The bedrock geology of the 7.5-minute Port Henry quadrangle consists of deformed and metamorphosed Mesoproterozoic gneisses of the Adirondack Highlands unconformably overlain by weakly deformed lower Paleozoic sedimentary rocks of the Champlain Valley. The Mesoproterozoic rocks occur on the eastern edge of the Adirondack Highlands and represent an extension of the Grenville Province of Laurentia. Mesoproterozoic paragneiss, marble, and amphibolite hosted the emplacement of an anorthosite-mangerite-charnockite-granite (AMCG) suite, now exposed mostly as orthogneiss, at approximately 1.18–1.15 Ga (giga-annum). In the Port Henry quadrangle, the AMCG metaigneous rocks (Yhg, Ygb, Yanw) intruded older, mostly metasedimentary rocks of the Grenville Complex during the middle to late Shawinigan orogeny (~1,160–1,150 Ma [mega-annum]). All rocks were subsequently metamorphosed to upper amphibolite to granulite facies conditions during the 1,080–1,050 Ma Ottawan orogeny. New mapping reveals four periods of deformation: (1) D1 produced rarely preserved isoclinal folds in the paragneiss and marble and predates AMCG magmatism. (2) Subsequent D2 deformation produced the dominant gneissic fabric preserved in the rock, recumbent folding, and deformed all the Proterozoic units in the map area. Syn- to late-D2 felsic magmatism resulted in the regionally extensive Lyon Mountain Granite Gneiss, which hosts numerous magnetite ore bodies. (3) Mylonitic extensional shear zones and core complex formation marked the beginning of D3 deformation. Protracted D3 deformation resulted in F3 upright folding, dome and basin formation, pegmatite intrusion, reactivation of the S2 foliation, partial melting, metamorphism, metasomatism, iron-ore remobilization, and intrusion of magnetite-bearing pegmatite both as layer-parallel sills and crosscutting dikes. (4) D4 created northeast- and northwest-trending local high-grade ductile shear zones and boudinage, northwest-trending regional kilometer (km)-wide ductile shear zones, and crosscutting granitic pegmatite dikes. The development of the late-stage regional shear zones (D4) was likely due to the continuation of extensional doming and uplift from upper amphibolite facies conditions at the end of the Ottawan orogeny. The majority of iron-ore deposits in the Port Henry and adjacent Witherbee quadrangles are in the hanging wall of these extensional shear zones. In the Port Henry quadrangle, the km-wide Cheney Mountain shear zone is the result of D4 deformation. Kilometer-scale lineaments readily observed in lidar data are Ediacaran mafic dikes and Phanerozoic brittle faults. The Paleozoic rocks are part of the Early Cambrian to Late Ordovician carbonate bank on the ancient margin of Laurentia. The approximately 1-km-thick Cambrian to Ordovician stratigraphy records a transition from synrift clastics to passive-margin peritidal carbonate buildups to gradually deeper-water subtidal- to shelf-carbonates during foreland basin development associated with the Taconic orogeny. The Paleozoic rocks are weakly folded and block faulted. Large areas of the Champlain Valley are covered by undifferentiated glacial deposits, some of which contain mapped landslides. The map also shows waste rock piles and tailings from historical mining operations.
This study was undertaken to improve our understanding of the bedrock geology in the Adirondack Highlands, establish a modern framework for 1:24,000-scale bedrock geologic mapping in the Adirondacks, provide a context for historical iron mines in the eastern Adirondacks, and update the stratigraphy of the Champlain Valley in New York and Vermont. This Open-File Report includes a bedrock geologic map; a description of map units; a correlation of map units; and a geographic information system database that includes bedrock geologic units, faults, outcrops, and structural geologic information.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20261062","collaboration":"Prepared in cooperation with the State of Vermont, Vermont Agency of Natural Resources, Vermont Geological Survey and the State of New York, Department of Education, New York Geological Survey","programNote":"National Cooperative Geologic Mapping Program","usgsCitation":"Valley, P.M., Parker, M., Walsh, G.J., Orndorff, R.C., Walton, M.S., Jr., and Crider, E.A., Jr., 2026, Preliminary bedrock geologic map of the Port Henry quadrangle, Essex County, New York, and Addison County, Vermont: U.S. Geological Survey Open-File Report 2026–1062, 1 sheet, scale 1:24,000, https://doi.org/10.3133/ofr20261062.","productDescription":"1 Sheet: 63.17 x 30.58 inches; Data Release","numberOfPages":"1","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-158945","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":500360,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119212.htm","linkFileType":{"id":5,"text":"html"}},{"id":499704,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13HYFPM","text":"USGS data release","linkHelpText":"Database for the preliminary bedrock geologic map of the Port Henry quadrangle, Essex County, New York, and Addison County, Vermont"},{"id":499702,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2026/1062/coverthb4.jpg"},{"id":499703,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2026/1062/ofr20261062.pdf","text":"Sheet","size":"5.45 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2026-1062 PDF"}],"country":"United States","state":"New York, Vermont","county":"Addison County, Essex County","otherGeospatial":"Port Henry quadrangle","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.5,\n 44.125\n ],\n [\n -73.5,\n 44\n ],\n [\n -73.375,\n 44\n ],\n [\n -73.375,\n 44.125\n ],\n [\n -73.5,\n 44.125\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Florence Bascom Geoscience Center
U.S. Geological Survey
926A National Center
12201 Sunrise Valley Drive
Reston, VA 20192
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":70274696,"text":"70274696 - 2026 - Vulnerability of mangrove resources to sea-level rise on Sanibel Island, Florida, USA","interactions":[],"lastModifiedDate":"2026-04-06T15:07:30.716633","indexId":"70274696","displayToPublicDate":"2026-02-16T07:57:08","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1587,"text":"Estuarine, Coastal and Shelf Science","active":true,"publicationSubtype":{"id":10}},"title":"Vulnerability of mangrove resources to sea-level rise on Sanibel Island, Florida, USA","docAbstract":"Accelerating sea-level rise (SLR) is poised to reshape coastal environments over the coming decades, providing a challenge to land managers who need actionable information. Mangroves have an innate ability to keep pace with some SLR but may drown under the higher rates projected by the end of the century. Understanding local mangrove resilience to SLR requires understanding of historic and contemporary accretion rates, productivity, and forest elevations relative to tidal inundation. We applied the WARMER3 modeling framework to assess future mangrove resilience to SLR and blue carbon storage across the “Ding” Darling National Wildlife Refuge on Sanibel Island in southwest Florida, USA. We found that Sanibel mangroves are sensitive to the SLR scenario, with projected losses ranging from 19 to 70% by 2100. Across SLR scenarios, projected areal extent was similar until about 2035 and then diverge, coincident with projected acceleration in the rate of SLR. Threshold analysis indicates found that Sanibel mangroves are likely to submerge when rates exceed 6.5 mm yr−1. Currently, the mangrove forest of our study domain holds an estimated 214,000 Mg of carbon but is likely to decrease as mangroves convert to open water in the second half of this century. For “Ding” Darling, these site-specific projections identify when losses in mangrove resources are most likely, providing a basis to prioritize local management actions and conservation resources.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecss.2026.109775","usgsCitation":"Buffington, K.J., Krauss, K.W., Thorne, K., Conrad, J.R., Drexler, J.Z., and Zhu, Z., 2026, Vulnerability of mangrove resources to sea-level rise on Sanibel Island, Florida, USA: Estuarine, Coastal and Shelf Science, v. 333, 109775, 10 p., https://doi.org/10.1016/j.ecss.2026.109775.","productDescription":"109775, 10 p.","ipdsId":"IP-178704","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":502207,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Sanibel Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.1441386414859,\n 26.482734748273558\n ],\n [\n -82.15537584886745,\n 26.46118026405503\n ],\n [\n -82.13390475619293,\n 26.44806495228032\n ],\n [\n -82.0883539334147,\n 26.435668281802975\n ],\n [\n -82.05103035175483,\n 26.441058612733855\n ],\n [\n -82.05103035175483,\n 26.470880027687045\n ],\n [\n -82.09357120826992,\n 26.472137210863977\n ],\n [\n -82.1441386414859,\n 26.482734748273558\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"333","noUsgsAuthors":false,"publicationDate":"2026-02-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Buffington, Kevin J. 0000-0001-9741-1241 kbuffington@usgs.gov","orcid":"https://orcid.org/0000-0001-9741-1241","contributorId":4775,"corporation":false,"usgs":true,"family":"Buffington","given":"Kevin","email":"kbuffington@usgs.gov","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":958722,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Krauss, Ken W. 0000-0003-2195-0729","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":369271,"corporation":false,"usgs":false,"family":"Krauss","given":"Ken","middleInitial":"W.","affiliations":[{"id":87750,"text":"USGS-WARC","active":true,"usgs":false}],"preferred":false,"id":958723,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thorne, Karen M. 0000-0002-1381-0657","orcid":"https://orcid.org/0000-0002-1381-0657","contributorId":204579,"corporation":false,"usgs":true,"family":"Thorne","given":"Karen M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":958724,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Conrad, Jeremy R.","contributorId":369272,"corporation":false,"usgs":false,"family":"Conrad","given":"Jeremy","middleInitial":"R.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":958725,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Drexler, Judith Z. 0000-0002-0127-3866 jdrexler@usgs.gov","orcid":"https://orcid.org/0000-0002-0127-3866","contributorId":167492,"corporation":false,"usgs":true,"family":"Drexler","given":"Judith","email":"jdrexler@usgs.gov","middleInitial":"Z.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":958726,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zhu, Zhiliang 0000-0002-6860-6936","orcid":"https://orcid.org/0000-0002-6860-6936","contributorId":290659,"corporation":false,"usgs":false,"family":"Zhu","given":"Zhiliang","affiliations":[{"id":62470,"text":"U.S. Geological Survey, Reston, VA","active":true,"usgs":false}],"preferred":false,"id":958727,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70274127,"text":"70274127 - 2026 - Revisiting chlorophyll a thresholds for San Francisco Bay: Insights from observations of phytoplankton molecular abundance","interactions":[],"lastModifiedDate":"2026-02-26T17:04:58.420993","indexId":"70274127","displayToPublicDate":"2026-02-14T09:59:26","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1878,"text":"Harmful Algae","active":true,"publicationSubtype":{"id":10}},"title":"Revisiting chlorophyll a thresholds for San Francisco Bay: Insights from observations of phytoplankton molecular abundance","docAbstract":"Harmful Algal Blooms (HABs) are a hazard for coastal environments worldwide; identifying screening thresholds of chlorophyll-a (chl-a) associated with increased risk of HABs is a management priority. Molecular surveillance of coastal phytoplankton and bivalve biotoxins could be used to link chl-a with HAB risk, but requires an understanding of whether the HAB risks increase uniformly as chl-a rises, or whether some taxa are disproportionately favored, and if these relationships vary by season. In this study, we present a novel use of molecular abundance data to investigate the scientific bases for estuarine chl-a thresholds protective against HABs. In San Francisco Bay (SFB), California, the relationship between molecular relative abundance (as measured by 18S metabarcoding) of nine different HAB taxa, absolute quantitative polymerase chain reaction (qPCR) abundance, and mussel toxin concentrations of a subset of the taxa were investigated for thresholds as a function of increasing chl-a. Our results show most HAB taxa did not increase in absolute or relative abundance during SFB’s spring bloom interval, when chl-a levels were highest (>10 µg/L) but the assemblage was dominated by non-harmful diatoms. However, several flagellated, mixotrophic taxa did increase above their molecular baseline in fall, and the combined probability of any HAB occurring above baseline was elevated when chl-a reached ∼4.6 µg/L in the fall. This work demonstrates the promise of molecular approaches in disentangling the seasonally complex interplay between stressors and phytoplankton/HAB community responses and has the potential to provide clearer, more cost-effective monitoring and mitigation strategies for managers.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.hal.2026.103086","usgsCitation":"Killam, D., Bouma-Gregson, K., Sutula, M., Kudela, R., Hagy, J., Anderson, S., and Senn, D., 2026, Revisiting chlorophyll a thresholds for San Francisco Bay: Insights from observations of phytoplankton molecular abundance: Harmful Algae, v. 154, 103086, 16 p., https://doi.org/10.1016/j.hal.2026.103086.","productDescription":"103086, 16 p.","ipdsId":"IP-180259","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":500614,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.hal.2026.103086","text":"Publisher Index Page"},{"id":500555,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.71825250199157,\n 38.24752479202505\n ],\n [\n -122.71825250199157,\n 37.383291571958864\n ],\n [\n -121.97685049909562,\n 37.383291571958864\n ],\n [\n -121.97685049909562,\n 38.24752479202505\n ],\n [\n -122.71825250199157,\n 38.24752479202505\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"154","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Killam, Daniel 0000-0001-7569-1828","orcid":"https://orcid.org/0000-0001-7569-1828","contributorId":364654,"corporation":false,"usgs":false,"family":"Killam","given":"Daniel","affiliations":[{"id":12703,"text":"San Francisco Estuary Institute","active":true,"usgs":false}],"preferred":false,"id":956609,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bouma-Gregson, Keith 0000-0002-0304-6034","orcid":"https://orcid.org/0000-0002-0304-6034","contributorId":311235,"corporation":false,"usgs":true,"family":"Bouma-Gregson","given":"Keith","email":"","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":956610,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sutula, Martha","contributorId":191008,"corporation":false,"usgs":false,"family":"Sutula","given":"Martha","email":"","affiliations":[],"preferred":false,"id":956611,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kudela, Raphael","contributorId":196461,"corporation":false,"usgs":false,"family":"Kudela","given":"Raphael","affiliations":[],"preferred":false,"id":956612,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hagy, James","contributorId":196462,"corporation":false,"usgs":false,"family":"Hagy","given":"James","affiliations":[],"preferred":false,"id":956613,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Anderson, Stephanie","contributorId":367032,"corporation":false,"usgs":false,"family":"Anderson","given":"Stephanie","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":956614,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Senn, David","contributorId":349909,"corporation":false,"usgs":false,"family":"Senn","given":"David","affiliations":[{"id":83533,"text":"San Francisco Estuary Institute, Richmond, CA, USA","active":true,"usgs":false}],"preferred":false,"id":956615,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70275542,"text":"70275542 - 2026 - Invasion potential of nonnative fishes through a large western dam into an iconic and vulnerable ecosystem","interactions":[],"lastModifiedDate":"2026-05-19T15:44:07.710752","indexId":"70275542","displayToPublicDate":"2026-02-14T07:50:57","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1919,"text":"Hydrobiologia","onlineIssn":"1573-5117","printIssn":"0018-8158","active":true,"publicationSubtype":{"id":10}},"title":"Invasion potential of nonnative fishes through a large western dam into an iconic and vulnerable ecosystem","docAbstract":"Native fishes face imperilment due to habitat loss and fragmentation, megadrought, invasive species, and synergies amongst threats. These threats coalesce at Glen Canyon Dam (GCD), which impounds the Colorado River to create Lake Powell, a reservoir inhabited by a suite of nonnative fishes. Water level in Lake Powell has declined precipitously due to water overallocation and megadrought, increasing the risk of nonnative fish entrainment through the dam, as the penstocks draw water from the epilimnion. Our goal was to characterize the distribution and abundance of nonnative fishes in the vicinity of GCD and identify environmental conditions that may increase entrainment. We used active and passive sampling, hydroacoustics, and acoustic telemetry to assess the distribution and abundance of fishes. When the reservoir was stratified fish remained in the epilimnion, were present near the GCD penstocks, and thus at high risk of entrainment. When the reservoir was mixed, fish generally occupied shallower, warmer areas further from GCD, but this varied among species. The distribution of nonnative fish and accompanying risk of entrainment varied by season and epilimnion depth. Retaining more water in Lake Powell to draw water from below the thermocline would reduce the risk of nonnative fish entrainment into Grand Canyon.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s10750-026-06137-8","usgsCitation":"Friesen, B., Pennock, C.A., and Budy, P., 2026, Invasion potential of nonnative fishes through a large western dam into an iconic and vulnerable ecosystem: Hydrobiologia, v. 853, p. 3249-3268, https://doi.org/10.1007/s10750-026-06137-8.","productDescription":"20 p.","startPage":"3249","endPage":"3268","ipdsId":"IP-174501","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":503931,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":504169,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10750-026-06137-8","text":"Publisher Index Page"}],"country":"United States","state":"Arizona, Utah","otherGeospatial":"Glen Canyon Dam, Lake Powell","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.44896948736388,\n 37.25405554750887\n ],\n [\n -111.88286128357765,\n 36.87715647718177\n ],\n [\n -110.80300460888157,\n 36.824168436386316\n ],\n [\n -110.4477309484941,\n 37.20632124096176\n ],\n [\n -110.44321031550936,\n 37.62549296944364\n ],\n [\n -110.86061754284202,\n 37.59475093134766\n ],\n [\n -111.44896948736388,\n 37.25405554750887\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"853","noUsgsAuthors":false,"publicationDate":"2026-02-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Friesen, Barrett","contributorId":370978,"corporation":false,"usgs":false,"family":"Friesen","given":"Barrett","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":960841,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pennock, Casey A.","contributorId":370979,"corporation":false,"usgs":false,"family":"Pennock","given":"Casey","middleInitial":"A.","affiliations":[{"id":18155,"text":"The Ohio State University","active":true,"usgs":false}],"preferred":false,"id":960842,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Budy, Phaedra E. 0000-0002-9918-1678","orcid":"https://orcid.org/0000-0002-9918-1678","contributorId":228930,"corporation":false,"usgs":true,"family":"Budy","given":"Phaedra E.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":960843,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70273914,"text":"sir20265124 - 2026 - Bathymetric and velocimetric surveys at highway bridges crossing the Missouri River near Kansas City, Missouri, August 8–9, 2023","interactions":[],"lastModifiedDate":"2026-04-10T15:00:44.691323","indexId":"sir20265124","displayToPublicDate":"2026-02-13T11:09:36","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":"2026-5124","displayTitle":"Bathymetric and Velocimetric Surveys at Highway Bridges Crossing the Missouri River near Kansas City, Missouri, August 8–9, 2023","title":"Bathymetric and velocimetric surveys at highway bridges crossing the Missouri River near Kansas City, Missouri, August 8–9, 2023","docAbstract":"Bathymetric and velocimetric data were collected by the U.S. Geological Survey, in cooperation with the Missouri Department of Transportation, near 8 bridge crossings of the Missouri River near Kansas City, Missouri, on August 8–9, 2023. A multibeam echosounder mapping system was used to obtain channel-bed elevations for river reaches that extended about 1,550 to 1,640 feet longitudinally and generally extended laterally across the active channel from bank to bank during low floodflow to nonflood conditions. These surveys provided the channel geometry and hydraulic conditions of the river at the time of the surveys and provided characteristics of scour holes, which may be useful in developing or verifying predictive guidelines or equations for computing potential scour depth. The data collected from the surveys may also be useful to the Missouri Department of Transportation as a record of low floodflow conditions in regards to the stability and integrity of the bridges with respect to bridge scour. Bathymetric data were collected around every in-channel pier. Scour holes were at most piers where bathymetry could be obtained, except for those piers on banks or surrounded by riprap. All the bridge sites in this study were surveyed and documented in previous studies.
The average difference between the bathymetric surfaces ranged from 0.07 to 4.16 feet higher in 2023 than 2019, which indicates overall deposition between the survey dates, as might be expected based purely on streamflow at the time of the survey. However, the average difference between the bathymetric surfaces ranged from 1.44 feet higher to 1.88 feet lower in 2023 than 2015, which indicates a dynamic equilibrium of scour and deposition overall between those surveys, despite the lower flow conditions in 2023. Similarly, the average difference between the bathymetric surfaces ranged from 3.18 feet higher to 5.19 feet lower in 2023 than 2011, which indicates a relative equilibrium between scour and deposition overall, albeit the trend was toward scour as might be expected because of the substantial flood event in 2011.
Riprap blankets and alignment to flow had a substantial effect on the size of the scour hole for a given pier. Piers that were partially or fully surrounded by riprap blankets had scour holes that were substantially smaller (to nonexistent) compared to piers with no rock or riprap and effectively mitigated the scour holes historically observed at these piers. Several of the structures had piers that were skewed to primary approach flow. At most of the structures, the scour hole was deeper and longer on the side of the pier with impinging flow than the leeward side, with some amount of deposition on the leeward side, as typically observed at piers skewed to approach flow.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20265124","collaboration":"Prepared in cooperation with Missouri Department of Transportation","usgsCitation":"Huizinga, R.J., and Rivers, B.C., 2026, Bathymetric and velocimetric surveys at highway bridges crossing the Missouri River near Kansas City, Missouri, August 8–9, 2023: U.S. Geological Survey Scientific Investigations Report 2026–5124, 105 p., https://doi.org/10.3133/sir20265124.","productDescription":"Report: xi, 105 p.; Data Release; Dataset; 44 Oversize Map Figures: 17 x 11 inches","numberOfPages":"122","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-173988","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":500083,"rank":8,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2026/5124/images/"},{"id":500082,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1XUN9A8","text":"USGS data release","linkHelpText":"Bathymetry and velocity data from surveys at highway bridges crossing the Missouri River in Kansas City, Missouri, August 8–9, 2023"},{"id":500080,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20265124/full"},{"id":500359,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119211.htm","linkFileType":{"id":5,"text":"html"}},{"id":500081,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":500079,"rank":4,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2026/5124/downloads/","text":"Oversize figures","linkFileType":{"id":1,"text":"pdf"}},{"id":500078,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2026/5124/sir20265124.XML"},{"id":500077,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2026/5124/sir20265124.pdf","text":"Report","size":"31 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2026-5124"},{"id":500076,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2026/5124/coverthb.jpg"}],"country":"United States","state":"Kansas, Missouri","city":"Kansas City","otherGeospatial":"Missouri River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.667,\n 39.2\n ],\n [\n -94.23,\n 39.2\n ],\n [\n -94.23,\n 39.07\n ],\n [\n -94.667,\n 39.07\n ],\n [\n -94.667,\n 39.2\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
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.
We sought to assess bridle shiner (Notropis bifrenatus) habitat associations at local and regional scales across southern Maine and New Hampshire. We used local habitat data at 95 Maine sites to predict occupancy with classification and regression trees (CART). We then used ensemble species distribution models (SDMs) to model the historical (1898–2008) and current (2009–2022) ranges of the species. We used the BIOMOD platform to model the association between 35 environmental variables and bridle shiner presence during both time periods and at fine (pseudo-HUC14) and coarse (HUC12) spatial scales. We then calculated the change in predicted occupied drainages to estimate the change in the species' distribution at both scales. Within a site, bridle shiners were associated with submerged aquatic vegetation, organic substrate, and watermilfoil (Myriophyllum spp.). SDMs revealed an association with Appalachian (Hemlock-)Northern Hardwood Forest, sand substrate, and low-elevation terrain (at both spatial scales). Ensemble fine-scale SDMs suggest a substantial loss of historical bridle shiner habitat in both Maine (36% of drainages) and New Hampshire (16%), with comparable described losses (of 21% and 14%) at a coarse scale. Our local and regional models may be used to focus surveys on areas with high predicted habitat suitability or to inform habitat restoration efforts.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.72413","usgsCitation":"Katz, L.S., Coghlan, S.M., Carpenter, M.A., Kinnison, M.T., Zydlewski, J.D., 2026, Habitat-based predictions of bridle shiner (Notropis bifrenatus) in the northeastern U.S.: Ecology and Evolution, v. 16, no. 1, e72413, 19 p., https://doi.org/10.1002/ece3.72413.","productDescription":"e72413, 19 p.","ipdsId":"IP-158442","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":500597,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.72413","text":"Publisher Index Page"},{"id":500439,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine, New Hampshire","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -69.25429976691849,\n 47.475434309112075\n ],\n [\n -71.47637622335257,\n 45.210844648573726\n ],\n [\n -72.2134392335759,\n 43.872712909182994\n ],\n [\n -72.54074189044374,\n 42.739662976646855\n ],\n [\n -71.15118191830072,\n 42.68091951244142\n ],\n [\n -69.70815322174685,\n 43.6220110458515\n ],\n [\n -66.83114905877525,\n 44.72524582330068\n ],\n [\n -67.67285260858687,\n 45.844248966240016\n ],\n [\n -67.78415740622219,\n 47.198837764366964\n ],\n [\n -68.11347172586534,\n 47.38520895041782\n ],\n [\n -69.25429976691849,\n 47.475434309112075\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"1","noUsgsAuthors":false,"publicationDate":"2026-01-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Katz, Lara S.","contributorId":366795,"corporation":false,"usgs":false,"family":"Katz","given":"Lara","middleInitial":"S.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":956239,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coghlan, Stephen M. Jr.","contributorId":366796,"corporation":false,"usgs":false,"family":"Coghlan","given":"Stephen","suffix":"Jr.","middleInitial":"M.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":956240,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carpenter, Matthew A.","contributorId":366797,"corporation":false,"usgs":false,"family":"Carpenter","given":"Matthew","middleInitial":"A.","affiliations":[{"id":56597,"text":"New Hampshire Fish and Game Department","active":true,"usgs":false}],"preferred":false,"id":956241,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kinnison, Michael T.","contributorId":366798,"corporation":false,"usgs":false,"family":"Kinnison","given":"Michael","middleInitial":"T.","affiliations":[{"id":87507,"text":"Maine Center for Genetics in the Environment","active":true,"usgs":false}],"preferred":false,"id":956242,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zydlewski, Joseph D. 0000-0002-2255-2303 jzydlewski@usgs.gov","orcid":"https://orcid.org/0000-0002-2255-2303","contributorId":2004,"corporation":false,"usgs":true,"family":"Zydlewski","given":"Joseph","email":"jzydlewski@usgs.gov","middleInitial":"D.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":956243,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70273895,"text":"70273895 - 2026 - Inference of pattern-based geological CO2 sequestration and oil recovery potential in a commingled main pay and residual oil zone CO2-EOR flood","interactions":[],"lastModifiedDate":"2026-02-13T18:54:27.652376","indexId":"70273895","displayToPublicDate":"2026-02-12T09:51:46","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":16689,"text":"Geoenergy Science and Engineering","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Inference of pattern-based geological CO2 sequestration and oil recovery potential in a commingled main pay and residual oil zone CO2-EOR flood","title":"Inference of pattern-based geological CO2 sequestration and oil recovery potential in a commingled main pay and residual oil zone CO2-EOR flood","docAbstract":"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":38175,"text":"Toxics Substances Hydrology Program","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","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":70273891,"text":"70273891 - 2026 - Suspended-sediment and phosphorus response in a fire-affected central Montana headwater catchment","interactions":[],"lastModifiedDate":"2026-02-12T15:42:25.928091","indexId":"70273891","displayToPublicDate":"2026-02-11T08:33:51","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2457,"text":"Journal of Soils and Sediments","active":true,"publicationSubtype":{"id":10}},"title":"Suspended-sediment and phosphorus response in a fire-affected central Montana headwater catchment","docAbstract":"Purpose
Eutrophication and nuisance filamentous algal blooms (i.e. Cladophora) are increasingly common occurrences throughout much of the western United States. Wildfire may be contributing to the frequency and magnitude of algal blooms through excess sediment and nutrient loading to streams and rivers. Our objective was to evaluate the effects the 2021 Woods Creek Fire had on sediment yields and phosphorus (total and bioavailable) partitioning in Camas Creek, a major tributary to the Smith River in Montana where Cladophora are now consistently reaching nuisance levels.
Methods
We collected water quality samples during snowmelt pulsing events as well as fixed interval sampling using an established U.S. Geological Survey stream gage instrumented with a continuous water quality sonde and an automatic peristaltic pump sampler. Water samples were processed for total phosphorus (TP), sediment-bound bioavailable phosphorus (S-BioP), soluble reactive phosphorus (SRP), and suspended sediment concentrations and were evaluated using linear regression and other nonparametric statistical tests. Continuous turbidity and streamflow were evaluated using hysteresis analysis to determine sediment sourcing and connectivity.
Results
We found that the Woods Creek Fire did not significantly influence TP and S-BioP in Camas Creek. However, there was a significant increase in SRP and turbidity in both postfire years (2022 and 2023). Hysteresis analysis of 91 delineated events indicated positive (clockwise) hysteresis was the dominant event pattern during the snowmelt period. This may indicate a lower hillslope to channel connectivity, with the major sediment supply originating from the channel and/or riparian areas.
Conclusion
Results from this study demonstrate the benefits of combining discrete water quality samples with high-frequency turbidity sensors to characterize postfire sediment and phosphorus dynamics. While a lack of postfire response in TP and S-BioP is contrary to many other studies, our findings highlight the role climate and catchment morphology play in attenuating a disturbance effect.
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To better understand co-occurrence of pesticides and PFAS in agricultural streams, 10 streams were sampled in two agricultural valleys in northern California, USA during two periods of pesticide application (May and July of 2024). Water samples were analyzed for 183 pesticides and 57 PFAS. Sixty pesticides were detected: 23 herbicides, 18 insecticides, 18 fungicides, and 1 synergist. Overall detection frequencies (DF) ranged from 0 to 100%, and concentrations ranged from non-detect (ND) to 782 ng/L (fluxapyroxad). Fourteen PFAS were detected: 6 perfluoroalkyl carboxylates, 5 perfluoroalkyl sulfonates, 2 perfluoroalkyl acid precursors, and 1 perfluoroalkyl phosphinic acid with DFs ranging from 0 to 60% (PFOA) and concentrations ranging from ND to 20 ng/L (PFBA). Co-occurrence of PFAS and pesticides was observed in 80% of samples. Adopting the global definition of PFAS (includes fluorinated pesticides), pesticide applications are a likely source of instream PFAS. Examination of total organic fluorine and transformations to terminal and ultra-short PFAS could further elucidate pesticide contributions to environmental PFAS.","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.estlett.6c00132","usgsCitation":"Woodward, E.E., Hladik, M.L., Orlando, J., Uychutin, M., Gray, J.L., and Kolpin, D., 2026, Assessing pesticide application contributions of per- and polyfluoroalkyl substances (PFAS) in agricultural streams: Environmental Science & Technology Letters, v. 13, no. 4, p. 586-593, https://doi.org/10.1021/acs.estlett.6c00132.","productDescription":"8 p.","startPage":"586","endPage":"593","ipdsId":"IP-179149","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":503439,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.estlett.6c00132","text":"Publisher Index Page"},{"id":503267,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento Valley, San Joaquin Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.75011070222405,\n 40.04218778358725\n ],\n [\n -122.75011070222405,\n 37.04490168579687\n ],\n [\n -119.7854685554131,\n 37.04490168579687\n ],\n [\n -119.7854685554131,\n 40.04218778358725\n ],\n [\n -122.75011070222405,\n 40.04218778358725\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"13","issue":"4","noUsgsAuthors":false,"publicationDate":"2026-03-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Woodward, Emily E. 0000-0001-9196-1349 ewoodward@usgs.gov","orcid":"https://orcid.org/0000-0001-9196-1349","contributorId":177364,"corporation":false,"usgs":true,"family":"Woodward","given":"Emily","email":"ewoodward@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":959895,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hladik, Michelle L. 0000-0002-0891-2712","orcid":"https://orcid.org/0000-0002-0891-2712","contributorId":221229,"corporation":false,"usgs":true,"family":"Hladik","given":"Michelle","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":959896,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Orlando, James L. 0000-0002-0099-7221","orcid":"https://orcid.org/0000-0002-0099-7221","contributorId":357549,"corporation":false,"usgs":false,"family":"Orlando","given":"James L.","affiliations":[{"id":85456,"text":"USGS California Water Science Center","active":true,"usgs":false}],"preferred":false,"id":959897,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Uychutin, Matthew 0000-0003-2677-7902","orcid":"https://orcid.org/0000-0003-2677-7902","contributorId":339824,"corporation":false,"usgs":true,"family":"Uychutin","given":"Matthew","email":"","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":959898,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gray, James L. 0000-0002-0807-5635","orcid":"https://orcid.org/0000-0002-0807-5635","contributorId":205658,"corporation":false,"usgs":true,"family":"Gray","given":"James","email":"","middleInitial":"L.","affiliations":[{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true}],"preferred":true,"id":959899,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kolpin, Dana W. 0000-0002-3529-6505","orcid":"https://orcid.org/0000-0002-3529-6505","contributorId":205652,"corporation":false,"usgs":true,"family":"Kolpin","given":"Dana W.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":959900,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"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 Page"},{"id":500135,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon, Washington","otherGeospatial":"western Pacific Northwest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -125.13201264037224,\n 49.10187680299188\n ],\n [\n -125.13201264037224,\n 41.96932472868852\n ],\n [\n -120.71315520957631,\n 41.96932472868852\n ],\n [\n -120.71315520957631,\n 49.10187680299188\n ],\n [\n -125.13201264037224,\n 49.10187680299188\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","issue":"2","noUsgsAuthors":false,"publicationDate":"2026-02-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Wall, Sara","contributorId":238092,"corporation":false,"usgs":false,"family":"Wall","given":"Sara","email":"","affiliations":[{"id":33615,"text":"Carleton 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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 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Reports","active":true,"publicationSubtype":{"id":10}},"title":"Per- and polyfluoroalkyl substances in waters associated with oil and gas development in the Denver Basin","docAbstract":"Use of per- and polyfluoroalkyl substances (PFAS) in the petroleum industry could be a cause for concern due to the large volumes of produced water (PW) generated during oil and gas extraction, the reuse of these wastes in water-stressed regions, and adverse health outcomes related to PFAS exposures. However, PW PFAS characterization is nearly absent in the literature, and hydraulic fracturing (HF) chemical disclosures often omit the identities of additives as proprietary. Here we evaluate PFAS in PW samples from three petroleum wells in the Denver Basin during their first year of production. Total concentrations of targeted PFAS (Σ40PFAS) were < 35 ng/L in PW samples, with short-chain PFAS like perfluorobutanoic acid persisting throughout the sampled duration. Analysis of freshwater inputs for hydraulic fracturing (Σ40PFAS ~ 113 ng/L) and mixed fracture fluid (Σ40PFAS ~ 69 ng/L) indicated much of the targeted PFAS content was derived from the input water, and not from HF additives, however samples subjected to oxidation indicated the presence of PFAS precursors that would not be detected by targeted analysis. This study highlights that while PFAS content is low in the studied PWs, the potential for redistribution of PFAS in the environment may be a consideration for reuse applications.
","language":"English","publisher":"Springer Nature","doi":"10.1038/s41598-025-33394-9","usgsCitation":"Varonka, M., Jubb, A., McDevitt, B., Shelton, J.L., Barnhart, E.P., Akob, D., and Cozzarelli, I.M., 2026, Per- and polyfluoroalkyl substances in waters associated with oil and gas development in the Denver Basin: Scientific Reports, v. 16, 5743, 8 p., https://doi.org/10.1038/s41598-025-33394-9.","productDescription":"5743, 8 p.","ipdsId":"IP-173284","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":500246,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-025-33394-9","text":"Publisher Index Page"},{"id":500087,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Nebraska, Wyoming","otherGeospatial":"Denver Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105.7394986500792,\n 41.98806529832831\n ],\n [\n -105.7394986500792,\n 39.252214457834185\n ],\n [\n -103.39682614563382,\n 39.252214457834185\n ],\n [\n -103.39682614563382,\n 41.98806529832831\n ],\n [\n -105.7394986500792,\n 41.98806529832831\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","noUsgsAuthors":false,"publicationDate":"2026-02-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Varonka, Matthew S. 0000-0003-3620-5262","orcid":"https://orcid.org/0000-0003-3620-5262","contributorId":203231,"corporation":false,"usgs":true,"family":"Varonka","given":"Matthew S.","affiliations":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":955714,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jubb, Aaron M. 0000-0001-6875-1079","orcid":"https://orcid.org/0000-0001-6875-1079","contributorId":201978,"corporation":false,"usgs":true,"family":"Jubb","given":"Aaron M.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":955715,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McDevitt, Bonnie 0000-0001-8390-0028","orcid":"https://orcid.org/0000-0001-8390-0028","contributorId":291246,"corporation":false,"usgs":true,"family":"McDevitt","given":"Bonnie","email":"","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":955716,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shelton, Jenna L. 0000-0002-1377-0675","orcid":"https://orcid.org/0000-0002-1377-0675","contributorId":366369,"corporation":false,"usgs":false,"family":"Shelton","given":"Jenna","middleInitial":"L.","affiliations":[{"id":87467,"text":"Illinois State Water Survey, Prairie Research Institute, Champaign, IL, USA 61821","active":true,"usgs":false}],"preferred":false,"id":955717,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barnhart, Elliott P. 0000-0002-8788-8393","orcid":"https://orcid.org/0000-0002-8788-8393","contributorId":203225,"corporation":false,"usgs":true,"family":"Barnhart","given":"Elliott","middleInitial":"P.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":955718,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Akob, Denise M. 0000-0003-1534-3025","orcid":"https://orcid.org/0000-0003-1534-3025","contributorId":204701,"corporation":false,"usgs":true,"family":"Akob","given":"Denise M.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":955719,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cozzarelli, Isabelle M. 0000-0002-5123-1007 icozzare@usgs.gov","orcid":"https://orcid.org/0000-0002-5123-1007","contributorId":1693,"corporation":false,"usgs":true,"family":"Cozzarelli","given":"Isabelle","email":"icozzare@usgs.gov","middleInitial":"M.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":955720,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70273858,"text":"70273858 - 2026 - Evaluating machine learning approaches to identify and predict oil and gas produced water lithium concentrations","interactions":[],"lastModifiedDate":"2026-02-09T15:28:29.171335","indexId":"70273858","displayToPublicDate":"2026-02-06T08:20:00","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":23297,"text":"Data Science in Science","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating machine learning approaches to identify and predict oil and gas produced water lithium concentrations","docAbstract":"Recently, the demand for battery-grade lithium has substantially increased, largely due to electrification of the transportation sector. The search for new lithium sources has turned to produced waters (frequently brines), a large-volume wastewater by-product of oil and gas extraction. Geochemical analysis indicates the presence of varying concentrations of lithium from produced water samples collected across the United States and represented in the U.S. Geological Survey’s National Produced Water Geochemical Database, as well as mixtures of Marcellus Shale produced water included in the Pennsylvania Department of Environmental Protection’s Oil and Gas Well Waste Reports. We first examined whether the geochemical signature of the lithium-bearing produced waters is sufficiently distinct so that machine learning (ML) can be used to correctly classify samples to the formation of origin. The produced water sample data used to assess classification accuracy were from the Marcellus Shale, Utica Shale and Point Pleasant Formation (Utica), and Smackover Formation oil and gas wells. Further, we evaluated the potential for ML to accurately classify Marcellus Shale produced water spatially (i.e., northeast versus southwest Pennsylvania). We then investigated whether ML algorithms applied to a suite of geochemical concentration data (i.e. Ba, Br, Cl, K, Mg, Sr) may be used to predict the lithium concentration of an unknown sample. Finally, we applied an estimated economic lithium grade cutoff of 150 milligrams per liter (mg/l) and assessed the utility of ML to predict whether a produced water sample would fall above or below the grade cutoff based on the suite of geochemical parameters. Four machine learning algorithms—Random Forest (RF), Gradient Boosting Trees (GBT), Extreme Boosting (XGBoost), and Deep Neural Networks (DNN) were assessed. This study successfully demonstrates that all four machine learning methods can precisely and accurately estimate lithium concentrations and geologic formation classification. The products of this study contribute to the growing body of knowledge aimed at expanding the lithium resource base within the United States.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/26941899.2026.2624195","usgsCitation":"Attanasi, E., McDevitt, B., Freeman, P., and Coburn, T., 2026, Evaluating machine learning approaches to identify and predict oil and gas produced water lithium concentrations: Data Science in Science, v. 5, no. 1, 2624195, 18 p., https://doi.org/10.1080/26941899.2026.2624195.","productDescription":"2624195, 18 p.","ipdsId":"IP-180475","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":499937,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/26941899.2026.2624195","text":"Publisher Index Page"},{"id":499678,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Arkansas, Florida, Georgia, Louisiana, Mississippi, Oklahoma, South Carolina, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -100.73317123367434,\n 35.41758190253161\n ],\n [\n -100.73317123367434,\n 28.707661068137995\n ],\n [\n -83.28804352213777,\n 28.707661068137995\n ],\n [\n -83.28804352213777,\n 35.41758190253161\n ],\n [\n -100.73317123367434,\n 35.41758190253161\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"5","issue":"1","noUsgsAuthors":false,"publicationDate":"2026-02-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Attanasi, Emil 0000-0001-6845-7160 attanasi@usgs.gov","orcid":"https://orcid.org/0000-0001-6845-7160","contributorId":1809,"corporation":false,"usgs":true,"family":"Attanasi","given":"Emil","email":"attanasi@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":955297,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McDevitt, Bonnie 0000-0001-8390-0028","orcid":"https://orcid.org/0000-0001-8390-0028","contributorId":291246,"corporation":false,"usgs":true,"family":"McDevitt","given":"Bonnie","email":"","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":955298,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Freeman, Philip A. 0000-0002-0863-7431","orcid":"https://orcid.org/0000-0002-0863-7431","contributorId":347358,"corporation":false,"usgs":false,"family":"Freeman","given":"Philip A.","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":955299,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Coburn, Timothy 0000-0002-7309-0962","orcid":"https://orcid.org/0000-0002-7309-0962","contributorId":366116,"corporation":false,"usgs":false,"family":"Coburn","given":"Timothy","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":955300,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70276519,"text":"70276519 - 2026 - Occupancy rates of river otters (Lontra canadensis) in the St. Louis River Area of Concern","interactions":[],"lastModifiedDate":"2026-06-10T15:19:10.257613","indexId":"70276519","displayToPublicDate":"2026-02-05T12:26:09","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":24810,"text":"IUCN/SSC Otter Specialist Group Bulletin","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Occupancy rates of river otters (Lontra canadensis) in the St. Louis River Area of Concern","title":"Occupancy rates of river otters (Lontra canadensis) in the St. Louis River Area of Concern","docAbstract":"The St. Louis River Area of Concern (AOC), an estuarine system along the northeastern Minnesota-Wisconsin border, experienced significant ecological disturbances and degradation from unregulated industrial development during the 19th and 20th centuries. Recent restoration and remediation efforts in the AOC have led to substantial improvements in water quality, habitat, and fisheries populations. However, the status of wildlife populations following restoration efforts in the AOC remain largely unknown. We used remote cameras to estimate occupancy rates of river otters Lontra canadensis in the AOC and at two reference areas that had not been subjected to historical degradation. Because river otters occupy a high trophic level within these systems, we hypothesized that they would be an effective indicator species of overall habitat quality and recovery. Using data collected from June 2015 to February 2016, we developed single-season occupancy models for both summer and winter seasons. We found no differences in occupancy between the AOC and the reference sites, suggesting that river otter populations within the AOC were comparable to those in unaffected areas. Our results indicate that habitat restoration and remediation efforts in the St. Louis River estuary have been successful in promoting healthy wildlife populations.
","language":"English","publisher":"IUCN/SSC Otter Specialist Group","usgsCitation":"Evans, B.E., Tucker, M.C., Van Deelen, T.R., and Crimmins, S.M., 2026, Occupancy rates of river otters (Lontra canadensis) in the St. Louis River Area of Concern: IUCN/SSC Otter Specialist Group Bulletin, v. 43, no. 1, p. 24-34.","productDescription":"11 p.","startPage":"24","endPage":"34","ipdsId":"IP-152860","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":505271,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":505201,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://iucnosgbull.org/Volume43/Vol43_Iss1_Index.html"}],"country":"United States","state":"Minnesota, Wisconsin","otherGeospatial":"Boulder Lake, St. Croix River, St. Louis River Area of Concern","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.94600196159826,\n 46.77005207817896\n ],\n [\n -92.37221823661014,\n 46.77005207817896\n ],\n [\n -92.37221823661014,\n 46.026349615068455\n ],\n [\n -91.94600196159826,\n 46.026349615068455\n ],\n [\n -91.94600196159826,\n 46.77005207817896\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"43","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Evans, Bryn E.","contributorId":371903,"corporation":false,"usgs":false,"family":"Evans","given":"Bryn","middleInitial":"E.","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":962565,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tucker, Madeleine C.","contributorId":371904,"corporation":false,"usgs":false,"family":"Tucker","given":"Madeleine","middleInitial":"C.","affiliations":[{"id":36971,"text":"University of Alaska","active":true,"usgs":false}],"preferred":false,"id":962566,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Van Deelen, Timothy R.","contributorId":371905,"corporation":false,"usgs":false,"family":"Van Deelen","given":"Timothy","middleInitial":"R.","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":962567,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Crimmins, Shawn M. 0000-0001-6229-5543 scrimmins@usgs.gov","orcid":"https://orcid.org/0000-0001-6229-5543","contributorId":5498,"corporation":false,"usgs":true,"family":"Crimmins","given":"Shawn","email":"scrimmins@usgs.gov","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":962568,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70275576,"text":"70275576 - 2026 - Weather, water, and precipitation","interactions":[],"lastModifiedDate":"2026-05-04T15:43:51.730851","indexId":"70275576","displayToPublicDate":"2026-02-05T10:40:54","publicationYear":"2026","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"12","title":"Weather, water, and precipitation","docAbstract":"No abstract available.
","largerWorkTitle":"Mountain sheep in North America: Biology, ecology, conservation, and management","language":"English","publisher":"CRC Press","usgsCitation":"Cain, J.W., and van de Kerk, M., 2026, Weather, water, and precipitation, chap. 12 of Mountain sheep in North America: Biology, ecology, conservation, and management.","ipdsId":"IP-174390","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":503940,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Krausman, Paul R.","contributorId":356094,"corporation":false,"usgs":false,"family":"Krausman","given":"Paul R.","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":960968,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Jex, William","contributorId":371079,"corporation":false,"usgs":false,"family":"Jex","given":"William","affiliations":[],"preferred":false,"id":960969,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"Cain, James W. III 0000-0003-4743-516X jwcain@usgs.gov","orcid":"https://orcid.org/0000-0003-4743-516X","contributorId":4063,"corporation":false,"usgs":true,"family":"Cain","given":"James","suffix":"III","email":"jwcain@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":960926,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"van de Kerk, Madelon","contributorId":371051,"corporation":false,"usgs":false,"family":"van de Kerk","given":"Madelon","affiliations":[{"id":38118,"text":"Western Colorado University","active":true,"usgs":false}],"preferred":false,"id":960927,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70273385,"text":"sir20255104 - 2026 - Estimating the magnitude and frequency of floods at ungaged locations on urban streams in Tennessee and parts of Alabama, Georgia, Mississippi, North Carolina, and South Carolina, using data through the 2022 water year","interactions":[],"lastModifiedDate":"2026-02-05T22:06:06.078649","indexId":"sir20255104","displayToPublicDate":"2026-02-05T08:41:23","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":"2025-5104","displayTitle":"Estimating the Magnitude and Frequency of Floods at Ungaged Locations on Urban Streams in Tennessee and Parts of Alabama, Georgia, Mississippi, North Carolina, and South Carolina, Using Data Through the 2022 Water Year","title":"Estimating the magnitude and frequency of floods at ungaged locations on urban streams in Tennessee and parts of Alabama, Georgia, Mississippi, North Carolina, and South Carolina, using data through the 2022 water year","docAbstract":"In 2024, the U.S. Geological Survey, in cooperation with the Tennessee Department of Transportation, updated the methods for predicting the magnitude and frequency of floods at ungaged locations on streams in urban areas in Tennessee. The study area included 136 streamgages in urban areas in Tennessee, Mississippi, Alabama, Georgia, South Carolina, and North Carolina that had at least 10 percent developed imperviousness in their basins as indicated by data from the 2011 National Land Cover Database. Regression equations were developed to predict streamflows corresponding to the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities (AEPs) and were incorporated into the StreamStats application. In generalized least-squares regression, the base-10 logarithm of drainage area, the percentages of the streamgage basins in developed land use, and the percentages of the streamgage basins in the Piedmont and Ridge and Valley Level 3 ecoregions were statistically significant in explaining the variability in annual peak streamflows in the study area. Drainage areas ranged from 0.164 to 93.4 square miles, the percentage of the streamgage basins in developed land use ranged from 26 to 100 percent, and the percentage of the streamgage basins in Piedmont and Ridge and Valley Level 3 ecoregions ranged from 0 to 100 percent. Pseudo R-squared values for the regression equations ranged from 0.86, or 86 percent, for the 50- and 20-percent AEPs (2- and 5-year floods) to 0.71, or 71 percent, for the 0.2-percent AEP (500-year flood). The average variance of prediction (in log base-10 units) ranged from 0.023 for the 20- and 10-percent AEPs to 0.05 for the 0.2-percent AEP. The average variance of prediction can be reported as a percentage of the predicted value, known as the standard error of prediction, which ranged from 35.8 percent for the 20-percent AEP (5-year flood) to 55.4 percent for the 0.2-percent AEP (500-year flood). Methods are presented for estimating annual peak streamflows for gaged locations, ungaged locations on gaged streams, and locations on ungaged streams.
Contact Us- USGS Publications Warehouse
Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
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Digital flood-inundation maps for a 2.7-mile reach of Río Grande De Loíza in Caguas, Puerto Rico, were created by the U.S. Geological Survey. Water-surface profiles were computed for the stream reach by using a one-dimensional, steady-state, step-backwater model. The model was calibrated to the current (2025) stage-streamflow relation (rating curve) for the U.S. Geological Survey streamgage 50055000, Río Grande De Loíza, Puerto Rico. The resulting hydraulic model was then used to compute 16 water-surface profiles for water levels (flood stages) ranging from 19.00 to 34.00 feet at the streamgage; these flood stages range from “moderate flood stage” to above “major flood stage” as defined by the National Weather Service. The 34.00-foot stage exceeds the historical maximum peak stage of 33.20 feet, recorded at the streamgage in 1945. The simulated water-surface profiles were used in combination with a digital elevation model derived from light detection and ranging (lidar) data to map the inundated areas associated with each flood profile.
The flood-inundation maps and the supporting hydraulic model produced by this study can be used by emergency managers and local officials to assess flood-mitigation strategies and to define flood-hazard areas to help protect life and property, to coordinate flood-response activities such as evacuations and road closures, and to aid post-flood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255112","usgsCitation":"Ostheimer, C.J., Torres-Garcia, L.M., and Gomez-Fragoso, J.M., 2026, Flood-inundation maps for Río Grande De Loíza in and near Caguas, Puerto Rico, 2026: U.S. Geological Survey Scientific Investigations Report 2025–5112, 16 p., https://doi.org/10.3133/sir20255112.","productDescription":"Report: vii, 16 p.; Data Release","numberOfPages":"16","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-179822","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":499274,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5112/coverthb.jpg"},{"id":499277,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255112/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5112 HTML"},{"id":499276,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5112/sir20255112.pdf","size":"3.16 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5112 PDF"},{"id":499609,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119202.htm","linkFileType":{"id":5,"text":"html"}},{"id":499280,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1IZ6D5V","text":"USGS data release","linkHelpText":"Geospatial data sets and hydraulic model for Río Grande De Loíza in Caguas, Puerto Rico"},{"id":499279,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5112/images/"},{"id":499278,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5112/sir20255112.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2025-5112 XML"}],"country":"United States","otherGeospatial":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -66.09390482728071,\n 18.33190261334512\n ],\n [\n -66.09390482728071,\n 18.134783495693256\n ],\n [\n -65.97456945279221,\n 18.134783495693256\n ],\n [\n -65.97456945279221,\n 18.33190261334512\n ],\n [\n -66.09390482728071,\n 18.33190261334512\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
6460 Busch Blvd, Suite 100
Columbus, OH 43229-1737
Chinook salmon population declines span their geographic range with climate hypothesized as a major driver. Concerns of warming freshwater temperatures in their northern range gained urgency during 2019 when a heatwave coincided with premature mortality. This study examined heat stress during the 2019 heatwave compared to subsequent years and described water temperatures in western Alaska to understand the degree to which freshwater temperatures may be a stressor. Heat stress was prevalent among Chinook salmon captured in the 2019 heatwave (Kuskokwim tributaries: 90% in Kwethluk and 63% Takotna river), and variable in subsequent years (∼8% to 60% across Kuskokwim tributaries and Norton Sound rivers). A review of water temperature data indicated that potentially stressful temperatures (≥18 °C) were most common and prolonged in the Yukon River, moderately common and prolonged in the Kuskokwim River, and relatively rare in the Norton Sound region. Water temperatures in 2019 broke several records for overall maximum and frequency of temperatures ≥ 18 °C. Migration water temperatures and heat stress in northern Pacific salmon habitats vary more widely than previously recognized (up to 25 °C).
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2025-0109","usgsCitation":"von Biela, V.R., Regish, A.M., McCormick, S.D., Spaeder, J., Whitworth, K., Leon, J., Gillikin, D., Liller, Z., Ivanoff, R., Bell, J., Larson, S.D., Carey, M.P., and Zimmerman, C.E., 2026, Migration water temperature and heat stress assessments in western Alaska Chinook salmon overlapping the 2019 heatwave: Canadian Journal of Fisheries and Aquatic Sciences, https://doi.org/10.1139/cjfas-2025-0109.","ipdsId":"IP-171279","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true},{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":499750,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"western Alaska","edition":"Online First","noUsgsAuthors":false,"publicationDate":"2026-02-04","publicationStatus":"PW","contributors":{"authors":[{"text":"von Biela, Vanessa R. 0000-0002-7139-5981 vvonbiela@usgs.gov","orcid":"https://orcid.org/0000-0002-7139-5981","contributorId":3104,"corporation":false,"usgs":true,"family":"von Biela","given":"Vanessa","email":"vvonbiela@usgs.gov","middleInitial":"R.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":955346,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Regish, Amy M. 0000-0003-4747-4265","orcid":"https://orcid.org/0000-0003-4747-4265","contributorId":265360,"corporation":false,"usgs":true,"family":"Regish","given":"Amy","email":"","middleInitial":"M.","affiliations":[{"id":365,"text":"Leetown Science 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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.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255098","issn":"2328-0328","collaboration":"Prepared in cooperation with the City of Brownsville, Texas","usgsCitation":"Ockerman, D.J., and Choi, N., 2026, Water-budget simulations for selected watersheds in Cameron County, Texas, 2022–23: U.S. Geological Survey Scientific Investigations Report 2025–5098, 37 p., https://doi.org/10.3133/sir20255098.","productDescription":"Report: viii, 37 p.; Data Release","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-167896","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":499763,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255098/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5098 HTML"},{"id":499608,"rank":6,"type":{"id":36,"text":"NGMDB Index 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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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