Black Carp Mylopharyngodon piceus were imported into the United States in the 1970s and 1980s for use in aquaculture; escape occurred and reported wild captures increased. Lacking species-specific capture methods, we assessed fisheries dependent incidental Black Carp catches for a common method, hoop nets, by kernel density analysis to identify an area of increased reporting and compare frequency of reports for water temperature, river stage, and capture date to identify seasonality. We then used fisheries independent effort to identify co-occurrence of species via non-metric multi-dimensional scaling and fit Black Carp catch and environmental covariates by generalized linear models to assess site-specific environmental covariates facilitating capture. The best approximating distribution was refitted for predictions and inference. The greatest density of fisheries dependent hoop net captures (39 %) was near the confluence of the Missouri and Mississippi rivers, primarily from July-September. Captures were characterized by median water temperature 26.7°C, river stage 5.02 m, and 223 day-of-year (DOY; mid-August). Ordination of fisheries independent catch identified similarity in environmental covariates of Smallmouth Buffalo Ictiobus bubalus and Black Carp. The probability of capturing ≥ 1 Black Carp increased with DOY, decreased with increasing current velocity, and increased with depth. Most captures occurred in outside bends (87 %) or side channels (12 %). Probability of Black Carp capture was low but increased in summer and early fall when stage is lower, facilitating reduced current velocity and access to deeper areas. Results may be validated beyond this river segment to test if site-specific hydrology or habitat characteristics facilitated increased commercial and biologist capture and for replication.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.fishres.2025.107368","usgsCitation":"Kroboth, P., Colvin, M.E., and Broaddus, C., 2025, Fisheries dependent and independent data inform a capture technique for an emerging invasive fish species in the mainstem Mississippi River; Black Carp Mylopharyngodon piceus: Fisheries Research, v. 285, 107368, 12 p., https://doi.org/10.1016/j.fishres.2025.107368.","productDescription":"107368, 12 p.","ipdsId":"IP-167531","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":487576,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.fishres.2025.107368","text":"Publisher Index Page"},{"id":485444,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Missouri","otherGeospatial":"MIssissippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.22145042868262,\n 38.90746978465282\n ],\n [\n -90.22145042868262,\n 38.666188258783194\n ],\n [\n -90.1030809646113,\n 38.666188258783194\n ],\n [\n -90.1030809646113,\n 38.90746978465282\n ],\n [\n -90.22145042868262,\n 38.90746978465282\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"285","noUsgsAuthors":false,"publicationDate":"2025-04-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Kroboth, Patrick 0000-0002-9447-4818","orcid":"https://orcid.org/0000-0002-9447-4818","contributorId":216578,"corporation":false,"usgs":true,"family":"Kroboth","given":"Patrick","email":"","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":935820,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":935821,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Broaddus, Courtney 0000-0003-3851-3584","orcid":"https://orcid.org/0000-0003-3851-3584","contributorId":354595,"corporation":false,"usgs":true,"family":"Broaddus","given":"Courtney","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":935822,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70265982,"text":"sir20255029 - 2025 - Spatial stream network modeling of water temperature within the White River Basin, Mount Rainier National Park, Washington","interactions":[{"subject":{"id":70257569,"text":"70257569 - 2024 - Spatial variability of water temperature within the White River basin, Mount Rainier National Park Washington","indexId":"70257569","publicationYear":"2024","noYear":false,"title":"Spatial variability of water temperature within the White River basin, Mount Rainier National Park Washington"},"predicate":"SUPERSEDED_BY","object":{"id":70265982,"text":"sir20255029 - 2025 - Spatial stream network modeling of water temperature within the White River Basin, Mount Rainier National Park, Washington","indexId":"sir20255029","publicationYear":"2025","noYear":false,"title":"Spatial stream network modeling of water temperature within the White River Basin, Mount Rainier National Park, Washington"},"id":1}],"lastModifiedDate":"2025-08-07T21:05:21.759632","indexId":"sir20255029","displayToPublicDate":"2025-04-23T07:58:02","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":"2025-5029","displayTitle":"Spatial Stream Network Modeling of Water Temperature within the White River Basin, Mount Rainier National Park, Washington","title":"Spatial stream network modeling of water temperature within the White River Basin, Mount Rainier National Park, Washington","docAbstract":"Water temperature is a primary control on the occurrence and distribution of fish and other ectothermic aquatic species. In the Pacific Northwest, cold-water species such as Pacific salmon (Oncorhynchus spp.) and bull trout (Salvelinus confluentus) have specific temperature requirements during different life stages that must be met to ensure the viability of their populations. Rivers draining Mount Rainier in western Washington, including the White River along its northern flank, support a number of cold-water fish populations, but the spatial distribution of water temperatures, particularly during late-summer baseflow during August and September, and the climatic, hydrologic, and physical processes regulating it are not well constrained. Spatial stream network (SSN) models, which are generalized linear models that incorporate streamwise spatial autocovariance structures, were fit to mean and 7-day average daily maximum water temperature for August and September for the White River Basin. The SSN models were calibrated using water temperature measurements collected in 2010 through 2020. The extent of the models included the White River and its tributaries upstream from its confluence with Silver Creek in Mount Rainier National Park, Washington. SSN models incorporated covariates hypothesized to represent the climatic, hydrologic, and physical processes that influence water temperature. SSN models were fit to the measured data and compared to generalized linear models that lacked spatial autocovariance structures. Statistically significant covariates within the best-fit models included the proportion of ice cover and forest cover within the basin, mean August air temperature, the proportion of consolidated geologic units, and snow-water equivalent. Statistical models that included spatial autocovariance structures had better predictive performance than those that did not. Additionally, models of mean August and September water temperature had better predictive performance than those of 7-day average daily maximum temperature in August and September. Predictions of the spatial distribution of water temperature were similar between August and September with a general warming in the downstream part of the mainstem White River compared to cooler water temperatures in the high-elevation headwater streams. The proportion of ice cover emerged as an inversely related significant covariate to both mean August and September water temperature because streams that receive glacial meltwater are colder than non-glaciated streams. Water temperatures of the upper White River increased downstream and are attributed to warming of water temperature from accumulated solar radiation and inflow of non-glaciated tributaries. Estimated water temperatures for the upper White River model are 3–4 degrees Celsius (°C) warmer for tributaries, but 1–2 °C cooler for the mainstem compared to the regional-scale model. Differences between the upper White River SSN model and the regional-scale NorWeST model are attributed to the fact that the upper White River SSN included water temperature observations specific to the upper White River, whereas water temperature observations from lower elevation streams and downstream from the Mount Rainer National Park boundary were used in the regional scale model.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255029","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Gendaszek, A.S., Leach, A.C., and Jaeger, K.L., 2025, Spatial stream network modeling of water temperature within the White River Basin, Mount Rainier National Park, Washington (ver. 1.1, May 2025): U.S. Geological Survey\nScientific Investigations Report 2025–5029, 17 p., https://doi.org/10.3133/sir20255029. [Supersedes preprint https://doi.org/10.31223/X5712P.]","productDescription":"Report: vi, 17 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-168299","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":484931,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://www.sciencebase.gov/catalog/item/6542802dd34ee4b6e05bd2cb","text":"USGS data release","description":"USGS data release","linkHelpText":"Stream Temperature Models of White River Watershed, Mount Rainier National Park, Washington"},{"id":484872,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5029/sir20255029.XML"},{"id":484871,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5029/images"},{"id":484870,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255029/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5029"},{"id":484869,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5029/sir20255029.pdf","text":"Report","size":"4.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5029"},{"id":484868,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5029/coverthb2.jpg"},{"id":486241,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2025/5029/versionHistory.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"},"description":"Version History"},{"id":493767,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118576.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Washington","otherGeospatial":"Mount Rainier National Park, upper White River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.75,\n 47\n ],\n [\n -121.75,\n 46.8333\n ],\n [\n -121.5,\n 46.8333\n ],\n [\n -121.5,\n 47\n ],\n [\n -121.75,\n 47\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: April 23.2025; Version 1.1: May 20, 2025","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
The hydrogeologic framework at closed-loop geothermal sites in the Erie-Ontario Lowlands and Allegheny Plateau of central and western New York is the result of the complex interaction of bedrock geology, glacial geology, and groundwater hydrology, and the occurrence of petroleum and gas. Considerations for closed-loop geothermal bore installation include the thickness and character of glacial deposits, bedrock solubility and depth to competent rock, karst development, the distribution of highly permeable zones and their hydraulic heads, and the presence of saline water, gas, and oil. The hydrogeology of the Erie-Ontario Lowlands and Allegheny Plateau poses challenges to closed-loop geothermal bore drilling and casing; managing drill cuttings, discharge water, and gas; and grouting. The potential to encounter severe challenges typically increases with bore depth. This report highlights hydrogeologic considerations for closed-loop geothermal bore installation in New York’s Erie-Ontario Lowlands and Allegheny Plateau to help guide the efficient and safe development of geothermal resources in the regions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251013","usgsCitation":"Williams, J.H., Kappel, W.M., and Woda, J.C., 2025, Hydrogeologic framework and considerations for drilling and grouting of closed-loop geothermal bores in the Erie-Ontario Lowlands and Allegheny Plateau of New York State: U.S. Geological Survey Open-File Report 2025–1013, 11 p., https://doi.org/10.3133/ofr20251013.","productDescription":"v, 11 p.","numberOfPages":"11","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-160254","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":484773,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1013/coverthb.jpg"},{"id":484774,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1013/ofr20251013.pdf","text":"Report","size":"3.94 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1013 PDF"},{"id":484776,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1013/ofr20251013.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2025-1013 XML"},{"id":484775,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251013/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1013 HTML"},{"id":484777,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1013/images/"},{"id":493765,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118544.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New York","otherGeospatial":"Allegheny Plateau, Erie-Ontario Lowlands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -79.81166805859044,\n 42.45817957611891\n ],\n [\n -79.70949328319377,\n 41.9887101346182\n ],\n [\n -75.25480564049155,\n 41.98783382644345\n ],\n [\n -74.87533808065217,\n 41.42844676638893\n ],\n [\n -74.68982238759088,\n 44.66640491894796\n ],\n [\n -75.55631580076837,\n 44.66459292990035\n ],\n [\n -76.3423531557645,\n 44.2288156067778\n ],\n [\n -76.41053068058827,\n 43.979744421465796\n ],\n [\n -76.30005176487545,\n 43.60034279040653\n ],\n [\n -76.88563948226266,\n 43.34164590639\n ],\n [\n -77.46843046198688,\n 43.34036285729903\n ],\n [\n -78.42846779801299,\n 43.474518439670874\n ],\n [\n -79.08970961322291,\n 43.31146453979213\n ],\n [\n -78.97892018115594,\n 42.958836576630915\n ],\n [\n -78.81395126277387,\n 42.855237313375795\n ],\n [\n -79.81166805859044,\n 42.45817957611891\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
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Troy, NY 12180–8349
This study evaluates the potential water availability in Barka Slough and the effects of changing hydrological conditions on the aquatic habitat of five protected species. Barka Slough is a historically perennial wetland at the downstream western end of the San Antonio Creek Valley watershed (SACVW). A previously published hydrologic model of the SACVW for 1948–2018 was extended to include 2019–2021 and then modified to simulate the future years of 2022–2051. Two models simulating the future years of 2022–2051 were constructed, each with different climate inputs: (1) a repeated historical climate and (2) a 2070-centered Drier Extreme Warming climate (2070 DEW). The model with the 2070 DEW climate had warmer temperatures and an increase in average annual precipitation driven by larger, albeit more infrequent, precipitation events than the model with the historical climate. Simulated groundwater pumpage resulted in cumulative groundwater storage depletion and groundwater-level decline in Barka Slough in both future models. The simulations indicate that Barka Slough may transition from a perennial to an ephemeral wetland. Streamflow, stream disconnection, and depth to groundwater are key habitat metrics for federally listed species in Barka Slough. Future seasonal conditions for each metric are more likely to affect federally listed species’ habitats under 2070 DEW climatic conditions. Future seasonal streamflow volume may negatively impact unarmored threespine stickleback (Gasterosteus aculeatus williamsoni) and tidewater goby (Eucyclogobis newberryi) habitats. Future seasonal stream disconnection may negatively impact the unarmored threespine stickleback habitat. Future groundwater-level decline may negatively impact Gambel’s watercress (Nasturtium gambelii) and La Graciosa thistle (Cirsium scariosum var. loncholepis) habitats and could influence the ability to use Barka Slough as a restoration or reintroduction site for these species. Results from this study can be used to inform water management decisions to sustain future groundwater availability in the SACVW.
","language":"English","publisher":"MDPI","doi":"10.3390/w17081238","usgsCitation":"Cromwell, G., Culling, D., Young, M.J., and Larsen, J., 2025, Simulated effects of future water availability and protected species habitat in a perennial wetland, Santa Barbara County, California: Water, v. 17, no. 8, 1238, 29 p., https://doi.org/10.3390/w17081238.","productDescription":"1238, 29 p.","ipdsId":"IP-168161","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":488483,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w17081238","text":"Publisher Index Page"},{"id":484844,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Santa Barbara County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120.5333,\n 34.85\n ],\n [\n -120.5333,\n 34.6833\n ],\n [\n -120.1,\n 34.6833\n ],\n [\n -120.1,\n 34.85\n ],\n [\n -120.5333,\n 34.85\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"17","issue":"8","noUsgsAuthors":false,"publicationDate":"2025-04-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Cromwell, Geoffrey 0000-0001-8481-405X gcromwell@usgs.gov","orcid":"https://orcid.org/0000-0001-8481-405X","contributorId":5920,"corporation":false,"usgs":true,"family":"Cromwell","given":"Geoffrey","email":"gcromwell@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":934165,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Culling, Daniel Philip 0000-0002-6585-0650","orcid":"https://orcid.org/0000-0002-6585-0650","contributorId":299662,"corporation":false,"usgs":true,"family":"Culling","given":"Daniel Philip","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":934166,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Young, Matthew J. 0000-0001-9306-6866 mjyoung@usgs.gov","orcid":"https://orcid.org/0000-0001-9306-6866","contributorId":206255,"corporation":false,"usgs":true,"family":"Young","given":"Matthew","email":"mjyoung@usgs.gov","middleInitial":"J.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":934167,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Larsen, Joshua 0000-0002-1218-800X jlarsen@usgs.gov","orcid":"https://orcid.org/0000-0002-1218-800X","contributorId":272403,"corporation":false,"usgs":true,"family":"Larsen","given":"Joshua","email":"jlarsen@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":934168,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70267284,"text":"70267284 - 2025 - Using DNA barcoding to evaluate freshwater mussel and fish-host relationships in the Flint River (Georgia, USA)","interactions":[],"lastModifiedDate":"2025-05-19T15:32:21.932397","indexId":"70267284","displayToPublicDate":"2025-04-21T08:26:44","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1699,"text":"Freshwater Science","active":true,"publicationSubtype":{"id":10}},"title":"Using DNA barcoding to evaluate freshwater mussel and fish-host relationships in the Flint River (Georgia, USA)","docAbstract":"Freshwater mussels have a unique life history in which larval mussels (glochidia) act as obligate parasites to fish hosts. Host selectivity may be species specific, and identification of host fish is a critical step in conservation planning for individual mussel species. The Flint River harbors ~23% of the freshwater mussel (order Unionida) diversity in the state of Georgia, USA. Nine species in the basin are state or federally listed, and local diversity is threatened by shifting hydrologic conditions, increasing habitat loss, and sedimentation. However, knowledge on host species is lacking for nearly 40% of mussel species in the Flint River, limiting the efforts of conservation managers. In this study, we assessed the use of host fish by mussels by identifying the species of naturally encysted mussel larvae and transformed juveniles found on wild-caught fishes. Infested fishes were collected in the summers of 2021 and 2022 and held in laboratory conditions. Glochidia and juvenile mussels were collected as they excised from live hosts and were identified by DNA barcoding with the cytochrome oxidase c subunit I locus. Twenty-eight unique mussel–host relationships were identified, 27 of which were considered novel when cross-referenced to the existing mussel–host databases and peer-reviewed literature. Our data build upon knowledge of host use in unionids and further demonstrate the importance of understanding patterns in wild host use.
","language":"English","publisher":"University of Chicago Press","doi":"10.1086/734714","usgsCitation":"Robinson, H., Wares, J., Cowie, G., Williams, S., Scott, B.F., Rowe, M.T., Johnson, N., and Hazelton, P., 2025, Using DNA barcoding to evaluate freshwater mussel and fish-host relationships in the Flint River (Georgia, USA): Freshwater Science, 14 p., https://doi.org/10.1086/734714.","productDescription":"14 p.","ipdsId":"IP-160278","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":486159,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia","otherGeospatial":"Flint River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -85.09753023326218,\n 33.35794111464041\n ],\n [\n -85.09753023326218,\n 30.70192265047531\n ],\n [\n -83.10505296880376,\n 30.70192265047531\n ],\n [\n -83.10505296880376,\n 33.35794111464041\n ],\n [\n -85.09753023326218,\n 33.35794111464041\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Online First","noUsgsAuthors":false,"publicationDate":"2025-04-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Robinson, Hayley A.","contributorId":355549,"corporation":false,"usgs":false,"family":"Robinson","given":"Hayley A.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":937586,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wares, John P.","contributorId":355552,"corporation":false,"usgs":false,"family":"Wares","given":"John P.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":937587,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cowie, Gail M.","contributorId":355554,"corporation":false,"usgs":false,"family":"Cowie","given":"Gail M.","affiliations":[{"id":84771,"text":"Albany State University","active":true,"usgs":false}],"preferred":false,"id":937588,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Williams, Shayla D.","contributorId":355555,"corporation":false,"usgs":false,"family":"Williams","given":"Shayla D.","affiliations":[{"id":84771,"text":"Albany State University","active":true,"usgs":false}],"preferred":false,"id":937589,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Scott, Ben F","contributorId":334186,"corporation":false,"usgs":false,"family":"Scott","given":"Ben","email":"","middleInitial":"F","affiliations":[{"id":6608,"text":"San Diego State University","active":true,"usgs":false}],"preferred":false,"id":937590,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rowe, Matthew T.","contributorId":150928,"corporation":false,"usgs":false,"family":"Rowe","given":"Matthew","email":"","middleInitial":"T.","affiliations":[{"id":13588,"text":"Central Michigan University","active":true,"usgs":false}],"preferred":false,"id":937591,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Johnson, Nathan 0000-0001-5167-1988","orcid":"https://orcid.org/0000-0001-5167-1988","contributorId":216879,"corporation":false,"usgs":true,"family":"Johnson","given":"Nathan","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":937592,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hazelton, Peter D.","contributorId":340493,"corporation":false,"usgs":false,"family":"Hazelton","given":"Peter D.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":937593,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70265829,"text":"sir20255023 - 2025 - A framework for understanding the effects of subsurface agricultural drainage on downstream flows","interactions":[],"lastModifiedDate":"2025-04-18T14:23:34.614404","indexId":"sir20255023","displayToPublicDate":"2025-04-17T15:29:38","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":"2025-5023","displayTitle":"A Framework for Understanding the Effects of Subsurface Agricultural Drainage on Downstream Flows","title":"A framework for understanding the effects of subsurface agricultural drainage on downstream flows","docAbstract":"Understanding controls on streamflow volume and magnitude is important to water resource management applications, such as critical water and transportation structure design and floodplain mapping. Changes in land use and agricultural practices, such as subsurface agricultural drainage, may be contributing to changes in streamflow characteristics. Subsurface agricultural drainage, also known as tile drainage, is the practice of installing drains in the subsurface of agricultural fields to improve productivity. Because of the complex interactions between subsurface drainage systems, precipitation, local soil conditions, and land management practices, it is difficult to determine how subsurface agricultural drainage affects downstream flow. Previously developed subsurface agricultural drainage conceptual models under dry, saturated, and winter conditions are summarized, and current literature on the effects of subsurface agricultural drainage on downstream flows, focusing on peak flow, non-event flow, and total flow to develop frameworks for discussing these systems is compiled.
The effects that subsurface drainage has on hydrologic systems are expected to vary by site and are seasonally based on system design, soil type, moisture conditions, precipitation characteristics, and land conditions. Subsurface drainage can affect the magnitude of peak flow by converting surface runoff from a storm event to subsurface runoff. By increasing hydrologic connectivity of a catchment, subsurface drainage can increase non-event flow or the flow between two storm events, typically dependent on lateral flow through the subsurface and groundwater. Theoretically, by diverting water from groundwater recharge or by reducing water available for evapotranspiration, subsurface drainage may increase the total volume of flow. Precipitation changes may increase infiltration, excess overland flow, and flood risk regardless of the presence or absence of subsurface drainage.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255023","collaboration":"Prepared in cooperation with Illinois Department of Transportation, Iowa Department of Transportation, Michigan Department of Transportation, Minnesota Department of Transportation, Missouri Department of Transportation, Montana Department of Natural Resources and Conservation, North Dakota Department of Water Resources, South Dakota Department of Transportation, and Wisconsin Department of Transportation","usgsCitation":"Podzorski, H.L., and Ryberg, K.R., 2025, A framework for understanding the effects of subsurface agricultural drainage on downstream flows: U.S. Geological Survey Scientific Investigations Report 2025–5023, 24 p., https://doi.org/10.3133/sir20255023.","productDescription":"vi, 24 p.","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-161597","costCenters":[{"id":34685,"text":"Dakota Water Science 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\"}}]}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240
This review assesses gaps in water quality modeling, emphasizing opportunities to improve next-generation models that are essential for managing water quality and are integral to meeting goals of scientific and management agencies. In particular, this paper identifies gaps in water quality modeling capabilities that, if addressed, could support assessments, projections, and evaluations of management alternatives to support ecosystem health and human beneficial use of water resources. It covers surface water and groundwater quality modeling, dealing with a broad suite of physical, biogeochemical, and anthropogenic drivers. Modeling capabilities for six constituents (or constituent categories) are explored: water temperature, salinity, nutrients, sediment, geogenic constituents, and contaminants of emerging concern. Each constituent was followed through the coupled atmospheric-hydrologic-human system, with prominent modeling gaps described for a diverse array of relevant inputs, processes, and human activities. Commonly identified modeling gaps primarily fall under three types: (1) model gaps, (2) data gaps, and (3) process understanding gaps. In addition to potential solutions for addressing specific individual modeling limitations, some broad approaches (e.g., enhanced data collection and compilation, machine learning, reduced-complexity modeling) are discussed as ways forward for tackling multiple gaps. This gap analysis establishes a framework of diverse approaches that may support improved process representation, scale, and accuracy of models for a wide range of water quality issues.
","language":"English","publisher":"MDPI","doi":"10.3390/w17081200","usgsCitation":"Lucas, L., Brown, C., Robertson, D., Baker, N.T., Johnson, Z., Green, C., Cho, J., Erickson, M., Gellis, A.C., Jasmann, J.R., Knowles, N., Prein, A., and Stackelberg, P.E., 2025, Gaps in water quality modeling of hydrologic systems: Water, v. 17, no. 8, 1200, 98 p., https://doi.org/10.3390/w17081200.","productDescription":"1200, 98 p.","ipdsId":"IP-157684","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":488460,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w17081200","text":"Publisher Index Page"},{"id":484764,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"17","issue":"8","noUsgsAuthors":false,"publicationDate":"2025-04-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Lucas, Lisa 0000-0001-7797-5517 llucas@usgs.gov","orcid":"https://orcid.org/0000-0001-7797-5517","contributorId":260498,"corporation":false,"usgs":true,"family":"Lucas","given":"Lisa","email":"llucas@usgs.gov","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":933941,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brown, Craig J. 0000-0002-3858-3964","orcid":"https://orcid.org/0000-0002-3858-3964","contributorId":210450,"corporation":false,"usgs":true,"family":"Brown","given":"Craig J.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933942,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Robertson, Dale M. 0000-0001-6799-0596","orcid":"https://orcid.org/0000-0001-6799-0596","contributorId":217258,"corporation":false,"usgs":true,"family":"Robertson","given":"Dale M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933943,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Baker, Nancy T. 0000-0002-7979-5744","orcid":"https://orcid.org/0000-0002-7979-5744","contributorId":222870,"corporation":false,"usgs":true,"family":"Baker","given":"Nancy","email":"","middleInitial":"T.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933944,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnson, Zachary 0000-0002-0149-5223 zjohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-0149-5223","contributorId":190399,"corporation":false,"usgs":true,"family":"Johnson","given":"Zachary","email":"zjohnson@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":933945,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Green, Christopher 0000-0002-6480-8194","orcid":"https://orcid.org/0000-0002-6480-8194","contributorId":201642,"corporation":false,"usgs":true,"family":"Green","given":"Christopher","email":"","affiliations":[{"id":438,"text":"National Research Program - 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2025 - Lithium from magma to mine in an early Yellowstone hotspot caldera","interactions":[],"lastModifiedDate":"2025-07-10T14:50:14.042773","indexId":"70267815","displayToPublicDate":"2025-04-16T08:37:29","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1796,"text":"Geology","active":true,"publicationSubtype":{"id":10}},"title":"Lithium from magma to mine in an early Yellowstone hotspot caldera","docAbstract":"Renewable energy technologies rely on the extraction of metals not historically in high demand, such as lithium (Li), for which ore deposit models are incompletely understood. One of the world’s largest Li deposits is hosted in lake sediments of the 16.4 Ma McDermitt caldera, which formed during the early stages of Yellowstone hotspot volcanism in the western United States. Eruptive and posteruptive mobility of Li are major challenges in elucidating deposit formation. Melt inclusions preserved in quartz crystals provide a means to assess pre-eruptive magmatic Li contents. Concentrations of Li determined by ion microprobe for melt inclusions in a McDermitt rhyolite lava are 400−1350 ppm, compared to 20−70 ppm Li in matrix rhyolite glasses. Synthesis with melt inclusion data for eight additional calderas demonstrates a recurrence of Li-rich rhyolitic magmas (200−2000 ppm Li) in the western part of the Yellowstone hotspot track. However, unlike the multicyclic caldera complexes with overlapping fault networks that may have compromised Li retention, the McDermitt caldera remained a closed hydrologic system throughout its evolution. Modeling indicates 100 km3 of resurgent magma could yield 25−150 Mt Li in a magmatic fluid and supports accumulation of Li-rich magmatic fluid in a closed intracaldera lake, followed by evaporative concentration and sequestration of Li within clay minerals to generate the McDermitt deposit.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/G53140.1","usgsCitation":"Watts, K., 2025, Lithium from magma to mine in an early Yellowstone hotspot caldera: Geology, v. 53, no. 7, p. 592-596, https://doi.org/10.1130/G53140.1.","productDescription":"5 p.","startPage":"592","endPage":"596","ipdsId":"IP-167363","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":489475,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":490666,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/g53140.1","text":"Publisher Index Page"}],"country":"United States","state":"Idaho, Nevada, Oregon, Wyoming","otherGeospatial":"Yellowstone hotspot caldera","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.21830246150877,\n 45.126578896874065\n ],\n [\n -119.21693278725452,\n 45.126578896874065\n ],\n [\n -119.21693278725452,\n 41.23242701033587\n ],\n [\n -114.17059390138817,\n 40.859473447854995\n ],\n [\n -114.02540645163282,\n 42.00890055289802\n ],\n [\n -110.44161856797778,\n 41.981517173869975\n ],\n [\n -110.21830246150877,\n 45.126578896874065\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"53","issue":"7","noUsgsAuthors":false,"publicationDate":"2025-04-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Watts, Kathryn E. 0000-0002-6110-7499","orcid":"https://orcid.org/0000-0002-6110-7499","contributorId":204344,"corporation":false,"usgs":true,"family":"Watts","given":"Kathryn E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":939006,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70265700,"text":"sir20255003 - 2025 - Estimation of baseflow and flooding characteristics for East Canyon Creek, Summit and Morgan Counties, Utah","interactions":[],"lastModifiedDate":"2025-08-07T20:57:16.247704","indexId":"sir20255003","displayToPublicDate":"2025-04-16T07:09:29","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-5003","displayTitle":"Estimation of Baseflow and Flooding Characteristics for East Canyon Creek, Summit and Morgan Counties, Utah","title":"Estimation of baseflow and flooding characteristics for East Canyon Creek, Summit and Morgan Counties, Utah","docAbstract":"An improved understanding of hydrologic responses to changing climatic conditions is needed to better inform water management practices. East Canyon Creek, a perennial, snowmelt-dominated stream in the Wasatch Mountains of northern Utah, is subjected to increasing development and demands on water in the Snyderville Basin and adjacent areas. In this study, streamflow and specific conductance measured at three U.S. Geological Survey streamgages on East Canyon Creek were used to estimate daily baseflow for water years 2011–22. Trends in these estimates and correlations with climate data from two Natural Resource Conservation Service snow telemetry (SNOTEL) stations within the Snyderville Basin above East Canyon Reservoir, were quantified and reported. Peak annual streamflow also was assessed for flood potential on the study reach of East Canyon Creek. The hydrograph separations showed consistent baseflow indices among all sites, with a larger baseflow component during the fall–spring period (September–April; baseflow indices approximately equal to [≈] 0.751–0.835) and smaller component during the summer period (May–August; baseflow indices ≈ 0.428–0.532). In-stream specific conductance during spring (February–April) was influenced by road salt application, limiting the utility of the hydrograph separation approach. Annual streamflow and climate data were evaluated for trends using the nonparametric Mann–Kendall test, with inconclusive results. Related tests for trends, the Seasonal and Regional Kendall tests, were used to evaluate data at monthly timesteps and indicated a decreasing trend in total streamflow and baseflow at all streamgages. The rank-based Kendall’s tau test for correlation was used to measure the ordinal association with climatic data at co-located SNOTEL stations. Total streamflow and baseflow were strongly correlated with precipitation and snow-water equivalent. By incorporating a predictive regression model, the nonparametric Theil–Sen line, these correlations could support the development of streamflow forecast models using climate data from SNOTEL stations. Such models would provide water managers with tools to help make proactive decisions, such as reservoir or water reclamation releases and curtailment of withdrawals, in response to regional drought or varying snowpack and spring runoff in a given year.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255003","collaboration":"Prepared in cooperation with Snyderville Basin Water Reclamation District","usgsCitation":"Root, J.C., and Rumsey, C.A., 2025, Estimation of baseflow and flooding characteristics for East Canyon Creek, Summit and Morgan Counties, Utah: U.S. Geological Survey Scientific Investigations Report 2025–5003, 29 p., https://doi.org/10.3133/sir20255003.","productDescription":"Report: viii, 29 p.; Data Release","numberOfPages":"29","onlineOnly":"Y","ipdsId":"IP-162488","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":493759,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118539.htm","linkFileType":{"id":5,"text":"html"}},{"id":484540,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P14SJDMX","text":"USGS data release","description":"Root, J.C., 2025, Baseflow estimation and trend and correlation analysis results for East Canyon Creek, Summit and Morgan Counties, Utah, 2010–2022: U.S. Geological Survey data release, https://doi.org/10.5066/P14SJDMX.","linkHelpText":"Baseflow estimation and trend and correlation analysis results for East Canyon Creek, Summit and Morgan Counties, Utah, 2010–2022"},{"id":484539,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5003/images"},{"id":484538,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5003/sir20255003.XML","description":"SIR 2025-5003 XML"},{"id":484537,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255003/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5003 HTML"},{"id":484536,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5003/sir20255003.pdf","text":"Report","size":"8.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5003 PDF"},{"id":484535,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5003/coverthb.jpg"}],"country":"United States","state":"Utah","county":"Morgan County, Summit County","otherGeospatial":"East Canyon Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.85739630382633,\n 41.2514958778022\n ],\n [\n -111.85739630382633,\n 40.5798335667547\n ],\n [\n -110.91729451616551,\n 40.5798335667547\n ],\n [\n -110.91729451616551,\n 41.2514958778022\n ],\n [\n -111.85739630382633,\n 41.2514958778022\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
Utah Water Science Center
U.S. Geological Survey
2329 West Orton Circle
Salt Lake City, Utah 84119-2047
Nitrogen transported along groundwater flow paths in coastal aquifers can contribute substantially to nitrogen loading into surface water receptors, particularly in hydrologic systems dominated by groundwater discharge. Nitrogen entrained in the aquifer is a function of land use and associated nitrogen sources at the time of groundwater recharge, which may differ considerably from present-day sources. Legacy nitrogen can result in substantial discrepancies between observed present-day nitrogen loading to surface water receptors and loading estimated from present-day sources. Additionally, legacy nitrogen can continue to discharge into surface waters after nitrogen mitigation actions have been undertaken. Here, we use a numerical modeling framework to compare three methods of estimating time-varying historical nitrogen loads to four water bodies (receptors) on eastern Long Island, New York. The methods span a range of data requirements and process complexity, from instantaneous receptor loads calculated from steady-state groundwater contributing areas, to transient loads estimated by explicitly simulating legacy groundwater nitrogen transport over a century with large changes in nitrogen sources and hydrologic conditions. The effects of legacy nitrogen on estimated receptor loads varied temporally and spatially within the study area. Depending on antecedent nitrogen inputs and hydrologic conditions, historical annual nitrogen loads estimated from transient simulations accounting for legacy nitrogen can be quite similar (<10% difference) or substantially different (±100%) from those estimated from simpler instantaneous methods. Continued input of present-day nitrogen sources using methods that account for legacy nitrogen results in asymptotic increases in receptor nitrogen loads over time, indicating that simulated present-day receptor nitrogen loads are not in equilibrium with present-day inputs. For these receptors in disequilibrium, models simulating transient groundwater nitrogen transport could be used to account for legacy nitrogen lag times to help resource managers evaluate the potential effectiveness of proposed nitrogen mitigation actions.
","language":"English","publisher":"EarthArXiv","doi":"10.31223/X56Q8J","usgsCitation":"Jahn, K., and Walter, D.A., 2025, Assessing legacy nitrogen in groundwater using numerical models of the Long Island aquifer system, New York: EarthArXiv, https://doi.org/10.31223/X56Q8J.","productDescription":"38 p.","ipdsId":"IP-170367","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":497047,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jahn, Kalle 0000-0002-4976-0137","orcid":"https://orcid.org/0000-0002-4976-0137","contributorId":333053,"corporation":false,"usgs":true,"family":"Jahn","given":"Kalle","email":"","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":951352,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walter, Donald A. 0000-0003-0879-4477 dawalter@usgs.gov","orcid":"https://orcid.org/0000-0003-0879-4477","contributorId":1101,"corporation":false,"usgs":true,"family":"Walter","given":"Donald","email":"dawalter@usgs.gov","middleInitial":"A.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":951353,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70265508,"text":"sir20255005 - 2025 - Potential water-quality and hydrology stressors on freshwater mussels with development of environmental DNA assays for selected mussels and macroinvertebrates in Big Darby Creek Basin, Ohio, 2020–22","interactions":[],"lastModifiedDate":"2025-08-07T20:54:11.145385","indexId":"sir20255005","displayToPublicDate":"2025-04-14T12:55:00","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":"2025-5005","displayTitle":"Potential Water-Quality and Hydrology Stressors on Freshwater Mussels With Development of Environmental DNA Assays for Selected Mussels and Macroinvertebrates in Big Darby Creek Basin, Ohio, 2020–22","title":"Potential water-quality and hydrology stressors on freshwater mussels with development of environmental DNA assays for selected mussels and macroinvertebrates in Big Darby Creek Basin, Ohio, 2020–22","docAbstract":"The richness and abundance of freshwater mussels in the Big Darby Creek Basin has declined in recent decades, according to survey results published by the Ohio Biological Survey. In October 2016, a major mussel die-off of undetermined cause reportedly affected over 50 miles of Big Darby Creek; however, fishes and other wildlife were not noticeably impacted. Pollution, habitat destruction, climate change, and hydrologic modification have all been theorized as potential reasons for the widespread declines in freshwater mussel populations in North America. To better understand potential stressors to mussels and other aquatic organisms in the Big Darby Creek Basin, the U.S. Geological Survey, in cooperation with the Ohio Water Development Authority, evaluated water quality and temporal changes in hydrology at selected locations. In addition, environmental deoxyribonucleic acid (eDNA) quantitative polymerase chain reaction (qPCR) assays were developed to detect the presence of selected mussels and macroinvertebrates using stream water.
Time-weighted average concentrations of pesticides, organic wastewater compounds (OWCs), and polycyclic aromatic hydrocarbons (PAHs) were determined for selected locations within the Big Darby Creek Basin. Passive samplers designed to mimic the respiratory exposure of aquatic organisms and the bioconcentration of organic contaminants into their fatty tissues were deployed three times annually at three sites within the Big Darby Creek Basin in 2020 and 2021. Analyses were done for 204 pesticide compounds, 38 OWCs, and 33 PAHs. Of the 204 pesticide compounds, 70 were detected in at least one sample; 30 were detected in all samples. Herbicides and herbicide degradates were the pesticides most frequently detected and also had some of the highest concentrations of the pesticides detected in this study. Three herbicides (atrazine, ametryn, and metribuzin) were detected in at least 88 percent of samples and two fungicides (azoxystrobin and propiconazole) were detected in all samples. Of the 38 OWCs, 24 were detected in at least one sample; however, only one (N,N-diethyltoluamide [DEET]) was detected in all samples. Of the 33 PAHs, 29 were detected in at least one sample; 12 were detected in all samples.
A continuous water-quality monitor was operated seasonally on Big Darby Creek above Georgesville, Ohio, from 2020 to 2022. Dissolved oxygen concentrations generally followed a daily cycle, peaking in early evening and troughing around sunrise. There were occasional 24-hour swings in dissolved oxygen concentration that had a range exceeding 10 milligrams per liter. However, dissolved oxygen concentrations never fell below Ohio’s aquatic life criteria for warmwater habitats (outside of mixing zones) of 4.0 milligrams per liter as an instantaneous minimum and 5.0 milligrams per liter as a minimum 24-hour average. The Ohio water-quality criteria for temperatures are 29.4 degrees Celsius as an instantaneous maximum and 27.8 degrees Celsius as a 24-hour average maximum. In 2020, there were 10 days when the maximum instantaneous value for temperature was exceeded and 3 consecutive days when the maximum 24-hour average temperature was exceeded.
Streamflow time-series data from three gaging stations within the Big Darby Creek Basin were evaluated for trends in annual flow statistics and daily nonexceedance probabilities over time. In general, the evaluation of streamflow conditions at the Big Darby Creek gage (with 97 years of record) indicated that streamflow changed between water years 1922 and 2021. During that time span, flows in general increased, the number of high-flow pulses became more frequent, and low-flow pulses and extreme low-flow periods became less frequent. The only strong indication of trends over time in annual flow statistics for the relatively short records for the other two gages (on Little Darby Creek, with 25 years of record, and Hellbranch Run, with 29 years of record) was that as time went on, reversals between rising and falling periods became more frequent.
The U.S. Geological Survey Ohio Water Microbiology Laboratory developed eDNA qPCR assays to detect Epioblasma rangiana (northern riffleshell mussels), Chimarra obscura (a species of caddisfly), Maccaffertium pulchellum (a species of mayfly), and optimized a preexisting eDNA qPCR assay to detect for Ptychobranchus fasciolaris (kidneyshell mussels). The assays were validated by using environmental sampling methods. Assay sensitivity was established by determining the limits of detection and quantification. Water samples were collected at 12 sites in the Big Darby Creek Basin between 2020 and 2022 and analyzed for eDNA with the qPCR assays developed for this study.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255005","collaboration":"Prepared in cooperation with the Ohio Water Development Authority","usgsCitation":"Huitger, C.A., Koltun, G.F., Stelzer, E.A., and Lynch, L.D., 2025, Potential water-quality and hydrology stressors on freshwater mussels with development of environmental DNA assays for selected mussels and macroinvertebrates in Big Darby Creek Basin, Ohio, 2020–22: U.S. Geological Survey Scientific Investigations Report 2025–5005, 59 p., https://doi.org/10.3133/sir20255005.","productDescription":"Report: ix, 59 p.; 2 Appendices; 2 Data Releases","numberOfPages":"59","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-161896","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":484334,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13GN45M","text":"USGS data release","linkHelpText":"Pesticide, organic wastewater compound (OWC) and polycyclic aromatic hydrocarbon (PAH) data determined from samples collected with instream passive samplers in the Big Darby Creek Basin, Ohio, 2020–21"},{"id":484333,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1WELW7W","text":"USGS data release","linkHelpText":"Annual streamflow statistics for selected streamgages on Big and Little Darby Creeks and Hellbranch Run, Ohio (through water year 2021)"},{"id":484331,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2025/5005/sir20255005_app1_csv.zip","text":"Tables 1.1–1.17 (CSV)","size":"34.6 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"Appendix 1. Quality Control and Summary Information for Analyses of Pesticides, Organic Wastewater Compounds, and Polycyclic Aromatic Hydrocarbons"},{"id":484330,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2025/5005/sir20255005_app1_tables.xlsx","text":"Tables 1.1–1.17","size":"131 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"Appendix 1. Quality Control and Summary Information for Analyses of Pesticides, Organic Wastewater Compounds, and Polycyclic Aromatic Hydrocarbons"},{"id":484329,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5005/images/"},{"id":484328,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5005/sir20255005.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2025-5005 XML"},{"id":493757,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118527.htm","linkFileType":{"id":5,"text":"html"}},{"id":484327,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255005/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5005 HTML"},{"id":484326,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5005/sir20255005.pdf","size":"4.03 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5005 PDF"},{"id":484324,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5005/coverthb.jpg"}],"country":"United States","state":"Ohio","otherGeospatial":"Big Darby Creek basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.8333,\n 40.333\n ],\n [\n -83.8333,\n 39.5\n ],\n [\n -83,\n 39.5\n ],\n [\n -83,\n 40.333\n ],\n [\n -83.8333,\n 40.333\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
6460 Busch Blvd, Suite 100
Columbus, OH 43229
Tapwater (TW) safety and sustainability are priorities in the United States. Per/polyfluoroalkyl substance(s) (PFAS) contamination is a growing public-health concern due to prolific use, widespread TW exposures, and mounting human-health concerns. Historically-rural, actively-urbanizing communities that rely on surficial-aquifer private wells incur elevated risks of unrecognized TW exposures, including PFAS, due to limited private-well monitoring and contaminant-source proliferation in urbanizing landscapes. Here, a broad-analytical-scope TW-assessment was conducted in a hydrologically-vulnerable, Mississippi River alluvial-island community, where PFAS contamination of the shallow-alluvial drinking-water aquifer has been documented, but more comprehensive contaminant characterization to inform decision-making is currently lacking. In 2021, we analyzed 510 organics, 34 inorganics, and 3 microbial groups in 11 residential and community locations to assess (1) TW risks beyond recognized PFAS issues, (2) day-to-day and year-to-year risk variability, and (3) suitability of the underlying sandstone aquifer as an alternative source to mitigate TW-PFAS exposures. Seventy-six organics and 25 inorganics were detected. Potential human-health risks of detected TW exposures were explored based on cumulative benchmark-based toxicity quotients (∑TQ). Elevated risks (∑TQ ≥ 1) from organic and inorganic contaminants were observed in all alluvial-aquifer-sourced synoptic samples but not in sandstone-aquifer-sourced samples. Repeated sampling at 3 sites over 52–55 h indicated limited variability in risk over the short-term. Comparable PFAS-specific ∑TQ for spatial-synoptic, short-term (3 days) temporal, and long-term (3 years quarterly) temporal samples indicated that synoptic results provided useful insight into the risks of TW-PFAS exposures at French Island over the long-term. No PFAS detections in sandstone-aquifer-sourced samples over a 3 year period indicated no PFAS-associated risk and supported the sandstone aquifer as an alternative drinking-water source to mitigate community TW-PFAS exposures. This study illustrated the importance of expanded contaminant monitoring of private-well TW, beyond known concerns (in this case, PFAS), to reduce the risks of a range of unrecognized contaminant exposures.
","language":"English","publisher":"Royal Society of Chemistry","doi":"10.1039/D5EM00005J","usgsCitation":"Bradley, P., Romanok, K., Smalling, K., Donahue, L., Gaikowski, M., Hines, R.K., Breitmeyer, S.E., Gordon, S.E., Loftin, K.A., McCleskey, R., Meppelink, S.M., and Schreiner, M., 2025, Tapwater exposures, residential risk, and mitigation in a PFAS-impacted-groundwater community: Environmental Science: Processes and Impacts, 21 p., https://doi.org/10.1039/D5EM00005J.","productDescription":"21 p.","ipdsId":"IP-142462","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":488254,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1039/d5em00005j","text":"Publisher Index Page"},{"id":484592,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","city":"Campbell","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -91.3046523459501,\n 43.87386653369529\n ],\n [\n -91.3046523459501,\n 43.86407274973604\n ],\n [\n -91.28224710242334,\n 43.86407274973604\n ],\n [\n -91.28224710242334,\n 43.87386653369529\n ],\n [\n -91.3046523459501,\n 43.87386653369529\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Online First","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bradley, Paul M. 0000-0001-7522-8606","orcid":"https://orcid.org/0000-0001-7522-8606","contributorId":205668,"corporation":false,"usgs":true,"family":"Bradley","given":"Paul M.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933349,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Romanok, Kristin M. 0000-0002-8472-8765","orcid":"https://orcid.org/0000-0002-8472-8765","contributorId":205651,"corporation":false,"usgs":true,"family":"Romanok","given":"Kristin M.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933350,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smalling, Kelly 0000-0002-1214-4920","orcid":"https://orcid.org/0000-0002-1214-4920","contributorId":221234,"corporation":false,"usgs":true,"family":"Smalling","given":"Kelly","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933351,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Donahue, Lee","contributorId":353363,"corporation":false,"usgs":false,"family":"Donahue","given":"Lee","affiliations":[{"id":84381,"text":"Town of Campbell, WI","active":true,"usgs":false}],"preferred":false,"id":933352,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gaikowski, Mark P. 0000-0002-6507-9341 mgaikowski@usgs.gov","orcid":"https://orcid.org/0000-0002-6507-9341","contributorId":149357,"corporation":false,"usgs":true,"family":"Gaikowski","given":"Mark P.","email":"mgaikowski@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":933353,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hines, Randy K. 0000-0002-5135-3135 rkhines@usgs.gov","orcid":"https://orcid.org/0000-0002-5135-3135","contributorId":3340,"corporation":false,"usgs":true,"family":"Hines","given":"Randy","email":"rkhines@usgs.gov","middleInitial":"K.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":933354,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Breitmeyer, Sara E. 0000-0003-0609-1559 sbreitmeyer@usgs.gov","orcid":"https://orcid.org/0000-0003-0609-1559","contributorId":172622,"corporation":false,"usgs":true,"family":"Breitmeyer","given":"Sara","email":"sbreitmeyer@usgs.gov","middleInitial":"E.","affiliations":[{"id":37464,"text":"WMA - 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Using uniquely high-resolution records of fluvial suspended sediment and coastal morphology, we quantify sedimentary responses from a steep, 357-km2 watershed in California under extreme wet and dry hydrologic conditions. In years with multiple 2- to 10-year floods, fluvial sediment coarsened significantly as the wet season progressed, with late-season floods delivering dominantly sand-sized material to the coast. Greater and coarser sediment supply under wetter antecedent conditions affected nearshore geomorphic evolution for 4–5 years. The watershed and coastal changes we documented point to an increasing role of sediment-related hazards (flooding and hillslope erosion) and resources (nearshore accretion) as wet seasons intensify.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2025GL115232","usgsCitation":"East, A.E., Snyder, A.G., Stevens, A.W., Warrick, J.A., Topping, D.J., Thomas, M.A., and Ritchie, A., 2025, River floods under wetter antecedent conditions deliver coarser sediment to the coast: Geophysical Research Letters, v. 52, no. 8, e2025GL115232, 10 p., https://doi.org/10.1029/2025GL115232.","productDescription":"e2025GL115232, 10 p.","ipdsId":"IP-175612","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":488252,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2025gl115232","text":"Publisher Index Page"},{"id":484583,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Lorenzo River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.2795738662301,\n 37.28890656527331\n ],\n [\n -122.2795738662301,\n 36.95309077205526\n ],\n [\n -121.9584157989861,\n 36.95309077205526\n ],\n [\n -121.9584157989861,\n 37.28890656527331\n ],\n [\n -122.2795738662301,\n 37.28890656527331\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"52","issue":"8","noUsgsAuthors":false,"publicationDate":"2025-04-13","publicationStatus":"PW","contributors":{"authors":[{"text":"East, Amy E. 0000-0002-9567-9460 aeast@usgs.gov","orcid":"https://orcid.org/0000-0002-9567-9460","contributorId":196364,"corporation":false,"usgs":true,"family":"East","given":"Amy","email":"aeast@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":933368,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Snyder, Alexander G. 0000-0001-6250-4827 agsnyder@usgs.gov","orcid":"https://orcid.org/0000-0001-6250-4827","contributorId":171654,"corporation":false,"usgs":true,"family":"Snyder","given":"Alexander","email":"agsnyder@usgs.gov","middleInitial":"G.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":933369,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stevens, Andrew W. 0000-0003-2334-129X astevens@usgs.gov","orcid":"https://orcid.org/0000-0003-2334-129X","contributorId":139313,"corporation":false,"usgs":true,"family":"Stevens","given":"Andrew","email":"astevens@usgs.gov","middleInitial":"W.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":933370,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Warrick, Jonathan A. 0000-0002-0205-3814 jwarrick@usgs.gov","orcid":"https://orcid.org/0000-0002-0205-3814","contributorId":167736,"corporation":false,"usgs":true,"family":"Warrick","given":"Jonathan","email":"jwarrick@usgs.gov","middleInitial":"A.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":933371,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"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":933372,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Thomas, Matthew A. 0000-0002-9828-5539 matthewthomas@usgs.gov","orcid":"https://orcid.org/0000-0002-9828-5539","contributorId":200616,"corporation":false,"usgs":true,"family":"Thomas","given":"Matthew","email":"matthewthomas@usgs.gov","middleInitial":"A.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":933373,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ritchie, Andrew C. 0000-0001-5826-9983","orcid":"https://orcid.org/0000-0001-5826-9983","contributorId":333630,"corporation":false,"usgs":true,"family":"Ritchie","given":"Andrew C.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":933374,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70273122,"text":"70273122 - 2025 - Multi-Scale Graph Learning for anti-sparse downscaling","interactions":[],"lastModifiedDate":"2025-12-16T16:51:11.338092","indexId":"70273122","displayToPublicDate":"2025-04-11T10:46:24","publicationYear":"2025","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Multi-Scale Graph Learning for anti-sparse downscaling","docAbstract":"Water temperature can vary substantially even across short distances within the same sub-watershed. Accurate prediction of stream water temperature at fine spatial resolutions (i.e., fine scales, ≤ 1 km) enables precise interventions to maintain water quality and protect aquatic habitats. Although spatiotemporal models have made substantial progress in spatially coarse time series modeling, challenges persist in predicting at fine spatial scales due to the lack of data at that scale. To address the problem of insufficient fine-scale data, we propose a Multi-Scale Graph Learning (MSGL) method. This method employs a multi-task learning framework where coarse-scale graph learning, bolstered by larger datasets, simultaneously enhances fine-scale graph learning. Although existing multi-scale or multi-resolution methods integrate data from different spatial scales, they often overlook the spatial correspondences across graph structures at various scales. To address this, our MSGL introduces an additional learning task, cross-scale interpolation learning, which leverages the hydrological connectedness of stream locations across coarse- and fine-scale graphs to establish cross-scale connections, thereby enhancing overall model performance. Furthermore, we have broken free from the mindset that multi-scale learning is limited to synchronous training by proposing an Asynchronous Multi-Scale Graph Learning method (ASYNC-MSGL). Extensive experiments demonstrate the state-of-the-art performance of our method for anti-sparse downscaling of daily stream temperatures in the Delaware River Basin, USA, highlighting its potential utility for water resources monitoring and management.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the AAAI conference on artificial intelligence","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Association for the Advancement of Artificial Intelligence","doi":"10.1609/aaai.v39i27.35014","usgsCitation":"Fan, Y., Yu, R., Barclay, J.R., Appling, A.P., Sun, Y., Xie, Y., and Jia, X., 2025, Multi-Scale Graph Learning for anti-sparse downscaling, in Proceedings of the AAAI conference on artificial intelligence, v. 39, no. 27, p. 27969-27977, https://doi.org/10.1609/aaai.v39i27.35014.","productDescription":"9 p.","startPage":"27969","endPage":"27977","ipdsId":"IP-167502","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":497731,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1609/aaai.v39i27.35014","text":"Publisher Index Page"},{"id":497583,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"39","issue":"27","noUsgsAuthors":false,"publicationDate":"2025-04-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Fan, Yingda","contributorId":352470,"corporation":false,"usgs":false,"family":"Fan","given":"Yingda","affiliations":[{"id":84236,"text":"Department of Computer Science, University of Pittsburgh","active":true,"usgs":false}],"preferred":false,"id":952391,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yu, Runlong 0000-0003-4080-2377","orcid":"https://orcid.org/0000-0003-4080-2377","contributorId":352471,"corporation":false,"usgs":false,"family":"Yu","given":"Runlong","affiliations":[{"id":84236,"text":"Department of Computer Science, University of Pittsburgh","active":true,"usgs":false}],"preferred":false,"id":952392,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barclay, Janet R. 0000-0003-1643-6901 jbarclay@usgs.gov","orcid":"https://orcid.org/0000-0003-1643-6901","contributorId":222437,"corporation":false,"usgs":true,"family":"Barclay","given":"Janet","email":"jbarclay@usgs.gov","middleInitial":"R.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":952393,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Appling, Alison P. 0000-0003-3638-8572 aappling@usgs.gov","orcid":"https://orcid.org/0000-0003-3638-8572","contributorId":150595,"corporation":false,"usgs":true,"family":"Appling","given":"Alison","email":"aappling@usgs.gov","middleInitial":"P.","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":952394,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sun, Yiming","contributorId":352472,"corporation":false,"usgs":false,"family":"Sun","given":"Yiming","affiliations":[{"id":84236,"text":"Department of Computer Science, University of Pittsburgh","active":true,"usgs":false}],"preferred":false,"id":952395,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Xie, Yiqun","contributorId":297447,"corporation":false,"usgs":false,"family":"Xie","given":"Yiqun","email":"","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":952396,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jia, Xiaowei 0000-0001-8544-5233","orcid":"https://orcid.org/0000-0001-8544-5233","contributorId":237807,"corporation":false,"usgs":false,"family":"Jia","given":"Xiaowei","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":952397,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70265692,"text":"70265692 - 2025 - Identifying preferential flow from soil moisture time series: Review of methodologies","interactions":[],"lastModifiedDate":"2025-04-14T16:18:34.093186","indexId":"70265692","displayToPublicDate":"2025-04-10T09:14:52","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3674,"text":"Vadose Zone Journal","active":true,"publicationSubtype":{"id":10}},"title":"Identifying preferential flow from soil moisture time series: Review of methodologies","docAbstract":"Identifying and quantifying preferential flow (PF) through soil—the rapid movement of water through spatially-distinct pathways in the subsurface—is vital to understanding how the hydrologic cycle responds to climate, land cover, and anthropogenic changes. In recent decades, methods have been developed that use measured soil moisture time series to identify PF. Because they allow for continuous monitoring and are relatively easy to implement, these methods have become an important tool for recognizing when, where, and under what conditions PF occurs. The methods seek to identify a pattern or quantification that indicates the occurrence of PF. Most commonly, the chosen signature is either (1) a nonsequential response to infiltrated water, in which soil moisture responses do not occur in order of shallowest to deepest, or (2) a velocity criterion, in which newly infiltrated water is detected at depth earlier than is possible by nonpreferential flow processes. Alternative signatures have also been developed that have certain advantages but are less commonly utilized. Choosing among these possible signatures requires attention to their pertinent characteristics, including susceptibility to errors, possible bias toward false negatives or false positives, reliance on subjective judgments, and possible requirements for additional types of data. We review 77 studies that have applied such methods, to highlight important information for readers who want to identify PF from soil moisture data, and to inform those who aim to develop new methods or improve existing ones.","language":"English","publisher":"Soil Science Society of America","doi":"10.1002/vzj2.70017","usgsCitation":"Nimmo, J.R., Wiekenkamp, I., Araki, R., Groh, J., Singh, N., Crompton, O., Wyatt, B., Ajami, H., Gimenez, D., Hirmas, D., Sullivan, P., and Sprenger, M., 2025, Identifying preferential flow from soil moisture time series: Review of methodologies: Vadose Zone Journal, v. 24, no. 2, e70017, 25 p., https://doi.org/10.1002/vzj2.70017.","productDescription":"e70017, 25 p.","ipdsId":"IP-175711","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":488217,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/vzj2.70017","text":"Publisher Index Page"},{"id":484511,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"24","issue":"2","noUsgsAuthors":false,"publicationDate":"2025-04-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Nimmo, John R. 0000-0001-8191-1727 jrnimmo@usgs.gov","orcid":"https://orcid.org/0000-0001-8191-1727","contributorId":757,"corporation":false,"usgs":true,"family":"Nimmo","given":"John","email":"jrnimmo@usgs.gov","middleInitial":"R.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":933270,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wiekenkamp, Inge","contributorId":353318,"corporation":false,"usgs":false,"family":"Wiekenkamp","given":"Inge","affiliations":[{"id":52961,"text":"GFZ Potsdam","active":true,"usgs":false}],"preferred":false,"id":933271,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Araki, Ryoko","contributorId":353321,"corporation":false,"usgs":false,"family":"Araki","given":"Ryoko","affiliations":[{"id":84355,"text":"San Diego State U","active":true,"usgs":false}],"preferred":false,"id":933272,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Groh, Jannis","contributorId":353322,"corporation":false,"usgs":false,"family":"Groh","given":"Jannis","affiliations":[{"id":84358,"text":"Agrosphere Institute, Jülich, Germany","active":true,"usgs":false}],"preferred":false,"id":933273,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Singh, Nitin","contributorId":353323,"corporation":false,"usgs":false,"family":"Singh","given":"Nitin","affiliations":[{"id":84359,"text":"Auburn U","active":true,"usgs":false}],"preferred":false,"id":933274,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Crompton, Octavia","contributorId":353324,"corporation":false,"usgs":false,"family":"Crompton","given":"Octavia","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":933275,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wyatt, Briana","contributorId":353325,"corporation":false,"usgs":false,"family":"Wyatt","given":"Briana","affiliations":[{"id":84360,"text":"Texas A&M U","active":true,"usgs":false}],"preferred":false,"id":933276,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ajami, Hoori 0000-0001-6883-7630","orcid":"https://orcid.org/0000-0001-6883-7630","contributorId":303806,"corporation":false,"usgs":false,"family":"Ajami","given":"Hoori","email":"","affiliations":[{"id":36629,"text":"University of California","active":true,"usgs":false}],"preferred":false,"id":933277,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gimenez, Daniel","contributorId":353326,"corporation":false,"usgs":false,"family":"Gimenez","given":"Daniel","affiliations":[{"id":84361,"text":"Rutgers U","active":true,"usgs":false}],"preferred":false,"id":933278,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hirmas, Daniel","contributorId":353327,"corporation":false,"usgs":false,"family":"Hirmas","given":"Daniel","affiliations":[{"id":49949,"text":"Texas Tech U","active":true,"usgs":false}],"preferred":false,"id":933279,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Sullivan, Pamela","contributorId":190446,"corporation":false,"usgs":false,"family":"Sullivan","given":"Pamela","affiliations":[],"preferred":false,"id":933280,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Sprenger, Matthias 0000-0003-1221-2767","orcid":"https://orcid.org/0000-0003-1221-2767","contributorId":344277,"corporation":false,"usgs":false,"family":"Sprenger","given":"Matthias","email":"","affiliations":[{"id":82324,"text":"Lawrence Berkley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":933281,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70271313,"text":"70271313 - 2025 - Central Valley Hydrologic Model version 2 (CVHM2): Decision support tool for groundwater and land subsidence management","interactions":[],"lastModifiedDate":"2025-09-04T15:13:46.061817","indexId":"70271313","displayToPublicDate":"2025-04-09T08:06:56","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Central Valley Hydrologic Model version 2 (CVHM2): Decision support tool for groundwater and land subsidence management","docAbstract":"The San Joaquin Valley (SJV) of California is one of the world’s most productive agricultural regions. Reliance on groundwater has led to some of the greatest rates of human-induced land subsidence in the world in the 20th century, as well as more recently. The United States Geological Survey (USGS) has recently developed an integrated surface–subsurface hydrologic model, the Central Valley Hydrologic Model 2 (CVHM2), that represents the major components of the hydrologic system of California’s Central Valley. In this study, CVHM2 was applied as a decision support tool while simulating various management strategies to mitigate the land subsidence caused by the extraction of groundwater. CVHM2 was extended through to 2073 and applied to simulate management scenarios in terms of three primary drivers and their impact on subsidence along the Delta–Mendota Canal (DMC), a critical piece of infrastructure in the western SJV. The drivers considered were agricultural water demands, managed aquifer recharge (MAR), and changes in future climate. The results show that future subsidence is most sensitive to water demands, second most sensitive to future changes in climate, and relatively insensitive to MAR when it is applied as a surface application in the western SJV. However, we demonstrate via proof-of-concept scenarios that the MAR is capable of arresting subsidence when implemented via injection below the Corcoran Clay Member of the Tulare Formation instead of as a surface application. We also examine the uncertainty that is the result of climate variability and how to use the tool to identify the most appropriate strategies to constrain future subsidence to acceptable levels.
","language":"English","publisher":"MDPI","doi":"10.3390/w17081120","usgsCitation":"Nelson, K., Quinn, N., and Traum, J.A., 2025, Central Valley Hydrologic Model version 2 (CVHM2): Decision support tool for groundwater and land subsidence management: Water, v. 17, no. 8, 1120, 26 p., https://doi.org/10.3390/w17081120.","productDescription":"1120, 26 p.","ipdsId":"IP-175116","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":495188,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w17081120","text":"Publisher Index Page"},{"id":495166,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Joaquin Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.73121499117096,\n 38.53799288965703\n ],\n [\n -120.87600359152006,\n 36.83520659574968\n ],\n [\n -119.56082081779095,\n 35.04449080764874\n ],\n [\n -118.38309238355373,\n 35.35382368503207\n ],\n [\n -120.95162530913443,\n 38.878303068488464\n ],\n [\n -121.73121499117096,\n 38.53799288965703\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"17","issue":"8","noUsgsAuthors":false,"publicationDate":"2025-04-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Nelson, Kirk","contributorId":360939,"corporation":false,"usgs":false,"family":"Nelson","given":"Kirk","affiliations":[{"id":27228,"text":"Reclamation","active":true,"usgs":false}],"preferred":false,"id":947952,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Quinn, Nigel","contributorId":360940,"corporation":false,"usgs":false,"family":"Quinn","given":"Nigel","affiliations":[{"id":86123,"text":"Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":947953,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Traum, Jonathan A. 0000-0002-4787-3680 jtraum@usgs.gov","orcid":"https://orcid.org/0000-0002-4787-3680","contributorId":4780,"corporation":false,"usgs":true,"family":"Traum","given":"Jonathan","email":"jtraum@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":947954,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70265480,"text":"sir20255009 - 2025 - Application of Hydrologic Simulation Program—FORTRAN (HSPF) as part of an integrated hydrologic model for the Salinas Valley, California","interactions":[],"lastModifiedDate":"2025-08-07T20:33:11.257584","indexId":"sir20255009","displayToPublicDate":"2025-04-08T10:50:17","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":"2025-5009","displayTitle":"Application of Hydrologic Simulation Program—FORTRAN (HSPF) as Part of an Integrated Hydrologic Model for the Salinas Valley, California","title":"Application of Hydrologic Simulation Program—FORTRAN (HSPF) as part of an integrated hydrologic model for the Salinas Valley, California","docAbstract":"The U.S. Geological Survey (USGS), in cooperation with the Monterey County Water Resources Agency, conducted studies to help evaluate the surface-water and groundwater resources of the Salinas Valley study area, consisting of the entire Salinas River watershed and several smaller, adjacent coastal watersheds draining into Monterey Bay. The Salinas Valley study area is a highly productive agricultural region that depends on the coordinated use of surface water and groundwater to meet demand for irrigation and public water supply. To continue to meet these demands, a better understanding of the historical water balance and the effects of water-resource development on the long-term sustainability of water resources in the Salinas Valley study area is needed.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255009","collaboration":"Prepared in cooperation with the Monterey County Water Resources Agency","programNote":"Water Resource Mission Area—Water Availability and Use Science Program","usgsCitation":"Hevesi, J.A., Henson, W., Hanson, R.T., Jachens, E.R., Bond, S., Earll, M.M., and Herbert, D., 2025, Application of Hydrologic Simulation Program—FORTRAN (HSPF) as part of an integrated hydrologic model for the Salinas Valley, California: U.S. Geological Survey Scientific Investigations Report 2025–5009, 148 p., https://doi.org/10.3133/sir20255009.","productDescription":"Report: xii, 148 p.; Data Release","numberOfPages":"148","onlineOnly":"Y","ipdsId":"IP-129397","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":493743,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118516.htm","linkFileType":{"id":5,"text":"html"}},{"id":484307,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FJAWC4","text":"USGS data release","description":"Hevesi, J., Henson, W., Hanson, R.T., Earll, M.M., Herbert, D.M., and Jachens, E.R., 2025, Salinas Valley watershed model—Application of Hydrologic Simulation Program—FORTRAN (HSPF): U.S. Geological Survey data release, https://doi.org/10.5066/P9FJAWC4","linkHelpText":"Salinas Valley watershed model—Application of Hydrologic Simulation Program—FORTRAN (HSPF)"},{"id":484309,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5009/sir20255009.XML","description":"SIR 2025-5009 XML"},{"id":484304,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5009/coverthb.jpg"},{"id":484310,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5009/images"},{"id":484308,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255009/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5009 HTML"},{"id":484306,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5009/sir20255009.pdf","text":"Report","size":"38.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5009 PDF"}],"country":"United States","state":"California","otherGeospatial":"Salinas Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.67652901886302,\n 36.776695068396975\n ],\n [\n -121.78751170378024,\n 36.79802674348818\n ],\n [\n -121.86963889061894,\n 36.607609833119014\n ],\n [\n -121.92291057937909,\n 36.64679949204769\n ],\n [\n -121.97840192183737,\n 36.575530747588786\n ],\n [\n -120.9440432984108,\n 35.507662585291214\n ],\n [\n -120.48235532915595,\n 35.54921011874251\n ],\n [\n -120.6510490102299,\n 35.98505610457029\n ],\n [\n -121.40351161396718,\n 36.65036120011224\n ],\n [\n -121.67652901886302,\n 36.776695068396975\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Understanding fine-scale habitat selection of endangered Kootenai River white sturgeon (Acipenser transmontanus) is an important component for monitoring and recovery efforts. Fine-scale habitat selection and quantifying temporal changes in suitable habitat contributes to the work of addressing recruitment failure within the Kootenai River population. Habitat suitability indices were developed using over 96 000 acoustic telemetry sturgeon detections and two-dimensional hydrodynamic model simulations near Bonners Ferry, Idaho, USA. The selected habitat was assessed to develop habitat suitability indices for sturgeon; females undergoing spawn migrations and non-spawners. The most frequented locations were 8–9 m deep and water velocities of 0.3–0.7 m·s−1. These observations suggest sturgeon with different spawning capabilities selected similar habitat. Weighted usable area was calculated to understand temporal variability in habitat quality, which showed a positive relationship with increases in flow. Results help understand the habitat limiting factors in regulated hydrologic regimes; provide biologists insight for monitoring efforts in discrete habitat conditions; guidance for water managers and the regulation of upstream water resources; and guidance to restoration practitioners for in-stream structure designs.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2024-0244","usgsCitation":"Dudunake, T., Kenworthy, M.K., Smith, T., Stephenson, S., and Hardy, R.S., 2025, Influence of local river hydraulics on Kootenai River white sturgeon (Acipenser transmontanus) habitat selection during four spawning years, 2017–2020: Canadian Journal of Fisheries and Aquatic Sciences, v. 82, 16 p., https://doi.org/10.1139/cjfas-2024-0244.","productDescription":"16 p.","ipdsId":"IP-150378","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":488244,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1139/cjfas-2024-0244","text":"Publisher Index Page"},{"id":484574,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Kootenai River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.30893853974553,\n 48.70438566677956\n ],\n [\n -116.33409421325345,\n 48.70438566677956\n ],\n [\n -116.33409421325345,\n 48.691255343773804\n ],\n [\n -116.30893853974553,\n 48.691255343773804\n ],\n [\n -116.30893853974553,\n 48.70438566677956\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"82","noUsgsAuthors":false,"publicationDate":"2025-04-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Dudunake, Taylor 0000-0001-7650-2419 tdudunake@usgs.gov","orcid":"https://orcid.org/0000-0001-7650-2419","contributorId":191564,"corporation":false,"usgs":true,"family":"Dudunake","given":"Taylor","email":"tdudunake@usgs.gov","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933385,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kenworthy, Megan Kearney 0000-0001-7108-3016","orcid":"https://orcid.org/0000-0001-7108-3016","contributorId":304286,"corporation":false,"usgs":true,"family":"Kenworthy","given":"Megan","email":"","middleInitial":"Kearney","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933386,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Troy","contributorId":353368,"corporation":false,"usgs":false,"family":"Smith","given":"Troy","affiliations":[{"id":36224,"text":"Idaho Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":933387,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stephenson, Sarah","contributorId":353369,"corporation":false,"usgs":false,"family":"Stephenson","given":"Sarah","affiliations":[{"id":51972,"text":"British Columbia Ministry of Forests","active":true,"usgs":false}],"preferred":false,"id":933388,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hardy, Ryan S.","contributorId":167032,"corporation":false,"usgs":false,"family":"Hardy","given":"Ryan","email":"","middleInitial":"S.","affiliations":[{"id":6764,"text":"Idaho Department of Fish and Game, Nampa, Idaho","active":true,"usgs":false}],"preferred":false,"id":933389,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70272682,"text":"70272682 - 2025 - Uncertainty quantification of geophysical and hydrologic parameters estimated from borehole nuclear magnetic resonance data","interactions":[],"lastModifiedDate":"2025-12-04T16:46:03.167798","indexId":"70272682","displayToPublicDate":"2025-04-05T10:43:22","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":18010,"text":"JGR Machine Learning and Computation","active":true,"publicationSubtype":{"id":10}},"title":"Uncertainty quantification of geophysical and hydrologic parameters estimated from borehole nuclear magnetic resonance data","docAbstract":"Borehole nuclear magnetic resonance (bNMR) data are typically used to infer in situ hydrologic properties. Partial water content as a function of pore size is estimated by fitting the measured NMR response to a multi-exponential T2 distribution, and the sum of estimated T2 amplitudes equals the total volumetric water content. From these estimated parameters, several empirical relationships are commonly used to infer hydraulic conductivity from the NMR-estimated water content and T2 distribution. Often, parameters are estimated through deterministic inversion methods that produce a single best-fit estimate, but do not reflect uncertainties in model parameters. Here, a Bayesian Markov chain Monte Carlo (McMC) approach for analyzing bNMR data is developed that allows for comprehensive uncertainty quantification of NMR parameters and derived hydrologic properties. The underlying model that describes the T2 distribution is defined by a set of spline interpolation points. The number of interpolation points is allowed to vary in a trans-dimensional algorithm that naturally favors simple models with fewer interpolation points, allowing the data to inform the necessary level of model complexity. Additionally, data error is estimated as an unknown parameter. Analysis of the ensemble of models output from the McMC algorithm provides useful details on the range of plausible T2 distributions that can fit a measured bNMR decay curve, as well as uncertainty estimates of total water content. The ensemble of NMR parameters can also be propagated through commonly used relationships to produce uncertainty estimates on derived parameters such as bound/capillary/mobile water content or hydraulic conductivity.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024JH000461","usgsCitation":"Minsley, B.J., Phillips, S.N., and James, S.R., 2025, Uncertainty quantification of geophysical and hydrologic parameters estimated from borehole nuclear magnetic resonance data: JGR Machine Learning and Computation, v. 2, no. 2, e2024JH000461, 15 p., https://doi.org/10.1029/2024JH000461.","productDescription":"e2024JH000461, 15 p.","ipdsId":"IP-171454","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":497115,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024jh000461","text":"Publisher Index Page"},{"id":497066,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","issue":"2","noUsgsAuthors":false,"publicationDate":"2025-04-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Minsley, Burke J. 0000-0003-1689-1306","orcid":"https://orcid.org/0000-0003-1689-1306","contributorId":248573,"corporation":false,"usgs":true,"family":"Minsley","given":"Burke","email":"","middleInitial":"J.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":951326,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Phillips, Stephanie N. 0000-0002-2022-7726","orcid":"https://orcid.org/0000-0002-2022-7726","contributorId":214857,"corporation":false,"usgs":true,"family":"Phillips","given":"Stephanie","email":"","middleInitial":"N.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true}],"preferred":true,"id":951327,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"James, Stephanie R. 0000-0001-5715-253X","orcid":"https://orcid.org/0000-0001-5715-253X","contributorId":260620,"corporation":false,"usgs":true,"family":"James","given":"Stephanie","email":"","middleInitial":"R.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":951328,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70265808,"text":"70265808 - 2025 - Salinas Valley integrated hydrologic and reservoir operations models, Monterey and San Luis Obispo Counties, California","interactions":[],"lastModifiedDate":"2025-04-16T14:10:07.800114","indexId":"70265808","displayToPublicDate":"2025-04-05T09:01:07","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":"Salinas Valley integrated hydrologic and reservoir operations models, Monterey and San Luis Obispo Counties, California","docAbstract":"The area surrounding the Salinas Valley groundwater basin in Monterey and San Luis Obispo Counties of California is a highly productive agricultural area, contributes significantly to the local economy, and provides a substantial portion of vegetables and other agricultural commodities to the Nation. This region of California provides about half of the Nation’s lettuce, celery, broccoli, and spinach each year. Thus, this agricultural area provides significant volumes of agricultural products not just for California but the entire United States. Changes in population and increased agricultural development, which includes a shift toward more water-intensive crops, and climate variability, have put increasing demand on both surface water and groundwater resources in the valley. This has resulted in water management challenges in the Salinas Valley that are predominantly related to distribution of water supply throughout the basin. Where and when the water is present in the surface and subsurface does not coincide with where and when the water is needed. To deal with the distribution issue, historically water has been used conjunctively in the valley. Conjunctive use is a water management strategy that coordinates surface water and groundwater use to maximize water availability. Groundwater is used throughout the Salinas Valley to meet water demands when surface water supplies are insufficient. Availability of surface water is constrained by climate. Precipitation and streamflow vary seasonally and year to year. Although there are two reservoirs in the Salinas Valley to capture and store water during wet periods, the only conveyance of reservoir water to coastal agricultural areas is the Salinas River. Increasing demand on groundwater and surface water resources throughout the Salinas Valley has resulted in undesirable effects of unsustainable water use, such as surface water depletion, groundwater level declines, storage depletion in the principal aquifers, and seawater intrusion. To address these escalating issues, local communities, water management agencies, and groundwater sustainability agencies are evaluating how to sustainably manage both their surface water and groundwater resources. To meet water demands and reduce undesirable effects of unsustainable water use, continued conjunctive management of surface water and groundwater would ideally incorporate strategies to deal with increases in demand and a variable climate. To evaluate the challenging water management issues in the Salinas Valley, the U.S. Geological Survey, Monterey County Water Resource Agency, and the Salinas Valley Basin Groundwater Sustainability Agency developed a comprehensive suite of models that represent the Salinas Valley Hydrogeologic system called the Salinas Valley System Model. The Salinas Valley Geologic Framework was developed to characterize the subsurface using various topographic and geologic data sources, including information on hydrogeologic units, their surfaces and extents, geologic structures, lithology, and elevations from borehole data and cross sections, as well as details on faults and existing models. The Salinas Valley Watershed Model simulates the entire Salinas River watershed. Monthly surface water inflows into the integrated hydrologic model domain were simulated using the Salinas Valley Watershed Model. The historical model uses historical climate data, water and land use data, and reservoir releases to simulate agricultural operations, including landscape water demands, diversions, and reclaimed wastewater. The operational model adds an embedded reservoir operations framework to the simulation of the historical model that allows specified operational rules to simulate reservoir releases and changes in reservoir storage. The operational model assumes current reservoir operations and constant land use, which differs from historical conditions. Thus, the operational model is a hypothetical baseline model that can be used by local water managers to evaluate and quantify potential benefits of water supply projects. Together, the geologic framework, watershed, historical, and operational models form a tool that can be used to simulate irrigated agriculture and associated reservoir operations of the integrated hydrologic system of the Salinas Valley.
","language":"English","publisher":"Eartharxiv","doi":"10.31223/X5ZD9N","usgsCitation":"Henson, W.R., Hanson, R., Boyce, S.E., Hevesi, J.A., and Jachens, E.R., 2025, Salinas Valley integrated hydrologic and reservoir operations models, Monterey and San Luis Obispo Counties, California: EarthArXiv, https://doi.org/10.31223/X5ZD9N.","productDescription":"312 p.","ipdsId":"IP-172765","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":488263,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.31223/x5zd9n","text":"Publisher Index Page"},{"id":484636,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Henson, Wesley R. 0000-0003-4962-5565 whenson@usgs.gov","orcid":"https://orcid.org/0000-0003-4962-5565","contributorId":384,"corporation":false,"usgs":true,"family":"Henson","given":"Wesley","email":"whenson@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933598,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hanson, Randy 0000-0002-9819-7141","orcid":"https://orcid.org/0000-0002-9819-7141","contributorId":216356,"corporation":false,"usgs":false,"family":"Hanson","given":"Randy","affiliations":[{"id":37374,"text":"Retired USGS","active":true,"usgs":false}],"preferred":false,"id":933667,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boyce, Scott E. 0000-0003-0626-9492 seboyce@usgs.gov","orcid":"https://orcid.org/0000-0003-0626-9492","contributorId":4766,"corporation":false,"usgs":true,"family":"Boyce","given":"Scott","email":"seboyce@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933668,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hevesi, Joseph A. 0000-0003-2898-1800 jhevesi@usgs.gov","orcid":"https://orcid.org/0000-0003-2898-1800","contributorId":1507,"corporation":false,"usgs":true,"family":"Hevesi","given":"Joseph","email":"jhevesi@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933669,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jachens, Elizabeth Rae 0000-0001-5885-8892","orcid":"https://orcid.org/0000-0001-5885-8892","contributorId":294690,"corporation":false,"usgs":true,"family":"Jachens","given":"Elizabeth","email":"","middleInitial":"Rae","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933670,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70267524,"text":"70267524 - 2025 - Streamflow response to glacier mass loss varies with basin precipitation across Alaska","interactions":[],"lastModifiedDate":"2025-05-28T14:28:22.882379","indexId":"70267524","displayToPublicDate":"2025-04-04T09:24:54","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":"Streamflow response to glacier mass loss varies with basin precipitation across Alaska","docAbstract":"Diminishing glaciers affect streamflow, and given the extent of glaciers in Alaska and adjacent Canada, continued glacier mass loss is likely to have profound effects on ecosystems sensitive to runoff. The effects of glacier mass loss on streamflow are likely to vary across the wide ranges of basin size, glacier cover, and precipitation in this region. In this study, we use U.S. Geological Survey (USGS) streamflow data with satellite-based glacier volume change estimates to quantify how glacier mass loss subsidized streamflow over the 2000–2019 period for 116 glacierized basins. We examine interannual variability in that subsidy at three USGS-monitored glaciers to explore the ability of the subsidy to buffer streamflow derived solely from precipitation. We found the relative importance of percent glacier cover on streamflow magnitude increases in drier basins. In the driest basins, glaciers produced 40 times greater percent glacier mass loss subsidies to streamflow for the percent glacier cover compared to the wettest basins. While the subsidy from glacier mass loss buffers interannual variability in streamflow to varying degrees, it can also increase streamflow variability. Smaller amounts of percent glacier cover are needed to produce summer-melt-dominated seasonal flow regimes in drier basins than in wetter basins. Decreasing glacier cover will eventually decrease summer streamflow, increasing spring streamflow in drier basins, and attenuating seasonality with increasing spring and autumnal streamflow in wetter basins. Quantifying the downstream effects of continued glacier mass loss without the computational expense of a hydrological model is broadly applicable in this changing climate.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024WR037859","usgsCitation":"Curran, J.H., Rick, B., Littell, J., and Sass, L., 2025, Streamflow response to glacier mass loss varies with basin precipitation across Alaska: Water Resources Research, v. 61, no. 4, e2024WR037859, 18 p., https://doi.org/10.1029/2024WR037859.","productDescription":"e2024WR037859, 18 p.","ipdsId":"IP-165191","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":490156,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024wr037859","text":"Publisher Index Page"},{"id":486640,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Janet H. 0000-0002-3899-6275 jcurran@usgs.gov","orcid":"https://orcid.org/0000-0002-3899-6275","contributorId":690,"corporation":false,"usgs":true,"family":"Curran","given":"Janet","email":"jcurran@usgs.gov","middleInitial":"H.","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":938483,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rick, Brianna 0000-0002-0516-7577","orcid":"https://orcid.org/0000-0002-0516-7577","contributorId":350547,"corporation":false,"usgs":false,"family":"Rick","given":"Brianna","affiliations":[{"id":83769,"text":"USGS Alaska Climate Adaptation Center","active":true,"usgs":false}],"preferred":false,"id":938484,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Littell, Jeremy S. 0000-0002-5302-8280","orcid":"https://orcid.org/0000-0002-5302-8280","contributorId":205907,"corporation":false,"usgs":true,"family":"Littell","given":"Jeremy","middleInitial":"S.","affiliations":[{"id":107,"text":"Alaska Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":938485,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sass, Louis C. 0000-0003-4677-029X lsass@usgs.gov","orcid":"https://orcid.org/0000-0003-4677-029X","contributorId":3555,"corporation":false,"usgs":true,"family":"Sass","given":"Louis C.","email":"lsass@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":938486,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70261844,"text":"70261844 - 2025 - Evaluating the applicability of the generalized power-law rating curve model: With applications to paired discharge-stage data from Iceland, Sweden, and the United States","interactions":[],"lastModifiedDate":"2024-12-30T15:09:17.858395","indexId":"70261844","displayToPublicDate":"2025-04-01T08:00:28","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating the applicability of the generalized power-law rating curve model: With applications to paired discharge-stage data from Iceland, Sweden, and the United States","docAbstract":"Hydrologic research and operations make extensive use of streamflow time series. In most applications, these time series are estimated from rating curves, which relate flow to some easy-to-measure surrogate, typically stage. The conventional stage-discharge rating takes the form of a segmented power law, with one segment for each hydrologic control at the stream gauge. However, these ratings are notoriously difficult to estimate with numerical methods, so that most are still developed manually. A few automated algorithms have emerged, but their use is sporadic, and their relative merits have not been rigorously assessed. One recently developed approach, the generalized power-law, avoids the segmenting problem by representing the power-law exponent as a Gaussian process. On the one hand, this representation is more flexible and easier to fit, but its flexibility might allow unrealistic solutions, so it needs to be tested under a range of conditions to assess its operational viability. This study evaluates the generalized power-law rating curve model by applying it to observations from 180 streams in Iceland, Sweden, and the United States. Overall, the model proved flexible and computationally robust, generating convincing rating curves across a range of geographic settings and was comparable to curves generated by a segmented rating model. Lastly, we propose a model-selection algorithm based on information theory to help identify the best rating curve model for a particular stream gauge.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2024.132537","usgsCitation":"Vias, R., Hrafnkelsson, B., Hodson, T.O., Rögnvaldsson, S., Jansson, A., and Gardarsson, S., 2025, Evaluating the applicability of the generalized power-law rating curve model: With applications to paired discharge-stage data from Iceland, Sweden, and the United States: Journal of Hydrology, v. 651, 132537, 19 p., https://doi.org/10.1016/j.jhydrol.2024.132537.","productDescription":"132537, 19 p.","ipdsId":"IP-167791","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":488042,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2024.132537","text":"Publisher Index 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Center","active":true,"usgs":true}],"preferred":true,"id":922011,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rögnvaldsson, Sölvi","contributorId":347574,"corporation":false,"usgs":false,"family":"Rögnvaldsson","given":"Sölvi","affiliations":[{"id":36649,"text":"University of Iceland","active":true,"usgs":false}],"preferred":false,"id":922012,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jansson, Axel Örn","contributorId":347575,"corporation":false,"usgs":false,"family":"Jansson","given":"Axel Örn","affiliations":[{"id":36649,"text":"University of Iceland","active":true,"usgs":false}],"preferred":false,"id":922013,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gardarsson, Sigurdur M.","contributorId":347576,"corporation":false,"usgs":false,"family":"Gardarsson","given":"Sigurdur M.","affiliations":[{"id":36649,"text":"University of Iceland","active":true,"usgs":false}],"preferred":false,"id":922014,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70268133,"text":"70268133 - 2025 - Per- and polyfluoroalkyl substances (PFAS) mass flux and mass balance at an aqueous film-forming foam release site in semiarid eastern New Mexico, USA","interactions":[],"lastModifiedDate":"2025-06-13T15:50:56.988751","indexId":"70268133","displayToPublicDate":"2025-03-31T10:41:18","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2233,"text":"Journal of Contaminant Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Per- and polyfluoroalkyl substances (PFAS) mass flux and mass balance at an aqueous film-forming foam release site in semiarid eastern New Mexico, USA","docAbstract":"Passive flux meters (PFMs) directly measure groundwater chemistry mass flux and Darcy flux, providing insight into contaminant source-zone architecture and transport properties. This study uses PFMs to characterize PFAS flux in groundwater at a semiarid site with a thick (greater than 90-m) unsaturated zone where groundwater has been contaminated with per- and polyfluoroalkyl substances (PFAS) related to the use of aqueous film-forming foam (AFFF) for fire training and fire suppression. PFAS mass discharge (PFAS mass flux integrated over a control plane) in groundwater downgradient from several PFAS release areas is calculated using PFM results. In groundwater downgradient from fire-training areas, total PFAS mass discharge (summed across 14 compounds) was estimated to be between 6.0 and 31 g per day in 2020 and between 5.9 and 23 g per day in 2021. Site-specific documentation, generic information on AFFF properties, and literature values of PFAS concentration in AFFF are used to estimate site-specific PFAS-application rates at fire-training areas. These PFAS-application rates are compared to groundwater PFAS-discharge rates. Results suggest that transformation processes (exact pathways unknown) have led to increased discharge of measured PFAS in groundwater relative to initial AFFF formulations. The mass balance approach has broad applicability as a high-level approach that can provide insight into PFAS transport at AFFF sites.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jconhyd.2025.104550","usgsCitation":"Gray, E., Potteiger, S., Brannon, T., Norton, S., Cho, J., and Annable, M., 2025, Per- and polyfluoroalkyl substances (PFAS) mass flux and mass balance at an aqueous film-forming foam release site in semiarid eastern New Mexico, USA: Journal of Contaminant Hydrology, v. 272, 104550, 11 p., https://doi.org/10.1016/j.jconhyd.2025.104550.","productDescription":"104550, 11 p.","ipdsId":"IP-160587","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":491002,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jconhyd.2025.104550","text":"Publisher Index Page"},{"id":490714,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Cannon Air Force Base","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -103.33908374205211,\n 34.41604524683224\n ],\n [\n -103.33908374205211,\n 34.359881732275326\n ],\n [\n -103.29149858549228,\n 34.359881732275326\n ],\n [\n -103.29149858549228,\n 34.41604524683224\n ],\n [\n -103.33908374205211,\n 34.41604524683224\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"272","noUsgsAuthors":false,"publicationDate":"2025-03-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Gray, Erin Louise 0000-0002-3945-6393","orcid":"https://orcid.org/0000-0002-3945-6393","contributorId":295317,"corporation":false,"usgs":true,"family":"Gray","given":"Erin Louise","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":940312,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Potteiger, Samuel Edwin 0009-0006-3293-7246","orcid":"https://orcid.org/0009-0006-3293-7246","contributorId":339925,"corporation":false,"usgs":true,"family":"Potteiger","given":"Samuel Edwin","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":940313,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brannon, Trevor Dylan 0009-0005-6030-8140","orcid":"https://orcid.org/0009-0005-6030-8140","contributorId":344656,"corporation":false,"usgs":true,"family":"Brannon","given":"Trevor Dylan","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":940314,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Norton, Stuart Bryan 0000-0002-4870-7481","orcid":"https://orcid.org/0000-0002-4870-7481","contributorId":295316,"corporation":false,"usgs":true,"family":"Norton","given":"Stuart Bryan","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":940315,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cho, Jay","contributorId":239518,"corporation":false,"usgs":false,"family":"Cho","given":"Jay","email":"","affiliations":[{"id":47898,"text":"BSEE","active":true,"usgs":false}],"preferred":false,"id":940316,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Annable, Michael D. 0000-0002-8736-9411","orcid":"https://orcid.org/0000-0002-8736-9411","contributorId":356873,"corporation":false,"usgs":false,"family":"Annable","given":"Michael D.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":940317,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70265697,"text":"70265697 - 2025 - Understanding predator-prey-competitor dynamics between Lower Missouri River Macrhybopsis and Scaphirhynchus using a population—bioenergetics model ensemble","interactions":[],"lastModifiedDate":"2025-04-15T14:57:29.302648","indexId":"70265697","displayToPublicDate":"2025-03-29T07:49:57","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":16139,"text":"Ecological Modeling","active":true,"publicationSubtype":{"id":10}},"title":"Understanding predator-prey-competitor dynamics between Lower Missouri River Macrhybopsis and Scaphirhynchus using a population—bioenergetics model ensemble","docAbstract":"The pallid sturgeon Scaphirhynchus albus is a long-lived, endangered fish in the Missouri River. Individuals become piscivorous as adults, so recruitment from stocking or reproduction could reduce populations of prey, including Macrhybopsis chubs. We constructed an individual- and age-based, multi-species, predator-prey-competitor model (IAMP) to represent the benthic community (sturgeons, chubs, and chironomids) of the Lower Missouri River (LMR) to explore scenarios of potential predator-prey-competitor dynamics. Our simulations suggest that chubs alone are unlikely able to support a level of LMR pallid sturgeon similar to historical or current populations. These simulations also suggest that adult pallid sturgeon may need to shift to non-chub prey fish to achieve the greater sizes observed in the Upper Missouri River. When annual hydrologic regimes were included, we found a negative relationship between chub relative abundance and previous year 30-day minimum flows. Inclusion of temporal environmental variability made it clear that large chub populations may be necessary to support LMR pallid sturgeon. When full stochasticity was included in the IAMP, chub population sizes needed to increase further to ensure continued reproduction and recruitment of both chubs and pallid sturgeon. These results support the hypothesis that the pallid sturgeon population in the Lower Missouri River may be food-limited. However, the full extent of this limitation and the management changes needed to address this will require more research on the biology and population dynamics of this fish community, on pallid sturgeon interactions with prey species, and on how sympatric species may be affected during the pallid sturgeon recovery process.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2025.111097","usgsCitation":"Wildhaber, M.L., Albers, J.L., and Green, N., 2025, Understanding predator-prey-competitor dynamics between Lower Missouri River Macrhybopsis and Scaphirhynchus using a population—bioenergetics model ensemble: Ecological Modeling, v. 504, 111097, 28 p., https://doi.org/10.1016/j.ecolmodel.2025.111097.","productDescription":"111097, 28 p.","ipdsId":"IP-164525","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":488248,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolmodel.2025.111097","text":"Publisher Index Page"},{"id":484578,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Kansas, Minnesota, Missouri, Montana, Nebraska, North Dakota, South Dakota, Wyoming","otherGeospatial":"Missouri River","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-111.048974,44.474072],[-111.323669,44.724474],[-111.50494,44.635746],[-111.469185,44.552044],[-112.258665,44.569516],[-112.387389,44.448058],[-112.749011,44.491233],[-112.844859,44.358221],[-113.134824,44.752763],[-113.455071,44.865424],[-113.802955,45.592631],[-114.015633,45.696127],[-114.345019,45.459916],[-114.559038,45.565706],[-114.422963,45.855381],[-114.527096,46.146218],[-114.322912,46.642938],[-114.76689,46.696901],[-115.294785,47.220914],[-115.731348,47.433381],[-115.72377,47.696671],[-116.049153,47.999923],[-116.049193,49.000912],[-95.153711,48.998903],[-95.153314,49.384358],[-94.878454,49.333193],[-94.640803,48.741171],[-93.818375,48.534442],[-92.984963,48.623731],[-92.634931,48.542873],[-92.698824,48.494892],[-92.341207,48.23248],[-92.066269,48.359602],[-91.542512,48.053268],[-90.88548,48.245784],[-90.703702,48.096009],[-89.489226,48.014528],[-90.735927,47.624343],[-92.058888,46.809938],[-92.025789,46.710839],[-92.189091,46.717541],[-92.291976,46.503997],[-92.33859,46.050111],[-92.869193,45.717568],[-92.646602,45.441635],[-92.807362,44.758909],[-91.410555,43.970892],[-91.244135,43.774667],[-91.243183,43.540309],[-96.591213,43.500514],[-96.439335,43.113916],[-96.630311,42.770885],[-96.396107,42.484095],[-96.272901,42.047281],[-96.129186,41.965136],[-96.081843,41.580407],[-95.850188,41.184798],[-95.885349,40.721093],[-95.758045,40.613759],[-91.625161,40.5435],[-91.452458,40.375501],[-91.510322,40.127994],[-91.369953,39.745042],[-90.721593,39.23273],[-90.653164,38.916141],[-90.113327,38.849306],[-90.367013,38.250054],[-89.952499,37.883218],[-89.516685,37.692762],[-89.438275,37.161287],[-89.102879,36.9697],[-89.120437,36.782071],[-89.429311,36.481875],[-89.55264,36.577178],[-89.527029,36.341679],[-89.703511,36.243412],[-89.615128,36.113816],[-89.733095,36.000608],[-90.368718,35.995812],[-90.075934,36.281485],[-90.157136,36.484317],[-94.617919,36.499414],[-94.699735,36.998805],[-109.045223,36.999084],[-109.050076,41.000659],[-111.046723,40.997959],[-111.048974,44.474072]]]},\"properties\":{\"name\":\"Colorado\",\"nation\":\"USA \"}}]}","volume":"504","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wildhaber, Mark L. 0000-0002-6538-9083 mwildhaber@usgs.gov","orcid":"https://orcid.org/0000-0002-6538-9083","contributorId":1386,"corporation":false,"usgs":true,"family":"Wildhaber","given":"Mark","email":"mwildhaber@usgs.gov","middleInitial":"L.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":933316,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Albers, Janice L. 0000-0002-6312-8269 jalbers@usgs.gov","orcid":"https://orcid.org/0000-0002-6312-8269","contributorId":3972,"corporation":false,"usgs":true,"family":"Albers","given":"Janice","email":"jalbers@usgs.gov","middleInitial":"L.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":933317,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Green, Nicholas S.","contributorId":301918,"corporation":false,"usgs":false,"family":"Green","given":"Nicholas S.","affiliations":[{"id":65362,"text":"Kennesaw State University","active":true,"usgs":false}],"preferred":false,"id":933318,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70267431,"text":"70267431 - 2025 - Estuarine tidal cycles may preserve thermal refugia as global temperatures increase","interactions":[],"lastModifiedDate":"2025-05-23T16:24:15.426836","indexId":"70267431","displayToPublicDate":"2025-03-28T09:15:04","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1584,"text":"Estuaries and Coasts","active":true,"publicationSubtype":{"id":10}},"title":"Estuarine tidal cycles may preserve thermal refugia as global temperatures increase","docAbstract":"Climate change is affecting coastal ecosystems worldwide as water temperatures increase, hydrologic regimes change, and sea levels rise. Consequently, estuaries risk declines in ecosystem functioning due to increasing temperatures and other hydrologic factors. Characterizing and predicting estuarine water temperature are challenging because these systems are highly dynamic. Statistical models have been used to accurately assess air temperature-water temperature relationships in lakes and streams but have not been effectively applied to tidally influenced ecosystems like estuaries. We used 6 years of continuous monitoring data from the Nisqually River Delta in Puget Sound, Washington, U.S.A., to parameterize and run a non-linear statistical model and generate spatially explicit model predictions. Our goal was to examine spatiotemporal patterns in estuarine water temperature and thermal refugia given current estimates of climactic change. The performance of the parameterized model was similar to that of non-linear stream temperature models (NSE = 0.76; RMSE = 2.34 °C). Scenarios incorporating forecasted high-emission air temperatures through the year 2100 (+ 7 °C) predicted a corresponding 3.55 ± 0.63 °C increase in average water temperatures; however, moderate and high rates of sea-level rise offset temperature increases by 3–20% and substantially reduced the amount of time temperatures exceeded the thermal stress threshold of 20 °C for juvenile salmon. These findings demonstrate how the effects of one climate stressor (sea-level rise) may offset another (temperature increases) to maintain thermal refugia for coldwater fishes. Similar exercises may allow managers to explore mitigation options like the planting of riparian vegetation or modified flooding regimes to further offset rising water temperatures.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s12237-025-01510-7","usgsCitation":"Davis, M.J., Woo, I., and De La Cruz, S.E., 2025, Estuarine tidal cycles may preserve thermal refugia as global temperatures increase: Estuaries and Coasts, v. 48, 90, 19 p., https://doi.org/10.1007/s12237-025-01510-7.","productDescription":"90, 19 p.","ipdsId":"IP-129413","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":486525,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Nisqually River Delta, Puget Sound, Salish Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.55330830655157,\n 48.98716401827198\n ],\n [\n -123.55330830655157,\n 47.20224465518157\n ],\n [\n -121.87955279406418,\n 47.20224465518157\n ],\n [\n -121.87955279406418,\n 48.98716401827198\n ],\n [\n -123.55330830655157,\n 48.98716401827198\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"48","noUsgsAuthors":false,"publicationDate":"2025-03-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Davis, Melanie J. 0000-0003-1734-7177","orcid":"https://orcid.org/0000-0003-1734-7177","contributorId":202773,"corporation":false,"usgs":true,"family":"Davis","given":"Melanie","email":"","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":938184,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Woo, Isa 0000-0002-8447-9236 iwoo@usgs.gov","orcid":"https://orcid.org/0000-0002-8447-9236","contributorId":2524,"corporation":false,"usgs":true,"family":"Woo","given":"Isa","email":"iwoo@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":938185,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"De La Cruz, Susan E.W. 0000-0001-6315-0864","orcid":"https://orcid.org/0000-0001-6315-0864","contributorId":202774,"corporation":false,"usgs":true,"family":"De La Cruz","given":"Susan","email":"","middleInitial":"E.W.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":938186,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}