Oil and gas reservoirs represent suitable containers to sequester carbon dioxide (CO2) in a supercritical state because they are accessible, reservoir properties are known, and they previously contained stored buoyant fluids. However, planners must quantify the relative magnitude of the CO2 storage resource in these reservoirs to formulate a comprehensive strategy for CO2 mitigation. Even reconnaissance-type estimates of CO2 storage resources of known oil and gas reservoirs may require complicated calculations involving 1) estimates of recoverable oil and gas, 2) reservoir properties (depth, temperature, pressure, etc.), and 3) the physical qualities of the retained fluids. We demonstrate the application of machine learning (ML) algorithms to bypass these computations to yield more rapid estimates of CO2 storage resources in reservoirs capable of hosting CO2 in a supercritical state. ML algorithms are computationally efficient because they do not impose the strong assumptions on the data-generating process that standard statistical or engineering procedures require. Further, ML algorithms can capture highly complex, particularly nonlinear, relationships among predictor variables. We demonstrate the application of four different ML algorithms using data from onshore and offshore oil and gas reservoirs in Europe, and show they perform well when predictions are compared to engineering estimates. The proposed methods and models provide an effective and novel way to more rapidly and directly determine the subsurface CO2 storage capacity of oil and gas reservoirs around the world, information that operators, researchers, and policymakers alike require to meet energy transition and decarbonization goals.
","language":"English","publisher":"frontiers","doi":"10.3389/fenvs.2025.1562087","usgsCitation":"Attanasi, E., Freeman, P., and Coburn, T.C., 2025, Machine learning provides reconnaissance-type estimates of carbon dioxide storage resources in oil and gas reservoirs: Frontiers in Enviornmental Science, v. 13, 1562087, 14 p., https://doi.org/10.3389/fenvs.2025.1562087.","productDescription":"1562087, 14 p.","ipdsId":"IP-166626","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":487848,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fenvs.2025.1562087","text":"Publisher Index Page"},{"id":485138,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"western Europe","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -13.428924366035233,\n 54.504852491541925\n ],\n [\n -13.428924366035233,\n 41.74320075493446\n ],\n [\n 28.707525813502826,\n 41.74320075493446\n ],\n [\n 28.707525813502826,\n 54.504852491541925\n ],\n [\n -13.428924366035233,\n 54.504852491541925\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"13","noUsgsAuthors":false,"publicationDate":"2025-04-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Attanasi, Emil 0000-0001-6845-7160 attanasi@usgs.gov","orcid":"https://orcid.org/0000-0001-6845-7160","contributorId":1809,"corporation":false,"usgs":true,"family":"Attanasi","given":"Emil","email":"attanasi@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":934732,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Freeman, Philip A. 0000-0002-0863-7431 pfreeman@usgs.gov","orcid":"https://orcid.org/0000-0002-0863-7431","contributorId":193093,"corporation":false,"usgs":true,"family":"Freeman","given":"Philip A.","email":"pfreeman@usgs.gov","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":934733,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coburn, Timothy C.","contributorId":26011,"corporation":false,"usgs":true,"family":"Coburn","given":"Timothy","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":934734,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70266320,"text":"70266320 - 2025 - Prospectivity modeling of the NASA VIPER landing site at Mons Mouton near the Lunar South Pole","interactions":[],"lastModifiedDate":"2025-05-02T15:25:36.189617","indexId":"70266320","displayToPublicDate":"2025-04-25T10:22:57","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17061,"text":"Planetary Science Journal","active":true,"publicationSubtype":{"id":10}},"title":"Prospectivity modeling of the NASA VIPER landing site at Mons Mouton near the Lunar South Pole","docAbstract":"We use a high-resolution digital elevation model and a numerical thermal model to produce a variety of inputs for a water-ice prospectivity model for the Volatiles Investigating Polar Exploration Rover (VIPER) landing site. These input data are maps of topography, surface slope, surface aspect, surface curvature, maximum temperature, depth to ice stability, permanently shadowed regions (PSRs), distance to PSRs, and PSR density. This model predicts where water ice is most likely within the top meter of regolith, assuming plausible relationships between ice concentration and the various inputs. The model is designed to be adjusted in near-real time as data are collected during the VIPER mission. As such, it is a tool for both analyzing data from the mission as well as planning operations. Since the current model, at this point, relies only on orbital remote sensing, the final version will also be a tool to extrapolate the VIPER mission results across the lunar poles.
","language":"English","publisher":"American Astronomical Society","doi":"10.3847/PSJ/adbc6c","usgsCitation":"Coyan, J.A., Siegler, M., Martinez-Comacho, J., Beyer, R.A., and Shirley, M., 2025, Prospectivity modeling of the NASA VIPER landing site at Mons Mouton near the Lunar South Pole: Planetary Science Journal, v. 6, no. 5, 105, 9 p., https://doi.org/10.3847/PSJ/adbc6c.","productDescription":"105, 9 p.","ipdsId":"IP-168617","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":487929,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3847/psj/adbc6c","text":"Publisher Index Page"},{"id":485333,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Mons Mouton, Moon","volume":"6","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-04-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Coyan, Joshua Aaron 0000-0002-8450-7364","orcid":"https://orcid.org/0000-0002-8450-7364","contributorId":247291,"corporation":false,"usgs":true,"family":"Coyan","given":"Joshua","email":"","middleInitial":"Aaron","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":935581,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Siegler, Matthew A.","contributorId":237898,"corporation":false,"usgs":false,"family":"Siegler","given":"Matthew","middleInitial":"A.","affiliations":[{"id":24584,"text":"PSI","active":true,"usgs":false}],"preferred":false,"id":935582,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martinez-Comacho, José 0000-0003-0542-7866","orcid":"https://orcid.org/0000-0003-0542-7866","contributorId":354404,"corporation":false,"usgs":false,"family":"Martinez-Comacho","given":"José","affiliations":[{"id":84624,"text":"University of Hawai’i at Manoa, Hawaii Institute for Geophysics and Planetology, 1680 East-West Road, POST Building, Honolulu, HI 96822","active":true,"usgs":false}],"preferred":false,"id":935583,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Beyer, Ross A.","contributorId":204235,"corporation":false,"usgs":false,"family":"Beyer","given":"Ross","email":"","middleInitial":"A.","affiliations":[{"id":36890,"text":"Sagan Center at the SETI Institute and NASA Ames Research Center","active":true,"usgs":false}],"preferred":false,"id":935584,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shirley, Mark 0000-0001-8767-1760","orcid":"https://orcid.org/0000-0001-8767-1760","contributorId":354405,"corporation":false,"usgs":false,"family":"Shirley","given":"Mark","affiliations":[{"id":84625,"text":"SETI Institute/NASA Ames Research Center","active":true,"usgs":false}],"preferred":false,"id":935585,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70270067,"text":"70270067 - 2025 - Broadband stochastic simulation of earthquake ground motions with multiple strong phases with an application to the 2023 Kahramanmaraş, Turkey (Türkiye), earthquake","interactions":[],"lastModifiedDate":"2025-08-08T14:30:32.119094","indexId":"70270067","displayToPublicDate":"2025-04-25T09:26:17","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1436,"text":"Earthquake Spectra","active":true,"publicationSubtype":{"id":10}},"title":"Broadband stochastic simulation of earthquake ground motions with multiple strong phases with an application to the 2023 Kahramanmaraş, Turkey (Türkiye), earthquake","docAbstract":"Stochastic ground motion simulation models are often less accurate at lower frequencies than at higher frequencies when fitting recorded data unless supplemented by a deterministic forward directivity velocity pulse model. Moreover, time-modulated stochastic models, which adjust ground motion amplitudes over time, typically use functions that fail to capture multiple strong-motion phases. The February 2023 Turkey (Türkiye) earthquake exhibited diverse recordings, including near-fault and far-field motions with pulse-like and non-pulse-like characteristics, along with single and multiple strong-motion phases. To better represent such a diverse set of recordings, this study enhances a fully non-stationary site-based stochastic model without combining it with a deterministic model. Improvements include a new band-pass filter with upper- and lower-frequency limits, which refines the representation of the low-frequency content. Moreover, a time-modulating function that can represent energy arrival in multiple strong phases is introduced. The reference model’s parameters are identified by fitting to the energy content, zero-level crossings, and cumulative counts of positive-minima and negative-maxima of a target accelerogram. This fitting procedure is modified to address the increased number of parameters. These improvements broaden the reference model’s applicability while preserving its simplicity, a key aspect appealing to engineering practitioners. The improved model’s applicability is demonstrated by simulating a dataset from the February 2023 Türkiye earthquake, and the accuracy is tested using a pulse-like Next Generation Attenuation Relationships for Western United States dataset. Validations are performed based on total energy, zero-level crossings, Fourier amplitude spectrum, elastic response spectra, and peak ground motion parameters. Validations are performed schematically in the time and frequency domains and quantitatively using goodness-of-fit scores, various validation-metrics errors, and inter-period correlations. Overall, the improved stochastic model can effectively simulate a set of diverse ground motion recordings, including near-fault pulse-like records, records with multiple strong phases, and far-field motions across a broad frequency range.
","language":"English","publisher":"Sage Publications","doi":"10.1177/87552930251331981","usgsCitation":"Hussaini, S.M., Karimzadeh, S., Rezaeian, S., and Lourenco, P., 2025, Broadband stochastic simulation of earthquake ground motions with multiple strong phases with an application to the 2023 Kahramanmaraş, Turkey (Türkiye), earthquake: Earthquake Spectra, v. 41, no. 3, p. 2399-2435, https://doi.org/10.1177/87552930251331981.","productDescription":"37 p.","startPage":"2399","endPage":"2435","ipdsId":"IP-174088","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":494180,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1177/87552930251331981","text":"Publisher Index Page"},{"id":493834,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Turkey","city":"Kahramanmaraş","volume":"41","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-04-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Hussaini, S. M. Sajad","contributorId":359418,"corporation":false,"usgs":false,"family":"Hussaini","given":"S.","middleInitial":"M. Sajad","affiliations":[{"id":85799,"text":"University of Minho, Portugal","active":true,"usgs":false}],"preferred":false,"id":945288,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Karimzadeh, Shaghayegh","contributorId":359419,"corporation":false,"usgs":false,"family":"Karimzadeh","given":"Shaghayegh","affiliations":[{"id":85799,"text":"University of Minho, Portugal","active":true,"usgs":false}],"preferred":false,"id":945289,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rezaeian, Sanaz 0000-0001-7589-7893","orcid":"https://orcid.org/0000-0001-7589-7893","contributorId":238513,"corporation":false,"usgs":true,"family":"Rezaeian","given":"Sanaz","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":945290,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lourenco, Paulo B.","contributorId":359420,"corporation":false,"usgs":false,"family":"Lourenco","given":"Paulo B.","affiliations":[{"id":85799,"text":"University of Minho, Portugal","active":true,"usgs":false}],"preferred":false,"id":945291,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70273016,"text":"70273016 - 2025 - Daily survival rate and nest-site selection of Zone-tailed Hawks (Buteo albonotatus) in the Chihuahuan Desert ecoregion of Texas","interactions":[],"lastModifiedDate":"2025-12-12T15:31:33.490472","indexId":"70273016","displayToPublicDate":"2025-04-25T09:25:46","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2442,"text":"Journal of Raptor Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Daily survival rate and nest-site selection of Zone-tailed Hawks (Buteo albonotatus) in the Chihuahuan Desert ecoregion of Texas","title":"Daily survival rate and nest-site selection of Zone-tailed Hawks (Buteo albonotatus) in the Chihuahuan Desert ecoregion of Texas","docAbstract":"The Zone-tailed Hawk (Buteo albonotatus) is one of the least studied raptors in North America and lacks contemporary literature allowing informed management decisions for this species. Zone-tailed Hawks occupy rugged areas in the southwestern region of the United States and are listed as state threatened in Texas. Our objectives were to assess habitat, productivity, and daily survival rate (DSR) of Zone-tailed Hawk nests in riparian zones of the Chihuahuan Desert Ecoregion of Texas. We surveyed for Zone-tailed Hawk nests along ∼30 km of 12 riparian corridors in Brewster, Jeff Davis, and Presidio Counties, Texas. We monitored 11 and 15 Zone-tailed Hawk nests in 2018 and 2019, respectively, and conducted vegetation surveys at the nest tree, nest site (11.3-m radius), and paired random locations. We used nest survival modeling to evaluate the effects of eight habitat variables (nest tree diameter at breast height [DBH], nest tree height, nest height, nest distance to main stem, nest to tree height ratio, mean stand height, number of trees within nest site, and mean nest site DBH) on nest DSR. DSR was positively correlated with nest to tree height ratio and nest tree DBH. Zone-tailed Hawk nests had an estimated 0.991 (standard error [SE] = 0.004, 95% CI = 0.980–0.996) constant DSR and ultimately a 51.4% chance of nest success (SE = 0.0943) across the nesting season. Our results suggest that by selecting larger trees for nesting as well as placing nests higher within the tree, Zone-tailed Hawks may increase their chances of successfully fledging young.
","language":"English","publisher":"Raptor Research Foundation","doi":"10.3356/jrr2436","usgsCitation":"Skidmore, C., Boal, C.W., Skipper, B.R., and Martin, R., 2025, Daily survival rate and nest-site selection of Zone-tailed Hawks (Buteo albonotatus) in the Chihuahuan Desert ecoregion of Texas: Journal of Raptor Research, v. 59, no. 2, p. 1-9, https://doi.org/10.3356/jrr2436.","productDescription":"9","startPage":"1","endPage":"9","ipdsId":"IP-165271","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":497700,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3356/jrr2436","text":"Publisher Index Page"},{"id":497468,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Chihuahuan Desert ecoregion","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -103.16016896102632,\n 29.02969764012022\n ],\n [\n -102.6187673940317,\n 29.82558478770983\n ],\n [\n -102.34533119746091,\n 29.825904876682998\n ],\n [\n -103.31770853819764,\n 31.40373063096237\n ],\n [\n -105.27499788066719,\n 30.839874155801937\n ],\n [\n -104.70567543260988,\n 30.257905616135616\n ],\n [\n -104.50920724722273,\n 29.627352150897323\n ],\n [\n -103.81983396318309,\n 29.241412262608875\n ],\n [\n -103.16016896102632,\n 29.02969764012022\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"59","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Skidmore, Caroline","contributorId":363911,"corporation":false,"usgs":false,"family":"Skidmore","given":"Caroline","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":952099,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boal, Clint W. 0000-0001-6008-8911 cboal@usgs.gov","orcid":"https://orcid.org/0000-0001-6008-8911","contributorId":1909,"corporation":false,"usgs":true,"family":"Boal","given":"Clint","email":"cboal@usgs.gov","middleInitial":"W.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":952100,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Skipper, Ben R.","contributorId":198462,"corporation":false,"usgs":false,"family":"Skipper","given":"Ben","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":952101,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Martin, Russell","contributorId":267876,"corporation":false,"usgs":false,"family":"Martin","given":"Russell","affiliations":[{"id":27442,"text":"Texas parks and Wildlife Department","active":true,"usgs":false}],"preferred":false,"id":952102,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70269917,"text":"70269917 - 2025 - Reproductive habitat mismatch influences chytrid infection dynamics in a tropical amphibian community","interactions":[],"lastModifiedDate":"2025-08-07T14:34:46.482133","indexId":"70269917","displayToPublicDate":"2025-04-25T09:23:15","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3871,"text":"Global Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Reproductive habitat mismatch influences chytrid infection dynamics in a tropical amphibian community","docAbstract":"Batrachochytrium dendrobatidis (Bd) has been decimating amphibian populations globally; previous work indicates that infection risk increases with moisture and thermal mismatch from a host’s optimum. We hypothesized that, in addition to these abiotic influences, mismatch of hosts from their reproductive habitat heightens infection risk via exposure and/or susceptibility mechanisms. We evaluated this “reproductive habitat mismatch hypothesis” by quantifying the interplay of host breeding mode, habitat, and rainfall on Bd infection dynamics using two years of frog survey data—including swab data for 3427 captures representing 44 species—from Brazil’s Atlantic Forest. We modeled infection prevalence, infection intensity, and the number of frogs captured as a function of rainfall, reproductive mode (aquatic or terrestrial), and habitat (aquatic or terrestrial) using hierarchical models. High rainfall was associated with increases in infection prevalence and infection intensity; however, these increases were particularly apparent for species in habitats that were mismatched from the species’ reproductive habitat. Tropical regions experiencing increases in precipitation will likely see higher Bd risk, and our results indicate that such increases in rainfall will be particularly problematic for species that are forced to move from their reproductive habitats by factors such as habitat loss or thermal stress.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2025.e03599","usgsCitation":"Gilbert, N.A., Bell, R.C., Catenazzi, A., Martins, R.A., Buttimer, S., Neely, W.J., Lambertini, C., Saenz Calderon, V., Haddad, C.F., Becker, C.G., and DiRenzo, G.V., 2025, Reproductive habitat mismatch influences chytrid infection dynamics in a tropical amphibian community: Global Ecology and Conservation, v. 60, e03599, 12 p., https://doi.org/10.1016/j.gecco.2025.e03599.","productDescription":"e03599, 12 p.","ipdsId":"IP-171930","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":494048,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13TRTVG","text":"USGS data release","linkHelpText":"Code for reproductive habitat mismatch influences chytrid infection dynamics in a tropical amphibian community"},{"id":493796,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2025.e03599","text":"Publisher Index Page"},{"id":493706,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Brazil","otherGeospatial":"Parque Estadual da Serra do Mar–Núcleo Santa Virgínia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -45.079106081456246,\n -23.236053619691134\n ],\n [\n -45.236468001362624,\n -23.236053619691134\n ],\n [\n -45.236468001362624,\n -23.443275890477395\n ],\n [\n -45.079106081456246,\n -23.443275890477395\n ],\n [\n -45.079106081456246,\n -23.236053619691134\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"60","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gilbert, Neil A.","contributorId":359068,"corporation":false,"usgs":false,"family":"Gilbert","given":"Neil","middleInitial":"A.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":944942,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bell, Rayna C.","contributorId":359069,"corporation":false,"usgs":false,"family":"Bell","given":"Rayna","middleInitial":"C.","affiliations":[{"id":12937,"text":"California Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":944943,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Catenazzi, Alessandro","contributorId":359070,"corporation":false,"usgs":false,"family":"Catenazzi","given":"Alessandro","affiliations":[{"id":7017,"text":"Florida International University","active":true,"usgs":false}],"preferred":false,"id":944944,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Martins, Renato A.","contributorId":359071,"corporation":false,"usgs":false,"family":"Martins","given":"Renato","middleInitial":"A.","affiliations":[{"id":85744,"text":"Universidade Federal de São Carlo","active":true,"usgs":false}],"preferred":false,"id":944945,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Buttimer, Shannon","contributorId":359073,"corporation":false,"usgs":false,"family":"Buttimer","given":"Shannon","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":944946,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Neely, Wesley J.","contributorId":359075,"corporation":false,"usgs":false,"family":"Neely","given":"Wesley","middleInitial":"J.","affiliations":[{"id":6677,"text":"Texas State University","active":true,"usgs":false}],"preferred":false,"id":944947,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lambertini, Carolina","contributorId":359077,"corporation":false,"usgs":false,"family":"Lambertini","given":"Carolina","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":944948,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Saenz Calderon, Veronica","contributorId":359079,"corporation":false,"usgs":false,"family":"Saenz Calderon","given":"Veronica","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":944949,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Haddad, Célio F.B.","contributorId":359081,"corporation":false,"usgs":false,"family":"Haddad","given":"Célio","middleInitial":"F.B.","affiliations":[{"id":48854,"text":"Universidade Estadual Paulista","active":true,"usgs":false}],"preferred":false,"id":944950,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Becker, C. Guilherme","contributorId":359083,"corporation":false,"usgs":false,"family":"Becker","given":"C.","middleInitial":"Guilherme","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":944951,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"DiRenzo, Graziella Vittoria 0000-0001-5264-4762","orcid":"https://orcid.org/0000-0001-5264-4762","contributorId":243404,"corporation":false,"usgs":true,"family":"DiRenzo","given":"Graziella","email":"","middleInitial":"Vittoria","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":944952,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70269918,"text":"70269918 - 2025 - Satellite imagery can predict bird species occupancy and inform multispecies management in pine savannas","interactions":[],"lastModifiedDate":"2025-08-07T15:09:06.947409","indexId":"70269918","displayToPublicDate":"2025-04-25T07:59:58","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9101,"text":"Ornithological Applications","printIssn":"0010-5422","active":true,"publicationSubtype":{"id":10}},"title":"Satellite imagery can predict bird species occupancy and inform multispecies management in pine savannas","docAbstract":"Multispecies management can contribute to meeting growing challenges of preserving biodiversity, yet current game and threatened species management often focuses on individual species. Satellite imagery available at high spatial and temporal resolution provides a potential tool to overcome the challenge posed by multispecies management of linking patterns of habitat use among species. We sought to determine whether satellite imagery could be used to describe patterns of species occupancy and inform multispecies management in pine savannas in Georgia, USA. We conducted point-count surveys at 7 sites in 2022 for 3 bird species: Colinus virginianus (Northern Bobwhite), Dryobates borealis (Red-cockaded Woodpecker), and Peucaea aestivalis (Bachman’s Sparrow). We built single-season occupancy models comparing a set of models using covariates collected from field vegetation surveys and another set using covariates extracted from Sentinel-2 satellite imagery. We then used a multi-objective optimization algorithm to identify quasi-optimal management solutions (i.e., sets of covariate values from top satellite imagery metric models). We found that models created using satellite imagery performed well at predicting occupancy of all 3 species as measured by the area under the receiver operating characteristic curve (AUC > 0.8) and had higher AUC scores than field-derived habitat covariate-based models. We found combinations of metrics that could result in high rates of predicted probability of occupancy for all species (within 86% of highest possible occupancy probability), but these combinations did not exist at any of the sites. Our results demonstrate that (1) satellite imagery can allow users to build reliable occupancy models without intensive field-based vegetation surveys; and (2) C. virginianus, D. borealis, and P. aestivalis in pine savanna ecosystems could be simultaneously managed through more frequent burning, changes in canopy cover or by producing suitable heterogeneity of habitats after identifying an appropriate scale of management.
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\"}}]}","edition":"Online First","noUsgsAuthors":false,"publicationDate":"2025-04-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Allred, Cory R.","contributorId":350504,"corporation":false,"usgs":false,"family":"Allred","given":"Cory","middleInitial":"R.","affiliations":[{"id":34764,"text":"Virginia Tech, Department of Fish and Wildlife Conservation","active":true,"usgs":false}],"preferred":false,"id":944953,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schneider, Todd M.","contributorId":359085,"corporation":false,"usgs":false,"family":"Schneider","given":"Todd","middleInitial":"M.","affiliations":[{"id":36378,"text":"Georgia Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":944954,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hunter, Elizabeth Ann 0000-0003-4710-167X","orcid":"https://orcid.org/0000-0003-4710-167X","contributorId":288535,"corporation":false,"usgs":true,"family":"Hunter","given":"Elizabeth","email":"","middleInitial":"Ann","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":944955,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70266112,"text":"70266112 - 2025 - Statistical approaches for modeling correlated grade and tonnage distributions and applications for mineral resource assessments","interactions":[],"lastModifiedDate":"2025-04-25T15:35:26.584633","indexId":"70266112","displayToPublicDate":"2025-04-24T10:32:31","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":14424,"text":"Applied Computing and Geosciences","active":true,"publicationSubtype":{"id":10}},"title":"Statistical approaches for modeling correlated grade and tonnage distributions and applications for mineral resource assessments","docAbstract":"Atmospheric rivers (ARs) drive most riverine floods on the United States (U.S.) West Coast. However, estimating flood risk based solely on AR intensity and duration is challenging because precipitation phase, antecedent conditions, and physical watershed characteristics (e.g., slope and soil depth) can influence the magnitude of floods. Here, we analyze how antecedent soil moisture (ASM) conditions contribute to variability in streamflow during AR events and how that changes across climatic regimes and physiography in 122 U.S. West Coast watersheds. We identify a robust non-linear relationship between streamflow and ASM during ARs in 89% of watersheds. The inflection point in this relationship represents a watershed-specific critical ASM threshold, above which event maximum streamflow is, on average, two to four and a half times larger. Wet ASM conditions amplify the hydrologic impacts of more frequent but weaker, lower moisture transport AR events, while dry ASM conditions attenuate the hydrologic impacts that stronger, higher moisture transport AR events could otherwise cause. Our research shows that watersheds prone to ASM-amplified streamflows have higher evaporation ratios, lower cold-season precipitation, lower snow-to-rain ratios, and shallower, clay-rich soils. Higher evaporation and lower precipitation lead to greater ASM variability during the cold season, increasing streamflow during wet periods and buffering streamflow during dry periods. Lower snow fraction and shallower soils limit the antecedent water storage capacity of a watershed, contributing to greater sensitivity of streamflow peaks to ASM variability. Incorporating ASM thresholds into hydrologic models in these regions prone to AR-amplified streamflow could improve forecasts and decrease uncertainty.
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Existing snow cover products do not reliably distinguish seasonal snow from glacier ice and firn, preventing their use for glacier snow cover detection. Despite previous efforts to classify glacier surface facies using machine learning on local scales, currently there is no published comparison of machine learning models for classifying glacier snow cover across different satellite image products. We present an automated snow detection workflow for mountain glaciers using supervised machine-learning-based image classifiers and Landsat 8 and 9, Sentinel-2, and PlanetScope satellite imagery. We develop the image classifiers by testing numerous machine learning algorithms with training and validation data from the U.S. Geological Survey Benchmark Glacier Project glaciers. The workflow produces daily to twice monthly time series of several glacier mass balance and snowmelt indicators (snow-covered area, accumulation area ratio, and seasonal snow line) from 2013 to present. Workflow performance is assessed by comparing automatically classified images and snow lines to manual interpretations at each glacier site. The image classifiers exhibit overall accuracies of 92%–98%, K scores of 84%–96%, and F scores of 93%–98% for all image products. The median difference between automatically and manually delineated median snow line altitudes is 31m (IQR of 73to0m)across all image products. The Sentinel-2 classifier (support vector machine) produces the most accurate glacier mass balance and snowmelt indicators and distinguishes snow from ice and f irn the most reliably. Although they are less accurate, the Landsat- and PlanetScope-derived estimates greatly enhance the temporal coverage of observations. The transient accumulation area ratio produces the least noisy time series, making it the most reliable indicator for characterizing seasonal snow trends. The temporally detailed accumulation area ratio time series reveal that the timing of minimum snow cover conditions varies by up to a month between Arctic (63°N) and midlatitude (48°N) sites, underscoring the potential for bias when estimating glacier minimum snow cover conditions from a single late-summer image. Widespread application of our automated snow detection workflow has the potential to improve regional assessments of glacier mass balance, land ice representations within Earth system models, water resources, and the impacts of climate change on snow cover across broad spatial scales.
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/tc-19-1675-2025","usgsCitation":"Aberle, R., Enderlin, E., O'Neel, S., Florentine, C., Sass, L., Dickson, A., Marshall, H., and Flores, A., 2025, Automated snow cover detection on mountain glaciers usingspaceborne imagery and machine learning: The Cryosphere, v. 19, p. 1675-1693, https://doi.org/10.5194/tc-19-1675-2025.","productDescription":"19 p.","startPage":"1675","endPage":"1693","ipdsId":"IP-161789","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":487924,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/tc-19-1675-2025","text":"Publisher Index Page"},{"id":485326,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, Unite States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.76360091465392,\n 47.06180837633883\n ],\n [\n -121.3176201884703,\n 48.805343460206615\n ],\n [\n -120.0015482436147,\n 50.335300241584264\n ],\n [\n -130.45623413084917,\n 62.431155673423405\n ],\n [\n -144.1767604606605,\n 64.77803371822662\n ],\n [\n -156.74065293077143,\n 62.94965967944643\n ],\n [\n -153.58716512013862,\n 58.34813914012679\n ],\n [\n -151.25294354192766,\n 56.670611414741046\n ],\n [\n -147.2303378681512,\n 59.785680900702204\n ],\n [\n -139.72127967128716,\n 59.315677918279675\n ],\n [\n -134.82069406634898,\n 55.48786621236388\n ],\n [\n -131.59648302534626,\n 51.752911462025\n ],\n [\n -123.76360091465392,\n 47.06180837633883\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"19","noUsgsAuthors":false,"publicationDate":"2025-04-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Aberle, Rainey","contributorId":354302,"corporation":false,"usgs":false,"family":"Aberle","given":"Rainey","affiliations":[],"preferred":false,"id":935376,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Enderlin, Ellyn","contributorId":187445,"corporation":false,"usgs":false,"family":"Enderlin","given":"Ellyn","email":"","affiliations":[],"preferred":false,"id":935377,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O'Neel, Shad 0000-0002-9185-0144","orcid":"https://orcid.org/0000-0002-9185-0144","contributorId":289666,"corporation":false,"usgs":false,"family":"O'Neel","given":"Shad","affiliations":[{"id":62222,"text":"Cold Regions Research Laboratory","active":true,"usgs":false}],"preferred":false,"id":935378,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Florentine, Caitlyn 0000-0002-7028-0963","orcid":"https://orcid.org/0000-0002-7028-0963","contributorId":205964,"corporation":false,"usgs":true,"family":"Florentine","given":"Caitlyn","email":"","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":935380,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sass, Louis C. 0000-0003-4677-029X lsass@usgs.gov","orcid":"https://orcid.org/0000-0003-4677-029X","contributorId":3555,"corporation":false,"usgs":true,"family":"Sass","given":"Louis C.","email":"lsass@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":935381,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dickson, Adam","contributorId":354305,"corporation":false,"usgs":false,"family":"Dickson","given":"Adam","affiliations":[],"preferred":false,"id":935383,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Marshall, Hans-Peter","contributorId":330964,"corporation":false,"usgs":false,"family":"Marshall","given":"Hans-Peter","email":"","affiliations":[{"id":33038,"text":"Department of Geosciences, Boise State University","active":true,"usgs":false}],"preferred":false,"id":935379,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Flores, Alejandro","contributorId":221466,"corporation":false,"usgs":false,"family":"Flores","given":"Alejandro","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":935382,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70268481,"text":"70268481 - 2025 - A review of post-wildfire adaptations of surface-water-quality models: Synthesis, gaps, and opportunities","interactions":[],"lastModifiedDate":"2025-06-27T15:13:12.47879","indexId":"70268481","displayToPublicDate":"2025-04-24T08:09:24","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"A review of post-wildfire adaptations of surface-water-quality models: Synthesis, gaps, and opportunities","docAbstract":"As wildfires increasingly affect water-supply watersheds, the demand for models to predict water-quality responses is increasing. This work reviews and synthesizes existing post-wildfire applications of water-quality models in the context of geographic and ecohydrological distribution, hydrologic and water-quality response process representation, model parameterization, model and input data scales, model calibration data availability, as well as calibration and performance evaluation approaches. Emphasis is placed on models that simulate water-quality output, rather than sediment and erosional response as the primary focus. Here, identified gaps and opportunities to advance the post-wildfire application of water-quality models include: 1. applying models in under-represented geographic and ecohydrologic regions, 2. simulating multiple streamflow generation mechanisms, including groundwater, with an emphasis on shifting dominant flow pathways as the landscape recovers following wildfire, 3. adding studies that include the simulation of metals, 4. incorporating more biogeochemical and in-stream processes to model applications, 5. applying finer spatial and temporal resolution of precipitation data input as well as finer spatial resolution hydrologic response units, 6. implementing fully distributed grid or element models or finer resolution response units to capture burn severity heterogeneity, 7. collecting enhanced water-quality data for model calibration and validation, 8. conducting model-intercomparison studies, and 9. developing model parameter value guidance in post-wildfire applications. These identified gaps and opportunities may assist users in deciding on key processes and approaches to consider in modeling post-wildfire water-quality conditions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2025.179435","usgsCitation":"Shephard, Z.M., Partridge, T.F., Murphy, S.F., Walvoord, M.A., and Ebel, B., 2025, A review of post-wildfire adaptations of surface-water-quality models: Synthesis, gaps, and opportunities: Science of the Total Environment, v. 979, 179435, 15 p., https://doi.org/10.1016/j.scitotenv.2025.179435.","productDescription":"179435, 15 p.","ipdsId":"IP-165381","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":491530,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"979","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Shephard, Zachary M. 0000-0003-2994-3355","orcid":"https://orcid.org/0000-0003-2994-3355","contributorId":222581,"corporation":false,"usgs":true,"family":"Shephard","given":"Zachary","email":"","middleInitial":"M.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":941495,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Partridge, Trevor Fuess 0000-0003-1589-4783","orcid":"https://orcid.org/0000-0003-1589-4783","contributorId":302668,"corporation":false,"usgs":true,"family":"Partridge","given":"Trevor","email":"","middleInitial":"Fuess","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":941496,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Murphy, Sheila F. 0000-0002-5481-3635 sfmurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-5481-3635","contributorId":1854,"corporation":false,"usgs":true,"family":"Murphy","given":"Sheila","email":"sfmurphy@usgs.gov","middleInitial":"F.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":941497,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Walvoord, Michelle A. 0000-0003-4269-8366","orcid":"https://orcid.org/0000-0003-4269-8366","contributorId":211843,"corporation":false,"usgs":true,"family":"Walvoord","given":"Michelle","email":"","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":941498,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ebel, Brian A. 0000-0002-5413-3963","orcid":"https://orcid.org/0000-0002-5413-3963","contributorId":211845,"corporation":false,"usgs":true,"family":"Ebel","given":"Brian A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":941499,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70266130,"text":"70266130 - 2025 - HarvestStat Africa – Harmonized subnational crop statistics for sub-Saharan Africa","interactions":[],"lastModifiedDate":"2025-04-30T14:55:18.955047","indexId":"70266130","displayToPublicDate":"2025-04-24T07:42:44","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12552,"text":"Scientific Data - Nature","active":true,"publicationSubtype":{"id":10}},"title":"HarvestStat Africa – Harmonized subnational crop statistics for sub-Saharan Africa","docAbstract":"Sub-Saharan Africa (SSA) faces severe agricultural data scarcity amidst high food insecurity and a large agricultural yield gap, making crop production data crucial for understanding and enhancing food systems. To address this gap, HarvestStat Africa presents the largest compilation of open-access subnational crop statistics and time-series across SSA. Based on agricultural statistics collated by USAID’s Famine Early Warning Systems Network, the subnational crop statistics are standardized and calibrated across changing administrative units to produce consistent and continuous time-series. The dataset includes 546,605 records, primarily spanning from 1980 to 2022, detailing crop production, harvested areas, and yields for 33 countries and 90 crop types, including key cereals in SSA such as wheat, maize, rice, sorghum, barley, millet, and fonio. This new dataset enhances our understanding of how climate variability and change influence agricultural production, supports subnational food system analysis, and aids in operational yield forecasting. As an open-source resource, it sets an important precedent for sharing subnational crop statistics to inform decision-making and modeling efforts.","language":"English","publisher":"Springer Nature","doi":"10.1038/s41597-025-05001-z","usgsCitation":"Lee, D., Anderson, W., Chen, X., Davenport, F., Shukla, S., Sahajpal, R., Budde, M., Rowland, J., Verdin, J., You, L., Ahouangbenon, M., Frankel Davis, K., Kebede, E., Ehrmann, S., Justice, C., and Meyer, C., 2025, HarvestStat Africa – Harmonized subnational crop statistics for sub-Saharan Africa: Scientific Data - Nature, v. 12, 690, 13 p., https://doi.org/10.1038/s41597-025-05001-z.","productDescription":"690, 13 p.","ipdsId":"IP-171185","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":487839,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41597-025-05001-z","text":"Publisher Index 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Matthieu","contributorId":353905,"corporation":false,"usgs":false,"family":"Ahouangbenon","given":"Matthieu","affiliations":[{"id":84528,"text":"Department of Geography and Spatial Sciences, University of Delaware, Newark, DE 19716 USA","active":true,"usgs":false}],"preferred":false,"id":934718,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Frankel Davis, Kyle","contributorId":209958,"corporation":false,"usgs":false,"family":"Frankel Davis","given":"Kyle","email":"","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":934719,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Kebede, Endalkachew","contributorId":353906,"corporation":false,"usgs":false,"family":"Kebede","given":"Endalkachew","affiliations":[{"id":84528,"text":"Department of Geography and Spatial Sciences, University of Delaware, Newark, DE 19716 USA","active":true,"usgs":false}],"preferred":false,"id":934720,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Ehrmann, Steffen","contributorId":353907,"corporation":false,"usgs":false,"family":"Ehrmann","given":"Steffen","affiliations":[{"id":13099,"text":"German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany","active":true,"usgs":false}],"preferred":false,"id":934721,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Justice, Christina","contributorId":347086,"corporation":false,"usgs":false,"family":"Justice","given":"Christina","email":"","affiliations":[{"id":37106,"text":"Cherokee Nation","active":true,"usgs":false}],"preferred":false,"id":934722,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Meyer, Carsten","contributorId":193124,"corporation":false,"usgs":false,"family":"Meyer","given":"Carsten","email":"","affiliations":[],"preferred":false,"id":934723,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70266022,"text":"ofr20211030T - 2025 - System characterization report on Resourcesat-2A Linear Imaging Self Scanning-3 sensor","interactions":[{"subject":{"id":70266022,"text":"ofr20211030T - 2025 - System characterization report on Resourcesat-2A Linear Imaging Self Scanning-3 sensor","indexId":"ofr20211030T","publicationYear":"2025","noYear":false,"chapter":"T","displayTitle":"System Characterization Report on Resourcesat-2A Linear Imaging Self Scanning-3 Sensor","title":"System characterization report on Resourcesat-2A Linear Imaging Self Scanning-3 sensor"},"predicate":"IS_PART_OF","object":{"id":70221266,"text":"ofr20211030 - 2021 - System characterization of Earth observation sensors","indexId":"ofr20211030","publicationYear":"2021","noYear":false,"title":"System characterization of Earth observation sensors"},"id":1}],"isPartOf":{"id":70221266,"text":"ofr20211030 - 2021 - System characterization of Earth observation sensors","indexId":"ofr20211030","publicationYear":"2021","noYear":false,"title":"System characterization of Earth observation sensors"},"lastModifiedDate":"2025-04-24T14:11:03.291909","indexId":"ofr20211030T","displayToPublicDate":"2025-04-23T12:23:04","publicationYear":"2025","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":"2021-1030","chapter":"T","displayTitle":"System Characterization Report on Resourcesat-2A Linear Imaging Self Scanning-3 Sensor","title":"System characterization report on Resourcesat-2A Linear Imaging Self Scanning-3 sensor","docAbstract":"This report addresses system characterization of the Indian Space Research Organisation Resourcesat-2A Linear Imaging Self Scanning-3 sensor and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence since 2021. These reports present and detail the methodology and procedures for characterization, present technical and operational information about the specific sensing system being evaluated, and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
Resourcesat-2A is identical to Resourcesat-2 and was launched in 2016 on the Polar Satellite Launch Vehicle-C36 for continuity of data and improved temporal resolution. The Resourcesat-2 platform (which includes Resourcesat-2A) is of Indian Remote Sensing Satellites-1C/1D–P3 heritage and was built by the Indian Space Research Organisation. Resourcesat-2 and Resourcesat-2A carry the Linear Imaging Self Scanning-3 and Linear Imaging Self Scanning-4 sensors for medium-resolution imaging. More information on Indian Space Research Organisation satellites and sensors is available in the “2022 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://www.isro.gov.in/.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances.
To summarize the results, we have determined that this sensor provides an interior geometric performance with mean offsets in the range of 1.75 meters (m; 0.06 pixel) to 6.83 m (0.23 pixel) in easting and −1.83 m (−0.06 pixel) to 1.81 m (0.06 pixel) in northing in band-to-band registration and a root mean square error in the range of 3.81 m (0.13 pixel) to 8.19 m (0.27 pixel) in easting and 2.21 m (0.09 pixel) to 4.72 m (0.16 pixel) in northing.
We have measured an exterior geometric error offset in the range of −21.29 to 6.88 m in easting and −7.35 to −2.63 m in northing, and the root mean square error is in the range of 7.19 to 21.43 m in easting and 3.64 to 8.19 m in northing in comparison to the Landsat 8 Operational Land Imager.
The measured radiometric performance was in the range of −0.002 to 0.031 in offset and 0.701 to 0.940 in slope, and the spatial performance was in the range of 1.204 to 1.265 pixels for full width at half maximum with a modulation transfer function at a Nyquist frequency in the range of 0.251 to 0.277.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030T","usgsCitation":"Park, S., Shrestha, M., Kim, M., Sampath, A., and Clauson, J., 2025, System characterization report on Resourcesat-2A Linear Imaging Self Scanning-3 sensor, chap. T of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 17 p., https://doi.org/10.3133/ofr20211030T.","productDescription":"v, 17 p.","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-170097","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":484900,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1030/t/images/"},{"id":484899,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1030/t/ofr20211030t.XML"},{"id":484898,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/t/ofr20211030t.pdf","text":"Report","size":"3.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1030-T"},{"id":484897,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/t/coverthb.jpg"},{"id":484901,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20211030T/full"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Many coastal urban areas are prone to extreme pluvial flooding due to limitations in stormwater system capacity, with the additional potential for flooding compounded by storm surge, tides, and waves. Understanding and simulating these processes can improve prediction and flood risk management. Here, we adapt the Coupled Ocean–Atmosphere–Wave–Sediment Transport modeling framework (COAWST) to simulate pluvial flooding from post-tropical Cyclone Ida (2021) in the Jamaica Bay watershed of New York City (NYC). We modify the model to capture the volumetric effects of rainfall and parameterize soil infiltration and a stormwater conveyance system as the drainage rate. We generate a spatially continuous flood map of Ida with a root-mean-square error (RMSE) of 20 cm when compared to high-water marks, useful for understanding Ida's impacts and subsequent mitigation planning. Results show that over 23 km2 and 4621 buildings were flooded deeper than 0.3 m during Ida. Sensitivity analyses are used to study the broader risk from events like Ida (pluvial flooding) as well as potential compound (pluvial–coastal) flooding. Spatial shifting of the storm track within a typical 12 h forecast uncertainty reveals a worst-case scenario that increases this flooded area to 62 km2 (5907 buildings). Shifting Ida's rainfall to coincide with high tide increases this flooded area by 1 km2, a relatively small change due to the lack of significant storm surge. The application of COAWST to this storm event addresses a broader goal of developing the capability to model compound pluvial–coastal flooding by simultaneously representing coastal storm processes such as rain, tide, waves, erosion, and atmosphere–wave–ocean interactions. The sensitivity analysis results underscore the need for detailed flood risk assessments, showing that Ida, already NYC's worst rain event, could have been even more devastating with slight shifts in the storm track.
","language":"English","publisher":"European Geosciences Union","doi":"10.5194/hess-29-2043-2025","usgsCitation":"Kasaei, S., Orton, P.M., Ralston, D.K., and Warner, J., 2025, Pluvial and potential compound flooding in a coupled coastal modeling framework: New York City during post-tropical Cyclone Ida (2021): Hydrology and Earth System Sciences, v. 29, no. 8, p. 2043-2058, https://doi.org/10.5194/hess-29-2043-2025.","productDescription":"16 p.","startPage":"2043","endPage":"2058","ipdsId":"IP-168323","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":502086,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/hess-29-2043-2025","text":"Publisher Index Page"},{"id":502009,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","city":"New York City","otherGeospatial":"Jamaica Bay watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.01902277171598,\n 40.81541595710692\n ],\n [\n -74.01902277171598,\n 40.5619948325492\n ],\n [\n -73.61280152477734,\n 40.5619948325492\n ],\n [\n -73.61280152477734,\n 40.81541595710692\n ],\n [\n -74.01902277171598,\n 40.81541595710692\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"29","issue":"8","noUsgsAuthors":false,"publicationDate":"2025-04-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Kasaei, Shima","contributorId":369142,"corporation":false,"usgs":false,"family":"Kasaei","given":"Shima","affiliations":[{"id":28243,"text":"Stevens Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":958528,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Orton, Phillip M.","contributorId":369143,"corporation":false,"usgs":false,"family":"Orton","given":"Phillip","middleInitial":"M.","affiliations":[{"id":28243,"text":"Stevens Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":958529,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ralston, David K.","contributorId":369144,"corporation":false,"usgs":false,"family":"Ralston","given":"David","middleInitial":"K.","affiliations":[{"id":36711,"text":"Woods Hole Oceanographic Institution","active":true,"usgs":false}],"preferred":false,"id":958530,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Warner, John C. 0000-0002-3734-8903 jcwarner@usgs.gov","orcid":"https://orcid.org/0000-0002-3734-8903","contributorId":2681,"corporation":false,"usgs":true,"family":"Warner","given":"John C.","email":"jcwarner@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":958531,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70266396,"text":"70266396 - 2025 - Fisheries dependent and independent data inform a capture technique for an emerging invasive fish species in the mainstem Mississippi River; Black Carp Mylopharyngodon piceus","interactions":[],"lastModifiedDate":"2025-05-06T14:11:54.318665","indexId":"70266396","displayToPublicDate":"2025-04-23T09:02:42","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1661,"text":"Fisheries Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Fisheries dependent and independent data inform a capture technique for an emerging invasive fish species in the mainstem Mississippi River; Black Carp Mylopharyngodon piceus","title":"Fisheries dependent and independent data inform a capture technique for an emerging invasive fish species in the mainstem Mississippi River; Black Carp Mylopharyngodon piceus","docAbstract":"Black Carp Mylopharyngodon piceus were imported into the United States in the 1970s and 1980s for use in aquaculture; escape occurred and reported wild captures increased. Lacking species-specific capture methods, we assessed fisheries dependent incidental Black Carp catches for a common method, hoop nets, by kernel density analysis to identify an area of increased reporting and compare frequency of reports for water temperature, river stage, and capture date to identify seasonality. We then used fisheries independent effort to identify co-occurrence of species via non-metric multi-dimensional scaling and fit Black Carp catch and environmental covariates by generalized linear models to assess site-specific environmental covariates facilitating capture. The best approximating distribution was refitted for predictions and inference. The greatest density of fisheries dependent hoop net captures (39 %) was near the confluence of the Missouri and Mississippi rivers, primarily from July-September. Captures were characterized by median water temperature 26.7°C, river stage 5.02 m, and 223 day-of-year (DOY; mid-August). Ordination of fisheries independent catch identified similarity in environmental covariates of Smallmouth Buffalo Ictiobus bubalus and Black Carp. The probability of capturing ≥ 1 Black Carp increased with DOY, decreased with increasing current velocity, and increased with depth. Most captures occurred in outside bends (87 %) or side channels (12 %). Probability of Black Carp capture was low but increased in summer and early fall when stage is lower, facilitating reduced current velocity and access to deeper areas. Results may be validated beyond this river segment to test if site-specific hydrology or habitat characteristics facilitated increased commercial and biologist capture and for replication.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.fishres.2025.107368","usgsCitation":"Kroboth, P., Colvin, M.E., and Broaddus, C., 2025, Fisheries dependent and independent data inform a capture technique for an emerging invasive fish species in the mainstem Mississippi River; Black Carp Mylopharyngodon piceus: Fisheries Research, v. 285, 107368, 12 p., https://doi.org/10.1016/j.fishres.2025.107368.","productDescription":"107368, 12 p.","ipdsId":"IP-167531","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":487576,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.fishres.2025.107368","text":"Publisher Index Page"},{"id":485444,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Missouri","otherGeospatial":"MIssissippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.22145042868262,\n 38.90746978465282\n ],\n [\n -90.22145042868262,\n 38.666188258783194\n ],\n [\n -90.1030809646113,\n 38.666188258783194\n ],\n [\n -90.1030809646113,\n 38.90746978465282\n ],\n [\n -90.22145042868262,\n 38.90746978465282\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"285","noUsgsAuthors":false,"publicationDate":"2025-04-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Kroboth, Patrick 0000-0002-9447-4818","orcid":"https://orcid.org/0000-0002-9447-4818","contributorId":216578,"corporation":false,"usgs":true,"family":"Kroboth","given":"Patrick","email":"","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":935820,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Colvin, Michael E. 0000-0002-6581-4764","orcid":"https://orcid.org/0000-0002-6581-4764","contributorId":331490,"corporation":false,"usgs":true,"family":"Colvin","given":"Michael","email":"","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":935821,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Broaddus, Courtney 0000-0003-3851-3584","orcid":"https://orcid.org/0000-0003-3851-3584","contributorId":354595,"corporation":false,"usgs":true,"family":"Broaddus","given":"Courtney","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":935822,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70265982,"text":"sir20255029 - 2025 - Spatial stream network modeling of water temperature within the White River Basin, Mount Rainier National Park, Washington","interactions":[{"subject":{"id":70257569,"text":"70257569 - 2024 - Spatial variability of water temperature within the White River basin, Mount Rainier National Park Washington","indexId":"70257569","publicationYear":"2024","noYear":false,"title":"Spatial variability of water temperature within the White River basin, Mount Rainier National Park Washington"},"predicate":"SUPERSEDED_BY","object":{"id":70265982,"text":"sir20255029 - 2025 - Spatial stream network modeling of water temperature within the White River Basin, Mount Rainier National Park, Washington","indexId":"sir20255029","publicationYear":"2025","noYear":false,"title":"Spatial stream network modeling of water temperature within the White River Basin, Mount Rainier National Park, Washington"},"id":1}],"lastModifiedDate":"2025-08-07T21:05:21.759632","indexId":"sir20255029","displayToPublicDate":"2025-04-23T07:58:02","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-5029","displayTitle":"Spatial Stream Network Modeling of Water Temperature within the White River Basin, Mount Rainier National Park, Washington","title":"Spatial stream network modeling of water temperature within the White River Basin, Mount Rainier National Park, Washington","docAbstract":"Water temperature is a primary control on the occurrence and distribution of fish and other ectothermic aquatic species. In the Pacific Northwest, cold-water species such as Pacific salmon (Oncorhynchus spp.) and bull trout (Salvelinus confluentus) have specific temperature requirements during different life stages that must be met to ensure the viability of their populations. Rivers draining Mount Rainier in western Washington, including the White River along its northern flank, support a number of cold-water fish populations, but the spatial distribution of water temperatures, particularly during late-summer baseflow during August and September, and the climatic, hydrologic, and physical processes regulating it are not well constrained. Spatial stream network (SSN) models, which are generalized linear models that incorporate streamwise spatial autocovariance structures, were fit to mean and 7-day average daily maximum water temperature for August and September for the White River Basin. The SSN models were calibrated using water temperature measurements collected in 2010 through 2020. The extent of the models included the White River and its tributaries upstream from its confluence with Silver Creek in Mount Rainier National Park, Washington. SSN models incorporated covariates hypothesized to represent the climatic, hydrologic, and physical processes that influence water temperature. SSN models were fit to the measured data and compared to generalized linear models that lacked spatial autocovariance structures. Statistically significant covariates within the best-fit models included the proportion of ice cover and forest cover within the basin, mean August air temperature, the proportion of consolidated geologic units, and snow-water equivalent. Statistical models that included spatial autocovariance structures had better predictive performance than those that did not. Additionally, models of mean August and September water temperature had better predictive performance than those of 7-day average daily maximum temperature in August and September. Predictions of the spatial distribution of water temperature were similar between August and September with a general warming in the downstream part of the mainstem White River compared to cooler water temperatures in the high-elevation headwater streams. The proportion of ice cover emerged as an inversely related significant covariate to both mean August and September water temperature because streams that receive glacial meltwater are colder than non-glaciated streams. Water temperatures of the upper White River increased downstream and are attributed to warming of water temperature from accumulated solar radiation and inflow of non-glaciated tributaries. Estimated water temperatures for the upper White River model are 3–4 degrees Celsius (°C) warmer for tributaries, but 1–2 °C cooler for the mainstem compared to the regional-scale model. Differences between the upper White River SSN model and the regional-scale NorWeST model are attributed to the fact that the upper White River SSN included water temperature observations specific to the upper White River, whereas water temperature observations from lower elevation streams and downstream from the Mount Rainer National Park boundary were used in the regional scale model.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255029","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Gendaszek, A.S., Leach, A.C., and Jaeger, K.L., 2025, Spatial stream network modeling of water temperature within the White River Basin, Mount Rainier National Park, Washington (ver. 1.1, May 2025): U.S. Geological Survey\nScientific Investigations Report 2025–5029, 17 p., https://doi.org/10.3133/sir20255029. [Supersedes preprint https://doi.org/10.31223/X5712P.]","productDescription":"Report: vi, 17 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-168299","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":484931,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://www.sciencebase.gov/catalog/item/6542802dd34ee4b6e05bd2cb","text":"USGS data release","description":"USGS data release","linkHelpText":"Stream Temperature Models of White River Watershed, Mount Rainier National Park, Washington"},{"id":484872,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5029/sir20255029.XML"},{"id":484871,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5029/images"},{"id":484870,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255029/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5029"},{"id":484869,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5029/sir20255029.pdf","text":"Report","size":"4.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5029"},{"id":484868,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5029/coverthb2.jpg"},{"id":486241,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2025/5029/versionHistory.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"},"description":"Version History"},{"id":493767,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118576.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Washington","otherGeospatial":"Mount Rainier National Park, upper White River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.75,\n 47\n ],\n [\n -121.75,\n 46.8333\n ],\n [\n -121.5,\n 46.8333\n ],\n [\n -121.5,\n 47\n ],\n [\n -121.75,\n 47\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: April 23.2025; Version 1.1: May 20, 2025","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
Previous efforts to characterize tsunami threats to people have focused primarily on individual scenarios in specific areas but have not recognized multiple scenarios across an entire country. This study addresses this gap by quantifying population exposure and evacuation potential in the United States to 102 earthquake-related, tsunami-hazard zones, including 92 local scenarios, 8 distant scenarios, and 2 probabilistic products. Geospatial path-distance modeling quantified evacuation potential and the influence of departure delays. We focused on residents to support other national, multi-hazard risk analyses. Millions of residents are in distant-tsunami zones, and hundreds of thousands of residents are in local-tsunami zones. In 41 scenarios, there is at least one resident that may have insufficient time to evacuate before wave arrival. Tens of thousands of residents may have insufficient time to evacuate from local tsunamis that impact the U.S. Pacific Northwest or Puerto Rican coastlines. The largest improvements in evacuation potential may come from reducing departure delays in some areas but may involve vertical-evacuation structures or changing land use in other areas.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ijdrr.2025.105511","usgsCitation":"Wood, N.J., Peters, J., Sheehan, A., and Bausch, D., 2025, National population exposure and evacuation potential in the United States to earthquake-generated tsunami threats: International Journal of Disaster Risk Reduction, v. 123, 105511, 18 p., https://doi.org/10.1016/j.ijdrr.2025.105511.","productDescription":"105511, 18 p.","ipdsId":"IP-176718","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":485209,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"123","noUsgsAuthors":false,"publicationDate":"2025-04-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Wood, Nathan J. 0000-0002-6060-9729 nwood@usgs.gov","orcid":"https://orcid.org/0000-0002-6060-9729","contributorId":3347,"corporation":false,"usgs":true,"family":"Wood","given":"Nathan","email":"nwood@usgs.gov","middleInitial":"J.","affiliations":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true}],"preferred":true,"id":934864,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peters, Jeff 0000-0003-4312-0590 jpeters@usgs.gov","orcid":"https://orcid.org/0000-0003-4312-0590","contributorId":4711,"corporation":false,"usgs":true,"family":"Peters","given":"Jeff","email":"jpeters@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":934865,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sheehan, Anne 0009-0005-0636-6892","orcid":"https://orcid.org/0009-0005-0636-6892","contributorId":358952,"corporation":false,"usgs":false,"family":"Sheehan","given":"Anne","affiliations":[{"id":30786,"text":"FEMA","active":true,"usgs":false}],"preferred":false,"id":934866,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bausch, Doug","contributorId":195191,"corporation":false,"usgs":false,"family":"Bausch","given":"Doug","email":"","affiliations":[{"id":34169,"text":"Pacific Disaster Center","active":true,"usgs":false}],"preferred":false,"id":934867,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70273768,"text":"70273768 - 2025 - Seasonal movements and demographics of the endangered White River Spinedace to inform restoration and translocation","interactions":[],"lastModifiedDate":"2026-01-28T16:54:16.957569","indexId":"70273768","displayToPublicDate":"2025-04-22T09:46:46","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Seasonal movements and demographics of the endangered White River Spinedace to inform restoration and translocation","docAbstract":"Objective
Translocation is a tool being explored to restart extirpated populations or facilitate new populations of endangered spring-dependent fish populations. Our objective was to provide information on habitat requirements for endangered White River Spinedace Lepidomeda albivallis during all seasons of the year and the population demographics that are necessary to plan conservation translocations of this species
Methods
We tagged and released White River Spinedace with passive integrated transponders during four twice-a-year events. Fish were subsequently recaptured or detected on six passive antennas placed throughout the Flag Springs Complex, Nevada. We evaluated movement data to understand seasonal habitat use patterns, used a Barker model to estimate monthly survival rates, adjusted counts to account for capture probability and estimate abundance, and applied reverse-time mark–recapture models to estimate recruitment to 70 mm total length.
Results
White River Spinedace were more active but used similar habitats during spawning seasons than during nonspawning seasons. Median life expectancy was about 5 months after tagging, and only 1% of adult White River Spinedace survived 3–4 years posttagging. The estimated population size in the Flag Springs Complex during our sampling period (November 2020 to June 2022) was fewer than a thousand White River Spinedace, and this estimate has been steady or slightly increasing.
Conclusions
Complex spring habitats with water temperatures ranging about 13°C to 21°C that are free from piscivorous fish are appropriate for White River Spinedace. The White River Spinedace population at Flag Springs is small but stable or increasing in size.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/tafafs/vnaf007","usgsCitation":"Burdick, S.M., Harter, J.F., Beckstrand, M., Paul-Wilson, R.K., Hayes, B., Perry, R.W., and Smith, C.D., 2025, Seasonal movements and demographics of the endangered White River Spinedace to inform restoration and translocation: Transactions of the American Fisheries Society, v. 154, no. 3, p. 246-261, https://doi.org/10.1093/tafafs/vnaf007.","productDescription":"16 p.","startPage":"246","endPage":"261","ipdsId":"IP-165644","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":499182,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"154","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-04-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Burdick, Summer M. 0000-0002-3480-5793 sburdick@usgs.gov","orcid":"https://orcid.org/0000-0002-3480-5793","contributorId":3448,"corporation":false,"usgs":true,"family":"Burdick","given":"Summer","email":"sburdick@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":954695,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harter, James F.","contributorId":365736,"corporation":false,"usgs":false,"family":"Harter","given":"James","middleInitial":"F.","affiliations":[{"id":87201,"text":"United States Fish and Wildlife Service, Las Vegas, Nevada","active":true,"usgs":false}],"preferred":false,"id":954696,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beckstrand, Mark","contributorId":365737,"corporation":false,"usgs":false,"family":"Beckstrand","given":"Mark","affiliations":[{"id":87202,"text":"Nevada Department of Wildlife, Eli, Nevada","active":true,"usgs":false}],"preferred":false,"id":954697,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Paul-Wilson, Rachael Katelyn 0000-0002-8213-1084","orcid":"https://orcid.org/0000-0002-8213-1084","contributorId":298894,"corporation":false,"usgs":true,"family":"Paul-Wilson","given":"Rachael","email":"","middleInitial":"Katelyn","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":954698,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hayes, Brian S. 0000-0001-8229-4070","orcid":"https://orcid.org/0000-0001-8229-4070","contributorId":37022,"corporation":false,"usgs":true,"family":"Hayes","given":"Brian S.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":false,"id":954699,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Perry, Russell W. 0000-0003-4110-8619","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":214553,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","middleInitial":"W.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":954700,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Smith, Collin D. 0000-0003-4184-5686 cdsmith@usgs.gov","orcid":"https://orcid.org/0000-0003-4184-5686","contributorId":3111,"corporation":false,"usgs":true,"family":"Smith","given":"Collin","email":"cdsmith@usgs.gov","middleInitial":"D.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":954701,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70270100,"text":"70270100 - 2025 - Discovery of late Holocene-aged Acropora palmata reefs in Dry Tortugas National Park, Florida, USA: The past as a key to the future?","interactions":[],"lastModifiedDate":"2025-08-11T15:39:28.971692","indexId":"70270100","displayToPublicDate":"2025-04-22T08:35:46","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5781,"text":"The Depositional Record","active":true,"publicationSubtype":{"id":10}},"title":"Discovery of late Holocene-aged Acropora palmata reefs in Dry Tortugas National Park, Florida, USA: The past as a key to the future?","docAbstract":"Emblematic of global coral-reef ecosystem decline, the coral ecosystem-engineer Acropora palmata is now rare throughout much of the western Atlantic. Understanding when and where this foundation species occurred during the past can provide information about the environmental limits defining its distribution through space and time. In this paper, the present, historical and newly dated geological records of A. palmata are compared to reveal novel insights into the environmental constraints on its occurrence in Dry Tortugas National Park, a subtropical reef system at the south-western terminus of the Florida reef tract. Although past geological investigation found little evidence of the species in the park, a single, moderately sized A. palmata reef existed throughout historical times (1881 Common Era [CE] to present day; ‘historical population’, termed herein). Over the last 140 years, repeated population declines occurred with little to no recovery, culminating in the extirpation of A. palmata from the area during the 2023–2024 CE global coral bleaching event. Reported here for the first time is a significant record of Late Holocene A. palmata populations that existed from ca 4500 to 375 years before present (‘Late Holocene population,’ termed herein) in three broadly distributed areas of the shallow Dry Tortugas platform. This discovery challenges previous assumptions regarding the species' limited contribution to reef development in the area by providing data that extend the known spatial and stratigraphic extent of Holocene populations in this location. It is posited that, although the Late Holocene climate largely suppressed regional reef development, the new records provide evidence for centennial-scale periods of more favourable and stable climate that allowed for short-term expansions of A. palmata populations in the Dry Tortugas. In conclusion, the species' prospects for future success in this and other subtropical location
","language":"English","publisher":"Wiley","doi":"10.1002/dep2.70005","usgsCitation":"Stathakopoulos, A., Toth, L., Modys, P.A., Johnson, S.A., and Kuffner, I.B., 2025, Discovery of late Holocene-aged Acropora palmata reefs in Dry Tortugas National Park, Florida, USA: The past as a key to the future?: The Depositional Record, v. 11, no. 3, p. 808-828, https://doi.org/10.1002/dep2.70005.","productDescription":"21 p.","startPage":"808","endPage":"828","ipdsId":"IP-169190","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":494189,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/dep2.70005","text":"Publisher Index Page"},{"id":493935,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Dry Tortugas National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.9661549521622,\n 24.68040740481777\n ],\n [\n -82.9661549521622,\n 24.595463709079198\n ],\n [\n -82.8127098632192,\n 24.595463709079198\n ],\n [\n -82.8127098632192,\n 24.68040740481777\n ],\n [\n -82.9661549521622,\n 24.68040740481777\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"11","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-04-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Stathakopoulos, Anastasios 0000-0002-4404-035X astathakopoulos@usgs.gov","orcid":"https://orcid.org/0000-0002-4404-035X","contributorId":147744,"corporation":false,"usgs":true,"family":"Stathakopoulos","given":"Anastasios","email":"astathakopoulos@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":945450,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Toth, Lauren T. 0000-0002-2568-802X ltoth@usgs.gov","orcid":"https://orcid.org/0000-0002-2568-802X","contributorId":181748,"corporation":false,"usgs":true,"family":"Toth","given":"Lauren","email":"ltoth@usgs.gov","middleInitial":"T.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":945451,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Modys, Peter Alexander Bacon 0000-0002-2948-5983","orcid":"https://orcid.org/0000-0002-2948-5983","contributorId":336719,"corporation":false,"usgs":true,"family":"Modys","given":"Peter","email":"","middleInitial":"Alexander Bacon","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":945452,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Selena Anne-Marie 0000-0003-1015-1788","orcid":"https://orcid.org/0000-0003-1015-1788","contributorId":296373,"corporation":false,"usgs":true,"family":"Johnson","given":"Selena","email":"","middleInitial":"Anne-Marie","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":945453,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kuffner, Ilsa B. 0000-0001-8804-7847 ikuffner@usgs.gov","orcid":"https://orcid.org/0000-0001-8804-7847","contributorId":3105,"corporation":false,"usgs":true,"family":"Kuffner","given":"Ilsa","email":"ikuffner@usgs.gov","middleInitial":"B.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":945454,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70265960,"text":"70265960 - 2025 - Simulated effects of future water availability and protected species habitat in a perennial wetland, Santa Barbara County, California","interactions":[],"lastModifiedDate":"2025-04-23T13:18:13.267716","indexId":"70265960","displayToPublicDate":"2025-04-21T11:18:45","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Simulated effects of future water availability and protected species habitat in a perennial wetland, Santa Barbara County, California","docAbstract":"This study evaluates the potential water availability in Barka Slough and the effects of changing hydrological conditions on the aquatic habitat of five protected species. Barka Slough is a historically perennial wetland at the downstream western end of the San Antonio Creek Valley watershed (SACVW). A previously published hydrologic model of the SACVW for 1948–2018 was extended to include 2019–2021 and then modified to simulate the future years of 2022–2051. Two models simulating the future years of 2022–2051 were constructed, each with different climate inputs: (1) a repeated historical climate and (2) a 2070-centered Drier Extreme Warming climate (2070 DEW). The model with the 2070 DEW climate had warmer temperatures and an increase in average annual precipitation driven by larger, albeit more infrequent, precipitation events than the model with the historical climate. Simulated groundwater pumpage resulted in cumulative groundwater storage depletion and groundwater-level decline in Barka Slough in both future models. The simulations indicate that Barka Slough may transition from a perennial to an ephemeral wetland. Streamflow, stream disconnection, and depth to groundwater are key habitat metrics for federally listed species in Barka Slough. Future seasonal conditions for each metric are more likely to affect federally listed species’ habitats under 2070 DEW climatic conditions. Future seasonal streamflow volume may negatively impact unarmored threespine stickleback (Gasterosteus aculeatus williamsoni) and tidewater goby (Eucyclogobis newberryi) habitats. Future seasonal stream disconnection may negatively impact the unarmored threespine stickleback habitat. Future groundwater-level decline may negatively impact Gambel’s watercress (Nasturtium gambelii) and La Graciosa thistle (Cirsium scariosum var. loncholepis) habitats and could influence the ability to use Barka Slough as a restoration or reintroduction site for these species. Results from this study can be used to inform water management decisions to sustain future groundwater availability in the SACVW.
","language":"English","publisher":"MDPI","doi":"10.3390/w17081238","usgsCitation":"Cromwell, G., Culling, D., Young, M.J., and Larsen, J., 2025, Simulated effects of future water availability and protected species habitat in a perennial wetland, Santa Barbara County, California: Water, v. 17, no. 8, 1238, 29 p., https://doi.org/10.3390/w17081238.","productDescription":"1238, 29 p.","ipdsId":"IP-168161","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":488483,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w17081238","text":"Publisher Index Page"},{"id":484844,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Santa Barbara County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120.5333,\n 34.85\n ],\n [\n -120.5333,\n 34.6833\n ],\n [\n -120.1,\n 34.6833\n ],\n [\n -120.1,\n 34.85\n ],\n [\n -120.5333,\n 34.85\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"17","issue":"8","noUsgsAuthors":false,"publicationDate":"2025-04-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Cromwell, Geoffrey 0000-0001-8481-405X gcromwell@usgs.gov","orcid":"https://orcid.org/0000-0001-8481-405X","contributorId":5920,"corporation":false,"usgs":true,"family":"Cromwell","given":"Geoffrey","email":"gcromwell@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":934165,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Culling, Daniel Philip 0000-0002-6585-0650","orcid":"https://orcid.org/0000-0002-6585-0650","contributorId":299662,"corporation":false,"usgs":true,"family":"Culling","given":"Daniel Philip","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":934166,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Young, Matthew J. 0000-0001-9306-6866 mjyoung@usgs.gov","orcid":"https://orcid.org/0000-0001-9306-6866","contributorId":206255,"corporation":false,"usgs":true,"family":"Young","given":"Matthew","email":"mjyoung@usgs.gov","middleInitial":"J.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":934167,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Larsen, Joshua 0000-0002-1218-800X jlarsen@usgs.gov","orcid":"https://orcid.org/0000-0002-1218-800X","contributorId":272403,"corporation":false,"usgs":true,"family":"Larsen","given":"Joshua","email":"jlarsen@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":934168,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70266049,"text":"70266049 - 2025 - Mahi-mahi metacouplings: Quantifying human–nature interactions in dolphinfish (Coryphaena hippurus) fisheries","interactions":[],"lastModifiedDate":"2025-04-24T16:03:22.35888","indexId":"70266049","displayToPublicDate":"2025-04-21T11:01:01","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":21212,"text":"Global Sustainability","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Mahi-mahi metacouplings: Quantifying human–nature interactions in dolphinfish (Coryphaena hippurus) fisheries","title":"Mahi-mahi metacouplings: Quantifying human–nature interactions in dolphinfish (Coryphaena hippurus) fisheries","docAbstract":"Fisheries encompass humans and fish, but fisheries researchers rarely model human–nature interactions over space and time. I filled this information gap for dolphinfish (Coryphaena hippurus), a popular, widely distributed species that supports industrial, artisanal, recreational, and subsistence fisheries. Dolphinfish human–nature interactions showed a long-term up-and-down pattern in 1950–2019. Recent declines in catch mirror decreases in abundance and size that have been observed in parts of the species’ range. This research provides a robust perspective on the recreational, economic, cultural, and nutritional significance of dolphinfish while creating an approach for evaluating human–nature interactions in fisheries worldwide.","language":"English","publisher":"Cambridge University Press","doi":"10.1017/sus.2025.3","usgsCitation":"Carlson, A.K., 2025, Mahi-mahi metacouplings: Quantifying human–nature interactions in dolphinfish (Coryphaena hippurus) fisheries: Global Sustainability, https://doi.org/10.1017/sus.2025.3.","ipdsId":"IP-167357","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":487905,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1017/sus.2025.3","text":"Publisher Index Page"},{"id":484991,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"Online First","noUsgsAuthors":false,"publicationDate":"2025-04-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Carlson, Andrew Kenneth 0000-0002-6681-0853","orcid":"https://orcid.org/0000-0002-6681-0853","contributorId":340581,"corporation":false,"usgs":true,"family":"Carlson","given":"Andrew","email":"","middleInitial":"Kenneth","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":934452,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70273070,"text":"70273070 - 2025 - Interspecific effects of invasive wild pigs (Sus scrofa) on native nine-banded armadillos (Dasypus novemcinctus)","interactions":[],"lastModifiedDate":"2025-12-15T14:45:22.18155","indexId":"70273070","displayToPublicDate":"2025-04-21T08:24:54","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2373,"text":"Journal of Mammalogy","onlineIssn":"1545-1542","printIssn":"0022-2372","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Interspecific effects of invasive wild pigs (Sus scrofa) on native nine-banded armadillos (Dasypus novemcinctus)","title":"Interspecific effects of invasive wild pigs (Sus scrofa) on native nine-banded armadillos (Dasypus novemcinctus)","docAbstract":"Biological invasions pose significant risks to ecosystems and native species. Wild pigs (Sus scrofa) are a highly detrimental invasive species in North America, directly and indirectly affecting native species. Co-occurrence of wild pigs and native species may lead to interspecific interactions that alter ecological communities. Accordingly, we investigated spatial and temporal factors influencing detection and occupancy of Eurasian Wild Pig and Nine-banded Armadillo (Dasypus novemcinctus) before examining interspecific effects. We analyzed camera-trap data collected from August to September 2021 using a hierarchical modeling framework to estimate detection and occupancy of both species individually (single-species analyses) and concurrently (conditional co-occurrence analyses). We observed higher Wild Pig detection rates and space use in late summer and in areas with greater riparian cover, respectively. Armadillo detection increased linearly throughout our sampling season and in response to precipitation. Moreover, armadillo detection was 3.5 to 5.1× higher at sites used by wild pigs, regardless of whether wild pigs were detected during a survey period. Occupancy of armadillo was best explained by a quadratic trend in site elevation but did not depend on the presence of wild pigs. Our results indicate that wild pigs may influence armadillo detection (or site-use intensity), but not occupancy, therefore revealing nuanced interspecific interactions. Between species, we observed high overlap in diel activity but significantly different activity peaks, with armadillos being strictly nocturnal and wild pigs being crepuscular but with more cathemeral activity, suggesting that fine-scale temporal partitioning may have occurred. Our results provide insights into the influence of a large-bodied and destructive invasive species (Wild Pig) on a smaller, ecologically important native species (Nine-banded Armadillo).
","language":"English","publisher":"Oxford Academic","doi":"10.1093/jmammal/gyaf023","usgsCitation":"Broadway, M.S., Todaro, H.M., Koeck, M.M., Dotterweich, C.N., Cain, S.A., Chitwood, M., and Lonsinger, R.C., 2025, Interspecific effects of invasive wild pigs (Sus scrofa) on native nine-banded armadillos (Dasypus novemcinctus): Journal of Mammalogy, v. 106, no. 4, p. 976-988, https://doi.org/10.1093/jmammal/gyaf023.","productDescription":"13 p.","startPage":"976","endPage":"988","ipdsId":"IP-163684","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":497716,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/jmammal/gyaf023","text":"Publisher Index Page"},{"id":497469,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma","otherGeospatial":"James Collins Wildlife Management Area, Sans Bois Wildlife Management Area, southeast Oklahoma","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -98.05973004796076,\n 35.30322610651754\n ],\n [\n -98.05973004796076,\n 33.72739259313137\n ],\n [\n -94.40186391242315,\n 33.72739259313137\n ],\n [\n -94.40186391242315,\n 35.30322610651754\n ],\n [\n -98.05973004796076,\n 35.30322610651754\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"106","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-04-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Broadway, Matthew S.","contributorId":364085,"corporation":false,"usgs":false,"family":"Broadway","given":"Matthew","middleInitial":"S.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":952222,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Todaro, Holly M.","contributorId":364088,"corporation":false,"usgs":false,"family":"Todaro","given":"Holly","middleInitial":"M.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":952223,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Koeck, Molly M.","contributorId":364091,"corporation":false,"usgs":false,"family":"Koeck","given":"Molly","middleInitial":"M.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":952224,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dotterweich, Courtney N.","contributorId":364094,"corporation":false,"usgs":false,"family":"Dotterweich","given":"Courtney","middleInitial":"N.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":952225,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cain, Sarah A.","contributorId":364097,"corporation":false,"usgs":false,"family":"Cain","given":"Sarah","middleInitial":"A.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":952226,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chitwood, M. 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Time-lapse photography can be particularly useful for studying snakes that often fail to trigger a camera's infrared motion sensor due to their ectothermic nature. However, the large datasets produced by time-lapse photography require labor-intensive classification, limiting their use in large-scale studies. While many artificial intelligence-based object detection models are effective at identifying mammals in images, their ability to detect snakes is unproven. Here, we used camera data to evaluate the efficacy of an object detection model to rapidly and accurately detect snakes. We classified images manually to the species level and compared this with a hybrid review workflow where the model removed blank images followed by a manual review. Using a ≥0.05 model confidence threshold, our hybrid review workflow correctly identified 94.5% of blank images, completed image classification 6× faster, and detected large (>66 cm) snakes as well as manual review. Conversely, the hybrid review method often failed to detect all instances of a snake in a string of images and detected fewer small (<66 cm) snakes than manual review. However, most relevant ecological information requires only a single detection in a sequence of images, and study design changes could likely improve the detection of smaller snakes. Our findings suggest that an object detection-assisted hybrid workflow can greatly reduce time spent manually classifying data-heavy time-lapse snake studies and facilitate ecological monitoring for large snakes.
","language":"English","publisher":"Zoological Society of London","doi":"10.1002/rse2.70009","usgsCitation":"Miller, S., Kirkland, M., Hart, K., and McCleery, R.A., 2025, Object detection-assisted workflow facilitates cryptic snake monitoring: Remote Sensing in Ecology and Conservation, v. 11, no. 5, p. 606-617, https://doi.org/10.1002/rse2.70009.","productDescription":"12 p.","startPage":"606","endPage":"617","ipdsId":"IP-171959","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":488156,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rse2.70009","text":"Publisher Index Page"},{"id":485551,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.3162930521356,\n 25.76295442989739\n ],\n [\n -80.73353749057956,\n 25.76295442989739\n ],\n [\n -80.73353749057956,\n 25.272501320110464\n ],\n [\n -80.3162930521356,\n 25.272501320110464\n ],\n [\n -80.3162930521356,\n 25.76295442989739\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"11","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-04-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Miller, Storm","contributorId":354750,"corporation":false,"usgs":false,"family":"Miller","given":"Storm","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":936283,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kirkland, Michael","contributorId":301069,"corporation":false,"usgs":false,"family":"Kirkland","given":"Michael","email":"","affiliations":[{"id":36603,"text":"SFWMD","active":true,"usgs":false}],"preferred":false,"id":936284,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hart, Kristen 0000-0002-5257-7974","orcid":"https://orcid.org/0000-0002-5257-7974","contributorId":220333,"corporation":false,"usgs":true,"family":"Hart","given":"Kristen","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":936285,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCleery, Robert A.","contributorId":139849,"corporation":false,"usgs":false,"family":"McCleery","given":"Robert","email":"","middleInitial":"A.","affiliations":[{"id":12557,"text":"University of Florida, FLREC","active":true,"usgs":false}],"preferred":false,"id":936286,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70270754,"text":"70270754 - 2025 - Habitat and predator influences on the spatial ecology of nine-banded armadillos","interactions":[],"lastModifiedDate":"2025-08-22T17:13:03.576077","indexId":"70270754","displayToPublicDate":"2025-04-19T10:02:36","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1398,"text":"Diversity","active":true,"publicationSubtype":{"id":10}},"title":"Habitat and predator influences on the spatial ecology of nine-banded armadillos","docAbstract":"Mesopredator suppression has implications for community structure, biodiversity, and ecosystem function, but mesopredators with physical defenses may not avoid apex predators. We investigated nine-banded armadillos (Dasypus novemcinctus) in southwestern Oklahoma (USA) to evaluate if a species with physical defenses was influenced by a dominant predator, the coyote (Canis latrans). We sampled nine-banded armadillos and coyotes with motion-activated cameras. We used single-species and conditional two-species occupancy models to assess the influences of environmental factors and coyotes on nine-banded armadillo occurrence and site-use intensity (i.e., detection). We used camera-based detections to characterize the diel activity of each species and their overlap. Nine-banded armadillo occupancy was greater at sites closer to cover, with lower slopes, and further from water, whereas coyote space use was greater at higher elevations; both species were positively associated with recent burns. Nine-banded armadillo occurrence was not influenced by coyotes, but site-use intensity was suppressed by the presence of coyotes. Nine-banded armadillos (strictly nocturnal) and coyotes (predominantly nocturnal) had a high overlap in summer diel activity. Nine-banded armadillos are ecosystem engineers but are often considered a threat to species of concern and/or a nuisance. Thus, understanding the role of interspecific interactions on nine-banded armadillos has important implications for conservation and management.
","language":"English","publisher":"MDPI","doi":"10.3390/d17040290","usgsCitation":"Lonsinger, R.C., Murley, B.P., McDonald, D.T., Fallon, C.E., and White, K.M., 2025, Habitat and predator influences on the spatial ecology of nine-banded armadillos: Diversity, v. 17, no. 4, 290, 19 p., https://doi.org/10.3390/d17040290.","productDescription":"290, 19 p.","ipdsId":"IP-176619","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":495050,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/d17040290","text":"Publisher Index Page"},{"id":494540,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma","otherGeospatial":"Wichita Mountains Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -98.82516483790342,\n 34.84404355598089\n ],\n [\n -98.82516483790342,\n 34.674776209073414\n ],\n [\n -98.51270854835755,\n 34.674776209073414\n ],\n [\n -98.51270854835755,\n 34.84404355598089\n ],\n [\n -98.82516483790342,\n 34.84404355598089\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"17","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-04-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Lonsinger, Robert Charles 0000-0002-1040-7299","orcid":"https://orcid.org/0000-0002-1040-7299","contributorId":340524,"corporation":false,"usgs":true,"family":"Lonsinger","given":"Robert","email":"","middleInitial":"Charles","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":946998,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Murley, Ben P.","contributorId":360371,"corporation":false,"usgs":false,"family":"Murley","given":"Ben","middleInitial":"P.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":946999,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McDonald, Daniel T.","contributorId":360373,"corporation":false,"usgs":false,"family":"McDonald","given":"Daniel","middleInitial":"T.","affiliations":[{"id":25470,"text":"U.S. Fish & Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":947000,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fallon, Christine E.","contributorId":360375,"corporation":false,"usgs":false,"family":"Fallon","given":"Christine","middleInitial":"E.","affiliations":[{"id":25470,"text":"U.S. Fish & Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":947001,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"White, Kara M.","contributorId":360378,"corporation":false,"usgs":false,"family":"White","given":"Kara","middleInitial":"M.","affiliations":[{"id":7211,"text":"University of Alaska, Fairbanks","active":true,"usgs":false}],"preferred":false,"id":947002,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70266024,"text":"70266024 - 2025 - Microbiome data management in action workshop: Atlanta, GA, USA, June 12–13, 2024","interactions":[],"lastModifiedDate":"2025-04-24T15:08:09.125922","indexId":"70266024","displayToPublicDate":"2025-04-19T09:59:15","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17060,"text":"Environmental Microbiome","active":true,"publicationSubtype":{"id":10}},"title":"Microbiome data management in action workshop: Atlanta, GA, USA, June 12–13, 2024","docAbstract":"Microbiome research is revolutionizing human and environmental health, but the value and reuse of microbiome data are significantly hampered by the limited development and adoption of data standards. While several ongoing efforts are aimed at improving microbiome data management, significant gaps still remain in terms of defining and promoting adoption of consensus standards for these datasets. The Strengthening the Organization and Reporting of Microbiome Studies (STORMS) guidelines for human microbiome research have been endorsed and successfully utilized by many research organizations, publishers, and funding agencies, and have been recognized as a consensus community standard. No equivalent effort has occurred for environmental, synthetic, and non-human host-associated microbiomes. To address this growing need within the microbiome research community, we convened the Microbiome Data Management in Action Workshop (June 12–13, 2024, in Atlanta, GA, USA), to bring together key decision makers in microbiome science including researchers, publishers, funders, and data repositories. The 50 attendees, representing the diverse and interdisciplinary nature of microbiome research, discussed recent progress and challenges, and brainstormed actionable recommendations and paths forward for coordinated environmental microbiome data management and the modifications necessary for the STORMS guidelines to be applied to environmental, non-human host, and synthetic microbiomes. The outcomes of this workshop will form the basis of a formalized data management roadmap to be implemented across the field. These best practices will drive scientific innovation now and in years to come as these data continue to be used not only in targeted reanalyses but in large-scale models and machine learning efforts.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s40793-025-00702-9","usgsCitation":"Kelliher, J., Aljumaah, M., Bordenstein, S., Brister, J., Chain, P., Dunduore-Arias, J., Emerson, J.B., Ferdandes, V., Flores, R., Gonzalez, A., Hansen, Z., Hatcher, E., Jackson, S., Kellogg, C.A., Madupu, R., Miller, C., Mirzayi, C., Mongodin, E., Moustafa, A., Mungall, C., Oliver, A., Pariente, N., Pett-Ridge, J., Record, S., Reji, L., Reysenbach, A., Rich, V., Richardson, L., Schriml, L., Shabman, R., Sierra, M., Sullivan, M., Sundaramurthy, P., Thibault, K.M., Thompson, L., Tighe, S.W., Vereen, E., and Eloe-Fadrosh, E., 2025, Microbiome data management in action workshop: Atlanta, GA, USA, June 12–13, 2024: Environmental Microbiome, v. 20, 40, 8 p., https://doi.org/10.1186/s40793-025-00702-9.","productDescription":"40, 8 p.","ipdsId":"IP-169821","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":487902,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40793-025-00702-9","text":"Publisher Index Page"},{"id":484981,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"20","noUsgsAuthors":false,"publicationDate":"2025-04-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Kelliher, Julia 0000-0003-4100-9119","orcid":"https://orcid.org/0000-0003-4100-9119","contributorId":353689,"corporation":false,"usgs":false,"family":"Kelliher","given":"Julia","affiliations":[{"id":84466,"text":"Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, USA; New Mexico Consortium, Los Alamos, NM, USA","active":true,"usgs":false}],"preferred":false,"id":934366,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Aljumaah, Mashael 0000-0003-2477-7239","orcid":"https://orcid.org/0000-0003-2477-7239","contributorId":353690,"corporation":false,"usgs":false,"family":"Aljumaah","given":"Mashael","affiliations":[{"id":84468,"text":"UNC Microbiome Core, Center for Gastrointestinal Biology and Disease (CGIBD), School of Medicine, University of North Carolina, Chapel Hill, NC,","active":true,"usgs":false}],"preferred":false,"id":934367,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bordenstein, Sarah R. 0000-0001-6092-1950","orcid":"https://orcid.org/0000-0001-6092-1950","contributorId":353691,"corporation":false,"usgs":false,"family":"Bordenstein","given":"Sarah R.","affiliations":[{"id":84470,"text":"Departments of Biology & Entomology, Pennsylvania State University, University Park, PA, USA; One Health Microbiome Center, Pennsylvania State University, University Park, PA, USA","active":true,"usgs":false}],"preferred":false,"id":934368,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brister, J. Rodney 0000-0002-2249-975X","orcid":"https://orcid.org/0000-0002-2249-975X","contributorId":353692,"corporation":false,"usgs":false,"family":"Brister","given":"J. 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Department of Pediatrics.","active":true,"usgs":false}],"preferred":false,"id":934375,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Hansen, Zoe A. 0000-0002-6238-0104","orcid":"https://orcid.org/0000-0002-6238-0104","contributorId":353698,"corporation":false,"usgs":false,"family":"Hansen","given":"Zoe A.","affiliations":[{"id":84476,"text":"Department of Plant Pathology, University of Minnesota, Saint Paul, MN, USA","active":true,"usgs":false}],"preferred":false,"id":934376,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Hatcher, Eneida L. 0009-0004-8858-9645","orcid":"https://orcid.org/0009-0004-8858-9645","contributorId":353699,"corporation":false,"usgs":false,"family":"Hatcher","given":"Eneida L.","affiliations":[{"id":84471,"text":"National Center for Biotechnology Information, National Library of Medicine, National Institutes of 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Changes in land use and agricultural practices, such as subsurface agricultural drainage, may be contributing to changes in streamflow characteristics. Subsurface agricultural drainage, also known as tile drainage, is the practice of installing drains in the subsurface of agricultural fields to improve productivity. Because of the complex interactions between subsurface drainage systems, precipitation, local soil conditions, and land management practices, it is difficult to determine how subsurface agricultural drainage affects downstream flow. Previously developed subsurface agricultural drainage conceptual models under dry, saturated, and winter conditions are summarized, and current literature on the effects of subsurface agricultural drainage on downstream flows, focusing on peak flow, non-event flow, and total flow to develop frameworks for discussing these systems is compiled.
The effects that subsurface drainage has on hydrologic systems are expected to vary by site and are seasonally based on system design, soil type, moisture conditions, precipitation characteristics, and land conditions. Subsurface drainage can affect the magnitude of peak flow by converting surface runoff from a storm event to subsurface runoff. By increasing hydrologic connectivity of a catchment, subsurface drainage can increase non-event flow or the flow between two storm events, typically dependent on lateral flow through the subsurface and groundwater. Theoretically, by diverting water from groundwater recharge or by reducing water available for evapotranspiration, subsurface drainage may increase the total volume of flow. Precipitation changes may increase infiltration, excess overland flow, and flood risk regardless of the presence or absence of subsurface drainage.
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U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240