The U.S. Army Corps of Engineers, Jacksonville District, is deepening the St. Johns River channel in Jacksonville, Florida, from 40 to 47 feet along 13 miles of the river channel beginning at the mouth of the river at the Atlantic Ocean, in order to accommodate larger, fully loaded cargo vessels. The U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, monitored stage, discharge, and (or) water temperature and salinity at 26 continuous data collection stations in the St. Johns River and its tributaries.
This is the fifth annual report by the U.S. Geological Survey on data collection for the Jacksonville Harbor deepening project. The report contains information pertinent to data collection during the 2020 water year, from October 2019 to September 2020. The addition of water-quality data collection at St. Johns River at Buffalo Bluff near Satsuma was the only modification to the previously installed network.
Discharge and salinity varied widely during the data collection period, which included above-average rainfall for 3 of the 5 counties in the study area. Total annual rainfall for all counties ranked third among the annual totals computed for the 5 years considered for this study. Annual mean discharge at Clapboard Creek was highest among the tributaries, followed by Ortega River, Durbin Creek, Pottsburg Creek at U.S. 90, Cedar River, Trout River, Julington Creek, Pottsburg Creek near South Jacksonville, Dunn Creek, and Broward River, whose annual mean was lowest. Annual mean discharge at 8 of the 10 tributary monitoring sites was higher for the 2020 water year than for the 2019 water year, and the computed annual mean flow at Clapboard Creek was the highest over the 5 years considered for this study. The annual mean discharge for each of the main-stem sites was higher for the 2020 water year than for the 2019 water year except for Buffalo Bluff, which remained the same.
Among the tributary sites, annual mean salinity was highest at Clapboard Creek, the site closest to the Atlantic Ocean, and was lowest at Durbin Creek, the site farthest from the ocean. Annual mean salinity data from the main-stem sites on the St. Johns River indicate that salinity decreased with distance upstream from the ocean, which was expected. Relative to annual mean salinity calculated for the 2019 water year, annual mean salinity at all monitoring locations was higher for the 2020 water year except at the tributary sites of Trout River, Dunn Creek, and Clapboard Creek, which were lower, and Durbin Creek, which remained the same. The 2020 annual mean salinity on the main-stem of the St. Johns River was the highest since the beginning of the study in 2016 at Dancy Point, Racy Point, Shands Bridge, below Shands Bridge, above Buckman Bridge, and Jacksonville (Acosta Bridge). Among the tributary sites, annual mean salinity rankings for 2020 were highest for Julington Creek and Ortega River, which were the second-highest on record for those sites.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221024","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Ryan, P.J., 2022, Continuous stream discharge, salinity, and associated data collected in the lower St. Johns River and its tributaries, Florida, 2020: U.S. Geological Survey Open-File Report 2022–1024, 48 p., https://doi.org/10.3133/ofr20221024.","productDescription":"Report: ix, 48 p.; Dataset","numberOfPages":"62","onlineOnly":"Y","ipdsId":"IP-133884","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":400657,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1024/coverthb.jpg"},{"id":400658,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1024/ofr20221024.pdf","text":"Report","size":"3.73 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1024"},{"id":400659,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1024/ofr20221024.XML"},{"id":400660,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1024/images"},{"id":400661,"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":401171,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20221024/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":501767,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113057.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Florida","otherGeospatial":"St. Johns River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.27935791015625,\n 29.14736383122664\n ],\n [\n -80.38970947265625,\n 29.14736383122664\n ],\n [\n -80.38970947265625,\n 30.56226095049944\n ],\n [\n -82.27935791015625,\n 30.56226095049944\n ],\n [\n -82.27935791015625,\n 29.14736383122664\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
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Groundwater-quality data collected from 1997 through 2018 from 68 monitoring locations open to the Missouri River alluvial aquifer (hereafter referred to as the “alluvial aquifer”) near the Independence, Missouri, well field were analyzed by the U.S. Geological Survey, in cooperation with the City of Independence, Missouri. This analysis was done to assess the quality of the water in the alluvial aquifer near the well field, identify trends in water quality in the alluvial aquifer from 1997 through 2018, assess hydraulic interaction between the Missouri River and the groundwater system, identify potential threats to the potability of the water extracted from the well field, and identify ways to improve the monitoring effort. Water-quality data indicate that water from the Missouri River recharges the alluvial aquifer. Recharge is exacerbated by pumping from the well field so that the quality of the water pumped from the well field is similar to that of the river for many constituents. Water-quality data indicate that the alluvial aquifer is under oxygen- and nitrate-reducing conditions, and iron- and manganese-reducing conditions are present in most of the alluvial aquifer. Sulfate-reducing conditions are present along the northern and western parts of the monitoring network north of the Missouri River. Maximum contaminant levels for antimony, arsenic, barium, lead, selenium, and uranium were exceeded in at least one sample, and the median concentrations of arsenic exceeded the maximum contaminant level in several monitoring wells on the periphery of the well field. Secondary maximum contaminant levels were exceeded for iron, manganese, and sulfate in multiple wells. Low concentrations of a variety of organic compounds, primarily derived from recharge from the Missouri River with lesser amounts potentially derived from application at land surface in the study area, are present in the alluvial aquifer and in water extracted from the well field.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225027","collaboration":"Prepared in cooperation with the City of Independence, Missouri","usgsCitation":"Kay, R.T., Krempa, H.M., and Hulsey, K.M., 2022, Water quality in the Missouri River alluvial aquifer near the Independence, Missouri, well field, 1997–2018: U.S. Geological Survey Scientific Investigations Report 2022–5027, 63 p., https://doi.org/10.3133/sir20225027.","productDescription":"Report: vi, 63 p.; Appendixes; Dataset","numberOfPages":"74","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-113521","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":400531,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5027/images"},{"id":400528,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5027/coverthb.jpg"},{"id":400529,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5027/sir20225027.pdf","text":"Report","size":"3.89 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5027"},{"id":400530,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5027/sir20225027.XML"},{"id":400533,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2022/5027/sir20225027_appendix1.xlsx","text":"Appendix 1","size":"204 kB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"—Tables 1.1 to 1.70"},{"id":400534,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2022/5027/sir20225027_appendix1.zip","text":"Appendix 1","size":"61 kB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"—Tables 1.1 to 1.70"},{"id":400535,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2022/5027/sir20225027_appendix2.xlsx","text":"Appendix 2","size":"68.8 kB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"—Tables 2.1 to 2.30"},{"id":400536,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2022/5027/sir20225027_appendix2.zip","text":"Appendix 2","size":"16 kB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"—Tables 2.1 to 2.30"},{"id":400537,"rank":9,"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":400723,"rank":10,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225027/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":502384,"rank":11,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113058.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Missouri","city":"Independence","otherGeospatial":"Missouri River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.46765899658203,\n 39.08876842889136\n ],\n [\n -94.37633514404297,\n 39.08876842889136\n ],\n [\n -94.37633514404297,\n 39.165471994238374\n ],\n [\n -94.46765899658203,\n 39.165471994238374\n ],\n [\n -94.46765899658203,\n 39.08876842889136\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
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A cooperative study between the National Park Service (NPS) and the U.S. Geological Survey (USGS) characterized groundwater quality and hydrogeology in parts of Pinnacles National Park. The water-quality investigation assessed the geochemistry of springs, wells, surface water, and precipitation and analyzed geochemistry of rock formations that affect the water chemistry through water-rock interaction. The hydrogeology investigation used geophysical and groundwater level data to characterize groundwater-flow processes in the alluvial deposits of Bear Valley and the Chalone Creek watershed.
Analysis of aqueous geochemical parameters in water samples from perennial springs, water-supply wells, and surface waters was conducted for samples collected after the dry season (autumnal) and after the wet season (vernal) to assess changes in geochemistry due to changes in groundwater levels or flow resulting from precipitation. The chemistry of bulk precipitation collected during the wet season was also analyzed. Bedrock samples were analyzed for geochemical parameters to help constrain groundwater sources, flow paths, and weathering. The geochemical investigations show a correspondence between the source rock and the spring-water chemistry that can be attributed to the mineralogy of the source rock. The narrow range of strontium isotopes in water samples, sourced in geochemically and mineralogically disparate rocks, indicates that the bedrock groundwater is relatively old and has reached quasi-steady state with respect to weathering of susceptible minerals.
Groundwater-level monitoring indicated that the water table is shallow—from 0 to 10 meters (m) below land surface. In southern Bear Valley and in the Chalone Creek alluvium, water levels rose and declined by several meters over each annual cycle of this study. In northern Bear Valley, water levels rose modestly over two wet seasons but declined during a third wet season. In Bear Valley, groundwater/surface-water interaction occurs along the perennial reach of Sandy Creek. Groundwater discharges to the upstream part of the reach, becomes surface water and is partly consumed by evapotranspiration, and infiltrates farther downstream. In the Chalone Creek alluvium, runoff-generated surface-water flow in intermittent stream reaches is a major component of groundwater recharge. After the onset of significant streamflow, creek water rapidly recharges groundwater until water levels rise to nearly the creek level. Groundwater levels generally remain high throughout the wet season, then gradually decline after the creek becomes dry.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221026","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Scheiderich, K., Tiedeman, C.R., Hsieh, P.A., 2022, Aqueous geochemistry of waters and hydrogeology of alluvial deposits, Pinnacles National Park, California: U.S. Geological Survey Open-File Report 2022-1026, 39 p., https://doi.org/10.3133/ofr20221026.","productDescription":"Report: viii, 39 p.; 3 Data Releases","numberOfPages":"39","onlineOnly":"Y","ipdsId":"IP-129434","costCenters":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"links":[{"id":400733,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IZXRC0","text":"Streamflow data collected by the wading method, Pinnacles National Park, California, 2018","description":"Tiedeman, C.R., Ingebritsen, S.E., and Hsieh, P.A., 2021, Streamflow data collected by the wading method, Pinnacles National Park, California, 2018: U.S. Geological Survey data release, https://doi.org/10.5066/P9IZXRC0."},{"id":400732,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AMDH71","text":"Passive Seismic Data Collected for the Horizontal-to-Vertical Spectral Ratio (HVSR) Method, Pinnacles National Park, California, 2018-2020","description":"Tiedeman, C.R., and Hsieh, P.A., 2021, Passive Seismic Data Collected for the Horizontal-to-Vertical Spectral Ratio (HVSR) Method, Pinnacles National Park, California, 2018-2020: U.S. Geological Survey data release, https://doi.org/10.5066/P9AMDH71."},{"id":400435,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1026/covrthb.jpg"},{"id":400731,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BMM0XG","text":"Geochemistry of rocks, precipitation, and water sources from Pinnacles National Park, California, 2016-2017","description":"Scheiderich, K.D., 2021, Geochemistry of rocks, precipitation, and water sources from Pinnacles National Park, California, 2016-2017: U.S. Geological Survey data release, https://doi.org/10.5066/P9BMM0XG."},{"id":400436,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1026/ofr20221026.pdf","text":"Report","size":"8 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"California","otherGeospatial":"Pinnacles National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.20666503906249,\n 36.4729263733008\n ],\n [\n -121.2070083618164,\n 36.458430880233415\n ],\n [\n -121.20237350463867,\n 36.45856894533104\n ],\n [\n -121.20254516601562,\n 36.444761218880885\n ],\n [\n -121.2070083618164,\n 36.44448503928196\n ],\n [\n -121.20769500732422,\n 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WMA- Laboratory & Analytical Services Division
U.S. Geological Survey
USGS Headquarters
12201 Sunrise Valley Drive
Reston, VA 20192
The Extending Government Funding and Delivering Emergency Assistance Act (Public Law 117-43) was enacted on September 30, 2021. The U.S. Geological Survey received $26.3 million in supplemental funding to repair and replace facilities and equipment, collect high-resolution elevation data, and complete scientific assessments to support direct recovery and rebuilding decisions in areas affected by declared disasters—earthquakes, wildfires, hurricanes, and floods—that occurred between 2019 and 2021.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223031","usgsCitation":"Hinck, J.E., and Stachyra, J., 2022, 2022 Emergency Assistance Act—USGS recovery activities: U.S. Geological Survey Fact Sheet 2022–3031, 4 p., https://doi.org/10.3133/fs20223031.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","ipdsId":"IP-139600","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":400692,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3031/fs20223031.XML"},{"id":400691,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3031/fs20223031.pdf","text":"Report","size":"3.55 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022-3031"},{"id":400690,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3031/coverthb.jpg"}],"contact":"Associate Director, Natural Hazards Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Producing accurate hindcasts and forecasts with coupled models is challenging due to complex parameterizations that are difficult to ground in observational data. We present a calibration workflow that utilizes a series of machine learning algorithms paired with Windsurf, a coupled beach-dune model (Aeolis, the Coastal Dune Model, and XBeach), to produce hindcasts and forecasts of morphologic change along Bogue Banks, North Carolina. Neural networks paired with genetic algorithms allow us to fine tune calibration parameters for the hindcast, and then a long short-term memory neural network, trained on the hindcast, produces a 4-year forecast. We compare our hindcasts to observations from 2016 to 2017 and find they successfully reproduce observed modes of dune and beach change except for seaward growth of the dune face. We compare our forecasts to observations from 2016 to 2020 and find that they produce reasonably accurate predictions of dune change except when there are significant instances of erosion during the forecast period.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2022.105404","usgsCitation":"Itzkin, M., Moore, L.J., Ruggiero, P., Hovenga, P.A., and Hacker, S.D., 2022, Combining process-based and data-driven approaches to forecast beach and dune change: Environmental Modelling & Software, v. 153, 105404, 14 p., https://doi.org/10.1016/j.envsoft.2022.105404.","productDescription":"105404, 14 p.","ipdsId":"IP-134588","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":487468,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2022.105404","text":"Publisher Index Page"},{"id":400757,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","otherGeospatial":"Bogue Banks","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.10479736328125,\n 34.6252978589571\n ],\n [\n -76.66534423828124,\n 34.6252978589571\n ],\n [\n -76.66534423828124,\n 34.74838307098529\n ],\n [\n -77.10479736328125,\n 34.74838307098529\n ],\n [\n -77.10479736328125,\n 34.6252978589571\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"153","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Itzkin, Michael 0000-0003-0693-0607","orcid":"https://orcid.org/0000-0003-0693-0607","contributorId":291846,"corporation":false,"usgs":true,"family":"Itzkin","given":"Michael","email":"","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":843218,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moore, Laura J.","contributorId":195973,"corporation":false,"usgs":false,"family":"Moore","given":"Laura","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":843219,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ruggiero, Peter","contributorId":15709,"corporation":false,"usgs":false,"family":"Ruggiero","given":"Peter","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":843220,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hovenga, Paige A. 0000-0002-3569-0123","orcid":"https://orcid.org/0000-0002-3569-0123","contributorId":267191,"corporation":false,"usgs":false,"family":"Hovenga","given":"Paige","email":"","middleInitial":"A.","affiliations":[{"id":55435,"text":"College of Engineering, Oregon State University, Corvallis, OR, USA","active":true,"usgs":false}],"preferred":false,"id":843221,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hacker, Sally D.","contributorId":291847,"corporation":false,"usgs":false,"family":"Hacker","given":"Sally","email":"","middleInitial":"D.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":843222,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70234249,"text":"70234249 - 2022 - Hot spots and hot moments in the Critical Zone: Identification of and incorporation into reactive transport models","interactions":[],"lastModifiedDate":"2022-08-05T13:52:04.318024","indexId":"70234249","displayToPublicDate":"2022-05-17T08:46:59","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Hot spots and hot moments in the Critical Zone: Identification of and incorporation into reactive transport models","docAbstract":"Biogeochemical processes are often spatially discrete (hot spots) and temporally isolated (hot moments) due to variability in controlling factors like hydrologic fluxes, lithological characteristics, bio-geomorphic features, and external forcing. Although these hot spots and hot moments (HSHMs) account for a high percentage of carbon, nitrogen and nutrient cycling within the Critical Zone, the ability to identify and incorporate them into reactive transport models remains a significant challenge. This chapter provides an overview of the hot spots hot moments (HSHMs) concepts, where past work has largely focused on carbon and nitrogen dynamics within riverine systems. This work is summarized in the context of process-based and data-driven modeling approaches, including a brief description of recent research that casts a wider net to incorporate Hg, Fe and other Critical Zone elements, and focuses on interdisciplinary approaches and concepts. The broader goal of this chapter is to provide an overview of the gaps in our current understanding of HSHMs, and the opportunities therein, while specifically focusing on the underlying parameters and processes leading to their prognostic and diagnostic representation in reactive transport models.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Biogeochemistry of the Critical Zone","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer Nature","doi":"10.1007/978-3-030-95921-0_2","usgsCitation":"Arora, B., Briggs, M., Zarnetske, J.P., Stegen, J., Gomez-Velez, J., and Dwivedi, D., 2022, Hot spots and hot moments in the Critical Zone: Identification of and incorporation into reactive transport models, chap. of Biogeochemistry of the Critical Zone, p. 9-47, https://doi.org/10.1007/978-3-030-95921-0_2.","productDescription":"39 p.","startPage":"9","endPage":"47","ipdsId":"IP-114081","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":404874,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2022-05-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Arora, Bhavna 0000-0001-7841-886X","orcid":"https://orcid.org/0000-0001-7841-886X","contributorId":290532,"corporation":false,"usgs":false,"family":"Arora","given":"Bhavna","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":848330,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Briggs, Martin A. 0000-0003-3206-4132","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":222756,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":848331,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zarnetske, Jay P.","contributorId":210073,"corporation":false,"usgs":false,"family":"Zarnetske","given":"Jay","email":"","middleInitial":"P.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":848332,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stegen, James","contributorId":242792,"corporation":false,"usgs":false,"family":"Stegen","given":"James","affiliations":[{"id":48525,"text":"Earth and Biological Sciences Division, Pacific Northwest National Laboratory","active":true,"usgs":false}],"preferred":false,"id":848333,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gomez-Velez, Jesus","contributorId":219087,"corporation":false,"usgs":false,"family":"Gomez-Velez","given":"Jesus","affiliations":[{"id":36656,"text":"Vanderbilt University","active":true,"usgs":false}],"preferred":false,"id":848334,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dwivedi, D.","contributorId":294554,"corporation":false,"usgs":false,"family":"Dwivedi","given":"D.","affiliations":[{"id":36254,"text":"LBNL","active":true,"usgs":false}],"preferred":false,"id":848335,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70234225,"text":"70234225 - 2022 - Velocity modeling of supercritical pore fluids through porous media under reservoir conditions with applications for petroleum secondary migration and carbon sequestration plumes","interactions":[],"lastModifiedDate":"2022-08-04T13:38:44.407482","indexId":"70234225","displayToPublicDate":"2022-05-17T08:31:43","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":11447,"text":"SEG-AAPG Interpretation","active":true,"publicationSubtype":{"id":10}},"title":"Velocity modeling of supercritical pore fluids through porous media under reservoir conditions with applications for petroleum secondary migration and carbon sequestration plumes","docAbstract":"Computational methods to characterize secondary migration in porous media traditionally rely on fluid transport equations with assumptions of time invariance, such as flowpath modeling of buoyancy vectors, statistical percolation algorithms, capillary pressure curves, or a form of Darcy’s Law which presumes instantaneous fluid transport. However, in petroleum systems modeling, the timeframe of secondary migration from source to reservoir is important to quantify in relation to other geologic factors such as timing of petroleum generation, fault movement, and seal formation. Additionally, quantifying migration velocities enables an estimation of the distance a plume of geologically sequestered carbon dioxide travels over time, as well as the identification of low-permeability strata appropriate for long-term containment. This study introduces a method to quantify transport velocities of supercritical fluids in low-permeability lithologies for a broad range of rock and fluid properties likely encountered in the sedimentary sequence. A time-dependent form of Darcy’s Law for pressure-driven viscous flow through homogeneous isotropic porous media was used to model flow velocities within a carrier bed. Thermodynamic equations of state were used to determine thermophysical properties of supercritical pore fluids under reservoir pressures ranging from 0–200 MPa (0–29,000 psi) to constrain the momentum equations. Three case studies were examined that (1) estimated fluid flow velocities of methane within the low-permeability Upper Jurassic Haynesville Formation, (2) defined permeability-based flow units to evaluate saline formations for long-term geologic carbon sequestration, and (3) calculated the migration distance of carbon dioxide plumes at the Decatur, Illinois injection and sequestration project.","language":"English","publisher":"Society of Economic Geologists","doi":"10.1190/int-2021-0182.1","usgsCitation":"Burke, L.A., 2022, Velocity modeling of supercritical pore fluids through porous media under reservoir conditions with applications for petroleum secondary migration and carbon sequestration plumes: SEG-AAPG Interpretation, v. 10, no. 3, p. SG1-SG9, https://doi.org/10.1190/int-2021-0182.1.","productDescription":"9 p.","startPage":"SG1","endPage":"SG9","ipdsId":"IP-126541","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":447759,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1190/int-2021-0182.1","text":"Publisher Index Page"},{"id":435846,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GT9TWK","text":"USGS data release","linkHelpText":"Data tables associated with velocity modeling of supercritical pore fluids through porous media at reservoir conditions with applications for petroleum secondary migration and carbon sequestration plumes"},{"id":404814,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-05-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Burke, Lauri A. 0000-0002-2035-8048 lburke@usgs.gov","orcid":"https://orcid.org/0000-0002-2035-8048","contributorId":3859,"corporation":false,"usgs":true,"family":"Burke","given":"Lauri","email":"lburke@usgs.gov","middleInitial":"A.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":848241,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70233241,"text":"70233241 - 2022 - Revealing active Mars with HiRISE digital terrain models","interactions":[],"lastModifiedDate":"2022-07-19T12:14:27.972653","indexId":"70233241","displayToPublicDate":"2022-05-17T07:09:52","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Revealing active Mars with HiRISE digital terrain models","docAbstract":"No abstract available.
","language":"English","publisher":"Wiley","doi":"10.1111/sjtg.12360","usgsCitation":"Krauss, K., 2022, Use of ‘accommodation space’ in tidal wetlands. A commentary on Kerrylee Rogers’ ‘Accommodation space as a framework for assessing the response of mangroves to relative sea-level rise’: Singapore Journal of Tropical Geography, v. 42, no. 2, p. 184-189, https://doi.org/10.1111/sjtg.12360.","productDescription":"6 p.","startPage":"184","endPage":"189","ipdsId":"IP-119386","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":404989,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"42","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-05-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Krauss, Ken 0000-0003-2195-0729","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":223022,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":848602,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70231682,"text":"70231682 - 2022 - Decadal trends of mercury cycling and bioaccumulation within Everglades National Park","interactions":[],"lastModifiedDate":"2022-06-01T15:37:49.966824","indexId":"70231682","displayToPublicDate":"2022-05-17T06:45:23","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Decadal trends of mercury cycling and bioaccumulation within Everglades National Park","docAbstract":"Mercury (Hg) contamination has been a persistent concern in the Florida Everglades for over three decades due to elevated atmospheric deposition and the system's propensity for methylation and rapid bioaccumulation. Given declines in atmospheric Hg concentrations in the conterminous United States and efforts to mitigate nutrient release to the greater Everglades ecosystem, it was vital to assess how Hg dynamics responded on temporal and spatial scales. This study used a multimedia approach (water and biota) to examine Hg and methylmercury (MeHg) dynamics across a 76-site network within the southernmost portion of the region, Everglades National Park (ENP), from 2008 to 2018. Atmospheric Hg deposition was evaluated over time using a long-term monitoring station. Hg concentrations across matrices showed that air, water, and biota from the system were inextricably linked. Temporal patterns across matrices were driven primarily by hydrologic and climatic changes in the park and no evidence of a decline in atmospheric Hg deposition from 2008 to 2018 was observed, unlike other regions of the United States. In the Shark River Slough (SRS), excess dissolved organic carbon and sulfate were also consistently delivered from upgradient canals and showed no evidence of decline over the study period. Within the SRS a strong positive correlation was observed between MeHg concentrations in surface water and resident fish. Within distinct geographic regions of ENP (SRS, Marsh, Coastal), the geochemical controls on MeHg dynamics differed and highlighted regions susceptible to higher MeHg bioaccumulation, particularly in the SRS and Coastal regions. This study demonstrates the strong influence that dissolved organic carbon and sulfate loads have on spatial and temporal distributions of MeHg across the ENP. Importantly, improved water quality and flow rates are two key restoration targets of the nearly 30-year Everglades restoration program, which if achieved, this study suggests would lead to reduced MeHg production and exposure.
The clean energy transition will require a vast increase in metal supply, yet new mineral deposit discoveries are declining, due in part to challenges associated with exploring under sedimentary and volcanic cover. Recently, several case studies have demonstrated links between lithospheric electrical conductors imaged using magnetotelluric (MT) data and mineral deposits, notably Iron Oxide Copper Gold (IOCG). Adoption of MT methods for exploration is therefore growing but the general applicability and relationship with many other deposit types remains untested. Here, we compile a global inventory of MT resistivity models from Australia, North and South America, and China and undertake the first quantitative assessment of the spatial association between conductors and three mineral deposit types commonly formed in convergent margin settings. We find that deposits formed early in an orogenic cycle such as volcanic hosted massive sulfide (VHMS) and copper porphyry deposits show weak to moderate correlations with conductors in the upper mantle. In contrast, deposits formed later in an orogenic cycle, such as orogenic gold, show strong correlations with mid-crustal conductors. These variations in resistivity response likely reflect mineralogical differences in the metal source regions of these mineral systems and suggest a metamorphic-fluid source for orogenic gold is significant. Our results indicate the resistivity structure of mineralized convergent margins strongly reflects late-stage processes and can be preserved for hundreds of millions of years. Discerning use of MT is therefore a powerful tool for mineral exploration.
","language":"English","publisher":"Springer Nature","doi":"10.1038/s41598-022-11921-2","usgsCitation":"Kirkby, A., Czarnota, K., Huston, D.L., Champion, D.C., Doublier, M.P., Bedrosian, P.A., Duan, J., and Heinson, G., 2022, Lithospheric conductors reveal source regions of convergent margin mineral systems: Scientific Reports, v. 12, 8190, 10 p., https://doi.org/10.1038/s41598-022-11921-2.","productDescription":"8190, 10 p.","ipdsId":"IP-130445","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":501609,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-022-11921-2","text":"Publisher Index Page"},{"id":501583,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Australia, China","otherGeospatial":"North America, South America","volume":"12","noUsgsAuthors":false,"publicationDate":"2022-05-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Kirkby, Alison 0000-0003-1361-440X","orcid":"https://orcid.org/0000-0003-1361-440X","contributorId":222461,"corporation":false,"usgs":false,"family":"Kirkby","given":"Alison","email":"","affiliations":[{"id":35920,"text":"Geoscience Australia","active":true,"usgs":false}],"preferred":false,"id":957955,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Czarnota, Karol","contributorId":259291,"corporation":false,"usgs":false,"family":"Czarnota","given":"Karol","affiliations":[{"id":35920,"text":"Geoscience Australia","active":true,"usgs":false}],"preferred":false,"id":957956,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Huston, David L.","contributorId":259293,"corporation":false,"usgs":false,"family":"Huston","given":"David","middleInitial":"L.","affiliations":[{"id":35920,"text":"Geoscience Australia","active":true,"usgs":false}],"preferred":false,"id":957957,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Champion, David C.","contributorId":259290,"corporation":false,"usgs":false,"family":"Champion","given":"David","middleInitial":"C.","affiliations":[{"id":35920,"text":"Geoscience Australia","active":true,"usgs":false}],"preferred":false,"id":957958,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Doublier, Michael P.","contributorId":259292,"corporation":false,"usgs":false,"family":"Doublier","given":"Michael","middleInitial":"P.","affiliations":[{"id":35920,"text":"Geoscience Australia","active":true,"usgs":false}],"preferred":false,"id":957959,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bedrosian, Paul A. 0000-0002-6786-1038 pbedrosian@usgs.gov","orcid":"https://orcid.org/0000-0002-6786-1038","contributorId":839,"corporation":false,"usgs":true,"family":"Bedrosian","given":"Paul","email":"pbedrosian@usgs.gov","middleInitial":"A.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":957960,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Duan, Jinming","contributorId":367954,"corporation":false,"usgs":false,"family":"Duan","given":"Jinming","affiliations":[{"id":35920,"text":"Geoscience Australia","active":true,"usgs":false}],"preferred":false,"id":957961,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Heinson, Graham","contributorId":211596,"corporation":false,"usgs":false,"family":"Heinson","given":"Graham","email":"","affiliations":[{"id":36897,"text":"University of Adelaide","active":true,"usgs":false}],"preferred":false,"id":957962,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70232265,"text":"70232265 - 2022 - Global cycling and climate effects of aeolian dust controlled by biological soil crusts","interactions":[],"lastModifiedDate":"2022-06-21T16:35:28.748212","indexId":"70232265","displayToPublicDate":"2022-05-16T11:31:05","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2845,"text":"Nature Geoscience","active":true,"publicationSubtype":{"id":10}},"title":"Global cycling and climate effects of aeolian dust controlled by biological soil crusts","docAbstract":"Biological soil crusts (biocrusts) cover ~12% of the global land surface. They are formed by an intimate association between soil particles, photoautotrophic and heterotrophic organisms, and they effectively stabilize the soil surface of drylands. Quantitative information on the impact of biocrusts on the global cycling and climate effects of aeolian dust, however, is not available. Here, we combine the currently limited experimental data with a global climate model to investigate the effects of biocrusts on regional and global dust cycling under current and future conditions. We estimate that biocrusts reduce the global atmospheric dust emissions by ~60%, preventing the release of ~0.7 Pg dust per year. Until 2070, biocrust coverage is expected to be severely reduced by climate change and land-use intensification. The biocrust loss will cause an increased dust burden, leading to a reduction of the global radiation budget of around 0.12 to 0.22 W m−2, corresponding to about 50% of the total direct forcing of anthropogenic aerosols. This biocrust control on dust cycling and its climate impacts have important implications for human health, biogeochemical cycling and the functioning of the ecosystems, and thus should be considered in the modelling, mitigation and management of global change.
","language":"English","publisher":"Springer Nature","doi":"10.1038/s41561-022-00942-1","usgsCitation":", R., Stanelle, T., Egerer, S., Cheng, Y., Suess, H.E., Canton, Y., Belnap, J., Andreae, M.O., Tegen, I., Reick, C., Poschl, U., and Weber, B., 2022, Global cycling and climate effects of aeolian dust controlled by biological soil crusts: Nature Geoscience, v. 15, p. 458-463, https://doi.org/10.1038/s41561-022-00942-1.","productDescription":"5 p.","startPage":"458","endPage":"463","ipdsId":"IP-137868","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":447771,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41561-022-00942-1","text":"Publisher Index Page"},{"id":402400,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","noUsgsAuthors":false,"publicationDate":"2022-05-16","publicationStatus":"PW","contributors":{"authors":[{"text":" Rodriguez-Caballero","contributorId":292505,"corporation":false,"usgs":false,"given":"Rodriguez-Caballero","email":"","affiliations":[{"id":62919,"text":"Agronomy Dept., University of Almeria, Carretera Sacramento s/n, 04120 La 6 Cañada de San Urbano (Almería), Spain; Multiphase Chemistry Department, Max Planck Institute for Chemistry, Hahn-8 Meitner-Weg 1, 55128 Mainz, Germany","active":true,"usgs":false}],"preferred":false,"id":844907,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stanelle, T","contributorId":292506,"corporation":false,"usgs":false,"family":"Stanelle","given":"T","email":"","affiliations":[{"id":62920,"text":"Institute for Atmospheric and Climate Science, ETH Zurich, Universitätstrasse 16, 10 8092 Zürich, Switzerland; Now at: Department of Waste, Water, Energy and Air, Canton of Zurich, Walcheplatz 12 2, 8090 Zurich, Switzerland","active":true,"usgs":false}],"preferred":false,"id":844908,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Egerer, S","contributorId":292507,"corporation":false,"usgs":false,"family":"Egerer","given":"S","email":"","affiliations":[{"id":62921,"text":"Climate Service Center Germany (GERICS), Fischertwiete 1, 20095 Hamburg, 14 Germany; Max Planck Institute for Meteorology, Bundesstraße 53, 20146 Hamburg, Germany","active":true,"usgs":false}],"preferred":false,"id":844909,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cheng, Yang","contributorId":211352,"corporation":false,"usgs":false,"family":"Cheng","given":"Yang","email":"","affiliations":[],"preferred":false,"id":844910,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Suess, H. 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afirnstahl@usgs.gov","orcid":"https://orcid.org/0000-0003-2686-7596","contributorId":168296,"corporation":false,"usgs":true,"family":"Firnstahl","given":"Aaron","email":"afirnstahl@usgs.gov","middleInitial":"D.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":843293,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bradbury, Ken","contributorId":214587,"corporation":false,"usgs":false,"family":"Bradbury","given":"Ken","affiliations":[],"preferred":false,"id":843294,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Muldoon, Moe","contributorId":291890,"corporation":false,"usgs":false,"family":"Muldoon","given":"Moe","email":"","affiliations":[{"id":39043,"text":"Wisconsin Geological and Natural History Survey","active":true,"usgs":false}],"preferred":false,"id":843295,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kieke, Burney A","contributorId":195802,"corporation":false,"usgs":false,"family":"Kieke","given":"Burney","email":"","middleInitial":"A","affiliations":[],"preferred":false,"id":843296,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70234165,"text":"70234165 - 2022 - Friction in clay-bearing faults increases with the ionic radius of interlayer cations","interactions":[],"lastModifiedDate":"2022-08-02T12:04:29.583516","indexId":"70234165","displayToPublicDate":"2022-05-16T07:00:54","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8956,"text":"Communications Earth & Environment","active":true,"publicationSubtype":{"id":10}},"title":"Friction in clay-bearing faults increases with the ionic radius of interlayer cations","docAbstract":"Smectite can dramatically reduce the strength of crustal faults and may cause creep on natural faults without great earthquakes; however, the frictional mechanism remains unexplained. Here, our shear experiments reveal systematic increase in shear strength with the increase of the ionic radius of interlayer cations among lithium-, sodium-, potassium-, rubidium-, and cesium-montmorillonites, a smectite commonly found in faults. Using density-functional-theory calculations, we find that relatively small sodium ions fit in the ditrigonal cavities on the montmorillonite surfaces, resulting in weakening of interlayer repulsion during sliding. On the other hand, relatively large potassium ions do not fit in the ditrigonal cavities, resulting in a larger resistance to sliding due to electrostatic repulsion between potassium ions. Calculated shear strength is consistent with our shear experiments by considering the partial dehydration of the frictional contact area. These results provide the basis for developing a quantitative model of smectite-bearing fault rheology.
Early detection and rapid response (EDRR) can help mitigate and control invasive species outbreaks early on but its success is dependent on accurate identification of invasive species. We evaluated a novel outbreak in San Diego County, California of the Sonoran Spotted Whiptail (Aspidoscelis sonorae) in order to confirm their spread as well as quantify how to better detect and potentially manage this invasive species in California. We found that A. sonorae went undetected for over two years due to its morphological similarity to native whiptails and that it has spread rapidly since they were first observed. There are two species of native California whiptails with which A. sonorae can be confused locally, the Orange-throated Whiptail (Aspidoscelis hyperythrus), and to a lesser extent the Tiger Whiptail (Aspidoscelis tigris). We review key diagnostic features to distinguish A. sonorae from native California whiptails. We also discuss how to efficiently use widely available community science tools to rapidly assess a novel invasive species outbreak and outline suggestions to help manage cryptic invasive species.
Identifying animal behaviors, life history states, and movement patterns is a prerequisite for many animal behavior analyses and effective management of wildlife and habitats. Most approaches classify short-term movement patterns with high frequency location or accelerometry data. However, patterns reflecting life history across longer time scales can have greater relevance to species biology or management needs, especially when available in near real-time. Given limitations in collecting and using such data to accurately classify complex behaviors in the long-term, we used hourly GPS data from 5 waterfowl species to produce daily activity classifications with machine-learned models using “automated modelling pipelines”.
Automated pipelines are computer-generated code that complete many tasks including feature engineering, multi-framework model development, training, validation, and hyperparameter tuning to produce daily classifications from eight activity patterns reflecting waterfowl life history or movement states. We developed several input features for modeling grouped into three broad categories, hereafter “feature sets”: GPS locations, habitat information, and movement history. Each feature set used different data sources or data collected across different time intervals to develop the “features” (independent variables) used in models.
Automated modelling pipelines rapidly developed easily reproducible data preprocessing and analysis steps, identification and optimization of the best performing model and provided outputs for interpreting feature importance. Unequal expression of life history states caused unbalanced classes, so we evaluated feature set importance using a weighted F1-score to balance model recall and precision among individual classes. Although the best model using the least restrictive feature set (only 24 hourly relocations in a day) produced effective classifications (weighted F1 = 0.887), models using all feature sets performed substantially better (weighted F1 = 0.95), particularly for rarer but demographically more impactful life history states (i.e., nesting).
Automated pipelines generated models producing highly accurate classifications of complex daily activity patterns using relatively low frequency GPS and incorporating more classes than previous GPS studies. Near real-time classification is possible which is ideal for time-sensitive needs such as identifying reproduction. Including habitat and longer sequences of spatial information produced more accurate classifications but incurred slight delays in processing.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s40462-022-00324-7","usgsCitation":"Overton, C.T., Casazza, M.L., Bretz, J., McDuie, F., Matchett, E., Mackell, D.A., Lorenz, A., Mott, A., Herzog, M.P., and Ackerman, J.T., 2022, Machine learned daily life history classification using low frequency tracking data and automated modelling pipelines: Application to North American waterfowl: Movement Ecology, v. 10, 23, 13 p., https://doi.org/10.1186/s40462-022-00324-7.","productDescription":"23, 13 p.","ipdsId":"IP-133430","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":447785,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-022-00324-7","text":"Publisher Index Page"},{"id":435848,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XBZKZ8","text":"USGS data release","linkHelpText":"Hourly GPS Locations, Associated Habitat 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0000-0003-3657-5941","orcid":"https://orcid.org/0000-0003-3657-5941","contributorId":222610,"corporation":false,"usgs":true,"family":"Lorenz","given":"Austen","email":"","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":857613,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mott, Andrea 0000-0001-9586-9590","orcid":"https://orcid.org/0000-0001-9586-9590","contributorId":299367,"corporation":false,"usgs":false,"family":"Mott","given":"Andrea","affiliations":[{"id":64822,"text":"USGS WERC (name not found)","active":true,"usgs":false}],"preferred":false,"id":857614,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Herzog, Mark P. 0000-0002-5203-2835 mherzog@usgs.gov","orcid":"https://orcid.org/0000-0002-5203-2835","contributorId":131158,"corporation":false,"usgs":true,"family":"Herzog","given":"Mark","email":"mherzog@usgs.gov","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":857615,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Ackerman, Joshua T. 0000-0002-3074-8322","orcid":"https://orcid.org/0000-0002-3074-8322","contributorId":202848,"corporation":false,"usgs":true,"family":"Ackerman","given":"Joshua","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":857616,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70256672,"text":"70256672 - 2022 - Wildlife associates of nine-banded armadillo (Dasypus novemcinctus) burrows in Arkansas","interactions":[],"lastModifiedDate":"2024-08-30T14:24:25.771746","indexId":"70256672","displayToPublicDate":"2022-05-15T09:16:03","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Wildlife associates of nine-banded armadillo (Dasypus novemcinctus) burrows in Arkansas","title":"Wildlife associates of nine-banded armadillo (Dasypus novemcinctus) burrows in Arkansas","docAbstract":"The Nine-banded Armadillo (Dasypus novemcinctus) is a widespread burrowing species with an expanding geographic range across the southeastern and midwestern United States. Armadillos dig numerous, large burrows within their home ranges and these burrows are likely used by a diverse suite of wildlife species as has been reported for other burrowing ecosystem engineers such as Gopher Tortoises (Gopherus polyphemus), Desert Tortoises (Gopherus agassizi), and Black-tailed Prairie Dogs (Cynomys ludovicianus). We used motion-triggered game cameras at 35 armadillo burrows in 4 ecoregions of Arkansas and documented 19 species of mammals, 4 species of reptile, 1 species of amphibian, and 40 species of bird interacting with burrows. Bobcat (Lynx rufus), Coyote (Canis latrans), Eastern Cottontail (Sylvilagus floridanus), Gray Fox (Urocyon cinereoargenteus), Gray Squirrel (Sciurus carolinensis), Northern Raccoon (Procyon lotor), Virginia Opossum (Didelphis virginiana), and unidentified rodents (mice and rats) were documented using burrows in all four ecoregions. We documented wildlife hunting, seeking shelter, rearing young in, and taking over and modifying armadillo burrows. The rate of use was highest in the Mississippi Alluvial Valley, a landscape dominated by agriculture, where natural refugia may be limited and rodents are abundant. Armadillo burrows are clearly visited and used by numerous wildlife species to fulfill various life stage requirements, and this list will likely expand if more attention is devoted to understanding the role of armadillos burrows. Armadillos are important ecosystem engineers, and their ecological role warrants more investigation and attention as opposed to only being viewed and managed as agricultural and garden pests.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.8858","usgsCitation":"DeGregorio, B.A., Veon, J.T., and Massey, A., 2022, Wildlife associates of nine-banded armadillo (Dasypus novemcinctus) burrows in Arkansas: Ecology and Evolution, v. 12, no. 5, e8858, 10 p., https://doi.org/10.1002/ece3.8858.","productDescription":"e8858, 10 p.","ipdsId":"IP-139270","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":447786,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.8858","text":"Publisher Index Page"},{"id":433366,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"12","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-05-15","publicationStatus":"PW","contributors":{"authors":[{"text":"DeGregorio, Brett Alexander 0000-0002-5273-049X","orcid":"https://orcid.org/0000-0002-5273-049X","contributorId":243214,"corporation":false,"usgs":true,"family":"DeGregorio","given":"Brett","email":"","middleInitial":"Alexander","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908595,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Veon, John T.","contributorId":341550,"corporation":false,"usgs":false,"family":"Veon","given":"John","email":"","middleInitial":"T.","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":908596,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Massey, Andrhea","contributorId":341551,"corporation":false,"usgs":false,"family":"Massey","given":"Andrhea","email":"","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":908597,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70267788,"text":"70267788 - 2022 - Attraction, entrance, and passage efficiency of Arctic Grayling, trout, and suckers at Denil fishways in the Big Hole River basin, Montana","interactions":[],"lastModifiedDate":"2025-06-02T15:31:35.247658","indexId":"70267788","displayToPublicDate":"2022-05-14T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Attraction, entrance, and passage efficiency of Arctic Grayling, trout, and suckers at Denil fishways in the Big Hole River basin, Montana","docAbstract":"The Big Hole River basin in southwestern Montana supports the only indigenous, self‐sustaining fluvial population of Arctic Grayling Thymallus arcticus in the conterminous United States, but the basin is fragmented by numerous low‐head irrigation diversion dams. Denil fishways at 63 diversion dams provide Arctic Grayling and other fishes opportunities for year‐round access to critical habitats; however, their efficiency has not been evaluated. We quantified attraction, entrance, and passage for hatchery‐reared Arctic Grayling, wild trout (Brook Trout Salvelinus fontinalis and Brown Trout Salmo trutta), and wild suckers (White Sucker Catostomus commersonii and Longnose Sucker C. catostomus) during 14 field trials conducted at six Denil fishways over a representative range of fishway slopes and hydraulic conditions using passive integrated transponder telemetry. Attraction (60.4–84.3%) and entrance (44.3–78.6%) efficiencies were variable across test conditions and reduced overall fishway efficiencies (19.1–55.8%). In contrast, upon entry, passage efficiencies were high (96.2–97.0%) for all taxa across all test conditions. Attraction of hatchery‐reared Arctic Grayling increased with upstream depth (a surrogate for fishway discharge) and attraction flow, but attraction of wild fish was less affected by these conditions. Entrance of Arctic Grayling, Brook Trout, and Brown Trout decreased with upstream depth and fishway slope, especially when plunging entrance conditions associated with shallow downstream depths were present. However, entrance of Arctic Grayling and both trout species increased with downstream depth, and submerged fishway entrances demonstrated promise for increasing entrance efficiency at fishways with high discharges and steep slopes. We demonstrate that comprehensive evaluations of fishway efficiency components can identify specific solutions that improve fishway efficiency; application of these engineering solutions at individual fishways (as needed) could improve their efficiency and further enhance aquatic connectivity for fishes in the Big Hole River basin and elsewhere.
","language":"English","publisher":"Oxford Academic","doi":"10.1002/tafs.10362","usgsCitation":"Triano, B., Kappenman, K., McMahon, T., Blank, M., Heim, K., Parker, A., Zale, A.V., Platt, N., and Plymesser, K., 2022, Attraction, entrance, and passage efficiency of Arctic Grayling, trout, and suckers at Denil fishways in the Big Hole River basin, Montana: Transactions of the American Fisheries Society, v. 151, no. 4, p. 453-473, https://doi.org/10.1002/tafs.10362.","productDescription":"21 p.","startPage":"453","endPage":"473","ipdsId":"IP-132814","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":490656,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/tafs.10362","text":"Publisher Index Page"},{"id":489403,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Big Hole River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.60253591434008,\n 45.88362120565034\n ],\n [\n -112.60253591434008,\n 45.38912468392812\n ],\n [\n -112.20177404522745,\n 45.38912468392812\n ],\n [\n -112.20177404522745,\n 45.88362120565034\n ],\n [\n -112.60253591434008,\n 45.88362120565034\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"151","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-05-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Triano, Ben","contributorId":356217,"corporation":false,"usgs":false,"family":"Triano","given":"Ben","affiliations":[{"id":84938,"text":"Ecology Department","active":true,"usgs":false}],"preferred":false,"id":938890,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kappenman, Kevin M.","contributorId":356218,"corporation":false,"usgs":false,"family":"Kappenman","given":"Kevin M.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":938891,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McMahon, Thomas E.","contributorId":356219,"corporation":false,"usgs":false,"family":"McMahon","given":"Thomas E.","affiliations":[{"id":84938,"text":"Ecology Department","active":true,"usgs":false}],"preferred":false,"id":938892,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Blank, Matt","contributorId":356220,"corporation":false,"usgs":false,"family":"Blank","given":"Matt","affiliations":[{"id":84940,"text":"Western Transportation Institute","active":true,"usgs":false}],"preferred":false,"id":938893,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Heim, Kurt C.","contributorId":356221,"corporation":false,"usgs":false,"family":"Heim","given":"Kurt C.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":938894,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Parker, Albert E.","contributorId":356222,"corporation":false,"usgs":false,"family":"Parker","given":"Albert E.","affiliations":[{"id":80194,"text":"Center for Biofilm Engineering","active":true,"usgs":false}],"preferred":false,"id":938895,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Zale, Alexander V. 0000-0003-1703-885X","orcid":"https://orcid.org/0000-0003-1703-885X","contributorId":244099,"corporation":false,"usgs":true,"family":"Zale","given":"Alexander","email":"","middleInitial":"V.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":938889,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Platt, Nolan","contributorId":356223,"corporation":false,"usgs":false,"family":"Platt","given":"Nolan","affiliations":[{"id":36226,"text":"U.S. Department of Agriculture Forest Service","active":true,"usgs":false}],"preferred":false,"id":938896,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Plymesser, Katey","contributorId":356224,"corporation":false,"usgs":false,"family":"Plymesser","given":"Katey","affiliations":[{"id":84941,"text":"Department of Civil Engineering","active":true,"usgs":false}],"preferred":false,"id":938897,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70236468,"text":"70236468 - 2022 - Credit where credit is due","interactions":[],"lastModifiedDate":"2023-03-30T13:00:41.605746","indexId":"70236468","displayToPublicDate":"2022-05-13T16:55:45","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7458,"text":"Eos Science News","active":true,"publicationSubtype":{"id":10}},"title":"Credit where credit is due","docAbstract":"Credit is the currency of science. 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","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022EO220239","usgsCitation":"Parsons, M.A., Katz, D.S., Langseth, M., Ramapriyan, H., and Ramdeen, S., 2022, Credit where credit is due: Eos Science News, HTML Document, https://doi.org/10.1029/2022EO220239.","productDescription":"HTML Document","ipdsId":"IP-143013","costCenters":[{"id":38128,"text":"Science Analytics and Synthesis","active":true,"usgs":true}],"links":[{"id":447787,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022eo220239","text":"Publisher Index Page"},{"id":406349,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Parsons, Mark A. 0000-0002-7723-0950","orcid":"https://orcid.org/0000-0002-7723-0950","contributorId":296275,"corporation":false,"usgs":false,"family":"Parsons","given":"Mark","email":"","middleInitial":"A.","affiliations":[{"id":36730,"text":"University of Alabama","active":true,"usgs":false}],"preferred":false,"id":851123,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Katz, Daniel S. 0000-0001-5934-7525","orcid":"https://orcid.org/0000-0001-5934-7525","contributorId":296276,"corporation":false,"usgs":false,"family":"Katz","given":"Daniel","email":"","middleInitial":"S.","affiliations":[{"id":16984,"text":"University of Illinois at Urbana-Champaign","active":true,"usgs":false}],"preferred":false,"id":851124,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Langseth, Madison 0000-0002-4472-9106 mlangseth@usgs.gov","orcid":"https://orcid.org/0000-0002-4472-9106","contributorId":191744,"corporation":false,"usgs":true,"family":"Langseth","given":"Madison","email":"mlangseth@usgs.gov","affiliations":[{"id":38128,"text":"Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":851125,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ramapriyan, Hampapuram 0000-0002-8425-8943","orcid":"https://orcid.org/0000-0002-8425-8943","contributorId":296277,"corporation":false,"usgs":false,"family":"Ramapriyan","given":"Hampapuram","email":"","affiliations":[{"id":7239,"text":"Science Systems and Applications, Inc.","active":true,"usgs":false}],"preferred":false,"id":851126,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ramdeen, Sarah 0000-0003-1135-5942","orcid":"https://orcid.org/0000-0003-1135-5942","contributorId":296278,"corporation":false,"usgs":false,"family":"Ramdeen","given":"Sarah","email":"","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":851127,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70231492,"text":"sim3487 - 2022 - Geologic maps of the Stephenson and Winchester quadrangles, Frederick and Clarke Counties, Virginia, and Inwood and White Hall quadrangles, Berkeley and Jefferson Counties, West Virginia","interactions":[],"lastModifiedDate":"2026-04-01T15:15:19.750245","indexId":"sim3487","displayToPublicDate":"2022-05-13T11:20:00","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":"3487","displayTitle":"Geologic Maps of the Stephenson and Winchester Quadrangles, Frederick and Clarke Counties, Virginia, and Inwood and White Hall Quadrangles, Berkeley and Jefferson Counties, West Virginia","title":"Geologic maps of the Stephenson and Winchester quadrangles, Frederick and Clarke Counties, Virginia, and Inwood and White Hall quadrangles, Berkeley and Jefferson Counties, West Virginia","docAbstract":"The study area consists of four contiguous 7.5-minute quadrangles and is located in Frederick and Clarke Counties, Virginia, and Berkeley and Jefferson Counties, West Virginia. The individual quadrangles are Stephenson, Winchester, Inwood, and White Hall. The study area lies within the Great Valley subprovince of the Valley and Ridge physiographic province where about 23,000 feet (ft) (7,000 meters [m]) of Middle Cambrian to Upper Devonian sedimentary rocks are exposed and are overlain by Holocene and older surficial deposits. The area of the four maps is divided into three geologic regions based on the following primary lithologies: (1) Cambrian and Ordovician carbonate rocks of the Great Valley southeast of the North Mountain fault zone and east and west of the core of the Massanutten synclinorium; (2) shale, graywacke, and calcareous shale of the Ordovician Martinsburg Formation of the Great Valley and Massanutten synclinorium; and (3) Ordovician through Devonian clastic rocks and minor limestone and dolostone northwest of and within the North Mountain fault zone. Rocks of all three regions were folded and faulted during the late Paleozoic Alleghanian orogeny (roughly 320 to 250 million years before present). The terrain of this portion of the Great Valley generally is gently to moderately rolling with low local relief with elevations in the study area ranging from about 425 ft (130 m) where Opequon Creek flows out of the eastern edge of the Inwood quadrangle to about 950 ft (290 m) adjacent to Round Hill in the western part of the Winchester quadrangle. Sinkholes and other karst features are common in the carbonate rocks of the Great Valley. The area west of the North Mountain fault zone is underlain by middle Paleozoic strata and consists of a series of ridges and valleys with higher local relief, with elevations ranging from about 785 ft (240 m) in the vicinity of Green Spring in the central part of the White Hall quadrangle to about 1,435 ft (437 m) at the summit of North Mountain in the northeastern part of the White Hall quadrangle.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3487","usgsCitation":"Weary, D.J., Doctor, D.H., and Orndorff, R.C., 2022, Geologic maps of the Stephenson and Winchester quadrangles, Frederick and Clarke Counties, Virginia, and Inwood and White Hall quadrangles, Berkeley and Jefferson Counties, West Virginia: U.S. Geological Survey Scientific Investigations Map 3487, 4 sheets, scale 1:24,000, 33-p. pamphlet, https://doi.org/10.3133/sim3487.","productDescription":"Pamphlet: viii, 33 p.; 4 Sheets: 28.00 x 42.00 inches or smaller; Base Map; Metadata; Database; Read Me","numberOfPages":"33","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-009285","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":501931,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113056.htm","linkFileType":{"id":5,"text":"html"}},{"id":400509,"rank":8,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sim/3487/sim3487_openaccess.zip","text":"Open Access","size":"10.5 MB","linkFileType":{"id":6,"text":"zip"}},{"id":400508,"rank":7,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sim/3487/sim3487_basemaps.zip","text":"Base Maps","size":"540 MB","linkFileType":{"id":6,"text":"zip"}},{"id":400507,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3487/sim3487_mapsheets.zip","text":"Map Sheets 1–4","size":"604 MB","linkFileType":{"id":6,"text":"zip"}},{"id":400502,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3487/coverthb3.jpg"},{"id":400505,"rank":4,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3487/sim3487_metadata.zip","text":"Metadata","size":"108 KB","linkFileType":{"id":6,"text":"zip"}},{"id":400506,"rank":5,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3487/sim3487_readme.txt","text":"Read Me","size":"8.50 KB","linkFileType":{"id":2,"text":"txt"}},{"id":400504,"rank":3,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3487/sim3487_database.zip","text":"Database","size":"36.2 MB","linkFileType":{"id":6,"text":"zip"}},{"id":400503,"rank":2,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sim/3487/sim3487_pamphlet.pdf","text":"Pamphlet","size":"9.18 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3487"}],"country":"United States","state":"Virginia, West Virginia","county":"Berkeley County, Clarke County, Frederick County, Jefferson County","otherGeospatial":"Inwood, Stephenson, White Hall and Winchester quadrangles","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.25,\n 39.375\n ],\n [\n -78,\n 39.375\n ],\n [\n -78,\n 39.125\n ],\n [\n -78.25,\n 39.125\n ],\n [\n -78.25,\n 39.375\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Florence Bascom Geoscience Center
U.S. Geological Survey
926A National Center
12201 Sunrise Valley Drive
Reston, VA 20192
To examine controls on the local stress field at Augustine Volcano, Alaska, before its 2006 eruption, we calculated fault plane solutions for volcano-tectonic earthquakes from 2002 to 2006. The P-axis orientation was first aligned to the regional maximum compression (NW) and then rotated by about 90° (perpendicular to the dike alignment) after the onset of surface deformation in mid-August 2005. Using 3D finite element models, we systematically evaluated the effects of tectonic stresses, volcanic edifice densities, and dike overpressures on the local stress field orientation. Combining data and models to generate “phase diagrams” of different stress controls by these competing effects, we argue that moderate tectonic stress of 2–3 MPa at 600 m above sea level slightly exceeded the edifice loading before the precursory deformation and was then overprinted by a local stress field from dike opening with an overpressure of ~15 MPa.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022GL097958","usgsCitation":"Zhan, Y., Roman, D., Le Mevel, H., and Power, J., 2022, Earthquakes indicated stress field change during the 2006 unrest of Augustine Volcano, Alaska: Geophysical Research Letters, v. 49, e2022GL097958, 9 p., https://doi.org/10.1029/2022GL097958.","productDescription":"e2022GL097958, 9 p.","ipdsId":"IP-137090","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":463353,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Augustine Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -153.5968722856842,\n 59.431514142471286\n ],\n [\n -153.5968722856842,\n 59.29604332497132\n ],\n [\n -153.3209313297999,\n 59.29604332497132\n ],\n [\n -153.3209313297999,\n 59.431514142471286\n ],\n [\n -153.5968722856842,\n 59.431514142471286\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"49","noUsgsAuthors":false,"publicationDate":"2022-05-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Zhan, Yan","contributorId":345673,"corporation":false,"usgs":false,"family":"Zhan","given":"Yan","email":"","affiliations":[{"id":82691,"text":"Carnegie Institution for Science, Washington, DC","active":true,"usgs":false}],"preferred":false,"id":917229,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roman, Diana","contributorId":237832,"corporation":false,"usgs":false,"family":"Roman","given":"Diana","affiliations":[{"id":47620,"text":"Dept. of Terrestrial Magnetism, Carnegie Institution for Science, Washington DC 20015","active":true,"usgs":false}],"preferred":false,"id":917230,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Le Mevel, Helene","contributorId":345674,"corporation":false,"usgs":false,"family":"Le Mevel","given":"Helene","affiliations":[{"id":82691,"text":"Carnegie Institution for Science, Washington, DC","active":true,"usgs":false}],"preferred":false,"id":917231,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Power, John 0000-0002-7233-4398","orcid":"https://orcid.org/0000-0002-7233-4398","contributorId":215240,"corporation":false,"usgs":true,"family":"Power","given":"John","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":917232,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70231610,"text":"70231610 - 2022 - OPERA Product Description Document","interactions":[],"lastModifiedDate":"2022-05-31T16:21:24.236411","indexId":"70231610","displayToPublicDate":"2022-05-13T11:00:22","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesNumber":"JPL D-107389 Rev. A","title":"OPERA Product Description Document","docAbstract":"No abstract available.
","language":"English","publisher":"NASA","usgsCitation":"Bato, G., Bekaert, D., Brancato, V., Fattahi, H., Hansen, M., Jones, J., Logan, T., Lu, Z., Marshak, C., Meyer, F., Pikens, A., and Shiroma, G., 2022, OPERA Product Description Document, 33 p.","productDescription":"33 p.","ipdsId":"IP-137221","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":401372,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":401371,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.jpl.nasa.gov/go/opera/products"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bato, Grace","contributorId":292158,"corporation":false,"usgs":false,"family":"Bato","given":"Grace","email":"","affiliations":[],"preferred":false,"id":843941,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bekaert, David 0000-0002-1062-6221","orcid":"https://orcid.org/0000-0002-1062-6221","contributorId":270718,"corporation":false,"usgs":false,"family":"Bekaert","given":"David","email":"","affiliations":[{"id":36711,"text":"Woods Hole Oceanographic Institution","active":true,"usgs":false}],"preferred":false,"id":843942,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brancato, Virginiia","contributorId":292159,"corporation":false,"usgs":false,"family":"Brancato","given":"Virginiia","email":"","affiliations":[],"preferred":false,"id":843943,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fattahi, Heresh","contributorId":292160,"corporation":false,"usgs":false,"family":"Fattahi","given":"Heresh","email":"","affiliations":[],"preferred":false,"id":843944,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hansen, Matt","contributorId":61330,"corporation":false,"usgs":true,"family":"Hansen","given":"Matt","email":"","affiliations":[],"preferred":false,"id":843945,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jones, John W. 0000-0001-6117-3691 jwjones@usgs.gov","orcid":"https://orcid.org/0000-0001-6117-3691","contributorId":2220,"corporation":false,"usgs":true,"family":"Jones","given":"John","email":"jwjones@usgs.gov","middleInitial":"W.","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":843108,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Logan, Thomas","contributorId":292161,"corporation":false,"usgs":false,"family":"Logan","given":"Thomas","email":"","affiliations":[],"preferred":false,"id":843946,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lu, Zhong","contributorId":199794,"corporation":false,"usgs":false,"family":"Lu","given":"Zhong","affiliations":[],"preferred":false,"id":843947,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Marshak, Charles","contributorId":292162,"corporation":false,"usgs":false,"family":"Marshak","given":"Charles","email":"","affiliations":[],"preferred":false,"id":843948,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Meyer, Franz","contributorId":219958,"corporation":false,"usgs":false,"family":"Meyer","given":"Franz","affiliations":[{"id":40098,"text":"Geophysical Institute, 2156 Koyukuk Drive, University of Alaska Fairbanks, Fairbanks, AK 99775","active":true,"usgs":false}],"preferred":false,"id":843949,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Pikens, Amy","contributorId":292163,"corporation":false,"usgs":false,"family":"Pikens","given":"Amy","email":"","affiliations":[],"preferred":false,"id":843950,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Shiroma, Gustavo","contributorId":292164,"corporation":false,"usgs":false,"family":"Shiroma","given":"Gustavo","email":"","affiliations":[],"preferred":false,"id":843951,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70231597,"text":"70231597 - 2022 - Value of information: Exploring behavioral and social factors","interactions":[],"lastModifiedDate":"2022-05-17T13:31:49.83722","indexId":"70231597","displayToPublicDate":"2022-05-13T08:28:58","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5738,"text":"Frontiers in Environmental Science","active":true,"publicationSubtype":{"id":10}},"title":"Value of information: Exploring behavioral and social factors","docAbstract":"There is growing interest within and beyond the economics community in assessing the value of information (VOI) used in decision making. VOI assessments often do not consider the complex behavioral and social factors that affect the perception, valuation, and use of information by individuals and groups. Additionally, VOI assessments frequently do not examine the full suite of interactions and outcomes affecting different groups or individuals. The behavioral and social factors that we mention are often (but not always) innately-derived, less-than-conscious influences that reflect human and societal adaptations to the past. We first discuss these concepts in the context of the recognition and use of information for decision making. We then find fifteen different aspects of value and information pertinent to VOI assessments. We examine methodologies and issues related to current VOI estimation practices in economics. Building on this examination, we explore the perceptions, social factors, and behavioral factors affecting information sharing, prioritization, valuation, and discounting. Information and valuation issues are then considered in the context of information production, information trading and controls, and information communication pathologies. Lastly, we describe issues relating to information useability and actionability. Our examples mention the value and use of geospatial information, and more generally concern societal issues relating to the management of natural resources, environments, and natural and anthropogenic hazards. Our paper aims to be instrumentally relevant to anyone interested in the use and value of science.
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