Invasive species such as grass carp (Ctenopharyngodon idella) pose substantial ecological threats to North American freshwater ecosystems. Understanding their early life stage behavior is critical for management efforts. From spawning to hatching, invasive carp eggs must remain suspended in the water column while drifting downstream for the best chance of survival. This highly vulnerable life stage is a potential target for population control to reduce recruitment. However, studying egg transport and potential dispersal control techniques is challenging, because the availability of live eggs and time period for experimentation are extremely limited. Additionally, accurately replicating the physical characteristics and transport mechanisms of fish eggs using surrogates in laboratory and field studies is not trivial. This study presents a novel method to create fluorescein-dyed, preserved grass carp eggs as surrogates for live eggs in transport and dispersal control experiments. This technique enables year-round studies of grass carp egg transport, offering managers a reliable tool for developing and testing dispersal control and passive sampling methods for invasive carp eggs. In this study, we rehydrate and dye preserved grass carp eggs in varying concentrations of aqueous fluorescein for a range of rehydration times, evaluate dye retention and egg visibility under ultraviolet light (UV-A), and measure diameters and settling velocities for comparison with live eggs. Eggs rehydrated in 0.100 g per liter fluorescein for 30 min maintain adequate brightness for up to 40 min in mixed conditions and exhibit mean settling velocities and densities similar to live eggs, making them ideal for laboratory experiments using quantitative imaging techniques.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.70124","usgsCitation":"Doyle, H.F., Stahlschmidt, B.H., Herndon, A.M., Prasad, V., George, A.E., Fischer, J.R., Jackson, P.R., Cory D. Suski, and Tinoco, R.O., 2026, Development and assessment of fluorescent-dyed, preserved invasive grass carp (Ctenopharyngodon idella) eggs as surrogates for live eggs in transport and dispersal control experiments: River Research and Applications, v. 42, no. 5, p. 976-988, https://doi.org/10.1002/rra.70124.","productDescription":"13 p.","startPage":"976","endPage":"988","ipdsId":"IP-172670","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":502166,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":502461,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rra.70124","text":"Publisher Index Page"}],"volume":"42","issue":"5","noUsgsAuthors":false,"publicationDate":"2026-03-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Doyle, Henry F. 0000-0001-9942-8602","orcid":"https://orcid.org/0000-0001-9942-8602","contributorId":369222,"corporation":false,"usgs":false,"family":"Doyle","given":"Henry","middleInitial":"F.","affiliations":[{"id":16984,"text":"University of Illinois at Urbana-Champaign","active":true,"usgs":false}],"preferred":false,"id":958621,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stahlschmidt, Benjamin H. 0000-0001-6197-662X","orcid":"https://orcid.org/0000-0001-6197-662X","contributorId":211250,"corporation":false,"usgs":true,"family":"Stahlschmidt","given":"Benjamin","email":"","middleInitial":"H.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":958622,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Herndon, Anne Marie 0000-0002-7057-0303","orcid":"https://orcid.org/0000-0002-7057-0303","contributorId":332776,"corporation":false,"usgs":true,"family":"Herndon","given":"Anne","email":"","middleInitial":"Marie","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":958623,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Prasad, Vindhyawasini 0000-0003-0585-7217","orcid":"https://orcid.org/0000-0003-0585-7217","contributorId":296287,"corporation":false,"usgs":false,"family":"Prasad","given":"Vindhyawasini","email":"","affiliations":[{"id":16984,"text":"University of Illinois at Urbana-Champaign","active":true,"usgs":false}],"preferred":false,"id":958624,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"George, Amy E. 0000-0003-1150-8646 ageorge@usgs.gov","orcid":"https://orcid.org/0000-0003-1150-8646","contributorId":3950,"corporation":false,"usgs":true,"family":"George","given":"Amy","email":"ageorge@usgs.gov","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":958625,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fischer, Jesse Robert 0000-0002-9071-7931","orcid":"https://orcid.org/0000-0002-9071-7931","contributorId":329677,"corporation":false,"usgs":true,"family":"Fischer","given":"Jesse","email":"","middleInitial":"Robert","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":958626,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jackson, P. Ryan 0000-0002-3154-6108 pjackson@usgs.gov","orcid":"https://orcid.org/0000-0002-3154-6108","contributorId":194529,"corporation":false,"usgs":true,"family":"Jackson","given":"P.","email":"pjackson@usgs.gov","middleInitial":"Ryan","affiliations":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":958627,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Cory D. Suski","contributorId":369224,"corporation":false,"usgs":false,"family":"Cory D. Suski","affiliations":[{"id":16984,"text":"University of Illinois at Urbana-Champaign","active":true,"usgs":false}],"preferred":false,"id":958628,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Tinoco, Rafael O.","contributorId":211779,"corporation":false,"usgs":false,"family":"Tinoco","given":"Rafael","email":"","middleInitial":"O.","affiliations":[{"id":38317,"text":"Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL","active":true,"usgs":false}],"preferred":false,"id":958629,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70274277,"text":"70274277 - 2026 - Satellite time series analysis to quantify changing climax ciénegas using a state and transition model approach","interactions":[],"lastModifiedDate":"2026-03-24T17:12:07.583859","indexId":"70274277","displayToPublicDate":"2026-03-07T10:02:44","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Satellite time series analysis to quantify changing climax ciénegas using a state and transition model approach","docAbstract":"Ciénegas are rare wetlands in arid landscapes of the North American Southwest, historically providing critical ecological and hydrological functions but increasingly threatened by changing climate and land use pressures. This study quantifies changes in ciénega condition and floodplain dynamics using a state-and-transition model (STM) informed by expert knowledge and remote sensing. Key factors include woody plant encroachment, water availability, and soil aggradation. We mapped 31 ciénegas with high-resolution imagery and analyzed Landsat data (1985–2023) to assess vegetation health and moisture using the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Infrared Index (NDII). Results show substantial interannual variability in phenology, water stress, and soil moisture, with regional drying and elevation strongly influencing ciénega resilience. We classified ciénegas into three functional states—healthy, desiccated, and dormant—and mapped their 2023 condition. Trend analyses indicate most ciénegas exhibit greening despite drought, though localized variability underscores the need for site-specific management. None are in a stable climax (reference) state; rather, they transition among states in response to external drivers. Increasing woody plant cover and surface drying, likely linked to declining regional water tables, favor deep-rooted species over wetland grasses—a pattern mirrored in adjacent control plots. Spatially explicit analysis revealed intra-ciénega variability often masked by aggregated data, highlighting the importance of high-resolution monitoring. Seasonal and long-term trends provide context for understanding ciénega dynamics, including degradation and restoration pathways. This study emphasizes the importance of groundwater conservation and demonstrates how remote sensing supports long-term monitoring. The STM framework offers a practical tool for adaptive management to sustain freshwater resources in arid environments.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2026.114741","usgsCitation":"Norman, L., Petrakis, R.E., Wilson, N.R., Middleton, B.R., Villarreal, M.L., Pollock, M., Minckley, T.A., and Hendrickson, D., 2026, Satellite time series analysis to quantify changing climax ciénegas using a state and transition model approach: Ecological Indicators, v. 184, 114741, 16 p., https://doi.org/10.1016/j.ecolind.2026.114741.","productDescription":"114741, 16 p.","ipdsId":"IP-179305","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":501684,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2026.114741","text":"Publisher Index Page"},{"id":501477,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","state":"Arizona, New Mexico","otherGeospatial":"Sonora","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.05152972005978,\n 33.0768867725987\n ],\n [\n -112.05152972005978,\n 29.88732922369421\n ],\n [\n -108.36301240182003,\n 29.88732922369421\n ],\n [\n -108.36301240182003,\n 33.0768867725987\n ],\n [\n -112.05152972005978,\n 33.0768867725987\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"184","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Norman, Laura M. 0000-0002-3696-8406","orcid":"https://orcid.org/0000-0002-3696-8406","contributorId":203300,"corporation":false,"usgs":true,"family":"Norman","given":"Laura M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":957547,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Petrakis, Roy E. 0000-0001-8932-077X rpetrakis@usgs.gov","orcid":"https://orcid.org/0000-0001-8932-077X","contributorId":174623,"corporation":false,"usgs":true,"family":"Petrakis","given":"Roy","email":"rpetrakis@usgs.gov","middleInitial":"E.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":957548,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wilson, Natalie R. 0000-0001-5145-1221 nrwilson@usgs.gov","orcid":"https://orcid.org/0000-0001-5145-1221","contributorId":214982,"corporation":false,"usgs":true,"family":"Wilson","given":"Natalie","email":"nrwilson@usgs.gov","middleInitial":"R.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":957549,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Middleton, Barry R.","contributorId":367728,"corporation":false,"usgs":false,"family":"Middleton","given":"Barry","middleInitial":"R.","affiliations":[{"id":36921,"text":"Ret. USGS","active":true,"usgs":false}],"preferred":false,"id":957550,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Villarreal, Miguel L. 0000-0003-0720-1422 mvillarreal@usgs.gov","orcid":"https://orcid.org/0000-0003-0720-1422","contributorId":214980,"corporation":false,"usgs":true,"family":"Villarreal","given":"Miguel","email":"mvillarreal@usgs.gov","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":957551,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pollock, Michael","contributorId":367729,"corporation":false,"usgs":false,"family":"Pollock","given":"Michael","affiliations":[{"id":38436,"text":"National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":957552,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Minckley, Thomas A.","contributorId":367730,"corporation":false,"usgs":false,"family":"Minckley","given":"Thomas","middleInitial":"A.","affiliations":[{"id":87617,"text":"University of Wyoming, Department of Geology and Geophysics, Laramie, WY 82071-2000","active":true,"usgs":false}],"preferred":false,"id":957553,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hendrickson, Dean","contributorId":367731,"corporation":false,"usgs":false,"family":"Hendrickson","given":"Dean","affiliations":[{"id":87618,"text":"University of Texas at Austin, College of Natural Sciences, Austin, TX 78712","active":true,"usgs":false}],"preferred":false,"id":957554,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70274196,"text":"ofr20261063 - 2026 - Evaluation of turbidity corrections for EXO fluorescent dissolved organic matter (fDOM) sensors","interactions":[],"lastModifiedDate":"2026-03-06T21:45:10.353284","indexId":"ofr20261063","displayToPublicDate":"2026-03-06T11:20:00","publicationYear":"2026","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":"2026-1063","displayTitle":"Evaluation of Turbidity Corrections for EXO Fluorescent Dissolved Organic Matter (fDOM) Sensors","title":"Evaluation of turbidity corrections for EXO fluorescent dissolved organic matter (fDOM) sensors","docAbstract":"The use of field-deployable fluorescence sensors to better understand dissolved organic matter concentrations and composition has grown immensely in recent years. Applications of these sensors to critical monitoring efforts have also grown, encompassing post-fire monitoring, wastewater tracking, and use as a proxy for various contaminants. Despite the growth, it is well known that these sensors require corrections for temperature (Watras and others, 2011) and are subject to many light-field interferences caused by both scattering and absorbance due to dissolved and particulate substances (Downing and others, 2012; Lee and others, 2015; Booth and others, 2023). The most common fluorescence sensors used by the U.S. Geological Survey (USGS) include those targeting fluorescent dissolved organic matter (fDOM) and chlorophylls. Because fDOM sensors primarily measure fluorescence in the dissolved to colloidal phases, corrections to the interferences caused by particulates can be made relatively easily. By the end of 2024, the USGS had 69 fDOM sensors deployed within official water quality monitoring networks included on the USGS National Water Dashboard (https://dashboard.waterdata.usgs.gov/app/nwd/en/) and numerous others used in surveys and research applications across the Nation.
Although temperature corrections are widely applicable across sensor models, interference corrections can be model specific due to differences in design specifications across manufacturers and models (Booth and others, 2023). The corrections are also potentially subject to changes in manufacturing within a specific sensor model. Recently, USGS staff obtained information regarding possible changes in the manufacturing of its most widely-used fDOM sensor model, raising concerns about data consistency and quality in the USGS fDOM sensor networks.
Furthermore, changes in turbidity sensors since the corrections guidance was performed may also affect the performance of the corrections. The turbidity sensor used in the original experiments (Downing and others, 2012) was determined to have a signal output approximately 1.3 times higher than the output of the turbidity sensor currently used in an extensive field comparison study (Messner and others, 2023). With these changes, it is imperative that the corrections be reevaluated to maintain data consistency and continuity across the USGS.
In this study, we evaluated turbidity corrections for fDOM sensors over a range of serial numbers covering manufacturing dates 2015 through 2022 and turbidity serial numbers covering the range 2013 through 2022. The goal was to determine whether reported changes in the manufacturing process of the fDOM and turbidity sensors affected the correction approach developed by Downing and others (2012) such that additional guidance would be required to address this manufacturing change. To evaluate, we repeated a laboratory-based test similar to that performed by Downing and others (2012) in which a series of tank experiments with multiple sensors were deployed in a suspension of Elliot Silt Loam (ESL). High turbidities of the ESL suspension were maintained throughout the tank by turbulent recirculation using submersible pumps. Particulates were removed using a recirculated line equipped with a capsule filter (0.45 micron). Measurements were collected throughout the filtration until turbidities reached approximately 5 formazin nephelometric units (FNU; data available in Baxter and others, 2023). Each experimental run included a mixture of unique sensor combinations to account for variability imposed by the turbidity and temperature sensors. The fDOM correction factor was calculated for each combination of fDOM and turbidity sensors included in the test.
We observed no systematic change in fDOM correction coefficients across serial numbers representing manufacturing years 2015 through 2022. However, the results highlighted questions raised about the corrections for high-turbidity samples, as noted in USGS Techniques and Methods (Booth and others, 2023). Applying the inverse of the commonly-used fDOM ratio with a quadratic fit performed better than the exponential fits when correcting fDOM data for turbidity in the ESL laboratory filtration test and generated a simple scale factor correction equation. This approach also served as a better indicator of data quality than the exponential fit approach. Similar to fDOM, more rigorous quality assurance measures may be necessary to evaluate turbidity sensor calibrations and performance. Sensors exceeding a certain age may need to be replaced despite passing quality assurance checks during calibration. Further testing of the turbidity corrections for different sediment and water types is warranted to better understand the variations in the fits and correctable ranges of turbidity in different systems.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20261063","programNote":"Water Resources Mission Area","usgsCitation":"Fleck, J.A., Baxter, T.J., and Hansen, A.M., 2026, Evaluation of turbidity corrections for fluorescent dissolved organic matter (fDOM) sensors: U.S. Geological Survey Open-File Report 2026–1063, 30 p., https://doi.org/10.3133/ofr20261063.","productDescription":"Report: vi, 30 p.; Data Release","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-171907","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":500842,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2026/1063/coverthb.jpg"},{"id":500843,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2026/1063/ofr20261063.pdf","text":"Report","size":"2.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2026-1063 PDF"},{"id":500844,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20261063/full","linkFileType":{"id":5,"text":"html"},"description":"OFR 2026-1063 HTML"},{"id":500845,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2026/1063/ofr20261063.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2026-1063 XML"},{"id":500846,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2026/1063/images"},{"id":500847,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OB430E","text":"USGS data release","linkHelpText":"Fluorescence sensor measurements in sediment suspensions to evaluate turbidity corrections"}],"contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Restoration of impaired floodplains is an increasingly prevalent strategy for alleviating water quality concerns and reducing downstream flooding at watershed scales. Floodplains temporarily store water and slow flow velocity to promote sedimentation during overbank flooding and remove inorganic nitrogen from floodwater and groundwater via denitrification. Evaluating the impacts of different restoration strategies on denitrification can inform more strategic investments into floodplain modifications that improve water quality outcomes. Our research investigates how denitrification rates in floodplains respond to environmental factors that are actionable from an engineering perspective through design and water resources management. We seasonally measured soil denitrification enzyme activity and various environmental characteristics in 4 floodplains with different restoration design and management approaches at the confluence of the Wabash and Tippecanoe Rivers in Indiana, United States. Our results showed that denitrification rates in an agricultural floodplain were significantly lower than in restored floodplains with native vegetation. Certain soil conditions characteristic of floodplain wetlands were associated with higher denitrification, particularly elevated total nitrogen, moisture, silt, and organic matter contents. Vegetation species composition was correlated with denitrification rates. This link may reflect the direct effects of vegetation on soil conditions, such as supplying labile organic carbon, or indirect effects, such as vegetation acting as an indicator of hydrologic regime and land use. Denitrification seasonally varied, peaking in winter when nitrate supply from rivers draining agricultural watersheds in the region is also high. Substrate limitation of soil denitrification enzyme activity was most significant during the summer when overbank flooding, which replenishes soil nitrogen stocks, rarely occurs. Our findings indicate that denitrification capacity will likely be maximized in riverine floodplains that are restored as wetlands with diverse native vegetation and enhanced hydrologic connectivity. Such restoration activities promote higher denitrification rates via elevated moisture, fine sediment deposition, and soil organic matter.
","language":"English","publisher":"University of Vermont Press","doi":"10.70793/jeed.13","usgsCitation":"Lay, D.W., McMillan, S.W., Hosen, J.D., Dey, S., and Noe, G.E., 2026, Assessing environmental drivers of denitrification in restored riverine floodplains: Journal of Ecological Engineering Design, v. 4, no. 1, 17 p., https://doi.org/10.70793/jeed.13.","productDescription":"17 p.","ipdsId":"IP-179949","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":502474,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.70793/jeed.13","text":"Publisher Index Page"},{"id":502206,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Tippecanoe River, Wabash River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -86.73421342718163,\n 40.54578001376902\n ],\n [\n -86.77854172394578,\n 40.56893409536539\n ],\n [\n -86.85226822336595,\n 40.486689152088985\n ],\n [\n -86.83272067583472,\n 40.46994703338217\n ],\n [\n -86.73421342718163,\n 40.54578001376902\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"4","issue":"1","noUsgsAuthors":false,"publicationDate":"2026-03-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Lay, Danielle Winter","contributorId":369252,"corporation":false,"usgs":false,"family":"Lay","given":"Danielle","middleInitial":"Winter","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":958691,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McMillan, Sara W.","contributorId":369253,"corporation":false,"usgs":false,"family":"McMillan","given":"Sara","middleInitial":"W.","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":958692,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hosen, Jacob D.","contributorId":369254,"corporation":false,"usgs":false,"family":"Hosen","given":"Jacob","middleInitial":"D.","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":958693,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dey, Sayan","contributorId":369255,"corporation":false,"usgs":false,"family":"Dey","given":"Sayan","affiliations":[{"id":30787,"text":"Saint Louis University","active":true,"usgs":false}],"preferred":false,"id":958694,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Noe, Gregory E. 0000-0002-6661-2646 gnoe@usgs.gov","orcid":"https://orcid.org/0000-0002-6661-2646","contributorId":139100,"corporation":false,"usgs":true,"family":"Noe","given":"Gregory","email":"gnoe@usgs.gov","middleInitial":"E.","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":958695,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70275205,"text":"70275205 - 2026 - Stream macroinvertebrate responses vary with region, land use and management practice type","interactions":[],"lastModifiedDate":"2026-04-22T14:34:11.340833","indexId":"70275205","displayToPublicDate":"2026-03-06T09:21:44","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Stream macroinvertebrate responses vary with region, land use and management practice type","docAbstract":"Intensive land use alters hydrology and water quality, threatening freshwater benthic macroinvertebrates. Over 200,000 management practices (MPs) have been implemented across the Chesapeake Bay watershed since the 1980s, yet biological responses remain inconsistent. We synthesized 29 studies from 4 physiographic provinces covering 8 MP categories and evaluated macroinvertebrate responses along MP gradients using structural (richness), functional (biomass), tolerance, and biotic metrics. We hypothesized that MPs enhancing habitat complexity or restoring flow regimes would benefit taxa sensitive to sediment, hydrologic instability and organic pollution, with outcomes shaped by regional context, land use, and chosen metrics. Four themes emerged. (i) Agricultural Riparian Forest Buffers (RFBs) consistently improved sensitive metrics related to abundance, biomass and richness. (ii) Urban streams with Stream Habitat Improvement and Management (SHIM) showed improved richness and diversity, but biomass and tolerance metrics declined or remained neutral, indicating unresolved hydrologic and pollutant stress. (iii) Structural and functional responses diverged: effect sizes for total and feeding-group biomasses (functional metrics) were negative, whereas genus-level Ephemeroptera-Plecoptera-Trichoptera (EPT) richness (structural metric) was positive, indicating that structural shifts may not track underlying production changes. (iv) Physiographic comparisons showed counterintuitive patterns, as RFBs improved EPT richness in Piedmont streams but had negative effects in the Coastal Plain. Evaluating MP effectiveness requires distinguishing a no-MP pathway (stressors → instream conditions → assemblages → responses) from an MP-mediated pathway (practice regime → modified stressors → instream conditions → assemblages → responses), underscoring the need for region-specific, multi-metric monitoring and improved understanding of MP density thresholds and recovery lags.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2026.129172","collaboration":"Virginia Tech, USGS","usgsCitation":"Sabat-Bonilla, S.A., Belvin, A.C., Noe, G.E., Maloney, K.O., Frimpong, E.A., Angermeier, P., and Entrekin. Sally E., 2026, Stream macroinvertebrate responses vary with region, land use and management practice type: Journal of Environmental Management, v. 403, 129172, 14 p., https://doi.org/10.1016/j.jenvman.2026.129172.","productDescription":"129172, 14 p.","ipdsId":"IP-181470","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":503441,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jenvman.2026.129172","text":"Publisher Index Page"},{"id":503299,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"eastern contiguous United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -101.0436249,\n 29.3328733\n ],\n [\n -99.6425005,\n 27.5074215\n ],\n [\n -98.8640981,\n 26.211199\n ],\n [\n -97.3591867,\n 25.8848423\n ],\n [\n -96.9440387,\n 27.8291374\n ],\n [\n -93.6747485,\n 29.4233134\n ],\n [\n -89.2638014,\n 28.9703115\n ],\n [\n -85.7350438,\n 29.5136731\n ],\n [\n -84.0744519,\n 29.6490614\n ],\n [\n -81.7392446,\n 25.0885121\n ],\n [\n -80.1824398,\n 24.9003778\n ],\n [\n -79.8191853,\n 26.5366431\n ],\n [\n -81.1684162,\n 31.3037444\n ],\n [\n -75.0968772,\n 35.2926383\n ],\n [\n -75.4082382,\n 37.5061126\n ],\n [\n -73.2287114,\n 40.0542222\n ],\n [\n -71.9832675,\n 41.0008498\n ],\n [\n -69.3885927,\n 41.5856895\n ],\n [\n -70.0632082,\n 42.2421607\n ],\n [\n -72.450309,\n 41.1573197\n ],\n [\n -73.5400724,\n 41.1963789\n ],\n [\n -73.6438594,\n 43.1574323\n ],\n [\n -77.6396585,\n 43.1574323\n ],\n [\n -81.7911381,\n 41.2744275\n ],\n [\n -86.3577657,\n 34.4838855\n ],\n [\n -88.5372925,\n 37.5884002\n ],\n [\n -92.5849851,\n 34.5266496\n ],\n [\n -97.9300151,\n 34.6121119\n ],\n [\n -98.8122046,\n 31.4809337\n ],\n [\n -101.0436249,\n 29.3328733\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"403","noUsgsAuthors":false,"publicationDate":"2026-03-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Sabat-Bonilla, Sergio A.","contributorId":370289,"corporation":false,"usgs":false,"family":"Sabat-Bonilla","given":"Sergio","middleInitial":"A.","affiliations":[{"id":25550,"text":"Virginia Polytechnic Institute and State University","active":true,"usgs":false}],"preferred":false,"id":960116,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Belvin, Abigail C.","contributorId":370290,"corporation":false,"usgs":false,"family":"Belvin","given":"Abigail","middleInitial":"C.","affiliations":[{"id":25550,"text":"Virginia Polytechnic Institute and State University","active":true,"usgs":false}],"preferred":false,"id":960117,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Noe, Gregory E. 0000-0002-6661-2646 gnoe@usgs.gov","orcid":"https://orcid.org/0000-0002-6661-2646","contributorId":139100,"corporation":false,"usgs":true,"family":"Noe","given":"Gregory","email":"gnoe@usgs.gov","middleInitial":"E.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":960118,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Maloney, Kelly O. 0000-0003-2304-0745 kmaloney@usgs.gov","orcid":"https://orcid.org/0000-0003-2304-0745","contributorId":4636,"corporation":false,"usgs":true,"family":"Maloney","given":"Kelly","email":"kmaloney@usgs.gov","middleInitial":"O.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":960119,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Frimpong, Emmanuel A.","contributorId":370293,"corporation":false,"usgs":false,"family":"Frimpong","given":"Emmanuel","middleInitial":"A.","affiliations":[{"id":25550,"text":"Virginia Polytechnic Institute and State University","active":true,"usgs":false}],"preferred":false,"id":960120,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Angermeier, Paul L. 0000-0003-2864-170X","orcid":"https://orcid.org/0000-0003-2864-170X","contributorId":204519,"corporation":false,"usgs":true,"family":"Angermeier","given":"Paul L.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":960121,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Entrekin. Sally E.","contributorId":370299,"corporation":false,"usgs":false,"family":"Entrekin. Sally E.","affiliations":[{"id":25550,"text":"Virginia Polytechnic Institute and State University","active":true,"usgs":false}],"preferred":false,"id":960122,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70274250,"text":"70274250 - 2026 - A framework for integrating spatiotemporal deep learning methods with landsat for annual land cover and impervious surface mapping","interactions":[],"lastModifiedDate":"2026-03-19T19:31:01.642826","indexId":"70274250","displayToPublicDate":"2026-03-05T14:20:03","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"A framework for integrating spatiotemporal deep learning methods with landsat for annual land cover and impervious surface mapping","docAbstract":"The San Francisco Bay supports thousands of breeding waterbirds annually and hosts large populations of American avocets (Recurvirostra americana), black-necked stilts (Himantopus mexicanus), and Forster’s terns (Sterna forsteri). These three species have relied largely on former commercial salt ponds in south San Francisco Bay, which provide wetland foraging habitat and island nesting habitat. The South Bay Salt Pond Restoration Project is in the process of restoring as much as 15,100 acres of these former salt ponds to tidal marsh and tidal mudflats. Although this restoration is expected to have numerous benefits, including providing habitat for tidal wetland-dependent species, improving water quality, buffering against storm surge, and protecting inland areas from sea level rise, the reduction in former salt-pond habitat and nesting islands may negatively affect breeding waterbirds. To address the reduction in former salt-pond habitat available to waterbirds, the South Bay Salt Pond Restoration Project will maintain some pond habitat for wildlife and provide enhancements such as the construction of new islands for nesting. The South Bay Salt Pond Restoration Project follows an adaptive management plan in which waterbird response to the changing landscape is monitored over time to ensure that existing breeding waterbird populations are maintained.
In this report, we provide results of waterbird nest monitoring in south San Francisco Bay during the 2024 breeding season and present these results in the context of annual nest monitoring in south San Francisco Bay since 2005. Overall, Forster’s tern nest abundance in 2024 (1,808 nests) was the highest recorded between 2005 and 2024, and it maintained the high abundance first observed in 2022 (1,727 nests), which reversed the historically low abundance observed during 2015–17. In contrast, nest abundance remained at or near 20-year lows for American avocets (222 nests) and black-necked stilts (126 nests) in 2024, but both species had small increases in their nesting population sizes compared to 2022. In 2024, there were only 3 Forster’s tern, 5 American avocet, and 3 black-necked stilt major colony nesting sites, which is down from the annual averages of 6.6, 12.4, and 6.6 observed during 2005–09. Nest success (73 percent for American avocets, 54 percent for black-necked stilt, and 64 percent for Forster’s terns) increased compared to 2022 (30 percent for American avocets, 29 percent for black-necked stilt, and 53 percent for Forster’s terns) and during 2005–10 (37 percent for American avocets, 24 percent for black-necked stilt, and 61 percent for Forster’s terns). Nest success in 2024 was above (American avocets and black-necked stilts) or slightly below (Forster’s terns) baseline values established for the South Bay Salt Pond Restoration Project. Average egg-hatching success was lower for American avocets (86 percent) and Forster’s terns (86 percent) and similar for black-necked stilts (96 percent) than the values observed during 2005–10. Average clutch sizes for American avocets (3.87 eggs), black-necked stilts (3.88 eggs), and Forster’s terns (2.73 eggs) were greater than what was observed in 2022 and during 2005–10. Average nest-initiation dates in 2024 were substantially earlier among all three species (April 19 for American avocets, April 25 for black-necked stilts, and May 12 for Forster’s terns) than in 2022 (May 4 for American avocets, May 13 for black-necked stilts, and May 20 for Forster’s terns) and during 2005–10 (May 15 for American avocets, May 3 for black-necked stilts, and May 30 for Forster’s terns). Finally, the enhanced managed ponds with newly constructed islands (Ponds A16 and SF2) supported 52 percent of American avocet nests, 47 percent of black-necked stilt nests, and 94 percent of all the Forster’s tern nests recorded in south San Francisco Bay in 2024.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20261064","collaboration":"Prepared in cooperation with the California State Coastal Conservancy, California Wildlife Foundation, California Department of Fish and Wildlife, U.S. Fish and Wildlife Service, and South Bay Salt Pond Restoration Project","programNote":"Ecosystems Mission Area—Land Management Research Program and Species Management Research Program","usgsCitation":"Ackerman, J.T., Hartman, C.A., and Herzog, M., 2026, Monitoring nesting waterbirds for the South Bay Salt Pond Restoration Project—2024 breeding season: U.S. Geological Survey Open-File Report 2026–1064, 27 p., https://doi.org/10.3133/ofr20261064.","productDescription":"Report: vi, 27 p.; Data Release","numberOfPages":"27","onlineOnly":"Y","ipdsId":"IP-177737","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":500738,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13VVTPR","text":"USGS data release","linkHelpText":"Waterbird nest abundance in south San Francisco Bay"},{"id":500737,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2026/1064/images"},{"id":500736,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2026/1064/ofr20261064.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2026-1064 XML"},{"id":500733,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2026/1064/coverthb.jpg"},{"id":500734,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2026/1064/ofr20261064.pdf","text":"Report","size":"4.15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2026-1064 PDF"},{"id":500735,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20261064/full","linkFileType":{"id":5,"text":"html"},"description":"OFR 2026-1064 HTML"}],"country":"United States","state":"California","otherGeospatial":"south San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.25,\n 37.667\n ],\n [\n -122.25,\n 37.4167\n ],\n [\n -121.9,\n 37.4167\n ],\n [\n -121.9,\n 37.667\n ],\n [\n -122.25,\n 37.667\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Following a heavy, multiday rainfall event that took place between June 20 and June 22, 2024, widespread flooding occurred in parts of northwestern Iowa. Ten U.S. Geological Survey (USGS) streamgages with periods of record ranging from 56 to 99 years in length experienced new peaks of record, three of which were more than double the previous peak-of-record: 06483500 (Rock River near Rock Valley, Iowa), 06605850 (Little Sioux River at Linn Grove, Iowa), and 06606600 (Little Sioux River at Correctionville, Iowa). A Presidential declaration of a major disaster for the State of Iowa was approved on June 24, 2024, and the cost of the flooding is estimated at over $310 million. The severity of this flooding prompted the USGS, in cooperation with the Iowa Department of Transportation, to summarize the meteorological and hydrological conditions preceding the flooding, compile estimates of the magnitude of peak flows resulting from the flooding, and update estimates of peak-flow frequency for selected USGS streamgages. Of the 33 streamgages analyzed, a peak streamflow occurred that corresponded to an annual exceedance probability of less than 4 percent at 13 streamgages, an annual exceedance probability of less than 1 percent at 6 streamgages, and an annual exceedance probability of less than 0.2 percent at 1 streamgage.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20261066","collaboration":"Prepared in cooperation with the Iowa Department of Transportation","usgsCitation":"Marti, M.K., and O’Shea, P.S., 2026, Floods of June 2024 in northwestern Iowa: U.S. Geological Survey Open-File Report 2026–1066, 16 p., https://doi.org/10.3133/ofr20261066.","productDescription":"Report: vi, 16 p.; Data Release","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-175807","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":500762,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20261066/full"},{"id":500761,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2026/1066/images/"},{"id":500760,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2026/1066/ofr20261066.XML"},{"id":500759,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2026/1066/ofr20261066.pdf","text":"Report","size":"2.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2026-1066"},{"id":500758,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2026/1066/coverthb.jpg"},{"id":501165,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119301.htm","linkFileType":{"id":5,"text":"html"}},{"id":500763,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1JFCNSZ","text":"USGS data release","linkHelpText":"Peak-flow frequency analysis for U.S. Geological Survey streamgages in northwestern Iowa, based on data through water year 2024"}],"country":"United States","state":"Iowa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -96.667,\n 43.6\n ],\n [\n -96.667,\n 41.667\n ],\n [\n -93,\n 41.667\n ],\n [\n -93,\n 43.6\n ],\n [\n -96.667,\n 43.6\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, Iowa 52240
A new oceanographic radar instrument package was developed by the U.S. Geological Survey (USGS) to measure storm waves and water levels in the nearshore, capable of being deployed rapidly and transmitting data in near real-time. To test the performance and accuracy of the sensor, multiple years of data were collected over various hydrodynamic conditions and compared to long-term monitoring data collected at the U.S. Army Corps of Engineers (USACE) Field Research Facility in Duck, North Carolina, USA. The oceanographic radars were highly reliable, with less than 1% of the record being erroneous spikes or missing data points. At the end of the pier, the radar was highly accurate, with nearly perfect agreement in water level (r2 = 0.997) compared to a nearby National Oceanic and Atmospheric Administration (NOAA) tide gauge, and good agreement in significant wave height (r2 = 0.98) and peak wave period (r2 = 0.65) compared to a nearby USACE sensor. This work demonstrates the potential of the USGS radar for rapid response storm deployments and collecting reliable and accurate hydrodynamic measurements in the nearshore for validating coastal impact models.
","conferenceTitle":"Coastal Dynamics 2025","conferenceDate":"April 7-11, 2025","conferenceLocation":"Aveiro, Portugal","language":"English","publisher":"Springer","doi":"10.1007/978-3-032-15473-6_106","usgsCitation":"Brown, J., McClenney, B.J., and Dickhudt, P., 2026, Measuring storm waves and water levels from a fixed structure with a rapidly deployable oceanographic radar, Coastal Dynamics 2025, Aveiro, Portugal, April 7-11, 2025, p. 696-702, https://doi.org/10.1007/978-3-032-15473-6_106.","productDescription":"7 p.","startPage":"696","endPage":"702","ipdsId":"IP-174186","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":500987,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":501099,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/978-3-032-15473-6_106","text":"Publisher Index Page"}],"country":"United States","state":"North Carolina","city":"Duck","noUsgsAuthors":false,"publicationDate":"2026-03-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Brown, Jenna A. 0000-0003-3137-7073","orcid":"https://orcid.org/0000-0003-3137-7073","contributorId":208564,"corporation":false,"usgs":true,"family":"Brown","given":"Jenna A.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":956978,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McClenney, Bryce J 0009-0007-6454-2078","orcid":"https://orcid.org/0009-0007-6454-2078","contributorId":367183,"corporation":false,"usgs":true,"family":"McClenney","given":"Bryce","middleInitial":"J","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":956979,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dickhudt, Patrick J. ","contributorId":169593,"corporation":false,"usgs":false,"family":"Dickhudt","given":"Patrick J. ","affiliations":[{"id":25562,"text":"(former) Woods Hole Coastal and Marine Science Center employee","active":true,"usgs":false}],"preferred":false,"id":956980,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70274219,"text":"70274219 - 2026 - Groundwater dependency and hydroclimatic influences on riparian and upland vegetation productivity, Upper San Pedro, Arizona, United States","interactions":[],"lastModifiedDate":"2026-03-13T15:02:27.64804","indexId":"70274219","displayToPublicDate":"2026-03-04T09:37:40","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Groundwater dependency and hydroclimatic influences on riparian and upland vegetation productivity, Upper San Pedro, Arizona, United States","docAbstract":"In arid and semi-arid regions, groundwater sustains vegetation through subsurface water access, yet the responses of groundwater-dependent ecosystems (GDEs) to changing hydroclimate and groundwater availability are relatively understudied. This study investigates seasonal and spatial patterns in vegetation greenness using Landsat Enhanced Vegetation Index (EVI) values across riparian and upland zones in the semi-arid Upper San Pedro (USP) watershed, southern Arizona, which experiences a bimodal precipitation regime. We paired 25 years (2000–2024) of EVI and depth to groundwater (DTG) data from 89 wells and climate metrics (precipitation and vapour pressure deficit) to quantify the sensitivity of vegetation to subsurface moisture as well as atmospheric moisture supply and demand. Vegetation at wells near the USP riparian area showed strong associations between EVI and DTG anomalies during the monsoon season, indicating sustained groundwater use even during this wet period when summer precipitation is abundant. In contrast, upland vegetation that lacked access to groundwater showed minimal sensitivity in EVI to DTG and was generally less responsive to vapour pressure deficit. Interestingly, the riparian GDEs were not decoupled from precipitation and climate variability. These results underscore the importance of groundwater for maintaining riparian productivity and highlight the utility of remote sensing in identifying vegetation-climate-groundwater linkages across heterogeneous dryland landscapes.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.70405","usgsCitation":"Bromley, F., Borxton, P., Zhang, J., van Leeuwen, W.J., Nagler, P., and Hu, J., 2026, Groundwater dependency and hydroclimatic influences on riparian and upland vegetation productivity, Upper San Pedro, Arizona, United States: Hydrological Processes, v. 40, no. 3, e70405, 18 p., https://doi.org/10.1002/hyp.70405.","productDescription":"e70405, 18 p.","ipdsId":"IP-180542","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":501360,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.70405","text":"Publisher Index Page"},{"id":501145,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Upper San Pedro watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.31987279028372,\n 31.82228360728554\n ],\n [\n -110.31987279028372,\n 31.379497469636988\n ],\n [\n -109.98150823782808,\n 31.379497469636988\n ],\n [\n -109.98150823782808,\n 31.82228360728554\n ],\n [\n -110.31987279028372,\n 31.82228360728554\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"40","issue":"3","noUsgsAuthors":false,"publicationDate":"2026-03-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Bromley, Fern 0000-0003-0596-1487","orcid":"https://orcid.org/0000-0003-0596-1487","contributorId":367222,"corporation":false,"usgs":false,"family":"Bromley","given":"Fern","affiliations":[{"id":36671,"text":"School of Natural Resources and the Environment, University of Arizona","active":true,"usgs":false}],"preferred":false,"id":957082,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Borxton, Patrick 0000-0002-2665-6820","orcid":"https://orcid.org/0000-0002-2665-6820","contributorId":248510,"corporation":false,"usgs":false,"family":"Borxton","given":"Patrick","email":"","affiliations":[{"id":49935,"text":"2University of Arizona, School of Natural Resources and the Environment","active":true,"usgs":false}],"preferred":false,"id":957083,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhang, Jiaqi","contributorId":202467,"corporation":false,"usgs":false,"family":"Zhang","given":"Jiaqi","email":"","affiliations":[{"id":36453,"text":"University of Texas, Arlington, TX, USA","active":true,"usgs":false}],"preferred":false,"id":957084,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"van Leeuwen, Willem J.D. 0000-0002-3188-7172","orcid":"https://orcid.org/0000-0002-3188-7172","contributorId":191856,"corporation":false,"usgs":false,"family":"van Leeuwen","given":"Willem","middleInitial":"J.D.","affiliations":[],"preferred":false,"id":957085,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nagler, Pamela L. 0000-0003-0674-103X","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":363777,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","middleInitial":"L.","affiliations":[],"preferred":true,"id":957086,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hu, Jia","contributorId":367226,"corporation":false,"usgs":false,"family":"Hu","given":"Jia","affiliations":[],"preferred":false,"id":957087,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70273889,"text":"70273889 - 2026 - Changing drivers of regional large magnitude avalanche frequency throughout Colorado, USA","interactions":[],"lastModifiedDate":"2026-03-23T14:02:07.561392","indexId":"70273889","displayToPublicDate":"2026-03-04T08:59:53","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2824,"text":"Natural Hazards and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Changing drivers of regional large magnitude avalanche frequency throughout Colorado, USA","docAbstract":"Large magnitude snow avalanches (destructive size ≥ D3) impact settlements, transportation corridors, and public safety worldwide. In Colorado, United States, avalanches have killed more people than any other natural hazard since 1950. In March 2019, a large magnitude avalanche cycle occurred throughout the entire mountainous portion of Colorado resulting in more than 1000 reported avalanches during a two-week period. Nearly 200 of these avalanches were size D4 or larger with at least three D5 avalanches. However, placing this 2019 large magnitude avalanche cycle in historic context requires data prior to the instrumental record. Here, we paired tree disturbance data from dendrochronology (1698 to 2020) with meteorological data from the modeled and instrumental record (1901 to 2020) to understand the frequency and climate drivers of large magnitude snow avalanche cycles. The extensive number of downed trees from the 2019 avalanche cycle allowed us to collect 1,188 cross-sections and cores from 1023 individual trees within 24 avalanche paths across the state. From these samples we identified 4135 avalanche-related growth disturbances. We employed a strategic nested sampling design to spatially aggregate avalanche frequency from individual avalanche paths, to counties, to three major sub-regions (i.e., north, central, and south), and across the entire region (i.e., state of Colorado). Over a period spanning more than three centuries (1698 to 2020), we identified 76 avalanche years within 24 individual avalanche paths. Large magnitude avalanche event frequency varied across paths and sub-regions with several notable region-wide avalanche cycles. Both tree-ring and historical written records highlighted 1899 as a year with widespread and large magnitude avalanche activity similar to the March 2019 avalanche cycle. Since the early-20th century (1900 to 2020) regional avalanche probability declined significantly in parallel with decreasing snowpack throughout Colorado. Similarly, dominant avalanche regimes shifted from large magnitude regional cycles driven by above average snowfall years over most of the record, to regional avalanche cycles occurring more commonly in average to low snow years since 1988. In recent decades, a lack of December precipitation and above average March precipitation characterized years with regional large magnitude avalanche activity. Even with declining snow water equivalent, truly extreme regional large magnitude avalanche cycles remain possible – as demonstrated by the 2019 cycle. This underscores that rare but high-impact events are not eliminated by long-term trends. Understanding the changing snow and weather drivers and subsequent behavior of large magnitude avalanche cycles across multiple spatial scales may improve avalanche forecasting and the products and mitigations strategies developed by structural engineers to mitigate avalanche danger. This can decrease the avalanche risk to the public and improve infrastructure design in avalanche terrain.
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/nhess-26-1059-2026","usgsCitation":"Peitzsch, E.H., Martin, J.T., Greene, E.M., Eckert, N., Favillier, A., Konigsberg, J., Kichas, N., Stahle, D.K., Birkeland, K.W., Elder, K., and Pederson, G.T., 2026, Changing drivers of regional large magnitude avalanche frequency throughout Colorado, USA: Natural Hazards and Earth System Sciences, v. 26, p. 1059-1074, https://doi.org/10.5194/nhess-26-1059-2026.","productDescription":"16 p.","startPage":"1059","endPage":"1074","ipdsId":"IP-175486","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":501654,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/nhess-26-1059-2026","text":"Publisher Index Page"},{"id":499809,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -109.129166385724,\n 41.04962213955744\n ],\n [\n -109.129166385724,\n 36.99334376580887\n ],\n [\n -102.04655644997314,\n 36.99334376580887\n ],\n [\n -102.04655644997314,\n 41.04962213955744\n ],\n [\n -109.129166385724,\n 41.04962213955744\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"26","noUsgsAuthors":false,"publicationDate":"2026-03-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Peitzsch, Erich H. 0000-0001-7624-0455","orcid":"https://orcid.org/0000-0001-7624-0455","contributorId":202576,"corporation":false,"usgs":true,"family":"Peitzsch","given":"Erich","middleInitial":"H.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science 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California","interactions":[],"lastModifiedDate":"2026-04-10T15:34:37.991321","indexId":"ofr20261065","displayToPublicDate":"2026-03-03T12:16:41","publicationYear":"2026","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":"2026-1065","displayTitle":"Evaluation of Pathogen Risks and Testing Considerations for Chinook Salmon Egg Movements Between New Zealand and California","title":"Evaluation of pathogen risks and testing considerations for Chinook salmon egg movements between New Zealand and California","docAbstract":"Oncorhynchus tshawytscha (Walbaum in Artedi, 1792; Chinook salmon) were historically abundant in the McCloud River but are now extirpated from this tributary owing to dam construction and lack of passage. Planning efforts to restore populations above Shasta and Keswick Dams are currently underway, including an evaluation of potential source populations. One potential source is New Zealand Chinook salmon, which are believed to have originated from tributaries of the Sacramento River. These fish could be returned to California if reintroduction risks, including risks of pathogen introduction, could be sufficiently mitigated. The U.S. Geological Survey was contracted to provide scientific support for reintroduction efforts, including evaluating the risks of pathogen transmission via the movement of Chinook salmon eggs from New Zealand to the McCloud River. This report estimates pathogen risks associated with egg movement and considers epidemiological and biosecurity measures to minimize these risks.
Pathogen risks associated with the movement of Chinook salmon eggs from New Zealand were evaluated based on pathogen virulence, transmission route, and geographic distribution. These criteria identified 14 moderate- and high-risk pathogens out of the 30 pathogens evaluated. Pathogen species and strains were considered high risk if they have the potential for vertical transmission (that is, transmission from parent to offspring), are moderately or highly virulent, and are exotic to the Sacramento River Basin. According to these criteria, we identified the following pathogens as high risk:
Strategic use of testing and biosecurity measures can minimize pathogen risks associated with the movement of eggs. The most effective measures include iodophor treatment of eggs to remove external pathogens, testing of all the adult fish from which gametes are obtained, and a quarantine period after transport to confirm pathogen testing results. Additional measures to enhance biosecurity could include testing the quarantined fish following emergence and (or) developing a fish health history of the source population through pathogen monitoring.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20261065","collaboration":"Prepared in cooperation with California Department of Fish and Wildlife, Anchor QEA, and HDR","programNote":"Land Management Research Program and Species Management Research Program","usgsCitation":"Couch, C.E., Powell, D.B., and Lovy, J., 2026, Evaluation of pathogen risks and testing considerations for Chinook salmon egg movements between New Zealand and California: U.S. Geological Survey Open-File Report 2026–1065, 18 p., https://doi.org/10.3133/ofr20261065.","productDescription":"vi, 18 p.","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-182977","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":500703,"rank":5,"type":{"id":39,"text":"HTML 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\"}}]}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
5501-A Cook Underwood Road
Cook, Washington 98605-9717
This report presents the results of a cooperative study by the U.S. Geological Survey and Missouri Department of Natural Resources to quantify sediment transport source contributions in the Medicine Creek drainage basin. Understanding relative source contributions provides valuable information for selecting the conservation practices that may be most effective in reducing sediment and sediment-associated nutrient transport in the Medicine Creek drainage basin and similar areas of the Lower Grand River drainage basin. Sediment samples were collected from potential contributing areas (source samples) and from fluvial-transported samples (target samples). Source sample types included streambanks, row crop fields, and a combined pastures and forests category. Samples were analyzed for particle size and quantity of carbon, nitrogen, stable isotopes of carbon and nitrogen, and 49 mineral elements as potential tracers. Results for the carbon stable isotope ratio of carbon-13/carbon-12 (δ13C) and concentrations of total carbon, total nitrogen, calcium, potassium, and copper were selected by discriminant function analysis as the best combination of multiple tracers to differentiate each source type. The discriminant function analysis poorly differentiated pastures and forests, so these source types were combined. The sources defined by the discriminant function analysis were then used in an unmixing model to apportion sources for each target sample.
In the study area, transported sediment was predominantly bank sediment, with an overall average of 86.9 percent of suspended-sediment samples and depositional streambed samples attributed to bank material. Suspended-sediment samples from the mainstem of Medicine Creek were dominated by bank sediments (average of 95.8 percent), and depositional streambed samples from throughout the drainage basin had more variable source contributions with an average of 71.1 percent attributed to bank material. The relative importance of upland sources (row crop fields and the combined pastures and forests category) varied seasonally and with streamflow but was not related to land use or drainage basin size. Relative contributions from upland sources were greater in the summer through winter rather than spring and during lower streamflow, though this may be driven by the seasonality of streamflow. These results indicate management practices that reduce bank erosion could be effective strategies for managing the dominant source of sediment and sediment-associated phosphorus.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20265121","collaboration":"Prepared in cooperation with Missouri Department of Natural Resources","usgsCitation":"Garrett, J.D., 2026, Stream sediment sources in Medicine Creek, northern Missouri and southern Iowa: U.S. Geological Survey Scientific Investigations Report 2026–5121, 11 p., https://doi.org/10.3133/sir20265121.","productDescription":"Report: vi, 11 p.; Data Release; Dataset","numberOfPages":"22","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-164057","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":501166,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119303.htm","linkFileType":{"id":5,"text":"html"}},{"id":500681,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":500680,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13EN5TA","text":"USGS data release","linkHelpText":"Chemical and physical data for sediment source and fluvial target samples for fingerprinting of suspended and bed sediment in Medicine Creek, Missouri and Iowa"},{"id":500679,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20265121/full"},{"id":500678,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2026/5121/images/"},{"id":500677,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2026/5121/sir20265121.XML"},{"id":500676,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2026/5121/sir20265121.pdf","text":"Report","size":"1.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2026-5121"},{"id":500675,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2026/5121/coverthb.jpg"}],"country":"United States","state":"Iowa, Missouri","otherGeospatial":"Medicine Creek drainage basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.1667,\n 40.75\n ],\n [\n -93.5,\n 40.75\n ],\n [\n -93.5,\n 40\n ],\n [\n -93.1667,\n 40\n ],\n [\n -93.1667,\n 40.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
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400 South Clinton Street, Suite 269
Iowa City, IA 52240
Antimicrobial resistance can be an indicator of anthropogenic contamination in surface waters and is a potential public health threat. Methodological standardization for characterization of antimicrobial resistance in the environment is lacking. Quantitative PCR (qPCR) is used for rapid assessment of antibiotic resistance genes (ARGs) from environmental sources, including surface water. Here we describe the validation and application of a qPCR assay for 47 bacterial gene targets intended for surface water samples. The qPCR assay displayed excellent sensitivity (97.66%) and specificity (98.71%) for detecting ARGs when compared to whole genome sequencing of bacterial isolates. The qPCR assay was able to detect up to 6/8 (75.0%) of ARGs spiked into sterile water at varying concentrations and four sample ultrafiltration volumes. Nineteen different ARGs were detected across six samples sites at three national parks in Alaska using ultrafiltered surface water samples. The number of unique ARGs detected was higher at sites within parks with greater visitation. The relative abundance of ARGs/16S from Exit Creek in Kenai Fjords National Park, downstream from a visitor center was greater than all other sampled sites. We have demonstrated a robust qPCR assay for monitoring ARGs in surface waters, including those that are minimally human impacted.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-026-35635-x","usgsCitation":"Scott, L.C., Ahlstrom, C., Woksepp, H., Bonnedahl, J., Mckeeman, C.M., Miller, M.E., Shirokauer, D., and Ramey, A.M., 2026, Validation and application of a standardized quantitative PCR assay for the assessment of antimicrobial resistance genes in surface water: Scientific Reports, v. 16, 11597, 14 p., https://doi.org/10.1038/s41598-026-35635-x.","productDescription":"11597, 14 p.","ipdsId":"IP-172114","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":503763,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-026-35635-x","text":"Publisher Index Page"},{"id":503518,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.99719849243888,\n 64.323291045561\n ],\n [\n -139.26423702947662,\n 64.323291045561\n ],\n [\n -139.26423702947662,\n 58.766526235513254\n ],\n [\n -155.99719849243888,\n 58.766526235513254\n ],\n [\n -155.99719849243888,\n 64.323291045561\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","noUsgsAuthors":false,"publicationDate":"2026-03-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Scott, Laura Celeste 0000-0003-0303-5340","orcid":"https://orcid.org/0000-0003-0303-5340","contributorId":306143,"corporation":false,"usgs":true,"family":"Scott","given":"Laura","email":"","middleInitial":"Celeste","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":960233,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ahlstrom, Christina 0000-0001-5414-8076","orcid":"https://orcid.org/0000-0001-5414-8076","contributorId":214540,"corporation":false,"usgs":true,"family":"Ahlstrom","given":"Christina","email":"","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":960234,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Woksepp, Hanna","contributorId":207263,"corporation":false,"usgs":false,"family":"Woksepp","given":"Hanna","email":"","affiliations":[],"preferred":false,"id":960235,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bonnedahl, Jonas","contributorId":181800,"corporation":false,"usgs":false,"family":"Bonnedahl","given":"Jonas","email":"","affiliations":[],"preferred":false,"id":960236,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mckeeman, Cherie Marie 0000-0001-9868-2502","orcid":"https://orcid.org/0000-0001-9868-2502","contributorId":334651,"corporation":false,"usgs":true,"family":"Mckeeman","given":"Cherie","email":"","middleInitial":"Marie","affiliations":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"preferred":true,"id":960237,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Miller, Mark E.","contributorId":367864,"corporation":false,"usgs":false,"family":"Miller","given":"Mark","middleInitial":"E.","affiliations":[{"id":87633,"text":"NPS Wrangell-St. Elias National Park and Preserve","active":true,"usgs":false}],"preferred":false,"id":960238,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Shirokauer, Dave","contributorId":353626,"corporation":false,"usgs":false,"family":"Shirokauer","given":"Dave","affiliations":[],"preferred":false,"id":960239,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ramey, Andrew M. 0000-0002-3601-8400 aramey@usgs.gov","orcid":"https://orcid.org/0000-0002-3601-8400","contributorId":1872,"corporation":false,"usgs":true,"family":"Ramey","given":"Andrew","email":"aramey@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":960240,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70274656,"text":"70274656 - 2026 - Detection of Naegleria fowleri in thermally impacted recreational waters of western United States national parks","interactions":[],"lastModifiedDate":"2026-04-02T15:33:42.37614","indexId":"70274656","displayToPublicDate":"2026-03-02T10:21:18","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10742,"text":"ACS ES&T Water","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Detection of Naegleria fowleri in thermally impacted recreational waters of western United States national parks","title":"Detection of Naegleria fowleri in thermally impacted recreational waters of western United States national parks","docAbstract":"Naegleria fowleri is a thermophilic free-living amoeba (FLA) and the causative agent of primary amoebic meningoencephalitis, posing public health risks in warm freshwater environments. This multiyear, multiagency study surveyed 40 thermally impacted recreational waters across five western United States national parks and recreation areas–Yellowstone National Park, Grand Teton National Park, Olympic National Park, Newberry National Volcanic Monument, and Lake Mead National Recreation Area–to assess N. fowleri presence, concentration, and associated environmental conditions. A total of 185 water samples were analyzed by qPCR and Sanger sequencing, revealing widespread detection of N. fowleri in 34% of samples with positive detections from Lake Mead, Yellowstone, and Grand Teton hot springs and thermally impacted waters, with concentrations ranging from 4.9 to 115.7 cells/L. Multiple codetections of N. fowleri with nonpathogenic species including Naegleria australiensis were identified, suggesting they may inhabit similar ecological niches in the natural systems in contrast to engineered systems. These findings indicate that N. fowleri is present in thermally impacted areas across the western United States and underscore the use of enhanced monitoring, public awareness, and risk management strategies in thermally influenced recreational waters.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acsestwater.5c01243","usgsCitation":"Shikany, J.I., Banks, M.M., Barnhart, E.P., Kinsey, S., Wright, P.R., Kageyama, S.A., Merkes, C.M., Kulesza, N., Wylie, J., Halonen, S., Ortega-Villa, A.M., Long, C.M., Peyton, B.M., and Puzon, G., 2026, Detection of Naegleria fowleri in thermally impacted recreational waters of western United States national parks: ACS ES&T Water, v. 6, no. 3, p. 1704-1715, https://doi.org/10.1021/acsestwater.5c01243.","productDescription":"12 p.","startPage":"1704","endPage":"1715","ipdsId":"IP-184597","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":502067,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acsestwater.5c01243","text":"Publisher Index 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Carrie M.","contributorId":369192,"corporation":false,"usgs":false,"family":"Long","given":"Carrie","middleInitial":"M.","affiliations":[{"id":87735,"text":"Laboratory of Bacteriology, Division of Intramural Research, National Institute of Allergy and Infectious Disease, National Institutes of Health, Hamilton, MT, USA","active":true,"usgs":false}],"preferred":false,"id":958593,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Peyton, Brent M.","contributorId":369193,"corporation":false,"usgs":false,"family":"Peyton","given":"Brent","middleInitial":"M.","affiliations":[{"id":87731,"text":"Center for Biofilm Engineering, Chemical and Biological Engineering Department, Montana State University, 366 Barnard Hall, Bozeman, Montana, United States","active":true,"usgs":false}],"preferred":false,"id":958594,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Puzon, 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Though glaciers in the Alaska region (as defined by glaciologists this includes both Alaska and portions of adjacent Canada) range from sea level to nearly 6200 m (20,320 ft), the majority of glacier area in the Alaska region is concentrated between 900 and 2100 m (2950 to 6890 ft). Long term glacier monitoring in Alaska by the U.S. Geological Survey (USGS) Benchmark Glacier Project is on moderate-sized glaciers with distributions of glacier area in this elevation range. These are some of the longest in-situ records of glacier mass change in the world. The process-based understanding of glacier change on those “Benchmark Glaciers” is robust, but it is limited to the range of conditions present on those particular glaciers—at moderate elevations—where large amounts of melt water and rain pass through the glacier and into the downstream ecosystem on an annual basis.","language":"English","publisher":"U.S. National Park Sevice","usgsCitation":"Sass, L., McNeil, C., Baker, E.A., Frederick, Z.A., and Loso, M., 2026, Brewing change in the (glacier) percolation zone: Alaska Park Science, v. 24, no. 1, p. 2-15.","productDescription":"14 p.","startPage":"2","endPage":"15","ipdsId":"IP-182134","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":501727,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":501707,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://irma.nps.gov/DataStore/Reference/Profile/2317596"}],"country":"United 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Service","active":true,"usgs":false}],"preferred":false,"id":958021,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70274497,"text":"70274497 - 2026 - Understanding flooding and channel dynamics along the Taiya River: Providing context for resource management","interactions":[],"lastModifiedDate":"2026-03-27T17:08:43.02844","indexId":"70274497","displayToPublicDate":"2026-03-01T12:05:40","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":691,"text":"Alaska Park Science","printIssn":"1545- 496","active":true,"publicationSubtype":{"id":10}},"title":"Understanding flooding and channel dynamics along the Taiya River: Providing context for resource management","docAbstract":"Flooding and channel change in the Taiya River Basin in recent decades have directly affected\npark infrastructure and cultural resources. The complexities of flooding and channel change are compounded by the changing sediment and flow regime from a changing climate and\nshrinking glaciers, which will continue to drive dynamic riverine change. Streamflow data and\ngeomorphic interpretation helped us place these events in context to inform decision making that\ntakes dynamic natural processes into account.","language":"English","publisher":"National Park Service","usgsCitation":"Curran, J.H., 2026, Understanding flooding and channel dynamics along the Taiya River: Providing context for resource management: Alaska Park Science, v. 24, no. 1, p. 26-35.","productDescription":"10 p.","startPage":"26","endPage":"35","ipdsId":"IP-182307","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":501726,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":501705,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://irma.nps.gov/DataStore/Reference/Profile/2317596"}],"country":"United Statesq","state":"Alaska","otherGeospatial":"Taiya River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -135.40358674059615,\n 59.43440777292025\n ],\n [\n -135.16293100255444,\n 59.465315065891104\n ],\n [\n -135.17678488217464,\n 59.63505469200757\n ],\n [\n -135.48037695028358,\n 59.796116294791744\n ],\n [\n -135.80292468090636,\n 59.69771870037559\n ],\n [\n -135.45205191120436,\n 59.41808754397522\n ],\n [\n -135.40358674059615,\n 59.43440777292025\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"24","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Curran, Janet H. 0000-0002-3899-6275 jcurran@usgs.gov","orcid":"https://orcid.org/0000-0002-3899-6275","contributorId":690,"corporation":false,"usgs":true,"family":"Curran","given":"Janet","email":"jcurran@usgs.gov","middleInitial":"H.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":958013,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70274284,"text":"70274284 - 2026 - Hyperspectral retrieval of phytoplankton absorption and community composition from NASA’s PACE-OCI in estuarine–coastal waters using a hybrid framework combining mixture-of-experts and Variational Autoencoder","interactions":[],"lastModifiedDate":"2026-03-24T17:58:00.328721","indexId":"70274284","displayToPublicDate":"2026-02-28T10:36:32","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Hyperspectral retrieval of phytoplankton absorption and community composition from NASA’s PACE-OCI in estuarine–coastal waters using a hybrid framework combining mixture-of-experts and Variational Autoencoder","docAbstract":"Retrieving the phytoplankton absorption coefficient (aphy; m−1), one of the most spectrally rich inherent optical properties, remains challenging in optically complex coastal waters worldwide. Leveraging NASA's new hyperspectral mission, PACE, we introduce Hyper-MoE-VAE, a deep-learning architecture that integrates a Mixture-of-Experts with a Variational Autoencoder to retrieve high-dimensional aphy and subsequent estimation of phytoplankton community composition (PCC) from PACE-OCI hyperspectral remote sensing reflectance (Rrs). Pre-trained on global hyperspectral bio-optical datasets and fine-tuned using regional field Rrs–aphy pairings from inland– estuarine–coastal waters, Hyper-MoE-VAE demonstrated strong transferability and effective adaptation across regions. Validation with in-situ Rrs showed accurate aphy retrievals in Lake Erie (NRMSE = 0.12, ε = 17.10), Lake Pontchartrain (NRMSE = 0.11, ε = 37.12), and the Barataria–Terrebonne Estuary (NRMSE = 0.14, ε = 38.89). Using same-day PACE-OCI Level 2 Rrs, the model achieved comparable performance in Lake Erie (NRMSE = 0.19, ε = 55.19), Lake Pontchartrain (NRMSE = 0.14, ε = 51.39), and the Barataria–Terrebonne Estuary (NRMSE = 0.17, ε = 47.92). Hyper-MoE-VAE derived PACE-OCI hyperspectral aphy was further decomposed against mass-specific absorption spectra to estimate group-specific contributions to total chlorophyll a. The resulting PCC showed strong agreement with HPLC–CHEMTAX in Lake Erie (R2= 0.692) and Gulf estuarine–coastal systems (R2 = 0.732). Monte Carlo noise experiments further revealed group-dependent sensitivities, with diatoms and dinoflagellates showing moderate susceptibility to noise, while cyanobacteria and cryptophytes exhibited narrow uncertainty distributions. These results demonstrate Hyper-MoE-VAE's capability for regional, operational water-quality monitoring with PACE-OCI and its adaptability to current and future hyperspectral missions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2026.115327","usgsCitation":"Bai, X., Liu, B., Li, J., Xiong, Y., D'Sa, E.J., Baustian, M.M., Zhang, X., Grunert, B.K., Emeghiebo, C.O., Glasspie, C., and Yuan, X., 2026, Hyperspectral retrieval of phytoplankton absorption and community composition from NASA’s PACE-OCI in estuarine–coastal waters using a hybrid framework combining mixture-of-experts and Variational Autoencoder: Remote Sensing of Environment, v. 337, 115327, 21 p., https://doi.org/10.1016/j.rse.2026.115327.","productDescription":"115327, 21 p.","ipdsId":"IP-183464","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":501687,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2026.115327","text":"Publisher Index 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Lafayette","active":true,"usgs":false}],"preferred":false,"id":957612,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Glasspie, Cassie","contributorId":367744,"corporation":false,"usgs":false,"family":"Glasspie","given":"Cassie","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":957613,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Yuan, Xu","contributorId":367734,"corporation":false,"usgs":false,"family":"Yuan","given":"Xu","affiliations":[{"id":13359,"text":"University of Delaware","active":true,"usgs":false}],"preferred":false,"id":957614,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70274263,"text":"70274263 - 2026 - Mercury cycling across a U.S. semi-arid mountain ecosystem elevation gradient","interactions":[],"lastModifiedDate":"2026-03-24T14:10:42.104231","indexId":"70274263","displayToPublicDate":"2026-02-28T09:05:05","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9326,"text":"JGR Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Mercury cycling across a U.S. semi-arid mountain ecosystem elevation gradient","docAbstract":"Mountains comprise ∼30% of the Earth's surface, but mercury (Hg) cycling in these regions remains understudied, particularly in the semi-arid western U.S. where strong climatic and ecological gradients in mountainous landscapes influence Hg deposition, retention, and bioaccumulation. In this study, we quantified growing season inputs, storage, and bioaccumulation of Hg along a ∼2,000 m elevation gradient in the Colorado Rocky Mountains, spanning the plains to the alpine. We measured Hg in atmospheric deposition, vegetation, soil, and 12-day-old chickadees. Accounting for percent canopy cover, open precipitation was the largest component of atmospheric deposition at all elevations, followed by throughfall and litterfall fluxes. Atmospheric Hg fluxes peaked at mid-elevations, likely due to cloud-cap dynamics and denser canopy cover. Total gaseous Hg and precipitation fluxes were highest at low elevations, likely reflecting local emissions and meteorological pooling. Surface soil Hg storage was more strongly predicted by organic matter content (R2 = 0.49; p < 0.01) and water retention (R2 = 0.45; p < 0.01) than by elevation (R2 = 0.21; p < 0.05). Alpine soils (66.3 ± 25.3 ng g−1) had significantly higher total Hg concentrations than lower elevations (<41.0 ± 12.7 ng g−1; p < 0.01), likely reflecting slower organic matter turnover. Soils on north-facing slopes also retained significantly higher pools of Hg in surface soils compared with south- and east-facing slopes. Vegetation Hg pools were greatest in the alpine region, likely due to long-lived plant species. Methylmercury (MeHg) concentrations in chickadee feathers peaked at mid-elevations (205 ± 155 ng g−1), corresponding to higher ecosystem Hg inputs via throughfall. Our results show that deposition, canopy cover, and meteorological conditions—not elevation alone—predict Hg retention and bioaccumulation.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2025JG009556","usgsCitation":"Miller, H.R., Janssen, S., Taylor, S.A., Gerson, J.R., McIntosh, T.L., and Hinckley, E.S., 2026, Mercury cycling across a U.S. semi-arid mountain ecosystem elevation gradient: JGR Biogeosciences, v. 131, no. 3, e2025JG009556, 19 p., https://doi.org/10.1029/2025JG009556.","productDescription":"e2025JG009556, 19 p.","ipdsId":"IP-177248","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":501443,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"headwaters of the Boulder Creek Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105.58,\n 40.06\n ],\n [\n -105.58,\n 39.98\n ],\n [\n -105.27,\n 39.98\n ],\n [\n -105.27,\n 40.06\n ],\n [\n -105.58,\n 40.06\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"131","issue":"3","noUsgsAuthors":false,"publicationDate":"2026-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Miller, Hannah R.","contributorId":367690,"corporation":false,"usgs":false,"family":"Miller","given":"Hannah","middleInitial":"R.","affiliations":[{"id":16144,"text":"University of Colorado-Boulder","active":true,"usgs":false}],"preferred":false,"id":957444,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Janssen, Sarah E. 0000-0003-4432-3154","orcid":"https://orcid.org/0000-0003-4432-3154","contributorId":210991,"corporation":false,"usgs":true,"family":"Janssen","given":"Sarah E.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":957445,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Taylor, Scott A.","contributorId":367691,"corporation":false,"usgs":false,"family":"Taylor","given":"Scott","middleInitial":"A.","affiliations":[{"id":16144,"text":"University of Colorado-Boulder","active":true,"usgs":false}],"preferred":false,"id":957446,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gerson, Jacqueline R.","contributorId":367692,"corporation":false,"usgs":false,"family":"Gerson","given":"Jacqueline","middleInitial":"R.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":957447,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McIntosh, Tyler L.","contributorId":367693,"corporation":false,"usgs":false,"family":"McIntosh","given":"Tyler","middleInitial":"L.","affiliations":[{"id":16144,"text":"University of Colorado-Boulder","active":true,"usgs":false}],"preferred":false,"id":957448,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hinckley, Eve-Lyn S.","contributorId":367694,"corporation":false,"usgs":false,"family":"Hinckley","given":"Eve-Lyn","middleInitial":"S.","affiliations":[{"id":16144,"text":"University of Colorado-Boulder","active":true,"usgs":false}],"preferred":false,"id":957449,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70274197,"text":"70274197 - 2026 - Terrestrial ecosystem response to changing temperature and seasonality in the Paleocene-Eocene Thermal Maximum: Shallow marine records from the Salisbury Embayment, USA","interactions":[],"lastModifiedDate":"2026-03-10T13:41:31.319054","indexId":"70274197","displayToPublicDate":"2026-02-28T08:12:37","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5790,"text":"Paleoceanography and Paleoclimatology","active":true,"publicationSubtype":{"id":10}},"title":"Terrestrial ecosystem response to changing temperature and seasonality in the Paleocene-Eocene Thermal Maximum: Shallow marine records from the Salisbury Embayment, USA","docAbstract":"The Paleocene-Eocene thermal maximum (PETM, ∼56 Ma) is marked by a massive and rapid rise in atmospheric CO2 and ∼5°C of global warming. It is globally characterized by a negative carbon isotope excursion (CIE), and, at least locally, is preceded by a pre-onset excursion (POE). We present palynological and bioclimatic analyses from stratigraphically expanded marginal marine sediment sections from the eastern United States. Late Paleocene forests were dominated by needle-leaved gymnosperms and broad-leaved angiosperms characteristic of warm climates. The POE is marked by a minor expansion of angiosperms and pteridophytes, warmer winters, and altered seasonal precipitation, followed by a return to pre-POE conditions. Increased terrestrial palynomorph concentrations before the CIE are suggestive of increased fluvial discharge before the PETM. Early PETM assemblages are characterized by dominance of ferns, loss of conifers, and expansion of broad-leaved angiosperm forests. Bioclimatic analyses indicate warmer mean atmospheric temperatures in early PETM time, driven primarily by winter warming of ∼3°C. A shift in seasonality, associated with increased severity of storms and floods that scoured the late Paleocene floodplain, facilitated establishment of riparian fern communities at the CIE onset. These flooding events persisted through the early part of the PETM and were severe enough to transport Westphalian-age (Middle Pennsylvanian) reworked material from the central Appalachian Basin and flush large amounts of terrestrial material and carbon onto the continental shelf, resulting in decreased salinity, increased productivity, and water-column stratification.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2025PA005278","usgsCitation":"Willard, D., Nelissen, M., Sluijs, A., Brinkhuis, H., Reichgelt, T., Robinson, M., and Self-Trail, J., 2026, Terrestrial ecosystem response to changing temperature and seasonality in the Paleocene-Eocene Thermal Maximum: Shallow marine records from the Salisbury Embayment, USA: Paleoceanography and Paleoclimatology, v. 41, no. 3, e2025PA005278, 19 p., https://doi.org/10.1029/2025PA005278.","productDescription":"e2025PA005278, 19 p.","ipdsId":"IP-171569","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":501095,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2025pa005278","text":"Publisher Index Page"},{"id":500858,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, Maryland, New Jersey, Virginia","otherGeospatial":"Salisbury Embayment","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74,\n 40\n ],\n [\n -77,\n 40\n ],\n [\n -77,\n 37\n ],\n [\n -74,\n 37\n ],\n [\n -74,\n 40\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"41","issue":"3","noUsgsAuthors":false,"publicationDate":"2026-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Willard, Debra A. 0000-0003-4878-0942","orcid":"https://orcid.org/0000-0003-4878-0942","contributorId":269840,"corporation":false,"usgs":true,"family":"Willard","given":"Debra A.","affiliations":[],"preferred":true,"id":956904,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nelissen, Mei","contributorId":362170,"corporation":false,"usgs":false,"family":"Nelissen","given":"Mei","affiliations":[{"id":36885,"text":"Utrecht University","active":true,"usgs":false}],"preferred":false,"id":956905,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sluijs, Appy","contributorId":215371,"corporation":false,"usgs":false,"family":"Sluijs","given":"Appy","email":"","affiliations":[{"id":36885,"text":"Utrecht University","active":true,"usgs":false}],"preferred":false,"id":956906,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brinkhuis, Henk","contributorId":328591,"corporation":false,"usgs":false,"family":"Brinkhuis","given":"Henk","affiliations":[{"id":36885,"text":"Utrecht University","active":true,"usgs":false}],"preferred":false,"id":956907,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reichgelt, Tammo","contributorId":215367,"corporation":false,"usgs":false,"family":"Reichgelt","given":"Tammo","email":"","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":956908,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Robinson, Marci M. 0000-0002-9200-4097","orcid":"https://orcid.org/0000-0002-9200-4097","contributorId":261664,"corporation":false,"usgs":true,"family":"Robinson","given":"Marci M.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":956909,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Self-Trail, Jean 0000-0002-3018-4985 jstrail@usgs.gov","orcid":"https://orcid.org/0000-0002-3018-4985","contributorId":147370,"corporation":false,"usgs":true,"family":"Self-Trail","given":"Jean","email":"jstrail@usgs.gov","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":956910,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70274266,"text":"70274266 - 2026 - Extreme precipitation variability and soil texture controls on water-table response","interactions":[],"lastModifiedDate":"2026-03-24T16:31:28.917628","indexId":"70274266","displayToPublicDate":"2026-02-27T09:28:04","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Extreme precipitation variability and soil texture controls on water-table response","docAbstract":"Extreme precipitation events (EPEs), a key class of hydrometeorological extremes, are intensifying globally under climate change; however, their effects on water-table dynamics across varying soil textures remain poorly understood. To better understand the impacts of EPEs, we conducted one-dimensional modeling to evaluate water-table response time, displacement, recession time, and total recharge under EPEs of 0.20 m, 0.40 m, and 0.60 m amounts, applied over 1-, 7-, and 20-day durations across twelve soil textures. The results show that coarse soils (i.e., sand) respond within days, while fine soils (i.e., clay) may take over 200 days. Water-table displacement ranged from 0.30 to 1.64 m and increased with EPE magnitude. The time it took for water tables to recede ranged from 1.2 to 3.0 years. A first-order estimate of total possible recharge, calculated from porosity and displacement, ranged from 17% (clay) to 97% (sand), averaging ~63% across soil textures. These findings highlight that recharge is primarily governed by EPE magnitude and soil properties, not event duration. This modeling effort provides new insight into how soil texture modulates groundwater response to extreme precipitation, informing future water budget and resilience assessments.
","language":"English","publisher":"MDPI","doi":"10.3390/w18050587","usgsCitation":"Corona, C.R., Ge, S., Anderson, S.P., and Dickinson, J.E., 2026, Extreme precipitation variability and soil texture controls on water-table response: Water, v. 18, no. 5, 587, 20 p., https://doi.org/10.3390/w18050587.","productDescription":"587, 20 p.","ipdsId":"IP-160684","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":501680,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w18050587","text":"Publisher Index Page"},{"id":501472,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"18","issue":"5","noUsgsAuthors":false,"publicationDate":"2026-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Corona, Claudia R.","contributorId":152548,"corporation":false,"usgs":false,"family":"Corona","given":"Claudia","middleInitial":"R.","affiliations":[{"id":6690,"text":"San Francisco State University","active":true,"usgs":false}],"preferred":false,"id":957469,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ge, Shemin","contributorId":203465,"corporation":false,"usgs":false,"family":"Ge","given":"Shemin","email":"","affiliations":[{"id":36627,"text":"University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":957470,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, Suzanne P. 0000-0002-6796-6649","orcid":"https://orcid.org/0000-0002-6796-6649","contributorId":172732,"corporation":false,"usgs":false,"family":"Anderson","given":"Suzanne","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":957471,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dickinson, Jesse E. 0000-0002-0048-0839 jdickins@usgs.gov","orcid":"https://orcid.org/0000-0002-0048-0839","contributorId":152545,"corporation":false,"usgs":true,"family":"Dickinson","given":"Jesse","email":"jdickins@usgs.gov","middleInitial":"E.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":957472,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70274150,"text":"70274150 - 2026 - Urbanization alters riverine fluorescent dissolved organic matter characteristics in a forested city – metropolitan Atlanta, Georgia (USA)","interactions":[],"lastModifiedDate":"2026-03-02T14:49:19.406602","indexId":"70274150","displayToPublicDate":"2026-02-27T08:39:47","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1561,"text":"Environmental Research","active":true,"publicationSubtype":{"id":10}},"title":"Urbanization alters riverine fluorescent dissolved organic matter characteristics in a forested city – metropolitan Atlanta, Georgia (USA)","docAbstract":"Streams and rivers in urban watersheds are predicted to export more bioreactive, autochthonous dissolved organic matter (DOM) relative to forested watersheds. However, the spatial and temporal variations of DOM quality in forested urban watersheds remain uncertain, and their relationships with socioeconomic conditions, biological characteristics, and the built environment are understudied. We measured optical properties of fluorescent DOM (FDOM) in 93 streams spanning a gradient of land-use and land cover during four seasons in metropolitan Atlanta, Georgia, USA. Streamwater FDOM was dominated by humic substances from anthropogenic (41%) and terrestrial origin (41.5%). Impervious surface cover was the strongest predictor, which was positively correlated with anthropogenically- and autochthonously-derived FDOM. Overwater canopy cover was positively associated with autochthonous FDOM, and housing age increased diagenetic FDOM. FDOM was more proteinaceous during low-flow conditions (fall, winter), and more allochthonous humic-like FDOM was detected during periods of higher flows (spring, summer). Interestingly, wastewater-related FDOM proxies were highest during low flows, suggesting that sewer exfiltration is a pervasive source and is diluted by other inputs during high flows. Overall, seasonal patterns in FDOM quality were associated with changes in hydrology, and FDOM was primarily humic throughout the year, a pattern likely driven by ubiquitous forest canopy cover. Our results highlight the importance of urban forests in mediating aquatic carbon cycling and provide a template for future studies that integrate sociodemographic and infrastructure information into studies of watershed biogeochemistry, especially in regions undergoing rapid, intense, and localized urban development.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envres.2026.124085","usgsCitation":"Chen, S., Hale, R., Hopkins, K.G., Ortiz Muñoz, L., Kominoski, J., Ledford, S., and Capps, K., 2026, Urbanization alters riverine fluorescent dissolved organic matter characteristics in a forested city – metropolitan Atlanta, Georgia (USA): Environmental Research, v. 297, 124085, 15 p., https://doi.org/10.1016/j.envres.2026.124085.","productDescription":"124085, 15 p.","ipdsId":"IP-183715","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":500667,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia","city":"Atlanta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -84.1667,\n 34\n ],\n [\n -84.7,\n 34\n ],\n [\n -84.7,\n 33.5\n ],\n [\n -84.1667,\n 33.5\n ],\n [\n -84.1667,\n 34\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"297","noUsgsAuthors":false,"publicationDate":"2026-02-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Chen, Shuo","contributorId":343806,"corporation":false,"usgs":false,"family":"Chen","given":"Shuo","affiliations":[{"id":13510,"text":"Smithsonian Environmental Research Center","active":true,"usgs":false}],"preferred":false,"id":956692,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hale, Rebecca","contributorId":348368,"corporation":false,"usgs":false,"family":"Hale","given":"Rebecca","affiliations":[{"id":38154,"text":"Idaho State University","active":true,"usgs":false}],"preferred":false,"id":956693,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hopkins, Kristina G. 0000-0003-1699-9384 khopkins@usgs.gov","orcid":"https://orcid.org/0000-0003-1699-9384","contributorId":195604,"corporation":false,"usgs":true,"family":"Hopkins","given":"Kristina","email":"khopkins@usgs.gov","middleInitial":"G.","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":956694,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ortiz Muñoz, Liz","contributorId":343807,"corporation":false,"usgs":false,"family":"Ortiz Muñoz","given":"Liz","affiliations":[{"id":7017,"text":"Florida International University","active":true,"usgs":false}],"preferred":false,"id":956695,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kominoski, John","contributorId":298258,"corporation":false,"usgs":false,"family":"Kominoski","given":"John","affiliations":[{"id":7017,"text":"Florida International University","active":true,"usgs":false}],"preferred":false,"id":956696,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ledford, Sarah","contributorId":300624,"corporation":false,"usgs":false,"family":"Ledford","given":"Sarah","email":"","affiliations":[{"id":52554,"text":"Georgia State University","active":true,"usgs":false}],"preferred":false,"id":956697,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Capps, Krista A.","contributorId":270490,"corporation":false,"usgs":false,"family":"Capps","given":"Krista A.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":956698,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70274109,"text":"sir20265118 - 2026 - Groundwater budget for the Mountain Home area, southern Idaho, 2022–23","interactions":[],"lastModifiedDate":"2026-02-27T21:32:48.272408","indexId":"sir20265118","displayToPublicDate":"2026-02-26T15:10:00","publicationYear":"2026","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":"2026-5118","displayTitle":"Groundwater Budget for the Mountain Home Area, Southern Idaho, 2022–23","title":"Groundwater budget for the Mountain Home area, southern Idaho, 2022–23","docAbstract":"The U.S. Geological Survey, with funding from the Idaho Department of Water Resources, developed a groundwater budget for the Mountain Home area in southern Idaho for irrigation year 2023 (November 1, 2022–October 31, 2023). This study focused on the water balance across the Cinder Cone Butte Critical Groundwater Area (CGWA), Mountain Home Groundwater Management Area (GWMA), and the rest of the study area (RoSA), compiling data from various sources, including precipitation records, groundwater level measurements, metered groundwater pumpage data, surface water diversions and evapotranspiration (ET) estimates derived from remote sensing satellite imagery, and ground-based reference data. Key inflow components included recharge from applied surface water irrigation (which incorporates incidental recharge from irrigation practices and conveyance losses), estimated tributary streamflow, and estimated mountain block recharge. The key outflow components were groundwater pumpage for irrigation, municipal, industrial, and domestic uses, and ET. Recharge from applied irrigation and mountain block recharge were the largest inflows, and groundwater pumpage for irrigation was the largest outflow.
The CGWA had a positive groundwater budget residual of 2,170 acre-feet (acre-ft), which contrasts with observed long-term groundwater level declines and historical trends of storage depletion. This positive residual is likely associated with unquantified outflows, including lateral groundwater flow out of the subregion, or other complexities, such as overestimated tributary contributions relative to the actual recharge for the 2023 water budget. The GWMA exhibited a positive residual of 56,563 acre-ft, primarily owing to recharge from applied surface water irrigation and areal recharge during a wetter-than-average year, which allowed irrigation entities to deliver more water from in-basin and out-of-basin reservoirs. The RoSA showed a large positive residual of 124,933 acre-ft. The interpretation of these positive residuals must account for significant uncertainties, including estimations of areal recharge, tributary streamflow (particularly losses and diversions), ET, the volume of surface water loss to the Snake River, lateral groundwater flows between subregions and across study area boundaries, and the unquantified groundwater discharge to the Snake River. These uncertainties, in combination with the complex hydrogeologic controls on water movement and limitations of remotely sensed data, directly affect the accuracy of water availability assessments.
Future data collection efforts would help reduce these uncertainties and support water resource management decisions in the Mountain Home area. Key efforts could include installing additional streamflow gaging stations (particularly to quantify tributary losses and gains and surface water losses to the Snake River), improving groundwater pumpage metering, and validating remotely sensed ET data with ground-based measurements. Furthermore, to better quantify unrepresented or highly uncertain fluxes, focused investigations on groundwater discharge to the Snake River, lateral groundwater flows between subregions and across study area boundaries, and a more robust determination of the actual influence and volume of mountain block recharge would help refine future water availability assessments for the Mountain Home area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20265118","collaboration":"Prepared in cooperation with the Idaho Department of Water Resources","usgsCitation":"Thomas, P.M., 2026, Groundwater budget for the Mountain Home area, southern Idaho, 2022–23: U.S. Geological Survey Scientific Investigations Report 2026–5118, 41 p., https://doi.org/10.3133/sir20265118.","productDescription":"Report: ix, 41 p.; Data Release","numberOfPages":"56","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-140358","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":500654,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119277.htm","linkFileType":{"id":5,"text":"html"}},{"id":500532,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13ZG67D","text":"USGS data release","linkHelpText":"Supporting data for 2022–2023 groundwater budget for the Mountain Home area, southern Idaho"},{"id":500531,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2026/5118/images/"},{"id":500530,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2026/5118/sir20265118.XML"},{"id":500528,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2026/5118/coverthb.jpg"},{"id":500529,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2026/5118/sir20265118.pdf","text":"Report","size":"4.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2026-5118"},{"id":500533,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20265118/full"}],"country":"United States","state":"Idaho","otherGeospatial":"Mountain Home area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.5,\n 43.5\n ],\n [\n -116.5,\n 42.833\n ],\n [\n -115.0833,\n 42.833\n ],\n [\n -115.0833,\n 43.5\n ],\n [\n -116.5,\n 43.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Rd
Boise, Idaho 83702-4520
Intersections of drainage networks and road networks represent a critical nexus between natural waterways and human infrastructure. Managing these systems involves decisions related to management of infrastructure, hydrologic and geomorphic processes, and ecological connectivity. Interactions among these systems influence multiple values, including the intactness of transportation networks, public safety, water quality, and ecosystem function that collectively amount to billions of dollars. Despite the importance of road-stream crossings, there are countless gaps in knowing where and what they are. These gaps limit the degree to which managers can inventory and assess stream and road networks to inform decisions. To address this first-level need, we developed RoadxStr (road-stream crossings): a survey tool that effectively characterizes road-stream crossings across the full stream and drainage network. This document describes the RoadxStr Field Form, available within a mobile application, which is designed for rapid and standardized data collection involving assessment of a road-stream crossing, including the road, crossing structure(s), and the nearby hydrologic channel. This document provides instructions on how to (1) access and download the RoadxStr Field Form within the mobile application service and (2) use and complete a RoadxStr Field Form survey.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm18B1","collaboration":"Prepared in cooperation with the Bureau of Land Management and U.S. Forest Service","usgsCitation":"Heaston, E., Winter, S., Bauer, S., Ronningen, T., and Dunham, J., 2026, RoadxStr user’s guide—For collection of road-stream crossing assessment field observations: U.S. Geological Survey Techniques and Methods, book 18, chap. B1, 32 p., https://doi.org/10.3133/tm18B1.","productDescription":"vii, 32 p.","numberOfPages":"44","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-176750","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":500545,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/18/b1/coverthb.jpg"},{"id":500546,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/18/b1/tm18B1.pdf","text":"Report","size":"15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 18-B1"},{"id":500547,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/tm/18/b1/tm18B1.XML"},{"id":500549,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/tm18B1/full"},{"id":500548,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/tm/18/b1/images/"}],"contact":"Director, Forest and Rangeland Ecosystem Science Center Corvallis Research Group
3200 SW Jefferson Way
Corvallis, OR 97331
fresc_outreach@usgs.gov