Sex‐related differences in mortality are widespread in the animal kingdom. Although studies have shown that sex determination systems might drive lifespan evolution, sex chromosome influence on aging rates have not been investigated so far, likely due to an apparent lack of demographic data from clades including both XY (with heterogametic males) and ZW (heterogametic females) systems. Taking advantage of a unique collection of capture–recapture datasets in amphibians, a vertebrate group where XY and ZW systems have repeatedly evolved over the past 200 million years, we examined whether sex heterogamy can predict sex differences in aging rates and lifespans. We showed that the strength and direction of sex differences in aging rates (and not lifespan) differ between XY and ZW systems. Sex‐specific variation in aging rates was moderate within each system, but aging rates tended to be consistently higher in the heterogametic sex. This led to small but detectable effects of sex chromosome system on sex differences in aging rates in our models. Although preliminary, our results suggest that exposed recessive deleterious mutations on the X/Z chromosome (the “unguarded X/Z effect”) or repeat‐rich Y/W chromosome (the “toxic Y/W effect”) could accelerate aging in the heterogametic sex in some vertebrate clades.
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Estimating costs for other species of YNP amphibians produced more negative results. For Columbia Spotted Frogs (Rana luteiventris) and Boreal Chorus Frogs (Pseudacris maculata) (both more aquatic and less adapted to terrestrial habitats), movement costs increased by about 2–15X. Reduced frequency or duration of rain events might limit the nocturnal movements of Western Tiger Salamanders (Ambystoma mavortium). Climate change may not have negative impacts on all amphibians throughout YNP, but increased movement costs for terrestrial habitats will accentuate effects of drying wetlands in at least parts of YNP. Land management actions that preserve habitat structure of both forest and low shrub cover may help mitigate continued drying conditions of climate change.
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To confront these challenges effectively, managers need to develop proactive adaptation strategies to prepare for and deal with the effects of climate change. We engaged managers and biologists from several midwestern U.S. Fish and Wildlife Service field stations to understand recent and future climate change effects, identify adaptation barriers and opportunities, and pilot an approach for integrating adaptation thinking into management planning. To start, three structured discussions informed our understanding of how managers currently deal with climate change effects, the strategies being implemented to cope, and the barriers that limit climate change adaptation efforts. We used these insights to develop a multiday virtual workshop geared toward identifying potential adaptation strategies for managed wetlands. First, we developed a conceptual model to visualize how management actions are used to meet habitat objectives within wetland management systems. Next, we discussed how climate change may affect management actions and objectives; we used this understanding of potential effects to spatially assess vulnerability of managed wetlands to climate change. Using a scenario planning approach, we incorporated multiple potential future conditions and identified effects and adaptation strategies that could be considered for each scenario. As a result, several adaptation strategies for managed wetlands under dry and wet future scenarios were identified that can be applied when developing site-specific adaptation plans. Based on our piloted approach, we determined it would be important to have an adaptation team composed of scientists and managers to facilitate discussions, develop appropriate scenarios, and identify realistic adaptation options. We document the tools, findings, and adaptation thinking process taken to enhance adaptation efforts of managed wetlands. The adaptation thinking process can be applied to advance adaptation efforts in other habitats, ecosystems, and site-specific land management.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211103","usgsCitation":"Delaney, J.T., Bouska, K.L., and Eash, J.D., 2021, Climate Change Adaptation Thinking for Managed Wetlands: U.S. Geological Survey Open-File Report 2021–1103, 25 p., https://doi.org/10.3133/ofr20211103.","productDescription":"Report: vi, 25 p.; 3 Data Releases","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-128227","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":394943,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AL7GZM","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Watershed-based Midwest Climate Change Vulnerability Assessment Tool"},{"id":394942,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AL7GZM","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"R code: Scripts used to analyze data for the Midwest Climate Change Vulnerability Assessment"},{"id":394941,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AL7GZM","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Model inputs: Midwest climate change vulnerability assessment for the U.S. Fish and Wildlife Service"},{"id":394938,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1103/ofr20211103.pdf","text":"Report","size":"44.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1103"},{"id":394937,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1103/coverthb.jpg"},{"id":501530,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112325.htm","linkFileType":{"id":5,"text":"html"}},{"id":394940,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1103/images"},{"id":394939,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1103/ofr20211103.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2021–1103 XML"}],"contact":"Director, Upper Midwest Environmental Sciences Center
U.S. Geological Survey
2630 Fanta Reed Road
La Crosse, WI 54602
The San Antonio Creek Valley watershed (SACVW) is located in western Santa Barbara County, about 15 miles south of Santa Maria and 55 miles north of Santa Barbara, California. The SACVW is about 135 square miles and encompasses the San Antonio Creek Valley groundwater basin; the SACVW is separated from adjacent groundwater basins by the Casmalia and Solomon Hills to the north, and the Purisima Hills to the south. At the western, downstream part of the valley, uplifted, consolidated rocks cause groundwater to discharge at land surface at Barka Slough. Since the late 1800s, groundwater has been the primary source of water for agricultural, military, municipal, and domestic uses. Groundwater withdrawal by pumping exceeded the amount of water replenishing the aquifer system during water years 1948–2018, causing groundwater-level declines of more than 150 feet in parts of the valley and reducing base flow at Barka Slough. Reliance on groundwater for agricultural water use (primarily for the irrigation and frost protection of vineyards, and fruit and berry crops) continues to strain the sustainability of the groundwater system.
Through a cooperative agreement, the Santa Barbara County Water Agency and Vandenberg Space Force Base invited the U.S. Geological Survey to address declines in groundwater levels, develop a better understanding of the hydrogeologic system, and provide tools to help evaluate and manage the effects of future development of the San Antonio Creek Valley groundwater basin within the encompassing San Antonio Creek Valley watershed (SACVW). The objectives of this study were to (1) refine the hydrogeologic framework of the San Antonio Creek Valley watershed, (2) quantify the hydrologic budget of the valley, and (3) develop hydrologic modeling tools to evaluate and aid in managing the groundwater resource. This report focuses on the first and second objectives to construct a hydrogeologic framework and characterize the historical and present-day hydrologic conditions of the SACVW during water years 1948–2018. As part of the second objective, work included quantifying the hydrologic budget and evaluating the hydrogeologic system using a combination of existing data and geologic and hydrologic data collected for this study.
The groundwater-flow system in the SACVW consists of five hydrogeologic units. These separate water-bearing units were identified based on hydrogeologic properties, such as sediment grain size, vertical-head differences in multiple-depth, monitoring-well sites, long-term groundwater level responses to pumping and climate, and the chemical character of groundwater and groundwater age in the mostly semi-consolidated to unconsolidated basin-fill sediments. The hydrogeologic units that comprise the different aquifers vary in their lithologic composition. The upper and lower aquifers (upper Paso Robles Formation, and lower Paso Robles Formation and Careaga Sandstone, respectively) are relatively coarse grained and are comprised of sand, gravel, and clay; the middle confining unit (the middle Paso Robles Formation) is relatively fine grained and is comprised of primarily clay, silt, and sand. The Pezzoni-Casmalia and Los Alamos faults, which are inferred to transect the SACVW between the western and eastern areas of the valley floor, do not appear to substantially affect the groundwater system.
Present-day recharge to the study area occurs primarily as infiltration from precipitation and streams in the upland areas of the Casmalia Hills and Solomon Hills, and along the main channel of San Antonio Creek. Reported estimates of annual natural recharge during water years 1948–2018 generally ranged from about 5,000 acre-feet to more than about 30,000 acre-feet. Stable and radioactive isotopes show that groundwater from the lower aquifer is old and probably was recharged as infiltration from precipitation and streams in the eastern upland areas of the Solomon Hills; however, the infiltration and recharge from these sources probably does not occur under present-day climatic conditions. Anthropogenic recharge, from sources such as return flow from agricultural irrigation, municipal water systems, and wastewater effluent, was estimated to range from about 600 acre-feet in 1948 to about 6,600 acre-feet in 2018. The average annual amount of groundwater removed from the SACVW by pumping during 1948–2018 was estimated to be about 17,200 acre-feet per year, increasing from about 3,000 acre-feet in 1948 to about 32,600 acre-feet in 2018. Estimates of annual pumpage generally exceeded estimates of annual recharge beginning in the mid-1970s and continuing through 2018. The predominant direction of groundwater flow under historical and present-day conditions was from the eastern uplands in the Solomon Hills to the west along San Antonio Creek to the discharge area in Barka Slough, and from the northern uplands in the Casmalia Hills south to San Antonio Creek.
Pumpage since the early 1900s and the subsequent groundwater-level declines have substantially reduced the amount of natural groundwater discharge at Barka Slough. Estimates of base flow to San Antonio Creek at the western, downstream extent of the SACVW have varied over time in response to changes in groundwater pumpage and climate; however, there was an overall decline in base flow during water years 1956–2018, decreasing from an average of about 1,700 acre-feet per year during 1956–69, to about 300 acre-feet per year during 2016–18. The long-term extraction of groundwater correlates with a decrease in groundwater levels by more than about 150 feet since the early 1940s in the eastern part of the basin near Los Alamos, and as much as about 50 feet in the upland areas and in the western part of the basin. At Barka Slough, groundwater levels have declined below land surface in some places, altering native riparian vegetation in and around the slough.
Surface-water quality in the SACVW varied depending on location and the time of year the samples were collected and on the amount of annual precipitation Most groundwater in the SACVW was calcium-bicarbonate-type water with total dissolved-solids concentrations of about 500–800 milligrams per liter generally representing water naturally recharged as infiltration from precipitation and streams. Total dissolved-solids concentrations in some wells ranged from 800 to 8,000 milligrams per liter, suggesting mixing of naturally recharged infiltrated water with water associated with oil-bearing geologic formations, agricultural products, or the evaporation of shallow groundwater. Concentrations of total dissolved solids and the chemical constituents chloride, nitrate plus nitrite (as nitrogen), calcium, and magnesium at selected wells generally increased during water years 1980–2018; increasing concentrations of these constituents may be associated with the expansion of agriculture in the watershed over time and the corresponding increase in the use of nitrates and calcium- and magnesium-based fertilizers and soil additives in modern agricultural practices.
The predominant direction of groundwater flow during historical and present-day conditions was from the eastern uplands in the Solomon Hills to the west along San Antonio Creek toward Barka Slough, and from the western uplands in the Casmalia Hills south to San Antonio Creek. The age of groundwater in the SACVW was evaluated using radioactive isotopes, and the flow of groundwater within the SACVW was evaluated using radioactive and stable isotopes. Modern groundwater (recharged after 1952) was generally found adjacent to San Antonio Creek and its tributaries in wells with perforated depths that averaged about 270 feet below land surface. Pre-modern groundwater (recharged before 1952) was found in wells that had average perforation depths of about 540 ft below land surface. Pre-modern groundwater identified in wells in the eastern upland area is interpreted to have had long, slow travel times to the western part of the SACVW where it was eventually discharged as base flow at Barka Slough or extracted as groundwater pumpage.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225001","collaboration":"Prepared in cooperation with Santa Barbara County Water Agency and Vandenberg Space Force Base","programNote":"Groundwater Availability and Use Assessments","usgsCitation":"Cromwell, G., Sweetkind, D.S., Densmore, J.N., Engott, J.A., Seymour, W.A., Larsen, J.D., Ely, C.P., Stamos, C.L., and Faunt, C.C., 2022, Hydrogeologic characterization of the San Antonio Creek Valley watershed, Santa Barbara County, California: U.S. Geological Survey Scientific Investigations Report 2022–5001, 124 p., https://doi.org/10.3133/sir20225001.","productDescription":"Report: xiv, 124 p.; Data Release","numberOfPages":"124","onlineOnly":"Y","ipdsId":"IP-106483","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":395158,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AD7DL8","linkHelpText":"Data release of hydrogeologic data from the San Antonio Creek Valley watershed, Santa Barbara County, California"},{"id":395160,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5001/covrthb.jpg"},{"id":395161,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5001/sir20225001.pdf","text":"Report","size":"15 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":395162,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5001/sir20225001.xml"},{"id":395163,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5001/images"}],"country":"United States","state":"California","county":"Santa Barbara County","otherGeospatial":"San Antonio Creek Valley watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.50079345703125,\n 34.71113805795655\n ],\n [\n -120.09292602539062,\n 34.71113805795655\n ],\n [\n -120.09292602539062,\n 34.854382885097905\n ],\n [\n -120.50079345703125,\n 34.854382885097905\n ],\n [\n -120.50079345703125,\n 34.71113805795655\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
The objectives of the study are to (1) develop an updated assessment of the hydrogeology and geochemistry of the Petaluma valley watershed (PVW) and (2) develop an integrated hydrologic model for the PVW. The purpose of this report is to describe the conceptual model of the hydrologic, hydrogeologic, and water-quality characteristics of the PVW and a numerical groundwater-flow model of PVW.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225009","collaboration":"Prepared in cooperation with the Sonoma County Water Agency and the City of Petaluma","programNote":"Water Availability and Use Science Program","usgsCitation":"Traum, J.A., Teague, N.F., Sweetkind, D.S., and Nishikawa, T., 2022, Hydrologic and geochemical characterization of the Petaluma River watershed, Sonoma County, California: U.S. Geological Survey Scientific Investigations Report 2022–5009, 217 p., https://doi.org/10.3133/sir20225009.","productDescription":"Report: xviii, 217 p.; Executive Summmary: 5 p.; 4 Data Releases","numberOfPages":"217","onlineOnly":"Y","ipdsId":"IP-081057","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":395152,"rank":6,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5009/sir20225009.pdf","text":"Report","size":"130 MB"},{"id":395151,"rank":5,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5009/covrthb.png"},{"id":395147,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P965IDQZ","linkHelpText":"MODFLOW-OWHM used to characterize the flow system of the Petaluma River watershed, Sonoma County, California"},{"id":395146,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IQDHIT","linkHelpText":"Petaluma Model GIS Data"},{"id":395166,"rank":9,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5009/sir20225009_execSummary.pdf","text":"Executive Summary","size":"200 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Full Executive Summary from this report"},{"id":395149,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NL90P8","linkHelpText":"Data release of three-dimensional hydrogeologic framework model of the Petaluma Valley watershed, Sonoma County, California"},{"id":395150,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IRYFMB","linkHelpText":"Selected chemical and physical properties and inorganic constituents and time-series nitrate in samples from selected wells and/or springs, Petaluma Valley watershed, Sonoma County, California, 1959–2015"},{"id":395153,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5009/sir20225009.xml"},{"id":395154,"rank":8,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5009/images"}],"country":"United States","state":"California","county":"Sonoma County","otherGeospatial":"Petaluma River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.44674682617188,\n 38.11943249695316\n ],\n [\n -122.61428833007814,\n 38.37503882134334\n ],\n [\n -122.74887084960936,\n 38.361041528596026\n ],\n [\n -122.77359008789062,\n 38.293170153420135\n ],\n [\n -122.728271484375,\n 38.19718009396176\n ],\n [\n -122.48382568359374,\n 38.07187927827001\n ],\n [\n -122.44674682617188,\n 38.11943249695316\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Climate change is transforming ecosystems and affecting ecosystem goods and services. Along the Gulf of Mexico and Atlantic coasts of the southeastern United States, the frequency and intensity of extreme freeze events greatly influences whether coastal wetlands are dominated by freeze-sensitive woody plants (mangrove forests) or freeze-tolerant grass-like plants (salt marshes). In response to warming winters, mangroves have been expanding and displacing salt marshes at varying degrees of severity in parts of north Florida, Louisiana, and Texas. As winter warming accelerates, mangrove range expansion is expected to increasingly modify wetland ecosystem structure and function. Because there are differences in the ecological and societal benefits that salt marshes and mangroves provide, coastal environmental managers are challenged to anticipate effects of mangrove expansion on critical wetland ecosystem services, including those related to carbon sequestration, wildlife habitat, storm protection, erosion reduction, water purification, fisheries support, and recreation. Mangrove range expansion may also affect wetland stability in the face of extreme climatic events and rising sea levels. Here, we review current understanding of the effects of mangrove range expansion and displacement of salt marshes on wetland ecosystem services in the southeastern United States. We also identify critical knowledge gaps and emerging research needs regarding the ecological and societal implications of salt marsh displacement by expanding mangrove forests. One consistent theme throughout our review is that there are ecological trade-offs for consideration by coastal managers. Mangrove expansion and marsh displacement can produce beneficial changes in some ecosystem services, while simultaneously producing detrimental changes in other services. Thus, there can be local-scale differences in perceptions of the impacts of mangrove expansion into salt marshes. For very specific local reasons, some individuals may see mangrove expansion as a positive change to be embraced, while others may see mangrove expansion as a negative change to be constrained.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.16111","usgsCitation":"Osland, M., Hughes, A.R., Armitage, A.R., Scyphers, S.B., Cebrian, J., Swinea, S.H., Shepard, C., Allen, M.S., Feher, L., Nelson, J., O’Brien, C.L., Sanspree, C.R., Smee, D.L., Snyder, C.M., Stetter, A.P., Stevens, P.W., Swanson, K., Williams, L.H., Brush, J.M., Marchionno, J., and Bardou, R., 2022, The impacts of mangrove range expansion on wetland ecosystem services in the southeastern United States: Current understanding, knowledge gaps, and emerging research needs: Global Change Biology, v. 28, no. 10, p. 3163-3187, https://doi.org/10.1111/gcb.16111.","productDescription":"25 p.","startPage":"3163","endPage":"3187","ipdsId":"IP-132601","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":467202,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://repository.library.noaa.gov/view/noaa/43126","text":"External Repository"},{"id":395545,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -102.12890625,\n 17.14079039331665\n ],\n [\n -79.1015625,\n 17.14079039331665\n ],\n [\n -79.1015625,\n 33.284619968887675\n ],\n [\n -102.12890625,\n 33.284619968887675\n ],\n [\n -102.12890625,\n 17.14079039331665\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"28","issue":"10","noUsgsAuthors":false,"publicationDate":"2022-02-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Osland, Michael 0000-0001-9902-8692","orcid":"https://orcid.org/0000-0001-9902-8692","contributorId":219805,"corporation":false,"usgs":true,"family":"Osland","given":"Michael","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":833324,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hughes, A. Randall","contributorId":177827,"corporation":false,"usgs":false,"family":"Hughes","given":"A.","email":"","middleInitial":"Randall","affiliations":[],"preferred":false,"id":833325,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Armitage, Anna R.","contributorId":218913,"corporation":false,"usgs":false,"family":"Armitage","given":"Anna","email":"","middleInitial":"R.","affiliations":[{"id":39935,"text":"Texas A&M Galveston, Galveston, TX USA","active":true,"usgs":false}],"preferred":false,"id":833326,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Scyphers, Steven B.","contributorId":274810,"corporation":false,"usgs":false,"family":"Scyphers","given":"Steven","middleInitial":"B.","affiliations":[{"id":56654,"text":"Northeastern University Marine Science Center, 430 Nahant Rd, Nahant, Massachusetts, USA","active":true,"usgs":false}],"preferred":false,"id":833327,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cebrian, 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USA","active":true,"usgs":false}],"preferred":false,"id":833344,"contributorType":{"id":1,"text":"Authors"},"rank":21}]}} ,{"id":70227735,"text":"sir20215098 - 2022 - Bathymetric and velocimetric surveys at highway bridges crossing the Missouri River near Kansas City, Missouri, August 2019, August 2020, and October 2020","interactions":[],"lastModifiedDate":"2026-04-02T19:39:16.740242","indexId":"sir20215098","displayToPublicDate":"2022-01-31T10:11:35","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":"2021-5098","displayTitle":"Bathymetric and Velocimetric Surveys at Highway Bridges Crossing the Missouri River near Kansas City, Missouri, August 2019, August 2020, and October 2020","title":"Bathymetric and velocimetric surveys at highway bridges crossing the Missouri River near Kansas City, Missouri, August 2019, August 2020, and October 2020","docAbstract":"Bathymetric and velocimetric data were collected by the U.S. Geological Survey, in cooperation with the Missouri Department of Transportation, near 9 bridges at 8 highway crossings of the Missouri River near Kansas City, Missouri, on August 13–14, 2019. A multibeam echosounder mapping system was used to obtain channel-bed elevations for river reaches about 1,550 to 1,660 feet longitudinally and generally extending laterally across the active channel from bank to bank during moderate flood-flow conditions. These surveys indicated the channel conditions at the time of the surveys and provided characteristics of scour holes that may be useful in developing predictive guidelines or equations for scour holes. These data also may be useful to the Missouri Department of Transportation as a low to moderate flood-flow assessment of the bridges for stability and integrity issues with respect to bridge scour during floods.
Bathymetric data were collected around every pier that was in water, except around the nose of one pier that was surrounded by a persistent debris raft. Scour holes were present at most piers for which bathymetry could be obtained, except those on banks or surrounded by riprap. The observed scour holes at the surveyed bridges generally were examined with respect to shape and depth.
Comparisons between bathymetric surfaces from previous surveys and this study do not indicate any consistent correlation in channel-bed elevations with streamflow conditions at the times of the surveys. The predominant overall scour observed between the various surveys implies the channel bed in the 2019 surveys might have been rebounding from more substantial scour caused by the high streamflow earlier in March and June 2019, which was the highest streamflow since 1993. Pier size and nose shape had a substantial effect on the size of the scour hole observed at a given pier. Many of the piers at the Kansas City area bridges have wide or blunt noses caused by exposed footings, seal courses, or caissons, which resulted in large, deep scour holes at most piers. Several of the structures had piers that were skewed to primary approach flow; and, at most of the structures, the scour hole was deeper and longer on the side of the pier with impinging flow than the leeward side, with some amount of deposition on the leeward side, as typically has been observed at piers skewed to approach flow.
Limited additional bathymetric data were collected by the U.S. Geological Survey, in cooperation with Clarkson Construction, near the main channel piers of the U.S. Highway 169 (Broadway) and the Interstate 435 (Randolph) bridges on August 17 and October 23, 2020, to determine the channel-bed conditions before and after installation of scour countermeasures near those piers. Survey results from before and after installation of these countermeasures show these features had a substantial effect on mitigating the observed scour at these piers, particularly when compared to piers at other sites without such features.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215098","collaboration":"Prepared in cooperation with the Missouri Department of Transportation and Clarkson Construction","usgsCitation":"Huizinga, R.J., 2022, Bathymetric and velocimetric surveys at highway bridges crossing the Missouri River near Kansas City, Missouri, August 2019, August 2020, and October 2020: U.S. Geological Survey Scientific Investigations Report 2021–5098, 112 p., https://doi.org/10.3133/sir20215098.","productDescription":"Report: xii, 112 p.; Data Release; Dataset","numberOfPages":"128","onlineOnly":"Y","ipdsId":"IP-124626","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":395010,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96TX8AE","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Bathymetry and velocity data from surveys at highway bridges crossing the Missouri River in Kansas City, Missouri, in August 2019, August 2020, and October 2020"},{"id":395008,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5098/coverthb.jpg"},{"id":395013,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5098/images"},{"id":395012,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5098/sir20215098.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2021–5098 XML"},{"id":395011,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"},{"id":395009,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5098/sir20215098.pdf","text":"Report","size":"38.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5098"},{"id":502114,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112326.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Missouri","city":"Kansas City","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 -94.68086242675781,\n 39.102357437817595\n ],\n [\n -94.48722839355467,\n 39.102357437817595\n ],\n [\n -94.48722839355467,\n 39.193948213963665\n ],\n [\n -94.68086242675781,\n 39.193948213963665\n ],\n [\n -94.68086242675781,\n 39.102357437817595\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Tidal wetlands provide myriad ecosystem services across local to global scales. With their uncertain vulnerability or resilience to rising sea levels, there is a need for mapping flooding drivers and vulnerability proxies for these ecosystems at a national scale. However, tidal wetlands in the conterminous USA are diverse with differing elevation gradients, and tidal amplitudes, making broad geographic comparisons difficult. To address this, a national-scale map of relative tidal elevation (Z*MHW), a physical metric that normalizes elevation to tidal amplitude at mean high water (MHW), was constructed for the first time at 30 × 30-m resolution spanning the conterminous USA. Contrary to two study hypotheses, watershed-level median Z*MHW and its variability generally increased from north to south as a function of tidal amplitude and relative sea-level rise. These trends were also observed in a reanalysis of ground elevation data from the Pacific Coast by Janousek et al. (Estuaries and Coasts 42 (1): 85–98, 2019). Supporting a third hypothesis, propagated uncertainty in Z*MHW increased from north to south as light detection and ranging (LiDAR) errors had an outsized effect under narrowing tidal amplitudes. The drivers of Z*MHW and its variability are difficult to determine because several potential causal variables are correlated with latitude, but future studies could investigate highest astronomical tide and diurnal high tide inequality as drivers of median Z*MHW and Z*MHW variability, respectively. Watersheds of the Gulf Coast often had propagated Z*MHW uncertainty greater than the tidal amplitude itself emphasizing the diminished practicality of applying Z*MHW as a flooding proxy to microtidal wetlands. Future studies could focus on validating and improving these physical map products and using them for synoptic modeling of tidal wetland carbon dynamics and sea-level rise vulnerability analyses.
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lwindham-myers@usgs.gov","orcid":"https://orcid.org/0000-0003-0281-9581","contributorId":2449,"corporation":false,"usgs":true,"family":"Windham-Myers","given":"Lisamarie","email":"lwindham-myers@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":832240,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70256719,"text":"70256719 - 2022 - Tracking spatial regimes in animal communities: Implications for resilience-based management","interactions":[],"lastModifiedDate":"2024-09-03T16:17:05.511321","indexId":"70256719","displayToPublicDate":"2022-01-29T11:08:28","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Tracking spatial regimes in animal communities: Implications for resilience-based management","docAbstract":"Spatial regimes (the spatial extents of ecological states) exhibit strong spatiotemporal order as they expand or contract in response to retreating or encroaching adjacent spatial regimes (e.g., woody plant invasion of grasslands) and human management (e.g., fire treatments). New methods enable tracking spatial regime boundaries via vegetation landcover data, and this approach is being used for strategic management across biomes. A clear advancement would be incorporating animal community data to track spatial regime boundaries alongside vegetation data. In a 41,170-hectare grassland experiencing woody plant encroachment, we test the utility of using animal community data to track spatial regimes via two hypotheses. (H1) Spatial regime boundaries identified via independent vegetation and animal datasets will exhibit spatial synchrony; specifically, grassland:woodland bird community boundaries will synchronize with grass:woody vegetation boundaries. (H2) Negative feedbacks will stabilize spatial regimes identified via animal data; specifically, frequent fire treatments will stabilize grassland bird community boundaries. We used 26 years of bird community and vegetation data alongside 32 years of fire history data. We identified spatial regime boundaries with bird community data via a wombling approach. We identified spatial regime boundaries with vegetation data by calculating spatial covariance between remotely-sensed grass and woody plant cover per pixel. For fire history data, we calculated the cumulative number of fires per pixel. Setting bird boundary strength (wombling R2 values) as the response variable, we tested our hypotheses with a hierarchical generalized additive model (HGAM). Both hypotheses were supported: animal boundaries synchronized with vegetation boundaries in space and time, and grassland bird communities stabilized as fire frequency increased (HGAM explained 38% of deviance). We can now track spatial regimes via animal community data pixel-by-pixel and year-by-year. Alongside vegetation boundary tracking, tracking animal community boundaries can inform the scale of management necessary to maintain animal communities endemic to desirable ecological states. Our approach will be especially useful for conserving animal communities requiring large-scale, unfragmented landscapes—like grasslands and steppes.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2022.108567","usgsCitation":"Roberts, C.P., Uden, D.R., Allen, C., Angeler, D., Powell, L., Allred, B.W., Jones, M., Maestas, J.D., and Twidwell, D., 2022, Tracking spatial regimes in animal communities: Implications for resilience-based management: Ecological Indicators, v. 136, 108567, 9 p., https://doi.org/10.1016/j.ecolind.2022.108567.","productDescription":"108567, 9 p.","ipdsId":"IP-133356","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":448996,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2022.108567","text":"Publisher Index Page"},{"id":433414,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kansas","otherGeospatial":"Fort Riley Army Base","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -96.96213921773204,\n 39.311060889325915\n ],\n [\n -96.96422797821519,\n 39.2045692635035\n ],\n [\n -96.9059515607264,\n 39.170753927787935\n ],\n [\n -96.87441127742586,\n 39.12982552331178\n ],\n [\n -96.87065150855537,\n 39.06172160474132\n ],\n [\n -96.83117393541791,\n 39.03739558273512\n ],\n [\n -96.75242766519114,\n 39.027994666034715\n ],\n [\n -96.70313291778122,\n 39.08988085180364\n ],\n [\n -96.68057430455956,\n 39.133608110581775\n ],\n [\n -96.68057430455947,\n 39.2068138331922\n ],\n [\n -96.74490812745047,\n 39.242505115215266\n ],\n [\n -96.84683963904419,\n 39.30135970662323\n ],\n [\n -96.96213921773204,\n 39.311060889325915\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"136","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Roberts, Caleb Powell 0000-0002-8716-0423","orcid":"https://orcid.org/0000-0002-8716-0423","contributorId":288567,"corporation":false,"usgs":true,"family":"Roberts","given":"Caleb","email":"","middleInitial":"Powell","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908767,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Uden, Daniel R.","contributorId":74258,"corporation":false,"usgs":true,"family":"Uden","given":"Daniel","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":908768,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Allen, Craig R.","contributorId":246029,"corporation":false,"usgs":false,"family":"Allen","given":"Craig R.","affiliations":[{"id":36892,"text":"University of Nebraska","active":true,"usgs":false}],"preferred":false,"id":908769,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Angeler, David G.","contributorId":25027,"corporation":false,"usgs":true,"family":"Angeler","given":"David G.","affiliations":[],"preferred":false,"id":908770,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Powell, Larkin A.","contributorId":15100,"corporation":false,"usgs":true,"family":"Powell","given":"Larkin A.","affiliations":[],"preferred":false,"id":908771,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Allred, Brady W","contributorId":216378,"corporation":false,"usgs":false,"family":"Allred","given":"Brady","email":"","middleInitial":"W","affiliations":[{"id":39397,"text":"W.A. Franke College of Forestry and Conservation University of Montana, Missoula","active":true,"usgs":false}],"preferred":false,"id":908772,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jones, Matthew O.","contributorId":341488,"corporation":false,"usgs":false,"family":"Jones","given":"Matthew O.","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":908773,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Maestas, Jeremy D","contributorId":191086,"corporation":false,"usgs":false,"family":"Maestas","given":"Jeremy","email":"","middleInitial":"D","affiliations":[],"preferred":false,"id":908774,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Twidwell, Dirac","contributorId":341491,"corporation":false,"usgs":false,"family":"Twidwell","given":"Dirac","affiliations":[{"id":16610,"text":"University of Nebraska-Lincoln","active":true,"usgs":false}],"preferred":false,"id":908775,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70227859,"text":"70227859 - 2022 - Condition of macroinvertebrate communities in the Buffalo River Area of Concern following sediment remediation","interactions":[],"lastModifiedDate":"2022-02-01T17:43:25.670538","indexId":"70227859","displayToPublicDate":"2022-01-28T11:37:21","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Condition of macroinvertebrate communities in the Buffalo River Area of Concern following sediment remediation","docAbstract":"The lower 10 km of the Buffalo River, a tributary to Lake Erie, was designated as an Area of Concern (AOC) in 1987 through the Great Lakes Water Quality Agreement because sediment contamination and habitat alteration from past industrialization caused several Beneficial Use Impairments (BUIs). Extensive remediation efforts conducted between 2011 and 2015 removed approximately 688,100 cubic meters of contaminated sediment from the Buffalo River AOC, and subsequent chemical analysis of sediments indicated that most remedial goals had been achieved. Benthic macroinvertebrate communities and sediment toxicity were evaluated in the AOC and an upstream reference area in 2017 and 2020 to determine whether remediation has improved benthic conditions sufficiently that the benthos BUI designation can be removed. Community condition was characterized using the New York State multi-metric index of biological integrity and bed sediments were used for 10-day toxicity tests with Chironomus dilutus and Hyalella azteca. Macroinvertebrate communities were classified as moderately to slightly impacted at most AOC sites compared to slightly impacted at most reference sites, but toxicity tests did not identify any evidence of toxicity in sediments from the AOC. A linear mixed effects model indicated that total organic carbon concentration in sediments, distance upstream from the river mouth, and the relative dominance of zebra mussels Dreissena polymorpha were the primary predictors of macroinvertebrate community condition. These findings are consistent with those from other AOCs in New York which indicate that contemporary benthic communities are generally shaped by legacy habitat alterations rather than AOC-specific sediment contamination and toxicity.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2021.11.002","usgsCitation":"George, S.D., Duffy, B.T., Baldigo, B., Skaros, D., and Smith, A., 2022, Condition of macroinvertebrate communities in the Buffalo River Area of Concern following sediment remediation: Journal of Great Lakes Research, v. 48, no. 1, p. 183-194, https://doi.org/10.1016/j.jglr.2021.11.002.","productDescription":"12 p.","startPage":"183","endPage":"194","ipdsId":"IP-129186","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":449003,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2021.11.002","text":"Publisher Index Page"},{"id":395221,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Buffalo River area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.87908935546874,\n 42.83015652099459\n ],\n [\n -78.74862670898438,\n 42.83015652099459\n ],\n [\n -78.74862670898438,\n 42.895585521720584\n ],\n [\n -78.87908935546874,\n 42.895585521720584\n ],\n [\n -78.87908935546874,\n 42.83015652099459\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"48","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"George, Scott D. 0000-0002-8197-1866 sgeorge@usgs.gov","orcid":"https://orcid.org/0000-0002-8197-1866","contributorId":3014,"corporation":false,"usgs":true,"family":"George","given":"Scott","email":"sgeorge@usgs.gov","middleInitial":"D.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":832426,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duffy, Brian T.","contributorId":272971,"corporation":false,"usgs":false,"family":"Duffy","given":"Brian","email":"","middleInitial":"T.","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":832427,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baldigo, Barry P. 0000-0002-9862-9119","orcid":"https://orcid.org/0000-0002-9862-9119","contributorId":25174,"corporation":false,"usgs":true,"family":"Baldigo","given":"Barry P.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":832428,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Skaros, Damianos","contributorId":272972,"corporation":false,"usgs":false,"family":"Skaros","given":"Damianos","email":"","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":832429,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, Alexander J.","contributorId":140345,"corporation":false,"usgs":false,"family":"Smith","given":"Alexander J.","affiliations":[{"id":13464,"text":"Environmental Analyst, NY State Dept of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":832430,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70238963,"text":"70238963 - 2022 - Forecasting species distributions: Correlation does not equal causation","interactions":[],"lastModifiedDate":"2022-12-19T14:24:41.325188","indexId":"70238963","displayToPublicDate":"2022-01-28T08:19:56","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1399,"text":"Diversity and Distributions","active":true,"publicationSubtype":{"id":10}},"title":"Forecasting species distributions: Correlation does not equal causation","docAbstract":"Identifying the mechanisms influencing species' distributions is critical for accurate climate change forecasts. However, current approaches are limited by correlative models that cannot distinguish between direct and indirect effects.
New Hampshire and Vermont, USA.
Using causal and correlational models and new theory on range limits, we compared current (2014–2019) and future (2080s) distributions of ecologically important mammalian carnivores and competitors along range limits in the northeastern US under two global climate models (GCMs) and a high-emission scenario (RCP8.5) of projected snow and forest biomass change.
Our hypothesis that causal models of climate-mediated competition would result in different distribution predictions than correlational models, both in the current and future periods, was well-supported by our results; however, these patterns were prominent only for species pairs that exhibited strong interactions. The causal model predicted the current distribution of Canada lynx (Lynx canadensis) more accurately, likely because it incorporated the influence of competitive interactions mediated by snow with the closely related bobcat (Lynx rufus). Both modeling frameworks predicted an overall decline in lynx occurrence in the central high-elevation regions and increased occurrence in the northeastern region in the 2080s due to changes in land use that provided optimal habitat. However, these losses and gains were less substantial in the causal model due to the inclusion of an indirect buffering effect of snow on lynx.
Our comparative analysis indicates that a causal framework, steeped in ecological theory, can be used to generate spatially explicit predictions of species distributions. This approach can be used to disentangle correlated predictors that have previously hampered understanding of range limits and species' response to climate change.
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Modeling subsurface performance of a geothermal reservoir using machine learning","interactions":[],"lastModifiedDate":"2022-05-11T11:44:14.982345","indexId":"70231484","displayToPublicDate":"2022-01-28T06:42:19","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10757,"text":"Energies","active":true,"publicationSubtype":{"id":10}},"title":"Modeling subsurface performance of a geothermal reservoir using machine learning","docAbstract":"Physical processes driving barrier island change during storms are important to understand to mitigate coastal hazards and to evaluate conceptual models for barrier evolution. Spatial variations in barrier island topography, landcover characteristics, and nearshore and back-barrier hydrodynamics can yield complex morphological change that requires models of increasing resolution and physical complexity to predict. Using the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) modeling system, we investigated two barrier island breaches that occurred on Fire Island, NY during Hurricane Sandy (2012) and at Matanzas, FL during Hurricane Matthew (2016). The model employed a recently implemented infragravity (IG) wave driver to represent the important effects of IG waves on nearshore water levels and sediment transport. The model simulated breaching and other changes with good skill at both locations, resolving differences in the processes and evolution. The breach simulated at Fire Island was 250 m west of the observed breach, whereas the breach simulated at Matanzas was within 100 m of the observed breach. Implementation of the vegetation module of COAWST to allow three-dimensional drag over dune vegetation at Fire Island improved model skill by decreasing flows across the back-barrier, as opposed to varying bottom roughness that did not positively alter model response. Analysis of breach processes at Matanzas indicated that both far-field and local hydrodynamics influenced breach creation and evolution, including remotely generated waves and surge, but also surge propagation through back-barrier waterways. This work underscores the importance of resolving the complexity of nearshore and back-barrier systems when predicting barrier island change during extreme events.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021JF006307","usgsCitation":"Hegermiller, C., Warner, J.C., Olabarrieta, M., Sherwood, C.R., and Kalra, T., 2022, Modeling of barrier breaching during Hurricanes Sandy and Matthew: JGR-Earth Surface, v. 127, no. 3, e2021JF006307, 20 p., https://doi.org/10.1029/2021JF006307.","productDescription":"e2021JF006307, 20 p.","ipdsId":"IP-130367","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":449023,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2021jf006307","text":"External Repository"},{"id":413242,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida, New York","city":"Matanzas","otherGeospatial":"Fire Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.23320426968054,\n 41.082997080822736\n ],\n [\n -74.23320426968054,\n 40.32905270617809\n ],\n [\n -71.43741334729009,\n 40.32905270617809\n ],\n [\n -71.43741334729009,\n 41.082997080822736\n ],\n [\n -74.23320426968054,\n 41.082997080822736\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.335863306947,\n 26.295094198443238\n ],\n [\n -78.26241414026661,\n 27.205116096340547\n ],\n [\n -80.7016669317273,\n 31.630079958177035\n ],\n [\n -82.69413025464559,\n 30.949177652812494\n ],\n [\n -80.2897567468504,\n 26.26830908028633\n ],\n [\n -80.335863306947,\n 26.295094198443238\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"127","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-03-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Hegermiller, Christie 0000-0002-6383-7508","orcid":"https://orcid.org/0000-0002-6383-7508","contributorId":241895,"corporation":false,"usgs":true,"family":"Hegermiller","given":"Christie","affiliations":[{"id":36711,"text":"Woods Hole Oceanographic Institution","active":true,"usgs":false}],"preferred":true,"id":864757,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Warner, John C. 0000-0002-3734-8903 jcwarner@usgs.gov","orcid":"https://orcid.org/0000-0002-3734-8903","contributorId":258015,"corporation":false,"usgs":true,"family":"Warner","given":"John","email":"jcwarner@usgs.gov","middleInitial":"C.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":864758,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Olabarrieta, Maitane 0000-0002-7619-7992 molabarrieta@usgs.gov","orcid":"https://orcid.org/0000-0002-7619-7992","contributorId":211373,"corporation":false,"usgs":false,"family":"Olabarrieta","given":"Maitane","email":"molabarrieta@usgs.gov","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":864759,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sherwood, Christopher R. 0000-0001-6135-3553 csherwood@usgs.gov","orcid":"https://orcid.org/0000-0001-6135-3553","contributorId":2866,"corporation":false,"usgs":true,"family":"Sherwood","given":"Christopher","email":"csherwood@usgs.gov","middleInitial":"R.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":864760,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kalra, Tarandeep S. 0000-0001-5468-248X tkalra@usgs.gov","orcid":"https://orcid.org/0000-0001-5468-248X","contributorId":178820,"corporation":false,"usgs":true,"family":"Kalra","given":"Tarandeep S.","email":"tkalra@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":864762,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70256727,"text":"70256727 - 2022 - Influences of channel and floodplain modification on expansion of woody vegetation into Catahoula Lake, Louisiana, USA","interactions":[],"lastModifiedDate":"2024-09-03T16:44:50.779458","indexId":"70256727","displayToPublicDate":"2022-01-26T11:39:54","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1425,"text":"Earth Surface Processes and Landforms","active":true,"publicationSubtype":{"id":10}},"title":"Influences of channel and floodplain modification on expansion of woody vegetation into Catahoula Lake, Louisiana, USA","docAbstract":"Ecosystem structure of wetlands in managed floodplains depends on hydrological processes controlled by geomorphology and water management. Overlapping effects of direct modifications and geomorphic adjustments to management can combine to trigger changes to floodplain ecosystem structure. We examined the case of woody vegetation encroaching into the depressional Catahoula Lake, Louisiana, in the context of regional hydrologic and geomorphic modification in the floodplain of the Mississippi River. Historical aerial photographs indicated woody encroachment into Catahoula Lake for at least 80 years, and the rate of expansion has increased in recent decades. Historical stage analysis revealed that the downstream Red–Atchafalaya–Mississippi River system controls the lower limit of the lake water level when the large rivers are high, but channel enlargement and other hydrological changes there have reduced the frequency of backwater flooding by 42% since 1880. In addition, operation of the water control structure on the lake has altered its hydrological regime to be more regular among years. Historic stage analysis revealed current lake levels are lower in the high-water spring, less variable in the dry period, and lack the extreme high-water events of 100+ years ago, all of which facilitate the expansion of woody vegetation.
","language":"English","publisher":"Wiley","doi":"10.1002/esp.5328","usgsCitation":"Keim, R., Dugue, L., Latuso, K., Joshi, S., King, S.L., and Willis, F., 2022, Influences of channel and floodplain modification on expansion of woody vegetation into Catahoula Lake, Louisiana, USA: Earth Surface Processes and Landforms, v. 47, no. 6, p. 1466-1479, https://doi.org/10.1002/esp.5328.","productDescription":"14 p.","startPage":"1466","endPage":"1479","ipdsId":"IP-130218","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":449025,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/esp.5328","text":"Publisher Index Page"},{"id":433416,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Catahoula Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92.20285714912983,\n 31.44173705449458\n ],\n [\n -92.12418940417722,\n 31.44557223555043\n ],\n [\n -92.0297881102343,\n 31.532780176376406\n ],\n [\n -92.03877870965712,\n 31.576832357365504\n ],\n [\n -92.0803602319895,\n 31.574917477714266\n ],\n [\n -92.13655147838381,\n 31.541400717897048\n ],\n [\n -92.17925682564407,\n 31.505955624529022\n ],\n [\n -92.21521922333639,\n 31.45899413301514\n ],\n [\n -92.20285714912983,\n 31.44173705449458\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"47","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-02-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Keim, R.F.","contributorId":264646,"corporation":false,"usgs":false,"family":"Keim","given":"R.F.","affiliations":[{"id":54524,"text":"Lousiiana State University","active":true,"usgs":false}],"preferred":false,"id":908787,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dugue, L.","contributorId":341705,"corporation":false,"usgs":false,"family":"Dugue","given":"L.","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":908788,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Latuso, K.D.","contributorId":341706,"corporation":false,"usgs":false,"family":"Latuso","given":"K.D.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":908789,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Joshi, S.","contributorId":341707,"corporation":false,"usgs":false,"family":"Joshi","given":"S.","email":"","affiliations":[{"id":13314,"text":"Columbia River Inter-Tribal Fish Commission","active":true,"usgs":false}],"preferred":false,"id":908790,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"King, Sammy L. 0000-0002-5364-6361 sking@usgs.gov","orcid":"https://orcid.org/0000-0002-5364-6361","contributorId":557,"corporation":false,"usgs":true,"family":"King","given":"Sammy","email":"sking@usgs.gov","middleInitial":"L.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908791,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Willis, F.L.","contributorId":341708,"corporation":false,"usgs":false,"family":"Willis","given":"F.L.","email":"","affiliations":[{"id":81776,"text":"Willis Engineering and Scientific","active":true,"usgs":false}],"preferred":false,"id":908792,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70227681,"text":"70227681 - 2022 - The potential of wave energy conversion to mitigate coastal erosion from hurricanes","interactions":[],"lastModifiedDate":"2022-01-26T17:12:54.664026","indexId":"70227681","displayToPublicDate":"2022-01-26T11:03:34","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2380,"text":"Journal of Marine Science and Engineering","active":true,"publicationSubtype":{"id":10}},"title":"The potential of wave energy conversion to mitigate coastal erosion from hurricanes","docAbstract":"Wave energy conversion technologies have recently attracted more attention as part of global efforts to replace fossil fuels with renewable energy resources. While ocean waves can provide renewable energy, they can also be destructive to coastal areas that are often densely populated and vulnerable to coastal erosion. There have been a variety of efforts to mitigate the impacts of wave- and storm-induced erosion; however, they are either temporary solutions or approaches that are not able to adapt to a changing climate. This study explores a green and sustainable approach to mitigating coastal erosion from hurricanes through wave energy conversion. A barrier island, Dauphin Island, off the coast of Alabama, is used as a test case. The potential use of wave energy converter farms to mitigate erosion due to hurricane storm surges while simultaneously generating renewable energy is explored through simulations that are forced with storm data using the XBeach model. It is shown that wave farms can impact coastal morphodynamics and have the potential to reduce dune and beach erosion, predominantly in the western portion of the island. The capacity of wave farms to influence coastal morphodynamics varies with the storm intensity.
","language":"English","publisher":"MDPI AG","doi":"10.3390/jmse10020143","usgsCitation":"Ozkan, C., Mayo, T., and Passeri, D., 2022, The potential of wave energy conversion to mitigate coastal erosion from hurricanes: Journal of Marine Science and Engineering, v. 10, no. 2, p. 1-26, https://doi.org/10.3390/jmse10020143.","productDescription":"143, 26 p.","startPage":"1","endPage":"26","ipdsId":"IP-126398","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":449028,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/jmse10020143","text":"Publisher Index Page"},{"id":394882,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama","otherGeospatial":"Dauphin Island, Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.35411071777344,\n 30.22317846163011\n ],\n [\n -88.06777954101562,\n 30.22317846163011\n ],\n [\n -88.06777954101562,\n 30.355397662121728\n ],\n [\n -88.35411071777344,\n 30.355397662121728\n ],\n [\n -88.35411071777344,\n 30.22317846163011\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-01-21","publicationStatus":"PW","contributors":{"editors":[{"text":"Morales, Rafael","contributorId":272228,"corporation":false,"usgs":false,"family":"Morales","given":"Rafael","email":"","affiliations":[],"preferred":false,"id":831787,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Ozkan, Cigdem","contributorId":272200,"corporation":false,"usgs":false,"family":"Ozkan","given":"Cigdem","email":"","affiliations":[{"id":18879,"text":"University of Central Florida","active":true,"usgs":false}],"preferred":false,"id":831708,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mayo, Talea","contributorId":272201,"corporation":false,"usgs":false,"family":"Mayo","given":"Talea","email":"","affiliations":[{"id":40432,"text":"Emory University","active":true,"usgs":false}],"preferred":false,"id":831709,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Passeri, Davina 0000-0002-9760-3195 dpasseri@usgs.gov","orcid":"https://orcid.org/0000-0002-9760-3195","contributorId":166889,"corporation":false,"usgs":true,"family":"Passeri","given":"Davina","email":"dpasseri@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":831710,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70227685,"text":"70227685 - 2022 - Testing the potential of streamflow data to predict spring migration of an ungulate herds","interactions":[],"lastModifiedDate":"2022-01-26T16:07:24.226926","indexId":"70227685","displayToPublicDate":"2022-01-26T09:51:49","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Testing the potential of streamflow data to predict spring migration of an ungulate herds","docAbstract":"In mountainous and high latitude regions, migratory animals exploit green waves of emerging vegetation coinciding with rising daily mean temperatures initiating snowmelt across the landscape. Snowmelt also causes rivers and streams draining these regions to swell, a process referred to as to as the ‘spring pulse.’ Networks of streamgages measuring streamflow in these regions often have long-term and continuous periods of record available in real-time and at the daily time step, and thus produce data with potential to predict temporal migration patterns for species exploiting green waves. We tested the potential of models informed by streamflow data to predict timing of spring migration of mule deer (Odocoileus hemionus) herds in a headwater basin of the Colorado River. Models using streamflow data were compared with those informed by traditional temperature-derived measures of the onset of spring. Non-parametric linear-regression techniques were used to test for temporal stationarity in each variable, and logistic-regression models were used to produce probabilities of migration initiation. Our analysis indicates that models using daily streamflow data can perform as well as those using temperature-derived data to predict past-migration patterns, and nearly as well in potential to forecast future migrations. The best performing model was used to generate probabilities of onset of migration for mule deer herds over the 69-year period-of-record from a streamgage. That model indicated spring migration has been trending toward earlier initiations, with modeled median initiations shifting from a Julian day of 123 in the mid 20th century to Julian day 115 over the most recent two decades. The period of 1960 to 1979 had the latest modeled median initiations with Julian day of 128. The analyses demonstrate promise for merging existing hydrologic and biological data collection platforms in these regions to explore timing of past migration patterns and predict migration onsets in real-time.
","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pone.0262078","usgsCitation":"Alexander, J.S., Murr, M.L., and Eddy-Miller, C.A., 2022, Testing the potential of streamflow data to predict spring migration of an ungulate herds: PLoS ONE, v. 17, no. 1, p. 1-18, https://doi.org/10.1371/journal.pone.0262078.","productDescription":"e0262078, 18 p.","startPage":"1","endPage":"18","ipdsId":"IP-125176","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":449034,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0262078","text":"Publisher Index Page"},{"id":394871,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Wyoming","otherGeospatial":"Little Snake River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.45703125,\n 40.45321727150385\n ],\n [\n -108.00933837890625,\n 40.70562793820589\n ],\n [\n -107.46826171874999,\n 40.84913799774759\n ],\n [\n -107.0892333984375,\n 40.86991083161536\n ],\n [\n -107.05078125,\n 41.00477542222947\n ],\n [\n -107.490234375,\n 41.539421883822854\n ],\n [\n -108.446044921875,\n 41.54764462357737\n ],\n [\n -108.8031005859375,\n 41.20552261955812\n ],\n [\n -108.45703125,\n 40.45321727150385\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"17","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-01-21","publicationStatus":"PW","contributors":{"editors":[{"text":"Grignolio, Stefano","contributorId":272227,"corporation":false,"usgs":false,"family":"Grignolio","given":"Stefano","email":"","affiliations":[{"id":35987,"text":"Department of Life Sciences and Biotechnology, University of Ferrara, Ferrara, Italy","active":true,"usgs":false}],"preferred":false,"id":831783,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Alexander, Jason S. 0000-0002-1602-482X jalexand@usgs.gov","orcid":"https://orcid.org/0000-0002-1602-482X","contributorId":261330,"corporation":false,"usgs":true,"family":"Alexander","given":"Jason","email":"jalexand@usgs.gov","middleInitial":"S.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":831739,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Murr, Marissa L.","contributorId":252938,"corporation":false,"usgs":false,"family":"Murr","given":"Marissa","email":"","middleInitial":"L.","affiliations":[{"id":50476,"text":"Department of Geology and Geophysics, University of Wyoming, Laramie, Wyoming","active":true,"usgs":false}],"preferred":false,"id":831740,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eddy-Miller, Cheryl A. 0000-0002-4082-750X","orcid":"https://orcid.org/0000-0002-4082-750X","contributorId":195780,"corporation":false,"usgs":true,"family":"Eddy-Miller","given":"Cheryl","email":"","middleInitial":"A.","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":false,"id":831741,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70227686,"text":"70227686 - 2022 - Oxygen isotopes of land snail shells in high latitude regions","interactions":[],"lastModifiedDate":"2022-01-26T15:51:22.676505","indexId":"70227686","displayToPublicDate":"2022-01-26T09:41:08","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3219,"text":"Quaternary Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Oxygen isotopes of land snail shells in high latitude regions","docAbstract":"The present study investigates the environmental significance of the oxygen isotopic composition of several modern land snail species collected along two north-to-south transects in Alaska and Scandinavia at latitudes between 60 and 70 °N. We tested the hypothesis that land snail shell δ18O values primarily track precipitation δ18O. The results show that shell δ18O values from Scandinavia were ∼5.1‰ enriched in 18O with respect to snails from Alaska, equivalent to differences in precipitation δ18O values between the two regions. Within the Alaskan transect, shell δ18O values increased with observed increasing air temperature and precipitation δ18O, whereas shell δ18O values from Scandinavia did not correlate to instrumental climate data because of a reduced climatic gradient across the locations sampled. In addition, shell δ18O values differed significantly among sympatric species, with larger species consistently exhibiting higher δ18O values, which implies that species-level isotopic variations should be considered at the local and microhabitat scale. However, when snail shell δ18O values from this study are combined with previously published data from North America and Europe, we see evidence that shell δ18O values track precipitation δ18O across latitudes, even when different species are combined because climate gradients are greater than variations among taxa.
This article investigates methods to improve earthquake early warning (EEW) predictions of shaking levels for residents of tall buildings. In the current U.S. Geological Survey ShakeAlert EEW system, regions far from an epicenter will not receive alerts due to low predicted ground‐shaking intensities. However, residents of tall buildings in those areas may still experience significant shaking due to the acceleration amplification caused by tall buildings’ dynamic behavior, as recently experienced by residents of the 52‐story building in downtown Los Angeles (DTLA) during the 2019 M 7.1 Ridgecrest earthquake. Using more than 400 recorded response data acquired from 77 instrumented buildings in California, here we compare the Federal Emergency Management Agency (FEMA) P‐58 and American Society of Civil Engineers (ASCE) 7‐16 simplified equations for peak floor acceleration (PFA), finding that the ASCE estimation is close to the median of data recorded in large and long‐distance events, whereas the current FEMA estimation is not suitable. In the second part of this article, four instrumented tall buildings in DTLA are extensively studied, and the performance of the simplified and response spectrum (RS) methods giving both an estimation of the free‐field horizontal peak ground acceleration (PGA) and pseudospectral acceleration is evaluated. The results show that the RS method is as accurate as the response history analysis as long as the ground‐motion RS is accurate, whereas the ASCE 7‐16 prediction is conservative. However, when ground‐motion RS or PGA is estimated for DTLA using a ground‐motion model (GMM), the performance of the RS method significantly degrades due to underestimation by the GMM at long periods. The results of this study imply that a nonergodic GMM, which may give more accurate prediction in Los Angeles, could improve the results for PFA when the building’s behavior is dominated by a few long‐period fundamental modes, as is the case for the 52‐story building in DTLA.
In the United States, the Bald and Golden Eagle Protection Act prohibits take of golden eagles (Aquila chrysaetos) unless authorized by permit, and stipulates that all permitted take must be sustainable. Golden eagles are unintentionally killed in conjunction with many lawful activities (e.g., electrocution on power poles, collision with wind turbines). Managers who issue permits for incidental take of golden eagles must determine allowable take levels and manage permitted take accordingly. To aid managers in making these decisions in the western United States, we used an integrated population model to obtain estimates of golden eagle vital rates and population size, and then used those estimates in a prescribed take level (PTL) model to estimate the allowable take level. Estimated mean annual survival rates for golden eagles ranged from 0.70 (95% credible interval = 0.66–0.74) for first-year birds to 0.90 (0.88–0.91) for adults. Models suggested a high proportion of adult female golden eagles attempted to breed and breeding pairs fledged a mean of 0.53 (0.39–0.72) young annually. Population size in the coterminous western United States has averaged ~31,800 individuals for several decades, with λ = 1.0 (0.96–1.05). The PTL model estimated a median allowable take limit of ~2227 (708–4182) individuals annually given a management objective of maintaining a stable population. We estimate that take averaged 2572 out of 4373 (59%) deaths annually, based on a representative sample of transmitter-tagged golden eagles. For the subset of golden eagles that were recovered and a cause of death determined, anthropogenic mortality accounted for an average of 74% of deaths after their first year; leading forms of take over all age classes were shooting (~670 per year), collisions (~611), electrocutions (~506), and poisoning (~427). Although observed take overlapped the credible interval of our allowable take estimate and the population overall has been stable, our findings indicate that additional take, unless mitigated for, may not be sustainable. Our analysis demonstrates the utility of the joint application of integrated population and prescribed take level models to management of incidental take of a protected species.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2544","usgsCitation":"Milsap, B., Zimmerman, G.S., Kendall, W.L., Barnes, J., Braham, M., Bedrosian, B.E., Bell, D.A., Bloom, P.H., Crandall, R.H., Domenech, R., Driscoll, D., Duerr, A.E., Gerhardt, R., Gibbs, S.E., Harmata, A.R., Jacobson, K., Katzner, T., Knight, R., Lockhart, J.M., McIntyre, C., Murphy, R.K., Slater, S.J., Smith, B.W., Smith, J., Stahlecker, D.W., and Watson, J.W., 2022, Age-specific survival rates, causes of death, and allowable take of golden eagles in the western United States: Ecological Applications, v. 32, no. 3, e2544, 22 p., https://doi.org/10.1002/eap.2544.","productDescription":"e2544, 22 p.","ipdsId":"IP-125525","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"links":[{"id":449040,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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2022 - Hydrology of the Yucaipa groundwater subbasin: Characterization and integrated numerical model, San Bernardino and Riverside Counties, California","interactions":[{"subject":{"id":70228448,"text":"sir20215118A - 2022 - Hydrogeologic characterization of the Yucaipa groundwater subbasin","indexId":"sir20215118A","publicationYear":"2022","noYear":false,"chapter":"A","displayTitle":"Hydrogeologic Characterization of the Yucaipa Groundwater Subbasin","title":"Hydrogeologic characterization of the Yucaipa groundwater subbasin"},"predicate":"IS_PART_OF","object":{"id":70227651,"text":"sir20215118 - 2022 - Hydrology of the Yucaipa groundwater subbasin: Characterization and integrated numerical model, San Bernardino and Riverside Counties, California","indexId":"sir20215118","publicationYear":"2022","noYear":false,"title":"Hydrology of the Yucaipa groundwater subbasin: Characterization and integrated numerical model, San Bernardino and Riverside Counties, California"},"id":1},{"subject":{"id":70228449,"text":"sir20215118B - 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As the population has increased throughout southern California, so has the demand for water. The Yucaipa groundwater subbasin (hereafter referred to as “Yucaipa subbasin”), one of nine groundwater subbasins in what the California Department of Water Resources (DWR) refers to as the Upper Santa Ana Valley groundwater basin (California Department of Water Resources, 2016; the DWR naming convention is used within this report), is no exception; steady population growth since the 1940s and changes in water use has forced local water purveyors to regularly adapt their water infrastructure to meet demand. Groundwater has historically been the dominant source of water in the Yucaipa subbasin although recently, imported water via the California State Water Project has augmented the total water supply. Despite the influx of imported water, overall demand for groundwater continues to rise, and there is concern by local water managers that groundwater levels may adversely impact water supply and (or) decline to a point where it will be uneconomical to produce water, severely limiting the ability of local agencies to meet water-supply demand.
To better understand the hydrogeology and water resources in the Yucaipa subbasin, the U.S. Geological Survey (USGS) and the San Bernardino Valley Municipal Water District initiated a cooperative study to understand the hydrogeologic system of the Yucaipa subbasin and in the encompassing Yucaipa Valley watershed (YVW). A three-dimensional hydrogeologic framework model was constructed to quantify the structure and extent of hydrogeologic units. Historical and present-day groundwater conditions were characterized to evaluate the groundwater-flow system. Lastly, the Yucaipa Integrated Hydrological Model (YIHM) was developed to simulate the integrated surface-water and groundwater systems, including natural and anthropogenic (that is, human influenced) recharge and discharge throughout the study area from 1947 to 2014.
The Yucaipa subbasin is an inland groundwater basin located about 12 miles (mi) southeast of the City of San Bernardino and about 75 mi east of Los Angeles, California. The subbasin encompasses about 39 square miles (mi2), including the City of Yucaipa. The geographic extent of the Yucaipa subbasin was established by the California Department of Water Resources, who defined the boundaries of the subbasin based on hydrogeologic transitions between crystalline rock and basin-fill sediments, active fault strands, surface-water drainage divides, and a portion of an adjudicated groundwater management boundary. Two groundwater subbasins of the Upper Santa Ana Valley groundwater basin are adjacent to the Yucaipa subbasin, the San Bernardino groundwater subbasin to the west and the San Timoteo groundwater subbasin to the south.
The Yucaipa subbasin is encompassed by the YVW, which is in turn comprised of three sub-watersheds that represent surface-water flow across and within the Yucaipa subbasin. Although the Yucaipa subbasin is the specific area of interest for this study, the entire YVW was considered for the purposes of characterizing the hydrogeology of the Yucaipa subbasin and for development of the YIHM.
The purposes of this report are to (1) describe the hydrologic and hydrogeologic settings of the Yucaipa subbasin and aquifer system, (2) describe the construction and calibration of the fully coupled groundwater and surface-water flow model for the Yucaipa subbasin and the encompassing YVW, referred to as the YIHM, and (3) present numerical results, including water budgets and hydraulic heads, and the effect of pumping and climate stresses (precipitation and temperature) on water-budget components.
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California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Global Navigation Satellite Systems (GNSSs) have undergone notable advancement in the last few decades, leading to the availability of a dataset with capabilities well beyond its original intended purpose. The proliferation of high‐rate (1 Hz or greater) GNSS receivers in areas of seismological interest now allows for routine consideration of dynamic earthquake ground motions, with centimeter‐level displacement accuracy via precise point positioning methods. Real‐time (RT) GNSS observations, from stations that are both telemetered and processed to displacement with minimal latency, have lower accuracy compared to post‐processed (PP) GNSS displacements due to imprecise knowledge of atmospheric conditions, satellite clocks, and satellite orbits in RT. Whether the quality of RT high‐rate GNSS is sufficient for use in rapid response products remains to be thoroughly examined. Here, we highlight RT GNSS displacement time series processed during the 2019
We present a kinematic slip model of the July 8, 2021 Antelope Valley earthquake from a finite-source inversion based on regional seismic waveforms and static offsets from GPS and InSAR. Seismic waveforms are employed at 6s dominant period out to 100 km from the epicenter, and the combined GPS and InSAR datasets cover the near field and far field out to ∼ 100 km and constrain the overall rupture size. The aftershock pattern defines a nearly north-striking, 50◦ east-dipping fault plane. We find a unilateral rupture along this fault plane propagating southward and updip with predominantly normal slip up to ∼ 1.5m. The estimated seismic moment of 8.47 × 10 22 17 Nm is equivalent to Mw 5.92. A finite-source inversion that retains seismic waveforms and GPS static offsets but omits InSAR range changes yields a seismic moment of 1.08 × 10 25 18 Nm (Mw 5.99). Despite vigorous aftershock activity between 10 km and Earth’s surface, coseismic slip is concentrated in the depth interval 7 - 10 km.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0320210043","usgsCitation":"Pollitz, F., Wicks, C., and Hammond, W.M., 2022, Kinematic slip model of the July 8, 2021 M6.0 Antelope Valley, California, earthquake: The Seismic Record, v. 2, no. 1, p. 20-28, https://doi.org/10.1785/0320210043.","productDescription":"9 p.","startPage":"20","endPage":"28","ipdsId":"IP-133557","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":449047,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1785/0320210043","text":"Publisher Index Page"},{"id":421846,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Antelope Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120.00,\n 39.00\n ],\n [\n -120.00,\n 38.00\n ],\n [\n -119.00,\n 38.00\n ],\n [\n -119.00,\n 39.00\n ],\n [\n -120.00,\n 39.00\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"2","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-01-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Pollitz, Frederick 0000-0002-4060-2706 fpollitz@usgs.gov","orcid":"https://orcid.org/0000-0002-4060-2706","contributorId":139578,"corporation":false,"usgs":true,"family":"Pollitz","given":"Frederick","email":"fpollitz@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":885944,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wicks, Charles 0000-0002-0809-1328","orcid":"https://orcid.org/0000-0002-0809-1328","contributorId":9023,"corporation":false,"usgs":true,"family":"Wicks","given":"Charles","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":885945,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hammond, William M","contributorId":292777,"corporation":false,"usgs":false,"family":"Hammond","given":"William","email":"","middleInitial":"M","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":885946,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70262545,"text":"70262545 - 2022 - Redundancy analysis reveals complex den use patterns by eastern spotted skunks, a conditional specialist","interactions":[],"lastModifiedDate":"2025-01-22T18:32:02.773642","indexId":"70262545","displayToPublicDate":"2022-01-26T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Redundancy analysis reveals complex den use patterns by eastern spotted skunks, a conditional specialist","docAbstract":"Wildlife managers tasked with understanding habitat and resource selection at the population level attempt to characterize patterns in nature that aid and inform conservation. Resource selection functions (RSFs), such as discrete choice analyses, are the standard convention to characterize the effects of habitat attributes on resource selection patterns. These tools are invaluable for wildlife management and conservation and have proven successful in numerous studies. However, the analysis of small datasets using RSF becomes problematic when attempting to account for complex sources of variation, and the inclusion of factors such as weather or intrinsic variation on target species' response may produce models with poor predictive ability. We compared the application of generalized linear mixed-effects modeling (GLMM) and redundancy analysis (RDA) on Appalachian spotted skunk (Spilogale putorius putorius) den selection data at four study sites within the George Washington, Jefferson, and Monongahela National Forests, and surrounding private lands in the Appalachian Mountains of western Virginia and northeastern West Virginia. We assessed the need for the inclusion of alternative sources of variation (i.e., weather conditions and individual intrinsic variation) in addition to standard habitat attributes to better identify sources of variation in den selection. The RDA elucidated complex and opposing relationships, whereby den type use was based on reproductive status or weather condition, which were not evident in the GLMM model that relied solely on habitat measures. Our results demonstrated the importance of examining resource selection data using multivariate techniques in addition to conventional discrete choice analyses to better understand intricate habitat–species relationships, especially for small datasets. Furthermore, from our analyses, we proposed that spotted skunks are neither a true generalist nor specialist species. We introduced and define the term “conditional specialist” to represent a species that is conditionally selective of a given resource in response to one or more current environmental or intrinsic conditions.
","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.3913","usgsCitation":"Thorne, E., and Ford, W., 2022, Redundancy analysis reveals complex den use patterns by eastern spotted skunks, a conditional specialist: Ecosphere, v. 13, no. 1, e3913, 20 p., https://doi.org/10.1002/ecs2.3913.","productDescription":"e3913, 20 p.","ipdsId":"IP-120640","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481095,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3913","text":"Publisher Index Page"},{"id":480946,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Viginia, West 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Mark","email":"wford@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":924521,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70227465,"text":"sir20215135 - 2022 - Groundwater hydrology in the area of Savannah and Gunstocker Creeks in northeastern Hamilton, southern Meigs, and northwestern Bradley Counties, Tennessee, 2007–09","interactions":[],"lastModifiedDate":"2022-01-26T12:05:52.319711","indexId":"sir20215135","displayToPublicDate":"2022-01-25T13:59:52","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":"2021-5135","displayTitle":"Groundwater Hydrology in the Area of Savannah and Gunstocker Creeks in Northeastern Hamilton, Southern Meigs, and Northwestern Bradley Counties, Tennessee, 2007–09","title":"Groundwater hydrology in the area of Savannah and Gunstocker Creeks in northeastern Hamilton, southern Meigs, and northwestern Bradley Counties, Tennessee, 2007–09","docAbstract":"The U.S. Geological Survey, in cooperation with the Savannah Valley Utility District, evaluated the groundwater hydrology of the Valley and Ridge carbonate rock aquifer in northeastern Hamilton, southern Meigs, and northwestern Bradley Counties, Tennessee, from 2007 through 2009. The evaluation included, and built on, the results of test drilling conducted in the area in 1974 to determine the potential for groundwater as a source of public supply for the utility and the results of an investigation conducted to define recharge areas for wells used by groundwater-source public-supply water systems throughout Hamilton County in the early 1990s.
Groundwater-level data collected from wells open to the aquifer in the study area were used to prepare potentiometric-surface maps for fall 1992, spring and fall 1993, summer 2008, and spring 2009 conditions. Two primary groundwater basins were delineated from the maps—the larger of which coincides with the watershed of Savannah Creek in the southern part of the study area and the smaller of which coincides with the watershed of Gunstocker Creek in the northern part of the study area. Both basins are characterized by potentiometric surfaces that contain a central area of low-altitude groundwater levels and low gradients relative to the basin margins that reflect the orientation of enhanced permeability along dissolution-enlarged features that have developed parallel to strike in the aquifer. The recharge area of the Savannah Creek groundwater basin is estimated to be about 31 square miles, and the recharge area of the Gunstocker Creek groundwater basin is estimated to be about 17 square miles.
Recharge to the aquifer in the Savannah Creek and Gunstocker Creek groundwater basins primarily occurs in the uplands area along White Oak Mountain in the eastern part of the study area and along the western boundaries of the basins. Groundwater flows toward the potentiometric lows in each basin, discharging as base flow to the streams and to springs locally. Groundwater withdrawals for public supply by the utility influence the potentiometric low in the north-central part of the Savannah Creek groundwater basin and disrupt flow in the creek and nearby Anderson Spring, particularly during the summer and fall seasons. No large groundwater withdrawals currently occur in the Gunstocker Creek basin, but there is potential for groundwater supply development in the basin.
A conceptual model of the groundwater hydrology of the area developed from the evaluation indicates that Chickamauga Lake is the base-level control on groundwater discharge from the Savannah Creek and Gunstocker Creek basins and that lake stage affects the potentiometric surfaces and groundwater discharge in the most downgradient parts of the basins as a result of inferred hydraulic connection between the aquifer and the lake. The model also infers that captured surface water from sections of Savannah Creek and the Hiwassee River that are embayed by the lake could recharge the aquifer and serve as a source of water withdrawn by wells in each basin if the potentiometric surfaces were lowered to altitudes less than the stage of the lake, particularly under potential future groundwater-development scenarios in the Gunstocker Creek basin.
Geochemical analysis of samples collected from six wells for the study indicate that groundwater in the Valley and Ridge aquifer in the area generally is a calcium-magnesium-bicarbonate type, and although the water generally is hard, it is suitable for most uses. Trace-element concentrations were less than primary drinking-water criteria in all the samples.
Results of the investigation indicate that options are available for additional groundwater withdrawal in the study area. Water-level data collected since 1975 at the Savannah Valley Utility District Smith Road well site indicate that some additional amount of groundwater is available for withdrawal from the aquifer in the Savannah Creek groundwater basin. The potentiometric low within the Gunstocker Creek groundwater basin indicates that an area with enhanced permeability is present as a northeastern counterpart to the potentiometric low within the Savannah Creek basin. Because the Gunstocker Creek basin is about one-half the total area of the Savannah Creek basin, a commensurate decrease in available groundwater storage is likely. Furthermore, groundwater withdrawal locations in the Gunstocker Creek basin would be closer to—and possibly connected hydraulically to—the Hiwassee River, thus increasing the potential for induced surface-water recharge in the basin if sustained drawdown from pumping lowered groundwater levels to altitudes less than the stage of the river.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215135","isbn":"978-1-4113-4435-8","collaboration":"Prepared in cooperation with the Savannah Valley Utility District","programNote":"Water Availability and Use Science Program","usgsCitation":"Carmichael, J.K., 2022, Groundwater hydrology in the area of Savannah and Gunstocker Creeks in northeastern Hamilton, southern Meigs, and northwestern Bradley Counties, Tennessee, 2007–09: U.S. Geological Survey Scientific Investigations Report 2021–5135, 31 p., 5 pls., https://doi.org/10.3133/sir20215135.","productDescription":"Report: vii, 31 p.; Data Release; 5 Plates: 20.00 x 30.00 inches or smaller","numberOfPages":"44","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-104265","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":394458,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QVHDI5","text":"USGS Data Release","linkHelpText":"Geospatial data for groundwater potentiometric-surface maps in northeastern Hamilton, southern Meigs, and northwestern Bradley Counties, Tennessee, fall 1992, spring and fall 1993, summer 2008, and spring 2009"},{"id":394455,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5135/coverthb.jpg"},{"id":394456,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5135/sir20215135.pdf","text":"Report","size":"3.43 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5135"},{"id":394457,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2021/5135/sir20215135_plates.pdf","text":"Plates 1–5","size":"2.29 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5135 Plates"}],"country":"United States","state":"Tennessee","county":"Meigs County, Bradley 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U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
The West Napa Fault has previously been mapped as extending ~45 kilometers (km) from northern Vallejo to southern Saint Helena, California, dominantly running along the western edge of Napa Valley. A zone of fault strands (some previously unmapped) along a ~15-km section of the fault ruptured during the 2014 magnitude 6.0 South Napa earthquake, illustrating the need for further investigation of this little-studied structure. Based on light detection and ranging (lidar) topography and field examination, the fault zone likely extends an additional 10 km or more northward past Saint Helena. In this vicinity, geomorphology suggests two fault strands, one along the range front and another associated with a line of rounded hills that rise 5–10 meters above the middle of the valley. In 2017, we excavated two trenches across an apparent fault scarp on the east side of one elongate hill near Ehlers Lane north of Saint Helena. Examination of the walls revealed three main sedimentary packages. The oldest package, weakly lithified alluvial fan gravels with local sand and silt layers, is tilted 25°–35° to the west. Overlying these tilted strata are two younger sets of strata. On the west side, underlying the crest of the scarp, are alluvial fan gravels with local sand and silt lenses, potentially tilted a few degrees to the west. On the east side, deposited against the scarp, are much finer grained (dominantly fine sand to silt) subhorizontal fluvial strata, likely overbank deposits from the Napa River. We obtained age control on the two younger units through a combination of radiocarbon, infrared-stimulated luminescence, and obsidian hydration dating, establishing that they are latest Pleistocene to modern in age. Although there are no prominent unconformities within the alluvial fan sediments, sample dating indicates there are two generations, one in the 10–20 thousand year (ka) age range and one in the <3 ka age range. Owing to a general lack of well-defined laterally continuous alluvial fan units, it is difficult to distinguish contacts between the two generations except in the immediate proximity of dated samples. The river sediments approximately span the Holocene. No faults were apparent in either trench, indicating that any fault related to the observed surface deformation has not ruptured to the surface at this site during the Holocene and is likely blind.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221002","usgsCitation":"Philibosian, B.E., Sickler, R.R., Prentice, C.S., Pickering, A.J., Gannon, P., Broudy, K.N., Mahan, S.A., Titular, J.N., Turner, E.A., Folmar, C., Patterson, S.F., and Bowman, E.E., 2022, Photomosaics and logs associated with study of West Napa Fault at Ehlers Lane, north of Saint Helena, California: U.S. Geological Survey Open-File Report 2022–1002, 1 sheet, pamphlet 8 p., https://doi.org/10.3133/ofr20221002.","productDescription":"Report: iv, 8 p.; 1 Sheet: 82.00 x 43.00 inches","numberOfPages":"8","additionalOnlineFiles":"Y","ipdsId":"IP-114127","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":501537,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112153.htm","linkFileType":{"id":5,"text":"html"}},{"id":394764,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2022/1002/ofr20221002_sheet.pdf","size":"80 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":394763,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1002/ofr20221002_pamphlet.pdf","text":"Pamphlet","size":"300 KB","linkFileType":{"id":1,"text":"pdf"}},{"id":394762,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1002/covrthb.jpg"}],"country":"United States","state":"California","city":"Saint Helena","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.58407592773438,\n 38.47294404791815\n ],\n [\n -122.38494873046875,\n 38.47294404791815\n ],\n [\n -122.38494873046875,\n 38.586820096127674\n ],\n [\n -122.58407592773438,\n 38.586820096127674\n ],\n [\n -122.58407592773438,\n 38.47294404791815\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information, Menlo Park, Calif.
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345 Middlefield Road, MS 977
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