The U.S. Geological Survey (USGS), in cooperation with the Louisiana Department of Transportation and Development, collected water-withdrawal and water-use data from a 2020 inventory of water withdrawals in Louisiana. In 2020, approximately 8,700 million gallons per day (Mgal/d) of water was withdrawn from groundwater and surface-water sources in Louisiana, which represented a 0.22-percent decrease from 2015. Total groundwater withdrawals were about 1,900 Mgal/d, an increase of 7.1 percent from 2015, and total surface-water withdrawals were about 6,800 Mgal/d, a decrease of 2.1 percent from 2015 to 2020.
Total water withdrawals, in million gallons per day, in 2020 for the various categories of use were as follows: public supply, 720; industry, 2,100; power generation, 4,100; rural domestic, 39; livestock, 7.0; rice irrigation, 930; general irrigation, 250; and aquaculture, 590. From 2015 to 2020, Louisiana’s total withdrawals for public supply increased by 1.4 percent, industry decreased by 2.3 percent, power generation decreased by 4.9 percent, rural domestic decreased by 1.2 percent, livestock increased by 11 percent, rice irrigation increased by 13 percent, general irrigation increased by 12 percent, and aquaculture increased by 20 percent.
About 51 percent (approximately 960 Mgal/d) of all groundwater withdrawn was from the Chicot aquifer system and 24 percent (approximately 450 Mgal/d) was withdrawn from the Mississippi River alluvial aquifer. Since 2015, withdrawals from the Chicot aquifer system increased by 13 percent, and withdrawals from the Mississippi River alluvial aquifer increased by 18 percent. About 72 percent (4,900 Mgal/d) of all surface water withdrawn was from the Mississippi River main stem. This value represents a 1.1-percent decrease in withdrawals from 2015 to 2020.
All water-withdrawal and water-use data presented in this report should be considered estimates. Because of rounding, totals and percentages presented in the tables, figures, and text in the report may differ slightly from totals or percentages calculated individually.
Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Contact Us- USGS Publications Warehouse
","tableOfContents":"Provenance analysis is a powerful tool for investigating sediment delivery networks, constraining magmatic histories, and reconstructing the tectonic evolution of orogenic belts and basins. Basin analysis studies increasingly use detrital zircon (DZ) U-Pb forward mixture modeling to enhance provenance interpretations by quantifying the relative contributions of different sources. Forward mixture modeling requires significant a priori knowledge that limits deep-time applications. This challenge is overcome with an inverse mixture modeling approach non-negative matrix factorization to reconstruct the number and age distributions of paleo-source regions of Proterozoic metasedimentary rocks in the southwestern United States. This analysis indicates eight reconstructed end-member distributions representing unique sediment sources: two multi-modal end members characterized by ages older than ca. 1.8 Ga from cratonic Laurentia, five unimodal age distributions between ca. 1.80 Ga and 1.65 Ga consistent with Paleoproterozoic arc magmatic sources, and a ca. 1.6−1.5 Ga end member likely derived from exotic cratons in supercontinent Nuna (Columbia). Sediments deposited between ca. 1.80 Ga and 1.73 Ga yield heterogeneous age distributions suggesting multiple arc-backarc systems and several phases of slab roll back, contraction, and accretionary orogenesis, including input from pre−1.8 Ga Laurentian cratons. Homogenization of DZ signatures during the Yavapai orogeny (ca. 1.72−1.68 Ga) reflect crustal assembly as well as the uplift of Paleoproterozoic arcs in the orogenic hinterland. Detrital zircon age distributions from strata deposited during the Mazatzal orogeny (ca. 1.65−1.60 Ga) suggest the Mazatzal Province is a continental arc constructed on older crust. Mesoproterozoic samples are consistent with multiple basins derived from local recycling and long-distance sediment transport. Collectively, these data record the tectonic transition from the episodic accretion of disparate crustal domains to an increasingly integrated continental margin. These results provide new insights into the Proterozoic tectonic and paleogeographic evolution of the southwestern United States at basin to orogen scales and highlight the power of inverse DZ modeling to extract geologically meaningful quantitative mixture models from sedimentary records alone, offering a powerful tool for deep-time tectonic and basin analysis.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/B38713.1","usgsCitation":"Hillenbrand, I.W., and Thomson, K.D., 2026, Reconstructing ancient sedimentary source-to-sink systems – Examples from southern Laurentia’s Proterozoic accretionary orogens: GSA Bulletin, https://doi.org/10.1130/B38713.1.","ipdsId":"IP-180279","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":504696,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Colorado, New Mexico, Nevada, Texas, Utah, Wyoming","otherGeospatial":"southwestern United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115,\n 45\n ],\n [\n -100,\n 45\n ],\n [\n -100,\n 30\n ],\n [\n -115,\n 30\n ],\n [\n -115,\n 45\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Online First","noUsgsAuthors":false,"publicationDate":"2026-05-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Hillenbrand, Ian William 0000-0003-2801-3674","orcid":"https://orcid.org/0000-0003-2801-3674","contributorId":299032,"corporation":false,"usgs":true,"family":"Hillenbrand","given":"Ian","email":"","middleInitial":"William","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":961944,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thomson, Kelly David 0000-0003-3378-1432","orcid":"https://orcid.org/0000-0003-3378-1432","contributorId":301019,"corporation":false,"usgs":true,"family":"Thomson","given":"Kelly","email":"","middleInitial":"David","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":961945,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70275706,"text":"70275706 - 2026 - Life history traits and population dynamics of Freshwater Drum across large river gradients","interactions":[],"lastModifiedDate":"2026-05-13T14:10:53.539574","indexId":"70275706","displayToPublicDate":"2026-05-07T09:05:02","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Life history traits and population dynamics of Freshwater Drum across large river gradients","docAbstract":"Monitoring and assessment of nongame native fishes is limited, but conservation interest in these species is growing. Freshwater Drum Aplodinotus grunniens are a wide-ranging species that serve important functional roles and could serve as an indicator for similar but less common species. Our overall objectives were to quantify and compare population dynamic rates and life history of Freshwater Drum among study reaches in the upper Mississippi and Illinois rivers and relate these metrics to hypothesized environmental and anthropogenic factors.
We integrated recently collected age data with monitoring data to estimate age and size distributions, growth curves, maturation schedules, mortality rates, and young-to-adult ratios of Freshwater Drum in six study reaches spanning 1,500 km of river. Principal component analyses and linear regression were used to relate environmental and anthropogenic gradients (latitude, commercial harvest, hydrologic dynamics, primary productivity) to life history traits and population dynamic rates.
We found latitudinal gradients in life history traits and population dynamic rates whereby Freshwater Drum in upstream, higher-latitude study reaches generally exhibited later maturity, slower growth, smaller maximum size, and lower mortality rates compared with those in lower-latitude study reaches. Further, young-to-adult ratios positively corresponded with chlorophyll-a concentration. No clear relationships were apparent between population dynamic rates and hydrologic variation or commercial harvest.
Latitude is an important structuring component of life history traits and population dynamics of Freshwater Drum in the upper Mississippi and Illinois rivers likely due to both temperature seasonality and disturbance regimes. The presence of demographic structure in a widespread, common species such as Freshwater Drum suggests similar patterns likely exist in other long-lived native fishes.
As the population of New Jersey continues to remain dense, the need for water supply will likely continue to be high, which can lead to water managers needing to make difficult decisions about managing drinking-water supply. Streamgaging weirs like the ones used by the U.S. Geological Survey (USGS) play a critical role in providing accurate and stable streamflow data, but their presence can affect the passage of diadromous fish species such as river herring (Alosa pseudoharengus [alewife], Alosa aestivalis [blueback herring], and Alosa sapidissima [American shad]). In some situations, weirs existing in rivers and streams are no longer used because they were part of a farm irrigation system or some type of industrial operation. The weir at the USGS streamgage 01402000 Millstone River at Blackwells Mills, New Jersey, was purposefully built as a hydraulic-control structure that provides a precise and stable control for the measurement of stage and computation of continuous streamflow. To satisfy the dual need of maintaining accurate streamflow data and providing improved fish passage for select species of fish during migration season, the USGS proposed the development and evaluation of two alternative weir designs that would meet the criteria established for successful passage of American shad, alewife, and blueback herring during their yearly migration. The designs were also required to maintain adequate control of the upstream pool elevation necessary for the precise computation of streamflow used by State agencies for municipal water-supply purposes for surrounding communities.
Two alternative weir design modifications were incorporated at the center of the Blackwells Mills weir and modeled using two-dimensional hydraulic modeling software and three-dimensional computational fluid-dynamics software to simultaneously evaluate conditions for passage of the target fish species and effects to streamflow computations at the streamgage. The models were calibrated to existing conditions around the weir location using surveyed-elevation data and recorded stage, streamflow, and velocity in the Millstone River. The alternative weir designs lowered the weir crest by 1.02 feet (ft) and the resulting simulations showed an effective increase in depth of 0.98 ft at the median streamflow of 251 cubic feet per second (ft3/s) and 0.96 ft at the 95-percent exceedance streamflow of 98 ft3/s. The alternative weir designs were also found to increase streamflow depth across the shallowest portions of the weir structure at the downstream anti-scour skirt by lowering the skirt about 4 inches, allowing for two or more body depths of water for American shad, alewife, and blueback herring at the median migration streamflow of 251 ft3/s. The alternative weir designs also reduced the highest stream velocities across the downstream weir sill and anti-scour skirt from about 9 to 10 feet per second, and the depth-averaged velocity to about 7 to 8 feet per second. The sensitivity of the weir with respect to the computation of streamflow was increased from about 1.8 cubic feet per second per hundredth foot to 1.6 cubic feet per second per hundredth foot for streamflows of about 10–100 cubic feet per second.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20265002","collaboration":"Prepared in cooperation with the New Jersey Department of Environmental Protection","usgsCitation":"Suro, T.P., Niemoczynski, M.J., and Mulligan, K.B., 2026, Analysis of alternative weir designs for improved passage of select fish at the U.S. Geological Survey streamgaging weir at Blackwells Mills, New Jersey: U.S. Geological Survey Scientific Investigations Report 2026–5002, 31 p., https://doi.org/10.3133/sir20265002.","productDescription":"Report: ix, 31 p.; Data Release","numberOfPages":"31","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-179030","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":504269,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119406.htm","linkFileType":{"id":5,"text":"html"}},{"id":503276,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2026/5002/coverthb.jpg"},{"id":503277,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2026/5002/sir20265002.pdf","text":"Report","size":"56.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2026-5002 PDF"},{"id":503278,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20265002/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2026-5002 HTML"},{"id":503279,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2026/5002/sir20265002.XML","text":"SIR 2026-5002 XML","description":"SIR 2026-5002 XML"},{"id":503280,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2026/5002/images"},{"id":503281,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P14T93HI","text":"USGS data release","linkHelpText":"HEC-RAS and FLOW 3-D HYDRO models used to evaluate alternative weir designs for the Millstone River at Blackwells Mills, New Jersey"}],"country":"United States","state":"New Jersey","otherGeospatial":"Blackwells Mills","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.57920795080669,\n 40.47768788237764\n ],\n [\n -74.57239357394151,\n 40.47768788237764\n ],\n [\n -74.57239357394151,\n 40.47245781646623\n ],\n [\n -74.57920795080669,\n 40.47245781646623\n ],\n [\n -74.57920795080669,\n 40.47768788237764\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville, NJ 08648
Prioritization is a central component of natural resource management because conservation needs routinely exceed available resources. Waterfowl and wetland conservation programs in North America are at the forefront of landscape-scale prioritization and transboundary management decisions due to the migratory nature of ducks, geese, and swans. The growing availability of geographic information systems (GIS) and geospatial technologies has accelerated the development of multi-objective landscape prioritization models, including applications of structured decision making and multi-criteria decision analysis to spatial planning for waterfowl and wetlands at the continental scale. However, regional managers and conservationists could benefit from flexibility in downscaling continental tools, selecting objectives, and assigning weights for rapid production of spatial prioritization models at smaller spatial scales without extensive computer coding or GIS analysis. We developed a spatial value model that prioritizes landscapes at sub-continental scales (e.g., states and provinces, bird conservation regions, etc.) and provides flexibility for users to select waterfowl conservation objectives of interest and weights. Our model can be used for direct downscaling of an existing continental geospatial model or further customized with region-specific geospatial data. We illustrate how regional prioritization can vary with the spatial scale selected by the user. The spatial value modeling framework and the downscaling tool presented here could increase the use of multi-criteria decision analysis and linear value modeling in spatial landscape prioritization, while also providing flexibility for selecting scales, objectives, and weights. Our spreadsheet tool was developed specifically for use by regional biologists, conservationists, and managers and does not require knowledge of GIS software (although results can be exported from the spreadsheet for spatial analysis using GIS). Together, the model outputs and the accompanying spreadsheet tool provide a bridge between continental waterfowl conservation and regional implementation, enabling rapid, stakeholder-driven, value-explicit prioritization.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wsb.70027","usgsCitation":"Couvillon, A., Soulliere, G., Gordon, D., Eggeman, D., Al-Saffar, M.A., Humburg, D.D., and Lyons, J., 2026, Regional conservation planning tool: A spreadsheet model to support spatial prioritization and resource allocation decisions: Wildlife Society Bulletin, https://doi.org/10.1002/wsb.70027.","ipdsId":"IP-168494","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":504385,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wsb.70027","text":"Publisher Index Page"},{"id":504242,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"Online First","noUsgsAuthors":false,"publicationDate":"2026-05-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Couvillon, Anastasia","contributorId":371246,"corporation":false,"usgs":false,"family":"Couvillon","given":"Anastasia","affiliations":[{"id":63963,"text":"University of Louisiana","active":true,"usgs":false}],"preferred":false,"id":961373,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Soulliere, Gregory J.","contributorId":353609,"corporation":false,"usgs":false,"family":"Soulliere","given":"Gregory J.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":961374,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gordon, David H.","contributorId":221670,"corporation":false,"usgs":false,"family":"Gordon","given":"David H.","affiliations":[],"preferred":false,"id":961375,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Eggeman, Diane","contributorId":371247,"corporation":false,"usgs":false,"family":"Eggeman","given":"Diane","affiliations":[{"id":81180,"text":"Ducks Unlimited, Inc","active":true,"usgs":false}],"preferred":false,"id":961376,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Al-Saffar, Mohammed A","contributorId":292215,"corporation":false,"usgs":false,"family":"Al-Saffar","given":"Mohammed","email":"","middleInitial":"A","affiliations":[{"id":62842,"text":"USWFS","active":true,"usgs":false}],"preferred":false,"id":961377,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Humburg, Dale D.","contributorId":79357,"corporation":false,"usgs":false,"family":"Humburg","given":"Dale","email":"","middleInitial":"D.","affiliations":[{"id":13073,"text":"Ducks Unlimited, Inc.","active":true,"usgs":false}],"preferred":false,"id":961378,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lyons, James E. 0000-0002-9810-8751","orcid":"https://orcid.org/0000-0002-9810-8751","contributorId":228916,"corporation":false,"usgs":true,"family":"Lyons","given":"James E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":961379,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70275756,"text":"70275756 - 2026 - USGS 2025 critical minerals review","interactions":[],"lastModifiedDate":"2026-05-18T15:39:31.131997","indexId":"70275756","displayToPublicDate":"2026-05-01T10:37:48","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2755,"text":"Mining Engineering","active":true,"publicationSubtype":{"id":10}},"title":"USGS 2025 critical minerals review","docAbstract":"The United States Geological Survey (USGS) provides scientific information for the Department of Interior and the nation, consistent with its original mission expressed in the Organic Act of 1879 (43 U.S.C. 31): “the classification of the public lands and examination of the geological structure, mineral resources, and products within and outside the national domain.” Legislation such as the Energy Act of 2020 and the 2022 Infrastructure Investment and Jobs Act (43 USC 31l) and recent executive actions (Executive Orders 14154 , 14153, 14241 Secretary’s Orders 3417, 3418, 3422, 3436) underscore the importance of mineral resources and focus USGS activities on mapping and assessing mineral resources, with a particular focus on those presently identified as critical, both in ground and above ground in mine wastes.
This article reviews selected activities and accomplishments by the USGS Mineral Resources Program related to critical minerals in 2025. Highlights include a new List of Critical Minerals, a first-ever national mine waste inventory, international minerals partnerships, mineral resource assessment advancements, and national data collection activities and outcomes of the Earth Mapping Resources Initiative (Earth MRI). The selected contributions are not comprehensive but are intended to demonstrate USGS leadership in critical mineral mapping and assessment, the importance of domestic and global partnerships, and the breadth of research activities that are responsive to national needs and priorities.
","language":"English","publisher":"Society for Mining, Metallurgy & Exploration","usgsCitation":"Jones, J.V., Gallegos, T., Kunledare, M.A., and Riggs, C.E., 2026, USGS 2025 critical minerals review: Mining Engineering, v. 78, no. 5, p. 42-57.","productDescription":"16 p.","startPage":"42","endPage":"57","ipdsId":"IP-188633","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":504481,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":504466,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://me.smenet.org/usgs-2025-critical-minerals-review/"}],"volume":"78","issue":"5","noUsgsAuthors":false,"publicationDate":"2026-05-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Jones, James V. III 0000-0002-6602-5935 jvjones@usgs.gov","orcid":"https://orcid.org/0000-0002-6602-5935","contributorId":201245,"corporation":false,"usgs":true,"family":"Jones","given":"James","suffix":"III","email":"jvjones@usgs.gov","middleInitial":"V.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":961655,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gallegos, Tanya 0000-0003-3350-6473 tgallegos@usgs.gov","orcid":"https://orcid.org/0000-0003-3350-6473","contributorId":222082,"corporation":false,"usgs":true,"family":"Gallegos","given":"Tanya","email":"tgallegos@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":961656,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kunledare, Mojisola Abosede 0009-0007-3629-4855","orcid":"https://orcid.org/0009-0007-3629-4855","contributorId":371350,"corporation":false,"usgs":true,"family":"Kunledare","given":"Mojisola","middleInitial":"Abosede","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":961657,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Riggs, Charlotte E. 0000-0002-3597-0328","orcid":"https://orcid.org/0000-0002-3597-0328","contributorId":302492,"corporation":false,"usgs":true,"family":"Riggs","given":"Charlotte","email":"","middleInitial":"E.","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":961658,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70275653,"text":"70275653 - 2026 - An overview and participatory framework for choosing spatial boundaries in social–ecological systems modeling","interactions":[],"lastModifiedDate":"2026-05-07T15:15:49.50451","indexId":"70275653","displayToPublicDate":"2026-05-01T10:11:55","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5685,"text":"ISPRS International Journal of Geo-Information ","printIssn":"2220-9964","active":true,"publicationSubtype":{"id":10}},"title":"An overview and participatory framework for choosing spatial boundaries in social–ecological systems modeling","docAbstract":"A common challenge when modeling social–ecological systems (SESs) is defining the spatial extent of the system. Boundaries that do not adequately capture both social and ecological processes and their interactions can lead to mischaracterization of the system, while expanding boundaries too widely can impact model complexity and required resources. Socially, boundaries can invoke and influence identity, culture, power, and sense of place. Boundary decisions benefit from flexible, iterative approaches and the expertise of local communities. Here, we use a structured database search supplemented with citation searching to identify and review the literature that addresses choosing or defining spatial boundaries in SESs mapping or modeling and, when applicable, how participatory methods were used in the research process. In a review of the resulting 79 studies, we discovered that pre-existing social or ecological boundaries were used most frequently (36 and 18 publications, respectively). Twenty-one publications combined social and ecological boundaries or data to create custom boundaries, and four studies used an alternative approach to conventional boundaries. Informed by the literature review, we present a general framework for defining boundaries at the outset of SES research. We then connect the framework to a specific case study based on a collaborative project with Tribal, university, and federal scientists to develop a social–ecological climate adaptation plan. We present guiding questions alongside candidate boundaries for our study system and explore the tradeoffs of these boundary options, which can function as a useful template for other social–ecological research collaborations.
","language":"English","publisher":"MPDI","doi":"10.3390/ijgi15050196","usgsCitation":"Perella, C.D., Vukomanovic, J., Hickman, C., Terando, A.J., Eaton, M.J., and Schaefer, M., 2026, An overview and participatory framework for choosing spatial boundaries in social–ecological systems modeling: ISPRS International Journal of Geo-Information, v. 15, no. 5, 196, 35 p., https://doi.org/10.3390/ijgi15050196.","productDescription":"196, 35 p.","ipdsId":"IP-181199","costCenters":[{"id":40926,"text":"Southeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":504220,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/ijgi15050196","text":"Publisher Index Page"},{"id":504094,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"5","noUsgsAuthors":false,"publicationDate":"2026-05-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Perella, Christina D.","contributorId":371222,"corporation":false,"usgs":false,"family":"Perella","given":"Christina","middleInitial":"D.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":961313,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vukomanovic, Jelena","contributorId":316275,"corporation":false,"usgs":false,"family":"Vukomanovic","given":"Jelena","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":961314,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hickman, Caleb R.","contributorId":356386,"corporation":false,"usgs":false,"family":"Hickman","given":"Caleb R.","affiliations":[{"id":84985,"text":"Eastern Band of Cherokee Indians","active":true,"usgs":false}],"preferred":false,"id":961315,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Terando, Adam J.","contributorId":371223,"corporation":false,"usgs":false,"family":"Terando","given":"Adam","middleInitial":"J.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":961316,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Eaton, Mitchell J. 0000-0001-7324-6333","orcid":"https://orcid.org/0000-0001-7324-6333","contributorId":213526,"corporation":false,"usgs":true,"family":"Eaton","given":"Mitchell","middleInitial":"J.","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":961317,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schaefer, Marie","contributorId":371224,"corporation":false,"usgs":false,"family":"Schaefer","given":"Marie","affiliations":[],"preferred":false,"id":961318,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70275754,"text":"70275754 - 2026 - Revisiting the utility of regional-scale, high-quality geophysical data in mineral exploration - A case study featuring the Mammoth Magnetic Anomaly, Pinal County, Arizona","interactions":[],"lastModifiedDate":"2026-05-18T15:10:18.71482","indexId":"70275754","displayToPublicDate":"2026-05-01T09:58:40","publicationYear":"2026","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Revisiting the utility of regional-scale, high-quality geophysical data in mineral exploration - A case study featuring the Mammoth Magnetic Anomaly, Pinal County, Arizona","docAbstract":"Regional aeromagnetic surveys passively measure the total magnetic intensity (TMI) and are a foundational tool used in mineral exploration (Airo, 2015). With the increased global demand and the number of critical mineral resources required for manufacturing high-tech devices, developing high-quality, regional-scale geophysical surveys could aid critical mineral exploration efforts and geologic mapping. In 2019, the U. S. Geological Survey launched the Earth Mapping Resources Initiative (Earth MRI) to modernize the geologic and geophysical mapping of regions that have the potential to contain critical mineral resources within the United States. In support of planning Earth MRI geophysical surveys, Drenth and Grauch (2019) defined five aeromagnetic data quality rankings (rank 1 through rank 5) applying them to the airborne geophysical survey inventory of the United States (Johnson et al., 2021). Rank 1 aeromagnetic surveys are of the highest quality, meeting modern standards and allowing best practices for qualitative and quantitative interpretation; whereas rank 5 aeromagnetic surveys are of the lowest quality, being useful only for qualitative interpretation of broad features. Through the Earth MRI effort, 48 high-quality, regional-scale rank 1 and 2 airborne magnetic and radiometric geophysical surveys have been planned, collected, or publicly release through May 2025 (U. S. Geological Survey, 2025). Here, a portion of a rank 1 Earth MRI aeromagnetic survey in southeast Arizona is presented and compared to a legacy rank 5 aeromagnetic survey over the Mammoth Magnetic Anomaly (MMA), demonstrating how modern, high-quality aeromagnetic data improves our view of crustal geology, aiding mineral exploration.","conferenceTitle":"Mineral Prospectivity and Exploration Targeting – MinProXT 2025","conferenceDate":"October 21-24, 2025","language":"English","publisher":"Geological Survey of Finland","usgsCitation":"Walter, C.A., 2026, Revisiting the utility of regional-scale, high-quality geophysical data in mineral exploration - A case study featuring the Mammoth Magnetic Anomaly, Pinal County, Arizona, Mineral Prospectivity and Exploration Targeting – MinProXT 2025, October 21-24, 2025, p. 54-57.","productDescription":"4 p.","startPage":"54","endPage":"57","ipdsId":"IP-183052","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":504478,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":504465,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://urn.fi/URN:NBN:fi:gtk-1.2.246.563.1.131543"}],"country":"United States","state":"Arizona","county":"Pinal County","otherGeospatial":"Mammoth Magnetic Anomaly","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.6527,\n 32.85\n ],\n [\n -110.4398,\n 32.85\n ],\n [\n -110.4398,\n 32.716522\n ],\n [\n -110.6527,\n 32.716522\n ],\n [\n -110.6527,\n 32.85\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2026-05-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Walter, Callum Andrew 0000-0001-7955-2016","orcid":"https://orcid.org/0000-0001-7955-2016","contributorId":360911,"corporation":false,"usgs":true,"family":"Walter","given":"Callum","middleInitial":"Andrew","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":961654,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70274338,"text":"70274338 - 2026 - The United States Magnetotelluric Array and the National Impedance Map","interactions":[],"lastModifiedDate":"2026-05-01T14:26:55.789731","indexId":"70274338","displayToPublicDate":"2026-05-01T09:02:26","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3283,"text":"Reviews of Geophysics","active":true,"publicationSubtype":{"id":10}},"title":"The United States Magnetotelluric Array and the National Impedance Map","docAbstract":"The United States Magnetotelluric Array (USMTArray) data set, collected in the years 2006–2024, consists of more than 1,700 long-period magnetotelluric stations covering the entirety of the contiguous United States on a quasi-regular 70 km grid. Funding across multiple federal agencies was critical to sustaining this effort to its completion. Important components of the project included active guidance and participation from the MT community, the open and timely availability of all data, and the application of consistent instrumentation and robust data processing. Together with parallel advancement in the development of publicly available three-dimensional (3D) inversion codes, the USMTArray has revitalized the US magnetotelluric community and increased the visibility of magnetotellurics within the Earth-science community. Taken as a whole, these data are visualized as the National Impedance Map, which, together with a 3D synthesis conductivity model of the nation, reveals the electrical architecture of the contiguous US. USMTArray data are used by researchers worldwide for fundamental and applied studies, including investigations of continental architecture and evolution, estimation of hazards to critical infrastructure due to geomagnetic storms, and assessment of the nation's undiscovered geothermal and mineral resources. We here review the history and development of the project, discuss the challenges and successes in its execution, present the National Impedance Map and synthesis conductivity model, and highlight the breadth of research stemming from this rich data set.
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pbedrosian@usgs.gov","orcid":"https://orcid.org/0000-0002-6786-1038","contributorId":839,"corporation":false,"usgs":true,"family":"Bedrosian","given":"Paul","email":"pbedrosian@usgs.gov","middleInitial":"A.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":957946,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schultz, Adam","contributorId":197380,"corporation":false,"usgs":false,"family":"Schultz","given":"Adam","affiliations":[],"preferred":false,"id":957947,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Egbert, Gary D.","contributorId":187462,"corporation":false,"usgs":false,"family":"Egbert","given":"Gary","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":957948,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pellerin, Louise","contributorId":367953,"corporation":false,"usgs":false,"family":"Pellerin","given":"Louise","affiliations":[{"id":87654,"text":"Green Geophysics","active":true,"usgs":false}],"preferred":false,"id":957949,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Love, Jeffrey J. 0000-0002-3324-0348 jlove@usgs.gov","orcid":"https://orcid.org/0000-0002-3324-0348","contributorId":760,"corporation":false,"usgs":true,"family":"Love","given":"Jeffrey","email":"jlove@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":957950,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Frassetto, Andy","contributorId":350836,"corporation":false,"usgs":false,"family":"Frassetto","given":"Andy","affiliations":[{"id":83843,"text":"Earthscope","active":true,"usgs":false}],"preferred":false,"id":957951,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Murphy, Benjamin S. 0000-0001-7636-3711","orcid":"https://orcid.org/0000-0001-7636-3711","contributorId":221483,"corporation":false,"usgs":false,"family":"Murphy","given":"Benjamin S.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":957952,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70275313,"text":"sir20265018 - 2026 - Understanding the occurrence and distribution of per- and polyfluoroalkyl substances (PFAS) in surface waters of the nontidal Passaic River Basin","interactions":[],"lastModifiedDate":"2026-05-01T16:47:30.87071","indexId":"sir20265018","displayToPublicDate":"2026-04-30T15:25:00","publicationYear":"2026","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations 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In 2025, 37 sites in the Wanaque, Ramapo, Pompton, and Passaic River watersheds were sampled in January, March, July, and September under base-flow conditions and a subset of sites was sampled during two rain events. Samples were analyzed for 40 individual PFAS and total organic carbon and a subset of samples was analyzed for 1,4-dioxane and trace elements. Fifteen PFAS were detected at least once, with individual concentrations ranging from 0.42 to 28 nanograms per liter (ng/L; median, 2.8 ng/L). Perfluorooctanoate (PFOA) and perfluorooctane sulfonate (PFOS) were widespread and detected in 100 and 97 percent of the samples, respectively. Concentrations of PFOA and PFOS ranged from 1.2 to 28 ng/L (median, 7.7 ng/L) and from 0.52 to 12 ng/L (median, 3.8 ng/L), respectively. Generally, concentrations were lower in the Wanaque and Ramapo River watersheds compared to the Pompton and Passaic River watersheds. Concentrations of PFOA and PFOS were highest in July and September when flows were low. During rain events, median concentrations of PFOS were elevated compared to those observed under base-flow conditions, indicating potential inputs from non-point sources. To understand potential drivers of PFAS concentrations, land cover and potential PFAS sources were summarized for each sampling site, and an accumulated wastewater model was used to estimate the percentage of wastewater from upstream municipal and industrial sources in all flowlines of the Passaic River Basin. Developed land, the number of potential sources, and the mean-annual accumulated wastewater percentage were highly correlated with PFAS concentrations and Deciduous Forests were negatively related to concentrations. Data provided by this study can be used by water purveyors and resource managers to make treatment and mitigation decisions to minimize PFAS in local surface waters used as drinking-water resources.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20265018","collaboration":"Prepared in cooperation with the North Jersey District Water Supply Commission","usgsCitation":"Schreiner, M.L., Romanok, K.M., Gray, J.T., Brown, E.J., Williams, B.M., Kneser, M., Capuzzi, A.J., Boerner, J., Giunta, L., Serillo, P., Trainor, J.J., and Smalling, K.L., 2026, Understanding the occurrence and distribution of per- and polyfluoroalkyl substances (PFAS) in surface waters of the nontidal Passaic River Basin: U.S. Geological Survey Scientific Investigations Report 2026–5018, 64 p., https://doi.org/10.3133/sir20265018.","productDescription":"Report: ix, 64 p.; Data Release","numberOfPages":"64","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-184195","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":503901,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119389.htm","linkFileType":{"id":5,"text":"html"}},{"id":503606,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1RGG9YQ","text":"USGS data release","linkHelpText":"Per- and polyfluoroalkyl substances (PFAS) concentration results in the Wanaque, Ramapo, Pompton and Passaic River watersheds, New Jersey 2025"},{"id":503604,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2026/5018/sir20265018.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2026-5018 XML"},{"id":503603,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20265018/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2026-5018 HTML"},{"id":503605,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2026/5018/images/"},{"id":503602,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2026/5018/sir20265018.pdf","text":"Report","size":"4.73 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2026-5018 PDF"},{"id":503601,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2026/5018/coverthb.jpg"}],"country":"United States","state":"New Jersey, New York","otherGeospatial":"Passaic River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.83442630336236,\n 41.42203271609333\n ],\n [\n -74.7558402457881,\n 41.42203271609333\n ],\n [\n -74.7558402457881,\n 40.74669233601534\n ],\n [\n -73.83442630336236,\n 40.74669233601534\n ],\n [\n -73.83442630336236,\n 41.42203271609333\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville, NJ 08648
U.S. Geological Survey researchers, in cooperation with the North Jersey District Water Supply Commission, determined that per- and polyfluoroalkyl substances (PFAS) are present in northern New Jersey rivers that are used as drinking-water sources. During their 2025 study, the researchers sampled 37 locations across the Wanaque, Ramapo, Pompton, and Passaic River watersheds. The researchers tested each sample for 40 types of PFAS. Of these, 15 were detected at least once. Two PFAS, perfluorooctanoate (PFOA) and perfluorooctane sulfonate (PFOS), were present in nearly every sample. PFAS concentrations varied by watershed and season. The lowest were detected in the Wanaque and Ramapo River watersheds, and the highest, in the Pompton and Passaic River watersheds. Concentrations of PFOA and PFOS were highest under base-flow conditions in July and September.
","publicationDate":"2026-04-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Schreiner, Molly L. 0000-0001-9306-5564","orcid":"https://orcid.org/0000-0001-9306-5564","contributorId":296363,"corporation":false,"usgs":true,"family":"Schreiner","given":"Molly L.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":960544,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Romanok, Kristin M. 0000-0002-8472-8765 kromanok@usgs.gov","orcid":"https://orcid.org/0000-0002-8472-8765","contributorId":204640,"corporation":false,"usgs":true,"family":"Romanok","given":"Kristin","email":"kromanok@usgs.gov","middleInitial":"M.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":960545,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gray, Jacob T. 0000-0002-9374-0336","orcid":"https://orcid.org/0000-0002-9374-0336","contributorId":330273,"corporation":false,"usgs":true,"family":"Gray","given":"Jacob","middleInitial":"T.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":960546,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brown, Eileen J. 0000-0003-3417-0203 ejbrown@usgs.gov","orcid":"https://orcid.org/0000-0003-3417-0203","contributorId":361968,"corporation":false,"usgs":true,"family":"Brown","given":"Eileen","email":"ejbrown@usgs.gov","middleInitial":"J.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":960547,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Williams, Brianna M. 0000-0003-3389-8251","orcid":"https://orcid.org/0000-0003-3389-8251","contributorId":204714,"corporation":false,"usgs":false,"family":"Williams","given":"Brianna","middleInitial":"M.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":960548,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kneser, Maureen","contributorId":370591,"corporation":false,"usgs":false,"family":"Kneser","given":"Maureen","affiliations":[{"id":88047,"text":"North Jersey District Water Supply Commission","active":true,"usgs":false}],"preferred":false,"id":960549,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Capuzzi, Albert J.","contributorId":370592,"corporation":false,"usgs":false,"family":"Capuzzi","given":"Albert","middleInitial":"J.","affiliations":[{"id":88047,"text":"North Jersey District Water Supply Commission","active":true,"usgs":false}],"preferred":false,"id":960550,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Boerner, Jason","contributorId":370593,"corporation":false,"usgs":false,"family":"Boerner","given":"Jason","affiliations":[{"id":88047,"text":"North Jersey District Water Supply Commission","active":true,"usgs":false}],"preferred":false,"id":960551,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Giunta, Luke","contributorId":370594,"corporation":false,"usgs":false,"family":"Giunta","given":"Luke","affiliations":[{"id":88047,"text":"North Jersey District Water Supply Commission","active":true,"usgs":false}],"preferred":false,"id":960552,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Serillo, Paul","contributorId":370595,"corporation":false,"usgs":false,"family":"Serillo","given":"Paul","affiliations":[{"id":88047,"text":"North Jersey District Water Supply Commission","active":true,"usgs":false}],"preferred":false,"id":960553,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Trainor, John J. 0000-0002-6603-2684 jtrainor@usgs.gov","orcid":"https://orcid.org/0000-0002-6603-2684","contributorId":5408,"corporation":false,"usgs":true,"family":"Trainor","given":"John","email":"jtrainor@usgs.gov","middleInitial":"J.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":960554,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Smalling, Kelly L. 0000-0002-1214-4920","orcid":"https://orcid.org/0000-0002-1214-4920","contributorId":221234,"corporation":false,"usgs":true,"family":"Smalling","given":"Kelly","middleInitial":"L.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":960555,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70275239,"text":"sir20265142 - 2026 - Assessment of long-term trends in streamflow statistics within and near the Mobile Bay and Perdido Bay watersheds, United States, 1950–2022","interactions":[],"lastModifiedDate":"2026-05-01T16:45:34.897463","indexId":"sir20265142","displayToPublicDate":"2026-04-30T10:23:48","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":"2026-5142","displayTitle":"Assessment of Long-Term Trends in Streamflow Statistics Within and Near the Mobile Bay and Perdido Bay Watersheds, United States, 1950–2022","title":"Assessment of long-term trends in streamflow statistics within and near the Mobile Bay and Perdido Bay watersheds, United States, 1950–2022","docAbstract":"The U.S. Geological Survey, in cooperation with the Gulf Coast Ecosystem Restoration Council, assessed monotonic trends for a variety of streamflow statistics for 69 long-term U.S. Geological Survey streamgages within either the Mobile Bay or Perdido Bay watersheds that were active through at least at the end of calendar year 2019. Long-term data were defined for this investigation as having at least 50 years of cumulative record within the period since January 1, 1950, with a requirement for a complete record of streamflow during the 2010s (2010–19). The 69 streamgages have at least 54 years and as many as 73 years of daily mean streamflow data; the median period of record is 72 years; and 15 of the streamgages are identified as “major nodes” on the basis of the criteria described. The occurrence of statistically monotonic significant trends for the 69 streamgages at the 0.05 significance level is spatially shown for six statistics. For the major node streamgages, the study depicts (1) time-series graphics of annual mean, annual harmonic mean, decadal 10th, 50th, and 90th-percentile streamflows, and (2) a variation on Quantile-Kendall plots of Kendall’s tau and streamflow nonexceedance probabilities for each of the 365 days of a year. Trend assessment synthesis shows that, except for a few streamgages with relatively greater counts of statistically significant trends than others, the majority (about 93 percent) of individual trend tests indicate no trend in the streamflow and ecological metrics considered.
Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Contact Us- USGS Publications Warehouse
","tableOfContents":"Many hydrologic applications require basic information on the size and shape of river channels, but measuring cross section (XS) geometry in the field or via remote sensing can be costly and often provides only partial coverage. Given these challenges, we capitalized upon an existing data set of 46,971 XS from gaging stations to evaluate various approximations of channel shape. After screening and pre-processing these data, we fit four model types to each XS, including a new approach that involves Stacking PDFs (probability density functions) to Approximate River Channel Shapes (SPARCS). This framework produced depth estimates that closely matched field measurements, with typical cross-sectional area errors <1% and a median R2 of 0.77 for comparison of observed and predicted depths. SPARCS model parameters can be interpreted in terms of channel characteristics: mean depth, asymmetry, bar convexity, and flatness of the bed. The model performed well for the XS included in the database, which was biased toward straight, uniform channels conducive to operational streamflow measurement. Neither model parameters nor accuracy were dependent on discharge. We also assessed the potential of SPARCS to fill in measurement gaps and found that although the model can help, the accuracy of inferred depths decreased as the observable fraction of the channel decreased. An important limitation of SPARCS is that mid-channel bars or multi-threaded morphologies cannot be produced. Graphical tools can help visualize how model parameters affect simulated river forms. SPARCS could facilitate satellite-based discharge estimation by providing prior information on channel shape.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2025WR041177","usgsCitation":"Legleiter, C.J., and Kinzel, P.J., 2026, Evaluating approximations of river channel shape using a national cross section database: Water Resources Research, v. 62, no. 5, e2025WR041177, 35 p., https://doi.org/10.1029/2025WR041177.","productDescription":"e2025WR041177, 35 p.","ipdsId":"IP-179311","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":504157,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2025wr041177","text":"Publisher Index Page"},{"id":503881,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"62","issue":"5","noUsgsAuthors":false,"publicationDate":"2026-04-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":960758,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kinzel, Paul J. 0000-0002-6076-9730 pjkinzel@usgs.gov","orcid":"https://orcid.org/0000-0002-6076-9730","contributorId":743,"corporation":false,"usgs":true,"family":"Kinzel","given":"Paul","email":"pjkinzel@usgs.gov","middleInitial":"J.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":960759,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70275718,"text":"70275718 - 2026 - Changes in suspended sediment concentration along tidal rivers of the Chesapeake Bay: The tidal freshwater “sediment shadow”","interactions":[],"lastModifiedDate":"2026-05-15T13:15:07.488526","indexId":"70275718","displayToPublicDate":"2026-04-30T08:34:23","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1587,"text":"Estuarine, Coastal and Shelf Science","active":true,"publicationSubtype":{"id":10}},"title":"Changes in suspended sediment concentration along tidal rivers of the Chesapeake Bay: The tidal freshwater “sediment shadow”","docAbstract":"Transport of terrigenic sediment from nontidal watersheds into estuaries has important impacts on coastal habitat quality, pollutant transport, and resilience to sea-level rise. However, relatively little is known about changes in suspended sediment as nontidal rivers encounter tide, transition into tidal rivers through the tidal freshwater zone (TFZ), and enter saline portions of estuaries. The goal of this paper is to identify spatial and temporal patterns in suspended sediment concentration (SS) changes across tidal and salinity gradients over multiple tidal rivers, using a robust monitoring long-term dataset from the Chesapeake Bay. The multiple TFZs in the Chesapeake Bay consistently have a “sediment shadow” shown by a local spatial minimum in SS compared to upstream nontidal and downgradient oligohaline river reaches. Similarly, freshwater inputs from nontidal rivers have diminishing influence on tidal SS temporal dynamics with distance downstream from the head-of-tide. Therefore, little of the contemporary watershed sediment load is likely transported past the TFZ except during extreme floods when some sediment may be delivered to saline portions of the estuary. Tidal freshwater and brackish portions of the estuary have spatially variable trends in SS over time, both increases and decreases. However, the more saline downstream ends of tidal rivers and the mainstem of the Chesapeake Bay have had a consistent average 25% decline in SS over the past decades. In summary, the presence of “sediment shadows” suggests watershed loads of sediment are currently mostly not transported through the TFZ into the saline estuary, and likely generate sediment deficits for tidal freshwater wetlands.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecss.2026.109931","usgsCitation":"Noe, G.E., Murphy, R., and Krauss, K., 2026, Changes in suspended sediment concentration along tidal rivers of the Chesapeake Bay: The tidal freshwater “sediment shadow”: Estuarine, Coastal and Shelf Science, v. 337, 109931, 14 p., https://doi.org/10.1016/j.ecss.2026.109931.","productDescription":"109931, 14 p.","ipdsId":"IP-178405","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":504324,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":504375,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecss.2026.109931","text":"Publisher Index Page"}],"country":"United States","state":"Delaware, Maryland, Pennsylvania","otherGeospatial":"Chesapeake Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78,\n 40\n ],\n [\n -75.2,\n 40\n ],\n [\n -75.2,\n 37\n ],\n [\n -78,\n 37\n ],\n [\n -78,\n 40\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"337","noUsgsAuthors":false,"publicationDate":"2026-04-30","publicationStatus":"PW","contributors":{"authors":[{"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":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"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}],"preferred":true,"id":961522,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Murphy, Rebecca","contributorId":331418,"corporation":false,"usgs":false,"family":"Murphy","given":"Rebecca","affiliations":[{"id":79204,"text":"UMCES","active":true,"usgs":false}],"preferred":false,"id":961523,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Krauss, Ken 0000-0003-2195-0729","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":210857,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":961524,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70275267,"text":"sir20265132 - 2026 - Hydrologic investigation of water level fluctuations at Moreau Lake, Moreau Lake State Park, town of Moreau, New York","interactions":[],"lastModifiedDate":"2026-05-01T16:44:17.159946","indexId":"sir20265132","displayToPublicDate":"2026-04-29T11:00:00","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":"2026-5132","displayTitle":"Hydrologic Investigation of Water Level Fluctuations at Moreau Lake, Moreau Lake State Park, Town of Moreau, New York","title":"Hydrologic investigation of water level fluctuations at Moreau Lake, Moreau Lake State Park, town of Moreau, New York","docAbstract":"The causes of water level fluctuations at Moreau Lake, within Moreau Lake State Park in the town of Moreau, New York, were investigated from 2016 to 2021 after lake water levels dropped between 2015 and 2016, raising concerns about the loss of a shallow swimming area at the park beach. Annual variation in precipitation records from the area did not account for the lake water level decline. Two possible causes for the low lake water levels were investigated: the increase in groundwater withdrawals from new residential development since about 2000 and seasonal changes (nongrowing and growing seasons) in precipitation.
Investigation of the potential effects of nearby groundwater withdrawals required the compilation and collection of well-log data, seismic surveys, and measurements of lake and groundwater levels, field chemical parameters, and water isotopes to define the hydrogeologic system and to estimate water use. The net result of this work was the determination that Moreau Lake is a “flow though” lake with no surface water outlet; groundwater enters the lake on the upgradient side and exits through the downgradient side, however, groundwater does not flow southward from the lake toward nearby groundwater withdrawals from the semiconfined aquifer, and thus groundwater withdrawals were unlikely to have an effect on lake water levels.
Investigation of the historic precipitation records during nongrowing (November through April) and growing (May through October) indicated that (1) nongrowing season precipitation from 2011–12 to 2015–16 was more deficient than any similar period during the past 78 years and (2) since about 2000, nongrowing seasons have been drier overall and growing seasons have been considerably wetter. Initiation of lake water level monitoring in 2016 provided an opportunity to compare seasonal precipitation with seasonal lake water level changes. Nongrowing season lake water levels are very sensitive to precipitation, such that high precipitation (40 percent above the seasonal median) resulted in a 5-foot rise in lake water level. In contrast, the growing season lake water levels are sensitive to dry conditions; for example, deficient rainfall (about 6 percent below the seasonal median) resulted in a decline of lake water levels of about 3.5 feet. However, lake water levels are insensitive to high growing season rainfall inputs (about 10 to 47 percent above the seasonal median); lake water levels consistently declined about by 0.8 feet above this range of seasonal excessive precipitation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20265132","collaboration":"Prepared in cooperation with New York State Department of Parks, Recreation and Historic Preservation","usgsCitation":"Heisig, P.M., 2026, Hydrologic investigation of water level fluctuations at Moreau Lake, Moreau Lake State Park, town of Moreau, New York: U.S. Geological Survey Scientific Investigations Report 2026–5132, 55 p., https://doi.org/10.3133/sir20265132.","productDescription":"Report: viii, 55 p., 3 Data Releases","numberOfPages":"55","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-148237","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":503535,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2026/5132/coverthb.jpg"},{"id":503899,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119387.htm","linkFileType":{"id":5,"text":"html"}},{"id":503541,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9R49VRO","text":"USGS data release","linkHelpText":"Horizontal-to-vertical spectral ratio (HVSR) soundings and depth-to-bedrock data for the Moreau Lake area, town of Moreau, N.Y."},{"id":503540,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K0LSHJ","text":"USGS data release","linkHelpText":"Hydrologic data from the Moreau Lake area, town of Moreau, N.Y."},{"id":503539,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JPZ1R5","text":"USGS data release","linkHelpText":"Geospatial data from the Moreau Lake area, town of Moreau, N.Y."},{"id":503538,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2026/5132/images"},{"id":503537,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2026/5132/sir20265132.XML","description":"SIR 2026-5132 XML"},{"id":503536,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20265132/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2026-5132 HTML"},{"id":503534,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2026/5132/sir20265132.pdf","text":"Report","size":"36.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2026-5132 PDF"}],"country":"United States","state":"New York","otherGeospatial":"Moreau Lake, Moreau Lake State Park, Town of Moreau","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.7367112856191,\n 43.25254327180468\n ],\n [\n -73.67250609715373,\n 43.25254327180468\n ],\n [\n -73.67250609715373,\n 43.20548515764352\n ],\n [\n -73.7367112856191,\n 43.20548515764352\n ],\n [\n -73.7367112856191,\n 43.25254327180468\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180
The transition from Landsat Collection 1 to Collection 2 introduced significant improvements in radiometric and geometric accuracy. However, the improvements cause location misalignment between the existing Landsat-derived land cover products and the new collection. The legacy National Land Cover Database (NLCD) has been used as a cornerstone land cover source for a variety of research. Therefore, a method aligning the legacy NLCD product to Collection 2 is required to ensure its continuity and consistency of service. We developed a strategy to not only align legacy NLCD to match new Collection 2 geometric locations but also improve land cover labeling in the region that was affected by the geometric shifts. The method identifies boundary pixels of homogeneous land cover patches as potential problem areas that are likely impacted by geometric shifts and generates candidate labels from 3 × 3 window with the target pixel at the center and segmentation-derived majority label. Standard phenology patterns of each candidate land cover type are established based on the random samples except boundary pixels within a 1000-pixels × 1000-pixels processing window region. The phenological distance to each standard land cover type pattern is calculated through a penalty dynamic time warping (DTW) method for each target pixel in the boundary region. Finally, the method determines the most suitable label based on the phenological distance from the candidate labels. Both visual and accuracy assessment results demonstrate that the alignment preserves the overall land cover patterns in the original legacy NLCD product while reducing the spatial discrepancies between the Landsat Collection 2 and land cover. In addition, it enhances the accuracy of land cover labeling of boundary pixels. The overall accuracy (OA) was increased by 7% in the land cover boundary regions after alignment. The quality and confusion matrix comparison between the alignment results and the original legacy NLCD confirm the reliability of the method. Our alignment method has the potential to serve as a framework for aligning other Landsat-derived land cover products to future collections.
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sjin@usgs.gov","orcid":"https://orcid.org/0000-0001-9919-8077","contributorId":4397,"corporation":false,"usgs":true,"family":"Jin","given":"Suming","email":"sjin@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":961847,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70275078,"text":"sir20265129 - 2026 - Shallow hydrogeologic framework of the Tully Valley mudboil area, Onondaga County, New York","interactions":[],"lastModifiedDate":"2026-05-01T16:42:15.609499","indexId":"sir20265129","displayToPublicDate":"2026-04-29T09:32:47","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":"2026-5129","displayTitle":"Shallow Hydrogeologic Framework of the Tully Valley Mudboil Area, Onondaga County, New York","title":"Shallow hydrogeologic framework of the Tully Valley mudboil area, Onondaga County, New York","docAbstract":"Mudboils have been documented in the Tully Valley in southern Onondaga County, New York, since the late 1890s. Sediment-laden water from the mudboils flows into Onondaga Creek, which empties into Onondaga Lake at Syracuse 15 miles to the north. Turbidity from the mudboils has degraded the water quality of Onondaga Creek despite a series of mitigation efforts that began in the early 1990s. Turbidity mitigation actions presently (2025) being considered include creek relocation and offline sediment settling. In support of these proposed actions during 2021–23, the U.S. Geological Survey, in cooperation with the New York State Department of Environmental Conservation, U.S. Environmental Protection Agency, Onondaga Nation, Onondaga Environmental Institute, and Central New York Regional Planning and Development Board, collected and analyzed geologic, hydrologic, geophysical, and geotechnical data to characterize the shallow hydrogeology along four proposed creek-relocation paths and in the proposed offline settling basin area.
The investigation indicated that the four proposed creek-relocation paths, two east of Onondaga Creek and two west of Onondaga Creek, are underlain by sediments including muck, alluvium, mudboil deposits, alluvial-fan sand and gravel, and lacustrine fines. The proposed excavations would penetrate partially to fully saturated conditions: generally, the water table is shallow near the creek and deep on the alluvial fans. The shallowest excavation, about 5 feet below land surface, would be near the creek and primarily in alluvium, and the deepest excavation, as much as 30 feet below land surface, would be in the alluvial-fan deposits. Brackish waters would be penetrated by proposed channel excavations on the eastern side of Onondaga Creek in an area downgradient from a potentially leaking historical salt-exploration borehole and near the main mudboil area. Excavation in these areas likely would provide a continuous source of brackish groundwater to the relocated creek. Proposed channel excavations of muck, soft to very soft lacustrine fines, and mudboil-type sediments in mudboil and suspected mudboil areas would pose an excavation and slope stability challenge and would have the greatest potential to create new mudboils. Proposed channel excavations below the water table on the Rattlesnake Gulf and Rainbow Creek alluvial fans would intercept groundwater and make the constructed streambank susceptible to seepage-induced slope instability. The substantial water-level fluctuation in the sediments of both alluvial fans would aggravate the stability condition. In addition, excavation on the Rattlesnake Gulf alluvial fan would have the potential to affect water-supply springs at the toe of the fan.
The proposed offline settling basin area is in the northern part of the Rattlesnake Gulf alluvial fan. Natural and man-made diversions of Rattlesnake Gulf have resulted in saturated conditions in the general area of the proposed basin. The proposed offline settling basin would be excavated in, and berms would be constructed on, alluvial-fan deposits and lacustrine fines. In the proposed basin area, the alluvial deposits overlying the lacustrine fines are less than 10 feet thick. Excavation, berm construction, and loading of the saturated, soft to very soft lacustrine fines may be problematic and require soil strengthening.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20265129","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation, U.S. Environmental Protection Agency, Onondaga Nation, Onondaga Environmental Institute, and Central New York Regional Planning and Development Board","usgsCitation":"Williams, J.H., Terry, N.C., Kappel, W.M., Heisig, P.M., Glas, R.L., and Woda, J.C., 2026, Shallow hydrogeologic framework of the Tully Valley mudboil area, Onondaga County, New York: U.S. Geological Survey Scientific Investigations Report 2026–5129, 58 p., https://doi.org/10.3133/sir20265129.","productDescription":"Report: ix, 58 p.; Data Release","numberOfPages":"58","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-160204","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":503898,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119386.htm","linkFileType":{"id":5,"text":"html"}},{"id":502792,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2026/5129/images"},{"id":502793,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P149P35X","text":"USGS data release","linkHelpText":"Surface-geophysical, geotechnical, hydraulic-slug test, specific conductance, and geospatial data for shallow hydrogeologic investigations of the Tully Valley mudboil area, Onondaga County, New York"},{"id":502791,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2026/5129/sir20265129.XML","description":"SIR 2026-5129 XML"},{"id":502790,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20265129/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2026-5129 HTML"},{"id":502789,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2026/5129/coverthb2.jpg"},{"id":502786,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2026/5129/sir20265129.pdf","text":"Report","size":"21.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2026-5129 PDF"}],"country":"United States","state":"New York","county":"Onondaga County","otherGeospatial":"Tully Valley Mudboil Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.30484467796983,\n 43.13842576064047\n ],\n [\n -76.30484467796983,\n 42.78187367077939\n ],\n [\n -76.00789969132674,\n 42.78187367077939\n ],\n [\n -76.00789969132674,\n 43.13842576064047\n ],\n [\n -76.30484467796983,\n 43.13842576064047\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Laminated soil carbonate rinds are a Quaternary paleoclimate archive whose isotope composition is linked to soil formation conditions. At Rio Mesa, Utah (USA), we investigated the fidelity of rind records in a river terrace setting by determining the seasonal timing of rind formation and testing for inter-record replication. We infer soil carbonate formed in the spring season, contrasting with our prior inference of summer formation at Teasdale, Utah, ≈200 km distant. This apparent discrepancy occurs because of differences in the timing of the largest annual infiltration (spring vs. summer). At Rio Mesa, modern soil data show that soil carbonate δ13C would have high values (−2 to 2‰ VPDB) regardless of seasonal activity of C3 versus C4 plants because respiration rate is a strong control. We accordingly suggest reassessment of published records interpreting soil carbonate δ13C only via C3 versus C4 plant abundance. Three rind δ13C and δ18O records generally replicated. Intriguingly, rind δ13C may inversely correlate with summer insolation, evidence for global-scale influence on soils. Rind δ18O is not as clearly correlated with published western USA paleoclimate records, potentially due to regional differences in climate and because rinds record soil-specific processes. Our results support the fidelity of the soil carbonate rind paleoarchive and suggest that because rind formation seasonality is intimately tied to infiltration seasonality, spatial transects of rind records might be used to delineate boundaries between areas dominated by spring and summer infiltration, permitting reconstruction of the geographic extent of large-scale hydrologic phenomena such as the North American Monsoon.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2025GC012660","usgsCitation":"Huth, T.E., Cerling, T.E., Marchetti, D.W., Ellwein, A.L., Mahan, S.A., Bowling, D.R., Passey, B.H., Polyak, V.J., and Asmerom, Y., 2026, Springtime formation of laminated soil carbonate rinds and changes in fluvial terrace soils on orbital timescales at Rio Mesa, Utah, USA: Geochemistry, Geophysics, Geosystems, v. 27, no. 5, e2025GC012660, 20 p., https://doi.org/10.1029/2025GC012660.","productDescription":"e2025GC012660, 20 p.","ipdsId":"IP-179258","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":504152,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2025gc012660","text":"Publisher Index Page"},{"id":504055,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13EMTYG","text":"USGS data release","linkHelpText":"Luminescence data for: Springtime Formation of Laminated Soil Carbonate Rinds and Changes in Fluvial Terrace Soils on Orbital Timescales at Rio Mesa, Utah, USA"},{"id":503670,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Rio Mesa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.19947650382221,\n 38.81144940086827\n ],\n [\n -109.15369813510924,\n 38.81144940086827\n ],\n [\n -109.15369813510924,\n 38.77688465318985\n ],\n [\n -109.19947650382221,\n 38.77688465318985\n ],\n [\n -109.19947650382221,\n 38.81144940086827\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"27","issue":"5","noUsgsAuthors":false,"publicationDate":"2026-04-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Huth, Tyler E. 0000-0002-3436-7009","orcid":"https://orcid.org/0000-0002-3436-7009","contributorId":370676,"corporation":false,"usgs":false,"family":"Huth","given":"Tyler","middleInitial":"E.","affiliations":[{"id":37383,"text":"Washington University","active":true,"usgs":false}],"preferred":false,"id":960678,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cerling, Thure E.","contributorId":370677,"corporation":false,"usgs":false,"family":"Cerling","given":"Thure","middleInitial":"E.","affiliations":[{"id":13252,"text":"University of Utah","active":true,"usgs":false}],"preferred":false,"id":960679,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Marchetti, David W.","contributorId":370678,"corporation":false,"usgs":false,"family":"Marchetti","given":"David","middleInitial":"W.","affiliations":[{"id":6693,"text":"Western State Colorado University","active":true,"usgs":false}],"preferred":false,"id":960680,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ellwein, Amy L. 0000-0003-3591-6601","orcid":"https://orcid.org/0000-0003-3591-6601","contributorId":370679,"corporation":false,"usgs":false,"family":"Ellwein","given":"Amy","middleInitial":"L.","affiliations":[{"id":49195,"text":"Rocky Mountain Biological Laboratory","active":true,"usgs":false}],"preferred":false,"id":960681,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mahan, Shannon A. 0000-0001-5214-7774 smahan@usgs.gov","orcid":"https://orcid.org/0000-0001-5214-7774","contributorId":147159,"corporation":false,"usgs":true,"family":"Mahan","given":"Shannon","email":"smahan@usgs.gov","middleInitial":"A.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":960682,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bowling, David R.","contributorId":370684,"corporation":false,"usgs":false,"family":"Bowling","given":"David","middleInitial":"R.","affiliations":[{"id":13252,"text":"University of Utah","active":true,"usgs":false}],"preferred":false,"id":960683,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Passey, Benjamin H.","contributorId":370685,"corporation":false,"usgs":false,"family":"Passey","given":"Benjamin","middleInitial":"H.","affiliations":[{"id":37387,"text":"University of Michigan","active":true,"usgs":false}],"preferred":false,"id":960684,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Polyak, Victor J.","contributorId":370686,"corporation":false,"usgs":false,"family":"Polyak","given":"Victor","middleInitial":"J.","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":960685,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Asmerom, Yemane","contributorId":295388,"corporation":false,"usgs":false,"family":"Asmerom","given":"Yemane","affiliations":[{"id":16658,"text":"UNM","active":true,"usgs":false}],"preferred":false,"id":960686,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70275363,"text":"70275363 - 2026 - Modeling chronic wasting disease transmission risk in mule deer related to habitat characteristics","interactions":[],"lastModifiedDate":"2026-05-01T13:56:46.724745","indexId":"70275363","displayToPublicDate":"2026-04-29T08:52:37","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Modeling chronic wasting disease transmission risk in mule deer related to habitat characteristics","docAbstract":"Chronic wasting disease (CWD) is a prion disease of cervids that spreads to uninfected individuals through direct transmission (contact with infected individuals), vertical transmission (from mother to offspring), or indirect transmission (exposure to contaminated environments). The risk of indirect transmission is unevenly distributed on the landscape, and risk levels are expected to be controlled by patterns of habitat use by infected and uninfected individuals as well as environmental properties that alter the length of time prions remain infectious and available for uptake. Despite evidence from controlled or laboratory studies identifying environmental properties likely to affect patterns of CWD prion locations on the landscape, it remains difficult to connect mechanisms to realized increased or decreased risk of disease transmission, and few studies have attempted to detect patterns of different CWD risk in different environments. Using data from GPS-collared mule deer in Wyoming that were CWD-tested annually, we constructed models predicting annual probability of disease transmission contingent on environmental properties extracted from GPS use points. We compared models that emphasized different pathways of disease transmission by including or excluding sets of covariates that described deer density, habitat selection, and covariates expected to affect prion persistence in the environment. Results indicated that key habitat characteristics often selected by mule deer, such as proximity to secondary roads, were also associated with higher risk of testing positive for CWD, which supports the hypothesis that disease risk was correlated to patterns of habitat use by deer. We also found increased risk associated with spatial properties that were not selected-for by deer, such as areas where topography collects moisture, suggesting that prion retention mechanisms also play a role in risk. Incorporating these spatially-varying risk factors into our understanding of CWD transmission and outbreak progression can support managers in designing data collection and disease management strategies.","language":"English","publisher":"PLoS","doi":"10.1371/journal.pone.0346077","usgsCitation":"Christensen, E.M., Kleist, N.J., Edmunds, D.R., Heinrichs, J., Saher, D., Whipple, A.L., DeVivo, M., and Aldridge, C.L., 2026, Modeling chronic wasting disease transmission risk in mule deer related to habitat characteristics: PLoS ONE, v. 21, no. 4, e0346077, 24 p., https://doi.org/10.1371/journal.pone.0346077.","productDescription":"e0346077, 24 p.","ipdsId":"IP-179327","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":504156,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0346077","text":"Publisher Index Page"},{"id":504056,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1K3QFC8","text":"USGS data release","linkHelpText":"Raster maps of relative risk of chronic wasting disease transmission based on environmental covariates for the South Converse Mule Deer Herd, Converse County, Wyoming"},{"id":503880,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.91552517753678,\n 43.139560980857084\n ],\n [\n -104.18803524946959,\n 43.139560980857084\n ],\n [\n -104.18803524946959,\n 41.60389872904699\n ],\n [\n -106.91552517753678,\n 41.60389872904699\n ],\n [\n -106.91552517753678,\n 43.139560980857084\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"21","issue":"4","noUsgsAuthors":false,"publicationDate":"2026-04-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Christensen, Erica Meta 0000-0002-5635-2502","orcid":"https://orcid.org/0000-0002-5635-2502","contributorId":370740,"corporation":false,"usgs":true,"family":"Christensen","given":"Erica","middleInitial":"Meta","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":960708,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kleist, Nathan J. 0000-0002-2468-4318","orcid":"https://orcid.org/0000-0002-2468-4318","contributorId":260598,"corporation":false,"usgs":true,"family":"Kleist","given":"Nathan","email":"","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":960709,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Edmunds, David R. 0000-0002-5212-8271 dedmunds@usgs.gov","orcid":"https://orcid.org/0000-0002-5212-8271","contributorId":152210,"corporation":false,"usgs":true,"family":"Edmunds","given":"David","email":"dedmunds@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":960710,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Heinrichs, Julie A. 0000-0001-7733-5034","orcid":"https://orcid.org/0000-0001-7733-5034","contributorId":240888,"corporation":false,"usgs":false,"family":"Heinrichs","given":"Julie A.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":960711,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Saher, D. Joanne 0000-0002-2452-2570","orcid":"https://orcid.org/0000-0002-2452-2570","contributorId":288928,"corporation":false,"usgs":false,"family":"Saher","given":"D. Joanne","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":960712,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Whipple, Ashley L. 0000-0002-0304-7643","orcid":"https://orcid.org/0000-0002-0304-7643","contributorId":300552,"corporation":false,"usgs":true,"family":"Whipple","given":"Ashley","email":"","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":960713,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"DeVivo, Melia","contributorId":198647,"corporation":false,"usgs":false,"family":"DeVivo","given":"Melia","email":"","affiliations":[],"preferred":false,"id":960714,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":960715,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70275326,"text":"70275326 - 2026 - Characterizing the long-term (1981–2023) temperature and precipitation dynamics in the Trans-Mountain regions of Kazakhstan, Central Asia","interactions":[],"lastModifiedDate":"2026-04-29T14:27:38.481172","indexId":"70275326","displayToPublicDate":"2026-04-28T09:15:49","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":"Characterizing the long-term (1981–2023) temperature and precipitation dynamics in the Trans-Mountain regions of Kazakhstan, Central Asia","docAbstract":"Mountain regions are highly climate-sensitive, yet long-term observational evidence of elevation and seasonal climate dynamics in Central Asia remains limited. This study examines spatiotemporal trends in temperature (Tmean, Tmax, Tmin, and diurnal temperature range [DTR]) and precipitation across Kazakhstan’s transmountain regions using 74 meteorological stations (1981–2023). Data were analyzed using the Mann–Kendall test and Sen’s slope estimator, stratified across six elevation zones from lowlands (<400 m) to high mountains (>1500 m). Results reveal a robust, spatially coherent warming signal across all zones. Annual Tmean increased at a median rate of ~0.30 °C decade−1, peaking at 0.36 °C decade−1 above 1500 m, corresponding to an absolute increase exceeding 1.5 °C. Warming exhibited strong seasonal and diurnal asymmetries. Spring warmed most rapidly, with Tmean increasing >0.60 °C decade−1 (approaching 3 °C total). Winter warming was driven by Tmin increases (up to 0.44 °C decade−1), causing widespread DTR contraction, whereas summer warming was driven by Tmax increases, expanding DTR at higher elevations. Tmin showed the strongest elevation amplification overall. In stark contrast, precipitation trends were weak, spatially heterogeneous, and largely non-significant. Annual changes ranged from −6.63 to +14.35 mm decade−1, with seasonal tendencies indicating modest, non-significant winter/spring wetting and summer drying. Ultimately, the results demonstrate a profound decoupling between strong, elevation-dependent warming and weak precipitation changes. The acute amplification of temperature, particularly during spring and summer at high elevations, has severe implications for snowmelt timing, glacier mass balance, evapotranspiration demand, and long-term water security in Kazakhstan.
","language":"English","publisher":"MDPI","doi":"10.3390/w18091046","usgsCitation":"Duisebek, B., Senay, G.B., Usmanov, T., Kyrgyzbay, K., Sagin, J., Mukanov, Y., Samarkhanov, K., Wang, X., Danierhan, S., and Pan, X., 2026, Characterizing the long-term (1981–2023) temperature and precipitation dynamics in the Trans-Mountain regions of Kazakhstan, Central Asia: Water, v. 18, no. 9, 1046, 26 p., https://doi.org/10.3390/w18091046.","productDescription":"1046, 26 p.","ipdsId":"IP-187896","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":503778,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w18091046","text":"Publisher Index Page"},{"id":503620,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Kazakhstan","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[70.96231,42.26615],[70.38896,42.08131],[69.07003,41.38424],[68.63248,40.66868],[68.2599,40.66232],[67.98586,41.13599],[66.71405,41.16844],[66.51065,41.98764],[66.02339,41.99465],[66.09801,42.99766],[64.90082,43.72808],[63.18579,43.65007],[62.0133,43.50448],[61.05832,44.40582],[60.23997,44.78404],[58.68999,45.50001],[58.50313,45.5868],[55.92892,44.99586],[55.96819,41.30864],[55.45525,41.25986],[54.75535,42.04397],[54.07942,42.32411],[52.94429,42.11603],[52.50246,41.78332],[52.44634,42.02715],[52.69211,42.4439],[52.50143,42.7923],[51.34243,43.13297],[50.89129,44.03103],[50.33913,44.28402],[50.30564,44.60984],[51.2785,44.51485],[51.3169,45.246],[52.16739,45.40839],[53.04088,45.25905],[53.22087,46.23465],[53.04274,46.85301],[52.04202,46.80464],[51.19195,47.0487],[50.03408,46.60899],[49.10116,46.39933],[48.59324,46.56103],[48.69473,47.07563],[48.05725,47.74375],[47.31523,47.71585],[46.46645,48.39415],[47.04367,49.15204],[46.7516,49.35601],[47.54948,50.4547],[48.57784,49.87476],[48.70238,50.60513],[50.76665,51.69276],[52.32872,51.71865],[54.53288,51.02624],[55.71694,50.62172],[56.77796,51.04355],[58.36329,51.06365],[59.64228,50.54544],[59.93281,50.84219],[61.33742,50.79907],[61.588,51.27266],[59.96753,51.96042],[60.92727,52.44755],[60.73999,52.71999],[61.69999,52.98],[60.97807,53.66499],[61.43659,54.00626],[65.17853,54.35423],[65.66688,54.60127],[68.1691,54.97039],[69.06817,55.38525],[70.86527,55.16973],[71.18013,54.13329],[72.22415,54.37666],[73.50852,54.03562],[73.42568,53.48981],[74.38485,53.54686],[76.8911,54.49052],[76.52518,54.177],[77.80092,53.40441],[80.03556,50.86475],[80.56845,51.38834],[81.94599,50.8122],[83.383,51.06918],[83.93511,50.88925],[84.41638,50.3114],[85.11556,50.1173],[85.54127,49.69286],[86.82936,49.82667],[87.35997,49.21498],[86.59878,48.54918],[85.76823,48.45575],[85.72048,47.45297],[85.16429,47.00096],[83.18048,47.33003],[82.45893,45.53965],[81.94707,45.31703],[79.96611,44.91752],[80.86621,43.18036],[80.18015,42.92007],[80.25999,42.35],[79.64365,42.49668],[79.14218,42.85609],[77.65839,42.96069],[76.00035,42.98802],[75.63696,42.8779],[74.21287,43.29834],[73.6453,43.09127],[73.48976,42.50089],[71.84464,42.8454],[71.18628,42.70429],[70.96231,42.26615]]]},\"properties\":{\"name\":\"Kazakhstan\"}}]}","volume":"18","issue":"9","noUsgsAuthors":false,"publicationDate":"2026-04-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Duisebek, Baktybek","contributorId":360498,"corporation":false,"usgs":false,"family":"Duisebek","given":"Baktybek","affiliations":[{"id":86016,"text":"Kazakh British Technical University","active":true,"usgs":false}],"preferred":false,"id":960567,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":166812,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":960568,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Usmanov, 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Yerbolat","contributorId":370602,"corporation":false,"usgs":false,"family":"Mukanov","given":"Yerbolat","affiliations":[{"id":88050,"text":"Gumilyov Eurasian National University, AKazakhstan","active":true,"usgs":false}],"preferred":false,"id":960572,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Samarkhanov, Kanat","contributorId":370637,"corporation":false,"usgs":false,"family":"Samarkhanov","given":"Kanat","affiliations":[],"preferred":false,"id":960603,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wang, Xuejia","contributorId":370638,"corporation":false,"usgs":false,"family":"Wang","given":"Xuejia","affiliations":[],"preferred":false,"id":960573,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Danierhan, Sulitan","contributorId":370603,"corporation":false,"usgs":false,"family":"Danierhan","given":"Sulitan","affiliations":[{"id":88051,"text":"Chinese Academy of Sciences, China","active":true,"usgs":false}],"preferred":false,"id":960574,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Pan, Xiaohui","contributorId":370604,"corporation":false,"usgs":false,"family":"Pan","given":"Xiaohui","affiliations":[{"id":88051,"text":"Chinese Academy of Sciences, China","active":true,"usgs":false}],"preferred":false,"id":960575,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70275190,"text":"sir20255047 - 2026 - Peak-, mean-, and low-streamflow regional-regression equations for natural streamflow in central and western Colorado, 2019","interactions":[],"lastModifiedDate":"2026-05-18T16:07:47.996983","indexId":"sir20255047","displayToPublicDate":"2026-04-24T13:10:00","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":"2025-5047","displayTitle":"Peak-, Mean-, and Low-Streamflow Regional-Regression Equations for Natural Streamflow in Central and Western Colorado, 2019","title":"Peak-, mean-, and low-streamflow regional-regression equations for natural streamflow in central and western Colorado, 2019","docAbstract":"The U.S. Geological Survey (USGS), in cooperation with the Colorado Department of Transportation, developed peak-, mean-, and low-streamflow regional-regression equations for estimating various statistics for natural streamflow in hydrologic regions of central and western Colorado. The peak-streamflow regression equations were developed using data from 418 streamgages, consisting of 15,202 years of record and a mean of approximately 36 years of record per streamgage. The mean- and low-streamflow regional-regression equations were developed using data from 323 streamgages where daily streamflow data were collected year-round. The annual exceedance-probability discharges for each streamgage were computed using the USGS software program PeakFQ. Mean monthly and 7-day minimum and maximum streamflows were computed using the USGS software program SWToolbox. Streamflow-duration values were computed using an R script. The regional-regression equations were determined using data for the period of record for a given streamgage through water year 2019. Geographic information systems datasets were used to develop 55 basin and 42 climatic characteristics, which were evaluated as candidate explanatory variables in the regression analysis.
For the peak-streamflow regional-regression equations, the study area was divided into four hydrologic regions based on mean basin elevation, including the Plateau (less than 8,014 feet), Mid-Elevation (8,015 feet to 9,492 feet), Sub-Alpine (9,493 feet to 10,490 feet), and Alpine (greater than 10,490 feet) regions. For the peak-streamflow equations, the selection of basin and climatic characteristics was based on the 1-percent annual exceedance-probability discharge for each hydrologic region.
For the mean streamflow, streamflow-duration values, and 7-day minimum and maximum streamflows, the study area was divided into four hydrologic regions based on river basin, including the (1) Colorado-East Slope Headwaters, (2) Green River, (3) Rio Grande, and (4) San Juan-Dolores. For mean streamflows, basin and climatic characteristics were evaluated separately for the annual period and each month for each hydrologic region. Regional regression equations published in this report are available for use in the USGS web-based program StreamStats.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20255047","collaboration":"Prepared in cooperation with the Colorado Department of Transportation","usgsCitation":"Kohn, M.S., Mast, M.A., and Gross, T.A., 2026, Peak-, mean-, and low-streamflow regional-regression equations for natural streamflow in central and western Colorado, 2019: U.S. Geological Survey Scientific Investigations Report 2025–5047, 38 p., https://doi.org/10.3133/sir20255047.","productDescription":"Report: viii, 38 p.; 2 Tables; Data Release","onlineOnly":"Y","ipdsId":"IP-140049","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":503285,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5047/sir20255047.pdf","text":"Report","size":"5.86 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5047"},{"id":504484,"rank":9,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255047/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5047"},{"id":504422,"rank":8,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5047/sir20255047.xml"},{"id":504421,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5047/images"},{"id":503897,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119385.htm","linkFileType":{"id":5,"text":"html"}},{"id":503290,"rank":5,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sir/2025/5047/sir20255047_table1.2.csv","text":"Table 1.2","size":"16.0 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2025-5047 Table 2","linkHelpText":"Basin and climate characteristics evaluated for use in the peak-, mean-, and low-streamflow regional-regression equations in central and western Colorado, 2019"},{"id":503284,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5047/coverthb.jpg"},{"id":503289,"rank":4,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sir/2025/5047/sir20255047_table1.1.csv","text":"Table 1.1","size":"72.0 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2025-5047 Table 1","linkHelpText":"Summary of the streamgages used in the regression analysis of natural streams in central and western Colorado, 2019"},{"id":503286,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Q5AMFV","text":"USGS data release","linkHelpText":"Streamflow data and basin characteristics of natural streams in central and western Colorado, 2019"}],"country":"United States","state":"Colorado","otherGeospatial":"central and western Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.5157587,\n 37.0336449\n ],\n [\n -104.2371905,\n 37.7437419\n ],\n [\n -104.4345097,\n 38.3014981\n ],\n [\n -104.9220039,\n 39.2695668\n ],\n [\n -104.5737937,\n 39.5207192\n ],\n [\n -104.8523603,\n 41.026094\n ],\n [\n -109.0540968,\n 40.9910589\n ],\n [\n -109.0557679,\n 37.0087015\n ],\n [\n -104.5157587,\n 37.0336449\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
Denver, CO 80225
Interest in nuclear power for the generation of electricity has risen with the increase in the need for more diverse baseload power, enhanced energy security, and the development of new technologies, such as small modular reactors (SMRs), which could provide power for remote areas, industrial applications, and artificial intelligence (AI) data centers. In 2024, the U.S. Department of Energy received $2.7 billion in congressional funding to bolster the domestic uranium production and nuclear fuel supply chain and address reliance on imports from foreign suppliers. In 2025, the U.S. Government issued several Executive and Secretary’s orders aimed at revitalizing the U.S. nuclear sector. If SMRs are to be as widely deployed in the United States and worldwide as envisioned, demand for uranium (nuclear reactor fuel) will likely increase.
After the Fukushima nuclear accident in 2011, the market spot price of uranium began a decline, followed by a decrease in U.S. and global uranium exploration and mine development expenditures that led to a uranium supply deficit until 2020, when prices started to recover, prompting a resurgence in uranium exploration and development. In January of 2024, the uranium spot price rose to a 17-year high $106 (U.S. dollars) per pound of U3O8 (triuranium oxide, commonly known as “yellowcake”), which is expected to increase uranium exploration, mine development, and uranium production domestically and worldwide.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/fs20253057","programNote":"Mineral Resources Program","usgsCitation":"Mihalasky, M.J., 2026, Uranium—Deposits, production and resources, market dynamics, and supply chain risks: U.S. Geological Survey Fact Sheet 2025-3057, 6 p., https://doi.org/10.3133/fs20253057.","productDescription":"6 p.","onlineOnly":"N","ipdsId":"IP-183501","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":503531,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119374.htm","linkFileType":{"id":5,"text":"html"}},{"id":503325,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20253057/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2025-3057"},{"id":499486,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2025/3057/coverthb.jpg"},{"id":499488,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2025/3057/fs20253057.pdf","text":"Report","size":"10.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2025-3057"},{"id":503248,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2025/3057/fs20253057.xml"},{"id":503247,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2025/3057/images"}],"geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -163.27895957198476,\n 82.71495374821887\n ],\n [\n 179.9,\n 82.71495374821887\n ],\n [\n 179.9,\n -58.79868573338722\n ],\n [\n -163.27895957198476,\n -58.79868573338722\n ],\n [\n -163.27895957198476,\n 82.71495374821887\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Geology, Minerals, Energy, and Geophysics Science Center
U.S. Geological Survey
Building 19, 350 N. Akron Rd.
P.O. Box 158
Moffett Field, CA 94035
Cyanobacterial harmful algal blooms (cyanoHABs) pose risks to human and animal health.
We investigated the relationship between cyanoHABs and asthma or wheeze-related emergency department (ED) visits near three Wisconsin cities (Green Bay, Madison, and Oshkosh) during 2017–2019. CyanoHAB exposure was approximated using the Cyanobacterial Assessment Network remotely sensed satellite indicator of cyanobacterial biomass, a chlorophyl algorithm (ChlBS) aggregated by water-adjacent ZIP Code Tabulation Areas (ZCTA), and distance weighted from the nearest waterbody. Weekly counts of ED visits for asthma or wheeze were aggregated by ZCTA. Poisson generalized linear models estimated the association between the weekly number of ED visits and weekly ChlBS, adjusting for maximum temperature, dewpoint, fine particulate matter (PM2.5), month, and correlation within ZCTA.
During 2017–2019, 7,057 ED visits for asthma or wheeze occurred in the study area (42 ZCTAs). Peaks in ChlBS occurred between June and October, with higher values in Lake Winnebago and Lake Mendota compared to Green Bay. ChlBS was not associated with ED visits for asthma or wheeze (adjusted rate ratio = 1.00, 95% confidence interval = 0.99, 1.00), and the presence of onshore winds did not change this result. Monthly aggregations of ED visits and ChlBS showed a monotonic trend between increasing ChlBS and ED visits during July–September.
This study demonstrates the utility of remote sensing data in environmental health research. Future studies could explore individual-level exposure and outcomes to refine health risks associated with cyanoHABs.
","language":"English","publisher":"Wolters Kluwer","doi":"10.1097/EE9.0000000000000439","collaboration":"Center for Disease Control and Prevention, Wisconsin Dept of Health Services, United States Environmental Protection Agency, National Aeronautics and Space Administration, Morgan State University","usgsCitation":"Lavery, A.M., Murray, J., Pennington, A.F., Schaeffer, B., Seegers, B., Hilborn, E.D., Loftin, K., Scroggins, S., and Backer, L., 2026, Cyanobacterial bloom occurrence and emergency department visits for asthma or wheeze, Wisconsin, 2017–2019: Environmental Epidemiology, v. 10, no. 3, e439, https://doi.org/10.1097/EE9.0000000000000439.","productDescription":"e439","ipdsId":"IP-178648","costCenters":[{"id":84311,"text":"Central Plains Water Science Center","active":true,"usgs":true}],"links":[{"id":503551,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":503769,"rank":1,"type":{"id":40,"text":"Open Access 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Washington","interactions":[{"subject":{"id":70275112,"text":"70275112 - 2025 - Development of a two-stage life cycle model to inform the Trap and Haul Program for Coho salmon in the Lewis River, Washington","indexId":"70275112","publicationYear":"2025","noYear":false,"title":"Development of a two-stage life cycle model to inform the Trap and Haul Program for Coho salmon in the Lewis River, Washington"},"predicate":"SUPERSEDED_BY","object":{"id":70275077,"text":"ofr20261004 - 2026 - Development of a two-stage lifecycle model to inform the trap-and-haul program for Oncorhynchus kisutch (coho salmon) in the Lewis River, Washington","indexId":"ofr20261004","publicationYear":"2026","noYear":false,"title":"Development of a two-stage lifecycle model to inform the trap-and-haul program for Oncorhynchus kisutch (coho salmon) in the Lewis River, Washington"},"id":1}],"lastModifiedDate":"2026-04-23T13:56:24.60608","indexId":"ofr20261004","displayToPublicDate":"2026-04-22T14:45:00","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":"2026-1004","displayTitle":"Development of a Two-Stage Lifecycle Model to Inform the Trap-and-Haul Program for Oncorhynchus kisutch (Coho Salmon) in the Lewis River, Washington","title":"Development of a two-stage lifecycle model to inform the trap-and-haul program for Oncorhynchus kisutch (coho salmon) in the Lewis River, Washington","docAbstract":"Restoration of salmon populations in the upper Lewis River Basin, Washington, depends on a trap-and-haul program owing to the Lewis River Hydroelectric Project (hereinafter referred to as “Project”) operated by PacifiCorp and Cowlitz Public Utilities District (hereinafter referred to as “Utilities”), which has been a barrier to salmon passage since the 1930s. Thus, sustaining the Oncorhynchus kisutch (Walbaum, 1792; coho salmon) population upstream from the Project currently depends on two fundamental factors: (1) the collection of upstream migrating adult coho salmon at Merwin Dam, the lowermost dam within the Project, and transporting them by truck to spawn above Swift Dam, the uppermost dam within the Project; and (2) the collection of out-migrating juvenile coho salmon at the downstream collection facility at Swift Dam for transport and release below the Project. The reintroduction program began once the downstream collection facility at Swift Dam was commissioned in late 2012, with the first year of transport data being collected in 2013. Over the past decade, the Utilities have been collecting data on juvenile outmigrants and adult fish returns at the dams. The need to construct a lifecycle model for Lewis River anadromous fish was identified by the Lewis River Aquatic Technical Subgroup, with the understanding that many years (more than 15 years) of data collection are needed to adequately measure the lifecycle production of salmon. The U.S. Geological Survey was contracted to develop and apply the model to past data at the Lewis River dams to help inform future data collection and provide a framework that can be updated annually to measure trap-and-haul program performance within a lifecycle context.
Because coho salmon can live as long as 5 years, estimating demographic parameters for coho salmon populations over their lifecycle requires at least 10 or more years of data collection. Over the past decade, PacifiCorp has been collecting data on fish collection efficiency and the numbers of adult and juvenile salmon transported around the Lewis River dams, making this an ideal time to formulate a lifecycle model that can guide future data collection efforts and provide preliminary information to resource managers. The goal of the statistical lifecycle model is to estimate annual production and survival during two critical life-stage transitions: (1) the freshwater production from escapement of adults released upstream from Swift Dam, and the collection of downstream migrating juveniles at the downstream passage facility at Swift Dam; and (2) the smolt-to-adult survival from the time of collection at Swift Dam to their return as adults. We used the Beverton-Holt stock-recruitment model to estimate juvenile production from the number of spawners (Beverton and Holt, 1957). This approach allowed us to test for density dependence at current spawner abundances while estimating annual productivity, defined as the number of juveniles produced per spawner at low spawner abundance. Productivity was then expressed as a function of the number of juveniles collected and transported downstream from the Project. Because juvenile fish collection efficiency (FCE) directly affects the number of juveniles that survive to continue downstream migration, FCE is a primary determinant of fish production. Consequently, the modeling framework is well suited to evaluate the performance of trap-and-haul programs within a lifecycle context.
The objectives of this study were to (1) gather and collate available data on adult and juvenile coho salmon at Merwin and Swift Dams; (2) quantify adult escapement, juvenile abundance, and the age at outmigration and adult return; (3) describe, formulate and fit the integrated population model to the data; and (4) summarize our findings, identify data gaps, and identify opportunities for future studies that could improve model estimation and inference. Our key findings were: (1) over and above the number of spawning females, FCE was the primary factor affecting productivity of coho salmon above Swift Dam; (2) smolt-to-adult return (SAR) rates were relatively high considering that harvest was included in the estimate, averaging about 4.5 percent and ranging as high as 12.9 percent; and (3) juvenile capacity upstream from Swift Dam was difficult to estimate due to the limited range in spawning females over the time series of data, suggesting the model may be improved by collecting data at higher spawner abundances. In addition, by including FCE in the model, we estimated that the median pre-collection productivity, defined as the number of juveniles produced per spawner when FCE=1, was 64 juveniles per spawner. Because the two-stage lifecycle model partitions factors that affect fish production in rivers versus the ocean, the model estimates may help inform fishery managers about the overall role that fish collection at Swift Dam plays in the recovery and sustainability of Lewis River coho salmon. By providing the model with (1) more years of data, (2) higher numbers of spawning females, and (3) data on age at juvenile migration in relation to age at adult return, greater certainty in the estimates of capacity and SAR can be attained. Ultimately, information provided by the model may assist in the evaluation and continued improvement of the current trap-and-haul program to support anadromous fishes in the Lewis River Basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20261004","collaboration":"Prepared in cooperation with PacifiCorp","usgsCitation":"Plumb, J.M., and Perry, R.W., 2026, Development of a two-stage lifecycle model to inform the trap-and-haul program for Oncorhynchus kisutch (coho salmon) in the Lewis River, Washington: U.S. Geological Survey Open-File Report 2026–1004, 24 p., https://doi.org/10.3133/ofr20261004. [Supersedes preprint https://doi.org/10.1101/2025.04.30.651546.]","productDescription":"vii, 24 p.","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-170103","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":502780,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2026/1004/coverthb.jpg"},{"id":502781,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2026/1004/ofr20261004.pdf","size":"5.95 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2026-1004 PDF"},{"id":502782,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20261004/full","linkFileType":{"id":5,"text":"html"},"description":"OFR 2026-1004 HTML"},{"id":502783,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2026/1004/ofr20261004.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2026-1004 XML"},{"id":502784,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2026/1004/images/"}],"country":"United States","state":"Washington","otherGeospatial":"Lewis River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.99926718616892,\n 46.128547340095906\n ],\n [\n -122.8039992422735,\n 46.12907889994935\n ],\n [\n -122.80484882535518,\n 45.8612743686528\n ],\n [\n -122.00013863515652,\n 45.86266272702096\n ],\n [\n -121.99926718616892,\n 46.128547340095906\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
5501-A Cook Underwood Road
Cook, Washington 98605-9717
Body and caudal fin locomotion is ubiquitous in aquatic vertebrates, and kinematic models describing it are used in robotics, biomechanics and fisheries research. This paper presents a new algorithm to translate continuous body midlines of fish into a series of interconnected segments by identifying favorable joint positions along the body. The algorithm employs binary search to generate parsimonious kinematic models, aiming at minimizing the number of segments yet keeping approximation error below a user-defined threshold. To achieve this, the algorithm maximizes the length of each segment by determining the most distal joint position through repetitive shrinking of the search space. Theoretical and empirical analysis using two different datasets show that the binary search algorithm is substantially faster when compared to segment growing algorithm, which employs linear search to generate its models. There is four-fold improvement in computation time when generating models with less than 10 segments, which are typically sufficient to describe fish and fish-inspired robot movements. Furthermore, the multi-segment models generated by the binary search algorithm matched the ground truth models obtained through dynamic programming in over 97% of cases, and on average, contained one fewer segment than those produced by the Ramer–Douglas–Peucker algorithm, which is widely used in curvature simplification tasks. Our findings suggest that the binary search algorithm provides a computationally efficient approach for generating compact kinematic models and may facilitate the analysis of large datasets with high temporal and spatial resolution.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.compag.2026.111789","usgsCitation":"Sterling, R.M., Goerig, E.M., Buzdalov, M., Castro-Santos, T., and Akanyeti, O., 2026, Fish body midline segmentation using binary search: Computers and Electronics in Agriculture, v. 248, 111789, 14 p., https://doi.org/10.1016/j.compag.2026.111789.","productDescription":"111789, 14 p.","ipdsId":"IP-171912","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":503451,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.compag.2026.111789","text":"Publisher Index Page"},{"id":503348,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"248","noUsgsAuthors":false,"publicationDate":"2026-04-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Sterling, Robert M.H.","contributorId":370360,"corporation":false,"usgs":false,"family":"Sterling","given":"Robert","middleInitial":"M.H.","affiliations":[{"id":16758,"text":"Aberystwyth University","active":true,"usgs":false}],"preferred":false,"id":960152,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Goerig, Elsa Marie-Catherine 0000-0003-1430-4657","orcid":"https://orcid.org/0000-0003-1430-4657","contributorId":370312,"corporation":false,"usgs":true,"family":"Goerig","given":"Elsa","middleInitial":"Marie-Catherine","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":960153,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Buzdalov, M","contributorId":370313,"corporation":false,"usgs":false,"family":"Buzdalov","given":"M","affiliations":[{"id":16758,"text":"Aberystwyth University","active":true,"usgs":false}],"preferred":false,"id":960154,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Castro-Santos, Theodore 0000-0003-2575-9120","orcid":"https://orcid.org/0000-0003-2575-9120","contributorId":315433,"corporation":false,"usgs":true,"family":"Castro-Santos","given":"Theodore","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":960155,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Akanyeti, O.","contributorId":269927,"corporation":false,"usgs":false,"family":"Akanyeti","given":"O.","email":"","affiliations":[{"id":16758,"text":"Aberystwyth University","active":true,"usgs":false}],"preferred":false,"id":960156,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}