This standard operating procedure (SOP) provides instructions and considerations for conducting mobile acoustic surveys along road transects to collect bat acoustic data following the North American Bat Monitoring Program (NABat) protocol and sample design. This report discusses measures for ensuring the safety of surveyors and efficiency of mobile transect surveys. This guidance is intended to aid surveyors in equipment setup and operation, including how to mount and orient equipment. Finally, this document provides guidance on driving the route, performing end of survey procedures, and steps for data management.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm2C3","usgsCitation":"Martin, J., Hall, M., Ferrall, E., Li, H., Rae, J., Straw, B., and Reichert, B., 2022, North American Bat Monitoring Program (NABat) mobile acoustic transect surveys standard operating procedure 3—Conducting mobile transect surveys: U.S. Geological Survey Techniques and Methods 2–C3, 6 p., https://doi.org/10.3133/tm2C3.","productDescription":"vi, 6 p.","onlineOnly":"Y","ipdsId":"IP-129332","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":410860,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/tm/02/c3/images"},{"id":410378,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/tm2C2","text":"USGS Techniques and Methods 2-C2—","linkHelpText":"North American Bat Monitoring Program (NABat) Mobile Acoustic Transect Surveys Standard Operating Procedure 2—Field Season and Survey Preparation"},{"id":410377,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/tm2C1","text":"USGS Techniques and Methods 2-C1—","linkHelpText":"North American Bat Monitoring Program (NABat) Mobile Acoustic Transect Surveys Standard Operating Procedure 1—Locating and Establishing Mobile Transect Routes"},{"id":409930,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/02/c3/tm2c3.pdf","text":"Report","size":"1.19 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T and M 2-C3"},{"id":411438,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/tm2C3/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"T and M 2-C3"},{"id":410861,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/tm/02/c3/tm2c3.xml"},{"id":409929,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/02/c3/coverthb.jpg"}],"contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526-8118
This document is the first of three standard operating procedures (SOPs) providing instructions and considerations for conducting mobile acoustic surveys along road transects to collect bat acoustic data following the North American Bat Monitoring Program (NABat) protocol and sample design. This SOP focuses specifically on selecting NABat grid cells and establishing mobile transect survey routes using online tools available through the NABat Partner Portal. Intended audiences for this document include those in charge of facilitating surveys within their region (for example, State or provincial managers, and NABat regional hub coordinators), and project leaders or survey coordinators responsible for setting up and organizing NABat mobile transect monitoring for their organization or area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm2C1","usgsCitation":"Martin, J., Smith, D., Li, H., Hall, M., Ferrall, E., Rae, J., Straw, B., and Reichert, B., 2022, North American Bat Monitoring Program (NABat) mobile acoustic transect surveys standard operating procedure 1—Locating and establishing mobile transect routes: U.S. Geological Survey Techniques and Methods 2–C1, 7 p., https://doi.org/10.3133/tm2C1.","productDescription":"vi, 7 p.","onlineOnly":"Y","ipdsId":"IP-129323","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":411436,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/tm2C1/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"T and M 2-C1"},{"id":411159,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/tm/02/c1/tm2c1.xml"},{"id":411158,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/tm/02/c1/images"},{"id":410000,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/tm2C3","text":"USGS Techniques and Methods 2-C3—","linkHelpText":"North American Bat Monitoring Program (NABat) Mobile Acoustic Transect Surveys Standard Operating Procedure 3—Conducting Mobile Transect Surveys"},{"id":409927,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/02/c1/coverthb.jpg"},{"id":410375,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/tm2C2","text":"USGS Techniques and Methods 2-C2—","linkHelpText":"North American Bat Monitoring Program (NABat) Mobile Acoustic Transect Surveys Standard Operating Procedure 2—Field Season and Survey Preparation"},{"id":409928,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/02/c1/tm2c1.pdf","text":"Report","size":"756 kB","linkFileType":{"id":1,"text":"pdf"},"description":"T and M 2-C1"}],"contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526-8118
A significant number of the world’s approximately 1,400 subaerial volcanoes with Holocene eruptions are unmonitored by ground-based sensors yet constitute a potential hazard to nearby residents and infrastructure, as well as air travel and global commerce. Data from an international constellation of more than 60 current satellite instruments provide a cost-effective means of tracking activity and potentially forecasting hazards at volcanoes around the world. These data span the electromagnetic spectrum: ultraviolet, optical, infrared, and microwave (synthetic aperture radar). They can measure volcanic thermal and gas emissions, ground displacement, and surface and topographic change, providing information that addresses one of the grand challenges in volcanology—to overcome our incomplete understanding of the relation between volcanic unrest and eruption, which is currently based on only a few well-studied volcanoes.
Although the potential of volcano remote sensing has been recognized for decades, there are many hurdles to clear before remote sensing data can be used fully by all volcano observatories. These include: (1) the limited temporal and spatial coverage of active volcanoes by satellites and the delayed distribution of those data; (2) the lack of background data acquired at all volcanoes; and (3) limited access to, and utilization of, remote sensing data in some areas owing to a lack of expertise, licensing, user-friendly formats, data access portals, or computational infrastructure.
While remote sensing data will never replace ground-based monitoring, a joint observation strategy provides a powerful means of assessing volcanic activity before, during, and after hazardous eruptions, especially given the unique spatial, temporal, and spectral perspective provided by remote measurements. A coordinated international remote sensing observation strategy for volcanoes—similar to one used by the cryosphere community—along with a volcano space task group to maximize the utility of satellite data for volcano monitoring would be highly beneficial. Such a vision could facilitate (1) global coordination of satellite observations (as done for polar regions) for background monitoring and eruption response, (2) open data that can be rapidly distributed during crises, (3) communication tools and forums for discussion of satellite data, (4) integrated ground and satellite databases of unrest, and (5) global capacity building.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225116","usgsCitation":"Pritchard, M.E., Poland, M., Reath, K., Andrews, B., Bagnardi, M., Biggs, J., Carn, S., Coppola, D., Ebmeier, S.K., Furtney, M.A., Girona, T., Griswold, J., Lopez, T., Lundgren, P., Ogburn, S., Pavolonis, M., Rumpf, E., Vaughan, G., Wauthier, C., Wessels, R., Wright, R., Anderson, K.R., Bato, M.G., and Roman, A., 2022, Optimizing satellite resources for the global assessment and mitigation of volcanic hazards—Suggestions from the USGS Powell Center Volcano Remote Sensing Working Group: U.S. Geological Survey Scientific Investigations Report 2022–5116, 69 p., https://doi.org/10.3133/sir20225116.","productDescription":"Report: ix, 69 p.; 1 Table","onlineOnly":"Y","ipdsId":"IP-110516","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":410734,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5116/sir20225116_appendix1_table1.1.xlsx","text":"Table 1.1","size":"243 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2022-5116 Table 1.1"},{"id":410733,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5116/sir20225116.pdf","text":"Report","size":"5.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5116"},{"id":410732,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5116/coverthb2.jpg"}],"contact":"Director, Volcano Science Center
U.S. Geological Survey
1300 SE Cardinal Court
Vancouver, WA 38683
Jewel Cave National Monument is in the western Black Hills of South Dakota and contains an extensive cave network, including various subterranean water bodies (cave lakes) that are believed to represent the regionally important Madison aquifer. Recent investigations have sought to improve understanding of hydrogeologic characteristics of cave lakes in Jewel Cave. The U.S. Geological Survey, in cooperation with the National Park Service, collected water-level and water-chemistry data within and near Jewel Cave to better understand groundwater interactions in Jewel Cave and to evaluate recharge characteristics of cave lakes. Continuous water-level data were collected at two cave lakes (Hourglass and New Years Lakes) from 2018 to 2021, and discrete measurements were collected by National Park Service staff from 2015 to 2021. Water samples were collected from one stream, one rain collector, three springs, and two cave lakes. The approach for this study included comparing water-level data collected from two cave lakes to historical climate data and using multivariate statistical analyses to evaluate water samples collected during this study and from previous investigations. This study builds on interpretations from previous investigations that collected similar datasets and performed similar analyses.
Hydrographs of Hourglass and News Years Lakes from 2015 to 2021 demonstrated the variability of groundwater levels in Jewel Cave in response to dry and wet climate conditions. Hourglass Lake displayed small (up to 4.8 feet), gradual water-level changes, whereas New Years Lake displayed relatively large (up to at least 27.5 feet) and rapid water-level changes. Hourglass and New Years Lakes are about 0.4 mile apart at the land surface, and the water-level elevation between the lakes varied from 61 to 93.5 feet from 2016 to 2021. The proximity and relatively small elevation difference of Hourglass and New Years Lakes indicated different recharge sources and (or) mechanisms were responsible for hydrograph dissimilarities. Water-level changes at Hourglass Lake were similar to water-level changes at a well completed in the Madison aquifer about 9 miles south of Jewel Cave National Monument, which indicated Hourglass Lake may be recharged similar to the regional Madison aquifer along outcrops north of Jewel Cave. New Years Lake displayed almost no similarities to the well completed in the Madison aquifer—indicating a more direct connection to local recharge rather than solely from outcrops recharging the regional Madison aquifer.
Results from multivariate statistical analyses of water-chemistry data were used to evaluate recharge observations from water-level data. The water chemistry of Hourglass Lake indicated its water was chemically more similar to precipitation than other groundwater sites sampled. A conceptual karst recharge model indicated that the dominant recharge source to Hourglass Lake was diffuse allogenic recharge from vertical movement of infiltrated precipitation through vertical or near-vertical fractures that extend through the Minnelusa Formation and unsaturated zone of the Madison Limestone. The water chemistry of New Years Lake was chemically similar to Hell Canyon Creek about 0.2 mile from New Years Lake at the land surface. Streamflow loss zones (concentrated allogenic recharge) along Hell Canyon Creek have not been mapped, but their presence in the Jewel Cave area has been speculated by previous investigations. A fault observed in the cave ceiling above New Years Lake by National Park Service staff could provide a natural conduit for direct recharge from Hell Canyon Creek to New Years Lake if the fault is extensive. Additional water-chemistry and water-level data, as well as streamflow data upstream and downstream of the potential streamflow loss zone along Hell Canyon Creek, are needed to prove the presence of this loss zone and discern further correlations between streamflow and water levels in New Years Lake. Observations from previous investigations and this study indicated recharge to Jewel Cave is complex and occurs on various timescales that are affected temporally by precipitation patterns and spatially by hydrologic connection with the overlying Minnelusa aquifer of the Minnelusa Formation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225108","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Medler, C.J., 2022, Hydrogeologic characteristics of Hourglass and New Years Cave Lakes at Jewel Cave National Monument, South Dakota, from water-level and water-chemistry data, 2015–21: U.S. Geological Survey Scientific Investigations Report 2022–5108, 47 p., https://doi.org/10.3133/sir20225108.","productDescription":"Report: viii, 47 p.; Dataset","numberOfPages":"60","onlineOnly":"Y","ipdsId":"IP-137086","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":410716,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5108/sir20225108.XML"},{"id":410714,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5108/coverthb.jpg"},{"id":410715,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5108/sir20225108.pdf","text":"Report","size":"8.03 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5108"},{"id":410717,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5108/images"},{"id":410718,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":410721,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225108/full","text":"Report"}],"country":"United States","state":"South Dakota","otherGeospatial":"Jewel Cave National Monument","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -103.2,\n 43.738808274610875\n ],\n [\n -104.0,\n 43.738808274610875\n ],\n [\n -104.0,\n 43.35675372367402\n ],\n [\n -103.2,\n 43.35675372367402\n ],\n [\n -103.2,\n 43.738808274610875\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
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A new set of resources is now available that describe global shoreline characteristics. High resolution (30 m), globally comprehensive Coastal Segment Units (CSUs) and Ecological Coastal Units (ECUs) were developed in a collaboration between the U.S. Geological Survey (USGS), Esri, and the Marine Biodiversity Observation Network (MBON). The data were produced from a segmentation and characterization of a global shoreline vector extracted from year 2014 Landsat imagery. A total of 4 million 1 km shoreline segments were attributed with values from ten variables which describe the ecological settings in which the coastline occurs, including water-side properties, landside properties, and properties of the coastline itself. These data were developed as part of a Group on Earth Observations (GEO) global ecosystem mapping initiative called GEO Ecosystems (GEO ECO). The development of the resource and its intended utility are reviewed herein.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Oceans 2022, Hampton Roads","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Oceans 2022, Hampton Roads","conferenceDate":"Oct 17-20, 2022","conferenceLocation":"Hampton Roads, VA","language":"English","publisher":"IEEE","doi":"10.1109/OCEANS47191.2022.9977390","usgsCitation":"Sayre, R., Butler, K., Van Graafeiland, K., Breyer, S., and Wright, D., 2022, Ecological Coastal Units – Standardized global shoreline characteristics, in Oceans 2022, Hampton Roads, Hampton Roads, VA, Oct 17-20, 2022, 4 p., https://doi.org/10.1109/OCEANS47191.2022.9977390.","productDescription":"4 p.","ipdsId":"IP-146446","costCenters":[{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"links":[{"id":412943,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sayre, Roger 0000-0001-6703-7105","orcid":"https://orcid.org/0000-0001-6703-7105","contributorId":302356,"corporation":false,"usgs":true,"family":"Sayre","given":"Roger","affiliations":[{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"preferred":true,"id":864110,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Butler, Kevin","contributorId":267714,"corporation":false,"usgs":false,"family":"Butler","given":"Kevin","affiliations":[{"id":38832,"text":"Esri","active":true,"usgs":false}],"preferred":false,"id":864111,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Van Graafeiland, Keith","contributorId":245012,"corporation":false,"usgs":false,"family":"Van Graafeiland","given":"Keith","affiliations":[],"preferred":false,"id":864112,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Breyer, Sean","contributorId":267716,"corporation":false,"usgs":false,"family":"Breyer","given":"Sean","affiliations":[{"id":38832,"text":"Esri","active":true,"usgs":false}],"preferred":false,"id":864113,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wright, Dawn","contributorId":200268,"corporation":false,"usgs":false,"family":"Wright","given":"Dawn","affiliations":[],"preferred":false,"id":864114,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"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":70238904,"text":"sir20225094 - 2022 - Groundwater quality and geochemistry of the western wet gas part of the Marcellus Shale Oil and Gas Play in West Virginia","interactions":[],"lastModifiedDate":"2022-12-19T12:01:47.434879","indexId":"sir20225094","displayToPublicDate":"2022-12-16T19:15:00","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":"2022-5094","displayTitle":"Groundwater Quality and Geochemistry of the Western Wet Gas Part of the Marcellus Shale Oil and Gas Play in West Virginia","title":"Groundwater quality and geochemistry of the western wet gas part of the Marcellus Shale Oil and Gas Play in West Virginia","docAbstract":"Thirty rural residential water wells in the wet gas region of the Marcellus Shale oil and gas play in northwestern West Virginia were sampled by the U.S. Geological Survey (USGS) in 2018, in cooperation with West Virginia State agencies, to analyze for a range of water-quality constituents, including major ions, trace metals, radionuclides, bacteria, and methane and other dissolved hydrocarbon gases. The groundwater-quality data collected for this study were used to assess the overall quality of groundwater in the study area in relation to public drinking-water standards. The groundwater-quality data were also evaluated with respect to geology, well depth, topographic setting, and proximity to oil and gas wells to identify possible relations to these factors.
The presence of total coliform bacteria in groundwater is a potential indicator of surface contamination. The presence of Escherichia coli bacteria is indicative of fecal contamination of groundwater from either human or animal sources and may be considered an indicator of other related pathogens such as viruses. Total coliforms were detected in 26 of the 30 (87 percent) wells sampled. Eleven of the 30 (37 percent) wells sampled had detections of Escherichia coli bacteria.
Sodium concentrations in 24 of 30 (80 percent) samples exceeded the U.S. Environmental Protection Agency (EPA) 20-milligram per Liter (mg/L) health-based value (HBV). Manganese, aluminum, and iron concentrations exceeded the EPA 50, 2.0, and 300 micrograms per liter (μg/L) secondary maximum contaminant level (SMCL) drinking-water standards at 14 (47 percent), 7 (23 percent), and 5 (17 percent) of the 30 wells sampled. Two of the 30 (7 percent) wells sampled had concentrations of manganese that exceeded the 300-μg/L USGS health-based screening level (HBSL). Arsenic concentrations at 7 of 30 (23 percent) wells sampled exceeded the 10-μg/L EPA maximum contaminant level (MCL) health-based drinking water standard. The EPA maximum contaminant level goal (MCLG) for arsenic is 0 μg/L and 29 of 30 wells sampled contained detectable concentrations of arsenic.
None of the 30 wells sampled exceeded the U.S. Office of Surface Mining Reclamation and Enforcement (OSMRE) 28-mg/L immediate action level (IAL) for methane in groundwater and only 1 of 30 (3 percent) sites exceeded the 10-mg/L OSMRE level of concern (LOC) for methane in groundwater. Of the 28 wells sampled for radon-222 all 28 (100 percent) exceeded the EPA proposed 300-picocuries per liter (pCi/L) MCL for radon. None of the samples exceeded the 4,000-pCi/L alternate maximum contaminant level (AMCL) which is applicable to public drinking water systems that have adopted radon mitigation programs.
Wilcoxon Signed Rank Tests indicated statistically significant differences at a 95 percent confidence interval (p less than 0.05) in radium-226, barium, and ethane groundwater concentrations with respect to the density of oil and gas wells present within a 500-meter (m) radius around the rural residential wells sampled for the study. Samples from residential wells that had four or fewer oil and gas wells in the surrounding 500-m radius had statistically lower concentrations of radium-226, bromide, and ethane than samples from residential wells sampled that had five or more oil and gas wells in the surrounding 500-m radius. Given the available data, the relationship between concentrations of radium-226, bromide, and ethane for wells sampled in this study and oil and gas development or natural geochemical processes is not clear.
Groundwater-age tracers (chlorofluorocarbons, tritium, and sulfur hexafluoride) were sampled at 17 of the 30 wells. All 17 samples contained a fraction of young, post-1950s groundwater. Many of the groundwater samples collected for this study have high calcium to sodium ratios and low total dissolved solids concentrations, indicating they are dominated by recently recharged water. A subset of samples had chloride to bromide mass ratios between 70 and 200, indicating that deep Appalachian basin brines mixed with the shallow groundwater. For most of the samples in this study, the C1 through C6 hydrocarbons have characteristics that reflect a biogenic gas signature that has, to varying degrees, undergone oxidation processes during transport. None of the samples show a characteristic thermogenic cracking pattern among the hydrocarbon ratios.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225094","isbn":"978-1-4113-4489-1","collaboration":"Prepared in cooperation with the West Virginia Department of Environmental Protection Division of Water and Waste Management and the West Virginia Department of Health and Human Resources Office of Environmental Health Services","usgsCitation":"Kozar, M.D., McAdoo, M.A., and Haase, K.B., 2022, Groundwater quality and geochemistry of the western wet gas part of the Marcellus Shale Oil and Gas Play in West Virginia: U.S. Geological Survey Scientific Investigations Report 2022–5094, 88 p., https://doi.org/10.3133/sir20225094.","productDescription":"Report: xiv, 88 p.; Data Release; Appendix","numberOfPages":"88","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-139572","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":410548,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9L98L0Y","text":"USGS data release","linkHelpText":"Dataset of C1-C6 dissolved trace hydrocarbon measurements in the western “Wet Gas” part of the Marcellus Shale Oil and Gas Play in West Virginia, U.S.A. collected between June and August 2018"},{"id":410543,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5094/coverthb.jpg"},{"id":410544,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5094/sir20225094.pdf","text":"Report","size":"8.52 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5094"},{"id":410545,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225094/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5094"},{"id":410546,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5094/sir20225094.XML"},{"id":410547,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5094/images/"},{"id":410549,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2022/5094/sir20225094_appendix1.xlsx","text":"Appendix 1","size":"78.1 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Correlation matrix showing Spearman’s correlation coefficients of statistical significance at a confidence interval of 99 percent for 58 variables, including 45 chemical constituents, 4 principal component analysis scores, 8 land use classifications, and well depth"},{"id":410550,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2022/5094/sir20225094_appendix1_csv.zip","text":"Appendix 1","size":"18.4 KB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- In CSV format"}],"country":"United States","state":"West 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Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, Virginia 23228
Ungulates (hooved mammals) have a broad distribution across the western United States and play an important role in maintaining predator-prey dynamics, affecting vegetation communities, and providing economic benefits to regional communities through tourism and hunting. Throughout the diverse landscapes they occupy, many ungulate populations undertake seasonal migrations to exploit spatially and temporally variable resources and to avoid predation or other threats. As the human footprint continues to expand across the western United States, ungulates increasingly face more obstacles on their migratory journeys. These obstacles threaten the long-term persistence of existing migrations. As a result, wildlife management agencies across the western United States have worked to identify and protect (or enhance) ungulate migration corridors and seasonal ranges identified from global positioning system (GPS) collar data. These efforts garnered additional support through the U.S. Department of the Interior Secretarial Order (SO) 3362, which was initiated in 2018 and provided Federal support for enhancing habitat quality of big-game winter ranges and migration corridors across the western states.
Further, SO 3362 prompted the U.S. Geological Survey (USGS) to establish the Corridor Mapping Team (CMT): a collaboration between USGS and participating State and Federal wildlife management agencies, as well as numerous Tribal Nations. The CMT works collaboratively to map ungulate migrations and seasonal ranges throughout the western United States within the Ungulate Migrations of the Western United States report series. Volume 1 of the series was published in 2020 and contained migrations and winter ranges from 42 herds across 5 states. Volume 2 was published in 2022 and contained migrations and seasonal ranges from an additional 65 herds. This report, Volume 3 in the series, details migrations and seasonal ranges from an additional 45 herds throughout most western states. In aggregate, the report series has detailed and mapped the migrations and seasonal ranges of 152 ungulate herds and serves as a map-based inventory of the documented ungulate migrations across the western United States. The data layers for most of the herds included in the report series are also available to the public by the USGS. In addition to the included herd maps, this volume provides an overview of the many ways the mapping efforts associated with the CMT are being integrated into local conservation, management, and policy throughout the western United States.
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U.S. Geological Survey
Box 25046, MS-980
Denver, CO 80225-0046
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":70238833,"text":"ofr20221107 - 2022 - Black Abalone surveys at Naval Base Ventura County, San Nicolas Island, California—2021, annual report","interactions":[],"lastModifiedDate":"2023-09-18T20:00:12.711841","indexId":"ofr20221107","displayToPublicDate":"2022-12-13T13:23:14","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":"2022-1107","displayTitle":"Black Abalone Surveys at Naval Base Ventura County, San Nicolas Island, California: 2021, Annual Report","title":"Black Abalone surveys at Naval Base Ventura County, San Nicolas Island, California—2021, annual report","docAbstract":"The U.S. Geological Survey monitors a suite of intertidal black abalone sites at San Nicolas Island, California, in cooperation with the U.S. Navy, which owns the island. The nine rocky intertidal sites were established in 1980 to study the potential effect of translocated sea otters on the intertidal black abalone population at the island. The sites were monitored from 1981 to 1997, usually annually or biennially. Monitoring resumed in 2001 and has been completed annually since then. Since 2018, the work has been carried out by the U.S. Geological Survey Western Ecological Research Center. The study sites became particularly important, from a management perspective, after a virulent disease decimated black abalone populations throughout southern California beginning in the mid-1980s. The disease, withering syndrome, was first observed on San Nicolas Island in 1992 and during the next few years, it reduced the population there by more than 99 percent. The species was subsequently listed as endangered under the Endangered Species Act in 2009.
The subject of this report is the 2021 survey of the sites and how the measured population status compares to the long-term data (collected over several decades) at San Nicolas Island. During the last two decades, the total monitored black abalone population at the island has grown approximately ten-fold after the disease related decline, from about 200 to more than 2,000 abalone. Since it was first consistently measured in 2005, the mean distance between adjacent black abalone has decreased substantially from approximately 50 centimeters to less than 15 centimeters, indicating that abalone are close enough together at several of the sites to reproduce successfully. The 2021 counts were the first since 2016 to indicate a possible decline in the monitored population at San Nicolas Island. Although still more than ten times the population counted in 2001, counts on the transects dropped by 13.6 percent from the survey count in 2020. The most significant decline was the loss of 341 abalone, from the previous count of 547, on a transect that since 2002 had the highest count of all 44 transects. Between 2020 and 2021, there were increases and decreases at the sites and the transects at each site. Although the 2021 count was lower than the 2020 count, it was the second highest since 1997. Based on the number of small abalone counted, recruitment rates were similar to most years since 2008 and higher than the rates observed before the population declines resulting from withering syndrome.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221107","collaboration":"Prepared in cooperation with the U.S. Navy","programNote":"Ecosystems Mission Area—Species Management Research Program","usgsCitation":"Kenner, M.C., and Yee, J.L., 2022, Black Abalone surveys at Naval Base Ventura County, San Nicolas Island, California—2021, annual report: U.S. Geological Survey Open-File Report 2022–1107, 34 p., https://doi.org/10.3133/ofr20221107.","productDescription":"vii, 34 p.","onlineOnly":"Y","ipdsId":"IP-144353","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":410409,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1107/ofr20221107.XML"},{"id":410407,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221107/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1107"},{"id":410408,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1107/images"},{"id":410406,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1107/ofr20221107.pdf","text":"Report","size":"3.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1107"},{"id":410405,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1107/coverthb.jpg"}],"country":"United States","state":"California","county":"Ventura County","otherGeospatial":"San Nicolas Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.59425743862556,\n 33.2937160305206\n ],\n [\n -119.59425743862556,\n 33.20294676390226\n ],\n [\n -119.4170269925379,\n 33.20294676390226\n ],\n [\n -119.4170269925379,\n 33.2937160305206\n ],\n [\n -119.59425743862556,\n 33.2937160305206\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
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
2885 Mission St
Santa Cruz, CA 95060
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. This report addresses the initial stage, which is 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 report provides a description of the methods and results of this synthesis. The findings from this work were used to refine study area boundaries and develop sampling plans for stage two of the project.
Stage two of the project, the results of which will be published separately, will be the collection, processing, and synthesis of new data, including high-resolution geophysical surveys and sediment cores within the three study areas. The data collected will provide new information about the three study areas including sediment thickness, grain-size distributions, and total organic carbon. The description and results of stage two of the work is included in another USGS report (Warrick and others, 2022).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221094","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, 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: U.S. Geological Survey Open-File Report 2022–1094, 21 p., https://doi.org/10.3133/ofr20221094.","productDescription":"vii, 21 p.","onlineOnly":"Y","ipdsId":"IP-142746","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":410369,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1094/ofr20221094.pdf","text":"Report","size":"6.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1094"},{"id":410368,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1094/coverthb.jpg"},{"id":501836,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113942.htm","linkFileType":{"id":5,"text":"html"}}],"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
2885 Mission St
Santa Cruz, CA 95060
Accurate and complete data on fire locations and burned areas are needed to quantify trends and patterns of fire occurrence, characterize drivers of fire, project future fire pattern behavior, and help with assessments of fire effects on natural and social systems. The Landsat Collection 2 Level-3 Burned Area science product is designed to identify burned areas across all ecosystems (for example, forests, shrublands, and grasslands) for Landsat 4–9 data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223083","usgsCitation":"U.S. Geological Survey, 2022, Landsat Collection 2 Level-3 Burned Area science product (ver. 1.1, June 2023): U.S. Geological Survey Fact Sheet 2022–3083, 2 p., https://doi.org/10.3133/fs20223083.","productDescription":"Report: 2 p.; Dataset","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-139624","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":418240,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3083/coverthb2.jpg"},{"id":410296,"rank":1,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS database","linkHelpText":"—EarthExplorer"},{"id":418243,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2022/3083/versionHist.txt","size":"1 kB","linkFileType":{"id":2,"text":"txt"}},{"id":418241,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3083/fs20223083.pdf","text":"Report","size":"1.54 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022–3038"}],"edition":"Version 1.0: December 13, 2022; Version 1.1: June 20, 2023","contact":"Customer Services, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
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
Islands support unique plants, animals, and human societies found nowhere else on the Earth. Local and global stressors threaten the persistence of island ecosystems, with invasive species being among the most damaging, yet solvable, stressors. While the threat of invasive terrestrial mammals on island flora and fauna is well recognized, recent studies have begun to illustrate their extended and destructive impacts on adjacent marine environments. Eradication of invasive mammals and restoration of native biota are promising tools to address both island and ocean management goals. The magnitude of the marine benefits of island restoration, however, is unlikely to be consistent across the globe. We propose a list of six environmental characteristics most likely to affect the strength of land–sea linkages: precipitation, elevation, vegetation cover, soil hydrology, oceanographic productivity, and wave energy. Global databases allow for the calculation of comparable metrics describing each environmental character across islands. Such metrics can be used today to evaluate relative potential for coupled land–sea conservation efforts and, with sustained investment in monitoring on land and sea, can be used in the future to refine science-based planning tools for integrated land–sea management. As conservation practitioners work to address the effects of climate change, ocean stressors, and biodiversity crises, it is essential that we maximize returns from our management investments. Linking efforts on land, including eradication of island invasive mammals, with marine restoration and protection should offer multiplied benefits to achieve concurrent global conservation goals.
","language":"English","publisher":"National Academy of Sciences","doi":"10.1073/pnas.2122354119","usgsCitation":"Sandin, S.A., Becker, P.A., Becker, C., Brown, K., Erazo, N.G., Figuerola, C., Fisher, R., Friedlander, A., Fukami, T., Graham, N.A., Gruner, D.S., Holmes, N.D., Holthuijzen, W.A., Jones, H.P., Rios, M., Samaniego, A., Sechrest, W., Semmens, B.X., Thornton, H.E., Thurber, R.V., Wails, C., Wolf, C.A., and Zgliczynski, B.J., 2022, Harnessing island–ocean connections to maximize marine benefits of island conservation: PNAS, v. 119, no. 51, e2122354119, 9 p., https://doi.org/10.1073/pnas.2122354119.","productDescription":"e2122354119, 9 p.","ipdsId":"IP-144780","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":445677,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9907155","text":"Publisher Index Page"},{"id":412678,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"119","issue":"51","noUsgsAuthors":false,"publicationDate":"2022-12-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Sandin, Stuart A.","contributorId":301995,"corporation":false,"usgs":false,"family":"Sandin","given":"Stuart","email":"","middleInitial":"A.","affiliations":[{"id":35051,"text":"Scripps Institution of Oceanography, UC San Diego","active":true,"usgs":false}],"preferred":false,"id":863263,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Becker, Penny A.","contributorId":173445,"corporation":false,"usgs":false,"family":"Becker","given":"Penny","email":"","middleInitial":"A.","affiliations":[{"id":27230,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":863264,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Becker, Ceiba","contributorId":301997,"corporation":false,"usgs":false,"family":"Becker","given":"Ceiba","email":"","affiliations":[{"id":35051,"text":"Scripps Institution of Oceanography, UC San Diego","active":true,"usgs":false}],"preferred":false,"id":863265,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brown, Kate","contributorId":301999,"corporation":false,"usgs":false,"family":"Brown","given":"Kate","email":"","affiliations":[{"id":65382,"text":"Global Island Partnership, New Zealand","active":true,"usgs":false}],"preferred":false,"id":863266,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Erazo, Natalia G.","contributorId":302000,"corporation":false,"usgs":false,"family":"Erazo","given":"Natalia","email":"","middleInitial":"G.","affiliations":[{"id":35051,"text":"Scripps Institution of Oceanography, UC San Diego","active":true,"usgs":false}],"preferred":false,"id":863267,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Figuerola, Cielo","contributorId":302001,"corporation":false,"usgs":false,"family":"Figuerola","given":"Cielo","email":"","affiliations":[{"id":26976,"text":"Island Conservation, Santa Cruz, CA","active":true,"usgs":false}],"preferred":false,"id":863268,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fisher, Robert N. 0000-0002-2956-3240","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":51675,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863269,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Friedlander, Alan M.","contributorId":302003,"corporation":false,"usgs":false,"family":"Friedlander","given":"Alan M.","affiliations":[{"id":65384,"text":"National Geographic Society, Washington DC","active":true,"usgs":false}],"preferred":false,"id":863270,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Fukami, Tadashi","contributorId":195506,"corporation":false,"usgs":false,"family":"Fukami","given":"Tadashi","email":"","affiliations":[],"preferred":false,"id":863271,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Graham, Nicholas A. J.","contributorId":302005,"corporation":false,"usgs":false,"family":"Graham","given":"Nicholas","email":"","middleInitial":"A. J.","affiliations":[{"id":65385,"text":"Lancaster Environment Centre, Lancaster University, UK","active":true,"usgs":false}],"preferred":false,"id":863272,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Gruner, Daniel S.","contributorId":195507,"corporation":false,"usgs":false,"family":"Gruner","given":"Daniel","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":863273,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Holmes, Nick D.","contributorId":172091,"corporation":false,"usgs":false,"family":"Holmes","given":"Nick","email":"","middleInitial":"D.","affiliations":[{"id":26976,"text":"Island Conservation, Santa Cruz, CA","active":true,"usgs":false}],"preferred":false,"id":863274,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Holthuijzen, Wieteke A.","contributorId":302006,"corporation":false,"usgs":false,"family":"Holthuijzen","given":"Wieteke","email":"","middleInitial":"A.","affiliations":[{"id":65387,"text":"University of Tennessee - Knoxville, TN","active":true,"usgs":false}],"preferred":false,"id":863275,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Jones, Holly P.","contributorId":270944,"corporation":false,"usgs":false,"family":"Jones","given":"Holly","email":"","middleInitial":"P.","affiliations":[{"id":56228,"text":"Northern Illinois University, Department of Biological Sciences; Northern Illinois University, Institute for the Study of the Environment, Sustainability, and Energy","active":true,"usgs":false}],"preferred":false,"id":863276,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Rios, Mariela","contributorId":302007,"corporation":false,"usgs":false,"family":"Rios","given":"Mariela","email":"","affiliations":[{"id":35051,"text":"Scripps Institution of Oceanography, UC San Diego","active":true,"usgs":false}],"preferred":false,"id":863277,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Samaniego, Araceli","contributorId":302008,"corporation":false,"usgs":false,"family":"Samaniego","given":"Araceli","email":"","affiliations":[{"id":65388,"text":"Manaaki Whenua – Landcare Research, New Zealand","active":true,"usgs":false}],"preferred":false,"id":863278,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Sechrest, Wes","contributorId":302009,"corporation":false,"usgs":false,"family":"Sechrest","given":"Wes","email":"","affiliations":[{"id":65389,"text":"Re:wild, Austin, TX","active":true,"usgs":false}],"preferred":false,"id":863279,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Semmens, Brice X.","contributorId":149775,"corporation":false,"usgs":false,"family":"Semmens","given":"Brice","email":"","middleInitial":"X.","affiliations":[{"id":17820,"text":"Scripps Institution of Oceanography, University of California, San Diego","active":true,"usgs":false}],"preferred":false,"id":863280,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Thornton, Hazel E.","contributorId":302010,"corporation":false,"usgs":false,"family":"Thornton","given":"Hazel","email":"","middleInitial":"E.","affiliations":[{"id":65390,"text":"United Nations Environment Programme, World Conservation Monitoring Centre, UK","active":true,"usgs":false}],"preferred":false,"id":863281,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Thurber, Rebecca Vega","contributorId":302011,"corporation":false,"usgs":false,"family":"Thurber","given":"Rebecca","email":"","middleInitial":"Vega","affiliations":[{"id":65391,"text":"Oregon State University, Corvallis, OR","active":true,"usgs":false}],"preferred":false,"id":863282,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Wails, Christy","contributorId":302012,"corporation":false,"usgs":false,"family":"Wails","given":"Christy","affiliations":[{"id":65392,"text":"Virginia Tech, Blacksburg, VA","active":true,"usgs":false}],"preferred":false,"id":863283,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Wolf, Coral A.","contributorId":302013,"corporation":false,"usgs":false,"family":"Wolf","given":"Coral","email":"","middleInitial":"A.","affiliations":[{"id":26976,"text":"Island Conservation, Santa Cruz, CA","active":true,"usgs":false}],"preferred":false,"id":863284,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Zgliczynski, Brian J.","contributorId":302014,"corporation":false,"usgs":false,"family":"Zgliczynski","given":"Brian","email":"","middleInitial":"J.","affiliations":[{"id":35051,"text":"Scripps Institution of Oceanography, UC San Diego","active":true,"usgs":false}],"preferred":false,"id":863285,"contributorType":{"id":1,"text":"Authors"},"rank":23}]}} ,{"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.
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
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 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 California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.27831636230019,\n 41.12301709043055\n ],\n [\n -122.27831636230019,\n 39.963081252129996\n ],\n [\n -120.53795346715106,\n 39.963081252129996\n ],\n [\n -120.53795346715106,\n 41.12301709043055\n ],\n [\n -122.27831636230019,\n 41.12301709043055\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"19","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-12-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Jobe, Jessica Ann Thompson 0000-0001-5574-4523","orcid":"https://orcid.org/0000-0001-5574-4523","contributorId":295377,"corporation":false,"usgs":true,"family":"Jobe","given":"Jessica","email":"","middleInitial":"Ann Thompson","affiliations":[{"id":300,"text":"Geologic Hazards Science 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0000-0002-0945-2172 sdelong@usgs.gov","orcid":"https://orcid.org/0000-0002-0945-2172","contributorId":5240,"corporation":false,"usgs":true,"family":"DeLong","given":"Stephen","email":"sdelong@usgs.gov","middleInitial":"B.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":943091,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hille, Madeline 0000-0001-7240-8214","orcid":"https://orcid.org/0000-0001-7240-8214","contributorId":315582,"corporation":false,"usgs":false,"family":"Hille","given":"Madeline","email":"","affiliations":[{"id":37387,"text":"University of Michigan","active":true,"usgs":false}],"preferred":false,"id":943092,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Delano, Jaime 0000-0003-2601-2600","orcid":"https://orcid.org/0000-0003-2601-2600","contributorId":225594,"corporation":false,"usgs":false,"family":"Delano","given":"Jaime","affiliations":[{"id":6605,"text":"USGS","active":true,"usgs":false}],"preferred":false,"id":943093,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Johnstone, Samuel 0000-0002-3945-2499","orcid":"https://orcid.org/0000-0002-3945-2499","contributorId":207545,"corporation":false,"usgs":true,"family":"Johnstone","given":"Samuel","email":"","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":943094,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Pickering, Alexandra 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