We explore how and when Hells Canyon, North America’s deepest river gorge (~2,400 m deep), formed, addressing these fundamental questions first posed by W. Lindgren [The Gold Belt of the Blue Mountains of Oregon (1901)]. Existing hypotheses about the canyon’s formation and timing of incision remain speculative due to a lack of direct constraints and geomorphic analysis in the canyon. Herein, we combine cosmogenic nuclide dating of cave-bound river deposits, river profile analysis, and numerical modeling to provide the first direct age constraints and systematic analysis of incision processes at work in Hells Canyon. Our study reveals a significant drainage capture triggered rapid incision at ~2.1 ± 1.0 Ma, establishing the Snake River’s modern route into the Columbia River system. The increased drainage area and subsequent increase in stream power resulted in the rapid incision of Hells Canyon and the formation of tributary knickpoints (KPs) that decrease in elevation away from the capture location. Cosmogenic dating of cave deposits indicates incision rates increased from ~0.01 to ~0.16 mm y−1. Numerical modeling of the stream capture supports these observations, demonstrating how abrupt drainage area increase drives rapid river incision. Our findings from Hells Canyon provide a well-constrained example of how drainage capture can dramatically shape the evolution of a major river gorge.
","language":"English","publisher":"National Academy of Sciences","doi":"10.1073/pnas.2413069122","usgsCitation":"Morriss, M., Mitchell, N., Yanites, B., Staisch, L.M., and Korup, O., 2025, Cave records reveal recent origin of North America’s deepest canyon: Proceedings of the National Academy of Sciences, v. 122, no. 21, e2413069122, 9 p., https://doi.org/10.1073/pnas.2413069122.","productDescription":"e2413069122, 9 p.","ipdsId":"IP-167780","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":490153,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1073/pnas.2413069122","text":"Publisher Index Page"},{"id":486579,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Oregon, Washington","otherGeospatial":"Hell's Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.54194789662489,\n 46.1007348974789\n ],\n [\n -117.54194789662489,\n 44.98900450879145\n ],\n [\n -116.26934949051348,\n 44.98900450879145\n ],\n [\n -116.26934949051348,\n 46.1007348974789\n ],\n [\n -117.54194789662489,\n 46.1007348974789\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"122","issue":"21","noUsgsAuthors":false,"publicationDate":"2025-05-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Morriss, Matthew","contributorId":355886,"corporation":false,"usgs":false,"family":"Morriss","given":"Matthew","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":938354,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mitchell, Nate","contributorId":355887,"corporation":false,"usgs":false,"family":"Mitchell","given":"Nate","affiliations":[{"id":37145,"text":"Indiana University","active":true,"usgs":false}],"preferred":false,"id":938355,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yanites, Brian","contributorId":91373,"corporation":false,"usgs":true,"family":"Yanites","given":"Brian","affiliations":[],"preferred":false,"id":938356,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Staisch, Lydia M. 0000-0002-1414-5994 lstaisch@usgs.gov","orcid":"https://orcid.org/0000-0002-1414-5994","contributorId":167068,"corporation":false,"usgs":true,"family":"Staisch","given":"Lydia","email":"lstaisch@usgs.gov","middleInitial":"M.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":938357,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Korup, Oliver","contributorId":218071,"corporation":false,"usgs":false,"family":"Korup","given":"Oliver","email":"","affiliations":[{"id":39735,"text":"Institute of Earth and Environmental Science, University of Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":938358,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70269525,"text":"70269525 - 2025 - Infrasonic directivity of monopole, dipole, and bipole ground-surface reflected sources","interactions":[],"lastModifiedDate":"2025-07-25T14:07:33.783578","indexId":"70269525","displayToPublicDate":"2025-05-19T09:06:48","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1803,"text":"Geophysical Journal International","active":true,"publicationSubtype":{"id":10}},"title":"Infrasonic directivity of monopole, dipole, and bipole ground-surface reflected sources","docAbstract":"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":70267840,"text":"70267840 - 2025 - Cultivating reciprocity and supporting Indigenous lifeways through the cultural transformation of natural resource management in North America","interactions":[],"lastModifiedDate":"2025-06-04T13:50:11.217859","indexId":"70267840","displayToPublicDate":"2025-05-19T08:47:04","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5936,"text":"People and Nature","active":true,"publicationSubtype":{"id":10}},"title":"Cultivating reciprocity and supporting Indigenous lifeways through the cultural transformation of natural resource management in North America","docAbstract":"Objective
Nonnative fishes can modify ecosystems and harm economies when they are introduced to new environments. Climate change is likely to assist the spread and establishment of some nonnative fishes (e.g., warmwater species), but spatiotemporal gaps in water temperature monitoring and modeling may prevent ecologists and managers from forecasting thermal habitat suitability for these taxa. The purpose of this study was to develop a predictive model of winter water temperatures and thermal habitat suitability for two priority nonnative armored catfish, Vermiculated Sailfin Catfish Pterygoplichthys disjunctivus and Orinoco Sailfin Catfish P. multiradiatus, in the Suwannee River, Florida and Georgia.
Methods
Precipitation- and groundwater-corrected air–water temperature models were developed and evaluated using a model selection procedure to predict water temperatures at four sites in the Suwannee River. These models were chosen because they blend the simplicity of air–water temperature models with the accuracy of hydrometeorological models to create an efficient, economical, management-relevant approach for analyzing and forecasting water temperature.
Results
Most of the top-performing water temperature models (92%) had precipitation or groundwater corrections to air–water temperature formulations. Projected mean and maximum water temperatures increased as simulated climate change intensified. All four Suwannee River sites studied were projected to be thermally hospitable to the survival of Vermiculated Sailfin Catfish. Lower river sites, noticeably warmer than upper river sites, were conducive to the survival of Orinoco Sailfin Catfish throughout the winter months. The upper river sites were too cold for Orinoco Sailfin Catfish survival in some climate-change scenarios, but the Suwannee River has an abundance of constant-temperature springs that are likely hospitable to Vermiculated Sailfin Catfish and Orinoco Sailfin Catfish throughout the year.
Conclusions
The findings suggest that winter water temperatures will likely not be a barrier to the survival of Pterygoplichthys catfish in the Suwannee River, amplifying the importance of conservation and management approaches to inhibit their spread and establishment. If the Pterygoplichthys population remains small and isolated and decision makers are able to devote required staff time and resources to managing these species, removal and eradication at local if not broader scales may be reasonable goals. This study provides a water temperature modeling approach that can aid ecologists and managers in prioritizing sites to prevent the introduction, slow the dispersal, eradicate, and control Pterygoplichthys catfish and other nonnative fishes in the Suwannee River and beyond.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/tafafs/vnaf018","usgsCitation":"Carlson, A.K., 2025, Protected from Pterygoplichthys? Predicting thermal habitat suitability for nonnative armored catfish in the Suwannee River: Transactions of the American Fisheries Society, v. 154, no. 4, p. 398-413, https://doi.org/10.1093/tafafs/vnaf018.","productDescription":"16 p.","startPage":"398","endPage":"413","ipdsId":"IP-171826","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":494463,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/tafafs/vnaf018","text":"Publisher Index Page"},{"id":494386,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida, Georgia","otherGeospatial":"Suwannee River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.76698997402998,\n 31.112972868570907\n ],\n [\n -83.18475784303796,\n 30.177311490235\n ],\n [\n -83.16967351009271,\n 29.211264445611363\n ],\n [\n -82.78703329930818,\n 29.017838843725343\n ],\n [\n -82.37936790717382,\n 30.074113688375725\n ],\n [\n -82.26360657954834,\n 31.112972868570907\n ],\n [\n -82.76698997402998,\n 31.112972868570907\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"154","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-05-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Carlson, Andrew Kenneth 0000-0002-6681-0853","orcid":"https://orcid.org/0000-0002-6681-0853","contributorId":340581,"corporation":false,"usgs":true,"family":"Carlson","given":"Andrew","email":"","middleInitial":"Kenneth","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":946650,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70267512,"text":"70267512 - 2025 - A joint Gaussian process model of geochemistry, geophysics, and temperature for groundwater TDS in the San Ardo Oil Field, California, USA","interactions":[],"lastModifiedDate":"2025-05-28T14:15:05.548599","indexId":"70267512","displayToPublicDate":"2025-05-18T09:08:06","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"A joint Gaussian process model of geochemistry, geophysics, and temperature for groundwater TDS in the San Ardo Oil Field, California, USA","docAbstract":"Although prevalent for the late Holocene, relative sea level (RSL) constraints during and immediately after the Last Glacial Maximum (LGM) are sparse. 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.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.quascirev.2025.109408","usgsCitation":"Medri, E., Simms, A., Kluesner, J., Johnson, S., Nishenko, S., Greene, H., Conrad, J.E., and Rand, D., 2025, Reconstructing late Pleistocene relative sea levels on transgressed shelves: An example from central California: Quaternary Science Reviews, v. 361, 109408, 19 p., https://doi.org/10.1016/j.quascirev.2025.109408.","productDescription":"109408, 19 p.","ipdsId":"IP-175358","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":491717,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.quascirev.2025.109408","text":"Publisher Index Page"},{"id":491527,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"central California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.06130678370785,\n 35.50970569830827\n ],\n [\n -121.06130678370785,\n 35.40292660321283\n ],\n [\n -120.89512889522845,\n 35.40292660321283\n ],\n [\n -120.89512889522845,\n 35.50970569830827\n ],\n [\n -121.06130678370785,\n 35.50970569830827\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"361","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Medri, Elisa","contributorId":357458,"corporation":false,"usgs":false,"family":"Medri","given":"Elisa","affiliations":[{"id":37180,"text":"UC Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":941487,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Simms, Alexander","contributorId":357459,"corporation":false,"usgs":false,"family":"Simms","given":"Alexander","affiliations":[{"id":37180,"text":"UC Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":941488,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kluesner, Jared W. 0000-0003-1701-8832","orcid":"https://orcid.org/0000-0003-1701-8832","contributorId":206367,"corporation":false,"usgs":true,"family":"Kluesner","given":"Jared W.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":941489,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Samuel Y. 0000-0001-7972-9977","orcid":"https://orcid.org/0000-0001-7972-9977","contributorId":221270,"corporation":false,"usgs":true,"family":"Johnson","given":"Samuel Y.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":941490,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nishenko, Stuart","contributorId":357460,"corporation":false,"usgs":false,"family":"Nishenko","given":"Stuart","affiliations":[{"id":64958,"text":"Pacific Gas and Electric","active":true,"usgs":false}],"preferred":false,"id":941491,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Greene, H. Gary","contributorId":357461,"corporation":false,"usgs":false,"family":"Greene","given":"H. 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Land managers in urban areas along the Rio Grande corridor and in the State’s rural northwest and southeast also have concerns about existing and developing roads, buildings, and other infrastructure. Federal, State, Tribal, and local organizations actively manage and monitor New Mexico’s water resources. Critical applications that meet the State’s management needs depend on light detection and ranging (lidar) data that provide a highly detailed three-dimensional (3D) model of the Earth’s surface and aboveground features. The 3D Elevation Program (3DEP) is managed by the U.S. Geological Survey in partnership with Federal, State, Tribal, U.S. territorial, and local agencies to acquire consistent lidar coverage at quality level 2 or better to meet the many needs of the Nation and New Mexico. The status of available and in-progress 3DEP baseline lidar data in New Mexico is shown. 3DEP baseline lidar data include quality level 2 or better, 1-meter or better digital elevation models, and lidar point clouds, and must meet the Lidar Base Specification version 1.2 or newer requirements. The National Enhanced Elevation Assessment identified user requirements and conservatively estimated that availability of lidar data would result in at least $9.32 million in new benefits annually to the State.
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Email: 3DEP@usgs.gov
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601 SW Second Avenue, Suite 1950
Portland, Oregon 97204
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
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.
","language":"English","publisher":"Springer","doi":"10.1007/s11538-024-01394-x","usgsCitation":"Mennatallah, G., Powell, J., McClure, J., Walsh, D.P., and Storm, D., 2025, Characterization of the long-distance dispersal kernel of white-tailed deer and evaluating its impact on chronic wasting disease spread in Wisconsin: Bulletin of Mathematical Biology, v. 87, 52, https://doi.org/10.1007/s11538-024-01394-x.","productDescription":"52","ipdsId":"IP-166201","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":486515,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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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":70267261,"text":"70267261 - 2025 - Biocrust mosses and cyanobacteria exhibit distinct carbon uptake responses to variations in precipitation amount and frequency","interactions":[],"lastModifiedDate":"2025-05-19T14:58:24.230313","indexId":"70267261","displayToPublicDate":"2025-05-15T07:53:17","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1466,"text":"Ecology Letters","active":true,"publicationSubtype":{"id":10}},"title":"Biocrust mosses and cyanobacteria exhibit distinct carbon uptake responses to variations in precipitation amount and frequency","docAbstract":"Dryland organisms exhibit varied responses to changes in precipitation, including event size, frequency, and soil moisture duration, influencing carbon uptake and reserve management strategies. This principle, central to the pulse-reserve paradigm, has not been thoroughly evaluated in biological soil crusts (biocrusts), essential primary producers on dryland surfaces. We conducted two experiments to investigate carbon uptake in biocrusts under different precipitation regimes. In the first, we applied a gradient of watering amounts to biocrusts dominated by moss or cyanobacteria, hypothesising distinct pulse-response strategies. The second experiment extended watering treatments over three months, varying pulse size and frequency. Our results revealed distinct carbon uptake patterns: moss crusts exhibited increased CO2 uptake with larger, less frequent watering events, whereas cyanobacteria crusts maintained similar carbon uptake across all event sizes. These findings suggest divergent pulse-response strategies across biocrust types, with implications for modelling dryland carbon dynamics and informing land management under changing precipitation regimes.","language":"English","publisher":"Wiley","doi":"10.1111/ele.70125","usgsCitation":"Young, K., Sala, O.E., Darrouzet-Nardi, A., Tucker, C.L., Finger-Higgens, R.A., Starbuck, M., and Reed, S., 2025, Biocrust mosses and cyanobacteria exhibit distinct carbon uptake responses to variations in precipitation amount and frequency: Ecology Letters, v. 28, no. 5, e70125, 10 p., https://doi.org/10.1111/ele.70125.","productDescription":"e70125, 10 p.","ipdsId":"IP-171245","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":489080,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/2566615","text":"External Repository"},{"id":486154,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Colorado Plateau, southeastern Utah","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.03847502985644,\n 38.34758364168215\n ],\n [\n -111.03847502985644,\n 37.01866208836557\n ],\n [\n -109.01970607796514,\n 37.01866208836557\n ],\n [\n -109.01970607796514,\n 38.34758364168215\n ],\n [\n -111.03847502985644,\n 38.34758364168215\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"28","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-05-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Young, Kristina E.","contributorId":195945,"corporation":false,"usgs":false,"family":"Young","given":"Kristina E.","affiliations":[],"preferred":false,"id":937537,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sala, Osvaldo E.","contributorId":139047,"corporation":false,"usgs":false,"family":"Sala","given":"Osvaldo","email":"","middleInitial":"E.","affiliations":[{"id":12629,"text":"Arizona State University, Tempe, AZ (DETAIL TO BE ADDED)","active":true,"usgs":false}],"preferred":false,"id":937538,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Darrouzet-Nardi, Anthony adarrouzet-nardi@usgs.gov","contributorId":207292,"corporation":false,"usgs":false,"family":"Darrouzet-Nardi","given":"Anthony","email":"adarrouzet-nardi@usgs.gov","affiliations":[],"preferred":false,"id":937539,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tucker, Colin L","contributorId":270737,"corporation":false,"usgs":false,"family":"Tucker","given":"Colin","email":"","middleInitial":"L","affiliations":[{"id":56205,"text":"U.S. National Forest Service, Northern Research Station, Houghton, MI 49931","active":true,"usgs":false}],"preferred":false,"id":937540,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Finger-Higgens, Rebecca A 0000-0002-7645-504X","orcid":"https://orcid.org/0000-0002-7645-504X","contributorId":290211,"corporation":false,"usgs":true,"family":"Finger-Higgens","given":"Rebecca","email":"","middleInitial":"A","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":937541,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Starbuck, Megan Elyse 0000-0002-1363-6994","orcid":"https://orcid.org/0000-0002-1363-6994","contributorId":355528,"corporation":false,"usgs":true,"family":"Starbuck","given":"Megan Elyse","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":937542,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Reed, Sasha C. 0000-0002-8597-8619","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":205372,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":937543,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"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
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
Lampreys (Petromyzontiformes) are an ancient group of fishes with complex life histories. We created a life cycle model that includes an R Shiny interactive web application interface to simulate abundance by life stage. This will allow scientists and managers to connect available demographic information in a framework that can be applied to questions regarding lamprey biology and conservation. We used Pacific lamprey (Entosphenus tridentatus) as a case study to highlight the utility of this model. We applied a global sensitivity analysis to explore the importance of individual life stage parameters to overall population size, and to better understand the implications of existing gaps in knowledge. We also provided example analyses of selected management scenarios (dam passage, fish translocations, and hatchery additions) influencing Pacific lamprey in fresh water. These applications illustrate how the model can be applied to inform conservation efforts. This tool will provide new capabilities for users to explore their own questions about lamprey biology and conservation. Simulations can hone hypotheses and predictions, which can then be empirically tested in the real world.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0323408","usgsCitation":"Gomes, D.G., Benjamin, J.R., Clemens, B.J., Lampman, R., and Dunham, J., 2025, New technology for an ancient fish: A lamprey life cycle modeling tool with an R Shiny application: PLoS ONE, v. 20, no. 5, e0323408, 25 p., https://doi.org/10.1371/journal.pone.0323408.","productDescription":"e0323408, 25 p.","ipdsId":"IP-172919","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":489170,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0323408","text":"Publisher Index Page"},{"id":486169,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"20","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-05-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Gomes, Dylan Gerald-Everett 0000-0002-2642-3728","orcid":"https://orcid.org/0000-0002-2642-3728","contributorId":346160,"corporation":false,"usgs":true,"family":"Gomes","given":"Dylan","email":"","middleInitial":"Gerald-Everett","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":937518,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Benjamin, Joseph R. 0000-0003-3733-6838 jbenjamin@usgs.gov","orcid":"https://orcid.org/0000-0003-3733-6838","contributorId":3999,"corporation":false,"usgs":true,"family":"Benjamin","given":"Joseph","email":"jbenjamin@usgs.gov","middleInitial":"R.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":937519,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clemens, Benjamin J.","contributorId":195098,"corporation":false,"usgs":false,"family":"Clemens","given":"Benjamin","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":937520,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lampman, Ralph","contributorId":215233,"corporation":false,"usgs":false,"family":"Lampman","given":"Ralph","email":"","affiliations":[],"preferred":true,"id":937521,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dunham, Jason 0000-0002-6268-0633","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":220078,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":937522,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70267437,"text":"70267437 - 2025 - Regional analysis of the dependence of peak-flow quantiles on climate with application to adjustment to climate trends","interactions":[],"lastModifiedDate":"2025-05-23T15:07:59.713754","indexId":"70267437","displayToPublicDate":"2025-05-14T10:05:31","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10778,"text":"Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Regional analysis of the dependence of peak-flow quantiles on climate with application to adjustment to climate trends","docAbstract":"Standard flood-frequency analysis methods rely on an assumption of stationarity, but because of growing understanding of climatic persistence and concern regarding the effects of climate change, the need for methods to detect and model nonstationary flood frequency has become widely recognized. In this study, a regional statistical method for estimating the effects of climate variations on annual maximum (peak) flows that allows for the effect to vary by quantile is presented and applied. The method uses a panel–quantile regression framework based on a location-scale model with two fixed effects per basin. The model was fitted to 330 selected gauged basins in the midwestern United States, filtered to remove basins affected by reservoir regulation and urbanization. Precipitation and discharge simulated using a water-balance model at daily and annual time scales were tested as climate variables. Annual maximum daily discharge was found to be the best predictor of peak flows, and the quantile regression coefficients were found to depend monotonically on annual exceedance probability. Application of the models to gauged basins is demonstrated by estimating the peak-flow distributions at the end of the study period (2018) and, using the panel model, to the study basins as-if-ungauged by using leave-one-out cross validation, estimating the fixed effects using static basin characteristics, and parameterizing the water-balance model discharge using median parameters. The errors of the quantiles predicted as-if-ungauged approximately doubled compared to the errors of the fitted panel model.
","language":"English","publisher":"MDPI","doi":"10.3390/hydrology12050119","usgsCitation":"Over, T.M., Marti, M.K., and Podzorski, H.L., 2025, Regional analysis of the dependence of peak-flow quantiles on climate with application to adjustment to climate trends: Hydrology, v. 12, no. 5, 119, 43 p., https://doi.org/10.3390/hydrology12050119.","productDescription":"119, 43 p.","ipdsId":"IP-167316","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":487957,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/hydrology12050119","text":"Publisher Index Page"},{"id":486509,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-05-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Over, Thomas M. 0000-0001-8280-4368","orcid":"https://orcid.org/0000-0001-8280-4368","contributorId":204650,"corporation":false,"usgs":true,"family":"Over","given":"Thomas","email":"","middleInitial":"M.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"preferred":true,"id":938195,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Marti, Mackenzie K. 0000-0001-8817-4969 mmarti@usgs.gov","orcid":"https://orcid.org/0000-0001-8817-4969","contributorId":289738,"corporation":false,"usgs":true,"family":"Marti","given":"Mackenzie","email":"mmarti@usgs.gov","middleInitial":"K.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":938196,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Podzorski, Hannah Lee 0000-0001-5204-2606 hpodzorski@usgs.gov","orcid":"https://orcid.org/0000-0001-5204-2606","contributorId":333626,"corporation":false,"usgs":true,"family":"Podzorski","given":"Hannah","email":"hpodzorski@usgs.gov","middleInitial":"Lee","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":938197,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70271181,"text":"70271181 - 2025 - The Hardscrabble Creek complex: A newly discovered, mostly buried, Mesoproterozoic mafic-ultramafic pluton in the Wet Mountains, Colorado, USA","interactions":[],"lastModifiedDate":"2025-09-02T14:49:24.55281","indexId":"70271181","displayToPublicDate":"2025-05-14T09:44:44","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1723,"text":"GSA Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"The Hardscrabble Creek complex: A newly discovered, mostly buried, Mesoproterozoic mafic-ultramafic pluton in the Wet Mountains, Colorado, USA","docAbstract":"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":70267215,"text":"70267215 - 2025 - Paleo-scours within the layered sulfate-bearing unit at Gale crater, Mars: Evidence for intense wind erosion","interactions":[],"lastModifiedDate":"2025-05-20T13:19:43.188052","indexId":"70267215","displayToPublicDate":"2025-05-14T08:15:31","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9967,"text":"JGR Planets","active":true,"publicationSubtype":{"id":10}},"title":"Paleo-scours within the layered sulfate-bearing unit at Gale crater, Mars: Evidence for intense wind erosion","docAbstract":"The surface of modern Mars is largely shaped by wind, but the influence of past wind activity is less well constrained. Sedimentary rocks exposed in the lower foothills of Aeolis Mons, the central mound within Gale crater, record a transition from predominantly lacustrine deposition in the Murray formation to aeolian deposition in the Mirador formation. Here, we report a series of enigmatic decameter-wide, concave-up scour-and-fill structures within the Mirador formation and discuss their formation mechanisms. Using panoramic images of stratigraphy exposed in cliff faces acquired by the Curiosity rover, we map the extent, distribution and orientation of the scour-and-fill structures and document the sedimentary facies within and surrounding these structures. The scours are grouped into two classes: (A) scours with a simple, symmetric morphology and light-toned, draping infill; and (B) scours with lateral pinching and dark-toned infill. We find that the scour-enclosing environment is composed of planar, even-in-thickness laminations with a pin-stripe pattern which we interpret as wind-ripple strata formed within an aeolian sandsheet environment. Class B contains cm-scale cross-bedding and a wing-shaped feature making this scour-and-fill structure consistent with fluvial processes. We interpret scour fill of class A as an aeolian infill due to similarities with the surrounding sandsheet strata. The broad morphologies and distribution of class A are also consistent with the geometry of blowout structures formed by localized, enhanced wind deflation. These paleo-blowout structures occur clustered within the same stratigraphic interval, which may imply that they record an interval of intensified wind activity at Gale crater.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024JE008680","usgsCitation":"Roberts, A., Gupta, S., Banhan, S., Cowart, A., Edgar, L.A., Rapin, W., Dietrich, W., Kite, E., Davis, J., Caravaca, G., Mondro, C., Gasda, P., Johnson, J., Le Mouelic, S., Fey, D., Bryk, A., Paar, G., Harris, R., Fraeman, A., and Vasavada, A., 2025, Paleo-scours within the layered sulfate-bearing unit at Gale crater, Mars: Evidence for intense wind erosion: JGR Planets, v. 130, no. 5, e2024JE008680, 32 p., https://doi.org/10.1029/2024JE008680.","productDescription":"e2024JE008680, 32 p.","ipdsId":"IP-169999","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":489183,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024je008680","text":"Publisher Index Page"},{"id":486069,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Gale Crater, Mars","volume":"130","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-05-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Roberts, A.L.","contributorId":355429,"corporation":false,"usgs":false,"family":"Roberts","given":"A.L.","affiliations":[{"id":84748,"text":"Department of Earth Science & Engineering, Imperial College London","active":true,"usgs":false}],"preferred":false,"id":937306,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gupta, S.","contributorId":177658,"corporation":false,"usgs":false,"family":"Gupta","given":"S.","email":"","affiliations":[],"preferred":false,"id":937307,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Banhan, S.G.","contributorId":355430,"corporation":false,"usgs":false,"family":"Banhan","given":"S.G.","affiliations":[{"id":84748,"text":"Department of Earth Science & Engineering, Imperial College London","active":true,"usgs":false}],"preferred":false,"id":937308,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cowart, A.","contributorId":355431,"corporation":false,"usgs":false,"family":"Cowart","given":"A.","affiliations":[{"id":13179,"text":"Planetary Science Institute","active":true,"usgs":false}],"preferred":false,"id":937309,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Edgar, Lauren A. 0000-0001-7512-7813 ledgar@usgs.gov","orcid":"https://orcid.org/0000-0001-7512-7813","contributorId":167501,"corporation":false,"usgs":true,"family":"Edgar","given":"Lauren","email":"ledgar@usgs.gov","middleInitial":"A.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":937310,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rapin, W.","contributorId":173218,"corporation":false,"usgs":false,"family":"Rapin","given":"W.","affiliations":[{"id":27192,"text":"IRAP","active":true,"usgs":false}],"preferred":false,"id":937311,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dietrich, W.E.","contributorId":351711,"corporation":false,"usgs":false,"family":"Dietrich","given":"W.E.","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":937312,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kite, E.S.","contributorId":351720,"corporation":false,"usgs":false,"family":"Kite","given":"E.S.","affiliations":[{"id":36705,"text":"University of Chicago","active":true,"usgs":false}],"preferred":false,"id":937313,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Davis, J.M.","contributorId":352402,"corporation":false,"usgs":false,"family":"Davis","given":"J.M.","affiliations":[{"id":84208,"text":"Imperial College, London, UK","active":true,"usgs":false}],"preferred":false,"id":937314,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Caravaca, G.","contributorId":290214,"corporation":false,"usgs":false,"family":"Caravaca","given":"G.","affiliations":[],"preferred":false,"id":937315,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Mondro, C.A.","contributorId":351708,"corporation":false,"usgs":false,"family":"Mondro","given":"C.A.","affiliations":[{"id":13711,"text":"Caltech","active":true,"usgs":false}],"preferred":false,"id":937316,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Gasda, P.J.","contributorId":355434,"corporation":false,"usgs":false,"family":"Gasda","given":"P.J.","affiliations":[{"id":13447,"text":"Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":937319,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Johnson, J.R.","contributorId":296826,"corporation":false,"usgs":false,"family":"Johnson","given":"J.R.","email":"","affiliations":[{"id":7166,"text":"Johns Hopkins University Applied Physics Laboratory","active":true,"usgs":false}],"preferred":false,"id":937320,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Le Mouélic, Stéphane","contributorId":92786,"corporation":false,"usgs":false,"family":"Le Mouélic","given":"Stéphane","affiliations":[],"preferred":false,"id":937321,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Fey, D.M.","contributorId":355435,"corporation":false,"usgs":false,"family":"Fey","given":"D.M.","affiliations":[{"id":36716,"text":"Malin Space Science Systems","active":true,"usgs":false}],"preferred":false,"id":937322,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Bryk, A.B.","contributorId":351718,"corporation":false,"usgs":false,"family":"Bryk","given":"A.B.","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":937323,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Paar, G.","contributorId":252879,"corporation":false,"usgs":false,"family":"Paar","given":"G.","email":"","affiliations":[{"id":50456,"text":"Joanneum Research, Graz, Austria","active":true,"usgs":false}],"preferred":false,"id":937395,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Harris, R.A.","contributorId":355436,"corporation":false,"usgs":false,"family":"Harris","given":"R.A.","affiliations":[{"id":84750,"text":"Department of Earth Sciences, Natural History Museum, London","active":true,"usgs":false}],"preferred":false,"id":937324,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Fraeman, A.","contributorId":177657,"corporation":false,"usgs":false,"family":"Fraeman","given":"A.","affiliations":[],"preferred":false,"id":937325,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Vasavada, A.R.","contributorId":351725,"corporation":false,"usgs":false,"family":"Vasavada","given":"A.R.","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":937326,"contributorType":{"id":1,"text":"Authors"},"rank":20}]}} ,{"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":118,"text":"Alaska Science Center Geography","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","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":70268342,"text":"70268342 - 2025 - Shifting baselines of coral-reef species composition from the Late Pleistocene to the present in the Florida Keys","interactions":[],"lastModifiedDate":"2025-06-23T14:20:17.616459","indexId":"70268342","displayToPublicDate":"2025-05-13T09:14:05","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5781,"text":"The Depositional Record","active":true,"publicationSubtype":{"id":10}},"title":"Shifting baselines of coral-reef species composition from the Late Pleistocene to the present in the Florida Keys","docAbstract":"The ongoing global-scale reassembly of modern coral reefs is unprecedented compared with the observed stability of most late Quaternary reef assemblages. One notable exception is the marine isotope stage (MIS) 5e (ca 130–116 thousand years ago [ka]) reefs in the Florida Keys, where the ubiquitous shallow-water coral, Acropora palmata, was near absent. Little is known, however, about reefs that grew during MIS5d–a (ca 116–74 ka), between MIS5e and the Holocene. It is therefore unclear whether Florida's unique MIS5e coral assemblages represent a geologically brief anomaly or a more persistent departure from the western Atlantic coral-reef archetype. We addressed that question by reconstructing the composition of MIS5d–a reefs within 29 coral-reef cores collected throughout the Florida Keys. We then compared the relative composition of corals during MIS5d–a to existing datasets from MIS5e, Holocene and modern (1996 and 2022) reefs to evaluate how far today's reef assemblages have diverged from geological baselines. We show that although the proportion of reef frameworks built by corals was remarkably consistent (ca 38%), species composition changed significantly through time. Acropora palmata was rare throughout MIS5, which we hypothesise was due to greater cold-temperature stress in Florida's subtropical reefs compared with the more climatically stable tropics. In contrast, the massive reef-building coral, Orbicella spp., was regionally dominant throughout the late Quaternary, but has become increasingly rare on modern reefs. By 2022, reefs in the Florida Keys were characterised by a truly novel coral assemblage dominated by Porites astreoides and Siderastrea siderea. In many ways, Florida's reefs defy the concept of a natural baseline; instead, their most persistent characteristic since the Late Pleistocene is their uniqueness. Yet, as reefs are increasingly subjected to unprecedented levels of environmental change, the exceptions to what was normal in the past could, paradoxically, provide the best geological analogues for the future.
","language":"English","publisher":"WIley","doi":"10.1002/dep2.70009","usgsCitation":"Toth, L., Stathakopoulos, A., Hsia, S., and Weinstein, D.A., 2025, Shifting baselines of coral-reef species composition from the Late Pleistocene to the present in the Florida Keys: The Depositional Record, v. 11, no. 3, p. 893-916, https://doi.org/10.1002/dep2.70009.","productDescription":"24 p.","startPage":"893","endPage":"916","ipdsId":"IP-173487","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":491456,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/dep2.70009","text":"Publisher Index Page"},{"id":491097,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Florida Keys","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.17917140030995,\n 25.383351188271973\n ],\n [\n -81.59701661611159,\n 24.90866922937429\n ],\n [\n -83.02465696293855,\n 24.77579446049141\n ],\n [\n -83.06138870428032,\n 24.366054515738625\n ],\n [\n -81.8345485434568,\n 24.395013657422012\n ],\n [\n -80.65178647224371,\n 24.557720553123204\n ],\n [\n -80.17917140030995,\n 25.383351188271973\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"11","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-05-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Toth, Lauren T. 0000-0002-2568-802X ltoth@usgs.gov","orcid":"https://orcid.org/0000-0002-2568-802X","contributorId":181748,"corporation":false,"usgs":true,"family":"Toth","given":"Lauren","email":"ltoth@usgs.gov","middleInitial":"T.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":940869,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stathakopoulos, Anastasios 0000-0002-4404-035X astathakopoulos@usgs.gov","orcid":"https://orcid.org/0000-0002-4404-035X","contributorId":147744,"corporation":false,"usgs":true,"family":"Stathakopoulos","given":"Anastasios","email":"astathakopoulos@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":940870,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hsia, Scarlette Shan-Hwei 0000-0002-2230-9004","orcid":"https://orcid.org/0000-0002-2230-9004","contributorId":346523,"corporation":false,"usgs":true,"family":"Hsia","given":"Scarlette Shan-Hwei","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":940871,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Weinstein, David A.","contributorId":206027,"corporation":false,"usgs":false,"family":"Weinstein","given":"David","email":"","middleInitial":"A.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":940872,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"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 - Scenario E2","size":"22.1 MB","description":"Scenario E2","linkHelpText":"- Scenario E2"},{"id":485744,"rank":14,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5133/sir20245133_app4_scenarioD2.mp4","text":"Appendix 4 - Scenario D2","size":"26.5 MB","description":"Scenario D2","linkHelpText":"- Scenario D2"},{"id":485732,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245133/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5133 HTML"},{"id":485738,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5133/sir20245133_app4_scenarioA2.mp4","text":"Appendix 4 - Scenario A2","size":"41.7 MB","description":"Scenario A2","linkHelpText":"- Scenario A2"},{"id":485848,"rank":16,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118573.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Washington","otherGeospatial":"Middle Fork Nooksack River, Mount Baker","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.667,\n 49\n ],\n [\n -122.667,\n 49\n ],\n [\n -122.667,\n 48.6667\n ],\n [\n -121.667,\n 48.6667\n ],\n [\n -121.667,\n 49\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"David A. Johnston Cascades Volcano Observatory
U.S. Geological Survey
1300 SE Cardinal Court
Building 10, Suite 100
Vancouver, WA 98683
Email: askCVO@usgs.gov
","tableOfContents":"Gravity data require submeter elevation accuracy for data processing, and differential global navigation satellite system (dGNSS) equipment is commonly used to acquire three-dimensional positional data to achieve such accuracy. However, lidar (light detection and ranging) data are commonly used to develop digital elevation models (DEMs) of Earth’s surface. Therefore, using elevations from lidar-derived DEMs for gravity-data acquisition and reduction may improve field efficiency and reduce cost. This study examines the feasibility of using DEMs for gravity-data reduction by comparing dGNSS elevation data from 435 gravity stations in Michigan, Wyoming, and Colorado with their respective DEM elevations. The results show that the average difference between DEM and dGNSS elevations is 13 centimeters (cm) and that 93 percent of those differences are less than 50 cm, even in areas with steep terrain. Because an elevation discrepancy of 50 cm corresponds to an error of roughly 0.1 milligals (mGal) in the simple Bouguer gravity anomaly, the results suggest that lidar-derived DEMs are a viable source for acquiring the elevation data needed to process gravity data, thus improving both the cost and efficiency of data collection for regional surveys where an accuracy of less than 1.0 mGal is desired.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251019","programNote":"Mineral Resources Program","usgsCitation":"Murchek, J.T., Drenth, B.J., Reitman, J.J., Anderson, E.D., Magnin, B.P., and DeGraff, J.M., 2025, The feasibility of using lidar-derived digital elevation models for gravity data reduction (ver. 1.1, July 2025): U.S. Geological Survey Open-File Report 2025–1019, 33 p., https://doi.org/10.3133/ofr20251019.","productDescription":"vii, 33 p.","numberOfPages":"33","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-163043","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":491565,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1019/coverthb2.jpg"},{"id":491633,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118572.htm"},{"id":491634,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1019/ofr20251019.pdf","text":"Report","size":"12.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1019 PDF"},{"id":491636,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1019/ofr20251019.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2025-1019 XML"},{"id":491635,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251019/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1019 HTML"},{"id":491638,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2025/1019/versionHist.txt","size":"654 B","linkFileType":{"id":2,"text":"txt"}},{"id":491637,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1019/images/"}],"edition":"Version 1.0: May 12, 2025; Version 1.1: July 1, 2025","contact":"Director, Energy and Minerals Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192-0002
The San Luis Valley in Colorado, USA, an agriculturally dominated stopover area, is used by the Rocky Mountain population of Antigone canadensis tabida (Greater Sandhill Crane) and some midcontinental individuals of A. c. canadensis (Lesser Sandhill Crane) during migration. While the numbers of both subspecies are stable, the effects of continued water scarcity and declines in grain output on the energetics of cranes in the San Luis Valley are unclear. We conducted roadside counts of A. c. tabida and A. c. canadensis on agricultural fields to determine the effects of crop type, roost distance, and tillage intensity on their selection and abundance on crop fields. Antigone canadensis varied in their use of the San Luis Valley for foraging. In autumn, both subspecies selected barley and other grains over other crop types. In spring, cranes preferred to forage in barley fields, and selection declined as distance to roosts increased. Both subspecies also selected barley fields that were lightly or not tilled. We modeled covariates on abundance for A. c. tabida only and found that more cranes were found close to roosts early in the season in autumn. As the season progressed, the number of A. c. tabida increased as roost distance increased. In spring, abundance was influenced by an interaction between time and crop, with the highest numbers found on barley and pasture around mid-March. Our results suggest that A. canadensis may switch to other crop types as resources are depleted near roosts but appear to prefer to fly farther for grains. Grains that are left idle or moderately tilled and are located near roosts will help ensure A. canadensis are able to maintain adequate nutrient reserves at agriculturally dominated stopover areas during migration.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/ornithapp/duaf027","collaboration":"U. S. Fish and Wildlife Service","usgsCitation":"Vanausdall, R., Kendall, W.L., and Collins, D., 2025, Antigone canadensis (Sandhill Crane) foraging patterns influenced by crop type, roost distance, and tillage intensity during spring and autumn migration at a primary stopover area: Ornithological Applications, v. 127, duaf027, 17 p., https://doi.org/10.1093/ornithapp/duaf027.","productDescription":"duaf027, 17 p.","ipdsId":"IP-170214","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":490643,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/ornithapp/duaf027","text":"Publisher Index Page"},{"id":489266,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"San Luis Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.19980544186409,\n 37.483870494045064\n ],\n [\n -106.19980544186409,\n 36.996256317805006\n ],\n [\n -105.66654470738654,\n 36.996256317805006\n ],\n [\n -105.66654470738654,\n 37.483870494045064\n ],\n [\n -106.19980544186409,\n 37.483870494045064\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"127","noUsgsAuthors":false,"publicationDate":"2025-05-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Vanausdall, Rachel A.","contributorId":356156,"corporation":false,"usgs":false,"family":"Vanausdall","given":"Rachel A.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":938810,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kendall, William L. 0000-0003-0084-9891","orcid":"https://orcid.org/0000-0003-0084-9891","contributorId":204844,"corporation":false,"usgs":true,"family":"Kendall","given":"William","email":"","middleInitial":"L.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":938811,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Collins, Daniel P.","contributorId":356157,"corporation":false,"usgs":false,"family":"Collins","given":"Daniel P.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":938812,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70266804,"text":"70266804 - 2025 - Nature-based solutions extend the lifespan of a regional levee system under climate change","interactions":[],"lastModifiedDate":"2025-05-13T15:40:10.266447","indexId":"70266804","displayToPublicDate":"2025-05-09T10:36:34","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7146,"text":"Nature Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Nature-based solutions extend the lifespan of a regional levee system under climate change","docAbstract":"Nature-based solutions are receiving increasing attention as a cost-effective climate adaptation strategy. Horizontal levees are nature-based adaptation solutions that include a sloping wetland habitat buffer fronting a levee. They can offer a hybrid solution to reinforce traditional levees in estuarine areas—plants on the horizontal levee can provide wave attenuation benefits as well as habitat benefits, but how the design of horizontal levees influences risk of levee failure remains unquantified. We use a hydrodynamic model, XBeach non-hydrostatic (XB-NH), to assess the stability and sustainability of existing levees and determine how hybrid nature-based climate adaptation measures can reduce the risk of overtopping on levees in San Francisco Bay. We compare overtopping rates in the existing levee system and in a variety of nature-based adaptation scenarios using a range of widths and slopes of horizontal levees to assess how horizontal levees perform in reducing risk of flooding, both with present day conditions and sea level rise. We show that climate change will challenge existing levee flood defenses in San Francisco Bay and increase the risk of overtopping, and that the nature-based solution of horizontal levees can meaningfully reduce risk of overtopping while simultaneously supporting marsh habitat. Flood risk reduction and habitat provision are both maximized with more gradually sloping and wider horizontal levee designs. Results show that the risk of overtopping can be reduced by up to 30% with horizontal levees. This analysis provides insight into horizontal levee design considerations and a methodological approach to adapt levees to prepare for climate change in urban wave-exposed estuaries. We show that horizontal levees can support preparation for the projected impacts of sea level rise (SLR) while simultaneously providing new intertidal wetland habitat.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-025-99762-7","usgsCitation":"Taylor-Burns, R.M., Reguero, B.G., Barnard, P.L., and Beck, M.W., 2025, Nature-based solutions extend the lifespan of a regional levee system under climate change: Nature Scientific Reports, v. 15, no. 1, 16218, 11 p., https://doi.org/10.1038/s41598-025-99762-7.","productDescription":"16218, 11 p.","ipdsId":"IP-162956","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":488196,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-025-99762-7","text":"Publisher Index Page"},{"id":485822,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.52930652577294,\n 37.836817243666715\n ],\n [\n -122.52930652577294,\n 37.38775011750823\n ],\n [\n -121.90448236358971,\n 37.38775011750823\n ],\n [\n -121.90448236358971,\n 37.836817243666715\n ],\n [\n -122.52930652577294,\n 37.836817243666715\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-05-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Taylor-Burns, Rae M. 0000-0003-4963-6643","orcid":"https://orcid.org/0000-0003-4963-6643","contributorId":312507,"corporation":false,"usgs":false,"family":"Taylor-Burns","given":"Rae","email":"","middleInitial":"M.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":936805,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reguero, Borja G. 0000-0001-5526-7157","orcid":"https://orcid.org/0000-0001-5526-7157","contributorId":193831,"corporation":false,"usgs":false,"family":"Reguero","given":"Borja","email":"","middleInitial":"G.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":true,"id":936807,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barnard, Patrick L. 0000-0003-1414-6476 pbarnard@usgs.gov","orcid":"https://orcid.org/0000-0003-1414-6476","contributorId":140982,"corporation":false,"usgs":true,"family":"Barnard","given":"Patrick","email":"pbarnard@usgs.gov","middleInitial":"L.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":936806,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Beck, Michael W.","contributorId":259298,"corporation":false,"usgs":false,"family":"Beck","given":"Michael","email":"","middleInitial":"W.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":true,"id":936808,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70271374,"text":"70271374 - 2025 - Global methane budget 2000-2020","interactions":[],"lastModifiedDate":"2025-09-10T14:32:19.040545","indexId":"70271374","displayToPublicDate":"2025-05-09T09:24:33","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1426,"text":"Earth System Science Data","active":true,"publicationSubtype":{"id":10}},"title":"Global methane budget 2000-2020","docAbstract":"Understanding and quantifying the global methane (CH4) budget is important for assessing realistic pathways to mitigate climate change. CH4 is the second most important human-influenced greenhouse gas in terms of climate forcing after carbon dioxide (CO2), and both emissions and atmospheric concentrations of CH4 have continued to increase since 2007 after a temporary pause. The relative importance of CH4 emissions compared to those of CO2 for temperature change is related to its shorter atmospheric lifetime, stronger radiative effect, and acceleration in atmospheric growth rate over the past decade, the causes of which are still debated. Two major challenges in quantifying the factors responsible for the observed atmospheric growth rate arise from diverse, geographically overlapping CH4 sources and from the uncertain magnitude and temporal change in the destruction of CH4 by short-lived and highly variable hydroxyl radicals (OH). To address these challenges, we have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to improve, synthesise, and update the global CH4 budget regularly and to stimulate new research on the methane cycle. Following Saunois et al. (2016, 2020), we present here the third version of the living review paper dedicated to the decadal CH4 budget, integrating results of top-down CH4 emission estimates (based on in situ and Greenhouse Gases Observing SATellite (GOSAT) atmospheric observations and an ensemble of atmospheric inverse-model results) and bottom-up estimates (based on process-based models for estimating land surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations). We present a budget for the most recent 2010–2019 calendar decade (the latest period for which full data sets are available), for the previous decade of 2000–2009 and for the year 2020.
The revision of the bottom-up budget in this 2025 edition benefits from important progress in estimating inland freshwater emissions, with better counting of emissions from lakes and ponds, reservoirs, and streams and rivers. This budget also reduces double counting across freshwater and wetland emissions and, for the first time, includes an estimate of the potential double counting that may exist (average of 23 Tg CH4 yr−1). Bottom-up approaches show that the combined wetland and inland freshwater emissions average 248 [159–369] Tg CH4 yr−1 for the 2010–2019 decade. Natural fluxes are perturbed by human activities through climate, eutrophication, and land use. In this budget, we also estimate, for the first time, this anthropogenic component contributing to wetland and inland freshwater emissions. Newly available gridded products also allowed us to derive an almost complete latitudinal and regional budget based on bottom-up approaches.
For the 2010–2019 decade, global CH4 emissions are estimated by atmospheric inversions (top-down) to be 575 Tg CH4 yr−1 (range 553–586, corresponding to the minimum and maximum estimates of the model ensemble). Of this amount, 369 Tg CH4 yr−1 or ∼ 65 % is attributed to direct anthropogenic sources in the fossil, agriculture, and waste and anthropogenic biomass burning (range 350–391 Tg CH4 yr−1 or 63 %–68 %). For the 2000–2009 period, the atmospheric inversions give a slightly lower total emission than for 2010–2019, by 32 Tg CH4 yr−1 (range 9–40). The 2020 emission rate is the highest of the period and reaches 608 Tg CH4 yr−1 (range 581–627), which is 12 % higher than the average emissions in the 2000s. Since 2012, global direct anthropogenic CH4 emission trends have been tracking scenarios that assume no or minimal climate mitigation policies proposed by the Intergovernmental Panel on Climate Change (shared socio-economic pathways SSP5 and SSP3). Bottom-up methods suggest 16 % (94 Tg CH4 yr−1) larger global emissions (669 Tg CH4 yr−1, range 512–849) than top-down inversion methods for the 2010–2019 period. The discrepancy between the bottom-up and the top-down budgets has been greatly reduced compared to the previous differences (167 and 156 Tg CH4 yr−1 in Saunois et al. (2016, 2020) respectively), and for the first time uncertainties in bottom-up and top-down budgets overlap. Although differences have been reduced between inversions and bottom-up, the most important source of uncertainty in the global CH4 budget is still attributable to natural emissions, especially those from wetlands and inland freshwaters.
The tropospheric loss of methane, as the main contributor to methane lifetime, has been estimated at 563 [510–663] Tg CH4 yr−1 based on chemistry–climate models. These values are slightly larger than for 2000–2009 due to the impact of the rise in atmospheric methane and remaining large uncertainty (∼ 25 %). The total sink of CH4 is estimated at 633 [507–796] Tg CH4 yr−1 by the bottom-up approaches and at 554 [550–567] Tg CH4 yr−1 by top-down approaches. However, most of the top-down models use the same OH distribution, which introduces less uncertainty to the global budget than is likely justified.
For 2010–2019, agriculture and waste contributed an estimated 228 [213–242] Tg CH4 yr−1 in the top-down budget and 211 [195–231] Tg CH4 yr−1 in the bottom-up budget. Fossil fuel emissions contributed 115 [100–124] Tg CH4 yr−1 in the top-down budget and 120 [117–125] Tg CH4 yr−1 in the bottom-up budget. Biomass and biofuel burning contributed 27 [26–27] Tg CH4 yr−1 in the top-down budget and 28 [21–39] Tg CH4 yr−1 in the bottom-up budget.
We identify five major priorities for improving the CH4 budget: (i) producing a global, high-resolution map of water-saturated soils and inundated areas emitting CH4 based on a robust classification of different types of emitting ecosystems; (ii) further development of process-based models for inland-water emissions; (iii) intensification of CH4 observations at local (e.g. FLUXNET-CH4 measurements, urban-scale monitoring, satellite imagery with pointing capabilities) to regional scales (surface networks and global remote sensing measurements from satellites) to constrain both bottom-up models and atmospheric inversions; (iv) improvements of transport models and the representation of photochemical sinks in top-down inversions; and (v) integration of 3D variational inversion systems using isotopic and/or co-emitted species such as ethane as well as information in the bottom-up inventories on anthropogenic super-emitters detected by remote sensing (mainly oil and gas sector but also coal, agriculture, and landfills) to improve source partitioning.
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/essd-17-1873-2025","usgsCitation":"Saunois, M., Martinez, A., Poulter, B., Zhang, Z., Raymond, P.A., Regnier, P., Canadell, J.G., Jackson, R.B., Patra, P.K., Bousquet, P., Ciais, P., Dlugokencky, E.J., Lan, X., Allen, G.H., Bastviken, D., Beerling, D.J., Belikov, D., Blake, D.R., Castaldi, S., Crippa, M., Deemer, B., Dennison, F., Etiope, G., Gedney, N., Höglund-Isaksson, L., Holgerson, M.A., Hopcroft, P.O., Hugelius, G., Ito, A., Jain, A.K., Janardanan, R., Johnson, M.S., Kleinen, T., Krummel, P.B., Lauerwald, R., Li, T., Liu, X., McDonald, K.C., Melton, J.R., Mühle, J., Müller, J., Murguia-Flores, F., Niwa, Y., Noce, S., Pan, S., Parker, R.J., Peng, C., Ramonet, M., Riley, W.J., Rocher-Ros, G., Rosentreter, J.A., Sasakawa, M., Segers, A., Smith, S.J., Stanley, E.H., Thanwerdas, J., Tian, H., Tsuruta, A., Tubiello, F.N., Weber, T.S., van der Werf, G.R., Worthy, D.E., Xi, Y., Yoshida, Y., Zhang, W., Zheng, B., Zhu, Q., Zhu, Q., and Zhuang, Q., 2025, Global methane budget 2000-2020: Earth System Science Data, v. 17, no. 5, p. 1873-1958, https://doi.org/10.5194/essd-17-1873-2025.","productDescription":"86 p.","startPage":"1873","endPage":"1958","ipdsId":"IP-163722","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":497355,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/essd-12-1561-2020","text":"Publisher Index Page"},{"id":495276,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"17","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-05-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Saunois, Marielle","contributorId":217394,"corporation":false,"usgs":false,"family":"Saunois","given":"Marielle","email":"","affiliations":[{"id":39615,"text":"Universite Paris-Saclay","active":true,"usgs":false}],"preferred":false,"id":948244,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Martinez, Adrien","contributorId":361117,"corporation":false,"usgs":false,"family":"Martinez","given":"Adrien","affiliations":[{"id":86187,"text":"Laboratoire des Sciences du Climat et de l’Environnement, LSCE-IPSL (CEA-CNRS-UVSQ), Université Paris-Saclay 91191 Gif-sur-Yvette, France","active":true,"usgs":false}],"preferred":false,"id":948245,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Poulter, Benjamin","contributorId":346344,"corporation":false,"usgs":false,"family":"Poulter","given":"Benjamin","affiliations":[{"id":82832,"text":"National Aeronautics and Space Administration, Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":948246,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zhang, Zhen 0000-0003-0899-1139","orcid":"https://orcid.org/0000-0003-0899-1139","contributorId":149173,"corporation":false,"usgs":false,"family":"Zhang","given":"Zhen","email":"","affiliations":[],"preferred":false,"id":948247,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Raymond, Peter A.","contributorId":361118,"corporation":false,"usgs":false,"family":"Raymond","given":"Peter","middleInitial":"A.","affiliations":[{"id":86188,"text":"Yale School of the Environment, Yale University, New Haven, CT 06511, USA","active":true,"usgs":false}],"preferred":false,"id":948248,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Regnier, Pierre","contributorId":304585,"corporation":false,"usgs":false,"family":"Regnier","given":"Pierre","email":"","affiliations":[{"id":66123,"text":"Department Geoscience, Environment & Society - BGEOSYS, Université Libre de Bruxelles, 1050 Bruxelles, Belgium","active":true,"usgs":false}],"preferred":false,"id":948249,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Canadell, Josep G.","contributorId":361119,"corporation":false,"usgs":false,"family":"Canadell","given":"Josep","middleInitial":"G.","affiliations":[{"id":86191,"text":"Global Carbon Project, CSIRO Environment, Canberra, ACT 2601, Australia","active":true,"usgs":false}],"preferred":false,"id":948250,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jackson, Robert B.","contributorId":361120,"corporation":false,"usgs":false,"family":"Jackson","given":"Robert","middleInitial":"B.","affiliations":[{"id":86192,"text":"Department of Earth System Science, Woods Institute for the Environment, and Precourt Institute for Energy, Stanford University, Stanford, CA 94305-2210, USA","active":true,"usgs":false}],"preferred":false,"id":948251,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Patra, Prabir K.","contributorId":361121,"corporation":false,"usgs":false,"family":"Patra","given":"Prabir","middleInitial":"K.","affiliations":[{"id":86193,"text":"Research Institute for Global Change, JAMSTEC, Kanazawa, Yokohama, Japan; Research Institute for Humanity and Nature, Kyoto, Japan","active":true,"usgs":false}],"preferred":false,"id":948252,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Bousquet, Philippe","contributorId":197935,"corporation":false,"usgs":false,"family":"Bousquet","given":"Philippe","email":"","affiliations":[{"id":35082,"text":"LSCE, CEA CNRS UVSQ IPSL, Université Paris Saclay, 91191 Gif sur Yvette, France","active":true,"usgs":false}],"preferred":false,"id":948253,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Ciais, Philippe","contributorId":361122,"corporation":false,"usgs":false,"family":"Ciais","given":"Philippe","affiliations":[{"id":86187,"text":"Laboratoire des Sciences du Climat et de l’Environnement, LSCE-IPSL (CEA-CNRS-UVSQ), Université Paris-Saclay 91191 Gif-sur-Yvette, France","active":true,"usgs":false}],"preferred":false,"id":948254,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Dlugokencky, Edward J.","contributorId":361123,"corporation":false,"usgs":false,"family":"Dlugokencky","given":"Edward","middleInitial":"J.","affiliations":[{"id":86194,"text":"NOAA GML, 325 Broadway, Boulder, CO 80305, USA","active":true,"usgs":false}],"preferred":false,"id":948255,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Lan, Xin","contributorId":361124,"corporation":false,"usgs":false,"family":"Lan","given":"Xin","affiliations":[{"id":86195,"text":"NOAA GML, 325 Broadway, Boulder, CO; Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, CO","active":true,"usgs":false}],"preferred":false,"id":948256,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Allen, George H.","contributorId":361125,"corporation":false,"usgs":false,"family":"Allen","given":"George","middleInitial":"H.","affiliations":[{"id":86196,"text":"Department of Geosciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA","active":true,"usgs":false}],"preferred":false,"id":948257,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Bastviken, David","contributorId":304579,"corporation":false,"usgs":false,"family":"Bastviken","given":"David","email":"","affiliations":[{"id":54595,"text":"Department of Thematic Studies - Environmental Change, Linköping University, Linköping, Sweden","active":true,"usgs":false}],"preferred":false,"id":948258,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Beerling, David J.","contributorId":361126,"corporation":false,"usgs":false,"family":"Beerling","given":"David","middleInitial":"J.","affiliations":[{"id":86197,"text":"School of Biosciences, University of Sheffield, 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Ambientali, Biologiche e Farmaceutiche, Università degli Studi della Campania Luigi 2 Vanvitelli, via Vivaldi 43, 81100 Caserta, Italy","active":true,"usgs":false}],"preferred":false,"id":948262,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Crippa, Monica","contributorId":361129,"corporation":false,"usgs":false,"family":"Crippa","given":"Monica","affiliations":[{"id":86200,"text":"European Commission, Joint Research Centre, Ispra, Italy; Unisystems S.A., Milan, Italy","active":true,"usgs":false}],"preferred":false,"id":948263,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Deemer, Bridget R. 0000-0002-5845-1002 bdeemer@usgs.gov","orcid":"https://orcid.org/0000-0002-5845-1002","contributorId":198160,"corporation":false,"usgs":true,"family":"Deemer","given":"Bridget","email":"bdeemer@usgs.gov","middleInitial":"R.","affiliations":[{"id":568,"text":"Southwest Biological Science 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Small waterbody conservation has been supported by different stakeholders aiming at improving water quality, enhancing floodwater storage, and supporting migratory bird breeding habitat. Conservation agencies are using hydrological and biological monitoring, modeling, and mapping to adaptively manage small waterbodies in the face of stressors such as invasive species and climate change. As remote sensing estimates of small waterbody surface water extent have become easier to access, understanding the capabilities and limitations of using remote sensing, especially in areas lacking surface water monitoring, is important for conservation decision making. Here, we used in situ monitoring and process-based hydrological modeling to explore remote sensing accuracy, especially related to waterbody size, emergent aquatic vegetation cover, and climatic conditions. Overall, we found that the accuracy of satellite and aerial imagery surface water mapping approaches vastly decreased for waterbodies smaller than 2 ha. We also found emergent vegetation could be masking surface water in waterbodies larger than 2 ha and that accuracy of some remote sensing estimates may decrease during wetter climatic periods. 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