Numerous mercury (Hg) sources can contribute to biological burdens within the Great Lakes, including atmospheric deposition (e.g., precipitation), non-point source land runoff (e.g., watershed), and legacy contamination. Due to these different environmental entry points, it is often difficult to ascertain if legacy Hg contamination contributes to contemporary fish consumption advisories within Areas of Concern (AOCs), as designated by the United States-Canada Great Lakes Water Quality Agreement. In this study, we aimed to assess the contributions of legacy Hg to sediments in nearshore wetland habitats and co-located prey items (dragonfly larvae and yellow perch) within the St. Louis River AOC using Hg stable isotopes. We observed that nearshore sediments had the same Hg source portfolio as previously examined main channel sites. Furthermore, this data confirmed that two major Hg sources were contributing to sediments within nearshore regions of the St. Louis River AOC: legacy and watershed Hg. The contribution of legacy Hg was estimated in biota and demonstrated that up to 64% of the Hg in fish tissue in the lower estuary (St. Louis Bay) was from legacy sources, but that this percentage declined substantially when examining upstream regions of the AOC. These data indicate the influence of legacy Hg to the food web varies spatially within the St. Louis River. We further found that watershed Hg sources are an important Hg contributor to the St. Louis River, which likely applies to other impacted and unimpacted tributaries across the Great Lakes region.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2024.102494","usgsCitation":"Janssen, S., Hoffman, J.C., and Krabbenhoft, D.P., 2024, New tools for a legacy problem: How isotope tracers inform area of concern actions in the St. Louis River in Lake Superior: Journal of Great Lakes Research (JGLR), v. 51, no. 1, 102494, 9 p., https://doi.org/10.1016/j.jglr.2024.102494.","productDescription":"102494, 9 p.","ipdsId":"IP-170550","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":497996,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2024.102494","text":"Publisher Index Page"},{"id":490198,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota, Wisconsin","otherGeospatial":"St. Louis River in Lake Superior","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92.25467395030219,\n 47.005307600372475\n ],\n [\n -92.25467395030219,\n 46.60173825387926\n ],\n [\n -91.04335848560507,\n 46.60173825387926\n ],\n [\n -91.04335848560507,\n 47.005307600372475\n ],\n [\n -92.25467395030219,\n 47.005307600372475\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"51","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Janssen, Sarah E. 0000-0003-4432-3154","orcid":"https://orcid.org/0000-0003-4432-3154","contributorId":210991,"corporation":false,"usgs":true,"family":"Janssen","given":"Sarah E.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":939289,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hoffman, Joel C.","contributorId":84244,"corporation":false,"usgs":false,"family":"Hoffman","given":"Joel","email":"","middleInitial":"C.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":939290,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Krabbenhoft, David P. 0000-0003-1964-5020 dpkrabbe@usgs.gov","orcid":"https://orcid.org/0000-0003-1964-5020","contributorId":1658,"corporation":false,"usgs":true,"family":"Krabbenhoft","given":"David","email":"dpkrabbe@usgs.gov","middleInitial":"P.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":939291,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70261770,"text":"sir20245124 - 2024 - Iodine-129 in the eastern Snake River Plain aquifer at and near the Idaho National Laboratory, Idaho, 2021–22","interactions":[],"lastModifiedDate":"2025-08-15T16:13:12.075619","indexId":"sir20245124","displayToPublicDate":"2024-12-20T13:41:26","publicationYear":"2024","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":"2024-5124","displayTitle":"Iodine-129 in the Eastern Snake River Plain Aquifer at and near the Idaho National Laboratory, Idaho, 2021–22","title":"Iodine-129 in the eastern Snake River Plain aquifer at and near the Idaho National Laboratory, Idaho, 2021–22","docAbstract":"Between the 1950s and 1980s, wastewater generated at the Idaho National Laboratory contained Iodine-129 (129I); this wastewater was discharged directly into the eastern Snake River Plain (ESRP) aquifer through a deep disposal well, unlined infiltration ponds, or leaked from distribution systems below industrial facilities. During 2021–22, the U.S. Geological Survey, in cooperation with the U.S. Department of Energy and the Idaho Department of Environmental Quality Idaho National Laboratory Oversight Program, collected groundwater samples from 64 monitoring wells in the ESRP aquifer, 6 of which are part of a multilevel monitoring system, to determine the concentration of 129I in the groundwater. These samples were analyzed by accelerator mass spectrometry as part of a long-term ongoing study to track trends and occurrences of this carcinogenic, long-lived radionuclide in the environment. Concentrations ranged from slightly above the locally determined background concentration of 5.4×10−6 picocuries per liter, to just below the U.S. Environmental Protection Agency’s maximum contaminant level of 1 picocurie per liter. Discharge of wastewater containing 129I has been discontinued to the aquifer, and long-term trends from a subset (n=15) of sampled wells show decreasing 129I concentrations over the last three decades. Concentrations of 129I in groundwater from monitoring wells near facilities at the Idaho National Laboratory are affected by episodic recharge from an ephemeral surface-water source and by the fracture-flow dominated hydrologic regime in the ESRP aquifer. The spatially focused sampling effort has also identified a low-level 129I plume that affects long-term water quality near and downgradient from the Advanced Test Reactor Complex in the southwestern part of the facility that had not been clearly defined in previous sampling efforts, although the definition of the plume is somewhat limited by available data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245124","collaboration":"Prepared in cooperation with the U.S. Department of Energy","programNote":"DOE/ID-22262","usgsCitation":"Treinen, K.C., Trcka, A.R., Krohe, N., and Lehotsky, G., 2024, Iodine-129 in the eastern Snake River Plain aquifer at and near the Idaho National Laboratory, Idaho, 2021–22: U.S. Geological Survey Scientific Investigations Report 2024–5124 (DOE/ID 22262), 27 p., https://doi.org/10.3133/sir20245124.","productDescription":"Report: vii, 27 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-150514","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":494219,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118236.htm","linkFileType":{"id":5,"text":"html"}},{"id":465410,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245124/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5124"},{"id":465409,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5124/sir20245124.pdf","text":"Report","size":"2.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5124"},{"id":465413,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5124/sir20245124.XML"},{"id":465412,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5124/images"},{"id":465411,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UWRYR4","text":"USGS data release","description":"USGS data release","linkHelpText":"Datasets for the U.S. Geological Survey—Idaho National Laboratory groundwater and surface-water monitoring networks, v1.1"},{"id":465408,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5124/coverthb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Eastern Snake River Plain aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.973611,\n 43.591667\n ],\n [\n -112.916667,\n 43.591667\n ],\n [\n -112.916667,\n 43.540278\n ],\n [\n -112.973611,\n 43.540278\n ],\n [\n -112.973611,\n 43.591667\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Rd
Boise, Idaho 83702-4250
The spatial heterogeneity of soil nutrients is crucial for the water bird and whole floodplain wetland ecosystem in large lakes, and it is influenced by the dramatic water level changes and sedimentation progress in West Dongting Lake (WDL). Soil samples were collected at various soil depths along the Yuan River and Li River that feed into WDL. The concentrations of soil total organic carbon (TOC), total nitrogen (TN), total phosphorus (TP), and soil grain size were tested. The stoichiometric ratios of C, N, P, and the mean value of soil grain size (Mz) were calculated. The differences of soil TOC, TN, TP and the stoichiometric ratio at different sites and soil depths were compared. Linear regression was used to explore the relationships of Mz and nutrient concentrations, and relationships between TOC, TN, and TP. Redundancy analysis was used to explore the relationship between soil nutrients, heavy metal concentrations, and plant community diversity. The results showed that the distributions of soil TOC, TN, and TP concentrations differed across regions in west Dongting Lake along the Yuan and Li Rivers. Total organic carbon concentration differed at different sedimentation depths. Soil grain size showed negative effect with soil TOC, TN, and TP concentrations in this region. Plant community diversity correlated positively with soil TOC and negatively with Hg. West Dongting Lake was N limited despite the high wet deposition of N. It could potentially be attributed to the insufficient presence of aerobic environments for microbes during intermittent flooding of the floodplain, coupled with feeble mineralization. This study can provide valuable insights for the conservation of water bird habitats and wetland ecosystems.
","language":"English","publisher":"MDPI","doi":"10.3390/w16243674","usgsCitation":"Lin, J., Wu, Y., Peng, D., Chen, M., Peng, L., Middleton, B., and Lei, T., 2024, Spatial differences in soil nutrients along a hydrographic gradient on floodplains in Dongting Lake: Water, v. 16, no. 24, 3674, 15 p., https://doi.org/10.3390/w16243674.","productDescription":"3674, 15 p.","ipdsId":"IP-132521","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":481042,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w16243674","text":"Publisher Index Page"},{"id":480985,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China","otherGeospatial":"West Dongting Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 111.958333,\n 29.0833\n ],\n [\n 111.958333,\n 28.8\n ],\n [\n 112.333,\n 28.8\n ],\n [\n 112.333,\n 29.0833\n ],\n [\n 111.958333,\n 29.0833\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"24","noUsgsAuthors":false,"publicationDate":"2024-12-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Lin, Jiayi","contributorId":348836,"corporation":false,"usgs":false,"family":"Lin","given":"Jiayi","affiliations":[{"id":80251,"text":"Southern University of Science and Technology, China","active":true,"usgs":false}],"preferred":false,"id":924846,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wu, Yuanmi","contributorId":349810,"corporation":false,"usgs":false,"family":"Wu","given":"Yuanmi","affiliations":[{"id":83517,"text":"Beijing Forestry University","active":true,"usgs":false}],"preferred":false,"id":924847,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peng, Dong","contributorId":224694,"corporation":false,"usgs":false,"family":"Peng","given":"Dong","email":"","affiliations":[{"id":40912,"text":"Beijing Forestry","active":true,"usgs":false}],"preferred":false,"id":924848,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chen, Mingzhu","contributorId":303338,"corporation":false,"usgs":false,"family":"Chen","given":"Mingzhu","email":"","affiliations":[{"id":65768,"text":"Shenzhen Landscape Institute, Shenzhen","active":true,"usgs":false}],"preferred":false,"id":924849,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Peng, Lingli","contributorId":349811,"corporation":false,"usgs":false,"family":"Peng","given":"Lingli","affiliations":[{"id":83517,"text":"Beijing Forestry University","active":true,"usgs":false}],"preferred":false,"id":924850,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Middleton, Beth 0000-0002-1220-2326","orcid":"https://orcid.org/0000-0002-1220-2326","contributorId":222689,"corporation":false,"usgs":true,"family":"Middleton","given":"Beth","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":924851,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lei, Ting","contributorId":245022,"corporation":false,"usgs":false,"family":"Lei","given":"Ting","affiliations":[{"id":40912,"text":"Beijing Forestry","active":true,"usgs":false}],"preferred":false,"id":924852,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70255373,"text":"tm6A63 - 2024 - SUTRA— A code for simulation of saturated-unsaturated, variable-density groundwater flow with solute or energy transport—Documentation of the version 4.0 enhancements—Freeze-thaw capability, saturation and relative-permeability relations, spatially varying properties, and enhanced budget and velocity outputs","interactions":[],"lastModifiedDate":"2024-12-20T15:08:35.83011","indexId":"tm6A63","displayToPublicDate":"2024-12-20T09:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"6-A63","displayTitle":"SUTRA: A Code for Simulation of Saturated-Unsaturated, Variable-Density Groundwater Flow With Solute or Energy Transport—Documentation of the Version 4.0 Enhancements—Freeze-Thaw Capability, Saturation and Relative-Permeability Relations, Spatially Varying Properties, and Enhanced Budget and Velocity Outputs","title":"SUTRA— A code for simulation of saturated-unsaturated, variable-density groundwater flow with solute or energy transport—Documentation of the version 4.0 enhancements—Freeze-thaw capability, saturation and relative-permeability relations, spatially varying properties, and enhanced budget and velocity outputs","docAbstract":"Version 4.0 of the Saturated-Unsaturated Transport (SUTRA) software code provides the capability to simulate the freezing and thawing of groundwater during energy transport simulations under saturated and unsaturated conditions. In addition to the types of hydrogeologic processes that SUTRA has been able to simulate in the past, this version can be used to study the effects of the freeze-thaw process on the flow and energy dynamics of hydrogeologic systems. The freeze-thaw simulation capability accounts for the latent heat of fusion and allows thermal property values to vary with changing total-water saturation, liquid-water saturation, and ice saturation. It allows the effective permeability of the porous medium to change as a result of freezing and thawing. This version also provides several user-selectable relations for the dependence of total-water saturation on fluid pressure, the dependence of liquid-water saturation on temperature during freezing and thawing, and the dependence of relative permeability on liquid saturation, as well as three user-selectable formulae for defining the bulk thermal conductivity of a mixture of solid grains, liquid water, ice, and air. For unsaturated simulations without freezing, the selectable total-water saturation relations eliminate the need for the user to program these and their associated relative-permeability functions, as had been required in previous SUTRA versions. Optional nonlinear dependence of fluid density on temperature, which covers the range from supercooled (about −50 degrees Celsius) to superheated (about 400 degrees Celsius), is also provided.
Additionally, this version makes it possible to spatially vary parameters that, in previous versions of SUTRA, were required to be spatially uniform: solid-matrix properties, adsorption parameters, and parameters for production of solute mass or energy. Spatial variation is also allowed for the newly included freeze-thaw process parameters. Additional enhancements provide (1) output of water-mass and energy budgets that include values of all component terms in the governing balance equations, and (2) output of Darcy velocities (fluid fluxes), in addition to the velocity output provided by previous SUTRA versions. These enhanced outputs allow fuller interpretation of simulation results, especially for freeze-thaw phenomena.
The set of processes simulated by this version of SUTRA are useful for studying a wide range of hydrogeologic system types, conditions, and questions. For cryohydrogeologic simulations, however, this version of the code is limited in that (1) it does not simulate thermomechanical effects of freeze-thaw, (2) pressure changes due to water density change during freezing are neglected, (3) ice saturation cannot exceed the initial porosity of the simulated medium, and (4) cryosuction, the migration of liquid water toward freezing fronts, is neglected. Furthermore, this version does not account for air flow or for water vaporization and sublimation under unsaturated conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm6A63","usgsCitation":"Voss, C.I., Provost, A.M., McKenzie, J.M., and Kurylyk, B.L., 2024, SUTRA—A code for simulation of saturated-unsaturated, variable-density groundwater flow with solute or energy transport—Documentation of the version 4.0 enhancements—Freeze-thaw capability, saturation and relative-permeability relations, spatially varying properties, and enhanced budget and velocity outputs: U.S. Geological Survey Techniques and Methods, book 6, chap. A63, 91 p., https://doi.org/10.3133/tm6A63.","productDescription":"Report: vii, 91 p.; Software Release","numberOfPages":"91","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-097937","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":430363,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/06/a63/tm6a63.pdf","text":"Report","size":"3.80 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 6-A63 PDF"},{"id":430364,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/tm6A63/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"TM 6-A63 HTML"},{"id":430365,"rank":4,"type":{"id":35,"text":"Software Release"},"url":"https://doi.org/10.5066/P9OL5IYX","text":"USGS software release","linkHelpText":"- SUTRA—A model for 2D or 3D saturated-unsaturated, variable-density groundwater flow with solute or energy transport"},{"id":430366,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/tm/06/a63/tm6a63.XML","description":"TM 6-A63 XML"},{"id":430367,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/tm/06/a63/images/"},{"id":430362,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/06/a63/coverthb.jpg"}],"contact":"Director, Earth System Processes Division
Water Resources Mission Area
U.S. Geological Survey
Great Salt Lake is a critical habitat for migratory birds that is threatened by elevated metal concentrations, including mercury (Hg) and lead (Pb), and is subject to severe hydrologic changes, such as declining lake level. When assessing metal profiles recorded in Great Salt Lake sediment, a large data gap exists regarding the sources of metals within the system, which is complicated by various source inputs to the lake and complex biogeochemistry. Here, we leverage Hg and Pb stable isotopes to track relative changes in metal source contributions to Great Salt Lake over time. Mercury and Pb concentrations increase in sediments deposited after 1920 and peak between 1965 and 1995, following closure of several local smelters and the onset of increased emission controls. The nominal associations above are confirmed via Hg stable isotopes in pre-1920 background sediments, which reflect atmospheric inputs from regional and global origin, whereas Hg and Pb stable isotopes together indicate that elevated metal concentrations in mid-late 20th century sediments reflect increased mining/smelting inputs. The observed minimal rebound towards pre-1920 Pb isotope signatures in 21st century sediments indicates that mining/smelting inputs, though reduced, remain a primary source of Pb to Great Salt Lake. In contrast, the more pronounced rebound of Hg stable isotope signatures to pre-1920 values indicate a greater contribution of atmospheric inputs of regional/global origin to current Hg inputs, though Hg concentrations are ∼10 times greater than pre-1920 background values due to global increases in atmospheric Hg concentrations or possibly slow recovery from local contamination. The importance of regional/global Hg sources to the system suggests that reductions in Hg bioaccumulation in the open water food webs of Great Salt Lake are more dependent on national and global reductions in Hg emissions and management strategies to limit methylmercury production within system. This work highlights the utility of using coupled Hg and Pb stable isotope values to assess trace metal pollution sources and pathways in aquatic systems.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2024.177374","usgsCitation":"Lopez, S.F., Janssen, S., Tate, M., Fernandez, D.P., Anderson, C.R., Armstrong, G.J., Wang, T.C., and Johnson, W.P., 2024, Using mercury and lead stable isotopes to assess mercury, lead, and trace metal source contributions to Great Salt Lake, Utah, USA: Science of the Total Environment, v. 957, 177374, 14 p., https://doi.org/10.1016/j.scitotenv.2024.177374.","productDescription":"177374, 14 p.","ipdsId":"IP-170245","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":488066,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2024.177374","text":"Publisher Index Page"},{"id":464466,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Great Salt Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -113.27532189699335,\n 41.764305005205784\n ],\n [\n -113.27532189699335,\n 40.550910675427446\n ],\n [\n -111.82239945429947,\n 40.550910675427446\n ],\n [\n -111.82239945429947,\n 41.764305005205784\n ],\n [\n -113.27532189699335,\n 41.764305005205784\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"957","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lopez, Samuel Francisco 0000-0002-3544-7465","orcid":"https://orcid.org/0000-0002-3544-7465","contributorId":344607,"corporation":false,"usgs":true,"family":"Lopez","given":"Samuel","email":"","middleInitial":"Francisco","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":919345,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Janssen, Sarah E. 0000-0003-4432-3154","orcid":"https://orcid.org/0000-0003-4432-3154","contributorId":210991,"corporation":false,"usgs":true,"family":"Janssen","given":"Sarah E.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":919346,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tate, Michael T. 0000-0003-1525-1219 mttate@usgs.gov","orcid":"https://orcid.org/0000-0003-1525-1219","contributorId":3144,"corporation":false,"usgs":true,"family":"Tate","given":"Michael T.","email":"mttate@usgs.gov","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":919347,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fernandez, Diego P.","contributorId":138701,"corporation":false,"usgs":false,"family":"Fernandez","given":"Diego","email":"","middleInitial":"P.","affiliations":[{"id":12499,"text":"Univ. of Utah","active":true,"usgs":false}],"preferred":false,"id":919348,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Anderson, Christopher R.","contributorId":346496,"corporation":false,"usgs":false,"family":"Anderson","given":"Christopher","email":"","middleInitial":"R.","affiliations":[{"id":13252,"text":"University of Utah","active":true,"usgs":false}],"preferred":false,"id":919349,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Armstrong, Grace Jane 0009-0009-8132-9011","orcid":"https://orcid.org/0009-0009-8132-9011","contributorId":332127,"corporation":false,"usgs":true,"family":"Armstrong","given":"Grace","email":"","middleInitial":"Jane","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":919350,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wang, Thomas Charng-Shuen 0009-0001-2214-4721","orcid":"https://orcid.org/0009-0001-2214-4721","contributorId":331024,"corporation":false,"usgs":true,"family":"Wang","given":"Thomas","email":"","middleInitial":"Charng-Shuen","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":919351,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Johnson, William P.","contributorId":107288,"corporation":false,"usgs":false,"family":"Johnson","given":"William","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":919352,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70261721,"text":"sir20245117 - 2024 - Hydrologic and hydraulic analyses of Silver Creek and selected tributaries associated with Scott Air Force Base, Illinois, 2022–24","interactions":[],"lastModifiedDate":"2025-08-15T16:14:31.530381","indexId":"sir20245117","displayToPublicDate":"2024-12-20T08:15:47","publicationYear":"2024","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":"2024-5117","displayTitle":"Hydrologic and Hydraulic Analyses of Silver Creek and Selected Tributaries Associated with Scott Air Force Base, Illinois, 2022–24","title":"Hydrologic and hydraulic analyses of Silver Creek and selected tributaries associated with Scott Air Force Base, Illinois, 2022–24","docAbstract":"A hydrologic model of the Silver Creek Basin in southwest Illinois, and a hydraulic model of a selected reach of Silver Creek and local tributaries on and near Scott Air Force Base, Illinois, were developed to assess the effects of temporal land-use development in the Silver Creek Basin, the potential effects of projected changes based on future precipitation, and the effects of added detention storage in selected tributaries near Scott Air Force Base. The hydrologic model consists of a total of 52 scenarios—24 scenarios for an assessment of basin-wide changes in hydrology, and 28 scenarios for the hydraulic analysis of a focus area of Silver Creek and tributaries on and near Scott Air Force Base. Scenarios were run for precipitation events of 2-year through 500-year recurrence intervals (50-percent through 0.2-percent annual exceedance probability) and 24-hour durations.
The effects of detention structures added to Silver Creek tributaries throughout Scott Air Force Base were greater on water-level profiles (about 1 to 3 feet) than the effects of projected (2050) changes in precipitation (about 1 foot or less) in these basins. The results indicated that despite the increases in water-surface elevations resulting from projected increases in precipitation, the detention structures could provide a net reduction in water-surface elevations in the flood-prone western tributaries on the base. The effects of detention structures and projected precipitation also were assessed using the mapped extent of inundation for the simulated probabilistic precipitation scenarios. As an example, limited inundation of a residential area along Ash Creek was evident in the 5-year recurrence interval event for the scenarios without detention storage, whereas the first indications of flooding in the residential area from the scenario with detention storage were in the 50-year recurrence interval event.
Changes in hydrologic conditions followed a spatial pattern similar to that of the changes in land-cover development, with the greatest changes in the downstream one-half of the Silver Creek Basin and most pronounced in subbasins on and surrounding Scott Air Force Base. There was up to an estimated 54.6-percent increase in peak streamflows in subbasins on or near Scott Air Force Base from historical (1992) to current (2019) conditions, but changes in peak streamflows of as much as 144 percent are anticipated under the planned (to about 2050) land cover plus projected (2050) precipitation. The changes in the timing of peak streamflows were towards earlier peaks, with cumulative changes between historical and projected conditions approaching 0.75 hour (45 minutes) for a 2-year recurrence interval event. Results of the percentage change in cumulative event volume were similar to those of percentage change in peak streamflows in terms of magnitude of change and temporal and spatial distribution of changes. The greatest magnitude of percentage change in the assessed hydrologic properties was associated with the 2-year recurrence interval event, and the magnitude of the percentage change decreased with increasing probabilistic event recurrence interval. Subbasins with a substantial change in runoff yield between historical and current conditions were primarily in the downstream one-half of the Silver Creek Basin and most were within or adjacent to Scott Air Force Base. The magnitude of runoff yield changes increased with recurrence interval, and maximum changes were associated with subbasins on base and with the changes between the historical and current conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245117","collaboration":"Prepared in cooperation with Scott Air Force Base","usgsCitation":"Cigrand, C.V., Heimann, D.C., and Rydlund, P.H., Jr., 2024, Hydrologic and hydraulic analyses of Silver Creek and selected tributaries associated with Scott Air Force Base, Illinois, 2022–24: U.S. Geological Survey Scientific Investigations Report 2024–5117, 87 p., https://doi.org/10.3133/sir20245117.","productDescription":"Report: x, 87 p.; Data Release; 2 Datasets","numberOfPages":"102","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-135331","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":494220,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118090.htm","linkFileType":{"id":5,"text":"html"}},{"id":465320,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GBYP2K","text":"USGS data release","linkHelpText":"Archive of hydrologic and hydraulic models used in the analyses of Silver Creek Basin and selected tributaries associated with Scott Air Force Base, Illinois, 1992–2050"},{"id":465321,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://datagateway.nrcs.usda.gov/GDGOrder.aspx","text":"U.S. Department of Agriculture, Natural Resources Conservation Service database","linkHelpText":"- GeoSpatial data gateway"},{"id":465322,"rank":8,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":465316,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5117/sir20245117.pdf","text":"Report","size":"97.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024–5117"},{"id":465317,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5117/sir20245117.XML"},{"id":465318,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5117/images/"},{"id":465315,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5117/coverthb.jpg"},{"id":465319,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245117/full"}],"country":"United States","state":"Illinois","otherGeospatial":"Scott Air Force Base, Silver Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.8831289278596,\n 38.57664573726461\n ],\n [\n -89.8831289278596,\n 38.50342611477362\n ],\n [\n -89.77601466850366,\n 38.50342611477362\n ],\n [\n -89.77601466850366,\n 38.57664573726461\n ],\n [\n -89.8831289278596,\n 38.57664573726461\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240
High-resolution elevation data for Kansas inform decision making to improve the State’s economy. Existing elevation data coverage is used to support State water planning initiatives, facilitate infrastructure management, and improve resilience to natural disasters. The expanding availability of current and more accurate elevation data helps better support natural resources conservation, agriculture and precision farming, flood risk management, water supply planning, infrastructure and construction management, and geologic resource assessment and hazard mitigation. Critical applications that meet the State’s management needs depend on light detection and ranging (lidar) data that provide a highly detailed three-dimensional (3D) model of the Earth’s surface and aboveground features.
The 3D Elevation Program (3DEP) is managed by the U.S. Geological Survey (USGS) in partnership with Federal, State, Tribal, U.S. territorial, and local agencies to acquire consistent lidar coverage at quality level 2 or better to meet the many needs of the Nation and Kansas. The status of available and in-progress 3DEP baseline lidar data in Kansas is shown in figure 1. 3DEP baseline lidar data include quality level 2 or better, 1-meter or better digital elevation models, and lidar point clouds, and must meet the Lidar Base Specification version 1.2 (https://www.usgs.gov/3dep/lidarspec) or newer requirements. The National Enhanced Elevation Assessment identified user requirements and conservatively estimated that availability of lidar data would result in at least $14.41 million in new benefits annually to the State. The top nine Kansas business uses for 3D elevation data, which are based on the estimated annual conservative benefits of 3DEP, are shown in table 2.
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\"}}]}","contact":"Director, National Geospatial Program
U.S. Geological Survey, MS 511
12201 Sunrise Valley Drive
Reston, VA 20192
Email: 3DEP@usgs.gov
","tableOfContents":"The U.S. Geological Survey (USGS), in cooperation with the West Virginia Department of Transportation, Division of Highways, compared time of concentration (Tc) and related runoff characteristics measured at four field sites in West Virginia to estimates of these values made using accepted methods. These four sites were selected to represent a range of basin size, length, and slope, and a range of estimated Tc. Instrumentation included a rain gage and a streamgage at all sites. Two streamgages, USGS station number (no.) 03159718 Grasslick Creek tributary above Interstate 77 near Fairplain, West Virginia, (referred to as Fairplain in this report) and USGS station no. 03159823 Grass Run tributary above Interstate 77 near Ripley, W. Va., (referred to as Ripley in this report) were near each other in northwestern West Virginia at the outlets of small basins with moderate slope. The largest, longest, and flattest basin in the study was upstream from USGS station no. 03190307 Hedricks Creek Tributary above US–19 near Hico, W. Va. (Hico). The final gaged basin in the study, that of USGS station no. 03197062 Cookman Fork at Interstate 79 near Wallback, W. Va., (Wallback) in central West Virginia, had a drainage area nearly as large as Hico, but the basin was more compact.
Precipitation and streamflow data were collected at the streamgages between October 2017 and July 2020. Storms were identified and classified through an iterative process relying on inspecting graphs created from the precipitation and streamflow data. Three hydrograph time metrics that represent Tc were computed for this study: time to rise, time to recede from a high point on the hydrograph to an inflection on the recession, and the time between an inflection on the hyetograph and an inflection on the recession of the hydrograph (precipitation inflection to recession inflection or PI-to-RI).
Hico had the slowest time metrics: the streamgage had an average Tc of 34 and 32 minutes for time to rise and time to recede, respectively. The time between the PI-to-RI at Hico, 38 minutes, was the longest for any of the characteristics at any of the streamgages. Wallback had the second slowest time metrics. At Wallback, average Tc for time to rise and time to recede was similar, 23 and 25 minutes, respectively. The average time between the PI-to-RI for Wallback was greater than its time to rise or time to recede, 32 minutes. At Fairplain and Ripley, time to rise was 18 and 19 minutes, time to recede was 14 and 16 minutes, and time between the PI-to-RI was 22 and 27 minutes, respectively. At Ripley, PI-to-RI and time to rise were significantly different from each other. Differences in metrics were not statistically significant (p ≤0.05) among streamgages.
At all streamgages, predictions made with the “Rational Method” were within one average standard deviation of the overall mean Tc. The Rational Method was applied following two different procedures— (1) channel geometry was estimated using professional judgment and (2) channel geometry estimates were adjusted using regional equations. The three different time metrics had an inconsistent relation with the estimates. Some of the predictions differed from individual hydrograph time metrics by more than one standard deviation. Predicted values for the 10-year storm were within the interquartile range (IQR) for 4 of 12 combinations of streamgages and time metrics. Adjusted Tc predictions were within the IQR of PI-to-RI for Fairplain and Wallback, longer than the IQR of observed PI-to-RI at Hico, and shorter than the IQR of observed PI-to-RI at Ripley. The adjusted predictions of Tc were within the IQR of time-to-rise for Hico and Fairplain and were longer than the IQR for Ripley and Wallback. At Ripley, the predictions were not within the IQR for either PI-to-RI or time to rise, but instead, were between them. These lines of evidence do not indicate large, systematic errors in Tc estimates.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245051","collaboration":"Prepared in cooperation with West Virginia Department of Transportation, Division of Highways","usgsCitation":"Messinger, T., Holmes, D.A., Scott, J.D., and Kirk, D.W., 2024, Improving time of concentration estimates for small rural watersheds in the Appalachian Plateaus physiographic province, West Virginia: U.S. Geological Survey Scientific Investigations Report 2024–5051, 34 p., https://doi.org/10.3133/sir20245051.","productDescription":"Report: vii, 34 p.; Data Release","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-135179","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":494223,"rank":7,"type":{"id":36,"text":"NGMDB Index 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U.S. Geological Survey
1730 East Parham Road
Richmond, VA 23228
The Russian River watershed is in northern Sonoma County and southern Mendocino County, California, in the northern part of the California Coast Ranges. The Russian River serves as a supply for agricultural irrigation and for municipal, domestic, and commercial uses. Through a cooperative agreement with the California State Water Resources Control Board and Sonoma County Water Agency, the U.S. Geological Survey has completed studies to better understand the hydrogeologic system and develop numerical hydrologic modeling tools to evaluate and aid in managing groundwater resources. This report focuses on the development of a digital three-dimensional hydrogeologic framework model of the Russian River watershed for use in groundwater resource assessment and numerical models.
The digital three-dimensional hydrogeologic framework model of the Russian River watershed portrays the altitude, thickness, and extent of five hydrogeologic units. These five hydrogeologic units include (1) a basement unit, (2) the Sonoma Volcanics, (3) a consolidated sedimentary rock unit, (4) an unconsolidated sediment unit, and (5) channel alluvium. Model input data were compiled from published geologic maps, interpreted well data, and a model of the top of basement derived from gravity data. These data were used to construct surfaces that represent the upper and lower subsurface boundaries of each hydrogeologic unit. Top surfaces were created for the five hydrogeologic units and then stacked in three dimensions to create a solid-volume digital model.
The digital three-dimensional hydrogeologic framework model described in this report and the corresponding data represent the generalized geometry of the subsurface geologic units; the model reproduces the input geologic data with reasonable accuracy and is consistent with previously published subsurface conceptualizations of the region. The model indicates the overall geometry of the basement within the watershed and the spatial extent, altitude, and thickness of the basin-filling units. The hydrogeologic framework model is at a scale and resolution appropriate for use as the foundation for a numerical hydrologic model of the study area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245083","collaboration":"Prepared in cooperation with the California State Water Resources Control Board and Sonoma County Water Agency","programNote":"Water Availability and Use Science Program—Water Resources Mission Area","usgsCitation":"Cromwell, G., Sweetkind, D.S., Langenheim, V.E., and Ely, C.P., 2024, Three-dimensional hydrogeologic framework model of the Russian River watershed, California: U.S. Geological Survey Scientific Investigations Report 2024–5083, 25 p., https://doi.org/10.3133/sir20245083.","productDescription":"Report: viii, 25 p.; Data Release","numberOfPages":"25","onlineOnly":"Y","ipdsId":"IP-122963","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":465343,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GPH5FW","text":"USGS Data Release","description":"Fenton, N.C., Cromwell, G., Sweetkind, D.S., Langenheim, V.E., Ely, C.P., Kohel, C.A., Martin, A.J., Morita, A.Y., Peterson, M.F., and Roberts, M.A., 2022, Hydrogeologic data of the Russian River watershed, Sonoma and Mendocino Counties, California (ver. 1.1, July 2023): U.S. Geological Survey data release, https://doi.org/10.5066/P9GPH5FW.","linkHelpText":"Hydrogeologic data of the Russian River watershed, Sonoma and Mendocino Counties, California (ver. 1.1, July 2023)"},{"id":465348,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5083/images"},{"id":465346,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5083/sir20245083.xml","linkFileType":{"id":8,"text":"xml"}},{"id":465347,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245083/full"},{"id":494221,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118084.htm"},{"id":465345,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5083/sir20245083.pdf","text":"Report","size":"8 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":465344,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5083/covrthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Russian River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.25,\n 39.25\n ],\n [\n -123.25,\n 38.333\n ],\n [\n -122.667,\n 38.333\n ],\n [\n -122.667,\n 39.25\n ],\n [\n -123.25,\n 39.25\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
Image velocimetry has become an effective method of mapping flow conditions in rivers, but this analysis is typically performed in a post-processing mode after data collection is complete. In this study, we evaluated the potential to infer flow velocities in approximately real time as thermal images are being acquired from an uncrewed aircraft system (UAS). The sensitivity of thermal image velocimetry to environmental conditions was quantified by conducting 20 flights over four days and assessing the accuracy of image-derived velocity estimates via comparison to direct field measurements made with an acoustic Doppler current profiler (ADCP). This analysis indicated that velocity mapping was most reliable when the air was cooler than the water. We also introduced a workflow for River Velocity Measurement in Approximately Real Time (RiVMART) that involved transferring brief image sequences from the UAS to a ground station as distinct data packets. The resulting velocity fields were as accurate as those generated via post-processing. A new particle image velocimetry (PIV) algorithm based on staggered image sequences increased the number of image pairs available for a given image sequence duration and slightly improved accuracy relative to a standard PIV implementation. Direct, automated geo-referencing of image-derived velocity vectors based on information on the position and orientation of the UAS acquired during flight led to poor alignment with vectors that were geo-referenced manually by selecting ground control points from an orthophoto. This initial proof-of-concept investigation suggests that our workflow could enable highly efficient characterization of flow fields in rivers and might help support applications that require rapid response to changing conditions.
","language":"English","publisher":"MDPI","doi":"10.3390/rs16244746","usgsCitation":"Legleiter, C.J., Kinzel, P.J., Dille, M., Vespignani, M., Wong, U., Anderson, I.E., Hyde, E., Gazoorian, C.L., and Cramer, J.M., 2024, Mapping river flow from thermal images in approximately real time: Proof of concept on the Sacramento River, California, USA: Remote Sensing, v. 16, no. 24, 4746, 32 p., https://doi.org/10.3390/rs16244746.","productDescription":"4746, 32 p.","ipdsId":"IP-170700","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":466704,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs16244746","text":"Publisher Index Page"},{"id":465404,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.98941342940537,\n 39.5356924740195\n ],\n [\n -122.01174409601143,\n 39.5356924740195\n ],\n [\n -122.01174409601143,\n 39.51821864106586\n ],\n [\n -121.98941342940537,\n 39.51821864106586\n ],\n [\n -121.98941342940537,\n 39.5356924740195\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"24","noUsgsAuthors":false,"publicationDate":"2024-12-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":921647,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kinzel, Paul J. 0000-0002-6076-9730 pjkinzel@usgs.gov","orcid":"https://orcid.org/0000-0002-6076-9730","contributorId":743,"corporation":false,"usgs":true,"family":"Kinzel","given":"Paul","email":"pjkinzel@usgs.gov","middleInitial":"J.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":921648,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dille, Michael","contributorId":331596,"corporation":false,"usgs":false,"family":"Dille","given":"Michael","email":"","affiliations":[{"id":79249,"text":"NASA Ames Research Center Intelligent Robotics Group","active":true,"usgs":false}],"preferred":false,"id":921649,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vespignani, Massimo 0000-0003-1408-7517","orcid":"https://orcid.org/0000-0003-1408-7517","contributorId":345569,"corporation":false,"usgs":false,"family":"Vespignani","given":"Massimo","email":"","affiliations":[{"id":79249,"text":"NASA Ames Research Center Intelligent Robotics Group","active":true,"usgs":false}],"preferred":false,"id":921650,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wong, Uland","contributorId":241700,"corporation":false,"usgs":false,"family":"Wong","given":"Uland","affiliations":[{"id":27071,"text":"NASA ARC","active":true,"usgs":false}],"preferred":false,"id":921651,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Anderson, Isaac E 0000-0003-3129-2440","orcid":"https://orcid.org/0000-0003-3129-2440","contributorId":347417,"corporation":false,"usgs":true,"family":"Anderson","given":"Isaac","email":"","middleInitial":"E","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":921652,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hyde, Elizabeth 0000-0001-5113-3581","orcid":"https://orcid.org/0000-0001-5113-3581","contributorId":347419,"corporation":false,"usgs":false,"family":"Hyde","given":"Elizabeth","email":"","affiliations":[{"id":66114,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":false}],"preferred":false,"id":921653,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gazoorian, Christopher L. 0000-0002-5408-6212 cgazoori@usgs.gov","orcid":"https://orcid.org/0000-0002-5408-6212","contributorId":2929,"corporation":false,"usgs":true,"family":"Gazoorian","given":"Christopher","email":"cgazoori@usgs.gov","middleInitial":"L.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":921654,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Cramer, Jennifer Marie 0000-0002-5899-8809","orcid":"https://orcid.org/0000-0002-5899-8809","contributorId":303769,"corporation":false,"usgs":true,"family":"Cramer","given":"Jennifer","email":"","middleInitial":"Marie","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":921655,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70261662,"text":"ofr20241072 - 2024 - Topographic and bathymetric survey in support of the effectiveness assessment of the living shoreline restoration in Gandys Beach, New Jersey","interactions":[],"lastModifiedDate":"2025-08-15T16:24:45.607243","indexId":"ofr20241072","displayToPublicDate":"2024-12-19T09:47:32","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-1072","displayTitle":"Topographic and Bathymetric Survey in Support of the Effectiveness Assessment of the Living Shoreline Restoration in Gandys Beach, New Jersey","title":"Topographic and bathymetric survey in support of the effectiveness assessment of the living shoreline restoration in Gandys Beach, New Jersey","docAbstract":"High resolution topobathymetric field surveys were conducted by the U.S. Geological Survey in collaboration with Northeastern University and in cooperation with the U.S. Fish and Wildlife Service and The Nature Conservancy in a selected shoreline along Gandys Beach, New Jersey, from January to April 2018. These data are a critical model input for hydrodynamic and wave models and can affect the accuracy of model outputs such as wave height, water surface elevation, current velocity, and sediment transport. Gandys Beach is a living shoreline where constructed oyster reefs (CORs) were built to protect the shoreline and enhance habitat for oyster and other species. Because of the complex topography and bathymetry of the study area, higher spatial resolution topobathymetric data are required to resolve the vertical variations near the CORs. During the field survey, the global navigation satellite system positioning method was used to establish the elevation of a benchmark referenced to the North American Vertical Datum of 1988. The topobathymetric data were collected using a total station. Horizontal accuracy of plus or minus 0.05 foot (ft) and vertical accuracy of plus or minus 0.10 ft were calculated using root mean square error between duplicate surveys. Two existing datasets were integrated with the survey data to create an updated topobathymetric dataset for model input and analysis: (1) the U.S. Geological Survey Coastal National Elevation Database 1-meter resolution data developed after Hurricane Sandy and (2) The Nature Conservancy 2017 elevation monitoring data at 10-meter resolution. A root mean square error analysis comparing survey data with the new topobathymetric dataset versus the survey data compared to the original Coastal National Elevation Data dataset showed errors of 0.31 and 2.61 ft, respectively. This improved dataset can be used for wave and hydrodynamic modeling in support of the effectiveness assessment of the CORs and living shoreline restoration along Gandys Beach.
Director, Wetland and Aquatic Research Center
U.S. Geological Survey
700 Cajundome Blvd.
Lafayette, LA 70506–3152
Contact Us- USGS Publications Warehouse
","tableOfContents":"Per- and polyfluoroalkyl substances (PFAS) are contaminants that can lead to adverse health effects in aquatic organisms, including reproductive toxicity and developmental abnormalities. To assess the ecological health risk of PFAS in Pennsylvania stream surface water, we conducted a comprehensive analysis that included both measured and predicted estimates. The potential combined exposure effects of 14 individual PFAS to aquatic biota were estimated using the sum of exposure-activity ratios (ΣEARs) in 280 streams. Additionally, machine learning techniques were utilized to predict potential PFAS exposure effects in unmonitored stream reaches, considering factors such as land use, climate, and geology. Leveraging a tailored convolutional neural network (CNN), a validation accuracy of 78% was achieved, directly outperforming traditional methods that were also used, such as logistic regression and gradient boosting (accuracies of ~65%). Feature importance analysis highlighted key variables that contributed to the CNN’s predictive power. The most influential features highlighted the complex interplay of anthropogenic and environmental factors contributing to PFAS contamination in surface waters. Industrial and urban land cover, rainfall intensity, underlying geology, agricultural factors, and their interactions emerged as key determinants. These findings may help to inform biotic sampling strategies, water quality monitoring efforts, and policy decisions aimed to mitigate the ecological impacts of PFAS in surface waters.
","language":"English","publisher":"MDPI","doi":"10.3390/toxics12120921","usgsCitation":"Breitmeyer, S.E., Williams, A., Conlon, M.D., Wertz, T.A., Heflin, B., Shull, D., and Duris, J.W., 2024, Predicted potential for aquatic exposure effects of per- and polyfluorinated alkyl substances (PFAS) in Pennsylvania’s statewide network of streams: Toxics, v. 12, no. 12, 921, 27 p., https://doi.org/10.3390/toxics12120921.","productDescription":"921, 27 p.","ipdsId":"IP-170831","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":466706,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/toxics12120921","text":"Publisher Index Page"},{"id":465482,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Brian","contributorId":347548,"corporation":false,"usgs":false,"family":"Heflin","given":"Brian","affiliations":[{"id":34928,"text":"Independent Researcher","active":true,"usgs":false}],"preferred":false,"id":921975,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shull, Dustin 0000-0002-0188-7964","orcid":"https://orcid.org/0000-0002-0188-7964","contributorId":347549,"corporation":false,"usgs":false,"family":"Shull","given":"Dustin","affiliations":[{"id":17703,"text":"Pennsylvania Department of Environmental Protection","active":true,"usgs":false}],"preferred":false,"id":921976,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Duris, Joseph W. 0000-0002-8669-8109 jwduris@usgs.gov","orcid":"https://orcid.org/0000-0002-8669-8109","contributorId":1981,"corporation":false,"usgs":true,"family":"Duris","given":"Joseph","email":"jwduris@usgs.gov","middleInitial":"W.","affiliations":[{"id":382,"text":"Michigan Water Science 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Methodology for the Public Drinking Water Source Water Assessment Program in Tennessee","title":"Status of public-supply water sources in 2022 and the development of a geographic information system methodology for the Public Drinking Water Source Water Assessment Program in Tennessee","docAbstract":"In 2021, the Tennessee Department of Environment and Conservation (TDEC) and the U.S. Geological Survey worked in cooperation to develop a geographic information system (GIS)-based methodology that systematically assesses the vulnerability of public-supply drinking water to potential contaminants consistent with the standards set forth in the Tennessee Source Water Assessment Program (SWAP). As of June 2022, public-supply water was provided by 643 active public water systems across Tennessee that withdrew water for public use from 1,378 individual water sources. With the newly developed methodology, referred to as “TN-SWAPyT,” TDEC can consistently delineate source water assessment zones and evaluate source susceptibility on the basis of information such as the proximity of contaminant sources, land-use activities, geologic information, and additional environmental spatial data. A major benefit of the TN-SWAPyT methodology is to provide TDEC with consistent reports that can be used as a starting point for assessing public supplies. Communities and public water systems can then build upon these reports by using local knowledge and site-specific information.
Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Contact Us- USGS Publications Warehouse
","tableOfContents":"Lithium-rich brine deposits occur throughout the United States, including in the Smackover Formation. The concentration of lithium in Smackover Formation brines was predicted across southern Arkansas by using a machine-learning model that incorporated lithium concentration data and geologic information. Between 5.1 and 19.0 million metric tons of lithium are calculated to be present in the brines of the Smackover Formation in southern Arkansas. The range in possible total lithium reflects the uncertainty in machine-learning predictions of lithium concentrations and the range of Smackover Formation porosity. This estimate quantifies the in-place lithium resource and does not consider the technological and economic feasibility of extracting the lithium from the brines.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20243052","issn":"2327-6916, 2327-6932","collaboration":"Prepared in cooperation with the Arkansas Department of Energy and Environment, Office of the State Geologist","programNote":"Energy Resources Program","usgsCitation":"Knierim, K.J., Masterson, A.L., Freeman, P.A., McDevitt, B., Herzberg, A.H., Li, P., Mills, C., Doolan, C., Jubb, A.M., Ausbrooks, S.M., and Chenault, J., 2024, Lithium resource in the Smackover Formation brines of southern Arkansas: U.S. Geological Survey Fact Sheet 2024–3052, 4 p., https://doi.org/10.3133/fs20243052.","productDescription":"Report: 4 p.; Data Release","numberOfPages":"4","onlineOnly":"N","ipdsId":"IP-172337","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":494229,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118086.htm","linkFileType":{"id":5,"text":"html"}},{"id":465210,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2024/3052/fs20243052.pdf","size":"1.79 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2024-3052"},{"id":465209,"rank":2,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2024/3052/images"},{"id":465208,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2024/3052/coverthb.jpg"},{"id":465231,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20243052/full","linkFileType":{"id":5,"text":"html"},"description":"FS 2024-3052 HTML"},{"id":465230,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2024/3052/fs20243052.XML","linkFileType":{"id":8,"text":"xml"},"description":"FS 2024-3052 XML"},{"id":465228,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/70259385","text":"Evaluation of the lithium resource in the Smackover Formation brines of southern Arkansas using machine learning"},{"id":465219,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QPRYZN","text":"USGS Data Release","linkHelpText":"- Lithium observations, machine-learning predictions, and mass estimates from the Smackover Formation brines in southern Arkansas"}],"country":"United States","state":"Arkansas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -94.00896297632752,\n 33.862680632060474\n ],\n [\n -94.00896297632752,\n 33.04929982263086\n ],\n [\n -92.24865389098971,\n 33.04929982263086\n ],\n [\n -92.24865389098971,\n 33.862680632060474\n ],\n [\n -94.00896297632752,\n 33.862680632060474\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Contact Us- USGS Publications Warehouse
","tableOfContents":"Water withdrawal from private groundwater wells is often unaccounted for in water planning studies, and water from private wells can be a source of exposure to environmental contaminants. The sizes of populations that depend on private wells for domestic water use and the amounts of water that are withdrawn from these wells are generally poorly represented in data collection efforts because of the challenges of locating, metering, or gathering withdrawal information from individual property owners. To address this problem, the U.S. Geological Survey, in cooperation with the Rhode Island Water Resources Board, estimated the volume of water withdrawn from domestic self-supply wells and the populations who use them for the State of Rhode Island at a 30-meter pixel spatial resolution and one-month temporal resolution between July 2014 and June 2021.
The number of people reliant on domestic self-supply wells has increased in Rhode Island over the study period; however, the statewide estimate of total water withdrawal has not statistically increased. Withdrawals from private wells are largest in the inland areas of the western part of the State, and the towns of Scituate and Charlestown have the highest estimated withdrawals. Statewide monthly withdrawals ranged from 3.987 million gallons per day in March 2018 to 7.767 million gallons per day in September 2016. The median per capita domestic water use rate was 46.0 gallons per capita per day.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245109","collaboration":"Prepared in cooperation with the Rhode Island Water Resources Board","usgsCitation":"Chamberlin, C.A., Armstrong, I.P., and Stagnitta, T.J., 2024, Estimating domestic self-supplied water use in Rhode Island, 2014–21: U.S. Geological Survey Scientific Investigations Report 2024–5109, 29 p., https://doi.org/10.3133/sir20245109.","productDescription":"Report: vii, 29 p.; Data Release","numberOfPages":"29","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-152675","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":464969,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WU48KY","text":"USGS data release","linkHelpText":"Monthly and annual population and self-supplied domestic water withdrawal maps of Rhode Island, 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U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
The “National Field Manual for the Collection of Water-Quality Data” (NFM) provides guidelines and procedures for U.S. Geological Survey (USGS) personnel who collect data used to assess the quality of the Nation’s surface-water and groundwater resources. This chapter, NFM A6.1, provides guidance and protocols for the measurement of temperature of air, of a surface-water body or in groundwater, which include the scientific basis of the measurement, selection and maintenance of equipment, calibration verification, troubleshooting, and procedures for measurement and reporting. It updates and supersedes USGS Techniques of Water-Resources Investigations, book 9, chapter A6.1, version 2.0, by Franceska D. Wilde.
Temperature of air and water is routinely measured when water samples are collected, is often measured continually at USGS streamgages, and is a parameter regularly measured during laboratory and field experiments. The field method for measuring temperature described in this chapter is applicable to air and most natural waters.
Before 2017, the NFM chapters were released in the USGS Techniques of Water-Resources Investigations series. Effective in 2018, new and revised NFM chapters are being released in the USGS Techniques and Methods series; this series change does not affect the content and format of the NFM. More information is in the general introduction to the NFM (USGS Techniques and Methods, book 9, chapter A0) at https://doi.org/10.3133/tm9A0. The authoritative current versions of NFM chapters are available in the USGS Publications Warehouse at https://pubs.usgs.gov/. Comments, questions, and suggestions related to the NFM can be addressed to nfm@usgs.gov.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm9A6.1","usgsCitation":"U.S. Geological Survey, 2024, Temperature: U.S. Geological Survey Techniques and Methods, book 9, chap. A6.1, 14 p., https://doi.org/10.3133/tm9A6.1. [Supersedes USGS Techniques of Water-Resources Investigations, book 9, chap. A6.1, version 2.0.]","productDescription":"v, 14 p.","numberOfPages":"14","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-157281","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":465033,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/tm/09/a6.1/images"},{"id":465035,"rank":7,"type":{"id":18,"text":"Project Site"},"url":"https://www.usgs.gov/mission-areas/water-resources/science/national-field-manual-collection-water-quality-data-nfm","text":"National Field Manual for the Collection of Water-Quality Data (NFM)"},{"id":465029,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/09/a6.1/coverthb.jpg"},{"id":465030,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/09/a6.1/tm9a6.1.pdf","text":"Report","size":"940 KB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 9-A6.1 PDF"},{"id":465031,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/tm9A6.1/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"TM 9-A6.1 HTML"},{"id":465032,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/tm/09/a6.1/tm9a6.1.XML","linkFileType":{"id":8,"text":"xml"},"description":"TM 9-A6.1 XML"},{"id":465034,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/tm9A0","text":"Techniques and Methods 9-A0","linkHelpText":"- General introduction for the “National Field Manual for the Collection of Water-Quality Data”"}],"contact":"Water Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
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","tableOfContents":"The Colorado River is an important water source in the southwestern United States and northern Mexico. High concentrations of dissolved solids in the river, sourced mainly from the Upper Colorado River Basin (UCOL), cause hundreds of millions of dollars in damages annually to crops and infrastructure. Determinations of total dissolved solids (TDS) in river and tributary samples often rely on summed concentrations of constituents in solution reported as the sum of constituents (SOC), which includes the bicarbonate concentration converted to the equivalent carbonate mass that would be present as a residue after drying. Alternatively, salinity, similar to SOC but including the entire concentration of bicarbonate in solution, may be used as a measure of dissolved solids. Use of SOC results may under-represent actual dissolved-solids concentrations and loads in streams where bicarbonate is a substantial component of the dissolved solids in solution. The objective of this manuscript is to evaluate the differences between the SOC and salinity determinations of dissolved solids in UCOL streams and rivers. Water-quality data from the U.S. Geological Survey were used to compute salinity concentrations at UCOL stream sites for comparison with SOC determinations. Results from 8,001 samples at 418 UCOL sites indicate a median increase in dissolved solids of 20% (13% and 30%, 25th and 75th percentiles, respectively) using the salinity method compared with SOC results. Differences in dissolved solids attributable to the computational approach for handling bicarbonate at UCOL sites were significantly greater than laboratory variability based on results from 890 replicate analyses. Salinity may be a more useful indicator of water quality than SOC in systems with substantial proportions of bicarbonate in the composition of dissolved solids, including the Colorado River and UCOL sites.
","language":"English","publisher":"PLoS","doi":"10.1371/journal.pwat.0000310","usgsCitation":"Tillman, F.D., Miller, M., Wise, D., McCleskey, R., and Day, N.K., 2024, Salinity or sum of constituents— Methods comparison for computing dissolved solids concentrations in streams of the Upper Colorado River Basin: PLOS Water, v. 3, no. 12, e0000310, 13 p., https://doi.org/10.1371/journal.pwat.0000310.","productDescription":"e0000310, 13 p.","ipdsId":"IP-151803","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":466711,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pwat.0000310","text":"Publisher Index Page"},{"id":465280,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, New Mexico, Utah, Wyoming","otherGeospatial":"Upper Colorado River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.07478126793694,\n 37.362761189486605\n ],\n [\n -110.7918874182937,\n 35.77503910288557\n ],\n [\n -106.92482149871722,\n 35.26377010349002\n ],\n [\n -105.78177189040801,\n 37.551686330409765\n ],\n [\n -106.7657709766068,\n 38.233772867977024\n ],\n [\n -105.31950823092234,\n 38.84018310941224\n ],\n [\n -104.67840732485422,\n 40.36065226066836\n ],\n [\n -106.71988351439666,\n 42.913593391958784\n ],\n [\n -109.98783943768271,\n 43.360892248620246\n ],\n [\n -110.9078832909733,\n 40.740953521572834\n ],\n [\n -112.07478126793694,\n 37.362761189486605\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"3","issue":"12","noUsgsAuthors":false,"publicationDate":"2024-12-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Tillman, Fred D. 0000-0002-2922-402X ftillman@usgs.gov","orcid":"https://orcid.org/0000-0002-2922-402X","contributorId":147809,"corporation":false,"usgs":true,"family":"Tillman","given":"Fred","email":"ftillman@usgs.gov","middleInitial":"D.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":921472,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, Matthew P. 0000-0002-2537-1823","orcid":"https://orcid.org/0000-0002-2537-1823","contributorId":220622,"corporation":false,"usgs":true,"family":"Miller","given":"Matthew P.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":921473,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wise, Daniel 0000-0002-1215-9612","orcid":"https://orcid.org/0000-0002-1215-9612","contributorId":217259,"corporation":false,"usgs":true,"family":"Wise","given":"Daniel","email":"","affiliations":[],"preferred":true,"id":921474,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCleskey, R. Blaine 0000-0002-2521-8052","orcid":"https://orcid.org/0000-0002-2521-8052","contributorId":205663,"corporation":false,"usgs":true,"family":"McCleskey","given":"R. Blaine","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":921475,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Day, Natalie K. 0000-0002-8768-5705","orcid":"https://orcid.org/0000-0002-8768-5705","contributorId":207302,"corporation":false,"usgs":true,"family":"Day","given":"Natalie","middleInitial":"K.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":921476,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70261593,"text":"sir20245113 - 2024 - Perchlorate, metals, organic compounds, and lead isotopes in groundwater, surface water, shallow groundwater, and soil within and near the Middleton Municipal Airport–Morey Field (C29), Middleton, Wisconsin, 2022","interactions":[],"lastModifiedDate":"2025-08-15T16:34:23.184884","indexId":"sir20245113","displayToPublicDate":"2024-12-17T09:08:14","publicationYear":"2024","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":"2024-5113","displayTitle":"Perchlorate, Metals, Organic Compounds, and Lead Isotopes in Groundwater, Surface Water, Shallow Groundwater, and Soil Within and Near the Middleton Municipal Airport–Morey Field (C29), Middleton, Wisconsin, 2022","title":"Perchlorate, metals, organic compounds, and lead isotopes in groundwater, surface water, shallow groundwater, and soil within and near the Middleton Municipal Airport–Morey Field (C29), Middleton, Wisconsin, 2022","docAbstract":"The Middleton Municipal Airport–Morey Field (C29) is in the City of Middleton and adjacent to the towns of Middleton and Springfield, Wisconsin. Nearby homes in the towns rely on private drinking water wells, and residents are concerned about the potential contamination of groundwater and surface water by airport activities, including flights by small aircraft that use leaded aviation fuel and a fireworks display in July 2021.
The U.S. Geological Survey, in cooperation with the Town of Middleton, completed a study in 2022 to characterize the occurrence and sources of perchlorate, metals (including lead), and organic compounds in samples of groundwater, surface water, shallow groundwater, and soils within and near the airport. Lead isotopes were also measured to determine sources of lead by comparing samples to environmental references.
Magnitudes of concentrations from samples of water and soil collected in 2022, and their spatial patterns across site locations, indicate the fireworks display in July 2021 was a likely source of perchlorate and metals in the airport study area. The highest perchlorate concentration was measured in surface water at the southeastern corner of the airport near the fireworks launch site; the highest concentrations of fireworks-associated metals were measured in shallow groundwater near the same location. Fireworks were not the only possible source of perchlorate and metals in the airport study area because both were also detected upgradient and away from the fireworks launch site.
Ratios of lead isotopes indicate that lead measured in water and soil within the airport study area was primarily sourced from background atmospheric lead deposition or Wisconsin galena lead ore. However, two groundwater samples (one upgradient and one downgradient from the airport; both with concentrations less than 1 microgram per liter) had isotopic signatures matching leaded aviation fuel sold at the airport.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245113","collaboration":"Prepared in cooperation with the Town of Middleton, Wisconsin","usgsCitation":"Schachter, L.A., and Stuntebeck, T.D., 2024, Perchlorate, metals, organic compounds, and lead isotopes in groundwater, surface water, shallow groundwater, and soil within and near the Middleton Municipal Airport–Morey Field (C29), Middleton, Wisconsin, 2022: U.S. Geological Survey Scientific Investigations Report 2024–5113, 55 p., https://doi.org/10.3133/sir20245113.","productDescription":"Report: viii, 55 p.; Data Release; Dataset","numberOfPages":"68","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-166313","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":494232,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118085.htm","linkFileType":{"id":5,"text":"html"}},{"id":465140,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245113/full"},{"id":465139,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":465137,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5113/images/"},{"id":465135,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5113/sir20245113.pdf","text":"Report","size":"9.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5113"},{"id":465134,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5113/coverthb.jpg"},{"id":465138,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IEJULZ","text":"USGS data release","linkHelpText":"Lead concentrations and isotope ratios for selected water and soil samples near Middleton Municipal Airport–Morey Field (C29), Middleton, WI, 2022"},{"id":465136,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5113/sir20245113.XML"}],"country":"United States","state":"Wisconsin","city":"Middleton","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.55428476671052,\n 43.12980057216322\n ],\n [\n -89.55428476671052,\n 43.11162168312745\n ],\n [\n -89.51852520964913,\n 43.11162168312745\n ],\n [\n -89.51852520964913,\n 43.12980057216322\n ],\n [\n -89.55428476671052,\n 43.12980057216322\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
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Long-term drought caused Lake Powell, a reservoir on the Colorado River (USA), to decline to its lowest elevation in >50 years during 2022–2023, allowing warm water to pass through intakes of Glen Canyon Dam and facilitating invasion by non-native Smallmouth Bass (Micropterus dolomieu). Establishment of bass downstream of the dam could threaten persistence of several native fishes, including two federally listed species. Subsequent detection of larval Smallmouth Bass in a spring-fed slough (river mile -12 slough) connected to the river in Glen Canyon National Recreation Area (NRA) increased urgency to stem further invasion. The National Park Service is evaluating proposed actions to limit effects from non-native predators on native species in the Colorado River, including potentially channelizing the slough. This locally rare, spring-fed waterbody provides habitat for other species, including Western Tiger Salamanders (Ambystoma mavortium subsp.) of uncertain origin. We found salamanders from the slough had two distinct mitochondrial DNA haplotypes identical to sequences from nearby Arizona Tiger Salamander (A. m. nebulosum) populations, confirming they are the native genotype. We detected Red-spotted Toads (Anaxyrus punctatus) and Woodhouse’s Toads (A. woodhousii) from three other sites in Glen Canyon NRA and 34 sites in adjacent, downstream Grand Canyon National Park (spanning ∼464 km of river) with environmental DNA and traditional surveys. However, we did not detect salamanders elsewhere, matching prior information that salamanders are rare in the Colorado River corridor below Glen Canyon Dam. Based on this information, we discuss management options for the local population of Arizona Tiger Salamanders.
","language":"English","publisher":"BioRxiv","doi":"10.1101/2024.12.15.628570","usgsCitation":"Hossack, B., Stemp, K.M., Goldberg, C.S., Duke, A.C., Preston, T., Arnold, J.A., and Ray, A.R., 2024, Rare habitats, rare species, and invasive predators highlight management complexities in the Colorado River system: BioRxiv, https://doi.org/10.1101/2024.12.15.628570.","productDescription":"21 p.","ipdsId":"IP-173018","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":496928,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1101/2024.12.15.628570","text":"External Repository"},{"id":496819,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hossack, Blake 0000-0001-7456-9564 blake_hossack@usgs.gov","orcid":"https://orcid.org/0000-0001-7456-9564","contributorId":207343,"corporation":false,"usgs":true,"family":"Hossack","given":"Blake","email":"blake_hossack@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":950844,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stemp, Kenzi Marie 0000-0001-7566-8513","orcid":"https://orcid.org/0000-0001-7566-8513","contributorId":362931,"corporation":false,"usgs":true,"family":"Stemp","given":"Kenzi","middleInitial":"Marie","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":950845,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Goldberg, Caren S","contributorId":362932,"corporation":false,"usgs":false,"family":"Goldberg","given":"Caren","middleInitial":"S","affiliations":[{"id":37380,"text":"Washington State University","active":true,"usgs":false}],"preferred":false,"id":950846,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Duke, Alexandra C.","contributorId":362933,"corporation":false,"usgs":false,"family":"Duke","given":"Alexandra","middleInitial":"C.","affiliations":[{"id":37380,"text":"Washington State University","active":true,"usgs":false}],"preferred":false,"id":950847,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Preston, Taryn","contributorId":292557,"corporation":false,"usgs":false,"family":"Preston","given":"Taryn","email":"","affiliations":[{"id":62075,"text":"National Park Service, Grand Canyon National Park","active":true,"usgs":false}],"preferred":false,"id":950848,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Arnold, J. 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The area being mapped using geophysical and geological data includes the SBNMS and the surrounding region, which totals approximately 3,700 square kilometers (km2) and is subdivided into 18 quadrangles. The seabed is a glaciated terrain that is topographically and texturally diverse. Quadrangle 2, the subject of this scientific investigations map, has a mapped area of 209 km2 and has water depths that range from about 19 meters (m) on the Stellwagen Bank crest to about 68 m in the Stellwagen Basin. Seven map types, each at a scale of 1:25,000, depict seabed topography, ruggedness, backscatter intensity, distribution of geologic substrates, sediment mobility, distribution of fine- and coarse-grained sand, and substrate mud content. These maps show the distribution of geologic substrates across the southwestern part of Stellwagen Bank, in Stellwagen Basin to the west and southwest of the bank, and in Little Stellwagen Basin and the western part of Race Point Channel to the south of the bank. Interpretations of multibeam sonar bathymetric and seabed backscatter imagery, photographs, video imagery, and grain-size analyses were used to create the geology-based maps. Data from 733 stations were analyzed, including 656 sediment samples. The geologic substrate maps of quadrangle 2 show the distribution of 19 geologic substrates that represent a wide range of textures, such as rippled and immobile sand, immobile muddy sand and sandy mud, sand that partially veneers gravel, and a boulder ridge. Mapped substrates are characterized by sediment grain-size composition, surface morphology, substrate layering, the mobility or immobility of substrate surfaces, and water depth range. This scientific investigations map portrays the major geological elements (substrates, topographic features, and processes) of environments in quadrangle 2. It is intended to provide a foundation for research into present and past sediment transport processes in a complex terrain, provide insights into the ecological requirements of invertebrate and vertebrate species that utilize the various substrates, and support seabed management in the region.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3530","collaboration":"Prepared in cooperation with the National Oceanic and Atmospheric Administration","programNote":"Coastal/Marine Hazards and Resources Program","usgsCitation":"Valentine, P.C., and Cross, V.A., 2024, Seabed maps showing topography, ruggedness, backscatter intensity, sediment mobility, and the distribution of geologic substrates in quadrangle 2 of the Stellwagen Bank National Marine Sanctuary region offshore of Boston, Massachusetts: U.S. Geological Survey Scientific Investigations Map 3530, 8 sheets, scale 1:25,000, 27-p. pamphlet, https://doi.org/10.3133/sim3530.","productDescription":"Pamphlet: v, 27 p.; 8 Sheets: 26.98 x 35.69 inches or smaller; Data Release","numberOfPages":"27","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-153982","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science 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Environmental DNA (eDNA) is a complex mixture of DNA, varying in particle sizes and distributed heterogeneously in aquatic systems. Optimizing eDNA sampling is crucial for maximizing species detection, particularly in high-risk scenarios like invasive species management. In this study, we compare two eDNA sampling methods - namely tow net and grab sample, where the tow nets process large volumes of water (3500–7000 L) through a 64 μm pore size and the grab samples process 1 L sample at a single point through 0.45–1.2 μm pore size membranes. We compared these methods to ascertain what most influences (1) the detection of invasive species (Dreissena mussels and Burmese pythons) using qPCR or ddPCR and (2) total diversity monitoring of metazoan, protist, and fungi community using a COI marker and plant communities using the ITS marker. Sampling was conducted across a wide geography and diverse aquatic environments in Minnesota and Florida, USA, and Switzerland. The tow net samples had significantly higher eDNA yield compared to grab samples; however, they exhibited equal or lower alpha diversity of OTUs (Operational Taxonomic Units). The two sampling methods measured different beta diversity of communities detected with the COI marker across all three regions, highlighting the impact of the sampling method on the diversity of eDNA captured. In comparison, the beta diversity of plant eDNA was less impacted by the sampling method. We found no clear difference in detection for the invasive species targets based on the eDNA sampling method. These results underscore the need for pilot studies before conducting biodiversity inventory and monitoring, and a need for a greater understanding of not just how much, but also what, eDNA is captured depending on method choice, considering both spatial and particle size heterogeneity.
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MT 59860, USA","active":true,"usgs":false}],"preferred":false,"id":924865,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Deiner, Kristy","contributorId":176866,"corporation":false,"usgs":false,"family":"Deiner","given":"Kristy","email":"","affiliations":[],"preferred":false,"id":924866,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70261452,"text":"sir20245122 - 2024 - Flood-inundation maps for the Cuyahoga River in and near Independence, Ohio, 2024","interactions":[],"lastModifiedDate":"2024-12-16T14:39:31.386387","indexId":"sir20245122","displayToPublicDate":"2024-12-16T08:30:00","publicationYear":"2024","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":"2024-5122","displayTitle":"Flood-Inundation Maps for the Cuyahoga River in and Near Independence, Ohio, 2024","title":"Flood-inundation maps for the Cuyahoga River in and near Independence, Ohio, 2024","docAbstract":"Digital flood-inundation maps for a 9.9-mile reach of the Cuyahoga River in and near Independence, Ohio, were created by the U.S. Geological Survey (USGS) in cooperation with the Northeast Ohio Regional Sewer District Board of Trustees. Water-surface profiles were computed for the stream reach by using a one-dimensional steady-state step-backwater model. The model was calibrated to the current (2024) stage-streamflow relation (rating curve 43.0) for the USGS streamgage 04208000, Cuyahoga River at Independence, Ohio. The resulting hydraulic model was then used to compute 13 water-surface profiles for water levels (flood stages) ranging from 14.00 to 26.00 feet. The flood stages range from “action stage” to above “major flood stage” as reported by the National Weather Service. The simulated water-surface profiles were then used in combination with a digital elevation model derived from light detection and ranging data to map the inundated areas associated with each flood profile.
The flood-inundation maps and the supporting hydraulic model produced by this study can be used by emergency managers and local officials to assess flood mitigation strategies and to define flood hazard areas to protect life and property, to coordinate flood response activities such as evacuations and road closures, and to aid postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245122","collaboration":"Prepared in cooperation with the Northeast Ohio Regional Sewer District Board of Trustees","usgsCitation":"Ostheimer, C.J., and Whitehead, M.T., 2024, Flood-inundation maps for the Cuyahoga River in and near Independence, Ohio, 2024: U.S. Geological Survey Scientific Investigations Report 2024–5122, 16 p., https://doi.org/10.3133/sir20245122.","productDescription":"Report: vi, 16 p.; Data Release","numberOfPages":"16","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-158401","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":464970,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5122/coverthb.jpg"},{"id":464971,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5122/sir20245122.pdf","text":"Report","size":"1.57 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5122 PDF"},{"id":464972,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245122/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5122 HTML"},{"id":464976,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20245115","text":"Scientific Investigations Report 2024–5115","linkHelpText":"Flood-Inundation Maps for the Cuyahoga River at Jaite, Ohio, 2024"},{"id":464973,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5122/sir20245122.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2024-5122 XML"},{"id":464974,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5122/images/"},{"id":464975,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZFZK0L","text":"USGS data release","linkHelpText":"Geospatial data sets and hydraulic model for the Cuyahoga River in and near the city of Independence, Ohio"}],"country":"United States","state":"Ohio","city":"Independence","otherGeospatial":"Cuyahoga River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.7238278506537,\n 41.48\n ],\n [\n -81.7238278506537,\n 41.373775959357204\n ],\n [\n -81.61067566475016,\n 41.373775959357204\n ],\n [\n -81.61067566475016,\n 41.48\n ],\n [\n -81.7238278506537,\n 41.48\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
Objective
Growth is one of the primary drivers of fish population dynamics and understanding factors influencing growth is vital to effective management of fish populations. This study investigated potential factors influencing growth of a recently established, non-native population of Walleye Sander vitreus in the Lake Pend Oreille system in northern Idaho.
Methods
We used relative growth index to describe growth of Walleyes relative to populations across North America. Mixed‐effects modeling was used to relate growth to abiotic (i.e., mean summer water temperature, river inflow) and biotic (i.e., kokanee Oncorhynchus nerka abundance and biomass; opossum shrimp Mysis diluviana density) variables. Models were ranked using Akaike's information criterion corrected for small sample size. Individual variability in growth was related to diet represented by stable isotopes (i.e., δ15N, δ13C) using linear regression for age‐1, age‐2, age‐3, and age‐5 individuals. Subsequently, for each age‐class, we evaluated differences in δ15N and δ13C between fast‐growing (i.e., 75th and higher percentiles of growth) and slow‐growing (i.e., 25th and lower percentiles of growth) individuals.
Results
The relative growth index suggested that Walleye grew fast relative to other populations, particularly those at similar latitudes to the Lake Pend Oreille system. Mixed-effects regression modeling indicated that growth of Walleyes was positively associated with temperature as well as abundance and biomass of kokanee; growth was negatively associated with inflow from the Clark Fork River and Mysis diluviana density. The top model explaining growth of Walleyes contained temperature and abundance of kokanee as environmental variables. The second equally plausible (i.e., within 2 AICc) model contained temperature. Growth of Walleyes varied among individuals. Generally, fast-growing Walleyes had higher δ15N than slow-growing Walleyes. Similarly, δ13C was more depleted in the fast-growing individuals for all age classes, except age 1, suggesting that age-1 individuals used higher proportions of littoral prey items compared to other age classes.
Conclusion
This study showed that kokanee abundance and temperature appeared to be important factors influencing growth of Walleyes in the Lake Pend Oreille system. Additionally, variability in growth appeared to be related to variability in diet, particularly for age-1 Walleyes. Impact statement Growth of Walleyes has been extensively studied, yet few studies have evaluated growth of Walleyes in novel systems or assessed individual variability in growth. Our research adds to the understanding of individual variability in growth and factors influencing population dynamics of non-native Walleyes.
","language":"English","publisher":"Oxford Academic","doi":"10.1002/nafm.11056","collaboration":"Idaho Department of Fish and Game","usgsCitation":"Frawley, S., Corsi, M., Dux, A.M., Hardy, R.S., and Quist, M.C., 2024, Abiotic and biotic factors related to growth of non-native Walleyes in Lake Pend Oreille, Idaho: North American Journal of Fisheries Management, v. 44, no. 6, p. 1325-1341, https://doi.org/10.1002/nafm.11056.","productDescription":"17 p.","startPage":"1325","endPage":"1341","ipdsId":"IP-163539","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":485351,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Lake Pend Oreille","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.79187320079157,\n 48.33908577891279\n ],\n [\n -116.79187320079157,\n 47.933452975307574\n ],\n [\n -116.12337645556701,\n 47.933452975307574\n ],\n [\n -116.12337645556701,\n 48.33908577891279\n ],\n [\n -116.79187320079157,\n 48.33908577891279\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"44","issue":"6","noUsgsAuthors":false,"publicationDate":"2024-12-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Frawley, Susan","contributorId":354288,"corporation":false,"usgs":false,"family":"Frawley","given":"Susan","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":935346,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Corsi, Matthew P.","contributorId":171811,"corporation":false,"usgs":false,"family":"Corsi","given":"Matthew P.","affiliations":[],"preferred":false,"id":935347,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dux, Andrew M.","contributorId":175256,"corporation":false,"usgs":false,"family":"Dux","given":"Andrew","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":935348,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":935349,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Quist, Michael C. 0000-0001-8268-1839","orcid":"https://orcid.org/0000-0001-8268-1839","contributorId":207142,"corporation":false,"usgs":true,"family":"Quist","given":"Michael","middleInitial":"C.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":935350,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}