Managers of the Central Valley Project (CVP) and State Water Projects (SWP) in California are confronted with difficult tradeoffs between water uses and associated values affected by water management decisions. These decisions involve altering the timing and magnitude of water releases from dams and reservoirs, which can affect habitats for economically important and Federally and State-listed endangered fish species, water deliveries for agriculture or municipalities, and water quality. In this report, we describe the results of a rapid structured decision-making process used to assist management agencies in evaluating tradeoffs while gathering input from cooperating agencies, rightsholders, or interested parties (hereafter participants) through facilitated workshops in spring 2025. Consideration of alternative water management actions was initiated by the continued decline of Hypomesus transpacificus (delta smelt) populations and the issuance of a new biological opinion for the CVP and SWP long-term operations on the effects on delta smelt and other Endangered Species Act-listed species in November 2024. An Executive Order was also issued in January 2025, directing the Bureau of Reclamation to maximize water deliveries. Participants, led by the U.S. Geological Survey and cooperating agencies, identified 8 fundamental values (hereafter objectives) and 11 alternative water management scenarios (or “alternative management actions” based on the PrOACT model). Using multicriteria decision analysis, we evaluated performance (or “consequences” based on a consequence table analysis) and analyzed tradeoffs of alternative water management actions to the fundamental objectives. We ranked the alternative water management actions based on four participants’ objective weights and composite utility scores calculated using a linear value function. The three highest ranking alternative water management actions had the poorest performance for delta smelt but performed best for CVP and SWP water exports and objectives related to coldwater pool operations for salmonids. An optimum strategy that could prevent the extinction of delta smelt was not determined for this study. However, insights gained from our rapid decision analysis suggested nonflow scenarios could benefit the delta smelt population, including in drier years, and could be considered to avoid curtailment of water exports.
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U.S. Geological Survey
2255 N. Gemini Drive
Flagstaff, AZ 86001
Decisions on how to store and distribute water in California’s Central Valley are made considering the use of water resources by people, fish and wildlife, and the effects on water quality. Water is stored behind dams throughout the Central Valley for later release into rivers and canals for distribution to meet different water needs. Declining water availability and increasing human demands for water over recent decades have made these decisions increasingly difficult, especially because different uses of water resources often conflict. This report summarizes a facilitated decision-making process, led by the U.S. Geological Survey, involving water, fish, wildlife managers, and those that have an interest in how water is used (interest holders) in the Central Valley. This process provides information for water managers to consider when deciding how to distribute water resources to meet the needs for endangered Hypomesus transpacificus (delta smelt), different runs of Oncorhynchus tshawytscha (Chinook salmon), and Oncorhynchus mykiss (Central Valley steelhead), while maximizing water deliveries for human use and maintaining water quality standards.
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$21B
Geologic maps save users an estimated 15% in annual costs: a value of between $14B and $21B.
$25.6B
in annual value to users of imagery from Landsat satellites, which were codeveloped by NASA and the USGS and operated through their lifespans by the USGS.
$13.5B
in annual benefits is generated by the USGS's 3D Elevation Program.
44%
USGS-identified undiscovered geothermal energy is equal to 44% of current U.S. electricity generation.
29.4B
barrels of oil and 391.6 trillion cubic feet of gas in recoverable resources are available on U.S. public lands based on USGS assessments.
$424B
in recent wildland fire damages highlight the need for USGS fire science, which supports efforts to protect communities and reduce risk.
USGS earthquake, volcano, landslide, and coastal hazard monitoring and information save lives and minimize costs; for example, $2.8M can be saved because of USGS enhanced information about a Mauna Loa eruption.
$4.5B
is the estimated cost of annual flooding. Through a network of over 11,885 streamgages, the USGS supports public safety and enables forecasts, early warning systems, and management actions that protect lives and property.
$3.1B
The USGS identified a $3.1B risk to the American economy if China restricts gallium imports. This is one example underscoring the importance of the USGS mapping critical minerals, investigating supply chains, and producing the Nation’s critical minerals list.
$21B
in estimated annual costs results from invasive species. The USGS’s invasive species research informs approaches used to reduce their effects on agriculture, water infrastructure, disease transmission, fisheries, and outdoor recreation.
USGS innovations support early warnings for harmful algal blooms—over $2M in yearly benefits are provided to Kansas alone.
$45B
USGS science informs the management of big game (such as deer and elk). The big-game hunting industry contributes $45B to the U.S. economy.
$4.1T
Mineral commodities are necessary for the $4.1T in value added to the GDP by major industries that consume processed mineral materials and employ 1 million workers. Because of this, USGS data on mineral supply, demand, and trade are highly valued.
45,000 metric tons
Rare earths power the growing technology economy, including cell phones, electric vehicles, and medical devices. For over 70 years, USGS work has supported the discovery of rare earth resources in California’s Mountain Pass area, which produced 45,000 metric tons of rare earth concentrates in 2024—over 11% of the global supply.
$70.2B
USGS science informs early warning systems and management strategies to mitigate disease outbreaks in agriculture—critical research on highly pathogenic avian influenza, for example, helps safeguard the $70B value in poultry and egg production.
$11.8B
USGS groundwater tools are vital for agriculture; for example, in the Mississippi Alluvial Plain, 65% of farming relies on groundwater to support its $11.8B annual industry.
1Values throughout are given in billions (B), millions (M), and trillions (T) of U.S. dollars. GDP is “Gross Domestic Product.” Percentages are shown as %.
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12201 Sunrise Valley Drive
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No abstract available.
","language":"English","publisher":"Nature","doi":"10.1038/s44221-025-00536-2","usgsCitation":"Archfield, S., 2025, Spatial connections between the timing of hydroclimatic extremes: Nature Water, v. 3, p. 1352-1353, https://doi.org/10.1038/s44221-025-00536-2.","productDescription":"2 p.","startPage":"1352","endPage":"1353","ipdsId":"IP-182869","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":498157,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"3","noUsgsAuthors":false,"publicationDate":"2025-12-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Archfield, Stacey 0000-0002-9011-3871 sarch@usgs.gov","orcid":"https://orcid.org/0000-0002-9011-3871","contributorId":214835,"corporation":false,"usgs":true,"family":"Archfield","given":"Stacey","email":"sarch@usgs.gov","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":953017,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70272699,"text":"sir20255086 - 2025 - Conceptual and numerical groundwater flow model of the Iowa River alluvial aquifer near Tama County, Iowa, 1980 through 2022","interactions":[],"lastModifiedDate":"2026-02-03T16:48:21.047788","indexId":"sir20255086","displayToPublicDate":"2025-12-08T13:13:22","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":"2025-5086","displayTitle":"Conceptual and Numerical Groundwater Flow Model of the Iowa River Alluvial Aquifer near Tama County, Iowa, 1980 through 2022","title":"Conceptual and numerical groundwater flow model of the Iowa River alluvial aquifer near Tama County, Iowa, 1980 through 2022","docAbstract":"The Iowa River alluvial aquifer is an important source of water on the Meskwaki Settlement in Tama County, Iowa, which is land owned by the Sac & Fox Tribe of the Mississippi in Iowa (commonly known as the Meskwaki Nation). The U.S. Geological Survey constructed a groundwater flow model, including a conceptual and numerical model, of the Iowa River alluvial aquifer and underlying hydrogeologic units near the Meskwaki Settlement in Tama County, Iowa, for the period of January 1980–August 2022 to estimate the fraction of water pumped from the Iowa River alluvial aquifer by Meskwaki Settlement wells that is derived from streamflow depletion in the Iowa River and its tributaries. Streamflow depletion is a reduction in streamflow caused by groundwater pumping and includes the interception by groundwater production wells of water that otherwise would have been discharged to streams (called “captured groundwater discharge”) and induced infiltration of streamflow to the production wells. Calibrated model runs were performed with no simulated pumping and simulated pumping only at Meskwaki Settlement wells, and the change in simulated flow rates between the groundwater system and streams for the two model runs represents the amount of streamflow depletion in the Iowa River and tributary streams resulting from pumping at the Meskwaki Settlement wells. Streamflow depletion in the Iowa River and its tributaries as a percentage of simulated pumping at the Meskwaki Settlement wells was calculated by dividing this difference by the total simulated pumping rate for the Meskwaki Settlement wells. The model results demonstrate that the mean monthly streamflow depletion, including induced infiltration and captured discharge, in the Iowa River and its tributary streams as a percentage of mean monthly pumping at the Meskwaki Settlement wells was 97.4 percent and ranged from 65.4 to 112 percent. Of the total streamflow depletion, mean monthly induced recharge was 20.9 percent and ranged from 4.9 to 37.2 percent. Mean monthly captured discharge was 76.5 percent and ranged from 57.1 to 97.1 percent. These results indicate that most of the water pumped from the Meskwaki Settlement wells is the result of streamflow depletion, in the form of both induced infiltration and captured discharge.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255086","collaboration":"Prepared in cooperation with the Sac & Fox Tribe of the Mississippi in Iowa","usgsCitation":"Goldstein, K.M.F., and Davis, K.W., 2025, Conceptual and numerical groundwater flow model of the Iowa River alluvial aquifer near Tama County, Iowa, 1980 through 2022: U.S. Geological Survey Scientific Investigations Report 2025–5086, 55 p., https://doi.org/10.3133/sir20255086.","productDescription":"Report: viii, 55 p.; Data Release; Dataset","numberOfPages":"68","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-154245","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":497068,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5086/coverthb.jpg"},{"id":497069,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5086/sir20255086.pdf","text":"Report","size":"20.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5086"},{"id":497070,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5086/sir20255086.XML"},{"id":497071,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5086/images/"},{"id":497074,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":497073,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1CPXXGM","text":"USGS data release","linkHelpText":"MODFLOW 6 groundwater flow model for the Iowa River alluvial aquifer near Tama, Iowa, 1980 through 2022"},{"id":497072,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255086/full"},{"id":497812,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119054.htm"}],"country":"United States","state":"Iowa","county":"Tama County","otherGeospatial":"Iowa River alluvial aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92.86911713753273,\n 42.130588756475845\n ],\n [\n -92.86911713753273,\n 41.830673568326176\n ],\n [\n -92.25633472266213,\n 41.830673568326176\n ],\n [\n -92.25633472266213,\n 42.130588756475845\n ],\n [\n -92.86911713753273,\n 42.130588756475845\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240
Miami-Dade County is part of a densely populated urban corridor in southeastern Florida. The Biscayne aquifer serves as Miami-Dade County’s primary drinking water source and is characterized by highly permeable karstic limestone and carbonate sand. The aquifer’s coastal location and permeable nature make it susceptible to saltwater intrusion. Monitoring the current inland extent and the rate of movement of the saltwater front in the aquifer can inform management strategies for conserving the long-term sustainability of the county’s water supply. In the 1950s, the U.S. Geological Survey published a map of the inland extent of saltwater intrusion in the Biscayne aquifer and has continued to update this map to monitor changes over time, with the most recent update published in 2018. An updated map has been created showing the approximate inland extent of saltwater intrusion in the Biscayne aquifer in eastern Miami-Dade County in 2022, with the 2018 extent shown for comparison. The inland extent of saltwater intrusion was mapped through the interpretation of borehole electromagnetic induction logs and measurements of chloride and specific conductance in groundwater samples. The location of the saltwater interface at the base of the Biscayne aquifer was represented by the 1,000-milligram-per-liter isochlor. This report describes changes in the location of the saltwater interface from 2018 to 2022. By 2022, the saltwater interface had moved farther inland in both the northern and southern parts of the county, advancing by as much as 0.3 kilometer in the north and up to 0.8 kilometer in the Model Land Area to the south. However, it remained relatively unchanged from its 2018 position in the east-central part of the county.
Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
7595 SW 33d St.
Davie, FL 33314
Contact Us- USGS Publications Warehouse
","tableOfContents":"The U.S. Geological Survey is developing nationally consistent water-use modeling approaches to replace previous methods relying on locally specific reported and estimated data. These national assessments require datasets that incorporate water withdrawal variability across the United States and over long periods. However, source data often have unclear definitions, missing or varied units, differing temporal resolutions, varied data quality, and inconsistent formats, which hinder automation and require individualized processing. The public-supply datasets described in this paper were used in machine learning models to estimate annual and monthly public-supply water use for 2000–2020 for the conterminous United States (CONUS) and in a model to estimate public-supply deliveries. Public-supply withdrawal data were acquired for the CONUS and the District of Columbia; however, 11 states had annual data for only 1 year, and 10 states had no monthly data. Annual withdrawal data were acquired for 81% of public-supply water service areas, and monthly withdrawal data were acquired for 47% for at least 1 year from 2000 to 2020. These datasets and methods provide the most comprehensive collection of reported public-supply withdrawals to date and can be used by water-use managers, the scientific community, and the broader public. The extensive data processing described herein can be applicable to datasets representing other categories of water use.
","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.70054","usgsCitation":"Luukkonen, C.L., Alzraiee, A.H., Herbert, D.M., Niswonger, R.G., Larsen, J., Buchwald, C.A., Houston, N., Dieter, C., Miller, L.D., and Stewart, J.S., 2025, Harmonization of a water withdrawal dataset for the conterminous United States: JAWRA Journal of the American Water Resources Association, v. 61, no. 6, e70054, 13 p., https://doi.org/10.1111/1752-1688.70054.","productDescription":"e70054, 13 p.","ipdsId":"IP-157002","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":498676,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1752-1688.70054","text":"Publisher Index Page"},{"id":498511,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"conterminous United States","geographicExtents":"{\n 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,{"id":70273138,"text":"70273138 - 2025 - Rice cultivation supports growth and survival of a threatened semi-aquatic reptile","interactions":[],"lastModifiedDate":"2025-12-16T16:18:00.57795","indexId":"70273138","displayToPublicDate":"2025-12-08T10:06:54","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Rice cultivation supports growth and survival of a threatened semi-aquatic reptile","docAbstract":"Integration of agroecosystems and other working landscapes with protected lands and waters is critical to the conservation of Earth's biodiversity. Rice agroecosystems support many species by providing aquatic habitat where natural wetlands have been altered or drained. In regions with long dry seasons, rice fields and associated irrigation canals provide essential habitat for wetland-dependent species. We quantified the spatial scale and magnitude of the effect of rice growing on the growth and survival of the giant gartersnake (Thamnophis gigas), a threatened species that persists primarily in areas of rice agriculture in the Central Valley of California, USA. We used structural causal models to identify drought condition as a key confounder to adjust for when estimating the total effect of rice growing on demographic rates. We analyzed capture-mark-recapture data from 19 populations of giant gartersnakes with an integrated growth–survival model and used distance-weighted covariates to account for the decline in influence of rice with increasing distance from our study sites. We found strong support for a positive effect of rice grown within 1.9 km of a canal on giant gartersnake growth. There was also support for a positive effect of rice on giant gartersnake survival, although the spatial scale extended out to 5 km or more. Our results demonstrate how active rice growing benefits giant gartersnakes inhabiting irrigation canals and demonstrate an approach for studying landscape effects on wildlife in agroecosystems.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.70139","usgsCitation":"Rose, J.P., Nguyen, A.M., Jordan, A., Macias, D., Schoenig, E.J., Napolitano, G., Kim, R., Ersan, J.S., Fulton, A.M., and Halstead, B., 2025, Rice cultivation supports growth and survival of a threatened semi-aquatic reptile: Ecological Applications, v. 35, no. 8, e70139, 15 p., https://doi.org/10.1002/eap.70139.","productDescription":"e70139, 15 p.","ipdsId":"IP-172102","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":497730,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.70139","text":"Publisher Index Page"},{"id":497649,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1JHAWVT","text":"USGS data release","linkHelpText":"Growth and Capture Mark Recapture Data from Giant Gartersnakes (Thamnophis gigas) in Rice Irrigation Canals 2018 to 2023 (ver. 2.0, July 2025)"},{"id":497576,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.17302271960432,\n 39.79287563400288\n ],\n [\n -122.17302271960432,\n 38.80561121822487\n ],\n [\n -121.10358373344488,\n 38.80561121822487\n ],\n [\n -121.10358373344488,\n 39.79287563400288\n ],\n [\n -122.17302271960432,\n 39.79287563400288\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"35","issue":"8","noUsgsAuthors":false,"publicationDate":"2025-12-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Rose, Jonathan P. 0000-0003-0874-9166 jprose@usgs.gov","orcid":"https://orcid.org/0000-0003-0874-9166","contributorId":199339,"corporation":false,"usgs":true,"family":"Rose","given":"Jonathan","email":"jprose@usgs.gov","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":952418,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nguyen, Allison M. 0000-0003-4408-5934","orcid":"https://orcid.org/0000-0003-4408-5934","contributorId":364275,"corporation":false,"usgs":true,"family":"Nguyen","given":"Allison","middleInitial":"M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":952419,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jordan, Anna 0000-0001-8834-4542 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Center","active":true,"usgs":true}],"preferred":true,"id":952427,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70273028,"text":"70273028 - 2025 - Seasonal movements of nonnative White Catfish in the Penobscot River estuary","interactions":[],"lastModifiedDate":"2026-01-22T16:41:17.477908","indexId":"70273028","displayToPublicDate":"2025-12-08T10:04:34","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Seasonal movements of nonnative White Catfish in the Penobscot River estuary","docAbstract":"White Catfish Ameiurus catus has been introduced to coastal watersheds across the United States. In the Penobscot River, Maine, this species has become increasingly common in upstream habitats that have been made accessible by recent dam removals. We characterized the movements of White Catfish to understand the temporal variation in their movement patterns and contextualize these findings within the recent changes in watershed connectivity.
We captured and tagged 10 adult White Catfish (mean fork length = 271 mm) with acoustic transmitters in the lower Penobscot River in July 2022. The movements of the tagged fish were monitored through April 2023 with a large network of stationary receivers.
The tagged catfish were detected up to 6 km upstream and 31 km downstream from the release site. The total distance that was traveled by individuals ranged from 0 to 154 km during the study. Fall and spring movements were associated with changes in river flow and water temperature, but fish were relatively stationary from December through March, when at least five individuals were assumed to have overwintered in lower river tributaries.
Our results show that individual White Catfish may move considerable distances within large river systems and that these movements are potentially facilitated by changing river conditions. Collectively, this study fills a long-standing knowledge gap about the movement ecology of this species, adds context to help explain a recent increase in observations within their introduced range, and shows how changes in river conditions may be used to predict when and where these fish will move within a tidal system.
Sediment bulk density (ρ-dry) and particle size are two important parameters for predicting sediment bed erosion. ρ-dry, however, is difficult to measure accurately. The units of ρdry have not been consistently reported in the literature, leading to confusion, particularly in the calculation of sediment budgets that typically require integrating mass-based and volumetric components. Relationships between ρdry and sediment composition have been developed for multiple regions and differ between systems. Developing a system-specific predictive model for ρdry can help fill data gaps and improve sediment budgets, model accuracy, and estimates of quantities of sediment needed for restoration. In this study, we investigate whether ρdry in San Francisco Estuary can be predicted from organic carbon content or percent of fines, which are more easily or frequently measured than ρdry. We compiled sediment properties from samples collected over the past decade throughout the intertidal and subtidal regions of San Francisco Bay and the Sacramento–San Joaquin Delta to examine this relationship. Sample composition ranged from 2.18 to 99.97% fines (particles < 0.0625 mm), ρ-dry ranged from 0.22 to 1.60 g cm-3, and organic carbon ranged from 0.06 to 7.98%. Regression analysis indicates that the percent of fines explains 93% of the variation of ρ-dry (p-value < 0.05, N = 81). The coefficient of determination decreased by ~1% when organic carbon was incorporated in the regression analysis. Comparison of this predictive ρ-dry model to four published models based on samples from other regions supports previous findings that the relationship between ρdry and grain size may vary by system. We also examined additional factors that may affect sediment erodibility, such as hydrographic and oceanographic conditions. Classification of sample sites as intertidal vs. subtidal or wavy vs. non-wavy each significantly explained the residuals from the ρdry model, and both intertidal and wavy conditions were associated with higher ρ-dry values.
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At the landfill, site cleanup has included the installation of a capillary barrier over former liquid waste lagoons and periodic monitoring of groundwater elevations and groundwater quality. At the refinery, remediation has focused on several petrochemical and crude oil release areas and included soil excavation, groundwater treatment, and regular monitoring of groundwater elevations and quality. Groundwater at both sites has higher concentrations of volatile organic compounds and trace metals than background aquifer concentrations. In 2022, the U.S. Geological Survey compiled the Lee Acres-Giant Bloomfield Refinery Database (LAGBRD), which contains publicly available groundwater-elevation data and organic and inorganic groundwater-quality data from both sites, spanning from 1985 to 2020. Data from the LAGBRD and precipitation data from other sources were used to better understand the cause of relatively high manganese concentrations observed in some groundwater wells at the site through comparison of groundwater chemistry to chemical end members, interpretation of spatial and temporal patterns in the groundwater chemistry, and interpretation of groundwater flow properties. In this study, elevated chloride concentrations in groundwater downgradient from the landfill have been attributed to landfill leachate based on the temporal and spatial variability of chloride concentrations and chloride-to-bromide ratios. Installation of a capillary barrier and surface-water runoff controls at the landfill in 2005 appears to have altered infiltration patterns at that site, resulting in a decrease in chloride at some wells but an increase in chloride and dissolved manganese at others. The timing and relation among groundwater elevation, chloride concentration, and manganese concentration suggest that leachate stored in the vadose zone provides a continued source of contamination to groundwater.
Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
Contact Us- USGS Publications Warehouse
","tableOfContents":"The National Atmospheric Deposition Program (NADP) collects atmospheric data to monitor air pollution effects on the quality of United States water supplies and ecosystems. The NADP requires consistent data collection at fixed locations and is governed by a committee with participation by many Federal and State agencies, universities, Tribes, and private companies. NADP conducts a spring business meeting where the Network Operations, Education and Outreach, and multiple science subcommittees adjourn to discuss all aspects of NADP. Similar to the spring meeting, a fall meeting occurs with the addition of a two-day scientific symposium where scientists present their research using NADP data.
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]\n}","contact":"National Atmospheric Deposition Program Coordinator
Observing Systems Division
Water Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
The Prairie Pothole Region (PPR) of North America is a globally important area hosting >50 % of North America’s breeding ducks. Ducks in the PPR depend on wetlands and grasslands which have experienced accelerated losses in extent and quality due to agriculture. While other bird populations have declined, duck abundance reached record highs recently (2013–2017). We explored this discontinuity by examining monitoring data for trends in pond numbers (wetlands with ponded water) and interannual dynamics (water-level dynamics indexed by interannual change in pond numbers) and how those factors influenced duck productivity in the PPR during 1976–2019. Over time, pond numbers increased but their interannual dynamics declined, indicating stabilization of an ecosystem evolved with a dynamic climate. Our models accounted for 67 and 71 % of variation in productivity of PPR-obligate gadwall (Mareca strepera) and redheads (Aythya americana), respectively. Breeding productivity of these sentinel species was positively correlated with pond abundance and dynamics, and systematically declined. Accordingly, our analyses revealed sensitivity of breeding ducks to systematic change in the PPR previously obscured by increasingly abundant pond numbers. Interannual pond dynamics improved duck productivity and pond dynamics have declined indicating a de facto 44-year decline in duck productivity which is likely driven by water-level stabilization decreasing quality of brood-rearing wetlands. Residual temporal effects indicated that productivity has also declined for other reasons, such as agricultural land use changes. While mechanisms behind these correlations are speculative, they demonstrate the importance of further understanding land use and climate changes in the PPR for conservation of these important species and ecosystems.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.indic.2025.101073","usgsCitation":"Anteau, M.J., Szymanski, M.L., and Pearse, A.T., 2025, Wetland hydrologic dynamics and duck productivity are declining in the Prairie Pothole Region, and they are linked: Environmental and Sustainability Indicators, v. 29, 101073, 11 p., https://doi.org/10.1016/j.indic.2025.101073.","productDescription":"101073, 11 p.","ipdsId":"IP-172342","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":499615,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.indic.2025.101073","text":"Publisher Index Page"},{"id":499373,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alberta, Iowa, Manitoba, Minnesota, Montana, North Dakota, South Dakota","otherGeospatial":"Prairie Pothole Region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.35951256057884,\n 55.830201295287566\n ],\n [\n -115.58338333574763,\n 47.847886890172305\n ],\n [\n -101.7900722437109,\n 47.49234945376459\n ],\n [\n -100.95405193901554,\n 43.77396787878899\n ],\n [\n -96.57786404894601,\n 43.798830626316516\n ],\n [\n -94.52621944089687,\n 42.26192084940283\n ],\n [\n -95.45523589381337,\n 46.222540001545326\n ],\n [\n -97.03293587652475,\n 51.31009651298103\n ],\n [\n -119.35951256057884,\n 55.830201295287566\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"29","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Anteau, Michael J. 0000-0002-5173-5870 manteau@usgs.gov","orcid":"https://orcid.org/0000-0002-5173-5870","contributorId":3427,"corporation":false,"usgs":true,"family":"Anteau","given":"Michael","email":"manteau@usgs.gov","middleInitial":"J.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":954798,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Szymanski, Michael L","contributorId":365789,"corporation":false,"usgs":false,"family":"Szymanski","given":"Michael","middleInitial":"L","affiliations":[{"id":87220,"text":"North Dakota Game and Fish Dept.","active":true,"usgs":false}],"preferred":false,"id":954799,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pearse, Aaron T. 0000-0002-6137-1556 apearse@usgs.gov","orcid":"https://orcid.org/0000-0002-6137-1556","contributorId":1772,"corporation":false,"usgs":true,"family":"Pearse","given":"Aaron","email":"apearse@usgs.gov","middleInitial":"T.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":954800,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70273235,"text":"70273235 - 2025 - Designs for cyanobacterial harmful algal bloom monitoring in the Sacramento–San Joaquin Delta, California","interactions":[],"lastModifiedDate":"2025-12-22T15:16:10.010119","indexId":"70273235","displayToPublicDate":"2025-12-05T08:48:02","publicationYear":"2025","noYear":false,"publicationType":{"id":27,"text":"Preprint"},"publicationSubtype":{"id":32,"text":"Preprint"},"seriesTitle":{"id":19891,"text":"ESS Open Archive","active":true,"publicationSubtype":{"id":32}},"title":"Designs for cyanobacterial harmful algal bloom monitoring in the Sacramento–San Joaquin Delta, California","docAbstract":"Spatio-temporal changes to our world’s surface water resources are escalating. Translating how these changes impact communities and ecosystems requires time-varying data of Global Surface Water Extents (GSWE). Traditionally, GSWE mapping has been limited to static estimates, with recent efforts focusing on annual averages, frequency and occurrence of long-term variations. Building upon these foundational capabilities, we harnessed remotely sensed Sentinel-2 based near real-time Dynamic World (DW) land cover products to produce the first-of-its-kind 10 m resolution GSWE dataset representing 2015–2023. Our dataset estimated 2.5 million km2 of permanent waters and 8 million km2 of seasonal waters worldwide. Comparing our Sentinel-2 based data to contemporary Landsat-based GSWE, we observed that our data mapped less water within the >50% probability of occurrence range, suggesting a lower presence of open permanent water especially in high latitudes, deviating from what we previously learnt from Landsat data. Statistical analysis compared to well-established observational products and widely used GSWE datasets across some of the world’s most ecologically significant regions, including Pantanal in South America and Haor in South Asia, supports the overall physical realism of our data in predicting global open surface water dynamics. Our key contribution is a prototype Open Science operational framework that extracts routinely available DW products, runs geospatial analytics, and creates actionable water information for educators, researchers, and stakeholders at any scale of practical interest. We present examples of this operational capability through instant mapping of flood in Spain and drought in Lake Urmia, Central Asia, frequent monitoring of river extent changes at the Ganges–Brahmaputra confluence, and above all, interoperability with other existing GSWE applications.
","language":"English","publisher":"IOP Publishing","doi":"10.1088/1748-9326/ae137b","usgsCitation":"Khare, A., Gupta, B.C., Rajib, A., Vanderhoof, M.K., Wu, Q., 2025, Estimates of global surface water dynamics harnessing near real-time land cover observations and open science geospatial capabilities: Environmental Research Letters, v. 20, no. 12, 124042, 17 p., https://doi.org/10.1088/1748-9326/ae137b.","productDescription":"124042, 17 p.","ipdsId":"IP-164592","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":500606,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ae137b","text":"Publisher Index Page"},{"id":500503,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"20","issue":"12","noUsgsAuthors":false,"publicationDate":"2025-12-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Khare, Arushi","contributorId":366982,"corporation":false,"usgs":false,"family":"Khare","given":"Arushi","affiliations":[{"id":50034,"text":"University of Texas, Arlington","active":true,"usgs":false}],"preferred":false,"id":956524,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gupta, Bikas C.","contributorId":366983,"corporation":false,"usgs":false,"family":"Gupta","given":"Bikas","middleInitial":"C.","affiliations":[{"id":50034,"text":"University of Texas, Arlington","active":true,"usgs":false}],"preferred":false,"id":956525,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rajib, Adnan","contributorId":365158,"corporation":false,"usgs":false,"family":"Rajib","given":"Adnan","affiliations":[{"id":50034,"text":"University of Texas, Arlington","active":true,"usgs":false}],"preferred":false,"id":956526,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vanderhoof, Melanie K. 0000-0002-0101-5533 mvanderhoof@usgs.gov","orcid":"https://orcid.org/0000-0002-0101-5533","contributorId":168395,"corporation":false,"usgs":true,"family":"Vanderhoof","given":"Melanie","email":"mvanderhoof@usgs.gov","middleInitial":"K.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":956527,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wu, Qiusheng","contributorId":208272,"corporation":false,"usgs":false,"family":"Wu","given":"Qiusheng","email":"","affiliations":[{"id":37769,"text":"Binghamton University","active":true,"usgs":false}],"preferred":false,"id":956528,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70272754,"text":"70272754 - 2025 - Imidacloprid in United States rivers, 2013–2022: Persistent presence and emerging chronic hazard","interactions":[],"lastModifiedDate":"2026-01-07T17:41:43.211623","indexId":"70272754","displayToPublicDate":"2025-12-04T08:33:36","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Imidacloprid in United States rivers, 2013–2022: Persistent presence and emerging chronic hazard","docAbstract":"Imidacloprid, a neonicotinoid insecticide, is used for agricultural and nonagricultural purposes and is toxic to nontarget organisms at low concentrations in aquatic ecosystems. A total of 12,547 water samples were collected from 2013 to 2022 from 77 rivers across the United States (U.S.) and were analyzed to evaluate detections and temporal trends in imidacloprid concentrations. Imidacloprid was detected in 44% of all samples, and the mean concentration, adjusted for nondetect samples, of 24.9 ng/L (median = 11.9 ng/L) was more than twice the chronic benchmark for freshwater invertebrates (10 ng/L). This potential hazard to aquatic life was persistent, with 44% of the sites having a median concentration exceeding the chronic benchmark. Half of the sites (n = 38) had increasing trends, including large river sites along the Mississippi River. The mean increase was 10.6 ng/L over the past decade, while only six sites indicated decreasing trends. The estimated total loading of imidacloprid delivered to the Gulf of America from 2013 to 2022 was 129,489 kg (142.7 U.S. ton). The extensive presence of imidacloprid in U.S. waterways, the high percentage of sites with trends of increasing concentrations, and the prevalence of concentrations exceeding chronic benchmarks suggest widespread persistent risks to ecosystem health.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.5c07311","usgsCitation":"Miller, S.A., Schmidt, T.S., Barber, L., Hladik, M.L., Kolpin, D., Shoda, M.E., and Stackpoole, S.M., 2025, Imidacloprid in United States rivers, 2013–2022: Persistent presence and emerging chronic hazard: Environmental Science & Technology, v. 59, no. 49, p. 26702-26715, https://doi.org/10.1021/acs.est.5c07311.","productDescription":"14 p.","startPage":"26702","endPage":"26715","ipdsId":"IP-177221","costCenters":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"links":[{"id":497189,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":497397,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.5c07311","text":"Publisher Index Page"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n 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Wake County, in central North Carolina, is experiencing rapid population growth, associated land development, and changing water use. Hydrogeologic data including groundwater levels, aquifer testing, borehole fracture flow measurements, water-quality samples, and groundwater age-dating tracers were collected, along with findings from previous investigations, to help inform a conceptual model of the flow system used to develop a modular three-dimensional finite-difference groundwater-flow model (MODFLOW) for simulating historical and future groundwater conditions from 2000 to 2070.
Hydraulic conductivity and transmissivity ranges were estimated from 17 slug tests and 21 borehole-flow measurements. Groundwater-quality analytical results from 19 sampling sites indicate that oxidation-reduction (redox) conditions varied within the regolith and bedrock and that minimal evaporation occurred before recharge entered the groundwater system. Age dating revealed mixtures of older and younger water, ranging from the 1940s to the 1990s—indicating variable flow pathways of recharge within permeable bedrock fracture zones.
To simplify the complex fractured-rock groundwater system, two layers representing the regolith and the fractured bedrock were used in the MODFLOW model. Model calibration included parameter estimation and provided a reasonable fit to observed groundwater levels and estimated stream base flows. The model forecast scenarios incorporated future climate-model data for two emissions scenarios with land cover change projections to simulate potential impacts to future groundwater levels, recharge, and base flows. Recharge and base flow projections were largely within historical ranges, with no apparent long-term trends, but did indicate a slight downward shift in median values—likely, in part, because of differences in spatial resolution of input climate datasets. Seasonal patterns were consistent with historical data, with projections of possible increases in future winter recharge. Model limitations are discussed, and additional monitoring and model refinement needs are highlighted to support decision making for local groundwater management.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255087","issn":"2328-0328","collaboration":"Prepared in cooperation with Wake County Environmental Services","usgsCitation":"Antolino, D.J., Gonthier, G.J., and Sanchez, G.M., 2025, Simulation of groundwater flow in Wake County, North Carolina, 2000 through 2070: U.S. Geological Survey Scientific Investigations Report 2025–5087, 77 p., https://doi.org/10.3133/sir20255087.","productDescription":"Report: xii, 77 p.; 2 Data Releases","numberOfPages":"94","onlineOnly":"Y","ipdsId":"IP-141136","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":497806,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119050.htm"},{"id":496949,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5087/coverthb.jpg"},{"id":497076,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255087/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5087 HTML"},{"id":497075,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5087/sir20255087.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2025-5087 XML"},{"id":496956,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UC8F3Z","text":"USGS Data Release","linkHelpText":"- Water-level data and results for slug tests performed in 17 wells in Wake County, North Carolina, 2020 and 2021"},{"id":496955,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9N3EQ86","text":"USGS Data Release","linkHelpText":"- MODFLOW-NWT model used to simulate groundwater flow in Wake County, North Carolina, 2000 through 2070"},{"id":496950,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5087/sir20255087.pdf","size":"21.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5087 PDF"},{"id":496958,"rank":2,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5087/images"}],"country":"United States","state":"North Carolina","county":"Wake County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-78.5465,36.0218],[-78.4307,35.9795],[-78.3969,35.9387],[-78.3567,35.9318],[-78.351,35.909],[-78.3385,35.9052],[-78.3347,35.8997],[-78.3302,35.896],[-78.3245,35.896],[-78.3177,35.8963],[-78.3137,35.8976],[-78.3081,35.8935],[-78.2948,35.8797],[-78.292,35.8792],[-78.2893,35.8741],[-78.2859,35.8713],[-78.2831,35.8681],[-78.2782,35.8631],[-78.2749,35.8567],[-78.2756,35.8494],[-78.2707,35.843],[-78.2657,35.8361],[-78.2652,35.8325],[-78.2613,35.8315],[-78.2591,35.826],[-78.2599,35.8183],[-78.3731,35.7523],[-78.4635,35.7072],[-78.4686,35.7087],[-78.4709,35.7078],[-78.4732,35.7046],[-78.4778,35.7011],[-78.5716,35.6255],[-78.708,35.5191],[-78.9196,35.5857],[-78.9956,35.6104],[-78.9796,35.6656],[-78.9439,35.7515],[-78.9421,35.756],[-78.9403,35.7615],[-78.9337,35.7859],[-78.9191,35.8216],[-78.9096,35.8506],[-78.9076,35.8678],[-78.89,35.8676],[-78.8298,35.8689],[-78.8056,35.9281],[-78.7609,35.9176],[-78.751,35.9307],[-78.7372,35.941],[-78.714,35.9729],[-78.7009,36.0068],[-78.6985,36.0131],[-78.7048,36.0091],[-78.7077,36.0087],[-78.7076,36.0132],[-78.7052,36.0223],[-78.7085,36.0287],[-78.7102,36.0287],[-78.713,36.0278],[-78.7164,36.0283],[-78.7232,36.0334],[-78.726,36.0343],[-78.7272,36.0334],[-78.7278,36.0289],[-78.7324,36.0267],[-78.7353,36.0199],[-78.7422,36.0209],[-78.75,36.026],[-78.7551,36.0283],[-78.7545,36.0301],[-78.7511,36.0323],[-78.7499,36.035],[-78.747,36.0395],[-78.7492,36.0427],[-78.7503,36.0468],[-78.7519,36.0491],[-78.7564,36.0532],[-78.7498,36.0718],[-78.7088,36.0768],[-78.6895,36.0752],[-78.5922,36.0378],[-78.5465,36.0218]]]},\"properties\":{\"name\":\"Wake\",\"state\":\"NC\"}}]}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
1770 Corporate Drive, suite 500
Norcross, GA 30093
Contact Us- USGS Publications Warehouse
","tableOfContents":"Bank erosion in Arctic rivers helps shape channel geometry, mobilizes carbon from permafrost and influences sediment delivery to the Arctic Ocean. On Alaska's Arctic coastal plain, rivers begin flowing during snowmelt in late spring while extensive river ice persists in channels, such that hydraulics are altered and water is kept cool. The effects of river ice on permafrost bank erosion are poorly understood, primarily due to a dearth of field observations and a lack of river ice in existing models.
To address this knowledge gap, we developed a numerical model to simulate the melt of substrate interstitial ice and bank collapse along individual permafrost river banks. We parameterize the model with field observations from riverbanks in three different channels on the Canning River delta, which are disparately impacted by river ice during snowmelt. We explore the bank erosion produced without river ice in the model and with modern river ice model scenarios that we drive with different stages and water temperature boundary conditions. We also compare predicted erosion rates to observations from satellite imagery to validate this approach.
In the model, banks are idealized as vertical profiles that rise 1–2 m above the river bed and are comprised of silt- to sand-sized sediment with dense roots in the active layer. Underneath, we generalize bank ice content underneath the active layer to represent ice-rich permafrost on the river corridor boundaries. The model predicts that these ice-rich river banks can erode by 2–6 m/yr. Scenarios without ice underpredict erosion in the distributary channels. Scenarios with varying river ice for different deltaic channels produce erosion rates similar to observations.
Our results suggest that the prolonged melt of thick river ice in a delta nonlinearly impacts permafrost bank erosion by blocking river discharge to certain branches, heightening stage across the distributary network and locally limiting river water warming. Given expected changes in air temperature and hydrology, future estimates of Arctic river bank erosion could be improved by considering river ice.
","language":"English","publisher":"Wiley","doi":"10.1002/esp.70189","usgsCitation":"Arcuri, J., Overeem, I., Repasch, M., Anderson, R.S., Anderson, S.P., Koch, J.C., and Urban, F., 2025, River ice controls permafrost bank erosion across an Arctic delta: Earth Surface Processes and Landforms, v. 50, no. 15, e70189, 16 p., https://doi.org/10.1002/esp.70189.","productDescription":"e70189, 16 p.","ipdsId":"IP-179882","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":497140,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Canning River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -144.96629304307663,\n 70.01100341463973\n ],\n [\n -146.12712314576996,\n 70.21006797383902\n ],\n [\n -146.58932980728264,\n 69.87830435250464\n ],\n [\n -146.27585557894227,\n 68.96347382420646\n ],\n [\n -145.52140711630287,\n 68.61915114052712\n ],\n [\n -144.96629304307663,\n 70.01100341463973\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"50","issue":"15","noUsgsAuthors":false,"publicationDate":"2025-12-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Arcuri, J","contributorId":363264,"corporation":false,"usgs":false,"family":"Arcuri","given":"J","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":951392,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Overeem, Irina","contributorId":197487,"corporation":false,"usgs":false,"family":"Overeem","given":"Irina","email":"","affiliations":[],"preferred":false,"id":951393,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Repasch, Marisa 0000-0003-2636-9896","orcid":"https://orcid.org/0000-0003-2636-9896","contributorId":334190,"corporation":false,"usgs":false,"family":"Repasch","given":"Marisa","email":"","affiliations":[],"preferred":false,"id":951394,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anderson, R. S.","contributorId":269710,"corporation":false,"usgs":false,"family":"Anderson","given":"R.","middleInitial":"S.","affiliations":[],"preferred":false,"id":951395,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Anderson, S. P.","contributorId":363265,"corporation":false,"usgs":false,"family":"Anderson","given":"S.","middleInitial":"P.","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":951396,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Koch, Joshua C. 0000-0001-7180-6982 jkoch@usgs.gov","orcid":"https://orcid.org/0000-0001-7180-6982","contributorId":202532,"corporation":false,"usgs":true,"family":"Koch","given":"Joshua","email":"jkoch@usgs.gov","middleInitial":"C.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":951397,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Urban, Frank 0000-0002-1329-1703 furban@usgs.gov","orcid":"https://orcid.org/0000-0002-1329-1703","contributorId":127827,"corporation":false,"usgs":true,"family":"Urban","given":"Frank","email":"furban@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":951398,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70272673,"text":"70272673 - 2025 - Present and future coastal flooding hazard for Long Island, NY and Long Island Sound (NY/CT), USA","interactions":[],"lastModifiedDate":"2025-12-03T16:09:30.659734","indexId":"70272673","displayToPublicDate":"2025-12-02T10:03:43","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":"Present and future coastal flooding hazard for Long Island, NY and Long Island Sound (NY/CT), USA","docAbstract":"Coastal flooding and the associated damages due to storms are increasing with sea level rise around the world, with regional variability in the severity of impacts., Researchers and resource managers need to better understand and predict the future shifts in coastal flooding due to these processes to plan for resilient and sustainable communities. Here we present an analysis of long-term historical records of water levels, tides, and modeled present-day wave climatologies, to characterize the present-day inundation extent in Long Island Sound and Long Island, NY. To understand the potential future changes in inundation extent, we provide a similar analysis of future climate projections of non-tidal residuals (storm surge) for the year 2050 and compare these projections with our present-day results. We examine both the magnitude of relatively frequent events with a 0.99 annual exceedance probability to more extreme events with a 0.01 annual exceedance probability (or the 1 in 100-year event). This range of events is relevant for local managers to understand the spatial variability in coastal inundation, in addition to planning for larger more catastrophic events.
","language":"English","publisher":"EarthArXiv","doi":"10.31223/X5117Q","usgsCitation":"Cook, S.E., and Herdman, L.M., 2025, Present and future coastal flooding hazard for Long Island, NY and Long Island Sound (NY/CT), USA: EarthArXiv, https://doi.org/10.31223/X5117Q.","productDescription":"39 p.","ipdsId":"IP-170004","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":497010,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Cook, Salme Ellen 0000-0003-1129-6209","orcid":"https://orcid.org/0000-0003-1129-6209","contributorId":303775,"corporation":false,"usgs":true,"family":"Cook","given":"Salme","email":"","middleInitial":"Ellen","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":951281,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Herdman, Liv M. 0000-0002-5444-6441 lherdman@usgs.gov","orcid":"https://orcid.org/0000-0002-5444-6441","contributorId":149964,"corporation":false,"usgs":true,"family":"Herdman","given":"Liv","email":"lherdman@usgs.gov","middleInitial":"M.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":951282,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70272733,"text":"70272733 - 2025 - Projecting management-relevant change of undeveloped coastal barriers with the Mesoscale Explicit Ecogeomorphic Barrier model (MEEB) v1.0","interactions":[],"lastModifiedDate":"2025-12-05T16:02:59.966789","indexId":"70272733","displayToPublicDate":"2025-12-02T09:59:19","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1818,"text":"Geoscientific Model Development","active":true,"publicationSubtype":{"id":10}},"title":"Projecting management-relevant change of undeveloped coastal barriers with the Mesoscale Explicit Ecogeomorphic Barrier model (MEEB) v1.0","docAbstract":"Models of coastal barrier geomorphic and ecologic change are valuable tools for understanding and predicting when, where, and how barriers evolve and transition between ecogeomorphic states. Few existing models of barrier systems are designed to operate over spatiotemporal scales congruous with effective management practices (i.e., decades/kilometers, referred to herein as “mesoscales”), incorporate important ecogeomorphic feedbacks, and provide probabilistic projections of future change. Here, we present a new numerical model designed to address these gaps by explicitly yet efficiently simulating coupled aeolian, marine, vegetation, and shoreline components of barrier evolution over spatiotemporal scales relevant to management. The Mesoscale Explicit Ecogeomorphic Barrier model (MEEB) simulates subaerial ecomorphologic change of undeveloped barrier systems over kilometers and decades using meter-scale spatial resolution and weekly time steps. MEEB applies simplified parameterizations to represent and couple key ecogeomorphic processes: dune growth, vegetation expansion and mortality, beach and foredune erosion, barrier overwash, and shoreline and shoreface change. The model is parameterized and calibrated with observed elevation, vegetation, and water level data for a case study site of North Core Banks, NC, USA. Simulated ecogeomorphic change in model hindcasts agrees well with observations, demonstrating both favorable skill scores and qualitatively correct behavior. We also describe an additional model framework for producing probabilistic projections that account for uncertainties related to future forcing conditions and intrinsic stochastic dynamics and demonstrate the probabilistic framework's utility with example forecast simulations. As a mesoscale model, MEEB is designed to investigate questions about future barrier ecogeomorphic change of moderate complexity, offering semi-qualitative predictions and semi-quantitative explanations. For example, MEEB can be used to investigate how climate-induced shifts in ecological composition may alter the likelihood of morphologic impacts or to generate probabilistic projections of ecogeomorphic state change.
","language":"English","publisher":"European Geosciences Union","doi":"10.5194/gmd-18-9319-2025","usgsCitation":"Reeves, I.R., Ashton, A.D., Lentz, E.E., Sherwood, C.R., Passeri, D., and Zeigler, S., 2025, Projecting management-relevant change of undeveloped coastal barriers with the Mesoscale Explicit Ecogeomorphic Barrier model (MEEB) v1.0: Geoscientific Model Development, v. 18, p. 9319-9348, https://doi.org/10.5194/gmd-18-9319-2025.","productDescription":"30 p.","startPage":"9319","endPage":"9348","ipdsId":"IP-170312","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":497392,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/gmd-18-9319-2025","text":"Publisher Index Page"},{"id":497142,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"18","noUsgsAuthors":false,"publicationDate":"2025-12-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Reeves, Ian Robert 0000-0002-6675-3756","orcid":"https://orcid.org/0000-0002-6675-3756","contributorId":363346,"corporation":false,"usgs":true,"family":"Reeves","given":"Ian","middleInitial":"Robert","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":951466,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ashton, Andrew D. 0000-0002-0241-3090","orcid":"https://orcid.org/0000-0002-0241-3090","contributorId":363347,"corporation":false,"usgs":false,"family":"Ashton","given":"Andrew","middleInitial":"D.","affiliations":[{"id":36711,"text":"Woods Hole Oceanographic Institution","active":true,"usgs":false}],"preferred":false,"id":951467,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lentz, Erika E. 0000-0002-0621-8954 elentz@usgs.gov","orcid":"https://orcid.org/0000-0002-0621-8954","contributorId":173964,"corporation":false,"usgs":true,"family":"Lentz","given":"Erika","email":"elentz@usgs.gov","middleInitial":"E.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":951468,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sherwood, Christopher R. 0000-0001-6135-3553 csherwood@usgs.gov","orcid":"https://orcid.org/0000-0001-6135-3553","contributorId":2866,"corporation":false,"usgs":true,"family":"Sherwood","given":"Christopher","email":"csherwood@usgs.gov","middleInitial":"R.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":951469,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Passeri, Davina 0000-0002-9760-3195 dpasseri@usgs.gov","orcid":"https://orcid.org/0000-0002-9760-3195","contributorId":166889,"corporation":false,"usgs":true,"family":"Passeri","given":"Davina","email":"dpasseri@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":951470,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zeigler, Sara 0000-0002-5472-769X","orcid":"https://orcid.org/0000-0002-5472-769X","contributorId":222703,"corporation":false,"usgs":true,"family":"Zeigler","given":"Sara","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":951471,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70274167,"text":"70274167 - 2025 - Day versus night relations between larval lake whitefish, cisco, and zooplankton onshore in Lakes Michigan, Huron, and Superior","interactions":[],"lastModifiedDate":"2026-03-03T15:01:45.882417","indexId":"70274167","displayToPublicDate":"2025-12-02T08:54:19","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Day versus night relations between larval lake whitefish, cisco, and zooplankton onshore in Lakes Michigan, Huron, and Superior","docAbstract":"Lake whitefish (Coregonus clupeaformis) populations in the upper Great Lakes have undergone declines in the past two decades, particularly in Lakes Michigan and Huron. However, cisco (Coregonus artedi) are recovering in parts of the Great Lakes. Population declines are hypothesized to be due, in part, to reduced zooplankton prey in areas that serve as critical habitat for larval coregonines. Larval lake whitefish, cisco, and zooplankton are commonly sampled only during daylight hours. Habitat use, community composition, catch rates, and abundance estimates of larval fish and zooplankton can change drastically at night versus day, necessitating diel comparisons for a more comprehensive understanding of the early life history of coregonines and their prey. We collected paired day and night onshore (≤ 1 m depth) zooplankton and larval coregonine samples from Lakes Michigan, Huron, and Superior in March–June 2021 to test if there were diel differences in lake whitefish and cisco abundance and zooplankton density and biomass. We also tested if relationships exist between larval coregonine abundance and zooplankton density and biomass and environmental variables (water temperature, dissolved oxygen concentration, pH, specific conductivity, substrate type). We observed consistently higher zooplankton density and biomass and larval lake whitefish and cisco abundance at night. Larval coregonine abundance was positively related to higher zooplankton population estimates but was not related to the environmental variables measured. Our results provide insight into sampling practices for larval lake whitefish, cisco, and zooplankton onshore in the Great Lakes to better understand factors influencing larval lake whitefish and cisco recruitment.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2025.102668","usgsCitation":"Freemon, S.D., Smith, J.B., Ackiss, A.S., Anweiler, K.V., Freeman, H.N., Hessell, C.R., Jonas, J., LaFaver, C.J., Olsen, E.J., and Doubek, J.P., 2025, Day versus night relations between larval lake whitefish, cisco, and zooplankton onshore in Lakes Michigan, Huron, and Superior: Journal of Great Lakes Research, v. 51, no. 6, 102668, 10 p., https://doi.org/10.1016/j.jglr.2025.102668.","productDescription":"102668, 10 p.","ipdsId":"IP-170585","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":500724,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan","otherGeospatial":"Lake Huron, Lake Michigan, Lake Superior","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -86,\n 47\n ],\n [\n -86,\n 44.8\n ],\n [\n -83,\n 44.8\n ],\n [\n -83,\n 47\n ],\n [\n -86,\n 47\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"51","issue":"6","noUsgsAuthors":false,"publicationDate":"2025-12-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Freemon, Simon D.D.","contributorId":367098,"corporation":false,"usgs":false,"family":"Freemon","given":"Simon","middleInitial":"D.D.","affiliations":[{"id":35243,"text":"Lake Superior State University","active":true,"usgs":false}],"preferred":false,"id":956752,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Jason B.","contributorId":367099,"corporation":false,"usgs":false,"family":"Smith","given":"Jason","middleInitial":"B.","affiliations":[{"id":79162,"text":"Sault Ste. 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New York, 347 U.S. 995), established the position of Delaware River Master within the U.S. Geological Survey. In addition, the Decree authorizes the diversion of water from the Delaware River Basin and requires that compensating releases from certain reservoirs owned by New York City be made under the supervision and direction of the River Master. The Decree stipulates that the River Master provide reports to the Court, not less frequently than annually. This report is the 65th annual report of the River Master of the Delaware River. The report covers the 2018 River Master report year, from December 1, 2017, to November 30, 2018.
During the report year, precipitation in the upper Delaware River Basin was 60.39 inches or 136 percent of the long-term average. On December 1, 2017, combined useable storage in the New York City reservoirs in the upper Delaware River Basin was 193.230 billion gallons or 71.3 percent of the combined useable storage capacity of 270.837 billion gallons. The reservoirs had a usable capacity of 99.5 percent on May 31, 2018. Combined storage remained high (above 80 percent combined capacity) and did not decline below 80 percent of combined capacity through November 30, 2018. River Master operations during the year were conducted as stipulated by the Decree and the Flexible Flow Management Program.
Diversions from the Delaware River Basin by New York City and New Jersey fully complied with the Decree. Reservoir releases were made as directed by the River Master at rates designed to meet the flow objective for the Delaware River at Montague, New Jersey, on 42 days during the report year. Interim Excess Release Quantity banks and conservation releases, designed to relieve thermal stress and protect the fishery and aquatic habitat in the tailwaters of the reservoirs, were also made during the report year.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251051","isbn":"978-1-4113-4631-4","usgsCitation":"Russell, K.L., Andrews, W.J., and McHugh, A.R., 2025, Report of the River Master of the Delaware River for the period December 1, 2017–November 30, 2018: U.S. Geological Survey Open-File Report 2025–1051, 79 p., https://doi.org/10.3133/ofr20251051.","productDescription":"x, 79 p.","numberOfPages":"79","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-170329","costCenters":[{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true}],"links":[{"id":496884,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1051/images/"},{"id":496883,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1051/ofr20251051.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2025-1051 XML"},{"id":496882,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251051/full","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1051 HTML"},{"id":496881,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1051/ofr20251051.pdf","text":"Report","size":"16.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1051 PDF"},{"id":496880,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1051/coverthb.jpg"}],"country":"United States","state":"Delaware, New Jersey, New York, Pennsylvania","otherGeospatial":"Delaware River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76,\n 42.75\n ],\n [\n -76,\n 39.7\n ],\n [\n -73.5,\n 39.7\n ],\n [\n -73.5,\n 42.75\n ],\n [\n -76,\n 42.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Delaware River Master
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U.S. Geological Survey
The San Luis Valley (SLV), Colorado is a critical stopover area for Rocky Mountain Population greater Sandhill Cranes (Antigone canadensis tabida). During spring and autumn, cranes use crops for foraging and water resources adjacent to foraging areas for roosting and loafing. However, surface water is becoming increasingly limited in the SLV. Understanding the factors that affect use by roosting, loafing, and foraging cranes and where habitat is the most limiting will inform water and habitat management under changing conditions. We used mixed-effects models to determine the effects of habitat variables, ownership, and landcover type on the selection of roosting, loafing, and foraging areas by cranes marked with GPS transmitters (2015–2021). We found that Sandhill Cranes selected for areas with a high amount of water, relatively short vegetation (< 5 m in autumn, < 10 m in spring), close to grain fields (< 5 km), and areas identified as open water for roosting. Loafing Sandhill Cranes also selected for areas with short vegetation and close to grain fields but that had less water and more sandbar and were identified as pastures or wetlands. Although selection was higher for private land overall, we found evidence of avoidance of private lands and a stronger preference for public lands with increasing surface water for roosting in spring. For foraging areas, selection was highest for barley in both seasons, but triticale and other grains had relatively high selection in autumn. Our research confirms the importance of providing roosting and loafing areas on both private and public lands close to foraging areas and provides evidence that roosting and loafing opportunities may be most limited on public lands in the SLV.
","language":"English","publisher":"The Resilience Alliance","doi":"10.5751/ACE-02924-200214","usgsCitation":"Vanausdall, R.A., Kendall, W.L., Collins, D.P., Donnelly, J.P., Hays, Q.R., 2025, Habitat selection by Rocky Mountain Population greater Sandhill Cranes (Antigone canadensis tabida) during spring and autumn migration at a key stopover area: Avian Conservation and Ecology., v. 20, no. 2, 14, 19 p., https://doi.org/10.5751/ACE-02924-200214.","productDescription":"14, 19 p.","ipdsId":"IP-167764","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":500589,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5751/ace-02924-200214","text":"Publisher Index Page"},{"id":500424,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"San Luis Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107,\n 38.5\n ],\n [\n -107,\n 37\n ],\n [\n -105,\n 37\n ],\n [\n -105,\n 38.5\n ],\n [\n -107,\n 38.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"20","issue":"2","noUsgsAuthors":false,"publicationDate":"2025-12-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Vanausdall, Rachel A.","contributorId":366817,"corporation":false,"usgs":false,"family":"Vanausdall","given":"Rachel","middleInitial":"A.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":956273,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kendall, William L. 0000-0003-0084-9891","orcid":"https://orcid.org/0000-0003-0084-9891","contributorId":204844,"corporation":false,"usgs":true,"family":"Kendall","given":"William","email":"","middleInitial":"L.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":956274,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Collins, Daniel P.","contributorId":366821,"corporation":false,"usgs":false,"family":"Collins","given":"Daniel","middleInitial":"P.","affiliations":[{"id":12428,"text":"U. 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Maximum CO2 injection rates can be enhanced by extracting brine from the CO2 storage unit. However, disposal of the extracted brine is both a technological and economic challenge. The lowest-cost option would likely be reinjection of the extracted brine into another formation above or below the CO2 storage unit. Therefore, it is important to estimate brine injectivity as it will constrain the potential to increase CO2 injectivity at an injection site that has access to multiple geologic storage units where either CO2 or brine can be injected. Using a simulation-optimization framework, coupled with a non-isothermal, multiphase CO2-water-salt equation-of-state module, we developed a computationally efficient method for evaluating optimization of simultaneous CO2 injection, brine extraction, and brine (re)injection at hypothetical injection sites deployed across a geologic basin. The Illinois basin is ideal for testing our methodology because it contains multiple geologic storage units with seals in between them to isolate injection of CO2 in one unit from interfering with the injection of either brine or CO2 in another unit above or below it. In addition, we investigated the relative effects of variation in key geologic parameters as well as two reservoir structures (hydrogeologic heterogeneity/anisotropy and homogeneity/isotropy) on CO2 injectivities and enhancement of CO2 injectivity through extracting brine. Results suggest that permeability, depth, and especially thickness of the storage unit could be the most influential parameters determining CO2 injectivity. They also suggest that only injecting CO2 into the storage unit with the greatest injectivity, enhancing that unit’s injectivity by extracting brine, and disposing of the produced brine in other suitable units could maximize total CO2 injectivity in limited regions of the basin. At the majority of simulated injection sites, however, we found that injecting CO2 into all of the accessible and suitable storage units was more likely to maximize the CO2 storage resource.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/feart.2025.1639952","usgsCitation":"Plampin, M.R., Anderson, S.T., Finsterle, S., and Wiens, A.M., 2025, Estimation of dynamic geologic CO2 storage resources in the Illinois Basin, including effects of brine extraction, anisotropy, and hydrogeologic heterogeneity: Frontiers in Earth Science, v. 13, 1639952, 18 p., https://doi.org/10.3389/feart.2025.1639952.","productDescription":"1639952, 18 p.","ipdsId":"IP-177734","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":497113,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2025.1639952","text":"Publisher Index Page"},{"id":497059,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Kentucky, Indiana","otherGeospatial":"Illinois Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -91.66105038187163,\n 41.36771728120675\n ],\n [\n -91.66105038187163,\n 37.13535863641968\n ],\n [\n -84.79728409671057,\n 37.13535863641968\n ],\n [\n -84.79728409671057,\n 41.36771728120675\n ],\n [\n -91.66105038187163,\n 41.36771728120675\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"13","noUsgsAuthors":false,"publicationDate":"2025-12-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Plampin, Michelle R. 0000-0003-4068-5801","orcid":"https://orcid.org/0000-0003-4068-5801","contributorId":363249,"corporation":false,"usgs":false,"family":"Plampin","given":"Michelle","middleInitial":"R.","affiliations":[{"id":86662,"text":"USGS, Geology, Energy & Minerals Science Center, DRP not in active directory","active":true,"usgs":false}],"preferred":false,"id":951354,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Steven T. 0000-0003-3481-3424 sanderson@usgs.gov","orcid":"https://orcid.org/0000-0003-3481-3424","contributorId":2532,"corporation":false,"usgs":true,"family":"Anderson","given":"Steven","email":"sanderson@usgs.gov","middleInitial":"T.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":951355,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Finsterle, Stefan","contributorId":299677,"corporation":false,"usgs":false,"family":"Finsterle","given":"Stefan","email":"","affiliations":[{"id":64929,"text":"Finsterle GeoConsulting, Inc.","active":true,"usgs":false}],"preferred":false,"id":951356,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wiens, Ashton M. 0000-0002-7030-0602","orcid":"https://orcid.org/0000-0002-7030-0602","contributorId":271176,"corporation":false,"usgs":true,"family":"Wiens","given":"Ashton","email":"","middleInitial":"M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":951357,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70273720,"text":"70273720 - 2025 - Detecting hidden sedimentary geothermal systems in the Upper Colorado River Basin","interactions":[],"lastModifiedDate":"2026-01-26T16:15:03.676695","indexId":"70273720","displayToPublicDate":"2025-12-01T09:57:32","publicationYear":"2025","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Detecting hidden sedimentary geothermal systems in the Upper Colorado River Basin","docAbstract":"Geothermal resources exist in sedimentary rock where circulation of water for efficient extraction or storage of heat is possible. Except in rare instances where hot water is expressed at the land surface, sedimentary geothermal resources are hidden, so the identification of these systems is optimally accomplished using predictive subsurface modeling. An integrated approach using detailed paleogeographic interpretations, subsurface geologic mapping, and numerical modeling has produced regional geologic and temperature models for the Upper Colorado River Basin, a large watershed in central North America that contains many sedimentary basins. These models identify areas of hidden sedimentary geothermal resource potential in low temperature (<90°C), moderate temperature (90–150°C), and high temperature (>150°C) fairways across the study area. These models incorporate maps of key horizons in outcrop and the subsurface to create a robust structural framework that can be used to target favorable geology for natural or engineered permeability. This framework is populated with lithologies derived from detailed palaeogeographical maps and over 40,000 bottom hole temperature (BHT) values were used to create a calibrated three-dimensional (3D) temperature model across the region. The resulting maps serve as a regional sedimentary geothermal play fairway screening tool for evaluating different grades of sedimentary geothermal resources and for identifying areas of interest where more detailed, prospect-scale studies can be undertaken.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Using the Earth to save the Earth","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Geothermal Resources Council","usgsCitation":"Gardner, R., Birdwell, J.E., Sweetkind, D., Sullivan, P., Eaton, M., Petermann, H., Clement, A., Hagadorn, J., and Woda, J., 2025, Detecting hidden sedimentary geothermal systems in the Upper Colorado River Basin, in Using the Earth to save the Earth, v. 49, p. 1512-1525.","productDescription":"14 p.","startPage":"1512","endPage":"1525","ipdsId":"IP-180873","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":499022,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":499003,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.geothermal-library.org/index.php?mode=pubs&action=view&record=1035309"}],"country":"United States","state":"Arizona, Colorado, New Mexico, Utah, Wyoming","otherGeospatial":"Upper Colorado River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105,\n 43.25\n ],\n [\n -113,\n 43.25\n ],\n [\n -113,\n 34\n ],\n [\n -105,\n 34\n ],\n [\n -105,\n 43.25\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"49","noUsgsAuthors":false,"publicationDate":"2025-12-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Gardner, Rand 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Holger","contributorId":365599,"corporation":false,"usgs":false,"family":"Petermann","given":"Holger","affiliations":[{"id":27833,"text":"Denver Museum of Nature and Science","active":true,"usgs":false}],"preferred":false,"id":954437,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Clement, Annaka","contributorId":365600,"corporation":false,"usgs":false,"family":"Clement","given":"Annaka","affiliations":[{"id":27833,"text":"Denver Museum of Nature and Science","active":true,"usgs":false}],"preferred":false,"id":954438,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hagadorn, James","contributorId":365601,"corporation":false,"usgs":false,"family":"Hagadorn","given":"James","affiliations":[{"id":27833,"text":"Denver Museum of Nature and Science","active":true,"usgs":false}],"preferred":false,"id":954439,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Woda, Joshua 0000-0002-2932-8013","orcid":"https://orcid.org/0000-0002-2932-8013","contributorId":290172,"corporation":false,"usgs":true,"family":"Woda","given":"Joshua","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":954440,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70274537,"text":"70274537 - 2025 - What is the (real) rate of soil health practice adoption? Making sense of three data sources","interactions":[],"lastModifiedDate":"2026-04-01T14:57:42.259853","indexId":"70274537","displayToPublicDate":"2025-12-01T09:53:26","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2456,"text":"Journal of Soil and Water Conservation","active":true,"publicationSubtype":{"id":10}},"title":"What is the (real) rate of soil health practice adoption? Making sense of three data sources","docAbstract":"Conservation stakeholders looking to quantify the impact of their investments to increase soil health practice adoption over time often face challenges in interpreting practice adoption data due to discrepancies in language and results among data sources. Similarly, efforts to estimate environmental outcomes of practice adoption, such as water quality and greenhouse gas emissions, can vary depending on different practice adoption input data. To help make sense of different adoption data sources, we compared county-level adoption data for winter cover crops (WCC), no-till (NT), and reduced tillage (RT) in three areas of the United States with contrasting climates and production systems: central Illinois (CIL), southern Illinois (SIL), and western New York (WNY). We analyzed data available during 2015 through 2022 from the Operational Tillage Information System (OpTIS, remote sensing), US Census of Agriculture (AgCensus, a farmer survey), and, specifically in Illinois, the Illinois Soil Conservation Transect Survey (Transect, a roadside survey). The magnitude of differences between the datasets depended on the practice and geographic location. For example, OpTIS and AgCensus tillage data were much more similar in Illinois (average difference of less than 4 percentage points) compared to New York (average differences of 20 percentage points). Similarly, there was less variability and smaller differences between OpTIS and AgCensus WCC data in Illinois compared to WNY. AgCensus tended to report lower WCC adoption for Illinois and greater adoption in WNY compared to OpTIS. All data sources agreed that the rate of change in tillage practices is slow (mainly –1% to 1%) and that adoption of WCC is low (assuming linear growth, it could take nearly a century to reach 50% WCC adoption in CIL). Differences among the datasets were attributed to definitional inconsistencies for RT and NT and how WCC data were acquired. For example, the AgCensus asks if a WCC was planted, whereas OpTIS and Transect evaluate the presence of a standing WCC. Data sources also reflect different time periods (calendar years or crop years) and types of cropland assessed (corn [Zea mays L.], soybean [Glycine max {L.} Merr.], or all cropland). We propose two recommendations to improve interpretation and consistency: (1) a working group to harmonize definitions and protocols and develop educational materials for data users, and (2) a research effort that integrates different adoption data types and produces publicly available adoption data at HUC-10 and county scales. Such activities could help improve data access and utility for evidence-based conservation decision-making and enhance the accuracy of environmental models that rely on adoption data as input.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/00224561.2025.2580218","usgsCitation":"McGill, B.M., Hively, W.D., Puntel, L.A., Shriver, J., Thieme, A.N., Manter, D.K., and Moore, J.M., 2025, What is the (real) rate of soil health practice adoption? Making sense of three data sources: Journal of Soil and Water Conservation, v. 80, no. 6, p. 724-733, https://doi.org/10.1080/00224561.2025.2580218.","productDescription":"10 p.","startPage":"724","endPage":"733","ipdsId":"IP-172017","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":501927,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"80","issue":"6","noUsgsAuthors":false,"publicationDate":"2026-01-27","publicationStatus":"PW","contributors":{"authors":[{"text":"McGill, Bonnie M.","contributorId":368946,"corporation":false,"usgs":false,"family":"McGill","given":"Bonnie","middleInitial":"M.","affiliations":[{"id":87675,"text":"American Farmland Trust","active":true,"usgs":false}],"preferred":false,"id":958154,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hively, W. Dean 0000-0002-5383-8064","orcid":"https://orcid.org/0000-0002-5383-8064","contributorId":201565,"corporation":false,"usgs":true,"family":"Hively","given":"W.","email":"","middleInitial":"Dean","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":958156,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Puntel, Laila A.","contributorId":368947,"corporation":false,"usgs":false,"family":"Puntel","given":"Laila","middleInitial":"A.","affiliations":[{"id":87676,"text":"Syngenta Group","active":true,"usgs":false}],"preferred":false,"id":958155,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shriver, John","contributorId":368948,"corporation":false,"usgs":false,"family":"Shriver","given":"John","affiliations":[{"id":87677,"text":"Regrow","active":true,"usgs":false}],"preferred":false,"id":958157,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thieme, Alison N.","contributorId":368949,"corporation":false,"usgs":false,"family":"Thieme","given":"Alison","middleInitial":"N.","affiliations":[{"id":87678,"text":"USDA-ARS-SASL","active":true,"usgs":false}],"preferred":false,"id":958158,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Manter, Daniel K.","contributorId":368950,"corporation":false,"usgs":false,"family":"Manter","given":"Daniel","middleInitial":"K.","affiliations":[{"id":87679,"text":"USDA-ARS-SMSBR","active":true,"usgs":false}],"preferred":false,"id":958159,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Moore, Jennifer M.","contributorId":368951,"corporation":false,"usgs":false,"family":"Moore","given":"Jennifer","middleInitial":"M.","affiliations":[{"id":87680,"text":"USDA-ARS-FSCRU","active":true,"usgs":false}],"preferred":false,"id":958160,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ]}