Water resources are limited in arid locations such as Tucson Basin. Residential development in the Tucson Mountains to the west of Tucson, Arizona, is limited by groundwater resources. Groundwater samples were collected from fractured bedrock and alluvial aquifers surrounding the Tucson Mountains to assess water quality and recharge history through measurement of stable O, H, and S isotopes; tritium; and 14C. Most groundwater is a mixture of different ages but is commonly several thousand years old. A few sampling locations indicated a component of water recharged after the above-ground nuclear testing of the mid 1950s, and these sites may represent locations near where the aquifer receives present-day recharge. The Tucson Mountains also host sulfide deposits associated with fractures and replacement zones; these locally contribute to poor-quality groundwater. Projections of future climate predict intensifying drought in southwestern North America. In the study area, a combination of strategies such as rainwater harvesting, exploitation of renewable water, and low groundwater use could be used for sustainable use of the groundwater supply.
","language":"English","publisher":"Wiley","doi":"10.1111/j.1936-704X.2021.3369.x","usgsCitation":"Eastoe, C.J., and Beisner, K.R., 2022, Understanding the water resources of a mountain-block aquifer: Tucson Mountains, Arizona: Journal of Contemporary Water Research & Education, v. 175, no. 1, https://doi.org/10.1111/j.1936-704X.2021.3369.x.","productDescription":"14 p.","startPage":"1-14","ipdsId":"IP-130604","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":447613,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/j.1936-704x.2021.3369.x","text":"Publisher Index Page"},{"id":401364,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Tucson Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.27296447753905,\n 32.155268542097815\n ],\n [\n -110.99761962890625,\n 32.155268542097815\n ],\n [\n -110.99761962890625,\n 32.377062004744786\n ],\n [\n -111.27296447753905,\n 32.377062004744786\n ],\n [\n -111.27296447753905,\n 32.155268542097815\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"175","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-05-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Eastoe, Christopher J.","contributorId":173510,"corporation":false,"usgs":false,"family":"Eastoe","given":"Christopher","email":"","middleInitial":"J.","affiliations":[{"id":6624,"text":"University of Arizona, Laboratory of Tree-Ring Research","active":true,"usgs":false}],"preferred":false,"id":843936,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beisner, Kimberly R. 0000-0002-2077-6899 kbeisner@usgs.gov","orcid":"https://orcid.org/0000-0002-2077-6899","contributorId":2733,"corporation":false,"usgs":true,"family":"Beisner","given":"Kimberly","email":"kbeisner@usgs.gov","middleInitial":"R.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":843937,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70231823,"text":"70231823 - 2022 - Impoundment increases methane emissions in Phragmites-invaded coastal wetlands ","interactions":[],"lastModifiedDate":"2022-07-08T13:36:32.552842","indexId":"70231823","displayToPublicDate":"2022-05-30T15:24:26","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Impoundment increases methane emissions in Phragmites-invaded coastal wetlands ","title":"Impoundment increases methane emissions in Phragmites-invaded coastal wetlands ","docAbstract":"Saline tidal wetlands are important sites of carbon sequestration and produce negligible methane (CH4) emissions due to regular inundation with sulfate-rich seawater. Yet, widespread management of coastal hydrology has restricted tidal exchange in vast areas of coastal wetlands. These ecosystems often undergo impoundment and freshening, which in turn cause vegetation shifts like invasion by Phragmites, that affect ecosystem carbon balance. Understanding controls and scaling of carbon exchange in these understudied ecosystems is critical for informing climate consequences of blue carbon restoration and/or management interventions. Here, we (1) examine how carbon fluxes vary across a salinity gradient (4–25 psu) in impounded and natural, tidally unrestricted Phragmites wetlands using static chambers and (2) probe drivers of carbon fluxes within an impounded coastal wetland using eddy covariance at the Herring River in Wellfleet, MA, United States. Freshening across the salinity gradient led to a 50-fold increase in CH4 emissions, but effects on carbon dioxide (CO2) were less pronounced with uptake generally enhanced in the fresher, impounded sites. The impounded wetland experienced little variation in water-table depth or salinity during the growing season and was a strong CO2 sink of −352 g CO2-C m−2 year−1 offset by CH4 emission of 11.4 g CH4-C m−2 year−1. Growing season CH4 flux was driven primarily by temperature. Methane flux exhibited a diurnal cycle with a night-time minimum that was not reflected in opaque chamber measurements. Therefore, we suggest accounting for the diurnal cycle of CH4 in Phragmites, for example by applying a scaling factor developed here of ~0.6 to mid-day chamber measurements. Taken together, these results suggest that although freshened, impounded wetlands can be strong carbon sinks, enhanced CH4 emission with freshening reduces net radiative balance. Restoration of tidal flow to impounded ecosystems could limit CH4 production and enhance their climate regulating benefits.
1. Ecological restoration and conservation efforts are increasing worldwide and the management of intraspecific genetic variation in plants and animals, an important component of biodiversity, is increasingly valued. As a result, tailorable, spatially explicit approaches to map genetic variation are needed to support decision-making and management frameworks related to the recovery of threatened and endangered species and the maintenance of genetic resources in species utilized by humans, such as for restoration or agricultural purposes.
2. Here, we describe and demonstrate a workflow to spatially interpolate patterns of genetic differentiation using novel functions in the R package POPMAPS (Population Management using Ancestry Probability Surfaces). Our approach uses empirical genetic data to estimate ancestry coefficients across a user-defined landscape correlated with patterns of differentiation in the focal species. The resulting surface, which we term the ancestry probability surface, includes two components: hard population boundaries and estimations of uncertainty that represent confidence in population assignments (i.e., ancestry probabilities).
3. An ancestry probability surface developed for Hilaria jamesii, an important graminoid utilized in restoration across the western United States, demonstrates the functionality of POPMAPS. Genetic distances among empirical sites correlated better with least-cost distances across suitable habitat than with geographic distances, informing the surface over which the interpolation was conducted (i.e., a model indicating habitat suitability). A jackknifing procedure identified parameter values resulting in robust population assignments across the species’ range, which were utilized in downstream analyses to estimate ancestry coefficients from empirical data. Ancestry coefficients were translated into ancestry probabilities, which tended to be low for cells that were intermediate in distance between empirical sampling locations representing different populations or when influenced by empirical sampling locations with mixed genetic ancestry.
4. POPMAPS allows users to tailor parameter values and analytical approaches and thereby incorporate species-specific biological characteristics and desired levels of uncertainty into maps illustrating patterns of genetic differentiation. Ancestry probability surfaces may be used to guide management or investigate further ecological or evolutionary hypotheses. We discuss how maps produced by POPMAPS can inform multiple management challenges including species recovery planning and the utilization of commonly used species in restoration.
","language":"English","publisher":"British Ecological Society","doi":"10.1111/2041-210X.13902","usgsCitation":"Massatti, R., and Winkler, D.E., 2022, Spatially explicit management of genetic diversity using ancestry probability surfaces: Methods in Ecology and Evolution, v. 13, no. 12, p. 2668-2681, https://doi.org/10.1111/2041-210X.13902.","productDescription":"14 p.","startPage":"2668","endPage":"2681","ipdsId":"IP-133238","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":447618,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/2041-210x.13902","text":"Publisher Index Page"},{"id":435837,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96VLOA5","text":"USGS data release","linkHelpText":"POPMAPS: An R package to estimate ancestry probability surfaces"},{"id":401362,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"12","noUsgsAuthors":false,"publicationDate":"2022-06-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Massatti, Robert 0000-0001-5854-5597","orcid":"https://orcid.org/0000-0001-5854-5597","contributorId":207294,"corporation":false,"usgs":true,"family":"Massatti","given":"Robert","email":"","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":843923,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Winkler, Daniel E. 0000-0003-4825-9073","orcid":"https://orcid.org/0000-0003-4825-9073","contributorId":206786,"corporation":false,"usgs":true,"family":"Winkler","given":"Daniel","email":"","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":843924,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70274304,"text":"70274304 - 2022 - Trans-crustal structural control of CO2-rich extensional magmatic systems revealed at Mount Erebus Antarctica","interactions":[],"lastModifiedDate":"2026-03-26T16:59:44.858337","indexId":"70274304","displayToPublicDate":"2022-05-30T11:52:03","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2842,"text":"Nature Communications","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Trans-crustal structural control of CO2-rich extensional magmatic systems revealed at Mount Erebus Antarctica","title":"Trans-crustal structural control of CO2-rich extensional magmatic systems revealed at Mount Erebus Antarctica","docAbstract":"Erebus volcano, Antarctica, with its persistent phonolite lava lake, is a classic example of an evolved, CO2-rich rift volcano. Seismic studies provide limited images of the magmatic system. Here we show using magnetotelluric data that a steep, melt-related conduit of low electrical resistivity originating in the upper mantle undergoes pronounced lateral re-orientation in the deep crust before reaching shallower magmatic storage and the summit lava lake. The lateral turn represents a structural fault-valve controlling episodic flow of magma and CO2 vapour, which replenish and heat the high level phonolite differentiation zone. This magmatic valve lies within an inferred, east-west structural trend forming part of an accommodation zone across the southern termination of the Terror Rift, providing a dilatant magma pathway. Unlike H2O-rich subduction arc volcanoes, CO2-dominated Erebus geophysically shows continuous magmatic structure to shallow crustal depths of < 1 km, as the melt does not experience decompression-related volatile supersaturation and viscous stalling.
","language":"English","publisher":"Nature","doi":"10.1038/s41467-022-30627-7","usgsCitation":"Hill, G.J., Wannamaker, P.E., Maris, V., Stodt, J.A., Kordy, M., Unsworth, M.J., Bedrosian, P.A., Wallin, E.L., Uhlmann, D.F., Ogawa, Y., and Kyle, P.R., 2022, Trans-crustal structural control of CO2-rich extensional magmatic systems revealed at Mount Erebus Antarctica: Nature Communications, v. 13, 2989, 10 p., https://doi.org/10.1038/s41467-022-30627-7.","productDescription":"2989, 10 p.","ipdsId":"IP-138531","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":501614,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-022-30627-7","text":"Publisher Index Page"},{"id":501590,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Antarctica, Mount Erebus","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 165,\n -78\n ],\n [\n 170,\n -78\n ],\n [\n 170,\n -77\n ],\n [\n 165,\n -77\n ],\n [\n 165,\n -78\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"13","noUsgsAuthors":false,"publicationDate":"2022-05-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Hill, Graham J","contributorId":367839,"corporation":false,"usgs":false,"family":"Hill","given":"Graham","middleInitial":"J","affiliations":[{"id":79730,"text":"Czech Academy of Science","active":true,"usgs":false}],"preferred":false,"id":957801,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wannamaker, Phil E","contributorId":367840,"corporation":false,"usgs":false,"family":"Wannamaker","given":"Phil","middleInitial":"E","affiliations":[{"id":13252,"text":"University of Utah","active":true,"usgs":false}],"preferred":false,"id":957802,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Maris, Virginie","contributorId":194006,"corporation":false,"usgs":false,"family":"Maris","given":"Virginie","affiliations":[],"preferred":false,"id":957803,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stodt, J. A.","contributorId":367843,"corporation":false,"usgs":false,"family":"Stodt","given":"J.","middleInitial":"A.","affiliations":[{"id":87627,"text":"Numerical Resources LLC","active":true,"usgs":false}],"preferred":false,"id":957804,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kordy, Michael","contributorId":367844,"corporation":false,"usgs":false,"family":"Kordy","given":"Michael","affiliations":[{"id":13252,"text":"University of Utah","active":true,"usgs":false}],"preferred":false,"id":957805,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Unsworth, Martyn J.","contributorId":367845,"corporation":false,"usgs":false,"family":"Unsworth","given":"Martyn","middleInitial":"J.","affiliations":[{"id":36696,"text":"University of Alberta","active":true,"usgs":false}],"preferred":false,"id":957806,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bedrosian, Paul A. 0000-0002-6786-1038 pbedrosian@usgs.gov","orcid":"https://orcid.org/0000-0002-6786-1038","contributorId":839,"corporation":false,"usgs":true,"family":"Bedrosian","given":"Paul","email":"pbedrosian@usgs.gov","middleInitial":"A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":957807,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wallin, Erin L.","contributorId":367846,"corporation":false,"usgs":false,"family":"Wallin","given":"Erin","middleInitial":"L.","affiliations":[{"id":47560,"text":"University of Hawaii Manoa","active":true,"usgs":false}],"preferred":false,"id":957808,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Uhlmann, Danny F.","contributorId":367847,"corporation":false,"usgs":false,"family":"Uhlmann","given":"Danny","middleInitial":"F.","affiliations":[{"id":35541,"text":"University of Lausanne","active":true,"usgs":false}],"preferred":false,"id":957809,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Ogawa, Yasuo","contributorId":302663,"corporation":false,"usgs":false,"family":"Ogawa","given":"Yasuo","email":"","affiliations":[{"id":38251,"text":"Tokyo Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":957810,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Kyle, Philip R.","contributorId":174414,"corporation":false,"usgs":false,"family":"Kyle","given":"Philip","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":957811,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70256653,"text":"70256653 - 2022 - Integrated animal movement and spatial capture–recapture models: Simulation, implementation, and inference","interactions":[],"lastModifiedDate":"2024-08-29T15:02:58.967417","indexId":"70256653","displayToPublicDate":"2022-05-30T09:59:16","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Integrated animal movement and spatial capture–recapture models: Simulation, implementation, and inference","docAbstract":"Over the last decade, spatial capture–recapture (SCR) models have become widespread for estimating demographic parameters in ecological studies. However, the underlying assumptions about animal movement and space use are often not realistic. This is a missed opportunity because interesting ecological questions related to animal space use, habitat selection, and behavior cannot be addressed with most SCR models, despite the fact that the data collected in SCR studies — individual animals observed at specific locations and times — can provide a rich source of information about these processes and how they relate to demographic rates. We developed SCR models that integrated more complex movement processes that are typically inferred from telemetry data, including a simple random walk, correlated random walk (i.e., short-term directional persistence), and habitat-driven Langevin diffusion. We demonstrated how to formulate, simulate from, and fit these models with standard SCR data using data-augmented Bayesian analysis methods. We evaluated their performance through a simulation study, in which we varied the detection, movement, and resource selection parameters. We also examined different numbers of sampling occasions and assessed performance gains when including auxiliary location data collected from telemetered individuals. Across all scenarios, the integrated SCR movement models performed well in terms of abundance, detection, and movement parameter estimation. We found little difference in bias for the simple random walk model when reducing the number of sampling occasions from T = 25 to T = 15. We found some bias in movement parameter estimates under several of the correlated random walk scenarios, but incorporating auxiliary location data improved parameter estimates and significantly improved mixing during model fitting. The Langevin movement model was able to recover resource selection parameters from standard SCR data, which is particularly appealing because it explicitly links the individual-level movement process with habitat selection and population density. We focused on closed population models, but the movement models developed here can be extended to open SCR models. The movement process models could also be easily extended to accommodate additional “building blocks” of random walks, such as central tendency (e.g., territoriality) or multiple movement behavior states, thereby providing a flexible and coherent framework for linking animal movement behavior to population dynamics, density, and distribution.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.3771","usgsCitation":"Gardner, B., McClintock, B., Converse, S.J., and Hostetter, N.J., 2022, Integrated animal movement and spatial capture–recapture models: Simulation, implementation, and inference: Ecology, v. 103, e3771, 13 p., https://doi.org/10.1002/ecy.3771.","productDescription":"e3771, 13 p.","ipdsId":"IP-130421","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":447622,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"text":"Publisher Index Page"},{"id":433312,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"103","noUsgsAuthors":false,"publicationDate":"2022-07-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Gardner, B.","contributorId":341497,"corporation":false,"usgs":false,"family":"Gardner","given":"B.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":908507,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McClintock, B.T.","contributorId":341498,"corporation":false,"usgs":false,"family":"McClintock","given":"B.T.","affiliations":[{"id":38436,"text":"National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":908508,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":908509,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hostetter, Nathan J. 0000-0001-6075-2157 nhostetter@usgs.gov","orcid":"https://orcid.org/0000-0001-6075-2157","contributorId":198843,"corporation":false,"usgs":true,"family":"Hostetter","given":"Nathan","email":"nhostetter@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":true,"id":908510,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70236349,"text":"70236349 - 2022 - P- and S-wave velocity estimation by ensemble Kalman inversion of dispersion data for strong motion stations in California","interactions":[],"lastModifiedDate":"2022-09-02T14:09:03.829255","indexId":"70236349","displayToPublicDate":"2022-05-30T09:01:38","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1803,"text":"Geophysical Journal International","active":true,"publicationSubtype":{"id":10}},"title":"P- and S-wave velocity estimation by ensemble Kalman inversion of dispersion data for strong motion stations in California","docAbstract":"This study uses an ensemble Kalman method for near-surface seismic site characterization of 154 network earthquake monitoring stations in California to improve the resolution of S-wave velocity (VS) and P-wave velocity (VP) profiles—up to the resolution depth—coupled with better quantification of uncertainties compared to previous site characterization studies at this network. These stations were part of the Yong et al. site characterization project, with selected stations based on future recordings of ground motions that are expected to exceed 10 per cent peak ground acceleration in 50 yr. To estimate VS and VP from experimental dispersion data, Yong et al. investigated these stations using linearized (local search and iteration) routines, and Yong et al. later studied a subset of these stations using nonlinear (global search and optimization) routines. In both studies, the selection of model parameters—that is, discretization of the VS and VP profiles with only five fixed thickness layers—was mainly based on trial and error. In contrast, this paper uses an approximate Bayesian method to assimilate experimental dispersion data and sequentially update an ensemble of particle estimates that span the VS and VP parameter spaces. Doing so, we systematically determine the most probable profiles conditioned on the experimental dispersion data, the introduced noise levels, and a priori knowledge in the form of physical constraints. We consider two configurations to discretize the soil depth from the surface to half of the maximum discernible wavelength obtained from the experimental dispersion data, namely refined and coarse models, and two initial models for each configuration to study solution multiplicity. Our results suggest that using the refined model for the top surface layers improves the resolution of near-surface site characteristics and the model’s success rate in capturing dispersion data at high frequencies. All models result in similar VS but distinct VP profiles, with increasing uncertainty at deeper layers, suggesting that the fundamental mode of Rayleigh wave dispersion data is not adequate to constrain the P-wave velocity profile and the S-wave velocity close to the resolution depth.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gji/ggac201","usgsCitation":"Bas, E.E., Seylabi, E., Yong, A., Tehrani, H., and Asimaki, D., 2022, P- and S-wave velocity estimation by ensemble Kalman inversion of dispersion data for strong motion stations in California: Geophysical Journal International, v. 231, no. 1, p. 536-551, https://doi.org/10.1093/gji/ggac201.","productDescription":"16 p.","startPage":"536","endPage":"551","ipdsId":"IP-132480","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":447625,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://resolver.caltech.edu/CaltechAUTHORS:20220804-250008000","text":"External Repository"},{"id":406134,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.58618164062499,\n 38.75408327579141\n ],\n [\n -122.684326171875,\n 37.900865092570065\n ],\n [\n -122.3876953125,\n 36.98500309285596\n ],\n [\n -121.46484375,\n 35.6126508187567\n ],\n [\n -120.4541015625,\n 34.32529192442733\n ],\n [\n -120.14648437499999,\n 33.60546961227188\n ],\n [\n -119.05883789062501,\n 32.8334428466495\n ],\n [\n -117.18017578125,\n 32.565333160841035\n ],\n [\n -114.47753906249999,\n 32.7503226078097\n ],\n [\n -114.42260742187499,\n 32.99023555965106\n ],\n [\n -114.6533203125,\n 33.073130945006625\n ],\n [\n -114.70825195312501,\n 33.38558626887102\n ],\n [\n -114.47753906249999,\n 33.63291573870479\n ],\n [\n -114.49951171875,\n 33.925129700072\n ],\n [\n -114.071044921875,\n 34.32529192442733\n ],\n [\n -114.444580078125,\n 34.67839374011646\n ],\n [\n -114.664306640625,\n 35.03899204678081\n ],\n [\n -116.46606445312499,\n 36.465471886798134\n ],\n [\n -119.58618164062499,\n 38.75408327579141\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"231","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-05-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Bas, Elif Ecem","contributorId":296121,"corporation":false,"usgs":false,"family":"Bas","given":"Elif","email":"","middleInitial":"Ecem","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":850703,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Seylabi, Elnaz","contributorId":296122,"corporation":false,"usgs":false,"family":"Seylabi","given":"Elnaz","email":"","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":850704,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yong, Alan K. 0000-0003-1807-5847","orcid":"https://orcid.org/0000-0003-1807-5847","contributorId":296123,"corporation":false,"usgs":true,"family":"Yong","given":"Alan K.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":850706,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tehrani, Hesam","contributorId":296124,"corporation":false,"usgs":false,"family":"Tehrani","given":"Hesam","email":"","affiliations":[],"preferred":false,"id":850707,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Asimaki, Domniki","contributorId":146598,"corporation":false,"usgs":false,"family":"Asimaki","given":"Domniki","email":"","affiliations":[{"id":7218,"text":"California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":850705,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70238968,"text":"70238968 - 2022 - Stream size, temperature, and density explain body sizes of freshwater salmonids across a range of climate conditions","interactions":[],"lastModifiedDate":"2022-12-19T14:57:53.446229","indexId":"70238968","displayToPublicDate":"2022-05-30T08:57:30","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Stream size, temperature, and density explain body sizes of freshwater salmonids across a range of climate conditions","docAbstract":"Climate change and anthropogenic activities are altering the body sizes of fishes, yet our understanding of factors influencing body size for many taxa remains incomplete. We evaluated the relationships between climate, environmental, and landscape attributes and the body size of different taxa of freshwater trout (Salmonidae) in the USA. Hierarchical spatial modeling across a gradient of habitats (5221 sites) illustrated the importance of watershed effects, which explained 17%–45% of the of the variation in body size across taxa. Stream size had a strong, positive relationship with body size, yet there was approximately tenfold difference in the strength of the relationship across taxa. Trout body size consistently declined with increasing density across taxa. Despite reliance on cold water, we found positive relationships between summer stream temperature and trout body size across most taxa. Our results highlight how providing trout access to larger, productive rivers for the expression of growth and life-history variation would promote body size diversity within and across populations.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2021-0343","usgsCitation":"Al-Chokhachy, R.K., Letcher, B., Muhlfeld, C.C., Dunham, J., Cline, T.J., Hitt, N.P., Roberts, J., and Schmetterling, D., 2022, Stream size, temperature, and density explain body sizes of freshwater salmonids across a range of climate conditions: Canadian Journal of Fisheries and Aquatic Sciences, v. 79, no. 10, p. 1729-1744, https://doi.org/10.1139/cjfas-2021-0343.","productDescription":"16 p.","startPage":"1729","endPage":"1744","ipdsId":"IP-131094","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":447627,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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jroberts@usgs.gov","orcid":"https://orcid.org/0000-0002-4193-610X","contributorId":5453,"corporation":false,"usgs":true,"family":"Roberts","given":"James","email":"jroberts@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":859453,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Schmetterling, David","contributorId":196555,"corporation":false,"usgs":false,"family":"Schmetterling","given":"David","affiliations":[],"preferred":false,"id":859454,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70231929,"text":"70231929 - 2022 - Plant pathogens provide clues to the potential origin of bat white-nose syndrome Pseudogymnoascus destructans","interactions":[],"lastModifiedDate":"2022-06-16T15:31:33.624892","indexId":"70231929","displayToPublicDate":"2022-05-30T08:47:02","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3698,"text":"Virulence","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Plant pathogens provide clues to the potential origin of bat white-nose syndrome Pseudogymnoascus destructans","title":"Plant pathogens provide clues to the potential origin of bat white-nose syndrome Pseudogymnoascus destructans","docAbstract":"White-nose syndrome has killed millions of bats, yet both the origins and infection strategy of the causative fungus, Pseudogymnoascus destructans, remain elusive. We provide evidence for a novel hypothesis that P. destructans emerged from plant-associated fungi and retained invasion strategies affiliated with fungal pathogens of plants. We demonstrate that P. destructans invades bat skin in successive biotrophic and necrotrophic stages (hemibiotrophic infection), a mechanism previously only described in plant fungal pathogens. Further, the convergence of hyphae at hair follicles suggests nutrient tropism. Tropism, biotrophy, and necrotrophy are often associated with structures termed appressoria in plant fungal pathogens; the penetrating hyphae produced by P. destructans resemble appressoria. Finally, we conducted a phylogenomic analysis of a taxonomically diverse collection of fungi. Despite gaps in genetic sampling of prehistoric and contemporary fungal species, we estimate an 88% probability the ancestral state of the clade containing P. destructans was a plant-associated fungus.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/21505594.2022.2082139","usgsCitation":"Meteyer, C., Dutheil, J.Y., Keel, M.K., Boyles, J., and Stukenbrock, E.H., 2022, Plant pathogens provide clues to the potential origin of bat white-nose syndrome Pseudogymnoascus destructans: Virulence, v. 13, no. 1, p. 1020-1031, https://doi.org/10.1080/21505594.2022.2082139.","productDescription":"12 p.","startPage":"1020","endPage":"1031","ipdsId":"IP-124944","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":447631,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/21505594.2022.2082139","text":"Publisher Index Page"},{"id":401681,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-06-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Meteyer, Carol 0000-0002-4007-3410","orcid":"https://orcid.org/0000-0002-4007-3410","contributorId":207215,"corporation":false,"usgs":true,"family":"Meteyer","given":"Carol","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"preferred":true,"id":844133,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dutheil, Julien Yann","contributorId":292251,"corporation":false,"usgs":false,"family":"Dutheil","given":"Julien","email":"","middleInitial":"Yann","affiliations":[{"id":62846,"text":"Molecular Systems Evolution, Max Planck Institute for Evolutionary Biology, 24306 Plön, Germany.","active":true,"usgs":false}],"preferred":false,"id":844134,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Keel, M. Kevin","contributorId":127729,"corporation":false,"usgs":false,"family":"Keel","given":"M.","email":"","middleInitial":"Kevin","affiliations":[{"id":7127,"text":"2Southeastern Cooperative Wildlife Disease Study, College of Veterinary Medicine, University of Georgia, Athens, GA 30602, USA.","active":true,"usgs":false}],"preferred":false,"id":844135,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boyles, Justin G.","contributorId":292252,"corporation":false,"usgs":false,"family":"Boyles","given":"Justin G.","affiliations":[{"id":62849,"text":"School of Biological Sciences, Southern Illinois University, Carbondale, Ill 62901","active":true,"usgs":false}],"preferred":false,"id":844136,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stukenbrock, Eva Holtgrewe","contributorId":292253,"corporation":false,"usgs":false,"family":"Stukenbrock","given":"Eva","email":"","middleInitial":"Holtgrewe","affiliations":[{"id":62850,"text":"5Max Planck Institute for Evolutionary Biology, 24306 Plön, Germany","active":true,"usgs":false}],"preferred":false,"id":844137,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70255807,"text":"70255807 - 2022 - Comparison of Digital Terrain Models from two photoclinometry methods","interactions":[],"lastModifiedDate":"2024-07-05T12:12:52.867955","indexId":"70255807","displayToPublicDate":"2022-05-30T07:04:59","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12997,"text":"International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Comparison of Digital Terrain Models from two photoclinometry methods","docAbstract":"We evaluate the horizontal resolution and vertical precision for digital topographic models (DTMs) of the Moon derived from image radiance information, a process known as photoclinometry (PC) or shape-from-shading (SfS). We use the implementations in two available planetary image processing software systems, single image PC in the U.S. Geological Survey Integrated Software for Imagers and Spectrometers (ISIS) system, and multi-image SfS in the Ames Stereo Pipeline (ASP), and test results obtained with and without use of a starting solution from stereo, with single and multiple images, and for varying illumination conditions. To obtain the higher quality reference DTMs against which the products can be evaluated, we derived DTMs by stereoanalysis of Lunar Reconnaissance Orbiter Narrow-Angle Camera (LROC NAC) images at their native pixel spacing of ∼0.5 m, then produced a 16-m/post stereo DTM from images downsampled to 4 m/pixel and refined it with images at 16 m/pixel. When used with a single image, both algorithms improved resolution (by a factor of 1.4 for PC and 2.4 for SfS compared to stereo). An albedo map produced in ISIS by ratioing the image to a simulation based on the stereo DTM was well correlated with one output by SfS. The albedo correction was crucial for PC with ∼60° incidence but not at ∼80°. DTMs produced by PC and SfS without a starting stereo DTM had larger errors but good detail, and could be useful for many applications. In SfS, it was necessary to increase smoothing to get a usable DTM when the weighting on an a priori DTM was reduced. Multi-image SfS including modeling of spatially varying albedo reduced vertical errors by factors of 1.5 or more compared to single-image SfS.
This paper documents a reversal in the groundwater salinity depth gradient in the North Coles Levee Oil Field in the San Joaquin Valley, California. Salinity, measured in mg/L, was mapped with water quality data from groundwater and oil and gas wells and salinity estimated from oil and gas well borehole geophysical logs using Archie's equation. The resulting three-dimensional salinity volume shows groundwater salinity increasing with depth through the Tulare and San Joaquin Formations to about 50,000 mg/L at 1100 m depth, then decreasing to 10,000–31,000 mg/L in the Etchegoin Formation at 1400 m depth. The high salinity zone occurs near the base of the San Joaquin Formation in sand lenses in shales that have been interpreted as representing a mudflat environment. The groundwater and produced water geochemistry show formation waters lie on the seawater dilution line, indicating the salinity structure is largely the result of dilution or evaporation of seawater and not due to water–rock interactions. Instead, changing depositional environments linked to decreasing sea level may be responsible for variably saline water at or near the time of deposition, leading to a salinity reversal preserved in connate waters. The steepness of the salinity reversal varies laterally, possibly due to post-depositional freshwater recharge allowed by thick sands, alternatively, by a change in connate water composition due to a lateral facies change present at the time of deposition. These results illustrate geologic and paleogeographic processes that drive the vertical salinity structure of groundwater in shallow alluvial basins.
Rare earth elements and yttrium (REYs) are critical elements and valuable commodities due to their limited availability and high demand in a wide range of applications and especially in high-technology products. The increased demand and geopolitical pressures motivate the search for alternative sources of REYs, and coal, coal waste, and coal ash are considered as new sources for these critical elements. This research evaluates the REY potential of coals from Indiana (USA). However, although coal data revealed REY potential, it suffered from sparse samples with complete REY measurements. Therefore, we explore the applicability of machine learning (ML) models and data augmentation techniques to demonstrate their applicability to evaluate REY potential in Indiana, and other areas in coal basins, using selected coal parameters (Al2O3, Fe2O3, C, Ash, S, P, Mo, Zn, and As contents) as covariates (indicators). Due to the relatively small sample size with complete REY data in the Indiana Coal Database, two data augmentation techniques (Random Over-Sampling Examples and Synthetic Minority Over-Sampling Technique) were used. Four machine learning algorithms (linear discriminate analysis, support vector machine, random forest, and artificial neural networks) were applied for modeling REY potential as a classification problem. The results show that application of Synthetic Minority Over-Sampling Technique prior to development of the support vector machine (SVM) models generated the best REY classification with an accuracy of 95%. The encouraging results based on Indiana coal data may suggest that a similar approach can be used for other coal basins for screening the locations with REY potential. Those locations then can be targeted for more detailed geochemical surveys to identify most promising areas and evaluate overall REY resources.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coal.2022.104054","usgsCitation":"Chatterjee, S., Mastalerz, M., Drobniak, A., and Karacan, C.O., 2022, Machine learning and data augmentation approach for identification of rare earth element potential in Indiana Coals, USA: International Journal of Coal Geology, v. 259, 104054, 14 p., https://doi.org/10.1016/j.coal.2022.104054.","productDescription":"104054, 14 p.","ipdsId":"IP-138032","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":402804,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Indiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.56103515625,\n 40.49709237269567\n ],\n [\n -87.5390625,\n 39.35129035526705\n ],\n [\n -87.56103515625,\n 38.839707613545144\n ],\n [\n -87.86865234374999,\n 38.06539235133249\n ],\n [\n -88.11035156249999,\n 37.90953361677018\n ],\n [\n -88.154296875,\n 37.77071473849609\n ],\n [\n -87.451171875,\n 37.92686760148135\n ],\n [\n -87.099609375,\n 37.87485339352928\n ],\n [\n -86.81396484375,\n 38.048091067457236\n ],\n [\n -86.572265625,\n 37.89219554724437\n ],\n [\n -86.396484375,\n 38.11727165830543\n ],\n [\n -86.63818359375,\n 38.95940879245423\n ],\n [\n -86.8359375,\n 40.111688665595956\n ],\n [\n -87.03369140625,\n 40.463666324587685\n ],\n [\n -87.56103515625,\n 40.49709237269567\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"259","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Chatterjee, Snahamoy","contributorId":292652,"corporation":false,"usgs":false,"family":"Chatterjee","given":"Snahamoy","email":"","affiliations":[{"id":16203,"text":"Michigan Technological university","active":true,"usgs":false}],"preferred":false,"id":845399,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mastalerz, Maria","contributorId":292654,"corporation":false,"usgs":false,"family":"Mastalerz","given":"Maria","affiliations":[{"id":62959,"text":"IU and Indiana Geological Survey","active":true,"usgs":false}],"preferred":false,"id":845400,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Drobniak, Agnieszka","contributorId":292655,"corporation":false,"usgs":false,"family":"Drobniak","given":"Agnieszka","email":"","affiliations":[{"id":62959,"text":"IU and Indiana Geological Survey","active":true,"usgs":false}],"preferred":false,"id":845401,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Karacan, C. Ozgen 0000-0002-0947-8241","orcid":"https://orcid.org/0000-0002-0947-8241","contributorId":201991,"corporation":false,"usgs":true,"family":"Karacan","given":"C.","email":"","middleInitial":"Ozgen","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":845402,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70256676,"text":"70256676 - 2022 - Modeling spatiotemporal abundance and movement dynamics using an integrated spatial capture–recapture movement model","interactions":[],"lastModifiedDate":"2024-08-30T15:04:17.421329","indexId":"70256676","displayToPublicDate":"2022-05-28T09:39:55","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Modeling spatiotemporal abundance and movement dynamics using an integrated spatial capture–recapture movement model","docAbstract":"Animal movement is a fundamental ecological process affecting the survival and reproduction of individuals, the structure of populations, and the dynamics of communities. Methods to quantify animal movement and spatiotemporal abundances, however, are generally separate and therefore omit linkages between individual-level and population-level processes. We describe an integrated spatial capture–recapture (SCR) movement model to jointly estimate (1) the number and distribution of individuals in a defined spatial region and (2) movement of those individuals through time. We applied our model to a study of polar bears (Ursus maritimus) in a 28,125 km2 survey area of the eastern Chukchi Sea, USA in 2015 that incorporated capture–recapture and telemetry data. In simulation studies, the model provided unbiased estimates of movement, abundance, and detection parameters using a bivariate normal random walk and correlated random walk movement process. Our case study provided detailed evidence of directional movement persistence for both male and female bears, where individuals regularly traversed areas larger than the survey area during the 36-day study period. Scaling from individual- to population-level inferences, we found that densities varied from <0.75 bears/625 km2 grid cell/day in nearshore cells to 1.6–2.5 bears/grid cell/day for cells surrounded by sea ice. Daily abundance estimates ranged from 53 to 69 bears, with no trend across days. The cumulative number of unique bears that used the survey area increased through time due to movements into and out of the area, resulting in an estimated 171 individuals using the survey area during the study (95% credible interval 124–250). Abundance estimates were similar to a previous multiyear integrated population model using capture–recapture and telemetry data (2008–2016; Regehr et al., Scientific Reports 8:16780, 2018). Overall, the SCR–movement model successfully quantified both individual- and population-level space use, including the effects of landscape characteristics on movement, abundance, and detection, while linking the movement and abundance processes to directly estimate density within a prescribed spatial region and temporal period. Integrated SCR–movement models provide a generalizable approach to incorporate greater movement realism into population dynamics and link movement to emergent properties including spatiotemporal densities and abundances.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.3772","usgsCitation":"Hostetter, N.J., Regehr, E., Wilson, R., Royle, A., and Converse, S.J., 2022, Modeling spatiotemporal abundance and movement dynamics using an integrated spatial capture–recapture movement model: Ecology, v. 103, no. 10, e3772, 13 p., https://doi.org/10.1002/ecy.3772.","productDescription":"e3772, 13 p.","ipdsId":"IP-130471","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":447644,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecy.3772","text":"Publisher Index Page"},{"id":433369,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Russia, United States","otherGeospatial":"eastern Chukchi Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -176.16804284685193,\n 70.64271222820804\n ],\n [\n -176.4405798143377,\n 63.26571385602864\n ],\n [\n -160.5704824801017,\n 63.43804844317145\n ],\n [\n -160.5759226643521,\n 70.4273607365736\n ],\n [\n -176.16804284685193,\n 70.64271222820804\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"103","issue":"10","noUsgsAuthors":false,"publicationDate":"2022-07-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Hostetter, Nathan J. 0000-0001-6075-2157 nhostetter@usgs.gov","orcid":"https://orcid.org/0000-0001-6075-2157","contributorId":198843,"corporation":false,"usgs":true,"family":"Hostetter","given":"Nathan","email":"nhostetter@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":true,"id":908609,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Regehr, E.V.","contributorId":341555,"corporation":false,"usgs":false,"family":"Regehr","given":"E.V.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":908610,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wilson, R.R.","contributorId":341556,"corporation":false,"usgs":false,"family":"Wilson","given":"R.R.","affiliations":[{"id":40296,"text":"United States Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":908611,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":146229,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","email":"aroyle@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":908612,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":908613,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70231912,"text":"70231912 - 2022 - Toxicological responses to sublethal anticoagulant rodenticide exposure in free-flying hawks","interactions":[],"lastModifiedDate":"2022-10-17T15:31:20.593592","indexId":"70231912","displayToPublicDate":"2022-05-28T08:58:40","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1564,"text":"Environmental Science and Pollution Research","active":true,"publicationSubtype":{"id":10}},"title":"Toxicological responses to sublethal anticoagulant rodenticide exposure in free-flying hawks","docAbstract":"An important component of assessing the hazards of anticoagulant rodenticides to non-target wildlife is observations in exposed free-ranging individuals. The objective of this study was to determine whether environmentally realistic, sublethal first-generation anticoagulant rodenticide (FGAR) exposures via prey can result in direct or indirect adverse effects to free-flying raptors. We offered black-tailed prairie dogs (Cynomys ludovicianus) that had fed on Rozol® Prairie Dog Bait (Rozol, 0.005% active ingredient chlorophacinone, CPN) to six wild-caught red-tailed hawks (RTHA, Buteo jamaicensis), and also offered black-tailed prairie dogs that were not exposed to Rozol to another two wild-caught RTHAs for 7 days. On day 6, blood was collected to determine CPN’s effects on blood clotting time. On day 7, seven of the eight RTHAs were fitted with VHF radio telemetry transmitters and the RTHAs were released the following day and were monitored for 33 days. Prothrombin time (PT) and Russell’s viper venom time confirmed that the CPN-exposed RTHAs were exposed to and were adversely affected by CPN. Four of the six CPN-exposed RTHAs exhibited ptiloerection, an indication of thermoregulatory dysfunction due to CPN toxicity, but no signs of intoxication were observed in the reference hawk or the remaining two CPN-exposed RTHAs. Of note is that PT values were associated with ptiloerection duration and frequency; therefore, sublethal CPN exposure can directly or indirectly evoke adverse effects in wild birds. Although our sample sizes were small, this study is a first to relate coagulation times to adverse clinical signs in free-ranging birds.
","language":"English","publisher":"Springer","doi":"10.1007/s11356-022-20881-z","usgsCitation":"Vyas, N.B., Rattner, B.A., Lockhart, J.M., Hulse, C., Rice, C., Kuncir, F., and Kritz, K., 2022, Toxicological responses to sublethal anticoagulant rodenticide exposure in free-flying hawks: Environmental Science and Pollution Research, v. 29, p. 74024-74037, https://doi.org/10.1007/s11356-022-20881-z.","productDescription":"14 p.","startPage":"74024","endPage":"74037","ipdsId":"IP-129660","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":401682,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Rocky Mountain Arsenal National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.90089416503906,\n 39.80220607474971\n ],\n [\n -104.78897094726562,\n 39.80115102364283\n ],\n [\n -104.79034423828125,\n 39.86969567045658\n ],\n [\n -104.86518859863281,\n 39.86969567045658\n ],\n [\n -104.8974609375,\n 39.84281323262067\n ],\n [\n -104.90226745605469,\n 39.828577091142016\n ],\n [\n -104.90089416503906,\n 39.80220607474971\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"29","noUsgsAuthors":false,"publicationDate":"2022-05-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Vyas, Nimish B. 0000-0003-0191-1319 nvyas@usgs.gov","orcid":"https://orcid.org/0000-0003-0191-1319","contributorId":4494,"corporation":false,"usgs":true,"family":"Vyas","given":"Nimish","email":"nvyas@usgs.gov","middleInitial":"B.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":844099,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rattner, Barnett A. 0000-0003-3676-2843 brattner@usgs.gov","orcid":"https://orcid.org/0000-0003-3676-2843","contributorId":4142,"corporation":false,"usgs":true,"family":"Rattner","given":"Barnett","email":"brattner@usgs.gov","middleInitial":"A.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":844100,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lockhart, J. Michael","contributorId":179117,"corporation":false,"usgs":false,"family":"Lockhart","given":"J.","email":"","middleInitial":"Michael","affiliations":[],"preferred":false,"id":844101,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hulse, Craig S.","contributorId":292224,"corporation":false,"usgs":false,"family":"Hulse","given":"Craig S.","affiliations":[{"id":37374,"text":"Retired USGS","active":true,"usgs":false}],"preferred":false,"id":844102,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rice, Clifford P.","contributorId":270789,"corporation":false,"usgs":false,"family":"Rice","given":"Clifford P.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":844103,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kuncir, Frank","contributorId":139801,"corporation":false,"usgs":false,"family":"Kuncir","given":"Frank","email":"","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":844104,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kritz, Kevin","contributorId":292226,"corporation":false,"usgs":false,"family":"Kritz","given":"Kevin","email":"","affiliations":[{"id":7199,"text":"US FWS","active":true,"usgs":false}],"preferred":false,"id":844105,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70232428,"text":"70232428 - 2022 - Watching the Cryosphere thaw: Seismic monitoring of permafrost degradation using distributed acoustic sensing during a controlled heating experiment","interactions":[],"lastModifiedDate":"2022-07-01T12:29:14.282307","indexId":"70232428","displayToPublicDate":"2022-05-28T07:27:47","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Watching the Cryosphere thaw: Seismic monitoring of permafrost degradation using distributed acoustic sensing during a controlled heating experiment","docAbstract":"Permafrost degradation is rapidly increasing in response to a warming Arctic climate, altering landscapes and damaging critical infrastructure. Solutions for monitoring permafrost thaw dynamics are essential to understand biogeochemical feedbacks as well as to issue warnings for hazardous geotechnical conditions. We investigate the feasibility of permafrost monitoring using permanently installed fiber-optic seismic networks. We conducted a 2-month seismic monitoring campaign during a controlled thaw experiment using a permanent surface orbital vibrator (SOV) and a 2D-array of distributed acoustic sensing (DAS) cables, and observed significant (15%) shear-wave velocity (Vs) reductions and approximately 2 m depression of the permafrost table beneath the heating zone. These observations were validated by time-lapse horizontal-to-vertical spectral ratio (HVSR) analysis from three co-located broadband seismometers. The combination of SOV and DAS provided unique seismic observations for permafrost monitoring at the field scale, as well as a basis for design and development of early warning systems for permafrost thaw.
We use the Multiple Element Limitation (MEL) model to examine responses of twelve ecosystems to elevated carbon dioxide (CO2), warming, and 20% decreases or increases in precipitation. Ecosystems respond synergistically to elevated CO2, warming, and decreased precipitation combined because higher water-use efficiency with elevated CO2 and higher fertility with warming compensate for responses to drought. Response to elevated CO2, warming, and increased precipitation combined is additive. We analyze changes in ecosystem carbon (C) based on four nitrogen (N) and four phosphorus (P) attribution factors: (1) changes in total ecosystem N and P, (2) changes in N and P distribution between vegetation and soil, (3) changes in vegetation C:N and C:P ratios, and (4) changes in soil C:N and C:P ratios. In the combined CO2 and climate change simulations, all ecosystems gain C. The contributions of these four attribution factors to changes in ecosystem C storage varies among ecosystems because of differences in the initial distributions of N and P between vegetation and soil and the openness of the ecosystem N and P cycles. The net transfer of N and P from soil to vegetation dominates the C response of forests. For tundra and grasslands, the C gain is also associated with increased soil C:N and C:P. In ecosystems with symbiotic N fixation, C gains resulted from N accumulation. Because of differences in N versus P cycle openness and the distribution of organic matter between vegetation and soil, changes in the N and P attribution factors do not always parallel one another. Differences among ecosystems in C-nutrient interactions and the amount of woody biomass interact to shape ecosystem C sequestration under simulated global change. We suggest that future studies quantify the openness of the N and P cycles and changes in the distribution of C, N, and P among ecosystem components, which currently limit understanding of nutrient effects on C sequestration and responses to elevated CO2 and climate change.
This report describes the findings of a U.S. Geological Survey study, completed in cooperation with the Bureau of Land Management, focused on better understanding the present-day (1975–2019) hydrogeology and groundwater quality of the San Agustin Basin in west-central New Mexico to support sustainable groundwater resource management. The basin hosts a relatively undeveloped basin-fill and alluvium aquifer system and is topographically divided into east and west subbasins by the McClure Hills. Groundwater chemistry and groundwater elevation data were compiled, collected, and interpreted in the context of groundwater flow and quality. The analyses presented in this report consider groundwater chemistry data collected within the last decade (2010–19) and groundwater elevation data collected from 1975 through 2019 to provide insight into present-day conditions. Groundwater elevations show that groundwater typically moves from the highlands to the lowlands, with a prominent east to west regional trend. Groundwater elevations were lowest in the southwestern portion of the west subbasin, where estimated flow directions suggest underflow through the local highlands into the northern East Fork Gila River watershed, which is further supported by historical groundwater elevation data from the northern East Fork Gila River watershed. Gradual groundwater elevation gradients (about 2 feet per mile) near the east and west subbasin divide suggest that groundwater slowly flows from the east subbasin to the west subbasin.
Quantitative analyses of groundwater chemistry data show that groundwater in both subbasins has similar chemical characteristics. A systematic east to west groundwater evolution in water chemistry was not observed despite evidenced subbasin connectivity. The absence of this pattern suggests that groundwater mixing is regionally prevalent, sediment reactivity is low and variable, and (or) recharge conditions are comparable in both subbasins. Groundwater chemistry was generally independent of aquifer type, suggesting that the aquifers are hydrologically well connected. Corrected carbon-14 groundwater age estimates in the basin ranged from 232 to 13,916 years before present with a median of 5,409 years. A wide range of groundwater ages is therefore present in the basin, with waters commonly being thousands of years old, thereby supporting generally slow regional groundwater movement. A component of relatively young groundwater, for which estimated ages could not be accurately computed, is also present in the basin, and it may commonly mix with older waters. The spatial distribution of categorical and quantitative groundwater ages indicates that most recharge likely occurs in the highlands through mountain-block recharge and as focused recharge within arroyos, although evidence of modern (1953 and after) groundwater was minimal at sampled sites.
Median annual gradients (groundwater elevation change over time) indicate that most groundwater elevations in the lowlands changed little (−0.2 to 0.2 foot per year) from 1975 through 2019. Groundwater elevations in the highlands varied more annually, which is likely due to recharge from precipitation events. These more variable groundwater elevations in the highlands compared with the lowlands, along with groundwater ages, provide further evidence that most groundwater recharge takes place in the highlands, with minimal recharge in the lowlands. Median groundwater elevation change for all sites was −0.05 foot per year. Temporal consistency of lowland groundwater elevations suggests that regional groundwater dynamics have been more or less stable through time under current climate and development conditions, although median annual gradients indicate that groundwater elevations may have slightly declined on average between 1975 and 2019.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225029","collaboration":"Prepared in cooperation with Bureau of Land Management and in collaboration with New Mexico Bureau of Geology and Mineral Resources","usgsCitation":"Pepin, J.D., Travis, R.E., Blake, J.M., Rinehart, A., and Koning, D., 2022, Hydrogeology and groundwater quality in the San Agustin Basin, New Mexico, 1975–2019: U.S. Geological Survey Scientific Investigations Report 2022–5029, 61 p., 4 app., https://doi.org/10.3133/sir20225029.","productDescription":"Report: x, 61 p.; 6 Tables; Dataset","numberOfPages":"76","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-120066","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":502386,"rank":12,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113080.htm","linkFileType":{"id":5,"text":"html"}},{"id":401145,"rank":11,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":401143,"rank":10,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029_table3.1.csv","text":"Table 3.1","size":"29.5 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2022-5029 Table 3.1"},{"id":401142,"rank":9,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029_table3.1.xlsx","text":"Table 3.1","size":"55.2 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2022-5029 Table 3.1"},{"id":401141,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029_table2.1.csv","text":"Table 2.1","size":"14.3 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2022-5029 Table 2.1"},{"id":401140,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029_table2.1.xlsx","text":"Table 2.1","size":"27.6 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2022-5029 Table 2.1"},{"id":401138,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029_table1.1.xlsx","text":"Table 1.1","size":"116 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2022-5029 Table 1.1"},{"id":401137,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5029/images"},{"id":401134,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5029/coverthb.jpg"},{"id":401135,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029.pdf","text":"Report","size":"8.37 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5029"},{"id":401139,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029_table1.1.csv","text":"Table 1.1","size":"146 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2022-5029 Table 1.1"},{"id":401136,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029.XML"}],"country":"United States","state":"New Mexico","otherGeospatial":"San Agustin Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.666,\n 34.5\n ],\n [\n -107.333,\n 34.5\n ],\n [\n -107.333,\n 33.333\n ],\n [\n -108.666,\n 33.333\n ],\n [\n -108.666,\n 34.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
Nearly half a century ago, two papers postulated the likelihood of lunar lava tube caves using mathematical models. Today, armed with an array of orbiting and fly-by satellites and survey instrumentation, we have now acquired cave data across our solar system—including the identification of potential cave entrances on the Moon, Mars, and at least six other planetary bodies. These discoveries gave rise to the study of planetary caves. To help advance this field, we leveraged the expertise of an interdisciplinary group to identify a strategy to explore caves beyond Earth. Focusing primarily on astrobiology, the cave environment, geology, robotics, instrumentation, and human exploration, our goal was to produce a framework to guide this subdiscipline through at least the next decade. To do this, we first assembled a list of 198 science and engineering questions. Then, through a series of social surveys, 114 scientists and engineers winnowed down the list to the top 53 highest priority questions. This exercise resulted in identifying emerging and crucial research areas that require robust development to ultimately support a robotic mission to a planetary cave—principally the Moon and/or Mars. With the necessary financial investment and institutional support, the research and technological development required to achieve these necessary advancements over the next decade are attainable. Subsequently, we will be positioned to robotically examine lunar caves and search for evidence of life within martian caves; in turn, this will set the stage for human exploration and potential habitation of both the lunar and martian subsurface.
","language":"English","publisher":"Wiley","doi":"10.1029/2022JE007194","usgsCitation":"Wynne, J.J., Titus, T.N., Agha-Mohammadi, A., Azua-Bustos, A., Boston, P.J., de Leon, P., Demirel-Floyd, C., de Waele, J., Jones, H., Malaska, M.J., Miller, A.Z., Sapers, H.M., Sauro, F., Sonderegger, D.L., Uckert, K., Wong, U.Y., Alexander, E.C., Chiao, L., Cushing, G.E., DeDecker, J., Fairen, A.G., Frumkin, A., Harris, G.L., Kearney, M.L., Kerber, L.A., Leveille, R.J., Manyapu, K., Massironi, M., Mylroie, J.E., Onac, B.P., Parazynski, S.E., Phillips-Lander, C.M., Prettyman, T.H., Schulze-Makuch, D., Wagner, R.V., Whittaker, W.L., and Williams, K.E., 2022, Fundamental science and engineering questions in planetary cave exploration: JGR Planets, v. 127, no. 11, e2022JE007194, 32 p., https://doi.org/10.1029/2022JE007194.","productDescription":"e2022JE007194, 32 p.","ipdsId":"IP-131152","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":447653,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2022je007194","text":"External Repository"},{"id":401298,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"127","issue":"11","noUsgsAuthors":false,"publicationDate":"2022-11-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Wynne, J. 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Detecting and quantifying groundwater discharge is challenging because rates of flow can be very small and difficult to measure, exchange is commonly highly heterogeneous both in space and time, and surface-water hydrodynamics can influence the exchange and hinder measurements. Fortunately, a growing number of methods developed during the last several decades has led to advancements in our capabilities to identify and quantify groundwater discharge to surface water, including better use of seepage meters, application of tracers such as heat or isotopes, and improved groundwater-modeling capabilities. This progress has led to coalescence in characterizing the complex mix of hydrological, biological, and chemical processes that occur at the groundwater-surface water interface, along with relevant societal effects. 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","language":"English","publisher":"Multidisciplinary Digital Publishing Institute","doi":"10.3390/w14111698","usgsCitation":"Duque, C., and Rosenberry, D., 2022, Advances in the study and understanding of groundwater discharge to surface water: Water, v. 14, no. 11, 1698, 5 p., https://doi.org/10.3390/w14111698.","productDescription":"1698, 5 p.","ipdsId":"IP-141343","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":447656,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w14111698","text":"Publisher Index Page"},{"id":401295,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"11","noUsgsAuthors":false,"publicationDate":"2022-05-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Duque, Carlos 0000-0001-5833-8483","orcid":"https://orcid.org/0000-0001-5833-8483","contributorId":245349,"corporation":false,"usgs":false,"family":"Duque","given":"Carlos","email":"","affiliations":[{"id":37318,"text":"Aarhus University","active":true,"usgs":false}],"preferred":false,"id":843722,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rosenberry, Donald O. 0000-0003-0681-5641","orcid":"https://orcid.org/0000-0003-0681-5641","contributorId":257638,"corporation":false,"usgs":true,"family":"Rosenberry","given":"Donald O.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":843723,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70231776,"text":"70231776 - 2022 - Constructing a large-scale landslide database across heterogeneous environments using task-specific model updates","interactions":[],"lastModifiedDate":"2022-06-16T15:29:22.216346","indexId":"70231776","displayToPublicDate":"2022-05-27T08:17:30","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1942,"text":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Constructing a large-scale landslide database across heterogeneous environments using task-specific model updates","docAbstract":"Preparation and mitigation efforts for widespread landslide hazards can be aided by a large-scale, well-labeled landslide inventory with high location accuracy. Recent smallscale studies for pixel-wise labeling of potential landslide areas in remotely-sensed images using deep learning (DL) showed potential but were based on data from very small, homogeneous regions with unproven model transferability. In this paper we consider a more realistic and practical setting for large-scale heterogeneous landslide data collection and DL-based labeling. In this setting, remotely sensed images are collected sequentially in temporal batches, where each batch focuses on images from a particular ecoregion, but different batches can focus on different ecoregions with distinct landscape characteristics. For such a scenario, we study the following questions: (1) How well do DL models trained in homogeneous regions perform when they are transferred to different ecoregions, (2) Does increasing the spatial coverage in the data improve model performance in a given ecoregion (even when the extra data do not come from the ecoregion), and (3) Can a landslide pixel labeling model be incrementally updated with new data, but without access to the old data and without losing performance on the old data (so that researchers can share models obtained from proprietary datasets)' We address these questions by extending the Learning without Forgetting framework, which is used for incremental training of image classification models, to the setting of incremental training of semantic segmentation models (e.g., identifying all landslide pixels in an image). We call the resulting extension Task-Specific Model Updates (TSMU). TSMU semantic segmentation framework consists of an encoder shared by all ecoregions to capture the similarities between them, and ecoregion-specific decoders to capture the nuances of each ecoregion. This framework is continually updated using a threestage training procedure for each new addition of an ecoregion without having to revisit data from old ecoregions and without losing performance on them.
","language":"English","publisher":"Institute of Electrical and Electronics Engineers","doi":"10.1109/JSTARS.2022.3177025","usgsCitation":"Nagendra, S., Kifer, D., Mirus, B., Pei, T., Lawson, K., Manjunatha, S.B., Li, W., Nguyen, H., Qiu, T., Tran, S., and Shen, C., 2022, Constructing a large-scale landslide database across heterogeneous environments using task-specific model updates: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, v. 15, p. 4349-4370, https://doi.org/10.1109/JSTARS.2022.3177025.","productDescription":"23 p.","startPage":"4349","endPage":"4370","ipdsId":"IP-137285","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":447657,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1109/jstars.2022.3177025","text":"Publisher Index Page"},{"id":401292,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nagendra, Savinay","contributorId":292084,"corporation":false,"usgs":false,"family":"Nagendra","given":"Savinay","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":843801,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kifer, Daniel","contributorId":292085,"corporation":false,"usgs":false,"family":"Kifer","given":"Daniel","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":843802,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mirus, Benjamin B. 0000-0001-5550-014X","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":267912,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":843803,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pei, Te","contributorId":292087,"corporation":false,"usgs":false,"family":"Pei","given":"Te","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":843804,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lawson, Kathryn","contributorId":292089,"corporation":false,"usgs":false,"family":"Lawson","given":"Kathryn","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":843805,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Manjunatha, Srikanth Banagere","contributorId":292090,"corporation":false,"usgs":false,"family":"Manjunatha","given":"Srikanth","email":"","middleInitial":"Banagere","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":843806,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Li, Weixin","contributorId":292093,"corporation":false,"usgs":false,"family":"Li","given":"Weixin","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":843807,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Nguyen, Hien","contributorId":292096,"corporation":false,"usgs":false,"family":"Nguyen","given":"Hien","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":843808,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Qiu, Tong","contributorId":292099,"corporation":false,"usgs":false,"family":"Qiu","given":"Tong","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":843809,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Tran, Sarah","contributorId":292102,"corporation":false,"usgs":false,"family":"Tran","given":"Sarah","email":"","affiliations":[{"id":37314,"text":"Google Inc.","active":true,"usgs":false}],"preferred":false,"id":843810,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Shen, Chaopeng","contributorId":152465,"corporation":false,"usgs":false,"family":"Shen","given":"Chaopeng","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":843811,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70238935,"text":"70238935 - 2022 - Global environmental changes more frequently offset than intensify detrimental effects of biological invasions","interactions":[],"lastModifiedDate":"2022-12-19T14:10:19.706047","indexId":"70238935","displayToPublicDate":"2022-05-27T07:50:03","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3164,"text":"Proceedings of the National Academy of Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Global environmental changes more frequently offset than intensify detrimental effects of biological invasions","docAbstract":"Human-induced abiotic global environmental changes (GECs) and the spread of nonnative invasive species are rapidly altering ecosystems. Understanding the relative and interactive effects of invasion and GECs is critical for informing ecosystem adaptation and management, but this information has not been synthesized. We conducted a meta-analysis to investigate effects of invasions, GECs, and their combined influences on native ecosystems. We found 458 cases from 95 published studies that reported individual and combined effects of invasions and a GEC stressor, which was most commonly warming, drought, or nitrogen addition. We calculated standardized effect sizes (Hedges’ d) for individual and combined treatments and classified interactions as additive (sum of individual treatment effects), antagonistic (smaller than expected), or synergistic (outside the expected range). The ecological effects of GECs varied, with detrimental effects more likely with drought than the other GECs. Invasions were more strongly detrimental, on average, than GECs. Invasion and GEC interactions were mostly antagonistic, but synergistic interactions occurred in >25% of cases and mostly led to more detrimental outcomes for ecosystems. While interactive effects were most often smaller than expected from individual invasion and GEC effects, synergisms were not rare and occurred across ecological responses from the individual to the ecosystem scale. Overall, interactions between invasions and GECs were typically no worse than the effects of invasions alone, highlighting the importance of managing invasions locally as a crucial step toward reducing harm from multiple global changes.
","language":"English","publisher":"National Academy of Sciences","doi":"10.1073/pnas.2117389119","usgsCitation":"Lopez, B., Allen, J., Dukes, J., Lenoir, J., Vila, M., Blumenthal, D., Beaury, E.M., Fusco, E.J., Laginhas, B.B., Morelli, T.L., O’Neill, M.W., Sorte, C.J., Maceda-Veiga, A., Whitlock, R., and Bradley, B., 2022, Global environmental changes more frequently offset than intensify detrimental effects of biological invasions: Proceedings of the National Academy of Sciences, v. 119, no. 22, e2117389119, 7 p., https://doi.org/10.1073/pnas.2117389119.","productDescription":"e2117389119, 7 p.","ipdsId":"IP-138217","costCenters":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":447659,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://idus.us.es/handle//11441/162137","text":"Publisher Index Page"},{"id":410699,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"119","issue":"22","noUsgsAuthors":false,"publicationDate":"2022-05-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Lopez, Bianca","contributorId":299985,"corporation":false,"usgs":false,"family":"Lopez","given":"Bianca","affiliations":[{"id":64995,"text":"University of Massachusetts, Northeast Climate Adaptation Science Center","active":true,"usgs":false}],"preferred":false,"id":859239,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Allen, Jenica","contributorId":299986,"corporation":false,"usgs":false,"family":"Allen","given":"Jenica","affiliations":[{"id":25495,"text":"Mount Holyoke College","active":true,"usgs":false}],"preferred":false,"id":859240,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dukes, Jeffrey","contributorId":299987,"corporation":false,"usgs":false,"family":"Dukes","given":"Jeffrey","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":859241,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lenoir, Jonathan","contributorId":167876,"corporation":false,"usgs":false,"family":"Lenoir","given":"Jonathan","email":"","affiliations":[{"id":24849,"text":"Université de Picardie Jules Verne","active":true,"usgs":false}],"preferred":false,"id":859242,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vila, Montserrat","contributorId":236834,"corporation":false,"usgs":false,"family":"Vila","given":"Montserrat","email":"","affiliations":[],"preferred":false,"id":859243,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Blumenthal, Dana","contributorId":70686,"corporation":false,"usgs":true,"family":"Blumenthal","given":"Dana","affiliations":[],"preferred":false,"id":859244,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Beaury, Evelyn M.","contributorId":236820,"corporation":false,"usgs":false,"family":"Beaury","given":"Evelyn","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":859245,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fusco, Emily J.","contributorId":236821,"corporation":false,"usgs":false,"family":"Fusco","given":"Emily","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":859246,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Laginhas, Brittany B.","contributorId":236823,"corporation":false,"usgs":false,"family":"Laginhas","given":"Brittany","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":859247,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Morelli, Toni Lyn 0000-0001-5865-5294 tmorelli@usgs.gov","orcid":"https://orcid.org/0000-0001-5865-5294","contributorId":197458,"corporation":false,"usgs":true,"family":"Morelli","given":"Toni","email":"tmorelli@usgs.gov","middleInitial":"Lyn","affiliations":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":859248,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"O’Neill, Mitchell W.","contributorId":299994,"corporation":false,"usgs":false,"family":"O’Neill","given":"Mitchell","email":"","middleInitial":"W.","affiliations":[{"id":12667,"text":"University of New Hampshire","active":true,"usgs":false}],"preferred":false,"id":859249,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Sorte, Cascade J.B.","contributorId":236835,"corporation":false,"usgs":false,"family":"Sorte","given":"Cascade","middleInitial":"J.B.","affiliations":[],"preferred":false,"id":859250,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Maceda-Veiga, Alberto","contributorId":299996,"corporation":false,"usgs":false,"family":"Maceda-Veiga","given":"Alberto","email":"","affiliations":[{"id":56742,"text":"Universitat de Barcelona","active":true,"usgs":false}],"preferred":false,"id":859251,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Whitlock, Raj","contributorId":299997,"corporation":false,"usgs":false,"family":"Whitlock","given":"Raj","email":"","affiliations":[{"id":16977,"text":"University of Liverpool","active":true,"usgs":false}],"preferred":false,"id":859252,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Bradley, Bethany A. 0000-0003-4912-4971","orcid":"https://orcid.org/0000-0003-4912-4971","contributorId":299998,"corporation":false,"usgs":true,"family":"Bradley","given":"Bethany A.","affiliations":[{"id":64995,"text":"University of Massachusetts, Northeast Climate Adaptation Science Center","active":true,"usgs":false}],"preferred":false,"id":859253,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70232104,"text":"70232104 - 2022 - #TheSmoreYouKnow and #emergencycute: A conceptual model on the use of humor by science agencies during crisis to create connection, empathy, and compassion","interactions":[],"lastModifiedDate":"2022-06-06T11:51:38.884858","indexId":"70232104","displayToPublicDate":"2022-05-27T06:48:57","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2036,"text":"International Journal of Disaster Risk Reduction","active":true,"publicationSubtype":{"id":10}},"title":"#TheSmoreYouKnow and #emergencycute: A conceptual model on the use of humor by science agencies during crisis to create connection, empathy, and compassion","docAbstract":"Studies from a variety of disciplines reveal that humor can be a useful method to reduce stress and increase compassion, connection, and empathy between agencies and people they serve during times of crisis. Despite this growing evidence base, humor's use during a geohazard (earthquake, volcanoes, landslides, and tsunami) to aid scientific agencies' crisis communication response has been rarely studied. A broad literature review of humor in crisis and an exploratory examination of several case studies reveal that scientific organizations, specifically those that respond to geohazards, can harness the power of humor to help create connection and empathy with the publics they seek to serve. We find evidence that supports our argument that the use of humor acknowledges a shared human experience, reducing the barriers between public officials, scientists, and the people most impacted by crisis. Public statements made by scientists and public officials during the U.S. Geological Survey (USGS) response to the Kīlauea eruption in 2018 in Hawai'i, United States, and GNS Science/GeoNet (GeoNet) response to the M7.8 Kaikōura/North Hurunui earthquake in 2016 in Aotearoa New Zealand, are used to inform the development of this conceptual model. We then posit a conceptual model which unifies concepts from the literature with our case studies to provide potential guidelines for those crisis communicators working for science agencies on how best to use humor to help people cope during times of crisis. This model can be further tested for future research to determine its effectiveness and utility for scientific agencies responding to geological crises.
Using a geology-based assessment methodology, the U.S. Geological Survey estimated undiscovered, technically recoverable mean resources of 906 million barrels of oil and 132.8 trillion cubic feet of gas in four geologic provinces of Western Australia.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213060","usgsCitation":"Schenk, C.J., Mercier, T.J., Woodall, C.A., Finn, T.M., Le, P.A., Marra, K.R., Leathers-Miller, H.M., and Drake, R.M., II, 2022, Assessment of undiscovered conventional oil and gas resources of the Perth Basin, NW Shelf, Browse Basin, and Bonaparte Gulf Basin provinces of Western Australia, 2020: U.S. Geological Survey Fact Sheet 2021−3060, 4 p., https://doi.org/10.3133/fs20213060.","productDescription":"Report: 2 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-122784","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":401244,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2021/3060/fs20213060.xml"},{"id":401243,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2021/3060/images"},{"id":401169,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3060/fs20213060.pdf","text":"Report","size":"1.62 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2021-3060"},{"id":401168,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3060/coverthb.jpg"},{"id":401170,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TRGOQC","text":"USGS data release","linkHelpText":"USGS National and Global Oil and Gas Assessment Project - Western Australia Assessment Unit Boundaries and Assessment Input Forms"}],"country":"Australia","otherGeospatial":"Perth Basin, NW Shelf, Browse Basin, and Bonaparte Gulf Basin provinces","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 108.19335937499999,\n -39.57182223734373\n ],\n [\n 132.5390625,\n -39.57182223734373\n ],\n [\n 132.5390625,\n -8.667918002363107\n ],\n [\n 108.19335937499999,\n -8.667918002363107\n ],\n [\n 108.19335937499999,\n -39.57182223734373\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
Using a geology-based assessment methodology, the U.S. Geological Survey estimated means of 806 million barrels of oil and 17.0 trillion cubic feet of gas within the greater Taranaki Basin and East Coast Basin of New Zealand.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213059","usgsCitation":"Schenk, C.J., Mercier, T.J., Tennyson, M.E., Ellis, G.S., Woodall, C.A., Le, P.A., Leathers-Miller, H.M., and Drake, R.M., II, 2022, Assessment of undiscovered conventional oil and gas resources of the greater Taranaki Basin and East Coast Basin of New Zealand, 2020: U.S. Geological Survey Fact Sheet 2021−3059, 4 p., https://doi.org/10.3133/fs20213059.","productDescription":"Report: 2 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-126854","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":401249,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2021/3059/fs20213059.xml"},{"id":401248,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2021/3059/images"},{"id":401159,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3059/coverthb.jpg"},{"id":401161,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WKWP6U","text":"USGS data release","linkHelpText":"USGS National and Global Oil and Gas Assessment Project-Greater Taranaki and East Coast Basins, New Zealand, Assessment Unit Boundaries and Assessment Input Forms"},{"id":401160,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3059/fs20213059.pdf","text":"Report","size":"2.75 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2021-3059"}],"country":"New Zealand","otherGeospatial":"Taranaki Basin, East Coast Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 171.123046875,\n -41.96765920367816\n ],\n [\n 180.08789062499997,\n -41.96765920367816\n ],\n [\n 180.08789062499997,\n -33.46810795527895\n ],\n [\n 171.123046875,\n -33.46810795527895\n ],\n [\n 171.123046875,\n -41.96765920367816\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
On January 18, 2022, groundwater levels were measured in selected wells in the Hālawa area, O‘ahu, Hawai‘i, constituting a synoptic groundwater-level survey (shortened herein to “synoptic survey”) of the area. Groundwater levels were measured mainly from 9:00 a.m. to 12:00 p.m. (times listed in Hawai‘i standard time) and provide a snapshot of groundwater levels during the survey period. Following a reported fuel release that affected groundwater quality in the Red Hill area, several production wells were shut down in the weeks prior to the synoptic survey. These wells include the Red Hill Shaft (shut down on November 28, 2021) and the Hālawa Shaft (shut down on December 3, 2021, except for weekly, short-duration operations for water-quality sampling). Groundwater levels measured in wells during the synoptic survey ranged from 16.81 to 20.19 feet above mean sea level. The groundwater levels measured on January 18, 2022, were about 0.3 to 0.6 feet higher than those measured at common sites during a synoptic groundwater-level survey on December 23, 2021.
The groundwater levels collected during the multiagency synoptic survey contain uncertainty because of several potential sources of error associated with (1) the accuracy of the measuring tapes used, (2) the accuracy of the measuring-point altitude at the top of each well, (3) well plumbness and alignment, (4) human error, and (5) changing conditions during the survey period. Because of these potential sources of error, comparability of groundwater-level measurements may be affected. Some of the sources of uncertainty can be addressed and lead to improved accuracy and comparability of the groundwater levels. For example, uncertainty associated with the measuring-point altitudes can be addressed by resurveying measuring-point altitudes to a common vertical datum using consistent surveying methods.
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Groundwater quality in the Sacramento Metropolitan Domestic-Supply Aquifer study unit (SacMetro-DSA) was studied from August to November 2017 as part of the second phase of the Priority Basin Project of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program. The study unit is in parts of Amador, Placer, Sacramento, and Sutter Counties, and the extent of the study unit was defined by the location of three California Department of Water Resources groundwater subbasins: the North American, the South American, and the Cosumnes. The SacMetro-DSA focused on groundwater resources used for domestic drinking-water supply, which generally correspond to shallower parts of aquifer systems than those of groundwater resources used for public drinking water supply in the same area. The assessments characterized the quality of untreated groundwater, not the quality of drinking water.
This study included two components: (1) a status assessment, which characterized the status of the quality of the groundwater resources used for domestic supply and (2) an understanding assessment, which evaluated the natural and human factors potentially affecting water quality in those resources. The first component of this study—the status assessment—was based on water-quality data collected from 49 sites sampled by the U.S. Geological Survey for the GAMA Priority Basin Project in 2017. The samples were analyzed for volatile organic compounds, pesticides, and naturally present inorganic constituents, such as major ions and trace elements. To provide context, concentrations of constituents measured in groundwater were compared to U.S. Environmental Protection Agency and California State Water Resources Control Board Division of Drinking Water regulatory and non-regulatory benchmarks for drinking-water quality. The status assessment used a grid-based method to estimate the proportion of the groundwater resources that had concentrations of water-quality constituents approaching or above benchmark concentrations. This method provides statistically unbiased results at the study-area scale and permits comparisons to other GAMA Priority Basin Project study areas. The second component of this study—the understanding assessment—identified the natural and human factors that potentially affect groundwater quality by evaluating land-use characteristics, groundwater age, and geochemical and hydrologic conditions of the domestic-supply aquifer and related these data to constituents identified in the status assessment for further evaluation.
In the SacMetro-DSA study unit, arsenic was the only inorganic constituent detected above health-based benchmarks and was detected in 10 percent of the domestic-supply aquifer system. Inorganic constituents were detected above the non-health-based California State Water Resources Control Board—Division of Drinking Water secondary maximum contaminant levels (SMCL-CA) in 16 percent of the system. The inorganic constituents detected above the SMCL-CA were chloride, iron, manganese, and total dissolved solids (TDS). Organic constituents (volatile organic compounds and pesticides) with health-based benchmarks were not detected above health-based benchmarks; however, chloroform was detected at concentrations higher than 10 percent of the health-based benchmark (80 micrograms per liter) in 2 percent of the domestic-supply aquifer system. Of the 310 organic constituents analyzed, 16 constituents were detected; however, only bentazon and chloroform had detection frequencies greater than 10 percent.
Inorganic constituents with health-based benchmarks that were evaluated in the understanding assessment included arsenic and hexavalent chromium. Arsenic and hexavalent chromium are natural constituents of aquifer sediments in the study unit and did not appear to be influenced by anthropogenic processes; rather, the presence of arsenic and hexavalent chromium appeared to be related to geochemical conditions controlled by oxidation–reduction reactions in the aquifer system. Naturally occurring inorganic constituents with SMCL-CAs evaluated in the understanding assessment were the trace elements iron and manganese, the major ion chloride, and TDS. Like arsenic and hexavalent chromium, the presence of iron and manganese was most strongly related to geochemical conditions in the aquifer system, specifically reducing conditions, which were most common near the western edge of the study unit close to the Sacramento River. Concentrations of chloride and TDS are indicators of salinity and were correlated with variables related to well location and included redox, agricultural land use, and elevation. Chloride and TDS were positively correlated to reducing conditions, and agricultural land use was negatively correlated to elevation and well depth. Observed correlations among variables were likely driven by the characteristics of the western part of the study unit, such as its higher proportion of agricultural land use and its relatively low elevation. A large portion of the western edge of the study unit is located in the center of the Sacramento Valley, defined by the location of the Sacramento River. The special-interest constituent perchlorate, also included in the understanding assessment, has natural and anthropogenic sources. Perchlorate was detected frequently and at moderate relative concentrations. In some areas of the study unit, concentrations of perchlorate were higher than what might be expected in nature; therefore, anthropogenic introduction of perchlorate or anthropogenically induced migration of native perchlorate could be occurring.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225021","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","programNote":"A product of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program","usgsCitation":"Bennett, G.L., V, 2022, Status and understanding of groundwater quality in the Sacramento Metropolitan Domestic-Supply Aquifer study unit, 2017—California GAMA Priority Basin Project: U.S. Geological Survey Scientific Investigations Report 2022–5021, 52 p., https://doi.org/10.3133/sir20225021.","productDescription":"Report: xi, 52 p.; Data Release","numberOfPages":"52","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-125530","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":401167,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9H4P0XF","text":"Potential explanatory variables for groundwater quality in the Sacramento Metropolitan Domestic-Supply Aquifer study unit, 2017—California GAMA Priority Basin Project","description":"Bennett, G.L., V, 2022, Potential explanatory variables for groundwater quality in the Sacramento Metropolitan Domestic-Supply Aquifer study unit, 2017—California GAMA Priority Basin Project: U.S. Geological Survey data release, available at https://doi.org/10.5066/P9H4P0XF."},{"id":401166,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5021/images"},{"id":401165,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5021/sir20225021.xml"},{"id":401164,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5021/sir20225021.pdf","text":"Report","size":"20 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Scientific Investigations Report 2022–5021"},{"id":401163,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5021/covrthb.jpg"},{"id":502376,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113075.htm","linkFileType":{"id":5,"text":"html"}},{"id":401191,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225021/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"Scientific Investigations Report 2022–5021"}],"country":"United States","state":"California","otherGeospatial":"Sacramento Metropolitan Domestic-Supply Aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.51953124999999,\n 37.87485339352928\n ],\n [\n -120.5419921875,\n 37.87485339352928\n ],\n [\n -120.5419921875,\n 39.232253141714885\n ],\n [\n -122.51953124999999,\n 39.232253141714885\n ],\n [\n -122.51953124999999,\n 37.87485339352928\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"GAMA Project Chief
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