Based on observation and data from meteorites and in situ scientific missions, experiments as well as models, the Martian mantle is assumed to share some compositional and mineralogical affinity with the terrestrial mantle. However, there might be subtle differences like the Martian mantle being more ferroan. Yet, we do not have any direct analysis of a Martian mantle rock to confirm this assumption. NASA’s Perseverance rover found olivine-rich boulder-sized float rocks on the upper Jezero fan (Mars). These boulders have an ultramafic composition and their mineralogy is dominantly composed of Fo73±3 olivine with high-Mg orthopyroxene, Cr-rich Ti-Fe oxides and minor plagioclase and high-Ca pyroxene. Microtextural and petrological analysis reveals that these minerals crystallized at equilibrium. In addition, these boulders are different from all the bedrocks analyzed by Perseverance along its traverse which are crustal igneous rocks and sediments. Comparing our data to Martian meteorites and available Mars bulk silicate models (BSM), we discuss that these boulders could represent primitive melts and/or lower crustal material, and we specifically hypothesize that they could be mantle peridotites. We propose that these putative mantle rocks could have been excavated by the succession of impacts from the shallow mantle or lower crust in the Isidis region where Jezero crater is located. These olivine-rich boulders could thereby constitute the first direct analysis of a Martian mantle rock.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2025.119746","usgsCitation":"Beyssac, O., Clave, E., Forni, O., Udry, A., Pascuzzo, A., Dehouck, E., Beck, P., Mandon, L., Quantin-Nataf, C., Mangold, N., Lopez-Reyes, G., Royer, C., Gasnault, O., Gabriel, T.S., Kah, L., Schroder, S., Johnson, J., Bertrand, T., Chide, B., Fouchet, T., Simon, J., Montmessin, F., Fau, A., Maurice, S., Wiens, R., and Cousin, A., 2025, Ultramafic float rocks at Jezero crater (Mars): Excavation of lower crustal rocks or mantle peridotites by impact cratering?: Earth and Planetary Science Letters, v. 675, 119746, 14 p., https://doi.org/10.1016/j.epsl.2025.119746.","productDescription":"119746, 14 p.","ipdsId":"IP-182722","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":497407,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.epsl.2025.119746","text":"Publisher Index Page"},{"id":497275,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"675","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Beyssac, O.","contributorId":290034,"corporation":false,"usgs":false,"family":"Beyssac","given":"O.","affiliations":[{"id":62313,"text":"Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, CNRS, Sorbonne Université","active":true,"usgs":false}],"preferred":false,"id":951826,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Clave, E.","contributorId":296842,"corporation":false,"usgs":false,"family":"Clave","given":"E.","affiliations":[{"id":64188,"text":"Planetary Exploration Team, Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":951827,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Forni, O.","contributorId":290037,"corporation":false,"usgs":false,"family":"Forni","given":"O.","affiliations":[{"id":62314,"text":"Institut de Recherche en Astrophysique et Planétologie, Université de Toulouse","active":true,"usgs":false}],"preferred":false,"id":951828,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Udry, A.","contributorId":290128,"corporation":false,"usgs":false,"family":"Udry","given":"A.","affiliations":[],"preferred":false,"id":951829,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pascuzzo, A.C.","contributorId":363588,"corporation":false,"usgs":false,"family":"Pascuzzo","given":"A.C.","affiliations":[{"id":86727,"text":"Department of Earth, Environmental, Planetary Science, Brown University","active":true,"usgs":false}],"preferred":false,"id":951830,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dehouck, E.","contributorId":290073,"corporation":false,"usgs":false,"family":"Dehouck","given":"E.","affiliations":[{"id":62330,"text":"Univ. 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Wake County, in central North Carolina, is experiencing rapid population growth, associated land development, and changing water use. Hydrogeologic data including groundwater levels, aquifer testing, borehole fracture flow measurements, water-quality samples, and groundwater age-dating tracers were collected, along with findings from previous investigations, to help inform a conceptual model of the flow system used to develop a modular three-dimensional finite-difference groundwater-flow model (MODFLOW) for simulating historical and future groundwater conditions from 2000 to 2070.
Hydraulic conductivity and transmissivity ranges were estimated from 17 slug tests and 21 borehole-flow measurements. Groundwater-quality analytical results from 19 sampling sites indicate that oxidation-reduction (redox) conditions varied within the regolith and bedrock and that minimal evaporation occurred before recharge entered the groundwater system. Age dating revealed mixtures of older and younger water, ranging from the 1940s to the 1990s—indicating variable flow pathways of recharge within permeable bedrock fracture zones.
To simplify the complex fractured-rock groundwater system, two layers representing the regolith and the fractured bedrock were used in the MODFLOW model. Model calibration included parameter estimation and provided a reasonable fit to observed groundwater levels and estimated stream base flows. The model forecast scenarios incorporated future climate-model data for two emissions scenarios with land cover change projections to simulate potential impacts to future groundwater levels, recharge, and base flows. Recharge and base flow projections were largely within historical ranges, with no apparent long-term trends, but did indicate a slight downward shift in median values—likely, in part, because of differences in spatial resolution of input climate datasets. Seasonal patterns were consistent with historical data, with projections of possible increases in future winter recharge. Model limitations are discussed, and additional monitoring and model refinement needs are highlighted to support decision making for local groundwater management.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255087","issn":"2328-0328","collaboration":"Prepared in cooperation with Wake County Environmental Services","usgsCitation":"Antolino, D.J., Gonthier, G.J., and Sanchez, G.M., 2025, Simulation of groundwater flow in Wake County, North Carolina, 2000 through 2070: U.S. Geological Survey Scientific Investigations Report 2025–5087, 77 p., https://doi.org/10.3133/sir20255087.","productDescription":"Report: xii, 77 p.; 2 Data Releases","numberOfPages":"94","onlineOnly":"Y","ipdsId":"IP-141136","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":497806,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119050.htm"},{"id":496949,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5087/coverthb.jpg"},{"id":497076,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255087/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5087 HTML"},{"id":497075,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5087/sir20255087.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2025-5087 XML"},{"id":496956,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UC8F3Z","text":"USGS Data Release","linkHelpText":"- Water-level data and results for slug tests performed in 17 wells in Wake County, North Carolina, 2020 and 2021"},{"id":496955,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9N3EQ86","text":"USGS Data Release","linkHelpText":"- MODFLOW-NWT model used to simulate groundwater flow in Wake County, North Carolina, 2000 through 2070"},{"id":496950,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5087/sir20255087.pdf","size":"21.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5087 PDF"},{"id":496958,"rank":2,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5087/images"}],"country":"United States","state":"North Carolina","county":"Wake County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-78.5465,36.0218],[-78.4307,35.9795],[-78.3969,35.9387],[-78.3567,35.9318],[-78.351,35.909],[-78.3385,35.9052],[-78.3347,35.8997],[-78.3302,35.896],[-78.3245,35.896],[-78.3177,35.8963],[-78.3137,35.8976],[-78.3081,35.8935],[-78.2948,35.8797],[-78.292,35.8792],[-78.2893,35.8741],[-78.2859,35.8713],[-78.2831,35.8681],[-78.2782,35.8631],[-78.2749,35.8567],[-78.2756,35.8494],[-78.2707,35.843],[-78.2657,35.8361],[-78.2652,35.8325],[-78.2613,35.8315],[-78.2591,35.826],[-78.2599,35.8183],[-78.3731,35.7523],[-78.4635,35.7072],[-78.4686,35.7087],[-78.4709,35.7078],[-78.4732,35.7046],[-78.4778,35.7011],[-78.5716,35.6255],[-78.708,35.5191],[-78.9196,35.5857],[-78.9956,35.6104],[-78.9796,35.6656],[-78.9439,35.7515],[-78.9421,35.756],[-78.9403,35.7615],[-78.9337,35.7859],[-78.9191,35.8216],[-78.9096,35.8506],[-78.9076,35.8678],[-78.89,35.8676],[-78.8298,35.8689],[-78.8056,35.9281],[-78.7609,35.9176],[-78.751,35.9307],[-78.7372,35.941],[-78.714,35.9729],[-78.7009,36.0068],[-78.6985,36.0131],[-78.7048,36.0091],[-78.7077,36.0087],[-78.7076,36.0132],[-78.7052,36.0223],[-78.7085,36.0287],[-78.7102,36.0287],[-78.713,36.0278],[-78.7164,36.0283],[-78.7232,36.0334],[-78.726,36.0343],[-78.7272,36.0334],[-78.7278,36.0289],[-78.7324,36.0267],[-78.7353,36.0199],[-78.7422,36.0209],[-78.75,36.026],[-78.7551,36.0283],[-78.7545,36.0301],[-78.7511,36.0323],[-78.7499,36.035],[-78.747,36.0395],[-78.7492,36.0427],[-78.7503,36.0468],[-78.7519,36.0491],[-78.7564,36.0532],[-78.7498,36.0718],[-78.7088,36.0768],[-78.6895,36.0752],[-78.5922,36.0378],[-78.5465,36.0218]]]},\"properties\":{\"name\":\"Wake\",\"state\":\"NC\"}}]}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
1770 Corporate Drive, suite 500
Norcross, GA 30093
Contact Us- USGS Publications Warehouse
","tableOfContents":"Using a geology-based assessment methodology, the U.S. Geological Survey estimated undiscovered, technically recoverable mean conventional and continuous resources of 4.7 trillion cubic feet of gas in the Mesaverde Group and Lance Formation in the Southwestern Wyoming Province, Wyoming, Utah, and Colorado.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/fs20253048","programNote":"National and Global Petroleum Assessment","usgsCitation":"Lagesse, J.H., Schenk, C.J., Hearon, J.S., Gelman, S.E., Finn, T.M., Johnson, B.G., Mercier, T.J., Le, P.A., Leathers-Miller, H.M., Cicero, A.D., and Drake, R.M., II, 2025, Assessment of undiscovered conventional and continuous gas resources in the Mesaverde Group and Lance Formation in the Southwestern Wyoming Province, Wyoming, Utah, and Colorado, 2025: U.S. Geological Survey Fact Sheet 2025–3048, 4 p., https://doi.org/10.3133/fs20253048.","productDescription":"Report: 4 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-178182","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":497804,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119049.htm"},{"id":497024,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20253048/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2025-3048"},{"id":497020,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2025/3048/fs20253048.xml"},{"id":497019,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2025/3048/images"},{"id":496957,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1NYUDGF","text":"USGS data release","linkHelpText":"USGS National and Global Oil and Gas Assessment Project-Southwestern Wyoming Province, Mesaverde Group and Lance Formation Conventional and Continuous Assessment Unit Boundaries, Assessment Input Data, and Fact Sheet Data Tables"},{"id":496954,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2025/3048/fs20253048.pdf","text":"Report","size":"10.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2025-3048"},{"id":496953,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2025/3048/coverthb.jpg"}],"country":"United States","state":"Colorado, Utah, Wyoming","otherGeospatial":"Southwestern Wyoming Province","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111,\n 43.5\n ],\n [\n -111,\n 39.75\n ],\n [\n -106,\n 39.75\n ],\n [\n -106,\n 43.5\n ],\n [\n -111,\n 43.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
Habitat degradation has been associated with the loss of many self-sustaining Muskellunge Esox masquinongy populations, including those in Green Bay, where stocking has provided an exceptional trophy fishery but restoration goals include establishing self-sustaining populations and there is little evidence of natural recruitment. Our objectives were to determine whether (1) Muskellunge spawning locations and occurrence of successful hatching were related to a suite of habitat characteristics, (2) proportions of Muskellunge spawning in or outside of tributaries to lower Green Bay were different, and (3) Muskellunge showed spawning site fidelity.
From 2017 to 2019, adult Muskellunge (N = 60) were surgically implanted with radio and acoustic transmitters to identify spawning locations, where we measured a suite of habitat variables and attempted to collect eggs and larvae. Side-scan sonar was used to quantify the amount of habitat available to Muskellunge for egg deposition in the Fox and Menominee rivers, which are tributaries to Green Bay.
Muskellunge eggs were collected at 58 locations, but only two larvae were collected from a single location. Bottom slope, depth, distance to shore, gravel substrate, organic matter, and dissolved oxygen best predicted the presence of Muskellunge eggs. We determined that little habitat associated with Muskellunge egg deposition was available in the Fox and Menominee rivers. However, approximately half of tagged Muskellunge appeared to spawn outside of tributaries. Muskellunge in Green Bay displayed moderate spawning site fidelity.
Our results suggest that successful hatching occurs at very low levels and the lack of suitable Muskellunge spawning habitat in Green Bay tributaries may be limiting natural reproduction. Changes in spatial allocation of stocked fish and enhancement of known spawning locations may increase egg deposition and subsequent natural reproduction.
Bank erosion in Arctic rivers helps shape channel geometry, mobilizes carbon from permafrost and influences sediment delivery to the Arctic Ocean. On Alaska's Arctic coastal plain, rivers begin flowing during snowmelt in late spring while extensive river ice persists in channels, such that hydraulics are altered and water is kept cool. The effects of river ice on permafrost bank erosion are poorly understood, primarily due to a dearth of field observations and a lack of river ice in existing models.
To address this knowledge gap, we developed a numerical model to simulate the melt of substrate interstitial ice and bank collapse along individual permafrost river banks. We parameterize the model with field observations from riverbanks in three different channels on the Canning River delta, which are disparately impacted by river ice during snowmelt. We explore the bank erosion produced without river ice in the model and with modern river ice model scenarios that we drive with different stages and water temperature boundary conditions. We also compare predicted erosion rates to observations from satellite imagery to validate this approach.
In the model, banks are idealized as vertical profiles that rise 1–2 m above the river bed and are comprised of silt- to sand-sized sediment with dense roots in the active layer. Underneath, we generalize bank ice content underneath the active layer to represent ice-rich permafrost on the river corridor boundaries. The model predicts that these ice-rich river banks can erode by 2–6 m/yr. Scenarios without ice underpredict erosion in the distributary channels. Scenarios with varying river ice for different deltaic channels produce erosion rates similar to observations.
Our results suggest that the prolonged melt of thick river ice in a delta nonlinearly impacts permafrost bank erosion by blocking river discharge to certain branches, heightening stage across the distributary network and locally limiting river water warming. Given expected changes in air temperature and hydrology, future estimates of Arctic river bank erosion could be improved by considering river ice.
","language":"English","publisher":"Wiley","doi":"10.1002/esp.70189","usgsCitation":"Arcuri, J., Overeem, I., Repasch, M., Anderson, R.S., Anderson, S.P., Koch, J.C., and Urban, F., 2025, River ice controls permafrost bank erosion across an Arctic delta: Earth Surface Processes and Landforms, v. 50, no. 15, e70189, 16 p., https://doi.org/10.1002/esp.70189.","productDescription":"e70189, 16 p.","ipdsId":"IP-179882","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":497140,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Canning River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -144.96629304307663,\n 70.01100341463973\n ],\n [\n -146.12712314576996,\n 70.21006797383902\n ],\n [\n -146.58932980728264,\n 69.87830435250464\n ],\n [\n -146.27585557894227,\n 68.96347382420646\n ],\n [\n -145.52140711630287,\n 68.61915114052712\n ],\n [\n -144.96629304307663,\n 70.01100341463973\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"50","issue":"15","noUsgsAuthors":false,"publicationDate":"2025-12-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Arcuri, J","contributorId":363264,"corporation":false,"usgs":false,"family":"Arcuri","given":"J","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":951392,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Overeem, Irina","contributorId":197487,"corporation":false,"usgs":false,"family":"Overeem","given":"Irina","email":"","affiliations":[],"preferred":false,"id":951393,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Repasch, Marisa 0000-0003-2636-9896","orcid":"https://orcid.org/0000-0003-2636-9896","contributorId":334190,"corporation":false,"usgs":false,"family":"Repasch","given":"Marisa","email":"","affiliations":[],"preferred":false,"id":951394,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anderson, R. S.","contributorId":269710,"corporation":false,"usgs":false,"family":"Anderson","given":"R.","middleInitial":"S.","affiliations":[],"preferred":false,"id":951395,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Anderson, S. P.","contributorId":363265,"corporation":false,"usgs":false,"family":"Anderson","given":"S.","middleInitial":"P.","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":951396,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Koch, Joshua C. 0000-0001-7180-6982 jkoch@usgs.gov","orcid":"https://orcid.org/0000-0001-7180-6982","contributorId":202532,"corporation":false,"usgs":true,"family":"Koch","given":"Joshua","email":"jkoch@usgs.gov","middleInitial":"C.","affiliations":[{"id":120,"text":"Alaska Science Center Water","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":951397,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Urban, Frank 0000-0002-1329-1703 furban@usgs.gov","orcid":"https://orcid.org/0000-0002-1329-1703","contributorId":127827,"corporation":false,"usgs":true,"family":"Urban","given":"Frank","email":"furban@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":951398,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70272673,"text":"70272673 - 2025 - Present and future coastal flooding hazard for Long Island, NY and Long Island Sound (NY/CT), USA","interactions":[],"lastModifiedDate":"2025-12-03T16:09:30.659734","indexId":"70272673","displayToPublicDate":"2025-12-02T10:03:43","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":"Present and future coastal flooding hazard for Long Island, NY and Long Island Sound (NY/CT), USA","docAbstract":"Coastal flooding and the associated damages due to storms are increasing with sea level rise around the world, with regional variability in the severity of impacts., Researchers and resource managers need to better understand and predict the future shifts in coastal flooding due to these processes to plan for resilient and sustainable communities. Here we present an analysis of long-term historical records of water levels, tides, and modeled present-day wave climatologies, to characterize the present-day inundation extent in Long Island Sound and Long Island, NY. To understand the potential future changes in inundation extent, we provide a similar analysis of future climate projections of non-tidal residuals (storm surge) for the year 2050 and compare these projections with our present-day results. We examine both the magnitude of relatively frequent events with a 0.99 annual exceedance probability to more extreme events with a 0.01 annual exceedance probability (or the 1 in 100-year event). This range of events is relevant for local managers to understand the spatial variability in coastal inundation, in addition to planning for larger more catastrophic events.
","language":"English","publisher":"EarthArXiv","doi":"10.31223/X5117Q","usgsCitation":"Cook, S.E., and Herdman, L.M., 2025, Present and future coastal flooding hazard for Long Island, NY and Long Island Sound (NY/CT), USA: EarthArXiv, https://doi.org/10.31223/X5117Q.","productDescription":"39 p.","ipdsId":"IP-170004","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":497010,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Cook, Salme Ellen 0000-0003-1129-6209","orcid":"https://orcid.org/0000-0003-1129-6209","contributorId":303775,"corporation":false,"usgs":true,"family":"Cook","given":"Salme","email":"","middleInitial":"Ellen","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":951281,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Herdman, Liv M. 0000-0002-5444-6441 lherdman@usgs.gov","orcid":"https://orcid.org/0000-0002-5444-6441","contributorId":149964,"corporation":false,"usgs":true,"family":"Herdman","given":"Liv","email":"lherdman@usgs.gov","middleInitial":"M.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":951282,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70272733,"text":"70272733 - 2025 - Projecting management-relevant change of undeveloped coastal barriers with the Mesoscale Explicit Ecogeomorphic Barrier model (MEEB) v1.0","interactions":[],"lastModifiedDate":"2025-12-05T16:02:59.966789","indexId":"70272733","displayToPublicDate":"2025-12-02T09:59:19","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1818,"text":"Geoscientific Model Development","active":true,"publicationSubtype":{"id":10}},"title":"Projecting management-relevant change of undeveloped coastal barriers with the Mesoscale Explicit Ecogeomorphic Barrier model (MEEB) v1.0","docAbstract":"Models of coastal barrier geomorphic and ecologic change are valuable tools for understanding and predicting when, where, and how barriers evolve and transition between ecogeomorphic states. Few existing models of barrier systems are designed to operate over spatiotemporal scales congruous with effective management practices (i.e., decades/kilometers, referred to herein as “mesoscales”), incorporate important ecogeomorphic feedbacks, and provide probabilistic projections of future change. Here, we present a new numerical model designed to address these gaps by explicitly yet efficiently simulating coupled aeolian, marine, vegetation, and shoreline components of barrier evolution over spatiotemporal scales relevant to management. The Mesoscale Explicit Ecogeomorphic Barrier model (MEEB) simulates subaerial ecomorphologic change of undeveloped barrier systems over kilometers and decades using meter-scale spatial resolution and weekly time steps. MEEB applies simplified parameterizations to represent and couple key ecogeomorphic processes: dune growth, vegetation expansion and mortality, beach and foredune erosion, barrier overwash, and shoreline and shoreface change. The model is parameterized and calibrated with observed elevation, vegetation, and water level data for a case study site of North Core Banks, NC, USA. Simulated ecogeomorphic change in model hindcasts agrees well with observations, demonstrating both favorable skill scores and qualitatively correct behavior. We also describe an additional model framework for producing probabilistic projections that account for uncertainties related to future forcing conditions and intrinsic stochastic dynamics and demonstrate the probabilistic framework's utility with example forecast simulations. As a mesoscale model, MEEB is designed to investigate questions about future barrier ecogeomorphic change of moderate complexity, offering semi-qualitative predictions and semi-quantitative explanations. For example, MEEB can be used to investigate how climate-induced shifts in ecological composition may alter the likelihood of morphologic impacts or to generate probabilistic projections of ecogeomorphic state change.
","language":"English","publisher":"European Geosciences Union","doi":"10.5194/gmd-18-9319-2025","usgsCitation":"Reeves, I.R., Ashton, A.D., Lentz, E.E., Sherwood, C.R., Passeri, D., and Zeigler, S., 2025, Projecting management-relevant change of undeveloped coastal barriers with the Mesoscale Explicit Ecogeomorphic Barrier model (MEEB) v1.0: Geoscientific Model Development, v. 18, p. 9319-9348, https://doi.org/10.5194/gmd-18-9319-2025.","productDescription":"30 p.","startPage":"9319","endPage":"9348","ipdsId":"IP-170312","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":497392,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/gmd-18-9319-2025","text":"Publisher Index Page"},{"id":497142,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"18","noUsgsAuthors":false,"publicationDate":"2025-12-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Reeves, Ian Robert 0000-0002-6675-3756","orcid":"https://orcid.org/0000-0002-6675-3756","contributorId":363346,"corporation":false,"usgs":true,"family":"Reeves","given":"Ian","middleInitial":"Robert","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":951466,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ashton, Andrew D. 0000-0002-0241-3090","orcid":"https://orcid.org/0000-0002-0241-3090","contributorId":363347,"corporation":false,"usgs":false,"family":"Ashton","given":"Andrew","middleInitial":"D.","affiliations":[{"id":36711,"text":"Woods Hole Oceanographic Institution","active":true,"usgs":false}],"preferred":false,"id":951467,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lentz, Erika E. 0000-0002-0621-8954 elentz@usgs.gov","orcid":"https://orcid.org/0000-0002-0621-8954","contributorId":173964,"corporation":false,"usgs":true,"family":"Lentz","given":"Erika","email":"elentz@usgs.gov","middleInitial":"E.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":951468,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sherwood, Christopher R. 0000-0001-6135-3553 csherwood@usgs.gov","orcid":"https://orcid.org/0000-0001-6135-3553","contributorId":2866,"corporation":false,"usgs":true,"family":"Sherwood","given":"Christopher","email":"csherwood@usgs.gov","middleInitial":"R.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":951469,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Passeri, Davina 0000-0002-9760-3195 dpasseri@usgs.gov","orcid":"https://orcid.org/0000-0002-9760-3195","contributorId":166889,"corporation":false,"usgs":true,"family":"Passeri","given":"Davina","email":"dpasseri@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":951470,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zeigler, Sara 0000-0002-5472-769X","orcid":"https://orcid.org/0000-0002-5472-769X","contributorId":222703,"corporation":false,"usgs":true,"family":"Zeigler","given":"Sara","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":951471,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70274167,"text":"70274167 - 2025 - Day versus night relations between larval lake whitefish, cisco, and zooplankton onshore in Lakes Michigan, Huron, and Superior","interactions":[],"lastModifiedDate":"2026-03-03T15:01:45.882417","indexId":"70274167","displayToPublicDate":"2025-12-02T08:54:19","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Day versus night relations between larval lake whitefish, cisco, and zooplankton onshore in Lakes Michigan, Huron, and Superior","docAbstract":"Lake whitefish (Coregonus clupeaformis) populations in the upper Great Lakes have undergone declines in the past two decades, particularly in Lakes Michigan and Huron. However, cisco (Coregonus artedi) are recovering in parts of the Great Lakes. Population declines are hypothesized to be due, in part, to reduced zooplankton prey in areas that serve as critical habitat for larval coregonines. Larval lake whitefish, cisco, and zooplankton are commonly sampled only during daylight hours. Habitat use, community composition, catch rates, and abundance estimates of larval fish and zooplankton can change drastically at night versus day, necessitating diel comparisons for a more comprehensive understanding of the early life history of coregonines and their prey. We collected paired day and night onshore (≤ 1 m depth) zooplankton and larval coregonine samples from Lakes Michigan, Huron, and Superior in March–June 2021 to test if there were diel differences in lake whitefish and cisco abundance and zooplankton density and biomass. We also tested if relationships exist between larval coregonine abundance and zooplankton density and biomass and environmental variables (water temperature, dissolved oxygen concentration, pH, specific conductivity, substrate type). We observed consistently higher zooplankton density and biomass and larval lake whitefish and cisco abundance at night. Larval coregonine abundance was positively related to higher zooplankton population estimates but was not related to the environmental variables measured. Our results provide insight into sampling practices for larval lake whitefish, cisco, and zooplankton onshore in the Great Lakes to better understand factors influencing larval lake whitefish and cisco recruitment.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2025.102668","usgsCitation":"Freemon, S.D., Smith, J.B., Ackiss, A.S., Anweiler, K.V., Freeman, H.N., Hessell, C.R., Jonas, J., LaFaver, C.J., Olsen, E.J., and Doubek, J.P., 2025, Day versus night relations between larval lake whitefish, cisco, and zooplankton onshore in Lakes Michigan, Huron, and Superior: Journal of Great Lakes Research, v. 51, no. 6, 102668, 10 p., https://doi.org/10.1016/j.jglr.2025.102668.","productDescription":"102668, 10 p.","ipdsId":"IP-170585","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":500724,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan","otherGeospatial":"Lake Huron, Lake Michigan, Lake Superior","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -86,\n 47\n ],\n [\n -86,\n 44.8\n ],\n [\n -83,\n 44.8\n ],\n [\n -83,\n 47\n ],\n [\n -86,\n 47\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"51","issue":"6","noUsgsAuthors":false,"publicationDate":"2025-12-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Freemon, Simon D.D.","contributorId":367098,"corporation":false,"usgs":false,"family":"Freemon","given":"Simon","middleInitial":"D.D.","affiliations":[{"id":35243,"text":"Lake Superior State University","active":true,"usgs":false}],"preferred":false,"id":956752,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Jason B.","contributorId":367099,"corporation":false,"usgs":false,"family":"Smith","given":"Jason","middleInitial":"B.","affiliations":[{"id":79162,"text":"Sault Ste. 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To accurately estimate some of these rates, the timing of the first daily ring must be estimated accurately. Variation in the timing of the first daily ring can be attributed to many factors, including biology of the species and experience of laboratory personnel. The amount of variation and the degree of differences, however, have not been quantified, hindering the utility of daily ring information to provide accurate estimates of spawning and hatching times. We conducted a review of studies for freshwater fishes in the continental United States to quantify variation in daily ring validation studies as it relates to timing of the first ring. We found 40 studies representing 12 orders, 15 families, and 35 species. Most studies investigated rings in the sagittae, although the lapilli and asterisci were also used for a few species. Variation in the timing of the first ring formation was evident, but not consistent among otolith types or groups of fishes. The first daily ring in sagittae varied from 31 d before hatch to 150 d after hatch. First daily ring formation in lapilli was consistent within families but formed before hatch in some families of fish and after hatch in other families. The first daily ring in asterisci were near universally formed after hatch, with the exception of one species of sturgeon (family Acipenseridae). Only three of the nine species where replicate studies existed were found to exhibit consistent first ring formation timing. Such findings suggest that differences among laboratories and personnel may play a larger role than differences among species or populations when inconsistent first ring formation timing results occur. For most species, error surrounding differences in timing formation is about 1 week, except for Salmoniformes, where error was up to a 150-d difference. Incorporating species biology along with uncertainty in temporal estimates based on otolith chronology would aid interpretation of results in field situations.
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Conservation","active":true,"usgs":false}],"preferred":false,"id":951797,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70272985,"text":"70272985 - 2025 - Review and synthesis of the applications of machine learning to coalbed methane recovery","interactions":[],"lastModifiedDate":"2025-12-12T14:56:51.475904","indexId":"70272985","displayToPublicDate":"2025-12-02T08:46:30","publicationYear":"2025","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Review and synthesis of the applications of machine learning to coalbed methane recovery","docAbstract":"Over the last 30 years, a substantial literature has evolved on the use of machine learning (ML) to assess, predict, and improve the efficiency of coalbed methane (CBM) recovery. In the United States, the production of CBM declined as shale gas production matured, but CBM continues to be an important energy resource in other parts of the world. ML applications that have the potential to improve CBM reservoir management and production forecasts, and to increase exploration and operational efficiency, are still of significant interest. The integration of geostatistical techniques into the CBM ML applications has been largely absent but represents an opportunity for improvement. The literature demonstrates the widespread interest in, and applicability of, ML algorithms applied to CBM problems, and that they continue to result in improvements in predictive performance. However, (1) much of the research is more academic than operational, (2) many results are based on simulations, or small or proprietary datasets, (3) ML performance information can be inconsistent and sometimes entirely omitted, (4) most methodologies are unique to the specific CBM situation and likely not generalizable, (5) no standard data repositories are available to directly compare the performance of competing algorithms, and (6) the spatial component is often omitted. Finally, relatively new ML protocols involving causality analysis and reinforced learning, as well as hybrid workflows combining both supervised and unsupervised learning, are anticipated to dominate the future investigations. Integration of geostatistical and geospatial analysis with ML should enhance performance.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Applied spatiotemporal data analytics and machine learning","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"intechOpen Limited","doi":"10.5772/intechopen.115671","usgsCitation":"Attanasi, E., Coburn, T., and Freeman, P., 2025, Review and synthesis of the applications of machine learning to coalbed methane recovery, chap. of Applied spatiotemporal data analytics and machine learning, https://doi.org/10.5772/intechopen.115671.","ipdsId":"IP-175372","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":497696,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5772/intechopen.115671","text":"Publisher Index Page"},{"id":497462,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"Online First","noUsgsAuthors":false,"publicationDate":"2025-12-02","publicationStatus":"PW","contributors":{"editors":[{"text":"Maucec, Marko","contributorId":364151,"corporation":false,"usgs":false,"family":"Maucec","given":"Marko","affiliations":[],"preferred":false,"id":952262,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Yarus, Jeffrey M.","contributorId":364152,"corporation":false,"usgs":false,"family":"Yarus","given":"Jeffrey","middleInitial":"M.","affiliations":[],"preferred":false,"id":952263,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Coburn, Timothy C.","contributorId":26011,"corporation":false,"usgs":true,"family":"Coburn","given":"Timothy","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":952264,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Pyrcz, Michael","contributorId":364153,"corporation":false,"usgs":false,"family":"Pyrcz","given":"Michael","affiliations":[],"preferred":false,"id":952265,"contributorType":{"id":2,"text":"Editors"},"rank":4}],"authors":[{"text":"Attanasi, Emil 0000-0001-6845-7160 attanasi@usgs.gov","orcid":"https://orcid.org/0000-0001-6845-7160","contributorId":1809,"corporation":false,"usgs":true,"family":"Attanasi","given":"Emil","email":"attanasi@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":952037,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coburn, Timothy","contributorId":245358,"corporation":false,"usgs":false,"family":"Coburn","given":"Timothy","affiliations":[],"preferred":false,"id":952038,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Freeman, Philip A. 0000-0002-0863-7431","orcid":"https://orcid.org/0000-0002-0863-7431","contributorId":347358,"corporation":false,"usgs":false,"family":"Freeman","given":"Philip A.","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":952039,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70274180,"text":"70274180 - 2025 - Aeromagnetic and magnetotelluric imaging of west-central Idaho and the Stibnite-Yellow Pine mining district: A regional to district perspective","interactions":[],"lastModifiedDate":"2026-03-04T22:38:45.947857","indexId":"70274180","displayToPublicDate":"2025-12-01T15:30:48","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1472,"text":"Economic Geology","active":true,"publicationSubtype":{"id":10}},"title":"Aeromagnetic and magnetotelluric imaging of west-central Idaho and the Stibnite-Yellow Pine mining district: A regional to district perspective","docAbstract":"Aeromagnetic and magnetotelluric (MT) data are used to better understand the geology and mineral resources near the Stibnite-Yellow Pine mining district in central Idaho. The reduced-to-pole (RTP) transformation of regional-scale aeromagnetic data shows that allochthonous island-arc rocks west of the Salmon River suture are significantly more magnetic than the Laurentian continental rocks east of the suture and that the granitoids of the Idaho batholith have moderate to low magnetization in both early, metaluminous, and late, peraluminous phases. Application of tilt derivative to aeromagnetic data highlights major crustal-scale structures. The 5-km upward continued magnetic data indicate island-arc rocks have deep magnetic sources. The 110-km-long MT profile images resistivity structure to depths around 30 km. At shallow depths, resistivity corresponds to mapped geologic units, with moderate resistivities underlying volcanic and roof-pendant metasedimentary rocks and moderate to high resistivities occurring beneath the Idaho batholith. Crustal-scale moderate resistivities beneath the suture image the results of tectonomagmatic processes that accompanied suturing and translating allochthonous terranes. Low resistivity values beneath and fringing the batholith are derived from metasedimentary rocks that may have served as a melt source and reductant during melt generation and provided metals during later ore formation.
In the Stibnite-Yellow Pine mining district, a high-resolution aeromagnetic compilation is shown to correlate with mapped lithologies and mineral deposit-related structures. The RTP transform distinguishes magnetic and nonmagnetic granitoid phases of the Idaho batholith. The tilt derivative highlights metasedimentary rocks, some of which are favorable ore hosts. The Meadow Creek fault hosts the Stibnite and Hangar Flats deposits and is imaged as a magnetic low due to hydrothermal alteration. Reconstructions of magnetic anomaly offsets and orebodies indicate around 3 km of post-95 Ma dextral separation, with some or all of the offset inferred to postdate the main Au mineralization episode (61–66 Ma).
","language":"English","publisher":"GeoScienceWorld","doi":"10.5382/econgeo.5182","usgsCitation":"Anderson, E., Rodriguez, B.D., Lund, K., Dail, C., and Breen, B., 2025, Aeromagnetic and magnetotelluric imaging of west-central Idaho and the Stibnite-Yellow Pine mining district: A regional to district perspective: Economic Geology, v. 120, no. 8, p. 1899-1923, https://doi.org/10.5382/econgeo.5182.","productDescription":"26 p.","startPage":"1899","endPage":"1923","ipdsId":"IP-114615","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":500851,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5382/econgeo.5182","text":"Publisher Index Page"},{"id":500769,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"west-central Idaho","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -115.63626406020117,\n 44.307766155511956\n ],\n [\n -115.63626406020117,\n 43.87879267849277\n ],\n [\n -114.41828968668513,\n 43.87879267849277\n ],\n [\n -114.41828968668513,\n 44.307766155511956\n ],\n [\n -115.63626406020117,\n 44.307766155511956\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"120","issue":"8","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Anderson, Eric D. 0000-0002-0138-6166","orcid":"https://orcid.org/0000-0002-0138-6166","contributorId":202072,"corporation":false,"usgs":true,"family":"Anderson","given":"Eric D.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":956794,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rodriguez, Brian D. 0000-0002-2263-611X brod@usgs.gov","orcid":"https://orcid.org/0000-0002-2263-611X","contributorId":836,"corporation":false,"usgs":true,"family":"Rodriguez","given":"Brian","email":"brod@usgs.gov","middleInitial":"D.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":956795,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lund, Karen 0000-0002-4249-3582 klund@usgs.gov","orcid":"https://orcid.org/0000-0002-4249-3582","contributorId":1235,"corporation":false,"usgs":true,"family":"Lund","given":"Karen","email":"klund@usgs.gov","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":956796,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dail, Christopher","contributorId":367119,"corporation":false,"usgs":false,"family":"Dail","given":"Christopher","affiliations":[{"id":87550,"text":"Midas Gold Idaho, Donnelly, ID 83615","active":true,"usgs":false}],"preferred":false,"id":956797,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Breen, Bill","contributorId":367120,"corporation":false,"usgs":false,"family":"Breen","given":"Bill","affiliations":[{"id":87551,"text":"Independent Consultant, Hope, Idaho 83836","active":true,"usgs":false}],"preferred":false,"id":956798,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70272621,"text":"ofr20251051 - 2025 - Report of the River Master of the Delaware River for the period December 1, 2017–November 30, 2018","interactions":[],"lastModifiedDate":"2026-02-03T16:42:39.284179","indexId":"ofr20251051","displayToPublicDate":"2025-12-01T14:45:00","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":"2025-1051","displayTitle":"Report of the River Master of the Delaware River for the Period December 1, 2017–November 30, 2018","title":"Report of the River Master of the Delaware River for the period December 1, 2017–November 30, 2018","docAbstract":"A Decree of the Supreme Court of the United States entered June 7, 1954 (New Jersey v. New York, 347 U.S. 995), established the position of Delaware River Master within the U.S. Geological Survey. In addition, the Decree authorizes the diversion of water from the Delaware River Basin and requires that compensating releases from certain reservoirs owned by New York City be made under the supervision and direction of the River Master. The Decree stipulates that the River Master provide reports to the Court, not less frequently than annually. This report is the 65th annual report of the River Master of the Delaware River. The report covers the 2018 River Master report year, from December 1, 2017, to November 30, 2018.
During the report year, precipitation in the upper Delaware River Basin was 60.39 inches or 136 percent of the long-term average. On December 1, 2017, combined useable storage in the New York City reservoirs in the upper Delaware River Basin was 193.230 billion gallons or 71.3 percent of the combined useable storage capacity of 270.837 billion gallons. The reservoirs had a usable capacity of 99.5 percent on May 31, 2018. Combined storage remained high (above 80 percent combined capacity) and did not decline below 80 percent of combined capacity through November 30, 2018. River Master operations during the year were conducted as stipulated by the Decree and the Flexible Flow Management Program.
Diversions from the Delaware River Basin by New York City and New Jersey fully complied with the Decree. Reservoir releases were made as directed by the River Master at rates designed to meet the flow objective for the Delaware River at Montague, New Jersey, on 42 days during the report year. Interim Excess Release Quantity banks and conservation releases, designed to relieve thermal stress and protect the fishery and aquatic habitat in the tailwaters of the reservoirs, were also made during the report year.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251051","isbn":"978-1-4113-4631-4","usgsCitation":"Russell, K.L., Andrews, W.J., and McHugh, A.R., 2025, Report of the River Master of the Delaware River for the period December 1, 2017–November 30, 2018: U.S. Geological Survey Open-File Report 2025–1051, 79 p., https://doi.org/10.3133/ofr20251051.","productDescription":"x, 79 p.","numberOfPages":"79","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-170329","costCenters":[{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true}],"links":[{"id":496884,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1051/images/"},{"id":496883,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1051/ofr20251051.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2025-1051 XML"},{"id":496882,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251051/full","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1051 HTML"},{"id":496881,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1051/ofr20251051.pdf","text":"Report","size":"16.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1051 PDF"},{"id":496880,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1051/coverthb.jpg"}],"country":"United States","state":"Delaware, New Jersey, New York, Pennsylvania","otherGeospatial":"Delaware River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76,\n 42.75\n ],\n [\n -76,\n 39.7\n ],\n [\n -73.5,\n 39.7\n ],\n [\n -73.5,\n 42.75\n ],\n [\n -76,\n 42.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Delaware River Master
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Chronic Wasting Disease (CWD) is a fatal and contagious neurological disease of cervid populations across North America. Collaborative efforts between government agencies, researchers, policymakers, and stakeholders are necessary to minimize CWD prevalence, spread, and impacts on animal and human health and well-being. However, critical information related to CWD epidemiology, management, and animal and human health risks was not effectively reaching Tribal Nations and their members. To understand these gaps and specific information needs and ensure meaningful participation in CWD management and control efforts, university researchers and Tribal members partnered to conduct semi-structured interviews that focused on deer hunting and the perceived impacts of CWD on Tribal communities. Interviews provided insights into information preferences, knowledge gaps, and perspectives on CWD, revealing a strong sense of responsibility toward deer and the environment. From this collaborative approach, we can create culturally tailored educational resources that address CWD concerns and align with Tribal values.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/08941920.2025.2572062","usgsCitation":"Faust, R., Bernstein, L.A., Fulton, D.C., Applegate, K., Ayres, A., May, P., Vig, A., Landon, A.C., Ruffing, S., Struck, M., Yoder, C., Schwabenlander, M.D., and Wolf, T.M., 2025, A community-based research approach to develop Chronic Wasting Disease outreach with Tribal communities: Society and Natural Resources, https://doi.org/10.1080/08941920.2025.2572062.","ipdsId":"IP-176947","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":497709,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/08941920.2025.2572062","text":"Publisher Index Page"},{"id":497489,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan, Minnesota, 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\"}}]}","edition":"Online First","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Faust, Roger","contributorId":364010,"corporation":false,"usgs":false,"family":"Faust","given":"Roger","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":952167,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bernstein, Lauren A.","contributorId":364012,"corporation":false,"usgs":false,"family":"Bernstein","given":"Lauren","middleInitial":"A.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":952168,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fulton, David C. 0000-0001-5763-7887","orcid":"https://orcid.org/0000-0001-5763-7887","contributorId":333043,"corporation":false,"usgs":true,"family":"Fulton","given":"David","email":"","middleInitial":"C.","affiliations":[{"id":79716,"text":"Minnesota Cooperative 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Indians,","active":true,"usgs":false}],"preferred":false,"id":952172,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Vig, Austin","contributorId":364019,"corporation":false,"usgs":false,"family":"Vig","given":"Austin","affiliations":[{"id":86754,"text":"Shakopee Mdewakanton Sioux Community","active":true,"usgs":false}],"preferred":false,"id":952173,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Landon, Adam C.","contributorId":364020,"corporation":false,"usgs":false,"family":"Landon","given":"Adam","middleInitial":"C.","affiliations":[{"id":6964,"text":"Minnesota Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":952174,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ruffing, Sarah","contributorId":364021,"corporation":false,"usgs":false,"family":"Ruffing","given":"Sarah","affiliations":[{"id":86753,"text":"Red Lake Band of Chippewa Indians,","active":true,"usgs":false}],"preferred":false,"id":952175,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Struck, Madeline","contributorId":364022,"corporation":false,"usgs":false,"family":"Struck","given":"Madeline","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":952176,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Yoder, Colin","contributorId":364023,"corporation":false,"usgs":false,"family":"Yoder","given":"Colin","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":952177,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Schwabenlander, Marc D.","contributorId":364024,"corporation":false,"usgs":false,"family":"Schwabenlander","given":"Marc","middleInitial":"D.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":952178,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Wolf, Tiffany M.","contributorId":364025,"corporation":false,"usgs":false,"family":"Wolf","given":"Tiffany","middleInitial":"M.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":952179,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70274042,"text":"70274042 - 2025 - Habitat selection by Rocky Mountain Population greater Sandhill Cranes (Antigone canadensis tabida) during spring and autumn migration at a key stopover area","interactions":[],"lastModifiedDate":"2026-02-23T17:04:09.417799","indexId":"70274042","displayToPublicDate":"2025-12-01T10:57:50","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":23304,"text":"Avian Conservation and Ecology.","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Habitat selection by Rocky Mountain Population greater Sandhill Cranes (Antigone canadensis tabida) during spring and autumn migration at a key stopover area","title":"Habitat selection by Rocky Mountain Population greater Sandhill Cranes (Antigone canadensis tabida) during spring and autumn migration at a key stopover area","docAbstract":"The San Luis Valley (SLV), Colorado is a critical stopover area for Rocky Mountain Population greater Sandhill Cranes (Antigone canadensis tabida). During spring and autumn, cranes use crops for foraging and water resources adjacent to foraging areas for roosting and loafing. However, surface water is becoming increasingly limited in the SLV. Understanding the factors that affect use by roosting, loafing, and foraging cranes and where habitat is the most limiting will inform water and habitat management under changing conditions. We used mixed-effects models to determine the effects of habitat variables, ownership, and landcover type on the selection of roosting, loafing, and foraging areas by cranes marked with GPS transmitters (2015–2021). We found that Sandhill Cranes selected for areas with a high amount of water, relatively short vegetation (< 5 m in autumn, < 10 m in spring), close to grain fields (< 5 km), and areas identified as open water for roosting. Loafing Sandhill Cranes also selected for areas with short vegetation and close to grain fields but that had less water and more sandbar and were identified as pastures or wetlands. Although selection was higher for private land overall, we found evidence of avoidance of private lands and a stronger preference for public lands with increasing surface water for roosting in spring. For foraging areas, selection was highest for barley in both seasons, but triticale and other grains had relatively high selection in autumn. Our research confirms the importance of providing roosting and loafing areas on both private and public lands close to foraging areas and provides evidence that roosting and loafing opportunities may be most limited on public lands in the SLV.
","language":"English","publisher":"The Resilience Alliance","doi":"10.5751/ACE-02924-200214","usgsCitation":"Vanausdall, R.A., Kendall, W.L., Collins, D.P., Donnelly, J.P., Hays, Q.R., 2025, Habitat selection by Rocky Mountain Population greater Sandhill Cranes (Antigone canadensis tabida) during spring and autumn migration at a key stopover area: Avian Conservation and Ecology., v. 20, no. 2, 14, 19 p., https://doi.org/10.5751/ACE-02924-200214.","productDescription":"14, 19 p.","ipdsId":"IP-167764","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":500589,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5751/ace-02924-200214","text":"Publisher Index Page"},{"id":500424,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"San Luis Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107,\n 38.5\n ],\n [\n -107,\n 37\n ],\n [\n -105,\n 37\n ],\n [\n -105,\n 38.5\n ],\n [\n -107,\n 38.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"20","issue":"2","noUsgsAuthors":false,"publicationDate":"2025-12-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Vanausdall, Rachel A.","contributorId":366817,"corporation":false,"usgs":false,"family":"Vanausdall","given":"Rachel","middleInitial":"A.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":956273,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kendall, William L. 0000-0003-0084-9891","orcid":"https://orcid.org/0000-0003-0084-9891","contributorId":204844,"corporation":false,"usgs":true,"family":"Kendall","given":"William","email":"","middleInitial":"L.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":956274,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Collins, Daniel P.","contributorId":366821,"corporation":false,"usgs":false,"family":"Collins","given":"Daniel","middleInitial":"P.","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":956275,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Donnelly, J. Patrick","contributorId":366822,"corporation":false,"usgs":false,"family":"Donnelly","given":"J.","middleInitial":"Patrick","affiliations":[{"id":81227,"text":"Intermountain West Joint Venture","active":true,"usgs":false}],"preferred":false,"id":956276,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hays, Quentin R.","contributorId":366823,"corporation":false,"usgs":false,"family":"Hays","given":"Quentin","middleInitial":"R.","affiliations":[{"id":87508,"text":"Geosystems Analysis","active":true,"usgs":false}],"preferred":false,"id":956277,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70271457,"text":"70271457 - 2025 - Multi-scale predictors of Northern Long-eared Bat (Myotis septentrionalis) occupancy in the United States","interactions":[],"lastModifiedDate":"2026-01-13T16:43:42.167219","indexId":"70271457","displayToPublicDate":"2025-12-01T10:40:43","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":19892,"text":"Journal of North American Bat Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Multi-scale predictors of Northern Long-eared Bat (Myotis septentrionalis) occupancy in the United States","title":"Multi-scale predictors of Northern Long-eared Bat (Myotis septentrionalis) occupancy in the United States","docAbstract":"Historically, Myotis septentrionalis (Northern Long eared Bat) was among the most common forest-interior species in North America. Largely due to high mortality from white-nose syndrome, this species has experienced severe population declines across its range. To create an updated species distribution map representing summer occupancy probabilities from 2017 to 2022, we integrated stationary acoustic data with live-capture data from the database of the North American Bat Monitoring Program into a multi-scale, multi-method occupancy modeling framework. Our results provide data-driven predictions with quantified uncertainty for summer occupancy probabilities for Northern Long-eared Bats at 2 spatial scales across the range of the species, while also accounting for inherent observation biases (e.g., imperfect detection).
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Maximum CO2 injection rates can be enhanced by extracting brine from the CO2 storage unit. However, disposal of the extracted brine is both a technological and economic challenge. The lowest-cost option would likely be reinjection of the extracted brine into another formation above or below the CO2 storage unit. Therefore, it is important to estimate brine injectivity as it will constrain the potential to increase CO2 injectivity at an injection site that has access to multiple geologic storage units where either CO2 or brine can be injected. Using a simulation-optimization framework, coupled with a non-isothermal, multiphase CO2-water-salt equation-of-state module, we developed a computationally efficient method for evaluating optimization of simultaneous CO2 injection, brine extraction, and brine (re)injection at hypothetical injection sites deployed across a geologic basin. The Illinois basin is ideal for testing our methodology because it contains multiple geologic storage units with seals in between them to isolate injection of CO2 in one unit from interfering with the injection of either brine or CO2 in another unit above or below it. In addition, we investigated the relative effects of variation in key geologic parameters as well as two reservoir structures (hydrogeologic heterogeneity/anisotropy and homogeneity/isotropy) on CO2 injectivities and enhancement of CO2 injectivity through extracting brine. Results suggest that permeability, depth, and especially thickness of the storage unit could be the most influential parameters determining CO2 injectivity. They also suggest that only injecting CO2 into the storage unit with the greatest injectivity, enhancing that unit’s injectivity by extracting brine, and disposing of the produced brine in other suitable units could maximize total CO2 injectivity in limited regions of the basin. At the majority of simulated injection sites, however, we found that injecting CO2 into all of the accessible and suitable storage units was more likely to maximize the CO2 storage resource.
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Except in rare instances where hot water is expressed at the land surface, sedimentary geothermal resources are hidden, so the identification of these systems is optimally accomplished using predictive subsurface modeling. An integrated approach using detailed paleogeographic interpretations, subsurface geologic mapping, and numerical modeling has produced regional geologic and temperature models for the Upper Colorado River Basin, a large watershed in central North America that contains many sedimentary basins. These models identify areas of hidden sedimentary geothermal resource potential in low temperature (<90°C), moderate temperature (90–150°C), and high temperature (>150°C) fairways across the study area. These models incorporate maps of key horizons in outcrop and the subsurface to create a robust structural framework that can be used to target favorable geology for natural or engineered permeability. This framework is populated with lithologies derived from detailed palaeogeographical maps and over 40,000 bottom hole temperature (BHT) values were used to create a calibrated three-dimensional (3D) temperature model across the region. The resulting maps serve as a regional sedimentary geothermal play fairway screening tool for evaluating different grades of sedimentary geothermal resources and for identifying areas of interest where more detailed, prospect-scale studies can be undertaken.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Using the Earth to save the Earth","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Geothermal Resources Council","usgsCitation":"Gardner, R., Birdwell, J.E., Sweetkind, D., Sullivan, P., Eaton, M., Petermann, H., Clement, A., Hagadorn, J., and Woda, J., 2025, Detecting hidden sedimentary geothermal systems in the Upper Colorado River Basin, in Using the Earth to save the Earth, v. 49, p. 1512-1525.","productDescription":"14 p.","startPage":"1512","endPage":"1525","ipdsId":"IP-180873","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":499022,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":499003,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.geothermal-library.org/index.php?mode=pubs&action=view&record=1035309"}],"country":"United States","state":"Arizona, Colorado, New Mexico, Utah, Wyoming","otherGeospatial":"Upper Colorado River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105,\n 43.25\n ],\n [\n -113,\n 43.25\n ],\n [\n -113,\n 34\n ],\n [\n -105,\n 34\n ],\n [\n -105,\n 43.25\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"49","noUsgsAuthors":false,"publicationDate":"2025-12-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Gardner, Rand 0000-0001-8711-5334","orcid":"https://orcid.org/0000-0001-8711-5334","contributorId":316831,"corporation":false,"usgs":true,"family":"Gardner","given":"Rand","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":954432,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Birdwell, Justin E. 0000-0001-8263-1452 jbirdwell@usgs.gov","orcid":"https://orcid.org/0000-0001-8263-1452","contributorId":3302,"corporation":false,"usgs":true,"family":"Birdwell","given":"Justin","email":"jbirdwell@usgs.gov","middleInitial":"E.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":954433,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sweetkind, Donald S. 0000-0003-0892-4796","orcid":"https://orcid.org/0000-0003-0892-4796","contributorId":210808,"corporation":false,"usgs":true,"family":"Sweetkind","given":"Donald S.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":954434,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sullivan, Patrick","contributorId":348055,"corporation":false,"usgs":false,"family":"Sullivan","given":"Patrick","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":954435,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Eaton, Melia","contributorId":365598,"corporation":false,"usgs":false,"family":"Eaton","given":"Melia","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":954436,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Petermann, Holger","contributorId":365599,"corporation":false,"usgs":false,"family":"Petermann","given":"Holger","affiliations":[{"id":27833,"text":"Denver Museum of Nature and Science","active":true,"usgs":false}],"preferred":false,"id":954437,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Clement, Annaka","contributorId":365600,"corporation":false,"usgs":false,"family":"Clement","given":"Annaka","affiliations":[{"id":27833,"text":"Denver Museum of Nature and Science","active":true,"usgs":false}],"preferred":false,"id":954438,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hagadorn, James","contributorId":365601,"corporation":false,"usgs":false,"family":"Hagadorn","given":"James","affiliations":[{"id":27833,"text":"Denver Museum of Nature and Science","active":true,"usgs":false}],"preferred":false,"id":954439,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Woda, Joshua 0000-0002-2932-8013","orcid":"https://orcid.org/0000-0002-2932-8013","contributorId":290172,"corporation":false,"usgs":true,"family":"Woda","given":"Joshua","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":954440,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70274537,"text":"70274537 - 2025 - What is the (real) rate of soil health practice adoption? Making sense of three data sources","interactions":[],"lastModifiedDate":"2026-04-01T14:57:42.259853","indexId":"70274537","displayToPublicDate":"2025-12-01T09:53:26","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2456,"text":"Journal of Soil and Water Conservation","active":true,"publicationSubtype":{"id":10}},"title":"What is the (real) rate of soil health practice adoption? Making sense of three data sources","docAbstract":"Conservation stakeholders looking to quantify the impact of their investments to increase soil health practice adoption over time often face challenges in interpreting practice adoption data due to discrepancies in language and results among data sources. Similarly, efforts to estimate environmental outcomes of practice adoption, such as water quality and greenhouse gas emissions, can vary depending on different practice adoption input data. To help make sense of different adoption data sources, we compared county-level adoption data for winter cover crops (WCC), no-till (NT), and reduced tillage (RT) in three areas of the United States with contrasting climates and production systems: central Illinois (CIL), southern Illinois (SIL), and western New York (WNY). We analyzed data available during 2015 through 2022 from the Operational Tillage Information System (OpTIS, remote sensing), US Census of Agriculture (AgCensus, a farmer survey), and, specifically in Illinois, the Illinois Soil Conservation Transect Survey (Transect, a roadside survey). The magnitude of differences between the datasets depended on the practice and geographic location. For example, OpTIS and AgCensus tillage data were much more similar in Illinois (average difference of less than 4 percentage points) compared to New York (average differences of 20 percentage points). Similarly, there was less variability and smaller differences between OpTIS and AgCensus WCC data in Illinois compared to WNY. AgCensus tended to report lower WCC adoption for Illinois and greater adoption in WNY compared to OpTIS. All data sources agreed that the rate of change in tillage practices is slow (mainly –1% to 1%) and that adoption of WCC is low (assuming linear growth, it could take nearly a century to reach 50% WCC adoption in CIL). Differences among the datasets were attributed to definitional inconsistencies for RT and NT and how WCC data were acquired. For example, the AgCensus asks if a WCC was planted, whereas OpTIS and Transect evaluate the presence of a standing WCC. Data sources also reflect different time periods (calendar years or crop years) and types of cropland assessed (corn [Zea mays L.], soybean [Glycine max {L.} Merr.], or all cropland). We propose two recommendations to improve interpretation and consistency: (1) a working group to harmonize definitions and protocols and develop educational materials for data users, and (2) a research effort that integrates different adoption data types and produces publicly available adoption data at HUC-10 and county scales. Such activities could help improve data access and utility for evidence-based conservation decision-making and enhance the accuracy of environmental models that rely on adoption data as input.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/00224561.2025.2580218","usgsCitation":"McGill, B.M., Hively, W.D., Puntel, L.A., Shriver, J., Thieme, A.N., Manter, D.K., and Moore, J.M., 2025, What is the (real) rate of soil health practice adoption? Making sense of three data sources: Journal of Soil and Water Conservation, v. 80, no. 6, p. 724-733, https://doi.org/10.1080/00224561.2025.2580218.","productDescription":"10 p.","startPage":"724","endPage":"733","ipdsId":"IP-172017","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":501927,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"80","issue":"6","noUsgsAuthors":false,"publicationDate":"2026-01-27","publicationStatus":"PW","contributors":{"authors":[{"text":"McGill, Bonnie M.","contributorId":368946,"corporation":false,"usgs":false,"family":"McGill","given":"Bonnie","middleInitial":"M.","affiliations":[{"id":87675,"text":"American Farmland Trust","active":true,"usgs":false}],"preferred":false,"id":958154,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hively, W. Dean 0000-0002-5383-8064","orcid":"https://orcid.org/0000-0002-5383-8064","contributorId":201565,"corporation":false,"usgs":true,"family":"Hively","given":"W.","email":"","middleInitial":"Dean","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":958156,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Puntel, Laila A.","contributorId":368947,"corporation":false,"usgs":false,"family":"Puntel","given":"Laila","middleInitial":"A.","affiliations":[{"id":87676,"text":"Syngenta Group","active":true,"usgs":false}],"preferred":false,"id":958155,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shriver, John","contributorId":368948,"corporation":false,"usgs":false,"family":"Shriver","given":"John","affiliations":[{"id":87677,"text":"Regrow","active":true,"usgs":false}],"preferred":false,"id":958157,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thieme, Alison N.","contributorId":368949,"corporation":false,"usgs":false,"family":"Thieme","given":"Alison","middleInitial":"N.","affiliations":[{"id":87678,"text":"USDA-ARS-SASL","active":true,"usgs":false}],"preferred":false,"id":958158,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Manter, Daniel K.","contributorId":368950,"corporation":false,"usgs":false,"family":"Manter","given":"Daniel","middleInitial":"K.","affiliations":[{"id":87679,"text":"USDA-ARS-SMSBR","active":true,"usgs":false}],"preferred":false,"id":958159,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Moore, Jennifer M.","contributorId":368951,"corporation":false,"usgs":false,"family":"Moore","given":"Jennifer","middleInitial":"M.","affiliations":[{"id":87680,"text":"USDA-ARS-FSCRU","active":true,"usgs":false}],"preferred":false,"id":958160,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70273263,"text":"70273263 - 2025 - The continued decline of the Palila (Loxioides bailleui) on Mauna Kea, Island of Hawaiʻi","interactions":[],"lastModifiedDate":"2025-12-29T15:58:04.83704","indexId":"70273263","displayToPublicDate":"2025-12-01T09:52:29","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":947,"text":"Avian Conservation and Ecology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"The continued decline of the Palila (Loxioides bailleui) on Mauna Kea, Island of Hawaiʻi","title":"The continued decline of the Palila (Loxioides bailleui) on Mauna Kea, Island of Hawaiʻi","docAbstract":"Palila (Loxioides bailleui) are critically endangered Hawaiian honeycreepers specializing on māmane (Sophora chrysophylla) seeds and restricted to Mauna Kea volcano on the Island of Hawaiʻi. Recently, the population was estimated to decline by 89% between 1998 and 2021, despite decades of ungulate removal, fence construction, māmane regeneration, fire suppression, and predator control. To inform managers with the most recent update on the status and trends of the Palila population, we analyzed annual bird survey data collected using point-transect distance sampling since 1998, including new annual survey data from 2022, 2023, and 2024. Prior to analysis, we predicted the population trajectory would change between 2021 and 2024 because of continued management actions promoting habitat recovery. We used distance sampling, log-linear regression, and state-space modeling to produce the new estimates and analyze trends across the time series. The 2022 population estimate was 367 to 742 birds (95% confidence interval; point estimate: 545), the lowest in recorded history. The 2023 and 2024 estimates of 374 to 842 birds (point estimate: 596) and 412 to 970 birds (point estimate: 666) were the second and third lowest in our time series, respectively. Our estimates for years before 2022 show population fluctuations between 4000 to 6800 birds from 1998 to 2005, then a steep decline through 2010. For the next decade, abundance fluctuated around 1000 birds, before declining again in 2021 to less than 700 birds. From 1998 to 2024, the population declined by more than 90%, or 205 birds per year, with 100% statistical support for an overall downward trend, despite significant management efforts and research. The greatest threats facing the Palila, if familiar, are not being eliminated swiftly enough to promote their recovery. The currently small and range-limited population is vulnerable to future climate-related events such as drought and fire. Continued monitoring can help to assess the response of Palila to adaptive management actions and changing environmental conditions.
","language":"English","publisher":"Resilience Alliance","doi":"10.5751/ACE-02920-200210","usgsCitation":"Hunt, N., Asing, C.K., Nietmann, L., Banko, P.C., and Camp, R.J., 2025, The continued decline of the Palila (Loxioides bailleui) on Mauna Kea, Island of Hawaiʻi: Avian Conservation and Ecology, v. 20, no. 2, 10, 18 p., https://doi.org/10.5751/ACE-02920-200210.","productDescription":"10, 18 p.","ipdsId":"IP-177477","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":498272,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5751/ace-02920-200210","text":"Publisher Index Page"},{"id":498146,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Mauna Kea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -155.62,\n 19.84\n ],\n [\n -155.62,\n 19.736678155129184\n ],\n [\n -155.4741502151662,\n 19.736678155129184\n ],\n [\n -155.4741502151662,\n 19.84\n ],\n [\n -155.62,\n 19.84\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"20","issue":"2","noUsgsAuthors":false,"publicationDate":"2025-12-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Hunt, Noah","contributorId":355564,"corporation":false,"usgs":false,"family":"Hunt","given":"Noah","affiliations":[{"id":13341,"text":"Hawai‘i Cooperative Studies Unit, University of Hawai‘i at Hilo","active":true,"usgs":false}],"preferred":false,"id":952938,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Asing, Chauncey K.","contributorId":272645,"corporation":false,"usgs":false,"family":"Asing","given":"Chauncey","email":"","middleInitial":"K.","affiliations":[{"id":40951,"text":"University of Hawai‘i - Mānoa","active":true,"usgs":false}],"preferred":false,"id":952939,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nietmann, Lindsey","contributorId":331548,"corporation":false,"usgs":false,"family":"Nietmann","given":"Lindsey","email":"","affiliations":[{"id":56397,"text":"State of Hawai‘i, Division of Forestry and Wildlife","active":true,"usgs":false}],"preferred":false,"id":952940,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Banko, Paul C. 0000-0002-6035-9803 pbanko@usgs.gov","orcid":"https://orcid.org/0000-0002-6035-9803","contributorId":3179,"corporation":false,"usgs":true,"family":"Banko","given":"Paul","email":"pbanko@usgs.gov","middleInitial":"C.","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true},{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":952941,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Camp, Richard J. 0000-0001-7008-923X rick_camp@usgs.gov","orcid":"https://orcid.org/0000-0001-7008-923X","contributorId":189964,"corporation":false,"usgs":true,"family":"Camp","given":"Richard","email":"rick_camp@usgs.gov","middleInitial":"J.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true},{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":952942,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70272168,"text":"70272168 - 2025 - Estimation of the accessible and useful resource base for electric-grade enhanced geothermal systems (EGS) resources of the Great Basin, USA","interactions":[],"lastModifiedDate":"2026-01-16T15:45:31.50898","indexId":"70272168","displayToPublicDate":"2025-12-01T09:43:12","publicationYear":"2025","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Estimation of the accessible and useful resource base for electric-grade enhanced geothermal systems (EGS) resources of the Great Basin, USA","docAbstract":"Scientists with the U.S. Geological Survey (USGS) recently completed a provisional assessment of the electric-grade geothermal resources associated with the low-permeability geologic formations of the Great Basin, USA, where resources are assumed to be accessible using enhanced geothermal systems (EGS) technologies (i.e., the engineering of sufficient permeability to facilitate efficient heat extraction). This assessment required estimation of the accessible resource base (electric-grade heat [>90ºC] at depths where drilling and stimulation are deemed achievable using current technology) and useful resource (heat that can be extracted from the accessible region). Electric-grade heat can be estimated from existing temperature models. The accessible resource base can be estimated as the electric-grade heat that exists at depths shallower than 6 km based on the limitations of current drilling and stimulation technologies, along with evidence for sustained natural fracture conductivity at depth. The useful part of the accessible heat can be estimated as the product of three efficiencies and factors: the heat extraction efficiency, the viable geology factor, and the reservoir spacing efficiency. The accessible and useful parts of the resource can be estimated in units of heat, or in units of electric power using an electrical conversion efficiency, which is a function of resource temperature. We also estimate the ranges for each of the efficiencies and describe the motivations behind the choice of best estimates used for the recent assessment. An analytic solution is provided for the useful resource above any depth (in units of electric power), where efficiency estimation assumes nearly steady heat extraction rates that cool reservoirs to 90ºC over 30 years of power generation.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Using Earth to save the Earth","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Geothermal Resource Council","usgsCitation":"Burns, E., Frash, L.P., and Williams, C.F., 2025, Estimation of the accessible and useful resource base for electric-grade enhanced geothermal systems (EGS) resources of the Great Basin, USA, in Using Earth to save the Earth, v. 49, p. 2020-2034.","productDescription":"15 p.","startPage":"2020","endPage":"2034","ipdsId":"IP-178656","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":498744,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":498743,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.geothermal-library.org/index.php?mode=pubs&action=view&record=1035334"}],"country":"United States","otherGeospatial":"Great Basin","volume":"49","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Burns, Erick R. 0000-0002-1747-0506","orcid":"https://orcid.org/0000-0002-1747-0506","contributorId":225412,"corporation":false,"usgs":true,"family":"Burns","given":"Erick R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":950291,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Frash, Luke P. 0000-0002-5424-4698","orcid":"https://orcid.org/0000-0002-5424-4698","contributorId":362313,"corporation":false,"usgs":false,"family":"Frash","given":"Luke","middleInitial":"P.","affiliations":[{"id":48588,"text":"Los Alamos National Lab","active":true,"usgs":false}],"preferred":false,"id":950292,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Williams, Colin F. 0000-0003-2196-5496 colin@usgs.gov","orcid":"https://orcid.org/0000-0003-2196-5496","contributorId":274,"corporation":false,"usgs":true,"family":"Williams","given":"Colin","email":"colin@usgs.gov","middleInitial":"F.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":950293,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70272571,"text":"sir20255097 - 2025 - Quality of groundwater used for domestic supply in the Gilroy-Hollister basin and surrounding areas, California, 2022","interactions":[],"lastModifiedDate":"2026-02-03T16:41:44.226534","indexId":"sir20255097","displayToPublicDate":"2025-12-01T09:37:12","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":"2025-5097","displayTitle":"Quality of Groundwater Used for Domestic Supply in the Gilroy-Hollister Basin and Surrounding Areas, California, 2022","title":"Quality of groundwater used for domestic supply in the Gilroy-Hollister basin and surrounding areas, California, 2022","docAbstract":"More than 2 million Californians rely on groundwater from domestic wells for drinking-water supply. This report summarizes a 2022 California Groundwater Ambient Monitoring and Assessment Priority Basin Project (GAMA-PBP) water-quality survey of 33 domestic and small-system drinking-water supply wells in the Gilroy-Hollister Valley groundwater basin and the surrounding areas, where more than 20,000 residents are estimated to utilize privately owned domestic wells. The study area includes the Llagas subbasin in the north, the North San Benito subbasin in the south, and the surrounding uplands. The study was focused on groundwater resources used for domestic drinking-water supply, which are mostly drawn from shallower parts of aquifer systems rather than those of groundwater resources used for public drinking-water supply in the same area. This assessment characterized the quality of ambient groundwater in the aquifer before filtration or treatment, rather than the quality of drinking water delivered to the tap.
To provide context, the measured concentrations of constituents in groundwater were compared to Federal and California State regulatory and non-regulatory benchmarks for drinking-water quality. A grid-based method was used to estimate the areal proportions of groundwater resources used for domestic drinking wells that have water-quality constituents present at high concentrations (above the benchmark), moderate concentrations (between one-half of the benchmark and the benchmark for inorganic constituents, or between one-tenth of the benchmark and the benchmark for organic constituents), and low concentrations (less than one-half or one-tenth the benchmark for inorganic and organic constituents, respectively). This method provides statistically representative results at the study-area scale and permits comparisons to other GAMA-PBP study areas. In the study area, inorganic constituents in groundwater were greater than regulatory benchmarks (U.S. Environmental Protection Agency [EPA] or State of California maximum contaminant levels [MCLs]) for public drinking-water quality in 24 percent of domestic groundwater resources. The inorganic constituents present at concentrations greater than MCLs for drinking water were nitrate (as nitrogen), barium, chromium, and selenium. Total dissolved solids (TDS) or manganese were present at concentrations greater than the secondary maximum contaminant levels (SMCLs) that the State of California uses as aesthetic-based benchmarks in 48 percent of domestic groundwater resources. No volatile organic compounds or pesticide constituents were present at concentrations greater than regulatory benchmarks. Total coliform bacteria and enterococci were detected in 4 percent of domestic groundwater resources. Per- and polyfluoroalkyl substances (PFAS) were detected in 19 percent of domestic groundwater resources, and 10 percent had concentrations greater than recently enacted (April 2024) EPA MCLs.
Physical and chemical factors from natural and anthropogenic sources that could affect the groundwater quality were evaluated using results from statistical testing of associations between constituent concentrations and potential explanatory variables. In this study, relevant physical factors include well construction characteristics, groundwater age, site proximity to groundwater recharge or discharge zones, and potential sources of contamination. Relevant chemical factors include the initial chemistry of the recharge water, the mineralogy of the aquifer sediments, and the subsequent shifts in chemistry as biologic and geologic reactions alter groundwater in the subsurface.
Nitrate concentrations were correlated to agricultural land use, distance from the boundary of the Gilroy-Hollister Valley groundwater basin, and the proportion of modern (post-1950s) water captured by the well. Denitrification under anoxic redox conditions can mitigate some nitrate derived from fertilizer application. Total dissolved solids primarily were derived from water-rock interactions with soils and aquifer materials in the study area, but there were high concentrations where agricultural practices contributed additional TDS. Mineralogy of aquifer sediments and rocks also affect barium, selenium, boron, and chromium concentrations in the Gilroy-Hollister Valley groundwater basin. PFAS were positively correlated with urban land use and the proportion of modern water captured by the well.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255097","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Faulkner, K.E., and Jurgens, B.C., 2025, Quality of groundwater used for domestic supply in the Gilroy-Hollister basin and surrounding areas, California, 2022: U.S. Geological Survey Scientific Investigations Report 2025–5097, 26 p., https://doi.org/10.3133/sir20255097.","productDescription":"viii, 26 p.","onlineOnly":"Y","ipdsId":"IP-160699","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":496802,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/sir/2025/5097/sir20255097.XML","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5097"},{"id":496800,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5097/coverthb.jpg"},{"id":497802,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119048.htm"},{"id":496804,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5097/sir20255097.XML"},{"id":496803,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5097/images"},{"id":496801,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5097/sir20255097.pdf","text":"Report","size":"6.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5097"}],"country":"United States","state":"California","otherGeospatial":"Gilroy-Hollister basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.61037451371827,\n 37.22507246909019\n ],\n [\n -121.7786749960862,\n 37.08414386069212\n ],\n [\n -121.42503094452843,\n 36.75628159886837\n ],\n [\n -121.29294616762297,\n 36.61961329116167\n ],\n [\n -121.07884238942088,\n 36.64012907347609\n ],\n [\n -121.29720693932853,\n 36.95316849961171\n ],\n [\n -121.61037451371827,\n 37.22507246909019\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
The U.S. Geological Survey works with the California State Water Resources Control Boards’ Groundwater Ambient Monitoring and Assessment Program to study the quality of groundwater used for drinking-water supplies across California. This report examines the quality of groundwater collected from 33 private domestic wells in the Gilroy-Hollister Valley groundwater basin and surrounding area in California’s Central Coast region. Groundwater samples were analyzed for human-made and naturally occurring substances that can be found dissolved in groundwater. They were also analyzed for geochemical tracers that can be used to help determined the age of the groundwater and processes affecting the concentrations of dissolved constituents. The water-quality data were compared to Federal and State benchmarks that are applied to public drinking water, such as regulatory maximum contaminant levels (MCLs). Nitrate was detected at concentrations greater than its Federal MCL benchmark in 17 percent of the groundwater samples. Nitrate concentrations above natural background levels were associated with greater agricultural land use near the well, wells tapping a higher proportion of younger groundwater, and absence of anoxic conditions that promote degradation of nitrate. No volatile organic compounds or pesticide constituents were detected at concentrations greater than MCLs, however per- and polyfluoroalkyl substances (PFAS) were detected at concentrations greater than the Federal MCLs enacted in April 2024 in about 10 percent of the groundwater samples. PFAS are used in many consumer products and industrial processes. Occurrences of these elevated concentrations of PFAS were not associated with known potential sources of PFAS contamination to groundwater but were positively correlated with urban land use and the proportion of younger groundwater tapped by the well. Total dissolved solids (TDS, a measure of salinity) were detected at concentrations about the State nonregulatory upper secondary MCL in 24 percent of the groundwater samples. TDS is primarily derived from natural interactions between water and aquifer materials although agricultural practices may contribute additional TDS is some areas. About 20,000 residents in the Gilroy-Hollister area, and more than 2 million people in California, use private domestic wells for drinking water. Therefore, assessing the quality of groundwater used by domestic wells and understanding the factors affecting that quality is important for protecting public health.
","publicationDate":"2025-12-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Faulkner, Kirsten E. 0000-0003-1628-2877","orcid":"https://orcid.org/0000-0003-1628-2877","contributorId":362930,"corporation":false,"usgs":false,"family":"Faulkner","given":"Kirsten","middleInitial":"E.","affiliations":[{"id":68550,"text":"California Water Science Center","active":true,"usgs":false}],"preferred":false,"id":950836,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jurgens, Bryant C. 0000-0002-1572-113X bjurgens@usgs.gov","orcid":"https://orcid.org/0000-0002-1572-113X","contributorId":127839,"corporation":false,"usgs":true,"family":"Jurgens","given":"Bryant C.","email":"bjurgens@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":950837,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}