Evaluating stream water chemistry patterns provides insight into catchment ecosystem and hydrologic processes. Spatially distributed patterns and controls of stream solutes are well-established for high-relief catchments where solute flow paths align with surface topography. However, the controls on solute patterns are poorly constrained for low-relief catchments where hydrogeologic heterogeneities and river corridor features, like wetlands, may influence water and solute transport. Here, we provide a data set of solute patterns from 58 synoptic surveys across 28 sites and over 32 months in a low-relief wetland-rich catchment to determine the major surface and subsurface controls along with wetland influence across the catchment. In this low-relief catchment, the expected wetland storage, processing, and transport of solutes is only apparent in solute patterns of the smallest subcatchments. Meanwhile, downstream seasonal and wetland influence on observed chemistry can be masked by large groundwater contributions to the main stream channel. These findings highlight the importance of incorporating variable groundwater contributions into catchment-scale studies for low-relief catchments, and that understanding the overall influence of wetlands on stream chemistry requires sampling across various spatial and temporal scales. Therefore, in low-relief wetland-rich catchments, given the mosaic of above and below ground controls on stream solutes, modeling efforts may need to include both surface and subsurface hydrological data and processes.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2025JG008989","usgsCitation":"Weidner, C., Zarnestke, J., Kendall, A., Martin, S., Nesheim, S., and Shogren, A., 2025, Wetlands, groundwater and seasonality influence the spatial distribution of stream chemistry in a low-relief catchment: JGR Biogeosciences, v. 130, no. 8, e2025JG008989, 19 p., https://doi.org/10.1029/2025JG008989.","productDescription":"e2025JG008989, 19 p.","ipdsId":"IP-179047","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":494438,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2025jg008989","text":"Publisher Index Page"},{"id":493705,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United Sates","state":"Michigan","otherGeospatial":"Augusta Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -85.37501891108799,\n 42.37891153154604\n ],\n [\n -85.37501891108799,\n 42.32980992829573\n ],\n [\n -85.34485255012329,\n 42.32980992829573\n ],\n [\n -85.34485255012329,\n 42.37891153154604\n ],\n [\n -85.37501891108799,\n 42.37891153154604\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"130","issue":"8","noUsgsAuthors":false,"publicationDate":"2025-08-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Weidner, Caroline R. 0009-0008-6994-0021","orcid":"https://orcid.org/0009-0008-6994-0021","contributorId":359353,"corporation":false,"usgs":false,"family":"Weidner","given":"Caroline R.","affiliations":[{"id":85775,"text":"Michigan State University Department of Earth and Environmental Sciences","active":true,"usgs":false}],"preferred":false,"id":945168,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zarnestke, Jay P. 0000-0001-7194-5245","orcid":"https://orcid.org/0000-0001-7194-5245","contributorId":359354,"corporation":false,"usgs":false,"family":"Zarnestke","given":"Jay P.","affiliations":[{"id":85775,"text":"Michigan State University Department of Earth and Environmental Sciences","active":true,"usgs":false}],"preferred":false,"id":945169,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kendall, Anthony D.","contributorId":357745,"corporation":false,"usgs":false,"family":"Kendall","given":"Anthony D.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":945170,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Martin, Sherry L. 0000-0001-7471-0476","orcid":"https://orcid.org/0000-0001-7471-0476","contributorId":343444,"corporation":false,"usgs":true,"family":"Martin","given":"Sherry","middleInitial":"L.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":945171,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nesheim, Samuel","contributorId":359355,"corporation":false,"usgs":false,"family":"Nesheim","given":"Samuel","affiliations":[{"id":85775,"text":"Michigan State University Department of Earth and Environmental Sciences","active":true,"usgs":false}],"preferred":false,"id":945172,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shogren, Arial J.","contributorId":359356,"corporation":false,"usgs":false,"family":"Shogren","given":"Arial J.","affiliations":[{"id":85776,"text":"The University of Alabama Biological Sciences Department","active":true,"usgs":false}],"preferred":false,"id":945173,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70269823,"text":"fs20253036 - 2025 - Applying U.S. Geological Survey science to understand effects to water supply in the Upper Colorado River Basin","interactions":[],"lastModifiedDate":"2026-02-03T14:46:41.794993","indexId":"fs20253036","displayToPublicDate":"2025-08-05T16:10:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-3036","displayTitle":"Applying U.S. Geological Survey Science to Understand Effects to Water Supply in the Upper Colorado River Basin","title":"Applying U.S. Geological Survey science to understand effects to water supply in the Upper Colorado River Basin","docAbstract":"The Colorado River Basin is a vital source of water to more than 40 million people in the Western United States and Mexico, including in major cities like Denver, Las Vegas, Phoenix, Tucson, Los Angeles, and San Diego, and supports irrigation for about 16,000 square kilometers of agricultural land. Since 2000, the southwestern United States has been unusually dry due to low precipitation and warm air temperatures, contributing to extreme water level declines of the two large reservoirs on the Colorado River, Lake Mead and Lake Powell. In 2021, these reservoirs reached their lowest levels on record, resulting in unprecedented restrictions on water usage in the basin. As much as 90 percent of the annual runoff in the Colorado River Basin originates in areas upstream from Lake Powell (hereafter, these areas will be referred to collectively as the “Upper Basin”). Consequently, understanding the processes that can affect water supply in the Upper Basin could be crucial for supporting human, agricultural, and ecological needs across a large spatial scale.
The U.S. Geological Survey (USGS) does a wide variety of science in cooperation with resource managers, municipalities, tribes, and local, State, and Federal agencies to help improve understanding of processes, such as streamflow and water quality, potentially affecting water supply in the Upper Basin. This fact sheet describes three key potential factors affecting water supply in the Upper Basin—snow processes and water storage, wildfire and basin hydrology, and salinity concentrations and water quality—and highlights associated USGS research activities in the basin.
Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225-0046
The U.S. Geological Survey (USGS) cooperates with resource managers, municipalities, tribes, and local, State, and Federal agencies to help improve understanding of processes potentially affecting water supply in the Colorado River Basin. This fact sheet describes three key potential factors affecting water supply in the upper portion of the basin—snow processes and water storage, wildfire and basin hydrology, and salinity concentrations and water quality—and highlights associated USGS research activities in the basin. The Colorado River Basin is an important water source for more than 40 million people in the Western United States and Mexico, providing water to major cities and irrigating agricultural land. However, since 2000, the region has faced prolonged drought conditions, leading to record low levels in Lake Mead and Lake Powell and resulting in water usage restrictions. The USGS plays a key role in studying the Colorado River Basin water supply. Understanding the processes that can affect water supply in the upper portion of the basin could be crucial for supporting human, agricultural, and ecological needs across a large spatial scale.
","publicationDate":"2025-08-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Day, Natalie K. 0000-0002-8768-5705","orcid":"https://orcid.org/0000-0002-8768-5705","contributorId":207302,"corporation":false,"usgs":true,"family":"Day","given":"Natalie","middleInitial":"K.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":944726,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Williams, Cory A. 0000-0003-1461-7848 cawillia@usgs.gov","orcid":"https://orcid.org/0000-0003-1461-7848","contributorId":689,"corporation":false,"usgs":true,"family":"Williams","given":"Cory","email":"cawillia@usgs.gov","middleInitial":"A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":944727,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70269614,"text":"70269614 - 2025 - Using imaging spectroscopy and elevation in machine learning to estimate soil salinity in intermittently tidal wetlands","interactions":[],"lastModifiedDate":"2025-08-06T15:04:23.764097","indexId":"70269614","displayToPublicDate":"2025-08-05T09:57:40","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Using imaging spectroscopy and elevation in machine learning to estimate soil salinity in intermittently tidal wetlands","docAbstract":"Coastal soil salinization patterns are changing due to drought, sea level rise (SLR), and changing freshwater inflow. These changes are expected to impact coastal wetland plant health and ecosystem function, such as changes to biomass and productivity. These impacts have led to greater interest in how we monitor soil salinization across spatial and temporal scales. Remote sensing is a promising tool for estimating soil salinity at the spatial scales required for decision making by land managers. However, the development of a remote sensing estimation approach for wetland soil salinity must account for two factors: (1) the high spatial and temporal heterogeneity of coastal wetlands and (2) the fact that soil salinity is the result of multiple historical land use, hydrological, and geomorphic processes. In spring 2022, a combined airborne-field campaign, known as SHIFT, collected a weekly time series of airborne visible to shortwave infrared (VSWIR) image spectroscopy data. This dataset provides a unique opportunity to assess the application of fine spatial (5 m) and temporal (weekly) resolution VSWIR data to estimate root zone soil salinity; when combined with environmental variables such as elevation, these data can account for some of these factors. In this study, we utilized VSWIR and elevation datasets in a random forest regression to predict and map soil salinity in an intermittently tidal estuary, Devereux Slough, located in Santa Barbara County, California. The final model combined spectral indices with elevation to better capture soil salinity dynamics despite lower correlation (r = 0.85) than solely using elevation (r = 0.92). This research demonstrates the utility of remote sensing datasets, namely, elevation and the modified Anthocyanin Reflectance Index (mARI), for predicting root zone soil salinity in intermittently tidal coastal wetlands. These findings are an important step in advancing coastal remote sensing by creating a gridded salinity dataset that can be used for salinity monitoring and other coastal applications, such as modeling change in vegetation communities or ecosystems facing the impacts of climatic variability and change.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.70356","usgsCitation":"Silva, G., Roberts, D., Byrd, K.B., Chadwick, D., Walker, I., and King, J., 2025, Using imaging spectroscopy and elevation in machine learning to estimate soil salinity in intermittently tidal wetlands: Ecosphere, v. 16, no. 8, e70356, 22 p., https://doi.org/10.1002/ecs2.70356.","productDescription":"e70356, 22 p.","ipdsId":"IP-172039","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":494433,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.70356","text":"Publisher Index Page"},{"id":493643,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Santa Barbara County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.86741425943123,\n 34.4236991476653\n ],\n [\n -119.88462619707985,\n 34.4236991476653\n ],\n [\n -119.88462619707985,\n 34.406950669793815\n ],\n [\n -119.86741425943123,\n 34.406950669793815\n ],\n [\n -119.86741425943123,\n 34.4236991476653\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"8","noUsgsAuthors":false,"publicationDate":"2025-08-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Silva, German","contributorId":358801,"corporation":false,"usgs":false,"family":"Silva","given":"German","affiliations":[{"id":37180,"text":"UC Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":944179,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roberts, Dar","contributorId":358803,"corporation":false,"usgs":false,"family":"Roberts","given":"Dar","affiliations":[{"id":37180,"text":"UC Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":944180,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Byrd, Kristin B. 0000-0002-5725-7486 kbyrd@usgs.gov","orcid":"https://orcid.org/0000-0002-5725-7486","contributorId":3814,"corporation":false,"usgs":true,"family":"Byrd","given":"Kristin","email":"kbyrd@usgs.gov","middleInitial":"B.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":944181,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chadwick, Dana","contributorId":358806,"corporation":false,"usgs":false,"family":"Chadwick","given":"Dana","affiliations":[{"id":27923,"text":"NASA JPL","active":true,"usgs":false}],"preferred":false,"id":944182,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walker, Ian","contributorId":358809,"corporation":false,"usgs":false,"family":"Walker","given":"Ian","affiliations":[{"id":37180,"text":"UC Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":944183,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"King, Jennifer","contributorId":358812,"corporation":false,"usgs":false,"family":"King","given":"Jennifer","affiliations":[{"id":37180,"text":"UC Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":944184,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70270917,"text":"70270917 - 2025 - Launching into societal benefits from the Surface Water and Ocean Topography (SWOT) mission","interactions":[],"lastModifiedDate":"2025-08-27T15:25:38.303891","indexId":"70270917","displayToPublicDate":"2025-08-05T08:01:40","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Launching into societal benefits from the Surface Water and Ocean Topography (SWOT) mission","docAbstract":"The 10th Surface Water and Ocean Topography (SWOT) Applications Meeting, held one year after the satellite's launch, highlighted significant milestones in mission progress and showcased the innovative work of SWOT Early Adopters (EA) using mission data products. Over 100 participants from diverse sectors convened to discuss operational applications leveraging SWOT's unprecedented water surface measurements. The meeting emphasized applied science efforts to enhance hydrology and oceanographic models. This summary highlights the breadth of operational and private-sector uses of SWOT data, emphasizing its potential to drive new innovations and deliver societal benefits, such as improved water resource management, flood prediction, and climate resilience.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024WR038436","usgsCitation":"Srinivasan, M., Tsontos, V., Bonnema, M., Pena-Luque, S., de Amorim-Teixiera, A., Alexandre Abdalla Araujo, Beighley, E., Birkett, C., Chen, C., Croneborg-Jones, L., David, C., Desai, S., Dib, A., Doorn, B., Dudley, R., Fatima, B., Fenoglio, L., de Moraes Frasson, R., Gangodagamage, C., Granger, S., Houghton, I., Jacobs, G., Jayaluxmi, I., Le Traon, P., Nickles, C., Picot, N., Schumann, G., Tchonang, B., Torre Zaffaroni, P., Van Oevelen, P., Wang, J., and Wegiel, J., 2025, Launching into societal benefits from the Surface Water and Ocean Topography (SWOT) mission: Water Resources Research, v. 61, no. 8, e2024WR038436, 8 p., https://doi.org/10.1029/2024WR038436.","productDescription":"e2024WR038436, 8 p.","ipdsId":"IP-167186","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":495067,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024wr038436","text":"Publisher Index Page"},{"id":494949,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"61","issue":"8","noUsgsAuthors":false,"publicationDate":"2025-08-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Srinivasan, Margaret","contributorId":360642,"corporation":false,"usgs":false,"family":"Srinivasan","given":"Margaret","affiliations":[{"id":7023,"text":"Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":947350,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tsontos, 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Zaffaroni","given":"Paula","affiliations":[{"id":63280,"text":"Universidad de Buenos Aires","active":true,"usgs":false}],"preferred":false,"id":947378,"contributorType":{"id":1,"text":"Authors"},"rank":29},{"text":"Van Oevelen, Peter","contributorId":360670,"corporation":false,"usgs":false,"family":"Van Oevelen","given":"Peter","affiliations":[{"id":86080,"text":"GEWEX","active":true,"usgs":false}],"preferred":false,"id":947379,"contributorType":{"id":1,"text":"Authors"},"rank":30},{"text":"Wang, Jinbo","contributorId":360671,"corporation":false,"usgs":false,"family":"Wang","given":"Jinbo","affiliations":[{"id":7023,"text":"Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":947380,"contributorType":{"id":1,"text":"Authors"},"rank":31},{"text":"Wegiel, Jerry","contributorId":360672,"corporation":false,"usgs":false,"family":"Wegiel","given":"Jerry","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":947381,"contributorType":{"id":1,"text":"Authors"},"rank":32}]}} ,{"id":70269796,"text":"sir20255065 - 2025 - Analysis of summer water temperatures of the lower Virgin River near Mesquite, Nevada, 2019–21","interactions":[],"lastModifiedDate":"2026-02-03T14:42:44.427211","indexId":"sir20255065","displayToPublicDate":"2025-08-01T13:50:56","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-5065","displayTitle":"Analysis of Summer Water Temperatures of the Lower Virgin River Near Mesquite, Nevada, 2019–21","title":"Analysis of summer water temperatures of the lower Virgin River near Mesquite, Nevada, 2019–21","docAbstract":"The lower Virgin River is a sandy, shallow reach of the Virgin River that flows from northern Arizona to Lake Mead in Nevada. The Virgin River hosts several native fish species, including two endangered fish, woundfin (Plagopterus argentissimu) and Virgin River chub (Gila seminuda). All native fish species in the lower Virgin River have experienced reductions in population sizes in the last several decades. Reduced stream flow (especially during summer low-flow conditions) often results in increased water temperatures, which can increase mortality, reduce breeding, limit population connectivity, and favor non-native fish species. This study investigated summer water temperatures and flow in the lower Virgin River near Mesquite, Nev., between Littlefield, Ariz., and Bunkerville, Nev., to evaluate how hydrologic conditions could be affecting native fish species. The 3-year monitoring project involved collection of continuous temperature and discrete discharge measurements at 15 sites from 2019 to 2021 during the summer months from June to September. Results indicate that the lower Virgin River is often greater than 5 degrees Celsius (°C) above the established critical thermal maximum of 31 °C, that the cooling effect of the Littlefield springs dissipates quickly downstream, and that water temperature is affected primarily by atmospheric conditions. Discharge and water temperature are poorly related at normal stable flow conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255065","collaboration":"Prepared in cooperation with the Bureau of Land Management and Nevada Department of Wildlife","usgsCitation":"Earp, K.J., 2025, Analysis of summer water temperatures of the lower Virgin River near Mesquite, Nevada, 2019–21: U.S. Geological Survey Scientific Investigations Report 2025–5065, 23 p., https://doi.org/10.3133/sir20255065.","productDescription":"viii, 23 p.","onlineOnly":"Y","ipdsId":"IP-104326","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":493355,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5065/sir20255065.XML"},{"id":493352,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5065/sir20255065.pdf","text":"Report","size":"7.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5065"},{"id":493351,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5065/coverthb.jpg"},{"id":493354,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5065/images"},{"id":493353,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255065/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5065"}],"country":"United States","state":"Arizona, Nevada","city":"Mesquite","otherGeospatial":"lower Virgin River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -113.89637038515828,\n 36.91617741482898\n ],\n [\n -114.22669808506164,\n 36.80414574994599\n ],\n [\n -114.25778641886733,\n 36.70566397893374\n ],\n [\n -113.95393733380085,\n 36.75799180706565\n ],\n [\n -113.89637038515828,\n 36.91617741482898\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road, Suite 3
Carson City, Nevada 89701
Models to estimate low-streamflow statistics at ungaged locations in New York, excluding Long Island and including hydrologically connected basins from bordering States, were developed for the first time by the U.S. Geological Survey, in cooperation with the New York State Department of Environmental Conservation. A total of 224 basin characteristics were developed for 213 unaltered streamgages (locations where the human effects on streamflow were limited), across the following categories: basin geometry, climate, land cover, soils, surficial geology, and other characteristics. The basins with unaltered streamgages were evaluated for potential redundancy, and streamgages in close proximity and with similar drainage areas were flagged and removed from the testing and cross-validation datasets to prevent data leaking from the training dataset to the testing dataset.
Random forest regression models were created by using basin characteristics as predictor variables and by developing a workflow to train, tune, and test the model. Models were developed to estimate the ungaged lowest annual 7-day and 30-day average streamflow that occurs (on average) once every 10 years (7Q10 and 30Q10). The top four basin characteristics used for the 7Q10 and 30Q10 models were drainage area, total stream length, perimeter of the basin, and length of the longest flow path. Results for the 7Q10 and 30Q10 models had coefficients of determination (R2) of 0.796 and 0.853, respectively. The output model results were bias-corrected for ungaged locations across New York and are available within the interactive StreamStats tool.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255060","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Stagnitta, T.J., Woda, J.C., and Graziano, A.P., 2025, Random forest regression models for estimating low-streamflow statistics at ungaged locations in New York, excluding Long Island: U.S. Geological Survey Scientific Investigations Report 2025–5060, 23 p., https://doi.org/10.3133/sir20255060.","productDescription":"Report: v, 23 p.; 2 Data Releases","numberOfPages":"23","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-167540","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":492987,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P146MTRS","text":"USGS data release","linkHelpText":"Random forest regression model archive for estimating low-streamflow statistics at ungaged locations in New York, excluding Long Island"},{"id":492986,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NOM6FR","text":"USGS data release","linkHelpText":"Low-flow statistics for New York State, excluding Long Island, computed through March 2022"},{"id":492985,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5060/images/"},{"id":492988,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20245055","text":"Scientific Investigations Report 2024–5055","linkHelpText":"- Low-Flow Statistics for Selected Streams in New York, Excluding Long Island"},{"id":492984,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5060/sir20255060.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2025-5060 XML"},{"id":492983,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255060/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5060 HTML"},{"id":492981,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5060/coverthb.jpg"},{"id":492982,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5060/sir20255060.pdf","text":"Report","size":"8.99 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5060 PDF"},{"id":492989,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://streamstats.usgs.gov/ss/","text":"StreamStats"}],"country":"United States","state":"New York","otherGeospatial":"New York excluding Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.8668360740237,\n 40.82174116460561\n ],\n [\n -73.64101370770591,\n 40.97341618058218\n ],\n [\n -73.641675229166,\n 41.369110620239354\n ],\n [\n -73.44442715633755,\n 41.42737341824312\n ],\n [\n -73.23400688811246,\n 42.735562128301694\n ],\n [\n -73.28689303884832,\n 45.063167069767246\n ],\n [\n -74.92263898531354,\n 45.049241271408505\n ],\n [\n -76.59790269295956,\n 44.152600935032865\n ],\n [\n -76.27472597825448,\n 43.63979148650773\n ],\n [\n -76.9594903941915,\n 43.33322253590592\n ],\n [\n -78.50182350844328,\n 43.459456447836146\n ],\n [\n -79.39789785747713,\n 43.19859290777666\n ],\n [\n -78.90328998597391,\n 42.86522759603616\n ],\n [\n -79.82520290314585,\n 42.24436339597355\n ],\n [\n -79.76467975802953,\n 41.964093469327736\n ],\n [\n -75.37056382699097,\n 42.00382656329654\n ],\n [\n -74.98586389700988,\n 41.54435731278656\n ],\n [\n -73.999903714982,\n 41.001270702844295\n ],\n [\n -73.96336292917533,\n 40.82357491793678\n ],\n [\n -73.8668360740237,\n 40.82174116460561\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Impervious cover (IC) is a common metric for assessing the degree of urbanisation in watersheds. However, there are different methods for determining IC, and use of IC correlation with urban watershed response to hydrologic and geochemical inputs can be strongly influenced by the end members (IC below 10% and above 40%). The resolution of the imagery (e.g., 1 m vs. 30 m) used to measure IC can influence the estimate of IC, with differences up to 15% observed between these two resolutions for 21 watersheds along the east coast of the United States. The differences are greatest in the middle range between 10% and 40% IC. When using IC for correlation with urban watershed responses such as discharge flashiness or median solute concentrations, fits with R2 between 0.4 and 0.78 were obtained when including end members of IC from 0% to 50%. However, when trying to distinguish behaviour between urban watersheds that fall in the middle ranges of IC, these same parameters do not correlate well with IC. Correlations fail significance tests, can switch direction, and fall below an R2 of 0.1 without the end members of very low or very high IC. Because of improved accuracy, the finest resolution is preferred when available, and mixing IC estimation methods should be avoided. Furthermore, using regressions that include end members may not contribute to differentiating how IC in the 10%–40% range impacts hydrologic and geochemical responses in urban watersheds. Understanding this middle range of IC is important for comparing urban and suburban watersheds or planning watershed development to minimise impacts.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.70219","usgsCitation":"Toran, L., Bain, D., Hopkins, K.G., Moore, J., and O'Donnell, E., 2025, Evaluating trends using total impervious cover as a metric for degree of urbanisation: Hydrological Processes, v. 39, no. 8, e70219, 9 p., https://doi.org/10.1002/hyp.70219.","productDescription":"e70219, 9 p.","ipdsId":"IP-173375","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":493566,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, Georgia, Maryland, New Jersy, New York, North Carolina, Pennsylvania, South Carolina, Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.45193313117295,\n 42.0101128534981\n ],\n [\n -85.58729060621017,\n 34.888304090891616\n ],\n [\n -84.9939132978826,\n 30.977848324458122\n ],\n [\n -81.00584486613695,\n 30.56241422580763\n ],\n [\n -75.17472217306889,\n 35.436628690715224\n ],\n [\n -72.87437915762574,\n 41.18374638044904\n ],\n [\n -76.45193313117295,\n 42.0101128534981\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"39","issue":"8","noUsgsAuthors":false,"publicationDate":"2025-08-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Toran, Laura","contributorId":81622,"corporation":false,"usgs":false,"family":"Toran","given":"Laura","email":"","affiliations":[{"id":34225,"text":"Temple University, Philadelphia, Pa.","active":true,"usgs":false}],"preferred":false,"id":944750,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bain, Daniel","contributorId":359003,"corporation":false,"usgs":false,"family":"Bain","given":"Daniel","affiliations":[{"id":12465,"text":"University of Pittsburgh","active":true,"usgs":false}],"preferred":false,"id":944751,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hopkins, Kristina G. 0000-0003-1699-9384 khopkins@usgs.gov","orcid":"https://orcid.org/0000-0003-1699-9384","contributorId":195604,"corporation":false,"usgs":true,"family":"Hopkins","given":"Kristina","email":"khopkins@usgs.gov","middleInitial":"G.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":944752,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Moore, Joel","contributorId":49034,"corporation":false,"usgs":false,"family":"Moore","given":"Joel","affiliations":[],"preferred":false,"id":944753,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"O'Donnell, Emily May 0000-0002-3202-159X","orcid":"https://orcid.org/0000-0002-3202-159X","contributorId":359005,"corporation":false,"usgs":true,"family":"O'Donnell","given":"Emily May","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":944754,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70269722,"text":"ofr20251035 - 2025 - Decision-support modeling and research priorities for establishing baseline conditions for outstandingly remarkable values, Obed Wild and Scenic River, Tennessee","interactions":[],"lastModifiedDate":"2026-02-03T14:41:11.637733","indexId":"ofr20251035","displayToPublicDate":"2025-08-01T07:31:50","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-1035","displayTitle":"Decision-Support Modeling and Research Priorities for Establishing Baseline Conditions for Outstandingly Remarkable Values, Obed Wild and Scenic River, Tennessee","title":"Decision-support modeling and research priorities for establishing baseline conditions for outstandingly remarkable values, Obed Wild and Scenic River, Tennessee","docAbstract":"The Obed River is the last undammed river in Tennessee. The Obed Wild and Scenic River is managed by the National Park Service and covers a protected area of the Obed River headwaters (including four contributing tributaries). The Obed Wild and Scenic River supports a unique ecosystem with eight federally listed species. The National Park Service is responsible for preserving the baseline free-flowing condition of the river and associated outstandingly remarkable values (ORVs). Previous studies have been mostly project-based with differing methods, thus complicating efforts to quantify long-term changes in environmental conditions. This report presents a science plan summarizing (1) ORV conditions, (2) recent results of a decision-support hydrologic model for OBRI, and (3) possible future research priorities. The decision-support model was created to model streamflow conditions and changes in the ORVs since park establishment in 1976 and during three additional time periods. Established baseline conditions could help with management of ORVs not dependent on streamflow.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251035","issn":"2331-1258","collaboration":"Prepared in cooperation with the National Park Service","programNote":"Water Availability and Use Science Program","usgsCitation":"Crowley-Ornelas, E.R., Schapansky, R., Blount, T., and Nicholas, N.S., 2025, Decision-support modeling and research priorities for establishing baseline conditions for outstandingly remarkable values, Obed Wild and Scenic River, Tennessee: U.S. Geological Survey Open-File Report 2025–1035, 18 p., https://doi.org/10.3133/ofr20251035.","productDescription":"viii, 18 p.","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-160489","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":493199,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251035/full","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1035 HTML"},{"id":493198,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1035/ofr20251035.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2025-1035 XML"},{"id":493197,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1035/ofr20251035.pdf","size":"1.35 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1035"},{"id":493200,"rank":2,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1035/images"},{"id":493196,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1035/coverthb.jpg"}],"country":"United States","state":"Tennessee","otherGeospatial":"Obed Wild and Scenic River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -84.95125744767695,\n 36.150994941624745\n ],\n [\n -84.95125744767695,\n 36.049079144332424\n ],\n [\n -84.64800767968804,\n 36.049079144332424\n ],\n [\n -84.64800767968804,\n 36.150994941624745\n ],\n [\n -84.95125744767695,\n 36.150994941624745\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Contact Us- USGS Publications Warehouse
","tableOfContents":"Population growth and recurring droughts in the Black Hills region raised interest in water resources and future availability. The Black Hills hydrology study (BHHS) was initiated in the early 1990s to address questions regarding water resources. Since completion of the BHHS in the early 2000s, the population of the Black Hills region increased by about 39 percent, which has renewed interest in water demand and availability in the Black Hills. The U.S. Geological Survey, in cooperation with the Western Dakota Regional Water System, completed a study to update hydrologic budgets from the BHHS for six of the most used aquifers in the Black Hills. Water availability was determined by comparing results from hydrologic budgets to modern well withdrawals (2003–22) and water rights information. Key updates to the BHHS budgets included adding available data from 1999 to 2022 and determining hydrologic budgets for six aquifers in nine smaller areas (called “subareas”).
Inflows for the hydrologic budget included recharge from precipitation and streamflow losses to aquifers. Total mean annual recharge for the six aquifers in the study area was estimated at 278,900 acre-feet, with 205,100 acre-feet from precipitation recharge and 73,800 acre-feet from streamflow recharge. Mean annual precipitation recharge for the Madison and Minnelusa aquifers together accounted for 76 percent of the total mean annual precipitation recharge, with the Madison aquifer contributing 57,000 acre-feet and the Minnelusa aquifer contributing 98,100 acre-feet. Outflow components estimated for the hydrologic budget include artesian springflow and well withdrawals. Total mean annual artesian springflow in the study area was estimated as 166,100 acre-feet for the combined Madison and Minnelusa aquifers. Mean total annual well withdrawals for 2003–22 in the study area were about 50,000 acre-feet. No increased well withdrawal patterns corresponding to population increases were observed between 2003 and 2022.
Water availability was determined by comparing total annual appropriations and mean and maximum annual well withdrawals for 2003–22 to mean annual recharge for 1931–2022 for each aquifer in subareas 1–9. Modern well withdrawals (mean and maximum for 2003–22) exceeded mean annual recharge for only the Deadwood and Inyan Kara aquifers in subareas 9 and 4, respectively. Additionally, total annual appropriations did not exceed mean annual recharge in most subareas, except most notably in subarea 4 (Rapid City area) where appropriations exceeded recharge for the Madison, Minnelusa, and Inyan Kara aquifers. Total annual appropriations also exceeded mean annual recharge for the Inyan Kara aquifer in subareas 3 and 5. In addition to recharge, water availability includes the water stored in pore spaces of aquifer materials. Estimates of total volume of recoverable water in storage were updated as part of this study to include the portion of aquifers in Wyoming, which were omitted during the BHHS. In total, the estimated total amount of recoverable water in storage in the study area was 356.9 million acre-feet for six major aquifers in the Black Hills area of South Dakota and Wyoming.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255067","collaboration":"Prepared in cooperation with the Western Dakota Regional Water System","usgsCitation":"Medler, C.J., Anderson, T.M., and Eldridge, W.G., 2025, Hydrologic budgets and water availability of six bedrock aquifers in the Black Hills area, South Dakota and Wyoming, 1931–2022: U.S. Geological Survey Scientific Investigations Report 2025–5067, 87 p., https://doi.org/10.3133/sir20255067.","productDescription":"Report: ix, 87 p.; Data Release","numberOfPages":"102","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-169475","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":493206,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1QWKUKP","text":"USGS data release","linkHelpText":"Datasets used in constructing hydrologic budgets for six bedrock aquifers in the Black Hills area of South Dakota and Wyoming, 1931–2022"},{"id":493201,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5067/coverthb.jpg"},{"id":493202,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5067/sir20255067.pdf","text":"Report","size":"27 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Sir 2025–5067"},{"id":493203,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5067/sir20255067.XML"},{"id":493204,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5067/images/"},{"id":493205,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255067/full"}],"country":"United States","state":"South Dakota, Wyoming","otherGeospatial":"Black Hills area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -104.5,\n 44.75\n ],\n [\n -104.5,\n 43.25\n ],\n [\n -103,\n 43.25\n ],\n [\n -103,\n 44.75\n ],\n [\n -104.5,\n 44.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
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Flood-frequency analysis provides the basis for flood risk estimates used by water-resource managers in land-use planning, and it informs the design of essential infrastructure such as bridges and culverts. Federal guidelines for flood-frequency analysis do not offer guidance on addressing changing climate and land-use conditions when estimating floods. However, failing to consider climatic and land-use changes that cause abrupt or gradual changes in flood regimes can result in a poor representation of the true flood risk.
In response to concerns about changing flood regimes, the U.S. Geological Survey, in cooperation with nine State agencies (Illinois Department of Transportation, Iowa Department of Transportation, Michigan Department of Transportation, Minnesota Department of Transportation, Missouri Department of Transportation, Montana Department of Natural Resources and Conservation, North Dakota Department of Water Resources, South Dakota Department of Transportation, and Wisconsin Department of Transportation) began a study to examine variability and change in hydrology and climate and the effects of urbanization and tile drainage on flooding. The analyses of patterns and changes in hydrology and climate were reported in a multichapter Scientific Investigations Report, the findings of which are summarized in this U.S. Geological Survey Circular. Additional analyses documenting changes in seasonality of flooding and the effects of urbanization and tile drainage were completed and published as separate studies and are also summarized in this Circular. These studies provide extensive exploratory analysis of peak streamflow, daily streamflow, and climate data, setting the stage for advancements in flood-frequency analysis.
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U.S. Geological Survey
821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702
In response to concerns about changing flood regimes, the U.S. Geological Survey, in cooperation with nine State agencies, began a study to examine variability and change in hydrology and climate and the effects of urbanization and tile drainage on flooding. The findings of that study are briefly summarized in this report.
","publicationDate":"2025-07-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Ryberg, Karen R. 0000-0002-9834-2046 kryberg@usgs.gov","orcid":"https://orcid.org/0000-0002-9834-2046","contributorId":1172,"corporation":false,"usgs":true,"family":"Ryberg","given":"Karen","email":"kryberg@usgs.gov","middleInitial":"R.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":942793,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Marti, Mackenzie K. 0000-0001-8817-4969 mmarti@usgs.gov","orcid":"https://orcid.org/0000-0001-8817-4969","contributorId":289738,"corporation":false,"usgs":true,"family":"Marti","given":"Mackenzie","email":"mmarti@usgs.gov","middleInitial":"K.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":942794,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barth, Nancy A. 0000-0002-7060-8244 nabarth@usgs.gov","orcid":"https://orcid.org/0000-0002-7060-8244","contributorId":298020,"corporation":false,"usgs":true,"family":"Barth","given":"Nancy","email":"nabarth@usgs.gov","middleInitial":"A.","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":true,"id":942795,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Over, Thomas M. 0000-0001-8280-4368","orcid":"https://orcid.org/0000-0001-8280-4368","contributorId":204650,"corporation":false,"usgs":true,"family":"Over","given":"Thomas","email":"","middleInitial":"M.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"preferred":true,"id":942796,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Levin, Sara B. 0000-0002-2448-3129","orcid":"https://orcid.org/0000-0002-2448-3129","contributorId":209947,"corporation":false,"usgs":true,"family":"Levin","given":"Sara B.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":942797,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Podzorski, Hannah Lee 0000-0001-5204-2606 hpodzorski@usgs.gov","orcid":"https://orcid.org/0000-0001-5204-2606","contributorId":333626,"corporation":false,"usgs":true,"family":"Podzorski","given":"Hannah","email":"hpodzorski@usgs.gov","middleInitial":"Lee","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":942798,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sando, Steven K. 0000-0003-1206-1030","orcid":"https://orcid.org/0000-0003-1206-1030","contributorId":203451,"corporation":false,"usgs":true,"family":"Sando","given":"Steven","email":"","middleInitial":"K.","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":true,"id":942799,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Williams-Sether, Tara 0000-0001-6515-9416","orcid":"https://orcid.org/0000-0001-6515-9416","contributorId":214143,"corporation":false,"usgs":true,"family":"Williams-Sether","given":"Tara","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":942800,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"O’Shea, Padraic S. 0000-0001-9005-8289 poshea@usgs.gov","orcid":"https://orcid.org/0000-0001-9005-8289","contributorId":196742,"corporation":false,"usgs":true,"family":"O’Shea","given":"Padraic","email":"poshea@usgs.gov","middleInitial":"S.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"preferred":true,"id":942801,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Chase, Katherine J. 0000-0002-5796-4148 kchase@usgs.gov","orcid":"https://orcid.org/0000-0002-5796-4148","contributorId":454,"corporation":false,"usgs":true,"family":"Chase","given":"Katherine","email":"kchase@usgs.gov","middleInitial":"J.","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":true,"id":942802,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70270164,"text":"70270164 - 2025 - Land-based nutrient flux to a fringing reef: Insights from Ofu Island, American Samoa","interactions":[],"lastModifiedDate":"2025-08-14T13:15:06.534259","indexId":"70270164","displayToPublicDate":"2025-07-30T08:07:07","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3912,"text":"Frontiers in Marine Science","onlineIssn":"2296-7745","active":true,"publicationSubtype":{"id":10}},"title":"Land-based nutrient flux to a fringing reef: Insights from Ofu Island, American Samoa","docAbstract":"Submarine groundwater discharge (SGD) is a critical driver of nutrient transport in coral reef ecosystems, shaping water quality, primary productivity, and overall reef health. This study quantifies SGD fluxes and associated nutrient dynamics in two reef flat pools within the Ofu Unit of the National Park of American Samoa: Papaloloa and Fatuana. A multi-method approach integrating unoccupied aerial system-based thermal infrared (UAS-TIR) surveys, radon-based SGD measurements, multichannel electrical resistivity tomography (ERT), and discrete water sampling was used to assess SGD rates and nutrient contributions. UAS-TIR imagery revealed cooler sea surface temperatures in both pools, indicative of SGD, with the higher fluxes observed in Papaloloa. Radon measurements revealed a strong inverse correlation between SGD rates and tidal stage, with a more immediate SGD response at Papaloloa due to its highly permeable calcareous sand and gravel substrate. In contrast, a 2–3-hour lag in SGD response at Fatuana suggests discharge from a more inland aquifer that has lower diffusivity. Nutrient concentrations correlated with temperature and salinity, confirming SGD as the dominant nutrient transport mechanism, whereas isotopic analyses indicated inputs from both groundwater and potential anthropogenic sources. Despite lower SGD flux at Fatuana, higher algal cover suggests additional factors influencing algal proliferation, including substrate availability and hydrodynamic conditions. Excess nutrient inputs from SGD may contribute to algal overgrowth, which threatens Ofu’s thermally tolerant corals by increasing competition for space and light. These findings underscore the complexity of SGD-mediated nutrient dynamics in reef environments and emphasize the need for integrated hydrological and ecological assessments to support effective reef conservation and management strategies. \n ","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmars.2025.1600827","usgsCitation":"Prouty, N.G., Oberle, F.K., Cheriton, O.M., Toth, L., Brown, E., and Storlazzi, C.D., 2025, Land-based nutrient flux to a fringing reef: Insights from Ofu Island, American Samoa: Frontiers in Marine Science, v. 12, 1600827, 15 p., https://doi.org/10.3389/fmars.2025.1600827.","productDescription":"1600827, 15 p.","ipdsId":"IP-176993","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":494196,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2025.1600827","text":"Publisher Index Page"},{"id":493956,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"American Samoa, Ofu Island, Olosega Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -169.70032612583023,\n -14.146100325769837\n ],\n [\n -169.70032612583023,\n -14.203250623210224\n ],\n [\n -169.59031247739568,\n -14.203250623210224\n ],\n [\n -169.59031247739568,\n -14.146100325769837\n ],\n [\n -169.70032612583023,\n -14.146100325769837\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2025-07-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Prouty, Nancy G. 0000-0002-8922-0688 nprouty@usgs.gov","orcid":"https://orcid.org/0000-0002-8922-0688","contributorId":215720,"corporation":false,"usgs":true,"family":"Prouty","given":"Nancy","email":"nprouty@usgs.gov","middleInitial":"G.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":945599,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Oberle, Ferdinand K.J. 0000-0001-8871-3619","orcid":"https://orcid.org/0000-0001-8871-3619","contributorId":214402,"corporation":false,"usgs":true,"family":"Oberle","given":"Ferdinand","middleInitial":"K.J.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":945600,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cheriton, Olivia M. 0000-0003-3011-9136","orcid":"https://orcid.org/0000-0003-3011-9136","contributorId":204459,"corporation":false,"usgs":true,"family":"Cheriton","given":"Olivia","middleInitial":"M.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":945601,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Toth, Lauren T. 0000-0002-2568-802X ltoth@usgs.gov","orcid":"https://orcid.org/0000-0002-2568-802X","contributorId":181748,"corporation":false,"usgs":true,"family":"Toth","given":"Lauren","email":"ltoth@usgs.gov","middleInitial":"T.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":945602,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brown, Eric K.","contributorId":359481,"corporation":false,"usgs":false,"family":"Brown","given":"Eric K.","affiliations":[{"id":85828,"text":"NPS American Samoa","active":true,"usgs":false}],"preferred":false,"id":945603,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Storlazzi, Curt D. 0000-0001-8057-4490","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":213610,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","middleInitial":"D.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":945604,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70275146,"text":"70275146 - 2025 - Characterizing the niche of Phalaris arundinacea (reed canarygrass) in floodplain forests of the Upper Mississippi River","interactions":[],"lastModifiedDate":"2026-04-20T15:35:48.147791","indexId":"70275146","displayToPublicDate":"2025-07-29T00:00:00","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Characterizing the niche of Phalaris arundinacea (reed canarygrass) in floodplain forests of the Upper Mississippi River","title":"Characterizing the niche of Phalaris arundinacea (reed canarygrass) in floodplain forests of the Upper Mississippi River","docAbstract":"Information on the favorable conditions for invasive species as well as potential constraints to their distribution can be valuable for management efforts. We used a niche modeling approach to analyze the patterns of species distributions along gradients of hypothesized influential environmental variables. Many ecological datasets may have incomplete coverage across the environmental gradients, infrequent sampling under some conditions, insufficient time for an invasive species to occupy all sites, and complex interactions among environmental variables (measured or unmeasured) that may result in species response curves that are difficult to interpret and may be ecologically misleading. To ensure the model and species response curves aligned with ecological niche theory, shape constraints were imposed to guarantee relationships follow a unimodal distribution to reflect the fundamental niche (where a species could occur). We compared a shape-constrained model to an unconstrained model and interpreted the species response curves from the constrained model to better characterize the ecological niche of reed canarygrass in floodplain forests of the Upper Mississippi River, USA. We found the probability of reed canarygrass occurrence decreases with increasing tree canopy cover, tree species richness, distance from forest edge, distance from invaded wet meadows, and island isolation. Probability of reed canarygrass presence exhibited bell-shaped curves in response to hydrology (inundation depth, frequency, and duration) and forest stress metrics indicating an optimum with less favorable conditions on either end of the ecological gradients. This information could be used to prioritize restoration efforts and enhance landcover change research in forested floodplains.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s13157-025-01956-2","usgsCitation":"Delaney, J.T., Van Appledorn, M., De Jager, N.R., Bouska, K.L., and Rohweder, J.J., 2025, Characterizing the niche of Phalaris arundinacea (reed canarygrass) in floodplain forests of the Upper Mississippi River: Wetlands, v. 45, 86, 12 p., https://doi.org/10.1007/s13157-025-01956-2.","productDescription":"86, 12 p.","ipdsId":"IP-164703","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":504066,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1NPO8BT","text":"USGS data release","linkHelpText":"Analysis data for reed canarygrass (Phalaris arundinacea) niche modeling in Upper Mississippi River floodplain forest understories"},{"id":503213,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"upper Mississippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.44020500286925,\n 45.20810051838578\n ],\n [\n -91.09888752607472,\n 41.81975291615561\n ],\n [\n -91.79588050539074,\n 40.07343528894683\n ],\n [\n -90.14935731160756,\n 36.647803520755886\n ],\n [\n -88.93474584520565,\n 37.11378048828301\n ],\n [\n -90.74845311041965,\n 39.84831202944948\n ],\n [\n -89.84649625728352,\n 41.9195697474386\n ],\n [\n -92.01609711969336,\n 45.28516191702806\n ],\n [\n -93.44020500286925,\n 45.20810051838578\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"45","noUsgsAuthors":false,"publicationDate":"2025-07-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Delaney, John T. 0000-0003-1038-0265","orcid":"https://orcid.org/0000-0003-1038-0265","contributorId":255630,"corporation":false,"usgs":true,"family":"Delaney","given":"John","middleInitial":"T.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":959651,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Van Appledorn, Molly 0000-0002-8029-0014","orcid":"https://orcid.org/0000-0002-8029-0014","contributorId":205785,"corporation":false,"usgs":true,"family":"Van Appledorn","given":"Molly","email":"","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":959652,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"De Jager, Nathan R. 0000-0002-6649-4125 ndejager@usgs.gov","orcid":"https://orcid.org/0000-0002-6649-4125","contributorId":3717,"corporation":false,"usgs":true,"family":"De Jager","given":"Nathan","email":"ndejager@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":959653,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bouska, Kristen L. 0000-0002-4115-2313 kbouska@usgs.gov","orcid":"https://orcid.org/0000-0002-4115-2313","contributorId":178005,"corporation":false,"usgs":true,"family":"Bouska","given":"Kristen","email":"kbouska@usgs.gov","middleInitial":"L.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":959654,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rohweder, Jason J. 0000-0001-5131-9773 jrohweder@usgs.gov","orcid":"https://orcid.org/0000-0001-5131-9773","contributorId":150539,"corporation":false,"usgs":true,"family":"Rohweder","given":"Jason","email":"jrohweder@usgs.gov","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":959655,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70269637,"text":"70269637 - 2025 - Wet meadow regeneration through restoration of biophysical feedbacks","interactions":[],"lastModifiedDate":"2025-07-29T15:05:50.007385","indexId":"70269637","displayToPublicDate":"2025-07-27T08:01:23","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5738,"text":"Frontiers in Environmental Science","active":true,"publicationSubtype":{"id":10}},"title":"Wet meadow regeneration through restoration of biophysical feedbacks","docAbstract":"Wet meadows are globally significant ecosystems that provide critical hydrological, ecological, and biogeochemical functions, yet their extent has declined dramatically due to land use changes and hydrologic alteration. These sedge-dominated wetlands exist at the drier end of the wetland gradient, maintained by shallow groundwater and periodic inundation. This paper is a global synthesis of the ecological, geomorphic, and hydrological dynamics of wet meadows, with an emphasis on alluvial systems, to inform effective restoration strategies. We compare wet meadows to other wetlands, classify them into palustrine, lacustrine, and alluvial types, then focus on alluvial wet meadows and discuss how their formation and persistence depend on ground and surface water interactions, sediment deposition and flow obstructions, all mediated by biological processes. In particular, we highlight the role of hydric graminoids in resisting erosion and maintaining soil cohesion, how beaver promote meadow persistence, and the significance of wet meadows as carbon sinks. We also present stratigraphic evidence demonstrating that incision, often triggered by anthropogenic activity or changing climate, is the primary mechanism of alluvial wet meadow degradation, resulting in water table decline and shifts in vegetation composition. Restoration requires reversing these incisional processes through techniques that elevate water tables, disperse flow and retain sediment—methods traditionally associated with either soil conservation or stream restoration. These include nature-based solutions that create obstructions such as beaver dams and their analogues, rock and wood-based obstructions and incision trench or gully filling and grading. Given their multifunctional value—including but not limited to flood attenuation, biodiversity support, and carbon sequestration—wet meadows warrant a focused restoration framework. This review advocates for a valley-floor scale restoration paradigm that integrates hydrological reconnection, sediment retention, and biological reinforcement to ensure long-term resilience of these systems in the face of changing climate and land use pressures.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fenvs.2025.1592036","usgsCitation":"Pollock, M., and Norman, L., 2025, Wet meadow regeneration through restoration of biophysical feedbacks: Frontiers in Environmental Science, v. 13, 1592036, 21 p., https://doi.org/10.3389/fenvs.2025.1592036.","productDescription":"1592036, 21 p.","ipdsId":"IP-172248","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":493323,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fenvs.2025.1592036","text":"Publisher Index Page"},{"id":493104,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","noUsgsAuthors":false,"publicationDate":"2025-07-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Pollock, Michael","contributorId":358835,"corporation":false,"usgs":false,"family":"Pollock","given":"Michael","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":944245,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Norman, Laura M. 0000-0002-3696-8406","orcid":"https://orcid.org/0000-0002-3696-8406","contributorId":203300,"corporation":false,"usgs":true,"family":"Norman","given":"Laura M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":944246,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70269656,"text":"70269656 - 2025 - A spatial analysis of the groundwater emergence flood hazard in Long Island, New York and near coastal areas surrounding Long Island Sound in New York, Connecticut, and Rhode Island","interactions":[],"lastModifiedDate":"2025-08-01T14:53:14.313529","indexId":"70269656","displayToPublicDate":"2025-07-25T09:42:32","publicationYear":"2025","noYear":false,"publicationType":{"id":27,"text":"Preprint"},"publicationSubtype":{"id":32,"text":"Preprint"},"seriesTitle":{"id":18346,"text":"EarthArXiv","active":true,"publicationSubtype":{"id":32}},"title":"A spatial analysis of the groundwater emergence flood hazard in Long Island, New York and near coastal areas surrounding Long Island Sound in New York, Connecticut, and Rhode Island","docAbstract":"Long Island, New York and near coastal areas surrounding Long Island Sound are densely populated and, like other coastal areas, are susceptible to flooding from several potential sources, including stormwater from precipitation events, tidal flooding and storm surge, and groundwater inundation or groundwater emergence flooding. The latter refers to the intersection of a rising water table with land surface or critical infrastructure. Many studies of flood drivers either neglect or only briefly discuss how shallow groundwater conditions may contribute to or exacerbate flood conditions. As part of a comprehensive study of compound flood hazards in the near coastal areas surrounding Long Island and Long Island Sound, a spatial analysis was completed, in cooperation with the Environmental Protection Agency’s Long Island Sound Study, using available regional datasets to characterize the potential hazard for groundwater emergence flooding.
The approximately 3,100 square mile study area was subdivided into 11,407 900-meter by 900-meter (approximately 3,000-feet by 3,000-feet) grid cells, for the purposes of integrating the spatial datasets to calculate and map the groundwater emergence flood hazard. The depth to the water table, hydrologic soil groups, and National Land Cover Database were harmonized to the common grid. A groundwater emergence flood hazard rank was calculated for each grid cell for current average conditions following a set of rules accounting for the depth to the water table and the percent of area within each cell with slow infiltrating soils. A higher sea level position scenario was also calculated for the Long Island part of the study area. The calculated groundwater emergence flood hazard rank was reviewed in concert with the National Land Cover Data Base to identify developed areas and associated infrastructure that may be at risk to groundwater emergence flooding.
Study results indicate that the groundwater emergence flood hazard is highest in coastal areas and near surface water where the water table is close to ground surface. Inland areas away from surface water bodies are not likely to be exposed to groundwater emergence flooding. For Long Island, under a scenario with higher sea level position, a greater groundwater emergence flood hazard is calculated in some locations closer to the coast and where land is submerged. Away from the coast and surface-water drainage, the groundwater emergence flood hazard is similar between the current average sea level condition and a higher sea level position scenario.
We review how ‘abrupt thaw’ has been used in published studies, compare these definitions to abrupt processes in other Earth science disciplines, and provide a definitive framework for how abrupt thaw should be used in the context of permafrost science.
We address several aspects of permafrost systems necessary for abrupt thaw to occur and propose a framework for classifying permafrost processes as abrupt thaw in the future. Based on a literature review and our collective expertise, we propose that abrupt thaw refers to thaw processes that lead to a substantial persistent environmental change within a few decades. Abrupt thaw typically occurs in ice-rich permafrost but may be initiated in ice-poor permafrost by external factors such as hydrologic change (i.e., increased streamflow, soil moisture fluctuations, altered groundwater recharge) or wildfire.
Permafrost thaw alters greenhouse gas emissions, soil and vegetation properties, and hydrologic flow, threatening infrastructure and the cultures and livelihoods of northern communities. The term ‘abrupt thaw’ has emerged in scientific discourse over the past two decades to differentiate processes that rapidly impact large depths of permafrost, such as thermokarst, from more gradual, top-down thaw processes that impact centimeters of near-surface permafrost over years to decades. However, there has been no formal definition for abrupt thaw and its use in the scientific literature has varied considerably. Our standardized definition of abrupt thaw offers a path forward to better understand drivers and patterns of abrupt thaw and its consequences for global greenhouse gas budgets, impacts to infrastructure and land-use, and Arctic policy- and decision-making.
","language":"English","publisher":"Springer","doi":"10.1007/s40641-025-00204-3","usgsCitation":"Webb, H., Fuchs, M., Abbott, B.W., Douglas, T.A., Elder, C.D., Ernakovich, J.G., Euskirchen, E., Göckede, M., Grosse, G., Hugelius, G., Jones, M.C., Koven, C., Kropp, H., Lathrop, E., Li, W., Loranty, M.M., Natali, S.M., Olefeldt, D., Christina Schädel, Schuur, E.A., Sonnentag, O., Strauss, J., Virkkala, A., and Merritt R. Turetsky, 2025, A review of abrupt permafrost thaw: Definitions, usage, and a proposed conceptual framework: Current Climate Change Reports, v. 11, 7, 15 p., https://doi.org/10.1007/s40641-025-00204-3.","productDescription":"7, 15 p.","ipdsId":"IP-178954","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":499934,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s40641-025-00204-3","text":"Publisher Index Page"},{"id":499649,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","noUsgsAuthors":false,"publicationDate":"2025-07-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Webb, Hailey","contributorId":366038,"corporation":false,"usgs":false,"family":"Webb","given":"Hailey","affiliations":[{"id":87335,"text":"Renewable and Sustainable Energy Institute, University of Colorado Boulder, Boulder, CO USA; Ecology and Evolutionary Biology, University of Colorado\nBoulder, Boulder, CO USA","active":true,"usgs":false}],"preferred":false,"id":955189,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fuchs, Matthias","contributorId":366057,"corporation":false,"usgs":false,"family":"Fuchs","given":"Matthias","affiliations":[{"id":87350,"text":"Renewable and Sustainable Energy Institute, University of Colorado Boulder, USA","active":true,"usgs":false}],"preferred":false,"id":955208,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Abbott, Benjamin W.","contributorId":366042,"corporation":false,"usgs":false,"family":"Abbott","given":"Benjamin","middleInitial":"W.","affiliations":[{"id":87338,"text":"Department of Plant & Wildlife Sciences, Brigham Young University, Provo, UT USA","active":true,"usgs":false}],"preferred":false,"id":955193,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Douglas, Thomas A. 0000-0003-1314-1905","orcid":"https://orcid.org/0000-0003-1314-1905","contributorId":64553,"corporation":false,"usgs":false,"family":"Douglas","given":"Thomas","email":"","middleInitial":"A.","affiliations":[{"id":33087,"text":"Cold Regions Research and Engineering Laboratory","active":true,"usgs":false}],"preferred":true,"id":955213,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Elder, Clayton D.","contributorId":201542,"corporation":false,"usgs":false,"family":"Elder","given":"Clayton","email":"","middleInitial":"D.","affiliations":[{"id":7023,"text":"Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":955197,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ernakovich, Jessica G. 0000-0002-4493-2489","orcid":"https://orcid.org/0000-0002-4493-2489","contributorId":257626,"corporation":false,"usgs":false,"family":"Ernakovich","given":"Jessica","email":"","middleInitial":"G.","affiliations":[{"id":12667,"text":"University of New Hampshire","active":true,"usgs":false}],"preferred":false,"id":955206,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Euskirchen, Eugenie","contributorId":330061,"corporation":false,"usgs":false,"family":"Euskirchen","given":"Eugenie","affiliations":[{"id":78786,"text":"University of Alaska Fairbanks, Fairbanks, AK, USA 99775","active":true,"usgs":false}],"preferred":false,"id":955201,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Göckede, Mathias","contributorId":366056,"corporation":false,"usgs":false,"family":"Göckede","given":"Mathias","affiliations":[{"id":52579,"text":"Max Planck Institute for Biogeochemistry, Jena, Germany","active":true,"usgs":false}],"preferred":false,"id":955207,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Grosse, Guido","contributorId":366051,"corporation":false,"usgs":false,"family":"Grosse","given":"Guido","affiliations":[{"id":87346,"text":"Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Permafrost Research Section, 14473 Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":955202,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hugelius, Gustaf 0000-0002-8096-1594","orcid":"https://orcid.org/0000-0002-8096-1594","contributorId":73863,"corporation":false,"usgs":false,"family":"Hugelius","given":"Gustaf","email":"","affiliations":[{"id":17850,"text":"Dept of Earth System Science, Stanford University, Stanford, CA 94305","active":true,"usgs":false},{"id":25546,"text":"Stockholm University, Sweden","active":true,"usgs":false}],"preferred":false,"id":955203,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Jones, Miriam C. 0000-0002-6650-7619","orcid":"https://orcid.org/0000-0002-6650-7619","contributorId":257239,"corporation":false,"usgs":true,"family":"Jones","given":"Miriam","email":"","middleInitial":"C.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":955210,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Koven, Charles","contributorId":51143,"corporation":false,"usgs":true,"family":"Koven","given":"Charles","affiliations":[],"preferred":false,"id":955194,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Kropp, Heather","contributorId":366053,"corporation":false,"usgs":false,"family":"Kropp","given":"Heather","affiliations":[{"id":87348,"text":"Environmental Studies Program, Hamilton College, Clinton, NY 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University of Colorado Boulder. Boulder CO 80309","active":true,"usgs":false}],"preferred":false,"id":955211,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Strauss, Jens","contributorId":223674,"corporation":false,"usgs":false,"family":"Strauss","given":"Jens","email":"","affiliations":[],"preferred":false,"id":955205,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Virkkala, Anna-Maria","contributorId":366040,"corporation":false,"usgs":false,"family":"Virkkala","given":"Anna-Maria","affiliations":[{"id":87336,"text":"Woodwell Climate Research Center, Falmouth, MA 02540 USA","active":true,"usgs":false}],"preferred":false,"id":955191,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Merritt R. Turetsky","contributorId":198334,"corporation":false,"usgs":false,"family":"Merritt R. Turetsky","affiliations":[],"preferred":false,"id":955190,"contributorType":{"id":1,"text":"Authors"},"rank":24}]}} ,{"id":70268977,"text":"sir20255027 - 2025 - Development of regression equations to estimate flow durations, low-flow frequencies, and mean flows at ungaged stream sites in Connecticut using data through water year 2022","interactions":[],"lastModifiedDate":"2026-02-03T14:32:36.443118","indexId":"sir20255027","displayToPublicDate":"2025-07-23T10:10:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-5027","displayTitle":"Development of Regression Equations to Estimate Flow Durations, Low-Flow Frequencies, and Mean Flows at Ungaged Stream Sites in Connecticut Using Data Through Water Year 2022","title":"Development of regression equations to estimate flow durations, low-flow frequencies, and mean flows at ungaged stream sites in Connecticut using data through water year 2022","docAbstract":"To aid Federal and State regulatory agencies in the effective management of water resources, the U.S. Geological Survey, in cooperation with the Connecticut Department of Energy and Environmental Protection and the Connecticut Department of Transportation, updated flow statistics for 118 streamgages and developed 47 regression equations to estimate selected flow duration, low flow, and mean flow statistics for the entire State of Connecticut, for the following: 1-, 5-, 10-, 25-, 50-, 75-, 90-, 99-percent flow durations; 7-day, 10-year low-flow frequency and 30-day, 2-year low-flow frequency; and mean flow, spring mean flow, and harmonic mean flow. In addition, regression equations were developed for monthly and seasonal flow durations, ranging from 25 to 99 percent for aquatic biological processes of salmonid spawning (November), overwinter (December–February), clupeid spawning (May), resident spawning (June), and rearing and growth (July–October) periods, and for flow durations ranging from 1 to 99 percent for the habitat forming (March–April) period. Statistics were derived from daily mean streamflow data collected from streamgages with at least 10 years of data through water year 2022 in southern New England and eastern New York.
Forty streamgages in Connecticut and adjacent areas of neighboring States were used in the regression analysis. Regression methods of weighted least squares and generalized least squares were used to derive the final coefficients and measures of uncertainty for the regression equations. The equations used to estimate selected streamflow statistics were developed by relating the flow statistics to different basin characteristics (physical, land cover, and climatic) at the 40 streamgages. Nine basin characteristics served as the explanatory variables in the statewide regression equations: drainage area, percentage of area with coarse-grained stratified deposits, stream density, mean basin slope, mean basin elevation, percentage of area with hydrologic soil group A, mean monthly precipitation for November, mean seasonal precipitation in the winter (December, January, and February), and mean annual temperature. The root mean square error of the 47 equations ranged from 7.9 to 121.9 percent, with an average of 27.9 percent. The equations estimate flows most accurately near the mean (50-percent flow duration), become less accurate for low flows, and are the least accurate for extreme low flows. The root mean square error for the 50-percent flow duration is 15.1 percent, with an average of 17.6 percent across the six periods. The extreme low flow statistics of 7-day, 10-year low-flow frequency, 99-percent flow duration, and 99-percent rearing and growth period flow durations have root mean square errors of 121.9, 105.1, and 121.9 percent, respectively. The adjusted coefficient of determination of the 47 equations ranged from 73.4 to 99.5 percent, with an average of 95.1 percent.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255027","collaboration":"Prepared in cooperation with the Connecticut Department of Energy and Environmental Protection and the Connecticut Department of Transportation","usgsCitation":"Ahearn, E.A., and Bent, G.C., 2025, Development of regression equations to estimate flow durations, low-flow frequencies, and mean flows at ungaged stream sites in Connecticut using data through water year 2022: U.S. Geological Survey Scientific Investigations Report 2025–5027, 54 p., https://doi.org/10.3133/sir20255027.","productDescription":"Report: vi, 54 p.; Data Release","numberOfPages":"54","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-165198","costCenters":[{"id":466,"text":"New England Water Science 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\"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
The U.S. Geological Survey, the Connecticut Department of Energy and Environmental Protection, and the Connecticut Department of Transportation collaboratively updated flow statistics for 118 streamgages and developed 47 regression equations to estimate key flow statistics in Connecticut. These included various flow durations and low-flow frequencies, as well as mean flow statistics for specific aquatic biological processes. The analysis used daily mean streamflow data from 40 streamgages with at least 10 years of data and incorporated basin characteristics such as drainage area and precipitation. The equations were most accurate near the mean flow (50-percent flow duration), with an average root mean square error of 27.9 percent, while accuracy decreased for low and extreme low flows. The adjusted coefficient of determination ranged from 73.4 to 99.5 percent, averaging 95.1 percent.
","publicationDate":"2025-07-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Ahearn, Elizabeth A. 0000-0002-5633-2640 eaahearn@usgs.gov","orcid":"https://orcid.org/0000-0002-5633-2640","contributorId":194658,"corporation":false,"usgs":true,"family":"Ahearn","given":"Elizabeth","email":"eaahearn@usgs.gov","middleInitial":"A.","affiliations":[{"id":377,"text":"Massachusetts-Rhode Island Water Science Center","active":false,"usgs":true},{"id":196,"text":"Connecticut Water Science Center","active":true,"usgs":true}],"preferred":false,"id":942790,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bent, Gardner C. 0000-0002-5085-3146","orcid":"https://orcid.org/0000-0002-5085-3146","contributorId":205226,"corporation":false,"usgs":true,"family":"Bent","given":"Gardner C.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":942791,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70269690,"text":"70269690 - 2025 - Daily fluctuating flows affect riparian plant species distributions from local to regional scales","interactions":[],"lastModifiedDate":"2025-07-30T15:16:37.98138","indexId":"70269690","displayToPublicDate":"2025-07-23T08:07:01","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":849,"text":"Applied Vegetation Science","active":true,"publicationSubtype":{"id":10}},"title":"Daily fluctuating flows affect riparian plant species distributions from local to regional scales","docAbstract":"Aims
The number of hydropower dams has grown globally over recent decades, with significant impacts on downstream riparian plant communities. Many of these dams generate daily fluctuating flows known as hydropeaking to meet sub-daily variation in energy demands. Hydropeaking can significantly impact riparian plant communities, with obligate riparian species tending to experience the greatest negative effects on habitat suitability. Whether this pattern holds in arid biomes where daily soil moisture enhancements could benefit some plants is an open question.
Location
Colorado River, Grand Canyon, Western USA.
Methods
We used occurrence records to model species responses to variation in daily flow fluctuations across 32 689 river segments in the Western United States. We then applied estimates of hydropeaking responses derived from those models to understanding the abundance and fine scale hydrologic niches of riparian plant species in the Colorado River ecosystem downstream of Glen Canyon Dam, which has experienced vegetation expansion attributed to river regulation, including hydropeaking that began in 1964.
Results
At the regional scale, species with greater wetland dependence exhibited increasingly negative responses to hydropeaking across 1 496 species, consistent with previous studies at smaller scales. At the local scale of the Colorado River, we found that species inhabiting near-channel habitat characterized by daily inundation and exposure had positive modeled responses to hydropeaking, consistent with a long history of selection for species tolerant of hydropeaking. In contrast, species inhabiting the zone immediately above peak daily river stage had negative modeled responses to hydropeaking, suggesting that they are being excluded from otherwise suitable habitat nearer the channel.
Conclusions
These results demonstrate that hydropeaking can impact species distributions from local to regional scales by excluding obligate wetland species and reducing habitat suitability for some facultative wetland species. These results from an arid river system are consistent with those reported from other biomes.
","language":"English","publisher":"Wiley","doi":"10.1111/avsc.70033","usgsCitation":"Butterfield, B.J., and Palmquist, E.C., 2025, Daily fluctuating flows affect riparian plant species distributions from local to regional scales: Applied Vegetation Science, v. 28, no. 3, e70033, 14 p., https://doi.org/10.1111/avsc.70033.","productDescription":"e70033, 14 p.","ipdsId":"IP-173588","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":493189,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Glen Canyon Dam to Lake Mead","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.69796878470245,\n 37.294312973717396\n ],\n [\n -114.69796878470245,\n 36.01181577939015\n ],\n [\n -111.34429886929873,\n 36.01181577939015\n ],\n [\n -111.34429886929873,\n 37.294312973717396\n ],\n [\n -114.69796878470245,\n 37.294312973717396\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"28","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-07-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Butterfield, Bradley J. 0000-0003-0974-9811","orcid":"https://orcid.org/0000-0003-0974-9811","contributorId":167009,"corporation":false,"usgs":false,"family":"Butterfield","given":"Bradley","email":"","middleInitial":"J.","affiliations":[{"id":24591,"text":"Merriam-Powell Center for Environmental Research and Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA","active":true,"usgs":false}],"preferred":false,"id":944450,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Palmquist, Emily C. 0000-0003-1069-2154 epalmquist@usgs.gov","orcid":"https://orcid.org/0000-0003-1069-2154","contributorId":5669,"corporation":false,"usgs":true,"family":"Palmquist","given":"Emily","email":"epalmquist@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":944451,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70269620,"text":"70269620 - 2025 - Hydrologic variability and groundwater age of springs in eastern Oregon and northern Nevada, USA","interactions":[],"lastModifiedDate":"2025-07-28T14:17:21.049353","indexId":"70269620","displayToPublicDate":"2025-07-20T09:10:13","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Hydrologic variability and groundwater age of springs in eastern Oregon and northern Nevada, USA","docAbstract":"The ecological importance of springs in semiarid regions is far greater than their small size and sparse distribution, yet little is known about the hydrologic functioning of these systems. During 2016–22, 261 springs were visited in the volcanic terrane of eastern Oregon and northern Nevada. When conditions were suitable, measurements of discharge, water temperature, and specific conductance were made, and samples for the analysis of carbon-14, tritium, and water stable isotopes (WSI) were collected. A subset of 60 springs was revisited during different seasons in the same year and during the dry season in multiple years to evaluate variability in discharge, chemistry, and groundwater age. Specific conductance and WSI varied considerably among springs across the study area but were unexpectedly stable across seasons and years at individual springs. Seasonal and interannual variability in spring discharge was related to the residence time of the discharging groundwater. Springs discharging older groundwater (103–104 years) had significantly less variability in their discharge compared to springs discharging younger groundwater (100–101 years). Variability among springs discharging younger groundwater included cessation of late-summer discharge at 18 % of the repeat-visit springs. A logistic regression model predicted the age of discharging spring water with 89 % accuracy using only the spring latitude, longitude, elevation, and δ2H value. This study framework provides a simple, inexpensive, and robust method to provisionally assess the hydrologic behavior of springs having little or no prior information in understudied, semiarid regions across the globe.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2025.133922","usgsCitation":"Johnson, H.M., 2025, Hydrologic variability and groundwater age of springs in eastern Oregon and northern Nevada, USA: Journal of Hydrology, v. 662, no. Part A, 133922, 13 p., https://doi.org/10.1016/j.jhydrol.2025.133922.","productDescription":"133922, 13 p.","ipdsId":"IP-123085","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":493312,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2025.133922","text":"Publisher Index Page"},{"id":492994,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada, Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120.31311680484953,\n 41.99539074875983\n ],\n [\n -120.0068581722988,\n 41.99391273807197\n ],\n [\n -119.90145747408332,\n 41.992434693061426\n ],\n [\n -119.84179670150823,\n 41.66048803137227\n ],\n [\n -118.9246520186846,\n 41.65959449142923\n ],\n [\n -118.93301194332504,\n 41.98445357532694\n ],\n [\n -117.02491929749873,\n 42.016929659002585\n ],\n [\n -117.01355889504242,\n 43.86708893750037\n ],\n [\n -116.91131527293696,\n 44.17343283492443\n ],\n [\n -117.20668573679785,\n 44.30365776197061\n ],\n [\n -117.26349474591721,\n 44.58751884415352\n ],\n [\n -117.18671855238352,\n 44.79442609102742\n ],\n [\n -117.77146876695777,\n 44.926186190521406\n ],\n [\n -117.91090405154984,\n 44.91839177439985\n ],\n [\n -117.97144831985975,\n 44.63186629043838\n ],\n [\n -118.6664325411721,\n 44.37656025661704\n ],\n [\n -119.868869617017,\n 44.396170474914186\n ],\n [\n -121.12677954373844,\n 44.318586844969104\n ],\n [\n -121.51042244069035,\n 42.944368334976076\n ],\n [\n -121.28368996303429,\n 42.40400912273424\n ],\n [\n -120.73802661060382,\n 42.42707264631977\n ],\n [\n -120.26821317970226,\n 41.99741467441126\n ],\n [\n -120.31311680484953,\n 41.99539074875983\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"662","issue":"Part A","noUsgsAuthors":false,"publicationDate":"2025-07-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Johnson, Henry M. 0000-0002-7571-4994 hjohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-7571-4994","contributorId":869,"corporation":false,"usgs":true,"family":"Johnson","given":"Henry","email":"hjohnson@usgs.gov","middleInitial":"M.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":944189,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70268790,"text":"sir20255054 - 2025 - Hydrogeologic framework and conceptual model of the Red River alluvial aquifer east of Lake Texoma, southeastern Oklahoma, 1980–2022","interactions":[],"lastModifiedDate":"2026-02-03T14:29:12.653472","indexId":"sir20255054","displayToPublicDate":"2025-07-18T13:39:29","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-5054","displayTitle":"Hydrogeologic Framework and Conceptual Model of the Red River Alluvial Aquifer East of Lake Texoma, Southeastern Oklahoma, 1980–2022","title":"Hydrogeologic framework and conceptual model of the Red River alluvial aquifer east of Lake Texoma, southeastern Oklahoma, 1980–2022","docAbstract":"The 1973 Oklahoma Groundwater Law (Oklahoma Statutes §82-1020.5) requires that the Oklahoma Water Resources Board conduct hydrologic investigations of the State’s groundwater basins to support a determination of the maximum annual yield for each groundwater basin. At present (2025), the Oklahoma Water Resources Board has not established a maximum annual yield for the Red River alluvial aquifer east of Lake Texoma. To support the evaluation and determination of a maximum annual yield, a hydrogeologic framework and conceptual groundwater-flow model were developed to assess groundwater availability in the Red River alluvial aquifer east of Lake Texoma.
The scope of this hydrologic investigation is the alluvium and terrace containing the Red River alluvial aquifer in Oklahoma between Lake Texoma, the Texas State line, and the Arkansas State line, an extent referred to in this report as “the eastern part of the Red River alluvial aquifer.” Parts of the alluvium and terrace extent in Arkansas and Texas are included in some analyses to address hydrologic influences from outside the aquifer’s boundaries in Oklahoma.
The eastern part of the Red River alluvial aquifer in southeastern Oklahoma consists of approximately 401,280 acres of Quaternary alluvium and terrace deposits associated with the Red River and its major tributaries. Mean annual recharge to the aquifer for the 1980–2022 study period was estimated to be 8.62 inches per year, or 17.98 percent of the mean annual precipitation over the same period (47.94 inches). This mean annual recharge rate is equivalent to an inflow of approximately 288,250 acre-feet per year for the eastern part of the Red River alluvial aquifer. Recharge estimated using the Soil-Water-Balance code accounts for 98.7 percent of the conceptual-model inflows to the eastern part of the Red River alluvial aquifer. Saturated-zone evapotranspiration accounts for 11.9 percent and net streambed seepage accounts for 87.4 percent of the outflows in the conceptual model.
Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
Contact Us- USGS Publications Warehouse
","tableOfContents":"The management of invasive Silver Carp Hypophthalmichthys molitrix in the Tennessee River basin focuses on removal, and there is interest in extending removal efforts to the tailwater environments of high-head locks and dams along the Tennessee River, such as Kentucky Dam. We used acoustic telemetry data from Silver Carp to understand important ecological associations underlying their residence in the Kentucky Dam tailwater, measured by daily fish counts and mean residence time.
We used time-series-informed regression models, variance partitioning, and cross-correlation function analysis to associate six predictors, including lock and dam operations (total, spill gate, and turbine discharge and number of lockages), hydrology (tailwater elevation), and water temperature, with two measures of Silver Carp residency (daily counts and mean residence time).
We found that spill-induced hydrology (total discharge + spill discharge + tailwater elevation) was negatively associated with daily counts but not with residence time, whereas temperature was positively associated with counts and negatively associated with residence times. Variance partitioning indicated that nearly all the variance in counts and residence times was jointly explained by temporal effects, lock and dam operations (discharge, tailwater elevation, and lockages), and temperature. The cross-correlations indicated that the counts were lagged by all predictors, sometimes up to 5 d, whereas residence times were lagged by both total and spill discharge and number of lockages.
We found that discharge and water temperature were principally associated with residency of Silver Carp in the Kentucky Dam tailwater. However, these associations were entirely temporally constrained, which can affect how strongly and how quickly Silver Carp respond to changing environmental conditions across different time scales. Managers can leverage these associations to plan removal periods where daily tailwater conditions/dam operations are favorable to invasive carp residence (e.g., >10°C and <2,500 m3/s) and adjust fishing effort to optimize removal rates in response to changing conditions.
The U.S. Geological Survey, in cooperation with the Alaska Department of Transportation and Public Facilities, inventoried selected special conditions for annual peak flows and identified extreme floods at streamgages in Alaska through water year 2022 to facilitate hydrologic analysis. Special conditions identified from U.S. Geological Survey gaging records and basin characteristics included regulation and diversion, urbanization, indeterminate drainage areas, drainage areas less than the minimum used in regional analyses, glacial lake outburst floods, other outburst floods, and snowmelt floods. For peak flows that occurred during calendar years 1980–2019, an atmospheric river dataset was used to identify atmospheric river presence or absence on the dates peak flows occurred. Extreme floods (defined as peak flows exceeding the 1-percent annual exceedance probability flood magnitude or an empirical measure of relative magnitude using Creager’s coefficient C) were identified and associated with flood-generating mechanisms using the other inventoried special conditions and other information.
The gaging record contained glacial lake outburst floods at 15 streamgages and other types of outburst floods at 10 streamgages. Non-outburst peak flows in Alaska resulted from a mixture of rainfall and melt-based flood-generating mechanisms in all but the most rain-dominated seasonal flow regime. Melt-based flood-generating mechanisms included snowmelt, high-elevation snow and ice melt, or rain-on-snow events. Atmospheric rivers were common in Alaska and conterminous basins in Canada, occurring in that region on 67 percent of the days in the calendar year 1980–2019 period. Atmospheric rivers were more common on the days of peak flows and even more common on the days of non-outburst extreme floods. The percentage of days when an atmospheric river was present increased to 78 percent for the days of peak flows in that period and to 83 percent for the days of non-outburst extreme floods in that period. Of 149 extreme floods in the gaging record, 38 were generated by outburst floods. Of the non-outburst extreme floods, 72 percent were generated by rainfall and 26 percent were generated by melt-based processes or a combination of rainfall and melt-based processes. Flood-generating mechanisms could not be determined for the final 2 percent of the non-outburst extreme floods because the month and day of the peak flows were unknown and no other information was available. Secondary factors strongly associated with extreme floods included antecedent rain and streamflow conditions and warm storm conditions that produced rain instead of snow or generated snowmelt.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255056","collaboration":"Prepared in cooperation with Alaska Department of Transportation and Public Facilities","usgsCitation":"Curran, J.H., 2025, Selected special conditions affecting peak streamflow and extreme floods in Alaska through water year 2022: U.S. Geological Survey Scientific Investigations Report 2025–5056, 41 p., https://doi.org/10.3133/sir20255056.","productDescription":"Report: viii, 41 p.; 5 Data Releases","onlineOnly":"Y","ipdsId":"IP-169678","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":499049,"rank":11,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118692.htm","linkFileType":{"id":5,"text":"html"}},{"id":492464,"rank":10,"type":{"id":31,"text":"Publication 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Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska, 99508
Designing aquatic nest boxes is rarely afforded detailed scientific account. Here we provide some historical context for nest boxes used in production of large-bodied fishes of the Australian freshwater cod genus Maccullochella. Our experience with eastern freshwater cod is used as a case study to: (a) convey aspects of the complexity of the nest box design process and to (b) demonstrate the importance of visual literacy in project communication across the variety of contributors to the eco-design process. Specifically, we describe a new, two-variant, triangular nest box design for application in rivers and modifications to a standard stainless steel nest box for hatchery-pond-based spawning of eastern freshwater cod M. ikei. We designed the boxes to test adult preference for single versus double entrance/exits to cavities in hatchery and field environments. An important consideration specific to hatchery production is harvesting demersal, adhesive eggs prior to hatching to minimise fungal infection of eggs and physical loss of larvae, in addition to providing critical first feeding of larvae. In contrast, field nest box design incorporated multiple factors and associated trade-offs related to both internal and external design, ranging from manufacturer capability, material types, cost, transportability, hydrological performance, biodegradability, retrievability, as well as biological and ecological function. Only preliminary findings from field nest box deployments are provided here, and we focus primarily on elements of visual language in the form of conceptual drawings, sketches and final schematics which have been central to our process. We emphasise the benefit of harnessing input from multiple fields of expertise and documenting and testing designs of nest boxes for cavity nesting fishes, under both controlled hatchery and more complex field conditions.
","language":"English","publisher":"Wiley","doi":"10.1002/aff2.70095","usgsCitation":"Ebner, B.C., Morris, S.S., St Vincent Welch, J., Ryan, P.C., Turner, M., Cameron, L.M., Poitras, N., Coonrod, B., Welsh, S.A., McLellan, M., Jess, L., Vidler, S., Ingram, B.A., Thurstan, S., Rowland, S.J., Blake, S., and Butler, G.L., 2025, Blueprints for riverine cod nest boxes draw from multiple design considerations: Aquaculture, Fish and Fisheries, v. 5, no. 4, e70095, 13 p., https://doi.org/10.1002/aff2.70095.","productDescription":"e70095, 13 p.","ipdsId":"IP-166252","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":494454,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/aff2.70095","text":"Publisher Index 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John","contributorId":359873,"corporation":false,"usgs":false,"family":"St Vincent Welch","given":"John","affiliations":[{"id":85927,"text":"New South Wales Department of Primary Industries and Regional Development","active":true,"usgs":false}],"preferred":false,"id":946363,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ryan, Paul C.","contributorId":359874,"corporation":false,"usgs":false,"family":"Ryan","given":"Paul","middleInitial":"C.","affiliations":[{"id":85927,"text":"New South Wales Department of Primary Industries and Regional Development","active":true,"usgs":false}],"preferred":false,"id":946364,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Turner, Mitch","contributorId":359875,"corporation":false,"usgs":false,"family":"Turner","given":"Mitch","affiliations":[{"id":85927,"text":"New South Wales Department of Primary Industries and Regional 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However, their accuracy varies across RSET estimation methods and diverse hydroclimate regions. While ET modeling efforts to account for biophysical processes and controlling parameters have made good progress in recent years, a parallel approach of integrating in-situ ET with RSET could reduce biases in RSET products. Basin water balance ET (WBET) and flux tower ET are widely applied to evaluate RSET accuracy, yet such ET measurements are rarely used for RSET bias corrections, especially for large area applications. To address this issue, we propose a novel approach: the water balance equivalence (WABE) method, which generates spatially continuous WBET for correcting biases in RSET products. The WABE method computes synthetic WBET by integrating observed WBET and flux tower-derived FLUXCOM ET, which fills the spatial gaps of observed WBET and generates a spatially continuous WBET dataset. Synthetic WBET (2002–2015 annual average) of eight-digit hydrologic unit code (HUC8) basins across the conterminous United States (CONUS), constituting 44 % (887 out of 2035 basins) of CONUS basins, was determined within 2.0 % (RMSE = 12 %) of observed WBET at CONUS and between 1–12 % (RMSE = 3–33 %) across 18 regions in CONUS. With WABE-based bias corrections, the overall annual bias of RSET decreased from 10 % (RMSE = 34 %) to 6 % (RMSE = 26 %) across 37 flux tower sites. The WABE method offers a new approach for RSET accuracy improvement and shows great promise for large area implementations with a potential to yield substantial benefits for building accurate basin water budgets and water management decisions.
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chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Chapter three - Global SSEBop actual evapotranspiration modeling and mapping using the VIIRS data","docAbstract":"AActual evapotranspiration (ETa) is an essential climate variable that can be used for drought monitoring and water availability assessment because of its close connection with vegetation, soil moisture, and the water cycle. An operational ETa using the Visible Infrared Imaging Radiometer Suite (VIIRS) and global weather datasets was developed through the Simplified Surface Energy Balance Model (SSEBop) model. An operational framework is established with the Famine Early Warning System Network (https://earlywarning.usgs.gov/fews) to generate and update global 1 km ETa at dekadal (∼10 day), monthly, and yearly time scales since February 2012. Modeled ETa at monthly and annual time scales was evaluated using 67 eddy covariance (EC) flux tower stations around the world and water balance-based ETa based on 810 United States eight-digit Hydrologic Unit Code (HUC8) and 18 Global Runoff Data Center (GRDC) basins. The correlation coefficient (r=0.68–0.94) shows relatively strong and consistent performance across the three datasets, capturing the spatiotemporal variability in HUC8 and GRDC basins and EC tower sites reliably. The bias (3%–15%) and root mean square error (RMSE: 13%–34%) showed relatively large errors and high variability among the three datasets. The evaluation results indicate the usefulness of the VIIRS ETa for drought monitoring and early warning applications without further adjustments, while bias-correction and calibration procedures may be required before using the VIIRS ETa data for localized water budget assessments. Availability of gridded actual ETa data from a combination of flux towers and basin-scale ETa is desired to establish bias-correction procedures to improve the absolute accuracy of remote-sensing ETa such as the SSEBop VIIRS operational products.
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