hydrogeophysics) or biologic and microbial processes (biogeophysics). Studies in environmental geophysics span a range of scales, from pore-scale laboratory tests to watershed-scale or regional field experiments. Methods vary by the nature of physics employed, the specific measurement acquired, and how that data is ultimately processed and analyzed to produce interpretable results. There exists further diversity in the acquisition logistics, geometry, and timing of data collection. Geophysical data can be collected in boreholes (one-dimensional, 1-D, vertical profiles), along survey lines (two-dimensional, 2-D, cross-sections), or in dense sensor arrays or gridded profiles (three-dimensional, 3-D, models). Regarding the temporal aspect, studies can conduct one-time geophysical surveys to obtain detailed imaging of subsurface structure or use timelapse and continuous monitoring to investigate variations in subsurface properties over time. The cumulation of all possible permutations of these factors (method, acquisition geometry, survey design, and target application) results in an immense diversity among environmental geophysical studies. Nevertheless, this field remains unified in the pursuit of understanding natural and human-impacted near-surface environments through geophysical investigations. Here we highlight some key references within environmental geophysics. Resources on geophysical theory, acquisition logistics, processing and inversion workflows, and example case studies are categorized into the most common geophysical classes within Geophysical Methods. Lastly, example references for the dominant types of applications in environmental geophysical studies are catalogued in Environmental Applications.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Oxford Bibliographies","largerWorkSubtype":{"id":11,"text":"Bibliography"},"language":"English","publisher":"Oxford University Press","doi":"10.1093/obo/9780199363445-0146","usgsCitation":"James, S.R., Glaser, D.R., and Garcia, A., 2025, Near-surface geophysics: Environmental applications, chap. <i>of</i> Oxford Bibliographies, HTML Document, https://doi.org/10.1093/obo/9780199363445-0146.","productDescription":"HTML Document","ipdsId":"IP-172909","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":501916,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2025-06-20","publicationStatus":"PW","contributors":{"authors":[{"text":"James, Stephanie R. 0000-0001-5715-253X","orcid":"https://orcid.org/0000-0001-5715-253X","contributorId":260620,"corporation":false,"usgs":true,"family":"James","given":"Stephanie","email":"","middleInitial":"R.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":958466,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Glaser, Dan R.","contributorId":292710,"corporation":false,"usgs":false,"family":"Glaser","given":"Dan","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":958467,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Garcia, Alejandro","contributorId":369112,"corporation":false,"usgs":false,"family":"Garcia","given":"Alejandro","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":958468,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70273971,"text":"70273971 - 2025 - Streamflow regime characterization in the changing boreal ecosystem: Wildfire impacts from stream-to-regional scales","interactions":[],"lastModifiedDate":"2026-02-20T17:06:56.875893","indexId":"70273971","displayToPublicDate":"2025-06-19T09:57:48","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Streamflow regime characterization in the changing boreal ecosystem: Wildfire impacts from stream-to-regional scales","docAbstract":"<p><span id=\"_mce_caret\" data-mce-bogus=\"1\" data-mce-type=\"format-caret\"><span>The boreal ecosystem has experienced significant changes over recent decades as wildfires become more frequent, intense, and severe. As streams are highly prevalent and ecologically relevant, understanding interactions among wildfire and hydrologic patterns is important for effective&nbsp;aquatic ecosystem&nbsp;management. This study used a Bayesian mixture model to classify&nbsp;streamflow&nbsp;regimes from modeled&nbsp;streamflow&nbsp;data for 32,730 stream reaches (totaling 295,880&nbsp;km) across the Yukon and Kuskokwim basins and the Northwestern Boreal Ecosystem in Alaska,&nbsp;USA, and Yukon Territory, Canada. We assessed time since burn and calculated the total length of stream (km) within burn perimeters for each streamflow class from 1985 to 2015. Additionally, we used field observations (2018–2022) to compare streamflow regimes in four burned and four unburned&nbsp;headwater&nbsp;streams (drainage basins ≤150&nbsp;km</span><sup>2</sup><span>) in interior Alaska. Modeled stream reaches were grouped into twenty-two classes and reduced to eleven metaclasses based on similarities in streamflow statistics. These metaclasses formed two broad groups: 1) large rivers with lower variability and strong seasonal signals, and 2) mid- to small-sized tributaries with high variability, frequent high flow events, and weaker seasonal signals. The stream length burned analysis indicated an average increase of 47&nbsp;km per year with first- and second-order streams experiencing more frequent fire. Empirical streamflow metrics from&nbsp;headwater&nbsp;stream gages revealed additional differences in streamflow patterns between burned and unburned streams. This streamflow classification establishes a baseline for understanding boreal stream responses to wildfire, detecting climate-induced regime shifts, and facilitating management and conservation of important boreal&nbsp;aquatic species.</span></span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2025.179770","usgsCitation":"Strohm, D.D., Sergeant, C.J., Paul, J.D., Falke, J.A., 2025, Streamflow regime characterization in the changing boreal ecosystem: Wildfire impacts from stream-to-regional scales: Science of the Total Environment, v. 991, 179770, 14 p., https://doi.org/10.1016/j.scitotenv.2025.179770.","productDescription":"179770, 14 p.","ipdsId":"IP-173153","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":500353,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Boreal Yukon-Kuskokwim study area","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -151.25073280649116,\n              65.22533418311923\n            ],\n            [\n              -151.08883708497373,\n              64.1874521027234\n            ],\n            [\n              -147.81756784832672,\n              64.90432777046252\n            ],\n            [\n              -144.75370718998238,\n              64.258653756473\n            ],\n            [\n              -144.56100126166737,\n              65.39083680482943\n            ],\n            [\n              -148.2119885011982,\n              65.53590677988976\n            ],\n            [\n              -151.25073280649116,\n              65.22533418311923\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"991","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Strohm, Deanna D.","contributorId":366469,"corporation":false,"usgs":false,"family":"Strohm","given":"Deanna","middleInitial":"D.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":955951,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sergeant, Christopher J.","contributorId":140496,"corporation":false,"usgs":false,"family":"Sergeant","given":"Christopher","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":955953,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Paul, Josh D.","contributorId":366470,"corporation":false,"usgs":false,"family":"Paul","given":"Josh","middleInitial":"D.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":955954,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Falke, Jeffrey A. 0000-0002-6670-8250 jfalke@usgs.gov","orcid":"https://orcid.org/0000-0002-6670-8250","contributorId":5195,"corporation":false,"usgs":true,"family":"Falke","given":"Jeffrey","email":"jfalke@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":955952,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70268334,"text":"70268334 - 2025 - Disparate groundwater responses to wildfire","interactions":[],"lastModifiedDate":"2025-06-23T14:34:47.223906","indexId":"70268334","displayToPublicDate":"2025-06-19T09:32:25","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5067,"text":"WIREs Water","active":true,"publicationSubtype":{"id":10}},"title":"Disparate groundwater responses to wildfire","docAbstract":"<p><span>Post-wildfire investigations of groundwater response reveal a range of outcomes, varying from substantial increases to notable decreases in recharge and baseflow, with some studies indicating negligible or short-lived effects. This review assesses these varied responses within five critical categories: climate, vegetation, hydrogeology, fire characteristics, and the cryosphere, examining both short-term (within 2 years) and intermediate (2–10 years post-fire) effects. Despite considerable variability, some consistent patterns emerge. For instance, in hydroclimatic settings where water input and evaporative demand cycles are out of sync, post-wildfire groundwater responses tend to be positive (i.e., increased flux or storage), whereas under low fire severity conditions or in vegetation types that quickly recover, groundwater responses tend to be negative (i.e., decreased flux or storage). We synthesize relevant findings into a compendium of testable hypotheses aimed at explaining the spatiotemporal variability in observed post-wildfire groundwater responses. A recurring theme is the critical influence of the pre-wildfire groundwater regime on expected response and recovery. We identify opportunities for specific improvements in post-wildfire monitoring and modeling that would further advance capabilities to predict groundwater response. A key area for further research is understanding how wildfire effects on snow dynamics and other cryospheric processes translate to changes in groundwater.</span></p>","language":"English","publisher":"Wiley Interdisciplinary Reviews","doi":"10.1002/wat2.70029","usgsCitation":"Walvoord, M.A., Ebel, B., Partridge, T.F., Rey, D., and Rosenberry, D., 2025, Disparate groundwater responses to wildfire: WIREs Water, v. 12, no. 3, e70029, 22 p., https://doi.org/10.1002/wat2.70029.","productDescription":"e70029, 22 p.","ipdsId":"IP-178452","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":491494,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wat2.70029","text":"Publisher Index Page"},{"id":491099,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-06-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Walvoord, Michelle A. 0000-0003-4269-8366","orcid":"https://orcid.org/0000-0003-4269-8366","contributorId":211843,"corporation":false,"usgs":true,"family":"Walvoord","given":"Michelle","email":"","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":940839,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ebel, Brian A. 0000-0002-5413-3963","orcid":"https://orcid.org/0000-0002-5413-3963","contributorId":211845,"corporation":false,"usgs":true,"family":"Ebel","given":"Brian A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":940840,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Partridge, Trevor Fuess 0000-0003-1589-4783","orcid":"https://orcid.org/0000-0003-1589-4783","contributorId":302668,"corporation":false,"usgs":true,"family":"Partridge","given":"Trevor","email":"","middleInitial":"Fuess","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":940841,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rey, David M. 0000-0003-2629-365X","orcid":"https://orcid.org/0000-0003-2629-365X","contributorId":211848,"corporation":false,"usgs":true,"family":"Rey","given":"David M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":940842,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rosenberry, D.O. 0000-0003-0681-5641","orcid":"https://orcid.org/0000-0003-0681-5641","contributorId":38500,"corporation":false,"usgs":true,"family":"Rosenberry","given":"D.O.","affiliations":[],"preferred":true,"id":940843,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70268363,"text":"70268363 - 2025 - Public supply water delivery analysis and estimation for the conterminous United States","interactions":[],"lastModifiedDate":"2025-06-25T13:12:44.133817","indexId":"70268363","displayToPublicDate":"2025-06-19T09:29:44","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Public supply water delivery analysis and estimation for the conterminous United States","docAbstract":"<p><span>Public supply water withdrawals represent 14% of all withdrawals in the conterminous United States (CONUS), supplying approximately 87% of the population with fresh water. Deliveries for public water supply are crucial for associating water use amounts with populations because they often differ from total withdrawals due to wholesales, transfers, losses, and other factors. Understanding these differences helps identify the drivers for each type of delivery. The goal of this study was to compile all available public water supply delivery data for the CONUS and develop a data-driven model to estimate deliveries for all water service areas within the CONUS. Annual deliveries were estimated between 2010 and 2020, encompassing total water deliveries; combined commercial, industrial, and institutional deliveries (CII); and domestic deliveries. Data were compiled for 2,744 water service areas to produce the most comprehensive public water supply delivery data set for the CONUS to date. Three ensemble modeling approaches were developed to estimate total, CII, and domestic per capita (DPC) deliveries using a gradient boosted regression tree modeling approach. Estimates of daily domestic and CII per capita deliveries were generated from these models for approximately 18,800 water service areas, covering most public water systems in the CONUS. Domestic delivery was found to be lowest in the midwestern region and higher in the southern and southwest regions of the United States. Results indicate that climate and land use can be associated with regional differences in DPC delivery. Population metrics and land use were identified as significant contributors to CII delivery estimates.</span></p>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024WR039271","usgsCitation":"Larsen, J., Alzraiee, A.H., Niswonger, R., Martin, D., Buchwald, C.A., Dieter, C., Luukkonen, C.L., Stewart, J.S., Paulinski, S., Miller, L.D., and Houston, N., 2025, Public supply water delivery analysis and estimation for the conterminous United States: Water Resources Research, v. 61, no. 6, e2024WR039271, 20 p., https://doi.org/10.1029/2024WR039271.","productDescription":"e2024WR039271, 20 p.","ipdsId":"IP-156956","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":491500,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024wr039271","text":"Publisher Index Page"},{"id":491183,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"conterminous United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"geometry\": {\n        \"type\": \"MultiPolygon\",\n        \"coordinates\": [\n          [\n            [\n              [\n                -94.81758,\n                49.38905\n              ],\n              [\n                -94.64,\n                48.84\n              ],\n              [\n                -94.32914,\n                48.67074\n              ],\n              [\n                -93.63087,\n                48.60926\n              ],\n              [\n                -92.61,\n                48.45\n              ],\n              [\n                -91.64,\n                48.14\n              ],\n              [\n                -90.83,\n            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]\n}","volume":"61","issue":"6","noUsgsAuthors":false,"publicationDate":"2025-06-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Larsen, Joshua 0000-0002-1218-800X jlarsen@usgs.gov","orcid":"https://orcid.org/0000-0002-1218-800X","contributorId":272403,"corporation":false,"usgs":true,"family":"Larsen","given":"Joshua","email":"jlarsen@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":941107,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Alzraiee, Ayman H. 0000-0001-7576-3449","orcid":"https://orcid.org/0000-0001-7576-3449","contributorId":272120,"corporation":false,"usgs":true,"family":"Alzraiee","given":"Ayman","email":"","middleInitial":"H.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":941108,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Niswonger, Richard G. rniswon@usgs.gov","contributorId":146547,"corporation":false,"usgs":false,"family":"Niswonger","given":"Richard G.","email":"rniswon@usgs.gov","affiliations":[],"preferred":false,"id":941109,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Martin, Donald 0000-0001-5913-2372 domartin@usgs.gov","orcid":"https://orcid.org/0000-0001-5913-2372","contributorId":4450,"corporation":false,"usgs":true,"family":"Martin","given":"Donald","email":"domartin@usgs.gov","affiliations":[],"preferred":true,"id":941110,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Buchwald, Cheryl A. 0000-0001-8968-5023 cabuchwa@usgs.gov","orcid":"https://orcid.org/0000-0001-8968-5023","contributorId":1943,"corporation":false,"usgs":true,"family":"Buchwald","given":"Cheryl","email":"cabuchwa@usgs.gov","middleInitial":"A.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science 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ldmiller@usgs.gov","orcid":"https://orcid.org/0000-0002-3523-0768","contributorId":1125,"corporation":false,"usgs":true,"family":"Miller","given":"Lisa","email":"ldmiller@usgs.gov","middleInitial":"D.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":941116,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Houston, Natalie 0000-0002-6071-4545","orcid":"https://orcid.org/0000-0002-6071-4545","contributorId":206533,"corporation":false,"usgs":true,"family":"Houston","given":"Natalie","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":941117,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}}
,{"id":70268390,"text":"70268390 - 2025 - Rapid emplacement of the Keaiwa Lava Flow of 1823 from the Great Crack in the Southwest Rift Zone of Kilauea volcano","interactions":[],"lastModifiedDate":"2025-06-24T14:27:46.198157","indexId":"70268390","displayToPublicDate":"2025-06-19T09:20:50","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Rapid emplacement of the Keaīwa Lava Flow of 1823 from the Great Crack in the Southwest Rift Zone of Kīlauea volcano","title":"Rapid emplacement of the Keaiwa Lava Flow of 1823 from the Great Crack in the Southwest Rift Zone of Kilauea volcano","docAbstract":"<p><span>The Keaīwa Lava Flow of 1823 in the Southwest Rift Zone of Kīlauea volcano is unusual for its expansive pāhoehoe sheet flow morphology and lack of constructive vent topography, despite having a similar tholeiitic basalt composition to other lavas erupted from Kīlauea. This lava flow issued from a&nbsp;∼10-km-long continuous fissure now known as the Great Crack, and has an unusually thin sheet flow morphology with margin thicknesses of ∼15–110&nbsp;cm (average of 42&nbsp;cm). Based on field observations of the lava flow at its fissure vent (e.g., drain-back features), we propose that the Great Crack formed, or at least significantly widened, just prior to and syn-eruptively with this 1823 eruption. The absence of pyroclastic cones or spatter ramparts indicates that the eruption consisted of a rapid outpouring of relatively degassed lava as the fissure unzipped. The rapidly moving lava flow overtopped pre-existing tumuli and scoria cones (e.g., Lava Plastered Cones) up to ∼10&nbsp;m tall. Glass and whole-rock chemistry yield homogeneous compositions for the lavas erupted from the Great Crack, with glass compositions of 6.40&nbsp;±&nbsp;0.10&nbsp;wt% MgO and whole-rock compositions of 7.39&nbsp;±&nbsp;0.07&nbsp;wt% MgO. Lava pads erupted from a short western fissure system are richer in mafic minerals (e.g., olivine and clinopyroxene), and show slightly more MgO-rich whole-rock compositions (7.79&nbsp;±&nbsp;0.05&nbsp;wt%). MgO-in-glass thermometry on juvenile spatter yield eruption temperatures of 1153&nbsp;±&nbsp;13°C that are typical of Kīlauea lavas. Thus, the extensive sheet-like lava flow morphology is not a direct consequence of unusual magmatic or rheological conditions (i.e., low viscosity). Instead, the flow morphology is associated with high effusion rates caused by sudden drainage of uprift magma as it erupted from the Great Crack. Lava flow modeling on a 2-m-resolution digital elevation model indicates that a minimum bulk effusion rate of ∼5800&nbsp;m</span><sup>3</sup><span>/s (∼3500&nbsp;m</span><sup>3</sup><span>/s dense rock equivalent) and a minimum flow velocity of ∼11&nbsp;m/s are required for the lava flow to overcome the topography of the Lava Plastered Cones. This effusion rate is among the highest inferred for eruptions in Hawaiʻi and around the world. This study highlights a less frequent eruption style at Hawaiian volcanoes characterized by a sudden outpouring of lava from an unusual fissure system. Local eyewitness accounts indicate that the 1823 eruption was preceded by seismicity. Given the complex magmatic-volcanic-tectonic relations across Kīlauea, we speculate that the south flank could have slipped over one or more events that ultimately triggered unzipping of the Great Crack and passive release of briefly stored uprift magma. An eruption similar to 1823 at Kīlauea or Mauna Loa, with an eruptive timeframe that could be as short as an hour, with high effusion rates and rapid flow front velocities, would not easily allow for a timely response.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2025.108391","usgsCitation":"Tonato, A., Shea, T., Downs, D.T., and Kelfoun, K., 2025, Rapid emplacement of the Keaiwa Lava Flow of 1823 from the Great Crack in the Southwest Rift Zone of Kilauea volcano: Journal of Volcanology and Geothermal Research, v. 466, 108391, 18 p., https://doi.org/10.1016/j.jvolgeores.2025.108391.","productDescription":"108391, 18 p.","ipdsId":"IP-169862","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":494405,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jvolgeores.2025.108391","text":"Publisher Index Page"},{"id":491181,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Great Crack in the Southwest Rift Zone of Kīlauea volcano, Keaīwa Lava Flow","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -155.2184297752859,\n              19.437317498221987\n            ],\n            [\n              -155.5,\n              19.437317498221987\n            ],\n            [\n              -155.5,\n              19.1667\n            ],\n            [\n              -155.2184297752859,\n              19.1667\n            ],\n            [\n              -155.2184297752859,\n              19.437317498221987\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"466","noUsgsAuthors":false,"publicationDate":"2025-06-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Tonato, Andrea","contributorId":352882,"corporation":false,"usgs":false,"family":"Tonato","given":"Andrea","affiliations":[{"id":64253,"text":"University of Hawaiʻi at Mānoa","active":true,"usgs":false}],"preferred":false,"id":941184,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shea, Thomas","contributorId":236886,"corporation":false,"usgs":false,"family":"Shea","given":"Thomas","affiliations":[{"id":47560,"text":"University of Hawaii Manoa","active":true,"usgs":false}],"preferred":false,"id":941185,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Downs, Drew T. 0000-0002-9056-1404 ddowns@usgs.gov","orcid":"https://orcid.org/0000-0002-9056-1404","contributorId":173516,"corporation":false,"usgs":true,"family":"Downs","given":"Drew","email":"ddowns@usgs.gov","middleInitial":"T.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":941186,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kelfoun, Karim","contributorId":333750,"corporation":false,"usgs":false,"family":"Kelfoun","given":"Karim","email":"","affiliations":[{"id":79967,"text":"Laboratoire Magmas et Volcans, Université Clermont Auvergne, Clermont-Ferrand, France","active":true,"usgs":false}],"preferred":false,"id":941187,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70269030,"text":"70269030 - 2025 - Navigating the possibilities and pitfalls of biocrust recovery in a changing climate","interactions":[],"lastModifiedDate":"2025-07-14T13:33:03.584001","indexId":"70269030","displayToPublicDate":"2025-06-19T08:31:26","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":724,"text":"American Journal of Botany","active":true,"publicationSubtype":{"id":10}},"title":"Navigating the possibilities and pitfalls of biocrust recovery in a changing climate","docAbstract":"<p><span>Biological soil crusts are complex communities composed of lichens, mosses, bacteria, and cyanobacteria that create a living skin on the soil surface across drylands worldwide. Although small in size, the vast area that biocrusts cover and the critical functions they provide make them a cornerstone of dryland health and resiliency. In addition to being important, biocrusts are exceptionally vulnerable to certain types of disturbance. Although they can withstand a wide range of temperatures and long periods without precipitation, biocrusts are highly sensitive to land-use change and are vulnerable to physical and compressional disturbance (i.e., trampling, vehicles, cattle, heavy machinery). In the face of these disturbances, a critical, long-standing question of interest to dryland ecologists is: Can biocrusts recover following disturbance without active intervention. If so, how long does it take? Early estimates of biocrust recovery suggested recovery can be incredibly slow (on the order of thousands of years), with more modern studies finding potential for faster recovery, especially with intervention. Multiple lines of evidence agree that recovery is context dependent, differing across climates, soils, and with the types of disturbance and biocrust. Additionally, active restoration of biocrusts is becoming more common as tractable strategies are developed for facilitating the establishment of biocrusts after disturbance. Here, we add to the body of knowledge about biocrust recovery following disturbances by reviewing recovery patterns, their connection to climate change, considerations for recovery in changing climates, and the role of restoration.</span></p>","language":"English","publisher":"Botanical Society of AMerica","doi":"10.1002/ajb2.70055","usgsCitation":"Phillips, M.L., Young, K.E., Lauria, C.M., Jech, S., Giraldo-Silva, A., and Reed, S., 2025, Navigating the possibilities and pitfalls of biocrust recovery in a changing climate: American Journal of Botany, v. 112, e70055, 8 p., https://doi.org/10.1002/ajb2.70055.","productDescription":"e70055, 8 p.","ipdsId":"IP-176307","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":492481,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ajb2.70055","text":"Publisher Index Page"},{"id":492193,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"112","noUsgsAuthors":false,"publicationDate":"2025-06-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Phillips, Michala Lee 0000-0001-7005-8740","orcid":"https://orcid.org/0000-0001-7005-8740","contributorId":245186,"corporation":false,"usgs":true,"family":"Phillips","given":"Michala","email":"","middleInitial":"Lee","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":942951,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Young, Kristina E.","contributorId":210572,"corporation":false,"usgs":false,"family":"Young","given":"Kristina","email":"","middleInitial":"E.","affiliations":[{"id":38116,"text":"Department of Biological Sciences, University of Texas at El Paso, El Paso, TX 79902, USA","active":true,"usgs":false}],"preferred":false,"id":942952,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lauria, Cara Marie 0000-0001-8914-8041","orcid":"https://orcid.org/0000-0001-8914-8041","contributorId":271066,"corporation":false,"usgs":true,"family":"Lauria","given":"Cara","email":"","middleInitial":"Marie","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":942953,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jech, Sierra","contributorId":292726,"corporation":false,"usgs":false,"family":"Jech","given":"Sierra","email":"","affiliations":[{"id":36627,"text":"University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":942954,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Giraldo-Silva, Ana","contributorId":357982,"corporation":false,"usgs":false,"family":"Giraldo-Silva","given":"Ana","affiliations":[{"id":85571,"text":"Public University of Navarre","active":true,"usgs":false}],"preferred":false,"id":942955,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Reed, Sasha C. 0000-0002-8597-8619","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":207498,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":942956,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
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