Rapid post-earthquake reconnaissance is important for emergency responses and rehabilitation by providing accurate and timely information about secondary hazards and impacts, including landslide, liquefaction, and building damage. Despite the extensive collection of geospatial data and satellite images, existing physics-based and data-driven methods suffer from low estimation performance due to the complex and event-specific causal dependencies underlying the cascading processes of earthquake-triggered hazards and impacts. Herein, we present a rapid seismic multi-hazard and impact estimation system that leverages advanced statistical causal inference and remote sensing techniques. The unique feature of this system is that it provides accurate and high-resolution estimations on a regional scale by jointly inferring multiple hazards and building damage from satellite images through modeling their causal dependencies. We evaluate our system on multiple seismic events from diverse countries around the globe. Our results corroborate that incorporating causal dependencies significantly improves large-scale estimation accuracy for multiple hazards and impacts compared to existing systems. The results also reveal quantitative causal mechanisms among earthquake-triggered multi-hazard and impact for multiple seismic events. Our system establishes a new way to extract and utilize the complex interactions of multiple hazards and impacts for effective disaster responses and advancing understanding of seismic geological processes.
","language":"English","publisher":"Springer","doi":"10.1038/s41467-022-35418-8","usgsCitation":"Xu, S., Dimasaka, J., Wald, D.J., and Noh, H.Y., 2022, Seismic multi-hazard and impact estimation via causal inference from satellite imagery: Nature Communications, v. 13, 7793, 13 p., https://doi.org/10.1038/s41467-022-35418-8.","productDescription":"7793, 13 p.","ipdsId":"IP-131046","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":445655,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-022-35418-8","text":"Publisher Index Page"},{"id":410700,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","noUsgsAuthors":false,"publicationDate":"2022-12-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Xu, Susu","contributorId":300127,"corporation":false,"usgs":false,"family":"Xu","given":"Susu","email":"","affiliations":[{"id":65025,"text":"Stony Brook University, NY, USA","active":true,"usgs":false}],"preferred":false,"id":859455,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dimasaka, Joshua","contributorId":300128,"corporation":false,"usgs":false,"family":"Dimasaka","given":"Joshua","email":"","affiliations":[{"id":65026,"text":"Stanford University, CA, USA","active":true,"usgs":false}],"preferred":false,"id":859456,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wald, David J. 0000-0002-1454-4514 wald@usgs.gov","orcid":"https://orcid.org/0000-0002-1454-4514","contributorId":795,"corporation":false,"usgs":true,"family":"Wald","given":"David","email":"wald@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":859457,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Noh, Hae Young","contributorId":265961,"corporation":false,"usgs":false,"family":"Noh","given":"Hae","email":"","middleInitial":"Young","affiliations":[{"id":54844,"text":"Carnegie Mellon University (now at Stanford University)","active":true,"usgs":false}],"preferred":false,"id":859458,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70238906,"text":"sim3497 - 2022 - Delineating the Pierre Shale from geophysical surveys east and southeast of Ellsworth Air Force Base, South Dakota, 2021","interactions":[],"lastModifiedDate":"2026-04-01T15:30:56.71177","indexId":"sim3497","displayToPublicDate":"2022-12-19T07:51:11","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3497","displayTitle":"Delineating the Pierre Shale from Geophysical Surveys East and Southeast of Ellsworth Air Force Base, South Dakota, 2021","title":"Delineating the Pierre Shale from geophysical surveys east and southeast of Ellsworth Air Force Base, South Dakota, 2021","docAbstract":"The U.S. Geological Survey, in cooperation with the U.S. Air Force Civil Engineer Center, used surface-geophysical methods to delineate the top of Cretaceous Pierre Shale along survey transects in selected areas east and southeast of Ellsworth Air Force Base, South Dakota, from April to September 2021. Two complementary geophysical methods—electrical resistivity and passive seismic—were used along 21 colocated transect surveys east and southeast of Ellsworth Air Force Base for a total of 24.7 line-kilometers. Electrical resistivity results were analyzed using EarthImager2D electrical resistivity tomography processing and inversion software. Two-dimensional earth models showing the electrical properties of the subsurface were evaluated by directly comparing the high and low subsurface resistivity values to a surficial-geologic map and nearby wells with drillers logs. Passive seismic data were analyzed using the horizontal-to-vertical spectral ratio method to determine the depth to the Cretaceous Pierre Shale at each survey point. The depth to the Pierre Shale along the transects ranged from 0.0 to about 19.8 meters, and the mean and median depths were about 6.1 and 5.6 meters, respectively. The elevation of the Pierre Shale and thickness of unconsolidated deposits generally increased with land-surface elevation from south to north; however, some transects displayed topographically high and low areas that did not correlate with land-surface topography.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3497","collaboration":"Prepared in cooperation with the U.S. Air Force Civil Engineer Center","usgsCitation":"Medler, C.J., 2022, Delineating the Pierre Shale from geophysical surveys east and southeast of Ellsworth Air Force Base, South Dakota, 2021: U.S. Geological Survey Scientific Investigations Map 3497, 3 sheets, 15-p. pamphlet, https://doi.org/10.3133/sim3497.","productDescription":"Report: vi, 15 p.; 3 Sheets: 64.00 × 53.33 inches or smaller; Data Release","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-137098","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":501938,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113997.htm","linkFileType":{"id":5,"text":"html"}},{"id":410625,"rank":7,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3497/sim3497_sheet03.pdf","text":"Sheet 3","size":"16.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3497, sheet 3","linkHelpText":"—Depth to Pierre Shale from Electrical Resistivity Tomography Inversion and Horizontal-to-Vertical Spectral Ratio Results for Transects 4A, 4B, 4D, 4E, 4FD3, 4FD4, 4FD5, 4G, 4H, and 5, Ellsworth Air Force Base, South Dakota"},{"id":410609,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3497/sim3497_sheet02.pdf","text":"Sheet 2","size":"14.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3497, sheet 2","linkHelpText":"—Depth to Pierre Shale from Electrical Resistivity Tomography Inversion and Horizontal-to-Vertical Spectral Ratio Results for Transects 2, 3A, 3B, 3D, 3E, and 3F, Ellsworth Air Force Base, South Dakota"},{"id":410608,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3497/sim3497_sheet01.pdf","text":"Sheet 1","size":"16.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3497, sheet 1","linkHelpText":"—Depth to Pierre Shale from Electrical Resistivity Tomography Inversion and Horizontal-to-Vertical Spectral Ratio Results for Transects 1A, 1C, 1D, 4F Alternate 1, and 4F Alternate 2, Ellsworth Air Force Base, South Dakota"},{"id":410607,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sim/3497/images"},{"id":410606,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sim/3497/sim3497.XML"},{"id":410605,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3497/sim3497.pdf","text":"Report","size":"8.72 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3497"},{"id":410604,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3497/coverthb.jpg"},{"id":410698,"rank":9,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sim3497/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":410626,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9X57BS0","text":"USGS data release","linkHelpText":"Electrical resistivity tomography (ERT) and horizontal-to-vertical spectral ratio (HVSR) data collected East and Southeast of Ellsworth Air Force Base, South Dakota, in 2021"}],"country":"United States","state":"South Dakota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -103.08,\n 44.06\n ],\n [\n -102.84286176348763,\n 44.06\n ],\n [\n -102.84286176348763,\n 44.2\n ],\n [\n -103.08,\n 44.2\n ],\n [\n -103.08,\n 44.06\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702
Algal blooms are pervasive in many freshwater environments and can pose risks to the health and safety of humans and other organisms. However, monitoring and tracking of potentially harmful blooms often relies on in-person observations by the public. Remote sensing has proven useful in augmenting in situ observations of algal concentration, but many hurdles hinder efficient application by end users. First, numerous approaches to estimate aquatic chlorophyll-a are available and can produce inconsistent results. Second, lack of quantitative in situ observations limits opportunities to train models for specific waterbodies, such that models developed for other systems must be used instead. We (1) implement univariate and multivariate logistic regression models to estimate the probability that aquatic chlorophyll-a concentrations exceed an accepted threshold beyond which harmful effects become likely and (2) evaluate the use of visually classified bloom/no-bloom satellite imagery to augment in situ training data. Using a binary classification of aquatic chlorophyll-a exceeding 10 μg / L, we found that (1) logistic regression models were ∼80 % accurate, (2) univariate models trained with visually classified data produce nearly the same accuracy (79%) as models trained with in situ observations (80%), and (3) augmenting in situ chlorophyll-a observations with visual classifications outperformed (82% accuracy) models trained on in situ observations alone (80% accuracy). These results provide a framework for evaluating multiple spectral indices in retrieving algal bloom presence or absence and illustrate that training data derived directly from satellite imagery can be useful in augmenting in situ observations.
","language":"English","publisher":"SPIE Digital Library","doi":"10.1117/1.JRS.16.044522","usgsCitation":"King, T.V., Hundt, S., Hafen, K., Stengel, V.G., and Ducar, S.D., 2022, Mapping the probability of freshwater algal blooms with various spectral indices and sources of training data: Journal of Applied Remote Sensing, v. 16, no. 4, 044522, 22 p., https://doi.org/10.1117/1.JRS.16.044522.","productDescription":"044522, 22 p.","ipdsId":"IP-127684","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":445656,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1117/1.jrs.16.044522","text":"Publisher Index Page"},{"id":435594,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GF0CBG","text":"USGS data release","linkHelpText":"Chlorophyll-a concentrations and algal bloom condition paired with Sentinel-2 aquatic reflectance values collected for Brownlee Reservoir, ID from 2015 through 2020"},{"id":410786,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.6105415720396,\n 45.15126198574853\n ],\n [\n -117.6105415720396,\n 43.793442297404255\n ],\n [\n -116.58806901557723,\n 43.793442297404255\n ],\n [\n -116.58806901557723,\n 45.15126198574853\n ],\n [\n -117.6105415720396,\n 45.15126198574853\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"King, Tyler V. 0000-0002-5785-3077","orcid":"https://orcid.org/0000-0002-5785-3077","contributorId":292424,"corporation":false,"usgs":true,"family":"King","given":"Tyler","middleInitial":"V.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":859498,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hundt, Stephen A. 0000-0002-6484-0637","orcid":"https://orcid.org/0000-0002-6484-0637","contributorId":204678,"corporation":false,"usgs":true,"family":"Hundt","given":"Stephen","middleInitial":"A.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":859499,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hafen, Konrad 0000-0002-1451-362X","orcid":"https://orcid.org/0000-0002-1451-362X","contributorId":215959,"corporation":false,"usgs":true,"family":"Hafen","given":"Konrad","email":"","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":859500,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stengel, Victoria G. 0000-0003-0481-3159 vstengel@usgs.gov","orcid":"https://orcid.org/0000-0003-0481-3159","contributorId":5932,"corporation":false,"usgs":true,"family":"Stengel","given":"Victoria","email":"vstengel@usgs.gov","middleInitial":"G.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":859501,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ducar, Scott D. 0000-0003-0781-5598","orcid":"https://orcid.org/0000-0003-0781-5598","contributorId":297547,"corporation":false,"usgs":true,"family":"Ducar","given":"Scott","email":"","middleInitial":"D.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":859502,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70238905,"text":"sir20225114 - 2022 - BFS—A non-linear, state-space model for baseflow separation and prediction","interactions":[],"lastModifiedDate":"2022-12-19T11:54:05.582352","indexId":"sir20225114","displayToPublicDate":"2022-12-16T12:26:01","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5114","displayTitle":"BFS—A Non-Linear, State-Space Model for Baseflow Separation and Prediction","title":"BFS—A non-linear, state-space model for baseflow separation and prediction","docAbstract":"Streamflow in rivers can be separated into a relatively steady component, or baseflow, that represents reliably available surface water and more dynamic components of runoff that typically represent a large fraction of total streamflow. A spatially aggregated numerical time-series model was developed to separate the baseflow component of a streamflow time-series using a state-space framework in which baseflow is a non-linear function of upstream storage, an unmeasured state variable. The state-space framework allows forecasting of baseflow for periods with no rainfall or snowmelt and estimation of residence times in contrast to other hydrograph separation models. The use of a non-linear relation between baseflow and storage maintains model performance over a wide range of time scales but will only provide reliable predictions for periods when the rate of streamflow recession as a fraction of streamflow decreases over time.
The baseflow separation model, BFS, is implemented as set of functions in the statistical computing language R. BFS is run using the main function, bf_sep, which reads model input (a time series of streamflow), calculates the baseflow component of streamflow, writes model output to a file, and returns an error to the user to facilitate automated calibration. The function, bf_sep, has six arguments, which a user must enter: a numerical vector with the time series of measured streamflow volume for each time step; a character string, timestep, that has a value of either “daily” or “hourly” indicating the time step; a character string, error_basis, indicating which simulated streamflow components are used for error calculations; a six-element numeric vector, flow, with parameters characterizing streamflow; a six-element vector, basin_char, with parameters characterizing the geometry of stream basin and reservoirs; and a six-element vector, gw_hyd, with hydraulic parameters. The function bf_sep calls a series of other functions to calculate surface and base reservoir storage and fluxes.
Calibration of a non-linear model for baseflow recession must confront three issues. First, baseflow is a component of streamflow, so it is always less than or equal to streamflow but there is no independent standard for the baseflow component of streamflow. Second, optimization routines can converge on a set of model parameters that result in relatively steady but minimal baseflow that does not exceed streamflow, Q, but has a limited dynamic range. Third, the power function used to generate non-linear first-order baseflow recession (dQ/dt)/Q ≠ constant) may only be sensitive to parameters over a limited range of values, which may not be found by optimization routines.
To address these issues, BFS calculates error as the mean of weighted differences between measured streamflow and either simulated baseflow or the sum of simulated baseflow and surface flow as a fraction of measured streamflow. The difference for each time step is weighted by an exponential function of the length of recession for each time step ranging from 0 for periods when streamflow increases and approaching 1 for long recessional periods. The weight is set to 1 for any time step when simulated streamflow exceeds measured streamflow. Error calculation incorporates limited precision of streamflow measurements.
A four-step calibration process was developed to find a set of viable parameters that maximize the baseflow component within the constraints of the conceptual model (a first-order recession rate that decreases during dry periods). BFS was calibrated at 13,208 U.S. Geological Survey streamgages with available daily streamflow records for at least 300 days from water years 1981 to 2020. The total simulated baseflow component as a fraction of streamflow (BFF) was generally less than the baseflow index (BFI) for 8,368 streamgages where BFF and BFI were available. The median difference was BFF–BFI = 0.11. Large differences were most common in the Interior West where streamflow in many rivers is regulated and is generated predominantly by snowmelt. The baseflow separation model generally allocates less streamflow to baseflow than graphical hydrograph separation in snowmelt rivers.
BFS can be used to forecast streamflow during dry periods by using a time series of real-time streamflow with values of Not Available (NA), appended to the time-series to represent missing (future) streamflow values. The forecast skill of BFS was evaluated in terms of difference between simulated baseflow and measured streamflow as a fraction of measured streamflow on the days of the annual maximum recession period at 5,916 of the sites with at least 10 years of record. The median annual error was less than 50 percent at one-half of the sites and generally improved for drier years with longer recession periods.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225114","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency and the Washington State Department of Ecology","usgsCitation":"Konrad, C.P., 2022, BFS—A non-linear, state-space model for baseflow separation and prediction: U.S. Geological Survey Scientific Investigations Report 2022–5114, 24 p., https://doi.org/10.3133/sir20225114.","productDescription":"Report: vii, 24 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-122969","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":410595,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5114/coverthb.jpg"},{"id":410596,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5114/sir20225114.pdf","text":"Report","size":"18.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5114"},{"id":410598,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AIPHEP","text":"USGS data release","description":"USGS data release","linkHelpText":"Non-linear baseflow separation model with parameters and results (ver. 2.0, October 2022)"},{"id":410599,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5114/images"},{"id":410600,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5114/sir20225114.XML"}],"contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
Green Lake, located in central Wisconsin USA within a watershed with land use dominated by agriculture, is listed as impaired under Sect. 303(d) of the Clean Water Act. The primary tributary, Silver Creek, is also impaired because of high total phosphorus (TP) concentrations. Silver Creek flows through a shallow marsh before reaching the lake. Prior to 2006, the marsh was turbid and free of macrophytes. Efforts to restrict carp (Cyprinus carpio) in the marsh and reduce the primary upstream phosphorus point source, resulted in the marsh becoming a clear-water, macrophyte-dominated system.
The point source reduction and marsh phytoplankton-to-macrophyte shift reduced the export of TP and suspended sediment (SS). These measured reductions at the marsh outlet exceeded the documented reductions in the upstream point source suggesting that the shift to a macrophyte-dominated system drove part of the TP reductions. TP loads at the marsh outlet significantly decreased in all seasons; however, SS loads significantly decreased in all seasons except winter, suggesting the vegetation shift was an important driver for these reductions. During 2012–2017, the marsh served as an overall sink for TP and SS, retaining on average 1.59 kg/day and 0.95 MT/day, respectively. Overall, this study documents benefits of a multi-stakeholder, collaborative ecological effort to restore a marsh from a turbid system to a macrophyte-dominated system, which resulted in significant reductions in downstream TP and SS loading to a major inland lake. This effort may serve as a model for similar restorations in other watersheds with land use dominated by agriculture.
","language":"English","publisher":"Springer","doi":"10.1007/s13157-022-01649-0","usgsCitation":"Fuller, S., Boswell, E.P., Thompson, A., and Robertson, D., 2022, Water-quality improvement of an agricultural watershed marsh after macrophyte establishment and point-source reduction: Wetlands, v. 42, 129, 13 p., https://doi.org/10.1007/s13157-022-01649-0.","productDescription":"129, 13 p.","ipdsId":"IP-139219","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":413530,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Green Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.08144298728385,\n 43.755719784168264\n ],\n [\n -88.8933819839438,\n 43.755719784168264\n ],\n [\n -88.8933819839438,\n 43.85676732584028\n ],\n [\n -89.08144298728385,\n 43.85676732584028\n ],\n [\n -89.08144298728385,\n 43.755719784168264\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"42","noUsgsAuthors":false,"publicationDate":"2022-12-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Fuller, Sarah","contributorId":302730,"corporation":false,"usgs":false,"family":"Fuller","given":"Sarah","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":865263,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boswell, Edward P 0000-0002-2644-4043","orcid":"https://orcid.org/0000-0002-2644-4043","contributorId":302732,"corporation":false,"usgs":false,"family":"Boswell","given":"Edward","email":"","middleInitial":"P","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":865264,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thompson, Anita M.","contributorId":200233,"corporation":false,"usgs":false,"family":"Thompson","given":"Anita M.","affiliations":[{"id":16128,"text":"Department of Biological System Engineering, University of Wisconsin—Madison, Madison, WI, USA","active":true,"usgs":false}],"preferred":false,"id":865265,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Robertson, Dale M. 0000-0001-6799-0596","orcid":"https://orcid.org/0000-0001-6799-0596","contributorId":217258,"corporation":false,"usgs":true,"family":"Robertson","given":"Dale M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":865266,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70270408,"text":"70270408 - 2022 - Examining landowners’ preferences for a chronic wasting disease management program","interactions":[],"lastModifiedDate":"2025-08-19T15:18:27.733358","indexId":"70270408","displayToPublicDate":"2022-12-15T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"Examining landowners’ preferences for a chronic wasting disease management program","docAbstract":"Private landowners are key partners in chronic wasting disease (CWD) management, especially in landscapes where there is limited public ownership. In this study, we evaluated landowners' preferences for alternative hypothetical CWD management programs using a stated choice experiment. We were particularly interested in understanding preferences for the use of financial incentives to motivate white-tailed deer harvest and facilitate hunter access to private lands as potential CWD management tools. We used latent class analysis to characterize preference heterogeneity among landowners stemming from patterns of choice. We compared means and distributions of auxiliary variables related to landowners' perceived risks, trust, attitudes toward management, and sociodemographics across latent classes stemming from choice model results. The pooled model demonstrated that reducing deer population density, providing payments to landowners for CWD-positive deer taken from their property, the form of incentives for public access, and banning recreational deer feeding had a small positive effect on respondents' choice of CWD management program. However, providing financial payments to hunters for harvesting CWD-positive deer and the use of targeted culling had the opposite effect on choice. Latent class models revealed that a majority of respondents exhibited a pattern of preference where all forms of incentives exerted a negative effect on choice, but smaller subsets of landowners positively evaluate the use of some incentives. Post-hoc contrasts revealed relationships between patterns of preferences and trust, risk, and attitudes toward CWD management with small to medium effects. Results demonstrated limited support for the use of financial incentives as a tool to manage access and harvest in the southeast Minnesota CWD management zone
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wsb.1401","usgsCitation":"Landon, A., Smith, K., Cornicelli, L., Fulton, D.C., McInenly, L.E., and Schroeder, S.A., 2022, Examining landowners’ preferences for a chronic wasting disease management program: Wildlife Society Bulletin, v. 47, no. 1, e1401, 19 p., https://doi.org/10.1002/wsb.1401.","productDescription":"e1401, 19 p.","ipdsId":"IP-122867","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":494457,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wsb.1401","text":"Publisher Index Page"},{"id":494314,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","county":"Fillmore County, Houston County, Wisconsin County","otherGeospatial":"southeastern Minnesota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.13879824080021,\n 44.7131470360637\n ],\n [\n -93.13879824080021,\n 43.4796764343609\n ],\n [\n -91.23563813665416,\n 43.4796764343609\n ],\n [\n -91.23137485428805,\n 43.89405306400184\n ],\n [\n -92.58709230231764,\n 44.71116401915873\n ],\n [\n -93.13879824080021,\n 44.7131470360637\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"47","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-12-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Landon, Adam","contributorId":279439,"corporation":false,"usgs":false,"family":"Landon","given":"Adam","affiliations":[{"id":34923,"text":"Minnesota DNR","active":true,"usgs":false}],"preferred":false,"id":946336,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Kyle","contributorId":359833,"corporation":false,"usgs":false,"family":"Smith","given":"Kyle","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":946340,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cornicelli, Louis","contributorId":359827,"corporation":false,"usgs":false,"family":"Cornicelli","given":"Louis","affiliations":[{"id":34923,"text":"Minnesota DNR","active":true,"usgs":false}],"preferred":false,"id":946337,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fulton, David C. 0000-0001-5763-7887","orcid":"https://orcid.org/0000-0001-5763-7887","contributorId":333043,"corporation":false,"usgs":true,"family":"Fulton","given":"David","email":"","middleInitial":"C.","affiliations":[{"id":79716,"text":"Minnesota Cooperative Unit","active":true,"usgs":false}],"preferred":true,"id":946335,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McInenly, Leslie E.","contributorId":359829,"corporation":false,"usgs":false,"family":"McInenly","given":"Leslie","middleInitial":"E.","affiliations":[{"id":34923,"text":"Minnesota DNR","active":true,"usgs":false}],"preferred":false,"id":946338,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schroeder, Susan A.","contributorId":359831,"corporation":false,"usgs":false,"family":"Schroeder","given":"Susan","middleInitial":"A.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":946339,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70238832,"text":"sir20225084 - 2022 - Precipitation-driven flood-inundation mapping of Muddy Creek at Harrisonville, Missouri","interactions":[],"lastModifiedDate":"2022-12-15T13:22:49.94251","indexId":"sir20225084","displayToPublicDate":"2022-12-14T10:28:07","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5084","displayTitle":"Precipitation-Driven Flood-Inundation Mapping of Muddy Creek at Harrisonville, Missouri","title":"Precipitation-driven flood-inundation mapping of Muddy Creek at Harrisonville, Missouri","docAbstract":"The U.S. Geological Survey, in cooperation with the city of Harrisonville, Missouri, assessed flooding of Muddy Creek resulting from varying precipitation magnitudes and durations, antecedent runoff conditions, and channel modifications (cleaned culverts and added detention storage). The precipitation scenarios were used to develop a library of flood-inundation maps that included a 3.8-mile reach of Muddy Creek and tributaries within and adjacent to the city.
Hydrologic and hydraulic models of the upper Muddy Creek study basin were used to assess streamflow magnitudes associated with simulated precipitation amounts and the resulting flood-inundation conditions. The U.S. Army Corps of Engineers Hydrologic Engineering Center-Hydrologic Modeling System (HEC–HMS; version 4.4.1) was used to simulate the amount of streamflow produced from a range of precipitation events. The Hydrologic Engineering Center-River Analysis System (HEC–RAS; version 5.0.7) was then used to route streamflows and map resulting areas of flood inundation.
The hydrologic and hydraulic models were calibrated to the September 28, 2019; May 27, 2021; and June 25, 2021, runoff events representing a range of antecedent runoff conditions and hydrologic responses. The calibrated HEC–HMS model was used to simulate streamflows from design rainfall events of 30-minute to 24-hour durations and ranging from a 100- to 0.1-percent annual exceedance probability. Flood-inundation maps were produced for reference stages of 1.0 foot (ft), or near bankfull, to 4.0 ft, or a stage exceeding the 0.1-percent annual exceedance probability interval precipitation, using the HEC–RAS model. The results of each precipitation duration-frequency value were represented by a 0.5-ft increment inundation map based on the generated peak streamflow from that rainfall event and the corresponding stage at the Muddy Creek reference location.
Seven scenarios were developed with the HEC–HMS hydrologic model with resulting streamflows routed in a HEC–RAS hydraulic model, and these scenarios varied by antecedent runoff condition and potential channel modifications. The same precipitation scenarios were used in each of the seven antecedent runoff and channel conditions, and the simulation results were assigned to a flood-inundation map condition based on the generated peak flow and corresponding stage at the Muddy Creek reference location.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225084","collaboration":"Prepared in cooperation with the city of Harrisonville, Missouri","usgsCitation":"Heimann, D.C., and Rydlund, P.H., 2022, Precipitation-driven flood-inundation mapping of Muddy Creek at Harrisonville, Missouri: U.S. Geological Survey Scientific Investigations Report 2022–5084, 18 p., https://doi.org/10.3133/sir20225084.","productDescription":"Report: viii, 18 p.; Data Release; Dataset; Application Site","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-135285","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":410386,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5084/sir20225084.pdf","text":"Report","size":"2.29 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5084"},{"id":410482,"rank":7,"type":{"id":4,"text":"Application Site"},"url":"https://ci.harrisonville.mo.us/1052/Stormwater-Management","text":"City of Harrisonville web page","linkHelpText":"—Flood-inundation mapping and model of Muddy Creek"},{"id":410385,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5084/coverthb.jpg"},{"id":410388,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5084/sir20225084.XML"},{"id":410389,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5084/images"},{"id":410390,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P969ZOLB","text":"USGS data release","linkHelpText":"Geospatial data and model archives associated with precipitation-driven flood-inundation mapping of Muddy Creek at Harrisonville, Missouri (ver. 2.0, December 2022)"},{"id":410391,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"}],"country":"United States","state":"Missouri","county":"Cass County","city":"Harrisonville","otherGeospatial":"Muddy Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -94.38510524959862,\n 38.66691333285476\n ],\n [\n -94.38510524959862,\n 38.60174153214416\n ],\n [\n -94.30827923799478,\n 38.60174153214416\n ],\n [\n -94.30827923799478,\n 38.66691333285476\n ],\n [\n -94.38510524959862,\n 38.66691333285476\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Field-based transplant gardens, including common and reciprocal garden experiments, are a powerful tool for studying genetic variation and gene-by-environment interactions. These experiments assume that individuals within the garden represent independent replicates growing in a homogenous environment. Plant neighborhood interactions are pervasive across plant populations and could violate assumptions of transplant garden experiments. We demonstrate how spatially explicit models for plant–plant interactions can provide novel insights on genotypes' performance in field-transplant garden designs. We used individual-based models, based on data from a sagebrush (Artemisia spp.) common garden, to simulate the impact of spatial plant–plant interactions on between-group differences in plant growth. We found that planting densities within the range of those used in many common gardens can bias experimental outcomes. Our results demonstrate that higher planting densities can lead to inflated group differences and may confound genotypes' competitive ability and genetically underpinned variation. Synthesis. We propose that spatially explicit models can help avoid biased results by informing the design and analysis of field-based transplant garden experiments. Alternately, including neighborhood effects in post hoc analyses of transplant garden experiments is likely to provide novel insights into the roles of biotic factors and density dependence in genetic differentiation.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.9630","usgsCitation":"Zaiats, A., Requena-Mullor, J.M., Germino, M., Forbey, J.S., Richardson, B.A., and Caughlin, T., 2022, Spatial models can improve the experimental design of field-based transplant gardens by preventing bias due to neighborhood crowding: Ecology and Evolution, v. 12, no. 12, e9630, 9 p., https://doi.org/10.1002/ece3.9630.","productDescription":"e9630, 9 p.","ipdsId":"IP-139927","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":445664,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.9630","text":"Publisher Index Page"},{"id":410630,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"12","noUsgsAuthors":false,"publicationDate":"2022-12-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Zaiats, Andrii 0000-0001-8978-4152","orcid":"https://orcid.org/0000-0001-8978-4152","contributorId":257072,"corporation":false,"usgs":false,"family":"Zaiats","given":"Andrii","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":859166,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Requena-Mullor, Juan M.","contributorId":218132,"corporation":false,"usgs":false,"family":"Requena-Mullor","given":"Juan","email":"","middleInitial":"M.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":859167,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Germino, Matthew J. 0000-0001-6326-7579","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":251901,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":859168,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Forbey, Jennifer S.","contributorId":194442,"corporation":false,"usgs":false,"family":"Forbey","given":"Jennifer","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":859169,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Richardson, Bryce A.","contributorId":207820,"corporation":false,"usgs":false,"family":"Richardson","given":"Bryce","email":"","middleInitial":"A.","affiliations":[{"id":37640,"text":"U.S.D.A. Forest Service Rocky Mountain Research Station, Provo, UT, 84606 USA","active":true,"usgs":false}],"preferred":false,"id":859170,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Caughlin, T. Trevor","contributorId":257076,"corporation":false,"usgs":false,"family":"Caughlin","given":"T. Trevor","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":859171,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70238856,"text":"70238856 - 2022 - Acetylenotrophic and diazotrophic Bradyrhizobium sp. strain I71 from TCE-contaminated soils","interactions":[],"lastModifiedDate":"2022-12-14T15:30:15.135816","indexId":"70238856","displayToPublicDate":"2022-12-14T09:10:46","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":850,"text":"Applied and Environmental Microbiology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Acetylenotrophic and diazotrophic Bradyrhizobium sp. strain I71 from TCE-contaminated soils","title":"Acetylenotrophic and diazotrophic Bradyrhizobium sp. strain I71 from TCE-contaminated soils","docAbstract":"The Sand Resources Project was established through collaborative agreements between the U.S. Geological Survey (USGS), the Bureau of Ocean Energy Management (BOEM), and the California Ocean Protection Council (OPC) with the purpose of evaluating sand and gravel resources in Federal and California State Waters for potential use in future beach-nourishment projects. Project partners worked in collaboration with California Coastal Sediment Management Workgroup (CSMW) members to define priority study areas for this work based on the potential for finding sand within the broader region and the needs for this sand as shown by beach erosion areas of concern in the adjacent littoral cells. The final study areas were defined to be (1) the San Francisco Littoral Cell, (2) the Oceanside Littoral Cell, and (3) the Silver Strand Littoral Cell.
A two-stage approach was used to assess the study areas. The initial stage was a synthesis of the existing geophysical and sediment-sampling data in each area. This allowed for evaluations of the data availability, data gaps, and general patterns of sediment thickness and grain size. This synthesis was published in a separate USGS open-file report (Warrick and others, 2022). The findings from this assessment were used to refine study area boundaries and develop sampling plans for stage two of the project.
Stage two of the project is the collection, processing, and synthesis of new data, including high-resolution geophysical surveys and sediment cores—this report addresses the second stage. The work focuses on two of the study areas—the San Francisco and the Oceanside Littoral Cells, where several research cruises have been conducted. A more limited, exploratory approach was used for the Silver Strand Littoral Cell, owing to the lack of existing high-resolution bathymetric data for this study area. The data collected provide new information about the three study areas, including sediment thickness, grain-size distributions, and total organic carbon.
Sediment in all three study areas of the Sand Resources Study was suitable for beach nourishment, as reflected by their grain-size distributions and sediment thicknesses. For example, sandy sediment in the San Francisco Littoral Cell study area was on and immediately outside of the ebb-tidal bar of the San Francisco Bay, a landform that has a strong influence on grain-size patterns of the region. The presence of thick sediment deposits in this area was interpreted to be a function of tectonics, which has caused physical features that include a graben north of the Golden Gate whose deposits were thicker and siltier than the remaining area. Sandy sediment on the inner and outer parts of the continental shelf in the Oceanside Littoral Cell may be useful for nourishment, whereas the midshelf between these areas was dominated by silty sediment. Sediment in the Silver Strand Littoral Cell, which was only sampled selectively, had the greatest potential for beach nourishment because of the greater prevalence of beach-comparable grain sizes, especially in the more distal and deeper areas where medium sands were found.
The Sand Resources Project did identify several sandy regions of the continental shelf that are deeper than dredging technologies currently (2022) available in the United States, which are generally limited to 30 meters (m) water depth or less. Although sandy sediment exists in all three study areas at water depths of 30 m or less, additional sediment supplies—most of which are in Federal Waters—are present in deeper settings, especially for the Oceanside and Silver Strand Littoral Cell study areas. Although the Silver Strand Littoral Cell study area was found to be considerably replete in sand resources, these conclusions are based on a limited sampling exercise across that study area. Thus, it may be beneficial to complete a more thorough characterization of the sediment resources in the Silver Strand Littoral Cell study area if it is determined that a need for sandy coastal sediment exists in this region.
As a result of the Sand Resources Project, several areas of sand resources in Federal and California State Waters were found where they were previously unknown. As such, this project may provide important data for future coastal-management decisions in California, and it should provide a model for future investigations of sediment resources in other regions of the State.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221095","collaboration":"Prepared in cooperation with the Bureau of Ocean Energy Management and the State of California Ocean Protection Council","usgsCitation":"Warrick, J.A., Conrad, J.E., Papesh, A., Lorenson, T., and Sliter, R.W., 2022, Assessment of significant sand resources in Federal and California State Waters of the San Francisco, Oceanside, and Silver Strand littoral cell study areas along the continental shelf of California: U.S. Geological Survey Open-File Report 2022–1095, 104 p., https://doi.org/10.3133/ofr20221095.","productDescription":"Report: viii, 104 p.; 3 Data Releases","onlineOnly":"Y","ipdsId":"IP-130530","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":501837,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113943.htm","linkFileType":{"id":5,"text":"html"}},{"id":410366,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UELSBU","text":"USGS data release","description":"USGS data release","linkHelpText":"Geophysical and sampling data collected offshore Oceanside, southern California during field activity 2017-686-FA from 2017-10-23 to 2017-10-31"},{"id":410365,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9690BEK","text":"USGS data release","description":"USGS data release","linkHelpText":"Geophysical and core sample data collected offshore Oceanside to San Diego, southern California, during field activity 2018-638-FA from 2018-05-21 to 2018-05-26"},{"id":410364,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LBG9H5","text":"USGS data release","description":"USGS data release","linkHelpText":"Geophysical and core sample data collected offshore San Francisco, California, during field activity 2019-649-FA from 2019-10-11 to 2019-10-18"},{"id":410367,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20221094","text":"OFR 2022-1094 —","linkHelpText":"Compilation of existing data for sand resource studies in Federal and California State Waters of the San Francisco, Oceanside, and Silver Strand littoral cell study areas along the continental shelf of California—Strategy for field studies and sand resource assessment"},{"id":410363,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1095/ofr20221095.pdf","text":"Report","size":"12.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1095"},{"id":410362,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1095/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco, Oceanside, and Silver Strand littoral cell study areas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.32845828309286,\n 37.818674364541195\n ],\n [\n -123.23587958971336,\n 37.818674364541195\n ],\n [\n -123.23587958971336,\n 36.79251013661299\n ],\n [\n -122.32845828309286,\n 36.79251013661299\n ],\n [\n -122.32845828309286,\n 37.818674364541195\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.13365815618306,\n 32.493660702226535\n ],\n [\n -117.13365815618306,\n 32.93175821350262\n ],\n [\n -117.80008333663935,\n 32.93175821350262\n ],\n [\n -117.80008333663935,\n 32.493660702226535\n ],\n [\n -117.13365815618306,\n 32.493660702226535\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.26754558635474,\n 33.52924818029179\n ],\n [\n -118.26754558635474,\n 33.1318551432673\n ],\n [\n -117.66427888472828,\n 33.1318551432673\n ],\n [\n -117.66427888472828,\n 33.52924818029179\n ],\n [\n -118.26754558635474,\n 33.52924818029179\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Pacific Coastal and Marine Science Center
U.S. Geological Survey
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The Landsat Collection 2 Level-3 Dynamic Surface Water Extent science product provides raster data that represent surface water inundation per pixel in Landsat 4–9 imagery. The Collection 2 Dynamic Surface Water Extent science product contains six acquisition-based raster products relating to surface water. Surface water extent is modulated by weather and climate, stream network hydrology, and geological processes such as isostatic rebound. Land use, ecosystem and service management, and overall water management also are affected by changes in surface water extent.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223084","usgsCitation":"U.S. Geological Survey, 2022, Landsat Collection 2 Level-3 Dynamic Surface Water Extent science product (ver. 1.1, June 2023): U.S. Geological Survey Fact Sheet 2022–3084, 2 p., https://doi.org/10.3133/fs20223084.","productDescription":"Report: 2 p.; Dataset","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-139625","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":410308,"rank":1,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS database","linkHelpText":"—EarthExplorer"},{"id":418247,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3084/coverthb2.jpg"},{"id":418249,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2022/3084/versionHist.txt","size":"1 kB","linkFileType":{"id":2,"text":"txt"}},{"id":418248,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3084/fs20223084.pdf","text":"Report","size":"1.72 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022–3084"}],"edition":"Version 1.0: December 13, 2022; Version 1.1: June 21, 2023","contact":"Customer Services, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Many organizations in the Delaware River Basin (DRB) monitor surface-water quality for regulatory, scientific, and decision-making purposes. In support of these purposes, over 260,000 water-quality records provided by 8 different organizations were compiled, screened, and used to generate water-quality trends in the DRB. These trends, for periods of record that end in 2018, were generated for 124 sites and up to 16 constituents using 2 trend methods: the Seasonal Kendall Test and the Weighted Regressions on Time, Discharge, and Season model. Seasonal Kendall Tests were performed on all water-quality records to detect monotonic trends in concentration over the period of record and for as many as four additional trend periods (1978–2018, 1998–2018, 2003–18, and 2008–18). The Weighted Regressions on Time, Discharge, and Season model was applied to water-quality records that passed more stringent screening criteria and was used to detect monontonic and nonmonotonic trends, account for variations in streamflow, and estimate annual concentrations. These two trend methods produced different trend directions less than 1 percent of the time, illustrating general agreement between the methods despite the different approaches and data input requirements. Overall, the changes in concentration for salinity constituents (specific conductance and total dissolved solids), chloride, and sodium were increases; those increases were some of the largest changes observed in the basin, and they occurred at faster rates over time. Total dissolved solids concentration trends at 4 of the 60 sites increased from below to above the level of concern threshold (a secondary drinking water threshold) over the period of record, indicating potentially meaningful degradation in water quality. Nutrient constituent (ammonia, nitrate, orthophosphate, total nitrogen, and total phosphorus) concentrations tended to decrease over the period of record, although fewer sites had significant trends and the changes in concentration were smaller compared to the salinity constituents. Total nitrogen and total phosphorus were the only nutrient constituents to have decreasing concentration trends that crossed from above to below the level of concern threshold, U.S. Environmental Protection Agency (EPA) ecoregional nutrient criteria, (EPA, undated c). This finding indicates water-quality improvement at sites with these trends (nine sites with total nitrogen trends and one site with a total phosphorus trend), although many sites were still in exceedance of the level of concern. Trends for total suspended solids and some major ions (calcium, magnesium, potassium) were largely nonsignificant or variable between sites, with no prevalent patterns across the DRB; however, sulfate concentrations decreased at most sites. Cumulative land-surface change within each watershed had a strong positive relation with changes in water-quality concentrations for the salinity constituents and most major ions, but not for the other constituents, indicating that land-surface changes are related to the sources and transport of these constituents. Investigating long-term trends (a decade or longer) in water quality can help the DRB water management community quantify the success of management practices and identify potential threats to water availability.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225097","programNote":"Water Availability and Use Science Program, National Water Quality Program","usgsCitation":"Shoda, M.E., and Murphy, J.C., 2022, Water-quality trends in the Delaware River Basin calculated using multisource data and two methods for trend periods ending in 2018: U.S. Geological Survey Scientific Investigations Report 2022–5097, 60 p., https://doi.org/10.3133/sir20225097.","productDescription":"Report v, 60 p.; 2 Data Releases","numberOfPages":"60","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-122487","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":410210,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KMWNJ5","text":"USGS data release","linkHelpText":"Water-quality trends for rivers and streams in the Delaware River Basin using Weighted Regressions on Time, Discharge, and Season (WRTDS) models, Seasonal Kendall Trend (SKT) tests, and multisource data, water years 1978–2018"},{"id":410211,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PX8LZO","text":"USGS data release","linkHelpText":"Multisource surface-water-quality data and U.S. Geological Survey streamgage match for the Delaware River Basin"},{"id":410212,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5097/sir20225097.XML"},{"id":410208,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5097/sir20225097.pdf","text":"Report","size":"16.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5097"},{"id":410207,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5097/coverthb.jpg"},{"id":433058,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/fs20233014","text":"USGS Fact Sheet 2023-3014","linkFileType":{"id":5,"text":"html"}},{"id":410209,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225097/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5097"},{"id":410213,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5097/images/"}],"country":"United States","state":"Delaware, New Jersey, New York, Pennsylvania","otherGeospatial":"Delaware River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.68904449140135,\n 38.5435194562952\n ],\n [\n -75.2278145853991,\n 38.47477347574127\n ],\n [\n -75.03014462568353,\n 38.95461543352394\n ],\n [\n -74.87640132368297,\n 39.566811726970116\n ],\n [\n -74.28339144453736,\n 40.70854805354401\n ],\n [\n -74.1735748002507,\n 41.56866114554592\n ],\n [\n -73.77823488082065,\n 42.41747384003048\n ],\n [\n -74.21750145796516,\n 42.64406475670245\n ],\n [\n -74.96425463911187,\n 42.44989429480822\n ],\n [\n -75.44744787397136,\n 42.043433704426945\n ],\n [\n -75.88671445111639,\n 41.486448364247764\n ],\n [\n -76.19420105511806,\n 40.79174109891525\n ],\n [\n -76.23812771283252,\n 40.37473571760768\n ],\n [\n -76.01849442425974,\n 39.702128532888\n ],\n [\n -75.75493447797302,\n 39.14224249538836\n ],\n [\n -75.68904449140135,\n 38.5435194562952\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Program Coordinator
Water Availability and Use Science Program
National Water Quality Program
U.S. Geological Survey
Geologic maps are the fundamental building blocks of surface and subsurface three-dimensional geologic framework models of the Earth’s crust. However, as the production and availability of geologic map databases continues to increase, inconsistent data models and the lack of synthesized, national geologic map data at scales appropriate for informed decision making negatively affect the functional integration of geologic map data with other national datasets. The Geologic Map Schema (GeMS) is the publication and archive database standard for geologic map data funded by the U.S. Geological Survey National Cooperative Geologic Mapping Program, and standardizes the organization and content of a single map database. However, synthesizing multiple databases into a seamless geologic map database creates a different set of challenges and database needs than GeMS was designed to accommodate. The Seamless Integrated Geologic Mapping (SIGMa) extension is designed to expand the capabilities of GeMS by enabling integration of map-based geoscience data. In particular, the SIGMa extension enables capturing a diverse and ever-changing set of map units, produced by many contributors operating independently, and by incremental and noncontiguous assembly and publication. Feature-level metadata fields allow data sources and digital compilation methods to be attributed separately and a relational structure is designed to support the link between data sources and features attributed with multiple data sources. Instead of paragraph-style map-unit descriptions that can be highly inconsistent, SIGMa parses fundamental map-unit attributes, including material, genetic process, and age, into thematically specific fields. The SIGMa extension uses a hierarchical map-unit organization to facilitate a dynamic and evolving, formation-level stratigraphic framework. The hierarchy is developed around geologic provinces that represent temporally restricted geologic events, processes, and settings. Geologic provinces can include magmatic events, depositional settings associated with tectonic processes or stable continental margins, and processes that are actively shaping the modern landscape. A geologic province hierarchy places map units into a geologic context at subregional to continental scales and provides the flexibility to support incremental assembly of the stratigraphy.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225115","usgsCitation":"Turner, K.J., Workman, J.B., Colgan, J.P., Gilmer, A.K., Berry, M.E., Johnstone, S.A., Warrell, K.F., Dechesne, M., VanSistine, D.P., Thompson, R.A., Hudson, A.M., Zellman, K.L., Sweetkind, D., and Ruleman, C.A., 2022, The Seamless Integrated Geologic Mapping (SIGMa) extension to the Geologic Map Schema (GeMS): U.S. Geological Survey Scientific Investigations Report 2022–5115, 33 p., https://doi.org/10.3133/sir20225115.","productDescription":"vii, 33 p.","onlineOnly":"Y","ipdsId":"IP-125234","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":410653,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225115/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5115"},{"id":410248,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5115/sir20225115.xml"},{"id":410247,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5115/images"},{"id":410246,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5115/sir20225115.pdf","text":"Report","size":"3.73 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5115"},{"id":410245,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5115/coverthb.jpg"}],"contact":"Director, Geosciences and Environmental Change Science Center
U.S. Geological Survey
Box 25046, MS-980
Denver, CO 80225-0046
Digital flood-inundation maps for a 2.4-mile reach of the Schoharie Creek in North Blenheim, New York, were created by the U.S. Geological Survey (USGS) in cooperation with the New York Power Authority. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science website at https://fim.wim.usgs.gov/fim/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage on the Schoharie Creek near North Blenheim, N.Y. (station number 01350212). Near-real-time stage at this streamgage may be obtained on the internet from the USGS National Water Information System at https://waterdata.usgs.gov/. Flood profiles were computed for the stream reach by means of a two-dimensional implicit finite-volume hydraulic model. The model was calibrated by using the active (as of April 2021) stage-discharge ratings at the USGS streamgages on the Schoharie Creek near North Blenheim (station number 01350212) and at North Blenheim (station number 01350180) and documented high-water marks in the study reach from the floods of August 28, 2011; January 19, 1996; and April 4, 1987.
The hydraulic model was used to compute 13 water-surface profiles for flood stages at 1-foot intervals referenced to the datum at the streamgage on the Schoharie Creek near North Blenheim, N.Y. (01350212). These profiles range from 14 feet, or near bankfull, to 26 feet, which is the highest whole-foot increment on the stage-discharge rating for the streamgage. The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from light detection and ranging data having a 0.52-foot vertical accuracy and 3.3-foot [1-meter] horizontal resolution) to delineate the area flooded at each stage. Flood inundation maps, along with near-real-time stage data from USGS streamgages, can provide emergency management personnel and residents with information critical for flood-response activities, such as evacuations and road closures, as well as for postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225119","collaboration":"Prepared in cooperation with the New York Power Authority","usgsCitation":"Nystrom, E.A., 2022, Flood-inundation maps for Schoharie Creek in North Blenheim, New York: U.S. Geological Survey Scientific Investigations Report 2022–5119, 14 p., https://doi.org/10.3133/sir20225119.","productDescription":"Report: vi, 14 p.; Data Release","numberOfPages":"14","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-122520","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":410180,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92YVB9V","text":"USGS data release","linkFileType":{"id":5,"text":"html"},"linkHelpText":"Geospatial dataset for flood inundation maps of Schoharie Creek in North Blenheim, New York"},{"id":410178,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5119/sir20225119.pdf","text":"Report","size":"49.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5119"},{"id":410181,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5119/sir20225119.XML"},{"id":410182,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5119/images/"},{"id":410179,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225119/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5119"},{"id":410177,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5119/coverthb.jpg"}],"country":"United States","state":"New York","city":"North Blenheim","otherGeospatial":"Schoharie Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.43435753120507,\n 42.481471549691946\n ],\n [\n -74.46929205403138,\n 42.481471549691946\n ],\n [\n -74.46929205403138,\n 42.457988603472074\n ],\n [\n -74.43435753120507,\n 42.457988603472074\n ],\n [\n -74.43435753120507,\n 42.481471549691946\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
We report a new sampling strategy for collecting representative samples of drill core. By splitting the core with a diamond saw into working and archive halves, the saw cuttings constitute a “channel” sample, the best subsample from which to obtain an average mineralogical and geochemical composition of a core. We apply this procedure to sampling core of the lower oceanic crust in the Hess Deep obtained during Expedition 345 of the Integrated Ocean Drilling Program (now International Ocean Discovery Program).
Our results show that particles produced by sawing range from sand to clay sizes. Sand- and silt-sized cuttings can be sampled with a spatula, whereas clay-sized particles remained in suspension after 12 h and could be collected only by settling, aided by centrifuge. X-ray diffraction (XRD) analysis and Rietveld refinement show that phyllosilicates were fractionated into the clay-sized fraction. Thus, collection of both the sedimented fraction and the clay-sized suspended fraction (commonly > 15 wt % of the total) is necessary to capture the whole sample. The strong positive correlation between the recovered sample mass (in grams) and length of core cut demonstrates that this sampling protocol was uniform and systematic, with almost 1.4 g sediment produced per centimeter of core cut. We show that major-element concentrations of our channel samples compare favorably with the compositions of billet-sized samples analyzed aboard the JOIDES Resolution, but the results show that individual billet analyses are rarely representative of the whole core recovered. A final test of the validity of our methods comes from the strong positive correlation between the loss on ignition (LOI) values of our channel samples and the H2O contents calculated from the modal mineralogy obtained by X-ray diffraction and Rietveld refinement. This sampling procedure shows that grain-sized fractionation modifies both mineralogical and chemical compositions; nevertheless, this channel sampling method is a reliable method of obtaining representative samples of bulk cores. With the ever-increasing precision offered by modern analytical instrumentation, this sampling protocol allows the accuracy of the analytical results to keep pace.
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/sd-31-71-2022","usgsCitation":"Wintsch, R.P., Meyer, R., Bish, D., Deasy, R.T., Nozaka, T., and Johnson, C., 2022, A channel sampling strategy for measurement of mineral modal and chemical composition of drill cores: Application to lower oceanic crustal rocks from IODP Expedition 345 to the Hess Deep rift: Scientific Drilling, v. 31, p. 71-84, https://doi.org/10.5194/sd-31-71-2022.","productDescription":"14 p.","startPage":"71","endPage":"84","ipdsId":"IP-133634","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":445680,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/sd-31-71-2022","text":"Publisher Index Page"},{"id":410280,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Hess Deep Rift, Pacific Ocean","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.57372796106893,\n 3.330136493398328\n ],\n [\n -111.57372796106893,\n -1.0981582918846584\n ],\n [\n -101.19437373696735,\n -1.0981582918846584\n ],\n [\n -101.19437373696735,\n 3.330136493398328\n ],\n [\n -111.57372796106893,\n 3.330136493398328\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"31","noUsgsAuthors":false,"publicationDate":"2022-10-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Wintsch, Robert P.","contributorId":192913,"corporation":false,"usgs":false,"family":"Wintsch","given":"Robert","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":858574,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Meyer, Romain","contributorId":148991,"corporation":false,"usgs":false,"family":"Meyer","given":"Romain","email":"","affiliations":[{"id":17609,"text":"Deutsche GeoForchungsZentrum Potsdam","active":true,"usgs":false}],"preferred":false,"id":858575,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bish, David","contributorId":291943,"corporation":false,"usgs":false,"family":"Bish","given":"David","affiliations":[{"id":37145,"text":"Indiana University","active":true,"usgs":false}],"preferred":false,"id":858576,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Deasy, Ryan T. 0000-0002-7530-803X","orcid":"https://orcid.org/0000-0002-7530-803X","contributorId":299762,"corporation":false,"usgs":true,"family":"Deasy","given":"Ryan","middleInitial":"T.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":858577,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nozaka, Toshio","contributorId":299763,"corporation":false,"usgs":false,"family":"Nozaka","given":"Toshio","email":"","affiliations":[{"id":64944,"text":"Okayama University","active":true,"usgs":false}],"preferred":false,"id":858578,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Johnson, Carley","contributorId":299764,"corporation":false,"usgs":false,"family":"Johnson","given":"Carley","email":"","affiliations":[{"id":64945,"text":"Marathon","active":true,"usgs":false}],"preferred":false,"id":858579,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70239015,"text":"70239015 - 2022 - Evaluating the sensitivity of multi-dimensional model predictions of salmon habitat to the source of remotely sensed river bathymetry","interactions":[],"lastModifiedDate":"2022-12-21T12:40:22.808277","indexId":"70239015","displayToPublicDate":"2022-12-12T06:37:09","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating the sensitivity of multi-dimensional model predictions of salmon habitat to the source of remotely sensed river bathymetry","docAbstract":"Multi-dimensional numerical models are fundamental tools for investigating biophysical processes in aquatic ecosystems. Remote sensing techniques increase the feasibility of applying such models at riverscape scales, but tests of model performance on large rivers have been limited. We evaluated the potential to develop two-dimensional (2D) and three-dimensional (3D) hydrodynamic models for a 1.6-km reach of a large gravel-bed river using three sources of remotely sensed river bathymetry. We estimated depth from hyperspectral image data acquired from conventional and uncrewed aircraft and multispectral satellite imagery. Our results indicated that modeled water depth errors were similar between 2D and 3D models, with depth residuals that were comparable to the uncertainty associated with the bathymetry used as input. We found good agreement between measured and modeled depth-averaged velocities generated by 2D and 3D models, while 3D models provided superior predictions of near-bed velocities. We found that optimal model performance occurred for lower flow resistance values than previously reported in the literature, possibly as a consequence of the high-resolution bathymetry used as model input. Model predictions of winter-run Chinook salmon (Oncorhynchus tshawytscha) spawning and rearing habitat were not sensitive to the source of bathymetric information, but bioenergetic predictions related to adult holding costs were influenced by the input bathymetry. Our results suggest that hyperspectral imagery acquired from piloted and/or uncrewed aircraft can be used to map the bathymetry of clear-flowing, relatively shallow large rivers with sufficient accuracy to support multi-dimensional flow model development; models developed from multispectral satellite imagery had more limited predictive capability.
Interspecific interactions among walleye Sander vitreus, lake whitefish Coregonus clupeaformis, and yellow perch Perca flavescens in Green Bay could influence the population status of each species, but potential trophic interactions are poorly understood. Our objectives were to determine if diet assemblages for each species and diet overlap among species varied spatially and temporally within Green Bay. Adult walleye (≥ 381 mm total length (TL); N = 981), lake whitefish (≥ 432 mm TL; N = 1507), and yellow perch (≥ 150 mm TL; N = 1174) were collected during May-October of 2018 and 2019 from multiple locations in southern and northern Green Bay. Diet assemblages of all three species varied between zones but walleye diets were more temporally variable (among months within zones and between years) than diets of lake whitefish or yellow perch. Lake whitefish represented a seasonally important prey item for walleye in southern Green Bay, composing 10% and 41% of walleye diets by weight in May and June, respectively. Yellow perch generally composed < 15% of walleye diets by weight but were consumed at a broader spatiotemporal scale than lake whitefish. Diet overlap between walleye and both lake whitefish and yellow perch was generally weak or moderate, whereas diet overlap between whitefish and perch was generally strong. Our assessment of adult trophic interactions suggests that changes in the population status of one species could influence fisheries for all three, and we identify additional research questions to address potential population-level effects of these trophic interactions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2022.09.005","usgsCitation":"Koeniga, L., Dembkowski, D., Hansen, S., Tsehaye, I., Tammie J. Paoli, Zorn, T., and Isermann, D.A., 2022, Diet composition and overlap for adult walleye, lake whitefish, and yellow perch in Green Bay, Lake Michigan: Journal of Great Lakes Research, v. 48, no. 6, p. 1681-1695, https://doi.org/10.1016/j.jglr.2022.09.005.","productDescription":"15 p.","startPage":"1681","endPage":"1695","ipdsId":"IP-140407","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":480917,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Green Bay, Lake Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.25433685919877,\n 45.59617874752695\n ],\n [\n -87.9984005897609,\n 44.918805118493594\n ],\n [\n -87.98617785518738,\n 44.57675244172867\n ],\n [\n -87.3651194846989,\n 44.78875465437872\n ],\n [\n -86.56980682225971,\n 45.71326930572798\n ],\n [\n -87.03874882070055,\n 45.85231391585981\n ],\n [\n -87.25433685919877,\n 45.59617874752695\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"48","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Koeniga, Lucas D.","contributorId":348678,"corporation":false,"usgs":false,"family":"Koeniga","given":"Lucas D.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":923697,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dembkowski, Daniel J.","contributorId":348681,"corporation":false,"usgs":false,"family":"Dembkowski","given":"Daniel J.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":923698,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hansen, Scott P.","contributorId":348684,"corporation":false,"usgs":false,"family":"Hansen","given":"Scott P.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923699,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tsehaye, Iyob","contributorId":348687,"corporation":false,"usgs":false,"family":"Tsehaye","given":"Iyob","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923700,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tammie J. Paoli","contributorId":348689,"corporation":false,"usgs":false,"family":"Tammie J. Paoli","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923701,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zorn, Troy G.","contributorId":348692,"corporation":false,"usgs":false,"family":"Zorn","given":"Troy G.","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923702,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Isermann, Daniel A. 0000-0003-1151-9097 disermann@usgs.gov","orcid":"https://orcid.org/0000-0003-1151-9097","contributorId":5167,"corporation":false,"usgs":true,"family":"Isermann","given":"Daniel","email":"disermann@usgs.gov","middleInitial":"A.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923703,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70238927,"text":"70238927 - 2022 - Decades of global sturgeon conservation efforts are threatened by an expanding captive culture industry","interactions":[],"lastModifiedDate":"2023-02-14T14:48:20.225927","indexId":"70238927","displayToPublicDate":"2022-12-08T09:50:45","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5686,"text":"Fisheries Magazine","active":true,"publicationSubtype":{"id":10}},"title":"Decades of global sturgeon conservation efforts are threatened by an expanding captive culture industry","docAbstract":"After centuries of overexploitation and habitat loss, many of the world's sturgeon (Acipenseridae) populations are at the brink of extinction. Although significant resources are invested into the conservation and restoration of imperiled sturgeons, the burgeoning commercial culture industry poses an imminent threat to the persistence of many populations. In the past decade, the number and distribution of captive sturgeon facilities has grown exponentially and now encompasses diverse interest groups ranging from hobby aquarists to industrial-scale commercial facilities. Expansion of sturgeon captive culture has largely fallen outside the purview of existing regulatory frameworks, raising concerns that continued growth of this industry has real potential to jeopardize conservation of global sturgeon populations. Here, we highlight some of the most significant threats commercial culture poses to wild populations, with particular emphasis on how releases can accelerate wild population declines through mechanisms such as hybridization, introgression, competition, and disease transmission. We also note that in some circumstances, commercial captive culture has continued to motivate harvest of wild populations, potentially accelerating species' declines. Given the prevalence and trajectory of sturgeon captive culture programs, we comment on modifications to regulatory frameworks that could improve the ability of captive culture to support wild sturgeon conservation.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/fsh.10865","usgsCitation":"White, S.L., Fox, D.A., Beridze, T., Bolden, S.K., Johnson, R.L., Savoy, T.F., Scheele, F., Schreier, A., and Kazyak, D., 2022, Decades of global sturgeon conservation efforts are threatened by an expanding captive culture industry: Fisheries Magazine, v. 48, no. 2, p. 54-61, https://doi.org/10.1002/fsh.10865.","productDescription":"8 p.","startPage":"54","endPage":"61","ipdsId":"IP-139797","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":467139,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://repository.library.noaa.gov/view/noaa/63487","text":"External Repository"},{"id":410632,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"48","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-12-08","publicationStatus":"PW","contributors":{"authors":[{"text":"White, Shannon L. 0000-0003-4687-6596","orcid":"https://orcid.org/0000-0003-4687-6596","contributorId":263424,"corporation":false,"usgs":true,"family":"White","given":"Shannon","email":"","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":859202,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fox, Dewayne A.","contributorId":117052,"corporation":false,"usgs":false,"family":"Fox","given":"Dewayne","email":"","middleInitial":"A.","affiliations":[{"id":12970,"text":"Department of Agriculture and Natural Resources, Delaware State University","active":true,"usgs":false}],"preferred":false,"id":859203,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beridze, Tamar","contributorId":299977,"corporation":false,"usgs":false,"family":"Beridze","given":"Tamar","email":"","affiliations":[{"id":63351,"text":"Ilia State University","active":true,"usgs":false}],"preferred":false,"id":859204,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bolden, Stephania K","contributorId":299978,"corporation":false,"usgs":false,"family":"Bolden","given":"Stephania","email":"","middleInitial":"K","affiliations":[{"id":64993,"text":"NMFS (retired)","active":true,"usgs":false}],"preferred":false,"id":859205,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnson, Robin L. 0000-0003-4314-3792 rjohnson1@usgs.gov","orcid":"https://orcid.org/0000-0003-4314-3792","contributorId":224717,"corporation":false,"usgs":true,"family":"Johnson","given":"Robin","email":"rjohnson1@usgs.gov","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":859206,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Savoy, Thomas F","contributorId":299979,"corporation":false,"usgs":false,"family":"Savoy","given":"Thomas","email":"","middleInitial":"F","affiliations":[{"id":62986,"text":"Connecticut Department of Energy and Environmental Protection","active":true,"usgs":false}],"preferred":false,"id":859207,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Scheele, Fleur","contributorId":299983,"corporation":false,"usgs":false,"family":"Scheele","given":"Fleur","email":"","affiliations":[],"preferred":false,"id":859219,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Schreier, Andrea D","contributorId":299980,"corporation":false,"usgs":false,"family":"Schreier","given":"Andrea D","affiliations":[{"id":12711,"text":"UC Davis","active":true,"usgs":false}],"preferred":false,"id":859208,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kazyak, David C. 0000-0001-9860-4045","orcid":"https://orcid.org/0000-0001-9860-4045","contributorId":202481,"corporation":false,"usgs":true,"family":"Kazyak","given":"David C.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":859209,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70238763,"text":"ofr20221088 - 2022 - Assessment of vulnerabilities and opportunities to restore marsh sediment supply at Nisqually River Delta, west-central Washington","interactions":[],"lastModifiedDate":"2022-12-09T20:48:54.83197","indexId":"ofr20221088","displayToPublicDate":"2022-12-08T08:00:44","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1088","displayTitle":"Assessment of Vulnerabilities and Opportunities to Restore Marsh Sediment Supply at Nisqually River Delta, West-Central Washington","title":"Assessment of vulnerabilities and opportunities to restore marsh sediment supply at Nisqually River Delta, west-central Washington","docAbstract":"A cascading set of hazards to coastal environments is intimately tied to sediment transport and includes the flooding and erosion of shorelines and habitats that support communities, industry, infrastructure, and ecosystem functions (for example, habitats critical to fisheries). This report summarizes modeling and measurement data used to evaluate the sediment budget of the Nisqually River Delta, the vulnerability of the largest estuary restoration project in Puget Sound at the Billy Frank Jr. Nisqually National Wildlife Refuge, and the role of coastal hydrodynamics and potential restoration alternatives for recovering sediment delivery to its marshes. The 2009 Brown’s Farm Restoration achieved many goals toward recovering salmon habitat, but the understanding of the delta and restoration area sediment budgets remain poorly quantified. Specifically, quantitative estimates of the amount of sediment delivered to the delta and restored marsh areas, which had subsided in response to historical diking and draining for grazing, were identified as important information needs. Forecasts of potential outcomes of proposed adaptive distributary channel restoration actions were also prioritized to inform potential solutions. These estimates can be used to evaluate whether sufficient sediment is available for marsh recovery downstream from Alder Lake, which traps about 90 percent the Nisqually River sediment load that could reach the delta. Additionally, quantitative sediment information was identified to help prioritize opportunities to recover and maintain the area marshes and guide ecosystem restoration investments across the delta to reduce the vulnerability of the system to drowning under projected sea level rise.
A coupled, numerical hydrodynamic-sediment transport model and measurements of the sediment load just upstream from the delta were used to evaluate the (1) availability of sediment for marsh recovery, (2) sediment transport dynamics across the estuary, and (3) potential outcomes of distributary reconnection alternatives under existing and projected conditions of streamflow and sea level. Modeling and measurements indicated that the volume of fluvial sediment load reaching and accumulating in the restoration area ranges from 7 to 32 percent and identified that restoration alternatives could recover about an additional 10–12 percent under current and projected sea-level rise by the year 2100. At these rates of sediment delivery, 85–200+ years may be necessary to fill for marsh vegetation development and maintenance. The model also reveals the sensitivity of sediment transport and accumulation to sediment properties, hydrodynamics, and wave conditions. The low sediment accumulation results in large part because of the role of waves in directing sediment transport offshore and challenges of restoring geomorphic processes suited to maintaining habitat structure where opportunity exists or least conflicts with land use. The findings therefore have implications for siting, phasing, and implementing strategies to route and retain sediment. This study shows that opportunities to recover sediment higher in the tidal prism, where a greater hydraulic gradient and gravity could promote progradation and greater sediment retention, may be more effective than alternatives lower in the tidal prism implemented to date and assessed in this study. Furthermore, the modeling indicates that distributary channel restoration also may provide additional benefits to society by reducing flood stage, and therefore, flood hazards surrounding the delta.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221088","collaboration":"Prepared in cooperation with Nisqually Indian Tribe, U.S. Fish and Wildlife Service, Billy Frank Jr. Nisqually National Wildlife Refuge, and Washington Department of Fish and Wildlife Estuary and Salmon Restoration Program","usgsCitation":"Grossman, E.E., Crosby, S.C., Stevens, A.W., Nowacki, D.J., vanAredonk, N.R., and Curran, C.A., 2022, Assessment of vulnerabilities and opportunities to restore marsh sediment supply at Nisqually River Delta, west-central Washington: U.S. Geological Survey Open-File Report 2022–1088, 50 p., https://doi.org/10.3133/ofr20221088.","productDescription":"Report: ix, 50 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-121432","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":410185,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7GF0SG7","text":"USGS data release","description":"USGS data release","linkHelpText":"Stage, water velocity and water quality data collected in the Lower Nisqually River, McAllister Creek and tidal channels of the Nisqually River Delta, Thurston County, Washington, February 11, 2016 to September 18, 2017 (ver. 1.1, December, 2019)"},{"id":410186,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95N6CIT","text":"USGS data release","description":"USGS data release","linkHelpText":"Topobathymetric Model of Puget Sound, Washington, 1887 to 2017"},{"id":410184,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1088/ofr20221088.pdf","text":"Report","size":"32.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1088"},{"id":410183,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1088/coverthb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Nisqually River Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.12450821055306,\n 47.12862354087443\n ],\n [\n -123.12450821055306,\n 45.666890715537136\n ],\n [\n -121.49348505325844,\n 45.666890715537136\n ],\n [\n -121.49348505325844,\n 47.12862354087443\n ],\n [\n -123.12450821055306,\n 47.12862354087443\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Pacific Coastal and Marine Science Center
U.S. Geological Survey
2885 Mission St.
Santa Cruz, CA 95060
Dynamic natural processes govern snow distribution in mountainous environments throughout the world. Interactions between these different processes create spatially variable patterns of snow depth across a landscape. Variations in accumulation and redistribution occur at a variety of spatial scales, which are well established for moderate mountain terrain. However, spatial patterns of snow depth variability in steep, complex mountain terrain have not been fully explored due to insufficient spatial resolutions of snow depth measurement. Recent advances in uncrewed aerial systems (UASs) and structure from motion (SfM) photogrammetry provide an opportunity to map spatially continuous snow depths at high resolutions in these environments. Using UASs and SfM photogrammetry, we produced 11 snow depth maps at a steep couloir site in the Bridger Range of Montana, USA, during the 2019–2020 winter. We quantified the spatial scales of snow depth variability in this complex mountain terrain at a variety of resolutions over 2 orders of magnitude (0.02 to 20 m) and time steps (4 to 58 d) using variogram analysis in a high-performance computing environment. We found that spatial resolutions greater than 0.5 m do not capture the complete patterns of snow depth spatial variability within complex mountain terrain and that snow depths are autocorrelated within horizontal distances of 15 m at our study site. The results of this research have the potential to reduce uncertainty currently associated with snowpack and snow water resource analysis by documenting and quantifying snow depth variability and snowpack evolution on relatively inaccessible slopes in complex terrain at high spatial and temporal resolutions.
","language":"English","publisher":"Copernicus Journals","doi":"10.5194/tc-16-4907-2022","usgsCitation":"Miller, Z., Peitzsch, E.H., Sproles, E.A., Birkeland, K.W., and Palomaki, R.T., 2022, Assessing the seasonal evolution of snow depth spatial variability and scaling in complex mountain terrain: The Cryosphere, v. 16, no. 12, p. 4907-4930, https://doi.org/10.5194/tc-16-4907-2022.","productDescription":"24 p.","startPage":"4907","endPage":"4930","ipdsId":"IP-139965","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":445693,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/tc-16-4907-2022","text":"Publisher Index Page"},{"id":435598,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YCIA1R","text":"USGS data release","linkHelpText":"2020 winter timeseries of UAS derived digital surface models (DSMs) from the Hourglass study site, Bridger Mountains, Montana, USA"},{"id":410274,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Bridger Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.94,\n 45.84\n ],\n [\n -110.94,\n 45.830\n ],\n [\n -110.93,\n 45.83\n ],\n [\n -110.93,\n 45.84\n ],\n [\n -110.94,\n 45.84\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"12","noUsgsAuthors":false,"publicationDate":"2022-12-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Miller, Zachary 0000-0002-6876-6710","orcid":"https://orcid.org/0000-0002-6876-6710","contributorId":214464,"corporation":false,"usgs":true,"family":"Miller","given":"Zachary","email":"","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":858561,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peitzsch, Erich H. 0000-0001-7624-0455","orcid":"https://orcid.org/0000-0001-7624-0455","contributorId":202576,"corporation":false,"usgs":true,"family":"Peitzsch","given":"Erich","middleInitial":"H.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":858562,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sproles, Eric A. 0000-0003-1245-1653","orcid":"https://orcid.org/0000-0003-1245-1653","contributorId":299760,"corporation":false,"usgs":false,"family":"Sproles","given":"Eric","email":"","middleInitial":"A.","affiliations":[{"id":64943,"text":"Montana State University Earth Sciences Department","active":true,"usgs":false}],"preferred":false,"id":858563,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Birkeland, Karl W.","contributorId":209943,"corporation":false,"usgs":false,"family":"Birkeland","given":"Karl","email":"","middleInitial":"W.","affiliations":[{"id":38033,"text":"U.S.D.A. Forest Service National Avalanche Center, Bozeman, Montana, USA","active":true,"usgs":false}],"preferred":false,"id":858564,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Palomaki, Ross T. 0000-0002-3304-9914","orcid":"https://orcid.org/0000-0002-3304-9914","contributorId":299761,"corporation":false,"usgs":false,"family":"Palomaki","given":"Ross","email":"","middleInitial":"T.","affiliations":[{"id":64943,"text":"Montana State University Earth Sciences Department","active":true,"usgs":false}],"preferred":false,"id":858565,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70243187,"text":"70243187 - 2022 - Quantifying permanent uplift due to lithosphere-hotspot interaction","interactions":[],"lastModifiedDate":"2023-05-03T11:51:00.692258","indexId":"70243187","displayToPublicDate":"2022-12-08T06:48:03","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3524,"text":"Tectonics","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying permanent uplift due to lithosphere-hotspot interaction","docAbstract":"Vertical motions that accompany the passage of the lithosphere over a mantle hotspot can shed light on the nature of the hotspot and its effect on the lithosphere. However, quantifying the temporal vertical and spatial extent, is challenging due to the paucity of evidence in the geological record. Here, we utilize dense seismic and well data covering the intersection of the Great Meteor Hotspot (GMH) track with the U.S. Atlantic continental margin to constrain the surface expression of the hotspot passage under the lithosphere. The continuous sedimentary record of the eastern North American margin during its passage over the hotspot allows determination of the timing, magnitude, width and rate of denudation. We find that a ∼300 km wide region was denuded by up to 850 m between ∼97 and 86 Ma, ∼10 m.y. after the passage of the GMH. Stratigraphic relationships suggest a decaying rock uplift rate with time and no subsequent sagging. The broad, long-lasting, and delayed uplift was modeled as a surface manifestation of either sub-lithospheric mantle depletion, permanently eroded base of the continental lithosphere, or intrusions of depleted magma. We consider sub-lithospheric depletion to be the most likely cause, based on seismic imaging results.
Soil moisture is a critical land surface variable, impacting the water, energy, and carbon cycles. While in situ soil moisture monitoring networks are still developing, there is no cohesive strategy or framework to coordinate, integrate, or disseminate these diverse data sources in a synergistic way that can improve our ability to understand climate variability at the national, state, and local levels. Thus, a national strategy is needed to guide network deployment, sustainable network operation, data integration and dissemination, and user-focused product development. The National Coordinated Soil Moisture Monitoring Network (NCSMMN) is a federally led, multi-institution effort that aims to address these needs by capitalizing on existing wide-ranging soil moisture monitoring activities, increasing the utility of observational data, and supporting their strategic application to the full range of decision-making needs. The goals of the NCSMMN are to 1) establish a national “network of networks” that effectively demonstrates data integration and operational coordination of diverse in situ networks; 2) build a community of practice around soil moisture measurement, interpretation, and application—a “network of people” that links data providers, researchers, and the public; and 3) support research and development (R&D) on techniques to merge in situ soil moisture data with remotely sensed and modeled hydrologic data to create user-friendly soil moisture maps and associated tools. The overarching mission of the NCSMMN is to provide coordinated high-quality, nationwide soil moisture information for the public good by supporting applications like drought and flood monitoring, water resource management, agricultural and forestry planning, and fire danger ratings.
The McMurdo Dry Valleys, Antarctica, are a polar desert populated with numerous closed-watershed, perennially ice-covered lakes primarily fed by glacial melt. Lake levels have varied by as much as 8 m since 1972 and are currently rising after a decade of decreasing. Precipitation falls as snow, so lake hydrology is dominated by energy available to melt glacier ice and to sublimate lake ice. To understand the energy and hydrologic controls on lake level changes and to explain the variability between neighboring lakes, only a few kilometers apart, we model the hydrology for the three largest lakes in Taylor Valley. We apply a physically based hydrological model that includes a surface energy balance model to estimate glacial melt and lake sublimation to constrain mass fluxes to and from the lakes. Results show that lake levels are very sensitive to small changes in glacier albedo, air temperature, and wind speed. We were able to balance the hydrologic budget in two watersheds using meltwater inflow and sublimation loss from the ice-covered lake alone. A third watershed, closest to the coast, required additional inflow beyond model uncertainties. We hypothesize a shallow groundwater system within the active layer, fed by dispersed snow patches, contributes 23% of the inflow to this watershed. The lakes are out of equilibrium with the current climate. If the climate of our study period (1996-2013) persists into the future, the lakes will reach equilibrium starting in 2300, with levels 2-17 m higher, depending on the lake, relative to the 2020 level.
The tectonic domains of Basin and Range extension, Cascadia subduction zone contraction, and Walker Lane dextral transtension converge in the Mushroom Rock region of northeastern California, USA. We combined analysis of high-resolution topographic data, bedrock mapping, 40Ar/39Ar geochronology, low-temperature thermochronology, and existing geologic and fault mapping to characterize an extensive dextral-normal-oblique fault system called the Pondosa fault zone. This fault zone extends north-northwest from the Pit River east of Soldier Mountain, California, into moderately high-relief volcanic topography as far north as the Bartle (California) townsite with normal and dextral offset apparent in geomorphology and fault exposures. New and existing 40Ar/39Ar and radiocarbon dating of offset lava flows provides ages of 12.4 ka to 9.6 Ma for late Cenozoic stratigraphic units. Scarp morphology and geomorphic expression indicate that the fault system was active in the late Pleistocene. The Pondosa fault zone may represent a dextral-oblique accommodation zone between north-south–oriented Basin and Range extensional fault systems and/or part of the Sierra Nevada–Oregon Coast block microplate boundary.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02450.1","usgsCitation":"Jobe, J.A., Briggs, R.W., Gold, R.D., DeLong, S.B., Hille, M., Delano, J., Johnstone, S., Pickering, A., Phillips, R., and Calvert, A.T., 2022, The Pondosa fault zone: A distributed dextral-normal-oblique fault system in northeastern California, USA: Geosphere, v. 19, no. 1, p. 179-205, https://doi.org/10.1130/GES02450.1.","productDescription":"27 p.","startPage":"179","endPage":"205","ipdsId":"IP-137700","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":492497,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02450.1","text":"Publisher Index Page"},{"id":492284,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"eastern 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the biogeographical and ecological response of western Atlantic coral reefs to past climate change that can be used to evaluate future climate impacts. However, previous studies have focused on millennial-scale change during the stable mid-Holocene, making it difficult to make inferences about the impact of shorter-term variability that is relevant to modern climate warming. Using uranium-series dating of newly discovered subfossil coral rubble deposits, we establish a new high-resolution record of coral community development off southeast Florida during a period of variable climate in the late Holocene. Our results indicate that coral communities dominated by reef-building Acropora palmata and Orbicella spp. persisted in the nearshore environments off southeast Florida ~75 km north of their primary historical ranges between ~3500 and 1800 years before present. This timing coincides with regional warming at the northern extent of the Atlantic Warm Pool, suggesting a likely link between regional oceanographic climate and the expansion of cold-sensitive reef-building coral communities to the high-latitude reefs off southeast Florida. These findings not only extend the record of coral-reef development in southeast Florida into the late Holocene, but they also have important implications for future range expansions of reef-building coral communities in response to modern climate change.