{"pageNumber":"120","pageRowStart":"2975","pageSize":"25","recordCount":41032,"records":[{"id":70247995,"text":"sir20235047 - 2023 - Compound flood model for the lower Nooksack River and delta, western Washington—Assessment of vulnerability and nature-based adaptation opportunities to mitigate higher sea level and stream flooding","interactions":[],"lastModifiedDate":"2026-03-09T16:02:11.243737","indexId":"sir20235047","displayToPublicDate":"2023-08-31T12:53:36","publicationYear":"2023","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":"2023-5047","displayTitle":"Compound Flood Model for the Lower Nooksack River and Delta, Western Washington—Assessment of Vulnerability and Nature-Based Adaptation Opportunities to Mitigate Higher Sea Level and Stream Flooding","title":"Compound flood model for the lower Nooksack River and delta, western Washington—Assessment of vulnerability and nature-based adaptation opportunities to mitigate higher sea level and stream flooding","docAbstract":"<p>Higher sea level and stream runoff associated with climate change is expected to lead to greater lowland flooding across the Pacific Northwest. Increases in stream runoff that range from 20 to 32 percent by the 2040s and from 52 to 72 percent by the 2080s is expected to steadily increase flood risk. Flood risk is also expected to increase in response to the landward shift in high tides and storm surge, which will retard downstream conveyance. The combination of higher stream runoff, which is expected to drive greater fluvial sediment delivery to the coast, and more frequent, higher coastal waters relative to present-day (2023) levels, which will retard streamflow, is projected to cause more sedimentation across coastal and estuarine systems, exacerbating the flood risk. In the Nooksack River delta of western Washington, as in many Puget Sound deltas, resilient adaptation planning to mitigate impacts to community assets and infrastructure, nationally essential agricultural areas, and valued habitats and restoration investments that support endangered and threatened salmon recovery are underway but are in need of more informed projections of compound flood hazards.</p><p>A Delft3D Flexible Mesh hydrodynamic model was constructed and used to assess changes in the extent, frequency, and timing of flood exposure associated with higher sea level and stream runoff projected to occur in the 2040s and 2080s. The model was also used to evaluate the change in and potential mitigating effects to flood exposure associated with individual and cumulative salmon-habitat-restoration strategies. Model simulations also evaluated the sensitivity of sedimentation to the individual and cumulative effects of higher fluvial delivery, trapping by sea-level rise, and changes in hydrodynamics associated with the rerouting of flows by proposed restoration strategies. The model performed well, having mean absolute errors for water levels below 1 foot (0.3 meters) when tested during a 2-year period for two recent flood events of record, the February 2, 2020, “Super Bowl flood” and the January 8, 2009, stream flood, both of which caused substantial flooding and damage across the study area. Fluvial discharge was found to dominate flood hazard at higher elevations in the study area, whereas near the coast, sea-level rise is computed to turn a less extreme 2-year (50 percent annual exceedance probability [AEP]) bankfull streamflow, which, at present (2023), causes nuisance flooding, into a more extreme 5-year (20 percent AEP) and 10 percent AEP stream-flood event by the 2050s and 2100, respectively.</p><p>The February 2020 Super Bowl flood was calculated to be a 10-year or 10 percent AEP peak-flow event, and the January 2009 flood was calculated to be a 25-year (4 percent AEP) peak-flow event. Extreme events such as the February 2020 Super Bowl flood and the January 2009 flood caused extensive damage across the Nooksack River floodplain, and model computations predict these magnitudes of events would have notably greater effect in the 2040s and 2080s in response to higher projected sea level and stream runoff. The modeled January 2009 flood is predicted to transform into a flood event, causing flood exposure that is comparable to the 100-year or 1 percent AEP flood by the 2040s. The modeled January 2009 flood is also predicted to exceed the flood exposure of the recent November 16, 2021, flood, which caused substantial damage in the lower Nooksack River floodplain and restricted access for emergency-management efforts on important arterial roadways in the area; the measured peak discharge during the November 16, 2021, flood exceeded that of the January 2009 flood.</p><p>Two of several identified alternative strategies that reroute floodwaters to restore salmon habitat were projected to reduce exposure to the increasingly impactful 10 and 4 percent AEP stream-flood events through the 2080s. The effects of the suggested alternatives, however, were found to reduce flow velocities, promote additional sedimentation, and reduce flow conveyance in the main-stem Nooksack River, a concern to flood-management efforts, navigation, and fishing. The model also suggests that main-stem channel sedimentation is likely, given projected climate change. Higher stream runoff that increases fluvial-sediment delivery and higher sea levels that retard downstream flow are expected to lead to greater sedimentation. Lastly, the model was used to assess the sensitivity of flood exposure to the individual and cumulative effects of climate changes, alternative strategies, and sedimentation, including recently observed decadal-scale aggradation patterns. These results indicate that sediment is likely to continue to be a challenge to flood-management efforts and that nature-based alternatives that benefit ecosystem restoration may also mitigate flood exposure for several decades.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235047","collaboration":"Prepared in cooperation with U.S. Environmental Protection Agency through Washington State Department of Fish and Wildlife <br> Prepared in collaboration with Whatcom County Flood Control Zone District, Nooksack Indian Tribe, and Lummi Tribe","usgsCitation":"Grossman, E.E., vanArendonk, N.R., and Nederhoff, K., 2023, Compound flood model for the lower Nooksack River and delta, western Washington—Assessment of vulnerability and nature-based adaptation opportunities to mitigate higher sea level and stream flooding: U.S. Geological Survey Scientific Investigations Report 2023–5047, 49 p., https://doi.org/10.3133/sir20235047.","productDescription":"Report: ix, 49 p.; Data Release","numberOfPages":"49","onlineOnly":"Y","ipdsId":"IP-134130","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":500924,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115235.htm","linkFileType":{"id":5,"text":"html"}},{"id":420306,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5047/covrthb.jpg"},{"id":420307,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5047/sir20235047.pdf","text":"Report","size":"12 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":420308,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DJM7X2","text":"Model input and projections of compound floodwater depths for the lower Nooksack River and delta, western Washington State","description":"Grossman, E.E., vanArendonk, N.R., Nederhoff, K., and Parker, K.A., 2023, Model input and projections of compound floodwater depths for the lower Nooksack River and delta, western Washington State: U.S. Geological Survey data release, https://doi.org/10.5066/P9DJM7X2."}],"country":"United States","state":"Washington","otherGeospatial":"lower Nooksack River and delta","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -122.69918794979601,\n              48.9\n            ],\n            [\n              -122.69918794979601,\n              48.70243139948016\n            ],\n            [\n              -122.46630483506905,\n              48.70\n            ],\n            [\n              -122.46630483506905,\n              48.9\n            ],\n            [\n              -122.69918794979601,\n              48.9\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p><a href=\"https://www.usgs.gov/centers/pcmsc\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/centers/pcmsc\">Pacific Coastal and Marine Science Center</a><br><a href=\"https://www.usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/\">U.S. Geological Survey</a><br>2885 Mission St.<br>Santa Cruz, CA 95060</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Results</li><li>Summary</li><li>References Cited</li><li>Appendix 1</li></ul>","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2023-08-31","noUsgsAuthors":false,"publicationDate":"2023-08-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Grossman, Eric E. 0000-0003-0269-6307 egrossman@usgs.gov","orcid":"https://orcid.org/0000-0003-0269-6307","contributorId":196610,"corporation":false,"usgs":true,"family":"Grossman","given":"Eric","email":"egrossman@usgs.gov","middleInitial":"E.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":881455,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"vanArendonk, Nathan R. 0000-0003-3911-995X","orcid":"https://orcid.org/0000-0003-3911-995X","contributorId":219469,"corporation":false,"usgs":false,"family":"vanArendonk","given":"Nathan","email":"","middleInitial":"R.","affiliations":[{"id":12723,"text":"Western Washington University","active":true,"usgs":false}],"preferred":false,"id":881456,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nederhoff, Cornelis M. 0000-0003-0552-3428","orcid":"https://orcid.org/0000-0003-0552-3428","contributorId":265889,"corporation":false,"usgs":false,"family":"Nederhoff","given":"Cornelis","email":"","middleInitial":"M.","affiliations":[{"id":33886,"text":"Deltares USA","active":true,"usgs":false}],"preferred":true,"id":881457,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70248002,"text":"sir20235095 - 2023 - Surrogate regression models estimating nitrate concentrations at six springs in Gooding County, south-central Idaho, 2018–22","interactions":[],"lastModifiedDate":"2026-03-12T21:18:37.918599","indexId":"sir20235095","displayToPublicDate":"2023-08-31T09:35:05","publicationYear":"2023","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":"2023-5095","displayTitle":"Surrogate Regression Models Estimating Nitrate Concentrations at Six Springs in Gooding County, South-Central Idaho, 2018–22","title":"Surrogate regression models estimating nitrate concentrations at six springs in Gooding County, south-central Idaho, 2018–22","docAbstract":"<p>Populations of endangered Banbury Springs limpet (<i>Idaholanx fresti</i>) and threatened Bliss Rapids snail (<i>Taylorconcha serpenticola</i>) are declining in springs north of the Snake River along the southern Gooding County boundary, in south-central Idaho. One hypothesis for the decline is that increased macrophyte growth, associated with elevated nitrate concentrations in the springs, is decreasing aquatic habitat for the limpet and snail populations. In support of U.S. Fish and Wildlife Service efforts to understand the population declines, the U.S. Geological Survey developed surrogate regression models to estimate nitrate concentrations at six springs influenced by upgradient agriculture, which results in an increase and decrease each year of streamflow, specific conductance, and nitrate concentrations. The surrogate regression models use continuous specific conductance data and streamflow data (available at two springs from existing U.S. Geological Survey streamgages).</p><p>The spring surrogate regression models showed that specific conductance can be an effective surrogate for nitrate in springs affected by agriculture and that the model results improved when streamflow data were included. Four of the six springs had surrogate regression models (using specific conductance and day of the year as explanatory variables) that performed well based on model summary statistics, and these models improved further with the inclusion of streamflow as an explanatory variable. The surrogate regression models at four springs had coefficient of determination (R<sup>2</sup>) values ranging from 0.79 to 0.94. The root mean squared error of the four models ranged from 0.07 to 0.11 milligrams per liter. Two of the six springs were not well modeled, with adjusted R<sup>2</sup> values of 0.15 and 0.80. The surrogate regression models for these two springs also did not meet the required assumption of linearity between explanatory and response variables for linear regression. The surrogate regression models show that specific conductance can be an effective surrogate for nitrate in springs affected by agriculture and that models are improved where streamflow data are included. These surrogates improve understanding of nitrate concentration variability in the springs.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235095","usgsCitation":"Skinner, K.D., 2023, Surrogate regression models estimating nitrate concentrations at six springs in Gooding County, south-central Idaho, 2018–22: U.S. Geological Survey Scientific Investigations Report 2023–5095, 22 p., https://doi.org/10.3133/sir20235095.","productDescription":"Report: vii, 22 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-147907","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":420343,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5095/coverthb.jpg"},{"id":420347,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5095/sir20235095.XML"},{"id":420346,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5095/images"},{"id":420345,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235095/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5095"},{"id":420344,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5095/sir20235095.pdf","text":"Report","size":"3.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5095"},{"id":420348,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BXIBF9","text":"USGS data release","description":"USGS data release","linkHelpText":"Surrogate regression model data for estimating nitrate concentrations at six springs in Gooding County, south-central Idaho"},{"id":501061,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115234.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Idaho","county":"Gooding County","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -115,\n              42.9167\n            ],\n            [\n              -115,\n              42.6\n            ],\n            [\n              -114.6,\n              42.6\n            ],\n            [\n              -114.6,\n              42.9167\n            ],\n            [\n              -115,\n              42.9167\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:dc_id@usgs.gov\" data-mce-href=\"mailto:dc_id@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/idaho-water-science-center\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/centers/idaho-water-science-center\">Idaho Water Science Center</a><br>U.S. Geological Survey<br>230 Collins Road<br>Boise, Idaho 83702-4520</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Nitrate Surrogate Regression Model Results</li><li>Discussion</li><li>Summary</li><li>References Cited</li></ul>","publishedDate":"2023-08-31","noUsgsAuthors":false,"publicationDate":"2023-08-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Skinner, Kenneth D. 0000-0003-1774-6565 kskinner@usgs.gov","orcid":"https://orcid.org/0000-0003-1774-6565","contributorId":1836,"corporation":false,"usgs":true,"family":"Skinner","given":"Kenneth","email":"kskinner@usgs.gov","middleInitial":"D.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":881486,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70248746,"text":"70248746 - 2023 - Quantifying site effects and their influence on earthquake source parameter estimations using a dense array in Oklahoma","interactions":[],"lastModifiedDate":"2024-06-27T16:55:25.440762","indexId":"70248746","displayToPublicDate":"2023-08-31T06:41:22","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6453,"text":"Journal of Geophysical Research Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying site effects and their influence on earthquake source parameter estimations using a dense array in Oklahoma","docAbstract":"<div class=\"article-section__content en main\"><p>We investigate the effects of site response on source parameter estimates using earthquakes recorded by the LArge-n Seismic Survey in Oklahoma (LASSO). While it is well known that near-surface unconsolidated sediments can cause an apparent breakdown of earthquake self-similarity, the influence of laterally varying site conditions remains unclear. We analyze site conditions across the 1825-station array on a river plain within an area of 40&nbsp;km by 23&nbsp;km using vertical ground motions from 14 regional earthquakes. While the source radiation pattern controls P-wave ground motions below 8&nbsp;Hz, the surface geology correlates with P-wave ground motions above 8&nbsp;Hz and S-wave ground motions at 2–21&nbsp;Hz. Stations installed in alluvial sediments have vertical ground motions that can exceed three times the array median. We use the variation of ground motion of regional earthquakes across the array as a proxy for site effects. The corner frequencies and stress drops of local earthquakes (<i>M</i><sub><i>L</i></sub>&nbsp;=&nbsp;0.01–3) estimated using a standard single-spectra approach show negative correlations with the site-effect proxy, while the seismic moments show positive correlations. In contrast, the spectral-ratio approach effectively shows no correlation. The overall bias is small as expected for this relatively homogeneous structure; accurate estimation of site-related biases requires at least 30 stations. Correcting for site-related biases reduces the standard deviations of the source parameters by less than 13% of the total variations. Remaining variations are partially associated with source directivity and model misfits— as small earthquakes can have complex ruptures.</p></div>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023JB027144","usgsCitation":"Chang, H., Abercrombie, R.E., Nakata, N., Pennington, C., Kemna, K.B., Cochran, E.S., and Harrington, R.M., 2023, Quantifying site effects and their influence on earthquake source parameter estimations using a dense array in Oklahoma: Journal of Geophysical Research Solid Earth, v. 128, no. 9, e2023JB027144, 19 p., https://doi.org/10.1029/2023JB027144.","productDescription":"e2023JB027144, 19 p.","ipdsId":"IP-147611","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":442261,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023jb027144","text":"Publisher Index Page"},{"id":420939,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -99.03088758444359,\n              37.03049544474975\n            ],\n            [\n              -99.03088758444359,\n              34.64496155871649\n            ],\n            [\n              -95.45088062769271,\n              34.64496155871649\n            ],\n            [\n              -95.45088062769271,\n              37.03049544474975\n            ],\n            [\n              -99.03088758444359,\n              37.03049544474975\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"128","issue":"9","noUsgsAuthors":false,"publicationDate":"2023-09-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Chang, Hilary","contributorId":293564,"corporation":false,"usgs":false,"family":"Chang","given":"Hilary","email":"","affiliations":[{"id":12444,"text":"Massachusetts Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":883416,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Abercrombie, Rachel E.","contributorId":329841,"corporation":false,"usgs":false,"family":"Abercrombie","given":"Rachel","email":"","middleInitial":"E.","affiliations":[{"id":13570,"text":"Boston University","active":true,"usgs":false}],"preferred":false,"id":883417,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nakata, Nori","contributorId":293565,"corporation":false,"usgs":false,"family":"Nakata","given":"Nori","affiliations":[{"id":12444,"text":"Massachusetts Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":883418,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pennington, Colin","contributorId":329842,"corporation":false,"usgs":false,"family":"Pennington","given":"Colin","affiliations":[{"id":16721,"text":"LLNL","active":true,"usgs":false}],"preferred":false,"id":883419,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kemna, Kilian B.","contributorId":329843,"corporation":false,"usgs":false,"family":"Kemna","given":"Kilian","email":"","middleInitial":"B.","affiliations":[{"id":47982,"text":"Ruhr-Universitat Bochum","active":true,"usgs":false}],"preferred":false,"id":883420,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cochran, Elizabeth S. 0000-0003-2485-4484 ecochran@usgs.gov","orcid":"https://orcid.org/0000-0003-2485-4484","contributorId":2025,"corporation":false,"usgs":true,"family":"Cochran","given":"Elizabeth","email":"ecochran@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":883421,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Harrington, Rebecca M.","contributorId":146633,"corporation":false,"usgs":false,"family":"Harrington","given":"Rebecca","email":"","middleInitial":"M.","affiliations":[{"id":16736,"text":"Dept. of Earth and Planetary Sci,.McGill Univ., Montreal, Quebec","active":true,"usgs":false}],"preferred":false,"id":883422,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70264030,"text":"70264030 - 2023 - Eastern Indigo snake (Drymarchon couperi) shelter site use In peninsular Florida, USA, and implicatIons for habItat conservatIon","interactions":[],"lastModifiedDate":"2025-03-05T17:28:54.304812","indexId":"70264030","displayToPublicDate":"2023-08-31T00:00:00","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1894,"text":"Herpetological Conservation and Biology","onlineIssn":"2151-0733","printIssn":"1931-7603","active":true,"publicationSubtype":{"id":10}},"title":"Eastern Indigo snake (Drymarchon couperi) shelter site use In peninsular Florida, USA, and implicatIons for habItat conservatIon","docAbstract":"<p>Shelters are critical for many species as protection from predators and extreme temperatures. Successful conservation of reptiles requires understanding both shelter site requirements and availability. The Eastern Indigo Snake (EIS; <i>Drymarchon couperi</i>) is endemic to the southeastern U.S. and is federally listed. Recovery has focused on maximizing unfragmented landscapes, with less attention on fine-scale features such as shelter sites. In the northern EIS range, Gopher Tortoise (<i>Gopherus polyphemus</i>) burrows are used extensively for shelter. Although EIS in peninsular Florida often shelter in tortoise burrows, they also use other shelters where tortoise burrows are scarce or absent. Solely focusing EIS survey and management efforts where Gopher Tortoises are present may overlook occupied habitats and misallocate resources. We investigated the importance of different shelter sites in central Florida using data from radio-tracked EIS. We modeled the use of shelter categories as a function of sex, season, and habitat using Bayesian multinomial Generalized Linear Models. Results showed that EIS in peninsular Florida used Gopher Tortoise burrows across all seasons and habitats. Tortoise burrow use was highest in xeric habitats and lowest in mesic habitats where burrows are most and least abundant, respectively. There was less variability in shelter site use in disturbed habitats and flatwoods. Tortoise burrow use by EIS in the cool season across sexes and habitats in our study was much lower than in southern Georgia. Our results indicate that EIS are less dependent on Gopher Tortoise burrows in peninsular Florida and that suitable habitats with few or no tortoise burrows could still provide conservation value for EIS.</p>","language":"English","publisher":"Herpetological Conservation and Biology","usgsCitation":"Bolt, M., Bauder, J.M., Legare, M., Jenkins, C., Rothermel, B., and Breininger, D., 2023, Eastern Indigo snake (Drymarchon couperi) shelter site use In peninsular Florida, USA, and implicatIons for habItat conservatIon: Herpetological Conservation and Biology, v. 18, no. 2, p. 362-373.","productDescription":"12 p.","startPage":"362","endPage":"373","ipdsId":"IP-139825","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":482919,"rank":1,"type":{"id":15,"text":"Index 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,{"id":70249665,"text":"70249665 - 2023 - Estimating fat content in barred owls (Strix varia) with predictive models developed from direct measures of proximate body composition","interactions":[],"lastModifiedDate":"2023-10-24T13:44:20.534571","indexId":"70249665","displayToPublicDate":"2023-08-30T08:42:28","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3919,"text":"Conservation Physiology","onlineIssn":"2051-1434","active":true,"publicationSubtype":{"id":10}},"title":"Estimating fat content in barred owls (Strix varia) with predictive models developed from direct measures of proximate body composition","docAbstract":"<p><span>Body condition indices and related metrics can help assess habitat quality and other ecological processes, and ideally, these metrics are based on measures of lipids directly extracted from the species of interest. In recent decades, barred owls (</span><i>Strix varia</i><span>) have become a species of conservation concern as they invaded older forests of the US Pacific Northwest, and caused population declines of the closely related and federally threatened northern spotted owl (</span><i>Strix occidentalis caurina</i><span>). A simple and effective measure of barred owl body condition could help to understand how habitat quality varies within their new range, which in turn can inform their management and other aspects of their ecology. Using 77 barred owl carcasses collected during experimental removals in Washington and Oregon, USA, we measured the amount of lipid in each specimen with proximate body composition analysis. We then fit and compared (with adjusted&nbsp;</span><i>R</i><sup>2</sup><span>&nbsp;values) alternative linear regression models to estimate the percent lipids in dry mass of the owls based on morphometric body condition indices, a qualitative fat score of subcutaneous breast fat, sex and the time of year females were collected (relative to egg production). Adjusted&nbsp;</span><i>R</i><sup>2</sup><span>&nbsp;values for all models ranged from 0.49 to 0.87, with the best model including mass divided by foot-pad length, fat score, sex and the time of year a female was collected. Most models generated comparable estimates of percent lipids at a population level and we provided correction factors to apply these models when used with live barred owls, allowing for site-specific comparisons of body condition among individuals inhabiting a diversity of environmental conditions.</span></p>","language":"English","publisher":"Oxford Academic","doi":"10.1093/conphys/coad069","usgsCitation":"Baumbusch, R.C., Dugger, K., and Wiens, D., 2023, Estimating fat content in barred owls (Strix varia) with predictive models developed from direct measures of proximate body composition: Conservation Physiology, v. 11, no. 1, coad069, 9 p., https://doi.org/10.1093/conphys/coad069.","productDescription":"coad069, 9 p.","ipdsId":"IP-139469","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":442265,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/conphys/coad069","text":"Publisher Index Page"},{"id":435202,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BY17SZ","text":"USGS data release","linkHelpText":"Fat content and morphometric data in barred owls (Strix varia) in the Pacific Northwest"},{"id":422066,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-08-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Baumbusch, Ryan C.","contributorId":331066,"corporation":false,"usgs":false,"family":"Baumbusch","given":"Ryan","email":"","middleInitial":"C.","affiliations":[{"id":79110,"text":"Oregon Cooperative Fish and Wildlife Research Unit, Department of Fisheries, Wildlife, and Conservation Sciences, Oregon State University","active":true,"usgs":false}],"preferred":false,"id":886640,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dugger, Katie M. 0000-0002-4148-246X cdugger@usgs.gov","orcid":"https://orcid.org/0000-0002-4148-246X","contributorId":4399,"corporation":false,"usgs":true,"family":"Dugger","given":"Katie","email":"cdugger@usgs.gov","middleInitial":"M.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":886641,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wiens, David 0000-0002-2020-038X","orcid":"https://orcid.org/0000-0002-2020-038X","contributorId":267230,"corporation":false,"usgs":true,"family":"Wiens","given":"David","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":886642,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70247959,"text":"70247959 - 2023 - Improvements and evaluation of the agro-hydrologic VegET model for large-area water budget analysis and drought monitoring","interactions":[],"lastModifiedDate":"2023-08-29T14:48:08.706864","indexId":"70247959","displayToPublicDate":"2023-08-29T09:26:17","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10778,"text":"Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Improvements and evaluation of the agro-hydrologic VegET model for large-area water budget analysis and drought monitoring","docAbstract":"<p><span>We enhanced the agro-hydrologic VegET model to include snow accumulation and melt processes and the separation of runoff into surface runoff and deep drainage. Driven by global weather datasets and parameterized by land surface phenology (LSP), the enhanced VegET model was implemented in the cloud to simulate daily soil moisture (SM), actual evapotranspiration (ETa), and runoff (R) for the conterminous United States (CONUS) and the Greater Horn of Africa (GHA). Evaluation of the VegET model with independent data showed satisfactory performance, capturing the temporal variability of SM (Pearson correlation r: 0.22–0.97), snowpack (r: 0.86–0.88), ETa (r: 0.41–0.97), and spatial variability of R (r: 0.81–0.90). Absolute magnitudes showed some biases, indicating the need of calibrating the model for water budget analysis. The seasonal Landscape Water Requirement Satisfaction Index (L-WRSI) for CONUS and GHA showed realistic depictions of drought hazard extent and severity, indicating the usefulness of the L-WRSI for the convergence of an evidence toolkit used by the Famine Early Warning System Network to monitor potential food insecurity conditions in different parts of the world. Using projected weather datasets and landcover-based LSP, the VegET model can be used not only for global monitoring of drought conditions, but also for evaluating scenarios on the effect of a changing climate and land cover on agriculture and water resources.</span></p>","language":"English","publisher":"MDPI","doi":"10.3390/hydrology10080168","usgsCitation":"Senay, G.B., Kagone, S., Parrish, G.E., Khand, K., Boiko, O., and Velpuri, N., 2023, Improvements and evaluation of the agro-hydrologic VegET model for large-area water budget analysis and drought monitoring: Hydrology, v. 10, no. 8, 168, 26 p., https://doi.org/10.3390/hydrology10080168.","productDescription":"168, 26 p.","ipdsId":"IP-155571","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":442273,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/hydrology10080168","text":"Publisher Index Page"},{"id":435203,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ILC6RP","text":"USGS data release","linkHelpText":"VegET v2.0 illustrative products and evaluation"},{"id":420242,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Burundi, Djibouti, Eritrea, Ethiopia, Kenya, Rwanda, Somalia, South Sudan, Sudan, Tanzania, Uganda, United States","otherGeospatial":"Horn of Africa","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"geometry\": {\n        \"type\": \"MultiPolygon\",\n        \"coordinates\": [\n          [\n            [\n              [\n                -94.81758,\n                49.38905\n              ],\n              [\n                -95.15609,\n                49.38425\n              ],\n              [\n                -95.15907,\n                49\n              ],\n              [\n                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,{"id":70249905,"text":"70249905 - 2023 - Macroscale analyses suggest invasive plant impacts depend more on the composition of invading plants than on environmental context","interactions":[],"lastModifiedDate":"2023-11-04T13:09:05.804684","indexId":"70249905","displayToPublicDate":"2023-08-29T08:02:37","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1839,"text":"Global Ecology and Biogeography","active":true,"publicationSubtype":{"id":10}},"title":"Macroscale analyses suggest invasive plant impacts depend more on the composition of invading plants than on environmental context","docAbstract":"<h3 id=\"geb13749-sec-0001-title\" class=\"article-section__sub-title section1\">Aim</h3><p>Native biodiversity is threatened by the spread of non-native invasive species. Many studies demonstrate that invasions reduce local biodiversity but we lack an understanding of how impacts vary across environments at the macroscale. Using ~11,500 vegetation surveys from ecosystems across the United States, we quantified how the relationship between non-native plant cover and native plant diversity varied across different compositions of invading plants (measured by non-native plant richness and evenness) and environmental contexts (measured by productivity and human activity).</p><h3 id=\"geb13749-sec-0002-title\" class=\"article-section__sub-title section1\">Location</h3><p>Continental United States.</p><h3 id=\"geb13749-sec-0003-title\" class=\"article-section__sub-title section1\">Time Period</h3><p>Surveys from 1990s-present.</p><h3 id=\"geb13749-sec-0004-title\" class=\"article-section__sub-title section1\">Major Taxa Studied</h3><p>Terrestrial plant communities.</p><h3 id=\"geb13749-sec-0005-title\" class=\"article-section__sub-title section1\">Methods</h3><p>We fit mixed effects models to understand how native plant richness, diversity and evenness varied with non-native cover. We tested how this relationship varied when non-native cover interacted with non-native plant richness and evenness, and with productivity and human activity.</p><h3 id=\"geb13749-sec-0006-title\" class=\"article-section__sub-title section1\">Results</h3><p>Across the United States, communities with greater cover of non-native plants had lower native plant richness and diversity but higher evenness, suggesting rare native plants can be lost while dominant plants decline in abundance. The relationship between non-native cover and native community diversity varied with non-native plant richness and evenness but was not associated with productivity and human activity. Negative associations were strongest in areas with low non-native richness and evenness, characterizing plant communities that were invaded by a dominant non-native plant.</p><h3 id=\"geb13749-sec-0007-title\" class=\"article-section__sub-title section1\">Main Conclusions</h3><p>Non-native plant cover provides a first approximation of invasion impacts on native community diversity, but the magnitude of impact depended on non-native plant richness and evenness. Relationships between non-native cover and native diversity were consistent in strength across continental scale gradients of productivity and human activity. Therefore, at the macroscale, invasive plant impacts on native plant communities likely depend more on the characteristics of the invading plants, that is the presence of a dominant invader, than on the environmental context.</p>","language":"English","publisher":"Wiley","doi":"10.1111/geb.13749","usgsCitation":"Beaury, E.M., Sofaer, H., Early, R., Pearse, I., Blumenthal, D.M., Corbin, J., Diez, J.M., Dukes, J., Barnett, D., Ibanez, I., Petri, L., Vilà, M., and Bradley, B., 2023, Macroscale analyses suggest invasive plant impacts depend more on the composition of invading plants than on environmental context: Global Ecology and Biogeography, v. 23, no. 11, p. 1964-1976, https://doi.org/10.1111/geb.13749.","productDescription":"13 p.","startPage":"1964","endPage":"1976","ipdsId":"IP-139929","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":442282,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/geb.13749","text":"Publisher Index Page"},{"id":422389,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"geometry\": {\n        \"type\": \"MultiPolygon\",\n        \"coordinates\": [\n          [\n            [\n              [\n                -94.81758,\n                49.38905\n              ],\n              [\n                -94.64,\n                48.84\n              ],\n              [\n                -94.32914,\n                48.67074\n              ],\n              [\n                -93.63087,\n                48.60926\n              ],\n              [\n                -92.61,\n                48.45\n              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               44.6159\n              ],\n              [\n                -123.89893,\n                45.52341\n              ],\n              [\n                -124.07963,\n                46.86475\n              ],\n              [\n                -124.39567,\n                47.72017\n              ],\n              [\n                -124.68721,\n                48.18443\n              ],\n              [\n                -124.5661,\n                48.37971\n              ],\n              [\n                -123.12,\n                48.04\n              ],\n              [\n                -122.58736,\n                47.096\n              ],\n              [\n                -122.34,\n                47.36\n              ],\n              [\n                -122.5,\n                48.18\n              ],\n              [\n                -122.84,\n                49\n              ],\n              [\n                -120,\n                49\n              ],\n            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\"name\": \"United States\"\n      }\n    }\n  ]\n}","volume":"23","issue":"11","noUsgsAuthors":false,"publicationDate":"2023-08-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Beaury, Evelyn M.","contributorId":236820,"corporation":false,"usgs":false,"family":"Beaury","given":"Evelyn","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":887630,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sofaer, Helen R. 0000-0002-9450-5223","orcid":"https://orcid.org/0000-0002-9450-5223","contributorId":216681,"corporation":false,"usgs":true,"family":"Sofaer","given":"Helen","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":887631,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Early, Regan","contributorId":236832,"corporation":false,"usgs":false,"family":"Early","given":"Regan","email":"","affiliations":[],"preferred":false,"id":887632,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pearse, Ian S. 0000-0001-7098-0495","orcid":"https://orcid.org/0000-0001-7098-0495","contributorId":211154,"corporation":false,"usgs":true,"family":"Pearse","given":"Ian","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":887633,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Blumenthal, Dana M.","contributorId":203896,"corporation":false,"usgs":false,"family":"Blumenthal","given":"Dana","email":"","middleInitial":"M.","affiliations":[{"id":36745,"text":"USDA-ARS Rangeland Resources Research Unit","active":true,"usgs":false}],"preferred":false,"id":887634,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Corbin, Jeffrey","contributorId":331412,"corporation":false,"usgs":false,"family":"Corbin","given":"Jeffrey","email":"","affiliations":[{"id":65470,"text":"Union College","active":true,"usgs":false}],"preferred":false,"id":887635,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Diez, Jeffrey M.","contributorId":169803,"corporation":false,"usgs":false,"family":"Diez","given":"Jeffrey","email":"","middleInitial":"M.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":887636,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dukes, Jeffrey","contributorId":299987,"corporation":false,"usgs":false,"family":"Dukes","given":"Jeffrey","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":887637,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Barnett, David","contributorId":174944,"corporation":false,"usgs":false,"family":"Barnett","given":"David","affiliations":[],"preferred":false,"id":887638,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Ibanez, Ines","contributorId":236833,"corporation":false,"usgs":false,"family":"Ibanez","given":"Ines","affiliations":[],"preferred":false,"id":887639,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Petri, Laís","contributorId":331416,"corporation":false,"usgs":false,"family":"Petri","given":"Laís","affiliations":[{"id":37387,"text":"University of Michigan","active":true,"usgs":false}],"preferred":false,"id":887640,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Vilà, Montserrat","contributorId":331419,"corporation":false,"usgs":false,"family":"Vilà","given":"Montserrat","affiliations":[{"id":64996,"text":"University of Sevilla","active":true,"usgs":false}],"preferred":false,"id":887641,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Bradley, Bethany A. 0000-0003-4912-4971","orcid":"https://orcid.org/0000-0003-4912-4971","contributorId":299998,"corporation":false,"usgs":true,"family":"Bradley","given":"Bethany A.","affiliations":[{"id":64995,"text":"University of Massachusetts, Northeast Climate Adaptation Science Center","active":true,"usgs":false}],"preferred":false,"id":887642,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}}
,{"id":70248868,"text":"70248868 - 2023 - Potential economic consequences along migratory flyways from reductions in breeding habitat of migratory waterbirds","interactions":[],"lastModifiedDate":"2023-11-03T16:34:21.911286","indexId":"70248868","displayToPublicDate":"2023-08-29T07:17:48","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Potential economic consequences along migratory flyways from reductions in breeding habitat of migratory waterbirds","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif text-s\"><div id=\"ab0005\" class=\"abstract author\" lang=\"en\"><div id=\"as0005\"><p id=\"sp0065\"><span>The migration of species, often across continents, makes it difficult to quantify the&nbsp;cumulative effects&nbsp;of local- and regional-scale conservation actions. Further, variation in stakeholder interests, differing jurisdictional governance processes, priorities, and monitoring abilities across the migratory range shapes place-specific differences in management actions. These differences may lead management of migratory species to benefit both species and stakeholders in some places more than others. In the case of North American waterfowl, possible reduction of wetland protection in breeding areas may lead to substantive shifts in benefits among stakeholders across their range by adversely affecting recreational viewing and hunting opportunities for these species. To understand possible consequences of wetland loss in the&nbsp;U.S.&nbsp;Prairie Pothole Region, the breeding region for 12 focal species of waterfowl, on the recreation economics for these species, we modeled a causal pathway linking wetland loss in the&nbsp;</span>breeding grounds<span> to changes in duck abundance and then assessed the consequences of that change in abundance on recreational hunting and viewing within migratory flyways. Under a scenario where wetland protections cease, we find annual economic activity associated with recreation may decrease as much as \\$489 million at the highest levels of predicted wetland loss, the majority of it coming from impacts to viewing behavior in the Mississippi Flyway. The number of hunters may decline by as much as 18,000, leading to \\$32 million less in annual economic activity. At highest levels of wetland loss, viewing value is expected to decline by more than one-quarter. Lost economic value associated with reductions in recreation in the Mississippi and Central Flyway states is not likely to be overcome by increases in agricultural economic output in drained wetlands of the Prairie Pothole Region. Our analyses indicate local effects of national water policies likely have far-reaching consequences because of the multi-dimensional connections arising from place-specific differences in management action, global and national agricultural economic drivers of crop expansion, and the biotic phenomena of transcontinental avian migration. Reductions in habitat in one location could ramify to economic consequences throughout the continent through connections fostered by migrating waterfowl.</span></p></div></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2023.110251","usgsCitation":"Thogmartin, W.E., Devries, J.H., Semmens, D., Diffendorfer, J., Dubovksy, J.A., Derbridge, J.J., and Mattsson, B., 2023, Potential economic consequences along migratory flyways from reductions in breeding habitat of migratory waterbirds: Biological Conservation, v. 285, 110251, 18 p., https://doi.org/10.1016/j.biocon.2023.110251.","productDescription":"110251, 18 p.","ipdsId":"IP-148477","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":442285,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.biocon.2023.110251","text":"Publisher Index Page"},{"id":435204,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UXWI30","text":"USGS data release","linkHelpText":"North American duck populations and the Central U.S. hunters who hunt them"},{"id":421125,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"285","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":883985,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Devries, James H.","contributorId":268336,"corporation":false,"usgs":false,"family":"Devries","given":"James","email":"","middleInitial":"H.","affiliations":[{"id":7182,"text":"Ducks Unlimited Canada","active":true,"usgs":false}],"preferred":true,"id":883986,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Semmens, Darius J. 0000-0001-7924-6529","orcid":"https://orcid.org/0000-0001-7924-6529","contributorId":64201,"corporation":false,"usgs":true,"family":"Semmens","given":"Darius J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":883987,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Diffendorfer, James E. 0000-0003-1093-6948 jediffendorfer@usgs.gov","orcid":"https://orcid.org/0000-0003-1093-6948","contributorId":3208,"corporation":false,"usgs":true,"family":"Diffendorfer","given":"James E.","email":"jediffendorfer@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":883988,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dubovksy, James A.","contributorId":330097,"corporation":false,"usgs":false,"family":"Dubovksy","given":"James","email":"","middleInitial":"A.","affiliations":[{"id":37976,"text":"US Fish and Wildlife Service (retired)","active":true,"usgs":false}],"preferred":false,"id":883989,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Derbridge, Jonathan J. 0000-0003-3074-3166","orcid":"https://orcid.org/0000-0003-3074-3166","contributorId":290285,"corporation":false,"usgs":false,"family":"Derbridge","given":"Jonathan","email":"","middleInitial":"J.","affiliations":[{"id":62394,"text":"The University of Arizona, Tucson","active":true,"usgs":false}],"preferred":false,"id":883990,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mattsson, Brady J.","contributorId":171612,"corporation":false,"usgs":false,"family":"Mattsson","given":"Brady J.","affiliations":[{"id":26928,"text":"Univ. of Vienna","active":true,"usgs":false}],"preferred":false,"id":883991,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70250459,"text":"70250459 - 2023 - Assessing contaminants of emerging concern in the Great Lakes Ecosystem: A decade of method development and practical application","interactions":[],"lastModifiedDate":"2023-12-12T12:49:10.90772","indexId":"70250459","displayToPublicDate":"2023-08-29T06:43:27","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Assessing contaminants of emerging concern in the Great Lakes Ecosystem: A decade of method development and practical application","docAbstract":"<div class=\"abstract-group  metis-abstract\"><div class=\"article-section__content en main\"><p>Assessing the ecological risk of contaminants in the field typically involves consideration of a complex mixture of compounds which may or may not be detected via instrumental analyses. Further, there are insufficient data to predict the potential biological effects of many detected compounds, leading to their being characterized as contaminants of emerging concern (CECs). Over the past several years, advances in chemistry, toxicology, and bioinformatics have resulted in a variety of concepts and tools that can enhance the pragmatic assessment of the ecological risk of CECs. The present Focus article describes a 10+- year multiagency effort supported through the U.S. Great Lakes Restoration Initiative to assess the occurrence and implications of CECs in the North American Great Lakes. State-of-the-science methods and models were used to evaluate more than 700 sites in about approximately 200 tributaries across lakes Ontario, Erie, Huron, Michigan, and Superior, sometimes on multiple occasions. Studies featured measurement of up to 500 different target analytes in different environmental matrices, coupled with evaluation of biological effects in resident species, animals from in situ and laboratory exposures, and in vitro systems. Experimental taxa included birds, fish, and a variety of invertebrates, and measured endpoints ranged from molecular to apical responses. Data were integrated and evaluated using a diversity of curated knowledgebases and models with the goal of producing actionable insights for risk assessors and managers charged with evaluating and mitigating the effects of CECs in the Great Lakes. This overview is based on research and data captured in approximately about 90 peer-reviewed journal articles and reports, including approximately about 30 appearing in a virtual issue comprised of highlighted papers published in<span>&nbsp;</span><i>Environmental Toxicology and Chemistry</i><span>&nbsp;</span>or<span>&nbsp;</span><i>Integrated Environmental Assessment and Management</i>.<span>&nbsp;</span><i>Environ Toxicol Chem</i><span>&nbsp;</span>2023;42:2506–2518. © 2023 SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.</p></div></div>","language":"English","publisher":"Society of Environmental Toxicology and Chemistry","doi":"10.1002/etc.5740","usgsCitation":"Ankley, G., Corsi, S., Custer, C.M., Ekman, D., Hummel, S.L., Kimbrough, K.L., Schoenfuss, H., and Villeneuve, D., 2023, Assessing contaminants of emerging concern in the Great Lakes Ecosystem: A decade of method development and practical application: Environmental Toxicology and Chemistry, v. 42, no. 12, p. 2506-2518, https://doi.org/10.1002/etc.5740.","productDescription":"13 p.","startPage":"2506","endPage":"2518","ipdsId":"IP-153930","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":442289,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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ccuster@usgs.gov","orcid":"https://orcid.org/0000-0003-0500-1582","contributorId":1143,"corporation":false,"usgs":true,"family":"Custer","given":"Christine","email":"ccuster@usgs.gov","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":889985,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ekman, Drew R.","contributorId":332308,"corporation":false,"usgs":false,"family":"Ekman","given":"Drew R.","affiliations":[{"id":12772,"text":"USEPA","active":true,"usgs":false}],"preferred":false,"id":889986,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hummel, Stephanie L.","contributorId":332309,"corporation":false,"usgs":false,"family":"Hummel","given":"Stephanie","email":"","middleInitial":"L.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":889987,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kimbrough, Kimani L.","contributorId":332310,"corporation":false,"usgs":false,"family":"Kimbrough","given":"Kimani","email":"","middleInitial":"L.","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":889988,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schoenfuss, Heiko L.","contributorId":332311,"corporation":false,"usgs":false,"family":"Schoenfuss","given":"Heiko L.","affiliations":[{"id":20306,"text":"St. Cloud State University","active":true,"usgs":false}],"preferred":false,"id":889989,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Villeneuve, Daniel L.","contributorId":332312,"corporation":false,"usgs":false,"family":"Villeneuve","given":"Daniel L.","affiliations":[{"id":12772,"text":"USEPA","active":true,"usgs":false}],"preferred":false,"id":889990,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70248371,"text":"70248371 - 2023 - Earth’s mantle composition revealed by mantle plumes","interactions":[],"lastModifiedDate":"2023-09-11T11:42:09.839773","indexId":"70248371","displayToPublicDate":"2023-08-29T06:37:52","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":16709,"text":"Nature Reviews in Earth and Environment","active":true,"publicationSubtype":{"id":10}},"title":"Earth’s mantle composition revealed by mantle plumes","docAbstract":"<div id=\"Abs1-section\" class=\"c-article-section\"><div id=\"Abs1-content\" class=\"c-article-section__content\"><p>Mantle plumes originate at depths near the core−mantle boundary (~2,800 km). As such, they provide invaluable information about the composition of the deep mantle and insight into convection, crustal formation, and&nbsp;crustal recycling, as well as&nbsp;global heat and volatile budgets. In this Review, we discuss the effectiveness&nbsp;and challenges of using isotopic analyses of plume-generated rocks to infer mantle composition and to constrain geodynamic&nbsp;models. Isotopic analyses of plume-derived ocean island basalts, including radiogenic (Sr, Nd, Pb, Hf, W, noble gas) and stable isotopes&nbsp;(Li, C, O, S, Fe, Tl), permit determination of mantle plume composition, which in turn generate&nbsp;insight into mantle plume origins, dynamics, mantle heterogeneities, early-formed mantle reservoirs, crustal recycling processes, core−mantle interactions and mantle evolution. Nevertheless, the magmatic flux, temperature, tectonic environment and compositions of mantle plumes can vary. Consequently, plumes and their melts are best evaluated along a spectrum that acknowledges their different properties, particularly mantle flux, before making interpretations about the interior of the Earth. To provide insight into specific mantle and plume processes, future work should document correlations across elemental and isotopic data sets on the same sample powder, coordinate targeting sampling strategies, and refine stable isotopic fractionation factors through experiments.&nbsp;Such work will benefit from collaboration across geochemical laboratories, as well as among geochemists, mineral physicists, seismologists and geodynamicists.</p></div></div>","language":"English","publisher":"Nature","doi":"10.1038/s43017-023-00467-0","usgsCitation":"Weis, D., Harpp, K., Harrison, L.N., Boyet, M., Chauvel, C., Farnetani, C., Finlayson, V., Lee, K., Parai, R., Shahar, A., and Williamson, N., 2023, Earth’s mantle composition revealed by mantle plumes: Nature Reviews in Earth and Environment, v. 4, p. 604-625, https://doi.org/10.1038/s43017-023-00467-0.","productDescription":"22 p.","startPage":"604","endPage":"625","ipdsId":"IP-147287","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":442291,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s43017-023-00467-0","text":"Publisher Index Page"},{"id":420695,"type":{"id":24,"text":"Thumbnail"},"url":"http://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","noUsgsAuthors":false,"publicationDate":"2023-08-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Weis, Dominique 0000-0002-6638-5543","orcid":"https://orcid.org/0000-0002-6638-5543","contributorId":304346,"corporation":false,"usgs":false,"family":"Weis","given":"Dominique","email":"","affiliations":[{"id":36972,"text":"University of British Columbia","active":true,"usgs":false}],"preferred":false,"id":882729,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harpp, Karen 0000-0002-5233-0461","orcid":"https://orcid.org/0000-0002-5233-0461","contributorId":329614,"corporation":false,"usgs":false,"family":"Harpp","given":"Karen","email":"","affiliations":[{"id":37669,"text":"Colgate University","active":true,"usgs":false}],"preferred":false,"id":882730,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harrison, Lauren N 0000-0002-6621-5958","orcid":"https://orcid.org/0000-0002-6621-5958","contributorId":300066,"corporation":false,"usgs":true,"family":"Harrison","given":"Lauren","email":"","middleInitial":"N","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":882731,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boyet, Maud 0000-0002-8945-2470","orcid":"https://orcid.org/0000-0002-8945-2470","contributorId":329615,"corporation":false,"usgs":false,"family":"Boyet","given":"Maud","email":"","affiliations":[{"id":78675,"text":"CNRS Délégation Rhône-Auvergne","active":true,"usgs":false}],"preferred":false,"id":882732,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chauvel, Catherine 0000-0002-3959-4665","orcid":"https://orcid.org/0000-0002-3959-4665","contributorId":329617,"corporation":false,"usgs":false,"family":"Chauvel","given":"Catherine","email":"","affiliations":[{"id":30776,"text":"Institut de Physique du Globe de Paris","active":true,"usgs":false}],"preferred":false,"id":882733,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Farnetani, Cinzia 0000-0002-6271-5568","orcid":"https://orcid.org/0000-0002-6271-5568","contributorId":329618,"corporation":false,"usgs":false,"family":"Farnetani","given":"Cinzia","email":"","affiliations":[{"id":30776,"text":"Institut de Physique du Globe de Paris","active":true,"usgs":false}],"preferred":false,"id":882734,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Finlayson, Val 0000-0003-2005-7410","orcid":"https://orcid.org/0000-0003-2005-7410","contributorId":329619,"corporation":false,"usgs":false,"family":"Finlayson","given":"Val","email":"","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":882735,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lee, Kanai","contributorId":329620,"corporation":false,"usgs":false,"family":"Lee","given":"Kanai","email":"","affiliations":[{"id":65265,"text":"Lawrence Livermore National Lab","active":true,"usgs":false}],"preferred":false,"id":882736,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Parai, Rita 0000-0002-9754-7349","orcid":"https://orcid.org/0000-0002-9754-7349","contributorId":329621,"corporation":false,"usgs":false,"family":"Parai","given":"Rita","email":"","affiliations":[{"id":16661,"text":"Washington University in Saint Louis","active":true,"usgs":false}],"preferred":false,"id":882737,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Shahar, Anat 0000-0002-0794-2717","orcid":"https://orcid.org/0000-0002-0794-2717","contributorId":329622,"corporation":false,"usgs":false,"family":"Shahar","given":"Anat","email":"","affiliations":[{"id":18922,"text":"Carnegie Institution of Washington","active":true,"usgs":false}],"preferred":false,"id":882738,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Williamson, Nicole 0000-0002-8767-8038","orcid":"https://orcid.org/0000-0002-8767-8038","contributorId":329623,"corporation":false,"usgs":false,"family":"Williamson","given":"Nicole","email":"","affiliations":[{"id":36972,"text":"University of British Columbia","active":true,"usgs":false}],"preferred":false,"id":882739,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}}
,{"id":70248803,"text":"70248803 - 2023 - Carbon sequestration and subsidence reversal in the Sacramento-San Joaquin Delta and Suisun Bay: Management opportunities for climate mitigation and adaptation","interactions":[],"lastModifiedDate":"2023-09-21T12:02:15.686453","indexId":"70248803","displayToPublicDate":"2023-08-28T06:58:41","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3331,"text":"San Francisco Estuary and Watershed Science","active":true,"publicationSubtype":{"id":10}},"title":"Carbon sequestration and subsidence reversal in the Sacramento-San Joaquin Delta and Suisun Bay: Management opportunities for climate mitigation and adaptation","docAbstract":"<div id=\"main\"><div data-reactroot=\"\"><div class=\"body\"><div><div class=\"c-columns--sticky-sidebar\"><div class=\"c-tabs\"><div class=\"c-tabs__content\"><div class=\"c-tabcontent\"><div class=\"c-clientmarkup\"><p>The aquatic landscapes of the Sacramento–San Joaquin Delta (hereafter, the Delta) and Suisun Bay represent both a significant past and future soil carbon stock. Historical alterations of hydrologic flows have led to depletion of soil carbon stocks via emissions of carbon dioxide (CO2), and loss of elevation as a result of subsidence. Optimizing ecosystem hydrology in the Delta and Suisun Bay could both reduce and reverse subsidence while also providing significant opportunities for climate mitigation and adaptation. Emissions of greenhouse gases (GHGs)—notably CO2, methane (CH4 ), and nitrous oxide (N2O)—contribute to global warming at different rates and intensities, requiring GHG accounting and modeling to assess the relative benefits of management options. Decades of data collection, model building, and map development suggest that past and current management actions have both caused—and can mitigate—losses of soil carbon. We review here the magnitude of potential GHG offsets, management options that may be achievable, and trade-offs of carbon storage under different land management. Using a land-use/land-cover framework to assess these management options, we describe the potential of three interventions (impoundment to reverse subsidence, agricultural management, and tidal reintroduction and/or maintained connectivity), both in acreage and radiative balance to clarify their relative influence on the region’s GHG balance today and in relation to its millennial history. From floodplains to farming to floating aquatic vegetation, we find specific scalable strategies to manage hydrology that can alter regional GHG balance. Preservation of soil carbon stocks and restoration of net atmospheric CO2 fluxes into soils are the primary route to net negative emissions in the Delta and Suisun Bay, with CH4 emission management occurring in a supporting role. Over a 40-year horizon of climate-mitigation markets, the resilience of different aquatic habitats introduces the most uncertainty, from expected and unexpected hydrologic changes associated with land, ocean, and operational water flows.</p></div></div></div></div></div></div></div></div></div>","language":"English","publisher":"University of California Davis","doi":"10.15447/sfews.2023v20iss4art7","usgsCitation":"Windham-Myers, L., Oikawa, P., Deverel, S., Chapple, D., Drexler, J.Z., and Stern, D., 2023, Carbon sequestration and subsidence reversal in the Sacramento-San Joaquin Delta and Suisun Bay: Management opportunities for climate mitigation and adaptation: San Francisco Estuary and Watershed Science, v. 20, no. 4, 7, 29 p., https://doi.org/10.15447/sfews.2023v20iss4art7.","productDescription":"7, 29 p.","ipdsId":"IP-144161","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":442295,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.15447/sfews.2023v20iss4art7","text":"Publisher Index Page"},{"id":421017,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento-San Joaquin Delta, Suisun Bay","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -122.41517582376721,\n              38.66686399623856\n            ],\n            [\n              -122.41517582376721,\n              37.643613493536606\n            ],\n            [\n              -121.10836346838857,\n              37.643613493536606\n            ],\n            [\n              -121.10836346838857,\n              38.66686399623856\n            ],\n            [\n              -122.41517582376721,\n              38.66686399623856\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"20","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-02-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Windham-Myers, Lisamarie 0000-0003-0281-9581 lwindham-myers@usgs.gov","orcid":"https://orcid.org/0000-0003-0281-9581","contributorId":2449,"corporation":false,"usgs":true,"family":"Windham-Myers","given":"Lisamarie","email":"lwindham-myers@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":883721,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Oikawa, Patty","contributorId":329976,"corporation":false,"usgs":false,"family":"Oikawa","given":"Patty","affiliations":[{"id":78755,"text":"California State University, Hayward","active":true,"usgs":false}],"preferred":false,"id":883722,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Deverel, Steve","contributorId":329977,"corporation":false,"usgs":false,"family":"Deverel","given":"Steve","email":"","affiliations":[{"id":78756,"text":"Hydrofocus, Inc.","active":true,"usgs":false}],"preferred":false,"id":883723,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chapple, Dylan","contributorId":329978,"corporation":false,"usgs":false,"family":"Chapple","given":"Dylan","email":"","affiliations":[{"id":78757,"text":"Delta Science Council","active":true,"usgs":false}],"preferred":false,"id":883724,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Drexler, Judith Z. 0000-0002-0127-3866 jdrexler@usgs.gov","orcid":"https://orcid.org/0000-0002-0127-3866","contributorId":167492,"corporation":false,"usgs":true,"family":"Drexler","given":"Judith","email":"jdrexler@usgs.gov","middleInitial":"Z.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":883725,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stern, Dylan","contributorId":329979,"corporation":false,"usgs":false,"family":"Stern","given":"Dylan","affiliations":[],"preferred":false,"id":883726,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70256565,"text":"70256565 - 2023 - Near surface sediments introduce low frequency noise into gravity models","interactions":[],"lastModifiedDate":"2024-08-01T14:42:01.361646","indexId":"70256565","displayToPublicDate":"2023-08-27T09:38:02","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":14424,"text":"Applied Computing and Geosciences","active":true,"publicationSubtype":{"id":10}},"title":"Near surface sediments introduce low frequency noise into gravity models","docAbstract":"<p><span>3D geologic modeling and mapping often relies on gravity modeling to identify key geologic structures, such as basin depth, fault offset, or fault dip. Such&nbsp;gravity models&nbsp;generally assume either homogeneous or spatially uncorrelated densities within modeled rock bodies and overlying sediments, with average densities typically derived from surface and drill-hole sampling. The noise contributed to the&nbsp;gravity anomaly&nbsp;by these density assumptions is zero in the homogeneous case and typically &lt;200 μGal in the uncorrelated case. Rock bodies and sediments, however, show both a range of densities and spatial correlation of these densities, in both surface and drill-hole samples, and this correlation causes an increase in power in the low frequency content of the resulting gravity anomaly. Spatial correlation of densities can be modeled as a Gaussian random field (GRF), with the random field parameters derived from drill-hole and geologic map data. Data from&nbsp;alluvial fan&nbsp;sediments in southern Nevada indicate correlation lengths of up to 300&nbsp;m in the vertical dimension and kilometers in the horizontal dimension. The resulting GRF density models show that the noise contributed to the measured gravity anomaly is of low frequency and can be several mGal in amplitude, contradicting the common attribution of lower frequencies to deeper sources. This low-frequency noise increases in power with an increase in&nbsp;sediment thickness. Its presence increases the ambiguity of interpretations of subsurface geologic body shape, such as&nbsp;basin analyses&nbsp;that attempt to quantify concealed basement fault depths, offsets, and dip angles. In the southwestern United States, where basin analyses are important for&nbsp;</span>natural resource<span>&nbsp;applications, such ambiguity increases the uncertainty of subsequent process modeling.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.acags.2023.100131","usgsCitation":"Phelps, G., and Cronkite-Ratcliff, C., 2023, Near surface sediments introduce low frequency noise into gravity models: Applied Computing and Geosciences, v. 19, 100131, 18 p., https://doi.org/10.1016/j.acags.2023.100131.","productDescription":"100131, 18 p.","ipdsId":"IP-146760","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":442296,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1016/j.acags.2023.100131","text":"Publisher Index Page"},{"id":432029,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"19","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Phelps, Geoffrey 0000-0003-1958-2736 gphelps@usgs.gov","orcid":"https://orcid.org/0000-0003-1958-2736","contributorId":127489,"corporation":false,"usgs":true,"family":"Phelps","given":"Geoffrey","email":"gphelps@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":908038,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cronkite-Ratcliff, Collin 0000-0001-5485-3832 ccronkite-ratcliff@usgs.gov","orcid":"https://orcid.org/0000-0001-5485-3832","contributorId":203951,"corporation":false,"usgs":true,"family":"Cronkite-Ratcliff","given":"Collin","email":"ccronkite-ratcliff@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":908039,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70248806,"text":"70248806 - 2023 - CGS: Coupled growth and survival model with cohort fairness","interactions":[],"lastModifiedDate":"2023-09-21T11:52:01.073506","indexId":"70248806","displayToPublicDate":"2023-08-27T06:46:51","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"CGS: Coupled growth and survival model with cohort fairness","docAbstract":"<div class=\"col-md-12\">Fish modeling in complex environments is critical for understanding drivers of population dynamics in aquatic systems. This paper proposes a Bayesian network method for modeling fish survival and growth over multiple connected rivers. Traditional fish survival models capture the effect of multiple environmental drivers (e.g., stream temperature, stream flow) by adding different variables, which increases model complexity and results in very long and impractical run times (i.e., weeks). We propose a coupled survival-growth model that leverages the observations from both sources simultaneously. It also integrates the Bayesian process into the neural network model to efficiently capture complex variable relationships in the system while also conforming to known survival processes used in existing fish models. To further reduce the performance disparity of fish body length across cohorts, we propose two approaches for enforcing fairness by the adjustment of training priorities and data augmentation. The results based on a real-world fish dataset collected in Massachusetts, US demonstrate that the proposed method can greatly improve prediction accuracy in modeling survival and body length compared to independent models on survival and growth, and effectively reduce the performance disparity across cohorts. The fish growth and movement patterns discovered by the proposed model are also consistent with prior studies in the same region, while vastly reducing run times and memory requirements.</div>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"International Joint Conference on Artificial Intelligence","doi":"10.24963/ijcai.2023/664","usgsCitation":"He, E., Wan, Y., Letcher, B., Fair, J.H., Xie, Y., and Jia, X., 2023, CGS: Coupled growth and survival model with cohort fairness, <i>in</i> Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, p. 5986-5994, https://doi.org/10.24963/ijcai.2023/664.","productDescription":"9 p.","startPage":"5986","endPage":"5994","ipdsId":"IP-151527","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":442301,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.24963/ijcai.2023/664","text":"Publisher Index Page"},{"id":421015,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"He, Erhu","contributorId":329980,"corporation":false,"usgs":false,"family":"He","given":"Erhu","email":"","affiliations":[{"id":12465,"text":"University of Pittsburgh","active":true,"usgs":false}],"preferred":false,"id":883729,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wan, Yue","contributorId":329981,"corporation":false,"usgs":false,"family":"Wan","given":"Yue","email":"","affiliations":[{"id":12465,"text":"University of Pittsburgh","active":true,"usgs":false}],"preferred":false,"id":883730,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Letcher, Benjamin 0000-0003-0191-5678","orcid":"https://orcid.org/0000-0003-0191-5678","contributorId":242666,"corporation":false,"usgs":true,"family":"Letcher","given":"Benjamin","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":883731,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fair, Jennifer H. 0000-0002-9902-1893","orcid":"https://orcid.org/0000-0002-9902-1893","contributorId":245941,"corporation":false,"usgs":true,"family":"Fair","given":"Jennifer","middleInitial":"H.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":883732,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Xie, Yiquin","contributorId":329982,"corporation":false,"usgs":false,"family":"Xie","given":"Yiquin","email":"","affiliations":[{"id":12465,"text":"University of Pittsburgh","active":true,"usgs":false}],"preferred":false,"id":883733,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jia, Xiaowei 0000-0001-8544-5233","orcid":"https://orcid.org/0000-0001-8544-5233","contributorId":237807,"corporation":false,"usgs":false,"family":"Jia","given":"Xiaowei","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":883734,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70247946,"text":"sir20235059 - 2023 - Modeling the water-quality effects to the Klamath River from recirculation in drains and canals, Oregon and California, 2006–15","interactions":[],"lastModifiedDate":"2026-03-09T16:35:33.967766","indexId":"sir20235059","displayToPublicDate":"2023-08-25T13:50:40","publicationYear":"2023","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":"2023-5059","displayTitle":"Modeling the Water-Quality Effects to the Klamath River from Recirculation in Drains and Canals, Oregon and California, 2006–15","title":"Modeling the water-quality effects to the Klamath River from recirculation in drains and canals, Oregon and California, 2006–15","docAbstract":"<p>The potential recirculation of Klamath Strait Drain (hereafter called by its local name, “Klamath Straits Drain”) water into Ady Canal to reduce the drain discharge of high nutrient loads into the Klamath River was assessed by the U.S. Geological Survey for the Bureau of Reclamation. To study the feasibility of recirculation, this investigation evaluated three recirculation scenarios over a 10-year period from 2006 to 2015, as a series of 1-year model simulations. A combination of two existing hydrodynamic, water-temperature, and water-quality models (CE-QUAL-W2) were used, including (1) the Link-Keno reach of the Klamath River, using Klamath Straits Drain as a tributary and for calendar years 2006–11, and (2) the same Link-Keno model used for calendar years 2012–15 in combination with an independent Klamath Straits Drain model from 2012 to 2015. Model simulations using the water-quality models were configured for the base case conditions and three different sets of recirculation scenarios: the maximum year-round recirculation without limits (scenario 1), limited year-round recirculation fixed by the current pipe flow configuration (scenario 2), and limited seasonal recirculation (May–September) also fixed by the current pipe flow configuration (scenario 3).</p><p>In the base case, estimates of annual average daily total nitrogen loads and daily total phosphorus loads exported to the Klamath River from the Klamath Straits Drain were as much as 3,060 and 457 pounds per day (lbs/day), respectively. Currently (2023), the Total Maximum Daily Loads allocations for the Klamath Straits Drain are 21 and 268 lbs/day for total phosphorus and total nitrogen, respectively, so these maximum estimates exceed the current Total Maximum Daily Loads by greater than an order of magnitude. With scenario 1, load reductions occurred year-round for all constituents evaluated (total nitrogen, total phosphorus, 5-day biochemical oxygen demand [BOD5], 5-day carbonaceous biochemical oxygen demand) for the Klamath Straits Drain discharging to the Klamath River. Scenario 2 also had large reductions in total nitrogen, total phosphorus, and BOD5 loads. Substantial reductions did occur for scenario 3 but were constrained to only the active recirculation period from May through September. Despite the restricted period, the average reductions in the annual average daily load for total phosphorus and total nitrogen were 32.1 percent and 26.5 percent, respectively.</p><p>The Ady Canal diverts high nutrient loads from the Klamath River, so the loading tradeoffs to the Klamath River between no recirculation and the recirculation scenarios were calculated. On an annual basis, the overall net balance between the Klamath Straits Drain and Ady Canal resulted in more total nitrogen and total phosphorus load reductions to the Klamath River for the three recirculation scenarios than the base case, for most years. In contrast, the net balance for BOD5 loads was higher to the Klamath River for the three recirculation scenarios than the base case, for most years.</p><p>With the recirculation scenarios, the optimal recirculation periods to benefit Ady Canal, Klamath River, and Klamath Straits Drain did not always coincide. Recirculation would be most effective at reducing loads toward the Klamath Straits Drain Total Maximum Daily Load allocations in the spring (March–May) of each year. However, recirculation during these months would also increase salinity in the Ady Canal. In summer, recirculation would reduce Klamath Straits Drain loads toward the Total Maximum Daily Load allocations, though recirculation could decrease Klamath River water quality mostly because of decreased withdrawals of Klamath River water by the Ady Canal. Scenario 3 avoided recirculation into Ady Canal in the early spring months when salinity concerns would be the highest, while still decreasing nutrient loads exported from the Klamath Straits Drain to the Klamath River in the summer months.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235059","collaboration":"Prepared in cooperation with Bureau of Reclamation","usgsCitation":"Smith, E.A., and Sullivan, A.B., 2023, Modeling the water-quality effects to the Klamath River from recirculation in drains and canals, Oregon and California, 2006–15: U.S. Geological Survey Scientific Investigations Report 2023–5059, 87 p., https://doi.org/10.3133/sir20235059.","productDescription":"Report: vii, 87 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-131325","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":420409,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235059/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5059"},{"id":420166,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5059/sir20235059.XML"},{"id":420165,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5059/images"},{"id":420163,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5059/sir20235059.pdf","text":"Report","size":"20.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5059"},{"id":420162,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5059/coverthb.jpg"},{"id":500940,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115220.htm","linkFileType":{"id":5,"text":"html"}},{"id":420167,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RWP4F9","text":"USGS data release","description":"USGS data release","linkHelpText":"CE–QUAL–W2 water-quality models for Klamath Straits Drain recirculation scenarios, Klamath River, Oregon, 2006–15"}],"country":"United States","state":"California, Oregon","otherGeospatial":"Klamath River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -122.21689162756053,\n              42.44595704887348\n            ],\n            [\n              -122.21689162756053,\n              41.58110381721761\n            ],\n            [\n              -121.27247261442969,\n              41.58110381721761\n            ],\n            [\n              -121.27247261442969,\n              42.44595704887348\n            ],\n            [\n              -122.21689162756053,\n              42.44595704887348\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 2nd Avenue, Suite 1950<br>Portland, OR 97204</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Model Results</li><li>Model Application</li><li>Summary</li><li>References Cited</li><li>Appendixes 1–3</li></ul>","publishedDate":"2023-08-25","noUsgsAuthors":false,"publicationDate":"2023-08-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Erik A. 0000-0001-8434-0798 easmith@usgs.gov","orcid":"https://orcid.org/0000-0001-8434-0798","contributorId":1405,"corporation":false,"usgs":true,"family":"Smith","given":"Erik","email":"easmith@usgs.gov","middleInitial":"A.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":881180,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sullivan, Annett B. 0000-0001-7783-3906 annett@usgs.gov","orcid":"https://orcid.org/0000-0001-7783-3906","contributorId":79821,"corporation":false,"usgs":true,"family":"Sullivan","given":"Annett B.","email":"annett@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":false,"id":881181,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70247906,"text":"sir20235072 - 2023 - Hydrogeology and simulated groundwater availability in reaches 3 and 4 of the Washita River aquifer, southern Oklahoma, 1980–2017","interactions":[],"lastModifiedDate":"2026-03-12T20:41:31.121532","indexId":"sir20235072","displayToPublicDate":"2023-08-25T11:36:00","publicationYear":"2023","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":"2023-5072","displayTitle":"Hydrogeology and Simulated Groundwater Availability in Reaches 3 and 4 of the Washita River Aquifer, Southern Oklahoma, 1980–2017","title":"Hydrogeology and simulated groundwater availability in reaches 3 and 4 of the Washita River aquifer, southern Oklahoma, 1980–2017","docAbstract":"<p>The 1973 Oklahoma Groundwater Law (Oklahoma Statutes §82–1020.5) requires that the Oklahoma Water Resources Board conduct hydrologic investigations of the State’s aquifers to determine the maximum annual yield for each groundwater basin. Because more than 20 years have elapsed since the final order was issued, the U.S. Geological Survey, in cooperation with the Oklahoma Water Resources Board, conducted an updated hydrologic investigation and evaluated the effects of potential groundwater withdrawals on groundwater flow and availability in reaches 3 and 4 of the Washita River aquifer in southern Oklahoma for a study period spanning 1980–2017. A hydrogeologic framework and conceptual model were developed to guide the construction and calibration of a numerical model of the Washita River aquifer. The numerical model was calibrated to water-table-altitude observations at selected wells, base-flow observations at selected U.S. Geological Survey streamgages, and the conceptual-model recharge.</p><p>Three types of groundwater-availability scenarios were run using the calibrated numerical model. These scenarios were used to (1) estimate equal-proportionate-share pumping rates, (2) quantify the potential effects of projected well withdrawals on groundwater storage over a 50-year period, and (3) simulate the potential effects of a hypothetical 10-year drought. With Washita River main-stem inflows, the 20-, 40-, and 50-year equal-proportionate-share pumping rates under normal recharge conditions were about 3.08 acre-feet per acre per year for reach 3 and about 3.80 acre-feet per acre per year for reach 4. Projected 50-year pumping scenarios were used to simulate the effects of modified well withdrawal rates. Because well withdrawals were less than 1 percent of the calibrated numerical-model water budget, changes to the well pumping rates had little effect on Washita River base flows and groundwater storage in the Washita River aquifer. A hypothetical 10-year drought scenario was used to simulate the potential effects of a prolonged period of reduced recharge on groundwater storage. Groundwater storage at the end of the drought period was 4.6&nbsp;percent less than the groundwater storage of the calibrated numerical model at the end of the drought period.<br></p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235072","issn":"2328-0328","collaboration":"Prepared in cooperation with the Oklahoma Water Resources Board","usgsCitation":"Rogers, I.M.J., Smith, S.J., Gammill, N.C., Gillard, N.J., Lockmiller, K.A., Fetkovich, E.J., Correll, J.S., and Hussey, S.P., 2023, Hydrogeology and simulated groundwater availability in reaches 3 and 4 of the Washita River aquifer, southern Oklahoma, 1980–2017: U.S. Geological Survey Scientific Investigations Report 2023–5072, 83 p., https://doi.org/10.3133/sir20235072.","productDescription":"Report: xii, 83 p.; 2 Data Releases","numberOfPages":"100","onlineOnly":"Y","ipdsId":"IP-128217","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":420079,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5072/coverthb.jpg"},{"id":420083,"rank":2,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5072/images"},{"id":420080,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5072/sir20235072.pdf","size":"57.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5072 pdf"},{"id":420085,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS water data for the Nation—U.S. Geological Survey National Water Information System database"},{"id":420183,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235072/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5072 HTML"},{"id":420081,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5072/sir20235072.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2023-5072 XML"},{"id":420084,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UET694","text":"U.S. Geological Survey data release—MODFLOW-NWT model used in simulation of groundwater availability in reaches 3 and 4 of the Washita River aquifer, southern Oklahoma, 1980–2017"},{"id":501037,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115236.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Oklahoma","otherGeospatial":"Washita River Aquifer","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -98.30,\n              35.30\n            ],\n            [\n              -98.3,\n              34.00\n            ],\n            [\n              -96.408,\n              34.00\n            ],\n            [\n              -96.40,\n              35.30\n            ],\n            [\n              -98.3,\n              35.30\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p>Director, <a data-mce-href=\"https://www.usgs.gov/centers/ot-water\" href=\"https://www.usgs.gov/centers/ot-water\">Oklahoma-Texas Water Science Center</a><br>U.S. Geological Survey<br>1505 Ferguson Lane<br>Austin, TX 78754–4501</p><p><a data-mce-href=\"../contact\" href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Hydrogeology of the Washita River Aquifer and Surrounding Units</li><li>Hydrogeologic Framework of the Washita River Aquifer</li><li>Conceptual Groundwater-Flow Model</li><li>Numerical Groundwater-Flow Model</li><li>Groundwater-Availability Scenarios</li><li>Model Limitations</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2023-08-25","noUsgsAuthors":false,"publicationDate":"2023-08-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Rogers, Ian M.J. 0000-0001-8492-5358","orcid":"https://orcid.org/0000-0001-8492-5358","contributorId":46036,"corporation":false,"usgs":true,"family":"Rogers","given":"Ian","email":"","middleInitial":"M.J.","affiliations":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"preferred":true,"id":880958,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, S. Jerrod 0000-0002-9379-8167 sjsmith@usgs.gov","orcid":"https://orcid.org/0000-0002-9379-8167","contributorId":981,"corporation":false,"usgs":true,"family":"Smith","given":"S.","email":"sjsmith@usgs.gov","middleInitial":"Jerrod","affiliations":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"preferred":true,"id":880959,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gammill, Nicole C. 0000-0003-3037-2668","orcid":"https://orcid.org/0000-0003-3037-2668","contributorId":328664,"corporation":false,"usgs":false,"family":"Gammill","given":"Nicole C.","affiliations":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"preferred":false,"id":880960,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gillard, Natalie J. 0000-0002-8823-5992","orcid":"https://orcid.org/0000-0002-8823-5992","contributorId":328665,"corporation":false,"usgs":false,"family":"Gillard","given":"Natalie","email":"","middleInitial":"J.","affiliations":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"preferred":true,"id":880961,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lockmiller, Kayla A. 0000-0001-7605-2286","orcid":"https://orcid.org/0000-0001-7605-2286","contributorId":269635,"corporation":false,"usgs":true,"family":"Lockmiller","given":"Kayla A.","affiliations":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":880962,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fetkovich, Evin J. 0000-0002-8899-8543","orcid":"https://orcid.org/0000-0002-8899-8543","contributorId":328666,"corporation":false,"usgs":true,"family":"Fetkovich","given":"Evin","email":"","middleInitial":"J.","affiliations":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":880963,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Correll, Jessica S. 0000-0000-0000-0001","orcid":"https://orcid.org/0000-0000-0000-0001","contributorId":150903,"corporation":false,"usgs":false,"family":"Correll","given":"Jessica S.","affiliations":[{"id":18135,"text":"Oklahoma Water Resources Board","active":true,"usgs":false}],"preferred":false,"id":880964,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hussey, Sean P. 0000-0000-0000-0001","orcid":"https://orcid.org/0000-0000-0000-0001","contributorId":328667,"corporation":false,"usgs":false,"family":"Hussey","given":"Sean","email":"","middleInitial":"P.","affiliations":[{"id":18135,"text":"Oklahoma Water Resources Board","active":true,"usgs":false}],"preferred":false,"id":880965,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70247940,"text":"70247940 - 2023 - Confirmation of significant sea turtle nesting activity on a remote island chain in the Gulf of Mexico","interactions":[],"lastModifiedDate":"2023-08-25T14:14:08.823257","indexId":"70247940","displayToPublicDate":"2023-08-25T09:03:51","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Confirmation of significant sea turtle nesting activity on a remote island chain in the Gulf of Mexico","docAbstract":"Globally, six of the seven sea turtle species are threatened or endangered and as such, monitoring reproductive activity for these species is necessary for effective population recovery. Remote beaches provide a challenge to conducting these surveys, which often results in data gaps that can hamper management planning. Throughout the summer of 2022, aerial surveys were conducted over the Chandeleur Islands in the Gulf of Mexico. Turtle crawls were photographed for subsequent review by 10 expert observers. Whenever possible, ground surveys were conducted, and samples of unhatched eggs or dead hatchlings were collected. A summary of historic reports of sea turtle nesting activity at this site was also compiled. On 11 days between May 4, 2022, and July 30, 2022, photographs of 55 potential sea turtle crawls were taken. Observers identified 54 of those as being made by a sea turtle. There was high-to-moderate certainty that 16 of those crawls were nests, that 14 were made by loggerheads, and that two were made by Kemp's ridleys. Observers were least certain of species identification when surveys were conducted during rainy weather. Genetic analyses based on mitochondrial and nuclear DNA were conducted on samples from five nests and those analyses confirmed that three nests were laid by Kemp's ridleys and two were laid by loggerheads. Historic records from the Chandeleur Islands substantiate claims that the Chandeleurs have supported sea turtle nesting activity for decades; however, the consistency of this activity remains unknown. Our aerial surveys, particularly when coupled with imaging, were a useful tool for documenting nesting activity on these remote islands. Future monitoring programs at this site could benefit from a standardized aerial survey program with a seaplane so trends in nesting activity could be determined particularly as the beach undergoes restoration.","language":"English","publisher":"John Wiley & Sons, Inc.","doi":"10.1002/ece3.10448","usgsCitation":"Lamont, M., Ingram, D., Baker, T., Weigel, M., and Shamblin, B.M., 2023, Confirmation of significant sea turtle nesting activity on a remote island chain in the Gulf of Mexico: Ecology and Evolution, v. 13, no. 8, e10448, 10 p., https://doi.org/10.1002/ece3.10448.","productDescription":"e10448, 10 p.","ipdsId":"IP-152524","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":442305,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.10448","text":"Publisher Index 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Warnell School of Forestry and Natural Resource, Athens Georiga","active":true,"usgs":false}],"preferred":false,"id":881148,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70247961,"text":"70247961 - 2023 - Identifying drivers of population dynamics for a stream breeding amphibian using time series of egg mass counts","interactions":[],"lastModifiedDate":"2023-08-29T13:49:07.077807","indexId":"70247961","displayToPublicDate":"2023-08-24T08:39:19","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Identifying drivers of population dynamics for a stream breeding amphibian using time series of egg mass counts","docAbstract":"<p><span>The decline in amphibian populations is one of the starkest examples of the biodiversity crisis. For stream breeding amphibians, alterations to natural flow regimes by dams, water diversions, and climate change have been implicated in declines and extirpations. Identifying drivers of amphibian declines requires long time series of abundance data because amphibian populations can exhibit high natural variability. Multiple population viability analysis (MPVA) models integrate abundance data and share information from different populations to estimate how environmental factors influence population growth. Flow alteration has been linked to declines and extirpations in the Foothill Yellow-legged Frog (</span><i>Rana boylii</i><span>), a stream breeding amphibian native to California and Oregon. To date, no study has jointly analyzed abundance data from populations throughout the range of&nbsp;</span><i>R. boylii</i><span>&nbsp;in an MPVA model. We compiled time series of egg mass counts (an index of adult female abundance) from&nbsp;</span><i>R. boylii</i><span>&nbsp;populations in 36 focal streams and fit an MPVA model to quantify how streamflow metrics, stream temperature, and surrounding land cover affect population growth. We found population growth was positively related to stream temperature and was higher in the years following a wet year with high total annual streamflow. Density dependence was weakest (i.e., carrying capacity was highest) for streams with high seasonality of streamflow and intermediate rates of change in streamflow during spring. Our results highlight how altered streamflow can further increase the risk of decline for&nbsp;</span><i>R. boylii</i><span>&nbsp;populations. Managing stream conditions to better match natural flow and thermal regimes would benefit the conservation of&nbsp;</span><i>R. boylii</i><span>&nbsp;populations.</span></p>","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4645","usgsCitation":"Rose, J.P., Kupferberg, S.J., Peek, R.A., Ashton, D., Bettaso, J.B., Bobzien, S., Bourque, R.M., Breedveld, K.G., Catenazzi, A., Drennan, J.E., Gonsolin, E., Grefsrud, M., Herman, A.E., House, M.R., Kluber, M.R., Lind, A.J., Marlow, K.R., Striegle, A., van Hattem, M., Wheeler, C.A., Wilcox, J.T., Wiseman, K.D., and Halstead, B., 2023, Identifying drivers of population dynamics for a stream breeding amphibian using time series of egg mass counts: Ecosphere, v. 14, no. 8, e4645, 22 p., https://doi.org/10.1002/ecs2.4645.","productDescription":"e4645, 22 p.","ipdsId":"IP-145406","costCenters":[{"id":651,"text":"Western Ecological Research 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University","active":true,"usgs":false}],"preferred":false,"id":881272,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Drennan, Joseph E.","contributorId":328782,"corporation":false,"usgs":false,"family":"Drennan","given":"Joseph","email":"","middleInitial":"E.","affiliations":[{"id":78494,"text":"Garcia and Associates (GANDA","active":true,"usgs":false}],"preferred":false,"id":881273,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Gonsolin, Earl","contributorId":316570,"corporation":false,"usgs":false,"family":"Gonsolin","given":"Earl","email":"","affiliations":[{"id":68645,"text":"0","active":true,"usgs":false}],"preferred":false,"id":881274,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Grefsrud, Marcia","contributorId":192076,"corporation":false,"usgs":false,"family":"Grefsrud","given":"Marcia","email":"","affiliations":[],"preferred":false,"id":881275,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Herman, Andrea 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J.","contributorId":328786,"corporation":false,"usgs":false,"family":"Lind","given":"Amy","email":"","middleInitial":"J.","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":881279,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Marlow, Karla R.","contributorId":328787,"corporation":false,"usgs":false,"family":"Marlow","given":"Karla","email":"","middleInitial":"R.","affiliations":[{"id":78495,"text":"Garcia and Associates (GANDA),","active":true,"usgs":false}],"preferred":false,"id":881280,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Striegle, Alan","contributorId":316576,"corporation":false,"usgs":false,"family":"Striegle","given":"Alan","email":"","affiliations":[{"id":68645,"text":"0","active":true,"usgs":false}],"preferred":false,"id":881281,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"van Hattem, Michael","contributorId":316577,"corporation":false,"usgs":false,"family":"van Hattem","given":"Michael","affiliations":[{"id":68645,"text":"0","active":true,"usgs":false}],"preferred":false,"id":881282,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Wheeler, Clara A.","contributorId":328788,"corporation":false,"usgs":false,"family":"Wheeler","given":"Clara","email":"","middleInitial":"A.","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":881283,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Wilcox, Jeffery T.","contributorId":328789,"corporation":false,"usgs":false,"family":"Wilcox","given":"Jeffery","email":"","middleInitial":"T.","affiliations":[{"id":78496,"text":"Sonoma Mountain Ranch Preservation Foundation","active":true,"usgs":false}],"preferred":false,"id":881284,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Wiseman, Kevin D.","contributorId":328790,"corporation":false,"usgs":false,"family":"Wiseman","given":"Kevin","email":"","middleInitial":"D.","affiliations":[{"id":78497,"text":"Garcia and Associates (GANDA)","active":true,"usgs":false}],"preferred":false,"id":881285,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"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":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":881286,"contributorType":{"id":1,"text":"Authors"},"rank":23}]}}
,{"id":70247929,"text":"70247929 - 2023 - Geographic and taxonomic variation in adaptive capacity among mountain-dwelling small mammals: implications for conservation status and actions","interactions":[],"lastModifiedDate":"2023-08-24T13:44:25.750726","indexId":"70247929","displayToPublicDate":"2023-08-24T07:57:22","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Geographic and taxonomic variation in adaptive capacity among mountain-dwelling small mammals: implications for conservation status and actions","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-gulliver text-s\"><div id=\"ab0005\" class=\"abstract author\" lang=\"en\"><div id=\"as0005\"><p id=\"sp0025\"><span>Contemporary climate change is modifying the distribution, morphology,&nbsp;phenology, physiology, evolution, and&nbsp;interspecific interactions&nbsp;of species.&nbsp;Effects of climate change&nbsp;are mediated not only through the magnitude of change experienced (exposure) and an animal's sensitivity to such changes, but also through the ability of the population or species to adjust to climatic variability and change genetically, behaviorally, or spatially (via its distribution) (i.e., adaptive capacity; AC). Here, we used an attribute-based framework to systematically evaluate and compare the AC of American pikas (</span><span><i>Ochotona</i><i>&nbsp;princeps</i></span>) against four other mountain-dwelling small mammals of North America to determine whether pikas are disproportionately vulnerable to climate change, as has been postulated. Unlike previous analyses, we also compared AC across<span>&nbsp;</span><i>O. princeps</i><span>&nbsp;</span>lineages and across three taxonomic (and thus, spatial) scales. Our results indicate that pikas have markedly lower adaptive capacity than all compared species except bushy-tailed woodrats (<span><i>Neotoma</i><i>&nbsp;cinerea</i></span>), and that our assessments of species generally align with earlier characterizations of climate-change vulnerability based on life-history characteristics. Although AC did not differ dramatically among pika lineages, some attributes are likely constraining AC differently in various parts of the geographic range. Comparisons across taxonomic levels of pikas illustrated that, although AC levels were comparable in pika lineages versus range-wide, AC was assessed as lower in interior-Great-Basin pikas than across the entire<span>&nbsp;</span><i>O.p. schisticeps</i><span>&nbsp;</span>lineage. We conclude that the comparatively lower AC of pikas results in particularly high susceptibility to anthropogenic climate change, corroborating results from numerous other recent investigations of pikas' climate-responsiveness. Adaptive-capacity evaluations appear useful as a consistent way to identify sentinel species or populations and for conservation prioritization.</p></div></div></div><ul id=\"issue-navigation\" class=\"issue-navigation u-margin-s-bottom u-bg-grey1\"></ul>","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2023.109942","usgsCitation":"Beever, E.A., Wilkening, J.L., Billman, P.D., Thurman, L., Ernest, K.A., Wright, D.H., Gill, A.M., Craighead, A.C., Helmstetter, N.A., Svancara, L.K., Camp, M.J., Bhattacharyya, S., Fitzgerald, J., Hirose, J.M., Westover, M.L., Gerraty, F.D., Klingler, K.B., Schmidt, D.A., Ryals, D.K., Brown, R.N., Clark, S., Clayton, N., Collins, G.H., Cutting, K., Doak, D.F., Epps, C., Foley, J.E., French, J., Hayes, C., Mills, Z.A., Moyer-Horner, L., Nichols, L.B., Orlofsky, K.B., Peacock, M., Penzel, N.C., Peterson, J., Ramsay, N.G., Rickman, T., Robinson, M.M., Robison, H.L., Rowe, K.M., Rowe, K.C., Russello, M., Smith, A., Stewart, J., Thompson, 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However, more than 7 m of phreatomagmatic Keanakākoʻi Tephra unit D deposits from explosive eruptions crown the southern rim of the summit caldera and provide a stark reminder of Kīlauea’s explosive past and future potential. We used detailed field observations as well as granulometric and morphological analysis of 100 samples from two proximal sections to assess the eruption style and fragmentation mechanism. The deposits can be divided into four subunits, six different lithofacies, and contain three juvenile tephra components. Each juvenile component shows distinct shape variability resulting from molten fuel-coolant interaction (MFCI) explosions of magma of variable vesicularity. Fragmentation of dense glass generates olive-green ash, fragmentation of low to moderately vesicular magma generates a dark gray ash-lapilli component, and fragmentation of highly vesicular magma generates light-yellow pumice. Our work shows that magma structure impacts MFCI explosion efficiency. Small-scale planar bedding throughout most of the deposit points to a general eruption style of small, frequent explosions generating low plumes. Thicker beds of accretionary lapilli of fine-extremely fine ash are related to very efficient magma-water mixing. Pyroclastic density current (PDC) deposits in the upper part of the stratigraphy contain at least three flows but show no significant dune or cross-bedding structures. We suggest that this is a function of the vent being situated in a caldera that was then ∼600 m deep, where the caldera wall acted as a barrier and changed the flow dynamics to very dilute overspills and co-PDC plume falls over the wall. Deconvolution modeling of the polymodal grain size distributions is used to assess grain size changes of each juvenile component for this deposit, which greatly improves interpretation of lithofacies generation and eruption dynamics. Size-correlated shape parameters show that shape data across a wide size range are needed to accurately track grain shapes. This study demonstrates how careful examination of grain size and shape of juvenile tephra clasts can help volcanologists understand how effusive basaltic volcanoes can become violently explosive.</p></div>","language":"English","publisher":"Frontiers","doi":"10.3389/feart.2023.1153288","usgsCitation":"Schmith, J., and Swanson, D., 2023, Complex styles of phreatomagmatic explosions at Kīlauea Volcano, Hawaii, controlled by magma structure: Frontiers in Earth Science - Volcanology, v. 11, 1153288, 25 p., https://doi.org/10.3389/feart.2023.1153288.","productDescription":"1153288, 25 p.","ipdsId":"IP-149856","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":442320,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2023.1153288","text":"Publisher Index Page"},{"id":435209,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97WS3WS","text":"USGS data release","linkHelpText":"Keanakākoʻi Tephra unit D, Kīlauea Volcano, Hawaii: sample details, grain size, and grain shape data for 100 tephra samples"},{"id":426359,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kīlauea Volcano","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -155.4259084319663,\n              19.5507272208674\n            ],\n            [\n              -155.4259084319663,\n              19.254964031182183\n            ],\n            [\n              -155.04401052855732,\n              19.254964031182183\n            ],\n            [\n              -155.04401052855732,\n              19.5507272208674\n            ],\n            [\n              -155.4259084319663,\n              19.5507272208674\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"11","noUsgsAuthors":false,"publicationDate":"2023-08-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Schmith, Jo 0000-0002-0912-7441","orcid":"https://orcid.org/0000-0002-0912-7441","contributorId":304399,"corporation":false,"usgs":true,"family":"Schmith","given":"Jo","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":896091,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Swanson, Donald A. 0000-0002-1680-3591","orcid":"https://orcid.org/0000-0002-1680-3591","contributorId":229682,"corporation":false,"usgs":true,"family":"Swanson","given":"Donald A.","affiliations":[],"preferred":true,"id":896092,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70247984,"text":"70247984 - 2023 - Evaluating a prospective fault-based stress-transfer forecast for the M7.9 Wenchuan earthquake region 15 years later","interactions":[],"lastModifiedDate":"2023-08-30T11:43:01.246632","indexId":"70247984","displayToPublicDate":"2023-08-24T06:39:23","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10542,"text":"The Seismic Record","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating a prospective fault-based stress-transfer forecast for the M7.9 Wenchuan earthquake region 15 years later","docAbstract":"<div id=\"137789579\" class=\"article-section-wrapper js-article-section js-content-section  \" data-section-parent-id=\"0\"><p>Four days after the 12 May 2008<span>&nbsp;</span><strong>M</strong>&nbsp;7.9 Wenchuan earthquake struck the Sichuan region of China, we submitted a prospective earthquake forecast based on transfer of stress from the mainshock onto significant faults crossing through populated areas. We identified where the largest aftershocks were likely to occur that could cause loss of life. We returned the revised article to the journal on 5 June 2008, marking the last day of our observation period. The primary testable features are locations and focal mechanisms of larger (<strong>M</strong><span>&nbsp;</span>≥ 4.5) earthquakes; did these events happen on or very near the faults we said they would? Did they have the same strikes, dips, and rakes as the faults we modeled? In retrospect, is the stress transfer method consistent with all<span>&nbsp;</span><strong>M</strong><span>&nbsp;</span>≥ 4.5 earthquakes that occurred? We find all but one<span>&nbsp;</span><strong>M</strong><span>&nbsp;</span>≥ 4.5 aftershock with known focal mechanisms located on stress‐increased faults, and their focal mechanism parameters overlap with geological characteristics we used in making calculations. Six of the seven lethal<span>&nbsp;</span><strong>M</strong><span>&nbsp;</span>&gt; 4.5 earthquakes that occurred in the region since 5 June 2008 were located on stress‐increased faults, with the lone exception triggered by hydraulic fracturing.</p></div>","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0320230021","usgsCitation":"Parsons, T.E., Ji, C., and Kirby, E., 2023, Evaluating a prospective fault-based stress-transfer forecast for the M7.9 Wenchuan earthquake region 15 years later: The Seismic Record, v. 3, no. 3, p. 218-227, https://doi.org/10.1785/0320230021.","productDescription":"10 p.","startPage":"218","endPage":"227","ipdsId":"IP-153067","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":442323,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1785/0320230021","text":"Publisher Index Page"},{"id":420297,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              102.07232120934884,\n              33.52855185269891\n            ],\n            [\n              102.07232120934884,\n              30.378770167475196\n            ],\n            [\n              105.34349709659051,\n              30.378770167475196\n            ],\n            [\n              105.34349709659051,\n              33.52855185269891\n            ],\n            [\n              102.07232120934884,\n              33.52855185269891\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"3","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-08-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Parsons, Thomas E. 0000-0002-0582-4338 tparsons@usgs.gov","orcid":"https://orcid.org/0000-0002-0582-4338","contributorId":2314,"corporation":false,"usgs":true,"family":"Parsons","given":"Thomas","email":"tparsons@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":881418,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ji, Chen","contributorId":62502,"corporation":false,"usgs":true,"family":"Ji","given":"Chen","email":"","affiliations":[],"preferred":false,"id":881419,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kirby, Eric 0000-0002-5701-8688","orcid":"https://orcid.org/0000-0002-5701-8688","contributorId":197171,"corporation":false,"usgs":false,"family":"Kirby","given":"Eric","email":"","affiliations":[],"preferred":false,"id":881420,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70247948,"text":"70247948 - 2023 - Multi-scale assessment of roost selection by ‘ōpe‘ape‘a, the Hawaiian hoary bat (Lasiurus semotus)","interactions":[],"lastModifiedDate":"2023-08-29T11:44:58.466128","indexId":"70247948","displayToPublicDate":"2023-08-24T06:39:17","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Multi-scale assessment of roost selection by ‘ōpe‘ape‘a, the Hawaiian hoary bat (Lasiurus semotus)","docAbstract":"<div class=\"abstract toc-section abstract-type-\"><div class=\"abstract-content\"><p>The Hawaiian hoary bat (<i>Lasiurus semotus</i>; Chiroptera: Vespertilionidae), commonly and locally known as ‘ōpe‘ape‘a, is a solitary, insectivorous, and foliage-roosting species distributed across a wide range of habitats in lowland and montane environments. The species, as with many others in the Hawaiian archipelago, are facing a suite of challenges due to habitat loss and degradation, introduced predators and pests, and climate change. An understanding of the roost requirements of foliage-roosting tree bats is critical to their conservation as these habitats provide several important benefits to survival and reproduction. Because little is known about ‘ōpe‘ape‘a roost ecology and considerable effort is needed to capture and track bats to roost locations, we examined resource selection at multiple spatial scales—perch location within a roost tree, roost tree, and forest stand. We used a discrete choice modeling approach to investigate day-roost selection and describe attributes of roost trees including those used as maternity roosts. ‘Ōpe‘ape‘a were found roosting in 19 tree species and in an assortment of landcover types including native and non-native habitats. Our results are largely consistent with findings of other studies of foliage-roosting, insectivorous tree bats where bats selected roost locations that may offer protection and thermoregulatory benefits.</p></div></div>","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pone.0288280","usgsCitation":"Montoya-Aiona, K., Gorresen, P., Courtot, K., Aguirre, A.A., Calderon, F.A., Casler, S.P., Ciarrachi, S.G., Hoeh, J.P., Tupu, J.L., and Zinn, T.L., 2023, Multi-scale assessment of roost selection by ‘ōpe‘ape‘a, the Hawaiian hoary bat (Lasiurus semotus): PLoS ONE, v. 18, no. 8, e0288280, 25 p., https://doi.org/10.1371/journal.pone.0288280.","productDescription":"e0288280, 25 p.","ipdsId":"IP-145606","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":442326,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0288280","text":"Publisher Index Page"},{"id":435211,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9X6G081","text":"USGS data release","linkHelpText":"Hawaiʻi Island ʻōpeʻapeʻa (Hawaiian hoary bat) roost data, 2018&amp;amp;amp;amp;ndash;2021"},{"id":435210,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9G4A2E3","text":"USGS data release","linkHelpText":"Hawai&amp;amp;amp;amp;lsquo;i Island ʻōpeʻapeʻa (Hawaiian hoary bat; Lasiurus semotus) mist netting effort, capture, and tag retention, 2018&amp;amp;amp;amp;ndash;2021"},{"id":420218,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -156.42668817478307,\n              20.37429981764066\n            ],\n            [\n              -156.42668817478307,\n              18.72897192098766\n            ],\n            [\n              -154.63668469640746,\n              18.72897192098766\n            ],\n            [\n              -154.63668469640746,\n              20.37429981764066\n            ],\n            [\n              -156.42668817478307,\n              20.37429981764066\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"18","issue":"8","noUsgsAuthors":false,"publicationDate":"2023-08-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Montoya-Aiona, Kristina 0000-0002-1776-5443 kmontoya-aiona@usgs.gov","orcid":"https://orcid.org/0000-0002-1776-5443","contributorId":5899,"corporation":false,"usgs":true,"family":"Montoya-Aiona","given":"Kristina","email":"kmontoya-aiona@usgs.gov","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true},{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":881185,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gorresen, P. Marcos 0000-0002-0707-9212","orcid":"https://orcid.org/0000-0002-0707-9212","contributorId":196628,"corporation":false,"usgs":false,"family":"Gorresen","given":"P. Marcos","affiliations":[{"id":13341,"text":"Hawai‘i Cooperative Studies Unit, University of Hawai‘i at Hilo","active":true,"usgs":false}],"preferred":false,"id":881186,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Courtot, Karen 0000-0002-8849-4054 kcourtot@usgs.gov","orcid":"https://orcid.org/0000-0002-8849-4054","contributorId":140002,"corporation":false,"usgs":true,"family":"Courtot","given":"Karen","email":"kcourtot@usgs.gov","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true},{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":881187,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Aguirre, Aaron A. 0000-0003-2256-0669","orcid":"https://orcid.org/0000-0003-2256-0669","contributorId":328746,"corporation":false,"usgs":false,"family":"Aguirre","given":"Aaron","email":"","middleInitial":"A.","affiliations":[{"id":78479,"text":"USGS - PIERC (Formerly)","active":true,"usgs":false}],"preferred":false,"id":881188,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Calderon, Flor A. 0000-0002-9449-3648","orcid":"https://orcid.org/0000-0002-9449-3648","contributorId":328747,"corporation":false,"usgs":false,"family":"Calderon","given":"Flor","email":"","middleInitial":"A.","affiliations":[{"id":78479,"text":"USGS - PIERC (Formerly)","active":true,"usgs":false}],"preferred":false,"id":881189,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Casler, Sean P. 0000-0001-6925-439X","orcid":"https://orcid.org/0000-0001-6925-439X","contributorId":328748,"corporation":false,"usgs":false,"family":"Casler","given":"Sean","email":"","middleInitial":"P.","affiliations":[{"id":78479,"text":"USGS - PIERC (Formerly)","active":true,"usgs":false}],"preferred":false,"id":881190,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ciarrachi, Sarah G.","contributorId":328749,"corporation":false,"usgs":false,"family":"Ciarrachi","given":"Sarah","email":"","middleInitial":"G.","affiliations":[{"id":78479,"text":"USGS - PIERC (Formerly)","active":true,"usgs":false}],"preferred":false,"id":881191,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hoeh, Julia P. S. 0000-0002-4552-8779","orcid":"https://orcid.org/0000-0002-4552-8779","contributorId":328750,"corporation":false,"usgs":false,"family":"Hoeh","given":"Julia","email":"","middleInitial":"P. S.","affiliations":[{"id":78479,"text":"USGS - PIERC (Formerly)","active":true,"usgs":false}],"preferred":false,"id":881192,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Tupu, Josephine L.","contributorId":328751,"corporation":false,"usgs":false,"family":"Tupu","given":"Josephine","email":"","middleInitial":"L.","affiliations":[{"id":78481,"text":"Hawaii Cooperative Studies Unit, University of Hawaii at Hilo","active":true,"usgs":false}],"preferred":false,"id":881193,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Zinn, Terry L.","contributorId":328752,"corporation":false,"usgs":false,"family":"Zinn","given":"Terry","email":"","middleInitial":"L.","affiliations":[{"id":78479,"text":"USGS - PIERC (Formerly)","active":true,"usgs":false}],"preferred":false,"id":881194,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70247674,"text":"tm6B10 - 2023 - Parameter estimation at the conterminous United States scale and streamflow routing enhancements for the National Hydrologic Model infrastructure application of the Precipitation-Runoff Modeling System (NHM-PRMS)","interactions":[],"lastModifiedDate":"2026-03-13T15:46:27.713556","indexId":"tm6B10","displayToPublicDate":"2023-08-23T11:15:00","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"6-B10","displayTitle":"Parameter Estimation at the Conterminous United States Scale and Streamflow Routing Enhancements for the National Hydrologic Model Infrastructure Application of the Precipitation-Runoff Modeling System (NHM-PRMS)","title":"Parameter estimation at the conterminous United States scale and streamflow routing enhancements for the National Hydrologic Model infrastructure application of the Precipitation-Runoff Modeling System (NHM-PRMS)","docAbstract":"<p>This report documents a three-part continental-scale calibration procedure and a new streamflow routing algorithm using the U.S. Geological Survey National Hydrologic Model (NHM) infrastructure along with an application of the Precipitation-Runoff Modeling System (PRMS). The traditional approach to hydrologic model calibration and evaluation, which relies on comparing observed and simulated streamflow, is not sufficient for accurately representing the non-streamflow parts of the water budget. If intermediate process variables computed by the hydrologic model are not examined, the variables could be characterized by parameter values that do not replicate those hydrological processes present in the physical system. In answer to this potential problem, alternative hydrologic process variables from the model (in addition to streamflow) are included in a calibration procedure applied to the conterminous United States (CONUS) domain.</p><p>The three-part calibration procedure presented in this report considers volume (calibration by hydrologic response unit [byHRU]), timing (calibration by headwater watershed [byHW]), and measured streamflow [byHWobs]). The first part, byHRU, is considered a water-balance volume calibration that uses five alternative (non-streamflow) hydrologic quantities (runoff, actual evapotranspiration, recharge, soil moisture, and snow-covered area) as calibration targets for each hydrologic response unit (HRU). These alternative data products were derived, with error bounds, from multiple sources for each of the 109,951 HRUs in the NHM on time scales varying from annual to daily. The second part of the calibration, byHW, is considered a streamflow timing calibration that uses statistically based streamflow simulations developed using ordinary kriging for 7,265 headwater watersheds that had drainage areas of less than 3,000 square kilometers (1,158 square miles) across the CONUS. Two streamflow routing algorithms were tested in this byHW calibration: (1) continuity without attenuation of the flood pulse and (2) a new formulation of the Muskingum routing method, which was added to the PRMS as part of this study. The third part of the calibration, byHWobs, refines the model parameters using available measured streamflow using 1,417 streamgage locations. A multiple-objective, stepwise, automated calibration procedure was used to identify the optimal set of parameters for each calibration procedure.</p><p>Using a variety of alternative datasets for calibration of the water budget provides users of the NHM-PRMS with improved initial parameters and helps alleviate the equifinality problem (getting the right answer for the wrong reason). Through a community effort, these alternative data products, with error bounds, can be used to improve and expand our understanding of hydrologic-process representation in models. The broader modeling community can use these data products, with error bounds, to calibrate and evaluate hydrologic models using more than streamflow.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm6B10","programNote":"Water Availability and Use Science Program","usgsCitation":"Hay, L.E., LaFontaine, J.H., Van Beusekom, A.E., Norton, P.A., Farmer, W.H., Regan, R.S., Markstrom, S.L., and Dickinson, J.E., 2023, Parameter estimation at the conterminous United States scale and streamflow routing enhancements for the National Hydrologic Model infrastructure application of the Precipitation-Runoff Modeling System (NHM-PRMS): U.S. Geological Survey Techniques and Methods 6–B10, 50 p., https://doi.org/10.3133/tm6B10.","productDescription":"Report: vii, 50 p.; Data Release","numberOfPages":"50","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-121885","costCenters":[{"id":13634,"text":"South Atlantic Water Science 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  [\n                -117.03121,\n                49\n              ],\n              [\n                -116.04818,\n                49\n              ],\n              [\n                -113,\n                49\n              ],\n              [\n                -110.05,\n                49\n              ],\n              [\n                -107.05,\n                49\n              ],\n              [\n                -104.04826,\n                48.99986\n              ],\n              [\n                -100.65,\n                49\n              ],\n              [\n                -97.22872,\n                49.0007\n              ],\n              [\n                -95.15907,\n                49\n              ],\n              [\n                -95.15609,\n                49.38425\n              ],\n              [\n                -94.81758,\n                49.38905\n              ]\n            ]\n          ]\n        ]\n      },\n      \"properties\": {\n        \"name\": \"United States\"\n      }\n    }\n  ]\n}","contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/sawsc\" data-mce-href=\"https://www.usgs.gov/centers/sawsc\">South Atlantic Water Science Center</a><br>U.S. Geological Survey<br>1770 Corporate Drive, Suite 500<br>Norcross, GA 30093</p><p><a href=\"https://pubs.er.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Results</li><li>Limitations of NHM-PRMS Modeling Application</li><li>Summary</li><li>Acknowledgments</li><li>References Cited</li><li>Appendix 1. National Hydrologic Model Infrastructure Application of the Precipitation-Runoff Modeling System (NHM-PRMS) Calibration Setup</li><li>Appendix 2. Documentation of the muskingum_mann Streamflow Routing Module for the Precipitation-Runoff Modeling System</li></ul>","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2023-08-23","noUsgsAuthors":false,"publicationDate":"2023-08-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Hay, Lauren E. 0000-0003-3763-4595","orcid":"https://orcid.org/0000-0003-3763-4595","contributorId":225584,"corporation":false,"usgs":false,"family":"Hay","given":"Lauren E.","affiliations":[{"id":39981,"text":"private consultant","active":true,"usgs":false}],"preferred":false,"id":880003,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"LaFontaine, Jacob H. 0000-0003-4923-2630 jlafonta@usgs.gov","orcid":"https://orcid.org/0000-0003-4923-2630","contributorId":2258,"corporation":false,"usgs":true,"family":"LaFontaine","given":"Jacob","email":"jlafonta@usgs.gov","middleInitial":"H.","affiliations":[{"id":316,"text":"Georgia Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":880004,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Van Beusekom, Ashley E. 0000-0002-6996-978X beusekom@usgs.gov","orcid":"https://orcid.org/0000-0002-6996-978X","contributorId":3992,"corporation":false,"usgs":true,"family":"Van Beusekom","given":"Ashley","email":"beusekom@usgs.gov","middleInitial":"E.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":880089,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Norton, Parker A. 0000-0002-4638-2601 pnorton@usgs.gov","orcid":"https://orcid.org/0000-0002-4638-2601","contributorId":2257,"corporation":false,"usgs":true,"family":"Norton","given":"Parker","email":"pnorton@usgs.gov","middleInitial":"A.","affiliations":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":880090,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"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":880007,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Regan, R. Steve 0000-0003-4803-8596 rsregan@usgs.gov","orcid":"https://orcid.org/0000-0003-4803-8596","contributorId":196973,"corporation":false,"usgs":true,"family":"Regan","given":"R.","email":"rsregan@usgs.gov","middleInitial":"Steve","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":880008,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Markstrom, Steven L. 0000-0001-7630-9547 markstro@usgs.gov","orcid":"https://orcid.org/0000-0001-7630-9547","contributorId":146553,"corporation":false,"usgs":true,"family":"Markstrom","given":"Steven","email":"markstro@usgs.gov","middleInitial":"L.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":880009,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dickinson, Jesse E. 0000-0002-0048-0839 jdickins@usgs.gov","orcid":"https://orcid.org/0000-0002-0048-0839","contributorId":152545,"corporation":false,"usgs":true,"family":"Dickinson","given":"Jesse","email":"jdickins@usgs.gov","middleInitial":"E.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":880091,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70254450,"text":"70254450 - 2023 - Fluid migration pathways to groundwater in mature oil fields: Exploring the roles of water injection/production and oil-well integrity in California, USA","interactions":[],"lastModifiedDate":"2024-05-24T11:45:46.389957","indexId":"70254450","displayToPublicDate":"2023-08-23T06:43:14","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Fluid migration pathways to groundwater in mature oil fields: Exploring the roles of water injection/production and oil-well integrity in California, USA","docAbstract":"<div id=\"ab0005\" class=\"abstract author\" lang=\"en\"><div id=\"as0005\"><p id=\"sp0045\"><span>Mature oil fields potentially contain multiple fluid migration pathways toward protected groundwater (total dissolved solids, TDS, in nonexempted aquifer &lt;10,000&nbsp;mg/L) because of their extensive development histories. Time-series data for water use, fluid pressures, oil-well construction, and&nbsp;geochemistry&nbsp;from the South Belridge and Lost Hills mature oil fields in California are used to explore the roles of injection/production of oil-field water and well-integrity issues in fluid migration. Injection/production of oil-field water modified hydraulic gradients in both oil fields, resulting in chemical transport from deeper groundwater and hydrocarbon-reservoir systems to aquifers in the oil fields. Those aquifers are used for&nbsp;</span>water supply<span>&nbsp;outside the oil-field boundaries. Oil wells drilled before 1976 can be fluid migration pathways because a relatively large percentage of them have &gt;10&nbsp;m of uncemented annulus that straddles oil-well casing damage and/or the base of groundwater with TDS &lt;10,000&nbsp;mg/L. The risk of groundwater-quality degradation is higher when wells with those risk factors occur in areas with upward hydraulic gradients created by positive net injection, groundwater withdrawals, or combinations of these variables. The complex changes in hydrologic conditions and&nbsp;groundwater chemistry&nbsp;likely would not have been discovered in the absence of years to decades of monitoring data for groundwater elevations and chemistry, and installation of monitoring wells in areas with overlapping risk factors. Important monitoring concepts based on results from this and other studies include monitoring hydrocarbon-reservoir and groundwater systems at multiple spatiotemporal scales and maintaining transparency and accessibility of data and analyses. This analysis focuses on two California oil fields, but the methods used and processes affecting fluid migration could be relevant in other oil fields where substantial injection/production of oil-field water occurs and oil-well integrity is of concern.</span></p></div></div><div id=\"ab0010\" class=\"abstract graphical\" lang=\"en\"><br></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2023.166400","usgsCitation":"McMahon, P.B., Landon, M.K., Stephens, M.J., Taylor, K.A., Gillespie, J.M., Davis, T., and Shimabukuro, D.H., 2023, Fluid migration pathways to groundwater in mature oil fields: Exploring the roles of water injection/production and oil-well integrity in California, USA: Science of the Total Environment, v. 900, 166400, 15 p., https://doi.org/10.1016/j.scitotenv.2023.166400.","productDescription":"166400, 15 p.","ipdsId":"IP-148641","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":442333,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2023.166400","text":"Publisher Index Page"},{"id":435212,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MHSTGS","text":"USGS data release","linkHelpText":"Oil well annular cement and casing damage data in mature oil fields undergoing hydraulic fracturing, South Belridge and Lost Hills Oil Fields, California"},{"id":429240,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              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Center","active":true,"usgs":true}],"preferred":true,"id":901402,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Landon, Matthew K. 0000-0002-5766-0494 landon@usgs.gov","orcid":"https://orcid.org/0000-0002-5766-0494","contributorId":392,"corporation":false,"usgs":true,"family":"Landon","given":"Matthew","email":"landon@usgs.gov","middleInitial":"K.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":901403,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stephens, Michael J. 0000-0001-8995-9928","orcid":"https://orcid.org/0000-0001-8995-9928","contributorId":205895,"corporation":false,"usgs":true,"family":"Stephens","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":901404,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Taylor, Kimberly A. 0000-0002-0095-6403 ktaylor@usgs.gov","orcid":"https://orcid.org/0000-0002-0095-6403","contributorId":1601,"corporation":false,"usgs":true,"family":"Taylor","given":"Kimberly","email":"ktaylor@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":901405,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gillespie, Janice M. 0000-0003-1667-3472","orcid":"https://orcid.org/0000-0003-1667-3472","contributorId":219675,"corporation":false,"usgs":true,"family":"Gillespie","given":"Janice","email":"","middleInitial":"M.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":901406,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Davis, Tracy 0000-0003-0253-6661 tadavis@usgs.gov","orcid":"https://orcid.org/0000-0003-0253-6661","contributorId":176921,"corporation":false,"usgs":true,"family":"Davis","given":"Tracy","email":"tadavis@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":901407,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Shimabukuro, David H. 0000-0002-6106-5284","orcid":"https://orcid.org/0000-0002-6106-5284","contributorId":208209,"corporation":false,"usgs":false,"family":"Shimabukuro","given":"David","email":"","middleInitial":"H.","affiliations":[{"id":37762,"text":"California State University, Sacramento","active":true,"usgs":false}],"preferred":false,"id":901408,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70247743,"text":"dr1180 - 2023 - Calibrating optical turbidity measurements with suspended-sediment concentrations from the Herring River in Wellfleet, Massachusetts, from November 2018 to November 2019","interactions":[],"lastModifiedDate":"2026-02-04T20:13:58.477377","indexId":"dr1180","displayToPublicDate":"2023-08-22T15:20:00","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":9318,"text":"Data Report","code":"DR","onlineIssn":"2771-9448","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1180","displayTitle":"Calibrating Optical Turbidity Measurements with Suspended-Sediment Concentrations from the Herring River in Wellfleet, Massachusetts, from November 2018 to November 2019","title":"Calibrating optical turbidity measurements with suspended-sediment concentrations from the Herring River in Wellfleet, Massachusetts, from November 2018 to November 2019","docAbstract":"The sediment budget in the tidally restricted Herring River in Wellfleet, Massachusetts, must be quantified so restoration options for the river can be evaluated. Platforms equipped with optical turbidity sensors were deployed seaward and landward of the Herring River restriction to measure a time series of turbidity, from which a time series of suspended-sediment concentration (SSC) can be estimated. Water samples were collected periodically from the Herring River from November 2018 to November 2019 and analyzed for SSC to derive a relationship to turbidity measurements given in nephelometric turbidity units. This report presents the data-collection methods used and the linear calibration model generated by repeated median regression to convert turbidity measurements to SSC.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1180","programNote":"Coastal and Marine Hazards and Resources Program","usgsCitation":"De Meo, O.A., Ganju, N.K., Bales, R.D., Marsjanik, E.D., and Suttles, S.E., 2023, Calibrating optical turbidity measurements with suspended-sediment concentrations from the Herring River in Wellfleet, Massachusetts, from November 2018 to November 2019: U.S. Geological Survey Data Report 1180, 8 p., https://doi.org/10.3133/dr1180.","productDescription":"Report: vi, 8 p.; 3 Data Releases","numberOfPages":"8","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-150029","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":499555,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115238.htm","linkFileType":{"id":5,"text":"html"}},{"id":419963,"rank":8,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1180/images/"},{"id":419962,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1180/dr1180.XML"},{"id":419824,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K3SCKY","text":"USGS data release","linkHelpText":"Water quality data from a multiparameter sonde collected in the Herring River during November 2018 to November 2019 in Wellfleet, MA"},{"id":419823,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95AE74D","text":"USGS data release","linkHelpText":"Time-series measurements of oceanographic and water quality data collected in the Herring River, Wellfleet, Massachusetts, USA, November 2018 to November 2019"},{"id":419822,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZL2IPN","text":"USGS data release","linkHelpText":"Suspended-sediment concentrations and loss-on-ignition from water samples collected in the Herring River during 2018-19 in Wellfleet, MA (ver 1.1, March 2023)"},{"id":419961,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/dr1180/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"DR 1180"},{"id":419821,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1180/dr1180.pdf","text":"Report","size":"4.57 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DR 1180"},{"id":419820,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1180/coverthb.jpg"}],"country":"United States","state":"Massachusetts","city":"Wellfleet","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -70.08466148489833,\n              41.94504968532215\n            ],\n            [\n              -70.08466148489833,\n              41.912369414289856\n            ],\n            [\n              -70.04897123149479,\n              41.912369414289856\n            ],\n            [\n              -70.04897123149479,\n              41.94504968532215\n            ],\n            [\n              -70.08466148489833,\n              41.94504968532215\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:WHSC_ science_ director@usgs.gov\" data-mce-href=\"mailto:WHSC_ science_ director@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/whcmsc\" data-mce-href=\"https://www.usgs.gov/centers/whcmsc\">Woods Hole Coastal and Marine Science Center</a><br>U.S. Geological Survey<br>384 Woods Hole Road<br>Quissett Campus<br>Woods Hole, MA 02543-1598</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Sensor Deployment and Water Sample Collection</li><li>Laboratory Determination of Suspended-Sediment Concentration</li><li>Calibration of the Optical Turbidity Sensor</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2023-08-22","noUsgsAuthors":false,"publicationDate":"2023-08-22","publicationStatus":"PW","contributors":{"authors":[{"text":"De Meo, Olivia A. 0000-0003-3937-1354","orcid":"https://orcid.org/0000-0003-3937-1354","contributorId":300810,"corporation":false,"usgs":true,"family":"De Meo","given":"Olivia","email":"","middleInitial":"A.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":880229,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ganju, Neil K. 0000-0002-1096-0465","orcid":"https://orcid.org/0000-0002-1096-0465","contributorId":202878,"corporation":false,"usgs":true,"family":"Ganju","given":"Neil K.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":880230,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bales, Robert D. 0000-0002-0807-9804","orcid":"https://orcid.org/0000-0002-0807-9804","contributorId":296652,"corporation":false,"usgs":true,"family":"Bales","given":"Robert","email":"","middleInitial":"D.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":880231,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Marsjanik, Eric D. 0000-0002-6065-0182 emarsjanik@usgs.gov","orcid":"https://orcid.org/0000-0002-6065-0182","contributorId":195777,"corporation":false,"usgs":true,"family":"Marsjanik","given":"Eric","email":"emarsjanik@usgs.gov","middleInitial":"D.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":880232,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Suttles, Steven E. 0000-0002-4119-8370 ssuttles@usgs.gov","orcid":"https://orcid.org/0000-0002-4119-8370","contributorId":192272,"corporation":false,"usgs":true,"family":"Suttles","given":"Steven","email":"ssuttles@usgs.gov","middleInitial":"E.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":880233,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70247877,"text":"sim3505 - 2023 - Aeromagnetic map of northeastern California","interactions":[],"lastModifiedDate":"2026-02-19T17:58:59.83292","indexId":"sim3505","displayToPublicDate":"2023-08-22T12:32:17","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3505","displayTitle":"Aeromagnetic Map of Northeastern California","title":"Aeromagnetic map of northeastern California","docAbstract":"<p>Aeromagnetic surveys were conducted to improve understanding of the geology and structure in northeastern California, a region predominantly covered by Quaternary and Tertiary, mainly Neogene, volcanic rocks including Medicine Lake volcano. New aeromagnetic data are a substantial improvement over existing data and reveal structural details not resolved by older surveys. Here we show how these data (1) do not support the presence of a northwest-striking structural feature across the Modoc Plateau, (2) reveal a northeast-striking fault-bounded block of predominantly reversely magnetized material that may influence tectonism at Medicine Lake volcano, and (3) constrain possible right-lateral offsets along the Likely Fault Zone and other faults that traverse the region. The data also highlight possible extensions of mapped faults, such as those in Fall River Valley and the Tule and Lower Klamath Lake areas.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3505","usgsCitation":"Langenheim, V.E. and Sweetkind, D.S., 2023, Aeromagnetic map of northeastern California: U.S. Geological Survey Scientific Investigations Map 3505, pamphlet 21 p., https://doi.org/10.3133/sim3505.","productDescription":"Pamphlet: iv, 21 p.; 3 Data Releases; 1 Sheet: 30.82 × 39.53 inches","numberOfPages":"21","additionalOnlineFiles":"Y","ipdsId":"IP-137584","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":500211,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115218.htm","linkFileType":{"id":5,"text":"html"}},{"id":435213,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TQGRDW","text":"USGS data release","linkHelpText":"Aeromagnetic and derivative gridded data, and magnetization boundaries of northeastern California"},{"id":420034,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91Y9L0H","text":"Data release of geologic data for the aeromagnetic map of northeastern California","description":"Sweetkind, D.S., and Langenheim, V.E., 2022, Data release of geologic data for the aeromagnetic map of northeastern California: U.S. Geological Survey data release, https://doi.org/10.5066/P91Y9L0H."},{"id":420033,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PUFYDD","text":"Aeromagnetic and derivative gridded data, and magnetization boundaries of northeastern California","description":"Langenheim, V.E., 2023, Aeromagnetic and derivative gridded data, and magnetization boundaries of northeastern California: U.S. Geological data release, https://doi.org/10.5066/P9PUFYDD."},{"id":420032,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LU37RC","text":"Aeromagnetic Data of Alturas, California, and Surrounding Areas","description":"Langenheim, V.E., 2022, Aeromagnetic Data of Alturas, California, and Surrounding Areas: U.S. Geological Survey data release, https://doi.org/10.5066/P9LU37RC."},{"id":420031,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3505/sim3505_sheet.pdf","text":"Map Sheet","size":"30 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":420030,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3505/sim3505_pamphlet.pdf","text":"Pamphlet","size":"10 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":420029,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3505/covrthb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -120.03188053680955,\n              42.02157881382186\n            ],\n            [\n              -122.00857149452494,\n              42.02157881382186\n            ],\n            [\n              -122.00857149452494,\n              40.50279454574354\n            ],\n            [\n              -120.03188053680955,\n              40.50279454574354\n            ],\n            [\n              -120.03188053680955,\n              42.02157881382186\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p><a href=\"https://www.usgs.gov/centers/gmeg\" data-mce-href=\"https://www.usgs.gov/centers/gmeg\">Geology, Minerals, Energy, &amp; Geophysics Science Center</a><br><a href=\"https://gcc02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fusgs.gov%2F&amp;data=05%7C01%7Cjtran%40usgs.gov%7C2acc9ccfe04c490508e208db57150e3b%7C0693b5ba4b184d7b9341f32f400a5494%7C0%7C0%7C638199520171483214%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&amp;sdata=M5pIPYGVMGFOGVgSlKnAjJ%2FMw0n5BBDivZ0f4E1wjFs%3D&amp;reserved=0\" data-mce-href=\"https://gcc02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fusgs.gov%2F&amp;data=05%7C01%7Cjtran%40usgs.gov%7C2acc9ccfe04c490508e208db57150e3b%7C0693b5ba4b184d7b9341f32f400a5494%7C0%7C0%7C638199520171483214%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&amp;sdata=M5pIPYGVMGFOGVgSlKnAjJ%2FMw0n5BBDivZ0f4E1wjFs%3D&amp;reserved=0\">U.S. Geological Survey</a><br>Building 19, 350 N. Akron Rd.<br>P.O. Box 158<br>Moffett Field, CA 94035</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Data</li><li>Filtering and Magnetization Boundaries</li><li>Results</li><li>Description of Map Units</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2023-08-22","noUsgsAuthors":false,"publicationDate":"2023-08-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Langenheim, Victoria E. 0000-0003-2170-5213 zulanger@usgs.gov","orcid":"https://orcid.org/0000-0003-2170-5213","contributorId":151042,"corporation":false,"usgs":true,"family":"Langenheim","given":"Victoria E.","email":"zulanger@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":880839,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sweetkind, Donald S. 0000-0003-0892-4796 dsweetkind@usgs.gov","orcid":"https://orcid.org/0000-0003-0892-4796","contributorId":139913,"corporation":false,"usgs":true,"family":"Sweetkind","given":"Donald","email":"dsweetkind@usgs.gov","middleInitial":"S.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":880840,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
]}