Fluvial export of dissolved carbon plays an important role in watershed-scale biogeochemistry. Predicted changes in climate are expected to impact watershed hydrologic regimes, and in turn, the sources and export of dissolved carbon from watersheds. Here, we utilize high resolution measurements of discharge and dissolved carbon concentration to examine how concentration-discharge (CQ) relationships vary seasonally and during high flow events over the main runoff season (May–October) in a temperate forested watershed in Southeast Alaska. Concentration-discharge relationships for dissolved organic carbon (DOC) and alkalinity demonstrated strong seasonal patterns, with more linear relationships in May and June versus other months. Changing power law model slopes (b values; the exponent in a power law regression between runoff and carbon yields) indicated potentially shifting watershed sources (biogenic vs. geologic) and contrasting dominant flowpaths (shallow vs. deeper groundwater) for DOC and alkalinity over the sampling period. During the largest storm event of the study, DOC and alkalinity b values shifted from an overall pattern of transport (mean b = 1.58 values >1.0 indicate transport limitation) and source limitation (mean b = 0.48, values <1.0 indicate source limitation) to chemostatic (DOC, b = 0.99; alkalinity, b = 1.019). In June through August, patterns in hysteresis index suggest that CQ relationships were altered when storms followed in close succession to each other. Together, these findings indicate that seasonal and antecedent flow conditions play a role in dissolved carbon export from forested watersheds. Understanding these dynamics, particularly during winter months, will become increasingly important as changes to hydroclimate impact riverine carbon export.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2025JG009090","usgsCitation":"Delbecq, C., Fellman, J.B., Bellmore, J.R., Whitney, E.J., Fitzgerald, K., Falke, J.A., 2026, Season and antecedent conditions impact concentration-discharge relationships for dissolved organic carbon and alkalinity in southeast Alaskan watershed: JGR Biogeosciences, v. 131, no. 1, e2025JG009090, 15 p., https://doi.org/10.1029/2025JG009090.","productDescription":"e2025JG009090, 15 p.","ipdsId":"IP-174690","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":500587,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2025jg009090","text":"Publisher Index Page"},{"id":500420,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Montana Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -136.63915248759182,\n 59.21990475595436\n ],\n [\n -136.63915248759182,\n 57.522914720234525\n ],\n [\n -134.63466688093874,\n 57.522914720234525\n ],\n [\n -134.63466688093874,\n 59.21990475595436\n ],\n [\n -136.63915248759182,\n 59.21990475595436\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"131","issue":"1","noUsgsAuthors":false,"publicationDate":"2026-01-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Delbecq, Claire","contributorId":337162,"corporation":false,"usgs":false,"family":"Delbecq","given":"Claire","email":"","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":955990,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fellman, Jason B.","contributorId":366494,"corporation":false,"usgs":false,"family":"Fellman","given":"Jason","middleInitial":"B.","affiliations":[{"id":16298,"text":"University of Alaska Southeast","active":true,"usgs":false}],"preferred":false,"id":955991,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bellmore, J. Ryan","contributorId":366495,"corporation":false,"usgs":false,"family":"Bellmore","given":"J.","middleInitial":"Ryan","affiliations":[{"id":27863,"text":"U. S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":955992,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Whitney, Emily J.","contributorId":366496,"corporation":false,"usgs":false,"family":"Whitney","given":"Emily","middleInitial":"J.","affiliations":[{"id":16298,"text":"University of Alaska Southeast","active":true,"usgs":false}],"preferred":false,"id":955993,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fitzgerald, Kevin","contributorId":332288,"corporation":false,"usgs":false,"family":"Fitzgerald","given":"Kevin","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":955994,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":955995,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70274052,"text":"70274052 - 2026 - Integrating climate data and river modeling to reveal Chinook salmon habitat conditions in subarctic river basins","interactions":[],"lastModifiedDate":"2026-02-23T15:27:36.372921","indexId":"70274052","displayToPublicDate":"2026-01-06T08:21:05","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Integrating climate data and river modeling to reveal Chinook salmon habitat conditions in subarctic river basins","docAbstract":"Climatic extremes can impact the productivity of aquatic species, affecting ecosystems and fishery-dependent communities. Advances in climate products, such as gridded datasets and downscaled projections, may be useful for quantifying freshwater habitat conditions and predicting climate change effects on fish. However, limited guidance exists for selecting climate products to develop indicators of freshwater habitat conditions that influence fish population dynamics. Here, we develop an approach for identifying streamflow and stream temperature models to address this need. We evaluated skill in predicted versus observed streamflow and stream temperature, with predictions depending on different models and gridded climate data as inputs. The best performing models were used in a case study exploring habitat conditions influencing Chinook salmon in the Yukon and Kuskokwim River basins, two remote high-latitude watersheds with few in situ habitat observations and recent salmon declines. Three modeled streamflow datasets had variable performance (median Nash–Sutcliffe efficiencies from 0.39 to 0.70). Three gridded temperature products differed in their ability to explain variation in weekly stream temperatures (median r2 from 0.42 to 0.76). We selected a single gridded air temperature dataset to compare two novel predictive stream temperature models, both of which had good accuracy (root mean squared error [RMSE] of 1.19 and 0.95°C). Stream temperature indicators calculated from modeled daily data, maximum temperatures during adult migration and cumulative temperatures during juvenile rearing, had high spatial correlation across tributaries within the Yukon and Kuskokwim River basins and showed significant warming over the past 40 years. Streamflow indicators calculated from modeled daily data, maximum flow during spawning and median flow during rearing, had few trends and were largely uncorrelated within the Yukon River basin and moderately correlated within the Kuskokwim River basin. Overall, we found that generic measures of model performance varied considerably, and it was important to consider the models best suited to our case study. For both streamflow and stream temperature, multiple high-performing models allowed estimation of ecologically relevant conditions affecting Chinook salmon. The approach we used to estimate local-scale habitat conditions has value to identify synchronous conditions that may influence multiple salmon populations under a changing subarctic climate.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.70399","usgsCitation":"Shaftel, R., Feddern, M.L., McAfee, S.A., Schoen, E.R., Cunningham, C., von Biela, V.R., Paul, J., Cheng, Y., Newman, A., Perdue, M., Schwenk, J., von Finster, A., Falke, J.A., 2026, Integrating climate data and river modeling to reveal Chinook salmon habitat conditions in subarctic river basins: Ecosphere, v. 17, no. 1, e70399, 25 p., https://doi.org/10.1002/ecs2.70399.","productDescription":"e70399, 25 p.","ipdsId":"IP-170801","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":500622,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.70399","text":"Publisher Index Page"},{"id":500403,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Kuskokwim River basin, Yukon River 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and evaluating an operational tool to enhance sub-seasonal to seasonal streamflow drought early warning for gaged locations","indexId":"70271720","publicationYear":"2025","noYear":false,"title":"Machine learning generated streamflow drought forecasts for the Conterminous United States (CONUS): Developing and evaluating an operational tool to enhance sub-seasonal to seasonal streamflow drought early warning for gaged locations"},"predicate":"SUPERSEDED_BY","object":{"id":70273497,"text":"70273497 - 2026 - Machine learning generated streamflow drought forecasts for the conterminous United States (CONUS): developing and evaluating an operational tool to enhance sub-seasonal to seasonal streamflow drought early warning for gaged locations","indexId":"70273497","publicationYear":"2026","noYear":false,"title":"Machine learning generated streamflow drought forecasts for the conterminous United States (CONUS): developing and evaluating an operational tool to enhance sub-seasonal to seasonal streamflow drought early warning for gaged locations"},"id":1}],"lastModifiedDate":"2026-01-20T15:17:42.806547","indexId":"70273497","displayToPublicDate":"2026-01-06T08:09:21","publicationYear":"2026","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":"Machine learning generated streamflow drought forecasts for the conterminous United States (CONUS): developing and evaluating an operational tool to enhance sub-seasonal to seasonal streamflow drought early warning for gaged locations","docAbstract":"Forecasts of streamflow drought, when streamflow declines below typical levels, are notably less available than for floods or meteorological drought, despite widespread impacts. We apply machine learning (ML) models to forecast streamflow drought 1–13 weeks ahead at 3,219 streamgages across the conterminous United States. We applied two ML methods (Long short-term memory neural networks; Light Gradient-Boosting Machine) and two benchmark models (persistence; Autoregressive Integrated Moving Average) to predict weekly streamflow percentiles with independent models for each forecast horizon. ML models outperformed benchmarks in predicting continuous streamflow percentiles below 30%. ML models generally performed worse than persistence models for discrete classification (moderate, severe, extreme) but exceeded the benchmark models for drought onset/termination. Performance was better for less intense droughts and shorter horizons, with predictive power for 1–4 weeks for severe droughts (10% threshold). This work highlights challenges and opportunities to advance hydrological drought forecasting and supports a new experimental forecasting tool.
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0000-0002-1460-6084","orcid":"https://orcid.org/0000-0002-1460-6084","contributorId":299035,"corporation":false,"usgs":true,"family":"Staub","given":"Leah","email":"","middleInitial":"Ellen","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":954013,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Watkins, David 0000-0002-7544-0700","orcid":"https://orcid.org/0000-0002-7544-0700","contributorId":317375,"corporation":false,"usgs":true,"family":"Watkins","given":"David","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":954014,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Wieczorek, Michael 0000-0003-0999-5457","orcid":"https://orcid.org/0000-0003-0999-5457","contributorId":207911,"corporation":false,"usgs":true,"family":"Wieczorek","given":"Michael","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":954015,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Wnuk, Kendall C. 0000-0001-7499-5256","orcid":"https://orcid.org/0000-0001-7499-5256","contributorId":305486,"corporation":false,"usgs":true,"family":"Wnuk","given":"Kendall C.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science 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USA","interactions":[],"lastModifiedDate":"2026-04-06T15:43:42.592143","indexId":"70273459","displayToPublicDate":"2026-01-06T08:06:52","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"Tectonic implications of transitional melting regimes from petrological, geochronological, and compositional characterization of the ophiolitic Seventymile terrane, Alaska, USA","docAbstract":"New geochemical, U-Pb geochronology, and Sr-Nd-Hf isotope data provide evidence for the tectonic evolution of the Seventymile terrane in interior Alaska, USA. Ultramafic and mafic rocks of the Seventymile terrane are thought to represent components of a dismembered ophiolite and provide unique constraints on regional terrane evolution and accretion. The Seventymile ophiolite represents fragments of the Devonian to Permian Slide Mountain Ocean (SMO) that separated allochthonous and parautochthonous continental fragments of western North America. It now occurs as multiple thrust sheets containing Permian mafic and ultramafic rocks overlying and/or possibly imbricated with amphibolite-facies supracrustal rocks of allochthonous Yukon-Tanana terrane and parautochthonous North America. Seventymile klippen contain variably serpentinized peridotite, primarily harzburgite, low-grade meta-mafic rocks, and minor oceanic sedimentary rocks (argillite, chert, limestone, and metasandstone). Mafic rocks include gabbro to diabase, typically as dikes, veinlets, or rare massive stocks intruding peridotite. Mafic rocks also include greenstones of the Seventymile assemblage in klippen structurally underlying, and in shear zone contact with, ultramafic klippen.
New trace element and radiogenic isotope data from mafic magmatic rocks associated with the Seventymile ultramafic bodies show evidence for a weakly subduction-modified mantle source, like the mantle source of normal mid-ocean-ridge basalt (N-MORB) or back-arc basin basalt (BABB). Seventymile assemblage greenstones are more heterogeneous. They range from N-MORB to enriched mid-ocean-ridge basalt (E-MORB) and ocean-island basalt (OIB), with a subset of samples indicative of continental arc affinity. These geochemistry results indicate that distinct tectonic environments are represented by at least two, and possibly three, lithological and structural units comprising the Seventymile terrane. Hf-Nd isotope systematics are consistent with a depleted MORB mantle (DMM)−like component that overlaps with Pacific MORB. Primary zircon is rare, but new in situ U-Pb data for gabbro and greenstone indicate ca. 274−272 Ma peak zircon and titanite crystallization. Scattered younger zircons define a ca. 255 Ma zircon peak and correspond to secondary crystallization associated with baddeleyite reaction of high-Si fluids during low-grade metamorphism. If Seventymile suites are contemporaneous, obduction associated with the closure of the SMO resulted in the stacking of ophiolitic packages representing distinct tectonomagmatic settings across the transition from pericontinental, to epicontinental, to distal ocean back-arc. Intrusions hosted in klippe of ultramafic rocks, plus the least subduction-modified greenstones underlying them, geologically and compositionally resemble Slide Mountain rocks of the Campbell Range formation in eastern Yukon and may provide a new piercing point across the Tintina fault.
","language":"English","publisher":"GeoScienceWorld","doi":"10.1130/GES02837.1","usgsCitation":"Todd, E., Caine, J., Bizimis, M., Kylander-Clark, A.R., Hammond, R.R., and Wypych, A., 2026, Tectonic implications of transitional melting regimes from petrological, geochronological, and compositional characterization of the ophiolitic Seventymile terrane, Alaska, USA: Geosphere, v. 22, no. 2, p. 296-339, https://doi.org/10.1130/GES02837.1.","productDescription":"44 p.","startPage":"296","endPage":"339","ipdsId":"IP-170876","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":498608,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":498699,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02837.1","text":"Publisher Index Page"}],"country":"Canada, United States","state":"Alaska, British Columbia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -151.54814578278632,\n 64.49262655255515\n ],\n [\n -151.54814578278632,\n 59.59805672240421\n ],\n [\n -133.60437046462778,\n 59.59805672240421\n ],\n [\n -133.60437046462778,\n 64.49262655255515\n ],\n [\n -151.54814578278632,\n 64.49262655255515\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"22","issue":"2","noUsgsAuthors":false,"publicationDate":"2026-01-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Todd, Erin 0000-0002-4871-9730 etodd@usgs.gov","orcid":"https://orcid.org/0000-0002-4871-9730","contributorId":202811,"corporation":false,"usgs":true,"family":"Todd","given":"Erin","email":"etodd@usgs.gov","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":953782,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Caine, Jonathan Saul 0000-0002-7269-6989 jscaine@usgs.gov","orcid":"https://orcid.org/0000-0002-7269-6989","contributorId":199295,"corporation":false,"usgs":true,"family":"Caine","given":"Jonathan Saul","email":"jscaine@usgs.gov","affiliations":[],"preferred":true,"id":953783,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bizimis, Michael","contributorId":192879,"corporation":false,"usgs":false,"family":"Bizimis","given":"Michael","email":"","affiliations":[],"preferred":false,"id":953784,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kylander-Clark, Andrew R.C. 0000-0002-4034-644X","orcid":"https://orcid.org/0000-0002-4034-644X","contributorId":302380,"corporation":false,"usgs":false,"family":"Kylander-Clark","given":"Andrew","middleInitial":"R.C.","affiliations":[{"id":36524,"text":"University of California, Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":953785,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hammond, Robert Reece","contributorId":365154,"corporation":false,"usgs":false,"family":"Hammond","given":"Robert","middleInitial":"Reece","affiliations":[{"id":37804,"text":"University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":953786,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wypych, Alicja","contributorId":216040,"corporation":false,"usgs":false,"family":"Wypych","given":"Alicja","email":"","affiliations":[{"id":39354,"text":"State of Alaska Department of Natural Resources DGGS Fairbanks","active":true,"usgs":false}],"preferred":false,"id":953787,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70275380,"text":"70275380 - 2026 - The Sand AbrasioN Device for Aeolian Research (SANDAR): A new experimental device for investigating how wind transport affects sand on Earth and Mars","interactions":[],"lastModifiedDate":"2026-05-01T13:46:59.560504","indexId":"70275380","displayToPublicDate":"2026-01-05T08:43:20","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":666,"text":"Aeolian Research","active":true,"publicationSubtype":{"id":10}},"title":"The Sand AbrasioN Device for Aeolian Research (SANDAR): A new experimental device for investigating how wind transport affects sand on Earth and Mars","docAbstract":"On Earth and Mars, aeolian transport causes sand grains to become abraded, resulting in mineralogic and textural changes. Understanding how sands evolve, or mature, with transport via experimental studies is important for understanding the origins, geologic history, and cycling of sediments, as well as dust production. Previous experimental works have used a variety of methods to simulate aeolian transport in the laboratory, but practical limitations and similitude concerns have limited such research. Here, we present and validate the Sand AbrasioN Device for Aeolian Research (SANDAR), a modified air mill that uses pressurized air to circulate sand around a small abrasion chamber, simulating the effects of aeolian transport. This device is re-circulating to simulate long-distance transport, and it allows for repeated analyses of well-constrained sediment samples, revealing their evolution over time. It is compatible with the grain sizes (74–500 μm) and grain impact velocities (∼0.6–3.7 m/s) typically expected for natural aeolian environments, and is also adaptable for diverse applications simulating different wind conditions. We show that the SANDAR achieves similitude of kinetic energy with respect to saltating sand on both Earth and Mars. SEM and optical microscope imaging reveal that the SANDAR produces microtextures on the surfaces of sand grains similar to those found with natural aeolian transport, demonstrating that it effectively simulates the mechanical effects of aeolian processes. Thus, the SANDAR is a valid tool for use in experimental research to improve our understanding of sedimentary processes across the Solar System.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.aeolia.2025.101027","usgsCitation":"Baker, A., Burr, D., Fry, R.L., Emery, J., and Loeffler, M., 2026, The Sand AbrasioN Device for Aeolian Research (SANDAR): A new experimental device for investigating how wind transport affects sand on Earth and Mars: Aeolian Research, v. 75, 101027, 16 p., https://doi.org/10.1016/j.aeolia.2025.101027.","productDescription":"101027, 16 p.","ipdsId":"IP-177776","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":504154,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.aeolia.2025.101027","text":"Publisher Index Page"},{"id":503878,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Earth, Mars","volume":"75","noUsgsAuthors":false,"publicationDate":"2026-01-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Baker, A.","contributorId":191683,"corporation":false,"usgs":false,"family":"Baker","given":"A.","affiliations":[],"preferred":false,"id":960794,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burr, Devon M.","contributorId":370803,"corporation":false,"usgs":false,"family":"Burr","given":"Devon","middleInitial":"M.","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":960795,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fry, Rachel Lynn 0009-0009-1600-5029","orcid":"https://orcid.org/0009-0009-1600-5029","contributorId":370805,"corporation":false,"usgs":true,"family":"Fry","given":"Rachel","middleInitial":"Lynn","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":960796,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Emery, Joshua P.","contributorId":370806,"corporation":false,"usgs":false,"family":"Emery","given":"Joshua","middleInitial":"P.","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":960797,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Loeffler, M.","contributorId":370807,"corporation":false,"usgs":false,"family":"Loeffler","given":"M.","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":960798,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70273442,"text":"70273442 - 2026 - Is satellite-derived bathymetry vertical accuracy dependent on satellite mission and processing method?","interactions":[],"lastModifiedDate":"2026-01-14T15:23:53.929478","indexId":"70273442","displayToPublicDate":"2026-01-05T08:18:13","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Is satellite-derived bathymetry vertical accuracy dependent on satellite mission and processing method?","docAbstract":"This research focusses on three satellite-derived bathymetry methods and optical satellite instruments: (1) a stereo photogrammetry bathymetry module (SaTSeaD) developed for the NASA Ames stereo pipeline open-source software (version 3.6.0) using stereo WorldView data; (2) physics-based radiative transfer equations (PBSDB) using Landsat data; and (3) a modified composite band-ratio method for Sentinel-2 (SatBathy) with an initial simplified calibration, followed by a more rigorous linear regression against in situ bathymetry data. All methods were tested in three different areas with different geological and environmental conditions, Cabo Rojo, Puerto Rico; Key West, Florida; and Cocos Lagoon and Achang Flat Reef Preserve, Guam. It is demonstrated that all satellite derived bathymetry (SDB) methods have increased accuracy when the results are aligned with higher-accuracy ICESat-2 ATL24 track bathymetry data using the iterative closest point (ICP). SDB vertical accuracy depends more on location characteristics than the method or optical satellite instrument used. All error metrics considered (mean absolute error, median absolute deviation, and root mean square error) can be less than 5% of the maximum bathymetry depth penetration for at least one method, although not necessarily for the same method for all sites. The SDB error distribution tends to be bimodal irrespective of method, satellite instrument, alignment, site, or maximum bathymetry depth, leading to the potential ineffectiveness of traditional error metrics, such as the root mean square error. However, our analysis demonstrates that performing detrending where possible can achieve an error distribution as close to normality as possible for which error metrics are more diagnostic.
","language":"English","publisher":"MDPI","doi":"10.3390/rs18020195","usgsCitation":"Palaseanu-Lovejoy, M., Danielson, J.J., Kim, M., Eder, B., Imahori, G., and Storlazzi, C.D., 2026, Is satellite-derived bathymetry vertical accuracy dependent on satellite mission and processing method?: Remote Sensing, v. 18, no. 2, 195, 30 p., https://doi.org/10.3390/rs18020195.","productDescription":"195, 30 p.","ipdsId":"IP-183102","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":498700,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs18020195","text":"Publisher Index Page"},{"id":498609,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Guam, Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.29886149831057,\n 31.035877030602435\n ],\n [\n -88.29886149831057,\n 24.818271329127427\n ],\n [\n -79.18887863828726,\n 24.818271329127427\n ],\n [\n -79.18887863828726,\n 31.035877030602435\n ],\n [\n -88.29886149831057,\n 31.035877030602435\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -67.91979115320188,\n 18.80997482085145\n ],\n [\n -67.91979115320188,\n 17.365111187758373\n ],\n [\n -64.61443502140921,\n 17.365111187758373\n ],\n [\n -64.61443502140921,\n 18.80997482085145\n ],\n [\n -67.91979115320188,\n 18.80997482085145\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 144.5400287592969,\n 13.788590651628851\n ],\n [\n 144.5400287592969,\n 13.156374228238235\n ],\n [\n 145.08326832494907,\n 13.156374228238235\n ],\n [\n 145.08326832494907,\n 13.788590651628851\n ],\n [\n 144.5400287592969,\n 13.788590651628851\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","issue":"2","noUsgsAuthors":false,"publicationDate":"2026-01-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Palaseanu-Lovejoy, Monica 0000-0002-3786-5118","orcid":"https://orcid.org/0000-0002-3786-5118","contributorId":305576,"corporation":false,"usgs":true,"family":"Palaseanu-Lovejoy","given":"Monica","affiliations":[],"preferred":true,"id":953719,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Danielson, Jeffrey J. 0000-0003-0907-034X daniels@usgs.gov","orcid":"https://orcid.org/0000-0003-0907-034X","contributorId":3996,"corporation":false,"usgs":true,"family":"Danielson","given":"Jeffrey","email":"daniels@usgs.gov","middleInitial":"J.","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":953720,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kim, Minsu 0000-0003-4472-0926","orcid":"https://orcid.org/0000-0003-4472-0926","contributorId":297371,"corporation":false,"usgs":false,"family":"Kim","given":"Minsu","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":false,"id":953721,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Eder, Bryan","contributorId":365118,"corporation":false,"usgs":false,"family":"Eder","given":"Bryan","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":953722,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Imahori, Gretchen","contributorId":365119,"corporation":false,"usgs":false,"family":"Imahori","given":"Gretchen","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":953723,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Storlazzi, Curt D. 0000-0001-8057-4490","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":213610,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","middleInitial":"D.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":953724,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70273333,"text":"70273333 - 2026 - Rapid characterization of the 2025 Mw 8.8 Kamchatka, Russia earthquake","interactions":[],"lastModifiedDate":"2026-01-07T15:03:07.193971","indexId":"70273333","displayToPublicDate":"2026-01-02T08:54:03","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10542,"text":"The Seismic Record","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Rapid characterization of the 2025 Mw 8.8 Kamchatka, Russia earthquake","title":"Rapid characterization of the 2025 Mw 8.8 Kamchatka, Russia earthquake","docAbstract":"The 29 July 2025 Mw 8.8 Kamchatka, Russia, earthquake was the sixth largest instrumentally recorded earthquake. This event was seismically well observed at regional and teleseismic distances, but publicly available near‐source data were sparse at the time of the event, presenting unique challenges for rapid source and impact characterization. The U.S. Geological Survey (USGS) National Earthquake Information Center provides global real‐time monitoring for earthquakes, including rapid response information products that estimate source characteristics, shaking, and the resulting impacts. We describe the USGS rapid response earthquake information products following the Kamchatka event and discuss their implications for ongoing hazards in the region. We describe potential improvements to our response workflows motivated by this event, including more rapid constraints on source geometries and the automated selection of fault geometries for finite‐fault inversions. The rapid response products together support the interpretation of a unilateral southwestward rupture with significant slip on the southwestern end of the rupture extent. The Mw 8.8–9.0 event in 1952, which ruptured a comparable extent of the Kuril–Kamchatka subduction interface, has many similarities to the 2025 rupture. This illustrates that slip deficits may remain following great earthquakes and highlights the usefulness of comparative studies between historic and modern events.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0320250038","usgsCitation":"Yin, H.Z., Allstadt, K.E., Barnhart, W.D., Clapp, S.A., Earle, P.S., Goldberg, D.E., Grant, A.R., Matt Herman, Jaiswal, K.S., McBride, S., Ringler, A.T., Schneider, M., Thompson, E.M., van der Elst, N., Wald, D., Wang, D., Worden, C., and Yeck, W.L., 2026, Rapid characterization of the 2025 Mw 8.8 Kamchatka, Russia earthquake: The Seismic Record, v. 6, no. 1, p. 1-12, https://doi.org/10.1785/0320250038.","productDescription":"12 p.","startPage":"1","endPage":"12","ipdsId":"IP-183627","costCenters":[{"id":122,"text":"Albuquerque Seismological Laboratory","active":false,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":498473,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1785/0320250038","text":"Publisher Index Page"},{"id":498374,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Russia","otherGeospatial":"Kuril-Kamchatka subduction zone","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 168,\n 56\n ],\n [\n 144,\n 56\n ],\n [\n 144,\n 41\n ],\n [\n 168,\n 41\n ],\n [\n 168,\n 56\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"6","issue":"1","noUsgsAuthors":false,"publicationDate":"2026-01-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Yin, Harriet Zoe 0000-0002-0670-6579","orcid":"https://orcid.org/0000-0002-0670-6579","contributorId":364882,"corporation":false,"usgs":true,"family":"Yin","given":"Harriet","middleInitial":"Zoe","affiliations":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"preferred":true,"id":953369,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Allstadt, Kate E. 0000-0003-4977-5248","orcid":"https://orcid.org/0000-0003-4977-5248","contributorId":138704,"corporation":false,"usgs":true,"family":"Allstadt","given":"Kate","email":"","middleInitial":"E.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":953370,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barnhart, William D 0000-0002-6576-068X","orcid":"https://orcid.org/0000-0002-6576-068X","contributorId":364883,"corporation":false,"usgs":true,"family":"Barnhart","given":"William","middleInitial":"D","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":953371,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Clapp, Samantha Ann 0009-0005-4418-2824","orcid":"https://orcid.org/0009-0005-4418-2824","contributorId":364884,"corporation":false,"usgs":true,"family":"Clapp","given":"Samantha","middleInitial":"Ann","affiliations":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"preferred":true,"id":953372,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Earle, Paul S. 0000-0002-3500-017X 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0000-0002-5096-4305","orcid":"https://orcid.org/0000-0002-5096-4305","contributorId":219066,"corporation":false,"usgs":true,"family":"Grant","given":"Alex","middleInitial":"R.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":953375,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Matt Herman 0000-0003-4684-9459","orcid":"https://orcid.org/0000-0003-4684-9459","contributorId":302616,"corporation":false,"usgs":false,"family":"Matt Herman","affiliations":[{"id":56050,"text":"California State University-Bakersfield","active":true,"usgs":false}],"preferred":false,"id":953376,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Jaiswal, Kishor S. 0000-0002-5803-8007 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emthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-6943-4806","contributorId":150897,"corporation":false,"usgs":true,"family":"Thompson","given":"Eric","email":"emthompson@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":953381,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"van der Elst, Nicholas 0000-0002-3812-1153 nvanderelst@usgs.gov","orcid":"https://orcid.org/0000-0002-3812-1153","contributorId":147858,"corporation":false,"usgs":true,"family":"van der Elst","given":"Nicholas","email":"nvanderelst@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":953382,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Wald, David 0000-0002-1454-4514 wald@usgs.gov","orcid":"https://orcid.org/0000-0002-1454-4514","contributorId":150898,"corporation":false,"usgs":true,"family":"Wald","given":"David","email":"wald@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":953383,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Wang, Dun 0000-0001-6435-9168","orcid":"https://orcid.org/0000-0001-6435-9168","contributorId":364891,"corporation":false,"usgs":false,"family":"Wang","given":"Dun","affiliations":[{"id":12433,"text":"China University of Geosciences","active":true,"usgs":false}],"preferred":false,"id":953384,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Worden, Charles 0000-0003-1181-685X cbworden@usgs.gov","orcid":"https://orcid.org/0000-0003-1181-685X","contributorId":152042,"corporation":false,"usgs":true,"family":"Worden","given":"Charles","email":"cbworden@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":953385,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Yeck, William L. 0000-0002-2801-8873 wyeck@usgs.gov","orcid":"https://orcid.org/0000-0002-2801-8873","contributorId":147558,"corporation":false,"usgs":true,"family":"Yeck","given":"William","email":"wyeck@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":953386,"contributorType":{"id":1,"text":"Authors"},"rank":18}]}} ,{"id":70273935,"text":"70273935 - 2026 - Characterization and modeling approach for planning restoration strategies in a complex basin affected by acid mine drainage","interactions":[],"lastModifiedDate":"2026-02-18T14:55:47.849711","indexId":"70273935","displayToPublicDate":"2026-01-01T07:42:51","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Characterization and modeling approach for planning restoration strategies in a complex basin affected by acid mine drainage","docAbstract":"The management of acid mine drainage (AMD) impacted catchments, such as the Odiel River basin, in southwestern Spain, prioritizes reclamation to meet water resources needs. Assessing water composition across its watercourses is needed to identify major AMD contributors and potentially guide remediation efforts. An equilibrium-based mixing model was developed to simulate AMD pollutant load reductions and estimate the impact of selective restorations on water quality in the Odiel River, particularly at the planned Alcolea Reservoir near its outlet. Sampling under varying flow conditions (average vs. high flow) showed a reduction in acidity transport (from 54.0 to 42.5 ton/day), attributed to greater neutralization effects during high flows. Over 90 % of metal-fluxes originate alone from the Riotinto (73.6 %), Tharsis (14.5 %), and San Telmo (5.00 %) mining districts, among many other mines. While geochemical model estimates fit well with observed data (R2 = 0.99), some deviations in non-conservative constituents (i.e. pH, Fe and Al) were observed (R2 = 0.73–0.99), likely due to uncertainties in solubility constants and redox/Fe speciation. After evaluating model reliability, two reduction scenarios (50 % and 100 %) were applied to the three primary sources. Full removal of contamination could subtantially improve impounded reservoir water quality, with pH values of 4.93 and 7.64, and a net acidity between 8.75 and 4.63 mg/L eq. CaCO3 in both average and high flows, respectively. Such differences may be related to flow regime effects on water quality. However, a 50 % reduction is insufficient to meet drinking or irrigation standards, highlighting the need for full and appropriate reclamation. The model offers a management tool for decision-making in the restoration of the Odiel River basin and could be transferable to similar AMD-affected basins worldwide.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2025.127486","usgsCitation":"Romero-Matos, J., Sánchez-López, L., León, R., Runkel, R.L., Nordstrom, D.K., Cánovas, C.R., Macías, F., and Nieto, J.M., 2026, Characterization and modeling approach for planning restoration strategies in a complex basin affected by acid mine drainage: Journal of Environmental Management, v. 397, 127486, 16 p., https://doi.org/10.1016/j.jenvman.2025.127486.","productDescription":"127486, 16 p.","ipdsId":"IP-178223","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":500250,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jenvman.2025.127486","text":"Publisher Index Page"},{"id":500136,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Spain","otherGeospatial":"Odiel River basin, southwestern Spain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -8.184000319431135,\n 37.77675639447594\n ],\n [\n -8.184000319431135,\n 37.181601306712864\n ],\n [\n -6.784070704852212,\n 37.181601306712864\n ],\n [\n -6.784070704852212,\n 37.77675639447594\n ],\n [\n -8.184000319431135,\n 37.77675639447594\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"397","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Romero-Matos, Jonatan","contributorId":366397,"corporation":false,"usgs":false,"family":"Romero-Matos","given":"Jonatan","affiliations":[{"id":87470,"text":"University of Huelva","active":true,"usgs":false}],"preferred":false,"id":955812,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sánchez-López, Laura","contributorId":366398,"corporation":false,"usgs":false,"family":"Sánchez-López","given":"Laura","affiliations":[{"id":87470,"text":"University of Huelva","active":true,"usgs":false}],"preferred":false,"id":955813,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"León, Rafael","contributorId":366399,"corporation":false,"usgs":false,"family":"León","given":"Rafael","affiliations":[{"id":87470,"text":"University of Huelva","active":true,"usgs":false}],"preferred":false,"id":955814,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Runkel, Robert L. 0000-0003-3220-481X runkel@usgs.gov","orcid":"https://orcid.org/0000-0003-3220-481X","contributorId":685,"corporation":false,"usgs":true,"family":"Runkel","given":"Robert","email":"runkel@usgs.gov","middleInitial":"L.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":955815,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nordstrom, D. 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We present a Monte Carlo method for computing that rate from seismicity catalogs while including a range of data and analysis uncertainties. This method is applied to regions for which the b value is assumed to be spatially invariant. Each region is broken down into epochs for which each epoch is estimated to have a uniform magnitude of completeness (Mc). The distribution of earthquake rates for M ≥ Mc is determined for each epoch by considering the Poisson likelihood of rates given the number of observed earthquakes with M ≥ Mc. We use a Monte Carlo process to include the uncertainty in b, Mc, and individual event magnitudes. The result for each epoch is the joint distribution of the Poisson rate of earthquakes with magnitudes larger than the minimum value used to calculate hazard (M1) and the Gutenberg–Richter b values, which control the extrapolation to other magnitudes. The rate for each region is either the duration‐weighted average over the epochs or, to better capture temporal variations, we also consider mixture models. The mixture models also provide an avenue to allow temporal variations in b values. To implement this joint distribution in a logic tree, we use the mean and 95% confidence branches, each of which is parameterized with an M ≥ M1 rate and b value. We explore different ways of defining those branches, as well as non‐Gutenberg–Richter branches, and their impact on hazard estimates. The mean hazard, but not the fractiles, is robust with respect to these choices. To illustrate these new methods, we use synthetic data and catalogs from recent U.S. Geological Survey National Seismic Hazard Models for the Central and Eastern United States and for Puerto Rico and the U.S. Virgin Islands.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120240245","usgsCitation":"Michael, A.J., and Llenos, A.L., 2026, Capturing the uncertainty of seismicity observations in earthquake rate estimates: Implications for probabilistic seismic hazard analysis and the USGS National Seismic Hazard Model: Bulletin of the Seismological Society of America, v. 116, no. 2, p. 786-809, https://doi.org/10.1785/0120240245.","productDescription":"24 p.","startPage":"786","endPage":"809","ipdsId":"IP-171147","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":498476,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1785/0120240245","text":"Publisher Index Page"},{"id":498394,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"central and eastern United States, Puerto Rico, U.S. Virgin Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -67.78074090569169,\n 18.563253258174967\n ],\n [\n -68.0988965908456,\n 17.877923675754772\n ],\n [\n -65.79027508404809,\n 17.94024820912165\n ],\n [\n -64.91802672378982,\n 17.61876483723642\n ],\n [\n -64.54841087453303,\n 17.674241141684107\n ],\n [\n -64.67276844737347,\n 18.36505205279029\n ],\n [\n -64.85854582812973,\n 18.494803755055287\n ],\n [\n -67.78074090569169,\n 18.563253258174967\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -103,\n 50\n ],\n [\n -103,\n 24\n ],\n [\n -60,\n 24\n ],\n [\n -60,\n 50\n ],\n [\n -103,\n 50\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"116","issue":"2","noUsgsAuthors":false,"publicationDate":"2025-12-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Michael, Andrew J. 0000-0002-2403-5019 michael@usgs.gov","orcid":"https://orcid.org/0000-0002-2403-5019","contributorId":1280,"corporation":false,"usgs":true,"family":"Michael","given":"Andrew","email":"michael@usgs.gov","middleInitial":"J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":953399,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Llenos, Andrea L. 0000-0002-4088-6737 allenos@usgs.gov","orcid":"https://orcid.org/0000-0002-4088-6737","contributorId":4455,"corporation":false,"usgs":true,"family":"Llenos","given":"Andrea","email":"allenos@usgs.gov","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":953400,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70273310,"text":"70273310 - 2026 - Natural source zone depletion of crude oil in the subsurface: Processes controlling mass losses of individual compounds","interactions":[],"lastModifiedDate":"2026-01-06T15:55:04.61116","indexId":"70273310","displayToPublicDate":"2025-12-30T09:46:03","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Natural source zone depletion of crude oil in the subsurface: Processes controlling mass losses of individual compounds","docAbstract":"At many petroleum hydrocarbon spill sites, residual spilled product forms a long-term source of groundwater contamination. The phrase source zone natural depletion is used to refer to the mass loss rates. Overall mass lost under environmental conditions was analyzed using conservative biomarker concentrations for a 1979 oil spill in northern Minnesota, USA. After 40–41 years, an average of 50% of the mass was lost with values ranging from 22% to 57% depending on location. It is also important to understand the composition changes in the source. To understand controls on the losses of individual compounds, concentrations of volatile hydrocarbons in oil samples were compared with aqueous solubilities, and pore-space oil saturations. The results of the comparison show that losses of the oil compounds were controlled by pore-space oil saturations, solubility, and susceptibility to degradation under methanogenic conditions. Compounds that degrade under methanogenic conditions, including toluene, o-xylene, and n-alkanes are more depleted compared to benzene, ethylbenzene, and m- and p-xylene for which losses are dominated by dissolution. These rates and compound-specific behaviors form a foundation for improved modeling approaches and risk analyses.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2025WR041964","usgsCitation":"Bekins, B., and Herkelrath, W., 2026, Natural source zone depletion of crude oil in the subsurface: Processes controlling mass losses of individual compounds: Water Resources Research, v. 62, no. 1, e2025WR041964, 19 p., https://doi.org/10.1029/2025WR041964.","productDescription":"e2025WR041964, 19 p.","ipdsId":"IP-139177","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":498472,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2025wr041964","text":"Publisher Index Page"},{"id":498361,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","otherGeospatial":"Bemidji crude oil site","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95.0916,\n 47.5742\n ],\n [\n -95.0916,\n 47.5733\n ],\n [\n -95.0895,\n 47.5733\n ],\n [\n -95.0895,\n 47.5742\n ],\n [\n -95.0916,\n 47.5742\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"62","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-12-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Bekins, Barbara 0000-0002-1411-6018 babekins@usgs.gov","orcid":"https://orcid.org/0000-0002-1411-6018","contributorId":139407,"corporation":false,"usgs":true,"family":"Bekins","given":"Barbara","email":"babekins@usgs.gov","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":953297,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Herkelrath, William 0000-0002-6149-5524","orcid":"https://orcid.org/0000-0002-6149-5524","contributorId":210576,"corporation":false,"usgs":true,"family":"Herkelrath","given":"William","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":953298,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70273351,"text":"70273351 - 2026 - Toward an efficient framework for remote sensing of river bathymetry: Comparing sensors and algorithms on an inaccessible proglacial river in Alaska","interactions":[],"lastModifiedDate":"2026-01-09T17:12:25.941374","indexId":"70273351","displayToPublicDate":"2025-12-27T11:01:08","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1801,"text":"Geomorphology","active":true,"publicationSubtype":{"id":10}},"title":"Toward an efficient framework for remote sensing of river bathymetry: Comparing sensors and algorithms on an inaccessible proglacial river in Alaska","docAbstract":"Remote sensing can provide reliable information on river depths and this approach might be particularly valuable in areas that are difficult to survey via conventional field methods. In this study, we assessed the potential to map the bathymetry of an inaccessible proglacial river in Alaska from both aerial orthophotos and a multispectral satellite image. In addition, we evaluated a variety of depth retrieval algorithms with different input data requirements, including some methods that require field measurements of water depth for calibration and other techniques that can be applied even when such field data are not available. These approaches might enable more efficient use of remote sensing methods by resource management agencies. Our results suggest that bathymetric mapping along the turquoise-colored river we examined was not only feasible but highly accurate ( R2 up to 0.94) for both types of image data. Algorithms that use paired observations of depth and reflectance to train depth retrieval models were the most accurate, with errors on the order of 15%–20% and little or no bias. Alternative techniques based on hydraulic and statistical concepts also led to strong agreement between predicted and observed depths but were more susceptible to systematic biases toward under- or over-estimation of depth. In contrast to clear-flowing streams, bathymetric mapping in this environment was enabled by a direct relationship between the depth and brightness of the water due to scattering by suspended sediment. In selecting an appropriate depth retrieval method, a compromise might need to be reached between the level of field effort invested and the accuracy of the resulting image-derived bathymetry. Standalone software for implementing these techniques is freely available.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.geomorph.2025.110140","usgsCitation":"Legleiter, C.J., Leonard, C.M., Burger, P.A., Pletcher, A.G., and Kinzel, P.J., 2026, Toward an efficient framework for remote sensing of river bathymetry: Comparing sensors and algorithms on an inaccessible proglacial river in Alaska: Geomorphology, v. 495, 110140, 24 p., https://doi.org/10.1016/j.geomorph.2025.110140.","productDescription":"110140, 24 p.","ipdsId":"IP-179757","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":498513,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United states","state":"Alaska","otherGeospatial":"Mulchatna River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -154.00824680332022,\n 60.789825438561536\n ],\n [\n -154.09788058802903,\n 60.789825438561536\n ],\n [\n -154.09788058802903,\n 60.770469283939605\n ],\n [\n -154.00824680332022,\n 60.770469283939605\n ],\n [\n -154.00824680332022,\n 60.789825438561536\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"495","noUsgsAuthors":false,"publicationDate":"2025-12-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":953420,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Leonard, Christina M. 0000-0002-5096-8103","orcid":"https://orcid.org/0000-0002-5096-8103","contributorId":360578,"corporation":false,"usgs":false,"family":"Leonard","given":"Christina","middleInitial":"M.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":953421,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Burger, Paul A. 0009-0005-7135-8290","orcid":"https://orcid.org/0009-0005-7135-8290","contributorId":360579,"corporation":false,"usgs":false,"family":"Burger","given":"Paul","middleInitial":"A.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":953422,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pletcher, Addison G. 0009-0002-3775-0852","orcid":"https://orcid.org/0009-0002-3775-0852","contributorId":360580,"corporation":false,"usgs":false,"family":"Pletcher","given":"Addison","middleInitial":"G.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":953423,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kinzel, Paul J. 0000-0002-6076-9730 pjkinzel@usgs.gov","orcid":"https://orcid.org/0000-0002-6076-9730","contributorId":743,"corporation":false,"usgs":true,"family":"Kinzel","given":"Paul","email":"pjkinzel@usgs.gov","middleInitial":"J.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":953424,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70273255,"text":"70273255 - 2026 - A 10-year continuous daily simulation of chloride flux from a suburban watershed in Fairfax County, Virginia, USA","interactions":[],"lastModifiedDate":"2025-12-29T15:17:40.106052","indexId":"70273255","displayToPublicDate":"2025-12-23T09:14:45","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"A 10-year continuous daily simulation of chloride flux from a suburban watershed in Fairfax County, Virginia, USA","docAbstract":"Increasing levels of chloride in surface water are associated with detrimental effects on water quality, aquatic ecosystems, infrastructure, and human health. Numerous mass-balance studies have inferred watershed transport processes by interpreting chloride inputs and outputs, but few represent internal dynamics explicitly. We constructed a coupled water/chloride mass balance model to gain insights into storage, residence time, and transport processes in a 10-km2 urban watershed. The model, which operates over a 10-year period at a daily time scale, represents storage in a dynamic soil-moisture reservoir, quick-flow runoff from storm events, and slow-flow runoff that sustains streamflow in dry weather. The calibrated model accurately represented (a)the observed transition from a streamflow enrichment regime in cold months to a dilution regime in warmer months, (b) the observed tendency for late-summer concentrations to be higher after winters with heavy snowfall, and (c) a period-of-record downward trend in chloride concentration likely associated with a downward trend in annual snowfall. Estimated chloride inputs averaged 195 metric tons per year, while the average output was 270 metric tons per year. In contrast, estimated storage was only 107 metric tons. The estimated mean residence time in groundwater was 1.27 years. This short residence time indicates that efforts to reduce inputs will manifest as decreased concentrations in streamflow on a management-relevant time scale of several years. The coupled mass balance model yielded insights into internal watershed dynamics that would not be possible from simple input/output analysis; such models can be useful tools for gaining insight into small watershed hydrology and pollutant transport.
","language":"English","publisher":"MDPI","doi":"10.3390/w18010043","usgsCitation":"Chanat, J.G., and Custer, C.A., 2026, A 10-year continuous daily simulation of chloride flux from a suburban watershed in Fairfax County, Virginia, USA: Water, v. 18, no. 1, 43, 25 p., https://doi.org/10.3390/w18010043.","productDescription":"43, 25 p.","ipdsId":"IP-184027","costCenters":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"links":[{"id":498300,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w18010043","text":"Publisher Index Page"},{"id":498099,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","county":"Fairfax 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Jeffrey G. 0000-0002-3629-7307 jchanat@usgs.gov","orcid":"https://orcid.org/0000-0002-3629-7307","contributorId":5062,"corporation":false,"usgs":true,"family":"Chanat","given":"Jeffrey","email":"jchanat@usgs.gov","middleInitial":"G.","affiliations":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":952891,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Custer, Christopher Allan 0000-0003-2072-1414","orcid":"https://orcid.org/0000-0003-2072-1414","contributorId":364564,"corporation":false,"usgs":true,"family":"Custer","given":"Christopher","middleInitial":"Allan","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"preferred":true,"id":952892,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70273476,"text":"70273476 - 2026 - Potential interactions between birds and floating photovoltaic solar energy: Spatially informed species vulnerabilities, techno-ecological risks, and sustainability trade-offs","interactions":[],"lastModifiedDate":"2026-01-20T15:30:26.944609","indexId":"70273476","displayToPublicDate":"2025-12-23T08:46:17","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5925,"text":"Environmental Science and Technology","active":true,"publicationSubtype":{"id":10}},"title":"Potential interactions between birds and floating photovoltaic solar energy: Spatially informed species vulnerabilities, techno-ecological risks, and sustainability trade-offs","docAbstract":"Floating photovoltaics (floating solar panels; FPV) can reduce the negative impacts of solar energy development in terrestrial environments, but their effects on freshwater ecosystems remain poorly understood. We examined potential FPV interactions with avian biodiversity, using previously modeled technical potential of FPV in the northeastern United States. We developed a vulnerability index based on avian species’: (i) morphological traits, (ii) level of conservation concern, and (iii) habitat requirements. Using eBird participatory science data, we assessed the risk of FPV to 291 species of birds occurring at FPV-suitable waterbodies based on their vulnerability index and estimated relative abundance at each location. The greatest risk of FPVs to birds was associated with waterbodies near the Atlantic coast and Great Lakes, and most (80%) of these high-risk waterbodies were smaller waterbodies. Because the avian risk at FPV-suitable waterbodies was not correlated with estimated energy generation, reducing risk to birds might not require energy trade-offs. Moreover, avoiding FPV installations at waterbodies predicted to pose high risk to birds is likely to return social and recreational benefits while minimizing risk of biofouling. Our analysis provides a framework for proactively evaluating risk, reducing potential adverse impacts of FPV to birds, and identifying cobenefits of sustainable FPV siting across waterscapes.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.5c09819","usgsCitation":"Binley, A.D., Gallaher, A., Rodewald, A.D., and Grodsky, S.M., 2026, Potential interactions between birds and floating photovoltaic solar energy: Spatially informed species vulnerabilities, techno-ecological risks, and sustainability trade-offs: Environmental Science and Technology, v. 60, no. 1, p. 510-621, https://doi.org/10.1021/acs.est.5c09819.","productDescription":"12 p.","startPage":"510","endPage":"621","ipdsId":"IP-181372","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":498920,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.5c09819","text":"Publisher Index Page"},{"id":498737,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont, Virginia, Washington DC, West Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -66.65501641197895,\n 44.74152548184259\n ],\n [\n -67.6968245772661,\n 45.73794783309825\n ],\n [\n -67.95270949892765,\n 47.379125614743884\n ],\n [\n -69.3915223409562,\n 47.4278826893804\n ],\n [\n -70.99089733033615,\n 45.39194803042125\n ],\n [\n -71.59816120202068,\n 45.0249102712188\n ],\n [\n -74.93724052894156,\n 44.993515254035685\n ],\n [\n -76.75034686370854,\n 43.91376278161334\n ],\n [\n -78.97980279465791,\n 43.429291703689586\n ],\n [\n -78.86279954021416,\n 42.897702704595986\n ],\n [\n -79.54055781839557,\n 42.48834669405045\n ],\n [\n -80.5152362296271,\n 42.22648847140036\n ],\n [\n -80.5372853679192,\n 40.61342359980179\n ],\n [\n -81.46839017947205,\n 39.28753079607813\n ],\n [\n -82.12840686018721,\n 39.08491548516051\n ],\n [\n -82.69161741334696,\n 38.02531835066708\n ],\n [\n -82.02995782117321,\n 37.54106137435666\n ],\n [\n -83.64361903153913,\n 36.60472689492755\n ],\n [\n -75.61198709812805,\n 36.516353887422284\n ],\n [\n -74.66765042081843,\n 38.89523397600536\n ],\n [\n -73.50324716106653,\n 40.2520538786232\n ],\n [\n -71.66037271059179,\n 41.01739604846372\n ],\n [\n -70.84438505823752,\n 41.23839424240364\n ],\n [\n -69.7808356241324,\n 41.14180012249446\n ],\n [\n -69.93670062740647,\n 42.203277879766915\n ],\n [\n -70.3951271076242,\n 42.825025843792105\n ],\n [\n -69.30407208470645,\n 43.759282507474296\n ],\n [\n -66.65501641197895,\n 44.74152548184259\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"60","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-12-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Binley, Allison D.","contributorId":365181,"corporation":false,"usgs":false,"family":"Binley","given":"Allison","middleInitial":"D.","affiliations":[{"id":36682,"text":"Cornell Lab of Ornithology","active":true,"usgs":false}],"preferred":false,"id":953873,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gallaher, Adam","contributorId":348210,"corporation":false,"usgs":false,"family":"Gallaher","given":"Adam","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":953874,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rodewald, Amanda D.","contributorId":365183,"corporation":false,"usgs":false,"family":"Rodewald","given":"Amanda","middleInitial":"D.","affiliations":[{"id":36682,"text":"Cornell Lab of Ornithology","active":true,"usgs":false}],"preferred":false,"id":953875,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Grodsky, Steven Mark 0000-0003-0846-7230","orcid":"https://orcid.org/0000-0003-0846-7230","contributorId":328517,"corporation":false,"usgs":true,"family":"Grodsky","given":"Steven","email":"","middleInitial":"Mark","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":953876,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70273446,"text":"70273446 - 2026 - An analysis of the linked decisions in the confiscation of illegally traded turtles","interactions":[],"lastModifiedDate":"2026-02-24T16:39:30.348704","indexId":"70273446","displayToPublicDate":"2025-12-22T08:46:47","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5803,"text":"Conservation Science and Practice","active":true,"publicationSubtype":{"id":10}},"title":"An analysis of the linked decisions in the confiscation of illegally traded turtles","docAbstract":"Over the last few decades, freshwater turtles have become more common in the illegal wildlife trade because of growing global demand. Illegally traded turtles may be intercepted by several different agencies with separate jurisdictions. When turtles are confiscated, uncertainties may make releasing them back to the wild difficult. We used tools from decision analysis to achieve the following three objectives: (1) map elements of the decision process and their relationships in the illegal turtle trade using conceptual models, (2) outline the linked decisions for turtle confiscation and repatriation using decision trees, and (3) evaluate the decision trees for two example scenarios, one with moderate uncertainty and one with high uncertainty. We used the wood turtle (Glyptemys insculpta) as a case study, which is a species of conservation concern in part due to illegal wildlife trafficking. We conducted 23 semi-structured interviews of decision makers in law enforcement, biologists, land managers, and zoo staff. Interviews revealed that decisions regarding the disposition of confiscated turtles are complicated by uncertainty in disease status and origin. Decision makers that handle confiscated turtles also recognize that their decisions are often made in sequence and dependent on the outcome of antecedent decisions. In evaluating our decision trees, we found that the optimal decisions for example scenarios were similar and insensitive to uncertainty. Future applications of the decision trees by decision makers would involve a decision analyst to parameterize and interpret the choices and consequences involved in working through these decision trees. Collectively, our work shows how the use of decision trees can help structure and evaluate risky decisions for repatriating confiscated wildlife.
","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/csp2.70165","usgsCitation":"Smith, D., DiRenzo, G.V., Fleming, J.E., McEachran, M.C., and Campbell Grant, E.H., 2025, An analysis of the linked decisions in the confiscation of illegally traded turtles: Conservation Science and Practice, e70165, 12 p., https://doi.org/10.1111/csp2.70165.","productDescription":"e70165, 12 p.","ipdsId":"IP-166499","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":498702,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/csp2.70165","text":"Publisher Index Page"},{"id":498616,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -97.61952248598104,\n 51.579179045299185\n ],\n [\n -97.61952248598104,\n 37.42140161216963\n ],\n [\n -66.83408644894973,\n 37.42140161216963\n ],\n [\n -66.83408644894973,\n 51.579179045299185\n ],\n [\n -97.61952248598104,\n 51.579179045299185\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"8","issue":"2","noUsgsAuthors":false,"publicationDate":"2025-12-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Desireé","contributorId":365126,"corporation":false,"usgs":false,"family":"Smith","given":"Desireé","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":953733,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DiRenzo, Graziella V.","contributorId":365127,"corporation":false,"usgs":false,"family":"DiRenzo","given":"Graziella","middleInitial":"V.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":953734,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fleming, Jillian Elizabeth 0000-0003-2570-914X","orcid":"https://orcid.org/0000-0003-2570-914X","contributorId":238931,"corporation":false,"usgs":true,"family":"Fleming","given":"Jillian","email":"","middleInitial":"Elizabeth","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":953735,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McEachran, Margaret C.","contributorId":365130,"corporation":false,"usgs":false,"family":"McEachran","given":"Margaret","middleInitial":"C.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":953736,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Campbell Grant, Evan H. 0000-0003-4401-6496 ehgrant@usgs.gov","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":150443,"corporation":false,"usgs":true,"family":"Campbell Grant","given":"Evan","email":"ehgrant@usgs.gov","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":953737,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70273308,"text":"70273308 - 2026 - Where to restore and conserve? A regional benefit cost analysis of coral reef protection and restoration for coastal flood resilience","interactions":[],"lastModifiedDate":"2026-01-06T14:49:27.601532","indexId":"70273308","displayToPublicDate":"2025-12-22T08:42:30","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Where to restore and conserve? A regional benefit cost analysis of coral reef protection and restoration for coastal flood resilience","docAbstract":"Momentum is growing for the management of coral reefs as a strategy to reduce climate risks in tropical coastlines. Yet, quantification of the life-time costs, impacts, and benefits remains limited. This study provides one of the first rigorous, spatially explicit, regional-scale Benefit:Cost Analyses (BCA) for coral reef restoration and was designed to meet the BCA requirements of major hazard mitigation funding and programs. This study simulates coastal flooding using a hydrodynamic model under different scenarios representing current coral reef conditions, reef degradation, and reef restoration. These coastal flood maps are used to estimate socioeconomic damages, which are included in a BCA to assess cost-effectiveness and priority areas for coral reef conservation and restoration. The United States Virgin Islands is used as a case study given recent impacts from storms and their new policy that declares reefs as natural infrastructure. The results show that flood risk across the islands of Saint Croix, Saint John, and Saint Thomas is $51.4 million USD per year. Annually, coral reefs prevent flood damages to 481 people and $43.6 million USD of infrastructure, which represents 87 % of the flood risk. These results identify the communities that could receive the greatest benefits from coral conservation and restoration, which overlap significantly. Coral reef restoration is found to be a cost-effective strategy for flood mitigation although how reefs are restored matters. Hybrid coral reef restoration provides greater flood mitigation benefits than ecological restoration, but its cost-effectiveness is overall lower given its greater costs. Many coastal areas have benefit:cost ratios exceeding 1.5, which complies with typical government agencies’ requirements for accessing hazard mitigation funds. This valuation framework helps provide rigorous regional-scale quantification of nature-based coastal protection solutions for coastal risk management decisions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2025.128166","usgsCitation":"Reguero, B., Gaido-Lassare, C., Storlazzi, C.D., McNulty, V., Perez, D., and Beck, M.W., 2026, Where to restore and conserve? A regional benefit cost analysis of coral reef protection and restoration for coastal flood resilience: Journal of Environmental Management, v. 397, 128166, 15 p., https://doi.org/10.1016/j.jenvman.2025.128166.","productDescription":"128166, 15 p.","ipdsId":"IP-170993","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":498342,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"U.S. Virgin Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -64.65441321858593,\n 18.38265061411741\n ],\n [\n -65.08683971208158,\n 18.38265061411741\n ],\n [\n -65.08683971208158,\n 18.287140701134334\n ],\n [\n -64.65441321858593,\n 18.287140701134334\n ],\n [\n -64.65441321858593,\n 18.38265061411741\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -64.91799534519654,\n 17.811855495330647\n ],\n [\n -64.91799534519654,\n 17.657306327448765\n ],\n [\n -64.54674404449649,\n 17.657306327448765\n ],\n [\n -64.54674404449649,\n 17.811855495330647\n ],\n [\n -64.91799534519654,\n 17.811855495330647\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"397","noUsgsAuthors":false,"publicationDate":"2025-12-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Reguero, Borja","contributorId":264485,"corporation":false,"usgs":false,"family":"Reguero","given":"Borja","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":953291,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gaido-Lassare, Camila","contributorId":364839,"corporation":false,"usgs":false,"family":"Gaido-Lassare","given":"Camila","affiliations":[{"id":17620,"text":"UCSC","active":true,"usgs":false}],"preferred":false,"id":953292,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Storlazzi, Curt D. 0000-0001-8057-4490","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":213610,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","middleInitial":"D.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":953293,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McNulty, Valerie","contributorId":341998,"corporation":false,"usgs":false,"family":"McNulty","given":"Valerie","affiliations":[{"id":33811,"text":"TNC","active":true,"usgs":false}],"preferred":false,"id":953294,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Perez, Denise","contributorId":341897,"corporation":false,"usgs":false,"family":"Perez","given":"Denise","email":"","affiliations":[{"id":33811,"text":"TNC","active":true,"usgs":false}],"preferred":false,"id":953295,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Beck, Michael W.","contributorId":259298,"corporation":false,"usgs":false,"family":"Beck","given":"Michael","email":"","middleInitial":"W.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":true,"id":953296,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70273304,"text":"70273304 - 2026 - Diurnal patterns of nitrous oxide fluxes from a seasonal prairie wetland","interactions":[],"lastModifiedDate":"2026-01-06T15:30:32.550008","indexId":"70273304","displayToPublicDate":"2025-12-18T09:23:57","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Diurnal patterns of nitrous oxide fluxes from a seasonal prairie wetland","docAbstract":"Wetlands have spatially and temporally dynamic nitrous oxide (N2O) fluxes. Understanding diurnal patterns in N2O fluxes in wetlands can reveal short-term drivers and improve process-based models. An automated chamber system was used to determine N2O flux rates every 2.5 to 4 h in a prairie pothole wetland in North Dakota during the 2013 to 2014 growing seasons under ponded, moist, and dry soil conditions. The wetland generally emitted N2O under all conditions, although median fluxes during ponded conditions were low over the two-year study. A significant diurnal N2O pattern was observed under dry soil conditions, but not in moist soil or ponded conditions. When soils were dry, daytime (~ 10:00–14:00; 2.43 ± 0.31 ng m-2 hr-1) and late-day (~ 14:00–18:00; 2.20 ± 0.16 ng m-2 hr-1) average N2O fluxes were 77% and 61% greater, respectively, than other times during the 24-hr diel cycle (< 1.37 ng m-2 hr-1). N2O flux was positively correlated to air (r = 0.58) and soil (r = 0.49) temperatures under dry conditions, while no significant correlations to environmental factors were observed under ponded and moist conditions. Incorporating diurnal patterns in N2O fluxes in wetlands and other ecosystems can improve extrapolations from daily to annual flux calculations and constrain global N2O inventories.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s13157-025-02017-4","usgsCitation":"Faust, D.R., Tangen, B., and Bansal, S., 2026, Diurnal patterns of nitrous oxide fluxes from a seasonal prairie wetland: Wetlands, v. 46, 1, 12 p., https://doi.org/10.1007/s13157-025-02017-4.","productDescription":"1, 12 p.","ipdsId":"IP-175229","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":498355,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota","county":"Stutsman County","otherGeospatial":"Cottonwood Lake Study Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -99.12,\n 47.12\n ],\n [\n -99.12,\n 47.09\n ],\n [\n -99.09,\n 47.09\n ],\n [\n -99.09,\n 47.12\n ],\n [\n -99.12,\n 47.12\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"46","noUsgsAuthors":false,"publicationDate":"2025-12-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Faust, Derek R.","contributorId":68232,"corporation":false,"usgs":true,"family":"Faust","given":"Derek","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":953286,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tangen, Brian 0000-0001-5157-9882 btangen@usgs.gov","orcid":"https://orcid.org/0000-0001-5157-9882","contributorId":216177,"corporation":false,"usgs":true,"family":"Tangen","given":"Brian","email":"btangen@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":953287,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bansal, Sheel 0000-0003-1233-1707 sbansal@usgs.gov","orcid":"https://orcid.org/0000-0003-1233-1707","contributorId":167295,"corporation":false,"usgs":true,"family":"Bansal","given":"Sheel","email":"sbansal@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":953288,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70273376,"text":"70273376 - 2026 - Phytoplankton biomass dynamics in wet (2019) and dry (2023) years in Lake Pontchartrain estuary, Louisiana from Sentinel 2-MSI and PACE-OCI observations","interactions":[],"lastModifiedDate":"2026-01-09T16:48:56.879977","indexId":"70273376","displayToPublicDate":"2025-12-17T10:44:40","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":23125,"text":"Science of the Total Environment.","active":true,"publicationSubtype":{"id":10}},"title":"Phytoplankton biomass dynamics in wet (2019) and dry (2023) years in Lake Pontchartrain estuary, Louisiana from Sentinel 2-MSI and PACE-OCI observations","docAbstract":"This study provides a comprehensive assessment of phytoplankton biomass dynamics in Lake Pontchartrain, Louisiana, by combining monthly water quality data with multispectral and hyperspectral satellite observations using a machine learning algorithm. A machine learning model based on Variational Autoencoder (VAE), globally applicable, was used to estimate phytoplankton biomass via chlorophyll-a (Chl-a) from Sentinel 2-MSI and NASA's new hyperspectral mission, PACE-OCI, enabling the first direct comparison between the two sensors. The model performed well in this complex estuarine system, with higher accuracy from PACE-OCI (MAE: 1.48, RMSE: 10.40, slope: 0.87) than Sentinel 2-MSI (MAE: 1.57, RMSE: 11.08, slope: 0.83). This approach enabled continuous high-resolution monitoring of phytoplankton biomass across space and time. Comparative analysis of 2019, a wet year with Bonnet Carré Spillway (BCS) openings, and 2023, a dry year with extremely low riverine inputs, revealed distinct biomass dynamics. In 2019, BCS discharge initially suppressed Chl-a within turbid waters (<5 mg Chl-a m−3) but later acted as a nutrient and hydrodynamic driver, transporting nutrients toward the lake outlet and Mississippi coast, promoting high biomass (25–45 mg Chl-a m−3) near the entrance. In contrast, dry conditions in 2023 led to more frequent-than-expected high biomass (>35 mg Chl-a m−3), persisting in the lake center. Similar spatial patterns were observed again in 2024, revealed for the first time by PACE-OCI. This study demonstrates the value of satellite-derived observations for capturing transient phytoplankton biomass events and highlights the potential of PACE-OCI's hyperspectral capabilities to better distinguish phytoplankton communities and improve understanding of their responses to freshwater inflows and associated processes driving pulses into estuaries.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2025.181126","usgsCitation":"Shrestha, S., Liu, B., Li, J., Huang, W., Baustian, M.M., D'Sa, E.J., Bargu, S., Messina, F., Georgiou, I.Y., Kumar, A., Freeman, A., and Mize, S., 2026, Phytoplankton biomass dynamics in wet (2019) and dry (2023) years in Lake Pontchartrain estuary, Louisiana from Sentinel 2-MSI and PACE-OCI observations: Science of the Total Environment., v. 1011, 181126, 18 p., https://doi.org/10.1016/j.scitotenv.2025.181126.","productDescription":"181126, 18 p.","ipdsId":"IP-182233","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":498677,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2025.181126","text":"Publisher Index Page"},{"id":498512,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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insights","interactions":[],"lastModifiedDate":"2026-01-22T16:45:57.886812","indexId":"70273226","displayToPublicDate":"2025-12-15T09:46:46","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Greater white-fronted goose habitat use in Louisiana provides water depth management insights","docAbstract":"Numerous waterfowl species have altered their geographic distribution in recent decades. The greater white-fronted goose (Anser albifrons) has shifted its wintering distribution from coastal marshes in Texas and Louisiana, USA, to interior landscapes, creating challenges for conservation managers. Although the range shift has been primarily attributed to landscape-scale changes in rice production, effects of fine-scale habitat conditions on greater white-fronted goose habitat use are unknown. Thus, information on fine-scale environmental conditions could provide information to managers working to mitigate effects of the shift. We used global positioning system neck collars to monitor 78 greater white-fronted geese in Louisiana each winter during 2016–2019 and visited all used locations to record vegetation height, water depth, and land cover. We modeled greater white-fronted goose habitat use in 4 regions across Louisiana (central, coastal, north, southwest), and evaluated effects of vegetation height, water depth, land cover, and interactions. In our candidate model set, we included a sampling null, which accounted for differences in sampling frequencies among hunt seasons and diel periods. The top model in the central, coastal, and north regions included water depth, whereas the top model in the southwest region included water depth, vegetation height, and land cover. Parameter estimates and confidence intervals indicated that goose habitat use was higher at sites with >5.1 cm of water compared to dry sites or sites with <5.1 cm of water. In the southwest region, goose habitat use was highest in areas identified as other land cover (e.g., corn, cotton, rye, sugarcane) and lowest in open water. Although Louisiana has substantial anthropogenic food resources in the form of rice agriculture and abundant grazing opportunities afforded by mild winters, our results indicate that wet areas provide better habitat to greater white-fronted geese than dry areas. New agricultural flooding regimes in Louisiana could improve habitat quality and potentially mitigate some effects of the winter distribution shift.
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\"}}]}","volume":"50","issue":"2","noUsgsAuthors":false,"publicationDate":"2025-12-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Beatty, William S. 0000-0003-0013-3113","orcid":"https://orcid.org/0000-0003-0013-3113","contributorId":224795,"corporation":false,"usgs":true,"family":"Beatty","given":"William S.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":952790,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Link, Paul T.","contributorId":364512,"corporation":false,"usgs":false,"family":"Link","given":"Paul","middleInitial":"T.","affiliations":[{"id":12717,"text":"Louisiana Department of Wildlife and Fisheries","active":true,"usgs":false}],"preferred":false,"id":952791,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Leach, Brett","contributorId":356397,"corporation":false,"usgs":false,"family":"Leach","given":"Brett","affiliations":[{"id":12717,"text":"Louisiana Department of Wildlife and Fisheries","active":true,"usgs":false}],"preferred":false,"id":952792,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Houdek, Steven C. 0000-0001-9452-6596 shoudek@usgs.gov","orcid":"https://orcid.org/0000-0001-9452-6596","contributorId":4423,"corporation":false,"usgs":true,"family":"Houdek","given":"Steven","email":"shoudek@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":952793,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Webb, Elisabeth B. 0000-0003-3851-6056 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involves shared learning through the development of a model or multiple models with a group of participants and a modelling team. PM has the potential to be used more frequently to address recreational fisheries problems. We focus on our experience working with small groups (usually <40 people) to address natural resource issues. This chapter focuses on building a PM team and the communications that occur before, during, and after a PM process. The participants usually include people like recreational and commercial fishers, members of environmental non-governmental organizations, and fishery managers or other decision makers. Ground rules, expectations, and end goals should be established early in the PM process. We emphasize that benefits arise from including the participants in the development and validation of the models in a process that includes feedback between the modellers and participants. Although participants are ideally involved in all stages, their inclusion during model development and testing can be critical for buy-in within the group. When the modellers listen carefully to participant concerns and respond to those concerns during model development, we have experienced favourable process outcomes such as improved participant engagement and building of trust.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Understanding recreational fishers: Disciplinary and interdisciplinary approaches for fisheries management","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-031-99739-6_22","usgsCitation":"Wilberg, M.J., Irwin, B., and Jones, M., 2026, Participatory modelling to support evaluation of management actions for recreational fisheries, chap. 22 of Understanding recreational fishers: Disciplinary and interdisciplinary approaches for fisheries management, p. 693-718, https://doi.org/10.1007/978-3-031-99739-6_22.","productDescription":"26 p.","startPage":"693","endPage":"718","ipdsId":"IP-149844","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":502491,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/978-3-031-99739-6_22","text":"Publisher Index Page"},{"id":502350,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2025-12-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Wilberg, Michael J.","contributorId":36494,"corporation":false,"usgs":true,"family":"Wilberg","given":"Michael","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":958906,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Irwin, Brian J. 0000-0002-0666-2641","orcid":"https://orcid.org/0000-0002-0666-2641","contributorId":280043,"corporation":false,"usgs":true,"family":"Irwin","given":"Brian J.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":958907,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jones, Michael L.","contributorId":126763,"corporation":false,"usgs":false,"family":"Jones","given":"Michael L.","affiliations":[{"id":6600,"text":"Qauntitative Fisheries Center, Department of Fisheries and Wildlife, Michigan State University","active":true,"usgs":false}],"preferred":false,"id":958908,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70274754,"text":"70274754 - 2026 - Integrating diverse stakeholder knowledge in recreational fisheries decision making","interactions":[],"lastModifiedDate":"2026-04-09T14:36:18.146592","indexId":"70274754","displayToPublicDate":"2025-12-15T09:29:32","publicationYear":"2026","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"20","title":"Integrating diverse stakeholder knowledge in recreational fisheries decision making","docAbstract":"Scientists and policymakers increasingly recognize that recreational fisheries decision making should in some way involve the knowledge of affected stakeholders. There are myriad studies about integrating stakeholder knowledge, but they span broad disciplines and topics. Lacking is (i) simple taxonomies that organize general approaches for integrating knowledge, and (ii) a review and examples of their application to specifically recreational fisheries and specifically diverse stakeholders. This chapter first defines the terms “stakeholders” and “recreational” and recognizes that globally, the decision making for non-commercial fisheries often occurs outside of the North American concept of management agencies and even purely leisure-based fisheries. Most of the chapter reviews common approaches for integrating stakeholder knowledge, from near-complete external governance with minimal input from stakeholders (top-down, command-and-control) to self-governance by stakeholders. Organizationally, we adapt the existing cooperative management continuum to first separate non-participatory from participatory approaches and then separate participatory approaches into those aimed at creating knowledge (to eventually inform future decisions) and those focused on decision making itself. The approaches we consider include observing stakeholders (e.g., via survey), incorporating multiple types of knowing (e.g., local traditional knowledge), collaborative data collection (e.g., citizen science), collaborative modelling (e.g., mental models and quantitative modelling), participatory decision making (co-management proper and the special cases of structured decision making and adaptive management), and finally self-governance (including private management of recreational fisheries). We define each according to a broader literature beyond recreational fisheries, describe the recognized advantages and disadvantages, describe or summarize applications to recreational fisheries, and finally mention relevance for integrating specifically diverse stakeholder knowledge. We then provide advice for how to select different approaches, including situations in which certain approaches are more likely to produce intended consequences. We conclude by describing potential challenges to the more useful integration of diverse stakeholder knowledge and needs in recreational fisheries decision making.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Understanding recreational fishers: Disciplinary and interdisciplinary approaches for fisheries management","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-031-99739-6_20","usgsCitation":"Camp, E.V., van Poorten, B.T., Robinson, K.F., Harrison, H., Nguyen, V., Crandall, C., Lorenzen, K., Casola, W., Dedual, M., and Klizentyte, K., 2026, Integrating diverse stakeholder knowledge in recreational fisheries decision making, chap. 20 of Understanding recreational fishers: Disciplinary and interdisciplinary approaches for fisheries management, p. 607-660, https://doi.org/10.1007/978-3-031-99739-6_20.","productDescription":"54 p.","startPage":"607","endPage":"660","ipdsId":"IP-150512","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":502490,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/978-3-031-99739-6_20","text":"Publisher Index Page"},{"id":502349,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2025-12-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Camp, Edward V.","contributorId":173095,"corporation":false,"usgs":false,"family":"Camp","given":"Edward","email":"","middleInitial":"V.","affiliations":[{"id":12557,"text":"University of Florida, FLREC","active":true,"usgs":false}],"preferred":false,"id":958924,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"van Poorten, Brett T.","contributorId":364222,"corporation":false,"usgs":false,"family":"van Poorten","given":"Brett","middleInitial":"T.","affiliations":[],"preferred":false,"id":958925,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Robinson, Kelly Filer 0000-0001-8109-9492","orcid":"https://orcid.org/0000-0001-8109-9492","contributorId":340631,"corporation":false,"usgs":true,"family":"Robinson","given":"Kelly","email":"","middleInitial":"Filer","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":958926,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harrison, Hannah","contributorId":369414,"corporation":false,"usgs":false,"family":"Harrison","given":"Hannah","affiliations":[{"id":24650,"text":"Dalhousie University","active":true,"usgs":false}],"preferred":false,"id":958927,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nguyen, Vivian M.","contributorId":166922,"corporation":false,"usgs":false,"family":"Nguyen","given":"Vivian M.","affiliations":[{"id":17786,"text":"Carleton University","active":true,"usgs":false}],"preferred":false,"id":958928,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Crandall, Chelsey","contributorId":340788,"corporation":false,"usgs":false,"family":"Crandall","given":"Chelsey","email":"","affiliations":[{"id":81665,"text":"Florida Fish and Wildlife Conservation Commission,","active":true,"usgs":false}],"preferred":false,"id":958929,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lorenzen, Kai","contributorId":169476,"corporation":false,"usgs":false,"family":"Lorenzen","given":"Kai","email":"","affiliations":[{"id":12557,"text":"University of Florida, FLREC","active":true,"usgs":false}],"preferred":false,"id":958930,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Casola, Will","contributorId":369418,"corporation":false,"usgs":false,"family":"Casola","given":"Will","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":958931,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Dedual, Michel","contributorId":369419,"corporation":false,"usgs":false,"family":"Dedual","given":"Michel","affiliations":[{"id":38703,"text":"New Zealand Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":958932,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Klizentyte, Kotryna","contributorId":369420,"corporation":false,"usgs":false,"family":"Klizentyte","given":"Kotryna","affiliations":[],"preferred":false,"id":958933,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70274535,"text":"70274535 - 2026 - Effects of ungulate browsing on forest assisted migration strategies to conserve ecosystem function","interactions":[],"lastModifiedDate":"2026-03-31T15:42:37.444512","indexId":"70274535","displayToPublicDate":"2025-12-13T10:36:25","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1321,"text":"Conservation Biology","active":true,"publicationSubtype":{"id":10}},"title":"Effects of ungulate browsing on forest assisted migration strategies to conserve ecosystem function","docAbstract":"Forest managers recognize that proactive management strategies, such as forest assisted migration (FAM) of tree species, intended to accelerate the pace of forest adaptation, may be necessary to maintain resilient forests and combat the stressors of climate change. However, the impact of interactions between climate change and ungulate browsers of trees, both of which have profound effects on the landscape, on the success of FAM efforts is unknown. We used a forest landscape model (LANDIS-II) to simulate assisted migration and browsing by ungulate (white-tailed deer [Odocoileus virginianus]) populations on a northern Wisconsin (USA) landscape under alternate climate futures. After accounting for effects of FAM strategy and climate change scenario, simulated ungulate browsing reduced species richness and the proportion of the landscape with tree species preferred by browsers and resulted in more of the landscape developing into novel forest communities that supported fewer ecosystem goods and services. Our results suggest that managers may need to select FAM species less preferred by ungulates or use seedling protection measures to mitigate the negative effects of chronically high ungulate populations in efforts to transition future forests to sustain ecosystem goods and services.
","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/cobi.70198","usgsCitation":"Gustafson, E.J., De Jager, N.R., McGraw, A.M., Kern, C.C., and Kabrick, J.M., 2026, Effects of ungulate browsing on forest assisted migration strategies to conserve ecosystem function: Conservation Biology, v. 40, no. 2, e70198, 14 p., https://doi.org/10.1111/cobi.70198.","productDescription":"e70198, 14 p.","ipdsId":"IP-171512","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":501867,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.9,\n 45.5\n ],\n [\n -88.9,\n 45\n ],\n [\n -88.333,\n 45\n ],\n [\n -88.333,\n 45.5\n ],\n [\n -88.9,\n 45.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"40","issue":"2","noUsgsAuthors":false,"publicationDate":"2025-12-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Gustafson, Eric J.","contributorId":368942,"corporation":false,"usgs":false,"family":"Gustafson","given":"Eric","middleInitial":"J.","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":958144,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"De Jager, Nathan R. 0000-0002-6649-4125 ndejager@usgs.gov","orcid":"https://orcid.org/0000-0002-6649-4125","contributorId":3717,"corporation":false,"usgs":true,"family":"De Jager","given":"Nathan","email":"ndejager@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":958145,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McGraw, Amanda M.","contributorId":368943,"corporation":false,"usgs":false,"family":"McGraw","given":"Amanda","middleInitial":"M.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":958146,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kern, Christel C.","contributorId":368944,"corporation":false,"usgs":false,"family":"Kern","given":"Christel","middleInitial":"C.","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":958147,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kabrick, John M.","contributorId":368945,"corporation":false,"usgs":false,"family":"Kabrick","given":"John","middleInitial":"M.","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":958148,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70273223,"text":"70273223 - 2026 - Multi-scale geophysical mapping of the brine and bedrock surfaces along the Dolores River, Paradox Valley, Colorado, December 2023","interactions":[],"lastModifiedDate":"2026-02-09T16:18:21.216938","indexId":"70273223","displayToPublicDate":"2025-12-12T09:32:25","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2850,"text":"Near Surface Geophysics","active":true,"publicationSubtype":{"id":10}},"title":"Multi-scale geophysical mapping of the brine and bedrock surfaces along the Dolores River, Paradox Valley, Colorado, December 2023","docAbstract":"Total dissolved solids derived from salt dome–sourced brine in the underlying alluvial aquifer substantially increase with distance in the reach of the Dolores River that passes through Paradox Valley in southwestern Colorado. The area has been the site of salinity control operations since the 1990s to reduce salt loading to the downstream Colorado River. Previous airborne and ground/water-based electromagnetic (EM) geophysical data have successfully mapped the top of the brine surface, albeit with relatively coarse near-surface resolution and limited spatial coverage. This present December 2023 study used ground-based high-resolution EM and passive seismic (horizontal-to-vertical spectral ratio, HVSR) tools to map in detail the depth and thickness of the brine zone in the alluvial aquifer (top of the brine down to bedrock contact) in areas immediately surrounding the Dolores River where previous airborne EM (AEM) results indicated brine within 10 m of land surface. Results indicate the deepest bedrock is generally associated with the shallowest brine and local depressions in the collapse breccia (caprock to the Paradox Formation salt) may facilitate vertical migration of brine into the alluvial aquifer. Additionally, the ground-based EM mapping corroborated general patterns in depth to brine that were observed in previous AEM results while also revealing additional detail, including suspected focused brine discharge zones to the Dolores River. A river-based survey wherein EM data, channel depth and river water–specific conductance information were collected augmented these findings and indicated specific areas on both the western and eastern sides of the river where focused brine discharge may occur. This study comprises a large-scale, ground- and water-based geophysical mapping effort, including hundreds of HVSR soundings and 100s of kilometres of EM data, which were successfully translated into digital brine and bedrock surfaces that could be incorporated into groundwater modelling efforts, future well siting or other decision-making.
","language":"English","publisher":"Wiley","doi":"10.1002/nsg.70032","usgsCitation":"Terry, N., Mast, M.A., Creighton, A.L., Homan, J.W., Newman, C.P., and Paschke, S.S., 2026, Multi-scale geophysical mapping of the brine and bedrock surfaces along the Dolores River, Paradox Valley, Colorado, December 2023: Near Surface Geophysics, v. 24, no. 1, p. 36-49, https://doi.org/10.1002/nsg.70032.","productDescription":"14 p.","startPage":"36","endPage":"49","ipdsId":"IP-171913","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":497869,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":498045,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/nsg.70032","text":"Publisher Index Page"}],"country":"United States","state":"Colorado","otherGeospatial":"Dolores River, Paradox Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108.932,\n 38.378\n ],\n [\n -108.932,\n 38.292\n ],\n [\n -108.797,\n 38.292\n ],\n [\n -108.797,\n 38.378\n ],\n [\n -108.932,\n 38.378\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"24","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-12-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Terry, Neil C. 0000-0002-3965-340X nterry@usgs.gov","orcid":"https://orcid.org/0000-0002-3965-340X","contributorId":192554,"corporation":false,"usgs":true,"family":"Terry","given":"Neil","email":"nterry@usgs.gov","middleInitial":"C.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true}],"preferred":true,"id":952784,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mast, M. Alisa 0000-0001-6253-8162","orcid":"https://orcid.org/0000-0001-6253-8162","contributorId":211054,"corporation":false,"usgs":true,"family":"Mast","given":"M.","email":"","middleInitial":"Alisa","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":952785,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Creighton, Andrea L. 0000-0003-3183-5396","orcid":"https://orcid.org/0000-0003-3183-5396","contributorId":268162,"corporation":false,"usgs":true,"family":"Creighton","given":"Andrea","email":"","middleInitial":"L.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":952786,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Homan, Joel William 0000-0002-6709-123X","orcid":"https://orcid.org/0000-0002-6709-123X","contributorId":315495,"corporation":false,"usgs":true,"family":"Homan","given":"Joel","email":"","middleInitial":"William","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":952787,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Newman, Connor P. 0000-0002-6978-3440","orcid":"https://orcid.org/0000-0002-6978-3440","contributorId":222596,"corporation":false,"usgs":true,"family":"Newman","given":"Connor","email":"","middleInitial":"P.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":952788,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Paschke, Suzanne S. 0000-0002-3471-4242 spaschke@usgs.gov","orcid":"https://orcid.org/0000-0002-3471-4242","contributorId":1347,"corporation":false,"usgs":true,"family":"Paschke","given":"Suzanne","email":"spaschke@usgs.gov","middleInitial":"S.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":952789,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70273084,"text":"70273084 - 2026 - A time-space model of graphite mineral systems","interactions":[],"lastModifiedDate":"2026-03-23T14:05:12.786795","indexId":"70273084","displayToPublicDate":"2025-12-12T09:03:53","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2746,"text":"Mineralium Deposita","active":true,"publicationSubtype":{"id":10}},"title":"A time-space model of graphite mineral systems","docAbstract":"Increasing demand for graphite in energy storage systems warrants review of graphite ore genesis in a mineral systems framework. Orogenic graphite encompasses the metamorphic and orogenic mineral systems that produce flake graphite and hydrothermal vein (lump and chip) graphite deposits, respectively. A common feature of orogenic graphite deposits is an association with upper amphibolite- to granulite-facies metasedimentary rocks in continent-continent or continent-island arc collisional orogens. Orogenic flake graphite deposits form primarily through graphitization of organic carbon during regional metamorphism, but strain localization and partial melting of pelitic protoliths are likely important processes for graphite grade and quality enrichment. Orogenic vein graphite deposits precipitate from hydrothermal fluids, possibly derived from metamorphism or anatexis at depth. Decarbonation reactions in mixed calcareous-carbonaceous metasedimentary sequences are the most likely carbon sources for the veins. In contrast, intrusion-related graphite includes magmatic-hydrothermal and metamorphic mineral systems that form primarily in continental arc settings via the interaction of magmas with carbonaceous sedimentary packages. Magmatic-hydrothermal flake graphite deposits are hosted in plutonic and volcanic rocks, and result from the exsolution of CO2-CH4-rich fluids from melts contaminated by such packages. Contact metamorphism of carbonaceous sedimentary rocks by plutons produces some microcrystalline (amorphous) graphite deposits, including many in China. Compilation of geologic data from known graphite deposits globally suggests that pulses of carbon deposition in the Paleoproterozoic, Mesoproterozoic, and Neoproterozoic provided source carbon material. Subsequent supercontinent orogenesis at ca. 2,100 to 1,700 Ma (Columbia), ca. 1,300 to 1,000 Ma (Rodinia), and ca. 650 to 500 Ma (Gondwana) resulted in the genesis of orogenic flake and vein graphite deposits, where favorable geologic components overlapped with organic ± carbonate carbon-rich strata. Cryogenian deposition of graphite protoliths and Cryogenian – Cambrian metamorphic mineralization account for nearly 75% of all known resources globally and coincide with profound carbon isotope excursions and climate variability, implying a link with the global carbon budget. Comparatively few graphite deposits are associated with Pangea-forming orogens, attributed to less exhumation and/or denudation. High-temperature metasedimentary belts containing organic carbon-rich protoliths are most favorable for hosting orogenic flake graphite deposits, whereas sequences that also contain carbonate protoliths are favorable for orogenic graphite veins. Continent-scale orogenic belts may host both deposit types along with vanadium deposits. Use of a time-space mineral systems framework for graphite deposits can improve exploration models needed to ensure future supply of this critical mineral and provide insights into Earth’s long-term carbon cycle.
","language":"English","publisher":"Springer","doi":"10.1007/s00126-025-01412-5","usgsCitation":"Case, G.N., 2026, A time-space model of graphite mineral systems: Mineralium Deposita, v. 61, p. 783-810, https://doi.org/10.1007/s00126-025-01412-5.","productDescription":"28 p.","startPage":"783","endPage":"810","ipdsId":"IP-177742","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":497464,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":497697,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00126-025-01412-5","text":"Publisher Index Page"}],"volume":"61","noUsgsAuthors":false,"publicationDate":"2025-12-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Case, George N.D. 0000-0001-9826-5661 gcase@usgs.gov","orcid":"https://orcid.org/0000-0001-9826-5661","contributorId":224941,"corporation":false,"usgs":true,"family":"Case","given":"George","email":"gcase@usgs.gov","middleInitial":"N.D.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":952260,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70273205,"text":"70273205 - 2026 - From sample to sonde to Sentinel-2: Insights from a multi-scale chlorophyll-a monitoring effort in the Hudson River, New York","interactions":[],"lastModifiedDate":"2025-12-19T14:48:05.059591","indexId":"70273205","displayToPublicDate":"2025-12-09T08:42:26","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1552,"text":"Environmental Monitoring and Assessment","onlineIssn":"1573-2959","printIssn":"0167-6369","active":true,"publicationSubtype":{"id":10}},"title":"From sample to sonde to Sentinel-2: Insights from a multi-scale chlorophyll-a monitoring effort in the Hudson River, New York","docAbstract":"Monitoring cyanobacteria and other nuisance phytoplankton in the Hudson River is of great interest given its societal and ecological importance. Satellite remote sensing provides a cost-effective method to monitor chlorophyll-a (chl-a), a common proxy for algal biomass; however, the dynamic nature of rivers complicates approaches traditionally applied to lakes and oceans. During 2021–2023, we collected discrete samples for laboratory measurement of chl-a and measured in situ chl-a fluorescence during a series of longitudinal boat surveys along a 220-km reach of the lower Hudson River. Surveys were timed to coincide with Sentinel-2 satellite overpasses. We first investigated relations between laboratory-measured chl-a concentration and field-measured chl-a fluorescence, observing a weak correlation (r2 = 0.25) that improved substantially after splitting data by day (mean r2 = 0.53). Separately, to estimate chl-a fluorescence using satellite data, we developed a series of random forest models leveraging the rich fluorescence dataset collected. We tested three model types: individual day models, leave-one-out models trained on all days except a holdout test day, and a single pooled model trained on all days. Generally, individual day models exhibited lowest error (mean of mean absolute error [MAE] = 0.16 relative fluorescence units [RFU]), followed by the single pooled model (MAE = 0.22 RFU). Daily holdout models showed highest error (mean MAE = 0.40 RFU); this approach was intended to represent model performance on a day unseen in the training set, providing a more conservative estimate of performance than the more traditional pooled approach. Findings from both analyses emphasize the importance of considering temporal variability when modeling riverine systems.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s10661-025-14844-3","usgsCitation":"Salls, W.B., Welk, R., King, T.V., Scavotto, N., Gorney, R.M., Gifford, S.R., Stouder, M.D., Nystrom, E.A., and Graham, J.L., 2026, From sample to sonde to Sentinel-2: Insights from a multi-scale chlorophyll-a monitoring effort in the Hudson River, New York: Environmental Monitoring and Assessment, v. 198, 25, 30 p., https://doi.org/10.1007/s10661-025-14844-3.","productDescription":"25, 30 p.","ipdsId":"IP-176108","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":498038,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10661-025-14844-3","text":"Publisher Index Page"},{"id":497763,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Jersey, New York","otherGeospatial":"Hudson River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.53385971301769,\n 42.80598359016898\n ],\n [\n -74.21892882912005,\n 42.80598359016898\n ],\n [\n -74.21892882912005,\n 40.6507605560374\n ],\n [\n -73.53385971301769,\n 40.6507605560374\n ],\n [\n -73.53385971301769,\n 42.80598359016898\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"198","noUsgsAuthors":false,"publicationDate":"2025-12-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Salls, Wilson Barg 0000-0001-7505-0828","orcid":"https://orcid.org/0000-0001-7505-0828","contributorId":364473,"corporation":false,"usgs":true,"family":"Salls","given":"Wilson","middleInitial":"Barg","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":952701,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Welk, Robert J. 0000-0003-0852-5584","orcid":"https://orcid.org/0000-0003-0852-5584","contributorId":202876,"corporation":false,"usgs":true,"family":"Welk","given":"Robert J.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":952702,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"King, Tyler V. 0000-0002-5785-3077","orcid":"https://orcid.org/0000-0002-5785-3077","contributorId":292424,"corporation":false,"usgs":true,"family":"King","given":"Tyler","middleInitial":"V.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":952703,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Scavotto, Natasha 0000-0002-0853-5355","orcid":"https://orcid.org/0000-0002-0853-5355","contributorId":362140,"corporation":false,"usgs":true,"family":"Scavotto","given":"Natasha","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":952704,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gorney, Rebecca M. 0000-0003-4406-261X","orcid":"https://orcid.org/0000-0003-4406-261X","contributorId":317259,"corporation":false,"usgs":true,"family":"Gorney","given":"Rebecca","middleInitial":"M.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":952705,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gifford, Sabina R. 0000-0002-0724-4986","orcid":"https://orcid.org/0000-0002-0724-4986","contributorId":310415,"corporation":false,"usgs":true,"family":"Gifford","given":"Sabina","email":"","middleInitial":"R.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":952706,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stouder, Michael D.W. 0000-0002-0446-2574","orcid":"https://orcid.org/0000-0002-0446-2574","contributorId":301805,"corporation":false,"usgs":true,"family":"Stouder","given":"Michael","middleInitial":"D.W.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":952707,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Nystrom, Elizabeth A. 0000-0002-0886-3439 nystrom@usgs.gov","orcid":"https://orcid.org/0000-0002-0886-3439","contributorId":1072,"corporation":false,"usgs":true,"family":"Nystrom","given":"Elizabeth","email":"nystrom@usgs.gov","middleInitial":"A.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":952708,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Graham, Jennifer L. 0000-0002-6420-9335 jlgraham@usgs.gov","orcid":"https://orcid.org/0000-0002-6420-9335","contributorId":202923,"corporation":false,"usgs":true,"family":"Graham","given":"Jennifer","email":"jlgraham@usgs.gov","middleInitial":"L.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":952709,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70273145,"text":"70273145 - 2026 - Growth of a passive lava lake during the 2020–2021 eruption of Kīlauea volcano, Hawaii","interactions":[],"lastModifiedDate":"2025-12-16T16:06:00.176799","indexId":"70273145","displayToPublicDate":"2025-12-05T10:01:09","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"Growth of a passive lava lake during the 2020–2021 eruption of Kīlauea volcano, Hawaii","docAbstract":"We investigate the growth of a passive lava lake in Halemaʻumaʻu crater during the December 2020 to May 2021 eruption of Kīlauea volcano, Hawaii. Fed by vents above their surfaces, the formation of passive lava lakes in topographic lows is an important process in the growth of basaltic volcanoes. We captured visible and thermal images during 17 helicopter overflights and applied structure-from-motion photogrammetry to create digital elevation models and orthomosaics of Halemaʻumaʻu. These data products allowed us to track eruptive activity and processes. The bulk time-averaged discharge rate (TADR) in December 2020 initially exceeded 100 m3 s–1 but decreased to < 10 m3 s–1 within seven days. By February 2021, TADR was < 2 m3 s–1 and continued to decrease until the eruption ended in May 2021. A total volume of 40.6 ± 0.5 × 106 m3 of lava filled Halemaʻumaʻu to a depth of 225 m. As TADR decreased, the lake progressively developed an immobile, solidified crust, beginning with surfaces farthest from the vent. This immobile surface rose endogenously, whereas exogenous surface rise occurred near the vent. Eruptive activity at a vent ended when the level of the lake surface exceeded that of the vent, which we attribute to the effects of lava sitting above the vent on ascending magma. Regular helicopter overflights, combined with field observations and the extensive monitoring network at Kīlauea, generated an unprecedented density of observations that provide insights into the emplacement of passive lava lakes and how these eruptions wane and end.
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