{"pageNumber":"65","pageRowStart":"1600","pageSize":"25","recordCount":46619,"records":[{"id":70255892,"text":"70255892 - 2024 - Use of Doppler velocity radars to monitor and predict debris and flood wave velocities and travel times in post-wildfire basins","interactions":[],"lastModifiedDate":"2024-07-10T14:33:11.692633","indexId":"70255892","displayToPublicDate":"2024-06-29T09:21:56","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5836,"text":"Journal of Hydrology X","onlineIssn":"2589-9155","active":true,"publicationSubtype":{"id":10}},"title":"Use of Doppler velocity radars to monitor and predict debris and flood wave velocities and travel times in post-wildfire basins","docAbstract":"<p id=\"sp0010\">The magnitude and timing of extreme events such as debris and floodflows (collectively referred to as floodflows) in post-wildfire basins are difficult to measure and are even more difficult to predict. To address this challenge, a sensor ensemble consisting of noncontact, ground-based (near-field), Doppler velocity (velocity) and pulsed (stage or gage height) radars, rain gages, and a redundant radio communication network was leveraged to monitor flood wave velocities, to validate travel times, and to compliment observations from NEXRAD weather radar. The sensor ensemble (DEbris and Floodflow Early warNing System, DEFENS) was deployed in Waldo Canyon, Pike National Forest, Colorado, USA, which was burned entirely (100 percent burned) by the Waldo Canyon fire during the summer of 2012 (<a class=\"anchor u-display-inline anchor-paragraph\" name=\"bb0185\" href=\"https://www.sciencedirect.com/science/article/pii/S2589915524000105?via%3Dihub#b0185\" data-sd-ui-side-panel-opener=\"true\" data-xocs-content-type=\"reference\" data-xocs-content-id=\"b0185\" data-mce-href=\"https://www.sciencedirect.com/science/article/pii/S2589915524000105?via%3Dihub#b0185\"><span class=\"anchor-text\">MTBS, 2020</span></a>).</p><p id=\"sp0015\">Surface velocity, stage, and precipitation time series collected during the DEFENS deployment on 10 August 2015 were used to monitor and predict flood wave velocities and travel times as a function of stream discharge (discharge; streamflow). The 10 August 2015 event exhibited spatial and temporal variations in rainfall intensity and duration that resulted in a discharge equal to 5.01 cubic meters per second (m<sup>3</sup>/s). Discharge was estimated post-event using a slope-conveyance indirect discharge method and was verified using velocity radars and the probability concept algorithm. Mean flood wave velocities – represented by the kinematic celerity<span> (</span><span class=\"math\"><span id=\"MathJax-Element-1-Frame\" class=\"MathJax_SVG\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;><mfenced is=&quot;true&quot;><mrow is=&quot;true&quot;><msub is=&quot;true&quot;><mi is=&quot;true&quot;>c</mi><mi is=&quot;true&quot;>k</mi></msub><mo is=&quot;true&quot;>=</mo><mn is=&quot;true&quot;>2.619</mn><mspace width=&quot;0.25em&quot; is=&quot;true&quot; /><mi is=&quot;true&quot;>m</mi><mi is=&quot;true&quot;>e</mi><mi is=&quot;true&quot;>t</mi><mi is=&quot;true&quot;>e</mi><mi is=&quot;true&quot;>r</mi><mi is=&quot;true&quot;>s</mi><mspace width=&quot;0.25em&quot; is=&quot;true&quot; /><mi is=&quot;true&quot;>p</mi><mi is=&quot;true&quot;>e</mi><mi is=&quot;true&quot;>r</mi><mspace width=&quot;0.25em&quot; is=&quot;true&quot; /><mi is=&quot;true&quot;>s</mi><mi is=&quot;true&quot;>e</mi><mi is=&quot;true&quot;>c</mi><mi is=&quot;true&quot;>o</mi><mi is=&quot;true&quot;>n</mi><mi is=&quot;true&quot;>d</mi><mo is=&quot;true&quot;>,</mo><mspace width=&quot;0.333333em&quot; is=&quot;true&quot; /><mi mathvariant=&quot;normal&quot; is=&quot;true&quot;>m</mi><mo is=&quot;true&quot;>/</mo><mi mathvariant=&quot;normal&quot; is=&quot;true&quot;>s</mi><mo is=&quot;true&quot;>&amp;#xB1;</mo><mn is=&quot;true&quot;>0.556</mn><mspace width=&quot;0.25em&quot; is=&quot;true&quot; /><mi is=&quot;true&quot;>p</mi><mi is=&quot;true&quot;>e</mi><mi is=&quot;true&quot;>r</mi><mi is=&quot;true&quot;>c</mi><mi is=&quot;true&quot;>e</mi><mi is=&quot;true&quot;>n</mi><mi is=&quot;true&quot;>t</mi></mrow></mfenced></math>\"><span class=\"MJX_Assistive_MathML\">\uD835\uDC50<sub>\uD835\uDC58 </sub>= 2.619 \uD835\uDC5A\uD835\uDC52\uD835\uDC61\uD835\uDC52\uD835\uDC5F\uD835\uDC60 \uD835\uDC5D\uD835\uDC52\uD835\uDC5F \uD835\uDC60\uD835\uDC52\uD835\uDC50\uD835\uDC5C\uD835\uDC5B\uD835\uDC51, m/s ± 0.556 \uD835\uDC5D\uD835\uDC52\uD835\uDC5F\uD835\uDC50\uD835\uDC52\uD835\uDC5B\uD835\uDC61)</span></span></span><span>&nbsp;</span>and dynamic celerity<span> (</span><span class=\"math\"><span id=\"MathJax-Element-2-Frame\" class=\"MathJax_SVG\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;><mfenced is=&quot;true&quot;><mrow is=&quot;true&quot;><msub is=&quot;true&quot;><mi is=&quot;true&quot;>c</mi><mi is=&quot;true&quot;>d</mi></msub><mo is=&quot;true&quot;>=</mo><mn is=&quot;true&quot;>3.533</mn><mspace width=&quot;0.333333em&quot; is=&quot;true&quot; /><mi mathvariant=&quot;normal&quot; is=&quot;true&quot;>m</mi><mo is=&quot;true&quot;>/</mo><mi mathvariant=&quot;normal&quot; is=&quot;true&quot;>s</mi><mo is=&quot;true&quot;>&amp;#xB1;</mo><mn is=&quot;true&quot;>0.181</mn><mspace width=&quot;0.25em&quot; is=&quot;true&quot; /><mi is=&quot;true&quot;>p</mi><mi is=&quot;true&quot;>e</mi><mi is=&quot;true&quot;>r</mi><mi is=&quot;true&quot;>c</mi><mi is=&quot;true&quot;>e</mi><mi is=&quot;true&quot;>n</mi><mi is=&quot;true&quot;>t</mi></mrow></mfenced><mi mathvariant=&quot;normal&quot; is=&quot;true&quot;>a</mi><mi mathvariant=&quot;normal&quot; is=&quot;true&quot;>n</mi><mi mathvariant=&quot;normal&quot; is=&quot;true&quot;>d</mi><mspace width=&quot;0.166667em&quot; is=&quot;true&quot; /><mi mathvariant=&quot;normal&quot; is=&quot;true&quot;>t</mi><mi mathvariant=&quot;normal&quot; is=&quot;true&quot;>h</mi><mi mathvariant=&quot;normal&quot; is=&quot;true&quot;>e</mi><mi mathvariant=&quot;normal&quot; is=&quot;true&quot;>i</mi><mi mathvariant=&quot;normal&quot; is=&quot;true&quot;>r</mi><mspace width=&quot;0.166667em&quot; is=&quot;true&quot; /><mi mathvariant=&quot;normal&quot; is=&quot;true&quot;>u</mi><mi mathvariant=&quot;normal&quot; is=&quot;true&quot;>n</mi><mi mathvariant=&quot;normal&quot; is=&quot;true&quot;>c</mi><mi mathvariant=&quot;normal&quot; is=&quot;true&quot;>e</mi><mi mathvariant=&quot;normal&quot; is=&quot;true&quot;>r</mi><mi mathvariant=&quot;normal&quot; is=&quot;true&quot;>t</mi><mi mathvariant=&quot;normal&quot; is=&quot;true&quot;>a</mi><mi mathvariant=&quot;normal&quot; is=&quot;true&quot;>i</mi><mi mathvariant=&quot;normal&quot; is=&quot;true&quot;>n</mi><mi mathvariant=&quot;normal&quot; is=&quot;true&quot;>t</mi><mi mathvariant=&quot;normal&quot; is=&quot;true&quot;>i</mi><mi mathvariant=&quot;normal&quot; is=&quot;true&quot;>e</mi><mi mathvariant=&quot;normal&quot; is=&quot;true&quot;>s</mi></math>\"><span class=\"MJX_Assistive_MathML\">\uD835\uDC50<sub>\uD835\uDC51</sub> = 3.533 m/s ± 0.181 \uD835\uDC5D\uD835\uDC52\uD835\uDC5F\uD835\uDC50\uD835\uDC52\uD835\uDC5B\uD835\uDC61) and their uncertainties</span></span></span><span>&nbsp;</span>were computed. L-moments were computed to establish probability density functions (PDFs) and associated statistics for each of the at-a-section hydraulic parameters to serve as a workflow for implementing alert networks in hydrologically similar basins that lack data.</p><p id=\"sp0020\">Measured flood wave velocities and travel times agreed well with predicted values. Absolute percent differences between predicted and measured flood wave velocities ranged from 1.6 percent to 49 percent and varied with water slope, hydraulic radius, and depth. The kinematic celerity was a better predictor for steep slopes and wide flood plains associated with the Upper Waldo and Middle Waldo radar streamgages; whereas, the dynamic celerity was a better surrogate for shallow slopes and incised channels such as the Lower Waldo radar streamgage.</p><p id=\"sp0025\">The method demonstrates the potential extensibility of a post-wildfire warning system by (1) leveraging multiple systems (i.e., weather radar, near-field velocity and stage radars, and rain gages) for accurate and timely warnings of debris and floodflows, (2) establishing an order of operations to site, install, and operate near-field radars and conventional rain gages to record floodflows, forecast travel times, and document geomorphic change in this basin and hydrologically similar basins that lack data, and (3) communicating data operationally with the Colorado Department of Transportation engineering staff, National Weather Service forecasters, and emergency managers.</p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.hydroa.2024.100180","usgsCitation":"Fulton, J.W., Hall, N.G., Hempel, L.A., Gourley, J., Henneberg, M.F., Kohn, M.S., Farmer, W., Asquith, W.H., Wasielewski, D., Stecklein, A.S., Mommandi, A., and Khan, A., 2024, Use of Doppler velocity radars to monitor and predict debris and flood wave velocities and travel times in post-wildfire basins: Journal of Hydrology X, v. 24, 100180, 17 p., https://doi.org/10.1016/j.hydroa.2024.100180.","productDescription":"100180, 17 p.","ipdsId":"IP-112029","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":439317,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.hydroa.2024.100180","text":"Publisher Index Page"},{"id":430892,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Waldo Canyon basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -105,\n              39\n            ],\n            [\n              -105,\n              38.8333\n            ],\n            [\n              -104.8167,\n              38.8333\n            ],\n            [\n              -104.8167,\n              39\n            ],\n            [\n              -105,\n              39\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"24","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fulton, John W, 0000-0002-5335-0720","orcid":"https://orcid.org/0000-0002-5335-0720","contributorId":213630,"corporation":false,"usgs":true,"family":"Fulton","given":"John","middleInitial":"W,","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":905913,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hall, Nicholas Graff 0000-0002-7331-8947","orcid":"https://orcid.org/0000-0002-7331-8947","contributorId":315497,"corporation":false,"usgs":true,"family":"Hall","given":"Nicholas","email":"","middleInitial":"Graff","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":905914,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hempel, Laura A. 0000-0001-5020-6056","orcid":"https://orcid.org/0000-0001-5020-6056","contributorId":224286,"corporation":false,"usgs":true,"family":"Hempel","given":"Laura","email":"","middleInitial":"A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":905915,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gourley, J.J.","contributorId":340018,"corporation":false,"usgs":false,"family":"Gourley","given":"J.J.","email":"","affiliations":[{"id":41181,"text":"NOAA National Severe Storms Laboratory","active":true,"usgs":false}],"preferred":false,"id":905916,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Henneberg, Mark F. 0000-0002-6991-1211 mfhenneb@usgs.gov","orcid":"https://orcid.org/0000-0002-6991-1211","contributorId":187481,"corporation":false,"usgs":true,"family":"Henneberg","given":"Mark","email":"mfhenneb@usgs.gov","middleInitial":"F.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":905917,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kohn, Michael S. 0000-0002-5989-7700 mkohn@usgs.gov","orcid":"https://orcid.org/0000-0002-5989-7700","contributorId":4549,"corporation":false,"usgs":true,"family":"Kohn","given":"Michael","email":"mkohn@usgs.gov","middleInitial":"S.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":905918,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Farmer, William H. 0000-0002-2865-2196","orcid":"https://orcid.org/0000-0002-2865-2196","contributorId":223181,"corporation":false,"usgs":true,"family":"Farmer","given":"William H.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":905919,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Asquith, William H. 0000-0002-7400-1861 wasquith@usgs.gov","orcid":"https://orcid.org/0000-0002-7400-1861","contributorId":1007,"corporation":false,"usgs":true,"family":"Asquith","given":"William","email":"wasquith@usgs.gov","middleInitial":"H.","affiliations":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":905920,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Wasielewski, Daniel","contributorId":340019,"corporation":false,"usgs":false,"family":"Wasielewski","given":"Daniel","email":"","affiliations":[{"id":41181,"text":"NOAA National Severe Storms Laboratory","active":true,"usgs":false}],"preferred":false,"id":905921,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Stecklein, Andrew S.","contributorId":340020,"corporation":false,"usgs":false,"family":"Stecklein","given":"Andrew","email":"","middleInitial":"S.","affiliations":[{"id":78854,"text":"Colorado Department of Transportation","active":true,"usgs":false}],"preferred":false,"id":905922,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Mommandi, Amanullah","contributorId":340021,"corporation":false,"usgs":false,"family":"Mommandi","given":"Amanullah","affiliations":[{"id":78854,"text":"Colorado Department of Transportation","active":true,"usgs":false}],"preferred":false,"id":905923,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Khan, Aziz","contributorId":340022,"corporation":false,"usgs":false,"family":"Khan","given":"Aziz","affiliations":[{"id":78854,"text":"Colorado Department of Transportation","active":true,"usgs":false}],"preferred":false,"id":905924,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}}
,{"id":70255757,"text":"70255757 - 2024 - Characterising, quantifying, and accessing eruption source parameters of explosive volcanic eruptions for operational simulation of tephra dispersion: A current view and future perspectives","interactions":[],"lastModifiedDate":"2024-07-03T12:02:59.820318","indexId":"70255757","displayToPublicDate":"2024-06-29T07:01:50","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"Characterising, quantifying, and accessing eruption source parameters of explosive volcanic eruptions for operational simulation of tephra dispersion: A current view and future perspectives","docAbstract":"<div id=\"Abs1-section\" class=\"c-article-section\"><div id=\"Abs1-content\" class=\"c-article-section__content\"><p>Eruption source parameters (ESPs) are crucial for characterising volcanic eruptions and are essential inputs to numerical models used for hazard assessment. Key ESPs of explosive volcanic eruptions include plume height, mass eruption rate, eruption duration, and grain-size distribution. Some of these ESPs can be directly observed during an eruption, but others are difficult to measure in real-time, or indeed, accurately and precisely quantify afterwards. Estimates of ESPs for eruptions that cannot be observed, for example, due to the remote location of a volcano or poor weather conditions, are often defined using expert judgement and data from past eruptions, both from the volcano of interest and analogue volcanoes farther afield. Analysis of such information is time intensive and difficult, particularly during eruption response. These difficulties have resulted in the production of datasets to aid quick identification of ESPs prior to or during an eruption for use in operational response settings such as those at volcano observatories and Volcanic Ash Advisory Centres. These resources include the Mastin et al. (<a id=\"ref-link-section-d6598425e472\" title=\"Mastin LG, Guffanti M, Ewert JW, Spiegel J (2009a), Spreadsheet of eruption source parameters for active volcanoes of the world, in U.S. Geological Survey open-file report 2009-1133.\" href=\"https://link.springer.com/article/10.1007/s00445-024-01706-y#ref-CR78\" data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 2009a\" data-mce-href=\"https://link.springer.com/article/10.1007/s00445-024-01706-y#ref-CR78\">2009a</a>) ESP dataset and the Catalogue of Icelandic Volcanoes and European Catalogue of Volcanoes aviation tables. Here, we review and compare these resources, which take different approaches to assigning ESPs. We identify future areas for development of these resources, highlighting the need for frequent updates as more knowledge of volcanic activity is gained and as modelling capabilities and requirements change.</p></div></div>","language":"English","publisher":"Springer","doi":"10.1007/s00445-024-01706-y","usgsCitation":"Engwell, S., Mastin, L.G., Bonadonna, C., Barsotti, S., Deligne, N.I., and Oladottir, B.A., 2024, Characterising, quantifying, and accessing eruption source parameters of explosive volcanic eruptions for operational simulation of tephra dispersion: A current view and future perspectives: Bulletin of Volcanology, v. 86, 67, 20 p., https://doi.org/10.1007/s00445-024-01706-y.","productDescription":"67, 20 p.","ipdsId":"IP-153885","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":439319,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00445-024-01706-y","text":"Publisher Index Page"},{"id":430754,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"86","noUsgsAuthors":false,"publicationDate":"2024-06-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Engwell, Samantha 0000-0001-7719-6257","orcid":"https://orcid.org/0000-0001-7719-6257","contributorId":251719,"corporation":false,"usgs":false,"family":"Engwell","given":"Samantha","email":"","affiliations":[{"id":25567,"text":"British Geological Survey","active":true,"usgs":false}],"preferred":false,"id":905555,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mastin, Larry G. 0000-0002-4795-1992","orcid":"https://orcid.org/0000-0002-4795-1992","contributorId":265985,"corporation":false,"usgs":true,"family":"Mastin","given":"Larry","email":"","middleInitial":"G.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":905556,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bonadonna, Contanza 0000-0002-2368-2193","orcid":"https://orcid.org/0000-0002-2368-2193","contributorId":339895,"corporation":false,"usgs":false,"family":"Bonadonna","given":"Contanza","email":"","affiliations":[{"id":62805,"text":"Université de Genève","active":true,"usgs":false}],"preferred":false,"id":905557,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barsotti, Sara","contributorId":199711,"corporation":false,"usgs":false,"family":"Barsotti","given":"Sara","email":"","affiliations":[],"preferred":false,"id":905558,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Deligne, Natalia I. 0000-0001-9221-8581","orcid":"https://orcid.org/0000-0001-9221-8581","contributorId":257389,"corporation":false,"usgs":true,"family":"Deligne","given":"Natalia","email":"","middleInitial":"I.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":905559,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Oladottir, Bergrun A. 0000-0002-6037-3330","orcid":"https://orcid.org/0000-0002-6037-3330","contributorId":339898,"corporation":false,"usgs":false,"family":"Oladottir","given":"Bergrun","email":"","middleInitial":"A.","affiliations":[{"id":40940,"text":"Icelandic Meteorological Office","active":true,"usgs":false}],"preferred":false,"id":905560,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70263882,"text":"70263882 - 2024 - Seismically detected cratering on Mars: Enhanced recent impact flux?","interactions":[],"lastModifiedDate":"2025-02-27T15:56:41.687204","indexId":"70263882","displayToPublicDate":"2024-06-28T08:52:22","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5010,"text":"Science Advances","active":true,"publicationSubtype":{"id":10}},"title":"Seismically detected cratering on Mars: Enhanced recent impact flux?","docAbstract":"<p><span>Seismic observations of impacts on Mars indicate a higher impact flux than previously measured. Using six confirmed seismic impact detections near the NASA InSight lander and two distant large impacts, we calculate appropriate scalings to compare these rates with lunar-based chronology models. We also update the impact rate from orbital observations using the most recent catalog of new craters on Mars. The snapshot of the current impact rate at Mars recorded seismically is higher than that found using orbital detections alone. The measured rates differ between a factor of 2 and 10, depending on the diameter, although the sample size of seismically detected impacts is small. The close timing of the two largest new impacts found on Mars in the past few decades indicates either a heightened impact rate or a low-probability temporal coincidence, perhaps representing recent fragmentation of a parent body. We conclude that seismic methods of detecting current impacts offer a more complete dataset than orbital imaging.</span></p>","language":"English","publisher":"American Association for the Advancement of Science","doi":"10.1126/sciadv.adk7615","usgsCitation":"Daubar, I.J., Garcia, R., Stott, A.E., Fernando, B., Collins, G.S., Dundas, C., Wojcicka, N., Zenhausern, G., McEwen, A.S., Stahler, S., Golombek, M.P., Charalambous, C., Giardini, D., Lognonne, P., and Banerdt, W., 2024, Seismically detected cratering on Mars: Enhanced recent impact flux?: Science Advances, v. 10, no. 26, eadk7615, 9 p., https://doi.org/10.1126/sciadv.adk7615.","productDescription":"eadk7615, 9 p.","ipdsId":"IP-152454","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":487204,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1126/sciadv.adk7615","text":"Publisher Index Page"},{"id":482566,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"26","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Daubar, Ingrid J.","contributorId":204233,"corporation":false,"usgs":false,"family":"Daubar","given":"Ingrid","email":"","middleInitial":"J.","affiliations":[{"id":7023,"text":"Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":928853,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Garcia, Raphael F.","contributorId":351538,"corporation":false,"usgs":false,"family":"Garcia","given":"Raphael F.","affiliations":[{"id":34610,"text":"Universite de Toulouse","active":true,"usgs":false}],"preferred":false,"id":928854,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stott, Alexander E.","contributorId":236698,"corporation":false,"usgs":false,"family":"Stott","given":"Alexander","email":"","middleInitial":"E.","affiliations":[{"id":47531,"text":"Department of Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London, SW7 2AZ, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":928855,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fernando, Benjamin","contributorId":351539,"corporation":false,"usgs":false,"family":"Fernando","given":"Benjamin","affiliations":[{"id":25447,"text":"University of Oxford","active":true,"usgs":false}],"preferred":false,"id":928856,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Collins, Gareth S.","contributorId":328863,"corporation":false,"usgs":false,"family":"Collins","given":"Gareth","email":"","middleInitial":"S.","affiliations":[{"id":24608,"text":"Imperial College London","active":true,"usgs":false}],"preferred":false,"id":928857,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dundas, Colin M. 0000-0003-2343-7224","orcid":"https://orcid.org/0000-0003-2343-7224","contributorId":237028,"corporation":false,"usgs":true,"family":"Dundas","given":"Colin M.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":928858,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wojcicka, Natalia","contributorId":351540,"corporation":false,"usgs":false,"family":"Wojcicka","given":"Natalia","affiliations":[{"id":24608,"text":"Imperial College London","active":true,"usgs":false}],"preferred":false,"id":928859,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Zenhausern, Geraldine","contributorId":351541,"corporation":false,"usgs":false,"family":"Zenhausern","given":"Geraldine","affiliations":[{"id":12483,"text":"ETH Zurich","active":true,"usgs":false}],"preferred":false,"id":928860,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"McEwen, Alfred S.","contributorId":61657,"corporation":false,"usgs":false,"family":"McEwen","given":"Alfred","email":"","middleInitial":"S.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":928861,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Stahler, Simon C.","contributorId":351542,"corporation":false,"usgs":false,"family":"Stahler","given":"Simon C.","affiliations":[{"id":12483,"text":"ETH Zurich","active":true,"usgs":false}],"preferred":false,"id":928862,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Golombek, Matthew P.","contributorId":175450,"corporation":false,"usgs":false,"family":"Golombek","given":"Matthew","email":"","middleInitial":"P.","affiliations":[{"id":7023,"text":"Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":928863,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Charalambous, Constantinos","contributorId":351543,"corporation":false,"usgs":false,"family":"Charalambous","given":"Constantinos","affiliations":[{"id":24608,"text":"Imperial College London","active":true,"usgs":false}],"preferred":false,"id":928864,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Giardini, Domenico","contributorId":351544,"corporation":false,"usgs":false,"family":"Giardini","given":"Domenico","affiliations":[{"id":12483,"text":"ETH Zurich","active":true,"usgs":false}],"preferred":false,"id":928865,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Lognonne, Philippe","contributorId":351545,"corporation":false,"usgs":false,"family":"Lognonne","given":"Philippe","affiliations":[{"id":84006,"text":"Universite Paris Cite","active":true,"usgs":false}],"preferred":false,"id":928866,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Banerdt, W. Bruce","contributorId":351546,"corporation":false,"usgs":false,"family":"Banerdt","given":"W. Bruce","affiliations":[{"id":36276,"text":"JPL","active":true,"usgs":false}],"preferred":false,"id":928867,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}}
,{"id":70255863,"text":"70255863 - 2024 - The National Ocean Biodiversity Strategy","interactions":[],"lastModifiedDate":"2025-01-24T20:43:15.098019","indexId":"70255863","displayToPublicDate":"2024-06-28T07:02:21","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"The National Ocean Biodiversity Strategy","docAbstract":"<p>President Biden has been clear that the ocean is central to life on Earth. As he has proclaimed, “the ocean powers millions of jobs; feeds and sustains us; and is a rejuvenating source of inspiration, exploration, and recreation.” The Biden-Harris Administration has worked hard to fulfill the President’s goal to protect and conserve at least 30% of U.S. waters by 2030. The ocean faces increased threats from warming, overfishing, increased acidity, and loss of biodiversity. It is now more important than ever to sustain the many benefits that the ocean, coasts, and Great Lakes provide, including food, a favorable climate, recreation, physical and mental health, and for many, a sense of cultural identity. Ocean life represents an irreplaceable heritage, the foundation of a habitable planet, and a vast trove of resources. Keeping our ocean healthy requires reliable information on the changing status of these living organisms, the drivers of biodiversity change, and options for effectively addressing those drivers. Over 2 million species are estimated to live in the ocean, yet only about 240,000 species have been described by scientists. Most of the ocean’s benefits result from those diverse species interacting with one another and the environment they create. To protect and conserve the ocean, we as a nation need to make better use of existing knowledge and prioritize acquiring new biodiversity knowledge to enable better policy and management decisions. The ability to monitor ocean species and habitats has expanded dramatically over the past decade, with innovations in technology, genomics, taxonomy, big data management and sharing, artificial intelligence, and machine learning. Yet large fractions of the U.S. ocean remain almost unknown. The National Ocean Biodiversity Strategy (strategy) reflects the urgent need to leverage these advances. The goals of this strategy must be guided by the nation’s diverse voices and ways of knowing, in order to maximize effective and equitable stewardship of the ocean’s diverse life and its benefits to people. The strategy is intended as a guiding document for government to advance three overarching goals: </p><p>● Goal 1: Drive delivery of ocean biodiversity knowledge at the national scale. Objectives include developing an Implementation Plan for achieving the strategy’s three goals; establishing a coordination mechanism to manage the implementation; and documenting gaps in biodiversity knowledge and the benefits of ocean biodiversity to people and economies. </p><p>● Goal 2: Strengthen tools and institutions to deliver ocean biodiversity knowledge. Objectives include establishing a robust information pipeline to support indicators and dynamic maps of ocean biodiversity, from the coasts to the deep sea. This pipeline should include expanded observing systems and comprehensive data management; science and technology solutions to accelerate the availability of biodiversity information; and plans to leverage previous investments to rebuild and expand the nation’s human capital and infrastructure to sustain foundational taxonomy and biodiversity science. </p><p>● Goal 3: Protect, conserve, restore, and sustainably use ocean biodiversity. Objectives include expanding the collection, delivery, and use of biodiversity knowledge to inform actions that advance ocean protection, conservation, restoration, and sustainable development. Government should lead in establishing and incentivizing diverse partnerships across scales and sectors to implement those actions and should educate and involve the public to discover and value the nation’s diverse ocean life. Achieving these goals will require commitments across society: new federal and private investments, coordination across sectors to address climate and equity challenges, and engagement of Indigenous Knowledge holders and frontline communities as full partners throughout planning and implementation. The Subcommittee on Ocean Science and Technology (SOST) IWG-Biodiversity will begin developing an Implementation Plan to describe and direct specific actions to implement the strategy. Successful implementation of the strategy will harmonize and expand collection and delivery of timely knowledge on ocean life to all of society. The strategy will also enable evidence-based management and protection of the ocean. Advancing the strategy will build human and institutional capital and partnerships that support both existing mandates and new needs to rebuild and sustain biodiversity, achieve healthy ocean ecosystems, and manage living resources. Implementing the strategy will deliver knowledge for monitoring, modeling, forecasting, and assessments that support food security, public health, and cultural values, and that more effectively protect, conserve, and restore nature.</p>","language":"English","publisher":"White House Office of Science, Technology, and Policy (OSTP)","collaboration":"National Oceanic and Atmospheric Administration, Smithsonian, Bureau of Ocean Energy Management, University Corporation for Atmospheric Research, Environmental Protection Agency, Office of Naval Research, National Science Foundation, National Aeronautics and Space Administration","usgsCitation":"Canonico, G., Duffy, J., Edmonson, M., Fillingham, K., Benson, A., Bisson, K., Demopoulos, A., Hinchey, B., Matsumoto, K., Meyer, C., Price, J., Shen, E., Turner, W., Weise, M., Vander Woude, A., and Wenzel, L., 2024, The National Ocean Biodiversity Strategy, vi, 18 p.","productDescription":"vi, 18 p.","ipdsId":"IP-166759","costCenters":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":430840,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":430823,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://bidenwhitehouse.archives.gov/wp-content/uploads/2024/06/NSTC_National-Ocean-Biodiversity-Strategy.pdf","linkFileType":{"id":1,"text":"pdf"}}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Canonico, Gabrielle","contributorId":217563,"corporation":false,"usgs":false,"family":"Canonico","given":"Gabrielle","email":"","affiliations":[{"id":39659,"text":"National Oceanographic and Atmospheric Administration, US Integrated Ocean Observing System, Silver Spring, MD, USA","active":true,"usgs":false}],"preferred":false,"id":905819,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duffy, J. Emmett","contributorId":270555,"corporation":false,"usgs":false,"family":"Duffy","given":"J. Emmett","affiliations":[],"preferred":false,"id":905820,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Edmonson, Masha","contributorId":339977,"corporation":false,"usgs":false,"family":"Edmonson","given":"Masha","email":"","affiliations":[{"id":36726,"text":"University Corporation for Atmospheric Research","active":true,"usgs":false}],"preferred":false,"id":905821,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fillingham, Katie","contributorId":339534,"corporation":false,"usgs":false,"family":"Fillingham","given":"Katie","email":"","affiliations":[{"id":36726,"text":"University Corporation for Atmospheric Research","active":true,"usgs":false}],"preferred":false,"id":905822,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Benson, Abigail 0000-0002-4391-107X","orcid":"https://orcid.org/0000-0002-4391-107X","contributorId":202078,"corporation":false,"usgs":true,"family":"Benson","given":"Abigail","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":905823,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bisson, Kelsey","contributorId":339979,"corporation":false,"usgs":false,"family":"Bisson","given":"Kelsey","email":"","affiliations":[{"id":37453,"text":"National Aeronautics and Space Administration","active":true,"usgs":false}],"preferred":false,"id":905824,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Demopoulos, Amanda 0000-0003-2096-4694","orcid":"https://orcid.org/0000-0003-2096-4694","contributorId":210316,"corporation":false,"usgs":true,"family":"Demopoulos","given":"Amanda","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":905825,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hinchey, Beth","contributorId":339981,"corporation":false,"usgs":false,"family":"Hinchey","given":"Beth","email":"","affiliations":[{"id":35215,"text":"Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":905826,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Matsumoto, Katsumi","contributorId":224012,"corporation":false,"usgs":false,"family":"Matsumoto","given":"Katsumi","email":"","affiliations":[],"preferred":false,"id":905827,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Meyer, Chris","contributorId":339540,"corporation":false,"usgs":false,"family":"Meyer","given":"Chris","email":"","affiliations":[],"preferred":false,"id":905828,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Price, James","contributorId":156327,"corporation":false,"usgs":false,"family":"Price","given":"James","affiliations":[{"id":20318,"text":"Bureau of Ocean Energy Management","active":true,"usgs":false}],"preferred":false,"id":905829,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Shen, Elaine","contributorId":339535,"corporation":false,"usgs":false,"family":"Shen","given":"Elaine","email":"","affiliations":[{"id":12642,"text":"National Science Foundation","active":true,"usgs":false}],"preferred":false,"id":905830,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Turner, Woody","contributorId":149221,"corporation":false,"usgs":false,"family":"Turner","given":"Woody","email":"","affiliations":[{"id":17679,"text":"Earth Science Division, NASA Headquarters, Washington D.C.","active":true,"usgs":false}],"preferred":false,"id":905831,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Weise, Mike","contributorId":339528,"corporation":false,"usgs":false,"family":"Weise","given":"Mike","email":"","affiliations":[{"id":63888,"text":"Office of Naval Research","active":true,"usgs":false}],"preferred":false,"id":905832,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Vander Woude, Andrea","contributorId":339983,"corporation":false,"usgs":false,"family":"Vander Woude","given":"Andrea","email":"","affiliations":[{"id":38436,"text":"National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":905833,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Wenzel, Lauren","contributorId":339985,"corporation":false,"usgs":false,"family":"Wenzel","given":"Lauren","email":"","affiliations":[{"id":38436,"text":"National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":905834,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}}
,{"id":70257140,"text":"70257140 - 2024 - Evaluating post-wildfire debris-flow rainfall thresholds and volume models at the 2020 Grizzly Creek Fire in Glenwood Canyon, Colorado, USA","interactions":[],"lastModifiedDate":"2024-08-12T11:42:08.195199","indexId":"70257140","displayToPublicDate":"2024-06-28T06:30:07","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":13453,"text":"NHESS","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating post-wildfire debris-flow rainfall thresholds and volume models at the 2020 Grizzly Creek Fire in Glenwood Canyon, Colorado, USA","docAbstract":"<div id=\"abstract\" class=\"abstract sec\"><div class=\"abstract-content show-no-js\"><p id=\"d1e310\">As wildfire increases in the western United States, so do postfire debris-flow hazards. The U.S. Geological Survey (USGS) has developed two separate models to estimate (1)&nbsp;rainfall intensity thresholds for postfire debris-flow initiation and (2)&nbsp;debris-flow volumes. However, the information necessary to test the accuracy of these models is seldom available. Here, we studied how well these models performed over a 2-year period in the 2020 Grizzly Creek Fire burn perimeter in Glenwood Canyon, Colorado, USA, through the development of a debris-flow response inventory. The study area had the advantage of a network of 11 rain gauges for rainfall intensity measurements and repeat lidar data for volume estimates. Our observations showed that 89 % of observed debris flows in the first year postfire were triggered by rainfall rates higher than the fire-wide rainfall threshold produced by the current USGS operational model (M1). No debris flows were observed in the second year postfire, despite eight rainstorms with intensities higher than the modeled rainfall threshold. We found that the operational model for debris-flow initiation rainfall thresholds works well in this region during the first year but may be too conservative in year&nbsp;2 due to vegetation recovery and sediment depletion. However, rainfall thresholds in the second year can be improved by using updated remote sensing imagery to recalculate the debris-flow initiation likelihood with the M1 model. The current volume model overestimates for this region by a median value of 4.4 times. However, the offset between the model estimates and observations is approximately linear, and the volumes from the Grizzly Creek debris flows had a similar magnitude to historic postfire debris flows in the region. Consequently, the current volume model could be adjusted with a regional correction factor.</p></div></div><div id=\"citation-footer\" class=\"sec\"><br></div>","language":"English","publisher":"European Geophysical Union","doi":"10.5194/nhess-24-2093-2024","usgsCitation":"Rengers, F.K., Bower, S., Knapp, A., Kean, J.W., vonLembke, D.W., Thomas, M.A., Kostelnik, J., Barnhart, K.R., Bethel, M., Gartner, J.E., Hille, M., Staley, D.M., Anderson, J.K., Roberts, E.K., DeLong, S.B., Lane, B., Ridgeway, P., and Murphy, B., 2024, Evaluating post-wildfire debris-flow rainfall thresholds and volume models at the 2020 Grizzly Creek Fire in Glenwood Canyon, Colorado, USA: NHESS, v. 24, no. 6, p. 2093-2114, https://doi.org/10.5194/nhess-24-2093-2024.","productDescription":"22 p.","startPage":"2093","endPage":"2114","ipdsId":"IP-153855","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":439328,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/nhess-24-2093-2024","text":"Publisher Index Page"},{"id":432480,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Glenwood Canyon","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -107.35373687363474,\n              39.70280123140964\n            ],\n            [\n              -107.35373687363474,\n              39.51857272600432\n            ],\n            [\n              -107.11050387434496,\n              39.51857272600432\n            ],\n            [\n              -107.11050387434496,\n              39.70280123140964\n     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0000-0003-4873-8111","orcid":"https://orcid.org/0000-0003-4873-8111","contributorId":342070,"corporation":false,"usgs":false,"family":"vonLembke","given":"Danielle","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":909596,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Thomas, Matthew A. 0000-0002-9828-5539 matthewthomas@usgs.gov","orcid":"https://orcid.org/0000-0002-9828-5539","contributorId":200616,"corporation":false,"usgs":true,"family":"Thomas","given":"Matthew","email":"matthewthomas@usgs.gov","middleInitial":"A.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":909548,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kostelnik, Jaime 0000-0002-1817-5461","orcid":"https://orcid.org/0000-0002-1817-5461","contributorId":300717,"corporation":false,"usgs":true,"family":"Kostelnik","given":"Jaime","email":"","affiliations":[{"id":300,"text":"Geologic Hazards Science 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jegartner@usgs.gov","contributorId":1876,"corporation":false,"usgs":true,"family":"Gartner","given":"Joseph","email":"jegartner@usgs.gov","middleInitial":"E.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":909597,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Hille, Madeline","contributorId":221221,"corporation":false,"usgs":false,"family":"Hille","given":"Madeline","affiliations":[],"preferred":false,"id":909598,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Staley, Dennis M. 0000-0002-2239-3402 dstaley@usgs.gov","orcid":"https://orcid.org/0000-0002-2239-3402","contributorId":4134,"corporation":false,"usgs":true,"family":"Staley","given":"Dennis","email":"dstaley@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":909599,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Anderson, Justin K. 0000-0001-7110-0293","orcid":"https://orcid.org/0000-0001-7110-0293","contributorId":342048,"corporation":false,"usgs":false,"family":"Anderson","given":"Justin","email":"","middleInitial":"K.","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":909553,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Roberts, Elizabeth K. 0000-0002-1701-5378","orcid":"https://orcid.org/0000-0002-1701-5378","contributorId":342049,"corporation":false,"usgs":false,"family":"Roberts","given":"Elizabeth","email":"","middleInitial":"K.","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":909554,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"DeLong, Stephen B. 0000-0002-0945-2172 sdelong@usgs.gov","orcid":"https://orcid.org/0000-0002-0945-2172","contributorId":5240,"corporation":false,"usgs":true,"family":"DeLong","given":"Stephen","email":"sdelong@usgs.gov","middleInitial":"B.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":909555,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Lane, Belize","contributorId":297493,"corporation":false,"usgs":false,"family":"Lane","given":"Belize","email":"","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":909556,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Ridgeway, Paxton 0000-0002-7500-7544","orcid":"https://orcid.org/0000-0002-7500-7544","contributorId":342050,"corporation":false,"usgs":false,"family":"Ridgeway","given":"Paxton","email":"","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":909557,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Murphy, Brendan","contributorId":139480,"corporation":false,"usgs":false,"family":"Murphy","given":"Brendan","affiliations":[],"preferred":false,"id":909558,"contributorType":{"id":1,"text":"Authors"},"rank":18}]}}
,{"id":70256555,"text":"70256555 - 2024 - Pasture and diurnal temperature are key predictors of regional Plains Spotted Skunk (Spilogale interrupta) distribution","interactions":[],"lastModifiedDate":"2024-10-08T16:28:41.215656","indexId":"70256555","displayToPublicDate":"2024-06-27T11:22:38","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2373,"text":"Journal of Mammalogy","onlineIssn":"1545-1542","printIssn":"0022-2372","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Pasture and diurnal temperature are key predictors of regional Plains Spotted Skunk (Spilogale interrupta) distribution","title":"Pasture and diurnal temperature are key predictors of regional Plains Spotted Skunk (Spilogale interrupta) distribution","docAbstract":"<p><span>The Plains Spotted Skunk (</span><i>Spilogale interrupta</i><span>) is a small carnivore native to central North America that has experienced significant population reductions, and there is a lack of information about the species that could inform conservation. Our study aimed to address knowledge gaps about the distribution and habitat associations of the species in South Dakota using species distribution modeling. We used species location data collected from state natural resource managers, trappers, and members of online social media groups dedicated to hunting and wildlife conservation; environmental predictors; and 6 predictive modeling algorithms (i.e., artificial neural networks, artificial classification tree analysis, generalized boosting models, maximum entropy, multivariate adaptive regression splines, and random forests) to develop climate and landcover ensemble distribution models. The most important climate and landcover predictors were mean temperature diurnal range (i.e., average monthly differences between daily high and low temperatures) and proportion of area classified as pasture. Ensemble model concordance identified approximately 31,300 km</span><sup>2</sup><span>&nbsp;of potential Plains Spotted Skunk habitat primarily in eastern South Dakota and between the watersheds of the Missouri and James rivers. Our results offer insights that can guide conservation and inform effective management strategies for conserving Plains Spotted Skunk populations in the northern Great Plains. The promotion of low-intensity agricultural practices such as maintaining pastures, farm buildings, fences rows, and the management of woodland encroachment may improve habitat suitability and facilitate the recovery of plains spotted skunks in the region.</span></p>","language":"English","publisher":"Oxford Academic","doi":"10.1093/jmammal/gyae063","usgsCitation":"White, K.M., Cheeseman, A.E., Stafford, J.D., and Lonsinger, R.C., 2024, Pasture and diurnal temperature are key predictors of regional Plains Spotted Skunk (Spilogale interrupta) distribution: Journal of Mammalogy, https://doi.org/10.1093/jmammal/gyae063.","ipdsId":"IP-154327","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":462702,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Dakota","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-104.054487,44.180381],[-104.055914,44.874986],[-104.057698,44.997431],[-104.039681,44.998041],[-104.040114,45.374214],[-104.045443,45.94531],[-100.430597,45.943638],[-99.005754,45.939944],[-98.414518,45.936504],[-96.56328,45.935238],[-96.564002,45.91956],[-96.56703,45.915682],[-96.56442,45.909415],[-96.568315,45.902902],[-96.568772,45.888072],[-96.571354,45.886673],[-96.571871,45.871846],[-96.574667,45.866816],[-96.572984,45.861602],[-96.574517,45.843098],[-96.583085,45.820024],[-96.596704,45.811801],[-96.612512,45.794442],[-96.627778,45.786239],[-96.638726,45.770171],[-96.641941,45.759871],[-96.652226,45.746809],[-96.662595,45.738682],[-96.672665,45.732336],[-96.711157,45.717561],[-96.745086,45.701576],[-96.75035,45.698782],[-96.760866,45.687518],[-96.835769,45.649648],[-96.844211,45.639583],[-96.852392,45.61484],[-96.857751,45.605962],[-96.801987,45.555414],[-96.79384,45.550724],[-96.76528,45.521414],[-96.745487,45.488712],[-96.743486,45.480649],[-96.738446,45.473499],[-96.732739,45.458737],[-96.692541,45.417338],[-96.680454,45.410499],[-96.617726,45.408092],[-96.60118,45.403181],[-96.562142,45.38609],[-96.521787,45.375645],[-96.489065,45.357071],[-96.469246,45.324941],[-96.468027,45.318619],[-96.46191,45.313884],[-96.453067,45.298115],[-96.451232,44.718375],[-96.453049,43.500415],[-96.598928,43.500457],[-96.599182,43.496011],[-96.586274,43.491099],[-96.580997,43.481384],[-96.586364,43.478251],[-96.584603,43.46961],[-96.587929,43.464878],[-96.600039,43.45708],[-96.60286,43.450907],[-96.594254,43.434153],[-96.587884,43.431685],[-96.575181,43.431756],[-96.570224,43.428601],[-96.573579,43.419228],[-96.562728,43.412782],[-96.557586,43.406792],[-96.537116,43.395063],[-96.531159,43.39561],[-96.529152,43.397735],[-96.525453,43.396317],[-96.521572,43.38564],[-96.521323,43.374607],[-96.526467,43.368314],[-96.527223,43.362257],[-96.526635,43.351833],[-96.524289,43.347214],[-96.534913,43.336473],[-96.528817,43.316561],[-96.525564,43.312467],[-96.530392,43.300034],[-96.553087,43.29286],[-96.555246,43.294803],[-96.56911,43.295535],[-96.573556,43.29917],[-96.581052,43.297118],[-96.579094,43.293797],[-96.577588,43.2788],[-96.580904,43.2748],[-96.582876,43.274594],[-96.582939,43.276536],[-96.586317,43.274319],[-96.58522,43.268878],[-96.576804,43.268308],[-96.564165,43.260239],[-96.554968,43.259998],[-96.552591,43.257769],[-96.552963,43.247281],[-96.565253,43.244241],[-96.571194,43.238961],[-96.568505,43.231554],[-96.56044,43.224219],[-96.554937,43.226775],[-96.540088,43.225698],[-96.535741,43.22764],[-96.526865,43.224071],[-96.519273,43.21769],[-96.500759,43.220767],[-96.496454,43.223652],[-96.485264,43.224183],[-96.476697,43.222014],[-96.470626,43.207225],[-96.473777,43.198766],[-96.473834,43.189804],[-96.472395,43.185644],[-96.465146,43.182971],[-96.467292,43.164066],[-96.466537,43.150281],[-96.459978,43.143516],[-96.450361,43.142237],[-96.443431,43.133825],[-96.440801,43.123129],[-96.436589,43.120842],[-96.439335,43.113916],[-96.462855,43.091419],[-96.462636,43.089614],[-96.455337,43.088129],[-96.454088,43.084197],[-96.455209,43.075053],[-96.46085,43.064033],[-96.468207,43.06186],[-96.473165,43.06355],[-96.476905,43.062383],[-96.490365,43.050789],[-96.501748,43.048632],[-96.510256,43.049917],[-96.518431,43.042068],[-96.509145,43.037297],[-96.512916,43.029962],[-96.510995,43.024701],[-96.499187,43.019213],[-96.49167,43.009707],[-96.496699,42.998807],[-96.509986,42.995126],[-96.512886,42.991424],[-96.512237,42.985937],[-96.516724,42.981458],[-96.520773,42.980385],[-96.515922,42.972886],[-96.506148,42.971348],[-96.503132,42.968192],[-96.500308,42.959391],[-96.504857,42.954659],[-96.509472,42.945151],[-96.519994,42.93976],[-96.516419,42.935438],[-96.516888,42.932512],[-96.525536,42.935511],[-96.541689,42.922576],[-96.536564,42.905656],[-96.542847,42.903737],[-96.539397,42.899964],[-96.536007,42.900901],[-96.528886,42.89795],[-96.526357,42.891852],[-96.540116,42.889678],[-96.537851,42.878475],[-96.546394,42.874464],[-96.549659,42.870281],[-96.550469,42.863742],[-96.546556,42.857273],[-96.541708,42.858871],[-96.545502,42.849956],[-96.554709,42.846142],[-96.554203,42.843648],[-96.549976,42.840705],[-96.551285,42.836606],[-96.556162,42.836675],[-96.560572,42.839373],[-96.56284,42.836309],[-96.563058,42.831051],[-96.565605,42.830434],[-96.571353,42.837155],[-96.581604,42.837521],[-96.58238,42.833657],[-96.577813,42.828719],[-96.585699,42.818041],[-96.596008,42.815044],[-96.595664,42.810426],[-96.590913,42.808987],[-96.595283,42.792982],[-96.602575,42.787767],[-96.603784,42.78372],[-96.61949,42.784034],[-96.626406,42.773518],[-96.632142,42.770863],[-96.632212,42.761512],[-96.628741,42.757532],[-96.621235,42.758084],[-96.619494,42.754792],[-96.630485,42.750378],[-96.639704,42.737071],[-96.631931,42.725086],[-96.624704,42.725497],[-96.624446,42.714294],[-96.630617,42.70588],[-96.6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Dakota\",\"nation\":\"USA  \"}}]}","edition":"Online First","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"White, Kara M.","contributorId":341115,"corporation":false,"usgs":false,"family":"White","given":"Kara","email":"","middleInitial":"M.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":907962,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cheeseman, Amanda E.","contributorId":341116,"corporation":false,"usgs":false,"family":"Cheeseman","given":"Amanda","email":"","middleInitial":"E.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":907963,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stafford, Joshua D. 0000-0001-7590-8708 jstafford@usgs.gov","orcid":"https://orcid.org/0000-0001-7590-8708","contributorId":267260,"corporation":false,"usgs":true,"family":"Stafford","given":"Joshua","email":"jstafford@usgs.gov","middleInitial":"D.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":907964,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lonsinger, Robert Charles 0000-0002-1040-7299","orcid":"https://orcid.org/0000-0002-1040-7299","contributorId":340524,"corporation":false,"usgs":true,"family":"Lonsinger","given":"Robert","email":"","middleInitial":"Charles","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907965,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70257547,"text":"70257547 - 2024 - Climate, food and humans predict communities of mammals in the United States","interactions":[],"lastModifiedDate":"2024-09-10T15:02:17.761832","indexId":"70257547","displayToPublicDate":"2024-06-27T10:18:40","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1399,"text":"Diversity and Distributions","active":true,"publicationSubtype":{"id":10}},"title":"Climate, food and humans predict communities of mammals in the United States","docAbstract":"<h3 id=\"ddi13900-sec-0001-title\" class=\"article-section__sub-title section1\">Aim</h3><p>The assembly of species into communities and ecoregions is the result of interacting factors that affect plant and animal distribution and abundance at biogeographic scales. Here, we empirically derive ecoregions for mammals to test whether human disturbance has become more important than climate and habitat resources in structuring communities.</p><h3 id=\"ddi13900-sec-0002-title\" class=\"article-section__sub-title section1\">Location</h3><p>Conterminous United States.</p><h3 id=\"ddi13900-sec-0003-title\" class=\"article-section__sub-title section1\">Time Period</h3><p>2010–2021.</p><h3 id=\"ddi13900-sec-0004-title\" class=\"article-section__sub-title section1\">Major Taxa Studied</h3><p>Twenty-five species of mammals.</p><h3 id=\"ddi13900-sec-0005-title\" class=\"article-section__sub-title section1\">Methods</h3><p>We analysed data from 25 mammal species recorded by camera traps at 6645 locations across the conterminous United States in a joint modelling framework to estimate relative abundance of each species. We then used a clustering analysis to describe 8 broad and 16 narrow mammal communities.</p><h3 id=\"ddi13900-sec-0006-title\" class=\"article-section__sub-title section1\">Results</h3><p>Climate was the most important predictor of mammal abundance overall, while human population density and agriculture were less important, with mixed effects across species. Seed production by forests also predicted mammal abundance, especially hard-mast tree species. The mammal community maps are similar to those of plants, with an east–west split driven by different dominant species of deer and squirrels. Communities vary along gradients of temperature in the east and precipitation in the west. Most fine-scale mammal community boundaries aligned with established plant ecoregions and were distinguished by the presence of regional specialists or shifts in relative abundance of widespread species. Maps of potential ecosystem services provided by these communities suggest high herbivory in the Rocky Mountains and eastern forests, high invertebrate predation in the subtropical south and greater predation pressure on large vertebrates in the west.</p><h3 id=\"ddi13900-sec-0007-title\" class=\"article-section__sub-title section1\">Main Conclusions</h3><p>Our results highlight the importance of climate to modern mammals and suggest that climate change will have strong impacts on these communities. Our new empirical approach to recognizing ecoregions has potential to be applied to expanded communities of mammals or other taxa.</p>","language":"English","publisher":"Wiley","doi":"10.1111/ddi.13900","usgsCitation":"Kays, R., Snider, M.H., Hess, G., Cove, M.V., Jensen, A., Shamon, H., McShea, W.J., Rooney, B., Allen, M.L., Pekins, C.E., Wilmers, C., Pendergast, M.E., Green, A.M., Suraci, J., Leslie, M.S., Nasrallah, S., Farkas, D., Jordan, M., Grigione, M., LaScaleia, M., Davis, M.L., Hansen, C., Millspaugh, J., Lewis, J.S., Havrda, M., Long, R., Remine, K.R., Jaspers, K.J., Lafferty, D.J., Hubbard, T., Studds, C.E., Barthelmess, E.L., Andy, K., Romero, A., O’Neill, B.J., Hawkins, M.T., Lombardi, J.V., Sergeyev, M., Fisher-Reid, M.C., Rentz, M.S., Nagy, C., Davenport, J.D., Rega-Brodsky, C.C., Appel, C.L., Lesmeister, D.B., Giery, S.T., Whittier, C.A., Alston, J., Sutherland, C., Rota, C., Murphy, T., Lee, T.E., Mortelliti, A., Bergman, D.L., Compton, J.A., Gerber, B.D., Burr, J., Rezendes, K., DeGregorio, B.A., Wehr, N.H., Benson, J.F., O’Mara, M.T., Jachowski, D., Gray, M., Beyer, D.E., Belant, J., Horan, R.V., Lonsinger, R.C., Kuhn, K.M., Hasstedt, S.C., Zimova, M., Moore, S.M., Herrera, D.J., Fritts, S., Edelman, A.J., Flaherty, E.A., Petroelje, T.R., Neiswenter, S.A., Risch, D.R., Iannarilli, F., van der Merwe, M., Maher, S.P., Farris, Z.J., Webb, S.L., Mason, D.S., Lashley, M.A., Wilson, A.M., Vanek, J.P., Wehr, S.R., Conner, L.M., Beasley, J.C., Bontrager, H.L., Baruzzi, C., Ellis-Felege, S.N., Proctor, M.D., Schipper, J., Weiss, K., Darracq, A.K., Barr, E.G., Alexander, P.D., Sekercioglu, C.H., Bogan, D.A., Schalk, C.M., Fantle-Lepczyk, J.E., Lepczyk, C.A., LaPoint, S., Whipple, L.S., Rowe, H.I., Mullen, K., Bird, T., Zorn, A., Brandt, L., Lathrop, R., McCain, C., Crupi, A.P., Clark, J., and Parsons, A., 2024, Climate, food and humans predict communities of mammals in the United States: Diversity and Distributions, v. 30, e13900, 16 p., 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,{"id":70255665,"text":"ofr20241034 - 2024 - Total phosphorus and suspended-sediment concentrations and loads from two main tributaries to Upper Klamath Lake, Oregon, 2014–20","interactions":[],"lastModifiedDate":"2026-01-29T19:46:30.854293","indexId":"ofr20241034","displayToPublicDate":"2024-06-27T09:16:43","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-1034","displayTitle":"Total Phosphorus and Suspended-Sediment Concentrations and Loads from Two Main Tributaries to Upper Klamath Lake, Oregon, 2014–20","title":"Total phosphorus and suspended-sediment concentrations and loads from two main tributaries to Upper Klamath Lake, Oregon, 2014–20","docAbstract":"<p>Total phosphorus (TP) and suspended-sediment concentrations (SSC) and loads were computed at two U.S. Geological Survey (USGS) streamgages in the upper Klamath River Basin on the Sprague (USGS site ID 11501000) and Williamson (USGS site ID 11502500) Rivers using high temporal resolution turbidity and streamflow data to develop surrogate regression models. Regression models were updated and validated for TP at the Williamson River site, and additional data improved a prior published TP model, increasing the coefficient of determination (R<sup>2</sup>) from 0.73 to 0.88. A new TP regression model was developed for the Sprague River site using 2 years of data and showed promising results with an R<sup>2</sup> of 0.93. Suspended-sediment concentration (SSC) surrogate models were also updated at these sites using a longer period of record than the TP models and improved characterization of sediment transport conditions at these monitoring sites.</p><p>Computations of TP loads were compared to the annual loading capacity dictated by the total maximum daily load (TMDL) for Upper Klamath Lake and showed that the combined TP load of the Williamson and Sprague Rivers approaches the annual loading capacity in water years with high annual streamflow. TP loads were also compared to loads computed by the Klamath Tribes using a long-term dataset and a regression and interpolation algorithm (RIA). The comparison showed that the two methods report similar annual loads, with the surrogate regression method generally reporting lower loads than the RIA, and the RIA annual loads falling within the range of uncertainty of the surrogate regression model results. Determining the effect of habitat and stream restoration on basin-scale TP and suspended-sediment loading is challenging using the surrogate regression method at these sites given the short period of record that TP and suspended-sediment load (SSL) data are available. However, long-term analysis by the Klamath Tribes in their larger monitoring network could provide insight into the impact of restoration at smaller spatial scales compared to the basin-wide assessment produced in this study.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241034","collaboration":"Prepared in cooperation with the Klamath Tribes and the Oregon Watershed Enhancement Board","usgsCitation":"Schenk, L.N., and Simeone, C., 2024, Total phosphorus and suspended-sediment concentrations and loads from two main tributaries to Upper Klamath Lake, Oregon, 2014–20: U.S. Geological Survey Open-File Report 2024–1034, 18 p., https://doi.org/10.3133/ofr20241034.","productDescription":"viii, 18 p.","onlineOnly":"Y","ipdsId":"IP-155613","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":430574,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1034/ofr20241034.pdf","text":"Report","size":"4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024-1034"},{"id":430573,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1034/ofr20241034.jpg"},{"id":430577,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1034/ofr20241034.XML"},{"id":430576,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1034/images"},{"id":430575,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241034/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2024-1034"},{"id":499255,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117101.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Oregon","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -122.39459049262162,\n              42.010424780518264\n            ],\n            [\n              -119.81143415588565,\n              42.010424780518264\n            ],\n            [\n              -119.81143415588565,\n              43.21983555337732\n            ],\n            [\n              -122.39459049262162,\n              43.21983555337732\n            ],\n            [\n              -122.39459049262162,\n              42.010424780518264\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:dc_or@usgs.gov\" data-mce-href=\"mailto:dc_or@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/oregon-water-science-center\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/centers/oregon-water-science-center\">Oregon Water Science Center</a><br>U.S. Geological Survey<br>601 SW Second Avenue, Suite 1950<br>Portland, Oregon 97204</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Data Collection and Methods</li><li>Results</li><li>Discussion</li><li>References Cited</li></ul>","publishedDate":"2024-06-27","noUsgsAuthors":false,"publicationDate":"2024-06-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Schenk, Liam N. 0000-0002-2491-0813 lschenk@usgs.gov","orcid":"https://orcid.org/0000-0002-2491-0813","contributorId":4273,"corporation":false,"usgs":true,"family":"Schenk","given":"Liam","email":"lschenk@usgs.gov","middleInitial":"N.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":905082,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Simeone, Caelan 0000-0003-3263-6452","orcid":"https://orcid.org/0000-0003-3263-6452","contributorId":221008,"corporation":false,"usgs":true,"family":"Simeone","given":"Caelan","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":905083,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70255930,"text":"70255930 - 2024 - Geographic principles applied to population dynamics: A spatially interpolated integrated population model","interactions":[],"lastModifiedDate":"2024-08-13T14:39:20.399987","indexId":"70255930","displayToPublicDate":"2024-06-27T07:24:09","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Geographic principles applied to population dynamics: A spatially interpolated integrated population model","docAbstract":"<div class=\"abstract-group  metis-abstract\"><div class=\"article-section__content en main\"><ol class=\"\"><li>A major impediment to wildlife conservation and management, from a quantitative perspective, is dealing with high degrees of uncertainty associated with population estimates. Integrated population models (IPMs) can help alleviate that challenge, but they are often limited to narrow spatial or temporal windows owing to the financial and logistical burdens of acquiring requisite datasets. To expand the spatiotemporal scope of practical IPM implementation, we developed a novel method that expresses demographic relatedness among sampled and unsampled locations using geographic principles of spatial autocorrelation.</li><li>We interpolated demographic parameters at unsampled locations using parameter estimates from data-informed locations. Errors attributable to the interpolative process were corrected using a joint likelihood and locally recorded count data (‘cheaper’ and broadly distributed). We evaluated the spatially interpolated IPM (SIIPM) for precision and accuracy under variable levels of spatial autocorrelation using simulated data and a Leave-One-Out Cross-Validation (LOOCV) technique. Conventional IPMs and state-space models (SSM) were fit to the same simulated datasets to provide a comparative assessment of the novel method. In a final, empirical demonstration we fit the SIIPM to data collected from Greater Sage-Grouse (<i>Centrocercus urophasianus</i>; sage-grouse) populations located in Nevada, U.S.A. during 2013–2021.</li><li>SIIPMs outperformed conventional IPMs when fit to data possessing moderate-to-high levels of spatial autocorrelation. Under moderate levels of autocorrelation, the average improvement in parameter estimation was 13.6% for survival, 65.3% for recruitment and 23.7% for rate of population change (\uD835\uDF06). When spatial autocorrelation was low, the SIIPM still outperformed contemporary approaches in areas that were geographically close (&lt;67 km) to sampling locations. Under low autocorrelation-near distance scenarios, we observed SIIPM parameters that were 30.8% (recruitment), 32.5% (\uD835\uDF06; IPM comparison) and 54.0% (\uD835\uDF06; SSM comparison) more precise than contemporary models.</li><li>Spatial autocorrelation is often assumed but rarely tested when comparing population dynamics across regions of large geographic extent. We demonstrated that SIIPMs can improve precision of species' vital rate estimation when extrapolating model inference beyond populations for which long-term monitoring data exists. Specific to sage-grouse, these results support previous conclusions of broad-scale spatial autocorrelation in population dynamics and a reproductive-survival trade-off previously documented at smaller scales.</li></ol></div></div>","language":"English","publisher":"British Ecological Society","doi":"10.1111/2041-210X.14334","usgsCitation":"Prochazka, B.G., Coates, P.S., O’Neil, S.T., Espinosa, S.P., and Aldridge, C.L., 2024, Geographic principles applied to population dynamics: A spatially interpolated integrated population model: Methods in Ecology and Evolution, v. 15, no. 8, p. 1394-1407, https://doi.org/10.1111/2041-210X.14334.","productDescription":"14 p.; 2 Data Releases","startPage":"1394","endPage":"1407","ipdsId":"IP-146509","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":439332,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/2041-210x.14334","text":"Publisher Index Page"},{"id":434935,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P139R33O","text":"USGS data release","linkHelpText":"Spatially Explicit Estimates of Greater Sage-Grouse (Centrocercus urophasianus) Survival, Recruitment, and Rate of Population Change in Nevada, 2013-2021"},{"id":434934,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13W3VKC","text":"USGS data release","linkHelpText":"Code for a spatially interpolated integrated population model applied to simulations of spatially autocorrelated Greater Sage-Grouse (Centrocercus urophasianus) population data"},{"id":430889,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"8","noUsgsAuthors":false,"publicationDate":"2024-06-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Prochazka, Brian G. 0000-0001-7270-5550 bprochazka@usgs.gov","orcid":"https://orcid.org/0000-0001-7270-5550","contributorId":174839,"corporation":false,"usgs":true,"family":"Prochazka","given":"Brian","email":"bprochazka@usgs.gov","middleInitial":"G.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":906054,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":906055,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O’Neil, Shawn T. 0000-0002-0899-5220","orcid":"https://orcid.org/0000-0002-0899-5220","contributorId":206589,"corporation":false,"usgs":true,"family":"O’Neil","given":"Shawn","email":"","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":906056,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Espinosa, Shawn P.","contributorId":195583,"corporation":false,"usgs":false,"family":"Espinosa","given":"Shawn","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":906057,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":906058,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70259266,"text":"70259266 - 2024 - An enhanced and expanded Toolbox for River Velocimetry using Images from Aircraft (TRiVIA)","interactions":[],"lastModifiedDate":"2024-10-03T14:48:46.639349","indexId":"70259266","displayToPublicDate":"2024-06-25T09:46:42","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"An enhanced and expanded Toolbox for River Velocimetry using Images from Aircraft (TRiVIA)","docAbstract":"<p><span>Detailed, accurate information on flow patterns in river channels can improve understanding of habitat conditions, geomorphic processes, and potential hazards to help inform water management. Data describing flow patterns in river channels can be obtained efficiently via image-based techniques that have become more widely used in recent years as the number of platforms for acquiring images has expanded and the number of algorithms for inferring velocities has grown. Image-based techniques have been incorporated into various software packages, including the Toolbox for River Velocimetry using Images from Aircraft (TRiVIA). TRiVIA is a freely available, standalone computer program that provides a comprehensive workflow for performing particle image velocimetry (PIV)-based analyses within a graphical interface. This paper summarizes major enhancements incorporated into the latest release of TRiVIA, version 2.1. For example, a new Tool for Input Parameter Selection (TIPS) provides guidance for specifying key inputs to the PIV algorithm by allowing users to explore relationships between flow velocity, pixel size, output vector spacing, and frame interval. Improved visualization capabilities include the ability to create streamlines and display PIV output on an interactive web map. The program now provides greater flexibility for importing field data in various formats and selecting which observations to use for accuracy assessment. The most substantial additions to TRiVIA 2.1 are the ability to integrate bathymetric information with image-derived velocity estimates to calculate river discharge and to use images acquired from moving aircraft to efficiently map long segments of large rivers to support habitat assessment, contaminant transport studies, and a range of other applications.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/rra.4333","usgsCitation":"Legleiter, C.J., and Kinzel, P.J., 2024, An enhanced and expanded Toolbox for River Velocimetry using Images from Aircraft (TRiVIA): River Research and Applications, v. 40, no. 8, p. 1602-1616, https://doi.org/10.1002/rra.4333.","productDescription":"15 p.","startPage":"1602","endPage":"1616","ipdsId":"IP-163908","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":466990,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rra.4333","text":"Publisher Index Page"},{"id":462540,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"40","issue":"8","noUsgsAuthors":false,"publicationDate":"2024-06-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":914715,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kinzel, Paul J. 0000-0002-6076-9730 pjkinzel@usgs.gov","orcid":"https://orcid.org/0000-0002-6076-9730","contributorId":743,"corporation":false,"usgs":true,"family":"Kinzel","given":"Paul","email":"pjkinzel@usgs.gov","middleInitial":"J.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":914716,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70255575,"text":"sir20245041 - 2024 - Representation of surface-water flows using Gradient-Related Discharge in an Everglades Network","interactions":[],"lastModifiedDate":"2026-02-03T19:22:10.1439","indexId":"sir20245041","displayToPublicDate":"2024-06-25T09:45:01","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5041","displayTitle":"Representation of Surface-Water Flows Using Gradient-Related Discharge in an Everglades Network","title":"Representation of surface-water flows using Gradient-Related Discharge in an Everglades Network","docAbstract":"<div class=\"user-content-block\"><p>The Everglades Depth Estimation Network interpolates water-level gage data to produce daily water-level elevations for the Everglades in south Florida. These elevations were used to estimate flow vectors (gradients and directions) and volumetric flow rates using the Gradient-Related Discharge in an Everglades Network (GARDEN) application developed by the U.S. Geological Survey in cooperation with the U.S. Army Corps of Engineers. Flow rates in both the east-west and north-south directions were computed on a 400-meter square grid using modified parameters in the Manning’s equation. The frictional resistance parameter in the Manning’s equation was calibrated to measured flow rates at coastal creeks fed by Everglades Depth Estimation Network boundary flows. Levees and other features that act as barriers to flow were defined as “no-flow” grid cells where vectors were set to zero.</p><p>The flow volume magnitudes were calibrated with 2020 daily values of coastal river flows, and verification was performed using 2021 data. Within a given day, the measured coastal river flows fluctuate more than the GARDEN boundary flows because of tidal and wind forcings. Because the GARDEN boundary flows were the upstream water source for the coastal rivers, calibration focused on matching average daily flow volumes rather than daily fluctuations. The Pearson’s correlation coefficient is 0.766 for the 2020 calibration period and 0.566 for the 2021 verification period.</p><p>Applying GARDEN to periods with hydraulic-control-structure releases allows the propagation of structure flows to be seen in the daily flow-vector maps along with the multiday response of flows farther downgradient. Flow vectors may be overestimated near control structures because of difficulties in resolving the water gradient downstream from the structure. Flow vectors farther from the structure are more accurate than those near the structure.</p></div>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245041","issn":"2328-0328","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","programNote":"Water Availability and Use Science Program","usgsCitation":"Swain, E., and Adams, T., 2024, Representation of surface-water flows using Gradient-Related Discharge in an Everglades Network: U.S. Geological Survey Scientific Investigations Report 2024–5041, 19 p., https://doi.org/10.3133/sir20245041.","productDescription":"Report: vi, 19 p.;2 Data Releases; Database; Software Release","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-148769","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":430460,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://sofia.usgs.gov/eden/garden/","text":"USGS Data Release","linkHelpText":"Gradient-Related Discharge in an Everglades Network (GARDEN) viewer"},{"id":430457,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245041/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5041 HTML"},{"id":430456,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5041/sir20245041.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2024-5041 XML"},{"id":499464,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117098.htm","linkFileType":{"id":5,"text":"html"}},{"id":430498,"rank":9,"type":{"id":35,"text":"Software Release"},"url":"https://doi.org/10.5066/P138WZSY","text":"Gradient-Related Discharge in an Everglades Network (GARDEN)","linkHelpText":"- Version 1.0.0 Initial release of the GARDEN flow vector tool for EDEN"},{"id":430451,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5041/coverthb.jpg"},{"id":430455,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5041/sir20245041.pdf","size":"4.42 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5041"},{"id":430459,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://waterdata.usgs.gov/nwis","text":"USGS Water Data for the Nation","linkHelpText":"USGS National Water Information System database"},{"id":430458,"rank":6,"type":{"id":9,"text":"Database"},"url":"https://www.sfwmd.gov/science-data/dbhydro","linkHelpText":"- South Florida Water Management District database"},{"id":430454,"rank":2,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5041/images"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -82.24296101320105,\n              26.830477146945583\n            ],\n            [\n              -82.24296101320105,\n              24.927823593384815\n            ],\n            [\n              -79.63920124757647,\n              24.927823593384815\n            ],\n            [\n              -79.63920124757647,\n              26.830477146945583\n            ],\n            [\n              -82.24296101320105,\n              26.830477146945583\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p>Director, <a data-mce-href=\"https://www.usgs.gov/centers/car-fl-water\" href=\"https://www.usgs.gov/centers/car-fl-water\">Caribbean-Florida Water Science Center</a><br>U.S. Geological Survey<br>4446 Pet Lane, Suite 108<br>Lutz, FL 33559<br></p><p><a id=\"LPlnk103145\" class=\"OWAAutoLink\" title=\"https://pubs.usgs.gov/contact\" href=\"https://pubs.usgs.gov/contact\" data-auth=\"NotApplicable\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Previous Development of the Everglades Depth Estimation Network (EDEN)</li><li>Methodology</li><li>Implementation of GARDEN Python Version 3.12.3 Script (App)</li><li>Results</li><li>Limitations</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2024-06-25","noUsgsAuthors":false,"publicationDate":"2024-06-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Swain, E. 0000-0001-7168-708X","orcid":"https://orcid.org/0000-0001-7168-708X","contributorId":339662,"corporation":false,"usgs":true,"family":"Swain","given":"E.","affiliations":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"preferred":true,"id":904803,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Adams, T. 0000-0002-3763-1098","orcid":"https://orcid.org/0000-0002-3763-1098","contributorId":339663,"corporation":false,"usgs":true,"family":"Adams","given":"T.","email":"","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":904804,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70255547,"text":"ofr20241035 - 2024 - Bibliography of water-quality studies in Gateway National Recreation Area, New York and New Jersey","interactions":[],"lastModifiedDate":"2026-01-29T19:48:21.704855","indexId":"ofr20241035","displayToPublicDate":"2024-06-25T08:30:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-1035","displayTitle":"Bibliography of Water-Quality Studies in Gateway National Recreation Area, New York and New Jersey","title":"Bibliography of water-quality studies in Gateway National Recreation Area, New York and New Jersey","docAbstract":"<p>The U.S. Geological Survey (USGS) provided technical assistance to the National Park Service (NPS) as part of the USGS-NPS Water-Quality Partnership, by gathering references related to water-quality research conducted in the three units of Gateway National Recreation Area (GATE): Jamaica Bay and Staten Island in New York, and Sandy Hook in New Jersey. As part of this effort, a literature search was performed to compile previous water-quality research conducted within the boundaries of GATE. The resulting bibliography is meant to assist GATE resource managers in understanding the extent of available data and developing plans to close data gaps.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241035","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Savoy, P., Marionkova, M., and Schubert, C., 2024, Bibliography of water-quality studies in Gateway National Recreation Area, New York and New Jersey: U.S. Geological Survey Open-File Report 2024–1035, 7 p., https://doi.org/10.3133/ofr20241035.","productDescription":"iii, 7 p.","numberOfPages":"7","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-161856","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":499256,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117097.htm","linkFileType":{"id":5,"text":"html"}},{"id":430428,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1035/images/"},{"id":430427,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1035/ofr20241035.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2024-1035 XML"},{"id":430426,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241035/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2024-1035 HTML"},{"id":430425,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1035/ofr20241035.pdf","text":"Report","size":"1.25 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024-1035 PDF"},{"id":430424,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1035/coverthb.jpg"}],"country":"United States","state":"New Jersey, New York","otherGeospatial":"Gateway National Recreation Area","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -73.85513729439843,\n              40.654551507838846\n            ],\n            [\n              -73.89381273583244,\n              40.62388973104002\n            ],\n            [\n              -73.9579556996699,\n              40.537441746266865\n            ],\n            [\n              -73.8554944605876,\n              40.56042638081682\n            ],\n            [\n              -73.85236012115627,\n              40.58267541112733\n            ],\n            [\n              -73.83876809711201,\n              40.58418418537727\n            ],\n            [\n              -73.82517607306771,\n              40.58569296761614\n            ],\n            [\n              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\"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:dc_ ny@usgs.gov\" data-mce-href=\"mailto:dc_ ny@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/new-york-water-science-center\" data-mce-href=\"https://www.usgs.gov/centers/new-york-water-science-center\">New York Water Science Center</a><br>U.S. Geological Survey<br>425 Jordan Road<br>Troy, NY 12180–8349</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Approach</li><li>Bibliography</li><li>Summary</li></ul>","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2024-06-25","noUsgsAuthors":false,"publicationDate":"2024-06-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Savoy, Philip 0000-0002-6075-837X","orcid":"https://orcid.org/0000-0002-6075-837X","contributorId":300288,"corporation":false,"usgs":true,"family":"Savoy","given":"Philip","email":"","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":904639,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Marionkova, Maria 0000-0002-3035-9466","orcid":"https://orcid.org/0000-0002-3035-9466","contributorId":339549,"corporation":false,"usgs":true,"family":"Marionkova","given":"Maria","email":"","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":904640,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schubert, Christopher 0000-0002-5137-1229 schubert@usgs.gov","orcid":"https://orcid.org/0000-0002-5137-1229","contributorId":138826,"corporation":false,"usgs":true,"family":"Schubert","given":"Christopher","email":"schubert@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":false,"id":904641,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70255719,"text":"70255719 - 2024 - In situ lung dust analysis by automated Field Emission Scanning Electron Microscopy with Energy Dispersive X-ray Spectroscopy: A method for assessing inorganic particles in tissue from coal miners","interactions":[],"lastModifiedDate":"2024-07-02T12:04:48.099252","indexId":"70255719","displayToPublicDate":"2024-06-25T07:01:12","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":891,"text":"Archives of Pathology","active":true,"publicationSubtype":{"id":10}},"title":"In situ lung dust analysis by automated Field Emission Scanning Electron Microscopy with Energy Dispersive X-ray Spectroscopy: A method for assessing inorganic particles in tissue from coal miners","docAbstract":"<div class=\"title -title\">Context.—</div><p>Overexposure to respirable coal mine dust can cause severe lung disease including progressive massive fibrosis (PMF). Field emission scanning electron microscopy with energy dispersive x-ray spectroscopy (FESEM-EDS) has been used for in situ lung dust particle analysis for evaluation of disease etiology. Automating such work can reduce time, costs, and user bias.</p><div class=\"title -title\">Objective.—</div><p>To develop and test an automated FESEM-EDS method for in situ analysis of inorganic particles in coal miner lung tissue.</p><div class=\"title -title\">Design.—</div><p>We programmed an automated FESEM-EDS procedure to collect particle size and elemental data, using lung tissue from 10 underground coal miners with PMF and 4 control cases. A statistical clustering approach was used to establish classification criteria based on particle chemistry. Data were correlated to PMF/non-PMF areas of the tissue, using corresponding brightfield microscopy images. Results for each miner case were compared with a separate corresponding analysis of particles recovered following tissue digestion.</p><div class=\"title -title\">Results.—</div><p>In situ analysis of miner tissues showed higher particle number densities than controls and densities were generally higher in PMF than non-PMF areas. Particle counts were typically dominated by aluminum silicates with varying percentages of silica. Compared to digestion results for the miner tissues, in situ results indicated lower density of particles (number per tissue volume), larger size, and a lower ratio of silica to total silicates—probably due to frequent particle clustering in situ.</p><div class=\"title -title\">Conclusions.—</div><p>Automated FESEM-EDS analysis of lung dust is feasible in situ and could be applied to a larger set of mineral dust–exposed lung tissues to investigate specific histologic features of PMF and other dust-related occupational diseases.</p>","language":"English","publisher":"Allen Press","doi":"10.5858/arpa.2024-0002-OA","usgsCitation":"Sarver, E.A., Keles, C., Lowers, H.A., Zell-Baran, L., Go, L.H., Hua, J., Cool, C., Rose, C., Green, F., Almberg, K.S., and Cohen, R.A., 2024, In situ lung dust analysis by automated Field Emission Scanning Electron Microscopy with Energy Dispersive X-ray Spectroscopy: A method for assessing inorganic particles in tissue from coal miners: Archives of Pathology, v. 148, no. 7, p. e154-e169, https://doi.org/10.5858/arpa.2024-0002-OA.","productDescription":"16 p.","startPage":"e154","endPage":"e169","ipdsId":"IP-160543","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":439351,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5858/arpa.2024-0002-oa","text":"Publisher Index Page"},{"id":430713,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"148","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-06-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Sarver, Emily A.","contributorId":265758,"corporation":false,"usgs":false,"family":"Sarver","given":"Emily","email":"","middleInitial":"A.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":905409,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Keles, C.","contributorId":339857,"corporation":false,"usgs":false,"family":"Keles","given":"C.","email":"","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":905410,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lowers, Heather A. 0000-0001-5360-9264 hlowers@usgs.gov","orcid":"https://orcid.org/0000-0001-5360-9264","contributorId":191307,"corporation":false,"usgs":true,"family":"Lowers","given":"Heather","email":"hlowers@usgs.gov","middleInitial":"A.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":905418,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zell-Baran, L.","contributorId":339860,"corporation":false,"usgs":false,"family":"Zell-Baran","given":"L.","affiliations":[{"id":36955,"text":"National Jewish Health","active":true,"usgs":false}],"preferred":false,"id":905413,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Go, Leonard H. T.","contributorId":306069,"corporation":false,"usgs":false,"family":"Go","given":"Leonard","email":"","middleInitial":"H. T.","affiliations":[],"preferred":false,"id":905485,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hua, J.","contributorId":339859,"corporation":false,"usgs":false,"family":"Hua","given":"J.","email":"","affiliations":[{"id":36955,"text":"National Jewish Health","active":true,"usgs":false}],"preferred":false,"id":905412,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cool, C.","contributorId":339858,"corporation":false,"usgs":false,"family":"Cool","given":"C.","affiliations":[{"id":36955,"text":"National Jewish Health","active":true,"usgs":false}],"preferred":false,"id":905411,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rose, Cecile","contributorId":204557,"corporation":false,"usgs":false,"family":"Rose","given":"Cecile","email":"","affiliations":[{"id":36955,"text":"National Jewish Health","active":true,"usgs":false}],"preferred":false,"id":905414,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Green, F.H.","contributorId":265748,"corporation":false,"usgs":false,"family":"Green","given":"F.H.","email":"","affiliations":[{"id":16660,"text":"University of Calgary","active":true,"usgs":false}],"preferred":false,"id":905419,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Almberg, K. S.","contributorId":265745,"corporation":false,"usgs":false,"family":"Almberg","given":"K.","email":"","middleInitial":"S.","affiliations":[{"id":36403,"text":"University of Illinois","active":true,"usgs":false}],"preferred":false,"id":905417,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Cohen, R. A.","contributorId":290338,"corporation":false,"usgs":false,"family":"Cohen","given":"R.","email":"","middleInitial":"A.","affiliations":[{"id":18133,"text":"University of Illinois Chicago","active":true,"usgs":false}],"preferred":false,"id":905416,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}}
,{"id":70255668,"text":"70255668 - 2024 - Application of normalized radar backscatter and hyperspectral data to augment rangeland vegetation fractional classification","interactions":[],"lastModifiedDate":"2024-06-28T11:44:29.88845","indexId":"70255668","displayToPublicDate":"2024-06-25T06:36:12","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Application of normalized radar backscatter and hyperspectral data to augment rangeland vegetation fractional classification","docAbstract":"<div class=\"art-abstract art-abstract-new in-tab hypothesis_container\">Rangeland ecosystems in the western United States are vulnerable to climate change, fire, and anthropogenic disturbances, yet classification of rangeland areas remains difficult due to frequently sparse vegetation canopies that increase the influence of soils and senesced vegetation, the overall abundance of senesced vegetation, heterogeneity of life forms, and limited ground-based data. The Rangeland Condition Monitoring Assessment and Projection (RCMAP) project provides fractional vegetation cover maps across western North America using Landsat imagery and artificial intelligence from 1985 to 2023 at yearly time-steps. The objectives of this case study are to apply hyperspectral data from several new data streams, including Sentinel Synthetic Aperture Radar (SAR) and Earth Surface Mineral Dust Source Investigation (EMIT), to the RCMAP model<strong>.<span>&nbsp;</span></strong>We run a series of five tests (Landsat-base model, base + SAR, base + EMIT, base + SAR + EMIT, and base + Landsat NEXT [LNEXT] synthesized from EMIT) over a difficult-to-classify region centered in southwest Montana, USA. Our testing results indicate a clear accuracy benefit of adding SAR and EMIT data to the RCMAP model, with a 7.5% and 29% relative increase in independent accuracy (<span class=\"html-italic\">R</span><sup>2</sup>), respectively. The ability of SAR data to observe vegetation height allows for more accurate classification of vegetation types, whereas EMIT’s continuous characterization of the spectral response boosts discriminatory power relative to multispectral data. Our spectral profile analysis reveals the enhanced classification power with EMIT is related to both the improved spectral resolution and representation of the entire domain as compared to legacy Landsat. One key finding is that legacy Landsat bands largely miss portions of the electromagnetic spectrum where separation among important rangeland targets exists, namely in the 900–1250 nm and 1500–1780 nm range. Synthesized LNEXT data include these gaps, but the reduced spectral resolution compared to EMIT results in an intermediate 18% increase in accuracy relative to the base run. Here, we show the promise of enhanced classification accuracy using EMIT data, and to a smaller extent, SAR.</div>","language":"English","publisher":"MDPI","doi":"10.3390/rs16132315","usgsCitation":"Rigge, M.B., Bunde, B., Postma, K., Oliver, S., and Mueller, N., 2024, Application of normalized radar backscatter and hyperspectral data to augment rangeland vegetation fractional classification: Remote Sensing, v. 16, no. 13, 2315, 19 p., https://doi.org/10.3390/rs16132315.","productDescription":"2315, 19 p.","ipdsId":"IP-164848","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":439353,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs16132315","text":"Publisher Index Page"},{"id":430592,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Montana, Wyoming","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -114.37184692226633,\n              45.84043830078252\n            ],\n            [\n              -114.37184692226633,\n              42.419568075570254\n            ],\n            [\n              -108.57106567226644,\n              42.419568075570254\n            ],\n            [\n              -108.57106567226644,\n              45.84043830078252\n            ],\n            [\n              -114.37184692226633,\n              45.84043830078252\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"16","issue":"13","noUsgsAuthors":false,"publicationDate":"2024-06-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Rigge, Matthew B. 0000-0003-4471-8009 mrigge@usgs.gov","orcid":"https://orcid.org/0000-0003-4471-8009","contributorId":751,"corporation":false,"usgs":true,"family":"Rigge","given":"Matthew","email":"mrigge@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":905125,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bunde, Brett 0000-0003-0228-779X","orcid":"https://orcid.org/0000-0003-0228-779X","contributorId":288364,"corporation":false,"usgs":false,"family":"Bunde","given":"Brett","affiliations":[{"id":61731,"text":"KBR","active":true,"usgs":false}],"preferred":false,"id":905126,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Postma, Kory 0000-0001-8058-498X","orcid":"https://orcid.org/0000-0001-8058-498X","contributorId":293879,"corporation":false,"usgs":false,"family":"Postma","given":"Kory","affiliations":[{"id":63548,"text":"KBRwyle, under contract to USGS","active":true,"usgs":false}],"preferred":false,"id":905127,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Oliver, Simon","contributorId":190986,"corporation":false,"usgs":false,"family":"Oliver","given":"Simon","email":"","affiliations":[],"preferred":false,"id":905128,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mueller, Norman","contributorId":190983,"corporation":false,"usgs":false,"family":"Mueller","given":"Norman","email":"","affiliations":[],"preferred":false,"id":905129,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70262884,"text":"70262884 - 2024 - Estimating biogeochemical rates using a computationally efficient Lagrangian approach","interactions":[],"lastModifiedDate":"2025-01-27T15:38:46.616909","indexId":"70262884","displayToPublicDate":"2024-06-24T08:29:40","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1584,"text":"Estuaries and Coasts","active":true,"publicationSubtype":{"id":10}},"title":"Estimating biogeochemical rates using a computationally efficient Lagrangian approach","docAbstract":"<div id=\"Abs1-section\" class=\"c-article-section\"><div id=\"Abs1-content\" class=\"c-article-section__content\"><p>Nutrient concentrations in many estuaries have increased over the past century due to increases in wastewater discharge and increased agricultural intensity, contributing to multiple environmental problems. Numerous biogeochemical and physical processes in estuaries influence nutrient concentrations during transport, resulting in complex spatial and temporal variability and challenges identifying predominant processes and their rates. Mechanistic models which require these rates to quantify biogeochemical processes become complex and difficult to calibrate as the number of processes and parameters grows, owing to the high dimensionality of the parameter space and the computational cost of simultaneously modeling the transport and transformations of constituents. We developed a modeling approach that decouples transport from transformations, enabling fast, data-driven exploration of the parameter space. The approach extracted information including water age, cumulative exposure to specific habitats, and mean water depth exposure from a hydrodynamic model. Using this information, a biogeochemical model was implemented to predict ammonium and nitrate concentrations in a Lagrangian frame. The model performed each simulation in milliseconds on a laptop computer, allowing the fitting of rate parameters for key transformations by optimization. The optimization used fixed station nitrate observations and the model was then validated against high-resolution mapping observations of ammonium and nitrate. The results suggest that the observed spatial and temporal variation can be largely represented with five transformation processes and their associated rates. Dissolved inorganic nitrogen (DIN) losses occurred only in shallow vegetated areas in the model, highlighting that biogeochemical processes in these areas should be included in DIN models.</p></div></div><h3 id=\"inline-recommendations\" class=\"c-article-recommendations-title\" data-gtm-vis-first-on-screen50443292_3866=\"47159\" data-gtm-vis-total-visible-time50443292_3866=\"100\" data-gtm-vis-has-fired50443292_3866=\"1\"><br></h3>","language":"English","publisher":"Springer Nature","doi":"10.1007/s12237-024-01381-4","usgsCitation":"Gross, E., Holleman, R., Kimmerer, W., Kraus, T.E., Bergamaschi, B.A., Burdick-Yahya, S., and Senn, D., 2024, Estimating biogeochemical rates using a computationally efficient Lagrangian approach: Estuaries and Coasts, v. 47, p. 1435-1455, https://doi.org/10.1007/s12237-024-01381-4.","productDescription":"21 p.","startPage":"1435","endPage":"1455","ipdsId":"IP-159738","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":489902,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1007/s12237-024-01381-4","text":"Publisher Index Page"},{"id":481266,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento-San Joaquin Delta, San Francisco Estuary","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -121.8779495399676,\n              38.44077465834883\n            ],\n            [\n              -121.8779495399676,\n              37.880419413418664\n            ],\n            [\n              -121.39550874625299,\n              37.880419413418664\n            ],\n            [\n              -121.39550874625299,\n              38.44077465834883\n            ],\n            [\n              -121.8779495399676,\n              38.44077465834883\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"47","noUsgsAuthors":false,"publicationDate":"2024-06-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Gross, Edward","contributorId":349905,"corporation":false,"usgs":false,"family":"Gross","given":"Edward","affiliations":[{"id":83529,"text":"Department of Civil and Environmental Engineering, University of California, Davis, CA, USA","active":true,"usgs":false}],"preferred":false,"id":925157,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Holleman, Rusty","contributorId":349906,"corporation":false,"usgs":false,"family":"Holleman","given":"Rusty","affiliations":[{"id":83530,"text":"Center for Watershed Sciences, University of California, Davis, CA, USA","active":true,"usgs":false}],"preferred":false,"id":925158,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kimmerer, Wim","contributorId":349907,"corporation":false,"usgs":false,"family":"Kimmerer","given":"Wim","affiliations":[{"id":83531,"text":"Estuary & Ocean Science Center, San Francisco State University, Tiburon, CA, USA","active":true,"usgs":false}],"preferred":false,"id":925159,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kraus, Tamara E. C. 0000-0002-5187-8644 tkraus@usgs.gov","orcid":"https://orcid.org/0000-0002-5187-8644","contributorId":147560,"corporation":false,"usgs":true,"family":"Kraus","given":"Tamara","email":"tkraus@usgs.gov","middleInitial":"E. C.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":925160,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bergamaschi, Brian A. 0000-0002-9610-5581 bbergama@usgs.gov","orcid":"https://orcid.org/0000-0002-9610-5581","contributorId":140776,"corporation":false,"usgs":true,"family":"Bergamaschi","given":"Brian","email":"bbergama@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":925161,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Burdick-Yahya, Scott","contributorId":349908,"corporation":false,"usgs":false,"family":"Burdick-Yahya","given":"Scott","affiliations":[{"id":83532,"text":"Resource Management Associates Inc., Davis, CA, USA","active":true,"usgs":false}],"preferred":false,"id":925162,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Senn, David","contributorId":349909,"corporation":false,"usgs":false,"family":"Senn","given":"David","affiliations":[{"id":83533,"text":"San Francisco Estuary Institute, Richmond, CA, USA","active":true,"usgs":false}],"preferred":false,"id":925163,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70256116,"text":"70256116 - 2024 - Computationally efficient emulation of spheroidal elastic deformation sources using machine learning models: a Gaussian-process-based approach","interactions":[],"lastModifiedDate":"2024-07-23T13:31:07.100914","indexId":"70256116","displayToPublicDate":"2024-06-24T08:27:07","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":18010,"text":"JGR Machine Learning and Computation","active":true,"publicationSubtype":{"id":10}},"title":"Computationally efficient emulation of spheroidal elastic deformation sources using machine learning models: a Gaussian-process-based approach","docAbstract":"Elastic continuum mechanical models are widely used to compute deformations due to pressure changes in buried cavities, such as magma reservoirs. In general, analytical models are fast but can be inaccurate as they do not correctly satisfy boundary conditions for many geometries, while numerical models are slow and may require specialized expertise and software. To overcome these limitations, we trained supervised machine learning emulators (model surrogates) based on parallel partial Gaussian processes which predict the output of a finite element numerical model with high fidelity but >1,000× greater computational efficiency. The emulators are based on generalized nondimensional forms of governing equations for finite non‐dipping spheroidal cavities in elastic halfspaces. Either cavity volume change or uniform pressure change boundary conditions can be specified, and the models predict both surface displacements and cavity (pore) compressibility. Because of their computational efficiency, using the emulators as numerical model surrogates can greatly accelerate data inversion algorithms such as those employing Bayesian Markov chain Monte Carlo sampling. The emulators also permit a comprehensive evaluation of how displacements and cavity compressibility vary with geometry and material properties, revealing the limitations of analytical models. Our open‐source emulator code can be utilized without finite element software, is suitable for a wide range of cavity geometries and depths, includes an estimate of uncertainties associated with emulation, and can be used to train new emulators for different source geometries.","language":"English","publisher":"Wiley","doi":"10.1029/2024JH000161","usgsCitation":"Anderson, K.R., and Gu, M., 2024, Computationally efficient emulation of spheroidal elastic deformation sources using machine learning models: a Gaussian-process-based approach: JGR Machine Learning and Computation, v. 1, e2024JH000161, 20 p., https://doi.org/10.1029/2024JH000161.","productDescription":"e2024JH000161, 20 p.","ipdsId":"IP-162883","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":439356,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024jh000161","text":"Publisher Index Page"},{"id":434939,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1NEG8BH","text":"USGS data release","linkHelpText":"spheroid90gp: Gaussian process emulation of vertical spheroidal elastic cavity models"},{"id":434938,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KAX1QP","text":"USGS data release","linkHelpText":"Trained emulators from the spheroid90gp software package"},{"id":431349,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"1","noUsgsAuthors":false,"publicationDate":"2024-07-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Anderson, Kyle R. 0000-0001-8041-3996 kranderson@usgs.gov","orcid":"https://orcid.org/0000-0001-8041-3996","contributorId":3522,"corporation":false,"usgs":true,"family":"Anderson","given":"Kyle","email":"kranderson@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":906758,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gu, Mengyang","contributorId":229680,"corporation":false,"usgs":false,"family":"Gu","given":"Mengyang","email":"","affiliations":[{"id":34029,"text":"U.C. Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":906759,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70255803,"text":"70255803 - 2024 - A reproducible manuscript workflow with a Quarto template","interactions":[],"lastModifiedDate":"2024-12-10T14:14:30.05609","indexId":"70255803","displayToPublicDate":"2024-06-24T06:24:20","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"A reproducible manuscript workflow with a Quarto template","docAbstract":"<div id=\"16083014\" class=\"article-section-wrapper js-article-section js-content-section  \" data-section-parent-id=\"0\"><p>Scientists and resource managers increasingly use Markdown-based tools to create reproducible reports and manuscripts. These workflows allow people to use standardized methods that are more reproducible, efficient, and transparent than other standard office tools. We present a Quarto template and demonstrate how this template may be used for a journal, the<span>&nbsp;</span><i>Journal of Fish and Wildlife Management</i>, in our article. This template may also be readily adapted to other journals that use Microsoft Word-based workflows and for other product types such as annual reports. We also provide a high-level overview of Quarto and other Markdown-based workflows as part of the document. Lastly, we provide examples of some features of the Quarto publishing system that may be helpful for authors when customizing Quarto templates for specific journal formatting requirements and other product types.</p></div>","language":"English","publisher":"Allen Press","doi":"10.3996/JFWM-24-003","usgsCitation":"Erickson, R.A., Archer, A.A., and Fienen, M., 2024, A reproducible manuscript workflow with a Quarto template: Journal of Fish and Wildlife Management, v. 15, no. 1, p. 251-258, https://doi.org/10.3996/JFWM-24-003.","productDescription":"8 p.; 2 Data Releases","startPage":"251","endPage":"258","ipdsId":"IP-158646","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":490025,"rank":4,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-24-003","text":"Publisher Index Page"},{"id":434941,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1GZPONT","text":"USGS data release","linkHelpText":"quarto-utils"},{"id":434940,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FPFKKS","text":"USGS data release","linkHelpText":"Quarto template for the Journal of Fish and Wildlife Management"},{"id":430788,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"1","noUsgsAuthors":false,"publicationDate":"2024-11-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":905644,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Archer, Althea A. 0000-0003-1927-0783","orcid":"https://orcid.org/0000-0003-1927-0783","contributorId":302489,"corporation":false,"usgs":true,"family":"Archer","given":"Althea","email":"","middleInitial":"A.","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":905645,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fienen, Michael N. 0000-0002-7756-4651","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":245632,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael N.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":905646,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70256017,"text":"70256017 - 2024 - Assessing the vertical accuracy of digital elevation models by quality level and land cover","interactions":[],"lastModifiedDate":"2024-07-15T11:16:55.557082","indexId":"70256017","displayToPublicDate":"2024-06-24T06:15:01","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3251,"text":"Remote Sensing Letters","active":true,"publicationSubtype":{"id":10}},"title":"Assessing the vertical accuracy of digital elevation models by quality level and land cover","docAbstract":"<div class=\"hlFld-Abstract\"><p class=\"last\">The vertical accuracy of elevation data in coastal environments is critical because small variations in elevation can affect an area’s exposure to waves, tides, and storm-related flooding. Elevation data contractors typically quantify the vertical accuracy of lidar-derived digital elevation models (DEMs) on a per-project basis to gauge whether the datasets meet quality and accuracy standards. Here, we collated over 5200 contractor elevation checkpoints along the Atlantic and Gulf of Mexico coasts of the United States that were collected for project-level analyses produced for assessing DEMs acquired for the U.S. Geological Survey’s Three-Dimensional Elevation Program. We used land cover data to quantify non-vegetated vertical accuracy and vegetated vertical accuracy statistics (overall and by point spacing bins) and assessed elevation error by land cover class. We found the non-vegetated vertical accuracy had an overall root mean square error of 6.9 cm and vegetated areas had a 95th percentile vertical error of 22.3 cm. Point spacing was generally positively correlated to elevation accuracy, but sample size limited the ability to interpret results from accuracy by land cover, particularly in wetlands. Based on the specific questions a researcher may be asking, use of literature or fieldwork could assist with enhancing error statistics in underrepresented classes.</p></div>","language":"English","publisher":"Taylor & Francis","doi":"10.1080/2150704X.2024.2368924","usgsCitation":"Han, M., Enwright, N., Gesch, D.B., Stoker, J.M., Danielson, J.J., and Amante, C.J., 2024, Assessing the vertical accuracy of digital elevation models by quality level and land cover: Remote Sensing Letters, v. 15, no. 7, p. 667-677, https://doi.org/10.1080/2150704X.2024.2368924.","productDescription":"11 p.","startPage":"667","endPage":"677","ipdsId":"IP-155247","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":431053,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-06-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Han, Minoo 0000-0002-6009-602X","orcid":"https://orcid.org/0000-0002-6009-602X","contributorId":332099,"corporation":false,"usgs":false,"family":"Han","given":"Minoo","email":"","affiliations":[{"id":79381,"text":"Han Consulting contracted to U.S. Geological Survey","active":true,"usgs":false}],"preferred":false,"id":906409,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Enwright, Nicholas 0000-0002-7887-3261","orcid":"https://orcid.org/0000-0002-7887-3261","contributorId":217771,"corporation":false,"usgs":true,"family":"Enwright","given":"Nicholas","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":906410,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gesch, Dean B. 0000-0002-8992-4933 gesch@usgs.gov","orcid":"https://orcid.org/0000-0002-8992-4933","contributorId":2956,"corporation":false,"usgs":true,"family":"Gesch","given":"Dean","email":"gesch@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":906411,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stoker, Jason M. 0000-0003-2455-0931 jstoker@usgs.gov","orcid":"https://orcid.org/0000-0003-2455-0931","contributorId":3021,"corporation":false,"usgs":true,"family":"Stoker","given":"Jason","email":"jstoker@usgs.gov","middleInitial":"M.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":906412,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Danielson, Jeffrey J. 0000-0003-0907-034X daniels@usgs.gov","orcid":"https://orcid.org/0000-0003-0907-034X","contributorId":3996,"corporation":false,"usgs":true,"family":"Danielson","given":"Jeffrey","email":"daniels@usgs.gov","middleInitial":"J.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":906426,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Amante, Christopher J.","contributorId":340045,"corporation":false,"usgs":false,"family":"Amante","given":"Christopher","email":"","middleInitial":"J.","affiliations":[{"id":81435,"text":"National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI)","active":true,"usgs":false}],"preferred":false,"id":906414,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70255572,"text":"70255572 - 2024 - A spatial machine learning model developed from noisy data requires multiscale performance evaluation: Predicting depth to bedrock in the Delaware River Basin, USA","interactions":[],"lastModifiedDate":"2024-06-24T14:17:47.470372","indexId":"70255572","displayToPublicDate":"2024-06-23T06:39:18","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7164,"text":"Environmental Modelling & Software","active":true,"publicationSubtype":{"id":10}},"title":"A spatial machine learning model developed from noisy data requires multiscale performance evaluation: Predicting depth to bedrock in the Delaware River Basin, USA","docAbstract":"<p><span>Spatial machine learning models can be developed from observations with substantial unexplainable variability, sometimes called ‘noise’. Traditional point-scale metrics (e.g., R</span><sup>2</sup><span>) alone can be misleading when evaluating these models. We present a multi-scale performance evaluation (MPE) using two additional scales (distributional and geostatistical). We apply the MPE framework to predictions of depth to bedrock (DTB) in the Delaware River Basin. Geostatistical analysis shows that approximately one third of the DTB variance is at spatial scale smaller than 2&nbsp;km. Hence, we interpret our point-scale R</span><sup>2</sup><span>&nbsp;of 0.3 (testing data) to be sufficient for regional-scale modelling. Bias-correction methods improve performance at two of the three MPE scales: point-scale change is negligible, while distributional and geostatistical performance improves. In contrast, bias correction applied to a global DTB model does not improve MPE performance. This work encourages scale-appropriate performance evaluations to enable effective model intercomparison.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2024.106124","usgsCitation":"Goodling, P.J., Belitz, K., Stackelberg, P.E., and Fleming, B.J., 2024, A spatial machine learning model developed from noisy data requires multiscale performance evaluation: Predicting depth to bedrock in the Delaware River Basin, USA: Environmental Modelling & Software, v. 179, 106124, 12 p., https://doi.org/10.1016/j.envsoft.2024.106124.","productDescription":"106124, 12 p.","ipdsId":"IP-160581","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":41514,"text":"Maryland-Delaware-District of Columbia  Water Science Center","active":true,"usgs":true}],"links":[{"id":439361,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2024.106124","text":"Publisher Index Page"},{"id":430446,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.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              -74.88099946089724,\n              38.58741180591247\n            ],\n            [\n              -74.71321333503417,\n              39.379784628066616\n            ],\n            [\n              -74.91854009408658,\n              39.623906471535875\n            ],\n            [\n              -74.56682974019151,\n              39.83490997578861\n            ],\n            [\n              -74.83087158557463,\n              40.43445755432647\n            ],\n            [\n              -74.69462804413362,\n              42.31099383658801\n            ],\n            [\n              -75.89851464683143,\n              42.243461978517985\n            ],\n            [\n              -76.67474108243883,\n              40.45538711590305\n            ],\n            [\n              -76.4341425039691,\n              39.732367924983954\n            ],\n            [\n              -75.81256791410406,\n              39.70992285882126\n            ],\n            [\n              -75.68892404289328,\n              38.72600697054469\n            ],\n            [\n              -74.88099946089724,\n              38.58741180591247\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"179","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Goodling, Phillip J. 0000-0001-5715-8579","orcid":"https://orcid.org/0000-0001-5715-8579","contributorId":239738,"corporation":false,"usgs":true,"family":"Goodling","given":"Phillip","email":"","middleInitial":"J.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia  Water Science Center","active":true,"usgs":true}],"preferred":true,"id":904792,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Belitz, Kenneth 0000-0003-4481-2345","orcid":"https://orcid.org/0000-0003-4481-2345","contributorId":201889,"corporation":false,"usgs":true,"family":"Belitz","given":"Kenneth","affiliations":[{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":904793,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stackelberg, Paul E. 0000-0002-1818-355X","orcid":"https://orcid.org/0000-0002-1818-355X","contributorId":204864,"corporation":false,"usgs":true,"family":"Stackelberg","given":"Paul","middleInitial":"E.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":904794,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fleming, Brandon J. 0000-0001-9649-7485 bjflemin@usgs.gov","orcid":"https://orcid.org/0000-0001-9649-7485","contributorId":4115,"corporation":false,"usgs":true,"family":"Fleming","given":"Brandon","email":"bjflemin@usgs.gov","middleInitial":"J.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":904795,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70255961,"text":"70255961 - 2024 - Back from the brink: Estimating daily and annual abundance of natural-origin salmon smolts from 30-years of mixed-origin capture-recapture data","interactions":[],"lastModifiedDate":"2024-07-11T14:25:45.706092","indexId":"70255961","displayToPublicDate":"2024-06-22T09:17:06","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1661,"text":"Fisheries Research","active":true,"publicationSubtype":{"id":10}},"title":"Back from the brink: Estimating daily and annual abundance of natural-origin salmon smolts from 30-years of mixed-origin capture-recapture data","docAbstract":"<p><span>Evaluating the status and trends of natural-origin anadromous fish populations over time requires robust estimates of out-migrating juvenile abundance. Information on abundance is typically acquired by capturing actively migrating fish as they pass stationary monitoring platforms. Challenges to estimation include protracted migration timing, temporally varying capture probabilities and the contemporaneous presence of unmarked hatchery-origin fish. The confounding effects of unmarked hatchery fish are especially pernicious in systems hosting multiple hatchery programs with variable mark-rates among releases. Here, we address this problem for a regionally and culturally important population of Chinook salmon (</span><i>Oncorhynchus tshawytscha</i><span>) supported by a hatchery-supplementation program implemented in response to the listing of this population under the U.S. Endangered Species Act. We developed a model to estimate daily and annual abundance of naturally produced age-0 fall Chinook salmon passing Lower Granite Dam (Snake River, USA) for each of the last 30 years. We accounted for variable hatchery marking rates by integrating two related data sources: 1) release-recapture data of fish with individually identifiable tags and 2) counts of marked and unmarked sample of fish captured each day. We fit joint parameters for daily fish arrival and capture probabilities to these data to estimate the daily abundance of hatchery- and natural-origin fish. Our results show that from 1992 to 2021, the annual abundance of juvenile natural-origin Snake River fall Chinook salmon increased by two orders of magnitude. These results are the first comprehensive evaluation of multi-decadal trends in abundance and run-timing for this population. Our approach can be adapted to other runs and locations within the Columbia River basin or similar systems where out-migrating fish are monitored at fixed locations.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.fishres.2024.107098","usgsCitation":"Hance, D., Plumb, J., Perry, R., and Tiffan, K., 2024, Back from the brink: Estimating daily and annual abundance of natural-origin salmon smolts from 30-years of mixed-origin capture-recapture data: Fisheries Research, v. 278, 107098, 18 p., https://doi.org/10.1016/j.fishres.2024.107098.","productDescription":"107098, 18 p.","ipdsId":"IP-160680","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":434942,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1EKDXW3","text":"USGS data release","linkHelpText":"Daily and annual abundances of natural- and hatchery-origin age-0 fall Chinook salmon (Oncorhynchus tshawytscha) passing Lower Granite Dam, Washington 1992 - 2021"},{"id":430961,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Oregon, Washington","otherGeospatial":"Snake River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -115.97264940326166,\n              47.24310996536303\n            ],\n            [\n              -118.075231715107,\n              47.27051186495126\n            ],\n            [\n              -118.03216123599826,\n              44.88742975557119\n            ],\n            [\n              -115.98127625140233,\n              44.90409917890665\n            ],\n            [\n              -115.97264940326166,\n              47.24310996536303\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"278","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hance, Dalton 0000-0002-4475-706X","orcid":"https://orcid.org/0000-0002-4475-706X","contributorId":220179,"corporation":false,"usgs":true,"family":"Hance","given":"Dalton","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":906151,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Plumb, John 0000-0003-4255-1612","orcid":"https://orcid.org/0000-0003-4255-1612","contributorId":220178,"corporation":false,"usgs":true,"family":"Plumb","given":"John","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":906152,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Perry, Russell 0000-0003-4110-8619","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":220189,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":906153,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tiffan, Kenneth 0000-0002-5831-2846","orcid":"https://orcid.org/0000-0002-5831-2846","contributorId":217812,"corporation":false,"usgs":true,"family":"Tiffan","given":"Kenneth","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":906154,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70256471,"text":"70256471 - 2024 - Viability modeling for decision support with limited data: A lizard case study","interactions":[],"lastModifiedDate":"2024-12-10T15:01:27.166066","indexId":"70256471","displayToPublicDate":"2024-06-21T15:27:27","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17797,"text":"Journal of Fish and Wildlife Managment","active":true,"publicationSubtype":{"id":10}},"title":"Viability modeling for decision support with limited data: A lizard case study","docAbstract":"<p>Plateau spot-tailed earless lizards,<i> Holbrookia lacerata,</i> are a species of ground lizard in central Texas that are under review for listing as endangered under the US Endangered Species Act, but heretofore no predictive models of population dynamics or viability have been developed. We used limited available data and published demographic rates in a PVA model to predict future status of these lizards under parametric and ecological uncertainty and temporal variability. Even in cases where data are sparse and life history information are limited, viability models can help clarify the consequences of management choices given the uncertainty. Our model predicted that on average populations will decline in in the future. Quasi-extinction probability was low 20 years into the future but up to 0.60. Extinction risk was highly dependent on the road mortality effect and the proportion of the population exposed to roadways, both of which are currently uncertain quantities. Despite these unknowns, our model enables managers to consider the future abundance and extinction risk for the species and make decisions about management to project the populations and also identifies key uncertainties for future research and monitoring.</p>","language":"English","publisher":"US Fish and Wildlife Service","doi":"10.3996/JFWM-23-024","usgsCitation":"Goode, A.B., Allan, N., and McGowan, C., 2024, Viability modeling for decision support with limited data: A lizard case study: Journal of Fish and Wildlife Managment, v. 15, no. 1, p. 70-86, https://doi.org/10.3996/JFWM-23-024.","productDescription":"17 p.","startPage":"70","endPage":"86","ipdsId":"IP-152348","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":487513,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-23-024","text":"Publisher Index Page"},{"id":432057,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","volume":"15","issue":"1","noUsgsAuthors":false,"publicationDate":"2024-11-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Goode, Ashley B.C.","contributorId":340756,"corporation":false,"usgs":false,"family":"Goode","given":"Ashley","email":"","middleInitial":"B.C.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":907519,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Allan, Nathan","contributorId":340757,"corporation":false,"usgs":false,"family":"Allan","given":"Nathan","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":907520,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McGowan, Conor P. 0000-0002-7330-9581 cmcgowan@usgs.gov","orcid":"https://orcid.org/0000-0002-7330-9581","contributorId":3381,"corporation":false,"usgs":true,"family":"McGowan","given":"Conor P.","email":"cmcgowan@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":907521,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70273359,"text":"70273359 - 2024 - Alaskan glacial dust is an important iron source to surface waters of the Gulf of Alaska","interactions":[],"lastModifiedDate":"2026-01-09T17:22:33.919542","indexId":"70273359","displayToPublicDate":"2024-06-21T11:17:31","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Alaskan glacial dust is an important iron source to surface waters of the Gulf of Alaska","docAbstract":"<p><span>This work evaluates glacial dust as a source of sediment, and associated iron (Fe), to the Fe-limited Gulf of Alaska (GoA). A reanalysis of GoA sediment data, using rare earth elements and thorium as provenance tracers, suggests a flux to the ocean surface of Copper River (AK) glacial dust, and associated Fe, that is comparable to the flux of dust from Asia, at least 1,000&nbsp;km from the narrow mountain valley glacial dust source area. This work suggests dust from Asia may not be the largest source of Fe to the GoA. Dust models fail to accurately simulate this glacial dust transport because their coarse resolution underestimates wind speeds, and the dust flux. This work suggests that glacial dust fluxes may have been important in the geologic past (e.g., the last glacial maximum) from locations where there was more extensive coverage by glaciers than at present.</span></p>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023GL106778","usgsCitation":"Crusius, J., Lao, C., Holmes, T.M., and Murray, J.W., 2024, Alaskan glacial dust is an important iron source to surface waters of the Gulf of Alaska: Geophysical Research Letters, v. 51, no. 12, e2023GL106778, 10 p., https://doi.org/10.1029/2023GL106778.","productDescription":"e2023GL106778, 10 p.","ipdsId":"IP-144402","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":498678,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023gl106778","text":"Publisher Index Page"},{"id":498516,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Gulf of Alaska","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -166,\n              61\n            ],\n            [\n              -166,\n              48\n            ],\n            [\n              -136,\n              48\n            ],\n            [\n              -136,\n              61\n            ],\n            [\n              -166,\n              61\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"51","issue":"12","noUsgsAuthors":false,"publicationDate":"2024-06-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Crusius, John 0000-0003-2554-0831 jcrusius@usgs.gov","orcid":"https://orcid.org/0000-0003-2554-0831","contributorId":2155,"corporation":false,"usgs":true,"family":"Crusius","given":"John","email":"jcrusius@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":953433,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lao, Carsten","contributorId":364912,"corporation":false,"usgs":false,"family":"Lao","given":"Carsten","affiliations":[{"id":87012,"text":"UW Dept of Chemistry","active":true,"usgs":false}],"preferred":false,"id":953434,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holmes, Thomas M. 0000-0001-8061-4325","orcid":"https://orcid.org/0000-0001-8061-4325","contributorId":364913,"corporation":false,"usgs":false,"family":"Holmes","given":"Thomas","middleInitial":"M.","affiliations":[{"id":87014,"text":"U. Tasmania","active":true,"usgs":false}],"preferred":false,"id":953435,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Murray, J. W. 0000-0002-8577-7964","orcid":"https://orcid.org/0000-0002-8577-7964","contributorId":364914,"corporation":false,"usgs":false,"family":"Murray","given":"J.","middleInitial":"W.","affiliations":[{"id":87015,"text":"UW School of Oceanography","active":true,"usgs":false}],"preferred":false,"id":953436,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70255562,"text":"70255562 - 2024 - Iron oxyhydroxide-rich hydrothermal deposits at the high-temperature Fåvne vent field, Mohns Ridge","interactions":[],"lastModifiedDate":"2024-06-24T14:00:38.413451","indexId":"70255562","displayToPublicDate":"2024-06-21T08:43:28","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1757,"text":"Geochemistry, Geophysics, Geosystems","active":true,"publicationSubtype":{"id":10}},"title":"Iron oxyhydroxide-rich hydrothermal deposits at the high-temperature Fåvne vent field, Mohns Ridge","docAbstract":"<p><span>The recently discovered Fåvne vent field, located at 3,040&nbsp;m depth on the slow-spreading Mohns mid-ocean ridge between Greenland and Norway, is a high-temperature (≥250°C) vent field that is characterized by Fe oxyhydroxide-rich and S-poor chimneys and mounds. The vent field is located on both the hanging wall and footwall of a normal fault with a ∼1.5&nbsp;km throw that forms the western edge of the ∼20&nbsp;km wide ridge axial valley. Data collected during exploration of the site using a remotely operated vehicle as well as mineralogical and geochemical analyses of rock samples and sediments are used to characterize the geological setting of the vent field and composition of the hydrothermal deposits. The chimney walls are highly porous and lack defined chalcopyrite lined conduits, typical of high-temperature chimneys. Overall, abundant Fe oxyhydroxide precipitation at high-temperature vents at Fåvne reflects an excess of Fe over reduced S in the fluid, leading to precipitation of Fe oxide and oxyhydroxide minerals at high to moderate temperature vents (&gt;100°C), and as microbially mediated and abiotic precipitation of Fe oxyhydroxide minerals at low-temperature diffuse vents (&lt;100°C). The mounds and chimneys exhibit low base metal and reduced S concentrations relative to globally averaged seafloor deposits and suggest subseafloor mixing of hydrothermal fluid with seawater, causing metal sulfide precipitation. Cobalt enrichment at Fåvne may reflect a subsurface influence of an ultramafic substrate on circulating fluids, although ultramafic rocks are absent on the seafloor and no other elements typical of ultramafic deposits are present.</span></p>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024GC011481","usgsCitation":"Gini, C., Jamieson, J., Reeves, E., Gartman, A., Barreyre, T., Babechuk, M.G., Jorgensen, S.L., and Robert, K., 2024, Iron oxyhydroxide-rich hydrothermal deposits at the high-temperature Fåvne vent field, Mohns Ridge: Geochemistry, Geophysics, Geosystems, v. 25, no. 6, e2024GC011481, 29 p., https://doi.org/10.1029/2024GC011481.","productDescription":"e2024GC011481, 29 p.","ipdsId":"IP-162095","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":439365,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024gc011481","text":"Publisher Index Page"},{"id":430445,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Fåvne Vent Field, Mohns Ridge","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -14.945593530960565,\n              74.30569199847616\n            ],\n            [\n              -14.945593530960565,\n              66.9804124151006\n            ],\n            [\n              3.9250034267149374,\n              66.9804124151006\n            ],\n            [\n              3.9250034267149374,\n              74.30569199847616\n            ],\n            [\n              -14.945593530960565,\n              74.30569199847616\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"25","issue":"6","noUsgsAuthors":false,"publicationDate":"2024-06-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Gini, Caroline","contributorId":334026,"corporation":false,"usgs":false,"family":"Gini","given":"Caroline","email":"","affiliations":[{"id":40744,"text":"Memorial University","active":true,"usgs":false}],"preferred":false,"id":904676,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jamieson, John","contributorId":339557,"corporation":false,"usgs":false,"family":"Jamieson","given":"John","affiliations":[{"id":40744,"text":"Memorial University","active":true,"usgs":false}],"preferred":false,"id":904677,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reeves, Eoghan P.","contributorId":339559,"corporation":false,"usgs":false,"family":"Reeves","given":"Eoghan P.","affiliations":[{"id":28158,"text":"University of Bergen","active":true,"usgs":false}],"preferred":false,"id":904678,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gartman, Amy 0000-0001-9307-3062 agartman@usgs.gov","orcid":"https://orcid.org/0000-0001-9307-3062","contributorId":177057,"corporation":false,"usgs":true,"family":"Gartman","given":"Amy","email":"agartman@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":904679,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barreyre, Thibaut","contributorId":339562,"corporation":false,"usgs":false,"family":"Barreyre","given":"Thibaut","email":"","affiliations":[{"id":28158,"text":"University of Bergen","active":true,"usgs":false}],"preferred":false,"id":904680,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Babechuk, Michael G.","contributorId":339563,"corporation":false,"usgs":false,"family":"Babechuk","given":"Michael","email":"","middleInitial":"G.","affiliations":[{"id":40744,"text":"Memorial University","active":true,"usgs":false}],"preferred":false,"id":904681,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jorgensen, Steffen L.","contributorId":339565,"corporation":false,"usgs":false,"family":"Jorgensen","given":"Steffen","email":"","middleInitial":"L.","affiliations":[{"id":28158,"text":"University of Bergen","active":true,"usgs":false}],"preferred":false,"id":904682,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Robert, Katleen","contributorId":339567,"corporation":false,"usgs":false,"family":"Robert","given":"Katleen","email":"","affiliations":[{"id":40744,"text":"Memorial University","active":true,"usgs":false}],"preferred":false,"id":904683,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70257766,"text":"70257766 - 2024 - From eDNA to decisions using a multi-method approach to restoration planning in streams","interactions":[],"lastModifiedDate":"2024-08-26T12:29:25.642251","indexId":"70257766","displayToPublicDate":"2024-06-21T07:24:58","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"From eDNA to decisions using a multi-method approach to restoration planning in streams","docAbstract":"<div id=\"Abs1-section\" class=\"c-article-section\"><div id=\"Abs1-content\" class=\"c-article-section__content\"><p>Reintroduction efforts are increasingly used to mitigate biodiversity losses, but are frequently challenged by inadequate planning and uncertainty. High quality information about population status and threats can be used to prioritize reintroduction and restoration efforts and can transform ad hoc approaches into opportunities for improving conservation outcomes at a landscape scale. We conducted comprehensive environmental DNA (eDNA) and visual encounter surveys to determine the distribution of native and non-native aquatic species in two high-priority watersheds to address key uncertainties—such as the distribution of threats and the status of existing populations—inherent in restoration planning. We then used these occurrence data to develop a menu of potential conservation actions and a decision framework to benefit an endangered vertebrate (foothill yellow-legged frog,<span>&nbsp;</span><i>Rana boylii</i>) in dynamic stream systems. Our framework combines the strengths of multiple methods, allowing managers and conservation scientists to incorporate conservation science and site-specific knowledge into the planning process to increase the likelihood of achieving conservation goals.</p></div></div>","language":"English","publisher":"Nature","doi":"10.1038/s41598-024-64612-5","usgsCitation":"Adams, A.J., Kamoroff, C., Norton, D.R., Grasso, R.L., Halstead, B., Kleeman, P.M., Mengelt, C., Powelson, K., Seaborn, T., and Goldberg, C., 2024, From eDNA to decisions using a multi-method approach to restoration planning in streams: Scientific Reports, v. 14, 14335, 11 p., https://doi.org/10.1038/s41598-024-64612-5.","productDescription":"14335, 11 p.","ipdsId":"IP-151835","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":439367,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-024-64612-5","text":"Publisher Index Page"},{"id":433155,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","noUsgsAuthors":false,"publicationDate":"2024-06-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Adams, Andrea J.","contributorId":202767,"corporation":false,"usgs":false,"family":"Adams","given":"Andrea","email":"","middleInitial":"J.","affiliations":[{"id":36524,"text":"University of California, Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":911628,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kamoroff, C.","contributorId":343666,"corporation":false,"usgs":false,"family":"Kamoroff","given":"C.","email":"","affiliations":[],"preferred":false,"id":911629,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Norton, Daniel R.","contributorId":64265,"corporation":false,"usgs":true,"family":"Norton","given":"Daniel","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":911630,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Halstead, Brian J. 0000-0002-5535-6528 bhalstead@usgs.gov","orcid":"https://orcid.org/0000-0002-5535-6528","contributorId":3051,"corporation":false,"usgs":true,"family":"Halstead","given":"Brian J.","email":"bhalstead@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":911632,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Grasso, R. L.","contributorId":343667,"corporation":false,"usgs":false,"family":"Grasso","given":"R.","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":911631,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kleeman, Patrick M. 0000-0001-6567-3239 pkleeman@usgs.gov","orcid":"https://orcid.org/0000-0001-6567-3239","contributorId":3948,"corporation":false,"usgs":true,"family":"Kleeman","given":"Patrick","email":"pkleeman@usgs.gov","middleInitial":"M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":911633,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mengelt, C.","contributorId":343668,"corporation":false,"usgs":false,"family":"Mengelt","given":"C.","affiliations":[],"preferred":false,"id":911634,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Powelson, K.","contributorId":343669,"corporation":false,"usgs":false,"family":"Powelson","given":"K.","affiliations":[],"preferred":false,"id":911635,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Seaborn, T.","contributorId":343670,"corporation":false,"usgs":false,"family":"Seaborn","given":"T.","affiliations":[],"preferred":false,"id":911636,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Goldberg, C.S.","contributorId":39551,"corporation":false,"usgs":true,"family":"Goldberg","given":"C.S.","email":"","affiliations":[],"preferred":false,"id":911637,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70255524,"text":"ofr20211030M - 2024 - System characterization report on the Gaofen-6","interactions":[{"subject":{"id":70255524,"text":"ofr20211030M - 2024 - System characterization report on the Gaofen-6","indexId":"ofr20211030M","publicationYear":"2024","noYear":false,"chapter":"M","displayTitle":"System Characterization Report on the Gaofen-6","title":"System characterization report on the Gaofen-6"},"predicate":"IS_PART_OF","object":{"id":70221266,"text":"ofr20211030 - 2021 - System characterization of Earth observation sensors","indexId":"ofr20211030","publicationYear":"2021","noYear":false,"title":"System characterization of Earth observation sensors"},"id":1}],"isPartOf":{"id":70221266,"text":"ofr20211030 - 2021 - System characterization of Earth observation sensors","indexId":"ofr20211030","publicationYear":"2021","noYear":false,"title":"System characterization of Earth observation sensors"},"lastModifiedDate":"2024-06-21T00:09:54.495479","indexId":"ofr20211030M","displayToPublicDate":"2024-06-20T15:21:37","publicationYear":"2024","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":"2021-1030","chapter":"M","displayTitle":"System Characterization Report on the Gaofen-6","title":"System characterization report on the Gaofen-6","docAbstract":"<h1>Executive Summary</h1><p>Gaofen-6 represents a series of Chinese high-resolution Earth observation satellites. More than 12 satellites have been launched in the Gaofen series, beginning with Gaofen-1 in 2013. Satellites within the series have varying infrared, radar, and optical imaging capabilities. The primary goal for the satellites in this series is to provide near real-time observations for climate change monitoring, geographical mapping, precision agriculture support, environmental and resource surveying, and disaster prevention. More information on Chinese satellites and sensors is available in the “2022 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium.”</p><p>The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances of Gaofen-6. Results of these analyses indicate that Gaofen-6 has an interior geometric performance root mean square error ranging from 2.84 meters (m; 0.18 pixel) to 7.42 m (0.46 pixel) in easting and from 2.84 m (0.18 pixel) to 11.57 m (0.72 pixel) in northing in band-to-band registration, an exterior geometric performance root mean square error ranging from 154.50 m (8.80 pixels) in easting to 14.65 m (0.80 pixel) in northing in comparison to a corresponding Sentinel-2 scene, a radiometric performance ranging from 0.018 to 0.055 (in offset) and from 0.620 to 0.858 (in slope), and a spatial performance ranging from 2.10 to 2.30 pixels at full width at half maximum, with a modulation transfer function at a Nyquist frequency ranging from 0.040 to 0.055.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030M","usgsCitation":"Sampath, A., Christopherson, J., Park, S., Kim, M., Stensaas, G.L., and Anderson, C., 2024, System characterization report on the Gaofen-6, chap. M <i>of</i> Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 9 p., https://doi.org/10.3133/ofr20211030M.","productDescription":"iv, 9 p.","numberOfPages":"18","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-133753","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":430388,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/m/coverthb.jpg"},{"id":430389,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/m/ofr20211030m.pdf","text":"Report","size":"1.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1030–M"},{"id":430390,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1030/m/ofr20211030m.XML"}],"contact":"<p>Director, <a data-mce-href=\"https://www.usgs.gov/centers/eros\" href=\"https://www.usgs.gov/centers/eros\">Earth Resources Observation and Science Center</a><br>U.S. Geological Survey<br>47914 252nd Street<br>Sioux Falls, SD 57198</p><p><a data-mce-href=\"../contact\" href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Executive Summary</li><li>Reference Cited</li><li>Introduction</li><li>System Description</li><li>Standardized Procedures</li><li>Measurements</li><li>Analysis</li><li>Summary and Conclusions</li><li>Selected References</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2024-06-20","noUsgsAuthors":false,"publicationDate":"2024-06-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Sampath, Aparajithan 0000-0002-6922-4913","orcid":"https://orcid.org/0000-0002-6922-4913","contributorId":222486,"corporation":false,"usgs":false,"family":"Sampath","given":"Aparajithan","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":false,"id":904500,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Christopherson, Jon 0000-0002-2472-0059","orcid":"https://orcid.org/0000-0002-2472-0059","contributorId":290324,"corporation":false,"usgs":false,"family":"Christopherson","given":"Jon","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":false,"id":904503,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Park, Seonkyung 0000-0003-3203-1998 seonkyungpark@contractor.usgs.gov","orcid":"https://orcid.org/0000-0003-3203-1998","contributorId":222488,"corporation":false,"usgs":false,"family":"Park","given":"Seonkyung","email":"seonkyungpark@contractor.usgs.gov","affiliations":[{"id":40547,"text":"United Support Services, Contractor to the USGS Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":904501,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kim, Minsu 0000-0003-4472-0926","orcid":"https://orcid.org/0000-0003-4472-0926","contributorId":297371,"corporation":false,"usgs":false,"family":"Kim","given":"Minsu","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":false,"id":904502,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stensaas, Gregory L. 0000-0001-6679-2416 stensaas@usgs.gov","orcid":"https://orcid.org/0000-0001-6679-2416","contributorId":2551,"corporation":false,"usgs":true,"family":"Stensaas","given":"Gregory","email":"stensaas@usgs.gov","middleInitial":"L.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":904504,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Anderson, Cody 0000-0001-5612-1889 chanderson@usgs.gov","orcid":"https://orcid.org/0000-0001-5612-1889","contributorId":195521,"corporation":false,"usgs":true,"family":"Anderson","given":"Cody","email":"chanderson@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":904505,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
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