Digital flood-inundation map libraries for two reaches that constitute 14.8 miles of Little and Big Papillion Creeks in Omaha, Nebraska, were created by the U.S. Geological Survey (USGS) in cooperation with the Papio-Missouri River Natural Resource District. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Program website at https://www.usgs.gov/mission-areas/water-resources/science/flood-inundation-mapping-fim-program, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at Little Papillion Creek at Irvington, Nebr. (USGS station 06610750), Little Papillion Creek at Ak-Sar-Ben at Omaha, Nebr. (USGS station 06610765), and Big Papillion Creek at Q Street at Omaha, Nebr. (USGS station 06610770) streamgages. Near-real-time stages at these streamgages may be obtained from the USGS National Water Information System database at https://doi.org/10.5066/F7P55KJN or from the National Weather Service Advanced Hydrologic Prediction Service at https://water.weather.gov/ahps/.
Flood profiles were computed for two different reaches that constitute 14.8 miles of stream length in the study area by using hydraulic models. The models were calibrated by adjusting roughness coefficients to best represent the current (2022) stage-streamflow relation at the streamgages within the study reach.
The hydraulic models were then used to compute water-surface profiles at 1-foot stage intervals for selected stage ranges to represent various flooding scenarios at the streamgages in each reach. The simulated water-surface profiles then were combined with a digital elevation model using a geographic information system, which had a 10-foot grid spacing to delineate the flooding extents and water depths for each stage. The availability of these flood-inundation maps, along with information regarding current stage from the USGS streamgages, can provide emergency management personnel and residents with information that is critical for flood response activities and post flood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255032","collaboration":"Prepared in cooperation with the Papio-Missouri River Natural Resource District","usgsCitation":"Strauch, K.R., and Hoefer, B.R., 2025, Flood-inundation maps for 14.8 miles of Little and Big Papillion Creeks in Omaha, Nebraska, 2023: U.S. Geological Survey Scientific Investigations Report 2025–5032, 14 p., https://doi.org/10.3133/sir20255032.","productDescription":"Report: vi, 14 p.; Data Release; Dataset","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-152753","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":493771,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118579.htm","linkFileType":{"id":5,"text":"html"}},{"id":485915,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5032/coverthb.jpg"},{"id":485916,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5032/sir20255032.pdf","text":"Report","size":"2.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5032"},{"id":485920,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5032/sir20255032.XML"},{"id":485921,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5032/images/"},{"id":485922,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":485923,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OU7E42","text":"USGS data release","linkHelpText":"Flood-inundation geospatial datasets for 14.8 miles of the Little and Big Papillion Creeks in Omaha, Nebraska, 2023"},{"id":485924,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255032/full"}],"country":"United States","state":"Nebraska","city":"Omaha","otherGeospatial":"Little and Big Papillion Creeks","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -96.15305796306968,\n 41.36725587936067\n ],\n [\n -96.15305796306968,\n 41.117996650104345\n ],\n [\n -95.92667071234595,\n 41.117996650104345\n ],\n [\n -95.92667071234595,\n 41.36725587936067\n ],\n [\n -96.15305796306968,\n 41.36725587936067\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512
In species inhabiting stressful environments, some individuals experience negative growth in body mass during their lives, potentially influencing survival, reproduction, populations, and ecosystems. Using data from a long-term (1997–2015) study of brook trout (Salvelinus fontinalis), brown trout (Salmo trutta), and juvenile Atlantic salmon (Salmo salar) in Massachusetts, USA, we analyzed the frequency, causes, and consequences of seasonal mass loss. Negative growth occurred in 14.8% of observations (3573 instances), with the highest prevalence during summer (June–September). Species differed markedly: 42.5% of juvenile Atlantic salmon, 24.9% of brook trout, but only 7.7% of brown trout exhibited negative growth. The likelihood of mass loss increased with size, age, and low stream flows, particularly in summer. Brook trout in the small or isolated tributaries exhibited less summer mass loss than those in the larger streams. Other factors, including temperature and population density, had minimal or inconsistent effects. Individuals experiencing negative growth, especially brook trout in summer and autumn, were less likely to be re-encountered. As we observed no association of mass loss with movement, this suggests a potential survival cost of negative body mass growth.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2024-0376","usgsCitation":"Letcher, B., Nislow, K., O’Donnell, M., Hayden, M.J., and Dubreuil, T., 2025, Negative growth in body mass of trout and salmon in a small stream network: Canadian Journal of Fisheries and Aquatic Sciences, v. 82, p. 1-14, https://doi.org/10.1139/cjfas-2024-0376.","productDescription":"14 p.","startPage":"1","endPage":"14","ipdsId":"IP-171146","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":501009,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hdl.handle.net/1807/144649","text":"External Repository"},{"id":494396,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"82","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Letcher, Benjamin H. 0000-0003-0191-5678","orcid":"https://orcid.org/0000-0003-0191-5678","contributorId":315442,"corporation":false,"usgs":true,"family":"Letcher","given":"Benjamin H.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":946666,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nislow, Keith","contributorId":201434,"corporation":false,"usgs":false,"family":"Nislow","given":"Keith","affiliations":[{"id":27110,"text":"U.S. Dept of Agriculture, Forest Service","active":true,"usgs":false}],"preferred":false,"id":946667,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O’Donnell, Matthew 0000-0002-9089-2377 mjodonnell@usgs.gov","orcid":"https://orcid.org/0000-0002-9089-2377","contributorId":167315,"corporation":false,"usgs":true,"family":"O’Donnell","given":"Matthew","email":"mjodonnell@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":946668,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hayden, Michael J. 0000-0002-9010-6831","orcid":"https://orcid.org/0000-0002-9010-6831","contributorId":291388,"corporation":false,"usgs":true,"family":"Hayden","given":"Michael","middleInitial":"J.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":946762,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dubreuil, Todd 0000-0003-0189-4336","orcid":"https://orcid.org/0000-0003-0189-4336","contributorId":217872,"corporation":false,"usgs":true,"family":"Dubreuil","given":"Todd","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":946670,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70267294,"text":"sir20255016 - 2025 - Flood of July 2023 in Vermont","interactions":[],"lastModifiedDate":"2025-08-07T21:13:56.630851","indexId":"sir20255016","displayToPublicDate":"2025-05-19T14:50:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-5016","displayTitle":"Flood of July 2023 in Vermont","title":"Flood of July 2023 in Vermont","docAbstract":"A major storm caused catastrophic flooding in many parts of Vermont on July 9–12, 2023, resulting in millions of dollars in damages. The high amount of rainfall caused several rivers to peak at record levels, in some cases exceeding records set during Tropical Storm Irene in 2011. The U.S. Geological Survey, in cooperation with the Federal Emergency Management Agency, collected and analyzed data that characterized the flood in Vermont. The data collected included peak water-surface elevations, taken from high-water marks at bridges, dams, and roads, and peak streamflow and annual exceedance probabilities (AEPs) at streamgages, lake gages, and selected ungaged locations. At 11 of the 80 streamgages with 12 to 94 years of record, the July 2023 peak streamflow was the peak of record. Ten streamgages recorded a peak streamflow with an AEP of less than or equal to 1 percent (greater than or equal to a 100-year recurrence interval).
The July 2023 flood affected many of the same communities as the historical flood caused by Tropical Storm Irene in 2011. Twenty of the 45 continuous-recording streamgages running during both events recorded greater peak streamflows during the July 2023 flood than during Tropical Storm Irene in 2011. Four of the 11 U.S. Geological Survey streamgages with period-of-record maximum peak streamflows observed during the July 2023 flood had previously recorded their maximum period-of-record peak streamflows during Tropical Storm Irene. There were 17 rivers in Vermont that were surveyed for high-water marks during both Tropical Storm Irene and the July 2023 flood. On those 17 rivers, a total of 103 sites contained surveyed high-water marks for both events. Thirty-two of these sites had higher surveyed elevations for the July 2023 flood than Tropical Storm Irene, including Black River in Newport, Black River in Springfield, Jewell Brook, Middlebury River, Missisquoi River, Ottauquechee River, Otter Creek, Wells River, Whetstone Brook, and Winooski River. Peak water-surface elevations were not collected on the Lamoille River in 2011.
Federal Emergency Management Agency flood insurance studies were evaluated in the context of the July 2023 flood. Peak streamflows at streamgages and nearby locations were assessed to determine the influence of the July 2023 flood on the AEPs used in past studies. Overall, 21 of 26 streamflow-computation locations in the flood insurance studies had more than a 10 percent difference in the 1-percent AEP streamflow. A hydraulic evaluation of surveyed water-surface elevations following the July 2023 flood was compared with the AEP profiles from past studies. Four of the 10 streamgages analyzed had poor alignment between the AEPs of the observed streamflows and the AEPs of the observed peak water-surface elevations as computed from flood insurance studies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255016","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Smith, T.L., Olson, S.A., LeNoir, J.M., Kalmon, R.D., and Ahearn, E.A., 2025, Flood of July 2023 in Vermont (ver. 1.1, May 22, 2025): U.S. Geological Survey Scientific Investigations Report 2025–5016, 21 p., https://doi.org/10.3133/sir20255016.","productDescription":"Report; vii, 21 p.; 2 Data Releases: Application Site","numberOfPages":"21","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-167054","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":493770,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118574.htm","linkFileType":{"id":5,"text":"html"}},{"id":486164,"rank":4,"type":{"id":31,"text":"Publication 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data viewer"},{"id":486167,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P14XBBAT","text":"USGS data release","linkHelpText":"Vermont Flood of July 2023 Data"},{"id":486291,"rank":8,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2025/5016/versionHist.txt","size":"879 B","linkFileType":{"id":2,"text":"txt"}},{"id":486405,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://www.sciencebase.gov/catalog/item/664e23efd34e702fe8744b3c","text":"USGS data release","linkHelpText":"Peak Streamflow, Peak Stage, and Annual Exceedance Probability Data of the July 2023 Flood in Vermont, with attached data tables 1.1 to 1.4 in Vermont_Flood_2023_tables.xlsx"}],"country":"United 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\"}}]}","edition":"Version 1.0: May 19, 2025; Version 1.1: May 22, 2025","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
A major storm caused catastrophic flooding in many parts of Vermont on July 9–12, 2023, resulting in millions of dollars in damages. The high amount of rainfall caused several rivers to peak at record levels, in some cases surpassing records set during Tropical Storm Irene in 2011. The U.S. Geological Survey, in cooperation with the Federal Emergency Management Agency, collected and analyzed data on peak water-surface elevations, peak streamflow, and annual exceedance probabilities at streamgages, lake gages, and selected ungaged locations. Of the 80 streamgages with records ranging from 12 to 94 years, 11 streamgages recorded peak streamflows during the July 2023 flood that were the highest on record, whereas 10 streamgages had peak streamflows with a recurrence interval of 100 years or more. The July 2023 flood affected many of the same communities as Tropical Storm Irene in 2011, but 20 of the 45 monitored streamgages recorded higher peak flows in the July 2023 flood. Seventeen rivers were surveyed for high-water marks during both Tropical Storm Irene and the July 2023 flood, and 32 of the 103 sites had higher water levels during the July 2023 flood compared with Tropical Storm Irene. Flood insurance studies by the Federal Emergency Management Agency were evaluated against the July 2023 flood data. Twenty-one of the 26 locations analyzed had more than a 10 percent difference in the 1-percent annual exceedance probability streamflow.
","publicationDate":"2025-05-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Travis L. 0000-0002-3448-2787 tlsmith@usgs.gov","orcid":"https://orcid.org/0000-0002-3448-2787","contributorId":297400,"corporation":false,"usgs":true,"family":"Smith","given":"Travis","email":"tlsmith@usgs.gov","middleInitial":"L.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":937647,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Olson, Scott A. 0000-0002-1064-2125","orcid":"https://orcid.org/0000-0002-1064-2125","contributorId":210173,"corporation":false,"usgs":true,"family":"Olson","given":"Scott A.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":937648,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"LeNoir, James M. 0000-0003-3295-4901","orcid":"https://orcid.org/0000-0003-3295-4901","contributorId":302690,"corporation":false,"usgs":true,"family":"LeNoir","given":"James","email":"","middleInitial":"M.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":937649,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kalmon, Rena D. 0000-0002-3210-3210","orcid":"https://orcid.org/0000-0002-3210-3210","contributorId":206320,"corporation":false,"usgs":true,"family":"Kalmon","given":"Rena","email":"","middleInitial":"D.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":937650,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ahearn, Elizabeth A. 0000-0002-5633-2640 eaahearn@usgs.gov","orcid":"https://orcid.org/0000-0002-5633-2640","contributorId":194658,"corporation":false,"usgs":true,"family":"Ahearn","given":"Elizabeth","email":"eaahearn@usgs.gov","middleInitial":"A.","affiliations":[{"id":196,"text":"Connecticut Water Science Center","active":true,"usgs":true},{"id":377,"text":"Massachusetts-Rhode Island Water Science Center","active":false,"usgs":true}],"preferred":false,"id":937651,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70267230,"text":"sir20235064G - 2025 - Peak streamflow trends in Montana and northern Wyoming and their relation to changes in climate, water years 1921–2020","interactions":[{"subject":{"id":70267230,"text":"sir20235064G - 2025 - Peak streamflow trends in Montana and northern Wyoming and their relation to changes in climate, water years 1921–2020","indexId":"sir20235064G","publicationYear":"2025","noYear":false,"chapter":"G","displayTitle":"Peak Streamflow Trends in Montana and Northern Wyoming and Their Relation to Changes in Climate, Water Years 1921–2020","title":"Peak streamflow trends in Montana and northern Wyoming and their relation to changes in climate, water years 1921–2020"},"predicate":"IS_PART_OF","object":{"id":70251152,"text":"sir20235064 - 2024 - Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin","indexId":"sir20235064","publicationYear":"2024","noYear":false,"title":"Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin"},"id":1}],"isPartOf":{"id":70251152,"text":"sir20235064 - 2024 - Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin","indexId":"sir20235064","publicationYear":"2024","noYear":false,"title":"Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin"},"lastModifiedDate":"2026-01-26T19:13:21.257304","indexId":"sir20235064G","displayToPublicDate":"2025-05-19T13:20:42","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-5064","chapter":"G","displayTitle":"Peak Streamflow Trends in Montana and Northern Wyoming and Their Relation to Changes in Climate, Water Years 1921–2020","title":"Peak streamflow trends in Montana and northern Wyoming and their relation to changes in climate, water years 1921–2020","docAbstract":"Frequency analysis on annual peak streamflow (hereinafter, peak flow) is essential to water-resources management applications, including critical structure design (for example, bridges and culverts) and floodplain mapping. Nonstationarity is a statistical property of a peak-flow series such that the distributional properties (the mean, variance, or skew) change either gradually (monotonic trend) or abruptly (shift, step change or change point) through time. Not incorporating or accounting for observed nonstationarity into peak-flow frequency analysis might result in a poor representation of the true probability of large floods and thus misrepresent the actual flood risks to life and property. This report summarizes how hydroclimatic variability might affect the temporal and spatial distributions of peak-flow data in the State of Montana (and northern Wyoming) and is part of a larger study to document peak-flow nonstationarity and hydroclimatic changes across a nine-State region consisting of Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin. A wide range of analyses and statistical approaches are applied to document the primary mechanisms controlling floods and characterize temporal changes in hydroclimatic variables and peak flows. This study was completed in cooperation with the Montana Department of Natural Resources and Conservation.
The purpose of this report is to characterize temporal and spatial patterns of nonstationarity in peak flows and hydroclimatology in Montana and northern Wyoming. In this evaluation, peak-flow, daily streamflow, and model-simulated gridded climatic data were examined for monotonic trends, change points, and other statistical properties that might indicate changing climatic and environmental conditions. This report includes background information on the study area, the history of U.S. Geological Survey peak-flow data collection and frequency analysis in Montana, and the review of research relating to hydroclimatic variability and change in Montana. This study might help provide a framework for addressing potential nonstationarity issues in peak-flow frequency updates that commonly are completed by the U.S. Geological Survey in cooperation with other agencies throughout the Nation.
The analytical structure of this study includes analyses of monotonic trends and change points in numerous hydroclimatic variables in assigned 30-, 50-, 75-, and 100-year analysis periods. For Montana and part of Wyoming, the 30-, 50-, 75, and 100-year analyses included 157, 70, 48, and 12 streamgages, respectively. For those streamgages, nonstationarities were analyzed in the following variables: (1) climatic variables, including annual and seasonal (winter, spring, summer, and fall) temperature and precipitation; (2) daily streamflow variables, including the annual center of volume duration, annual center of volume median, and peaks over threshold with a mean of four events per year; and (3) annual peak-flow variables, including peak-flow timing and magnitude. A likelihood approach was used to express statistical confidence and assign the nonstationarity results as likely upward or downward (highest statistical confidence), somewhat likely upward or downward (less statistical confidence), or about as likely as not (little statistical confidence; hereinafter, neutral). For the nonstationarity analyses of the climatic, daily streamflow, and peak-flow variables, the results are presented in detail and discussed with respect to statewide patterns and geographic variability. For each of the 30-, 50-, and 75-year analyses, peak-flow change-point and monotonic trend analyses were compiled for streamgages classified with likely downward or likely upward trends. For those streamgages, the associated basin characteristics and nonstationarity results for peak-flow timing, daily streamflow, and climatic variables were investigated and statistically compared to discern associations among other variables that might contribute to the peak-flow nonstationarity results.
The 50- and 75-year peak-flow nonstationarities identified in this study are mostly downward, in association with mostly upward temperature and potential evapotranspiration:precipitation monotonic trends. For the 50-, 75-, and 100-year analyses, the peak-flow change points are predominantly downward and are concentrated in the 1970s and 1980s, which indicates general consistency among the longer trend periods. These findings are in association with substantial research documenting globally rising temperature and atmospheric greenhouse gas concentrations that might be largely attributed to anthropogenic activities. Anthropogenic effects might represent long-term (on the order of several decades to more than a century) climate changes that might happen within highly variable natural climate fluctuations. Several paleo studies in the north-central United States have indicated that hydroclimatic extremes (that is, low- and high-streamflow conditions) before European settlement have been outside of extremes since the 1900s. Depending on the interactions of anthropogenic effects and natural climate variability, extreme high-streamflow conditions might occur in the future, even in the presence of long-term downward peak-flow trends.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235064G","collaboration":"Prepared in cooperation with the Montana Department of Natural Resources and Conservation","usgsCitation":"Sando, S.K., Barth, N.A., Sando, R., and Chase, K.J., 2025, Peak streamflow trends in Montana and northern Wyoming and their relation to changes in climate, water years 1921–2020, chap. G of Ryberg, K.R., comp., Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin: U.S. Geological Survey Scientific Investigations Report 2023–5064, 129 p., https://doi.org/10.3133/sir20235064G.","productDescription":"Report: x, 129 p.; Data Release; Dataset","numberOfPages":"144","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-159092","costCenters":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"links":[{"id":486055,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5064/g/sir20235064g.pdf","text":"Report","size":"19 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5064-G"},{"id":486054,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5064/g/coverthb.jpg"},{"id":486056,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5064/g/sir20235064g.XML"},{"id":486057,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5064/g/images/"},{"id":486059,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235064G/full"},{"id":499038,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118575.htm","linkFileType":{"id":5,"text":"html"}},{"id":486073,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":486058,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9R71WWZ","text":"USGS data release","linkHelpText":"Peak streamflow data, climate data, and results from investigating hydroclimatic trends and climate change effects on peak streamflow in the Central United States, 1921–2020"}],"country":"United States","state":"Montana, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.16658108866669,\n 49\n ],\n [\n -116.16658108866669,\n 43\n ],\n [\n -104.0598539498924,\n 43\n ],\n [\n -104.0598539498924,\n 49\n ],\n [\n -116.16658108866669,\n 49\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
Infrasound (acoustic waves below 20 Hz) can be used to detect, locate and quantify activity in the atmosphere such as volcanic eruptions and anthropogenic explosions. Attempts to quantify volcanic eruption parameters such as exit velocity, plume height and mass flow rate using infrasound data depend strongly on assumptions of the acoustic source type. Infrasonic sources may produce omnidirectional or directional wavefields, while propagation effects, such as interaction with topography, can induce further wavefield directivity that is measured by field instrumentation. Limited sampling of these wavefields can hinder our ability to infer the underlying source, and thus our understanding of the eruption characteristics. Equivalent sources are often used to represent acoustic source mechanisms and resultant wavefields. In this study, we review equivalent acoustic sources as they pertain to infrasonic scale and wavelengths commonly encountered in very local (<5 km range) geophysical field deployments. We highlight the equivalent infrasonic bipole source that can be induced by ground-reflection of an elevated monopole; we are not aware of any prior infrasound studies that use the bipole source concept. We use analytical and numerical methods to explore source directivity of monopole, dipole and bipole ground-reflected sources at infrasonic frequencies as well as the additional directivity complications introduced by interactions with topography. We illustrate that for typical volcano-infrasound wavelengths, increasing height above the ground as well as increasing source frequency leads to increased wavefield directivity. Numerical modelling using a simple omnidirectional monopole source embedded in topography further illustrates that both horizontal and vertical infrasound directionality can be induced by topography at the distance scales appropriate for local volcano infrasound monitoring. Information summarized in this analytical and numerical exploration of infrasound directivity may be used to help guide future volcano-infrasound field deployments intended to estimate source parameters or quantify wavefield directivity. Analytic solutions for simple whole-space or half-space atmospheres provide useful formulations for planning or initially analysing geophysical field-scale experimental data; however, especially at very local distances from the source (<5 km), 3-D simulations are necessary to account for complex topography commonly encountered in volcano-infrasound applications.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gji/ggaf180","usgsCitation":"Iezzi, A.M., Matoza, R.S., Opper, E., and Kim, K., 2025, Infrasonic directivity of monopole, dipole, and bipole ground-surface reflected sources: Geophysical Journal International, v. 242, no. 2, ggaf180, 23 p., https://doi.org/10.1093/gji/ggaf180.","productDescription":"ggaf180, 23 p.","ipdsId":"IP-175386","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":493308,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/gji/ggaf180","text":"Publisher Index Page"},{"id":492906,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"242","issue":"2","noUsgsAuthors":false,"publicationDate":"2025-05-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Iezzi, Alexandra M. 0000-0002-6782-7681","orcid":"https://orcid.org/0000-0002-6782-7681","contributorId":304206,"corporation":false,"usgs":true,"family":"Iezzi","given":"Alexandra","email":"","middleInitial":"M.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":943967,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Matoza, Robin S.","contributorId":257265,"corporation":false,"usgs":false,"family":"Matoza","given":"Robin","email":"","middleInitial":"S.","affiliations":[{"id":36524,"text":"University of California, Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":943968,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Opper, Emma V.","contributorId":358570,"corporation":false,"usgs":false,"family":"Opper","given":"Emma V.","affiliations":[{"id":85657,"text":"Department of Applied Mathematics, University of Colorado Boulder","active":true,"usgs":false}],"preferred":false,"id":943969,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kim, Keehoon","contributorId":252842,"corporation":false,"usgs":false,"family":"Kim","given":"Keehoon","email":"","affiliations":[{"id":27196,"text":"LANL","active":true,"usgs":false}],"preferred":false,"id":943970,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70267753,"text":"70267753 - 2025 - Serological assessment of pathogen exposure among desert bighorn sheep in southwestern Arizona","interactions":[],"lastModifiedDate":"2025-05-30T15:55:56.491711","indexId":"70267753","displayToPublicDate":"2025-05-19T08:50:13","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5991,"text":"The Southwestern Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"Serological assessment of pathogen exposure among desert bighorn sheep in southwestern Arizona","docAbstract":"Bighorn sheep (Ovis canadensis) are influenced by infectious diseases. Although Mycoplasma ovipneumoniae has been the main focus of bighorn sheep managers since early 2010, other pathogens may also influence bighorn sheep populations. We sampled desert bighorn sheep (Ovis canadensis mexicana) captured for a study on the Cabeza Prieta National Wildlife Refuge in southwestern Arizona, USA, 2001–2005, for a suite of pathogens: bluetongue (BT), epizootic hemorrhagic disease (EHD), parainfluenza 3, bovine respiratory syncytial virus, Clamydia, seven Leptospira serovars, bovine herpesvirus 1 (causative agent of infectious bovine rhinotracheitis), bovine viral diarrhea virus, and bovine respiratory syncytial virus. We recorded evidence of exposure to seven of these pathogens, with two Leptospira serovars (hardjo and bratislava), five strains of BT, and two strains of EHD detected. Seroprevalence rates of detected pathogens varied between 12–49%. We observed high levels of co-occurrence for EHD and BT. These results highlight that multiple pathogens may influence desert bighorn sheep populations. These data also provide historical context to pathogen exposure for a region where few such data are available.
","language":"English","publisher":"BioOne","doi":"10.1894/0038-4909-68.4.6","usgsCitation":"Padilla, C., Cain, J.W., Gompper, M., Krausman, P., and Devos, J., 2025, Serological assessment of pathogen exposure among desert bighorn sheep in southwestern Arizona: The Southwestern Naturalist, v. 68, no. 4, p. 294-300, https://doi.org/10.1894/0038-4909-68.4.6.","productDescription":"7 p.","startPage":"294","endPage":"300","ipdsId":"IP-158089","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":489284,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Cabeza Prieta National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.0165865297831,\n 32.53576263609473\n ],\n [\n -114.0165865297831,\n 31.947344458858936\n ],\n [\n -112.69784561865545,\n 31.947344458858936\n ],\n [\n -112.69784561865545,\n 32.53576263609473\n ],\n [\n -114.0165865297831,\n 32.53576263609473\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"68","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Padilla, Colton J.","contributorId":353982,"corporation":false,"usgs":false,"family":"Padilla","given":"Colton J.","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":938735,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cain, James W. III 0000-0003-4743-516X jwcain@usgs.gov","orcid":"https://orcid.org/0000-0003-4743-516X","contributorId":4063,"corporation":false,"usgs":true,"family":"Cain","given":"James","suffix":"III","email":"jwcain@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":938736,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gompper, Matthew E.","contributorId":353984,"corporation":false,"usgs":false,"family":"Gompper","given":"Matthew E.","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":938737,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Krausman, Paul R.","contributorId":356094,"corporation":false,"usgs":false,"family":"Krausman","given":"Paul R.","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":938738,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Devos, JIM","contributorId":356095,"corporation":false,"usgs":false,"family":"Devos","given":"JIM","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":938739,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70267322,"text":"70267322 - 2025 - Are wildfire risk mitigators more prepared to evacuate? Insights from communities in the western United States","interactions":[],"lastModifiedDate":"2025-05-20T15:10:39.570682","indexId":"70267322","displayToPublicDate":"2025-05-19T08:02:15","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1453,"text":"Ecological Economics","active":true,"publicationSubtype":{"id":10}},"title":"Are wildfire risk mitigators more prepared to evacuate? Insights from communities in the western United States","docAbstract":"As the realized experiences of wildfires threatening communities increase, the importance of proactive evacuation preparation and wildfire risk mitigation on private property to reduce the loss of lives and property is shaping wildfire policy and programs. To date, research has focused on pre-wildfire evacuation preparation and risk mitigation independently. This paper examines the substitutability or complementarity of these proactive risk-reducing actions. If mitigation and evacuation preparedness are substitutes, wildfire education programs may take a life-over-property approach. However, if proactive risk-reducing efforts are complements, wildfire education programs can confidently encourage residents to prepare for evacuation while also mitigating wildfire risk on their properties. This complementarity may also demonstrate that poorly mitigated households are less prepared to evacuate, compounding their risks. Using household survey data from 25 wildland-urban interface (WUI) communities across five Western states, we explore how wildfire risk mitigation actions affect evacuation preparedness. We find that improving household wildfire mitigation is associated with an improvement in wildfire evacuation preparedness. This complementary relationship between wildfire mitigation and evacuation preparedness actions highlights the potential benefits of a wildfire education approach that encourages residents to simultaneously prepare for evacuation and reduce wildfire risk on their properties before they are threatened by a wildfire.","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolecon.2025.108638","usgsCitation":"Webster, G., Brenkert-Smith, H., Champ, P.A., Meldrum, J., Wallace, K., Donovan, C., Wagner, C., Barth, C.M., Kuehn, J., Wittenbrink, S., and Taniguchi, C., 2025, Are wildfire risk mitigators more prepared to evacuate? 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This scarcity of data is particularly pronounced along mid-latitude shelves such as central California, which lack post LGM RSL constraints older than 12 ka. In this study we collected 7 sediment cores and high-resolution seismic data from Estero Bay to constrain RSLs across the central California shelf between ∼9 and ∼16 ka. We reconstructed these RSLs using two sea-level indicators found within our sediment cores: the wave ravinement shell hash burial surface (WRSHBS) and the sedimentary contact between offshore mud facies and ripple cross-laminated sands. To determine the indicative meaning of these two sea-level indicators, we examined the relationship between the local wave regime, modern bathymetric profiles, and the depth of preservation of each sea-level indicator. After correcting for tectonic uplift, we estimated sea levels in central California to have been ∼39 ± 7.5 and 49 ± 7.5 m below present sea level between 9 and 12 ka, in agreement with previous RSL reconstructions along this coast. Between 13.8 and 15.9 ka, we estimate sea levels to have reached ∼86 ± 8–99 ± 8 m below present sea level. Our findings offer a Late Pleistocene RSL reconstruction for central California and develop new methodologies for estimating past RSLs on similar mid-latitude shelves.
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Dozens of organizations from government agencies, nonprofits, and Tribal Nations have developed projects and monitoring programs to analyze and protect the water resources of the watershed and sound. The abundance of data and lack of an existing searchable index require a centralized metadata repository to allow users to find water resources data more efficiently. 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425 Jordan Road
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Email: 3DEP@usgs.gov
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601 SW Second Avenue, Suite 1950
Portland, Oregon 97204
The terrestrial management plan for Palmyra Atoll includes large-scale removal of coconut (Cocos nucifera) as part of native forest restoration and contaminant remediation that will leave soils and vegetation communities profoundly altered. To inform those efforts and provide baseline data for restoration monitoring, woody stem crowns and vegetation communities at Palmyra Atoll were mapped using existing datasets and manual photointerpretation of high-resolution aerial imagery collected using uncrewed aerial systems. Coconut palm, grand devil's claws (Pisonia grandis), and numerous other species were delineated as either individual crowns, crown portions, or species patches. The extent of land area at high tide was also delineated based on vegetation patterns, topographic indicators, and deposition lines of vegetation litter and flotsam. Finally, in a novel “bottom-up” approach to vegetation community mapping, crown maps were used to delineate U.S. National Vegetation Classification System vegetation communities and other management areas. Of the more than 44,000 mapped crowns, coconut was by far the most abundant species, comprising nearly half of the mapped stems. By establishing a quantitative baseline for current habitat conditions, this project facilitates the integration of contaminant remediation recovery activities with habitat restoration planning, implementation, and monitoring at Palmyra Atoll. Results illustrate the appropriateness of the mapping approach for plant species-level censuses and cover estimation over relatively small areas to aid in inventory and monitoring and to facilitate management planning. The relatively simple mapping methods used in this study are appropriate for resource managers with limited human and computational resources to support automated mapping.
","language":"English","publisher":"University of Hawaii Press","doi":"10.2984/78.3.4","usgsCitation":"Struckhoff, M., 2025, Pre-restoration woody species crown and vegetation community mapping using high-resolution uncrewed aerial system imagery, Palmyra Atoll: Pacific Science, v. 78, no. 3, p. 279-293, https://doi.org/10.2984/78.3.4.","productDescription":"15 p.","startPage":"279","endPage":"293","ipdsId":"IP-167800","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":486215,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Palmyra Atoll","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -162.11964162708554,\n 5.904086042937834\n ],\n [\n -162.11964162708554,\n 5.857794667611088\n ],\n [\n -162.03636083136453,\n 5.857794667611088\n ],\n [\n -162.03636083136453,\n 5.904086042937834\n ],\n [\n -162.11964162708554,\n 5.904086042937834\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"78","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-05-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Struckhoff, Matthew 0000-0002-4911-9956","orcid":"https://orcid.org/0000-0002-4911-9956","contributorId":201512,"corporation":false,"usgs":true,"family":"Struckhoff","given":"Matthew","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":937678,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70269069,"text":"70269069 - 2025 - If you build it, will they come? Assessing the response of tiger populations to elevated conservation efforts in lowland Nepal","interactions":[],"lastModifiedDate":"2025-07-16T13:42:27.704339","indexId":"70269069","displayToPublicDate":"2025-05-16T08:35:30","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3871,"text":"Global Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"If you build it, will they come? Assessing the response of tiger populations to elevated conservation efforts in lowland Nepal","docAbstract":"Thirteen countries within the distributional range of tigers adopted the St. Petersburg Declaration in 2010, committing to double their tiger populations by 2022. As a signatory to this document, Nepal elevated its tiger conservation efforts soon after the declaration was adopted. Using capture-mark-recapture (CMR) analyses of tiger survey data (2013–2022), we assessed the response of tiger populations to enhanced conservation measures in Banke and Bardia national parks (NP) in western lowland of Nepal. In Bardia NP, estimated tiger numbers increased from 49 in 2013 to 122 in 2022, and estimated population density increased from 3.39 in 2013 to 8.47 tigers/100 km2 in 2022). Female survival rate was consistently higher than male, with 87.06 % during 2013–2018 and 89.08 % during 2018–2022, compared to 72.9 % and 76.6 % for males in the respective periods. Additionally, the female population grew at a faster rate (6–7 % per year vs. ∼ 2 % per year for males). In Banke NP, tiger abundance increased from 3 to 22 between 2013 and 2022, and population density increased from 0.34 to 2.42/100 km2 over the same period. The remarkable 7-fold increase in tiger abundance within the Banke NP was facilitated by high male and female survival rates (≥ 90 % per year), as well as high recruitment rates (0.27 individual−1 year−1) from 2013 to 2018, leading to a robust (18–20 %) annual population growth rate. Tigers were recolonizing Banke NP at the beginning of this study, and the area offered ample space and a recovering prey base. This facilitated the recruitment of dispersers from the larger population in Bardia NP and allowed survival to be high by eliminating mortality due to density-dependent intra-specific aggression. Our results not only suggest that Nepal has met or exceeded its commitment to the St. Petersburg Declaration in Banke and Bardia NPs, but they highlight the ability of certain tiger populations to respond quickly and positively to enhanced conservation measures given suitable habitat and a healthy prey base.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2025.e03632","usgsCitation":"Lamichhane, S., Pathak, A., Karki, A., Khatiwada, A., Pokheral, C.P., Hines, J.E., Onorato, D.P., Stein, T., and Oli, M., 2025, If you build it, will they come? Assessing the response of tiger populations to elevated conservation efforts in lowland Nepal: Global Ecology and Conservation, v. 60, e03632, 14 p., https://doi.org/10.1016/j.gecco.2025.e03632.","productDescription":"e03632, 14 p.","ipdsId":"IP-172084","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":492499,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2025.e03632","text":"Publisher Index Page"},{"id":492341,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Nepal","otherGeospatial":"Banke National Park, Bardia National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 81.21243616640487,\n 28.814284168801763\n ],\n [\n 80.92541905887717,\n 28.44135810205067\n ],\n [\n 81.84220185865365,\n 27.8331018653778\n ],\n [\n 82.23232219898381,\n 28.00056066689831\n ],\n [\n 82.10971294916527,\n 28.27580010616687\n ],\n [\n 81.21243616640487,\n 28.814284168801763\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"60","noUsgsAuthors":false,"publicationDate":"2025-05-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Lamichhane, Saneer","contributorId":358054,"corporation":false,"usgs":false,"family":"Lamichhane","given":"Saneer","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":943205,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pathak, Abhinaya","contributorId":358055,"corporation":false,"usgs":false,"family":"Pathak","given":"Abhinaya","affiliations":[{"id":85578,"text":"Department of National Parks & Wildlife Conservation, Babar Mahal, Kathmandu","active":true,"usgs":false}],"preferred":false,"id":943206,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Karki, Ajay","contributorId":344545,"corporation":false,"usgs":false,"family":"Karki","given":"Ajay","email":"","affiliations":[{"id":82383,"text":"Department of National Parks and Wildlife Conservation, Babarmahal, Kathmandu","active":true,"usgs":false}],"preferred":false,"id":943207,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Khatiwada, Ambika P.","contributorId":358057,"corporation":false,"usgs":false,"family":"Khatiwada","given":"Ambika P.","affiliations":[{"id":85580,"text":"National Trust for Nature Conservation,","active":true,"usgs":false}],"preferred":false,"id":943208,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pokheral, Chiranjibi Prasad","contributorId":201696,"corporation":false,"usgs":false,"family":"Pokheral","given":"Chiranjibi","email":"","middleInitial":"Prasad","affiliations":[{"id":36232,"text":"National Trust for Nature Conservation, Khumaltar, POB 3712, Lalitpur, Nepal","active":true,"usgs":false}],"preferred":false,"id":943209,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hines, James E. 0000-0001-5478-7230 jhines@usgs.gov","orcid":"https://orcid.org/0000-0001-5478-7230","contributorId":146530,"corporation":false,"usgs":true,"family":"Hines","given":"James","email":"jhines@usgs.gov","middleInitial":"E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":943210,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Onorato, Dave P.","contributorId":171827,"corporation":false,"usgs":false,"family":"Onorato","given":"Dave","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":943211,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Stein, Taylor V.","contributorId":358058,"corporation":false,"usgs":false,"family":"Stein","given":"Taylor V.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":943212,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Oli, Madan K.","contributorId":352359,"corporation":false,"usgs":false,"family":"Oli","given":"Madan K.","affiliations":[{"id":84187,"text":"Department of Wildlife Ecology and Conservation, Newins-Zeigler Hall, University of Florida, Gainesville, FL 32611","active":true,"usgs":false}],"preferred":false,"id":943213,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70266894,"text":"ofr20251007 - 2025 - Mapping eelgrass (Zostera marina) cover and biomass at Izembek Lagoon, Alaska, using in-situ field data and Sentinel-2 satellite imagery","interactions":[{"subject":{"id":70261584,"text":"70261584 - 2024 - Mapping eelgrass cover and biomass at Izembek Lagoon, Alaska, using in-situ field data and Sentinel-2 satellite imagery","indexId":"70261584","publicationYear":"2024","noYear":false,"title":"Mapping eelgrass cover and biomass at Izembek Lagoon, Alaska, using in-situ field data and Sentinel-2 satellite imagery"},"predicate":"SUPERSEDED_BY","object":{"id":70266894,"text":"ofr20251007 - 2025 - Mapping eelgrass (Zostera marina) cover and biomass at Izembek Lagoon, Alaska, using in-situ field data and Sentinel-2 satellite imagery","indexId":"ofr20251007","publicationYear":"2025","noYear":false,"title":"Mapping eelgrass (Zostera marina) cover and biomass at Izembek Lagoon, Alaska, using in-situ field data and Sentinel-2 satellite imagery"},"id":1}],"lastModifiedDate":"2025-05-20T13:49:59.506555","indexId":"ofr20251007","displayToPublicDate":"2025-05-16T07:54:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-1007","displayTitle":"Mapping Eelgrass (Zostera marina) Cover and Biomass at Izembek Lagoon, Alaska, Using In-Situ Field Data and Sentinel-2 Satellite Imagery","title":"Mapping eelgrass (Zostera marina) cover and biomass at Izembek Lagoon, Alaska, using in-situ field data and Sentinel-2 satellite imagery","docAbstract":"The U.S. Geological Survey and the U.S. Fish and Wildlife Service have developed a three-tiered strategy for monitoring eelgrass (Zostera marina) beds at Izembek Lagoon, Alaska, that targets different spatial and temporal scales. The broadest-scale monitoring (tier-1) uses satellite imagery about every 5 years to delineate the spatial extent of eelgrass beds throughout the lagoon. This report describes the most recent (mid-2020s) tier-1 eelgrass monitoring at Izembek Lagoon. The monitoring effort began by canvasing all satellite imagery collected during summer, under clear daytime skies and at low-tide, since the last tier-1 effort in 2006. Two eelgrass maps of Izembek Lagoon were generated by first creating maps of spectrally unique classes from two Sentinel-2 satellite images collected on July 1, 2016, and August 14, 2020, then attributing those spectral classes with information about eelgrass conditions based on field data. Specifically, maps depicting various eelgrass metrics, such as percentage of cover and modeled biomass, were generated using summaries of the ground data that spatially intersected each spectral class. Comparisons of the 2016 and 2020 Sentinel-2 maps showing eelgrass distributional extent, as well as a 2006 Landsat map, indicated that areas where eelgrass presence may have declined during 2006–20 were most prevalent in the central part of Izembek Lagoon. More recently, during 2016-20, areas of possible biomass decline were more prevalent in the southern part of the lagoon. Monitoring eelgrass conditions at Izembek Lagoon with satellite imagery and concurrent ground data allows conditions to be compared over time, but the influences of tide levels, growing season phenology, and spatiotemporal co-registration accuracy should be considered when designing and interpreting change detection analyses.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251007","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","programNote":"Land Management Research Program","usgsCitation":"Douglas, D.C., Fleming, M.D., Patil, V.P., and Ward, D.H., 2025, Mapping eelgrass (Zostera marina) cover and biomass at Izembek Lagoon, Alaska, using in-situ field data and Sentinel-2 satellite imagery: U.S. Geological Survey Open-File Report 2025–1007, 30 p., https://doi.org/10.3133/ofr20251007. [Supersedes preprint https://doi.org/10.1101/2024.08.07.607047.]","productDescription":"Report: vii, 30 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-169599","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":485960,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1007/coverthb2.jpg"},{"id":485963,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1HLTAHD","text":"USGS data release","description":"USGS data release","linkHelpText":"Eelgrass (Zostera marina) maps from 2016 and 2020, at Izembek Lagoon, Alaska"},{"id":485961,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1007/ofr20251007.pdf","text":"Report","size":"10.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1007"},{"id":485962,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251007/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1007"},{"id":485964,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1007/images"},{"id":485965,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1007/ofr20251007.XML"}],"country":"United States","state":"Alaska","otherGeospatial":"Izembek Lagoon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -163.098854980302,\n 55.171004418891414\n ],\n [\n -162.87580882559715,\n 55.150219377165826\n ],\n [\n -162.79513255687405,\n 55.2819754305454\n ],\n [\n -162.63219813180595,\n 55.3494888427272\n ],\n [\n -162.52937543637472,\n 55.342292888511395\n ],\n [\n -162.47875503247008,\n 55.40162041992025\n ],\n [\n -162.50248334680037,\n 55.47879257840398\n ],\n [\n -162.74767592913727,\n 55.39443394016618\n ],\n [\n -162.92959300566955,\n 55.31259575418295\n ],\n [\n -163.098854980302,\n 55.171004418891414\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
Highly pathogenic H5N1 influenza A virus (HP H5N1) cases in wild mammals have been increasing globally. Carnivora has been the most affected mammal order; however, the extent of morbidity and mortality in carnivores exposed to HP H5N1 remains undefined. We assessed the presence of antibodies to H5 and N1 in the sera of 16 live-captured bobcats (Lynx rufus) in New York State, USA; reported on postcapture outcomes of HP H5N1-exposed bobcats; and reported on a case of HP H5N1 infection in one bobcat. In 2024, 4/16 bobcats (25%) tested positive for antibodies to H5 and N1. Two bobcats were confirmed alive as of November 2024 according to GPS data; the other two were alive as of June 2024, when their GPS collars stopped communicating. Another bobcat, which was negative for HP H5N1 antibodies at capture, died from HP H5N1 infection within 5 wks of capture. Our results provide evidence of bobcats both surviving and succumbing to HP H5N1 infection and highlight the importance of focused health studies paired with monitoring data to better understand exposure, infection, and outcomes for novel pathogens and species.
","language":"English","publisher":"BioOne","doi":"10.7589/jwd-d-24-00137","usgsCitation":"Turner, H., Fuller, A.K., Twining, J., Hitchener, G., Fadden, M., Stallknecht, D., Poulson, R., Carter, D., Watson, M., Schuler, K., and Bloodgood, J., 2025, Highly pathogenic avian influenza virus exposure and infection in free-ranging bobcats (Lynx rufus) in New York, USA: Journal of Wildlife Diseases, v. 61, no. 2, p. 515-521, https://doi.org/10.7589/jwd-d-24-00137.","productDescription":"7 p.","startPage":"515","endPage":"521","ipdsId":"IP-171388","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":493708,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New 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disease spread in Wisconsin","interactions":[],"lastModifiedDate":"2025-05-23T15:29:29.683831","indexId":"70267435","displayToPublicDate":"2025-05-15T10:22:59","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1107,"text":"Bulletin of Mathematical Biology","active":true,"publicationSubtype":{"id":10}},"title":"Characterization of the long-distance dispersal kernel of white-tailed deer and evaluating its impact on chronic wasting disease spread in Wisconsin","docAbstract":"Chronic wasting disease (CWD) is a fatal neurodegenerative disease infecting cervids. It is highly contagious and caused by misfolded prions that propagate via templated conformational conversion of the cervid’s normal prion protein. Prevalence of CWD in free-ranging deer in North America is mostly low, but in some regions local prevalence has reached 80%. CWD prions can be transmitted via direct contact with infected individuals or indirectly through the environment. Infected individuals shed prions through feces, urine, saliva or carcasses, and prions have long environmental persistence. Long-distance dispersal of infected deer poses a significant risk for CWD spread. We propose an integrodifference equation (IDE) model to capture CWD dynamics and the consequences of long-distance dispersal behavior in white-tailed deer (WTD, Odocoileus virginianus). A diffusion-settling model characterizes long-distance dispersal kernels, accommodating hypothetical dispersal behaviors through time-dependent settling rate functions. Three new closed-form dispersal kernels are approximated using Laplace’s method and parameterized with GPS location data collected from WTD in Wisconsin, USA. Settling rates reflecting ongoing sensitivity to stimuli which prompt deer to disperse from their natal home range give the most supported long-distance dispersal kernel. Impact of long-distance dispersal on CWD spread is quantified using the IDE model. At high population densities, long-distance dispersal can magnify CWD spread by a factor of four. At lower population densities single infected individuals cannot initiate an outbreak, but CWD may still spread due to the accumulation of environmental hazard from prions behind the wave of invasion, possibly presenting substantial management challenges.
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dwalsh@usgs.gov","orcid":"https://orcid.org/0000-0002-7772-2445","contributorId":4758,"corporation":false,"usgs":true,"family":"Walsh","given":"Daniel","email":"dwalsh@usgs.gov","middleInitial":"P.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":938193,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Storm, Daniel J.","contributorId":341059,"corporation":false,"usgs":false,"family":"Storm","given":"Daniel J.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":938194,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70266895,"text":"ofr20241070 - 2025 - Calibration of the Stream Salmonid Simulator (S3) model to estimate annual survival, movement, and food consumption by juvenile Chinook salmon (Oncorhynchus tshawytscha) in the restoration reach of the Trinity River, California, 2006–18","interactions":[],"lastModifiedDate":"2025-05-16T14:44:27.229198","indexId":"ofr20241070","displayToPublicDate":"2025-05-15T07:39:36","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-1070","displayTitle":"Calibration of the Stream Salmonid Simulator (S3) Model to Estimate Annual Survival, Movement, and Food Consumption by Juvenile Chinook Salmon (Oncorhynchus tshawytscha) in the Restoration Reach of the Trinity River, California, 2006–18","title":"Calibration of the Stream Salmonid Simulator (S3) model to estimate annual survival, movement, and food consumption by juvenile Chinook salmon (Oncorhynchus tshawytscha) in the restoration reach of the Trinity River, California, 2006–18","docAbstract":"The Trinity River is managed in two sections: (1) from the upper 64-kilometer “restoration reach” downstream from Lewiston Dam to the confluence with the North Fork Trinity River, and (2) the 120-kilometer lower Trinity River downstream from the restoration reach. The Stream Salmonid Simulator (S3) has been previously applied to these reaches and the Klamath River. To estimate fish growth, past S3 calibration efforts in the Trinity and Klamath Rivers used maximum likelihood methods that considered only the abundance of juvenile Chinook salmon (Oncorhynchus tshawytscha) passing a fish trap to estimate survival and movement parameters, but not fish consumption. Previous calibrations did not estimate the average proportion of maximum consumption (Cy) when estimating survival (Sy) and movement (M0y) parameters across years (y) of data, but because no other information was available in the literature a fixed value of Cy=0.66 was assumed. Therefore, the goal of this report is to present an alternative approach that calibrates the S3 model to multivariate data (that is, abundance and size), enabling the estimation of the average proportion of maximum consumption, in conjunction with survival and movement parameters for a particular migration year. We fit the S3 model to individual years of weekly trap abundance estimates and mean fish sizes (fork length) at the Pear Tree Gulch (hereafter referred to as Pear Tree) fish trap representing the restoration reach. We used the Earth Mover’s Distance (EMD) as the objective value to be minimized in parameter optimization. This approach estimated survival, movement, and consumption parameters for each migration year. Because we had information on the abundance of natural and hatchery produced juvenile salmon at the fish traps, we estimated survival and movement for natural and hatchery fish.
S3 is a deterministic life-stage-structured population model that tracks daily growth, movement, and survival of juvenile Chinook Salmon. A key theme of the model is that river discharge affects habitat availability and capacity, which in turn drives density-dependent population dynamics. To explicitly link population dynamics to habitat quality and quantity, the river environment is constructed as a one-dimensional series of linked habitat units, each of which has an associated daily timeseries of discharge, water temperature, and useable habitat area or carrying capacity. In turn, the physical characteristics of each habitat unit and the number of fish occupying each unit drive survival and growth within each habitat unit and movement of fish among habitat units.
The physical template of the restoration reach of the Trinity River was classified into 356 meso-habitat units comprised of runs, riffles, and pools. For each habitat unit, we developed a timeseries of daily discharge, water temperature, amount of available spawning habitat, and fry and parr carrying capacity. Capacity time series were constructed using state-of-the-art models of spatially explicit hydrodynamics and quantitative fish habitat relationships developed for the Trinity River. These variables were then used to drive population dynamics such as egg maturation and survival, and in turn, juvenile movement, growth, and survival.
We estimated movement, survival, and consumption parameters by calibrating the model to 12 years of weekly juvenile abundance estimates and fish sizes at the Pear Tree fish trap near the downstream end of the restoration reach. We estimated parameters for 12 years that included a wide range of female spawner abundances (1,414–11,494) and water year types (critically dry–extremely wet). We contrast the estimated parameters to the corresponding number of female spawners and the total annual volume of water discharged for the Trinity River (Trinity River Restoration Program; https://www.trrp.net/restoration/flows/summary/).
The calibration consisted of replicating historical conditions as closely as possible (for example, discharge; temperature; spawner abundance, spawning location and timing, and hatchery releases), and then running the model to predict weekly abundance passing the trap location from each brood year of adults and subsequent migration year of their juvenile progeny. Because density-dependent movement was favored in past evaluations, we estimated S3 parameters based on density-independent survival and density-dependent movement. Likewise, each year’s estimated survival parameter for natural (SNy) and hatchery (SHy) fish may be interpreted as the mean daily survival probability from emergence or hatchery release to the Pear Tree fish trap. Under density dependence, the estimated movement parameter for natural (M0Ny) and hatchery (M0Hy) fish represents the intercept of the Beverton-Holt model; the probability of remaining in a habitat at near-zero abundance.
We estimated Cy by using EMD and incorporating abundance and fish size into model calibration. Average daily proportions of maximum consumption, , across the years were generally high (=0.640; standard deviation (SD) SD=0.176), suggesting that fish were feeding at about two-thirds of expected maximum consumption rates. This average proportion of maximum consumption,is very similar to what has been assumed (=0.66) in previous Trinity and Klamath River S3 calibration and simulation efforts. In 2017, we estimated the lowest Cy, suggesting lower average consumption for juvenile salmon in high-discharge water years. When this high discharge year was excluded, there was no apparent trend in Cy with annual water volume. Estimates of survival showed little trend over the range in spawner abundances, but a trend towards higher natural and hatchery fish survival with higher annual volumes of water was apparent. Over the 12 years, the average survival of hatchery fish was =0.888 (SD=0.079) and the average survival natural fish was=0.969 (SD=0.01).
With respect to fish movement, we estimated higher M0Ny and M0Hy with higher annual volumes of water in the Trinity River. Higher MN0y or MH0y suggest greater probability of remaining in a habitat at low fish densities, with potential for density-dependent processes in movement to occur. The highest M0Ny = 0.676 was estimated during brood year 2012, and the overall average for natural fish was =0.276 (SD=0.188) and for hatchery fish was=0.467 (SD=0.235). Under the Beverton-Holt model, as M0Ny or M0Hy approach zero, there is less capacity for change in fish movement as fish density increases.
The S3 model was initialized with only the spatiotemporal distribution of spawners, so it performed well at capturing the essential outmigration features that are ultimately governed by rates of growth, movement, and mortality. We used a new optimization method that could accommodate multivariate data on abundance and fish size collected at the Pear Tree fish trap, enabling the calibration of S3 to estimate five parameters for 12 separate years of data. Incorporating weekly fish size data for each year in our parameter optimization process made the estimation of Cy possible and represents a step forward in the fitting of the S3 model to fish trap data for the purposes of parameter calibration and the estimation of growth parameters with respect to annual conditions. We identified lack of fit and adding important effects into the S3 model may improve the S3 estimation and simulation of water scenarios.
The Trinity River Restoration Program (TRRP) Science Advisory Board recommended that the TRRP focus on developing core elements of a decision support system (DSS; Buffington and others, 2014). Toward that end, the habitat and S3 models described in this report are both core elements of the DSS. The structure of S3 makes it a particularly useful fish production model for the DSS because population dynamics are sensitive to (1) water temperature, (2) daily discharge management, and (3) habitat quality and quantity. Each of these variables are key management parameters under consideration in the TRRP. As such, the S3 model may provide valuable insights into the potentially variable effects of different management decisions on the Trinity River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241070","collaboration":"Prepared in cooperation with U.S. Bureau of Reclamation","usgsCitation":"Plumb, J.M., Perry, R.W., and De Juilio, K., 2025, Calibration of the Stream Salmonid Simulator (S3) model to estimate annual survival, movement, and food consumption by juvenile Chinook salmon (Oncorhynchus tshawytscha) in the restoration reach of the Trinity River, California, 2006–18: U.S. Geological Survey Open-File Report 2024–1070, 21 p., https://doi.org/10.3133/ofr20241070.","productDescription":"vii, 22 p.","onlineOnly":"Y","ipdsId":"IP-156648","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":485969,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1070/images"},{"id":485968,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241070/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2024-1070"},{"id":485967,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1070/ofr20241070.pdf","text":"Report","size":"7.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024-1070"},{"id":485966,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1070/coverthb.jpg"},{"id":485970,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1070/ofr20241070.XML"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.1406578585185,\n 40.785753827016435\n ],\n [\n -123.1406578585185,\n 40.69151845163566\n ],\n [\n -122.79146166523228,\n 40.69151845163566\n ],\n [\n -122.79146166523228,\n 40.785753827016435\n ],\n [\n -123.1406578585185,\n 40.785753827016435\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
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6505 NE 65th Street
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The origin of prolific ca. 1.4 Ga ferroan magmatism between the southwestern USA and eastern Canada is enigmatic and has been explained by various models, including extensional, mantle plume, and convergent plate-margin models. Rare mafic plutons are associated with the ferroan plutons, which may help constrain their mantle source and tectonic setting. In the southwestern USA, only two such mafic plutons are known to exist. We present the first evidence for a third, mostly buried, potentially layered, mafic-ultramafic Mesoproterozoic pluton, informally referred to as the Hardscrabble Creek complex, in the central Wet Mountains of Colorado, USA. Recent geophysical data show an elliptical magnetic and gravity high spatially coincident with local gabbroic outcrops. New field and petrographic analyses of these exposed rocks reveal that they consist of ultramafic to mafic cumulates, including orthopyroxenite, olivine norite, norite, and anorthosite. High-precision U-Pb dating of zircon from orthopyroxenite and norite yield weighted mean 206Pb/238U dates of 1352.36 ± 1.60 Ma and 1352.37 ± 1.71 Ma, respectively. These dates indicate that the complex formed over a narrow timeframe, after the adjacent 1362 ± 7 Ma ferroan San Isabel Granite, and during the waning stages of the regional ca. 1.4 Ga ferroan magmatism. Whole-rock geochemistry and Nd-Sr-Pb isotope compositions of samples from the Hardscrabble Creek complex are similar to those of the San Isabel Granite, suggesting that they were derived from the same or a similar mantle source. The mineral chemistry of the samples is comparable to Proterozoic massif-type anorthosites and related mafic intrusions, indicating that the Hardscrabble Creek complex and San Isabel Granite together represent a rare anorthosite-mangerite-charnockite-granite (AMCG) suite in the southwestern USA. The Hardscrabble Creek complex is unique because it formed ~80 m.y. after the other few mafic plutons in the southwestern USA, and it contains an ultramafic section that is absent from these plutons and rare to the AMCG suite in general. A combination of arc-like whole-rock geochemistry, chondrite uniform reservoir-like Nd-Sr-Pb isotopes, and ocean island basalt (OIB)-like zircon trace element chemistry suggests that the complex was derived from a partial melt of OIB-like mantle and interacted with metasomatically enriched lithospheric mantle. The enriched lithospheric mantle signature, combined with the long ~160 m.y. duration of magmatism in the region, is consistent with a period of protracted convergent tectonism.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/B37903.1","usgsCitation":"Magnin, B.P., Brake, S.S., Kuiper, Y., Mohr, M.T., and Hanson, R.E., 2025, The Hardscrabble Creek complex: A newly discovered, mostly buried, Mesoproterozoic mafic-ultramafic pluton in the Wet Mountains, Colorado, USA: GSA Bulletin, v. 137, no. 9-10, p. 4558-4574, https://doi.org/10.1130/B37903.1.","productDescription":"17 p.","startPage":"4558","endPage":"4574","ipdsId":"IP-168096","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":495119,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Wet Mountains","volume":"137","issue":"9-10","noUsgsAuthors":false,"publicationDate":"2025-05-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Magnin, Benjamin Patrick 0000-0001-9951-4404","orcid":"https://orcid.org/0000-0001-9951-4404","contributorId":300679,"corporation":false,"usgs":true,"family":"Magnin","given":"Benjamin","email":"","middleInitial":"Patrick","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":947668,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brake, Sandra S.","contributorId":360805,"corporation":false,"usgs":false,"family":"Brake","given":"Sandra","middleInitial":"S.","affiliations":[{"id":17777,"text":"Indiana State University","active":true,"usgs":false}],"preferred":false,"id":947669,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kuiper, Yvette 0000-0002-8506-8180","orcid":"https://orcid.org/0000-0002-8506-8180","contributorId":299649,"corporation":false,"usgs":false,"family":"Kuiper","given":"Yvette","email":"","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":947670,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mohr, Michael T. 0009-0001-3814-6908","orcid":"https://orcid.org/0009-0001-3814-6908","contributorId":360807,"corporation":false,"usgs":false,"family":"Mohr","given":"Michael","middleInitial":"T.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":947671,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hanson, Richard E.","contributorId":360809,"corporation":false,"usgs":false,"family":"Hanson","given":"Richard","middleInitial":"E.","affiliations":[{"id":25471,"text":"Texas Christian University","active":true,"usgs":false}],"preferred":false,"id":947672,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70269366,"text":"70269366 - 2025 - Genetic connectivity in a cooperatively breeding carnivore between two protected areas","interactions":[],"lastModifiedDate":"2025-07-22T13:26:31.181174","indexId":"70269366","displayToPublicDate":"2025-05-14T09:37:05","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Genetic connectivity in a cooperatively breeding carnivore between two protected areas","docAbstract":"Wildlife populations are increasingly threatened by human activities. Most studies, however, are often short in duration or do not encompass the large spatial extent necessary to measure the potential effects of human activities on population vital rates. Furthermore, the life history features of species with high fecundity and excellent dispersal capabilities can act as buffers against the potential negative effects of human activities on their populations. We used a 30-year dataset of genetic samples from gray wolves (Canis lupus) in Alaska, USA, to examine genetic connectivity and diversity between National Park units separated by a region with recurrent human-caused mortality. We found that the two protected populations were genetically similar and that dispersal events occurred between them even though they are > 450 km apart. We posit that intact ecosystems and a history of continuous distribution of wolves surrounding the affected regions likely maintained the genetic connectivity of wolves in the two protected areas.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.71420","usgsCitation":"Cerreta, A., Adams, J., Borg, B., Sorum, M., Waits, L., and Ausband, D.E., 2025, Genetic connectivity in a cooperatively breeding carnivore between two protected areas: Ecology and Evolution, v. 15, no. 5, e71420, 10 p., https://doi.org/10.1002/ece3.71420.","productDescription":"e71420, 10 p.","ipdsId":"IP-158851","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":492874,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.71420","text":"Publisher Index Page"},{"id":492626,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Denali National Park and Preserve, Yukon- Charley Rivers National Preserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -148.7512872450975,\n 64.07437550215425\n ],\n [\n -153.00052847916817,\n 64.07437550215425\n ],\n [\n -153.00052847916817,\n 62.276194894069874\n ],\n [\n -148.7512872450975,\n 62.276194894069874\n ],\n [\n -148.7512872450975,\n 64.07437550215425\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -141.0182219902975,\n 65.5653487163298\n ],\n [\n -144.37903864723086,\n 65.5653487163298\n ],\n [\n -144.37903864723086,\n 64.4468015781325\n ],\n [\n -141.0182219902975,\n 64.4468015781325\n ],\n [\n -141.0182219902975,\n 65.5653487163298\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-05-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Cerreta, Ariana L.","contributorId":358318,"corporation":false,"usgs":false,"family":"Cerreta","given":"Ariana L.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":943537,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Adams, Jennifer R.","contributorId":358321,"corporation":false,"usgs":false,"family":"Adams","given":"Jennifer R.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":943539,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Borg, Bridget L.","contributorId":358323,"corporation":false,"usgs":false,"family":"Borg","given":"Bridget L.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":943540,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sorum, Mathew S.","contributorId":358326,"corporation":false,"usgs":false,"family":"Sorum","given":"Mathew S.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":943541,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Waits, Lisette P.","contributorId":358329,"corporation":false,"usgs":false,"family":"Waits","given":"Lisette P.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":943542,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ausband, David Edward 0000-0001-9204-9837","orcid":"https://orcid.org/0000-0001-9204-9837","contributorId":275329,"corporation":false,"usgs":true,"family":"Ausband","given":"David","email":"","middleInitial":"Edward","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":943538,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70266866,"text":"ofr20251025 - 2025 - Distribution and abundance of Least Bell’s Vireos (Vireo bellii pusillus) and Southwestern Willow Flycatchers (Empidonax traillii extimus) at the Mojave River Dam, San Bernardino County, California—2024 data summary","interactions":[],"lastModifiedDate":"2025-05-14T14:24:51.77589","indexId":"ofr20251025","displayToPublicDate":"2025-05-13T11:29:37","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-1025","displayTitle":"Distribution and Abundance of Least Bell’s Vireos (Vireo bellii pusillus) and Southwestern Willow Flycatchers (Empidonax traillii extimus) at the Mojave River Dam, San Bernardino County, California—2024 Data Summary","title":"Distribution and abundance of Least Bell’s Vireos (Vireo bellii pusillus) and Southwestern Willow Flycatchers (Empidonax traillii extimus) at the Mojave River Dam, San Bernardino County, California—2024 data summary","docAbstract":"We surveyed for Least Bell’s Vireos (Vireo bellii pusillus; vireo) and Southwestern Willow Flycatchers (Empidonax traillii extimus; flycatcher) at the Mojave River Dam study area near Hesperia, California, in 2024. Four vireo surveys were completed between April 17 and July 2, 2024, and three flycatcher surveys were completed between May 23 and July 2, 2024.
We detected three territorial male vireos, all of which were paired. No juveniles were observed during surveys. Vireo territories were reported in two habitat types: riparian scrub and willow-cottonwood. Red or arroyo willow (Salix laevigata or lasiolepis) was the dominant plant species in most vireo territories. No territorial or transient flycatchers were observed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251025","programNote":"Ecosystems Mission Area—Species Management Research Program","usgsCitation":"Howell, S.L., and Kus, B.E., 2025, Distribution and abundance of Least Bell’s Vireos (Vireo bellii pusillus) and Southwestern Willow Flycatchers (Empidonax traillii extimus) at the Mojave River Dam, San Bernardino County, California—2024 data summary: U.S. Geological Survey Open-File Report 2025–1025, 8 p., https://doi.org/10.3133/ofr20251025.","productDescription":"vi, 8 p.","onlineOnly":"Y","ipdsId":"IP-172384","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":485895,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1025/ofr20251025.XML"},{"id":485894,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1025/images"},{"id":485893,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251025/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1025"},{"id":485892,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1025/ofr20251025.pdf","text":"Report","size":"1.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1025"},{"id":485891,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1025/coverthb.jpg"}],"country":"United States","state":"California","county":"San Bernardino County","otherGeospatial":"Mojave River Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.2768615020681,\n 34.370295162773715\n ],\n [\n -117.2768615020681,\n 34.31872800675019\n ],\n [\n -117.21059336313478,\n 34.31872800675019\n ],\n [\n -117.21059336313478,\n 34.370295162773715\n ],\n [\n -117.2768615020681,\n 34.370295162773715\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Reliable population estimates are one of the most elementary needs for the management of wildlife, particularly for introduced ungulates on oceanic islands. We aimed to produce accurate and precise density estimates of Philippine deer (Rusa marianna) and wild pigs (Sus scrofa) on Guam using motion-triggered cameras combined with distance sampling to estimate densities from observations of unmarked animals while accounting for imperfect detection. We used an automated digital data processing pipeline for species recognition and to estimate the distance to detected species. Our density estimates were slightly lower than published estimates, consistent with management to reduce populations. We estimated the number of camera traps needed to obtain a 0.1 coefficient of variation was substantial, requiring > ten-fold increase in camera traps, while estimates with precision of 0.2 or 0.3 were more achievable, requiring doubling to quadrupling the number of camera traps. We provide best practices for establishing and conducting distance sampling with camera trap surveys for density estimation based on lessons learned during this study. Future studies should consider distance sampling with camera traps to efficiently survey and monitor unmarked animals, particularly medium-sized ungulates, in tropical, oceanic island ecosystems.
","language":"English","publisher":"Cambridge University Press","doi":"10.1017/S0266467425000033","usgsCitation":"Camp, R.J., Bak, T.M., Burt, M., and Vogt, S., 2025, Using distance sampling with camera traps to estimate densities of ungulates on tropical oceanic islands: Journal of Tropical Ecology, v. 41, e12, https://doi.org/10.1017/S0266467425000033.","productDescription":"e12","ipdsId":"IP-154449","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":486578,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Guam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 144.96784988323265,\n 13.61173385872614\n ],\n [\n 144.8366796899819,\n 13.68869399233894\n ],\n [\n 144.63621203614423,\n 13.440889342095488\n ],\n [\n 144.62136258030552,\n 13.248242573805925\n ],\n [\n 144.72778368049006,\n 13.221741661313231\n ],\n [\n 144.79955605038276,\n 13.274740600770016\n ],\n [\n 144.81688041552877,\n 13.419224264484484\n ],\n [\n 144.96042515531252,\n 13.525123785236971\n ],\n [\n 144.96784988323265,\n 13.61173385872614\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"41","noUsgsAuthors":false,"publicationDate":"2025-05-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Camp, Richard J. 0000-0001-7008-923X rick_camp@usgs.gov","orcid":"https://orcid.org/0000-0001-7008-923X","contributorId":189964,"corporation":false,"usgs":true,"family":"Camp","given":"Richard","email":"rick_camp@usgs.gov","middleInitial":"J.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true},{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":938391,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bak, Trevor M.","contributorId":317824,"corporation":false,"usgs":false,"family":"Bak","given":"Trevor","email":"","middleInitial":"M.","affiliations":[{"id":13341,"text":"Hawai‘i Cooperative Studies Unit, University of Hawai‘i at Hilo","active":true,"usgs":false}],"preferred":false,"id":938392,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Burt, Matthew D","contributorId":355925,"corporation":false,"usgs":false,"family":"Burt","given":"Matthew D","affiliations":[{"id":84860,"text":"Naval Facilities Marianas","active":true,"usgs":false}],"preferred":false,"id":938393,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vogt, Scott","contributorId":355926,"corporation":false,"usgs":false,"family":"Vogt","given":"Scott","affiliations":[{"id":84860,"text":"Naval Facilities Marianas","active":true,"usgs":false}],"preferred":false,"id":938394,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70267370,"text":"70267370 - 2025 - Fine-resolution satellite remote sensing improves spatially distributed snow modeling to near real time","interactions":[],"lastModifiedDate":"2025-05-21T14:36:05.59686","indexId":"70267370","displayToPublicDate":"2025-05-13T09:30:10","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Fine-resolution satellite remote sensing improves spatially distributed snow modeling to near real time","docAbstract":"Given the highly variable distribution of seasonal snowpacks in complex mountainous environments, the accurate snow modeling of basin-wide snow water equivalent (SWE) requires a spatially distributed approach at a sufficiently fine grid resolution (<500 m) to account for the important processes in the seasonal evolution of a snowpack (e.g., wind redistribution of snow to resolve patchy snow cover in an alpine zone). However, even well-validated snow evolution models, such as SnowModel, are prone to errors when key model inputs, such as the precipitation and wind speed and direction, are inaccurate or only available at coarse spatial resolutions. Incorporating fine-spatial-resolution remotely sensed snow-covered area (SCA) information into spatially distributed snow modeling has the potential to refine and improve fine-resolution snow water equivalent (SWE) estimates. This study developed 30 m resolution SnowModel simulations across the Big Thompson River, Fraser River, Three Lakes, and Willow Creek Basins, a total area of 4212 km2 in Colorado, for the water years 2000–2023, and evaluated the incorporation of a Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat SCA datasets into the model’s development and calibration. The SnowModel was calibrated spatially to the Landsat mean annual snow persistence (SP) and temporally to the MODIS mean basin SCA using a multi-objective calibration procedure executed using Latin hypercube sampling and a stepwise calibration process. The Landsat mean annual SP was also used to further optimize the SnowModel simulations through the development of a spatially variable precipitation correction field. The evaluations of the SnowModel simulations using the Airborne Snow Observatories’ (ASO’s) light detection and ranging (lidar)-derived SWE estimates show that the versions of the SnowModel calibrated to the remotely sensed SCA had an improved performance (mean error ranging from −28 mm to −6 mm) compared with the baseline simulations (mean error ranging from 69 mm to 86 mm), and comparable spatial patterns to those of the ASO, especially at the highest elevations. Furthermore, this study’s results highlight how a regularly updated 30 m resolution SCA could be used to further improve the calibrated SnowModel simulations to near real time (latency of 5 days or less).
","language":"English","publisher":"MDPI","doi":"10.3390/rs17101704","usgsCitation":"Sexstone, G., Akie, G.A., Selkowitz, D.J., Barnhart, T., Rey, D., León-Salazar, C., Carbone, E., and Bearup, L.A., 2025, Fine-resolution satellite remote sensing improves spatially distributed snow modeling to near real time: Remote Sensing, v. 17, no. 10, 1704, 24 p., https://doi.org/10.3390/rs17101704.","productDescription":"1704, 24 p.","ipdsId":"IP-174585","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":490140,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs17101704","text":"Publisher Index Page"},{"id":486286,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Rocky Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.33,\n 40.85\n ],\n [\n -106.33,\n 39.65\n ],\n [\n -105.17,\n 39.65\n ],\n [\n -105.17,\n 40.85\n ],\n [\n -106.33,\n 40.85\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"17","issue":"10","noUsgsAuthors":false,"publicationDate":"2025-05-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Sexstone, Graham A. 0000-0001-8913-0546","orcid":"https://orcid.org/0000-0001-8913-0546","contributorId":203850,"corporation":false,"usgs":true,"family":"Sexstone","given":"Graham A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":938011,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Akie, Garrett Alexander 0000-0002-6356-7106","orcid":"https://orcid.org/0000-0002-6356-7106","contributorId":290236,"corporation":false,"usgs":true,"family":"Akie","given":"Garrett","email":"","middleInitial":"Alexander","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":938012,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Selkowitz, David J. 0000-0003-0824-7051 dselkowitz@usgs.gov","orcid":"https://orcid.org/0000-0003-0824-7051","contributorId":3259,"corporation":false,"usgs":true,"family":"Selkowitz","given":"David","email":"dselkowitz@usgs.gov","middleInitial":"J.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"preferred":true,"id":938013,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barnhart, Theodore B. 0000-0002-9682-3217","orcid":"https://orcid.org/0000-0002-9682-3217","contributorId":202558,"corporation":false,"usgs":true,"family":"Barnhart","given":"Theodore B.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":938014,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rey, David M. 0000-0003-2629-365X","orcid":"https://orcid.org/0000-0003-2629-365X","contributorId":211848,"corporation":false,"usgs":true,"family":"Rey","given":"David M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":938015,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"León-Salazar, Claudia","contributorId":355707,"corporation":false,"usgs":false,"family":"León-Salazar","given":"Claudia","affiliations":[{"id":6736,"text":"Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":938016,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Carbone, Emily","contributorId":355708,"corporation":false,"usgs":false,"family":"Carbone","given":"Emily","affiliations":[{"id":84819,"text":"Northern Water","active":true,"usgs":false}],"preferred":false,"id":938017,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bearup, Lindsay A.","contributorId":139257,"corporation":false,"usgs":false,"family":"Bearup","given":"Lindsay","email":"","middleInitial":"A.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":938018,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70266760,"text":"sir20245133 - 2025 - Using the D-Claw software package to model lahars in the Middle Fork Nooksack River drainage and beyond, Mount Baker, Washington","interactions":[],"lastModifiedDate":"2025-07-03T14:16:54.133347","indexId":"sir20245133","displayToPublicDate":"2025-05-12T15:08:17","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5133","displayTitle":"Using the D-Claw Software Package to Model Lahars in the Middle Fork Nooksack River Drainage and Beyond, Mount Baker, Washington","title":"Using the D-Claw software package to model lahars in the Middle Fork Nooksack River drainage and beyond, Mount Baker, Washington","docAbstract":"Lahars, or volcanic mudflows, are the most hazardous eruption-related phenomena that will affect communities living along rivers that originate on Mount Baker. In the past 15,000 years, the largest lahars from Mount Baker have affected the Middle Fork Nooksack River drainage and beyond. Here we use the physics-based D-Claw software package to model nine lahar scenarios that are initiated as water-saturated landslides between Sherman Crater and the Roman Wall on the Mount Baker edifice and flow down the Middle Fork Nooksack River. The scenarios range in volume from 1 to 260 million cubic meters and have an initial hydraulic permeability from 10−12 to 10−10 meters squared. Model output includes data such as flow depth, velocity, runout distance, area inundated, arrival time, and sediment concentration as well as information that allows scientists to calculate other important hydrologic characteristics such as lahar discharge. These data are important to officials who have the responsibility to plan for, or take mitigation measures against, future Mount Baker lahars. To check the validity of the D-Claw results, we compare the scenarios to known geologic information. We also compare D-Claw results with empirical models that have been used in the past to determine potential inundation areas, runout distances, and arrival times. These comparisons highlight similarities and differences between empirical and physics-based models. We also present D-Claw scenario-based animations to help scientists, officials, and lay people alike to visualize how future lahars could affect communities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245133","usgsCitation":"Gardner, C.A., Benage, M.C., Cannon, C., and George, D.L., 2025, Using the D-Claw software package to model lahars in the Middle Fork Nooksack River drainage and beyond, Mount Baker, Washington: U.S. Geological Survey Scientific Investigations Report 2024–5133, 47 p., https://doi.org/10.3133/sir20245133.","productDescription":"Report: vii, 47 p.; 9 Animation Videos; Data Release","numberOfPages":"47","ipdsId":"IP-151680","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":485743,"rank":13,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5133/sir20245133_app4_scenarioC2.mp4","text":"Appendix 4 - Scenario C2","size":"35.9 MB","description":"Scenario C2","linkHelpText":"- Scenario C2"},{"id":485742,"rank":12,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5133/sir20245133_app4_scenarioB3.mp4","text":"Appendix 4 - Scenario B3","size":"47 MB","description":"Scenario B3","linkHelpText":"- Scenario B3"},{"id":485741,"rank":11,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5133/sir20245133_app4_scenarioB2.mp4","text":"Appendix 4 - Scenario B2","size":"37.6 MB","description":"Scenario B2","linkHelpText":"- Scenario B2"},{"id":485740,"rank":10,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5133/sir20245133_app4_scenarioB1.mp4","text":"Appendix 4 - Scenario B1","size":"25.6 MB","description":"Scenario B1","linkHelpText":"- Scenario B1"},{"id":485739,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5133/sir20245133_app4_scenarioA3.mp4","text":"Appendix 4 - Scenario A3","size":"50.4 MB","description":"Scenario A3","linkHelpText":"- Scenario A3"},{"id":485737,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5133/sir20245133_app4_scenarioA1.mp4","text":"Appendix 4 - Scenario A1","size":"35.4 MB","description":"Scenario A1","linkHelpText":"- Scenario A1"},{"id":485736,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1PEX7FS","text":"USGS data release","description":"George, D.L., Cannon, C.M., Benage, M.C., and Gardner, C.A., 2025, Simulated lahar extents and dynamics in the Middle Fork Nooksack River drainage, resulting from hypothetical landslide sources on the western summit of Mount Baker, Washington: U.S. Geological Survey data release, https://doi.org/10.5066/P1PEX7FS.","linkHelpText":"Simulated lahar extents and dynamics in the Middle Fork Nooksack River drainage, resulting from hypothetical landslide sources on the western summit of Mount Baker, Washington"},{"id":485734,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5133/images"},{"id":485733,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5133/sir20245133.XML","description":"SIR 2024-5133 XML"},{"id":485731,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5133/sir20245133.pdf","text":"Report","size":"12 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5133 PDF"},{"id":485730,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5133/coverthb.jpg"},{"id":485745,"rank":15,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5133/sir20245133_app4_scenarioE2.mp4","text":"Appendix 4 - 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Johnston Cascades Volcano Observatory
U.S. Geological Survey
1300 SE Cardinal Court
Building 10, Suite 100
Vancouver, WA 98683
Email: askCVO@usgs.gov
","tableOfContents":"