{"pageNumber":"16","pageRowStart":"375","pageSize":"25","recordCount":68790,"records":[{"id":70271974,"text":"70271974 - 2025 - Water withdrawal and consumption trends for thermoelectric-power plants in the conterminous United States, 2008-2020","interactions":[],"lastModifiedDate":"2025-12-01T16:46:15.609899","indexId":"70271974","displayToPublicDate":"2025-09-20T08:05:04","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":13280,"text":"Environmental Science and Technology: Water","active":true,"publicationSubtype":{"id":10}},"title":"Water withdrawal and consumption trends for thermoelectric-power plants in the conterminous United States, 2008-2020","docAbstract":"

Freshwater-using utility-scale thermoelectric (TE) plant water-use estimates were evaluated for annual trends from 2008 to 2020 across the conterminous United States (CONUS) and within hydrologic regions. Overall, TE water withdrawal and consumption trends declined across CONUS by 14,335 and 278 million liters/day, respectively. Decreasing water withdrawal and consumption trends for TE plants are driven largely by switching from coal-fired plants to other generation technologies. TE plant cooling system technology has also changed, with large declining trends for TE plants using once-through cooling systems and small increasing consumption trends for TE plants using recirculating tower cooling systems. Fifteen hydrologic regions have decreasing trends in withdrawals and consumption. The largest decreases are for coal-fired plants using once-through freshwater cooling systems in the Great Lakes and Ohio hydrologic regions. Natural gas combined cycle plants with recirculating tower cooling systems have increased water consumption trends across most of the CONUS hydrologic regions. Some TE plants with recirculating tower or once-through cooling systems withdraw water volumes that on average are close to or exceed average simulated streamflows. Most of these situations occur in the central and eastern U.S., potentially leading to water availability issues among competing water needs, ecosystem impacts from thermal pollution, and power generation constraints.

","language":"English","publisher":"American Chemical Society","doi":"10.1021/acsestwater.5c00360","usgsCitation":"Skinner, K.D., Niswonger, R.G., Harris, M.A., McCarthy, B.A., Chamberlin, C.A., Lombard, M.A., Diehl, T.H., Galanter, A.E., Gorman Sanisaca, L.E., and Stewart, J.S., 2025, Water withdrawal and consumption trends for thermoelectric-power plants in the conterminous United States, 2008-2020: Environmental Science and Technology: Water, v. 5, no. 10, p. 5280-5831, https://doi.org/10.1021/acsestwater.5c00360.","productDescription":"12 p.","startPage":"5280","endPage":"5831","ipdsId":"IP-150629","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":496324,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acsestwater.5c00360","text":"Publisher Index 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0000-0003-1711-3864","orcid":"https://orcid.org/0000-0003-1711-3864","contributorId":210381,"corporation":false,"usgs":true,"family":"Gorman Sanisaca","given":"Lillian","middleInitial":"E.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":949551,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Stewart, Jana S. 0000-0002-8121-1373","orcid":"https://orcid.org/0000-0002-8121-1373","contributorId":211037,"corporation":false,"usgs":true,"family":"Stewart","given":"Jana","middleInitial":"S.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":949552,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70271484,"text":"sir20255079 - 2025 - Microbial source tracking in Cedar and Crane Creeks near Curtice, Ohio, 2021","interactions":[],"lastModifiedDate":"2026-02-03T15:29:46.906418","indexId":"sir20255079","displayToPublicDate":"2025-09-19T12:25:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-5079","displayTitle":"Microbial Source Tracking in Cedar and Crane Creeks Near Curtice, Ohio, 2021","title":"Microbial source tracking in Cedar and Crane Creeks near Curtice, Ohio, 2021","docAbstract":"

Elevated concentrations of Escherichia coli (E. coli) bacteria and signs of sewage lead to impairment of Cedar and Crane Creeks near the town of Curtice, Ohio. In 2021, the U.S. Geological Survey, in cooperation with Ohio Environmental Protection Agency, collected samples and analyzed them for concentrations of E. coli and microbial source tracking (MST) markers to help characterize the locations and sources of fecal contamination and better inform potential remediation strategies. The study included a total of 118 samples collected at 12 sites (6 on Cedar Creek and 6 on Crane Creek) from May to September 2021 during wet and dry weather conditions.

All samples were analyzed for E. coli concentrations, and human and canine-associated MST markers. A subset of samples was analyzed for MST markers associated with swine, ruminant, cattle, horse, waterfowl, and poultry. Human-origin fecal contamination was found at all sites sampled in this study and concentrations of the human-associated MST marker HF183/BacR287 were significantly correlated with E. coli concentrations. The HF183/BacR287 marker was detected in 114 of 118 samples and the detection frequency in samples at each site ranged from 90 to 100 percent. E. coli concentrations exceeded the Ohio Environmental Protection Agency’s regulatory statistical threshold (410 most probable number of E. coli per 100 milliliters) in 91 percent of samples.

These findings verified that Cedar and Crane Creeks are impaired by bacteria, and the HF183/BacR287 marker results support that human-origin fecal contamination is the dominant contributor to that impairment. The canine-associated MST marker BacCan was also prevalent in collected samples (detected in 112 of 118 samples); however, BacCan can also be detected in human waste, so it is not feasible to ascertain whether canine feces is a source of contamination in these watersheds.

Human fecal contamination was nearly uniform among sites, but the Martin Williston Road ditch effluent site along Crane Creek had a significantly higher median HF183/BacR287 concentration than the other Crane Creek sites. Results indicate that the Martin Williston Road ditch is a potential source of human-origin fecal contamination to Crane Creek. There is likely additional human fecal contamination upstream from the study area.

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Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
6460 Busch Blvd, Suite 100
Columbus, OH 43229

","tableOfContents":"","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2025-09-19","noUsgsAuthors":false,"publicationDate":"2025-09-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Kephart, Christopher M. 0000-0002-3369-5596 ckephart@usgs.gov","orcid":"https://orcid.org/0000-0002-3369-5596","contributorId":1932,"corporation":false,"usgs":true,"family":"Kephart","given":"Christopher","email":"ckephart@usgs.gov","middleInitial":"M.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":948919,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lynch, Lauren D. 0000-0003-0209-1797","orcid":"https://orcid.org/0000-0003-0209-1797","contributorId":337141,"corporation":false,"usgs":true,"family":"Lynch","given":"Lauren","email":"","middleInitial":"D.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":948920,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70271479,"text":"sir20255082 - 2025 - Methods for estimating selected low-flow statistics at gaged and ungaged stream sites in Massachusetts","interactions":[],"lastModifiedDate":"2026-02-03T15:29:05.375527","indexId":"sir20255082","displayToPublicDate":"2025-09-19T09:50:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-5082","displayTitle":"Methods for Estimating Selected Low-Flow Statistics at Gaged and Ungaged Stream Sites in Massachusetts","title":"Methods for estimating selected low-flow statistics at gaged and ungaged stream sites in Massachusetts","docAbstract":"

The U.S. Geological Survey, in cooperation with the Massachusetts Department of Conservation and Recreation, Office of Water Resources, computed selected at-site streamflow statistics at U.S. Geological Survey streamgages in and near Massachusetts and developed regional regression equations for estimating selected streamflows at ungaged stream sites in Massachusetts. Two sets of regional regression equations were developed: (1) the “mainland” equations, for mainland Massachusetts excluding the area covered by the second set, and (2) the “southeastern” equations, for the Plymouth-Carver-Kingston-Duxbury aquifer area in southeastern Massachusetts and for Cape Cod. The regression equations and at-site statistics may be used by Federal, State, and local water managers in addressing water-resources issues relevant in Massachusetts.

Regional regression analyses for the mainland equations were developed to estimate the following 27 streamflow statistics: 99-, 98-, 95-, 90-, 85-, 80-, 75-, 70-, 60-, and 50-percent flow durations; monthly June, July, August, and September 90- and 50-percent flow durations; February, June, and August median of the monthly means; harmonic mean; and medians of the following annual low-flow frequency statistics: 7-day; 7-day, 2-year; 7-day, 10-year; 30-day, 2-year; and 30-day, 10-year. The analyses used 81 streamgages with minimal to no regulations in and near Massachusetts. The regression analyses determined that four basin characteristics—drainage area, combined hydrologic soils A and B, streamflow variability index, and annual mean temperature—were the only significant explanatory variables for the different mainland equations.

Regional regression equations were developed for the Plymouth-Carver-Kingston-Duxbury aquifer area in southeastern Massachusetts and Cape Cod, because surface-water drainage areas and groundwater contributing areas do not always coincide in this area of the State. The regression analyses to estimate 10 flow durations from the 99th to 50th percentiles used 18 streamflow sites with some occasional minor regulations—because there are few unregulated streams in southeastern Massachusetts. The analyses determined that groundwater contributing area and storage (combined water bodies and wetlands) were the only significant explanatory variables in the southeastern equations.

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\"}}]}","contact":"

Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532

","tableOfContents":"","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2025-09-19","noUsgsAuthors":false,"publicationDate":"2025-09-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Bent, Gardner C. 0000-0002-5085-3146","orcid":"https://orcid.org/0000-0002-5085-3146","contributorId":205226,"corporation":false,"usgs":true,"family":"Bent","given":"Gardner C.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":948892,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ahearn, Elizabeth A. 0000-0002-5633-2640 eaahearn@usgs.gov","orcid":"https://orcid.org/0000-0002-5633-2640","contributorId":194658,"corporation":false,"usgs":true,"family":"Ahearn","given":"Elizabeth","email":"eaahearn@usgs.gov","middleInitial":"A.","affiliations":[{"id":377,"text":"Massachusetts-Rhode Island Water Science Center","active":false,"usgs":true},{"id":196,"text":"Connecticut Water Science Center","active":true,"usgs":true}],"preferred":false,"id":948893,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fair, Jennifer H. 0000-0002-9902-1893","orcid":"https://orcid.org/0000-0002-9902-1893","contributorId":245941,"corporation":false,"usgs":true,"family":"Fair","given":"Jennifer","middleInitial":"H.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":948894,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70271743,"text":"70271743 - 2025 - Spatially resolved source apportionment of per- and polyfluoroalkyl substances (PFAS) within a post-industrial river catchment","interactions":[],"lastModifiedDate":"2025-09-23T14:33:54.417922","indexId":"70271743","displayToPublicDate":"2025-09-19T09:23:59","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Spatially resolved source apportionment of per- and polyfluoroalkyl substances (PFAS) within a post-industrial river catchment","docAbstract":"Source apportionment of per- and polyfluoroalkyl substances (PFAS) in rivers is typically based on water concentrations, which cannot quantify PFAS loads or define geographical source areas. This study applied a river catchment-scale approach to identify PFAS source zones and assess the relative importance of industrial PFAS sources in the River Mersey, UK – a post-industrial, densely populated catchment with diverse PFAS sources. Synoptic sampling and PFAS river load analysis identified key sub-catchments and river stretches contributing the majority of PFAS. Notably, the highest PFAS concentrations did not always correspond to the greatest loads. Most PFOS (64 %), PFOA (49 %), 6:2FTS (46 %) and PFHxS (56 %) were exported from the Upper Mersey sub-catchment, despite higher concentrations in northern sub-catchments, emphasising the importance of load-based monitoring. Mass balance analysis of loads highlighted substantial inputs from specific river stretches, notably the Lower Irwell (Bolton to Manchester City Centre), River Tame (Marple Bridge to Stockport), and Upper Mersey (Stockport to Urmston). While PFAS loads generally scaled with catchment area, yield (load per unit area) analysis identified disproportionately high exports from small headwater catchments, notably the upper River Roch (PFOA, PFHpA and PFHxA) and Glaze Brook (PFBS). Industrial sources in these sub-catchments (a waste management facility and landfills, respectively) were confirmed using gadolinium anomaly analysis and consented discharge records. More widely, gadolinium data suggested industrial discharges may contribute to PFAS occurrence at 62 % of our sample sites throughout the catchment. These findings demonstrate that spatial analysis of PFAS loads, rather than concentrations alone, is critical for identifying PFAS source areas. We present a scalable monitoring framework for PFAS source apportionment applied at the river catchment-scale that can be used by environmental managers to target and prioritise PFAS source areas for detailed monitoring and remediation.","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2025.180502","usgsCitation":"Byrne, P., Mayes, W.M., James, A.L., Comber, S., Biles, E., Riley, A.L., Verplanck, P., and Bradley, L., 2025, Spatially resolved source apportionment of per- and polyfluoroalkyl substances (PFAS) within a post-industrial river catchment: Science of the Total Environment, v. 1001, 180502, 12 p., https://doi.org/10.1016/j.scitotenv.2025.180502.","productDescription":"180502, 12 p.","ipdsId":"IP-180073","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":496352,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2025.180502","text":"Publisher Index Page"},{"id":495896,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United Kingdom","otherGeospatial":"River Mersey","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -2.4656527163469946,\n 53.60530248618926\n ],\n [\n -2.4656527163469946,\n 53.3355442876196\n ],\n [\n -1.966480261260216,\n 53.3355442876196\n ],\n [\n -1.966480261260216,\n 53.60530248618926\n ],\n [\n -2.4656527163469946,\n 53.60530248618926\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"1001","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Byrne, Patrick","contributorId":192845,"corporation":false,"usgs":false,"family":"Byrne","given":"Patrick","affiliations":[],"preferred":false,"id":949261,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mayes, William M.","contributorId":335073,"corporation":false,"usgs":false,"family":"Mayes","given":"William","email":"","middleInitial":"M.","affiliations":[{"id":40174,"text":"University of Hull","active":true,"usgs":false}],"preferred":false,"id":949262,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"James, Alun L.","contributorId":361704,"corporation":false,"usgs":false,"family":"James","given":"Alun","middleInitial":"L.","affiliations":[{"id":86333,"text":"Environment Agency UK","active":true,"usgs":false}],"preferred":false,"id":949263,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Comber, Sean","contributorId":335075,"corporation":false,"usgs":false,"family":"Comber","given":"Sean","email":"","affiliations":[{"id":80302,"text":"University of Plymouth,","active":true,"usgs":false}],"preferred":false,"id":949264,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Biles, Emma","contributorId":335077,"corporation":false,"usgs":false,"family":"Biles","given":"Emma","email":"","affiliations":[{"id":49583,"text":"Liverpool John Moores University","active":true,"usgs":false}],"preferred":false,"id":949265,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Riley, Alex L.","contributorId":361707,"corporation":false,"usgs":false,"family":"Riley","given":"Alex","middleInitial":"L.","affiliations":[{"id":40174,"text":"University of Hull","active":true,"usgs":false}],"preferred":false,"id":949266,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Verplanck, Philip L. 0000-0002-3653-6419","orcid":"https://orcid.org/0000-0002-3653-6419","contributorId":212813,"corporation":false,"usgs":true,"family":"Verplanck","given":"Philip","middleInitial":"L.","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":949267,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bradley, Lee","contributorId":361708,"corporation":false,"usgs":false,"family":"Bradley","given":"Lee","affiliations":[{"id":86332,"text":"John Moores University","active":true,"usgs":false}],"preferred":false,"id":949268,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70271720,"text":"70271720 - 2025 - Machine learning generated streamflow drought forecasts for the Conterminous United States (CONUS): Developing and evaluating an operational tool to enhance sub-seasonal to seasonal streamflow drought early warning for gaged locations","interactions":[{"subject":{"id":70271720,"text":"70271720 - 2025 - Machine learning generated streamflow drought forecasts for the Conterminous United States (CONUS): Developing and evaluating an operational tool to enhance sub-seasonal to seasonal streamflow drought early warning for gaged locations","indexId":"70271720","publicationYear":"2025","noYear":false,"title":"Machine learning generated streamflow drought forecasts for the Conterminous United States (CONUS): Developing and evaluating an operational tool to enhance sub-seasonal to seasonal streamflow drought early warning for gaged locations"},"predicate":"SUPERSEDED_BY","object":{"id":70273497,"text":"70273497 - 2026 - Machine learning generated streamflow drought forecasts for the conterminous United States (CONUS): developing and evaluating an operational tool to enhance sub-seasonal to seasonal streamflow drought early warning for gaged locations","indexId":"70273497","publicationYear":"2026","noYear":false,"title":"Machine learning generated streamflow drought forecasts for the conterminous United States (CONUS): developing and evaluating an operational tool to enhance sub-seasonal to seasonal streamflow drought early warning for gaged locations"},"id":1}],"supersededBy":{"id":70273497,"text":"70273497 - 2026 - Machine learning generated streamflow drought forecasts for the conterminous United States (CONUS): developing and evaluating an operational tool to enhance sub-seasonal to seasonal streamflow drought early warning for gaged locations","indexId":"70273497","publicationYear":"2026","noYear":false,"title":"Machine learning generated streamflow drought forecasts for the conterminous United States (CONUS): developing and evaluating an operational tool to enhance sub-seasonal to seasonal streamflow drought early warning for gaged locations"},"lastModifiedDate":"2026-01-26T16:29:56.651322","indexId":"70271720","displayToPublicDate":"2025-09-19T09:20:12","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":"Machine learning generated streamflow drought forecasts for the Conterminous United States (CONUS): Developing and evaluating an operational tool to enhance sub-seasonal to seasonal streamflow drought early warning for gaged locations","docAbstract":"

Forecasts of streamflow drought, when streamflow declines below typical levels, are notably less available than for floods or meteorological drought, despite widespread impacts. To address this gap, we apply machine learning (ML) models to forecast streamflow drought 1-13 weeks into the future at > 3,000 streamgage locations across the conterminous United States (CONUS). We applied two ML methods (Long short-term memory (LSTM) neural networks; Light Gradient-Boosting Machine - LightGBM) and two benchmark model approaches (persistence; Autoregressive Integrated Moving Average - ARIMA) to predict weekly streamflow percentiles with independent models for each forecast horizon. To explore whether a training focus on dry weeks improved performance, both ML models were trained using all percentiles (LSTM-all, LightGBM-all) and only percentiles below 30% (LSTM<30, LightGBM<30). We evaluated model performance regionally and nationally for drought occurrence (the classification performance for a future date) and for drought onset/termination (performance identifying drought starts and ends). ML models generally performed worse than the persistence model for discrete classification (moderate, severe, extreme drought) of drought occurrence but exceeded the benchmark models for onset/termination. ML models outperformed benchmarks in predicting continuous streamflow percentiles below 30%. Occurrence performance was better for less intense droughts and shorter forecast horizons, with the ML models having predictive power at 1-4 week horizons for severe droughts (10th percentile threshold). All models struggled to forecast onset, though the best ML model was the LSTM<30 (sensitivity of 22%). Termination performance was greater, with the drought termination performance greatest for the LightGBM-all model. When estimating model uncertainty, the LSTM<30 model had the narrowest 90% percentile interval with closest to optimal capture. This work highlights the challenges and opportunities to further advance hydrological drought forecasting and supports an experimental operational streamflow drought assessment and forecast tool.

","language":"English","publisher":"Earth ArXiv","doi":"10.31223/X56X77","usgsCitation":"Hammond, J., Goodling, P.J., Diaz, J.A., Corson-Dosch, H.R., Heldmyer, A.J., Hamshaw, S.D., McShane, R., Ross, J.C., Sando, R., Simeone, C., Smith, E., Staub, L.E., Watkins, D., Wieczorek, M., Wnuk, K., and Zwart, J.A., 2025, Machine learning generated streamflow drought forecasts for the Conterminous United States (CONUS): Developing and evaluating an operational tool to enhance sub-seasonal to seasonal streamflow drought early warning for gaged locations: EarthArXiv, https://doi.org/10.31223/X56X77.","productDescription":"55 p.","ipdsId":"IP-179826","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":495839,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Continental United States","geographicExtents":"{\n 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sampling activities in the U.S. Geological Survey national networks","interactions":[],"lastModifiedDate":"2026-02-03T15:26:53.6234","indexId":"gip260","displayToPublicDate":"2025-09-19T08:39:57","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":315,"text":"General Information Product","code":"GIP","onlineIssn":"2332-354X","printIssn":"2332-3531","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"260","displayTitle":"PFAS Sampling Activities in the U.S. Geological Survey National Networks","title":"PFAS sampling activities in the U.S. Geological Survey national networks","docAbstract":"

Per- and polyfluoroalkyl substances (PFAS), frequently called “forever chemicals,” are used for a wide variety of industrial purposes and are often found in common household and industrial items such as firefighting foams, non-stick cookware, and water-resistant materials. The contamination of water, air, and soil by PFAS is a national and global issue due to their widespread occurrence in multiple applications and resistance to biodegradation and other traditional treatment processes. Research indicates that many PFAS can be emitted to the atmosphere and transported and deposited long distances from the source.

The U.S. Geological Survey (USGS) Water Resources Mission Area received funding to implement a national-scale sampling effort to assess PFAS occurrence. To follow agency directives, the National Water Quality Network (NWQN) added PFAS sample monitoring for both surface water and groundwater, and also added PFAS monitoring to selected sites in the National Atmospheric Deposition Program (NADP).

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National Network Coordinators
Observing Systems Division
Water Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192

","tableOfContents":"","publishedDate":"2025-09-19","noUsgsAuthors":false,"publicationDate":"2025-09-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Riskin, Melissa L. 0000-0001-6499-3775 mriskin@usgs.gov","orcid":"https://orcid.org/0000-0001-6499-3775","contributorId":654,"corporation":false,"usgs":true,"family":"Riskin","given":"Melissa","email":"mriskin@usgs.gov","middleInitial":"L.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"preferred":true,"id":949171,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lindsey, Bruce D. 0000-0002-7180-4319 blindsey@usgs.gov","orcid":"https://orcid.org/0000-0002-7180-4319","contributorId":175346,"corporation":false,"usgs":true,"family":"Lindsey","given":"Bruce","email":"blindsey@usgs.gov","middleInitial":"D.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":949172,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCammon, Ryan Conner 0009-0003-2787-5878","orcid":"https://orcid.org/0009-0003-2787-5878","contributorId":346258,"corporation":false,"usgs":false,"family":"McCammon","given":"Ryan","email":"","middleInitial":"Conner","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"preferred":false,"id":949173,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70271926,"text":"70271926 - 2025 - Reservoir operational strategies for sustainable sand management in the Colorado River","interactions":[],"lastModifiedDate":"2025-09-24T15:24:43.186803","indexId":"70271926","displayToPublicDate":"2025-09-18T08:14:16","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Reservoir operational strategies for sustainable sand management in the Colorado River","docAbstract":"

Climate change and increasing societal demands for water pose challenges for the management of dam-regulated rivers. Management decisions impact the environment of these rivers, creating the need to balance societal needs with environmental conservation. Here we present a modeling framework that optimizes resource benefits within imposed water use goals for the Colorado River in Grand Canyon, where sandbars are a valued natural feature. The current sand-management paradigm utilizes controlled dam-release floods to build and maintain sandbars without exhausting the limited sand supplied by tributaries downstream from Glen Canyon Dam, which blocks all sand supplied from upriver. High monthly releases outside of controlled floods erode sandbars and cause net sand export from Grand Canyon, reducing the sand available to build sandbars. Releases are high in some months owing to the need to adjust flows to meet annual delivery targets, which can be updated throughout the year. Here, we present alternative strategies for operations that avoid high releases, while meeting water storage and delivery goals. We test these strategies using a simplified reservoir model which accounts for forecast uncertainty. We show how these strategies affect sand mass balance and sandbar size using previously developed models. Strategies optimal for sustainable sandbar building maintained sufficient reservoir elevations for implementing controlled floods, avoided high monthly releases by relaxing annual release constraints, and implemented controlled floods in fall immediately following tributary sand inputs. Coordinated modeling of reservoir operations and environmental resources is valuable for managers seeking to balance societal and environmental needs in regulated rivers worldwide.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024WR038315","usgsCitation":"Salter, G.L., Topping, D.J., Wang, J., Schmidt, J.C., Yackulic, C., Bair, L., Mueller, E., and Grams, P.E., 2025, Reservoir operational strategies for sustainable sand management in the Colorado River: Water Resources Research, v. 61, no. 9, e2024WR038315, 27 p., https://doi.org/10.1029/2024WR038315.","productDescription":"e2024WR038315, 27 p.","ipdsId":"IP-167426","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":496155,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024wr038315","text":"Publisher Index Page"},{"id":496013,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.0504953550317,\n 36.97634003343833\n ],\n [\n -114.0504953550317,\n 35.748902843127084\n ],\n [\n -111.3294352062603,\n 35.748902843127084\n ],\n [\n -111.3294352062603,\n 36.97634003343833\n ],\n [\n -114.0504953550317,\n 36.97634003343833\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"61","issue":"9","noUsgsAuthors":false,"publicationDate":"2025-09-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Salter, Gerard Lewis 0000-0001-6426-0133","orcid":"https://orcid.org/0000-0001-6426-0133","contributorId":333645,"corporation":false,"usgs":true,"family":"Salter","given":"Gerard","email":"","middleInitial":"Lewis","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":949399,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Topping, David J. 0000-0002-2104-4577","orcid":"https://orcid.org/0000-0002-2104-4577","contributorId":215068,"corporation":false,"usgs":true,"family":"Topping","given":"David","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":949400,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wang, Jianghao","contributorId":195004,"corporation":false,"usgs":false,"family":"Wang","given":"Jianghao","email":"","affiliations":[],"preferred":false,"id":949401,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schmidt, John C.","contributorId":361760,"corporation":false,"usgs":false,"family":"Schmidt","given":"John","middleInitial":"C.","affiliations":[{"id":86346,"text":"Center for Colorado River Studies, Department of Watershed Sciences, Utah State University, Logan, UT, USA","active":true,"usgs":false}],"preferred":false,"id":949402,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Yackulic, Charles B. 0000-0001-9661-0724","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":218825,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":949403,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bair, Lucas 0000-0002-9911-3624","orcid":"https://orcid.org/0000-0002-9911-3624","contributorId":248714,"corporation":false,"usgs":true,"family":"Bair","given":"Lucas","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":949404,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mueller, Erich R. 0000-0001-8202-154X","orcid":"https://orcid.org/0000-0001-8202-154X","contributorId":207750,"corporation":false,"usgs":false,"family":"Mueller","given":"Erich R.","affiliations":[{"id":37626,"text":"Department of Geography, University of Wyoming, Laramie, WY, USA","active":true,"usgs":false}],"preferred":false,"id":949405,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Grams, Paul E. 0000-0002-0873-0708","orcid":"https://orcid.org/0000-0002-0873-0708","contributorId":216115,"corporation":false,"usgs":true,"family":"Grams","given":"Paul","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":949406,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70273390,"text":"70273390 - 2025 - Habitat features influencing waterbird use of managed wetlands enrolled in a public-private partnership for land conservation: The California Waterfowl Habitat Program","interactions":[],"lastModifiedDate":"2026-01-12T14:53:19.867404","indexId":"70273390","displayToPublicDate":"2025-09-18T07:47:45","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Habitat features influencing waterbird use of managed wetlands enrolled in a public-private partnership for land conservation: The California Waterfowl Habitat Program","docAbstract":"

Draining, water diversion, and development have greatly reduced the availability of freshwater wetland habitat around the world, and many remaining wetlands are on private lands. Public–private partnership programs can be an important means for promoting habitat conservation and management on private lands. We investigated bird use of 117 wetlands enrolled in the California Waterfowl Habitat Program in California's Central Valley, where two-thirds of wetlands are under private ownership and management. Specifically, we quantified the influence of wetland habitat features and surrounding land cover on waterbird density and diversity in late winter and early spring and during the waterfowl breeding season. Dabbling duck and shorebird densities were highest in wetlands that had water depths < 20 cm, and waterbird densities decreased with water depth. Greater amounts of emergent vegetation, especially tall and dense emergent vegetation, had a negative effect on total waterbird density but a positive effect on species richness and secretive marsh bird density. Shorebird and breeding duck densities were lower in wetlands with a large number of trees and other potential perch sites, and waterbird densities decreased with the amount of nearby wetland habitat on the landscape. Overall, we estimated that during late winter and early spring, private properties that were enrolled in the California Waterfowl Habitat Program (8000–8500 ha each year) supported 480,000 birds per day during extreme drought conditions in 2022 and 280,000 birds per day in more normal, non-drought conditions in 2023. Over the 76-day winter and early spring survey period, this amounted to more than 20 million bird use days on wetlands enrolled in the California Waterfowl Habitat Program during late winter and early spring. These results demonstrate the value of public–private wetland conservation partnerships, the influence of wetland habitat features and surrounding land cover on waterbird abundance, and the benefits of habitat features that could be incorporated into management plans and wetland selection criteria for enrollment into public–private conservation programs.

","language":"English","publisher":"Wiley","doi":"10.1002/ece3.72032","usgsCitation":"Hartman, C.A., Ackerman, J.T., Peterson, S.H., Fettig, B.L., and Herzog, M.P., 2025, Habitat features influencing waterbird use of managed wetlands enrolled in a public-private partnership for land conservation: The California Waterfowl Habitat Program: Ecology and Evolution, v. 15, no. 9, e72032, 31 p., https://doi.org/10.1002/ece3.72032.","productDescription":"e72032, 31 p.","ipdsId":"IP-177295","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":498681,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.72032","text":"Publisher Index Page"},{"id":498542,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento Valley, San Joaquin Valley, Yolo-Delta and Suisun Marsh area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.95679541485802,\n 39.955731811890246\n ],\n [\n -122.95679541485802,\n 36.78915144435925\n ],\n [\n -120.20809606036002,\n 36.78915144435925\n ],\n [\n -120.20809606036002,\n 39.955731811890246\n ],\n [\n -122.95679541485802,\n 39.955731811890246\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"9","noUsgsAuthors":false,"publicationDate":"2025-09-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Hartman, C. Alex 0000-0002-7222-1633 chartman@usgs.gov","orcid":"https://orcid.org/0000-0002-7222-1633","contributorId":131157,"corporation":false,"usgs":true,"family":"Hartman","given":"C.","email":"chartman@usgs.gov","middleInitial":"Alex","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":953547,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ackerman, Joshua T. 0000-0002-3074-8322","orcid":"https://orcid.org/0000-0002-3074-8322","contributorId":202848,"corporation":false,"usgs":true,"family":"Ackerman","given":"Joshua","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":953548,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peterson, Sarah H. 0000-0003-2773-3901 sepeterson@usgs.gov","orcid":"https://orcid.org/0000-0003-2773-3901","contributorId":167181,"corporation":false,"usgs":true,"family":"Peterson","given":"Sarah","email":"sepeterson@usgs.gov","middleInitial":"H.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":953549,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fettig, Brady Lynn 0000-0002-3124-2606","orcid":"https://orcid.org/0000-0002-3124-2606","contributorId":302106,"corporation":false,"usgs":true,"family":"Fettig","given":"Brady","email":"","middleInitial":"Lynn","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":953550,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Herzog, Mark P. 0000-0002-5203-2835 mherzog@usgs.gov","orcid":"https://orcid.org/0000-0002-5203-2835","contributorId":131158,"corporation":false,"usgs":true,"family":"Herzog","given":"Mark","email":"mherzog@usgs.gov","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":953551,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70264311,"text":"70264311 - 2025 - Interrogating process deficiencies in large-scale hydrologic models with interpretable machine learning","interactions":[],"lastModifiedDate":"2025-11-26T16:47:08.362588","indexId":"70264311","displayToPublicDate":"2025-09-17T10:34:37","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1928,"text":"Hydrology and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Interrogating process deficiencies in large-scale hydrologic models with interpretable machine learning","docAbstract":"

Large-scale hydrologic models are increasingly being developed for operational use in the forecasting and planning of water resources. However, the predictive strength of such models depends on how well they resolve various functions of catchment hydrology, which are influenced by gradients in climate, topography, soils, and land use. Most assessments of hydrologic model uncertainty have been limited to traditional statistical methods. Here, we present a proof-of-concept approach that uses interpretable machine learning techniques to provide post hoc assessment of model sensitivity and process deficiency in hydrologic models. We train a random forest model to predict the Kling–Gupta efficiency (KGE) of National Water Model (NWM) and National Hydrologic Model (NHM) streamflow predictions for 4383 stream gauges in the conterminous United States. Thereafter, we explain the local and global controls that 48 catchment attributes exert on KGE prediction using interpretable Shapley values. Overall, we find that soil water content is the most impactful feature controlling successful model performance, suggesting that soil water storage is difficult for hydrologic models to resolve, particularly for arid locations. We identify nonlinear thresholds beyond which predictive performance decreases for NWM and NHM. For example, soil water content less than 210 mm, precipitation less than 900 mm yr−1, road density greater than 5 km km−2, and lake area percent greater than 10 % contributed to lower KGE values. These results suggest that improvements in how these influential processes are represented could result in the largest increases in NWM and NHM predictive performance. This study demonstrates the utility of interrogating process-based models using data-driven techniques, which has broad applicability and potential for improving the next generation of large-scale hydrologic models.

","language":"English","publisher":"Copernicus Publications","doi":"10.5194/hess-29-4457-2025","usgsCitation":"Husic, A., Hammond, J.C., Price, A.N., and Roundy, J., 2025, Interrogating process deficiencies in large-scale hydrologic models with interpretable machine learning: Hydrology and Earth System Sciences, v. 29, p. 4457-4472, https://doi.org/10.5194/hess-29-4457-2025.","productDescription":"16 p.","startPage":"4457","endPage":"4472","ipdsId":"IP-170300","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":496940,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/hess-29-4457-2025","text":"Publisher Index Page"},{"id":496907,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"conterminous United States","geographicExtents":"{\n \"type\": 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[\n -122.58736,\n 47.096\n ],\n [\n -122.34,\n 47.36\n ],\n [\n -122.5,\n 48.18\n ],\n [\n -122.84,\n 49\n ],\n [\n -120,\n 49\n ],\n [\n -117.03121,\n 49\n ],\n [\n -116.04818,\n 49\n ],\n [\n -113,\n 49\n ],\n [\n -110.05,\n 49\n ],\n [\n -107.05,\n 49\n ],\n [\n -104.04826,\n 48.99986\n ],\n [\n -100.65,\n 49\n ],\n [\n -97.22872,\n 49.0007\n ],\n [\n -95.15907,\n 49\n ],\n [\n -95.15609,\n 49.38425\n ],\n [\n -94.81758,\n 49.38905\n ]\n ]\n ]\n ]\n },\n \"properties\": {\n \"name\": \"United States\"\n }\n }\n ]\n}","volume":"29","noUsgsAuthors":false,"publicationDate":"2025-09-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Husic, Admin 0000-0002-4225-2252","orcid":"https://orcid.org/0000-0002-4225-2252","contributorId":340064,"corporation":false,"usgs":false,"family":"Husic","given":"Admin","email":"","affiliations":[{"id":81445,"text":"Assistant Professor (Kansas University)","active":true,"usgs":false}],"preferred":false,"id":930390,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hammond, John Christopher 0000-0002-6241-3551","orcid":"https://orcid.org/0000-0002-6241-3551","contributorId":302952,"corporation":false,"usgs":true,"family":"Hammond","given":"John","email":"","middleInitial":"Christopher","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":930391,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Price, Adam N. 0000-0002-7211-4758","orcid":"https://orcid.org/0000-0002-7211-4758","contributorId":295971,"corporation":false,"usgs":false,"family":"Price","given":"Adam","email":"","middleInitial":"N.","affiliations":[{"id":27155,"text":"University of California Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":930392,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Roundy, Joshua","contributorId":352231,"corporation":false,"usgs":false,"family":"Roundy","given":"Joshua","affiliations":[{"id":84135,"text":"Department of Civil, Environmental and Architectural Engineering, University of Kansas","active":true,"usgs":false}],"preferred":false,"id":930393,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70271488,"text":"70271488 - 2025 - Reduced Atlantic reef growth past 2 °C warming amplifies sea-level impacts","interactions":[],"lastModifiedDate":"2025-12-01T16:35:40.162981","indexId":"70271488","displayToPublicDate":"2025-09-17T09:09:26","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2840,"text":"Nature","active":true,"publicationSubtype":{"id":10}},"title":"Reduced Atlantic reef growth past 2 °C warming amplifies sea-level impacts","docAbstract":"

Coral reefs form complex physical structures that can help to mitigate coastal flooding risk1,2. This function will be reduced by sea-level rise (SLR) and impaired reef growth caused by climate change and local anthropogenic stressors3. Water depths above reef surfaces are projected to increase as a result, but the magnitudes and timescales of this increase are poorly constrained, which limits modelling of coastal vulnerability4,5. Here we analyse fossil reef deposits to constrain links between reef ecology and growth potential across more than 400 tropical western Atlantic sites, and assess the magnitudes of resultant above-reef increases in water depth through to 2100 under various shared socioeconomic pathway (SSP) emission scenarios. Our analysis predicts that more than 70% of tropical western Atlantic reefs will transition into net erosional states by 2040, but that if warming exceeds 2 °C (SSP2–4.5 and higher), nearly all reefs (at least 99%) will be eroding by 2100. The divergent trajectories of reef growth and SLR will thus magnify the effects of SLR; increases in water depth of around 0.3–0.5 m above the present are projected under all warming scenarios by 2060, but depth increases of 0.7–1.2 m are predicted by 2100 under scenarios in which warming surpasses 2 °C. This would increase the risk of flooding along vulnerable reef-fronted coasts and modify nearshore hydrodynamics and ecosystems. Reef restoration offers one pathway back to higher reef growth6,7, but would dampen the effects of SLR in 2100 only by around 0.3–0.4 m, and only when combined with aggressive climate mitigation.

","language":"English","publisher":"Nature","doi":"10.1038/s41586-025-09439-4","usgsCitation":"Perry, C.T., de Bakker, D., Webb, A., Comeau, S., Harvey, B., Cornwall, C., Alvarez-Filip, L., Perez-Cervantes, E., Morris, J.T., Enochs, I.C., Toth, L., O'Dea, A., Dillon, E.M., Meesters, E.H., and Precht, W., 2025, Reduced Atlantic reef growth past 2 °C warming amplifies sea-level impacts: Nature, v. 646, p. 619-626, https://doi.org/10.1038/s41586-025-09439-4.","productDescription":"8 p.","startPage":"619","endPage":"626","ipdsId":"IP-174363","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":495742,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41586-025-09439-4","text":"Publisher Index Page"},{"id":495706,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Bonaire, Mexico, United 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The Zuni Salt Lake is located in a maar in west-central New Mexico and contains hypersaline water that has long been used by Native Americans for religious purposes and the collection of salt. There have been several investigations suggesting different sources for the water and salt to the lake. Springs, seeps, and ephemeral streamflow have all been observed to contribute freshwater to the lake, and brackish to hypersaline seeps have been documented along the banks of the lake. This report summarizes the findings of a study that characterizes the lake’s hydrology, its water and salinity sources, and the hydrogeologic conceptual model. Regional groundwater levels indicate that each of the aquifers in the area have the potential to discharge groundwater to the lake. There is also evidence of vertical groundwater flow pathways at the maar that were likely created by the igneous intrusion that fractured the intersecting aquifers. A detailed water budget was constructed from continuous lake stage, precipitation, and evaporation data to estimate the groundwater inflow to the Zuni Salt Lake. It was determined that groundwater inflow to the lake is 441 ±94 acre-feet per year, which composes as much as 77 percent of the total inflows. The high sodium and chloride concentrations measured in two hypersaline samples collected near the lake indicate that the majority of the dissolved solids entering the lake are from a hypersaline groundwater source. The geochemical and isotopic compositions measured in the lake and surrounding features support the interpretation that hypersaline groundwater is the primary source of salts to the lake, which is likely sourced from the older (and deeper) Permian units. The hypersaline groundwater samples collected during this investigation have a unique aqueous chemistry relative to each of the mapped aquifers, and variability in groundwater compositions is interpreted to result from differences in minerology and residence time.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255057","issn":"2328-0328","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Robertson, A.J., Pepin, J.D., Gray, E.L., Collison, J.W., Brown, J., Ritchie, A., and Ball, G., 2025, Sources of water and salts for the Zuni Salt Lake in west-central New Mexico: U.S. Geological Survey Scientific Investigations Report 2025–5057, 40 p., https://doi.org/10.3133/sir20255057.","productDescription":"Report: viii, 40 p.; Data Release; 2 Datasets","numberOfPages":"52","onlineOnly":"Y","ipdsId":"IP-167505","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":496025,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118875.htm","linkFileType":{"id":5,"text":"html"}},{"id":493625,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS Dataset","linkHelpText":"- USGS water data for the Nation"},{"id":493624,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://data.usbr.gov/catalog/4699","text":"Bureau of Relamation Dataset","linkHelpText":"- Zuni Salt Lake weather monitoring data"},{"id":493647,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P16248E8","text":"USGS Data Release","linkHelpText":"- Aerial imagery, digital elevation model, orthomosaic image, ground control points, and bathymetry surveys to identify sources of water and salts for the Zuni Salt Lake in west-central New Mexico, United States"},{"id":493627,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255057/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5057 HTML"},{"id":493628,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5057/sir20255057.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2025-5057 XML"},{"id":493626,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5057/sir20255057.pdf","size":"10.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5057"},{"id":493629,"rank":2,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5057/images"},{"id":493630,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5057/coverthb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Zuni Salt Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -109,\n 34.75\n ],\n [\n -109,\n 34.333\n ],\n [\n -108.333,\n 34.333\n ],\n [\n -108.333,\n 34.75\n ],\n [\n -109,\n 34.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"

Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113

Contact Us- USGS Publications Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2025-09-17","noUsgsAuthors":false,"publicationDate":"2025-09-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Robertson, Andrew J. 0000-0003-2130-0347 ajrobert@usgs.gov","orcid":"https://orcid.org/0000-0003-2130-0347","contributorId":4129,"corporation":false,"usgs":true,"family":"Robertson","given":"Andrew","email":"ajrobert@usgs.gov","middleInitial":"J.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":944889,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pepin, Jeff D. 0000-0002-7410-9979","orcid":"https://orcid.org/0000-0002-7410-9979","contributorId":222161,"corporation":false,"usgs":true,"family":"Pepin","given":"Jeff","email":"","middleInitial":"D.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":944890,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gray, Erin L. 0000-0002-3945-6393","orcid":"https://orcid.org/0000-0002-3945-6393","contributorId":359054,"corporation":false,"usgs":true,"family":"Gray","given":"Erin","middleInitial":"L.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":944891,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Collison, Jake W. collison@usgs.gov","contributorId":5505,"corporation":false,"usgs":true,"family":"Collison","given":"Jake W.","email":"collison@usgs.gov","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":false,"id":944892,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brown, Jeb E. 0000-0001-7671-2379","orcid":"https://orcid.org/0000-0001-7671-2379","contributorId":225088,"corporation":false,"usgs":true,"family":"Brown","given":"Jeb E.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":944893,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ritchie, Andre 0000-0003-1289-653X abritchie@usgs.gov","orcid":"https://orcid.org/0000-0003-1289-653X","contributorId":195788,"corporation":false,"usgs":true,"family":"Ritchie","given":"Andre","email":"abritchie@usgs.gov","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":944894,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ball, Grady 0000-0003-3030-055X","orcid":"https://orcid.org/0000-0003-3030-055X","contributorId":220746,"corporation":false,"usgs":true,"family":"Ball","given":"Grady","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":944895,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70271523,"text":"70271523 - 2025 - Energetic value of Arctic forage-sized fish with implications for a nearshore seabird predator","interactions":[],"lastModifiedDate":"2025-09-18T15:25:56.527767","indexId":"70271523","displayToPublicDate":"2025-09-17T08:16:26","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2660,"text":"Marine Biology","active":true,"publicationSubtype":{"id":10}},"title":"Energetic value of Arctic forage-sized fish with implications for a nearshore seabird predator","docAbstract":"

Arctic cod (Boreogadus saida, also called polar cod) are considered the single most important Arctic forage fish due to their high abundance and nutritional quality. Because Arctic cod are strongly ice associated and prefer colder waters, their frequency in coastal waters has declined with warming, decreasing availability to nearshore predators. To consider the nutritional quality of alternative prey, we measured energy density and estimated whole-body energy of forage-size (39–200 mm) fishes collected during summers 2021–2023 (n = 274). The fishes sampled included 16 potential prey species from Foggy Island Bay (70.3°N, 147.5°W, near Prudhoe Bay) and Lion Bay (70.2°N, 146.4°W, near Flaxman Island), northern Alaska. Dry weight energy densities ranged from 16.2 to 27.5 kJ g-1 (mean ± SD = 22.0 ± 1.73 kJ g-1, n = 274) across individuals. Of common species, Arctic cod had the highest mean energy density (24.3 ± 1.1 kJ g-1, n = 25) and fourhorn sculpin (Myoxocephalus quadricornis) had the lowest (19.7 ± 0.8 kJ g-1, n = 20). To account for size differences among prey species, whole-body energy of typical fish sizes available to predators were modeled using whole-body energy to length relationships and length distributions. Juvenile salmonids (e.g., ciscoes and whitefishes) provided the most energy per individual and were four-fold greater than smaller-bodied Arctic cod. Predators that consume juvenile ciscoes and whitefishes may be more resilient to declines in Arctic cod availability than predators with smaller gapes.

","language":"English","publisher":"Springer Nature","doi":"10.1007/s00227-025-04705-5","usgsCitation":"Stanek, A.E., Uher-Koch, B.D., Dunton, K.H., and von Biela, V.R., 2025, Energetic value of Arctic forage-sized fish with implications for a nearshore seabird predator: Marine Biology, v. 172, 157, 13 p., https://doi.org/10.1007/s00227-025-04705-5.","productDescription":"157, 13 p.","ipdsId":"IP-171231","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":495747,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00227-025-04705-5","text":"Publisher Index Page"},{"id":495713,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Beaufort Sea coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -157.2117957640097,\n 71.3543754933907\n ],\n [\n -157.2117957640097,\n 69.83926146873208\n ],\n [\n -145.833226056111,\n 69.83926146873208\n ],\n [\n -145.833226056111,\n 71.3543754933907\n ],\n [\n -157.2117957640097,\n 71.3543754933907\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"172","noUsgsAuthors":false,"publicationDate":"2025-09-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Stanek, Ashley E. 0000-0001-5184-2126","orcid":"https://orcid.org/0000-0001-5184-2126","contributorId":290682,"corporation":false,"usgs":true,"family":"Stanek","given":"Ashley","email":"","middleInitial":"E.","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":948998,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Uher-Koch, Brian D. 0000-0002-1885-0260 buher-koch@usgs.gov","orcid":"https://orcid.org/0000-0002-1885-0260","contributorId":5117,"corporation":false,"usgs":true,"family":"Uher-Koch","given":"Brian","email":"buher-koch@usgs.gov","middleInitial":"D.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":948999,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dunton, Kenneth H. 0000-0003-3498-8021","orcid":"https://orcid.org/0000-0003-3498-8021","contributorId":361574,"corporation":false,"usgs":false,"family":"Dunton","given":"Kenneth","middleInitial":"H.","affiliations":[{"id":47685,"text":"Marine Science Institute, University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":949000,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"von Biela, Vanessa R. 0000-0002-7139-5981 vvonbiela@usgs.gov","orcid":"https://orcid.org/0000-0002-7139-5981","contributorId":3104,"corporation":false,"usgs":true,"family":"von Biela","given":"Vanessa","email":"vvonbiela@usgs.gov","middleInitial":"R.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":949001,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70273247,"text":"70273247 - 2025 - Variation and controls of sediment oxygen demand in backwater lakes of the Upper Mississippi River during winter","interactions":[],"lastModifiedDate":"2025-12-23T15:09:19.316964","indexId":"70273247","displayToPublicDate":"2025-09-17T08:02:00","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Variation and controls of sediment oxygen demand in backwater lakes of the Upper Mississippi River during winter","docAbstract":"

Many ecological processes affect the availability of winter dissolved oxygen (DO) concentrations in rivers, a key feature of overwintering fish habitat. Sediment oxygen demand (SOD) contributes to DO depletion, particularly during ice-covered periods, and may cause hypoxic conditions in backwater lakes, affecting the availability of suitable overwintering habitat. Understanding the drivers of SOD on habitat conditions during winter is critical for the management of the Upper Mississippi River System (UMRS). We measured SOD rates in three different habitat types (shallow, non-vegetated; deep, non-vegetated; and vegetated) within 12 backwater lakes in Pools 4, Pool 8, and Pool 13 of the UMRS in January and February 2022. Sediment physicochemical characteristics were measured to identify potential drivers of winter SOD rates. Measured SOD rates ranged from 0.04–0.44 g O2/(m2d) at in situ temperatures, and 0.14–1.46 g O2/(m2d) when corrected to 20°C. There were no statistically significant relations between in situ SOD and most sediment characteristics. SOD was positively associated with aquatic vegetation presence and negatively associated with sediment pH, water depth, flow velocity, and ice depth. SOD was typically higher at vegetated sites with lower flow velocity, with four of the five highest SOD rates measured at vegetated sites. Vegetation presence, depth, and flow velocity played greater roles in controlling SOD rates than sediment characteristics. Additionally, DO concentrations near the sediment–water interface at deep sites (> 1.5 m) were much lower than DO concentrations 0.2 m under the water surface, indicating that SOD was influencing DO concentrations and may be affecting overwintering habitat in backwater lakes.

","language":"English","publisher":"Wiley","doi":"10.1002/rra.70011","usgsCitation":"Perner, P.M., Kreiling, R.M., Jankowski, K.J., and Strauss, E.A., 2025, Variation and controls of sediment oxygen demand in backwater lakes of the Upper Mississippi River during winter: River Research and Applications, v. 41, no. 10, p. 2189-2204, https://doi.org/10.1002/rra.70011.","productDescription":"16 p.","startPage":"2189","endPage":"2204","ipdsId":"IP-170589","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":498054,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rra.70011","text":"Publisher Index Page"},{"id":497936,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Iowa, Minnesota, Missouri, Wisconsin","otherGeospatial":"Upper Mississippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.10097042912157,\n 44.97281290065325\n ],\n [\n -91.759506701898,\n 43.740062945434715\n ],\n [\n -90.9674401636216,\n 42.01556148014882\n ],\n [\n -91.743596973701,\n 39.81261700824835\n ],\n [\n -90.48342253124173,\n 38.34168124893585\n ],\n [\n -89.22324808878244,\n 36.870745489623346\n ],\n [\n -90.81905145778899,\n 40.275434086550305\n ],\n [\n -89.87713786574906,\n 41.79816956524569\n ],\n [\n -90.01573449349698,\n 42.334520209936784\n ],\n [\n -91.1511012598934,\n 43.92329321891202\n ],\n [\n -92.65275956586878,\n 45.089302207190485\n ],\n [\n -93.10097042912157,\n 44.97281290065325\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"41","issue":"10","noUsgsAuthors":false,"publicationDate":"2025-09-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Perner, Patrik Mathis 0000-0002-6142-518X","orcid":"https://orcid.org/0000-0002-6142-518X","contributorId":261675,"corporation":false,"usgs":true,"family":"Perner","given":"Patrik","email":"","middleInitial":"Mathis","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":952847,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kreiling, Rebecca M. 0000-0002-9295-4156","orcid":"https://orcid.org/0000-0002-9295-4156","contributorId":202193,"corporation":false,"usgs":true,"family":"Kreiling","given":"Rebecca","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":952848,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jankowski, Kathi Jo 0000-0002-3292-4182","orcid":"https://orcid.org/0000-0002-3292-4182","contributorId":207429,"corporation":false,"usgs":true,"family":"Jankowski","given":"Kathi","email":"","middleInitial":"Jo","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":952849,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Strauss, Eric A. 0000-0002-3134-2535","orcid":"https://orcid.org/0000-0002-3134-2535","contributorId":364544,"corporation":false,"usgs":false,"family":"Strauss","given":"Eric","middleInitial":"A.","affiliations":[{"id":47908,"text":"University of Wisconsin - La Crosse","active":true,"usgs":false}],"preferred":false,"id":952850,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70272270,"text":"70272270 - 2025 - Hydrologic connectivity in floodplain systems: A multiscale review of concepts, metrics and management","interactions":[],"lastModifiedDate":"2025-11-20T16:06:54.982885","indexId":"70272270","displayToPublicDate":"2025-09-16T10:03:43","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Hydrologic connectivity in floodplain systems: A multiscale review of concepts, metrics and management","docAbstract":"

Hydrologic connectivity (HC), particularly in floodplain systems, is pivotal in regulating ecosystem services by facilitating the movement of nutrients, sediments, chemicals, and biota. However, human interventions such as dam construction, levee installation, water management practices, and alterations in vegetation have significantly disrupted natural HC patterns globally. To provide a structured entry into the growing body of HC research, we conducted a systematic literature review of 1920 studies, analysing diverse definitions, influencing factors, quantification approaches, spatial and temporal scales, and management strategies. In addition to traditional review methods, our approach integrates keyword and cluster analysis to elucidate dominant research themes and trends across the literature. Our review reveals that the literature is heavily skewed towards research in North America and Europe (accounting for 72% of studies) and predominantly utilises field investigations, simulation modelling, and remote sensing integrated with geographic information systems. Although these methodologies have advanced our understanding, most studies focus on restricted spatial scales such as individual hillslopes, catchments, or stream networks and short temporal intervals, including single precipitation events or seasonal cycles. A narrow focus becomes a limitation when such studies do not contribute to broader efforts aimed at scaling insights across larger domains. These limitations highlight the potential benefits of innovative conceptual frameworks and quantification methods to better capture HC across broader environments and extended temporal scales. We conclude by discussing challenges in defining and quantifying floodplain HC and outlining potential future research directions to advance connectivity science and management, particularly in floodplain systems characterised by frequent hydrologic fluctuations, such as seasonal inundation and changing flow paths.

","language":"English","publisher":"Wiley","doi":"10.1002/hyp.70260","usgsCitation":"Hafez Ahmad, Miranda, L.E., Dunn, C.G., Melanie R. Boudreau, and Colvin, M.E., 2025, Hydrologic connectivity in floodplain systems: A multiscale review of concepts, metrics and management: Hydrological Processes, v. 39, no. 9, e70260, 23 p., https://doi.org/10.1002/hyp.70260.","productDescription":"e70260, 23 p.","ipdsId":"IP-177204","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":496691,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"39","issue":"9","noUsgsAuthors":false,"publicationDate":"2025-09-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Hafez Ahmad","contributorId":362594,"corporation":false,"usgs":false,"family":"Hafez Ahmad","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":950631,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miranda, Leandro E. 0000-0002-2138-7924 smiranda@usgs.gov","orcid":"https://orcid.org/0000-0002-2138-7924","contributorId":531,"corporation":false,"usgs":true,"family":"Miranda","given":"Leandro","email":"smiranda@usgs.gov","middleInitial":"E.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":950632,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dunn, Corey Garland 0000-0002-7102-2165","orcid":"https://orcid.org/0000-0002-7102-2165","contributorId":288691,"corporation":false,"usgs":true,"family":"Dunn","given":"Corey","email":"","middleInitial":"Garland","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":950633,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Melanie R. Boudreau","contributorId":362597,"corporation":false,"usgs":false,"family":"Melanie R. Boudreau","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":950634,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Colvin, Michael E. 0000-0002-6581-4764","orcid":"https://orcid.org/0000-0002-6581-4764","contributorId":331490,"corporation":false,"usgs":true,"family":"Colvin","given":"Michael","email":"","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":950735,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70271379,"text":"sir20255074 - 2025 - Using satellite imagery and soil data to understand occurrences and migration of soil conditions harmful to archaeological sites on Jamestown Island, Virginia","interactions":[],"lastModifiedDate":"2026-02-03T15:25:33.505229","indexId":"sir20255074","displayToPublicDate":"2025-09-16T10:00:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-5074","displayTitle":"Using Satellite Imagery and Soil Data To Understand Occurrences and Migration of Soil Conditions Harmful to Archaeological Sites on Jamestown Island, Virginia","title":"Using satellite imagery and soil data to understand occurrences and migration of soil conditions harmful to archaeological sites on Jamestown Island, Virginia","docAbstract":"

Many know Jamestown Island, Virginia, hereafter referred to as “the Island,” located near the mouth of the James River into the Chesapeake Bay, as the home of the first permanent English settlement in North America. However, the Island is home to 15,000 years’ worth of cultural artifacts and archaeological sites. In addition to its rich history, the Island is home to a variety of native plants and animals, including many rare, threatened, and endangered species. Preserving historical and natural resources is part of Colonial National Historic Park’s (COLO) enabling legislation. To this end, COLO has been seeking data to inform management decisions on how to prioritize resources to preserve archaeological sites and anticipate changes to natural systems from sea-level rise and other effects of climate change. The U.S. Geological Survey (USGS), in partnership with COLO, collected and analyzed data to help determine soil conditions detrimental to archaeological sites across the Island using a combination of soil samples and assessments of vegetative health as a proxy for soil conditions. This study combined normalized difference vegetative index raster grids spanning 8 years, 2010 to 2018, and soil data from 50 sites sampled in dry (June 2021) and wet months (March 2022) at two different soil horizons to investigate potential hazards to plant health and corrosive conditions in the unsaturated subsurface. The data suggest that access to the James River drives soil pH and soil conductivity. Areas of the Island that are subject to frequent inundation were observed to have both higher soil conductivity (as high as 4,845 millisiemens per meter [mS/m]) and lower pH (as low as 3.84). Higher soil conductivity, or salinity, and more acidity create corrosive environment, which can destroy buried artifacts and are detrimental to vegetative health. These conditions were not limited to the edges of the Island, like Black Point. Inland locations, such as the Pitch and Tar Swamp, were observed to have some of the highest conductivity values, which were likely caused by from a combination of inflow of James River water along Back Creek into the Pitch and Tar Swamp and proximity to the Visitor Center and other high-traffic areas of the Island. A difference of normalized difference vegetative index values from 2010 to 2018 raster grid appears to support this, showing an apparent loss of vegetative health in marsh grass in the Pitch and Tar Swamp. These data may inform COLO about areas of the Island that are currently most threatened by corrosive conditions and how those conditions are likely to migrate in the future.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255074","isbn":"978-1-4113-4629-1","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Caldwell, S.H., 2025, Using satellite imagery and soil data to understand occurrences and migration of soil conditions harmful to archaeological sites on Jamestown Island, Virginia (ver. 1.1, November 2025): U.S. Geological Survey Scientific Investigations Report 2025–5074, 22 p., https://doi.org/10.3133/sir20255074.","productDescription":"Report: vii, 22 p.; Data Release","numberOfPages":"22","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-167335","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":497790,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118874.htm"},{"id":496292,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2025/5074/versionHist.txt","size":"632 B","linkFileType":{"id":2,"text":"txt"}},{"id":495295,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13J32J4","text":"USGS data release","linkHelpText":"Satellite imagery products from 2010, 2011, 2018 and soil data from 2021–22 on Jamestown Island, Va."},{"id":495294,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5074/images/"},{"id":495293,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5074/sir20255074.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2025-5074 XML"},{"id":495292,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255074/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5074 HTML"},{"id":495291,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5074/sir20255074.pdf","text":"Report","size":"4.19 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5074 PDF"},{"id":495290,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5074/coverthb3.jpg"}],"country":"United States","state":"Virginia","otherGeospatial":"Jamestown Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.785,\n 37.22\n ],\n [\n -76.785,\n 37.19007232242731\n ],\n [\n -76.73168289008015,\n 37.19007232242731\n ],\n [\n -76.73168289008015,\n 37.22\n ],\n [\n -76.785,\n 37.22\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: September 16, 2025; Version 1.1: September 30, 2025","contact":"

Director, Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, Virginia 23228

","tableOfContents":"","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"publishedDate":"2025-09-16","revisedDate":"2025-11-17","noUsgsAuthors":false,"publicationDate":"2025-09-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Caldwell, Samuel H. 0000-0002-4444-7002","orcid":"https://orcid.org/0000-0002-4444-7002","contributorId":292520,"corporation":false,"usgs":true,"family":"Caldwell","given":"Samuel","email":"","middleInitial":"H.","affiliations":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"preferred":true,"id":948327,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70272995,"text":"70272995 - 2025 - Model‐based decomposition of spatially varying temporal shifts in seasonal streamflow across north temperate US rivers.","interactions":[],"lastModifiedDate":"2025-12-15T14:20:44.203219","indexId":"70272995","displayToPublicDate":"2025-09-16T07:54:44","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Model‐based decomposition of spatially varying temporal shifts in seasonal streamflow across north temperate US rivers.","docAbstract":"

Anthropogenically forced climate shifts disrupt the seasonal behavior of climatic and hydrologic processes. The seasonality of streamflow has significant implications for the ecology of riverine ecosystems and for meeting societal demands for water resources. We develop a hierarchical Bayesian model of daily streamflow to quantify how the shape of annual hydrographs are changing and to evaluate temporal trends in model-based hydrologic indices related to flow timing and magnitude shifts. We apply this model to 1,112 gages across the Northern US over the years 1965–2022. We identify large-scale patterns in temporal changes to streamflow profiles that are consistent with regional changes in hydroclimate, including decreasing seasonal flow variability in the Pacific Northwest and increasing winter flows in the northeastern United States. Within these regions we also observe fine-scale heterogeneity in streamflow timing and magnitude shifts, both of which have potentially significant implications for riverine ecosystem function and the ecosystem services they provide.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024wr039500","usgsCitation":"Collins, K.M., Schliep, E.M., Wagner, T., and Wikle, C.K., 2025, Model‐based decomposition of spatially varying temporal shifts in seasonal streamflow across north temperate US rivers.: Water Resources Research, v. 61, no. 9, e2024WR039500, 18 p., https://doi.org/10.1029/2024wr039500.","productDescription":"e2024WR039500, 18 p.","ipdsId":"IP-168920","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":497714,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024wr039500","text":"Publisher Index Page"},{"id":497463,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"northern United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -125.3458881096503,\n 49.29487267098142\n ],\n [\n -124.25341557040164,\n 42.38037865705249\n ],\n [\n -102.843031226844,\n 40.798858382917174\n ],\n [\n -102.32686478374309,\n 37.18541829571534\n ],\n [\n -93.79657291209023,\n 36.66945873456485\n ],\n [\n -90.39402222420446,\n 36.67882840785393\n ],\n [\n -82.35133965032877,\n 36.98587435527764\n ],\n [\n -75.04054116668894,\n 39.21009303966319\n ],\n [\n -68.1268180477979,\n 47.34769099450737\n ],\n [\n -80.2238519745004,\n 46.533866149748945\n ],\n [\n -91.9752488122572,\n 49.10266455472649\n ],\n [\n -125.3458881096503,\n 49.29487267098142\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"61","issue":"9","noUsgsAuthors":false,"publicationDate":"2025-09-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Collins, Kevin M.","contributorId":363830,"corporation":false,"usgs":false,"family":"Collins","given":"Kevin","middleInitial":"M.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":952061,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schliep, Erin M.","contributorId":363831,"corporation":false,"usgs":false,"family":"Schliep","given":"Erin","middleInitial":"M.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":952062,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":218091,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":952063,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wikle, Christopher K.","contributorId":363836,"corporation":false,"usgs":false,"family":"Wikle","given":"Christopher","middleInitial":"K.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":952064,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70272443,"text":"70272443 - 2025 - Potential for hydroacoustic technology to describe physical habitat for imperilled native freshwater mussels","interactions":[],"lastModifiedDate":"2025-11-21T19:16:54.700022","indexId":"70272443","displayToPublicDate":"2025-09-15T12:08:13","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1447,"text":"Ecohydrology","active":true,"publicationSubtype":{"id":10}},"title":"Potential for hydroacoustic technology to describe physical habitat for imperilled native freshwater mussels","docAbstract":"

The lack of information on what constitutes suitable habitat for native freshwater mussels can limit restoration efforts. While many species reside in silt–sand–gravel substrates, species such as the Spectaclecase (Cumberlandia monodonta) and Salamander (Simpsonaias ambigua) mussels are thought to be associated with rock structures (e.g., wing dams and rock outcrops) in rivers. Our objective was to assess if hydroacoustic technology could be used to quantify physical habitat features for C. monodonta and S. ambigua. Multibeam echosounder, acoustic Doppler current profiler, sidescan sonar and underwater videography were used to quantify water depth, substrate hardness, bed roughness and bed slope of the riverbed, water velocity, shear velocity and the degree of rock clustering at six sites in the Saint Croix River, Minnesota. The sites varied in type of rock structures and relative abundances of both species. The strength of the associations among physical habitat features and mussel abundance was weak; R2 values were typically < 0.5. However, species-specific differences in microhabitat were observed. For example, C. monodonta was typically observed at sites with higher velocity and shear velocity compared to S. ambigua. Mussel abundance was greatest at sites that contained crevices of sand surrounded by boulders and bedrock. Future refinements in hydroacoustic methods and post-processing computations could improve predictions. Information on habitat features from occupied and unoccupied sites could help resource managers characterize existing occupied habitats, identify potential reintroduction areas and implement restoration programmes.

","language":"English","publisher":"Wiley","doi":"10.1002/eco.70081","usgsCitation":"Hanson, J.L., Stone, J., Kitchel, L., Weinzinger, J., and Newton, T.J., 2025, Potential for hydroacoustic technology to describe physical habitat for imperilled native freshwater mussels: Ecohydrology, v. 18, no. 6, e70081, 15 p., https://doi.org/10.1002/eco.70081.","productDescription":"e70081, 15 p.","ipdsId":"IP-160742","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":496793,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota, Wisconsin","otherGeospatial":"Saint Croix River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92.62547203487303,\n 45.45770852010466\n ],\n [\n -92.77823906523427,\n 45.316776479418536\n ],\n [\n -92.78303275449902,\n 45.11481575379119\n ],\n [\n -92.74006801870277,\n 45.11481575379119\n ],\n [\n -92.72271624093813,\n 45.12507537508348\n ],\n [\n -92.68037840773216,\n 45.28795875844952\n ],\n [\n -92.62547203487303,\n 45.45770852010466\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","issue":"6","noUsgsAuthors":false,"publicationDate":"2025-09-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Hanson, Jenny L. 0000-0001-8353-6908 jhanson@usgs.gov","orcid":"https://orcid.org/0000-0001-8353-6908","contributorId":461,"corporation":false,"usgs":true,"family":"Hanson","given":"Jenny","email":"jhanson@usgs.gov","middleInitial":"L.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":950742,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stone, Jayme 0000-0002-0512-3072","orcid":"https://orcid.org/0000-0002-0512-3072","contributorId":251712,"corporation":false,"usgs":false,"family":"Stone","given":"Jayme","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":false,"id":950743,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kitchel, Lisie","contributorId":362829,"corporation":false,"usgs":false,"family":"Kitchel","given":"Lisie","affiliations":[{"id":82352,"text":"Wisconsin Department of Natural Resources (WI DNR)","active":true,"usgs":false}],"preferred":false,"id":950744,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Weinzinger, Jesse","contributorId":339829,"corporation":false,"usgs":false,"family":"Weinzinger","given":"Jesse","affiliations":[{"id":38155,"text":"WI DNR","active":true,"usgs":false}],"preferred":false,"id":950745,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Newton, Teresa J. 0000-0001-9351-5852","orcid":"https://orcid.org/0000-0001-9351-5852","contributorId":361878,"corporation":false,"usgs":false,"family":"Newton","given":"Teresa","middleInitial":"J.","affiliations":[{"id":85472,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":false}],"preferred":false,"id":950746,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70271485,"text":"70271485 - 2025 - Water temperature regimes and thermal drivers in semi-natural and flow-regulated rivers of the northern Great Plains","interactions":[],"lastModifiedDate":"2025-12-15T16:35:21.447687","indexId":"70271485","displayToPublicDate":"2025-09-15T09:39:41","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Water temperature regimes and thermal drivers in semi-natural and flow-regulated rivers of the northern Great Plains","docAbstract":"

Rivers of the northern Great Plains have lacked long-term, continuous water temperature assessments, and there is limited information on thermal regimes of these systems and factors driving water temperature. We collected and assembled 2001–2022 water temperature data from 18 sites on four reaches of three rivers that differ in anthropogenic impacts: semi-natural Yellowstone River (YR), flow-impacted Milk River (MK), 351-km of the Missouri River affected by hypolimnetic releases from Fort Peck Dam (FPD), and the semi-natural Missouri River (MR3093) upstream from FPD. Objectives were to: (1) compare May–September mean daily water temperature (Tw), day of year of maximum water temperature (Tmaxdoy), and maximum water temperature (Twmax) among reaches, (2) evaluate air temperature (Ta), river discharge (Qw), and dam-release water temperature (Twdam) as Tw drivers, and (3) model longitudinal recovery of Tw downstream from FPD. Mean Tw and Twmax were greatest at the YR, MR3093 and MK sites, and significantly less through 291-km downstream from FPD. Tmaxdoy at initial sites downstream from FPD was delayed 43–69 days relative to the semi-natural reach upstream from FPD. Ta was the primary correlate of Tw for the semi-natural sites; whereas, Twdam and Ta varied inversely as primary drivers for sites downstream from FPD. Thermal recovery from hypolimnetic releases was incomplete 291-km downstream from FPD and warming persisted 351-km downstream. Results quantify the varied water temperature regimes of rivers in the northern Great Plains and improve understanding of controls affecting Tw among reaches. Water temperature attributes of semi-natural reaches could be used as restoration targets for 300-km of Missouri River presently impacted by hypolimnetic releases.

","language":"English","publisher":"Wiley","doi":"10.1002/rra.70040","usgsCitation":"Braaten, P., Ritter, T.D., Haddix, T.M., Fuller, D.B., Hunziker, J.R., and Hargrave, J.G., 2025, Water temperature regimes and thermal drivers in semi-natural and flow-regulated rivers of the northern Great Plains: River Research and Applications, v. 41, no. 10, p. 2073-2091, https://doi.org/10.1002/rra.70040.","productDescription":"19 p.","startPage":"2073","endPage":"2091","ipdsId":"IP-173819","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":495744,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rra.70040","text":"Publisher Index Page"},{"id":495708,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, North Dakota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -109.74009289929737,\n 48.87888526247494\n ],\n [\n -109.74009289929737,\n 46.45771203249336\n ],\n [\n -103.34512548963949,\n 46.45771203249336\n ],\n [\n -103.34512548963949,\n 48.87888526247494\n ],\n [\n -109.74009289929737,\n 48.87888526247494\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"41","issue":"10","noUsgsAuthors":false,"publicationDate":"2025-09-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Braaten, Patrick 0000-0003-3362-420X pbraaten@usgs.gov","orcid":"https://orcid.org/0000-0003-3362-420X","contributorId":152682,"corporation":false,"usgs":true,"family":"Braaten","given":"Patrick","email":"pbraaten@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":948921,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ritter, T. David","contributorId":361493,"corporation":false,"usgs":false,"family":"Ritter","given":"T.","middleInitial":"David","affiliations":[{"id":78382,"text":"formerly Columbia Environmental Research Center","active":true,"usgs":false}],"preferred":false,"id":948922,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haddix, Tyler M.","contributorId":361495,"corporation":false,"usgs":false,"family":"Haddix","given":"Tyler","middleInitial":"M.","affiliations":[{"id":37431,"text":"Montana Fish, Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":948923,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fuller, David B.","contributorId":361497,"corporation":false,"usgs":false,"family":"Fuller","given":"David","middleInitial":"B.","affiliations":[{"id":37431,"text":"Montana Fish, Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":948924,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hunziker, John R.","contributorId":361499,"corporation":false,"usgs":false,"family":"Hunziker","given":"John","middleInitial":"R.","affiliations":[{"id":37431,"text":"Montana Fish, Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":948925,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hargrave, John G.","contributorId":361501,"corporation":false,"usgs":false,"family":"Hargrave","given":"John","middleInitial":"G.","affiliations":[{"id":13502,"text":"US Army Corps of Engineers","active":true,"usgs":false}],"preferred":false,"id":948926,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70271446,"text":"70271446 - 2025 - Hyperspectral imaging of river bathymetry using an ensemble of regression trees","interactions":[],"lastModifiedDate":"2025-09-16T14:29:31.078808","indexId":"70271446","displayToPublicDate":"2025-09-15T09:20:35","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1425,"text":"Earth Surface Processes and Landforms","active":true,"publicationSubtype":{"id":10}},"title":"Hyperspectral imaging of river bathymetry using an ensemble of regression trees","docAbstract":"

Remote sensing has emerged as an effective tool for characterizing river systems, and machine learning (ML) techniques could make this approach even more powerful. To explore this possibility, we developed an ML-based workflow for hyperspectral imaging of river bathymetry using an ensemble of regression trees (HIRBERT). This approach involves using paired observations of depth and reflectance to select wavelength bands as predictors and then train a depth retrieval model; applying the model to the image yields a spatially continuous bathymetric map. We used data from five rivers with diverse morphologies and optical characteristics to assess whether HIRBERT can (1) provide more accurate depth estimates than a band ratio-based algorithm and (2) extend the range of depths detectable via remote sensing. Relative to single band combinations identified via optimal band ratio analysis (OBRA), regression tree ensembles improved depth retrieval performance, with observed versus predicted (OP) regression R2 values increasing for all five sites. Similarly, HIRBERT provided more reliable depth estimates than OBRA over the full range of depths present along each river. These results suggest that by incorporating additional spectral information from multiple wavelength bands, ML could enhance bathymetric mapping across a range of river environments. In addition, we show how graphical tools can facilitate interpretation of ML-based depth retrieval models and yield insight regarding relationships between depth and reflectance. The HIRBERT workflow is packaged in free, standalone software developed to support applications in river research and management. Although ML can enhance remote sensing of river bathymetry, the limitations of this approach must also be acknowledged: Field measurements of water depth are required to train a depth retrieval model and the resulting model should only be applied to the image from which the training data were derived. The inherently image-specific nature of this approach implies that developing generalized regression tree ensembles that could be applied at larger scales would require additional research.

","language":"English","publisher":"Wiley","doi":"10.1002/esp.70155","usgsCitation":"Legleiter, C.J., Kinzel, P.J., Overstreet, B., and Harrison, L.R., 2025, Hyperspectral imaging of river bathymetry using an ensemble of regression trees: Earth Surface Processes and Landforms, v. 50, no. 12, e70155, 20 p., https://doi.org/10.1002/esp.70155.","productDescription":"e70155, 20 p.","ipdsId":"IP-176358","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":495594,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Idaho, Nebraska, Oregon, Wyoming","otherGeospatial":"Deschutes River, Kootenai River, Niobrara River, Sacramento River, Snake River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -126.50208043183841,\n 49\n ],\n [\n -126.50208043183841,\n 38.571428742902185\n ],\n [\n -99.8528658799654,\n 38.571428742902185\n ],\n [\n -99.8528658799654,\n 49\n ],\n [\n -126.50208043183841,\n 49\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"50","issue":"12","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":948790,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kinzel, Paul J. 0000-0002-6076-9730 pjkinzel@usgs.gov","orcid":"https://orcid.org/0000-0002-6076-9730","contributorId":743,"corporation":false,"usgs":true,"family":"Kinzel","given":"Paul","email":"pjkinzel@usgs.gov","middleInitial":"J.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":948791,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Overstreet, Brandon 0000-0001-7845-6671 boverstreet@usgs.gov","orcid":"https://orcid.org/0000-0001-7845-6671","contributorId":169201,"corporation":false,"usgs":true,"family":"Overstreet","given":"Brandon","email":"boverstreet@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":948792,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harrison, Lee R. 0000-0002-5219-9280","orcid":"https://orcid.org/0000-0002-5219-9280","contributorId":361416,"corporation":false,"usgs":false,"family":"Harrison","given":"Lee","middleInitial":"R.","affiliations":[{"id":18933,"text":"NOAA Southwest Fisheries Science Center","active":true,"usgs":false}],"preferred":false,"id":948793,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70272163,"text":"70272163 - 2025 - Evaluation of the acute toxicity of the piscicide TFM to Burbot","interactions":[],"lastModifiedDate":"2025-11-18T15:28:39.859386","indexId":"70272163","displayToPublicDate":"2025-09-15T08:23:55","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of the acute toxicity of the piscicide TFM to Burbot","docAbstract":"

Non-target animal sensitivity remains a concern when treating Laurentian Great Lakes streams with 4-nitro-3-(trifluoromethyl)phenol (TFM), the main pesticide used to control Sea Lamprey Petromyzon marinus as part of the bi-national Great Lakes Fishery Commission's Sea Lamprey Control Program. Populations of Burbot Lota lota, a historically and culturally important fish, inhabit some of the streams that are treated with TFM. While many species of fish inhabiting the Great Lakes streams have been assessed for sensitivity to TFM, we are not aware of previous research to assess the risk to Burbot. We assessed the sensitivity of Burbot to TFM using replicate 12-hour flow-through diluter toxicity tests. We found Burbot to have a median lethal concentration (LC50) of 9.74 mg/L, while the minimum lethal concentration (LC99.9) for Sea Lamprey was predicted to be 2.5 mg/L in similar waters. The resulting toxicity ratio (LC50 of non-target organism/LC99.9 of Sea Lamprey) of Burbot was 3.90, well above the toxicity ratios for known sensitive species. Our results suggest Burbot are not expected to be adversely affected during a typical TFM stream treatment.

","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.70110","usgsCitation":"Schloesser, N., Luoma, J., Kirkeeng, C., Wolfe, S.L., Schueller, J., and Thompson, H.M., 2025, Evaluation of the acute toxicity of the piscicide TFM to Burbot: Journal of Wildlife Management, v. 89, no. 8, e70110, 10 p., https://doi.org/10.1002/jwmg.70110.","productDescription":"e70110, 10 p.","ipdsId":"IP-164918","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":496584,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Great Lakes","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -91.69562748642463,\n 48.05802044710413\n ],\n [\n -92.51117172294536,\n 46.87833671243604\n ],\n [\n -87.99530971862121,\n 41.65815826878868\n ],\n [\n -81.14672357447232,\n 41.21219694603064\n ],\n [\n -75.24489409556779,\n 43.708024038812454\n ],\n [\n -82.95809831046053,\n 47.17556139162485\n ],\n [\n -85.26817194587964,\n 49.47106575418749\n ],\n [\n -91.69562748642463,\n 48.05802044710413\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"89","issue":"8","noUsgsAuthors":false,"publicationDate":"2025-09-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Schloesser, Nicholas 0000-0002-3815-5302","orcid":"https://orcid.org/0000-0002-3815-5302","contributorId":237025,"corporation":false,"usgs":true,"family":"Schloesser","given":"Nicholas","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":950285,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Luoma, James A. 0000-0003-3556-0190","orcid":"https://orcid.org/0000-0003-3556-0190","contributorId":355611,"corporation":false,"usgs":false,"family":"Luoma","given":"James A.","affiliations":[{"id":37196,"text":"Retired USGS employee","active":true,"usgs":false}],"preferred":false,"id":950286,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kirkeeng, Courtney A. 0000-0002-7141-1216","orcid":"https://orcid.org/0000-0002-7141-1216","contributorId":237026,"corporation":false,"usgs":true,"family":"Kirkeeng","given":"Courtney","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":950287,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wolfe, Samantha L. 0000-0001-8563-8836","orcid":"https://orcid.org/0000-0001-8563-8836","contributorId":274999,"corporation":false,"usgs":true,"family":"Wolfe","given":"Samantha","email":"","middleInitial":"L.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":950288,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schueller, Justin R. 0000-0002-7102-3889","orcid":"https://orcid.org/0000-0002-7102-3889","contributorId":213527,"corporation":false,"usgs":true,"family":"Schueller","given":"Justin","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":950289,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Thompson, Hannah Mann 0000-0001-8316-3232","orcid":"https://orcid.org/0000-0001-8316-3232","contributorId":362308,"corporation":false,"usgs":false,"family":"Thompson","given":"Hannah","middleInitial":"Mann","affiliations":[{"id":85472,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":false}],"preferred":false,"id":950290,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70271460,"text":"70271460 - 2025 - Uppermost Oligocene and Miocene diatom biostratigraphy of Ocean Drilling Program Sites 682 and 688 from the Peru Margin","interactions":[],"lastModifiedDate":"2025-09-16T14:40:56.866315","indexId":"70271460","displayToPublicDate":"2025-09-14T09:35:50","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3481,"text":"Stratigraphy","active":true,"publicationSubtype":{"id":10}},"title":"Uppermost Oligocene and Miocene diatom biostratigraphy of Ocean Drilling Program Sites 682 and 688 from the Peru Margin","docAbstract":"

The diatom biochronology of ODP (Ocean Drilling Program) Holes 682A and 688E provides a detailed framework for refiningMiocene diatom zonation in the East Pisco Basin of southern Peru, establishing both a nearly complete offshore reference section and a correlation tool for the fragmentary onshore vertebrate-bearing deposits. This new biostratigraphic record documents a complete succession of low latitude and/or northeastern Pacific Miocene diatom zones, with two notable exceptions: a dissolution and/or hiatus interval (*16.5–14 Ma) during the Middle Miocene Climatic Optimum and a likely earliest Miocene hiatus (*23.4–21.8 Ma). Although eastern equatorial Pacific diatom zones characterize the Upper Oligocene and Lower Miocene strata, an increased abundance of cool-water diatoms that lived during the Middle and Late Miocene allows better application of northeast Pacific diatom zones, except during the Messinian (7–6 Ma) when warm-water diatoms predominate. The effects of eustatic sea level and tectonics on depositional sequences in the EPB and in offshore cores off central Peru are discussed.

","language":"English","publisher":"Micropaleontology Press","doi":"10.29041/strat.22.3.01","usgsCitation":"Coenen, J., Barron, J.A., and Thomas J. DeVries, 2025, Uppermost Oligocene and Miocene diatom biostratigraphy of Ocean Drilling Program Sites 682 and 688 from the Peru Margin: Stratigraphy, v. 22, no. 3, p. 155-180, https://doi.org/10.29041/strat.22.3.01.","productDescription":"26 p.","startPage":"155","endPage":"180","ipdsId":"IP-175163","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":495595,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Peru","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80,\n -7.809435147132788\n ],\n [\n -80,\n -15\n ],\n [\n -75,\n -15\n ],\n [\n -75,\n -7.809435147132788\n ],\n [\n -80,\n -7.809435147132788\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"22","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Coenen, Jason 0000-0001-5848-5424","orcid":"https://orcid.org/0000-0001-5848-5424","contributorId":356809,"corporation":false,"usgs":false,"family":"Coenen","given":"Jason","affiliations":[{"id":16602,"text":"University of Nebraska, Lincoln","active":true,"usgs":false}],"preferred":false,"id":948829,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barron, John A. 0000-0002-9309-1145 jbarron@usgs.gov","orcid":"https://orcid.org/0000-0002-9309-1145","contributorId":2222,"corporation":false,"usgs":true,"family":"Barron","given":"John","email":"jbarron@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":948830,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thomas J. DeVries","contributorId":361444,"corporation":false,"usgs":false,"family":"Thomas J. DeVries","affiliations":[{"id":86275,"text":"Burke Museum, Washington University","active":true,"usgs":false}],"preferred":false,"id":948831,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70271692,"text":"70271692 - 2025 - Calcareous nannofossil biostratigraphy and floral response to environmental changes recorded in the Pliocene Yorktown Formation, southeastern Virginia, USA","interactions":[],"lastModifiedDate":"2025-09-19T14:00:53.937709","indexId":"70271692","displayToPublicDate":"2025-09-14T08:55:37","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3481,"text":"Stratigraphy","active":true,"publicationSubtype":{"id":10}},"title":"Calcareous nannofossil biostratigraphy and floral response to environmental changes recorded in the Pliocene Yorktown Formation, southeastern Virginia, USA","docAbstract":"

The Pliocene Yorktown Formation, deposited on the U.S. Mid-Atlantic Coastal Plain, has played an important role in advancing our knowledge of Pliocene paleoclimate. To refine the age and paleoenvironment of the Yorktown Formation, we analyzed the calcareous nannofossil assemblage and compared it with variations in lithology and calculated sea surface temperature (SST) from previous studies. The Yorktown Formation in the studied sections consists of, in ascending order, the Sunken Meadow, Rushmere, Morgarts Beach, and Moore House members. Sediment samples were collected from these units and analyzed for calcareous nannoplankton assemblages. The last occurrences of both Reticulofenestra pseudoumbilicus (3.82 Ma) and Sphenolithus spp. (3.61 Ma) were recognized within the Sunken Meadow Member. Discoaster tamalis and Discoaster surculus sporadically occurred within the Rushmere Member, but no specimens of the genus Sphenolithus were recorded, suggesting that this unit was deposited sometime between 3.61–2.76Ma. Rare occurrences of the genus Discoaster made it difficult to constrain the age of the Morgarts Beach and Moore House members, but they are most likely deposited before re-entrance of small Gephyrocapsa (ca. 2.5 Ma), supporting previous age estimates based on planktic foraminiferal biostratigraphy and variation in alkenone-based sea-surface temperature estimates. The abrupt decline of both cold-water species (Coccolithus pelagicus) and coastal species (Helicosphaera spp.) is associated with a rise in SST within the Rushmere Member just below the Morgarts Beach Member, and it may reflect a rapid transgression following the global sea-level low stand associated with Marine Isotope Stage (MIS)M2.

","language":"English","publisher":"Micropaleontological Press","doi":"10.29041/strat.22.3.02","usgsCitation":"Utsunomiya, M., and Dowsett, H.J., 2025, Calcareous nannofossil biostratigraphy and floral response to environmental changes recorded in the Pliocene Yorktown Formation, southeastern Virginia, USA: Stratigraphy, v. 22, no. 3, p. 181-193, https://doi.org/10.29041/strat.22.3.02.","productDescription":"13 p.","startPage":"181","endPage":"193","ipdsId":"IP-176133","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":495781,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","otherGeospatial":"Yorktown Formation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.98737121401622,\n 37.28802783610621\n ],\n [\n -76.98737121401622,\n 37.023565116443024\n ],\n [\n -76.52207462557801,\n 37.023565116443024\n ],\n [\n -76.52207462557801,\n 37.28802783610621\n ],\n [\n -76.98737121401622,\n 37.28802783610621\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"22","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-09-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Utsunomiya, Masayuki","contributorId":347801,"corporation":false,"usgs":false,"family":"Utsunomiya","given":"Masayuki","affiliations":[{"id":83252,"text":"Research Institute of Geology and Geoinformation, Geological Survey of Japan, National Institute of Advanced Industrial Science and Technology","active":true,"usgs":false}],"preferred":false,"id":949038,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dowsett, Harry J. 0000-0003-1983-7524","orcid":"https://orcid.org/0000-0003-1983-7524","contributorId":269579,"corporation":false,"usgs":true,"family":"Dowsett","given":"Harry","email":"","middleInitial":"J.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":949039,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70271453,"text":"70271453 - 2025 - Reframing fish passage prioritization for human nutrition outcomes","interactions":[],"lastModifiedDate":"2025-11-21T22:09:23.791782","indexId":"70271453","displayToPublicDate":"2025-09-13T09:43:53","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1547,"text":"Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Reframing fish passage prioritization for human nutrition outcomes","docAbstract":"

Water control infrastructure forms barriers that fragment river habitats, reducing aquatic biodiversity and the ecosystem services it provides. Irrigation infrastructure, for example, although implemented to support food production, highlights problematic trade-offs against wild food systems like inland fisheries which are a critical food resource for tens of millions of people, particularly in tropical countries. To reduce fragmentation at a broad range of barriers, fish passage technology is sometimes implemented to support migrating fish, aided by frameworks designed to prioritize barriers for remediation. This study critically evaluated 93 fish passage barrier prioritization frameworks globally to explore how they could strategically guide fish passage investments in tropical contexts and identify criteria relevant to delivering on nutrition security outcomes. Results showed prioritization frameworks were ill-equipped to support the broader human development goals that may drive fish passage investments in tropical countries, such as supporting human nutrition under United Nations Sustainable Development Goal (SDG) 2: Zero Hunger. Tropical contexts were underrepresented despite substantial recent fish passage investment, whereas temperate and conservation focused frameworks, particularly from North America, dominated. These findings prompt reflection on the inherent biases in fish passage barrier prioritization frameworks and criteria. Improving understanding of and collaboration with local partners to integrate SDG 2 into future prioritization frameworks could improve fish passage infrastructure and help support better nutrition and food production for communities.

","language":"English","publisher":"Springer","doi":"10.1007/s00267-025-02271-6","usgsCitation":"Duncan, N., Horta, A., Conallin, J., Marsden, T., Lynch, A.J., and Stuart, I., 2025, Reframing fish passage prioritization for human nutrition outcomes: Environmental Management, v. 75, p. 3401-3417, https://doi.org/10.1007/s00267-025-02271-6.","productDescription":"17 p.","startPage":"3401","endPage":"3417","ipdsId":"IP-179174","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":496350,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00267-025-02271-6","text":"Publisher Index Page"},{"id":495597,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"75","noUsgsAuthors":false,"publicationDate":"2025-09-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Duncan, Nicolette","contributorId":361437,"corporation":false,"usgs":false,"family":"Duncan","given":"Nicolette","affiliations":[{"id":40173,"text":"Charles Sturt University","active":true,"usgs":false}],"preferred":false,"id":948809,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Horta, Ana","contributorId":361438,"corporation":false,"usgs":false,"family":"Horta","given":"Ana","affiliations":[{"id":40173,"text":"Charles Sturt University","active":true,"usgs":false}],"preferred":false,"id":948810,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Conallin, John","contributorId":220478,"corporation":false,"usgs":false,"family":"Conallin","given":"John","email":"","affiliations":[{"id":40173,"text":"Charles Sturt University","active":true,"usgs":false}],"preferred":false,"id":948811,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Marsden, Tim","contributorId":361440,"corporation":false,"usgs":false,"family":"Marsden","given":"Tim","affiliations":[{"id":86273,"text":"Australasian Fish Passage Services Pty Ltd","active":true,"usgs":false}],"preferred":false,"id":948812,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lynch, Abigail J. 0000-0001-8449-8392","orcid":"https://orcid.org/0000-0001-8449-8392","contributorId":204271,"corporation":false,"usgs":true,"family":"Lynch","given":"Abigail","middleInitial":"J.","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":948813,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stuart, Ivor","contributorId":361442,"corporation":false,"usgs":false,"family":"Stuart","given":"Ivor","affiliations":[{"id":40173,"text":"Charles Sturt University","active":true,"usgs":false}],"preferred":false,"id":948814,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70273430,"text":"70273430 - 2025 - Toward a new framework to evaluate process-based model configurations and quantify data worth prior to calibration","interactions":[],"lastModifiedDate":"2026-01-13T15:35:23.650384","indexId":"70273430","displayToPublicDate":"2025-09-13T08:09:43","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Toward a new framework to evaluate process-based model configurations and quantify data worth prior to calibration","docAbstract":"Model criticism, discrimination, and selection methods often rely on calibrated model outputs. Because calibration can be computationally expensive, model criticism can first be undertaken by assessing model outputs obtained from limited prior parameter ensembles. However, such prior-based methods are often heuristic and do not formalize the notion of balancing model consistency with data and model complexity (i.e., model adequacy). We present a new framework to discriminate among candidate models prior to calibration that formalizes prior-to-calibration model adequacy into a metric to implicitly balance prior model output data coverage with model complexity represented by prior output (co)variance. The prior model adequacy metric “Mahalanobis distance deviation” quantifies the deviation of (a) the set of squared Mahalanobis distances of data from a prior model output distribution from (b) the set of squared Mahalanobis distances of data from their own distribution. A new data worth metric “discernment value” is also presented which quantifies the value of data for screening less-adequate models prior to calibration. Discernment value is calculated from the change in variance of a weighted average of prior model outputs from all candidate models due to less-adequate model outputs receiving lower weight. The framework is demonstrated using a one-dimensional groundwater flow model with eight possible configurations. A synthetic data network is used to test the framework. Results show the framework identifies the candidate models most similar to the true model used to create the synthetic data. Discernment values show variation in the value of different data types and locations for screening less-adequate models.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2025WR040323","usgsCitation":"Pleasants, M.S., Fienen, M., Essaid, H.I., Blomquist, J.D., Yang, J., and Ye, M., 2025, Toward a new framework to evaluate process-based model configurations and quantify data worth prior to calibration: Water Resources Research, v. 61, no. 9, e2025WR040323, 28 p., https://doi.org/10.1029/2025WR040323.","productDescription":"e2025WR040323, 28 p.","ipdsId":"IP-173111","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":498695,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2025wr040323","text":"Publisher Index Page"},{"id":498584,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"61","issue":"9","noUsgsAuthors":false,"publicationDate":"2025-09-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Pleasants, Mark Shannon 0000-0002-9864-5282","orcid":"https://orcid.org/0000-0002-9864-5282","contributorId":365071,"corporation":false,"usgs":true,"family":"Pleasants","given":"Mark","middleInitial":"Shannon","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":953659,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fienen, Michael N. 0000-0002-7756-4651","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":245632,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael N.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":953660,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Essaid, Hedeff I. 0000-0003-0154-8628 hiessaid@usgs.gov","orcid":"https://orcid.org/0000-0003-0154-8628","contributorId":2284,"corporation":false,"usgs":true,"family":"Essaid","given":"Hedeff","email":"hiessaid@usgs.gov","middleInitial":"I.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":953661,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Blomquist, Joel D. 0000-0002-0140-6534","orcid":"https://orcid.org/0000-0002-0140-6534","contributorId":215461,"corporation":false,"usgs":true,"family":"Blomquist","given":"Joel","middleInitial":"D.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":953662,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Yang, Jing","contributorId":192311,"corporation":false,"usgs":false,"family":"Yang","given":"Jing","affiliations":[],"preferred":false,"id":953663,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ye, Ming","contributorId":194184,"corporation":false,"usgs":false,"family":"Ye","given":"Ming","email":"","affiliations":[],"preferred":false,"id":953664,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70271352,"text":"cir1559 - 2025 - Summary of selenium in the lower Gunnison River Basin, Colorado—Information and data gaps","interactions":[],"lastModifiedDate":"2026-02-03T15:24:37.637334","indexId":"cir1559","displayToPublicDate":"2025-09-12T11:15:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":307,"text":"Circular","code":"CIR","onlineIssn":"2330-5703","printIssn":"1067-084X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1559","displayTitle":"Summary of Selenium in the Lower Gunnison River Basin, Colorado—Information and Data Gaps","title":"Summary of selenium in the lower Gunnison River Basin, Colorado—Information and data gaps","docAbstract":"

The Cretaceous Mancos Shale is a geologic source of selenium in the lower Gunnison River Basin. Natural weathering processes and human activity mobilize selenium from the Mancos Shale and derived materials, and surface water, groundwater, and sediment all affect the transport of selenium from source areas to receiving streams and biota. Selenium accumulates through the aquatic food chain, and its toxic effects can result in invertebrate mortality and mortality, decreased reproduction, and deformities to fish and birds. The Bureau of Reclamation, in cooperation with the State of Colorado and Gunnison River Basin water users, is implementing a Selenium Management Program to reduce selenium concentrations in the lower Gunnison River Basin of Colorado. Goals of the Selenium Management Program are to (1) achieve compliance with the State of Colorado chronic aquatic-life standard for dissolved selenium (4.6 micrograms per liter) in the Gunnison River near Grand Junction, Colorado; (2) sufficiently improve water-quality conditions to assist in the recovery of endangered species in the Gunnison and Colorado Rivers by reducing selenium concentrations; and (3) support continued water uses in the basin.

Many previous studies have contributed to the understanding of selenium in the environment; however, monitoring and research data gaps exist in the lower Gunnison River Basin. The purpose of this report is to summarize information regarding selenium in the lower Gunnison River Basin and describe strategies for scientific research and monitoring to potentially improve understanding of selenium sources; processes affecting the mobilization, transport, and fate of selenium; and the effects of selenium-mitigation projects in the lower Gunnison River Basin. Monitoring and research data gaps discussed in this report include geologic mapping and geochemical source characterization, long-term and ongoing monitoring of the surface-water and groundwater networks, developing and refining statistical models, characterizing selenium on suspended sediment, modeling selenium in the food web, evaluating best management practices, and more.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/cir1559","collaboration":"Prepared in cooperation with the Colorado Water Conservation Board, the Bureau of Reclamation, the Colorado River Water Conservation District, the U.S. Fish and Wildlife Service, the Bureau of Land Management, and the Natural Resources Conservation Service","usgsCitation":"Gidley, R.G., Leib, K.J., and Williams, C.A., 2025, Summary of selenium in the lower Gunnison River Basin, Colorado—Information and data gaps: U.S. Geological Survey Circular 1559, 44 p., https://doi.org/10.3133/cir1559.","productDescription":"vi, 44 p.","onlineOnly":"Y","ipdsId":"IP-139023","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":495449,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/cir1559/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"Circular 1559"},{"id":496026,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118831.htm","linkFileType":{"id":5,"text":"html"}},{"id":495259,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1559/cir1559.pdf","text":"Report","size":"36.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Circular 1559"},{"id":495258,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1559/coverthb.jpg"},{"id":495444,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/circ/1559/cir1559.xml"},{"id":495443,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/circ/1559/images"}],"country":"United States","state":"Colorado","otherGeospatial":"Lower Gunnison River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -109,\n 39.5\n ],\n [\n -109,\n 37.5\n ],\n [\n -106.5,\n 37.5\n ],\n [\n -106.5,\n 39.5\n ],\n [\n -109,\n 39.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"

Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
Denver, CO 80225

","tableOfContents":"","publishedDate":"2025-09-12","noUsgsAuthors":false,"publicationDate":"2025-09-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Gidley, Rachel G. 0000-0002-9840-8252","orcid":"https://orcid.org/0000-0002-9840-8252","contributorId":259315,"corporation":false,"usgs":true,"family":"Gidley","given":"Rachel","email":"","middleInitial":"G.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":948187,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Leib, Kenneth J. 0000-0002-0373-0768","orcid":"https://orcid.org/0000-0002-0373-0768","contributorId":202705,"corporation":false,"usgs":true,"family":"Leib","given":"Kenneth","middleInitial":"J.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":948188,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Williams, Cory A. 0000-0003-1461-7848 cawillia@usgs.gov","orcid":"https://orcid.org/0000-0003-1461-7848","contributorId":689,"corporation":false,"usgs":true,"family":"Williams","given":"Cory","email":"cawillia@usgs.gov","middleInitial":"A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":948189,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}