Acadia National Park has had increases in the frequency and magnitude of precipitation in recent years, leading to increased flood flows, stream erosion, and costly infrastructure damage. To improve infrastructure management in a changing climate, the U.S. Geological Survey, in cooperation with the National Park Service, has developed multiple datasets that can help natural resource managers identify stream reaches and stream crossings that have the highest potential for erosion and flood damage within Acadia National Park. To develop these datasets, we first created a lidar-derived hydrography based on a 1-meter digital elevation model and then estimated peak flows at stream crossings and along the stream network using regional regression equations for Maine. We assessed the erosion potential of stream reaches by computing channel morphologic and hydrologic metrics associated with erosive power, such as stream steepness, topographic openness, and percent storage in the contributing watershed. Stream crossing flood vulnerability was assessed by comparing estimated peak flows to stream crossing conveyance capacities. Our results indicate that stream reaches in the headwaters of the Acadia National Park highlands such as Sargent, Penobscot, and Cadillac Mountain, have the highest erosion potential and generally coincide with reaches that have had erosion and infrastructure damage in the past. Stream crossings with the highest flood vulnerability are distributed throughout Mount Desert Island and Acadia National Park, especially south of Jordan Pond, north of Sargent Mountain, and surrounding Eagle Lake. Over a quarter of the total stream crossings have insufficient information to compute flood vulnerability and are often on the parts of the stream with the highest potential for erosion. The datasets allow users to identify stream reaches with the highest erosion potential, stream crossings that are most vulnerable to flood damage, and to highlight areas where supplemental field assessments could most effectively be completed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20265116","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Armstrong, I.P., McCallister, M.A., Hyslop, K.M., and Benthem, A.J., 2026, Erosion potential and flood vulnerability of streams and stream crossings at Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2026–5116, 21 p., https://doi.org/10.3133/sir20265116.","productDescription":"Report: vii, 21 p.; Data Release","numberOfPages":"21","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-178032","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":499817,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2026/5116/coverthb.jpg"},{"id":499818,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2026/5116/sir20265116.pdf","size":"7.83 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2026-5116 PDF"},{"id":499819,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20265116/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2026-5116 HTML"},{"id":499820,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2026/5116/sir20265116.xml","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2026-5116 XML"},{"id":500656,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119275.htm","linkFileType":{"id":5,"text":"html"}},{"id":499821,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2026/5116/images/"},{"id":499822,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1EHZNHN","text":"USGS data release","linkHelpText":"Data for an erosion potential and flood vulnerability assessment of streams and stream crossings at Acadia National Park, Maine"},{"id":500517,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://geonarrative.usgs.gov/acadiaerosionfloodvulnerability/","text":"Interactive dashboard","linkHelpText":"- Erosion Potential and Flood Vulnerability of Streams and Stream Crossings at Acadia National Park"}],"country":"United States","state":"Maine","otherGeospatial":"Acadia National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -68.45003175798666,\n 44.44178922865794\n ],\n [\n -68.45003175798666,\n 44.21621316604151\n ],\n [\n -68.13514216440173,\n 44.21621316604151\n ],\n [\n -68.13514216440173,\n 44.44178922865794\n ],\n [\n -68.45003175798666,\n 44.44178922865794\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Rd.
Northborough, Massachusetts 01532
The U.S. Geological Survey, in cooperation with the National Park Service, has developed multiple datasets that can help natural resource managers identify stream reaches with the highest potential for erosion and stream crossings most vulnerable to flood damage within Acadia National Park. These datasets allow users to identify areas where supplemental field assessments could be most effectively completed.
","publicationDate":"2026-02-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Armstrong, Ian P. 0000-0002-8239-8029","orcid":"https://orcid.org/0000-0002-8239-8029","contributorId":344363,"corporation":false,"usgs":true,"family":"Armstrong","given":"Ian","email":"","middleInitial":"P.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":955710,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCallister, Meghan A. 0000-0001-8814-7725","orcid":"https://orcid.org/0000-0001-8814-7725","contributorId":358213,"corporation":false,"usgs":true,"family":"McCallister","given":"Meghan","middleInitial":"A.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":955711,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hyslop, Kristina M. 0009-0001-2525-5574","orcid":"https://orcid.org/0009-0001-2525-5574","contributorId":334465,"corporation":false,"usgs":true,"family":"Hyslop","given":"Kristina","middleInitial":"M.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":955712,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Benthem, Adam J. 0000-0003-2372-0281","orcid":"https://orcid.org/0000-0003-2372-0281","contributorId":220000,"corporation":false,"usgs":true,"family":"Benthem","given":"Adam","middleInitial":"J.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":955713,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70276374,"text":"70276374 - 2026 - A chromosome-level genome assembly of a vernal pool specialist amphibian, the Western Spadefoot, Spea hammondii","interactions":[{"subject":{"id":70276374,"text":"70276374 - 2026 - A chromosome-level genome assembly of a vernal pool specialist amphibian, the Western Spadefoot, Spea hammondii","indexId":"70276374","publicationYear":"2026","noYear":false,"displayTitle":"A chromosome-level genome assembly of a vernal pool specialist amphibian, the Western Spadefoot, Spea hammondii","title":"A chromosome-level genome assembly of a vernal pool specialist amphibian, the Western Spadefoot, Spea hammondii"},"predicate":"SUPERSEDED_BY","object":{"id":70276372,"text":"70276372 - 2026 - A chromosome-level genome assembly of a vernal pool specialist amphibian, the Western Spadefoot, Spea hammondii","indexId":"70276372","publicationYear":"2026","noYear":false,"title":"A chromosome-level genome assembly of a vernal pool specialist amphibian, the Western Spadefoot, Spea hammondii"},"id":1}],"supersededBy":{"id":70276372,"text":"70276372 - 2026 - A chromosome-level genome assembly of a vernal pool specialist amphibian, the Western Spadefoot, Spea hammondii","indexId":"70276372","publicationYear":"2026","noYear":false,"title":"A chromosome-level genome assembly of a vernal pool specialist amphibian, the Western Spadefoot, Spea hammondii"},"lastModifiedDate":"2026-06-02T13:33:41.991514","indexId":"70276374","displayToPublicDate":"2026-02-26T08:16:57","publicationYear":"2026","noYear":false,"publicationType":{"id":27,"text":"Preprint"},"publicationSubtype":{"id":32,"text":"Preprint"},"seriesTitle":{"id":19846,"text":"BioRxiv","active":true,"publicationSubtype":{"id":32}},"displayTitle":"A chromosome-level genome assembly of a vernal pool specialist amphibian, the Western Spadefoot, Spea hammondii","title":"A chromosome-level genome assembly of a vernal pool specialist amphibian, the Western Spadefoot, Spea hammondii","docAbstract":"We assembled and annotated a chromosome-level reference genome for the Western Spadefoot, Spea hammondii (Anura, Scaphiopodidae) representing one of only three amphibians included in the California Conservation Genomics Project (CCGP). Spea hammondii is a vernal pool breeding anuran native to California and northwestern Baja California which has undergone both range contractions and local extirpations across its distribution, primarily due to habitat loss and degradation and drought. The species is recognized by the state of California as a Species of Special Concern and is proposed for listing under the United States Endangered Species Act.
Using the established CCGP pipeline, this S. hammondii genome was produced using Pacific Biosciences HiFi long-reads and Omni-C proximity ligation, resulting in a de novo genome assembly 1.14 Gb in length, distributed across 479 scaffolds (scaffold N50 = 120.8 Mb; largest scaffold = 183.6 Mb) with a BUSCO completeness score of 90.9% using a conserved tetrapod ortholog set. Our assembly shows high base accuracy (QV = 63.7) and low frameshift error in coding regions (QV 50.42). Annotation of this genome yielded 20,434 genes with a BUSCO completeness score of 94.7%. This reference genome, in combination with range-wide resequencing data from CCGP, will facilitate statewide population genomic assessments to delineate conservation units, quantify inbreeding and genomic load, and test for adaptive variation associated with vernal pool hydrology and drought tolerance, all of which are important considerations in the proposed federal listing.
","language":"English","publisher":"BioRxiv","doi":"10.1101/2025.11.16.688715","usgsCitation":"Thompsky, B., Beraut, E., Cooper, R.D., Escalona, M., Espinoza, R.E., Fisher, R.N., Miller, C., Nguyen, O., Sacco, S., Sahasrabudhe, R., Seligmann, W.E., Tofflemier, E., Wang, I.J., and Schaffer, H.B., 2026, A chromosome-level genome assembly of a vernal pool specialist amphibian, the Western Spadefoot, Spea hammondii: BioRxiv, preprint posted February 26, 2026, https://doi.org/10.1101/2025.11.16.688715.","productDescription":"24 p.","ipdsId":"IP-186115","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":504945,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2026-02-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Thompsky, Ben","contributorId":371642,"corporation":false,"usgs":false,"family":"Thompsky","given":"Ben","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":962243,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beraut, Eric","contributorId":299352,"corporation":false,"usgs":false,"family":"Beraut","given":"Eric","email":"","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":962244,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cooper, Robert D.","contributorId":371650,"corporation":false,"usgs":false,"family":"Cooper","given":"Robert","middleInitial":"D.","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":962245,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Escalona, Merly","contributorId":299346,"corporation":false,"usgs":false,"family":"Escalona","given":"Merly","email":"","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":962246,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Espinoza, Robert E.","contributorId":371651,"corporation":false,"usgs":false,"family":"Espinoza","given":"Robert","middleInitial":"E.","affiliations":[{"id":36305,"text":"CSU Northridge","active":true,"usgs":false}],"preferred":false,"id":962247,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fisher, Robert N. 0000-0002-2956-3240 rfisher@usgs.gov","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":1529,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert","email":"rfisher@usgs.gov","middleInitial":"N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":962248,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Miller, Courtney","contributorId":371645,"corporation":false,"usgs":false,"family":"Miller","given":"Courtney","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":962249,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Nguyen, Oanh","contributorId":299348,"corporation":false,"usgs":false,"family":"Nguyen","given":"Oanh","email":"","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":962250,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sacco, Samuel","contributorId":299349,"corporation":false,"usgs":false,"family":"Sacco","given":"Samuel","email":"","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":962251,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Sahasrabudhe, Ruta","contributorId":367055,"corporation":false,"usgs":false,"family":"Sahasrabudhe","given":"Ruta","affiliations":[{"id":12711,"text":"UC Davis","active":true,"usgs":false}],"preferred":false,"id":962252,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Seligmann, William E.","contributorId":371658,"corporation":false,"usgs":false,"family":"Seligmann","given":"William","middleInitial":"E.","affiliations":[{"id":6948,"text":"UC Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":962253,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Tofflemier, Erin","contributorId":371647,"corporation":false,"usgs":false,"family":"Tofflemier","given":"Erin","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":962254,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Wang, Ian J.","contributorId":371659,"corporation":false,"usgs":false,"family":"Wang","given":"Ian","middleInitial":"J.","affiliations":[{"id":6609,"text":"UC Berkeley","active":true,"usgs":false}],"preferred":false,"id":962255,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Schaffer, H. 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These results show that the method scales across local to global domains and maintains high accuracy, providing a practical tool for near-real-time monitoring of floods, droughts, and water-resource management. Because the approach is sensor-agnostic, it can be ported to forthcoming L- and S-band missions such as NASA-ISRO synthetic aperture radar (NISAR), broadening its applicability to future hydrologic observations.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2026.115326","usgsCitation":"Jung, J., Fattahi, H., Jeong, S., Bonnema, M.G., Jones, J.W., Bekaert, D., Chan, S.K., and Handweger, A.L., 2026, Towards global mapping of dynamic surface water extents using Sentinel-1 SAR data: Remote Sensing of Environment, v. 337, 115326, 21 p., https://doi.org/10.1016/j.rse.2026.115326.","productDescription":"115326, 21 p.","ipdsId":"IP-183308","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":503010,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2026.115326","text":"Publisher Index Page"},{"id":502816,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"337","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jung, Jungkyo","contributorId":369929,"corporation":false,"usgs":false,"family":"Jung","given":"Jungkyo","affiliations":[{"id":64090,"text":"NASA Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":959401,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fattahi, Heresh","contributorId":292160,"corporation":false,"usgs":false,"family":"Fattahi","given":"Heresh","email":"","affiliations":[],"preferred":false,"id":959402,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jeong, Seongsu","contributorId":369930,"corporation":false,"usgs":false,"family":"Jeong","given":"Seongsu","affiliations":[{"id":64090,"text":"NASA Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":959403,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bonnema, Matthew G.","contributorId":369931,"corporation":false,"usgs":false,"family":"Bonnema","given":"Matthew","middleInitial":"G.","affiliations":[{"id":64090,"text":"NASA Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":959404,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jones, John W. 0000-0001-6117-3691 jwjones@usgs.gov","orcid":"https://orcid.org/0000-0001-6117-3691","contributorId":2220,"corporation":false,"usgs":true,"family":"Jones","given":"John","email":"jwjones@usgs.gov","middleInitial":"W.","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":959405,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bekaert, David","contributorId":267754,"corporation":false,"usgs":false,"family":"Bekaert","given":"David","affiliations":[{"id":13294,"text":"Woods Hole Oceanographic Institute","active":true,"usgs":false}],"preferred":false,"id":959406,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Chan, Steven K.","contributorId":369933,"corporation":false,"usgs":false,"family":"Chan","given":"Steven","middleInitial":"K.","affiliations":[{"id":64090,"text":"NASA Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":959407,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Handweger, Alexander L.","contributorId":369934,"corporation":false,"usgs":false,"family":"Handweger","given":"Alexander","middleInitial":"L.","affiliations":[{"id":64090,"text":"NASA Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":959408,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70274091,"text":"70274091 - 2026 - A tool to monitor hydrologic conditions on tree islands in the Everglades","interactions":[],"lastModifiedDate":"2026-02-26T16:50:16.661474","indexId":"70274091","displayToPublicDate":"2026-02-24T08:13:22","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"A tool to monitor hydrologic conditions on tree islands in the Everglades","docAbstract":"Tree islands are patchy upland forested habitats in Florida's Everglades that face degradation and disappearance due to altered hydrologic patterns. The U.S. Geological Survey coordinated with the Miccosukee Tribe of Indians of Florida and the Seminole Tribe of Florida to co-develop a decision-support tool based on tree-island hydrologic conditions. Everglades managers can use this tool to help with restoration planning and water operations decisions that affect tree-island conditions. After a series of organized workshops and meetings, a list of hydrologic metrics was selected as indicators of tree-island health, including hydroperiod, number of days since last dry, and maximum water depth at the head of the island. As a result, a web application tool, called ETree, has been developed and is publicly available online. This web application provides data on daily metrics for the current Everglades water year and annual summaries for past years, beginning in 2000.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2026.114640","usgsCitation":"Haider, S.M., van der Heiden, C., Bozas, M., and Romañach, S.S., 2026, A tool to monitor hydrologic conditions on tree islands in the Everglades: Ecological Indicators, v. 183, 114640, 7 p., https://doi.org/10.1016/j.ecolind.2026.114640.","productDescription":"114640, 7 p.","ipdsId":"IP-175495","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":500612,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2026.114640","text":"Publisher Index Page"},{"id":500509,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.08373270516965,\n 26.65815841176284\n ],\n [\n -82.08373270516965,\n 25.02905745196128\n ],\n [\n -80.69833784238166,\n 25.02905745196128\n ],\n [\n -80.69833784238166,\n 26.65815841176284\n ],\n [\n -82.08373270516965,\n 26.65815841176284\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"183","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Haider, Saira M. 0000-0001-9306-3454","orcid":"https://orcid.org/0000-0001-9306-3454","contributorId":206253,"corporation":false,"usgs":true,"family":"Haider","given":"Saira","middleInitial":"M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":956505,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"van der Heiden, Craig","contributorId":366978,"corporation":false,"usgs":false,"family":"van der Heiden","given":"Craig","affiliations":[{"id":87517,"text":"Seminole Tribe of Florida","active":true,"usgs":false}],"preferred":false,"id":956506,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bozas, Marcel","contributorId":366979,"corporation":false,"usgs":false,"family":"Bozas","given":"Marcel","affiliations":[{"id":87518,"text":"Miccosukee Tribe of Indians of Florida","active":true,"usgs":false}],"preferred":false,"id":956507,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Romañach, Stephanie S. 0000-0003-0271-7825","orcid":"https://orcid.org/0000-0003-0271-7825","contributorId":213745,"corporation":false,"usgs":true,"family":"Romañach","given":"Stephanie","middleInitial":"S.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":956508,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70275235,"text":"70275235 - 2026 - Snow simulations predict future changes in rain-on-snow events across the upper Gallatin River watershed, a Greater Yellowstone Ecosystem headwater system","interactions":[],"lastModifiedDate":"2026-04-23T15:02:12.132201","indexId":"70275235","displayToPublicDate":"2026-02-24T07:55:13","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3823,"text":"Journal of Hydrology: Regional Studies","active":true,"publicationSubtype":{"id":10}},"title":"Snow simulations predict future changes in rain-on-snow events across the upper Gallatin River watershed, a Greater Yellowstone Ecosystem headwater system","docAbstract":"Study region: The upper Gallatin River watershed, an alpine headwater system in the Greater Yellowstone Ecosystem, in Wyoming and Montana.
Study focus: As global and regional air temperatures rise, mountain headwaters across the Greater Yellowstone Ecosystem (GYE) are projected to see more precipitation falling as rain. While the hydrologic effects of this snow-to-rain transition depends on a variety of factors, it can lead to an increased occurrence of rain-on-snow (RoS) events. To investigate these changes, we used high-resolution (30 m) SnowModel simulations of the upper Gallatin River watershed. Simulations were run for 2001-2013 using two scenarios: (1) historical meteorology as control and (2) pseudo global warming (PGW) where control air temperature and precipitation conditions were perturbed to represent mean end-of-century conditions under a high-emissions scenario.
New hydrological insights for the region: SnowModel outputs show that changes in PGW precipitation and snow accumulation varied with elevation. Warmer air temperatures at low elevations (< 2,500 m) led to less snow accumulation and less precipitation falling as snow. Colder baseline air temperatures for elevations above 2,500 meters (m) resulted in minor reductions in winter snowfall fraction. For PGW simulations, spring (April-June) months were rainier, and elevations above 2,500 m experienced more RoS events. Snowpacks between 2,500-3,100 m generated more snowmelt during RoS events, which was reflected in the watershed average. More high-intensity melt events can affect aquatic habitat, water quality, and the accuracy of streamflow forecasts across the region.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ejrh.2026.103253","usgsCitation":"Newcomb, S.K., Barnhart, T., Heldmyer, A.J., and Storb, M.B., 2026, Snow simulations predict future changes in rain-on-snow events across the upper Gallatin River watershed, a Greater Yellowstone Ecosystem headwater system: Journal of Hydrology: Regional Studies, no. 64, 103253, 15 p., https://doi.org/10.1016/j.ejrh.2026.103253.","productDescription":"103253, 15 p.","ipdsId":"IP-175750","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":503450,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ejrh.2026.103253","text":"Publisher Index Page"},{"id":503345,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, Wyoming","otherGeospatial":"Gallatin River watershed, Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.1040556758394,\n 45.775196944106\n ],\n [\n -112.01972949762403,\n 44.55598521200966\n ],\n [\n -111.37153075418532,\n 44.6564938423231\n ],\n [\n -110.83513931044746,\n 44.25694322488387\n ],\n [\n -109.64451930770404,\n 44.03050575033751\n ],\n [\n -109.86085331075995,\n 44.82919751982631\n ],\n [\n -110.74738180936852,\n 45.88093570749882\n ],\n [\n -112.1040556758394,\n 45.775196944106\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","issue":"64","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Newcomb, Sarah Katherine 0000-0002-7832-5089","orcid":"https://orcid.org/0000-0002-7832-5089","contributorId":370359,"corporation":false,"usgs":true,"family":"Newcomb","given":"Sarah","middleInitial":"Katherine","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":960200,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barnhart, Theodore B. 0000-0002-9682-3217","orcid":"https://orcid.org/0000-0002-9682-3217","contributorId":202558,"corporation":false,"usgs":true,"family":"Barnhart","given":"Theodore B.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":960201,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Heldmyer, Aaron Joseph 0000-0001-8608-4927","orcid":"https://orcid.org/0000-0001-8608-4927","contributorId":302944,"corporation":false,"usgs":true,"family":"Heldmyer","given":"Aaron","email":"","middleInitial":"Joseph","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":960202,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Storb, Meryl Biesiot 0000-0002-4346-5022","orcid":"https://orcid.org/0000-0002-4346-5022","contributorId":305621,"corporation":false,"usgs":true,"family":"Storb","given":"Meryl","email":"","middleInitial":"Biesiot","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":960203,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70273927,"text":"sir20265114 - 2026 - Assessing natural recharge in Indian Wells Valley, California: A Basin Characterization Model case study","interactions":[],"lastModifiedDate":"2026-04-13T22:42:00.875548","indexId":"sir20265114","displayToPublicDate":"2026-02-18T12:45:00","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":"2026-5114","displayTitle":"Assessing Natural Recharge in Indian Wells Valley, California: A Basin Characterization Model Case Study","title":"Assessing natural recharge in Indian Wells Valley, California: A Basin Characterization Model case study","docAbstract":"The communities in Indian Wells Valley (IWV), in the northern Mojave Desert in California, rely on groundwater for domestic and agricultural use. Mountain front recharge from the surrounding Sierra Nevada is the main source of natural recharge to the valley. Increased urbanization, agricultural development, and groundwater pumping during recent decades put IWV in a state of critical overdraft. The U.S. Geological Survey Basin Characterization Model, version 8 (BCMv8) was used to evaluate historical and future climate and hydrologic conditions in IWV. The BCMv8 estimated natural recharge in IWV at 10.7 million cubic meters (Mm3) per year for the period from 1981 to 2010. Future patterns of water balance variables using three future climate scenarios, hot-wet, hot-dry, and warm-moderately wet, were calculated for mid-century (2040–69) and end-of-century (2070–99) periods. Results for both wet models projected an increase in recharge in both periods, whereas the hot-dry model projected a decrease in recharge in both periods. All models reported a large increase in seasonal variability in recharge, indicating more future availability and frequent occurrences of drought years. All climate scenarios projected an increase in climatic water deficit in both periods. These increases in irrigation demand and variability of water supply highlight the importance of strategic management planning for the sustainability of water resources in IWV.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20265114","collaboration":"Prepared in cooperation with Kern County, California","programNote":"Water Availability and Use Science Program","usgsCitation":"Saleh, D., Flint, L., and Stern, M., 2026, Assessing natural recharge in Indian Wells Valley, California—A Basin Characterization Model case study (ver. 1.1, March 2026): U.S. Geological Survey Scientific Investigations Report 2026–5114, 34 p., https://doi.org/10.3133/sir20265114.","productDescription":"vi, 34 p.","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-104255","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":501283,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119214.htm","linkFileType":{"id":5,"text":"html"}},{"id":500366,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2026/5114/coverthb2.jpg"},{"id":501280,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2026/5114/sir20265114.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2026-5114 XML"},{"id":501279,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20265114/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2026-5114 HTML"},{"id":501278,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2026/5114/sir20265114.pdf","text":"Report","size":"4.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2026-5114 PDF"},{"id":501281,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2026/5114/images"},{"id":501282,"rank":6,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2026/5114/versionHist.txt","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2026-5114 Version History"}],"country":"United States","state":"California","otherGeospatial":"Indian Wells Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.5,\n 36.5\n ],\n [\n -118.5,\n 35\n ],\n [\n -117,\n 35\n ],\n [\n -117,\n 36.5\n ],\n [\n -118.5,\n 36.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: February 18, 2026; Version 1.1: March 18, 2026","contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Chlorinated solvents, including trichloroethene (TCE) and other chlorinated volatile organic compounds (cVOCs), are widespread contaminants that can be treated by bioremediation approaches that enhance anaerobic reductive dechlorination. Reductive dechlorination can be enhanced either through the addition of an electron donor (biostimulation) or the addition of a known dechlorinating culture (bioaugmentation) along with an electron donor. Although bioremediation has been applied at many TCE-contaminated groundwater sites, application in source zones at sites where residual dense nonaqueous phase liquid (DNAPL) is present is more limited. In this study, laboratory and field treatability tests were completed to evaluate the potential application of anaerobic bioremediation for a shallow groundwater plume containing TCE in a perched alluvial aquifer at Site K, former Twin Cities Army Ammunition Plant, Arden Hills, Minnesota, which was on the National Priorities List as the New Brighton/Arden Hills Superfund site until 2019. In addition to the presence of residual DNAPL at the site, temporal variability in groundwater flow directions and input of oxygenated recharge were possible complicating factors for the application of enhanced anaerobic biodegradation in the shallow plume. The Site K plume extends beneath the footprint of Building 103, which was demolished in 2006, and soil excavations to a maximum depth of 6 feet (ft) below ground surface in 2014 were known to leave some deeper contaminated soil in place in the TCE source area. Groundwater treatment at the site, formalized as part of the 1997 Record of Decision, has been in operation since 1986 and consists of an extraction trench at the downgradient edge of the plume to collect groundwater, which is then pumped to an on-site air stripper. Groundwater concentrations in the plume have been relatively stable since treatment began, indicating a continued source of TCE in the aquifer. The desire for a destructive remedy that would enhance the removal of cVOCs in the aquifer at Site K and shorten the remediation timeframe led the U.S. Army to request that the U.S. Geological Survey conduct a groundwater treatability study to assess bioremediation. This report describes the U.S. Geological Survey bioremediation treatability study conducted during 2020–22, including pre-design site characterization to assist in formulating the bioremediation approach, laboratory experiments to support the design of the field pilot test, and implementation and 1-year performance monitoring results for the pilot test.
Pre-design site characterization included the collection of soil cores for cVOC analysis and lithologic descriptions and the re-installment of three wells to obtain hydrologic measurements and initial groundwater chemistry. Relatively flat head gradients were measured at the site, and substantial decreases in water-level elevations occurred from spring to summer (May–July 2021). Continuous water-level monitoring indicated a rapid response to precipitation. Groundwater flow velocities were consistently less than 0.5 foot per day, and the pilot bioremediation test was therefore designed with short lateral distances (about 5 ft) between injection and individual monitoring points. Soil analyses confirmed that high volatile organic compound contamination was left in place in the source area. The highest concentrations were near or in clay at the base of the perched aquifer. Concentrations of cVOCs measured in the replaced wells were consistent with historical data and had a maximum TCE concentration of 57,700 micrograms per liter (μg/L), indicative of nearby residual DNAPL based on the general rule of observed concentrations exceeding 1 percent of solubility. The primary TCE daughter product detected was 1,2-cis-dichloroethene (cisDCE), which indicated limited reductive dechlorination in the plume. Groundwater in both the source and downgradient areas was relatively reducing during the pre-design characterization, particularly in the source area where methane concentrations greater than 400 μg/L were measured.
Initial laboratory tests conducted using native aquifer microorganisms from the three replacement wells showed that anaerobic TCE biodegradation rates were low when biostimulated with the addition of sodium lactate as an electron donor, also known as a carbon donor, and resulted in the production of only cisDCE. Addition of a known dechlorinating culture, WBC-2, however, resulted in rapid biodegradation and production of ethene, verifying complete reductive dechlorination of TCE. Microcosms constructed with aquifer soil collected from the site were used to evaluate other electron donors besides lactate to support reductive dechlorination by WBC-2, including corn syrup as an alternative fast-release compound and whey, soy-based vegetable oil, and 3-D Microemulsion (Regenesis, San Clemente, California) as slow-release compounds. First-order rate constants for total organic chlorine removal in these WBC-2 amended microcosms were greatest with either lactate or vegetable oil as the donor, ranging between 0.061 and 0.047 per day or corresponding half-lives of 11–15 days. Testing of commercial products in other WBC-2-bioaugmented microcosms led to selection for the field pilot test of an emulsified vegetable oil product that also contained some sodium lactate as a fast-release donor. Delaying the addition of WBC-2 relative to the donor in the microcosms resulted in the most rapid overall biodegradation rates.
The selected design for the pilot test utilized three separate test plots, each about 30-ft wide and 60-ft long: plots GS1 and GS2 in the source area of the plume and plot GS3 in the downgradient area of the plume near the excavation trench. Each test plot had one injection well, one monitoring well upgradient from the injection point, and 12 surrounding monitoring wells in a grid to capture variable groundwater flow directions. Donor injections, which included a bromide tracer, were completed in October 2021, immediately following baseline sampling, and the WBC-2 culture was injected about 40 days later, between November 30 and December 2, 2021. Performance monitoring conducted until December 2022 included hydrologic measurements and analyses of cVOCs, redox-sensitive constituents, dissolved organic carbon, bromide, volatile fatty acids, compound-specific carbon isotopes, and microbial communities.
The biogeochemical data collected during the pilot tests in the three treatment plots showed that enhanced, complete reductive dechlorination of cVOCs in the groundwater was achieved in the GS1 and GS3 plots. In contrast, evidence of distribution of the injected amendments and subsequent biodegradation was limited in GS2, which was in an area of more heterogeneous soil lithology and low water table elevations. The molar composition of volatile organic compounds in the GS1 and GS3 plots was dominated by ethene in wells that were reached by the injected amendments by the end of the monitoring period. In the GS1 and GS3 plots, similar patterns were observed of cVOC concentrations decreasing to near detection levels, or below, at some wells sampled in July and October 2022, whereas ethene became dominant and indicated sustained complete reductive dechlorination. Baseline cVOC concentrations were more than a factor of 10 higher in the groundwater in the GS1 plot than in GS3, but no apparent inhibition of complete dechlorination occurred. As expected from the initial pre-design site data and the laboratory experiments, enhanced dissolution of residual DNAPL coupled to biodegradation was evident in the GS1 plot, where a marked increase in dichloroethene (DCE) above the initial baseline and upgradient TCE and DCE concentrations occurred. DCE concentrations subsequently declined where DNAPL dissolution was evident, concurrent with production of vinyl chloride and then predominantly ethene. Thus, overall biodegradation rates outpaced the DNAPL dissolution and desorption and DCE production in the source area. This success in complete degradation to predominantly ethene was achieved even in areas where the DCE concentrations reached a maximum of about 30,000 μg/L. Compound specific isotope analysis of carbon in TCE, cisDCE, trans-1,2-dichloroethene, and vinyl chloride was conducted to provide another line of evidence of the occurrence and extent of anaerobic biodegradation. Along a flow path in each plot that was affected by the injected amendments, carbon isotopes in the TCE and daughter cVOCs in the groundwater became isotopically heavier, indicating biodegradation.
Enhanced biodegradation rates calculated from the field tests in GS1 and GS3 showed half-lives of 36.9–75.3 days for DCE degradation and 9.48–38.5 days for ethene production. Notably, these ethene production rates calculated from the field tests are consistent with the results of WBC-2-bioaugmented microcosms amended with either lactate or vegetable oil, which had half-lives for total organic chlorine removal that ranged from 11 to 15 days. These rates indicated rapid enhanced biodegradation, which is promising for application of a full-scale bioremediation remedy. Ultimately, however, the mass of residual or sorbed TCE in the aquifer that remains accessible for dissolution and biodegradation would likely control the time required for a full-scale bioremediation effort to achieve performance goals for TCE and cisDCE specified in the Record of Decision for Site K.
The field pilot tests showed that the relatively low hydraulic head gradients and temporal changes in groundwater flow directions in the shallow aquifer would add complexity to a full-scale bioremediation effort. The radius of influence (ROI) at GS1 and GS3 (16.3 ft and 12.7 ft, respectively) were close to the design ROI of 15 ft. The estimated ROI at GS2 was about four times the design ROI, but may be less reliable at this location owing to groundwater flow direction. In addition, the low temperatures following WBC-2 injection in late November to early December 2021, in combination with the low hydraulic head gradients, were probably major factors in the delay observed before the onset of enhanced biodegradation following injection of the culture. Additional test injections could be beneficial to optimize the timing of donor and culture injections with the variable temperatures and hydraulic head in the shallow aquifer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255113","collaboration":"Prepared in cooperation with U.S. Army Environmental Command","usgsCitation":"Lorah, M.M., Majcher, E.H., Mumford, A.C., Foss, E.P., Needham, T.P., Psoras, A.W., Livdahl, C.T., Trost, J.J., Berg, A.M., Polite, B.F., Akob, D.M., and Cozzarelli, I.M., 2026, Treatability study to evaluate bioremediation of trichloroethene at Site K, former Twin Cities Army Ammunition Plant, Arden Hills, Minnesota, 2020–22: U.S. Geological Survey Scientific Investigations Report 2025–5113, 88 p., https://doi.org/10.3133/sir20255113.","productDescription":"Report: xii, 88 p.; Data Release","numberOfPages":"88","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-175852","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":500105,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5113/images/"},{"id":500361,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119213.htm","linkFileType":{"id":5,"text":"html"}},{"id":500106,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13QTBR7","text":"USGS data release","linkHelpText":"Former Twin Cities Army Ammunition Site K treatability test data including various field measurements, laboratory tests and degradation constituents in the bioremediation of trichloroethylene and dichloroethylene, Arden Hills, Minnesota 2020–2022"},{"id":500104,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5113/sir20255113.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2025-5113 XML"},{"id":500103,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255113/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5113 HTML"},{"id":500102,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5113/sir20255113.pdf","size":"6.92 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5113 PDF"},{"id":500101,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5113/coverthb.jpg"}],"country":"United States","state":"Minnesota","county":"Ramsey County","city":"Arden Hills","otherGeospatial":"Site K, former Twin Cities Army Ammunition Plant","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.17794646411902,\n 45.1090420800339\n ],\n [\n -93.17794646411902,\n 45.08000250215488\n ],\n [\n -93.14480906199879,\n 45.08000250215488\n ],\n [\n -93.14480906199879,\n 45.1090420800339\n ],\n [\n -93.17794646411902,\n 45.1090420800339\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Maryland-Delaware-D.C. Water Science Center
U.S. Geological Survey
5522 Research Park Drive
Catonsville, MD 21228
The Puyallup River drains a 990 square mile watershed in western Washington, with headwaters on the glacier-covered flanks of Mount Rainier. Major tributaries include the White, Carbon, and Mowich Rivers. In the levee-confined reaches of the lower watershed, loss of flood conveyance due to sand and gravel deposition has been a chronic issue. Over much of the 20th century, flood conveyance was maintained through sediment removal, but this practice ended in the late 1990s. Flood hazard management activities since the 1990s have primarily involved levee removal or setback projects. Assessments of 1984-2009 repeat cross sections suggested that sediment deposition rates were particularly high in reaches with recent levee setbacks. However, there have been no assessments of recent deposition rates since the 2009 surveys. There are also concerns that intensifying flood hydrology or increased sediment delivery from Mount Rainier may exacerbate deposition. However, assessment of those risks has been hindered by limited understanding of watershed-scale sediment delivery and routing, particularly for coarse sand and gravel.
The U.S. Geological Survey, in cooperation with Pierce County, initiated this study to improve understanding of sediment deposition in the lower Puyallup River watershed. This work is primarily based on differencing of multiple aerial lidar datasets collected during 2002–2022, supplemented by early 1990 photogrammetric elevation datasets, geomorphic assessments of streamgage data, historical topographic surveys from 1907, and previously collected sediment transport measurements. Analyses cover the Puyallup, Carbon, and Mowich Rivers, but do not include the White River.
During 2004–2020, repeat aerial lidar indicates that 1.3 ± 0.3 million yd3 of sediment accumulated in the lower 20 valley miles (VMs) of the Puyallup River, averaging 80,000 ± 20,000 cubic yards per year (yd3/yr). Deposition was observed during both 2004–11 and 2011–20 lidar differencing intervals. This continued a long-term depositional trend that extends back to at least 1977. From 2004 to 2011, deposition rates along the Soldiers Home levee setback reach, the only setback project downstream of VM 20 completed prior to 2011, were approximately four times higher than in adjacent unmodified reaches. From 2011 to 2020, two additional setback projects were completed; volumetric deposition rates over all three setback reaches were similar to adjacent unmodified reaches, suggesting elevated setback deposition in the 2004–11 interval may have been influenced by an extreme flood in November 2006. These levee setback projects increased the local cross-sectional area of the floodway, used as a rough proxy for relative flood conveyance, by 50 to 200 percent above 2004 conditions. If deposition continued at recent rates, cross-sectional area over the levee setback reaches would be reduced back to 2004 values by 2050-90.
Deposition also occurred over the lower six VMs of the Carbon River during 2004–20, though volumes (0.15 ± 0.09 million yd3) were an order of magnitude lower than along the Puyallup River. Relatively lower deposition rates in the Carbon River are most likely the combined result of modestly lower incoming sediment loads, modestly steeper channel slope, and the additional sediment transport capacity provided by two large non-glacial tributaries that enter the Carbon River near VM 5.
Upstream of the depositional reaches described above, 2002–22 sediment storage trends along the Puyallup, Carbon, and Mowich Rivers were predominately negative (net erosion) up to the Mount Rainier National Park boundary. Net erosion was the result of bank and bluff erosion exceeding deposition across wetted channel and bare gravel areas, as opposed to uniform vertical downcutting. Net erosion along these river valleys delivered 3.4 ± 0.6 million yd3 to the river system, equivalent to 190,000 ± 35,000 yd3/yr. Most of that volume was supplied by erosion of relatively low (4–10 ft) surfaces along the Puyallup and Mowich Rivers and tall (300 ft) glacial bluffs along the lower Carbon River. Substantial aggradation from 1984 to 2009 reported by Czuba and others (2010) along reaches of the Puyallup River (VM 19–22) where levee confinement has recently been removed was most likely an artifact of methodologic bias.
The Puyallup, Mowich, and Carbon Rivers drain five distinct glaciated watersheds on the flanks of Mount Rainier, four of which were assessed in this study. All four watersheds were impacted by an extreme November 2006 rainstorm. Between 2002 and 2008, debris flows occurred in all four headwater areas, collectively eroding at least 2.1 million yd3 of sediment. These debris flows formed distinct deposits one to two miles downstream of source areas, depositing 30-50 percent of the material eroded upstream. From 2008 to 2022, no headwater debris flows were observed and overall rates of geomorphic change in the headwaters were low. Rivers eroded into debris flow deposits emplaced over the 2002–08 interval, but re-deposited equivalent volumes of material within a half mile downstream.
Stage-discharge relations at five streamgages on upland rivers draining Mount Rainier show either net channel incision or dynamic variability with no long-term trend over the past 60–100 years. Observations of pervasive river valley erosion and stable or incising trends at long-term streamgages in the upper watershed do not support prior claims of widespread and accelerating aggradation of upland rivers draining Mount Rainier.
Erosion and deposition volumes estimated in this report were combined with sediment transport estimates from limited suspended sediment and bedload measurements, estimates of sub-glacial erosion rates, and sediment delivery from non-glacial tributaries to construct watershed-scale sediment budgets for the Puyallup River watershed. During 2004–20, the estimated sediment load entering the depositional lowlands was well balanced by estimated inputs from, in order of relative magnitude, subglacial erosion (33–60 percent of total sediment load), erosion along the major river valleys (25–45 percent), erosion in recently deglaciated headwater areas (7–17 percent) and non-glacial tributaries (3–9 percent). These results are specific to the study period and represent total sediment loads, most of which is fine material carried in suspension. The relative sourcing of sand and gravel may be different than implied by this sediment budget.
Downstream of VM 12, comparison of 1907 and 2009 channel surveys show net lowering of the channel thalweg of 4–12 ft. A long-term gage near VM 22 shows lowering of 4–5 ft through the 1960s. Lowering at both locations was inferred to be a channel response to the substantial straightening, and so steepening, of the river during major phases of levee construction through the early and mid-20th century.
Application of a simple empirical bedload-discharge power-law relation to an ensemble of model-estimated daily mean discharge records in the lower Puyallup River between 1977 and 2100 projects that annual bedload transport capacity in the lower Puyallup River will increase by 20–60 percent by the middle of the 21st century. Actual changes in bedload transport and deposition rates will depend on concurrent changes in sediment supply and local hydraulics governing deposition.
This report presents several key conclusions. First, the persistence and spatial patterns of sand and gravel deposition along the lower Puyallup River support prior claims that deposition is fundamentally caused by decreases in channel slope moving downstream. Given this underlying cause and the abundance of sand and gravel available to be transported downstream, deposition is likely to continue for the foreseeable future. Second, despite continued sediment deposition, recent levee setback projects in the lower Puyallup River will likely provide several decades of flood conveyance benefits relative to a no-action alternative. Third, while the rivers linking Mount Rainier to the Puget Sound lowlands have often been discussed as conduits that either pass or accumulate sediment from Mount Rainier, observations from 2002–22 show these river valleys acting as substantial sediment sources, delivering three times more sediment than recently deglaciated headwater areas on Mount Rainier. While the persistence and underlying cause of recent river valley erosion remain unknown, sediment storage dynamics along these river valleys are likely to be a major control on sand and gravel delivery to the lower watershed.
The duration, magnitude, and frequency of heatwaves are predicted to increase in the coming decades, a combination that can reduce the survival of many fish species. Across the world, there is broad interest in identifying thermal refuge for heat-intolerant fish species and exploring opportunities to enhance or protect these areas. Because deeper groundwater maintains a relatively constant temperature, groundwater-influenced areas along streams can provide cool-water refuge for fish during periods of extreme heat. A targeted approach was developed for identifying existing cold-water zones and areas of substantial groundwater discharge in four high priority Lake Ontario tributaries. Our approach included: (1) predicting where groundwater discharge is most likely with a simple geospatial model and (2) using model predictions to select field sites for intensive high-resolution study, including ground-based mapping of groundwater features (springs, seeps, tributaries) as well as drone-based optical and thermal infrared surveys. Results from field sites were used to both verify model performance and map different types and aerial extents of thermal anomalies. Geospatial modelling successfully predicted regions of widespread groundwater upwelling, later verified and mapped by field and drone surveys. Comparison of model and field survey results further highlighted specific geospatial layers, such as soil/bedrock types and topographic wetness index, as being particularly useful for predicting groundwater influence on streams in the study area. In addition, a comparison of geospatial model results with a model of fish abundances along the studied streams showed significant positive correlations for many heat-intolerant fish species over a wide geographic area. The approach developed in this study can be applied to other watersheds to highlight areas of probable groundwater discharge and could be used by fishery and water resource managers to support cold-water fish habitat management decision-making and resource conservation.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.70459","usgsCitation":"Woda, J., Terry, N., Kelley, D.J., Finkelstein, J., Gazoorian, C.L., and McKenna, J., 2026, A targeted approach for mapping groundwater discharge to surface water and fish thermal refuge in four Lake Ontario tributaries: Hydrologic Processes, v. 40, e70459, 16 p., https://doi.org/10.1002/hyp.70459.","productDescription":"e70459, 16 p.","ipdsId":"IP-176833","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":501102,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.70459","text":"Publisher Index Page"},{"id":500774,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"New York","otherGeospatial":"Lake Ontario","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.34397142741707,\n 44.03795020350384\n ],\n [\n -80.34397142741707,\n 42.83906785974037\n ],\n [\n -75.35823758327766,\n 42.83906785974037\n ],\n [\n -75.35823758327766,\n 44.03795020350384\n ],\n [\n -80.34397142741707,\n 44.03795020350384\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"40","noUsgsAuthors":false,"publicationDate":"2026-03-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Woda, Joshua C. 0000-0002-2932-8013","orcid":"https://orcid.org/0000-0002-2932-8013","contributorId":290172,"corporation":false,"usgs":true,"family":"Woda","given":"Joshua","middleInitial":"C.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":956839,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Terry, Neil C. 0000-0002-3965-340X nterry@usgs.gov","orcid":"https://orcid.org/0000-0002-3965-340X","contributorId":192554,"corporation":false,"usgs":true,"family":"Terry","given":"Neil","email":"nterry@usgs.gov","middleInitial":"C.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true}],"preferred":true,"id":956840,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kelley, David J 0000-0002-0143-0956","orcid":"https://orcid.org/0000-0002-0143-0956","contributorId":367137,"corporation":false,"usgs":true,"family":"Kelley","given":"David","middleInitial":"J","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":956841,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Finkelstein, Jason S. 0000-0002-7496-7236","orcid":"https://orcid.org/0000-0002-7496-7236","contributorId":202452,"corporation":false,"usgs":true,"family":"Finkelstein","given":"Jason S.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":956842,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gazoorian, Christopher L. 0000-0002-5408-6212 cgazoori@usgs.gov","orcid":"https://orcid.org/0000-0002-5408-6212","contributorId":2929,"corporation":false,"usgs":true,"family":"Gazoorian","given":"Christopher","email":"cgazoori@usgs.gov","middleInitial":"L.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":956843,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McKenna, James E. Jr. 0000-0002-1428-7597 jemckenna@usgs.gov","orcid":"https://orcid.org/0000-0002-1428-7597","contributorId":190798,"corporation":false,"usgs":true,"family":"McKenna","given":"James E.","suffix":"Jr.","email":"jemckenna@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":956844,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70273939,"text":"70273939 - 2026 - Characterizing operational signatures of reservoirs with the SWOT satellite by comparing natural lake and reservoir dynamics","interactions":[],"lastModifiedDate":"2026-02-18T15:12:53.887851","indexId":"70273939","displayToPublicDate":"2026-02-17T07:57:49","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Characterizing operational signatures of reservoirs with the SWOT satellite by comparing natural lake and reservoir dynamics","docAbstract":"Due to a lack of management operations data, hydrological models may represent reservoirs as natural lakes, leading to poor discharge predictions in regulated basins. To parse seasonal operational signatures, we compare the dynamics of natural lake and reservoir systems across North America using Surface Water and Ocean Topography (SWOT) satellite observations and derived discharge estimates. Overall, reservoirs and their adjacent river reaches exhibit significantly greater variability (in standard deviation) than their natural counterparts across almost all SWOT observed (e.g. water surface elevation) and inferred (e.g. discharge) variables. Natural lakes show strong same-day correlations between inflow and outflow discharge (median Spearman R = 0.8), whereas 76% of reservoirs exhibit maximum correlation when outflow is lagged, suggesting operations buffer seasonal flow variability. Our findings indicate operations not only affect reservoir dynamics themselves but also have upstream and downstream consequences, which, when integrated into models, will offer more realistic hydrologic conditions.
","language":"English","publisher":"IOP Publishing","doi":"10.1088/1748-9326/ae436e","usgsCitation":"Riggs, R.M., Dickinson, J.E., Brinkerhoff, C.B., Sikder, M.S., Wang, J., Gao, H., and Allen, G.H., 2026, Characterizing operational signatures of reservoirs with the SWOT satellite by comparing natural lake and reservoir dynamics: Environmental Research Letters, v. 21, 044008, 11 p., https://doi.org/10.1088/1748-9326/ae436e.","productDescription":"044008, 11 p.","ipdsId":"IP-177052","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":500251,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ae436e","text":"Publisher Index Page"},{"id":500137,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"North America","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -165.20917200792005,\n 72.32481180155446\n ],\n [\n -116.99838105786625,\n 14.863773568551608\n ],\n [\n -87.70978967120412,\n 18.449041713609518\n ],\n [\n 1.251170353917047,\n 74.7933097957411\n ],\n [\n -165.20917200792005,\n 72.32481180155446\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"21","noUsgsAuthors":false,"publicationDate":"2026-02-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Riggs, Ryan Matthew 0000-0001-6834-9469","orcid":"https://orcid.org/0000-0001-6834-9469","contributorId":359717,"corporation":false,"usgs":true,"family":"Riggs","given":"Ryan","middleInitial":"Matthew","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":955826,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dickinson, Jesse E. 0000-0002-0048-0839 jdickins@usgs.gov","orcid":"https://orcid.org/0000-0002-0048-0839","contributorId":152545,"corporation":false,"usgs":true,"family":"Dickinson","given":"Jesse","email":"jdickins@usgs.gov","middleInitial":"E.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":955827,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brinkerhoff, Craig B. 0000-0001-6701-4835","orcid":"https://orcid.org/0000-0001-6701-4835","contributorId":345546,"corporation":false,"usgs":false,"family":"Brinkerhoff","given":"Craig","middleInitial":"B.","affiliations":[{"id":37550,"text":"Yale University","active":true,"usgs":false}],"preferred":false,"id":955828,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sikder, Md. Safat 0000-0002-1910-1800","orcid":"https://orcid.org/0000-0002-1910-1800","contributorId":359718,"corporation":false,"usgs":false,"family":"Sikder","given":"Md.","middleInitial":"Safat","affiliations":[{"id":85904,"text":"Department of Geography and Geographic Information Science, University of Illinois Urbana-Champaign","active":true,"usgs":false}],"preferred":false,"id":955829,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wang, Jida","contributorId":333531,"corporation":false,"usgs":false,"family":"Wang","given":"Jida","email":"","affiliations":[{"id":79917,"text":"Department of Geography and Geospatial Sciences, Kansas State University, Manhattan, KS, USA.","active":true,"usgs":false}],"preferred":false,"id":955830,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gao, Huilin 0000-0001-7009-8005","orcid":"https://orcid.org/0000-0001-7009-8005","contributorId":359721,"corporation":false,"usgs":false,"family":"Gao","given":"Huilin","affiliations":[{"id":51860,"text":"Department of Civil Engineering, Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":955831,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Allen, George H. 0000-0001-8301-5301","orcid":"https://orcid.org/0000-0001-8301-5301","contributorId":225161,"corporation":false,"usgs":false,"family":"Allen","given":"George","middleInitial":"H.","affiliations":[{"id":41057,"text":"Department of Geography, Texas A&M University, College Station, TX, 77843","active":true,"usgs":false}],"preferred":false,"id":955832,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70274640,"text":"70274640 - 2026 - Environment, taxonomy, and socioeconomics predict non-imperilment in freshwater fishes","interactions":[],"lastModifiedDate":"2026-04-02T18:30:01.828215","indexId":"70274640","displayToPublicDate":"2026-02-16T11:24:32","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2842,"text":"Nature Communications","active":true,"publicationSubtype":{"id":10}},"title":"Environment, taxonomy, and socioeconomics predict non-imperilment in freshwater fishes","docAbstract":"Freshwater fishes are among the most threatened taxa, yet conservation assessments remain incomplete for many species. Freshwater fishes provide essential ecosystem services such as food security, recreational opportunities, and cultural significance. Despite heavy alterations to freshwater ecosystems, the reasons for species’ sensitivity and resistance to imperilment are unclear. To address this need, we develop a machine learning framework to predict global imperilment status for 10,631 freshwater fish species using a comprehensive set of environmental, socioeconomic, and intrinsic species-level predictors. Using updated IUCN Red List data, we train and validate Random Forest classifiers to distinguish imperiled (Vulnerable, Endangered, Critically Endangered) from non-imperiled species. We examine the relative influence of 52 variables derived from 12 global sources describing extrinsic environmental and socioeconomic factors and intrinsic species-specific characteristics. Our models achieve higher accuracy for non-imperiled species (90.1%) compared to imperiled species (81.8%), reflecting the greater heterogeneity of threats and conditions driving imperilment. Across models, key predictors include habitat variables, taxonomic order, hydrological characteristics, and disturbance indicators, underscoring the interplay between ecology, geography, and human pressures. This integrative, reproducible approach demonstrates the utility of machine learning for guiding proactive conservation and provides a scalable framework for global biodiversity risk assessment.
","language":"English","publisher":"Springer Nature","doi":"10.1038/s41467-025-68154-w","usgsCitation":"Murphy, C.A., Olivos, J.A., Arismendi, I., García-Berthou, E., Johnson, S.L., and Dunham, J., 2026, Environment, taxonomy, and socioeconomics predict non-imperilment in freshwater fishes: Nature Communications, v. 17, 1661, 11 p., https://doi.org/10.1038/s41467-025-68154-w.","productDescription":"1661, 11 p.","ipdsId":"IP-170109","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":502097,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-025-68154-w","text":"Publisher Index Page"},{"id":502030,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"17","noUsgsAuthors":false,"publicationDate":"2026-02-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Murphy, Christina Amy 0000-0002-3467-6610","orcid":"https://orcid.org/0000-0002-3467-6610","contributorId":335232,"corporation":false,"usgs":true,"family":"Murphy","given":"Christina","email":"","middleInitial":"Amy","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":958522,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Olivos, J. Andres","contributorId":369132,"corporation":false,"usgs":false,"family":"Olivos","given":"J.","middleInitial":"Andres","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":958523,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Arismendi, Ivan","contributorId":341108,"corporation":false,"usgs":false,"family":"Arismendi","given":"Ivan","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":958524,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"García-Berthou, Emili","contributorId":6293,"corporation":false,"usgs":false,"family":"García-Berthou","given":"Emili","affiliations":[],"preferred":false,"id":958525,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnson, Sherri L.","contributorId":369137,"corporation":false,"usgs":false,"family":"Johnson","given":"Sherri","middleInitial":"L.","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":958526,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dunham, Jason 0000-0002-6268-0633","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":220078,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":958527,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70273930,"text":"70273930 - 2026 - Effects of groundwater withdrawals for water bottling and municipal use, Wards Brook Valley, Maine and New Hampshire","interactions":[],"lastModifiedDate":"2026-02-18T15:14:50.497171","indexId":"70273930","displayToPublicDate":"2026-02-13T09:08:06","publicationYear":"2026","noYear":false,"publicationType":{"id":27,"text":"Preprint"},"publicationSubtype":{"id":32,"text":"Preprint"},"seriesTitle":{"id":18346,"text":"EarthArXiv","active":true,"publicationSubtype":{"id":32}},"title":"Effects of groundwater withdrawals for water bottling and municipal use, Wards Brook Valley, Maine and New Hampshire","docAbstract":"Hydrologic models for the Wards Brook valley near Fryeburg, Maine were developed for historical (2016 – 2021) and hypothetical future conditions (2046 – 2065 and 2080 – 2099) to understand the effects of groundwater withdrawals for bottled water and municipal use on hydrologic conditions (stream base flows and groundwater levels). Analyses showed that the simulated base flows in Wards Brook were reduced because of pumping for both municipal water supplies and for water bottling, and about half of the total pumping impact on the base flows in Wards Brook was from the bottled water extraction. Simulated flows were greater than the minimum recommended streamflow of 2,180 cubic meters per day (400 gallons per minute) throughout the historical period. Simulated groundwater levels at two of three nearby ponds (Round Pond and Davis Pond) were minimally affected by pumping conditions, and effects were primarily from the municipal well closest to the ponds.
Several estimates of future projected recharge were used to understand the potential effects of groundwater withdrawals on hydrologic conditions under multiple hypothetical climate conditions. Annual projected recharge rates in the mid- and late-21st century from two climate scenarios (stabilized greenhouse-gas emissions and high greenhouse-gas emissions) were similar to rates for 2016 – 2021. However, monthly recharge patterns for the future periods shifted toward more recharge in the winter months (December, January, and February) and less recharge in April, May, and October relative to 2016 – 2021.
The lowest mean monthly base flows from the future emission scenarios all remain larger than the minimum recommended streamflow and indicate no long-term declines in flow relative to historical conditions. However, simulated base flows during hypothetical 3-year drought scenarios declined below minimum recommended streamflow during the summer months in the stabilized- and high-emission scenarios in the mid-21st century. Although water is generally plentiful in the Wards Brook valley, reduced pumping may be needed to maintain streamflows in Wards Brook under future climate conditions similar to modeled drought scenarios.
Groundwater quality in and around oil fields in the Southern San Joaquin Valley is of interest to many California residents that rely heavily on groundwater for domestic, commercial, and agricultural use. To help assess the effects of historical oil-field activities and natural geologic sources on groundwater near the southwest margins of the Kern County Groundwater Subbasin, a multiple-well monitoring site was installed near the administrative boundary between the Midway-Sunset and Buena Vista Oil Fields in Kern County, California. The installation of the Midway-Sunset Buena Vista multiple-well monitoring site (MSBV) supports regional analysis of the relations of oil and gas sources to groundwater quality by providing information about the geology, hydrology, geophysical properties, and water quality of the alluvial and upper Tulare aquifers in areas where groundwater data were limited. Data collected from the site included drill cuttings, whole core samples, sidewall core samples, mud-gas analysis, borehole geophysical logs, depth to water measurements, and water quality samples. Whole cores were scanned using dual energy computed tomography. Subsamples of selected cores were analyzed for density, porosity, specific retention, and bulk minerology. Thin sections of the subsamples were prepared, photographed, and examined. Two samples were analyzed using scanning electron microscope technology to examine the microporosity of diatomite laden sediment. Instrumentation installed in the wells collect hourly depth to water measurements.
Analysis of the data show there is 355 feet of alluvium overlying the Tulare Formation at the well site. The contact between the two formations is an aquitard resulting in a perched aquifer in the alluvium and unconfined aquifer in the Tulare Formation. The alluvium is more heterogenous and finer grained than the Tulare Formation resulting in markedly higher porosity in the alluvium compared to the Tulare Formation. Higher specific retention observed in the alluvium is attributed to the finer grained sediment and greater abundance of reworked diatomite (as represented by opal-CT [cristobalite-tridymite]) compared to the Tulare Formation. Total dissolved solids (TDS) approached or exceeded 10,000 milligrams per liter (mg/L) in the alluvium from approximately 176 to 242 feet below land surface and at the top of the Amnicola clay at approximately 670 feet below land surface within the Tulare Formation. Elevated TDS, chloride, and boron concentrations in the alluvium and on top of the Amnicola clay likely reflect groundwater that is mixed with oil-field water. Water chemistry and modern-aged groundwater in the alluvial monitoring well (MSBV #3) are consistent with the oil-field water in the alluvium being derived from documented historical surface disposal of oil-field water upslope (northwest) of the site. Water chemistry and pre-modern groundwater age in the deeper Tulare monitoring well (MSBV #1) on top of the Amnicola clay are consistent with oil-field fluids derived from upslope natural geologic sources or old oil wells that leak in the subsurface. Shallow groundwater in the Tulare (MSBV #2) is not affected by mixing with oil-field sources.
In situ surface-water monitoring strategies are biased towards larger perennial streams and lakes and are generally not designed to track mechanisms of baseflow supply contributed by the dynamic storage of aquifers. Additionally, small (< 1 km2) groundwater-influenced lakes and wetlands globally have little in situ monitoring infrastructure. We explored the utility of remotely sensed Surface Water Ocean Topography Satellite (SWOT) data, collected from 2023 onward, to characterise the seasonal and multi-year water-level trends of groundwater flow-through kettle lakes distributed across the permeable sediments of eastern Massachusetts, USA. This analysis indicated that water levels for kettle lakes with areas down to approximately 0.05 km2 are resolvable in the study area. Our examination of 17 kettle lakes found that SWOT water-surface elevation data closely tracked groundwater levels in adjacent monitoring wells where available, including the timing of seasonal patterns (highest levels generally in late spring), although there was some variation between years and there was a substantial lag in the timing of high water levels for a lake located downgradient from a 30-m-thick vadose zone. Furthermore, SWOT-observed water-level increases in kettle lakes tracked with baseflow increases in two adjacent groundwater-dominated streams, as would be expected from increased hydraulic gradients. Unlike spectral remote sensing, SWOT data are generally not affected by cloud cover, resulting in a potential for groundwater-dominated lakes to be sentinels of dynamic storage patterns, including identification of baseflow drought lags, which are currently ill-defined hydrological processes. SWOT monitoring of groundwater-influenced surface waters shows potential for augmenting existing monitoring wells and streamgages as continuous monitors of groundwater levels and baseflow supply in permeable terrain.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.70414","usgsCitation":"Briggs, M.A., Harlan, M.E., Rey, D., Hare, D.K., LeBlanc, D.R., Boutt, D.F., and Gooseff, M.N., 2026, Tracking baseflow supply dynamics using SWOT data from small groundwater-dominated lakes: Hydrological Processes, v. 40, no. 2, e70414, 12 p., https://doi.org/10.1002/hyp.70414.","productDescription":"e70414, 12 p.","ipdsId":"IP-178175","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":500848,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.70414","text":"Publisher Index Page"},{"id":500722,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","otherGeospatial":"coastal southeastern Massachusetts","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -70.67620889081189,\n 42.11446080523638\n ],\n [\n -70.67620889081189,\n 41.52445687414806\n ],\n [\n -69.90003630265832,\n 41.52445687414806\n ],\n [\n -69.90003630265832,\n 42.11446080523638\n ],\n [\n -70.67620889081189,\n 42.11446080523638\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"40","issue":"2","noUsgsAuthors":false,"publicationDate":"2026-02-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Briggs, Martin A. 0000-0003-3206-4132","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":222756,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":956729,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harlan, Merritt Elizabeth 0000-0002-4019-4888","orcid":"https://orcid.org/0000-0002-4019-4888","contributorId":302672,"corporation":false,"usgs":true,"family":"Harlan","given":"Merritt","email":"","middleInitial":"Elizabeth","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"preferred":true,"id":956730,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rey, David M. 0000-0003-2629-365X","orcid":"https://orcid.org/0000-0003-2629-365X","contributorId":211848,"corporation":false,"usgs":true,"family":"Rey","given":"David M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":956731,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hare, Danielle K.","contributorId":219738,"corporation":false,"usgs":false,"family":"Hare","given":"Danielle","middleInitial":"K.","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":956732,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"LeBlanc, Denis R. 0000-0002-4646-2628","orcid":"https://orcid.org/0000-0002-4646-2628","contributorId":219907,"corporation":false,"usgs":true,"family":"LeBlanc","given":"Denis","email":"","middleInitial":"R.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":38175,"text":"Toxics Substances Hydrology Program","active":true,"usgs":true}],"preferred":true,"id":956733,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Boutt, David F.","contributorId":81095,"corporation":false,"usgs":false,"family":"Boutt","given":"David","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":956734,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gooseff, Michael N.","contributorId":367087,"corporation":false,"usgs":false,"family":"Gooseff","given":"Michael","middleInitial":"N.","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":956735,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70273941,"text":"70273941 - 2026 - Post-wildfire water quality and aquatic ecosystem response in the U.S. Pacific Northwest: science and monitoring gaps","interactions":[],"lastModifiedDate":"2026-02-18T14:40:29.316588","indexId":"70273941","displayToPublicDate":"2026-02-10T07:28:31","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":23283,"text":"Environmental Research: Water","active":true,"publicationSubtype":{"id":10}},"title":"Post-wildfire water quality and aquatic ecosystem response in the U.S. Pacific Northwest: science and monitoring gaps","docAbstract":"An increase in the occurrence of large, high severity wildfires in the western Pacific Northwest (PNW), USA, has created an urgent need for science to better inform forest management and policy decisions to maintain source water quality in the region. The western PNW faces similar challenges to other regions with shifting wildfire regimes and large population centers reliant on surface water from forested catchments. However, the uniquely wet and highly seasonal climate of the western PNW suggests that findings from other, more frequently burned regions may not be directly applicable. To identify science, monitoring, and management gaps and opportunities in the western PNW, this review was collaboratively undertaken by academics, non-government and industry representatives, and local, state, and federal government entities who have been working together since the 2020 Labor Day fires in Oregon. Focusing on Oregon and Washington, we found that monitoring networks for continuous water quantity and quality cover much of the state with greater representation in western U.S. ecoregions, but few studies have analyzed and published these data to capture and communicate the post-wildfire response. Approximately half of the streamgages in Oregon and Washington record major water quality parameters, and hundreds of sites in the area have discrete sampling for a wide range of water quality constituents. Still, numerous gaps exist in understanding the short- and long-term impacts of wildfire on hydrology, water chemistry, including pH and dissolved oxygen, mobilization of metals, aquatic ecosystems, and downstream drinking water treatment. Collective action to further collect, analyze, interpret, and publish the key data could help improve our understanding of post-wildfire water quality impacts in this and other increasingly wildfire-affected regions.
","language":"English","publisher":"IOP Publishing","doi":"10.1088/3033-4942/ae36cb","usgsCitation":"Wall, S., Compton, J.E., Coble, A.A., Haley, B.M., Lin, J., Myers-Pigg, A., Reale, J.K., Wampler, K., Swartz, A., Moffett, K., Bladon, K.D., Carpenter, K., Chang, H., Chen, J., Donahue, D., Eckley, C.S., Hohner, A.K., Kiffney, P.M., Miralha, L., Regier, P., Seeds, J., and River, M., 2026, Post-wildfire water quality and aquatic ecosystem response in the U.S. Pacific Northwest: science and monitoring gaps: Environmental Research: Water, no. 2, 015004, 31 P., https://doi.org/10.1088/3033-4942/ae36cb.","productDescription":"015004, 31 P.","ipdsId":"IP-181756","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":500249,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/3033-4942/ae36cb","text":"Publisher Index 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0000-0003-1448-9321","orcid":"https://orcid.org/0000-0003-1448-9321","contributorId":366413,"corporation":false,"usgs":false,"family":"Miralha","given":"Lorrayne","affiliations":[{"id":87477,"text":"Department of Food, Agricultural and Biological Engineering, The Ohio State University","active":true,"usgs":false}],"preferred":false,"id":955851,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Regier, Peter","contributorId":352457,"corporation":false,"usgs":false,"family":"Regier","given":"Peter","affiliations":[{"id":84230,"text":"Energy & Environment Directorate, Pacific Northwest National Laboratory, Richland, WA, USA","active":true,"usgs":false}],"preferred":false,"id":955852,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Seeds, Joshua","contributorId":366414,"corporation":false,"usgs":false,"family":"Seeds","given":"Joshua","affiliations":[{"id":87478,"text":"Oregon Department of Environmental Quality, Water Quality Division","active":true,"usgs":false}],"preferred":false,"id":955853,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"River, Mark 0009-0005-4967-8087","orcid":"https://orcid.org/0009-0005-4967-8087","contributorId":366415,"corporation":false,"usgs":false,"family":"River","given":"Mark","affiliations":[{"id":56610,"text":"Weyerhaeuser Company","active":true,"usgs":false}],"preferred":false,"id":955854,"contributorType":{"id":1,"text":"Authors"},"rank":22}]}} ,{"id":70273860,"text":"70273860 - 2026 - James Buttle Review: A synthesis of riparian plant water use over two decades in North American drylands","interactions":[],"lastModifiedDate":"2026-02-09T15:06:28.28691","indexId":"70273860","displayToPublicDate":"2026-02-05T07:57:07","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"James Buttle Review: A synthesis of riparian plant water use over two decades in North American drylands","docAbstract":"Assessing riparian ecosystem water use, particularly transpiration from vegetation and evaporation from soils (‘plant water use’, hereafter), is key to developing sound water management approaches. In western North America, a multidecadal drought is reducing water availability and increasing the use of detailed water budgets. Questions related to both removal of vegetation for water salvage and budgeting water to maintain valuable riparian areas have led to a wealth of studies on riparian plant water use across dryland river systems in North America. Towards evaluating broad patterns in riparian plant water use, we synthesise results from over two decades of research, with the goal of informing water management policies and planning. This study asks: (1) Do some riparian plant communities exhibit lower plant water use than others? (2) Do riparian plant communities have higher water use under hotter climates? (3) Can statistical models based on existing data, plant communities and climate data be used to predict water use for unmeasured locations? Using hierarchical Bayesian models to synthesise data on annual and daily-scale plant water use, we show that marshes, cottonwood-willow stands and tamarisk not impacted by biocontrol use larger amounts of water at the annual scale than other vegetation communities. All plant communities have higher annual water use in hotter climates, which is likely related to a longer growing season and higher evaporative demand. Statistical models based on existing water-use data, plant communities and climate provide bounds on plant water use that can be applied to unmeasured locations and used to evaluate the effects of plant community change on water use. This synthesis produces the most complete summary of riparian plant water use in North American drylands to date and provides water use predictions across different climate and community scenarios that can be used for current and future conditions.","language":"English","publisher":"Wiley","doi":"10.1002/hyp.70408","usgsCitation":"Palmquist, E.C., Nagler, P., Ogle, K., DiMartini, C., Kennedy, J.R., and Sankey, J., 2026, James Buttle Review: A synthesis of riparian plant water use over two decades in North American drylands: Hydrological Processes, v. 40, no. 2, e70408, 19 p., https://doi.org/10.1002/hyp.70408.","productDescription":"e70408, 19 p.","ipdsId":"IP-180955","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":499673,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","state":"Arizona, California, Colorado, Idaho, Nevada, New Mexico, Oregon, Texas, Wyoming","otherGeospatial":"western Pacific Coastal Plain, western Sierra Madre Piedmont","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.30356692213257,\n 42.70636820692536\n ],\n [\n -121.30356692213257,\n 26.163421921669297\n ],\n [\n -103.74141493477907,\n 26.163421921669297\n ],\n [\n -103.74141493477907,\n 42.70636820692536\n ],\n [\n -121.30356692213257,\n 42.70636820692536\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"40","issue":"2","noUsgsAuthors":false,"publicationDate":"2026-02-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Palmquist, Emily C. 0000-0003-1069-2154 epalmquist@usgs.gov","orcid":"https://orcid.org/0000-0003-1069-2154","contributorId":5669,"corporation":false,"usgs":true,"family":"Palmquist","given":"Emily","email":"epalmquist@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":955301,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nagler, Pamela L. 0000-0003-0674-103X","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":363777,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","middleInitial":"L.","affiliations":[],"preferred":true,"id":955302,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ogle, Kiona","contributorId":360747,"corporation":false,"usgs":false,"family":"Ogle","given":"Kiona","affiliations":[{"id":86099,"text":"School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, 86011, USA","active":true,"usgs":false}],"preferred":false,"id":955303,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DiMartini, Claudia 0009-0001-2457-0513","orcid":"https://orcid.org/0009-0001-2457-0513","contributorId":356930,"corporation":false,"usgs":false,"family":"DiMartini","given":"Claudia","affiliations":[{"id":85285,"text":"formerly: US Geological Survey, Southwest Biological Science Center, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":955304,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kennedy, Jeffrey R. 0000-0002-3365-6589 jkennedy@usgs.gov","orcid":"https://orcid.org/0000-0002-3365-6589","contributorId":176478,"corporation":false,"usgs":true,"family":"Kennedy","given":"Jeffrey","email":"jkennedy@usgs.gov","middleInitial":"R.","affiliations":[],"preferred":true,"id":955305,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sankey, Joel B. 0000-0003-3150-4992","orcid":"https://orcid.org/0000-0003-3150-4992","contributorId":261248,"corporation":false,"usgs":true,"family":"Sankey","given":"Joel B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":955306,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70273695,"text":"sir20255098 - 2026 - Water-budget simulations for selected watersheds in Cameron County, Texas, 2022–23","interactions":[],"lastModifiedDate":"2026-02-11T18:58:23.302299","indexId":"sir20255098","displayToPublicDate":"2026-02-04T09:07:41","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":"2025-5098","displayTitle":"Water-Budget Simulations for Selected Watersheds in Cameron County, Texas, 2022–23","title":"Water-budget simulations for selected watersheds in Cameron County, Texas, 2022–23","docAbstract":"The U.S. Geological Survey, in cooperation with the City of Brownsville, Texas, configured and calibrated a set of hydrologic models for a 217-square-mile study area in Cameron County in south Texas during 2022–23. The models were used for estimating runoff and quantities of water diverted from the Rio Grande/Rio Bravo del Norte (hereinafter referred to as the “Rio Grande”) to maintain water-surface elevations in the canals and resacas (former distributary channels cut off from the main channel of the Rio Grande). Resacas provide habitat to aquatic species and help reduce the effects of flooding.
Because of the large size of the study area and diversity of hydrologic conditions, the study area was divided into 11 watersheds, and separate hydrologic models were developed for 9 of the watersheds. Six of the nine modeled watersheds are drained mostly by canals (canal watersheds), and three of the modeled watersheds drain to resacas (resaca watersheds). The Hydrological Simulation Program—FORTRAN was selected for modeling the study area watersheds because it is flexible in simulating a wide variety of watershed conditions.
The models were calibrated with streamflow data collected during 2022–23. The calibrated models were used to simulate water budgets (streamflow, evapotranspiration, water-storage volumes, and water diversions and withdrawals) during 2022–23. Model simulations showed that the resaca watersheds required more diversions from the Rio Grande and released less runoff than did the canal watersheds. Management practices maintaining resaca water levels constrained their runoff.
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Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
Contact Us- USGS Publications Warehouse
","tableOfContents":"History matching of large hydrologic models is challenging due to data sparsity and non-unique process combinations (and associated parameters) that can produce similar model predictions. We develop an ensemble-based history matching (and uncertainty quantification) approach using an iterative ensemble smoother (iES) method for three cutouts of the National Hydrologic Model (NHM) and qualitatively compare the results and performance to the stepwise history matching approach. In the latter approach, subsets of parameters and observations were sequentially calibrated to a diverse range of observations to mitigate non-uniqueness and local minima. In iES, localization simulates the same causal connections between parameters and observations without the need (and computational cost) of sequential history matching steps. iES uses a weighted sum-of-squared-errors objective function which allows differential weighting of multiple data sources. Formal adoption of range observation also pushes results to within ranges of observation values rather than discrete values. Overall, the ensemble approach performs similarly to the stepwise approach. Both approaches performed poorly for the cutout representing a snowmelt-dominated watershed, indicating a structural issue in the process representation of the model. The main advantage of iES is quantification of uncertainty in both the history matching and the predictions of interest.
","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.70086","usgsCitation":"Fienen, M., Long, A.J., Markovich, K.H., Haj, A.E., and Barker, M., 2026, Ensemble methods for history matching and uncertainty quantification with a watershed model: Journal of the American Water Resources Association, v. 62, no. 1, e70086, 18 p., https://doi.org/10.1111/1752-1688.70086.","productDescription":"e70086, 18 p.","ipdsId":"IP-181945","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":499751,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n [\n [\n [\n -94.81758,\n 49.38905\n ],\n [\n -94.64,\n 48.84\n ],\n [\n -94.32914,\n 48.67074\n ],\n [\n -93.63087,\n 48.60926\n ],\n [\n 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evapotranspiration and impervious surfaces","interactions":[],"lastModifiedDate":"2026-02-19T15:12:36.147521","indexId":"70273955","displayToPublicDate":"2026-02-03T09:07:45","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Large streamflow differences between forested and urbanized watersheds in the energy-limited eastern United States: The role of evapotranspiration and impervious surfaces","docAbstract":"Urban forests and other green infrastructures have been viewed as part of the “Nature-based Solutions” (NbS) to mitigate emerging urban environmental change. This study focuses on the role of evapotranspiration (ET) in regulating water balances of small watersheds in the eastern United States. We compared streamflow and ET patterns at daily, monthly and annual scales and linked these hydrological variables to the physical properties of 11 paired watersheds dominated by forests (FW) or urban (UW) land covers. The annual precipitation ranged from 1028 mm to 1683 mm and potential ET (PET) from 815 mm to 1450 mm. The mean annual flow/precipitation (Q/P) ratios were 0.26 ± 0.13 and 0.41 ± 0.1 for FW and UW, respectively. Overall, UW had lower annual ET (772 mm in UW vs. 947 mm in FW), but higher mean annual and (∼58% higher), monthly water yield (17%–186% higher), and peakflow rates (up to 100 times higher) than FW. The streamflow differences between FW and UW were most pronounced during the growing season and early winter (June-November). The mean Q/P ratios for 30 large hurricane events (2016–2021) were 0.12 ± 0.11 and 0.38 ± 0.23 for FW and UW, respectively. The flow rates in the dormant season (around December-May) in UW were similar or lower than FW. We developed conceptual models to explain the seasonal and storm event streamflow differences using background climate (PET), ET, and land surface characteristics. Urban NbS designs should factor in strategies that maximize ET while minimizing impervious surfaces enhancing watershed “sponge” and “pump” functions.
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An eDNA autosampler was deployed May 27–June 02, 2025, which filtered and preserved four 2-L water samples every 12 hours (n = 48 samples). These samples were extracted by USGS and shipped to UCSD for targeted eDNA analyses of Argentine ant. Environmental DNA sampling kits were used to filter and preserve water samples at four sites along the creek spanning ~ 5 km of stream distance. Six 1-L samples were collected at each site May 27–28 and samples were analyzed for eDNA metabarcoding using a comprehensive panel, which encompasses species across the tree of life. In these samples, eDNA from 2,134 unique taxa spanning 30 taxonomic groups (e.g., amoebas, insects, plants) were found. These sampling efforts demonstrated how eDNA autonomous and manual sampling approaches can be applied by National Park Service, TNC, and partners at scale to provide otherwise difficult and expensive to acquire information about biological threats and biodiversity.
","language":"English","publisher":"National Park Service","doi":"10.36967/2317065","usgsCitation":"Sepulveda, A., and Theroux, S., 2026, Environmental DNA pilot monitoring program for invasive species and biodiversity assessments on Santa Cruz Island: Interim report, September 2025: Science Report NPS/SR—2026/381, vii, 8 p., https://doi.org/10.36967/2317065.","productDescription":"vii, 8 p.","ipdsId":"IP-183212","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":500193,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Santa Cruz Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.9346364010192,\n 34.10158409225852\n ],\n [\n -119.9346364010192,\n 33.95495636433603\n ],\n [\n -119.5117843852133,\n 33.95495636433603\n ],\n [\n -119.5117843852133,\n 34.10158409225852\n ],\n [\n -119.9346364010192,\n 34.10158409225852\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2026-02-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Sepulveda, Adam 0000-0001-7621-7028 asepulveda@usgs.gov","orcid":"https://orcid.org/0000-0001-7621-7028","contributorId":4187,"corporation":false,"usgs":true,"family":"Sepulveda","given":"Adam","email":"asepulveda@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":955924,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Theroux, Susanna","contributorId":244544,"corporation":false,"usgs":false,"family":"Theroux","given":"Susanna","affiliations":[],"preferred":false,"id":955925,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70275707,"text":"70275707 - 2026 - Thinking outside the rocks: Subsurface water storage, topography, and land cover are key modulators of large-scale riverine dissolved silicon dynamics","interactions":[],"lastModifiedDate":"2026-05-13T14:17:01.525783","indexId":"70275707","displayToPublicDate":"2026-01-28T09:12:22","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Thinking outside the rocks: Subsurface water storage, topography, and land cover are key modulators of large-scale riverine dissolved silicon dynamics","docAbstract":"Riverine dissolved silicon (DSi) dynamics reflect integrated geologic, hydrologic, climatic, and ecological controls. We compiled annual DSi data for 337 rivers across four continents and trained interpretable machine-learning models to predict concentrations and yields from 28 watershed variables. Both models reproduced testing data (R2 = 0.85 for concentration and 0.96 for yield) and withheld-site validation (R2 = 0.91 and 0.93). Lithology, especially volcanic rock fraction, strongly controlled DSi while subsurface storage, topography, and land cover further shaped DSi dynamics. DSi concentrations and yields exhibited nonlinear responses to basin slope, recession-curve slope, proportion of open-water cover, and nutrient availability. Concentrations showed sharper threshold responses to hydrologic and biotic variables, whereas yields varied more gradually with climate and lithology. These results provide a framework for forecasting DSi under land cover and climate change and for embedding realistic, nonlinear processes in mechanistic models.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2025GL118853","usgsCitation":"Bush, S.A., Johnson, K., Jankowski, K.J., Carey, J.C., Sethna, L.R., Lyon, N., and Sullivan, P.L., 2026, Thinking outside the rocks: Subsurface water storage, topography, and land cover are key modulators of large-scale riverine dissolved silicon dynamics: Geophysical Research Letters, v. 53, no. 2, e2025GL118853, 12 p., https://doi.org/10.1029/2025GL118853.","productDescription":"e2025GL118853, 12 p.","ipdsId":"IP-182450","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":504371,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2025gl118853","text":"Publisher Index Page"},{"id":504299,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"53","issue":"2","noUsgsAuthors":false,"publicationDate":"2026-01-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Bush, Sidney A. 0000-0002-8359-7927","orcid":"https://orcid.org/0000-0002-8359-7927","contributorId":265930,"corporation":false,"usgs":false,"family":"Bush","given":"Sidney","email":"","middleInitial":"A.","affiliations":[{"id":36627,"text":"University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":961459,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Keira 0000-0003-0671-3901","orcid":"https://orcid.org/0000-0003-0671-3901","contributorId":330720,"corporation":false,"usgs":false,"family":"Johnson","given":"Keira","email":"","affiliations":[{"id":78986,"text":"College of Earth, Ocean, and Atmospheric Science, Oregon State University, Corvallis, Oregon, 97331","active":true,"usgs":false}],"preferred":false,"id":961460,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jankowski, Kathi Jo 0000-0002-3292-4182","orcid":"https://orcid.org/0000-0002-3292-4182","contributorId":207429,"corporation":false,"usgs":true,"family":"Jankowski","given":"Kathi","email":"","middleInitial":"Jo","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":961461,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Carey, Joanna C. 0000-0003-2365-9185","orcid":"https://orcid.org/0000-0003-2365-9185","contributorId":363158,"corporation":false,"usgs":false,"family":"Carey","given":"Joanna","middleInitial":"C.","affiliations":[{"id":86633,"text":"Babson College","active":true,"usgs":false}],"preferred":false,"id":961462,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sethna, Lienne R. 0000-0003-1156-172X","orcid":"https://orcid.org/0000-0003-1156-172X","contributorId":330721,"corporation":false,"usgs":false,"family":"Sethna","given":"Lienne","email":"","middleInitial":"R.","affiliations":[{"id":78987,"text":"St. Croix Watershed Research Station, Marine on St. Croix, Minnesota 55047","active":true,"usgs":false}],"preferred":false,"id":961463,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lyon, Nicholas 0000-0003-3905-1078","orcid":"https://orcid.org/0000-0003-3905-1078","contributorId":332697,"corporation":false,"usgs":false,"family":"Lyon","given":"Nicholas","email":"","affiliations":[{"id":79584,"text":"1021 Anacapa St, Santa Barbara, CA 93101","active":true,"usgs":false}],"preferred":false,"id":961464,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sullivan, Pamela L. 0000-0001-8780-8501","orcid":"https://orcid.org/0000-0001-8780-8501","contributorId":330723,"corporation":false,"usgs":false,"family":"Sullivan","given":"Pamela","email":"","middleInitial":"L.","affiliations":[{"id":78986,"text":"College of Earth, Ocean, and Atmospheric Science, Oregon State University, Corvallis, Oregon, 97331","active":true,"usgs":false}],"preferred":false,"id":961465,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70273948,"text":"70273948 - 2026 - Modeling carbon fluxes in tidal forested wetlands in the Mississippi river deltaic plain under various hydrologic conditions: Implications for river diversions","interactions":[],"lastModifiedDate":"2026-03-02T17:49:22.623453","indexId":"70273948","displayToPublicDate":"2026-01-24T09:23:03","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3751,"text":"Wetlands Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Modeling carbon fluxes in tidal forested wetlands in the Mississippi river deltaic plain under various hydrologic conditions: Implications for river diversions","docAbstract":"Our understanding of the impacts of climate change, sea-level rise (SLR), and freshwater management on the magnitude and variability of carbon fluxes in tidal forested wetlands remains limited. In this study, we applied a process-driven wetland biogeochemistry model, Wetland Carbon Assessment Tool—DeNitrification-DeComposition (WCAT-DNDC) model to explore responses of carbon fluxes in tidal swamp forests to climate change-induced alterations in hydrologic conditions and to predict impacts of planned reintroduction of river flows. We selected twelve sites in three habitats (throughput, relict, degraded) inside the Lake Maurepas swamp forests (Louisiana, USA) to represent various hydrological and salinity regimes. Environmental scenarios included dry, average, and wet conditions, SLR (low and high), and a Mississippi River (MR) diversion. Simulation results showed that the responses of net ecosystem exchange (NEE), net primary productivity (NPP), ecosystem respiration (ER), methane (CH4) and nitrous oxide (N2O) emissions in the Lake Maurepas swamp forests varied substantially among sites. However, the overall net carbon uptake capacity of the Lake Maurepas swamp forests was high (NEE: − 1143 to − 1650 g C m−2 yr−1), suggesting that Lake Maurepas swamp forests are large carbon sinks. The high net carbon uptake capacity could be significantly affected by climate change induced drought, flooding, and SLR with the bi-directional changes (increase or decrease) depending on the direction and magnitude of the hydrologic regime changes. The response of the net carbon uptake capacity to MR diversion is also bi-directional and site-specific, but enhancement of the capacity of NEE of up to − 1957 g C m2 yr−1 is possible, implying that MR diversion into the swamp forests could be beneficial in the context of carbon cycling and carbon sequestration.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s11273-026-10111-5","usgsCitation":"Wang, H., Krauss, K.W., Shaffer, G.P., Patton, B., Kroes, D., Noe, G.E., Dai, Z., Dettwiller, L., and Trettin, C.C., 2026, Modeling carbon fluxes in tidal forested wetlands in the Mississippi river deltaic plain under various hydrologic conditions: Implications for river diversions: Wetlands Ecology and Management, v. 34, no. 1, 11, 27 p., https://doi.org/10.1007/s11273-026-10111-5.","productDescription":"11, 27 p.","ipdsId":"IP-180681","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":500188,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":500214,"rank":2,"type":{"id":42,"text":"Open Access USGS Document"},"url":"https://pubs.usgs.gov/publication/70273948/full"},{"id":500215,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/ja/70273948/70273948.XML"},{"id":500683,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/ja/70273948/images"}],"country":"United States","state":"Louisiana","otherGeospatial":"Lake Maurepas swamp forests","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.21451630425345,\n 30.450162758345343\n ],\n [\n -90.90340771654913,\n 30.450162758345343\n ],\n [\n -90.90340771654913,\n 29.978620193311116\n ],\n [\n -90.21451630425345,\n 29.978620193311116\n ],\n [\n -90.21451630425345,\n 30.450162758345343\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"34","issue":"1","noUsgsAuthors":false,"publicationDate":"2026-01-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Wang, Hongqing 0000-0002-2977-7732","orcid":"https://orcid.org/0000-0002-2977-7732","contributorId":222377,"corporation":false,"usgs":true,"family":"Wang","given":"Hongqing","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":955890,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Krauss, Ken W.","contributorId":366426,"corporation":false,"usgs":false,"family":"Krauss","given":"Ken","middleInitial":"W.","affiliations":[{"id":12699,"text":"Louisiana Universities Marine Consortium","active":true,"usgs":false}],"preferred":false,"id":955891,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shaffer, Gary P.","contributorId":366427,"corporation":false,"usgs":false,"family":"Shaffer","given":"Gary","middleInitial":"P.","affiliations":[{"id":28058,"text":"Southeastern Louisiana University","active":true,"usgs":false}],"preferred":false,"id":955892,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Patton, Brett 0000-0002-7396-3452 pattonb@usgs.gov","orcid":"https://orcid.org/0000-0002-7396-3452","contributorId":5458,"corporation":false,"usgs":true,"family":"Patton","given":"Brett","email":"pattonb@usgs.gov","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":955893,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kroes, Daniel 0000-0001-9104-9077 dkroes@usgs.gov","orcid":"https://orcid.org/0000-0001-9104-9077","contributorId":3830,"corporation":false,"usgs":true,"family":"Kroes","given":"Daniel","email":"dkroes@usgs.gov","affiliations":[{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":955894,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":37277,"text":"WMA - 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2026 - Surface variable‐based machine learning for scalable arsenic prediction in undersampled areas","interactions":[],"lastModifiedDate":"2026-01-28T15:44:10.069314","indexId":"70273771","displayToPublicDate":"2026-01-23T08:36:18","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":16135,"text":"GeoHealth","active":true,"publicationSubtype":{"id":10}},"title":"Surface variable‐based machine learning for scalable arsenic prediction in undersampled areas","docAbstract":"In the United States, private wells are not federally regulated, and many households do not test for Arsenic (As). Chronic exposure is linked with multiple health outcomes, and risk can change sharply over short distances and with well depth. Coarse maps or sparse sampling often miss exceedances. Most existing models operate at ∼1 km resolution and use groundwater chemistry or detailed geologic logs, which limits their use in undersampled areas where improved guidance is most needed. We overcome these limitations by developing a machine learning model for Minnesota, USA, that predicts As exposure risk using only surficial variables from remote sensing and global data sets. Variables related to surface water hydrology and geomorphology are selected based on mechanistic links that control redox conditions and As mobilization. Local training was essential, and surficial geology variables that are more sensitive to local conditions were needed to maximize model accuracy. The resulting complete model was sufficiently sensitive to generate accurate and detailed risk maps and depth profiles of As concentrations above the 10 μg/L maximum contaminant level. Accuracy depended on local training data density. We identified a training data density of 0.07 wells/km2 as a practical target for stable county-level performance. Maps of exceedance probabilities highlight priority areas for testing that are particularly important in rural communities that have received less sampling. These results support public health action by guiding where to install wells and where to test them, how much new sampling is needed, and where treatment outreach is most urgent.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2025GH001666","usgsCitation":"Azad, S., Stahl, M.O., Erickson, M., DeYoung, B.A., Connolly, C.T., Chillrud, L., Schilling, K., Navas-Acien, A., Basu, A., Mailloux, B., Bostick, B.C., and Chillrud, S.N., 2026, Surface variable‐based machine learning for scalable arsenic prediction in undersampled areas: GeoHealth, v. 10, no. 1, e2025GH001666, 18 p., https://doi.org/10.1029/2025GH001666.","productDescription":"e2025GH001666, 18 p.","ipdsId":"IP-177700","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":499326,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2025gh001666","text":"Publisher Index Page"},{"id":499171,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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We present a publicly available dataset consisting of 281,024 satellite and aerial images of U.S. rivers, constructed using an Application Programming Interface (API) and the U.S. Geological Survey’s National Hydrography Dataset. The dataset includes images, primary keys, and ancillary geospatial information. We use a manually labeled subset of the images to train models for detecting rapids, defined as areas where high velocity and turbulence lead to a wavy, rough, or even broken water surface visible in the imagery. To demonstrate the utility of this dataset, we develop an image segmentation model to identify rivers within images. This model achieved a mean test intersection-over-union (\uD835\uDC3C\uD835\uDC5C\uD835\uDC48) of 0.57, with performance rising to an actual \uD835\uDC3C\uD835\uDC5C\uD835\uDC48 of 0.89 on the subset of predictions with high confidence (predicted \uD835\uDC3C\uD835\uDC5C\uD835\uDC48 > 0.9). Following this initial segmentation of river channels within the images, we trained several convolutional neural network (CNN) architectures to classify the presence or absence of rapids. Our selected model reached an accuracy and F1 score of 0.93, indicating strong performance for the classification of rapids that could support consistent, efficient inventory and monitoring of rapids. These data provide new resources for recreation planning, habitat assessment, and discharge estimation. Overall, the dataset and tools offer a foundation for scalable, automated identification of geomorphic features to support riverine science and resource management.
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