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
Unprecedented wildfire frequency, fueled by invasive annual grasses, threatens sagebrush ecosystems. To suppress wildfire and conserve sagebrush, land management agencies have installed fuel breaks across the sagebrush biome. However, despite the potential reduction in wildfire, fuel breaks may have ecological costs. Determining an acceptable balance between risks and benefits of fuel breaks is needed to avoid accelerating sagebrush loss, annual grass invasion, and habitat degradation. To evaluate the potential for ecological trade-offs to occur, we characterized the contexts in which known fuel breaks currently exist. We synthesized spatial data on all known fuel breaks and a suite of variables that may contribute to fuel break risks and benefits, including burn probabilities, predicted fuel break effectiveness, linear infrastructure, invasive annual grass cover, soil moisture conditions that confer resistance to invasion and resilience to disturbance, and priority wildlife habitats across the sagebrush biome.
We found that within the sagebrush biome, fuel breaks are generally located in areas with high burn probability and are thus positioned well to intercept potential wildfires. However, fuel breaks are also frequently positioned in areas with lower predicted fuel break effectiveness relative to the sagebrush biome overall. Fuel breaks also are spatially associated with high invasive grass cover, indicating the need to better understand the causal relationship between fuel breaks and annual invasive grasses. We also show that the fuel break network is dense within priority wildlife habitats. Dense fuel breaks within wildlife habitats may trade off wildfire protection for decreased integrity of such habitats.
Our analyses describe the potential for fuel breaks to invoke ecological trade-offs and show that the balance of risks and benefits differs across sagebrush ecosystems. Strategic research and actions are needed to evaluate which factors tip the balance towards maximizing wildfire suppression while minimizing risk to sensitive ecological resources.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s42408-024-00334-3","usgsCitation":"Roche, M.D., Saher, D., Buchholtz, E.K., Crist, M., Shinneman, D.J., Aldridge, C.L., Brussee, B.E., Coates, P.S., Weise, C.L., and Heinrichs, J., 2024, Ecological trade-offs associated with fuel breaks in the sagebrush ecosystem: Fire Ecology, v. 20, 107, 18 p., https://doi.org/10.1186/s42408-024-00334-3.","productDescription":"107, 18 p.","ipdsId":"IP-145103","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":466703,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s42408-024-00334-3","text":"Publisher Index Page"},{"id":465427,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://doi.org/10.1186/s42408-024-00334-3"},{"id":465441,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, North Dakota, Oregon, South Dakota, Utah, Washington, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.40464933105906,\n 47.68825926160255\n ],\n [\n -120.82712524399327,\n 38.98548876525665\n ],\n [\n -116.566007458342,\n 34.861574632752706\n ],\n [\n -113.0521168639216,\n 34.87879900031098\n ],\n [\n -111.11535062559898,\n 36.83110418392951\n ],\n [\n -110.30926485046137,\n 34.69158883186793\n ],\n [\n -107.22899556426468,\n 33.55864330375027\n ],\n [\n -105.7874928285311,\n 33.88069041964181\n ],\n [\n -104.12987144374344,\n 41.124690075951804\n ],\n [\n -102.99792925457677,\n 47.70535579401806\n ],\n [\n -115.73449001953601,\n 45.314501829559646\n ],\n [\n -117.83550292526795,\n 47.80646424271862\n ],\n [\n -121.40464933105906,\n 47.68825926160255\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"20","noUsgsAuthors":false,"publicationDate":"2024-12-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Roche, Morgan Dake 0000-0002-2276-3944","orcid":"https://orcid.org/0000-0002-2276-3944","contributorId":345794,"corporation":false,"usgs":true,"family":"Roche","given":"Morgan","email":"","middleInitial":"Dake","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":921784,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Saher, D. Joanne 0000-0002-2452-2570","orcid":"https://orcid.org/0000-0002-2452-2570","contributorId":288928,"corporation":false,"usgs":false,"family":"Saher","given":"D. Joanne","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":921785,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Buchholtz, Erin K. 0000-0002-1985-9531","orcid":"https://orcid.org/0000-0002-1985-9531","contributorId":300162,"corporation":false,"usgs":true,"family":"Buchholtz","given":"Erin","middleInitial":"K.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":921786,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Crist, Michele R.","contributorId":178453,"corporation":false,"usgs":false,"family":"Crist","given":"Michele R.","affiliations":[],"preferred":false,"id":921787,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shinneman, Douglas J. 0000-0002-4909-5181 dshinneman@usgs.gov","orcid":"https://orcid.org/0000-0002-4909-5181","contributorId":147745,"corporation":false,"usgs":true,"family":"Shinneman","given":"Douglas","email":"dshinneman@usgs.gov","middleInitial":"J.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":921788,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":921789,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Brussee, Brianne E. 0000-0002-2452-7101 bbrussee@usgs.gov","orcid":"https://orcid.org/0000-0002-2452-7101","contributorId":4249,"corporation":false,"usgs":true,"family":"Brussee","given":"Brianne","email":"bbrussee@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":921790,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":921791,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Weise, Cali L.","contributorId":305785,"corporation":false,"usgs":false,"family":"Weise","given":"Cali","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":921792,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Heinrichs, Julie A. 0000-0001-7733-5034","orcid":"https://orcid.org/0000-0001-7733-5034","contributorId":240888,"corporation":false,"usgs":false,"family":"Heinrichs","given":"Julie A.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":921793,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70262810,"text":"70262810 - 2024 - Spatial differences in soil nutrients along a hydrographic gradient on floodplains in Dongting Lake","interactions":[],"lastModifiedDate":"2025-01-23T15:10:09.704636","indexId":"70262810","displayToPublicDate":"2024-12-20T09:02:53","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Spatial differences in soil nutrients along a hydrographic gradient on floodplains in Dongting Lake","docAbstract":"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":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":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":70262849,"text":"70262849 - 2024 - Geophysical characterization of an alkaline‑carbonatite complex using gravity and magnetic methods at Magnet Cove, Arkansas, USA","interactions":[],"lastModifiedDate":"2025-01-24T15:33:05.221746","indexId":"70262849","displayToPublicDate":"2024-12-20T08:23:15","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3525,"text":"Tectonophysics","active":true,"publicationSubtype":{"id":10}},"title":"Geophysical characterization of an alkaline‑carbonatite complex using gravity and magnetic methods at Magnet Cove, Arkansas, USA","docAbstract":"The Magnet Cove alkaline‑carbonatite complex (MCC), located in the Ouachita Mountains of south-central Arkansas in the United States, hosts an extensive variety of rare rock types and critical mineral resources with physical properties (density and magnetization) that contrast significantly with the sedimentary rocks into which they have intruded. Newly acquired ground-based gravity and magnetic data were used to develop two-dimensional and three-dimensional geophysical models of the Cretaceous-aged Magnet Cove intrusive complex. The models reveal that the MCC: (1) widens out at middle crustal depths to as much 22 km across, and may reach a depth of 20 km; (2) has a total volume (exposed and subsurface) that may be over 800 km3; (3) is likely connected at depth to other intrusions in the Arkansas alkaline province; and (4) has a geometry that is aligned with pre-existing structures such as the Reelfoot rift and the Ouachita orogenic belt, some of which were likely structurally controlled by the Precambrian crystalline basement and the continent-ocean transition zone buried beneath the Ouachita orogen. For the first time, the magnetic models of the MCC account for the presence of strong remanent magnetization. This results in a geophysical workflow necessary to accurately interpret magnetic anomalies over the much larger Arkansas alkaline province, its geologic and structural framework, and critical mineral potential.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.tecto.2024.230545","usgsCitation":"Amaral, C.M., Lamb, A., and Dumond, G., 2024, Geophysical characterization of an alkaline‑carbonatite complex using gravity and magnetic methods at Magnet Cove, Arkansas, USA: Tectonophysics, v. 893, 230545, 17 p., https://doi.org/10.1016/j.tecto.2024.230545.","productDescription":"230545, 17 p.","ipdsId":"IP-155830","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":489907,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.tecto.2024.230545","text":"Publisher Index Page"},{"id":481137,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas","otherGeospatial":"Magnet Cove","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92.87450349583777,\n 34.46740461224745\n ],\n [\n -92.87450349583777,\n 34.43682956355562\n ],\n [\n -92.80310027946261,\n 34.43682956355562\n ],\n [\n -92.80310027946261,\n 34.46740461224745\n ],\n [\n -92.87450349583777,\n 34.46740461224745\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"893","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Amaral, Chelsea Morgan 0000-0003-4632-4097","orcid":"https://orcid.org/0000-0003-4632-4097","contributorId":313539,"corporation":false,"usgs":true,"family":"Amaral","given":"Chelsea","email":"","middleInitial":"Morgan","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":925001,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lamb, Andrew P. 0000-0001-7214-516X","orcid":"https://orcid.org/0000-0001-7214-516X","contributorId":349870,"corporation":false,"usgs":false,"family":"Lamb","given":"Andrew P.","affiliations":[{"id":83523,"text":"University of Arkansas Department of Geosciences","active":true,"usgs":false}],"preferred":false,"id":925002,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dumond, Gregory 0000-0002-3296-0976","orcid":"https://orcid.org/0000-0002-3296-0976","contributorId":349871,"corporation":false,"usgs":false,"family":"Dumond","given":"Gregory","affiliations":[{"id":83523,"text":"University of Arkansas Department of Geosciences","active":true,"usgs":false}],"preferred":false,"id":925003,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"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":"Reed canarygrass (Phalaris arundinacea L.) is one of the most common invaders of floodplains and wetlands in North America. In the Upper Mississippi River floodplain, invasion by reed canarygrass in forest understories can inhibit forest regeneration when gaps form in the overstory. Understanding the distribution of reed canarygrass in forest understories is essential for effective management and control. We used an ensemble of species distribution models including Bayesian additive regression trees, boosted trees, and random forest algorithms to predict habitat suitability for reed canarygrass in forest understories across the Upper Mississippi River floodplain (~41,000 ha). Data from forest inventory study plots with reed canarygrass presence and absence were combined with 10 hypothesized environmental predictors of reed canarygrass invasion. We applied three approaches to better understand and incorporate the influence of spatial autocorrelation among our predictor variables, including random cross-validation, spatial cross-validation, and spatial cross-validation with Euclidean distance fields. Flood frequency, distance to contiguous floodplain, distance to forest edge, and distance to invaded wet meadow were among the most important environmental predictors across the three algorithms. Generally, the mean probability of reed canarygrass presence decreased with increasing flood depth, distance to contiguous floodplain, distance to invaded wet meadow, forest cover, and forest height, while relationships with other predictors were more variable. The ensemble of the three models (i.e., the average prediction) was used to map and summarize potential reed canary grass habitat suitability across the landscape. The maps generated quantified the habitat suitability for reed canarygrass and areas of agreement among the models in forest understories across the floodplain. This information can be used to better understand the extent of invasion, prioritize restoration efforts, and develop further research.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.70138","usgsCitation":"Delaney, J., Van Appledorn, M., De Jager, N.R., Bouska, K.L., and Rohweder, J.J., 2024, Spatial differences in predicted Phalaris arundinacea (reed canarygrass) occurrence in floodplain forest understories: Ecosphere, v. 15, no. 12, e70138, 19 p., https://doi.org/10.1002/ecs2.70138.","productDescription":"e70138, 19 p.","ipdsId":"IP-151054","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":487690,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.70138","text":"Publisher Index Page"},{"id":482505,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Indiana, Iowa, Minnesota, Missouri, Wisconsin","otherGeospatial":"Upper Mississippi River floodplain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95.10323647292768,\n 46.99782038374508\n ],\n [\n -95.10323647292768,\n 39.14905192203409\n ],\n [\n -86.81143448094211,\n 39.14905192203409\n ],\n [\n -86.81143448094211,\n 46.99782038374508\n ],\n [\n -95.10323647292768,\n 46.99782038374508\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"12","noUsgsAuthors":false,"publicationDate":"2024-12-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Delaney, John 0000-0003-1038-0265","orcid":"https://orcid.org/0000-0003-1038-0265","contributorId":255630,"corporation":false,"usgs":true,"family":"Delaney","given":"John","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":928659,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Van Appledorn, Molly 0000-0002-8029-0014","orcid":"https://orcid.org/0000-0002-8029-0014","contributorId":205785,"corporation":false,"usgs":true,"family":"Van Appledorn","given":"Molly","email":"","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":928660,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"De Jager, Nathan R. 0000-0002-6649-4125 ndejager@usgs.gov","orcid":"https://orcid.org/0000-0002-6649-4125","contributorId":3717,"corporation":false,"usgs":true,"family":"De Jager","given":"Nathan","email":"ndejager@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":928661,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bouska, Kristen L. 0000-0002-4115-2313 kbouska@usgs.gov","orcid":"https://orcid.org/0000-0002-4115-2313","contributorId":178005,"corporation":false,"usgs":true,"family":"Bouska","given":"Kristen","email":"kbouska@usgs.gov","middleInitial":"L.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":928662,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rohweder, Jason J. 0000-0001-5131-9773 jrohweder@usgs.gov","orcid":"https://orcid.org/0000-0001-5131-9773","contributorId":150539,"corporation":false,"usgs":true,"family":"Rohweder","given":"Jason","email":"jrohweder@usgs.gov","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":928663,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70261666,"text":"sir20245051 - 2024 - Improving time of concentration estimates for small rural watersheds in the Appalachian Plateaus physiographic province, West Virginia","interactions":[],"lastModifiedDate":"2025-08-15T16:20:41.023018","indexId":"sir20245051","displayToPublicDate":"2024-12-19T13:25:00","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":"2024-5051","displayTitle":"Improving Time of Concentration Estimates for Small Rural Watersheds in the Appalachian Plateaus Physiographic Province, West Virginia","title":"Improving time of concentration estimates for small rural watersheds in the Appalachian Plateaus physiographic province, West Virginia","docAbstract":"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,
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6000 J Street, Placer Hall
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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 - 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Existing elevation data coverage is used to improve resiliency to natural disasters, manage energy infrastructure, and assess natural resources. The expanding availability of current and more accurate elevation data helps better support natural resources conservation, agriculture and precision farming, flood risk management, infrastructure and construction management, geologic resource assessment and hazard mitigation, coastal zone management, and identification of features of interest or concern, such as archaeological and historic sites. 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 Texas. The status of available and in-progress 3DEP baseline lidar data in Texas 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 $53.1 million in new benefits annually to the State. The top 10 Texas 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":"Monitoring animal body condition can provide insight on population responses to environmental change. Pacific walruses (Odobenus rosmarus divergens) are experiencing loss of their sea ice habitat which has decreased the time that females spend foraging during a critical period of pregnancy and lactation. Here we investigate the potential for body condition to track demographic change and be monitored via two-dimensional aerial imagery by (1) examining whether walrus somatic growth and body mass data tracked estimated historic demographic changes, (2) collecting morphometric and body mass data and aerial imagery of walruses in human care to determine if sex, age group, and body size and condition can be determined from imagery, and (3) examining aerial imagery from a large coastal haulout used primarily by females and young to estimate potential sample sizes of measurable walruses. Body mass and growth in body length decreased between the last 1970s and early 1980s concurrent with a period when the population apparently approached carrying capacity and subsequently declined. Measures from aerial imagery (1) accurately distinguished reproductive age females from subadults and adult males and (2) enabled body mass estimates with 6-7% error using either areal footprint or a combination of length and width. We found a mean of 216 ± 77 walruses appropriately positioned for measurement from aerial surveys of the haulout enabling measurements of ≥7000 individuals annually via repeated daily imagery. Our results suggest that body mass of reproductive age females and growth of dependent young may be useful indicators to augment monitoring of the Pacific walrus population and can be achieved via non-invasive aerial imagery collections.
","language":"English","publisher":"Inter-Research","doi":"10.3354/meps14738","usgsCitation":"Rode, K.D., Fischbach, A.S., Synnott, M., Stewart, J., Northcraft, N., Allen, E., Trotto, K., Vancsok, C., Issenjou, N., Ploof, S., Rager, S., DiRocco, S., Owens, S., and Prahl, A., 2024, Photogrammetry-based body condition for monitoring an Arctic marine mammal experiencing habitat loss: Marine Ecology Progress Series, v. 751, p. 211-227, https://doi.org/10.3354/meps14738.","productDescription":"17 p.","startPage":"211","endPage":"227","ipdsId":"IP-167535","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":466705,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/meps14738","text":"Publisher Index Page"},{"id":465444,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Bering Sea, Chukchi Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -179.9,\n 71.04269895606964\n ],\n [\n -179.9,\n 57.55259327741362\n ],\n [\n -162.40817411659705,\n 57.55259327741362\n ],\n [\n -162.40817411659705,\n 71.04269895606964\n ],\n [\n -179.9,\n 71.04269895606964\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"751","noUsgsAuthors":false,"publicationDate":"2024-12-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Rode, Karyn D. 0000-0002-3328-8202 krode@usgs.gov","orcid":"https://orcid.org/0000-0002-3328-8202","contributorId":5053,"corporation":false,"usgs":true,"family":"Rode","given":"Karyn","email":"krode@usgs.gov","middleInitial":"D.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology 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Staci","contributorId":347485,"corporation":false,"usgs":false,"family":"Owens","given":"Staci","email":"","affiliations":[{"id":83173,"text":"SeaWorld Orlando","active":true,"usgs":false}],"preferred":false,"id":921783,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Prahl, Adriane","contributorId":347500,"corporation":false,"usgs":false,"family":"Prahl","given":"Adriane","email":"","affiliations":[],"preferred":false,"id":921864,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"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.
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\"}}]}","volume":"12","issue":"12","noUsgsAuthors":false,"publicationDate":"2024-12-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Breitmeyer, Sara E. 0000-0003-0609-1559 sbreitmeyer@usgs.gov","orcid":"https://orcid.org/0000-0003-0609-1559","contributorId":172622,"corporation":false,"usgs":true,"family":"Breitmeyer","given":"Sara","email":"sbreitmeyer@usgs.gov","middleInitial":"E.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":921971,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Williams, Amy 0000-0002-7679-181X","orcid":"https://orcid.org/0000-0002-7679-181X","contributorId":347547,"corporation":false,"usgs":false,"family":"Williams","given":"Amy","affiliations":[{"id":17703,"text":"Pennsylvania Department of Environmental 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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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The ECCOE Landsat Cal/Val Team continually monitors the geometric and radiometric performance of active Landsat missions and makes calibration adjustments, as needed, to maintain data quality at the highest level.
This report provides observed geometric and radiometric analysis results for Landsats 8 and 9 for quarter 2 (April–June) of 2024. All data used to compile the Cal/Val analysis results presented in this report are freely available from the U.S. Geological Survey EarthExplorer website: https://earthexplorer.usgs.gov.
This is the fourth quarterly report to include analysis results for Landsat 9, which was launched in September 2021. The inclusion of Landsat 9 analysis results was dependent on two factors: a complete reprocessing of the Landsat 9 data archive and enough time elapsing to begin formulating lifetime trends. In April 2023, all Landsat 9 image data acquired since the satellite’s launch were reprocessed to take advantage of calibration updates identified by the ECCOE Landsat Cal/Val Team. Additional information about the Landsat 9 reprocessing effort is available at https://www.usgs.gov/landsat-missions/news/upcoming-reprocessing-all-landsat-9-data. Additional information about Landsat 9 prelaunch, commissioning, and early on-orbit imaging performance is available at https://www.mdpi.com/journal/remotesensing/special_issues/15B4V2K92K.
This is the second quarterly report that does not include analysis results for Landsat 7 because Enhanced Thematic Mapper Plus imaging was suspended on January 19, 2024, after the satellite transitioned into full sunlight. The satellite has been drifting since early 2022 when it was lowered from the nominal orbit altitude, and the transition into full sunlight is a result of the satellite operating in its extended science mission. Additional information about the imaging suspension is available at https://www.usgs.gov/landsat-missions/news/landsat-7-imaging-suspended. Additional information about the Landsat 7 extended science mission is available at https://www.usgs.gov/landsat-missions/landsat-7-extended-science-mission.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241077","usgsCitation":"Haque, M.O., Hasan, M.N., Shrestha, A., Rengarajan, R., Lubke, M., Shaw, J.L., Ruslander, K., Micijevic, E., Choate, M.J., Anderson, C., Clauson, J., Thome, K., Kaita, E., Levy, R., Miller, J., and Ding, L., 2024, ECCOE Landsat quarterly Calibration and Validation report—Quarter 2, 2024: U.S. Geological Survey Open-File Report 2024–1077, 56 p., https://doi.org/10.3133/ofr20241077.","productDescription":"Report: viii, 56 p.; Dataset","numberOfPages":"68","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-168175","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":465270,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1077/coverthb.jpg"},{"id":465271,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1077/ofr20241077.pdf","text":"Report","size":"5.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024–1077"},{"id":465272,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1077/ofr20241077.XML"},{"id":465273,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1077/images/"},{"id":465274,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1077/images/"},{"id":465275,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS database","linkHelpText":"- EarthExplorer"},{"id":465277,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241077/full"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
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":"This book is an illustrative guide designed to introduce young minds to the exciting world of geologic hazards science careers. From geomorphologist to volcanologist, this book showcases a variety of science-based professions through fun and engaging activities. Each section of the book features a different science career and includes information about how the job got its name and what a typical day in the life of someone in that profession might look like. Readers will also have a chance to color, test their knowledge with trivia, and play a few games along the way. As a sequel to the biology-related science careers activity book, \"I Am a…Science Careers Book for Kids, Part 2\" is a fun and colorful way for kids to learn about the many exciting career opportunities available in the world of science.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip246","isbn":"978-1-4113-4592-8","programNote":"USGS Natural Hazards Mission Area","usgsCitation":"Sobieszczyk, S., 2024, I am a...Science careers book for kids, part 2: U.S. Geological Survey General Information Product 246, 51 p., https://doi.org/10.3133/gip246.","productDescription":"ii, 51 p.","numberOfPages":"51","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-168336","costCenters":[{"id":251,"text":"Ecosystems Mission Area","active":false,"usgs":true},{"id":5072,"text":"Office of Communication and Publishing","active":true,"usgs":true}],"links":[{"id":494227,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118087.htm"},{"id":465012,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/246/coverthb.jpg"},{"id":465013,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/246/gip246.pdf","text":"Report","size":"4.22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 246 PDF"}],"contact":"Youth and Education in Science
U.S. Geological Survey
12201 Sunrise Valley Dr.
Reston, VA 20192
Email: usgs_yes@usgs.gov
","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2024-12-18","noUsgsAuthors":false,"publicationDate":"2024-12-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Sobieszczyk, Steven 0000-0002-0834-8437","orcid":"https://orcid.org/0000-0002-0834-8437","contributorId":205030,"corporation":false,"usgs":true,"family":"Sobieszczyk","given":"Steven","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":920603,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70261664,"text":"fs20243052 - 2024 - Lithium resource in the Smackover Formation brines of Southern Arkansas","interactions":[],"lastModifiedDate":"2025-08-15T16:30:14.115117","indexId":"fs20243052","displayToPublicDate":"2024-12-18T10:51:05","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-3052","displayTitle":"Lithium Resource in the Smackover Formation Brines of Southern Arkansas","title":"Lithium resource in the Smackover Formation brines of Southern Arkansas","docAbstract":"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
Understanding the causes of mortality for a declining species is essential for developing effective conservation and management strategies, particularly when anthropogenic activities are the primary threat. Using a competing hazards framework allows for robust estimation of the cause-specific variation in risk that may exist across multiple dimensions, such as time and individual. Here, we estimated cause-specific rates of severe injury and mortality for North Atlantic right whales (Eubalaena glacialis), a critically endangered species that is currently in peril due to human-caused interactions. We developed a multistate capture–recapture model that leveraged 30 years of intensive survey effort yielding sightings of individuals with injury assessments and necropsies of carcass recoveries. We examined variation in the hazard rates of severe injury and mortality due to entanglements in fishing gear and vessel strikes as explained by temporal patterns and the age and reproductive status of the individual. We found strong evidence for increased rates of severe entanglement injuries after 2013 and for females with calves, with consequently higher marginal mortality. The model results also suggested that despite vessel strikes causing a lower average rate of severe injuries, the higher mortality rate conditional on injury results in significant total mortality risk, particularly for females resting from a recent calving event. Large uncertainty in the estimation of carcass recovery rate for vessel strike deaths permeated into the apportionment of mortality causes. The increased rates of North Atlantic right whale mortality in the last decade, particularly for reproducing females, has been responsible for the severe decline in the species. By apportioning the human-caused threats using a quantitative approach with estimation of relevant uncertainty, this work can guide development of conservation and management strategies to facilitate species recovery. Our approach is relevant to other monitored populations where cause-specific injuries from multiple threats can be observed in live and dead individuals.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.70086","usgsCitation":"Linden, D., Hostetler, J.A., Pace, R., Garrison, L.P., Knowlton, A., Lesage, V., Williams, R., and Runge, M.C., 2024, Quantifying uncertainty in anthropogenic causes of injury and mortality for an endangered baleen whale: Ecosphere, v. 15, no. 12, e70086, 12 p., https://doi.org/10.1002/ecs2.70086.","productDescription":"e70086, 12 p.","ipdsId":"IP-157942","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":488298,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.70086","text":"Publisher Index Page"},{"id":483337,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"12","noUsgsAuthors":false,"publicationDate":"2024-12-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Linden, Daniel W.","contributorId":229525,"corporation":false,"usgs":false,"family":"Linden","given":"Daniel W.","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":930730,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hostetler, J. A. 0000-0003-3669-1758","orcid":"https://orcid.org/0000-0003-3669-1758","contributorId":11319,"corporation":false,"usgs":true,"family":"Hostetler","given":"J.","middleInitial":"A.","affiliations":[],"preferred":true,"id":930731,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pace, Richard M III","contributorId":352277,"corporation":false,"usgs":false,"family":"Pace","given":"Richard M","suffix":"III","affiliations":[{"id":36612,"text":"National Marine Fisheries Service","active":true,"usgs":false}],"preferred":false,"id":930732,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Garrison, Lance P.","contributorId":296893,"corporation":false,"usgs":false,"family":"Garrison","given":"Lance","email":"","middleInitial":"P.","affiliations":[{"id":64230,"text":"NOAA-NMFS Southwest Fisheries Science Center","active":true,"usgs":false}],"preferred":false,"id":930733,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Knowlton, Amy R.","contributorId":352046,"corporation":false,"usgs":false,"family":"Knowlton","given":"Amy R.","affiliations":[{"id":37373,"text":"New England Aquarium","active":true,"usgs":false}],"preferred":false,"id":930734,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lesage, Veronique","contributorId":352311,"corporation":false,"usgs":false,"family":"Lesage","given":"Veronique","affiliations":[{"id":13677,"text":"Fisheries and Oceans Canada","active":true,"usgs":false}],"preferred":false,"id":930735,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Williams, Robert A. 0000-0002-2973-8493","orcid":"https://orcid.org/0000-0002-2973-8493","contributorId":203802,"corporation":false,"usgs":false,"family":"Williams","given":"Robert A.","affiliations":[{"id":36721,"text":"USGS-Emeritus","active":true,"usgs":false}],"preferred":false,"id":930736,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":930737,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70261711,"text":"70261711 - 2024 - A benchmark for computational analysis of animal behavior, using animal-borne tags","interactions":[],"lastModifiedDate":"2024-12-19T15:23:34.579209","indexId":"70261711","displayToPublicDate":"2024-12-18T09:18:45","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2792,"text":"Movement Ecology","active":true,"publicationSubtype":{"id":10}},"title":"A benchmark for computational analysis of animal behavior, using animal-borne tags","docAbstract":"Animal-borne sensors (‘bio-loggers’) can record a suite of kinematic and environmental data, which are used to elucidate animal ecophysiology and improve conservation efforts. Machine learning techniques are used for interpreting the large amounts of data recorded by bio-loggers, but there exists no common framework for comparing the different machine learning techniques in this domain. This makes it difficult to, for example, identify patterns in what works well for machine learning-based analysis of bio-logger data. It also makes it difficult to evaluate the effectiveness of novel methods developed by the machine learning community.
To address this, we present the Bio-logger Ethogram Benchmark (BEBE), a collection of datasets with behavioral annotations, as well as a modeling task and evaluation metrics. BEBE is to date the largest, most taxonomically diverse, publicly available benchmark of this type, and includes 1654 h of data collected from 149 individuals across nine taxa. Using BEBE, we compare the performance of deep and classical machine learning methods for identifying animal behaviors based on bio-logger data. As an example usage of BEBE, we test an approach based on self-supervised learning. To apply this approach to animal behavior classification, we adapt a deep neural network pre-trained with 700,000 h of data collected from human wrist-worn accelerometers.
We find that deep neural networks out-perform the classical machine learning methods we tested across all nine datasets in BEBE. We additionally find that the approach based on self-supervised learning out-performs the alternatives we tested, especially in settings when there is a low amount of training data available.
In light of these results, we are able to make concrete suggestions for designing studies that rely on machine learning to infer behavior from bio-logger data. Therefore, we expect that BEBE will be useful for making similar suggestions in the future, as additional hypotheses about machine learning techniques are tested. Datasets, models, and evaluation code are made publicly available at https://github.com/earthspecies/BEBE, to enable community use of BEBE.
","language":"English","publisher":"BMC","doi":"10.1186/s40462-024-00511-8","usgsCitation":"Hoffmann, B., Cusimano, M., Baglione, V., Canestrari, D., Chevallier, D., DeSantis, D.L., Jeantet, L., Ladds, M., Maekawa, T., Vicente, M., Moreno-Gonzalez, V., Pagano, A.M., Trapote, E., Vainio, O., Vehkaoja, A., Yoda, K., Zacarian, K., and Friedlaender, A., 2024, A benchmark for computational analysis of animal behavior, using animal-borne tags: Movement Ecology, v. 12, 78, 25 p., https://doi.org/10.1186/s40462-024-00511-8.","productDescription":"78, 25 p.","ipdsId":"IP-152357","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":466709,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-024-00511-8","text":"Publisher Index 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poultry highlight the disruptive impacts of emerging infectious diseases on public health, ecosystems, and economies. Infectious disease dynamics often depend on environmental conditions that drive occurrence, transmission, and outbreaks. Remote sensing can contribute to infectious disease research and management by providing standardized environmental data across broad spatial and temporal extents, often at no cost to the user. Here, we 1) conduct a systematic review of primary literature to quantify current uses of remote sensing in disease ecology and 2) synthesize qualitative information to identify opportunities for further integration of remote sensing into disease ecology. We identify that modern advances in airborne remote sensing are promoting early detection of forest pathogens and that satellite data is contributing to the study of geographically widespread human diseases. We discuss opportunities for increased use of data products that characterize vegetation, surface water, and soil; provide data at high spatio-temporal and spectral resolutions; and quantify uncertainty in measurements. Additionally, combining remote sensing with animal movement telemetry can provide novel insights into wildlife disease. Integrating these opportunities will advance research and management of infectious diseases.","language":"English","publisher":"The Royal Society Publishing","doi":"10.1098/rspb.2024.1712","usgsCitation":"Teitelbaum, C., Ferraz, A., De La Cruz, S.E., Gilmour, M., and Brosnan, I., 2024, The potential of remote sensing for improved infectious disease ecology research and practice: Proceedings of the Royal Society B: Biological Sciences, v. 291, 20241712, 12 p., https://doi.org/10.1098/rspb.2024.1712.","productDescription":"20241712, 12 p.","ipdsId":"IP-170705","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":466710,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1098/rspb.2024.1712","text":"Publisher Index Page"},{"id":465563,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"291","noUsgsAuthors":false,"publicationDate":"2024-12-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Teitelbaum, Claire S.","contributorId":337675,"corporation":false,"usgs":false,"family":"Teitelbaum","given":"Claire S.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":922111,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ferraz, António","contributorId":347661,"corporation":false,"usgs":false,"family":"Ferraz","given":"António","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":922112,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"De La Cruz, Susan E.W. 0000-0001-6315-0864","orcid":"https://orcid.org/0000-0001-6315-0864","contributorId":202774,"corporation":false,"usgs":true,"family":"De La Cruz","given":"Susan","email":"","middleInitial":"E.W.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":922113,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gilmour, Morgan E.","contributorId":245099,"corporation":false,"usgs":false,"family":"Gilmour","given":"Morgan E.","affiliations":[],"preferred":false,"id":922114,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brosnan, Ian G.","contributorId":347663,"corporation":false,"usgs":false,"family":"Brosnan","given":"Ian G.","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":922115,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70272012,"text":"70272012 - 2024 - Managing to survive despite the weather: Seeding decisions affecting simulated dryland restoration outcomes","interactions":[],"lastModifiedDate":"2025-09-30T15:53:37.174326","indexId":"70272012","displayToPublicDate":"2024-12-18T08:46:37","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3271,"text":"Restoration Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Managing to survive despite the weather: Seeding decisions affecting simulated dryland restoration outcomes","docAbstract":"Limited favorable weather windows for post-germination early seedling survival are associated with low restoration success in drylands. We examined whether post-fire seeding decisions could alter early seedling emergence and restoration success across western North American sagebrush ecosystems with a simulation approach. Seedling emergence estimates were based on germination of a specified percentile of sown seeds followed by favorable conditions for seedling development. We asked how three categories of seeding decisions affected emergence: post-fire seeding year, seasonal seeding delay (via seed coating technologies or later seeding timing), or native perennial grass seed source (via differing germination behavior). We also tested potential effects of mean annual precipitation on seeding decision outcomes (interactive effect) and included topographic microclimate and site (random effect) in models. Emergence was highest when the best seed source (of 10) was seeded in the best post-fire year (of 5). High emergence also resulted from seeding in the best post-fire year with an average seed source or seeding with the best source in the first post-fire year. Emergence was higher overall with higher mean precipitation. Seeding decision outcomes were sensitive to annual mean precipitation, with greater differences observed due to decisions at lower precipitation levels. While sensitive to assumptions related to seedling development and germination speed, these results suggest that restoration approaches linked to germination behavior and weather variability could improve restoration success rates in variable drylands like sagebrush ecosystems.
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