Accurate mapping of headwater streams and their flow status has important implications for understanding and managing water resources and land uses. However, accurate information is rare, especially in rugged, forested terrain. We developed a streamflow permanence classification model for forested lands in western Oregon using the latest light detection and ranging-derived hydrography published in the National Hydrography Dataset. Models were trained using 2,518 flow/no flow field observations collected in late summer 2019–2021 across headwaters of 129 sub-watersheds. The final model, the Western Oregon WeT DRy model, used Random Forest and 13 environmental covariates for classifying every 5-m stream sub-reach across 426 sub-watersheds. The most important covariates were annual precipitation and drainage area. Model output included probabilities of late summer surface flow presence and were subsequently categorized into three streamflow permanence classes—Wet, Dry, and Ambiguous. Ambiguous denoted model probabilities and associated prediction intervals that extended over the 50% classification threshold between wet and dry. Model accuracy was 0.83 for sub-watersheds that contained training data and decreased to 0.67 for sub-watersheds that did not have observations of late summer surface flow. The model identified where predictions extrapolated beyond the domain characterized by the training data. The combination of spatially continuous estimates of late summer streamflow status along with uncertainty and extrapolation estimates provide critical information for strategic project planning and designing additional field data collection.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2025WR040478","usgsCitation":"Burnett, J., Jaeger, K.L., Johnson, S.L., Wondzell, S., Dunham, J., Barker, M., Heaston, E.D., Chelgren, N., Wing, M., Staab, B., and Brown, M., 2025, A streamflow permanence classification model for forested streams that explicitly accounts for uncertainty and extrapolation: Water Resources Research, v. 61, no. 7, e2025WR040478, 29 p., https://doi.org/10.1029/2025WR040478.","productDescription":"e2025WR040478, 29 p.","ipdsId":"IP-166720","costCenters":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":496356,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2025wr040478","text":"Publisher Index 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Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-5054","displayTitle":"Hydrogeologic Framework and Conceptual Model of the Red River Alluvial Aquifer East of Lake Texoma, Southeastern Oklahoma, 1980–2022","title":"Hydrogeologic framework and conceptual model of the Red River alluvial aquifer east of Lake Texoma, southeastern Oklahoma, 1980–2022","docAbstract":"The 1973 Oklahoma Groundwater Law (Oklahoma Statutes §82-1020.5) requires that the Oklahoma Water Resources Board conduct hydrologic investigations of the State’s groundwater basins to support a determination of the maximum annual yield for each groundwater basin. At present (2025), the Oklahoma Water Resources Board has not established a maximum annual yield for the Red River alluvial aquifer east of Lake Texoma. To support the evaluation and determination of a maximum annual yield, a hydrogeologic framework and conceptual groundwater-flow model were developed to assess groundwater availability in the Red River alluvial aquifer east of Lake Texoma.
The scope of this hydrologic investigation is the alluvium and terrace containing the Red River alluvial aquifer in Oklahoma between Lake Texoma, the Texas State line, and the Arkansas State line, an extent referred to in this report as “the eastern part of the Red River alluvial aquifer.” Parts of the alluvium and terrace extent in Arkansas and Texas are included in some analyses to address hydrologic influences from outside the aquifer’s boundaries in Oklahoma.
The eastern part of the Red River alluvial aquifer in southeastern Oklahoma consists of approximately 401,280 acres of Quaternary alluvium and terrace deposits associated with the Red River and its major tributaries. Mean annual recharge to the aquifer for the 1980–2022 study period was estimated to be 8.62 inches per year, or 17.98 percent of the mean annual precipitation over the same period (47.94 inches). This mean annual recharge rate is equivalent to an inflow of approximately 288,250 acre-feet per year for the eastern part of the Red River alluvial aquifer. Recharge estimated using the Soil-Water-Balance code accounts for 98.7 percent of the conceptual-model inflows to the eastern part of the Red River alluvial aquifer. Saturated-zone evapotranspiration accounts for 11.9 percent and net streambed seepage accounts for 87.4 percent of the outflows in the conceptual model.
Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
Contact Us- USGS Publications Warehouse
","tableOfContents":"The management of invasive Silver Carp Hypophthalmichthys molitrix in the Tennessee River basin focuses on removal, and there is interest in extending removal efforts to the tailwater environments of high-head locks and dams along the Tennessee River, such as Kentucky Dam. We used acoustic telemetry data from Silver Carp to understand important ecological associations underlying their residence in the Kentucky Dam tailwater, measured by daily fish counts and mean residence time.
We used time-series-informed regression models, variance partitioning, and cross-correlation function analysis to associate six predictors, including lock and dam operations (total, spill gate, and turbine discharge and number of lockages), hydrology (tailwater elevation), and water temperature, with two measures of Silver Carp residency (daily counts and mean residence time).
We found that spill-induced hydrology (total discharge + spill discharge + tailwater elevation) was negatively associated with daily counts but not with residence time, whereas temperature was positively associated with counts and negatively associated with residence times. Variance partitioning indicated that nearly all the variance in counts and residence times was jointly explained by temporal effects, lock and dam operations (discharge, tailwater elevation, and lockages), and temperature. The cross-correlations indicated that the counts were lagged by all predictors, sometimes up to 5 d, whereas residence times were lagged by both total and spill discharge and number of lockages.
We found that discharge and water temperature were principally associated with residency of Silver Carp in the Kentucky Dam tailwater. However, these associations were entirely temporally constrained, which can affect how strongly and how quickly Silver Carp respond to changing environmental conditions across different time scales. Managers can leverage these associations to plan removal periods where daily tailwater conditions/dam operations are favorable to invasive carp residence (e.g., >10°C and <2,500 m3/s) and adjust fishing effort to optimize removal rates in response to changing conditions.
Cover crops play a critical role in providing agroecological services such as improving soil health, reducing erosion and nitrogen loss, and suppressing weeds, which are closely tied to their performance such as accumulated biomass. This study evaluated the Active Canopy Sensor (ACS) -214, an active proximal sensing device equipped with its own light-emitting red and near-infrared spectral reflectance sensors, a time-of-flight laser, and an ultrasonic sensor, for estimating winter cover crop biomass across 13 U.S. states from 2020 to 2024. We assessed 11 species from three functional groups – grasses (n = 797), legumes (n = 264), and brassicas (n = 181) – using Random Forest (RF) models and four cross-validation strategies. The ACS-214 showed moderate to strong prediction accuracy for grasses (R2 = 0.51 – 0.64) and legumes (R2 = 0.44 – 0.76), though performance declined in leave-one-region-out analyses (R2 = 0.06 – 0.46), indicating limited spatial generalizability. Brassica models had low prediction accuracy for all models (R2 < 0.30), likely due to flowering and patchy growth. Biomass prediction breakpoints were observed at ∼3000 kg ha−1 for legumes and ∼4000 kg ha−1 for grasses. We also evaluated the effectiveness of using ACS-214 data to train Sentinel-2 satellite imagery for estimating grass cover crop biomass using withheld, out of bag data from 2023 to 2024. Sentinel-2 RF models trained with ACS-214 data showed good agreement with field-sampled (R2 = 0.58 – 0.61) and ACS-214-estimated biomass (R2 = 0.70). While Sentinel-2 offers scalability, the ACS-214 enables finer-resolution biomass mapping and better accounts for within-field variability, making it an effective tool for localized management and monitoring. These findings support the integration of proximal and satellite sensing approaches to enhance cover crop biomass estimation and agroecological assessment.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.atech.2025.101201","usgsCitation":"Jennewein, J., Davis, B., Seehaver-Eagan, S., Nicolette, J., Pittman, J., Hively, W.D., Goldsmith, A., Hidalgo, C., Reberg-Horton, C., and Mirsky, S., 2025, Multi-sensor proximal remote sensing for cover crop biomass estimation at high and moderate spatial resolutions: Smart Agricultural Technology, v. 12, 101201, 22 p., https://doi.org/10.1016/j.atech.2025.101201.","productDescription":"101201, 22 p.","ipdsId":"IP-179201","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":493304,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.atech.2025.101201","text":"Publisher Index Page"},{"id":493188,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Florida, Indiana, Iowa, Kansas, Maryland, Missouri, North Carolina, Ohio, New Hampshire, Vermont, Virginia, Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -96.92714553560637,\n 43.58854319693313\n ],\n [\n -95.89232587289348,\n 37.62183341552925\n ],\n [\n -89.95992461869106,\n 37.07043638610585\n ],\n [\n -88.56407871751861,\n 30.53924077937387\n ],\n [\n -87.95786849494071,\n 30.079081766107564\n ],\n [\n -79.28622937957765,\n 30.005749406912585\n ],\n [\n -71.27060896702632,\n 45.07860396784778\n ],\n [\n -86.55806437204849,\n 45.188229262227445\n ],\n [\n -91.6664764308591,\n 46.72848518852835\n ],\n [\n -96.92714553560637,\n 43.58854319693313\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jennewein, Jyoti","contributorId":243442,"corporation":false,"usgs":false,"family":"Jennewein","given":"Jyoti","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":944485,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Davis, Brian W. 0000-0003-0714-5133","orcid":"https://orcid.org/0000-0003-0714-5133","contributorId":358921,"corporation":false,"usgs":false,"family":"Davis","given":"Brian W.","affiliations":[{"id":6758,"text":"USDA-ARS","active":true,"usgs":false}],"preferred":false,"id":944486,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Seehaver-Eagan, S. 0009-0002-1048-9623","orcid":"https://orcid.org/0009-0002-1048-9623","contributorId":358924,"corporation":false,"usgs":false,"family":"Seehaver-Eagan","given":"S.","affiliations":[{"id":85715,"text":"North Carolina State University (NCSU)","active":true,"usgs":false}],"preferred":false,"id":944487,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nicolette, J. 0000-0002-8904-2391","orcid":"https://orcid.org/0000-0002-8904-2391","contributorId":358925,"corporation":false,"usgs":false,"family":"Nicolette","given":"J.","affiliations":[{"id":6758,"text":"USDA-ARS","active":true,"usgs":false}],"preferred":false,"id":944488,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pittman, J.","contributorId":358926,"corporation":false,"usgs":false,"family":"Pittman","given":"J.","affiliations":[{"id":85718,"text":"BAER","active":true,"usgs":false}],"preferred":false,"id":944489,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hively, W. Dean 0000-0002-5383-8064","orcid":"https://orcid.org/0000-0002-5383-8064","contributorId":201565,"corporation":false,"usgs":true,"family":"Hively","given":"W.","email":"","middleInitial":"Dean","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":944490,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Goldsmith, Avi","contributorId":358927,"corporation":false,"usgs":false,"family":"Goldsmith","given":"Avi","affiliations":[{"id":13595,"text":"NCSU","active":true,"usgs":false}],"preferred":false,"id":944491,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hidalgo, C. 0009-0007-2566-5198","orcid":"https://orcid.org/0009-0007-2566-5198","contributorId":358928,"corporation":false,"usgs":false,"family":"Hidalgo","given":"C.","affiliations":[{"id":13595,"text":"NCSU","active":true,"usgs":false}],"preferred":false,"id":944492,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Reberg-Horton, C. 0000-0001-5002-106X","orcid":"https://orcid.org/0000-0001-5002-106X","contributorId":358929,"corporation":false,"usgs":false,"family":"Reberg-Horton","given":"C.","affiliations":[{"id":85719,"text":"NSCU","active":true,"usgs":false}],"preferred":false,"id":944493,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Mirsky, S.B.","contributorId":357633,"corporation":false,"usgs":false,"family":"Mirsky","given":"S.B.","affiliations":[{"id":62785,"text":"USDA-ARS Sustainable Agricultural Systems Laboratory","active":true,"usgs":false}],"preferred":false,"id":944494,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70269291,"text":"sir20255056 - 2025 - Selected special conditions affecting peak streamflow and extreme floods in Alaska through water year 2022","interactions":[],"lastModifiedDate":"2026-02-03T14:28:42.124178","indexId":"sir20255056","displayToPublicDate":"2025-07-17T14:22:08","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-5056","displayTitle":"Selected Special Conditions Affecting Peak Streamflow and Extreme Floods in Alaska Through Water Year 2022","title":"Selected special conditions affecting peak streamflow and extreme floods in Alaska through water year 2022","docAbstract":"The U.S. Geological Survey, in cooperation with the Alaska Department of Transportation and Public Facilities, inventoried selected special conditions for annual peak flows and identified extreme floods at streamgages in Alaska through water year 2022 to facilitate hydrologic analysis. Special conditions identified from U.S. Geological Survey gaging records and basin characteristics included regulation and diversion, urbanization, indeterminate drainage areas, drainage areas less than the minimum used in regional analyses, glacial lake outburst floods, other outburst floods, and snowmelt floods. For peak flows that occurred during calendar years 1980–2019, an atmospheric river dataset was used to identify atmospheric river presence or absence on the dates peak flows occurred. Extreme floods (defined as peak flows exceeding the 1-percent annual exceedance probability flood magnitude or an empirical measure of relative magnitude using Creager’s coefficient C) were identified and associated with flood-generating mechanisms using the other inventoried special conditions and other information.
The gaging record contained glacial lake outburst floods at 15 streamgages and other types of outburst floods at 10 streamgages. Non-outburst peak flows in Alaska resulted from a mixture of rainfall and melt-based flood-generating mechanisms in all but the most rain-dominated seasonal flow regime. Melt-based flood-generating mechanisms included snowmelt, high-elevation snow and ice melt, or rain-on-snow events. Atmospheric rivers were common in Alaska and conterminous basins in Canada, occurring in that region on 67 percent of the days in the calendar year 1980–2019 period. Atmospheric rivers were more common on the days of peak flows and even more common on the days of non-outburst extreme floods. The percentage of days when an atmospheric river was present increased to 78 percent for the days of peak flows in that period and to 83 percent for the days of non-outburst extreme floods in that period. Of 149 extreme floods in the gaging record, 38 were generated by outburst floods. Of the non-outburst extreme floods, 72 percent were generated by rainfall and 26 percent were generated by melt-based processes or a combination of rainfall and melt-based processes. Flood-generating mechanisms could not be determined for the final 2 percent of the non-outburst extreme floods because the month and day of the peak flows were unknown and no other information was available. Secondary factors strongly associated with extreme floods included antecedent rain and streamflow conditions and warm storm conditions that produced rain instead of snow or generated snowmelt.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255056","collaboration":"Prepared in cooperation with Alaska Department of Transportation and Public Facilities","usgsCitation":"Curran, J.H., 2025, Selected special conditions affecting peak streamflow and extreme floods in Alaska through water year 2022: U.S. Geological Survey Scientific Investigations Report 2025–5056, 41 p., https://doi.org/10.3133/sir20255056.","productDescription":"Report: viii, 41 p.; 5 Data Releases","onlineOnly":"Y","ipdsId":"IP-169678","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":499049,"rank":11,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118692.htm","linkFileType":{"id":5,"text":"html"}},{"id":492464,"rank":10,"type":{"id":31,"text":"Publication 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Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska, 99508
This paper hypothesizes that the Meteor Crater impact in Arizona, USA, 56,000 years ago triggered landslides in Grand Canyon that dammed the Colorado River and formed Nankoweap paleolake. This is compatible with shock and earthquake physics for the impact that infer a M5.4 seismic event, attenuated to an effective magnitude of M3.5 at Grand Canyon. Results that support the hypothesis include radiocarbon dating of driftwood and luminescence dating of associated slack-water lake sediments that are preserved in caves up to 60 m above the modern Colorado River. Radiocarbon ages from two locations, including Stanton’s Cave, date the driftwood as 55.25 ± 2.44 ka (n = 4). Sediments associated with the driftwood gave a luminescence age of 56.00 ± 6.39 ka (n = 2). These six Grand Canyon dates, and three published ages for the Meteor Crater impact, show statistically indistinguishable results that support the hypothesis for a geologically instantaneous series of events with a mean age of 55.60 ± 1.30 ka. This work highlights the value of radiocarbon dating near the limits of the technique, integration of multiple dating methods, and seismic and landslide hazards associated with meteorite impacts in regions of extreme topography like Grand Canyon.
Copper-based chemical treatments are commonly used to eradicate invasive mussels in small ponds and lakes, but their use in large rivers has been limited. In 2023, in response to a detection of invasive quagga mussels, a 10-km reach of the Snake River (Idaho, USA), was treated with an unprecedented 19,300 kg of chelated copper molluscicide to a target concentration of 1,000 µg/L for 10 days. We assessed the transport and fate of the copper and its exposure and effects on the nontarget benthic community downstream. Water samples were collected at seven locations throughout the treatment period, and sediment, periphyton, and benthic macroinvertebrates were collected pre- and posttreatment. Nearly half of the original mass of copper was removed from the water column via sedimentation, sorption to algae, or biological uptake within the 10-km treatment reach and the first 15 km downstream. Even so, dissolved copper concentrations exceeded the acute toxicity threshold at least as far as 28 km downstream for more than 2 weeks. Sediment copper increased by up to 8.3-fold, exceeding the consensus-based sediment quality threshold effect concentration at several sites. Effects on benthic macroinvertebrates varied by taxa. From 0–28 km downstream, invertebrate abundances decreased 52%–94%, with gastropods among the most affected. Of the unique taxa present at these sites pretreatment, 52%–64% were not found posttreatment but were replaced by other taxa, indicating a reorganization of the base of the food web. Additionally, from 0–15 km downstream, the percentage of individuals from tolerant taxa increased two to15-fold. Findings from this study can help watershed managers plan future invasive mussel responses while protecting culturally, economically, and ecologically important nontarget species in large rivers.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/etojnl/vgaf119","usgsCitation":"Baldwin, A.K., Murray, E., Zinsser, L.M., King, T.V., Ducar, S.D., Southern, I., Thom, T., and Mebane, C.A., 2025, Fate and effects to the benthic community of a copper treatment to eradicate invasive mussels in a large western river, USA: Environmental Toxicology and Chemistry, v. 44, no. 8, p. 2166-2180, https://doi.org/10.1093/etojnl/vgaf119.","productDescription":"15 p.","startPage":"2166","endPage":"2180","ipdsId":"IP-173841","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":494190,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/etojnl/vgaf119","text":"Publisher Index Page"},{"id":493934,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Snake River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.19538272232973,\n 43.32567469741488\n ],\n [\n -116.19538272232973,\n 42.22650789070423\n ],\n [\n -113.5905787354007,\n 42.22650789070423\n ],\n [\n -113.5905787354007,\n 43.32567469741488\n ],\n [\n -116.19538272232973,\n 43.32567469741488\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"44","issue":"8","noUsgsAuthors":false,"publicationDate":"2025-07-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Baldwin, Austin K. 0000-0002-6027-3823 akbaldwi@usgs.gov","orcid":"https://orcid.org/0000-0002-6027-3823","contributorId":4515,"corporation":false,"usgs":true,"family":"Baldwin","given":"Austin","email":"akbaldwi@usgs.gov","middleInitial":"K.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":945505,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Murray, Erin 0000-0002-5007-3449","orcid":"https://orcid.org/0000-0002-5007-3449","contributorId":205705,"corporation":false,"usgs":true,"family":"Murray","given":"Erin","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":945506,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zinsser, Lauren M. 0000-0002-8582-066X","orcid":"https://orcid.org/0000-0002-8582-066X","contributorId":205756,"corporation":false,"usgs":true,"family":"Zinsser","given":"Lauren","email":"","middleInitial":"M.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":945507,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"King, Tyler V. 0000-0002-5785-3077","orcid":"https://orcid.org/0000-0002-5785-3077","contributorId":292424,"corporation":false,"usgs":true,"family":"King","given":"Tyler","middleInitial":"V.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":945508,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ducar, Scott D. 0000-0003-0781-5598","orcid":"https://orcid.org/0000-0003-0781-5598","contributorId":297547,"corporation":false,"usgs":true,"family":"Ducar","given":"Scott","email":"","middleInitial":"D.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":945509,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Southern, India","contributorId":359460,"corporation":false,"usgs":false,"family":"Southern","given":"India","affiliations":[{"id":6912,"text":"Idaho Department of Environmental Quality","active":true,"usgs":false}],"preferred":false,"id":945510,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Thom, Theresa","contributorId":224436,"corporation":false,"usgs":false,"family":"Thom","given":"Theresa","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":945511,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mebane, Christopher A. 0000-0002-9089-0267 cmebane@usgs.gov","orcid":"https://orcid.org/0000-0002-9089-0267","contributorId":110,"corporation":false,"usgs":true,"family":"Mebane","given":"Christopher","email":"cmebane@usgs.gov","middleInitial":"A.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":945512,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70268980,"text":"fs20253033 - 2025 - The 3D Elevation Program—Supporting Vermont's economy","interactions":[],"lastModifiedDate":"2026-02-03T14:26:17.064965","indexId":"fs20253033","displayToPublicDate":"2025-07-15T07:45:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-3033","displayTitle":"The 3D Elevation Program—Supporting Vermont’s Economy","title":"The 3D Elevation Program—Supporting Vermont's economy","docAbstract":"The geographic information system (GIS) community in Vermont has a long history of interdisciplinary and cooperative projects that have facilitated the leveraging of geospatial technology on myriad data acquisitions across the State. High-resolution elevation data are proving to be a resource of great economic value in dealing with many important issues in Vermont. Vermont attained statewide coverage of quality level 2 coverage of topographic light detection and ranging (lidar) data in 2019. Having access to elevation data that are exponentially more accurate than what was previously available is enabling GIS professionals to better support and empower decision makers in economically important efforts such as environmental protection, public safety, watershed management and water quality, geology, transportation planning, forest and wildlife management, local planning, and flood plain management. In addition, developing a consistent and seamless statewide topographic framework supplants the traditionally time consuming and costly approach of extensive field data collection by requiring less time and money, therefore adding economic benefits. Critical applications that meet the State’s management needs depend on 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 (USGS) 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 Vermont. The status of available and in-progress 3DEP baseline lidar data in Vermont is shown in figure 1. 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 (https://www.usgs.gov/3dep/lidarspec) or newer requirements. The National Enhanced Elevation Assessment identified user requirements and conservatively estimated that availability of lidar data would result in at least $1.64 million in new benefits annually to the State. The top eight Vermont business uses for 3D elevation data, which are based on the estimated annual conservative benefits of 3DEP, are shown in table 2.
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\"}}]}","contact":"Director, National Geospatial Program
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 511
Reston, VA 20192
Email: 3DEP@usgs.gov
","tableOfContents":"The use of acoustic telemetry is steadily expanding to help answer questions related to habitat use, movement, and behavior of fishes. Significant time and resources are invested to start acoustic telemetry studies; therefore, careful planning is needed to limit post-release mortality of tagged individuals. Deep, cold-water species present additional challenges to acoustic tagging because of changes in temperature and pressure experienced during capture. The objective of our study was to determine if capture method, surface water temperature, water depth, or fish size influenced short-term post-release survival of a deep, cold-water species, Lake Trout Salvelinus namaycush. In 2023, 299 Lake Trout were captured with angling or gillnets across Lake Ontario (Laurentian Great Lake – U.S. & CAN) and surgically implanted with acoustic transmitters. We estimated 30-day post-release mortality and 24-h post-release distance traveled for tagged Lake Trout. We used Cox proportional hazards models to identify factors affecting survival probability and multiple linear regression to identify factors affecting post-release distance traveled. Thirty-day post-release mortality was minimal (9.03 %, 27/299 Lake Trout); however, mortality was 6.37 times more likely for Lake Trout captured in gillnets compare to angling (p = 0.003). Lake Trout length had a marginally significant effect on mortality (p = 0.052) but capture depth and temperature did not (p > 0.05). Lake Trout post-release distance traveled was not significantly influenced by capture gear, depth, temperature, or Lake Trout length (p = 0.61). Our results indicate that tagging-induced post-release mortality is minimal for Lake Trout tagged in the spring, but survival can be increased by avoiding use of gillnets.
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However, their accuracy varies across RSET estimation methods and diverse hydroclimate regions. While ET modeling efforts to account for biophysical processes and controlling parameters have made good progress in recent years, a parallel approach of integrating in-situ ET with RSET could reduce biases in RSET products. Basin water balance ET (WBET) and flux tower ET are widely applied to evaluate RSET accuracy, yet such ET measurements are rarely used for RSET bias corrections, especially for large area applications. To address this issue, we propose a novel approach: the water balance equivalence (WABE) method, which generates spatially continuous WBET for correcting biases in RSET products. The WABE method computes synthetic WBET by integrating observed WBET and flux tower-derived FLUXCOM ET, which fills the spatial gaps of observed WBET and generates a spatially continuous WBET dataset. Synthetic WBET (2002–2015 annual average) of eight-digit hydrologic unit code (HUC8) basins across the conterminous United States (CONUS), constituting 44 % (887 out of 2035 basins) of CONUS basins, was determined within 2.0 % (RMSE = 12 %) of observed WBET at CONUS and between 1–12 % (RMSE = 3–33 %) across 18 regions in CONUS. With WABE-based bias corrections, the overall annual bias of RSET decreased from 10 % (RMSE = 34 %) to 6 % (RMSE = 26 %) across 37 flux tower sites. The WABE method offers a new approach for RSET accuracy improvement and shows great promise for large area implementations with a potential to yield substantial benefits for building accurate basin water budgets and water management decisions.
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An operational ETa using the Visible Infrared Imaging Radiometer Suite (VIIRS) and global weather datasets was developed through the Simplified Surface Energy Balance Model (SSEBop) model. An operational framework is established with the Famine Early Warning System Network (https://earlywarning.usgs.gov/fews) to generate and update global 1 km ETa at dekadal (∼10 day), monthly, and yearly time scales since February 2012. Modeled ETa at monthly and annual time scales was evaluated using 67 eddy covariance (EC) flux tower stations around the world and water balance-based ETa based on 810 United States eight-digit Hydrologic Unit Code (HUC8) and 18 Global Runoff Data Center (GRDC) basins. The correlation coefficient (r=0.68–0.94) shows relatively strong and consistent performance across the three datasets, capturing the spatiotemporal variability in HUC8 and GRDC basins and EC tower sites reliably. The bias (3%–15%) and root mean square error (RMSE: 13%–34%) showed relatively large errors and high variability among the three datasets. The evaluation results indicate the usefulness of the VIIRS ETa for drought monitoring and early warning applications without further adjustments, while bias-correction and calibration procedures may be required before using the VIIRS ETa data for localized water budget assessments. Availability of gridded actual ETa data from a combination of flux towers and basin-scale ETa is desired to establish bias-correction procedures to improve the absolute accuracy of remote-sensing ETa such as the SSEBop VIIRS operational products.
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Researchers and managers benefit from understanding how to interpret and ensure the accuracy of fChl data collected by in situ fluorometers. Although fChl values produced by different manufacturers are often in agreement with discrete laboratory-derived Chlorophyll a (Chl a) concentration measurements, there are instances in which results significantly differ. Further, when measuring fChl side by side, different fluorometers may report values that differ significantly from each other, despite passing calibration checks prior to deployment. We compared environmental conditions and phytoplankton species composition associated with instances in which fChl measurements from three different fluorometers (EXO2 Total Algae Smart Sensor, YSI Inc./Xylem Inc., Yellow Springs, Ohio; FluoroProbe III, bbe Moldaenke GmbH, Kiel, Germany; WETStar, Sea-Bird Scientific, Bellevue, Washington) were significantly different from laboratory-derived Chl a concentrations. 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The method has been validated in reagent water, surface water, groundwater, and wastewater effluent. Other water types (for example, drinking water, untreated wastewater, and landfill leachate) have been analyzed by the method but not systematically validated. Recovery of isotope-dilution standards, added to each sample, may be used to assess method performance in nonvalidated matrices on a sample-by-sample basis.
Using this method, PFAS concentrations were determined in the range of 2–2,000 nanograms per liter in water samples. This range can be extended by diluting concentrated samples. At circumneutral pH, most compounds are present in the environment in their ionized form, and data are reported as such (for example, perfluorooctanoic acid is referred to as “perfluorooctanoate” [PFOA], perfluorooctane sulfonic acid is referred to as “perfluorooctane sulfonate” [PFOS]).
Sample preparation procedures were designed without the use of filtration and with minimum sample handling steps to mitigate procedural losses of target compounds due to sorption to surfaces. Further, isotope-dilution quantification allowed for the correction of bias that may result from procedural losses, matrix-induced signal suppression or enhancement, and other factors.
Validation experiments to characterize bias and variability, method detection level, and holding time were done in four distinct water matrices—reagent water, surface water, treated wastewater effluent, and groundwater—at multiple concentration levels. Mean PFAS recoveries met data quality objectives of bias and variability studies in all four validation matrices except for two compounds with low and variable recovery in the reagent water matrix only. Isotope-dilution standards, treated as surrogate compounds, were analyzed in more than 1,500 customer-submitted environmental samples with aggregate recovery of 102.5±6.5 percent (mean±standard deviation). Maximum holding times for all target compounds in the four validation matrices were 28 days for refrigerated samples and 90 days for frozen samples.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/tm5B13","collaboration":"Strategic Laboratory Science Branch and National Water Quality Laboratory","usgsCitation":"Gray, J.L., Kanagy, L.K., Kanagy, C.J., and Anderson, C.A., 2025, Determination of per- and polyfluoroalkyl substances in water by direct injection of matrix-modified centrifuge supernatant and liquid chromatography/tandem mass spectrometry with isotope dilution: U.S. Geological Survey Techniques and Methods, book 5, chap. B13, 121 p., https://doi.org/10.3133/tm5B13.","productDescription":"Report: xii, 121 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-144091","costCenters":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"links":[{"id":491919,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/05/b13/coverthb.jpg"},{"id":491984,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/tm/05/b13/images"},{"id":491920,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/05/b13/tm5B13.pdf","text":"Report","size":"5.35 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T and M 5-B13"},{"id":491921,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9P3YPXG","text":"USGS data release","linkHelpText":"Concentrations of per- and polyfluoroalkyl substances (PFAS) from validation experiments and custom sample analysis by U.S. Geological Survey (USGS) National Water Quality Laboratory (NWQL) Laboratory Code 9660, December 2020 to March 2022"},{"id":491985,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/tm/05/b13/tm5B13.xml"},{"id":492165,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/tm5B13/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"T and M 5-B13"}],"contact":"Chief, National Water Quality Laboratory
U.S. Geological Survey
Box 25046, Mail Stop 407
Denver, CO 80225-0585
Recent and ongoing collections of high-resolution elevation data in Louisiana are providing information that supports improved critical public safety modeling and enables the State to strengthen its efforts to fight the effects of land subsidence and sea-level rise. The availability of current and accurate three-dimensional (3D) elevation data supports numerous business activities, including flood risk management, infrastructure and construction management, coastal zone management, wildlife and habitat management, recreation, agriculture and precision farming, urban and regional planning, water supply and quality assessment, and natural resources conservation. Critical applications that meet the State’s management needs depend on light detection and ranging (lidar) data that provide a highly detailed 3D model of the Earth’s surface and aboveground features.
The 3D Elevation Program (3DEP) is managed by the U.S. Geological Survey (USGS) 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 Louisiana. The status of available and in-progress 3DEP baseline lidar data in Louisiana is shown in figure 1. 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 (https://www.usgs.gov/3dep/lidarspec) or newer requirements. The National Enhanced Elevation Assessment identified user requirements and conservatively estimated that availability of lidar data would result in at least $6.96 million in new benefits annually to the State. The top 10 Louisiana business uses for 3D elevation data, which are based on the estimated annual conservative benefits of 3DEP, are shown in table 2.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20253034","usgsCitation":"Cretini, C., 2025, The 3D Elevation Program—Supporting Louisiana's economy: U.S. Geological Survey Fact Sheet 2025–3034, 2 p., https://doi.org/10.3133/fs20253034.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-160425","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":491723,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2025/3034/images/"},{"id":491722,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2025/3034/fs20253034.XML","linkFileType":{"id":8,"text":"xml"},"description":"FS 2025-3034 XML"},{"id":491721,"rank":3,"type":{"id":39,"text":"HTML 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"nation\":\"USA \"}}]}","contact":"Director, National Geospatial Program
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 511
Reston, VA 20192
Email: 3DEP@usgs.gov
","tableOfContents":"Riverscapes are the integration of terrestrial and aquatic systems from headwaters to estuaries that provide habitat and ecosystem benefits when in good health. However, current riverscape degradation is pervasive, impairing the function and resulting benefits of these systems. Healthy riverscapes are adaptive and some can ‘heal’ after disturbance with minimal to no human assistance. As riverscape health is threatened, a need exists to address current degradation and understand the potential for riverscape restoration—concisely communicating what comprises healthy riverscapes is essential to direct limited resources and increase efficacy of restoration and conservation efforts. Healthy riverscapes have (i) space to interact within their valley bottom; (ii) natural flow, sediment, and vegetation regimes appropriate to the biophysical setting and river type; and (iii) structural forcing to support diversity and that creates varied residence times for water, sediment, and vegetation. These three principles are grounded in interdisciplinary science and lessons from riverscape scientists and restoration practitioners across the world. Understanding the context, anthropogenic influences, boundary conditions, and legacy effects influencing riverscapes is essential for the appropriate application of these principles in pursuit of achieving riverscape health. Emphasizing a holistic, biogeomorphic view of riverscapes through these principles can guide policies, restoration actions, and monitoring frameworks that ensure that riverscapes remain capable of accommodating and adjusting to disturbances while continuing to support biodiversity and human benefits.
","language":"English","publisher":"Wiley","doi":"10.1002/wat2.70028","usgsCitation":"Glassic, H.C., Al-Chokhachy, R., Wheaton, J., Macfarlane, W., Jordan, C., Murphy, B.M., Shahverdian, S., Bennett, S., Bouwes, N., Fryirs, K., Brierley, G., Ciotti, D., Bailey, P., Bartlet, K., Belletti, B., Bizzi, S., Brasington, J., Camp, R., Fairfax, E., Gilbert, J., Jimenez, J., Maestas, J.D., Mandish, T., McNamara, A., Miller, S.R., Marizot, B., Perle, M., Piegay, H., Reid, H., Reynolds, L.V., Saunders, W., Shallcross, A., Skidmore, P., Smith, R., Terrier, B., Wathen, G., and Weber, N., 2025, Principles of riverscape health: WIREsWATER (Wiley Interdisciplinary Reviews Primer), v. 12, no. 4, e70028, 20 p., https://doi.org/10.1002/wat2.70028.","productDescription":"e70028, 20 p.","ipdsId":"IP-172679","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":497739,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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In Northern California, the 2022 McKinney Fire burned 243 km2 of the Klamath National Forest, with 83% of the burned area classified as moderate to high severity. During the active wildfire, a high-intensity monsoonal rain event triggered sediment-laden flooding and runoff-initiated debris flows, causing extreme water-quality impairments and a 95 km fish kill zone along the main-stem Klamath River. This rain-on-wildfire event produced a flood wave that outpaced a sediment pulse, diminishing the dilution effect of the floodwaters. A network of high-frequency water-quality sensors recorded water-quality impairments that propagated 296 km downstream. Impairments at the nearest monitoring station, situated 71 km downstream from the fire perimeter, included dissolved oxygen sags to zero (anoxia) for 5.25 h, turbidity spikes exceeding 1000 FNU, a doubling of specific conductance from 175 to 415 µS/cm (at 25 °C), and pH anomalies of 0.5 units from 7.8 to 7.3. This novel rain-on-wildfire event triggered the first flush of fire-scar material during an active wildfire, resulting in water-quality impairments unprecedented in the historical monitoring data for the river spanning 2012 to 2022. This study provides new insights into the potential role of rain-on-wildfire events in generating extreme downstream water-quality and ecological impairments in a more fire-prone future.
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Many states are assessing PFAS in water resources, and insight from these statewide assessments can help guide future sampling efforts. A statewide assessment of 28 PFAS was conducted in New Mexico starting in 2020, and subsequent follow-up sampling has improved understanding of PFAS occurrence and sources throughout the state. PFAS were present in all major rivers of New Mexico (Rio Grande, Pecos River, San Juan River, Animas River, Canadian River, Gila River, Rio Chama, and Rio Puerco) with 13 of 28 analyzed PFAS (PFBA, PFPeA, PFHxA, PFHpA, PFOA, PFNA, PFDA, PFBs, PFPeS, PFHxS, PFOS, PFOSA, 6:2 FTS) detected from at least one sample for samples collected between 2020 and 2024. This study found high temporal and spatial variability—PFAS concentrations ranged from below the laboratory detection level to 156 ng/L, with concentrations generally increasing downstream on the major rivers. PFBS was the most frequently detected and highest concentration PFAS in this study, ranging from 1 to 93 ng/L, followed by PFBA and PFPeA, ranging from 0.9 to 32 ng/L. The average of the sum of PFAS detected increased by an order of magnitude from 4 to 46 ng/L in the Rio Grande as it flows through Albuquerque, the largest urban area in New Mexico. PFAS concentration increased by 58% after a stormflow pulse flushed over Albuquerque and contributed water to the Rio Grande. The contribution of wastewater to surface water resources varied diurnally as well as seasonally. Sampling multiple locations on major rivers across multiple seasons, taking into account known anthropogenic inputs, would enhance characterization of temporal and spatial variability of PFAS concentrations. Increased sampling frequency at sites with wastewater contribution and focused investigations in areas with higher than expected PFAS could increase understanding of potential sources and variability of source contributions.
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Selenium and other elements discharged by the mines into the waters of the United States can pose unknown risks to aquatic life. The U.S. Geological Survey Wyoming-Montana Water Science Center can collaborate with Montana Fish, Wildlife and Parks and other State and Federal agencies to design studies and to collect fish tissues to help fill this knowledge gap. This report describes the processes, techniques, and methods used to collect and analyze fish tissue collected from Lake Koocanusa; and procedures used to review and manage data, including quality assurance and quality control procedures used by the U.S. Geological Survey Wyoming-Montana Water Science Center and supporting analytical laboratories. These fish tissue collections began in 2021.
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U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
The purpose of this Wake Atoll Vessel Movement Biosecurity Program Efficacy document is to provide the United States Air Force (USAF) with an unbiased review of the current (2015; hereafter referred to as the 2015 Biosecurity Plan) biosecurity plan for the military base Wake Island Airfield (WIA) on Wake Atoll (hereafter Wake). Periodic reviews are an integral step for evaluating plan efficacy and updating plans with new information for improving plan effectiveness. The U.S. Geological Survey (USGS) acted as an external expert to provide the first unbiased assessment of the program and observe how it was being implemented. The USAF 2015 Wake Island Biosecurity Management Plan goes beyond sea vessel and container biosecurity; however, those aspects were not included in this evaluation.
We used several methods for a quality assurance evaluation of the 2015 sea vessel and shipping container biosecurity program specified in the Biosecurity Plan. Our evaluation included real-time observations in Hawai`i and at Wake. We surveyed cargo staging areas and empty shipping containers before supply shipment and the containers, barge, and marina at Wake after shipment. We used various detection tools and techniques (for example, visual encounter surveys, glue boards, chew cards, camera traps, and so on). We carried out an insect mortality experiment trial using one of the required shipping container biosecurity tools (dichlorvos impregnated pest strips). We also included a table-top review of documentation (largely the 2015 Biosecurity Plan) with respect to our observations to provide an assessment of how well the Biosecurity Plan protocols were carried out and how well they serve their intended purpose.
We observed biosecurity concerns in each focal area and stage of cargo handling (before and after barge movement) across all surveys of containers, flat racks, break bulk, warehouses, and dock areas. Using visual inspections, we recorded biosecurity concerns for every empty container we inspected before it was to be stuffed with cargo. Most containers had structural integrity issues (such as holes and damaged floorboards) and sanitation concerns, including live animals and plant matter or seeds. About one third of the containers had mold and a few had wet floorboards or standing water. We detected live animals on the break bulk, and flat racks were in poor condition. Next, we inspected cargo staging areas and noted extensive permeability of the building where cargo was staged for the 2018 resupply shipment and the building that had typically been used. We included the adjacent dock area used for staging break bulk, shipping containers and mooring the barge. We detected more than 5,000 individuals of 105 species. We also detected seeds in each location and scattered vegetation in the dock area, including growing in from the area outside separated by a chain link fence.
During surveys at Wake, we observed that 100 percent of the shipping containers, including all containers sent with required biosecurity tools, had live animals. The barge had only one unsecured snap trap for intercepting rodents aboard, we saw areas with fairly deep layers of dirt (or soil; we did not examine it to determine its properties), and there was plant matter with seed heads on the barge gangway that could easily be transported onto the barge. There was also only one snap trap station that was improperly placed on the dock. We also observed piled wood and vegetation nearby that could provide refuge to potential stowaway animals escaping.
Combined, surveys of the containers, staging areas, barges, and receiving area in Hawai`i and at Wake resulted in detection of more than 9,000 individuals of 131 animal species; nearly 4,000 individuals of 62 species were detected in surveys of containers once they had arrived at Wake. None of the species identified are known to be native to Wake. Our preliminary risk analysis of all species detected included eight species that we scored as high risk of potentially negative effects to biodiversity, infrastructure, or human health should they arrive at Wake and become established. Six of these species were only recorded using tools not clearly required by the Biosecurity Plan or being used to implement the plan.
We observed that the required biosecurity tools intended to intercept animals in the cargo staging area did not target the suite nor number of species present. Our analysis also indicated the required biosecurity tools intended to intercept animals in shipping containers were inadequate to handle the volume of organisms that were in the containers. The insect mortality trial experiment showed the pest strips were highly effective for only one of the three species tested, leaving uncertainty about how effective they are across the suite of potential species stowing away in cargo and containers.
Base Operating Support (BOS) did not carry out all Biosecurity Plan actions, but we also noted the document uses terminology such as “recommendation” as opposed to “requirement” which may lead contractors to consider those actions as optional. However, USAF provided evidence of BOS training and follow up; this included detailed identification of specific requirements for some of the biosecurity actions that we did not observe being carried out.
The 2015 Biosecurity Plan contains critical and useful components that seem to be well carried out. However, we also saw discrepancies, weaknesses, or both across methods and protocols currently used for Wake Atoll biosecurity. We observed shortcomings at each stage of our survey as well as in the plan as written, and we suggest general modifications to the Biosecurity Plan for consideration to potentially strengthen biosecurity overall.
Prevention is the most efficient and cost-effective biosecurity measure. Based on our findings, we see possible solutions to improve existing preventative biosecurity efforts and reduce potential incursion at Wake. These potential solutions include creating and implementing the following:
Management of invasive species enhances capability to protect human health and the environment as well as to advance mission accomplishment. Biosecurity plans are an integral component for addressing invasive species. Periodic evaluation of the efficacy of these plans is useful for identifying elements that are working well and for illuminating those that can be improved. Evaluations encourage consideration of new tools and adaptation of processes to achieve better outcomes and accommodate potential future threats more efficiently and more cost effectively.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251026","collaboration":"Prepared in cooperation with the U.S. Air Force","programNote":"Ecosystems Mission Area—Biological Threats and Invasive Species Research Program","usgsCitation":"Hathaway, S.A., Molden, J.C., Peck, R., Rex, K.R., Brehme, C.S., Black, T., and Fisher, R.N., 2025, Wake Atoll vessel movement biosecurity program efficacy: U.S. Geological Survey Open-File Report 2025–1026, 130 p., https://doi.org/10.3133/ofr20251026","productDescription":"x, 130 p.","onlineOnly":"Y","ipdsId":"IP-155305","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":491323,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1026/ofr20251026.pdf","text":"Report","size":"22.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1026"},{"id":491324,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251026/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1026"},{"id":491326,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1026/ofr20251026.XML"},{"id":491325,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1026/images"},{"id":491322,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1026/coverthb.jpg"}],"otherGeospatial":"Wake Atoll","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 166.58609930546095,\n 19.335566334904826\n ],\n [\n 166.58609930546095,\n 19.259871066135005\n ],\n [\n 166.6712110656557,\n 19.259871066135005\n ],\n [\n 166.6712110656557,\n 19.335566334904826\n ],\n [\n 166.58609930546095,\n 19.335566334904826\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Fluorinated organic contaminants, including per- and polyfluoroalkyl substances (PFASs) and fluorinated pesticides and pharmaceuticals (FPPs), pose a persistent threat to environmental health. Widely used liquid chromatography-tandem mass spectrometry (LC-MS/MS) methods fail to capture large fractions of total organofluorine in environmental samples, confounding the assessment of fluorinated contamination. Fluorine-19 nuclear magnetic resonance spectroscopy (19F-NMR) is an inclusive method for total and class-based organofluorine analysis. Here, we apply 19F-NMR to 31 effluent, surface water, and foam samples collected at 13 potential organofluorine point sources or source regions and compare the results to targeted LC-MS/MS for 34 or 64 PFASs. LC-MS/MS detected a median of 11.4% of total organofluorine detected by 19F-NMR (range: nondetect to 8190 nM F 19F-NMR; nondetect to 8010 nM F LC-MS/MS). The highest 19F-NMR total organofluorine concentrations, detected in wastewater treatment plant (WWTP)-associated foam and at a soy oil production facility, arose from resonances characteristic of per- and polyfluorinated alkyl chains. 19F-NMR resonances from aryl CF3 moieties were abundant in the WWTP-associated samples, consistent with prior reports of substantial contributions from pharmaceuticals to WWTP effluents. 19F-NMR enables the quantitative assessment of total organofluorine and qualitative insight into fluorinated structures, providing complementary analysis of organofluorine compounds missed by targeted mass spectrometry-based protocols.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.5c05079","usgsCitation":"Faber, K., Pomerantz, W., Gray, J., Hubbard, L.E., Kolpin, D., and Arnold, W., 2025, Revealing organofluorine contamination in effluents and surface waters with complementary analytical approaches: Fluorine-19 nuclear magnetic resonance spectroscopy (19F-NMR) and liquid chromatography-tandem mass spectrometry (LC-MS/MS): Environmental Science & Technology, v. 59, no. 28, p. 14695-14706, https://doi.org/10.1021/acs.est.5c05079.","productDescription":"12 p.","startPage":"14695","endPage":"14706","ipdsId":"IP-175830","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":492767,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"59","issue":"28","noUsgsAuthors":false,"publicationDate":"2025-07-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Faber, K.A.","contributorId":358440,"corporation":false,"usgs":false,"family":"Faber","given":"K.A.","affiliations":[{"id":85624,"text":"University of MN","active":true,"usgs":false}],"preferred":false,"id":943760,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pomerantz, W.C.K","contributorId":358441,"corporation":false,"usgs":false,"family":"Pomerantz","given":"W.C.K","affiliations":[{"id":85624,"text":"University of MN","active":true,"usgs":false}],"preferred":false,"id":943761,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gray, James L. 0000-0002-0807-5635","orcid":"https://orcid.org/0000-0002-0807-5635","contributorId":202726,"corporation":false,"usgs":true,"family":"Gray","given":"James L.","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":943762,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hubbard, Laura E. 0000-0003-3813-1500 lhubbard@usgs.gov","orcid":"https://orcid.org/0000-0003-3813-1500","contributorId":4221,"corporation":false,"usgs":true,"family":"Hubbard","given":"Laura","email":"lhubbard@usgs.gov","middleInitial":"E.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":943763,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kolpin, Dana W. 0000-0002-3529-6505","orcid":"https://orcid.org/0000-0002-3529-6505","contributorId":204154,"corporation":false,"usgs":true,"family":"Kolpin","given":"Dana W.","affiliations":[{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true},{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"preferred":true,"id":943764,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Arnold, W.A.","contributorId":358442,"corporation":false,"usgs":false,"family":"Arnold","given":"W.A.","affiliations":[{"id":85624,"text":"University of MN","active":true,"usgs":false}],"preferred":false,"id":943765,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70268852,"text":"gip254 - 2025 - U.S. Geological Survey Groundwater Climate Response Network, 2024","interactions":[],"lastModifiedDate":"2026-02-03T14:17:39.193886","indexId":"gip254","displayToPublicDate":"2025-07-08T09:09:09","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":315,"text":"General Information Product","code":"GIP","onlineIssn":"2332-354X","printIssn":"2332-3531","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"254","displayTitle":"U.S. Geological Survey Groundwater Climate Response Network, 2024","title":"U.S. Geological Survey Groundwater Climate Response Network, 2024","docAbstract":"As of October 2024, the U.S. Geological Survey (USGS) operated 588 sites across the United States and its territories as part of the Groundwater Climate Response Network (CRN). The CRN is comprised of wells selected to monitor the effects of climate variability, such as droughts, on groundwater levels nationwide. The CRN includes nearly 500 locations with real-time data and more than 100 sites with non-real-time data available to the public on the CRN web mapper and the USGS National Water Dashboard.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip254","usgsCitation":"Fine, J.M., and Caldwell, R.R., 2025, U.S. Geological Survey Groundwater Climate Response Network, 2024: U.S. Geological Survey General Information Product 254, 1 p., https://doi.org/10.3133/gip254.","productDescription":"1 p.","ipdsId":"IP-175295","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":491829,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/254/gip254.pdf","text":"Report","size":"1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 254"},{"id":491828,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/254/coverthb.jpg"}],"contact":"National Groundwater Networks Coordinator
Observing Systems Division
Water Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
During the past century, the proliferation of invasive species has contributed to loss of biodiversity and ecosystem degradation. In forests, invasive tree species can alter ecosystem function, but the underlying mechanisms of these changes are not fully understood. We use the ongoing invasion of P. cattleianum on the Island of Hawai’i to test the hypotheses that invasive structural changes drive changes to forest evapotranspiration (ET). The aim of our study is first to quantify the structural changes to native ‘ōhi‘a -dominated forest impacted by a gradient of P. cattleianum invasion. Our results suggest that invasive P. cattleianum causes significant changes to the vegetation density and structure of native forest on the Island of Hawai’i, including increased vegetation area index, decreased mean leaf height, and decreased structural heterogeneity. Second, we strove to understand the functional implications of structural changes through a biophysical modeling simulation, testing the sensitivity of ET to canopy structure under contrasting scenarios. Modeling the functional impact of structural change, we found that plots with P. cattleianum invasion importance value (IVinv) above 0.35 have a higher likelihood to increase ET compared to plots with P. cattleianum invasion less than 0.35 IVinv. Modeled increases in ET due to invasion ranged from 19 and 123% relative to native transects. The large variation in ET increases is caused by structural variation because the modeling scenarios did not include potential species differences in leaf physiology. Diagnostic scenario modeling shows the effect size of increased leaf area on modeled ET is constrained by the structural arrangement, that is vertical distribution, of the increased vegetation. Thus, invasion structure that increases vegetation density in taller, more sunlit forest strata will lead to a greater increase in ET compared to invasion structure that increases vegetation density in the shaded forest understory. Overall, we conclude the vertical distribution of vegetation is an important factor shaping the impact of invasive P. cattleianum on the forest water balance.
","language":"English","publisher":"Springer","doi":"10.1007/s10021-025-00974-9","usgsCitation":"Seely, T., Fortini, L., Liang, Y., and Battles, J.J., 2025, The structural and functional impacts of invasive Psidium cattleianum in forests on the Island of Hawai’i: Ecosystems, v. 28, 39, 17 p., https://doi.org/10.1007/s10021-025-00974-9.","productDescription":"39, 17 p.","ipdsId":"IP-166688","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":495749,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10021-025-00974-9","text":"Publisher Index Page"},{"id":495716,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Island of Hawaii","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n 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Center","active":false,"usgs":true}],"preferred":true,"id":948990,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Liang, Yutong","contributorId":361565,"corporation":false,"usgs":false,"family":"Liang","given":"Yutong","affiliations":[{"id":27526,"text":"Georgia Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":948991,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Battles, John J.","contributorId":102006,"corporation":false,"usgs":false,"family":"Battles","given":"John","email":"","middleInitial":"J.","affiliations":[{"id":6609,"text":"UC Berkeley","active":true,"usgs":false}],"preferred":false,"id":948992,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70268855,"text":"70268855 - 2025 - Chlorophyll trends are negative for lakes but positive for estuarine–coastal waters","interactions":[],"lastModifiedDate":"2025-07-09T15:17:42.154606","indexId":"70268855","displayToPublicDate":"2025-07-07T10:14:20","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2982,"text":"PNAS","active":true,"publicationSubtype":{"id":10}},"title":"Chlorophyll trends are negative for lakes but positive for estuarine–coastal waters","docAbstract":"Nutrient (nitrogen and phosphorus) pollution is an environmental problem of global concern because overenrichment of water bodies increases phytoplankton biomass and ecosystem metabolism, depletes oxygen in bottom waters, and increases the frequency and intensity of harmful algal blooms. These responses to nutrient pollution have motivated policies to reduce anthropogenic nutrient inputs. However, these policies have not been universally implemented and their success at reducing phytoplankton biomass is confounded by other components of global change that regulate the conversion of nutrients into biomass. These regulating processes themselves are changing in the Anthropocene. Our study is an assessment of changing phytoplankton biomass over the period 2000–2019, using chlorophyll a trends measured in 191 lakes and 159 estuarine-coastal sites. Our results show that phytoplankton biomass has decreased in most lakes, increased in most coastal sites, and the largest trends have been predominantly negative for lakes and positive for coastal sites. These results provide evidence of opposing directions of phytoplankton biomass change between lakes and coastal sites in this recent period of unprecedented global change. Nutrient pollution is a knotty environmental problem, and our study indicates that it might be a particularly challenging problem for ecosystems situated in densely populated landscapes where freshwater and sea water meet. Success at overcoming this challenge will require deeper scientific understanding of changes in processes that regulate the conversion of nutrients into phytoplankton biomass, substantial investments of time and resources to reduce nutrient inputs, and a flexible strategy designed to anticipate and adapt to a changing world.
","language":"English","publisher":"National Academy of Sciences","doi":"10.1073/pnas.2502289122","usgsCitation":"Cloern, J.E., and Jassby, A., 2025, Chlorophyll trends are negative for lakes but positive for estuarine–coastal waters: PNAS, v. 122, no. 122, e2502289122, 7 p., https://doi.org/10.1073/pnas.2502289122.","productDescription":"e2502289122, 7 p.","ipdsId":"IP-173995","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":492084,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1073/pnas.2502289122","text":"Publisher Index Page"},{"id":491901,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"122","issue":"122","noUsgsAuthors":false,"publicationDate":"2025-07-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Cloern, James E. 0000-0002-5880-6862 jecloern@usgs.gov","orcid":"https://orcid.org/0000-0002-5880-6862","contributorId":1488,"corporation":false,"usgs":true,"family":"Cloern","given":"James","email":"jecloern@usgs.gov","middleInitial":"E.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":942400,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jassby, Alan","contributorId":357740,"corporation":false,"usgs":false,"family":"Jassby","given":"Alan","affiliations":[{"id":16975,"text":"University of California Davis","active":true,"usgs":false}],"preferred":false,"id":942401,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}