{"pageNumber":"32","pageRowStart":"775","pageSize":"25","recordCount":46619,"records":[{"id":70265802,"text":"70265802 - 2025 - Volcanic gases reflect magma stalling and launching depths","interactions":[],"lastModifiedDate":"2025-04-16T15:01:09.279394","indexId":"70265802","displayToPublicDate":"2025-04-15T09:51:15","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1427,"text":"Earth and Planetary Science Letters","active":true,"publicationSubtype":{"id":10}},"title":"Volcanic gases reflect magma stalling and launching depths","docAbstract":"<p><span>Many open-vent arc volcanoes display two modes in their continuous gas emissions, one with a characteristic CO</span><sub>2</sub><span>/ S</span><sub>T</sub><span>&nbsp;ratio typical of periods of quiescent degassing and another punctuated by high CO</span><sub>2</sub><span>/ S</span><sub>T</sub><span>&nbsp;gas emitted in the weeks before eruption, a recently recognized eruption precursor. In this study we explore the origin of the two modes of degassing revealed by time-series gas data at Turrialba volcano (Costa Rica) in the context of new melt inclusion (MI) data. To reconstruct the c[CO</span><sub>2</sub><span>] of undegassed magma, we developed a rapid-quench piston-cylinder assembly to rehomogenize the vapor bubble commonly contained in MIs. We focus on olivine-hosted MIs from a mafic scoria sample erupted from Turrialba in 1864–1866. The reconstructed CO</span><sub>2</sub><span>&nbsp;contents in MIs decrease from ∼4000 to &lt;1000 ppmw as S contents decrease from 3500 to &lt;1000 ppmw. The highest reconstructed S and CO</span><sub>2</sub><span>&nbsp;in the MIs resulted in an initial magmatic CO</span><sub>2</sub><span>/ S</span><sub>T</sub><span>&nbsp;ratio (molar) of 0.83. Informed by the MI data, we modeled the decompression degassing of Turrialba magma and vapor composition using the Sulfur_X and EVo models. Instead of being controlled by initial magmatic CO</span><sub>2</sub><span>/S</span><sub>T</sub><span>&nbsp;ratio as suggested by previous studies, we find that the quiescent gas emitted from Turrialba during 2014–2018 (CO</span><sub>2</sub><span>/ S</span><sub>T</sub><span>&nbsp;= 2.3 ± 0.8, molar) appears to reflectequilibrium with magmas stored at 4–8 km (Sulfur_X) or 2 km (EVo) depth, when H</span><sub>2</sub><span>O is degassing extensively from the magma. A magma storage region at 4–8 km is also supported by seismic tomography. The second gas mode is noted by spikes in CO</span><sub>2</sub><span>/ S</span><sub>T</sub><span>&nbsp;∼ 7.9 ± 2 in the weeks prior to eruption. This gas reflects equilibrium with a magma at 12–18 km (Sulfur_X) or 4–8 km (EVo), where the ascending magma is saturated with a CO</span><sub>2</sub><span>-rich vapor. Thus, there are two important trans crustal depths beneath the volcano: one where the rate of H</span><sub>2</sub><span>O loss from the magma and thus magma viscosity increases, and one at greater depths where high CO</span><sub>2</sub><span>/S</span><sub>T</sub><span>&nbsp;vapor forms and may facilitate dike propagation. We interpret the shallower, H</span><sub>2</sub><span>O-loss region as the main site of magma stalling and storage, where quiescent gas is generated continuously. We interpret the greater depth (12–18 km) as the source of the precursory gas that precedes eruption, and where the mafic melt lastly equilibrated with a mush zone before ascending and triggering eruption weeks later. This hypothesis is ripe for testing at other volcanoes that exhibit two modes in gas geochemistry.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2025.119349","usgsCitation":"Ding, S., Plank, T., de Moor, J., Moussallam, Y., Brounce, M., and Kelly, P.J., 2025, Volcanic gases reflect magma stalling and launching depths: Earth and Planetary Science Letters, v. 660, 119349, 13 p., https://doi.org/10.1016/j.epsl.2025.119349.","productDescription":"119349, 13 p.","ipdsId":"IP-160372","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":484641,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Costa Rica","otherGeospatial":"Turrialba volcano","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -83.92,\n              10.0833\n            ],\n            [\n              -83.92,\n              9.9167\n            ],\n            [\n              -83.667,\n              9.9167\n            ],\n            [\n              -83.667,\n              10.0833\n            ],\n            [\n              -83.92,\n              10.0833\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"660","noUsgsAuthors":false,"publicationDate":"2025-04-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Ding, Shuo","contributorId":353454,"corporation":false,"usgs":false,"family":"Ding","given":"Shuo","affiliations":[{"id":84404,"text":"Lamont Doherty Earth Observatory, Columbia University, Palisades, NY, USA","active":true,"usgs":false}],"preferred":false,"id":933573,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Plank, Terry","contributorId":353455,"corporation":false,"usgs":false,"family":"Plank","given":"Terry","affiliations":[{"id":84404,"text":"Lamont Doherty Earth Observatory, Columbia University, Palisades, NY, USA","active":true,"usgs":false}],"preferred":false,"id":933574,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"de Moor, J. Maarten","contributorId":353456,"corporation":false,"usgs":false,"family":"de Moor","given":"J. Maarten","affiliations":[{"id":38348,"text":"Observatorio Vulcanológico y Sismológico de Costa Rica, Universidad Nacional, Heredia, Costa Rica","active":true,"usgs":false}],"preferred":false,"id":933575,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Moussallam, Yves","contributorId":353457,"corporation":false,"usgs":false,"family":"Moussallam","given":"Yves","affiliations":[{"id":84404,"text":"Lamont Doherty Earth Observatory, Columbia University, Palisades, NY, USA","active":true,"usgs":false}],"preferred":false,"id":933576,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brounce, Maryjo","contributorId":353458,"corporation":false,"usgs":false,"family":"Brounce","given":"Maryjo","affiliations":[{"id":84406,"text":"Earth & Planetary Sciences Department, University of California, Riverside","active":true,"usgs":false}],"preferred":false,"id":933577,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kelly, Peter J. 0000-0002-3868-1046 pkelly@usgs.gov","orcid":"https://orcid.org/0000-0002-3868-1046","contributorId":5931,"corporation":false,"usgs":true,"family":"Kelly","given":"Peter","email":"pkelly@usgs.gov","middleInitial":"J.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":933578,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70272696,"text":"70272696 - 2025 - Assessing legacy nitrogen in groundwater using numerical models of the Long Island aquifer system, New York","interactions":[],"lastModifiedDate":"2025-12-04T15:01:52.627998","indexId":"70272696","displayToPublicDate":"2025-04-15T08:56:58","publicationYear":"2025","noYear":false,"publicationType":{"id":27,"text":"Preprint"},"publicationSubtype":{"id":32,"text":"Preprint"},"seriesTitle":{"id":18346,"text":"EarthArXiv","active":true,"publicationSubtype":{"id":32}},"title":"Assessing legacy nitrogen in groundwater using numerical models of the Long Island aquifer system, New York","docAbstract":"<p><span>Nitrogen transported along groundwater flow paths in coastal aquifers can contribute substantially to nitrogen loading into surface water receptors, particularly in hydrologic systems dominated by groundwater discharge. Nitrogen entrained in the aquifer is a function of land use and associated nitrogen sources at the time of groundwater recharge, which may differ considerably from present-day sources. Legacy nitrogen can result in substantial discrepancies between observed present-day nitrogen loading to surface water receptors and loading estimated from present-day sources. Additionally, legacy nitrogen can continue to discharge into surface waters after nitrogen mitigation actions have been undertaken. Here, we use a numerical modeling framework to compare three methods of estimating time-varying historical nitrogen loads to four water bodies (receptors) on eastern Long Island, New York. The methods span a range of data requirements and process complexity, from instantaneous receptor loads calculated from steady-state groundwater contributing areas, to transient loads estimated by explicitly simulating legacy groundwater nitrogen transport over a century with large changes in nitrogen sources and hydrologic conditions. The effects of legacy nitrogen on estimated receptor loads varied temporally and spatially within the study area. Depending on antecedent nitrogen inputs and hydrologic conditions, historical annual nitrogen loads estimated from transient simulations accounting for legacy nitrogen can be quite similar (&lt;10% difference) or substantially different (±100%) from those estimated from simpler instantaneous methods. Continued input of present-day nitrogen sources using methods that account for legacy nitrogen results in asymptotic increases in receptor nitrogen loads over time, indicating that simulated present-day receptor nitrogen loads are not in equilibrium with present-day inputs. For these receptors in disequilibrium, models simulating transient groundwater nitrogen transport could be used to account for legacy nitrogen lag times to help resource managers evaluate the potential effectiveness of proposed nitrogen mitigation actions.</span></p>","language":"English","publisher":"EarthArXiv","doi":"10.31223/X56Q8J","usgsCitation":"Jahn, K., and Walter, D.A., 2025, Assessing legacy nitrogen in groundwater using numerical models of the Long Island aquifer system, New York: EarthArXiv, https://doi.org/10.31223/X56Q8J.","productDescription":"38 p.","ipdsId":"IP-170367","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":497047,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jahn, Kalle 0000-0002-4976-0137","orcid":"https://orcid.org/0000-0002-4976-0137","contributorId":333053,"corporation":false,"usgs":true,"family":"Jahn","given":"Kalle","email":"","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":951352,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walter, Donald A. 0000-0003-0879-4477 dawalter@usgs.gov","orcid":"https://orcid.org/0000-0003-0879-4477","contributorId":1101,"corporation":false,"usgs":true,"family":"Walter","given":"Donald","email":"dawalter@usgs.gov","middleInitial":"A.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":951353,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70270315,"text":"70270315 - 2025 - Multi-species telemetry quantifies current and future efficacy of a remote marine protected area","interactions":[],"lastModifiedDate":"2025-08-14T14:54:17.613606","indexId":"70270315","displayToPublicDate":"2025-04-15T07:47:36","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Multi-species telemetry quantifies current and future efficacy of a remote marine protected area","docAbstract":"<p><span>Large-scale marine protected areas (LSMPAs; &gt; 1000 km</span><sup>2</sup><span>) provide important refuge for large mobile species, but most do not encompass species' ranges. To better understand current and future LSMPA value, we concurrently tracked nine species (seabirds, cetaceans, pelagic fishes, manta rays, reef sharks) at Palmyra Atoll and Kingman Reef (PKMPA) in the U.S. Pacific Islands Heritage Marine National Monument. PKMPA and the U.S. Exclusive Economic Zone encompassed 39% and 54% of species movements (</span><i>n</i><span> = 83; tracking duration range: 0.5–350 days), respectively. Species distribution models indicated 73% of PKMPA contained highly suitable habitat. Under two projected future scenarios (SSP 1–2.6, “Sustainability”; SSP 3–7.0, “Rocky Road”), strong sea surface temperature gradients initially could cause abrupt oceanic change resulting in predicted habitat loss in 2040–2050, followed by an equilibrium response and regained habitat by 2090–2100. Current and future suitable habitats were available adjacent to PKMPA, suggesting that increased MPA size could enhance protection. Our three-tiered approach combining animal tracking with publicly available remote sensing data and future projected environmental scenarios could be used to design, study, and monitor protected areas throughout the world. Holistic approaches that encompass diverse species and habitat use can enhance assessments of protected area designs. Animal telemetry and remote sensing may be helpful for ascertaining the extent to which other MPAs protect large mobile species in the future.</span></p>","language":"English","publisher":"Wiley","doi":"10.1111/gcb.70138","usgsCitation":"Gilmour, M.E., Pollock, K., Adams, J., Block, B.A., Caselle, J.E., Filous, A., Friedlander, A.M., Game, E.T., Hazen, E.L., Hill, M., Holmes, N.D., Lafferty, K.D., Maxwell, S.M., McCauley, D.J., Schallert, R., Shaffer, S.A., Wolff, N.H., and Wegmann, A., 2025, Multi-species telemetry quantifies current and future efficacy of a remote marine protected area: Global Change Biology, v. 31, no. 4, e70138, 17 p., https://doi.org/10.1111/gcb.70138.","productDescription":"e70138, 17 p.","ipdsId":"IP-169295","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":494201,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gcb.70138","text":"Publisher Index Page"},{"id":494095,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Kingman Reef, Palmyra Atoll, U.S. Pacific Islands Heritage Marine National Monument.","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -162.47031093286282,\n              6.4554137414790915\n            ],\n            [\n              -162.47031093286282,\n              5.8142234034186515\n            ],\n            [\n              -161.9746083876704,\n              5.8142234034186515\n            ],\n            [\n              -161.9746083876704,\n              6.4554137414790915\n            ],\n            [\n              -162.47031093286282,\n              6.4554137414790915\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"31","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-04-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Gilmour, Morgan Elizabeth 0000-0002-2618-1095","orcid":"https://orcid.org/0000-0002-2618-1095","contributorId":289509,"corporation":false,"usgs":true,"family":"Gilmour","given":"Morgan","email":"","middleInitial":"Elizabeth","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":945997,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pollock, Kydd","contributorId":359650,"corporation":false,"usgs":false,"family":"Pollock","given":"Kydd","affiliations":[{"id":34601,"text":"Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":945998,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Adams, Josh 0000-0003-3056-925X","orcid":"https://orcid.org/0000-0003-3056-925X","contributorId":213442,"corporation":false,"usgs":true,"family":"Adams","given":"Josh","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":945999,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Block, Barbara A.","contributorId":359653,"corporation":false,"usgs":false,"family":"Block","given":"Barbara","middleInitial":"A.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":946000,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Caselle, Jennifer E.","contributorId":359655,"corporation":false,"usgs":false,"family":"Caselle","given":"Jennifer","middleInitial":"E.","affiliations":[{"id":37180,"text":"UC Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":946001,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Filous, Alexander","contributorId":272557,"corporation":false,"usgs":false,"family":"Filous","given":"Alexander","email":"","affiliations":[],"preferred":false,"id":946002,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Friedlander, Alan M.","contributorId":359658,"corporation":false,"usgs":false,"family":"Friedlander","given":"Alan","middleInitial":"M.","affiliations":[{"id":85893,"text":"National Geographic Society; Hawaiʻi Institute of Marine Biology","active":true,"usgs":false}],"preferred":false,"id":946003,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Game, Edward T.","contributorId":359659,"corporation":false,"usgs":false,"family":"Game","given":"Edward","middleInitial":"T.","affiliations":[{"id":34601,"text":"Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":946004,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hazen, Elliott L.","contributorId":359660,"corporation":false,"usgs":false,"family":"Hazen","given":"Elliott","middleInitial":"L.","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":946005,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hill, Marie","contributorId":359661,"corporation":false,"usgs":false,"family":"Hill","given":"Marie","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":946006,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Holmes, Nick D.","contributorId":359662,"corporation":false,"usgs":false,"family":"Holmes","given":"Nick","middleInitial":"D.","affiliations":[{"id":34601,"text":"Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":946007,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Lafferty, Kevin D. 0000-0001-7583-4593 klafferty@usgs.gov","orcid":"https://orcid.org/0000-0001-7583-4593","contributorId":1415,"corporation":false,"usgs":true,"family":"Lafferty","given":"Kevin","email":"klafferty@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":946008,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Maxwell, Sara M.","contributorId":359663,"corporation":false,"usgs":false,"family":"Maxwell","given":"Sara","middleInitial":"M.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":946009,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"McCauley, Douglas J.","contributorId":359664,"corporation":false,"usgs":false,"family":"McCauley","given":"Douglas","middleInitial":"J.","affiliations":[{"id":37180,"text":"UC Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":946010,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Schallert, Robert","contributorId":359665,"corporation":false,"usgs":false,"family":"Schallert","given":"Robert","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":946011,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Shaffer, Scott A.","contributorId":359666,"corporation":false,"usgs":false,"family":"Shaffer","given":"Scott","middleInitial":"A.","affiliations":[{"id":24620,"text":"San Jose State University","active":true,"usgs":false}],"preferred":false,"id":946012,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Wolff, Nicholas H.","contributorId":359667,"corporation":false,"usgs":false,"family":"Wolff","given":"Nicholas","middleInitial":"H.","affiliations":[{"id":34601,"text":"Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":946013,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Wegmann, Alex","contributorId":189488,"corporation":false,"usgs":false,"family":"Wegmann","given":"Alex","email":"","affiliations":[],"preferred":false,"id":946014,"contributorType":{"id":1,"text":"Authors"},"rank":18}]}}
,{"id":70266481,"text":"70266481 - 2025 - Seismic moment and local magnitude scales in Ridgecrest, CA from the SCEC/USGS Community Stress Drop Validation Study","interactions":[],"lastModifiedDate":"2025-05-28T14:57:24.741473","indexId":"70266481","displayToPublicDate":"2025-04-15T07:40:44","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Seismic moment and local magnitude scales in Ridgecrest, CA from the SCEC/USGS Community Stress Drop Validation Study","docAbstract":"<p>We illustrate the systematic difference between moment magnitude and local magnitude caused by underlying earthquake source physics, using seismic moments submitted to the Statewide California Earthquake Center/United States Geological Survey Community Stress Drop Validation Study 2019 Ridgecrest data set. While the relationship between seismic moment and moment magnitude (<strong>M</strong> or<strong><i> M</i><span style=\"font-size: 11.6667px;\" data-mce-style=\"font-size: 11.6667px;\">w</span></strong>) of log<sub>10</sub>(<strong><i>M</i><sub>0</sub></strong>) ~ 1.5* <strong>M</strong> is uniformly valid for all earthquake sizes by definition (Hanks and Kanamori, 1979), the relationship between local magnitude <i>M</i><sub>L</sub> and moment is itself magnitude dependent. For moderate events, ~3&lt; <strong>M</strong> &lt; ~6, <strong>M</strong> and <strong><i>M</i><sub>L</sub></strong> are coincident; for earthquakes smaller than ~3, <strong><i>M</i><sub>L</sub></strong> ~ 1.0 log<sub>10</sub> <strong><i>M</i><sub>0</sub></strong> (Hanks and Boore, 1984). This is a physical consequence of the corner frequency fc becoming larger than the upper frequency of observation and implies that <strong><i>M</i><sub>L</sub></strong> and M differ systematically by a factor of 1.5 for these small events. While this idea is not new, we propose a new, continuous relationship between local magnitude and moment, for magnitudes 2 to 6 which extrapolates to smaller and larger magnitudes, applicable to southern California specific to the Ridgecrest region. We make use of the plethora of seismic moments as submitted by many participants of the Community Stress Drop study, compared to the Southern California Seismic Network (SCSN) catalog magnitudes. Overall, the seismic moments in the Community Study recover moment magnitude well, so we use our new <strong><i>M</i><sub>L</sub></strong>-<strong><i>M</i><sub>0</sub></strong> to convert <strong><i>M</i><sub>L</sub></strong> to <strong>M</strong>, refining the SCSN operational <strong><i>M</i><sub>Lr</sub></strong>&nbsp;scale. This systematic difference of 50% in slope between local and moment magnitude at small magnitudes has implications for spectral stress drop estimates, earthquake ground motion modeling, as well as other magnitude scales and earthquake occurrence statistics.</p>","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120240162","usgsCitation":"Baltay Sundstrom, A.S., and Abercrombie, R., 2025, Seismic moment and local magnitude scales in Ridgecrest, CA from the SCEC/USGS Community Stress Drop Validation Study: Bulletin of the Seismological Society of America, v. 115, no. 3, p. 1279-1293, https://doi.org/10.1785/0120240162.","productDescription":"15 p.","startPage":"1279","endPage":"1293","ipdsId":"IP-167974","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":485557,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Ridgecrest","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -117.78392771143658,\n              35.70994070706057\n            ],\n            [\n              -117.78392771143658,\n              35.55296259649002\n            ],\n            [\n              -117.5741750312894,\n              35.55296259649002\n            ],\n            [\n              -117.5741750312894,\n              35.70994070706057\n            ],\n            [\n              -117.78392771143658,\n              35.70994070706057\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"115","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-04-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Baltay Sundstrom, Annemarie S. 0000-0002-6514-852X abaltay@usgs.gov","orcid":"https://orcid.org/0000-0002-6514-852X","contributorId":4932,"corporation":false,"usgs":true,"family":"Baltay Sundstrom","given":"Annemarie","email":"abaltay@usgs.gov","middleInitial":"S.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":936193,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Abercrombie, Rachel E.","contributorId":293131,"corporation":false,"usgs":false,"family":"Abercrombie","given":"Rachel E.","affiliations":[{"id":7208,"text":"Department of Earth and Environment, Boston University","active":true,"usgs":false}],"preferred":false,"id":936194,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70265520,"text":"ofr20241062 - 2025 - Characterizing Meteor Crater impact melts through geochemistry and textural analysis","interactions":[],"lastModifiedDate":"2025-08-07T20:55:19.201468","indexId":"ofr20241062","displayToPublicDate":"2025-04-14T15:15:42","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-1062","displayTitle":"Characterizing Meteor Crater Impact Melts Through Geochemistry and Textural Analysis","title":"Characterizing Meteor Crater impact melts through geochemistry and textural analysis","docAbstract":"<p>The U.S. Geological Survey Astrogeology Science Center houses the Meteor Crater sample collection, an assemblage of over 2,500 meters of cuttings from 161 drill holes into Meteor Crater’s rim, flanks, and ejecta blanket. We have utilized this unique collection to study the composition and spatial distribution of impact-generated materials from within the ejecta blanket. Meteor Crater has historically been known to have generated only a relatively small amount of impact melt compared to other terrestrial craters of similar size. A detailed compositional and textural dataset of impact-derived melts from this impact can therefore be a useful asset in improving our understanding of crater formation, and in particular impact melt formation.</p><p>We have characterized 42 impact-melt particles from Meteor Crater using a scanning electron microscope and an electron microprobe for textural and compositional analysis. We analyzed samples from six drill holes in the ejecta blanket, situated to the northwest, southeast, south, and southwest of the crater (ejecta northeast of the crater is devoid of impact melts). Impact melts were collected from drill cuttings at various depths within the ejecta blanket, ranging from a few centimeters below the surface down to ~6.5 meters.</p><p>Backscattered electron (BSE) images were acquired for each analyzed impact-melt particle. To characterize the various textures and phases present in each impact melt, we also took many detailed BSE images. Our geochemical analyses include full spectral profiles using energy dispersive X-ray spectrometry and well-calibrated wavelength dispersive spectrometry for a number of phases, including minerals (olivine, pyroxene, and so on), pristine glass, and metallic inclusions. The full dataset is available in ScienceBase as a data release (Gullikson and others, 2024), accessible at <a data-mce-href=\"https://doi.org/10.5066/P9OGAJ8P\" href=\"https://doi.org/10.5066/P9OGAJ8P\" target=\"_blank\" rel=\"noopener\">https://doi.org/10.5066/P9OGAJ8P</a>.</p><p>Our goal for this Open-File Report is to provide a summary of this immense dataset, details on data collection, descriptions of the different phases observed within impact-melt particles (both geochemically and texturally), and observable trends.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241062","usgsCitation":"Gullikson, A.L., Gaither, T.A., and Hagerty, J.J., 2024, Characterizing Meteor Crater impact melts through geochemistry and textural analysis: U.S. Geological Survey Open-File Report 2024–1062, 23 p., https://doi.org/10.3133/ofr20241062.","productDescription":"Report: vii, 23 p.; Data Release","numberOfPages":"23","onlineOnly":"Y","ipdsId":"IP-162108","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":493758,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118538.htm","linkFileType":{"id":5,"text":"html"}},{"id":484534,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241062/full"},{"id":484524,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1062/images"},{"id":484387,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OGAJ8P","text":"USGS Data Release","description":"Gullikson, A.L., Gaither, T.A., and Hagerty, J.J., 2024, Geochemistry and high-resolution backscattered electron imaging of Meteor Crater impact melts: U.S. Geological Survey data release, https://doi.org/10.5066/P9OGAJ8P.","linkHelpText":"Geochemistry and high-resolution backscattered electron imaging of Meteor Crater impact melts"},{"id":484384,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1062/covrthb.jpg"},{"id":484386,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1062/ofr20241062.XML","size":"200 KB","linkFileType":{"id":8,"text":"xml"}},{"id":484385,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1062/ofr20241062.pdf","text":"Report","size":"9 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Arizona","otherGeospatial":"Meteor Crater","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -111.03682408526112,\n              35.03726468620707\n            ],\n            [\n              -111.03682408526112,\n              35.01842427092913\n            ],\n            [\n              -111.01097319209285,\n              35.01842427092913\n            ],\n            [\n              -111.01097319209285,\n              35.03726468620707\n            ],\n            [\n              -111.03682408526112,\n              35.03726468620707\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p><a href=\"https://www.usgs.gov/centers/astrogeology-science-center\" data-mce-href=\"https://www.usgs.gov/centers/astrogeology-science-center\">Astrogeology Science Center</a><br><a href=\"https://www.usgs.gov/\" data-mce-href=\"https://www.usgs.gov/\">U.S. Geological Survey</a><br>2255 N. Gemini Dr.<br>Flagstaff, AZ 86001</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Geologic Setting of Meteor Crater</li><li>Purpose and Scope</li><li>Methods&nbsp;</li><li>Results</li><li>Discussion and Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2025-04-14","noUsgsAuthors":false,"publicationDate":"2025-04-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Gullikson, Amber L. 0000-0002-1505-3151","orcid":"https://orcid.org/0000-0002-1505-3151","contributorId":208679,"corporation":false,"usgs":true,"family":"Gullikson","given":"Amber","email":"","middleInitial":"L.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":932884,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gaither, Tenielle A. 0000-0003-4230-3678 tgaither@usgs.gov","orcid":"https://orcid.org/0000-0003-4230-3678","contributorId":4800,"corporation":false,"usgs":true,"family":"Gaither","given":"Tenielle","email":"tgaither@usgs.gov","middleInitial":"A.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":932885,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hagerty, Justin 0000-0003-3800-7948 jhagerty@usgs.gov","orcid":"https://orcid.org/0000-0003-3800-7948","contributorId":911,"corporation":false,"usgs":true,"family":"Hagerty","given":"Justin","email":"jhagerty@usgs.gov","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":932886,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70265508,"text":"sir20255005 - 2025 - Potential water-quality and hydrology stressors on freshwater mussels with development of environmental DNA assays for selected mussels and macroinvertebrates in Big Darby Creek Basin, Ohio, 2020–22","interactions":[],"lastModifiedDate":"2025-08-07T20:54:11.145385","indexId":"sir20255005","displayToPublicDate":"2025-04-14T12:55:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-5005","displayTitle":"Potential Water-Quality and Hydrology Stressors on Freshwater Mussels With Development of Environmental DNA Assays for Selected Mussels and Macroinvertebrates in Big Darby Creek Basin, Ohio, 2020–22","title":"Potential water-quality and hydrology stressors on freshwater mussels with development of environmental DNA assays for selected mussels and macroinvertebrates in Big Darby Creek Basin, Ohio, 2020–22","docAbstract":"<p>The richness and abundance of freshwater mussels in the Big Darby Creek Basin has declined in recent decades, according to survey results published by the Ohio Biological Survey. In October 2016, a major mussel die-off of undetermined cause reportedly affected over 50 miles of Big Darby Creek; however, fishes and other wildlife were not noticeably impacted. Pollution, habitat destruction, climate change, and hydrologic modification have all been theorized as potential reasons for the widespread declines in freshwater mussel populations in North America. To better understand potential stressors to mussels and other aquatic organisms in the Big Darby Creek Basin, the U.S. Geological Survey, in cooperation with the Ohio Water Development Authority, evaluated water quality and temporal changes in hydrology at selected locations. In addition, environmental deoxyribonucleic acid (eDNA) quantitative polymerase chain reaction (qPCR) assays were developed to detect the presence of selected mussels and macroinvertebrates using stream water.</p><p>Time-weighted average concentrations of pesticides, organic wastewater compounds (OWCs), and polycyclic aromatic hydrocarbons (PAHs) were determined for selected locations within the Big Darby Creek Basin. Passive samplers designed to mimic the respiratory exposure of aquatic organisms and the bioconcentration of organic contaminants into their fatty tissues were deployed three times annually at three sites within the Big Darby Creek Basin in 2020 and 2021. Analyses were done for 204 pesticide compounds, 38 OWCs, and 33 PAHs. Of the 204 pesticide compounds, 70 were detected in at least one sample; 30 were detected in all samples. Herbicides and herbicide degradates were the pesticides most frequently detected and also had some of the highest concentrations of the pesticides detected in this study. Three herbicides (atrazine, ametryn, and metribuzin) were detected in at least 88 percent of samples and two fungicides (azoxystrobin and propiconazole) were detected in all samples. Of the 38 OWCs, 24 were detected in at least one sample; however, only one (<i>N</i>,<i>N</i>-diethyltoluamide [DEET]) was detected in all samples. Of the 33 PAHs, 29 were detected in at least one sample; 12 were detected in all samples.</p><p>A continuous water-quality monitor was operated seasonally on Big Darby Creek above Georgesville, Ohio, from 2020 to 2022. Dissolved oxygen concentrations generally followed a daily cycle, peaking in early evening and troughing around sunrise. There were occasional 24-hour swings in dissolved oxygen concentration that had a range exceeding 10 milligrams per liter. However, dissolved oxygen concentrations never fell below Ohio’s aquatic life criteria for warmwater habitats (outside of mixing zones) of 4.0 milligrams per liter as an instantaneous minimum and 5.0 milligrams per liter as a minimum 24-hour average. The Ohio water-quality criteria for temperatures are 29.4 degrees Celsius as an instantaneous maximum and 27.8 degrees Celsius as a 24-hour average maximum. In 2020, there were 10 days when the maximum instantaneous value for temperature was exceeded and 3 consecutive days when the maximum 24-hour average temperature was exceeded.</p><p>Streamflow time-series data from three gaging stations within the Big Darby Creek Basin were evaluated for trends in annual flow statistics and daily nonexceedance probabilities over time. In general, the evaluation of streamflow conditions at the Big Darby Creek gage (with 97 years of record) indicated that streamflow changed between water years 1922 and 2021. During that time span, flows in general increased, the number of high-flow pulses became more frequent, and low-flow pulses and extreme low-flow periods became less frequent. The only strong indication of trends over time in annual flow statistics for the relatively short records for the other two gages (on Little Darby Creek, with 25 years of record, and Hellbranch Run, with 29 years of record) was that as time went on, reversals between rising and falling periods became more frequent.</p><p>The U.S. Geological Survey Ohio Water Microbiology Laboratory developed eDNA qPCR assays to detect <i>Epioblasma rangiana</i> (northern riffleshell mussels), <i>Chimarra obscura</i> (a species of caddisfly), <i>Maccaffertium pulchellum</i> (a species of mayfly), and optimized a preexisting eDNA qPCR assay to detect for <i>Ptychobranchus fasciolaris</i> (kidneyshell mussels). The assays were validated by using environmental sampling methods. Assay sensitivity was established by determining the limits of detection and quantification. Water samples were collected at 12 sites in the Big Darby Creek Basin between 2020 and 2022 and analyzed for eDNA with the qPCR assays developed for this study.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255005","collaboration":"Prepared in cooperation with the Ohio Water Development Authority","usgsCitation":"Huitger, C.A., Koltun, G.F., Stelzer, E.A., and Lynch, L.D., 2025, Potential water-quality and hydrology stressors on freshwater mussels with development of environmental DNA assays for selected mussels and macroinvertebrates in Big Darby Creek Basin, Ohio, 2020–22: U.S. Geological Survey Scientific Investigations Report 2025–5005, 59 p., https://doi.org/10.3133/sir20255005.","productDescription":"Report: ix, 59 p.; 2 Appendices; 2 Data Releases","numberOfPages":"59","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-161896","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":484334,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13GN45M","text":"USGS data release","linkHelpText":"Pesticide, organic wastewater compound (OWC) and polycyclic aromatic hydrocarbon (PAH) data determined from samples collected with instream passive samplers in the Big Darby Creek Basin, Ohio, 2020–21"},{"id":484333,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1WELW7W","text":"USGS data release","linkHelpText":"Annual streamflow statistics for selected streamgages on Big and Little Darby Creeks and Hellbranch Run, Ohio (through water year 2021)"},{"id":484331,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2025/5005/sir20255005_app1_csv.zip","text":"Tables 1.1–1.17 (CSV)","size":"34.6 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"Appendix 1. Quality Control and Summary Information for Analyses of Pesticides, Organic Wastewater Compounds, and Polycyclic Aromatic Hydrocarbons"},{"id":484330,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2025/5005/sir20255005_app1_tables.xlsx","text":"Tables 1.1–1.17","size":"131 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"Appendix 1. Quality Control and Summary Information for Analyses of Pesticides, Organic Wastewater Compounds, and Polycyclic Aromatic Hydrocarbons"},{"id":484329,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5005/images/"},{"id":484328,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5005/sir20255005.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2025-5005 XML"},{"id":493757,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118527.htm","linkFileType":{"id":5,"text":"html"}},{"id":484327,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255005/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5005 HTML"},{"id":484326,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5005/sir20255005.pdf","size":"4.03 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5005 PDF"},{"id":484324,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5005/coverthb.jpg"}],"country":"United States","state":"Ohio","otherGeospatial":"Big Darby Creek basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -83.8333,\n              40.333\n            ],\n            [\n              -83.8333,\n              39.5\n            ],\n            [\n              -83,\n              39.5\n            ],\n            [\n              -83,\n              40.333\n            ],\n            [\n              -83.8333,\n              40.333\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/oki-water\" data-mce-href=\"https://www.usgs.gov/centers/oki-water\">Ohio-Kentucky-Indiana Water Science Center</a><br>U.S. Geological Survey<br>6460 Busch Blvd, Suite 100<br>Columbus, OH 43229</p><p><a href=\"https://pubs.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Results and Discussion</li><li>Summary</li><li>References Cited</li><li>Appendix 1. Quality Control and Summary Information for Analyses of Pesticides, Organic Wastewater Compounds, and Polycyclic Aromatic Hydrocarbons</li></ul>","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2025-04-14","noUsgsAuthors":false,"publicationDate":"2025-04-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Huitger, Carrie A. 0000-0003-4534-3245 chuitger@usgs.gov","orcid":"https://orcid.org/0000-0003-4534-3245","contributorId":207180,"corporation":false,"usgs":true,"family":"Huitger","given":"Carrie","email":"chuitger@usgs.gov","middleInitial":"A.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":932859,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Koltun, G. F. 0000-0003-0255-2960 gfkoltun@usgs.gov","orcid":"https://orcid.org/0000-0003-0255-2960","contributorId":140048,"corporation":false,"usgs":true,"family":"Koltun","given":"G.","email":"gfkoltun@usgs.gov","middleInitial":"F.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":932860,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stelzer, Erin A. 0000-0001-7645-7603","orcid":"https://orcid.org/0000-0001-7645-7603","contributorId":220549,"corporation":false,"usgs":true,"family":"Stelzer","given":"Erin A.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":932861,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lynch, Lauren D. 0000-0003-0209-1797","orcid":"https://orcid.org/0000-0003-0209-1797","contributorId":337141,"corporation":false,"usgs":true,"family":"Lynch","given":"Lauren","email":"","middleInitial":"D.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":932862,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70265672,"text":"ofr20251009 - 2025 - Data gap analysis for estimation of agricultural return flows in the Upper Gunnison River Basin, Colorado","interactions":[],"lastModifiedDate":"2025-08-07T20:53:05.2169","indexId":"ofr20251009","displayToPublicDate":"2025-04-14T12:45:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-1009","displayTitle":"Data Gap Analysis for Estimation of Agricultural Return Flows in the Upper Gunnison River Basin, Colorado","title":"Data gap analysis for estimation of agricultural return flows in the Upper Gunnison River Basin, Colorado","docAbstract":"The Gunnison River and many tributaries in the Upper Gunnison River Basin provide water to irrigate agricultural crops. The application of irrigation water can recharge some aquifers locally by water percolating below the root zone and eventually flowing back to the stream or river through the subsurface. Diverting surface water for irrigation reduces streamflow during the irrigation season but can provide temporary storage of water and supplement streamflow after the snowmelt runoff season. Understanding the timing and quantity of agricultural return flows could help resource managers make informed decisions and adapt to potential changes in water management and availability that could affect irrigation practices. In 2024, the U.S. Geological Survey, in cooperation with the Upper Gunnison River Water Conservancy District, began a study to characterize agricultural return flows in the Upper Gunnison River Basin by using endmember mixing analysis and developing a groundwater model. Both approaches require data from multiple sources, but data gaps exist in the East River study reach and other reaches of interest (Ohio Creek, Tomichi Creek, and Cochetopa Creek). The East River Basin, which is the initial focus of the study, has fewer data gaps than the other basins. Data gaps could be addressed by installing additional surface water and groundwater monitoring sites, making regular streamflow measurements on tributaries, and completing tests to characterize local aquifer properties.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20251009","collaboration":"Prepared in cooperation with the Upper Gunnison River Water Conservancy District","usgsCitation":"Gidley, R.G., Miller, Q.M., and Belcher, W.R., 2025, Data gap analysis for estimation of agricultural return flows in the Upper Gunnison River Basin, Colorado: U.S. Geological Survey Open-File Report 2025-1009, 12 p., https://doi.org/10.3133/ofr20251009.","productDescription":"Report: iv, 12 p.; Database","onlineOnly":"Y","ipdsId":"IP-170914","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":484476,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1009/coverthb.jpg"},{"id":493755,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118526.htm","linkFileType":{"id":5,"text":"html"}},{"id":484572,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251009/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1009"},{"id":484516,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1009/ofr20251009.xml"},{"id":484515,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1009/images"},{"id":484478,"rank":3,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS data base","linkHelpText":"USGS water data for the Nation: U.S. Geological Survey National Water Information System database"},{"id":484477,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1009/ofr20251009.pdf","text":"Report","size":"2.86 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1009"}],"country":"United States","state":"Colorado","otherGeospatial":"Upper Gunnison River basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -106.5,\n              38.9167\n            ],\n            [\n              -107.0833,\n              38.9167\n            ],\n            [\n              -107.0833,\n              38.25\n            ],\n            [\n              -106.5,\n              38.25\n            ],\n            [\n              -106.5,\n              38.9167\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/colorado-water-science-center\" data-mce-href=\"https://www.usgs.gov/centers/colorado-water-science-center\">Colorado Water Science Center</a><br>U.S. Geological Survey<br>Box 25046, Mail Stop 415<br>Denver, CO 80225</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Previous Studies </li><li>Data Availability and Data Gaps</li><li>Summary</li><li>References Cited</li></ul>","publishedDate":"2025-04-14","noUsgsAuthors":false,"publicationDate":"2025-04-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Gidley, Rachel G. 0000-0002-9840-8252","orcid":"https://orcid.org/0000-0002-9840-8252","contributorId":259315,"corporation":false,"usgs":true,"family":"Gidley","given":"Rachel","email":"","middleInitial":"G.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933228,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, Quinn M. 0000-0002-9656-9685","orcid":"https://orcid.org/0000-0002-9656-9685","contributorId":353270,"corporation":false,"usgs":true,"family":"Miller","given":"Quinn M.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933229,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Belcher, Wayne R. 0000-0001-7255-916X wbelcher@usgs.gov","orcid":"https://orcid.org/0000-0001-7255-916X","contributorId":210577,"corporation":false,"usgs":true,"family":"Belcher","given":"Wayne","email":"wbelcher@usgs.gov","middleInitial":"R.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933230,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70265966,"text":"70265966 - 2025 - Lead exposure in waterfowl before contoxic shot requirements: A nationwide study, 1983−1986","interactions":[],"lastModifiedDate":"2025-04-22T16:43:50.38942","indexId":"70265966","displayToPublicDate":"2025-04-14T11:40:03","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Lead exposure in waterfowl before contoxic shot requirements: A nationwide study, 1983−1986","docAbstract":"<p><span>Before implementing nontoxic shot requirements for hunting waterfowl and American coots&nbsp;</span><i>Fulica americana</i><span>&nbsp;in the United States in 1991, the U.S. Fish and Wildlife Service monitored lead poisoning in waterfowl on federal and state wildlife hunting areas during 1983-1986. Federal and state collaborators collected gizzards and livers from 9,029 hunter-killed waterfowl (10 species of dabbling ducks&nbsp;</span><i>Anatinae</i><span>, 9 diving ducks&nbsp;</span><i>Aythyinae</i><span>, 5 geese&nbsp;</span><i>Anserinae</i><span>, and tundra swans&nbsp;</span><i>Cygnus columbianus</i><span>) across the four flyways. At the U.S. Fish and Wildlife Service National Wildlife Health Center, Madison, Wisconsin, waterfowl gizzards were examined for ingested lead and nontoxic shot and livers were analyzed for lead concentrations. Diving ducks had the greatest frequency (8.7%) of one or more ingested lead shot, followed by dabbling ducks (5.5%) and geese (1.3%). No ingested shot were found in tundra swans. The frequency of elevated (≥ 2.0 mg/kg wet weight) liver lead concentrations was also greatest in diving ducks, followed by dabbling ducks and geese. Within each species group, the frequency of elevated liver lead concentrations was greater than ingested lead shot, an indication that lead shot ingestion alone underrepresents lead exposure. Thus, lead in the liver may remain elevated after the erosion and excretion of lead pellets from the gizzard. Our results provide historical baseline data and summarize a nationwide study of lead exposure, using both ingested lead shot and liver lead concentrations, in waterfowl in the United States before the implementation of nontoxic shot regulations in 1991. These data can be compared with previous studies of lead exposure in waterfowl, as well as current and future assessments to evaluate the success of nontoxic shot regulations nationwide and specifically within previously sampled waterfowl management areas.</span></p>","language":"English","publisher":"U.S. Fish & Wildlife Service","doi":"10.3996/JFWM-24-041","usgsCitation":"Franson, J.C., and Bunck, C.M., 2025, Lead exposure in waterfowl before contoxic shot requirements: A nationwide study, 1983−1986: Journal of Fish and Wildlife Management, https://doi.org/10.3996/JFWM-24-041.","ipdsId":"IP-165936","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":488486,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-24-041","text":"Publisher Index Page"},{"id":484847,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"Online First","noUsgsAuthors":false,"publicationDate":"2025-04-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Franson, J. Christian 0000-0002-0251-4238 jfranson@usgs.gov","orcid":"https://orcid.org/0000-0002-0251-4238","contributorId":177499,"corporation":false,"usgs":true,"family":"Franson","given":"J.","email":"jfranson@usgs.gov","middleInitial":"Christian","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":934183,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bunck, Christine M. cbunck@usgs.gov","contributorId":731,"corporation":false,"usgs":true,"family":"Bunck","given":"Christine","email":"cbunck@usgs.gov","middleInitial":"M.","affiliations":[],"preferred":true,"id":934184,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70266423,"text":"70266423 - 2025 - A quantitative classification of the geography of non-native flora in the United States","interactions":[],"lastModifiedDate":"2025-05-06T15:03:25.831673","indexId":"70266423","displayToPublicDate":"2025-04-14T09:55:12","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1839,"text":"Global Ecology and Biogeography","active":true,"publicationSubtype":{"id":10}},"title":"A quantitative classification of the geography of non-native flora in the United States","docAbstract":"<h3 id=\"geb70041-sec-0001-title\" class=\"article-section__sub-title section1\">Aim</h3><p>Non-native plants have the potential to harm ecosystems. Harm is classically related to their distribution and abundance, but this geographical information is often unknown. Here, we assess geographical commonness as a potential indicator of invasive status for non-native flora in the United States. Geographical commonness could inform invasion risk assessments across species and ecoregions.</p><h3 id=\"geb70041-sec-0002-title\" class=\"article-section__sub-title section1\">Location</h3><p>Conterminous United States.</p><h3 id=\"geb70041-sec-0003-title\" class=\"article-section__sub-title section1\">Time Period</h3><p>Through 2022.</p><h3 id=\"geb70041-sec-0004-title\" class=\"article-section__sub-title section1\">Major Taxa Studied</h3><p>Plants.</p><h3 id=\"geb70041-sec-0005-title\" class=\"article-section__sub-title section1\">Methods</h3><p>We compiled and standardised occurrence and abundance data from 14 spatial datasets and used this information to categorise non-native species as uncommon or common based on three dimensions of commonness: area of occupancy, habitat breadth and local abundance. To assess consistency in existing categorizations, we compared commonness to invasive status in the United States. We identified species with higher-than-expected abundance relative to their occupancy, habitat breadth or residence time. We calculated non-native plant richness within United States ecoregions and estimated unreported species based on rarefaction/extrapolation curves.</p><h3 id=\"geb70041-sec-0006-title\" class=\"article-section__sub-title section1\">Results</h3><p>This comprehensive database identified 1874 non-native plant species recorded in 4,844,963 locations. Of these, 1221 species were locally abundant (&gt; 10% cover) in 797,759 unique locations. One thousand one hundred one non-native species (59%) achieved at least one dimension of commonness, including 565 species that achieved all three. Species with longer residence times tended to meet more dimensions of commonness. We identified 132 species with higher-than-expected abundance. Ecoregions in the central United States have the largest estimated numbers of unreported, abundant non-native plants.</p><h3 id=\"geb70041-sec-0007-title\" class=\"article-section__sub-title section1\">Main Conclusions</h3><p>A high proportion of non-native species have become common in the United States. However, existing categorizations of invasive species are not always consistent with species' abundance and distribution, even after considering residence time. Considering geographical commonness and higher-than-expected abundance revealed in this new dataset could support more consistent and proactive identification of invasive plants and lead to more efficient management practices.</p>","language":"English","publisher":"Wiley","doi":"10.1111/geb.70041","usgsCitation":"Bradley, B., Evans, A., Sofaer, H., Vilà, M., Barnett, D., Beaury, E.M., Blumenthal, D.M., Corbin, J., Dukes, J., Early, R., Ibanez, I., Pearse, I.S., Petri, L., and Sorte, C.J., 2025, A quantitative classification of the geography of non-native flora in the United States: Global Ecology and Biogeography, v. 34, e70041, 14 p., https://doi.org/10.1111/geb.70041.","productDescription":"e70041, 14 p.","ipdsId":"IP-157629","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":496397,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/geb.70041","text":"Publisher Index 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,{"id":70264820,"text":"ofr20251008 - 2025 - Suspended sediment and bedload transport along the Main and South Branches, Wild Rice River, northwestern Minnesota, 1979 through 2023","interactions":[],"lastModifiedDate":"2025-08-07T20:52:12.124261","indexId":"ofr20251008","displayToPublicDate":"2025-04-14T07:17:49","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-1008","displayTitle":"Suspended Sediment and Bedload Transport Along the Main and South Branches, Wild Rice River, Northwestern Minnesota, 1979 through 2023","title":"Suspended sediment and bedload transport along the Main and South Branches, Wild Rice River, northwestern Minnesota, 1979 through 2023","docAbstract":"<p>The geologic history and anthropogenic modifications of Minnesota’s Wild Rice River have caused major morphological adjustments, which induce erosion and excess fluvial sediment transport. The excess sediment deposits in the lower Wild Rice River, exacerbating flooding. To help mitigate these problems, the Wild Rice Watershed District has future plans to implement a river restoration on the lower Wild Rice River. The Wild Rice Watershed District collaborated with the U.S. Geological Survey to measure and analyze sediment transport along the Wild Rice River’s Main and South Branches to assess any potential changes in sediment transport among sites and time periods. Time differencing results indicated that all suspended-sediment constituents showed a significant difference between the two sampling periods at one South Branch site but not at the Main Branch site. Piecewise regression analysis better matched the suspended-sediment constituents transport process at most sites by differentiating no relation between suspended-sediment constituents at lower streamflows and a positive relation at higher streamflows at most Wild Rice River sites. Five of the sites showed elevated sediment transport with increasing streamflow. In contrast, the site farthest downstream showed a negative relation with increasing streamflow, indicating that that the lower Wild Rice River is supply limited and deposition is likely occurring upstream and (or) near the site. Overall, the uncertainty in results indicates the complexity of sediment transport in a river when using streamflow as the sole explanatory variable and suggests a need for multisite, multiyear, and multifaceted data.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251008","collaboration":"Prepared in cooperation with the Wild Rice Watershed District","usgsCitation":"Groten, J.T., Levin, S.B., Storey, G.G., Coenen, E.N., Blount, J.D., Lund, J.W., and Brannon, D.J., 2025, Suspended sediment and bedload transport along the Main and South Branches, Wild Rice River, northwestern Minnesota, 1979 through 2023: U.S. Geological Survey Open-File Report 2025–1008, 38 p., https://doi.org/10.3133/ofr20251008.","productDescription":"Report: vii, 38 p.; Dataset","numberOfPages":"50","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-154644","costCenters":[{"id":37947,"text":"Upper Midwest Water Science 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Nation"},{"id":483768,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251008/full"}],"country":"United States","state":"Minnesota","otherGeospatial":"Wild Rice River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -97,\n              47.5833\n            ],\n            [\n              -97,\n              47\n            ],\n            [\n              -95.25,\n              47\n            ],\n            [\n              -95.25,\n              47.5833\n            ],\n            [\n              -97,\n              47.5833\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/umid-water\" data-mce-href=\"https://www.usgs.gov/centers/umid-water\">Upper Midwest Water Science Center</a><br>U.S. Geological Survey<br>2280 Woodale Drive<br>Mounds View, MN 55112</p><p><a href=\"https://pubs.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods of Data Collection and Analysis</li><li>Streamflow, Suspended-Sediment Constituents, and Bedload Results</li><li>Discussion</li><li>Summary and Conclusions</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2025-04-14","noUsgsAuthors":false,"publicationDate":"2025-04-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Groten, Joel T. 0000-0002-0441-8442 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,{"id":70265714,"text":"70265714 - 2025 - River floods under wetter antecedent conditions deliver coarser sediment to the coast","interactions":[],"lastModifiedDate":"2025-04-15T15:10:08.066152","indexId":"70265714","displayToPublicDate":"2025-04-13T10:05:59","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"River floods under wetter antecedent conditions deliver coarser sediment to the coast","docAbstract":"<p><span>Increasing hydrologic volatility—more extreme rain, and larger variations between wet and dry years—has become apparent in some regions, but few data exist to determine how intensifying hydrologic extremes affect sedimentary systems. Using uniquely high-resolution records of fluvial suspended sediment and coastal morphology, we quantify sedimentary responses from a steep, 357-km</span><sup>2</sup><span>&nbsp;watershed in California under extreme wet and dry hydrologic conditions. In years with multiple 2- to 10-year floods, fluvial sediment coarsened significantly as the wet season progressed, with late-season floods delivering dominantly sand-sized material to the coast. Greater and coarser sediment supply under wetter antecedent conditions affected nearshore geomorphic evolution for 4–5&nbsp;years. The watershed and coastal changes we documented point to an increasing role of sediment-related hazards (flooding and hillslope erosion) and resources (nearshore accretion) as wet seasons intensify.</span></p>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2025GL115232","usgsCitation":"East, A.E., Snyder, A.G., Stevens, A.W., Warrick, J.A., Topping, D.J., Thomas, M.A., and Ritchie, A., 2025, River floods under wetter antecedent conditions deliver coarser sediment to the coast: Geophysical Research Letters, v. 52, no. 8, e2025GL115232, 10 p., https://doi.org/10.1029/2025GL115232.","productDescription":"e2025GL115232, 10 p.","ipdsId":"IP-175612","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science 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matthewthomas@usgs.gov","orcid":"https://orcid.org/0000-0002-9828-5539","contributorId":200616,"corporation":false,"usgs":true,"family":"Thomas","given":"Matthew","email":"matthewthomas@usgs.gov","middleInitial":"A.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":933373,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ritchie, Andrew C. 0000-0001-5826-9983","orcid":"https://orcid.org/0000-0001-5826-9983","contributorId":333630,"corporation":false,"usgs":true,"family":"Ritchie","given":"Andrew C.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":933374,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70273122,"text":"70273122 - 2025 - Multi-Scale Graph Learning for anti-sparse downscaling","interactions":[],"lastModifiedDate":"2025-12-16T16:51:11.338092","indexId":"70273122","displayToPublicDate":"2025-04-11T10:46:24","publicationYear":"2025","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Multi-Scale Graph Learning for anti-sparse downscaling","docAbstract":"<p><span>Water temperature can vary substantially even across short distances within the same sub-watershed. Accurate prediction of stream water temperature at fine spatial resolutions (i.e., fine scales, ≤ 1 km) enables precise interventions to maintain water quality and protect aquatic habitats. Although spatiotemporal models have made substantial progress in spatially coarse time series modeling, challenges persist in predicting at fine spatial scales due to the lack of data at that scale. To address the problem of insufficient fine-scale data, we propose a Multi-Scale Graph Learning (MSGL) method. This method employs a multi-task learning framework where coarse-scale graph learning, bolstered by larger datasets, simultaneously enhances fine-scale graph learning. Although existing multi-scale or multi-resolution methods integrate data from different spatial scales, they often overlook the spatial correspondences across graph structures at various scales. To address this, our MSGL introduces an additional learning task, cross-scale interpolation learning, which leverages the hydrological connectedness of stream locations across coarse- and fine-scale graphs to establish cross-scale connections, thereby enhancing overall model performance. Furthermore, we have broken free from the mindset that multi-scale learning is limited to synchronous training by proposing an Asynchronous Multi-Scale Graph Learning method (ASYNC-MSGL). Extensive experiments demonstrate the state-of-the-art performance of our method for anti-sparse downscaling of daily stream temperatures in the Delaware River Basin, USA, highlighting its potential utility for water resources monitoring and management.</span></p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the AAAI conference on artificial intelligence","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Association for the Advancement of Artificial Intelligence","doi":"10.1609/aaai.v39i27.35014","usgsCitation":"Fan, Y., Yu, R., Barclay, J.R., Appling, A.P., Sun, Y., Xie, Y., and Jia, X., 2025, Multi-Scale Graph Learning for anti-sparse downscaling, <i>in</i> Proceedings of the AAAI conference on artificial intelligence, v. 39, no. 27, p. 27969-27977, https://doi.org/10.1609/aaai.v39i27.35014.","productDescription":"9 p.","startPage":"27969","endPage":"27977","ipdsId":"IP-167502","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":497731,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1609/aaai.v39i27.35014","text":"Publisher Index Page"},{"id":497583,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"39","issue":"27","noUsgsAuthors":false,"publicationDate":"2025-04-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Fan, Yingda","contributorId":352470,"corporation":false,"usgs":false,"family":"Fan","given":"Yingda","affiliations":[{"id":84236,"text":"Department of Computer Science, University of Pittsburgh","active":true,"usgs":false}],"preferred":false,"id":952391,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yu, Runlong 0000-0003-4080-2377","orcid":"https://orcid.org/0000-0003-4080-2377","contributorId":352471,"corporation":false,"usgs":false,"family":"Yu","given":"Runlong","affiliations":[{"id":84236,"text":"Department of Computer Science, University of Pittsburgh","active":true,"usgs":false}],"preferred":false,"id":952392,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barclay, Janet R. 0000-0003-1643-6901 jbarclay@usgs.gov","orcid":"https://orcid.org/0000-0003-1643-6901","contributorId":222437,"corporation":false,"usgs":true,"family":"Barclay","given":"Janet","email":"jbarclay@usgs.gov","middleInitial":"R.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":952393,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Appling, Alison P. 0000-0003-3638-8572 aappling@usgs.gov","orcid":"https://orcid.org/0000-0003-3638-8572","contributorId":150595,"corporation":false,"usgs":true,"family":"Appling","given":"Alison","email":"aappling@usgs.gov","middleInitial":"P.","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":952394,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sun, Yiming","contributorId":352472,"corporation":false,"usgs":false,"family":"Sun","given":"Yiming","affiliations":[{"id":84236,"text":"Department of Computer Science, University of Pittsburgh","active":true,"usgs":false}],"preferred":false,"id":952395,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Xie, Yiqun","contributorId":297447,"corporation":false,"usgs":false,"family":"Xie","given":"Yiqun","email":"","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":952396,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jia, Xiaowei 0000-0001-8544-5233","orcid":"https://orcid.org/0000-0001-8544-5233","contributorId":237807,"corporation":false,"usgs":false,"family":"Jia","given":"Xiaowei","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":952397,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70266250,"text":"70266250 - 2025 - Detection of Giardia and Cryptosporidium in surface water of a subarctic city","interactions":[],"lastModifiedDate":"2025-05-02T14:34:36.525786","indexId":"70266250","displayToPublicDate":"2025-04-11T09:31:41","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":16699,"text":"Food and Waterborne Parasitology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Detection of <i>Giardia</i> and <i>Cryptosporidium</i> in surface water of a subarctic city","title":"Detection of Giardia and Cryptosporidium in surface water of a subarctic city","docAbstract":"Giardia and Cryptosporidium spp. are globally distributed protozoan parasites that can cause gastrointestinal disease in humans and animals. These zoonotic parasites and their ecological relationships have been understudied in Alaska and elsewhere, despite being identified as priority zoonotic pathogens. We aimed to detect and characterize Giardia and Cryptosporidium spp. in waterbodies within Anchorage, Alaska, USA using two methods, including the Environmental Protection Agency (EPA) Method 1623 that relies on microscopy and a molecular detection approach. The molecular approach was ultimately unsuccessful and therefore only data obtained using Method 1623 are presented. Giardia or Cryptosporidium spp. was detected from nine of 15 urban streams and lakes sampled (60%), six of which were positive for both parasites (40%). Fewer than 10 cysts or oocysts were detected in 10 L of surface water. Further research to characterize Giardia and Cryptosporidium beyond the genus level would help elucidate the zoonotic potential and ecology of these parasites within the region and more broadly in Alaska.","language":"English","publisher":"Elsevier","doi":"10.1016/j.fawpar.2025.e00262","usgsCitation":"Ahlstrom, C., Carey, M.P., Menning, D.M., O’Donnell, J.A., and Ramey, A.M., 2025, Detection of Giardia and Cryptosporidium in surface water of a subarctic city: Food and Waterborne Parasitology, v. 39, e00262, 6 p., https://doi.org/10.1016/j.fawpar.2025.e00262.","productDescription":"e00262, 6 p.","ipdsId":"IP-173920","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":487921,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.fawpar.2025.e00262","text":"Publisher Index Page"},{"id":485323,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","city":"Anchorage","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -149.71809212582164,\n              61.26288495513296\n            ],\n            [\n              -150.13097003492274,\n              61.26288495513296\n            ],\n            [\n              -150.13097003492274,\n              61.05787266884491\n            ],\n            [\n              -149.71809212582164,\n              61.05787266884491\n            ],\n            [\n              -149.71809212582164,\n              61.26288495513296\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"39","noUsgsAuthors":false,"publicationDate":"2025-04-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Ahlstrom, Christina 0000-0001-5414-8076","orcid":"https://orcid.org/0000-0001-5414-8076","contributorId":214540,"corporation":false,"usgs":true,"family":"Ahlstrom","given":"Christina","email":"","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":935084,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carey, Michael P. 0000-0002-3327-8995 mcarey@usgs.gov","orcid":"https://orcid.org/0000-0002-3327-8995","contributorId":5397,"corporation":false,"usgs":true,"family":"Carey","given":"Michael","email":"mcarey@usgs.gov","middleInitial":"P.","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":935085,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Menning, Damian M. 0000-0003-3547-3062 dmenning@usgs.gov","orcid":"https://orcid.org/0000-0003-3547-3062","contributorId":205131,"corporation":false,"usgs":true,"family":"Menning","given":"Damian","email":"dmenning@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":935086,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"O’Donnell, Jonathan A. 0000-0001-7031-9808","orcid":"https://orcid.org/0000-0001-7031-9808","contributorId":191423,"corporation":false,"usgs":false,"family":"O’Donnell","given":"Jonathan","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":935087,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ramey, Andrew M. 0000-0002-3601-8400 aramey@usgs.gov","orcid":"https://orcid.org/0000-0002-3601-8400","contributorId":1872,"corporation":false,"usgs":true,"family":"Ramey","given":"Andrew","email":"aramey@usgs.gov","middleInitial":"M.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":935088,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70265768,"text":"70265768 - 2025 - Development of a genotyping-in-thousands by sequencing (GT-seq) panel for identifying individuals and estimating relatedness among Alaska black bears (Ursus americanus)","interactions":[],"lastModifiedDate":"2025-04-15T14:34:50.502337","indexId":"70265768","displayToPublicDate":"2025-04-11T09:30:32","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Development of a genotyping-in-thousands by sequencing (GT-seq) panel for identifying individuals and estimating relatedness among Alaska black bears (<i>Ursus americanus</i>)","title":"Development of a genotyping-in-thousands by sequencing (GT-seq) panel for identifying individuals and estimating relatedness among Alaska black bears (Ursus americanus)","docAbstract":"<p><span>The management and conservation of large mammals, such as black bears (</span><i>Ursus americanus</i><span>), have long been informed by genetic estimates of population size and individual dispersal. Amplicon sequencing methods, also known as ‘genotyping-in-thousands-by sequencing’ (GT-seq), now enable the efficient and cost-effective genotyping of hundreds of loci and individuals in the same sequencing run. Here, we develop a GT-seq panel for individual identification and kinship inference in Alaska black bears. Using genomic data from restriction site-associated DNA sequencing of hunter-harvested bears from Southcentral Alaska (</span><i>n</i><span> = 85), we identified 170 microhaplotype and single nucleotide polymorphism (SNP) loci that were highly heterozygous in local populations. To enable sexing of individuals, we also included a previously published sex-linked locus in the GT-seq panel. We empirically validated the GT-seq panel using samples collected at different spatial scales. These samples included tissues (</span><i>n</i><span> = 82) obtained from bears within a small geographic area in Anchorage, Alaska, which were likely to be relatives as well as the hunter-harvested samples collected from geographically widespread locations throughout Southcentral Alaska. Empirical validation indicated high genotyping success and genotype reproducibility across replicate subsamples. Computer simulations demonstrated that the GT-seq panel had ample statistical power for distinguishing distinct individuals and first-order relatives (parent-offspring and full-sibling pairs) from unrelated individuals. As a final proof of concept, the panel was used to identify individual bears and close kin sampled from urban and wild habitats in Anchorage, Alaska. We anticipate that the GT-seq panel will be a useful genomic resource for the monitoring and management of Alaska black bear populations.</span>ons.</p>","language":"English","publisher":"Wiley","doi":"10.1002/ece3.71273","usgsCitation":"Petrou, E., Brandt, C., Spivey, T., Gruenthal, K., Mckeeman, C.M., Farley, S.D., Battle, D., Stantorf, C., and Ramey, A.M., 2025, Development of a genotyping-in-thousands by sequencing (GT-seq) panel for identifying individuals and estimating relatedness among Alaska black bears (Ursus americanus): Ecology and Evolution, v. 15, no. 4, e71273, 13 p., https://doi.org/10.1002/ece3.71273.","productDescription":"e71273, 13 p.","ipdsId":"IP-173133","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":488245,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.71273","text":"Publisher Index Page"},{"id":484575,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -144.37908579144883,\n              60.1880549532429\n            ],\n            [\n              -145.33789769841525,\n              61.28084387847426\n            ],\n            [\n              -147.5603688778254,\n              61.761383856172614\n            ],\n            [\n              -150.36520049953535,\n              61.47257901289217\n            ],\n            [\n              -151.5611076733203,\n              60.44966814671875\n            ],\n            [\n              -152.16087869317707,\n              59.10929453238546\n            ],\n            [\n              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Force","active":true,"usgs":false}],"preferred":false,"id":933481,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Spivey, Timothy J.","contributorId":353401,"corporation":false,"usgs":false,"family":"Spivey","given":"Timothy J.","affiliations":[{"id":7058,"text":"Alaska Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":933482,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gruenthal, Kristen M.","contributorId":353402,"corporation":false,"usgs":false,"family":"Gruenthal","given":"Kristen M.","affiliations":[{"id":7058,"text":"Alaska Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":933483,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mckeeman, Cherie Marie 0000-0001-9868-2502","orcid":"https://orcid.org/0000-0001-9868-2502","contributorId":334651,"corporation":false,"usgs":true,"family":"Mckeeman","given":"Cherie","email":"","middleInitial":"Marie","affiliations":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"preferred":true,"id":933484,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Farley, Sean D.","contributorId":340801,"corporation":false,"usgs":false,"family":"Farley","given":"Sean","email":"","middleInitial":"D.","affiliations":[{"id":81667,"text":"Alaska Department of Game and Fish","active":true,"usgs":false}],"preferred":false,"id":933485,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Battle, David","contributorId":353403,"corporation":false,"usgs":false,"family":"Battle","given":"David","affiliations":[{"id":7058,"text":"Alaska Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":933486,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Stantorf, Cory","contributorId":353404,"corporation":false,"usgs":false,"family":"Stantorf","given":"Cory","affiliations":[{"id":7058,"text":"Alaska Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":933487,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ramey, Andrew M. 0000-0002-3601-8400 aramey@usgs.gov","orcid":"https://orcid.org/0000-0002-3601-8400","contributorId":1872,"corporation":false,"usgs":true,"family":"Ramey","given":"Andrew","email":"aramey@usgs.gov","middleInitial":"M.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":933488,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70265521,"text":"ofr20251010 - 2025 - Benthic habitat map of Olowalu Reef, Maui, Hawaii—Geomorphological structure, biological cover, and geologic zonation determined with spectral, lidar, and acoustic data","interactions":[],"lastModifiedDate":"2025-08-07T20:42:30.599263","indexId":"ofr20251010","displayToPublicDate":"2025-04-10T14:01:28","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-1010","displayTitle":"Benthic Habitat Map of Olowalu Reef, Maui, Hawaii—Geomorphological Structure, Biological Cover, and Geologic Zonation Determined with Spectral, Lidar, and Acoustic Data","title":"Benthic habitat map of Olowalu Reef, Maui, Hawaii—Geomorphological structure, biological cover, and geologic zonation determined with spectral, lidar, and acoustic data","docAbstract":"<p>The fringing coral reef off Olowalu, Maui, Hawaii, has been identified as a local conservation priority site. In 2007, the National Oceanic and Atmospheric Administration (NOAA) produced a benthic habitat map of the Hawaiian Islands that was used as a foundation for this study. To support place-based management of the reef in the future, the U.S. Geological Survey (USGS) mapped the geologic zone, major and dominant geomorphological structure, biological cover type, and percent of biological cover for 11 square kilometers (km<sup>2</sup>) of Olowalu Reef at a minimum mapping unit (MMU) of 100 square meters (m<sup>2</sup>) to create a benthic habitat map. Heads-up digitization was employed on 0.50-meter (m) natural color satellite orthoimagery with ancillary 1-m acoustic backscatter imagery from single-scan sonar (sound navigation and ranging). A 1-m, 4-m, and 8-m digital bathymetric model (DBM) was interpolated from bathymetric lidar (light detection and ranging), and various geomorphometric layers derived from the DBMs were used for habitat interpretation. Still-frame imagery of the seafloor extracted from vessel-towed underwater video transects on Olowalu Reef served as ground validation points (<i>n</i>=870) during active mapping and accuracy assessment points (<i>n</i>=216) for thematic accuracy assessment. Thematic accuracy was cross-validated by the Hawai‘i Department of Land and Natural Resources Division of Aquatic Resources. Final thematic accuracy was 88.8 percent for major structure, 85.6 percent for dominant structure, 86.0 percent for major biological cover, and 78.6 percent for type and percent of major biological cover. Reef and hardbottom constituted 52 percent of the total mapped habitat, comprising mostly aggregate reef (31 percent) and pavement (11 percent), with large swaths of spur-and-groove (9 percent). Of this hardbottom, 17 percent was covered with moderate (10 to &lt;50 percent) coral and 27 percent with high coral cover (50 to &lt;90 percent). High (50 to &lt;90 percent) macroalgae cover dominated the continuous sand sheets in offshore bank/shelf zones.</p><p>The map created in this study supplements the NOAA 2007 map and expands on the observations made by USGS sampling of the reef. The NOAA 2007 map and our map differed in total areal extent by a negligible 6 m<sup>2</sup> and were in general thematic agreement. Our map is intended to serve as a baseline for public access, general research, local-level management, and reef change for future studies.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251010","usgsCitation":"Heberer, L.N., Alkins, K.A., Storlazzi, C.D., Cochran, S.A., Gibbs, A.E., Sparks, R., Stone, K., Silva, I., Martinez, T., Peralto, C., Levine, A.S., Stow, D., and Maloney, J., 2025, Benthic Habitat Map of Olowalu Reef, Maui, Hawaii—Geomorphological Structure, Biological Cover, and Geologic Zonation Determined with Spectral, Lidar, and Acoustic Data: U.S. Geological Survey Open-File Report 2025–1010, 32 p., https://doi.org/10.3133/ofr20251010.","productDescription":"Report: vi, 32 p.; Data Release","numberOfPages":"32","onlineOnly":"Y","ipdsId":"IP-152179","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":493749,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118524.htm","linkFileType":{"id":5,"text":"html"}},{"id":484394,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ICJ7CF","text":"USGS Data Release","description":"Heberer, L.N., Alkins, K.A., Storlazzi, C.D., Cochran, S.A., Gibbs, A.E., Sparks, R., Silva, I., Stone, K., Martinez, T., and Peralto, C., 2025, Benthic habitat map of the geomorphological structure, biological cover, and geologic zonation of Olowalu reef, Maui: U.S. Geological Survey data release, https://doi.org/10.5066/P9ICJ7CF.","linkHelpText":"Benthic habitat map of the geomorphological structure, biological cover, and geologic zonation of Olowalu reef, Maui"},{"id":484407,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1010/covrthb.jpg"},{"id":484408,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1010/ofr20251010.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Hawaii","otherGeospatial":"Maui, Olowalu Reef","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -156.66330945257621,\n              20.84439182546096\n            ],\n            [\n              -156.66330945257621,\n              20.763341421657273\n            ],\n            [\n              -156.5212712939503,\n              20.763341421657273\n            ],\n            [\n              -156.5212712939503,\n              20.84439182546096\n            ],\n            [\n              -156.66330945257621,\n              20.84439182546096\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p><a href=\"https://www.usgs.gov/centers/pcmsc\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/centers/pcmsc\">Pacific Coastal and Marine Science Center</a><br><a href=\"https://www.usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/\">U.S. Geological Survey</a><br>2885 Mission St.<br>Santa Cruz, CA 95060</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Data</li><li>Methods</li><li>Results</li><li>Discussion</li><li>References Cited</li><li>Appendix 1. U.S. Geological Survey Detailed Reef Classification Scheme</li></ul>","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2025-04-10","noUsgsAuthors":false,"publicationDate":"2025-04-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Heberer, Liana N.","contributorId":353085,"corporation":false,"usgs":false,"family":"Heberer","given":"Liana N.","affiliations":[{"id":6608,"text":"San Diego State University","active":true,"usgs":false}],"preferred":true,"id":932892,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Alkins, Kristen C. 0000-0003-3647-2678 kalkins@usgs.gov","orcid":"https://orcid.org/0000-0003-3647-2678","contributorId":333714,"corporation":false,"usgs":true,"family":"Alkins","given":"Kristen","email":"kalkins@usgs.gov","middleInitial":"C.","affiliations":[],"preferred":true,"id":932893,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Storlazzi, Curt D. 0000-0001-8057-4490 cstorlazzi@usgs.gov","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":140584,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","email":"cstorlazzi@usgs.gov","middleInitial":"D.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":932894,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cochran, Susan A. 0000-0002-2442-8787 scochran@usgs.gov","orcid":"https://orcid.org/0000-0002-2442-8787","contributorId":138976,"corporation":false,"usgs":true,"family":"Cochran","given":"Susan","email":"scochran@usgs.gov","middleInitial":"A.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":932895,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gibbs, Ann E. 0000-0002-0883-3774 agibbs@usgs.gov","orcid":"https://orcid.org/0000-0002-0883-3774","contributorId":2644,"corporation":false,"usgs":true,"family":"Gibbs","given":"Ann","email":"agibbs@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":932896,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sparks, Russell","contributorId":311128,"corporation":false,"usgs":false,"family":"Sparks","given":"Russell","affiliations":[{"id":17708,"text":"Hawai`i Department of Land and Natural Resources Division of Aquatic, Honolulu, Hawaii","active":true,"usgs":false}],"preferred":true,"id":932897,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stone, Kristy","contributorId":353086,"corporation":false,"usgs":false,"family":"Stone","given":"Kristy","affiliations":[{"id":17708,"text":"Hawai`i Department of Land and Natural Resources Division of Aquatic, Honolulu, Hawaii","active":true,"usgs":false}],"preferred":true,"id":932898,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Silva, Itana","contributorId":353087,"corporation":false,"usgs":false,"family":"Silva","given":"Itana","affiliations":[{"id":17708,"text":"Hawai`i Department of Land and Natural Resources Division of Aquatic, Honolulu, Hawaii","active":true,"usgs":false}],"preferred":true,"id":932899,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Martinez, Tatiana","contributorId":353088,"corporation":false,"usgs":false,"family":"Martinez","given":"Tatiana","affiliations":[{"id":17708,"text":"Hawai`i Department of Land and Natural Resources Division of Aquatic, Honolulu, Hawaii","active":true,"usgs":false}],"preferred":true,"id":932900,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Peralto, Cole","contributorId":353089,"corporation":false,"usgs":false,"family":"Peralto","given":"Cole","affiliations":[{"id":17708,"text":"Hawai`i Department of Land and Natural Resources Division of Aquatic, Honolulu, Hawaii","active":true,"usgs":false}],"preferred":true,"id":932901,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Levine, Arielle S.","contributorId":353090,"corporation":false,"usgs":false,"family":"Levine","given":"Arielle S.","affiliations":[{"id":6608,"text":"San Diego State University","active":true,"usgs":false}],"preferred":true,"id":932902,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Stow, Douglas A.","contributorId":38248,"corporation":false,"usgs":false,"family":"Stow","given":"Douglas","email":"","middleInitial":"A.","affiliations":[{"id":6608,"text":"San Diego State University","active":true,"usgs":false}],"preferred":true,"id":932903,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Maloney, Jillian","contributorId":353091,"corporation":false,"usgs":false,"family":"Maloney","given":"Jillian","affiliations":[{"id":6608,"text":"San Diego State University","active":true,"usgs":false}],"preferred":true,"id":932904,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}}
,{"id":70265692,"text":"70265692 - 2025 - Identifying preferential flow from soil moisture time series: Review of methodologies","interactions":[],"lastModifiedDate":"2025-04-14T16:18:34.093186","indexId":"70265692","displayToPublicDate":"2025-04-10T09:14:52","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3674,"text":"Vadose Zone Journal","active":true,"publicationSubtype":{"id":10}},"title":"Identifying preferential flow from soil moisture time series: Review of methodologies","docAbstract":"Identifying and quantifying preferential flow (PF) through soil—the rapid movement of water through spatially-distinct pathways in the subsurface—is vital to understanding how the hydrologic cycle responds to climate, land cover, and anthropogenic changes. In recent decades, methods have been developed that use measured soil moisture time series to identify PF. Because they allow for continuous monitoring and are relatively easy to implement, these methods have become an important tool for recognizing when, where, and under what conditions PF occurs. The methods seek to identify a pattern or quantification that indicates the occurrence of PF. Most commonly, the chosen signature is either (1) a nonsequential response to infiltrated water, in which soil moisture responses do not occur in order of shallowest to deepest, or (2) a velocity criterion, in which newly infiltrated water is detected at depth earlier than is possible by nonpreferential flow processes. Alternative signatures have also been developed that have certain advantages but are less commonly utilized. Choosing among these possible signatures requires attention to their pertinent characteristics, including susceptibility to errors, possible bias toward false negatives or false positives, reliance on subjective judgments, and possible requirements for additional types of data. We review 77 studies that have applied such methods, to highlight important information for readers who want to identify PF from soil moisture data, and to inform those who aim to develop new methods or improve existing ones.","language":"English","publisher":"Soil Science Society of America","doi":"10.1002/vzj2.70017","usgsCitation":"Nimmo, J.R., Wiekenkamp, I., Araki, R., Groh, J., Singh, N., Crompton, O., Wyatt, B., Ajami, H., Gimenez, D., Hirmas, D., Sullivan, P., and Sprenger, M., 2025, Identifying preferential flow from soil moisture time series: Review of methodologies: Vadose Zone Journal, v. 24, no. 2, e70017, 25 p., https://doi.org/10.1002/vzj2.70017.","productDescription":"e70017, 25 p.","ipdsId":"IP-175711","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":488217,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/vzj2.70017","text":"Publisher Index Page"},{"id":484511,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"24","issue":"2","noUsgsAuthors":false,"publicationDate":"2025-04-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Nimmo, John R. 0000-0001-8191-1727 jrnimmo@usgs.gov","orcid":"https://orcid.org/0000-0001-8191-1727","contributorId":757,"corporation":false,"usgs":true,"family":"Nimmo","given":"John","email":"jrnimmo@usgs.gov","middleInitial":"R.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":933270,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wiekenkamp, Inge","contributorId":353318,"corporation":false,"usgs":false,"family":"Wiekenkamp","given":"Inge","affiliations":[{"id":52961,"text":"GFZ Potsdam","active":true,"usgs":false}],"preferred":false,"id":933271,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Araki, Ryoko","contributorId":353321,"corporation":false,"usgs":false,"family":"Araki","given":"Ryoko","affiliations":[{"id":84355,"text":"San Diego State U","active":true,"usgs":false}],"preferred":false,"id":933272,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Groh, Jannis","contributorId":353322,"corporation":false,"usgs":false,"family":"Groh","given":"Jannis","affiliations":[{"id":84358,"text":"Agrosphere Institute,  Jülich, Germany","active":true,"usgs":false}],"preferred":false,"id":933273,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Singh, Nitin","contributorId":353323,"corporation":false,"usgs":false,"family":"Singh","given":"Nitin","affiliations":[{"id":84359,"text":"Auburn U","active":true,"usgs":false}],"preferred":false,"id":933274,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Crompton, Octavia","contributorId":353324,"corporation":false,"usgs":false,"family":"Crompton","given":"Octavia","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":933275,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wyatt, Briana","contributorId":353325,"corporation":false,"usgs":false,"family":"Wyatt","given":"Briana","affiliations":[{"id":84360,"text":"Texas A&M U","active":true,"usgs":false}],"preferred":false,"id":933276,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ajami, Hoori 0000-0001-6883-7630","orcid":"https://orcid.org/0000-0001-6883-7630","contributorId":303806,"corporation":false,"usgs":false,"family":"Ajami","given":"Hoori","email":"","affiliations":[{"id":36629,"text":"University of California","active":true,"usgs":false}],"preferred":false,"id":933277,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gimenez, Daniel","contributorId":353326,"corporation":false,"usgs":false,"family":"Gimenez","given":"Daniel","affiliations":[{"id":84361,"text":"Rutgers U","active":true,"usgs":false}],"preferred":false,"id":933278,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hirmas, Daniel","contributorId":353327,"corporation":false,"usgs":false,"family":"Hirmas","given":"Daniel","affiliations":[{"id":49949,"text":"Texas Tech U","active":true,"usgs":false}],"preferred":false,"id":933279,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Sullivan, Pamela","contributorId":190446,"corporation":false,"usgs":false,"family":"Sullivan","given":"Pamela","affiliations":[],"preferred":false,"id":933280,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Sprenger, Matthias 0000-0003-1221-2767","orcid":"https://orcid.org/0000-0003-1221-2767","contributorId":344277,"corporation":false,"usgs":false,"family":"Sprenger","given":"Matthias","email":"","affiliations":[{"id":82324,"text":"Lawrence Berkley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":933281,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}}
,{"id":70265711,"text":"70265711 - 2025 - Geochemistry and radiogenic isotopes constrain the mantle source region of the Mountain Pass Intrusive Suite, California","interactions":[],"lastModifiedDate":"2025-04-15T15:03:17.346697","indexId":"70265711","displayToPublicDate":"2025-04-10T07:58:12","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2588,"text":"LITHOS","active":true,"publicationSubtype":{"id":10}},"title":"Geochemistry and radiogenic isotopes constrain the mantle source region of the Mountain Pass Intrusive Suite, California","docAbstract":"<p><span>The Mountain Pass carbonatite stock is the largest rare earth element (REE) deposit and only active REE mine in the United States. The carbonatite intrusion and spatially associated alkaline silicate intrusions constitute the Mountain Pass Intrusive Suite, which is located within the Mojave Province in California. Both the carbonatite and the alkaline silicate rocks are enriched in large ion lithophile elements and light REEs, and less enriched to depleted in high field strength elements, indicating the mantle source region was metasomatically enriched in incompatible trace elements. The cause of this metasomatic mantle enrichment and the genetic relationship between the carbonatite and the alkaline silicate stocks are poorly understood. In this study, major and trace element geochemical data and isotopic (Rb-Sr, Sm-Nd, and Lu-Hf) data are presented to constrain genesis of the Mountain Pass Intrusive Suite, from mantle source region to the intrusion of the stocks. Our geochemical data are consistent with derivation of the alkaline silicate and carbonatite melts through partial melting from a shared mantle source region rather than through liquid immiscibility or fractional crystallization and separation of a carbothermal fluid. Although the Rb-Sr isotopic system in the Mountain Pass Intrusive Suite is disturbed at the whole-rock scale, the isotopic systems for whole-rock Sm-Nd (εNd</span><sub><i>i</i></sub><span>&nbsp;=&nbsp; ‐2.2&nbsp;±&nbsp;0.8) and zircon Lu-Hf (εHf</span><sub><i>i</i></sub><span>&nbsp;=&nbsp;0.1&nbsp;±&nbsp;1.1) are robust and support mantle derivation of the magmas. Geochemical modeling using experimentally derived partition coefficients was used to identify possible causes of enrichment in incompatible elements through metasomatism in the mantle source region. Modeling of metasomatism by melts derived by partial melting of deeply subducted carbonated sediments approximates observed Mountain Pass Intrusive Suite trace element chemistry. Scattered εHf</span><sub><i>i</i></sub><span>&nbsp;in inherited zircon (2.8&nbsp;±&nbsp;2.6) is consistent with derivation from an arc-related environment with substantial crustal contamination. Paleotectonic studies in the Mojave Province indicate that regional subduction preceded emplacement of the Mountain Pass Intrusive Suite by ∼300&nbsp;Ma. Melting of the Mountain Pass source region may have been caused by post-collisional thermal relaxation and extension.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.lithos.2025.108060","usgsCitation":"Benson, E.K., Watts, K., and Hillenbrand, I.W., 2025, Geochemistry and radiogenic isotopes constrain the mantle source region of the Mountain Pass Intrusive Suite, California: LITHOS, v. 508-509, 108060, 18 p., https://doi.org/10.1016/j.lithos.2025.108060.","productDescription":"108060, 18 p.","ipdsId":"IP-173126","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":488251,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.lithos.2025.108060","text":"Publisher Index Page"},{"id":484582,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Mojave Province","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -116.29333455287775,\n              36.19312110709426\n            ],\n            [\n              -116.29333455287775,\n              34.7648602212238\n            ],\n            [\n              -114.67184767081949,\n              34.7648602212238\n            ],\n            [\n              -114.67184767081949,\n              36.19312110709426\n            ],\n            [\n              -116.29333455287775,\n              36.19312110709426\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"508-509","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Benson, Erin Kay 0000-0003-3166-6043","orcid":"https://orcid.org/0000-0003-3166-6043","contributorId":346098,"corporation":false,"usgs":true,"family":"Benson","given":"Erin","email":"","middleInitial":"Kay","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":933365,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Watts, Kathryn E. 0000-0002-6110-7499","orcid":"https://orcid.org/0000-0002-6110-7499","contributorId":204344,"corporation":false,"usgs":true,"family":"Watts","given":"Kathryn E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":933366,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hillenbrand, Ian William 0000-0003-2801-3674","orcid":"https://orcid.org/0000-0003-2801-3674","contributorId":299032,"corporation":false,"usgs":true,"family":"Hillenbrand","given":"Ian","email":"","middleInitial":"William","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":933367,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70265548,"text":"70265548 - 2025 - The mountains are calling, but will visitors go? Modeling the effect of weather and air quality on visitation to Pacific Northwest parks and protected areas using mobile device data","interactions":[],"lastModifiedDate":"2025-04-14T15:58:28.046525","indexId":"70265548","displayToPublicDate":"2025-04-09T10:55:44","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":16703,"text":"PLOS Climate","active":true,"publicationSubtype":{"id":10}},"title":"The mountains are calling, but will visitors go? Modeling the effect of weather and air quality on visitation to Pacific Northwest parks and protected areas using mobile device data","docAbstract":"<p><span>We investigated how visitors to federal, state, and local parks and protected areas (PPAs) respond to weather and air quality conditions in the Pacific Northwest (PNW), United States. Specifically, we modeled the relationship between weekly visitation and mean weekly minimum and maximum temperature, precipitation, Air Quality Index (AQI), and particulate matter 2.5 concentration (PM2.5, often used as an indicator of wildfire smoke) during an extended peak visitation season from 2017 to 2021 in 91 PNW PPAs. We used mobile device data from&nbsp;</span><i>StreetLight Data Inc.</i><span>&nbsp;to estimate weekly vehicular visitation. Our findings indicate that increasing precipitation corresponded with decreased weekly visitation to all three types of PPAs and rising minimum temperatures corresponded with increased visitation. We found that rising maximum temperatures corresponded with increased visitation in federal and local PPAs, but corresponded with decreased visitation in local PPAs once temperatures reach a maximum threshold. We did not observe a maximum threshold effect in federal or state settings. Further, we found that the effect of air quality and smoke on visitation varies based on the metric used: increased PM2.5 concentrations (possibly indicating the presence of wildfire smoke) in federal and local PPAs corresponded with decreased visitation, while increased AQI in federal PPAs corresponded with increased visitation. These findings indicate that visitors may respond differently to different types of air pollution. Our results have implications for adapting peak- and shoulder- season visitor use management to current and future climate change within and beyond PPAs of the PNW.</span></p>","language":"English","publisher":"PLoS","doi":"10.1371/journal.pclm.0000537","usgsCitation":"Minehart, K., D'Antonio, A., and Wilkins, E.J., 2025, The mountains are calling, but will visitors go? Modeling the effect of weather and air quality on visitation to Pacific Northwest parks and protected areas using mobile device data: PLOS Climate, v. 4, no. 4, e0000537, 22 p., https://doi.org/10.1371/journal.pclm.0000537.","productDescription":"e0000537, 22 p.","ipdsId":"IP-167823","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":488214,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pclm.0000537","text":"Publisher Index Page"},{"id":484508,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Oregon, 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 \"}}]}","volume":"4","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-04-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Minehart, Kira 0009-0006-6007-0782","orcid":"https://orcid.org/0009-0006-6007-0782","contributorId":353124,"corporation":false,"usgs":false,"family":"Minehart","given":"Kira","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":933006,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"D'Antonio, Ashley","contributorId":353127,"corporation":false,"usgs":false,"family":"D'Antonio","given":"Ashley","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":933007,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wilkins, Emily J. 0000-0003-3055-4808","orcid":"https://orcid.org/0000-0003-3055-4808","contributorId":328409,"corporation":false,"usgs":true,"family":"Wilkins","given":"Emily","email":"","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":933008,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70265511,"text":"sir20255026 - 2025 - Time of travel of releases from Lake Wallenpaupack to the U.S. Geological Survey’s streamgage monitoring location on the Delaware River at Montague, New Jersey","interactions":[],"lastModifiedDate":"2025-08-07T20:39:55.109963","indexId":"sir20255026","displayToPublicDate":"2025-04-09T10:00:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-5026","displayTitle":"Time of Travel of Releases From Lake Wallenpaupack to the U.S. Geological Survey’s Streamgage Monitoring Location on the Delaware River at Montague, New Jersey","title":"Time of travel of releases from Lake Wallenpaupack to the U.S. Geological Survey’s streamgage monitoring location on the Delaware River at Montague, New Jersey","docAbstract":"<p>In 2016, the U.S. Geological Survey (USGS) carried out a hydraulic study within the upper Delaware River Basin for the purpose of determining the time of travel for water releases from the Brookfield Renewable U.S. hydroelectric plant at Lake Wallenpaupack, Pennsylvania, to reach the USGS streamgage located on the Delaware River at Montague, New Jersey (site number 01438500). From September 19 to October 14, 2016, Brookfield Renewable initiated repeated releases of approximately 650 cubic feet per second (ft<sup>3</sup>/s) and 1,440 ft<sup>3</sup>/s. Hydraulic signals from the releases were tracked at nine key locations between Lake Wallenpaupack and the USGS streamgage at Montague, New Jersey (01438500). Gage height data were recorded at streamgages at major confluences of the Lackawaxen and Delaware Rivers, Mongaup and Delaware Rivers, and the Neversink and Delaware Rivers. The time of travel to the USGS streamgage at Montague, New Jersey, was determined to be 1,185 minutes during the approximately 650 ft<sup>3</sup>/s releases and 960 minutes during the approximately 1,440 ft<sup>3</sup>/s releases. Elevated streamflow between Lake Wallenpaupack and the Montague streamgage from runoff events prior to the dam releases was found to decrease calculated time of travel identified during the study. The results from this study can be used as a guide to estimate time of travel while considering the water level of downstream streamflow, the volume of water being released, and other outside influences, such as precipitation and snowmelt.</p>","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255026","collaboration":"Prepared in cooperation with the Office of the Delaware River Master","usgsCitation":"Polcino, J., Trainor, J.J., and Collenburg, J.V., 2025, Time of travel of releases from Lake Wallenpaupack to the U.S. Geological Survey’s streamgage monitoring location on the Delaware River at Montague, New Jersey: U.S. Geological Survey Scientific Investigations Report 2025–5026, 20 p., https://doi.org/10.3133/sir20255026.","productDescription":"vii, 20 p.","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-150895","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":484348,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5026/images/"},{"id":484347,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5026/sir20255026.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2025-5026 XML"},{"id":484346,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255026/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5026 HTML"},{"id":484345,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5026/sir20255026.pdf","text":"Report","size":"13.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5026 PDF"},{"id":484344,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5026/coverthb.jpg"},{"id":493747,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118518.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New Jersey, New York, Pennsylvania","otherGeospatial":"Delaware River, Lake Wallenpaupack","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -75.28276534144987,\n              41.608544510962616\n            ],\n            [\n              -75.28276534144987,\n              41.13145977502657\n            ],\n            [\n              -74.30523052576332,\n              41.13145977502657\n            ],\n            [\n              -74.30523052576332,\n              41.608544510962616\n            ],\n            [\n              -75.28276534144987,\n              41.608544510962616\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:dc_nj@usgs.gov\" data-mce-href=\"mailto:dc_nj@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/new-jersey-water-science-center\" data-mce-href=\"https://www.usgs.gov/centers/new-jersey-water-science-center\">New Jersey Water Science Center</a><br>U.S. Geological Survey<br>3450 Princeton Pike, Suite 110<br>Lawrenceville, NJ 08648</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Purpose and Scope</li><li>Methods and Approach</li><li>Interpretation and Methodology</li><li>Results</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"publishedDate":"2025-04-09","noUsgsAuthors":false,"publicationDate":"2025-04-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Polcino, Jaclynne 0009-0008-5843-0448 jpolcino@usgs.gov","orcid":"https://orcid.org/0009-0008-5843-0448","contributorId":353082,"corporation":false,"usgs":true,"family":"Polcino","given":"Jaclynne","email":"jpolcino@usgs.gov","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":932865,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Trainor, John J. 0000-0002-6603-2684 jtrainor@usgs.gov","orcid":"https://orcid.org/0000-0002-6603-2684","contributorId":5408,"corporation":false,"usgs":true,"family":"Trainor","given":"John","email":"jtrainor@usgs.gov","middleInitial":"J.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":932866,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Collenburg, Jerilyn V. 0000-0002-3513-3116","orcid":"https://orcid.org/0000-0002-3513-3116","contributorId":224351,"corporation":false,"usgs":true,"family":"Collenburg","given":"Jerilyn V.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":932867,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70265510,"text":"sir20255015 - 2025 - Spatial and seasonal water-quality patterns and temporal water-quality trends in Lake Conroe on the West Fork San Jacinto River near Conroe, Texas, 1974–2021","interactions":[],"lastModifiedDate":"2025-04-09T16:30:31.689566","indexId":"sir20255015","displayToPublicDate":"2025-04-09T09:57:33","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-5015","displayTitle":"Spatial and Seasonal Water-Quality Patterns and Temporal Water-Quality Trends in Lake Conroe on the West Fork San Jacinto River Near Conroe, Texas, 1974–2021","title":"Spatial and seasonal water-quality patterns and temporal water-quality trends in Lake Conroe on the West Fork San Jacinto River near Conroe, Texas, 1974–2021","docAbstract":"<p>The impoundment of Lake Conroe in 1973 created an important water resource for greater Houston, Texas. The U.S. Geological Survey, in cooperation with the San Jacinto River Authority, analyzed water-quality data collected from 1974 to 2021 at upreservoir, mid-reservoir, and downreservoir sites in Lake Conroe. Water-column and seasonal variability of selected water-quality constituents (physiochemical properties, major ions, nutrients, and trace metals) were assessed, as well as thermal stratification. Water-quality trends were evaluated for 1974–2021 and 1993–2021.</p><p>Near-surface water (1–3 feet below the water surface) was warmer and contained higher dissolved-oxygen concentrations compared to near-bottom water (2–3 feet above the reservoir bottom). Dissolved-oxygen concentrations were lowest in summer and highest in winter. Specific conductance was higher near the bottom and varied seasonally, being lowest in winter and highest in summer. Values of pH were generally higher at the surface, with some variability between sites and seasons. Water transparency was higher downreservoir and seasonally lowest in summer.</p><p>Major-ion concentrations varied minimally within the water column and seasonally, except for sulfate, which was higher in winter and lower in summer. Most nutrient and trace metal concentrations were highest near the bottom during summer, notably at deeper sites. Thermal stratification in Lake Conroe begins in spring and peaks in summer and was limited to the deeper parts of the reservoir. The seasonal variability observed in dissolved constituent concentrations was driven by thermal stratification. Trend analyses for 1974–2021 indicated positive trends in water temperature, dissolved oxygen, pH, potassium, sodium, and silica. Negative trends were detected for calcium and magnesium near the reservoir bottom. During 1993–2021, positive trends were detected for near-surface dissolved-oxygen concentration, specific conductance, pH, all major ions excluding sulfate, and near-surface ammonia plus organic nitrogen concentration. Negative trends were determined for ammonia, iron, and manganese concentrations. Water transparency generally decreased over time.<br></p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255015","issn":"2328-0328","collaboration":"Prepared in cooperation with the San Jacinto River Authority","usgsCitation":"Adams, A.C., 2025, Spatial and seasonal water-quality patterns and temporal water-quality trends in Lake Conroe on the West Fork San Jacinto River near Conroe, Texas, 1974–2021: U.S. Geological Survey Scientific Investigations Report 2025–5015, 114 p., https://doi.org/10.3133/sir20255015.","productDescription":"Report: x, 114 p.; Dataset","numberOfPages":"128","onlineOnly":"Y","ipdsId":"IP-151416","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":484340,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5015/sir20255015.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2025-5015 XML"},{"id":484339,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5015/sir20255015.pdf","size":"5.81 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5015"},{"id":484338,"rank":2,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5015/images"},{"id":484337,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5015/coverthb.jpg"},{"id":484342,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://waterdata.usgs.gov/nwis","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":484341,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255015/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5015 HTML"}],"country":"United States","state":"Texas","city":"Conroe","otherGeospatial":"San Jacinto River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -95.40581137802859,\n              30.75\n            ],\n            [\n              -95.80947515533124,\n              30.75\n            ],\n            [\n              -95.80947515533124,\n              30.31860249296716\n            ],\n            [\n              -95.40581137802859,\n              30.31860249296716\n            ],\n            [\n              -95.40581137802859,\n              30.75\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p>Director, <a data-mce-href=\"https://www.usgs.gov/centers/ot-water\" href=\"https://www.usgs.gov/centers/ot-water\">Oklahoma-Texas Water Science Center</a><br>U.S. Geological Survey<br>1505 Ferguson Lane<br>Austin, TX 78754–4501</p><p><a id=\"LPlnkOWAb30f03cb-e6c0-c412-988f-235c353ce0b0\" class=\"OWAAutoLink\" href=\"https://pubs.usgs.gov/contact\" data-auth=\"NotApplicable\" data-olk-copy-source=\"MailCompose\" data-mce-href=\"../contact\">Contact Us- USGS Publications Warehouse</a></p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Spatial and Seasonal Water-Quality Patterns in Lake Conroe</li><li>Water-Quality Trends in Lake Conroe</li><li>Study Limitations and Considerations for Future Work</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2025-04-09","noUsgsAuthors":false,"publicationDate":"2025-04-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Adams, Alexandra C. 0000-0001-6136-9598 acadams@usgs.gov","orcid":"https://orcid.org/0000-0001-6136-9598","contributorId":330423,"corporation":false,"usgs":true,"family":"Adams","given":"Alexandra C.","email":"acadams@usgs.gov","affiliations":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":932864,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70267802,"text":"70267802 - 2025 - Geochemical and tectonic evolution of the Ordovician Bronson Hill arc and Silurian and Devonian Connecticut Valley–Gaspé trough: Eastern Vermont and western New Hampshire, USA","interactions":[],"lastModifiedDate":"2025-06-02T14:41:08.826524","indexId":"70267802","displayToPublicDate":"2025-04-09T09:31:06","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"Geochemical and tectonic evolution of the Ordovician Bronson Hill arc and Silurian and Devonian Connecticut Valley–Gaspé trough: Eastern Vermont and western New Hampshire, USA","docAbstract":"<p>We present major and trace element whole-rock geochemistry of 94 samples from the Bronson Hill arc (BHA) and Connecticut Valley–Gaspé trough (CVGT). These data, when combined with recent zircon U-Pb geochronology and a reexamination of existing whole-rock geochemistry, enable a new analysis of the tectonic history of the ancient Laurentian-Ganderian margin in the northern Appalachians of New England, USA, and southeastern Canada. The whole-rock geochemical data from the Ordovician BHA in western New Hampshire indicate that metamorphosed extrusive and intrusive rocks share the same temporally variable geochemical fingerprint. Mafic and felsic rocks form a bimodal distribution and plot as island arc magmas on geochemical discrimination diagrams. Approximately 80% of mafic greenstone and amphibolite samples plot as island arc tholeiites with a subset of samples that trend toward a more within-plate basalt geochemical signature. Where ages are known, older felsic rocks (ca. 475–460 Ma) in the BHA tend to be more sodic and less potassic than their younger (ca. 460–445 Ma) counterparts, and the geochemical results trend from volcanic arc granites toward syn-collisional granites through time. Prior to recent geochronology, it was thought that an age gap existed between the island arc magmas and the syn collisional granites. This led to the separation of felsic plutons into the predominantly older trondhjemitic magmas assigned to the Ammonoosuc Volcanics and a younger suite of granites designated as the Oliverian Plutonic Suite. This age gap was thought to represent a flip in subduction polarity which would also account for observed changes in pluton chemistry. With the addition of more recent U-Pb isotopic age data, we now know there is a continuum of ages, and a polarity flip is no longer required. When the isotopic ages are combined with the new geochemical data presented here, they suggest that as the BHA approached and collided with Laurentia ca. 455 Ma, continental material was assimilated into the magma, and there was a transition from trondhjemite to granodiorite and granite magmas in felsic rocks and from island arc tholeiite toward more alkali-rich basalt with a continental signature in mafic magmas. The similarities in whole-rock and trace element geochemistry, rock type, and range of isotopic ages from the Ammonoosuc Volcanics, Partridge Formation, and Oliverian Plutonic Suite, suggest they originated from the same magma source and were part of one evolving island arc system that persisted throughout the Ordovician, and the need to separate the Oliverian Plutonic Suite from the Ammonoosuc Volcanics is not necessary.</p><p>Most magmatism in the BHA ceased ca. 440 Ma. Following the Taconic orogeny, Silurian basin development was widespread along the length of the Laurentian-Ganderian suture. In New England and Québec, this resulted in the formation of the CVGT. The BHA and the CVGT are generally studied separately: the BHA in the context of arc-continent collision during the Taconic orogeny, and the CVGT as it relates to post-orogenic extension or the distal effects of the Salinic disturbance. When viewed collectively, the igneous geochemistry of the BHA and CVGT reveals an overlap between the waning stages of Taconic orogenesis and the onset of Silurian to Devonian basin development in the northern Appalachians. Metamorphosed bimodal volcanic and intrusive rocks are present in the CVGT (ca. 434–407 Ma) and Silurian cover sequence, which unconformably overlies the BHA. Mafic rocks in the CVGT are mostly tholeiitic basalts with a subset of alkali basalt in the Waits River Formation. Tectonic discrimination diagrams show that the mafic rocks are a mix of mid-ocean ridge basalt to within-plate basalt. The felsic rocks in the CVGT are mostly metamorphosed volcanic rocks that vary from island arc granite to within-plate granite. The geochemical signature of the CVGT is consistent with a post-collisional intra-arc basin, where slab breakoff or crustal attenuation played a key role before transitioning to a deepening foreland basin at the beginning of the Acadian orogeny.</p>","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02768.1","usgsCitation":"Valley, P.M., Walsh, G., Merschat, A.J., and McAleer, R.J., 2025, Geochemical and tectonic evolution of the Ordovician Bronson Hill arc and Silurian and Devonian Connecticut Valley–Gaspé trough: Eastern Vermont and western New Hampshire, USA: Geosphere, v. 21, no. 3, p. 418-445, https://doi.org/10.1130/GES02768.1.","productDescription":"28 p.","startPage":"418","endPage":"445","ipdsId":"IP-158690","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":490653,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02768.1","text":"Publisher Index Page"},{"id":489369,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Hampshire, Vermont","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -72.625,\n              43.75\n            ],\n            [\n              -72.625,\n              43.125\n            ],\n            [\n              -72.125,\n              43.125\n            ],\n            [\n              -72.125,\n              43.75\n            ],\n            [\n              -72.625,\n              43.75\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"21","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-04-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Valley, Peter M. 0000-0002-9957-0403 pvalley@usgs.gov","orcid":"https://orcid.org/0000-0002-9957-0403","contributorId":4809,"corporation":false,"usgs":true,"family":"Valley","given":"Peter","email":"pvalley@usgs.gov","middleInitial":"M.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":938935,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walsh, Gregory J. 0000-0003-4264-8836","orcid":"https://orcid.org/0000-0003-4264-8836","contributorId":265307,"corporation":false,"usgs":true,"family":"Walsh","given":"Gregory J.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":938936,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Merschat, Arthur J. 0000-0002-9314-4067 amerschat@usgs.gov","orcid":"https://orcid.org/0000-0002-9314-4067","contributorId":4556,"corporation":false,"usgs":true,"family":"Merschat","given":"Arthur","email":"amerschat@usgs.gov","middleInitial":"J.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":938937,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McAleer, Ryan J. 0000-0003-3801-7441 rmcaleer@usgs.gov","orcid":"https://orcid.org/0000-0003-3801-7441","contributorId":215498,"corporation":false,"usgs":true,"family":"McAleer","given":"Ryan","email":"rmcaleer@usgs.gov","middleInitial":"J.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":938938,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70265532,"text":"70265532 - 2025 - Variable phenology but consistent loss of ice cover of 1213 Minnesota lakes","interactions":[],"lastModifiedDate":"2025-08-04T15:36:27.834214","indexId":"70265532","displayToPublicDate":"2025-04-09T07:59:55","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5456,"text":"Limnology and Oceanography Letters","active":true,"publicationSubtype":{"id":10}},"title":"Variable phenology but consistent loss of ice cover of 1213 Minnesota lakes","docAbstract":"Lake ice cover is declining globally with important implications for lake ecosystems. Ice loss studies often rely on small numbers of lakes with long-term data. We analyzed variation and trends in ice cover phenology from 1,213 lakes over 74 years (1949-2022) in Minnesota (USA), during which ice cover duration declined at a rate of 2 days per decade (14 days total) and became more variable. Despite variation in phenology, just 10-20% of lakes differed from statewide phenological trends. Accounting for synchronous annual variation and estimating trends over long time periods (e.g., >40 years) were critical for obtaining robust estimates of ice loss. The constant rates estimated here were consistent with recent global estimates (1.7-1.9 days per decade) and suggest that, even if present, accelerating rates of ice loss would be difficult to detect in the midst of shorter-term periods of warming and increasing variability.","language":"English","publisher":"Association for the Sciences of Limnology and Oceanography","doi":"10.1002/lol2.70015","usgsCitation":"Walsh, J., Rounds, C., Vitense, K., Masui, H., Blumenfeld, K., Boulay, P., Thomas, S., Honsey, A.E., Blinick, N.S., Rude, C.L., Bacon, J., LaRoque, A., Leao, T., and Hansen, G., 2025, Variable phenology but consistent loss of ice cover of 1213 Minnesota lakes: Limnology and Oceanography Letters, v. 10, no. 4, p. 506-515, https://doi.org/10.1002/lol2.70015.","productDescription":"10 p.","startPage":"506","endPage":"515","ipdsId":"IP-171385","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":484498,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":488209,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/lol2.70015","text":"Publisher Index Page"}],"country":"United 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,{"id":70265476,"text":"fs20253018 - 2025 - The 3D Elevation Program—Supporting Rhode Island’s economy","interactions":[],"lastModifiedDate":"2025-05-15T00:36:15.96651","indexId":"fs20253018","displayToPublicDate":"2025-04-08T14:25:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-3018","displayTitle":"The 3D Elevation Program—Supporting Rhode Island’s Economy","title":"The 3D Elevation Program—Supporting Rhode Island’s economy","docAbstract":"<h1>Introduction &nbsp;</h1><p>High-resolution elevation data are critical to applications of landscape modeling and planning, both of which have a significant effect on Rhode Island’s economy. In these and other enterprises, program managers, while aiming to strike a balance between accuracy and cost, strive to obtain the best available elevation data to help them address a range of issues. Programs focused on climate change, environmental management, transportation design and asset management, aviation navigation and safety, riverine ecosystem management, wildlife habitat characterization and management, shellfish aquaculture, and the management and mapping of forests, parks and recreation areas, soils, wetlands, and impervious surfaces are also among the critical applications that meet the State’s management needs and depend on light detection and ranging (lidar) data that provide a highly detailed three-dimensional (3D) model of the Earth’s surface and aboveground features.</p><p>The 3D Elevation Program (3DEP) is managed by the U.S. Geological Survey (USGS) in partnership with Federal, State, Tribal, U.S. territorial, and local agencies to acquire consistent lidar coverage at quality level 2 or better to meet the many needs of the Nation and Rhode Island. The status of available and in-progress 3DEP baseline lidar data in Rhode Island is shown in figure 1. 3DEP baseline lidar data include quality level 2 or better, 1-meter or better digital elevation models, and lidar point clouds, and must meet the Lidar Base Specification version 1.2 (<a href=\"https://www.usgs.gov/3dep/lidarspec\" data-mce-href=\"https://www.usgs.gov/3dep/lidarspec\">https://www.usgs.gov/3dep/lidarspec</a>) or newer requirements. The National Enhanced Elevation Assessment identified user requirements and conservatively estimated that availability of lidar data would result in at least $178,560 in new benefits annually to the State. The top 10 Rhode Island business uses for 3D elevation data, which are based on the estimated annual conservative benefits of 3DEP, are shown in table 2.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20253018","usgsCitation":"Walters, D., 2025, The 3D Elevation Program—Supporting Rhode Island’s economy: U.S. Geological Survey Fact Sheet 2025–3018, 2 p., https://doi.org/10.3133/fs20253018.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-146497","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":484263,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20253018/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2025-3018 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Island\",\"nation\":\"USA  \"}}]}","contact":"<p>Director, <a href=\"https://www.usgs.gov/programs/national-geospatial-program\" data-mce-href=\"https://www.usgs.gov/programs/national-geospatial-program\">National Geospatial Program</a><br>U.S. Geological Survey, MS 511<br>12201 Sunrise Valley Drive<br>Reston, VA 20192</p><p>Email: <a href=\"mailto:3DEP@usgs.gov\" data-mce-href=\"mailto:3DEP@usgs.gov\">3DEP@usgs.gov</a></p>","tableOfContents":"<ul><li>Introduction</li><li>Status of 3DEP in Rhode Island</li><li>Flood Risk Management</li><li>Coastal Zone Management</li><li>Infrastructure and Construction Management</li><li>Urban and Regional Planning</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2025-04-08","noUsgsAuthors":false,"publicationDate":"2025-04-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Walters, Dan","contributorId":291381,"corporation":false,"usgs":true,"family":"Walters","given":"Dan","email":"","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":932794,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70265675,"text":"70265675 - 2025 - Historical coast snaps: Using centennial imagery to track shoreline change","interactions":[],"lastModifiedDate":"2025-04-14T16:09:31.957547","indexId":"70265675","displayToPublicDate":"2025-04-08T11:04:17","publicationYear":"2025","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":"Historical coast snaps: Using centennial imagery to track shoreline change","docAbstract":"<p><span>Understanding long-term coastal evolution requires historical data, yet accessing reliable information becomes increasingly challenging for extended periods. While vertical aerial imagery has been extensively used in coastal studies since the mid-20th century, and satellite-derived shoreline measurements are now revolutionizing shoreline change studies, ground-based images, such as historical photographs and picture postcards, provide an alternative source of shoreline data for earlier periods when other datasets are scarce. Despite their frequent use for documenting qualitative morphological changes, these valuable historical data sources have rarely supported quantitative assessments of coastal evolution. This study demonstrates the potential of historical ground-oblique images for quantitatively assessing shoreline position and long-term change. Using Conceição-Duquesa Beach (Cascais, Portugal) as a case study, we analyze shoreline evolution over 92 years by applying a novel methodology to historical photographs and postcards. The approach combines image registration, shoreline detection, coordinate transformation, and rectification while accounting for positional uncertainty. Results reveal a significant counterclockwise rotation of the shoreline between the 20th and 21st centuries, exceeding estimated uncertainty thresholds. This study highlights the feasibility of using historical ground-based imagery to reconstruct shoreline positions and quantify long-term coastal change. The methodology is straightforward, adaptable, and offers a promising avenue for extending the temporal range of shoreline datasets, advancing our understanding of coastal evolution.</span></p>","language":"English","publisher":"MDPI","doi":"10.3390/rs17081326","usgsCitation":"Valverde, F., Taborda, R., East, A.E., and Ponte Lira, C., 2025, Historical coast snaps: Using centennial imagery to track shoreline change: Remote Sensing, v. p., no. 8, 1326, 25, https://doi.org/10.3390/rs17081326.","productDescription":"1326, 25","ipdsId":"IP-174741","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":488216,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs17081326","text":"Publisher Index Page"},{"id":484510,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Portugal","city":"Cascais","otherGeospatial":"Conceição-Duquesa urban beach","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -9.42387719442334,\n              38.70744120223975\n            ],\n            [\n              -9.42387719442334,\n              38.69407237227725\n            ],\n            [\n              -9.383850065079855,\n              38.69407237227725\n            ],\n            [\n              -9.383850065079855,\n              38.70744120223975\n            ],\n            [\n              -9.42387719442334,\n              38.70744120223975\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"p.","issue":"8","noUsgsAuthors":false,"publicationDate":"2025-04-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Valverde, Fatima","contributorId":353271,"corporation":false,"usgs":false,"family":"Valverde","given":"Fatima","affiliations":[{"id":38277,"text":"University of Lisbon","active":true,"usgs":false}],"preferred":false,"id":933235,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Taborda, Rui","contributorId":353272,"corporation":false,"usgs":false,"family":"Taborda","given":"Rui","affiliations":[{"id":38277,"text":"University of Lisbon","active":true,"usgs":false}],"preferred":false,"id":933236,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"East, Amy E. 0000-0002-9567-9460 aeast@usgs.gov","orcid":"https://orcid.org/0000-0002-9567-9460","contributorId":196364,"corporation":false,"usgs":true,"family":"East","given":"Amy","email":"aeast@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":933237,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ponte Lira, Cristina","contributorId":353273,"corporation":false,"usgs":false,"family":"Ponte Lira","given":"Cristina","affiliations":[{"id":38277,"text":"University of Lisbon","active":true,"usgs":false}],"preferred":false,"id":933238,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70265686,"text":"70265686 - 2025 - A nationwide evaluation of crowd-sourced ambient temperature data","interactions":[],"lastModifiedDate":"2025-04-14T16:38:37.174103","indexId":"70265686","displayToPublicDate":"2025-04-07T09:33:36","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1708,"text":"Frontiers of Environmental Science and Engineering in China","active":true,"publicationSubtype":{"id":10}},"title":"A nationwide evaluation of crowd-sourced ambient temperature data","docAbstract":"Growing concerns about heat in urban areas paired with the sparsity of weather stations have resulted in individuals drawing on data from citizen science sensor networks to fill in data gaps. The PurpleAir are the most widely-used low-cost air quality sensors in the contiguous United States with 14,777 deployed between 1 Jan 2017 and 20 July 2021. Although the air quality data from PurpleAir sensors have been widely studied, less attention has been paid to reported temperature. We compare temperature metrics reported by PurpleAir sensors with a gridded temperature product, the North American Land Data Assimilation System (NLDAS)-2, which although not a gold-standard measure of temperature, is widely used in epidemiologic research. We evaluate the lag between indoor and outdoor PurpleAir temperatures. We report associations of the difference between outdoor PurpleAir temperatures and NLDAS-2 temperatures, an indicator of degradation, and the duration of sensor operation. Finally, based on the temperature range recorded by the outdoor PurpleAir sensors vis.a.vis NLDAS-2 temperatures, we provide a list of 271 (2.5%) sensors potentially misclassified as outdoor and likely located indoors. We observed that outdoor PurpleAir sensors agreed well with NLDAS-2 (R2 > 0.82). This association broke down under warm conditions (daily average NLDAS ≥ 21.1oC). Landcover and climate zone were significant modifiers of the association between PurpleAir and NLDAS-2 temperature metrics with the poorest PurpleAir- NLDAS-2 agreement in urban areas. Indoor PurpleAir temperatures lagged hourly NLDAS temperatures by two hours across almost all climate zones. The mean difference in hourly PurpleAir and NLDAS-2 temperatures increased by 0.57oC for every operational year, suggesting that careful attention must be paid to degradation. Overall, we find for use in epidemiological studies, that the PurpleAir sensor data is a reliable metric of daytime mean air temperature, but researchers should be aware of its limitations when examining extreme heat, or when aggregating sensor data across multiple years.","language":"English","publisher":"Frontiers Media","doi":"10.3389/fenvs.2025.1527855","usgsCitation":"deSouza, P., Ibsen, P.C., Westervelt, D., Kahn, R., Zaitchik, B., and Kinney, P., 2025, A nationwide evaluation of crowd-sourced ambient temperature data: Frontiers of Environmental Science and Engineering in China, v. 13, 1527855, 13 p., https://doi.org/10.3389/fenvs.2025.1527855.","productDescription":"1527855, 13 p.","ipdsId":"IP-171526","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":490097,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fenvs.2025.1527855","text":"Publisher Index 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